Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Pokhrel, Sarin Raj
Doctor of Philosophy - PhD
University of British Columbia
An urban water system (UWS) has three main service components: (i) drinking water; (ii) wastewater; and (iii) stormwater. Historically, each component in urban water development evolved with different objectives for “different” types of water. Even today, the trend continues, as urban water services are managed in silos. The silo-based approaches are less sustainable, resilient, and reliable, mainly because of significant pressures on freshwater supplies exerted by the increasing population, high living standards, rapid urbanization, and climate change uncertainties. To cope with these challenges, conventional thinking needs to change. An innovative paradigm, the “One Water” approach (OWA), which considers “urban water” as a single entity, is the need of the hour. Currently, Australia, USA, and Singapore are leading the implementation of the OWA whereas the EU nations have emphasized the need for integrating water resource management (reflecting the One Water concept) in an urban environment. Only a few Canadian municipalities have embraced OWA at a very preliminary level. This research aimed to develop an OWA-based framework to improve urban water sustainability, resiliency, and reliability. The research entailed five phases. Phase 1 identified Key Performance Indicators (KPIs) to evaluate drinking water, wastewater, and stormwater performance individually. Phase 2 developed a performance assessment model that benchmarked UWS performance, determined weaknesses, and recommended necessary interventions to enhance overall performance. Phase 3 provided a bigger picture of OWA (definition and scope) concept, existing practices, and challenges to implementing OWA in a real-world environment. In addition, it also developed an optimization model to identify the optimum water conservation strategies to reduce potable water use. Phase 4 developed and prioritized OWA indicators to evaluate integrated UWS (IUWS) performance and measure water systems’ sustainability, resiliency, and reliability. Phase 5 adopted a design thinking approach to develop best practices in implementing OWA in UWS. The results of this research will provide foundational knowledge to evaluate individual water system performance and set performance benchmarks for small and medium-sized UWSs. In addition, the findings from this study will open new avenues for urban water managers to adopt OWA in their existing systems to evaluate their IUWS performance. Thus, it will assist policy-makers in establishing new guidelines to improve their UWS holistically and help decision-makers make strategic decisions to address an increasing urban water service demand.
A PATH TOWARDS ‘ONE WATER’ COMMUNITY: PERFORMANCE ASSESSMENT AND BENCHMARKING by Sarin Raj Pokhrel MASc, Civil Engineering, The University of British Columbia, 2019 B.Tech. Civil Engineering, Invertis University, 2016 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) April 2023 ©Sarin Raj Pokhrel, 2023 ii The following individuals certify that they have read and recommended to the College of Graduate Studies for acceptance, the dissertation entitled: A Path towards ‘One Water’ Community: Performance Assessment and Benchmarking Submitted by Sarin Raj Pokhrel in partial fulfillment of the requirements for the degree of Doctor of Philosophy Examining Committee: Dr. Rehan Sadiq, School of Engineering Supervisor Dr. Kasun Hewage, School of Engineering Co-Supervisor Dr. Sepideh Pakpour, School of Engineering Supervisory Committee Member Dr. Nicolas Peleato, School of Engineering Supervisory Committee Member Dr. Nathan Pelletier, Biology, Faculty of Management University Examiner Dr. Usman Khan, York University, School of Engineering External Examiner iii Abstract An urban water system (UWS) has three main service components: (i) drinking water; (ii) wastewater; and (iii) stormwater. Historically, each component in urban water development evolved with different objectives for “different” types of water. Even today, the trend continues, as urban water services are managed in silos. The silo-based approaches are less sustainable, resilient, and reliable, mainly because of significant pressures on freshwater supplies exerted by the increasing population, high living standards, rapid urbanization, and climate change uncertainties. To cope with these challenges, conventional thinking needs to change. An innovative paradigm, the “One Water” approach (OWA), which considers “urban water” as a single entity, is the need of the hour. Currently, Australia, USA, and Singapore are leading the implementation of the OWA whereas the EU nations have emphasized the need for integrating water resource management (reflecting the One Water concept) in an urban environment. Only a few Canadian municipalities have embraced OWA at a very preliminary level. This research aimed to develop an OWA-based framework to improve urban water sustainability, resiliency, and reliability. The research entailed five phases. Phase 1 identified Key Performance Indicators (KPIs) to evaluate drinking water, wastewater, and stormwater performance individually. Phase 2 developed a performance assessment model that benchmarked UWS performance, determined weaknesses, and recommended necessary interventions to enhance overall performance. Phase 3 provided a bigger picture of OWA (definition and scope) concept, existing practices, and challenges to implementing OWA in a real-world environment. In addition, it also developed an optimization model to identify the optimum water conservation strategies to reduce potable water use. Phase 4 developed and prioritized OWA indicators to evaluate integrated UWS (IUWS) performance and measure water systems’ sustainability, resiliency, and reliability. Phase 5 adopted a design thinking approach to develop best practices in implementing OWA in UWS. The results of this research will provide foundational knowledge to evaluate individual water system performance and set performance benchmarks for small and medium-sized UWSs. In addition, the findings from this study will open new avenues for urban water managers to adopt OWA in their existing systems to evaluate their IUWS performance. Thus, it will assist policy-makers in establishing new guidelines to improve their UWS holistically and help decision-makers make strategic decisions to address an increasing urban water service demand. iv Lay Summary Conventional water systems are facing challenges of increasing population, ageing infrastructure, and climate change incidents. A shift in urban water management is a desired change for enhancing urban water services (i.e., drinking water, wastewater, and stormwater). A One Water Approach (OWA) concept fills the gap by accommodating the challenges posed by traditional systems as it integrates each urban water system (UWS) service component into a single entity. In this research, a framework based on OWA is proposed. It comprises a conceptual model for the One Water community, which is envisioned to minimize potable water uses by employing a combination of water management strategies closely interconnected with three urban water services. Further, integrated UWS performance is assessed holistically through the lens of OWA by identifying and prioritizing OWA indicators. Finally, implementing OWA in the existing system provides urban water managers and planners to evaluate the present status of utilities in achieving OWA goals. v Preface I, Sarin Raj Pokhrel, developed the research ideas presented in the thesis and conducted all analyses. Six journal articles and one conference article have been produced from this study. Three journal articles and a conference article have already been published; two are currently under 2nd review, and one is under the first review. The research was conducted under the supervision of Drs. Rehan Sadiq and Kasun Hewage (The University of British Columbia, Okanagan). Both supervisors have been a great help in terms of building research ideas, reviewing articles, and providing valuable suggestions for further enhancements. The published and submitted research articles are listed below. Journal Articles: Pokhrel, S.R., Chhipi-Shrestha, G.K., Hewage, K., and Sadiq, R. (2022). “Sustainable, Resilient, and Reliable Urban Water Systems: Making a case for 'One Water Approach'”. Environmental Reviews. https://doi.org/10.1139/er-2020-0090. (Chapter 3) Pokhrel, S.R., Chhipi-Shrestha, G.K., Hewage, K., and Sadiq, R. (2022). “Key Performance Indicators for Small and Medium-Sized Urban Water Systems in a Semi-arid Region: A Case Study of Okanagan Valley Canada”. Urban Water Journal. https://doi.org/10.1139/er-2020-0090. (Chapter 3) Pokhrel, S.R., Chhipi-Shrestha, G.K., Mian, H., Hewage, K., and Sadiq, R. (2023). “Integrated Performance Assessment of Urban Water Systems: Prioritization and Identification of OWA Indicators”. Sustainable Production and Consumption. https://doi.org/10.1016/j.spc.2022.12.005. (Chapter 7) Conference Article: Pokhrel, S.R., Chhipi-Shrestha, G.K., Mian, H., Hewage, K., and Sadiq, R. (2022). “Development of Performance Index for Small Drinking Water Systems”. Presented in Canadian Society of Civil Engineers Conference in May 2022. CSCE journal. (Chapter 5) Under Review Articles: vi Pokhrel, S.R., Chhipi-Shrestha, G.K., Mian, H., Hewage, K., and Sadiq, R. (2022). “Performance Assessment Model for Small and Medium- Sized Urban Water Systems: Development and Implementation”. Urban Water Journal. (Submission ID: 221791769) (Chapter 5) Pokhrel, S.R., Chhipi-Shrestha, G.K., Mian, H., Hewage, K., and Sadiq, R. (2022). “A Conceptual Model of One Water Community: A Path to Move Forward”. Submitted to the Journal of Cleaner Production. (Submission ID: JCLEPRO-D-22-17535) (Chapter 6) Pokhrel, S.R., Chhipi-Shrestha, G.K., Mian, H., Hewage, K., and Sadiq, R. (2022). “Incorporating Design Thinking in the implementation of One Water Approach for Urban Water Management”. Submitted to Utilities Policy. (Submission ID: JUIP-D-22-00680) (Chapter 8) vii Table of Contents ABSTRACT ................................................................................................................................. III LAY SUMMARY ........................................................................................................................ IV PREFACE ..................................................................................................................................... V TABLE OF CONTENTS ......................................................................................................... VII LIST OF TABLES ....................................................................................................................... X LIST OF FIGURES .................................................................................................................... XI LIST OF ABBREVIATIONS .................................................................................................. XII ACKNOWLEDGEMENTS .................................................................................................... XIV DEDICATION.......................................................................................................................... XVI INTRODUCTION ............................................................................................. 1 1.1 BACKGROUND .................................................................................................................... 1 1.2 RESEARCH GAPS AND MOTIVATION ..................................................................................... 3 1.3 RESEARCH OBJECTIVES ....................................................................................................... 5 1.4 THESIS ORGANIZATION ....................................................................................................... 6 RESEARCH METHODOLOGY ..................................................................... 8 2.1 PHASE 1 DEVELOPMENT OF CONVENTIONAL KEY PERFORMANCE INDICATORS ................... 8 2.2 PHASE 2 BENCHMARKING OF URBAN WATER SERVICES .................................................... 10 2.3 PHASE 3 DEVELOPMENT OF A CONCEPTUAL MODEL ......................................................... 11 2.4 PHASE 4 DEVELOPMENT OF OWA INDICATORS ................................................................ 12 2.5 PHASE 5 IMPLEMENTATION OF OWA FOR IUWM ............................................................ 12 LITERATURE REVIEW ............................................................................... 14 3.1 INTRODUCTION ................................................................................................................. 14 3.2 METHODS ......................................................................................................................... 16 3.3 TREND IN UWM ............................................................................................................... 17 3.4 INTERCONNECTION AMONG UWS SERVICE COMPONENTS ................................................ 21 3.5 ONE WATER APPROACH .................................................................................................... 27 3.6 WAY FORWARD FOR OWA IMPLEMENTATION .................................................................. 46 3.7 CONCLUSIONS .................................................................................................................. 50 DEVELOPMENT OF KEY PERFORMANCE INDICATORS ................ 51 4.1 INTRODUCTION ................................................................................................................. 51 4.2 METHODS ......................................................................................................................... 53 4.3 RESULT AND DISCUSSION .................................................................................................. 59 4.4 CONCLUSIONS .................................................................................................................. 70 viii PERFORMANCE ASSESSMENT MODEL FOR URBAN WATER SYSTEMS 72 5.1 INTRODUCTION ................................................................................................................. 72 5.2 METHODS ......................................................................................................................... 75 5.3 RESULTS AND DISCUSSION ................................................................................................ 80 5.4 CONCLUSIONS ................................................................................................................ 101 CONCEPTUAL MODEL OF ONE WATER COMMUNITY ................. 103 6.1 INTRODUCTION ............................................................................................................... 103 6.2 METHODS ....................................................................................................................... 106 6.3 RESULTS AND DISCUSSION .............................................................................................. 112 6.4 CONCLUSIONS ................................................................................................................ 121 IDENTIFICATION AND PRIORITIZATION OF ONE WATER APPROACH INDICATORS ................................................................................................... 123 7.1 INTRODUCTION ............................................................................................................... 123 7.2 METHODS ....................................................................................................................... 126 7.3 RESULTS AND DISCUSSIONS ............................................................................................ 133 7.4 DECISION CRITERIA ........................................................................................................ 141 7.5 STATISTICAL ANALYSIS .................................................................................................. 143 7.6 CONCLUSIONS ................................................................................................................ 145 IMPLEMENTATION OF ONE WATER APPROACH IN URBAN WATER SYSTEMS .................................................................................................................. 148 8.1 INTRODUCTION ............................................................................................................... 148 8.2 METHODS ....................................................................................................................... 151 8.3 RESULTS AND DISCUSSION .............................................................................................. 154 8.4 CONCLUSIONS ................................................................................................................ 171 CHAPTER 9 CONCLUSIONS AND RECOMMENDATIONS .......................................... 173 9.1 OVERVIEW .......................................................................................................................... 173 9.2 SUMMARY AND CONCLUSIONS ........................................................................................ 173 9.3 ORIGINALITY AND CONTRIBUTIONS ................................................................................ 176 9.4 LIMITATIONS .................................................................................................................. 177 9.5 FUTURE RESEARCH DIRECTIONS ..................................................................................... 177 REFERENCES .......................................................................................................................... 179 APPENDICES ........................................................................................................................... 220 APPENDIX A ............................................................................................................................ 220 APPENDIX B ............................................................................................................................ 237 APPENDIX C ............................................................................................................................ 244 APPENDIX D ............................................................................................................................ 247 ix APPENDIX E ............................................................................................................................ 249 APPENDIX F ............................................................................................................................ 251 APPENDIX G ............................................................................................................................ 253 APPENDIX H ............................................................................................................................ 262 APPENDIX I ............................................................................................................................. 270 x List of Tables Table 3-1 Distribution of urban populations in developed and developing countries in 2018 .... 15 Table 3-2. Volume of combined sewer flows in Canadian provinces (2017) .............................. 25 Table 3-3. Existing water management strategies in contrast to OWA ........................................ 33 Table 3-4. Institutions definition on OWA ................................................................................... 36 Table 4-1. Definition of the selection criteria ............................................................................... 55 Table 4-2. Developed Urban Water System KPIs ........................................................................ 59 Table 5-1. Inter-utility performance of drinking water ................................................................. 87 Table 5-2. Inter-utility performance of wastewater ...................................................................... 94 Table 5-3. Inter-utility performance of stormwater ...................................................................... 99 Table 5-4. Types of interventions ............................................................................................... 101 Table 6-1. Interventions and OWA guiding principles ............................................................... 107 Table 6-2. Interventions and parameters ..................................................................................... 108 Table 6-3. Results of percentiles calculation .............................................................................. 112 Table 6-4. Signal-to-noise ratios for L16 trials ........................................................................... 115 Table 6-5. Confirmation test results for the response variable ................................................... 119 Table 6-6. ANOVA results ......................................................................................................... 120 Table 7-1. Linguistic scale for rating decision criteria ............................................................... 129 Table 7-2. Rating schemes for fuzzy numbers ............................................................................ 130 Table 7-3. OWA indicators ......................................................................................................... 134 Table 7-4. Performance scores variability .................................................................................. 143 xi List of Figures Figure 2-1. A methodological framework for the proposed research ............................................. 9 Figure 3-1. Methodology for critical review ................................................................................. 17 Figure 3-2. Schematic diagram showing interactions among urban water system components .. 23 Figure 3-3. Water management triangle ....................................................................................... 29 Figure 3-4. Element contributing to the OWA concept ................................................................ 46 Figure 4-1. Methods adopted for the KPIs identification ............................................................. 54 Figure 5-1. Methodology adopted for the study ........................................................................... 75 Figure 5-2. Performance Scale ...................................................................................................... 79 Figure 5-3. Overall performance level of drinking water ............................................................. 81 Figure 5-4. Performance scores of drinking water criteria using KNN method ........................... 82 Figure 5-5. Overall performance level of wastewater .................................................................. 90 Figure 5-6. Performance scores of wastewater criteria using KNN method ................................ 91 Figure 5-7. Overall performance level of stormwater .................................................................. 96 Figure 5-8. Performance scores of stormwater criteria using KNN method ................................ 97 Figure 6-1. Methodology adopted for the current study ............................................................. 106 Figure 6-2. S/N ratio of RPWU at 90th percentile...................................................................... 117 Figure 6-3. S/N ratio of RPWU at the 50th percentile ................................................................ 118 Figure 6-4. S/N ratio of RPWU at the 10th percentile ................................................................ 118 Figure 7-1 Methodology adopted for OWA Identification ......................................................... 127 Figure 7-2. Prioritization scale .................................................................................................... 131 Figure 7-3. Spearman correlation of five performance objectives ............................................. 144 Figure 8-1 OWA Index Values ................................................................................................... 154 Figure 8-2 Indicators index value ............................................................................................... 156 Figure 8-3 Indicator performance scores under objective 1 ....................................................... 159 Figure 8-4 Indicators performance scores under objective 2 ...................................................... 162 Figure 8-5 Indicators performance scores under objective 3 ...................................................... 165 Figure 8-6 Indicators performance scores under objective 4 ...................................................... 168 Figure 8-7 Indicators performance scores under objective 5 ...................................................... 169 xii List of Abbreviations ADB Asian Development Bank AHP Analytic hierarchy process ANOVA Analysis of Variance AWM Adaptive water management AWWA American Water Works Association BC British Columbia BWN Boil water notice CSS Combined sewer system CSO Combined sewer overflow DPSIR Driver-Pressure-State-Impact Response DT Design thinking EU European Union F-AHP Fuzzy AHP FTO Full time operator F-TOPSIS Fuzzy TOPSIS GG Good governance GHG Greenhouse gas GL Gigalitres HAA Haloaceticacids IUWM Integrated urban water management IUWS Integrated urban water system IWA International Water Association IWM Integrated watershed management IWRM Integrated water resource management KNN K-nearest neighborhood KPI Key performance indicator LID Low Impact Development Lpd Liters per person per day LWS Large water system xiii MCDA Multi criteria decision analysis NRC National Research Council NWWBI National Water and Wastewater Benchmarking Initiative OBWB Okanagan Basin Water Board O&M Operation and monitoring OWA One Water Approach PAM Performance assessment model PI Performance indicator PL Performance level PROMETHEE Preference ranking organization method for enrichment evaluation RPWU Reduce potable water use RR Resource recovery RWU Reclaimed water use SMWS Small and medium-sized water systems S/N Signal to noise THM Trihalomethanes TOPSIS Technique for order performance by similarity to ideal solution UK United Kingdom UN United Nations US United States USEPA United States Environment Protection Agency UWS Urban water system UWM Urban water management VIKOR Vlekriterijumsko kompromisno rangiranje WFD Water framework directive WQA Water quality advisory WSA Water-saving appliances WSUD Water sensitive urban design WWTP Wastewater treatment plants xiv Acknowledgements I would like to express my deepest gratitude to Dr. Rehan Sadiq for his untiring support, guidance, and motivation throughout my graduate study. His mentorship was extremely valuable in completing this thesis. Despite of his extremely busy schedule, he was always available for discussions and provided timely feedback. He has always inspired me to strive hard in achieving goals and given me an utmost confidence to succeed in my graduate studies. Overall, Dr. Sadiq not only assisted me to become a good researcher but also a better human being. My special thanks goes to Dr. Kasun Hewage for his continuous motivation and words of encouragement at every single stage of my academic journey. His positive outlook and unparalleled support have provided me a greater confidence in successfully overcoming several obstacles that crossed my path. His critical feedback on my research work helped me to present my research ideas in a stronger manner. I am grateful for his support, motivation, and kindness. My sincere thanks goes to the supervisory committee of Dr. Sepideh Pakpour and Dr. Nicholas Peleator for their feedback, guidance, and encouragement. I extend my thanks to the School of Engineering staff, including Ms. Shannon Hohl, Ms. Angela Perry, Ms. Stephanie Oslund, Ms. Brittany Parr, and Ms. Renee Leboe for their continuous help and support. I extend my thanks to Dr. Gyan Chhipi Shrestha and Dr. Haroon R. Mian for their continuous involvement in my research. Both of them shared their critical feedback and suggestions in every stage of my academic research for which I am grateful towards their patience and dedication. I take this opportunity to thank Dr. Manjot Kaur and Dr. Michael De Coste for providing detailed feedback to my thesis. I thank Natural Sciences and Engineering Council of Canada for their financial support throughout the research. I would like to acknowledge all the participating municipalities across British Columbia for providing required data and valuable input to conduct this research. The support that I received from my colleagues at the Life Cycle Management Laboratory was immense and I truly thank them for their friendship. My warm gratitude goes to my amazing friends, including Dr. Haroon R. Mian, Dr. Majot Kaur, Dr. Sana Saleem, Dr. Guangji Hu, and Dr. Saeed Mohammadiun for their companionship. They have always remained by my side through xv thick and thin. I received tremendous support from my friends and their families in Kelowna, including Tracy Allen, Sudip Shrestha, and Ravi Sharma. This research would not have been possible without the support from my wonderful parents, Mr. Rishikesh Pokhrel and my late mom Mrs. Sandhya Pokhrel. She was and still my biggest source of inspiration. She trusted my potential and had faith in me more than anyone else. Although, I miss her physical presence, her blessings and utmost self-belief have helped me to become a better individual. My wife, Mrs. Prarkiti Bhattarai is my rock. Her support, love, and companionship is priceless. She has always stood by my side and provided me with that self-belief to accomplish anything even in the most unfavourable situation of my life. I shall always be indebted towards her evergreen support and cooperation. I would also like to thank my sister, brother in-law, grandparents, and in-laws for their enduring support over these years. xvi Dedication 𝑖𝑛 𝑡ℎ𝑒 𝑙𝑜𝑣𝑖𝑛𝑔 𝑚𝑒𝑚𝑜𝑟𝑦 𝑜𝑓 𝑚𝑦 𝑏𝑒𝑙𝑜𝑣𝑒𝑑 𝑚𝑜𝑚, 𝑀𝑟𝑠. 𝑆𝑎𝑛𝑑ℎ𝑦𝑎 𝑃𝑜𝑘ℎ𝑟𝑒𝑙 Introduction 1.1 Background Water is the main driver for socio-economic development, healthy ecosystems, and high quality of life. Drinking water, wastewater, and stormwater are three core service components that constitute a modern urban water system (UWS). It fulfils human needs by providing potable supplies for drinking, collecting and treating wastewater, and preventing flooding through prudent stormwater management (United Nations, 2020). Currently, UWSs are struggling to keep up with increasing urban water service demands, ageing infrastructure, and changing climate scenarios (Barraclough and Lucey, 2008). Based on the current water consumption, world populations are predicted to witness a water deficit of ~40% by 2030 (United Nations, 2015a). Besides, around 80% of global municipal and industrial wastewater is directly released into water sources, deteriorating source water quality and affecting aquatic organism and their habitats (UN Water, 2021). More than 100 million people have been affected by urban flooding every year over the last three decades (Jha and Davies, 2011). Canada, known for its seemingly endless availability of freshwater resources, is no exception. Some regions in Canada, including the Interior of British Columbia (BC), the Southern Prairies, and the high Arctic region, experience arid and semi-arid climatic conditions, leading to uncertainties in water availability soon (Government of Canada, 2013). The mentioned challenges will likely increase in the future, which will be incredibly challenging for effective urban water management (UWM). Conventionally, UWSs are managed individually (Furlong et al., 2015; Daigger, 2009), which has been a common trend since 1800s (Nelson, 2012). This type of UWM does not emphasize collaboration among water service components, water professionals, the public, and other relevant stakeholders (Santelmann et al., 2019). The siloed-based structure can be unsustainable in the circumstances because of rapid population growth, increased infrastructure needs, and ever-changing climate patterns (Pokhrel et al., 2022). The existing infrastructure and water governance highly depend on the 20th-century experience, immensely influenced by fragmentation and technologically invested solutions (Pahl-wostl, 2007; Wong et al., 2020). Furthermore, these technological innovations are applied individually to manage each UWS service component (Mukheibir et al., 2014). Recently, communities have also been experiencing summer water 2 shortages, fast drying of source waters, and limited green spaces. This situation has caused over- reliance on conventional water systems, which is why the issues mentioned above have raised serious questions about the attainment of urban water sustainability, resiliency and reliability (Marlow et al., 2013). Sustainability measures UWS capacity to meet the current needs without comprising the needs for the future while ensuring proper utilization of water resources. Resiliency assesses the UWSs capacity to withstand external disruptive forces and recover quickly if damages occur. Reliability measures service continuity (e.g. uninterrupted supply of clean water), predictability (e.g. intermittent supplies available at regular intervals), and functionality (e.g. no occurrence of system failures, pressure fluctuations, adverse water quality incidences). Overcoming the above challenges ha driven decision-makers to rethink current UWM practices and focus on alternative approaches that can accommodate the water service demands of today’s community in a sustainable manner. One such way is the ‘One Water Approach’ (OWA) which considers all three UWS components into a single entity and regards water by-products as important resources in achieving effective water management goals. Unlike the conventional approach, OWA focuses on various strategies that reduce water consumption, decrease wastewater generation and harvest stormwater; engage multi-stakeholders; assist in developing water policies; optimize financial resources, and build water leadership (Water Research Foundation, 2017). This approach makes a better option in adopting an integrated approach to manage water systems as there exist close interactions between UWS components (Pokhrel et al., 2022; Sapkota et al., 2015). OWA differs from other concepts, including net-zero water, net-positive water, wastewater reclamation, and integrated water resource management should not be intermixed. Some aspects of OWA are common to these terminologies, but they do not define the concept entirely. The details on the OWA differentiation from other practices and a comprehensive description of the concept is given in Chapter 3. The integration enhances overall UWS performance and promotes the longevity of urban water infrastructure. The benefits of integration include but are not limited to i) discerning alternative water sources and relationships between water, land use, and energy; ii) considering storage, distribution, treatment, reuse, and recycling as a part of a continuous urban water cycle; iii) building collaboration between institutions, government to foster public-private partnerships; iv) encouraging public participation in decision-making; and v) promoting efficiency, equity, and water sustainability (Furlong et al., 2017). 3 UWS is an essential but expensive public infrastructure. Therefore, it is important to evaluate the performance of UWS service components by formally conducting assessments. Performance assessment can be conducted by using various performance indicators (PIs). PIs are employed as valuable metrics and are broadly categorized into different criteria, including but not limited to environment, finance, infrastructure, operation and maintenance, social, and organizational. Some PIs are more important than others in measuring UWS functionality and overall performance. These PIs are termed Key Performance Indicators (KPIs). The KPIs classification system is widely adopted by various organizations across the globe, such as International Water Association (IWA), American Water Works Association (AWWA), Asian Development Bank (ADB), National Research Council (NRC), and National Water and Wastewater Benchmarking Initiative (NWWBI) (Haider et al., 2014). These organizations have established PIs, particularly for drinking water and wastewater. The stormwater PIs are less established as compared to the other two services. 1.2 Research gaps and motivation This research aims to develop a knowledge base on integrated urban water system (IUWS) management through a lens of One Water concept. Following knowledge gaps were identified through a comprehensive literature review. Inter-relationship between Urban Water System Components: The existing UWM is segregated, which makes them less sustainable, resilient, and reliable. Because UWS components are inter-related, it is important to integrate them and develop a holistic UWM approach. Therefore, understanding the synergetic relationship between urban water services is the first step towards integration, as a change in one system impacts others and vice-versa. IUWS is the need of the hour, which OWA underpins. The existing literature lacks a clear OWA definition and scope, including its current practices adopted by different countries, the challenges that UWSs have to face when introducing OWAs, and the absence of strategic plans. Thus, it is important to establish guiding principles and identify key benefits of OWA to ensure that utilities adopt this approach to enhance their overall system performance. Performance indicators and assessment for small and medium-sized semi-arid UWSs: PIs lay the foundation for performance assessment. This assessment is more important in small and medium-sized water systems (SMWSs) because these systems are grappled with distinct challenges (e.g., insufficient finance, inadequate human resources, and absence of advanced treatment facilities). Some SMWSs use the PIs developed for large water systems (LWSs). The 4 PIs developed for LWSs are more detailed as they have enough resources to conduct rigorous performance assessments. For example, SMWSs are not concerned with monitoring unregulated disinfection-by products as a part of their water compliance assessment, GHG emissions from pumps and treatment plants are not tracked, or financial indicators such as capital expenditure, economic real rate of return are not given a higher preference in assessment. Because SMWSs are struggling to recruit seasoned operators and full-time employees, they are experiencing more water quality issues due to insufficient revenue collection and consuming more water to meet increasing agriculture water demands. Therefore, developed PIs must be able to capture the unique situation faced by SMWSs. In addition, semi-arid regions (e.g., Okanagan Valley) have unique challenges, including mixed urban water demands, changing climatic patterns (e.g., floods, forest fires), and water quality incidents that require the formation of new PIs to conduct robust UWS performance assessments. Optimization model for building a holistic One Water Community: Considering the value of every water drop is essential in achieving One Water community goals. Reducing potable water use (RPWU) is the first step that urban water managers must be directed to and encouraged to meet those objectives. The current literature constitutes various tools and techniques and water management strategies to utilize water resources efficiently. However, the current literature has overlooked the development of an optimization model for reducing potable water use from a multi-dimensional facet. Therefore, an innovative approach is needed to develop an OWA-based model and its principles that paves the way for effective UWM. Building an optimal combination of water management strategies is a need of the hour for effective operations and sustainable urban systems. One Water Approach Indicators: Considering the existing knowledge, a huge void needs to be filled in establishing integrated indicators (OWA), which are required to measure holistic UWS performance. Unlike conventional systems, where indicators development was focused more on individual service components’ (drinking water, wastewater, and stormwater) performance, OWA indicators are established to evaluate IUWS performance against three decision criteria (sustainability, resiliency, and reliability). These criteria can serve as a tool for the decision-makers in developing holistic UWSs that ensure continuous management of urban water services while ensuring a smooth transition of a siloed system into an integrated system. Implementation of OWA in the existing UWSs: Numerous studies have been conducted in developing frameworks to measure IUWS performance from various interconnected dimensions, 5 including water resources, watersheds, and water quality. A wider gap is observed in the practical assessment of water systems performance, where OWA management needs to be prioritized. It is important to understand the existing UWSs status in building a One Water community and adopt practices to attain OWA goals. For this purpose, an OWA index must be developed that provides a numerical value to evaluate overall system performance. It also helps urban water managers identify gaps to reach the OWA scenario. Further, the indexing approach effectively allows urban water managers to navigate their interventions accordingly to enhance UWM. The primary motivation of this research originates from a clear need to replace conventional UWM systems with integrated management, which is underpinned by the OWA concept. OWA definition, scope, challenges, existing practices, and future implementation strategies have been overlooked in the current literature. A need to understand the OWA concept was a contributing factor in conducting this research. This concept can facilitate in evaluating IUWS performance by developing OWA indicators. Thus, it allows decision-makers to devise strategic plans and policies to improve UWS performance within BC and beyond. 1.3 Research objectives This research develops a framework based on the One Water Approach for small and medium-sized municipalities to achieve sustainable, resilient, and reliable urban water systems. Specific sub-objectives of this research are as follows: 1. Identify and develop key performance indicators for three urban water services (i.e., drinking water, wastewater, and stormwater) individually. 2. Conduct self and competitive performance benchmarking of the individual water services. 3. Develop a conceptual model of a ‘One Water’ community to evaluate various urban water intervention strategies. 4. Develop OWA-based key performance indicators to assess sustainability, resiliency, and reliability of UWSs. 5. Implement the OWA concept in the existing UWS and develop an OWA index. The proposed research has five main deliverables: 1. Key Performance Indicators (KPIs) are developed to assess the individual performance of small and medium-sized UWSs. Some KPIs developed in the study are unique to the Okanagan 6 Valley (BC, Canada) and were identified by conducting several workshops with municipal representatives. 2. A performance assessment model was developed to compare the existing performance of UWSs and recommend key interventions for enhancing overall system performance. Performance was assessed by developing an index which will facilitate relevant stakeholders in measuring UWS performance. 3. An optimization model for the ‘One Water’ community was developed using six guiding principles. The model can identify the optimal combination of alternatives for reducing potable water uses. 4. OWA indicators were developed and prioritized to measure IUWS performance using three decision criteria (sustainability, reliability, and resiliency) and understand the impact of each criterion. 5. OWA index was developed by implementing the OWA concept in the existing UWSs. Utilities’ existing performances were assessed using the OWA performance scale. 1.4 Thesis organization This thesis comprises nine chapters, including: Chapter 1: This chapter provides the background and foundation for the research. Various research gaps, motivations, objectives, and deliverables are extensively presented in this chapter. Chapter 2: This chapter summarizes the research methodology. It will also provide insight into the vital research phases and methods to attain the objectives. Chapter 3: A comprehensive literature review and content analysis are conducted on OWA (definition and scope), comprehensive existing practices, challenges, and strategies for its effective implementation in the current UWSs. Chapter 4: This chapter presents the identified KPIs for individual UWS components using a PROMETHE method. The KPIs were rated based on four decision criteria (relevancy, clarity, accuracy, and comparability). The important findings derived from the questionnaires and workshops are discussed in this chapter. Chapter 5: This chapter develops a performance assessment model is developed by collecting KPIs data from ten utilities in the Okanagan Valley. The data was analyzed using the TOPSIS method, and performance index values were determined for each utility. Necessary interventions were recommended to enhance overall system performance. 7 Chapter 6: In this chapter, a One Water Community is developed by employing a Taguchi-based optimization model to reduce potable water use (response variable). An optimal combination of control factors was determined to achieve the target. ANOVA analysis was conducted to determine the contribution of each control factor to the response variable. Chapter 7: In this chapter, OWA indicators are developed and prioritized. Twenty-nine indicators were developed based on the response achieved by 35 municipal representatives across BC. Fuzzy AHP and Fuzzy TOPSIS were used to prioritise indicators and determine sustainability, resiliency, and reliability scores for the integrated urban water system. Chapter 8: In this chapter, OWA concept is implemented across 12 BC municipalities for integrated UWS performance assessment. An OWA Index was developed employing VIKOR method. The index showed utilities present status in achieving OWA goals and calculated the distance measurement in achieving the ideal scenario (OWA). Chapter 9: In this chapter, conclusions derived from various methods and approaches have been provided. Research’s originality and contributions, limitation, and recommended future works are also mentioned. 8 Research Methodology This research aims to assess integrated urban water management (IUWM) performance through the lens of OWA. The research objectives mentioned in Section 1.3 were achieved through five phases: identifying KPIs for SMWSs, conducting self and competitive performance assessment, developing a conceptual model for the One Water community, establishing OWA indicators for holistic performance assessment, and implementing OWA in the existing UWSs. Data for identifying conventional KPIs and benchmarking of UWSs were gathered from ten utilities across the Okanagan Valley through questionnaires and semi-structured interviews. The relevant data for the OWA indicators identification were collected from 35 municipalities across BC, while the data sets for the OWA implementation were gathered from 12 BC municipalities. All data were collected through the questionnaire and/or telephone interview. This chapter provides a complete roadmap of the adopted methodology to achieve the study goal (Figure 2-1). A detailed description of the methods and procedures used in completing the research objectives are given under the relevant chapters. 2.1 Phase 1 Development of conventional key performance indicators In this Phase, a preliminary list of indicators was complied comprising drinking water, wastewater, and stormwater. The information was collected from AWWA, IWA NRC, NWWBI, National Water Commission, Office of the Water Services, World Bank, and ADB. Out of these indicators, 240 PIs (Drinking water (90), wastewater (78), and stormwater (72)) were short-listed for further selection. Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE II) was employed as a suitable multi-criteria decision analysis (MCDA) to identify the KPIs. The KPIs are broadly classified into six performance criteria: environmental, financial, infrastructure, operational and monitoring, social and institutional, and staff. The identified KPIs are primarily concerned with measuring water systems performance in the semi-arid regions. Delphi technique was also used to strengthen the identification process further. The details of the methodology used in this Phase are given in Chapter 4.9 Figure 2-1. A methodological framework for the proposed research10 2.2 Phase 2 Benchmarking of urban water services Based on the selected KPIs (Phase 1) across six performance criteria, relevant data were collected from ten major municipalities across the Okanagan Valley for drinking water, wastewater, and stormwater systems. For this purpose, questionnaires were designed in spreadsheets with the definition and formula for each KPI. Both self and competitive assessments were conducted. Some KPIs data had qualitative values. These values were converted into a quantitative score. The experts gave the scores based on their knowledge and expertise in UWM. The KPIs data were normalized individually for each performance criterion to form a normalized decision matrix. The weights for each performance criterion were calculated using Analytical Hierarchical Process (AHP) to build a weighted normalized decision matrix. The aggregation was conducted using a Technique for order performance by similarity to the ideal solution (TOPSIS) to determine a final performance index value for each municipality. The details of the methodology used in this Phase are given in Chapter 5. Data collection is an integral component of this research as data (mixture of qualitative and quantitative) were collected in all phases. All data have been gathered either through rigorous literature search or/ and where ever necessary expert opinions (e.g., academic researchers, industry personnel, and municipal representatives) have been sought. Academic experts included professors and post-doctoral research fellows whose expertise were primarily based on UWM, water sustainability and resiliency, and performance assessment and benchmarking. Throughout this research, questionnaires have been distributed to various municipalities across BC. Municipal representatives were chosen as the most appropriate participant in the study because in BC, municipality is responsible to supply drinking water, provide wastewater services, and manage stormwater for the majority of their residents (Pokhrel et al. 2020a). These representatives were selected because of their in-depth knowledge and expertise in the water area, previous experiences, and designations they held in their respective offices and were capable of creating direct or indirect influence in decision-making. The team’s expertise included but were not limited to sustainable urban water management, water policy and governance, public works and infrastructure, water quality and monitoring, and asset management. The contact information of each respondent was gathered from the official website of CivicInfoBC. In most cases, questionnaires were distributed via email to the higher authorities whose area of expertise was 11 directly related to the filed of water resources. Some authorities passed along the questionnaire to their managers, engineers, and other officers having a relevant knowledge and experience. Participants also opted for a Zoom meeting and telephone interview to complete the survey. A majority of respondents mentioned that they had a group of people working in the questionnaire such that biasness in providing the responses is minimized. The municipal respondents included director of water utilities, engineering managers, civil and water resource engineers, policy analysts, and sustainability coordinators. Although the municipal team was from diverse professional backgrounds, they possess a common interest of enhancing overall water system performance. On an average, 60% of respondents were females and 40% were males. While the relevant water experience in their current positions ranged between 5 -8 years, all respondents had more than 10 years of municipal working experience in various water positions. 2.3 Phase 3 Development of a conceptual model This phase is divided into two sub-phases. Content analysis on OWA practices In this phase, a critical content analysis of the existing literature was performed to review the latest trends in UWM and understand the state of the global practice of OWA. The content analysis was conducted by critically reviewing journal articles, technical reports, conference proceedings, and book chapters. A comprehensive review was done in three stages. First, the latest trends in UWM were studied, identifying the historical evolution of UWS service components and the current UWM process. Historically, each component in urban water development evolved with different objectives for “different” types of urban waters. Second, inter-relationships between UWS service components were identified based on the interactions. The interaction showed that the sustainability of an entire UWS could be achieved when UWS service components are managed holistically and viewed as “One Water” (Water Research Foundation 2017). Third, an overview of the OWA concept was provided, and various existing practices on OWA in different countries across the globe were discussed. The detailed methods used in this phase are presented in Chapter 3. Development of a conceptual model for One Water Community A conceptual model of One Water Community was developed. The conceptual model represents a dynamic system comprising elements and variables linked via equations (Mahmood et al., 2019). A Taguchi-based optimization model was developed to identify the optimal combination of 12 strategies and their corresponding parameters to reduce potable water use. The model scope was limited to water-saving appliances, reclaimed water use, resource recovery uses, stakeholder engagement activities, and building strong water governance to achieve OWA goals. Analysis of Variance (ANOVA) was conducted to determine the most influential factors in reducing potable water use at different percentiles. Confirmation tests were conducted to perform model validity and regression models were developed to determine the model’s effectiveness. The details of the methodology used in this Phase are given in Chapter 6. 2.4 Phase 4 Development of OWA Indicators In this phase, the literature and results from Phases 1 and 3 were utilized to select OWA indicators. These indicators were identified based on the rigorous literature search, academic experts, and municipal representative opinions across five performance objectives. The team’s expertise included but was not limited to sustainable UWM, water policy and governance, public works and infrastructure, water quality and monitoring, and asset management. The indicators developed represented essential characteristics of water systems relationship, global sustainability attributes, infrastructure features, increasing water services demands, and holistic building of One Water Community. Besides, these indicators assessed sustainability, resiliency, and reliability characteristics of UWSs, which also served as decision criteria in evaluating IUWS performance. Fuzzy- TOPSIS (F-TOPSIS) was employed to understand the impact of three decision criteria in identifying indicators and calculating their corresponding scores. In contrast, Fuzzy AHP (F-AHP) prioritized the OWA indicators. The detailed identification process and outcomes of this phase are given in Chapter 7. 2.5 Phase 5 Implementation of OWA for IUWM The OWA indicators developed in Phase 4 were employed to implement the OWA concept in the existing municipal utilities using a design thinking (DT) technique. This technique determined key challenges and investigated associated water system problems. It indicated that an integrated approach is required for effective UWM, which is underpinned by embracing OWA. For this purpose, a questionnaire was distributed to utilities to inquire the detailed information on each indicator that impacted their overall performance in attaining long-term One Water goals and developing an ideal One Water community. The percentage improvement required for each participating utility to attain the ideal scenario was determined based on the responses. Besides, an ideal One Water community was proposed as a prototype by developing the OWA index, which 13 was tested by employing OWA indicators. VIKOR was used as a suitable MCDA technique to determine the index value. The details of the methodology used in this Phase are given in Chapter 8. 14 Literature Review A version of this chapter has been published in the Environmental Reviews, titled “Sustainable, Resilient, and Reliable Urban Water Systems: A Case for One Water Approach”. 3.1 Introduction Drinking water systems include the supply of potable water to citizens for drinking, while in some cases fit-for-purpose water is supplied for non-potable uses such as laundry, toilet flushing, and irrigation (Ferguson et al., 2013). Wastewater refers to the polluted water generated by households and industries from water use that is collected, treated, and typically disposed of in receiving water bodies (Fam et al., 2014). Stormwater is produced after precipitation falls on the ground, is intended to be collected, transferred to nearby water bodies or end-of-pipe facilities, and managed locally (Fryd et al., 2012). Conventionally, drinking water demand is met through the construction of new supplies and infrastructure; wastewater is managed through proper collection, treatment, and disposal of water to the environment (Mukheibir et al., 2014). Stormwater is managed by constructing larger pipes to rapidly evacuate stormwater into receiving bodies to mitigate flooding risks (Andoh and Declerck, 2011). Unlike the natural water cycle, which is the storage and circulation of water between biosphere, atmosphere, lithosphere, and hydrosphere, an urban water cycle is a cyclic model that starts with source water abstraction and is followed by raw water treatment, distribution, use, collection, and wastewater treatment (Marsalek et al., 2016). The uncertainty in global temperature increases escalates climate change’s impacts, increasing average mean water vapour, precipitation, and evaporation, which can negatively affect the urban water cycle (Meehl et al., 2007). For example, extreme precipitation increases stormwater runoff that mixes with water bodies and pollutes the source water. The large volume of water from these extreme precipitation, sometimes cannot be handled by storm drains, resulting in flooding (City of Melbourne, 2020). As a result, it hinders water managers from gaining urban water sustainability. Water sustainability refers to “meeting present needs of water without compromising the needs for the future, while ensuring proper utilization of available energy and resources, leading to the holistic management of a UWS (Aquatech, 2019). Sustainable UWM faces constantly increasing challenges. Globally, only 2.5% of available water resources are freshwater. Among renewable 15 freshwater resources, 54% of water resources are already used for different purposes: agriculture (70%), industry (22%) and domestic use (8%) (Global Water Institute 2013). Currently, 55% of the world’s population lives in urban areas. The proportion is expected to increase to 68% by 2050 (United Nations, 2018). This population growth is observed more in developed countries than in developing nations. For example, in 2018, 81% of Canadians, 82% Of Americans, and 86% of Australians lived in urban areas (Statista, 2020). However, a few developing nations have higher urban populations compared to developed countries, including Argentina, Qatar, Saudi Arabia, and Brazil. Table 3-1 shows the distribution of urban populations in a selection of developed and developing countries (World Bank, 2018). The above-mentioned water problems show that these issues will increase in the future, which will be especially challenging for UWM. Table 3-1 Distribution of urban populations in developed and developing countries in 2018 Country (Developed) % Country (Developing) % Australia 86 Argentina 92 Canada 81 Bangladesh 37 Chile 88 China 59 Israel 92 India 34 Japan 92 Kenya 27 Netherlands 91 Malaysia 76 New Zealand 87 Mexico 80 Sweden 87 Nepal 20 United Kingdom 83 Philippines 47 United States of America 82 Qatar 99 To create a path for better UWM and an improved urban water cycle, it entails the development of an integrated water management approach that demonstrates connectivity and interdependence of urban water resources and anthropogenic activities (Marsalek et al., 2008). An in-depth understanding of this interrelationship is only possible when segregated water components are studied through a single lens. This lens should consider drinking water, wastewater, and stormwater as a single entity, where each UWS service component can be managed in an integrated fashion using the OWA. OWA is a new paradigm in water management, which regards all forms of water as a single entity, focussing on interdependencies among UWS components. Unlike the segregated management of UWS, OWA focuses on various strategies that reduce water use, decrease wastewater generation, and harvest stormwater. These strategies include: engaging multi-disciplinary stakeholders, developing water policies about each component of UWSs, optimizing 16 financial resources, and building water leadership (Water Research Foundation, 2017). For example, through water reuse, OWA can decrease freshwater withdrawal, reduce water age in the distribution network, and attain net energy savings despite the huge energy consumption needed for wastewater recycling (Liu et al., 2020). In addition, data sharing among individual components of UWSs through OWA, enhances technical innovation in asset monitoring and infrastructure planning (IWA, 2019b). Although a significant number of studies have been undertaken on OWA, limited studies have been conducted to understand this concept through the perspective of building sustainable urban water community. This Chapter attempted to address the above research gaps by investigating inter-relationships among UWS service components, understanding global OWA practices, highlighting the challenges in adopting OWA, proposing guiding principles for effective UWM practices, and evaluating the applicability of OWA to improve the existing UWM. 3.2 Methods An extensive literature review has been conducted to understand the evolution of UWS and the interactions among the three service components of UWS. The ongoing trends among OWA practices around the world. The literature for this review was obtained from journal articles, conference proceedings, book chapters, and reports compiled by various governmental and non-governmental bodies and international organizations [e.g., the United States Environmental Protection Agency (USEPA), Health Canada, Statistics Canada, and the United Nations]. Altogether, 151 publications were compiled, out of which 75% of the literature was published after 2009. Priority was placed on accessing up-to-date and high-quality literature as sources of information; thus, a large volume of literature selected was published after 2009. Fifty-one of these articles were derived from peer-reviewed journals and the remaining literature included reports, web sources, book chapters, and conference proceedings. Most articles were collected from peer-reviewed journals, including Water Research, Journal of Environmental Management, Urban Water Journal, Environmental Management, Water Policy, Science of Total Environment, Sustainability, Journal of Hydrology, and Environmental Reviews. The keywords used for the search include “urban water management”, “urban water cycle”, “one water approach”, “integrated water management”, “drinking water”, “wastewater”, and “stormwater”. Google Scholar, Science 17 Direct, Compendex Engineering Village were the web-based search engines used to collect literature for this review. Figure 3-1 shows the review process adopted in the current study. Figure 3-1. Methodology for critical review 3.3 Trend in UWM UWM dates back over one thousand years and is described in detail below: Drinking water Human civilizations, including Mesopotamia, Indus Valley, Ancient Egypt, Ancient Greek, Ancient China, and Rome, have each possessed their historical background on water supplies, distribution, and management that dates back thousands of years. Mesopotamia is the oldest human civilization, using rivers, canals, and wells as source waters. The Indus Valley Civilization significantly developed hydraulic engineering, where private wells were a popular source of water (Raju & Manasi, 2017). Ancient Egyptians irrigated some of the oldest agricultural lands while also depending heavily on natural water bodies for drinking purposes (Bazza, 2007). The 18 development works carried out by the Ancient Greeks can be compared to modern-day practices, such as the implementing hygienic living standards, using advanced technologies for water transportation, and construction for sediment control (Koutsoyiannis et al., 2008). During the Roman period, springs were common water sources and aqueducts were used to supply water for irrigation and urban purposes. Before the construction of aqueducts, the Romans obtained drinking water from wells. The aqueducts comprised open channels and pipes, and cisterns were utilized to store rainwater. The Ancient Chinese used wagons to deliver water and dams to store water (Du & Chen, 2007). The English philosopher and scientist Francis Bacon experimented with percolation, filtration, distillation, and coagulation during the 17th century. French scientist Philippe de La Haire took the initiative in the 18th century and proposed a home filter suitable for treating rainwater. He further found that underground aquifers were less polluted. A big leap for the water industry was made in the 19th century by developing simple filter facilities, water treatment plants, slow and rapid sand filters, and aeration processes. The 1830s to 1870s, when epidemics of diseases like typhoid and cholera affected several American and European cities, marked the establishment of a centralized structure for a water distribution network. The introduction of the centralized water system then improved urban dwellers’ hygiene (Chocat et al., 2001). Furthermore, disinfectants in drinking water started in the 20th century with ozone being used for the first time in France, sodium hypochlorite (Chlorine) in the United States, and chloramines in Canada (Angelakis et al., 2012). Wastewater Wastewater collection and management gathered considerable public attention when its close association with human health and environmental concerns were understood. The Ancient Greeks (Minoans and Hellenes), the Indus Valley Civilization, and the Romans are regarded as pioneers in developing basic sewage and drainage technologies that emphasized sanitation in an urban environment (De Feo et al., 2014). The use of sewers dates back to the Mesopotamian Empire (3500 BC) when human waste was carried to the stormwater drain system. However, people residing in these areas threw garbage and excreta on the unpaved streets. A centralized wastewater system comprised of drainage and sewage systems was conceived during the Indus Valley Civilization (Raju & Manasi, 2017). The Ancient Greeks used public toilets to drain wastes into sewers, which allowed the passage of wastewater and stormwater to a collection basin outside the city. These wastewaters were used in agriculture fields to flush sewers periodically (Cooper, 2001). 19 The Romans built a sewage system, Cloaca Maxima, responsible for wastewater and rainwater removal, as well as for swamp drainage (Hodge, 1995). The Romans used advanced sanitation technologies, including baths, and underground sewers and drains. The drains carried runoff intended to flush streets (De Feo et al., 2014). Stormwater In the past, most water systems relied on conveyance mains to transport both wastewater and stormwater. The expansion of urban infrastructure drove the industrial revolution and this era highlighted the importance of drainage systems to transport stormwater. Engineers believed that managing stormwater was equivalent to reducing downstream flooding (Hiraschman and Battiata, 2016). During the Mesopotamia Civilization, the drainage system was primarily used to drain surface runoff, whereas the rainwater was used for household and irrigation. In the Indus Valley Civilization, reservoirs were used for storing rainwater and excess water was stored in dams (Raju & Manasi, 2017). The ancient Egyptians diverted excess water into basins and stored water in fields for 40-60 days; any remaining water was drained into canals for the crop plantation (Bazza, 2007). The ancient Chinese used an earthen pipeline drainage system with a downspout for the drainage of both wastewater and stormwater (Du & Chen, 2007). A significant development in stormwater management was achieved during the Roman period. During this time, a few roads were graded to easily discharge the runoff into the drainage system (Burian and Edwards, 2002). In addition, rainfall was collected into a cistern and utilized locally, whereas large underground structures were built to store water (Hodge, 1995). Over time, engineers developed hydrologic models to predict maximum precipitation and the necessary infrastructure to protect public health. For example, in the United States (US), the history of stormwater management practice evolved over three eras: 1) pave and drain; 2) stream channel and flood protection, and 3) natural resource protection (Hiraschman & Battiata, 2016). In the pave and drain era, the drainage design was focused on two systems: the minor system (including curbs, gutters, inlets, and pipes) and the major system (including natural channels and flood plains). In the second era, stormwater and runoff were diverted to small streams through the drainage system as quickly as possible. The third era saw a need to understand the impact on source water quality due to urban runoff. This resulted in exploring a new stormwater management technique known as LID. LID mimics the natural hydrological cycle at a given site, thereby preserving natural resources while treating stormwater for various non-potable uses (Hiraschman & Battiata, 2016). 20 In summary, each component of urban water development evolved with different objectives that considered “different” types of water. This trend continues today as UWM is siloed (i.e., providing separate services for drinking water, wastewater, and stormwater). Modern water systems Modern UWS service components can achieve their objectives, such as drinking water provision, wastewater service, and stormwater management. Over the years, urban water managers have followed a simple template of providing water supplies, sewage facilities, and stormwater services to provide segregated services to customers at a low cost (Mitchell, 2006). However, this tactic has been criticized from a sustainability perspective. At present, UWS follows a conventional approach, which is based on a centralized structure. Centralized UWSs are effective in areas with high urbanization and economic activities are typically high. These systems use innovative technologies to meet water quality standards to fulfil human needs at an affordable cost (Leigh and Lee, 2019). The centralized water system experiences challenges in managing UWSs sustainably. Although it requires a centralized management structure, the management of these systems is disjointed (Daigger, 2009). For example, a drinking water system may not be responsible for managing wastewater or stormwater and vice versa. This system follows a linear approach, sometimes called the ‘take, make, and waste’ approach (Daigger, 2009). It divides drinking water, wastewater, and stormwater into three silos. Such a fragmented structure is detrimental to a new system that aims to integrate all urban water service components into a single entity (Leigh and Lee, 2019). Different cities across the globe follow a siloed-based UWM. A Canadian example that uses this approach includes Vancouver (British Columbia), where the metropolitan area consisting of 21 municipalities (Metro Vancouver) is responsible for ensuring high drinking water quality and monitoring water quality at the source. In contrast, its major urban centre, the City of Vancouver monitors drinking water quality before distribution. Metro Vancouver manages both drinking water and stormwater, while the City of Vancouver manages sewage and wastewater (City of Vancouver, 2020a; City of Vancouver, 2020b; Metro Vancouver, 2020). In Ontario (Canada), the City of Toronto distributes some of its treated water to the York Region (a regional municipality that includes nine municipalities), whereas the region manages drinking water and wastewater. York region’s stormwater works are overseen by the City of Toronto. A portion of sewage 21 generated by another regional municipality, Peel region (which includes three municipalities) is also managed by the city (City of Toronto, 2020). In Louisville (Kentucky, USA), drinking water, wastewater, and stormwater are operated individually and vary widely in their governance structure; however, they face similar water-related challenges (US Water Alliance, 2016). This independent nature of UWM may create problems within municipal water systems, where urban water issues are observed from a different perspective. Moreover, the conventional method of UWM is considered unsustainable because it leads to water stress, high-energy consumption, dispersion of nutrients into source water, and financially unstable water utilities (Daigger, 2009). Global water use trend Population growth is the primary reason for increasing water stress. According to the United Nations, if the increasing trend in global population continues (7.3 billion in 2015), it will reach 8.5 billion by 2030, 9.7 billion by 2050, and a staggering 11.2 billion by 2100 (United Nation 2015b). Remarkably, 30 – 40% of a municipality’s energy bill is invested in operating public drinking water and wastewater facilities (Copeland & Carter, 2017). Drinking water plants consume 80% of the energy needed to operate equipment for pumping. At wastewater treatment plants, a large quantity of electricity is used for aeration, pumping, and solids processing (Copeland & Carter, 2017), which is resource intensive. According to a study conducted at the Kralingseveer wastewater treatment plant in the Netherlands, the treatment plant emits various greenhouse gases, including nitrous oxide (78.4%), carbon dioxide (8.1%), and methane (13.5%) (Daelman et al., 2013) at different stages of the treatment process. Hence, if the current trend among UWSs and population growth does not change, it is very likely that the world will soon require the number of resources equivalent to approximately three planet earths, which is not sustainable (Daigger, 2009). 3.4 Interconnection among UWS service components The three urban water system service components interact with each other. Figure 3-2 represents the inter-relationship among these components, which are explained below: Drinking water-wastewater The relationship between drinking water and wastewater is important in the context of the urban water cycle. The increase in water demand is directly proportional to population growth that intensifies drinking water utilization and wastewater generation (Limphitakphong et al., 2016). 22 The rise in water consumption leads to a high volume of urban effluents, organic loads, and nutrients, resulting in a higher pollutant concentrations in source waters (Marsalek et al., 2008). The growing demand for water consumption also augments dry weather flows and combined sewer overflows leading to increased concentrations of ammonium and depletion of dissolved oxygen in source waters (Astaraie-imani et al., 2012). A recent report identifies that > 80% of the world’s wastewater and ≈95% in least developed countries are released into water bodies without treatment (UNESCO WWAP, 2017). The compromised water quality changes the physical characteristics of source waters (i.e., increased suspended solid particles and turbidity). In some instances, the treated sewage contains high concentrations of salt, leading to an increase in salinity of water supplies. This situation is usually observed in arid and semi-arid areas with high evaporation rates (Marsalek et al., 2008). In addition, the municipal wastewater systems collect sewage from homes, industries, business, institutions, melted snow, and surface runoff that drain into a sanitary sewer system. The collected sewage contains a high amount of organic wastes, nutrients, pathogens, microorganisms, and total suspended particles (CCME, 2019), which can influence the drinking water supplies especially through the direct migration of contaminants into groundwater sources (Geriesh and Ghodeif, 2004). All these particles pass through waterways or pipes before being transported to wastewater treatment plants. Wastewater characteristics are more abrasive in nature causing a serious impact on wastewater conveyance systems. 23 Figure 3-2. Schematic diagram showing interactions among urban water system componentsSource Water(Lake, river, groundwater)PumpingTreatment plantDistribution NetworkConsumersUrbanizationIndustrializationGHG emissionsIrregular weather patternsIncreased water demandHigh wastewater & waste generationChances of main failures, leakageENERGY CONSUMEDPressure on pipe networksExfiltrationContaminate drinking waterWastewater treatmentSludge producedGenerate heat &electricityLess pervious surfaceLow rainfallHigh rainfallDroughtLess water suppliesReduced infiltration capacitiesLandfillLeachateStorm run-off accumulationUrban floodingHigh temperatureQuick snow meltMelting snow pack on roadsPassage through drainsPipe blockage Wet soilHigh conductivityLand use pattern changePesticides rich water rapidly go into soilDisturbs groundwater qualityIncreased by-pass flowPRESSURECOMBINED SEWERLegendsStormwaterDrinking waterWastewaterReclaimed water use24 Drinking water mains and sewer mains are located either parallel or perpendicular to each other (i.e., with drinking water mains above wastewater mains) in municipal water systems. This layout obviously creates an opportunity for contaminant intrusion in the drinking water distribution network via sewage leakages and main failures (WSSC, 2015). For example, in the United States, as many as 75,000 leakages occur annually, leading to several billions of gallons of untreated wastewater released into surface water bodies (USEPA, 2014). The sewer mains are also subjected to several stresses that can lead to system failures (e.g., corrosion, pipe breaks, and flow obstacles). For example, corrosion results in tuberculation, leading to water quality issues and reduced flow and pressure, wall thinning causing weakened pipes, and holes causing water leakage and ruptures (Damvergis, 2014). All these failures directly or indirectly affect source waters and distribution networks. An aging infrastructure weakens and deteriorates the integrity of sewer pipes and with severe main breaks or exfiltration, will release untreated wastewater to the environment. The ex-filtrated wastewater gets mixed into the surrounding soil, affecting the drinking water sources (Roehrdanz et al., 2017). This effect is observed through groundwater migration into deep aquifers (Reynolds and Barrett, 2003) and in surface water via base flow recharge (Westbrook et al., 2005). Wastewater generated from industries (e.g., iron and steel, textile) have negative implications both for the environment and human health. The pollutants emitted from these industries are rich in biochemical oxygen demand, chemical oxygen demand, acid, cyanide, organic and inorganic contaminants, heavy metals, and mineral oils (Shi, 2009). These pollutants might infiltrate into underground sources, polluting the groundwater (Abdalla and Khalil, 2018). Typically, wastewater generated from pharmacy companies contain copious quantities of pharmaceutical compounds (Gadipelly et al., 2014) , including organic carbon, paracetamol, ketoprofen, and antibiotics (Wiest et al., 2018). The disposal of pharmaceuticals recovered from agricultural activities, industries, and hospitals immensely contribute to the presence of these compounds in water bodies (Klavarioti et al., 2009), while animal pharmaceuticals contaminate both surface water and groundwater via runoff from fields (Khetan and Collins, 2007). The above-mentioned examples demonstrate the inter-relationship between drinking water and wastewater. In addition, wastewater is reclaimed for different indirect potable and non-potable purposes. Among them, agriculture irrigation (32%), landscape irrigation (20%) industrial enhancement (19.3%), and environment enhancement (8%) are some of the primary wastewater reclamation applications (UN Water, 2017). 25 Wastewater-stormwater In many older and under-developed cities, combined sewer systems (CSS) are popular due to economic and technological constraints (Chhetri et al., 2016). During heavy rainfall events, CSSs are inundated with high flows, where some overflows are channelized to source waters without treatment. These overflows are referred to as combined sewer overflows (CSOs). (Government of Canada, 2013a). Although CSOs are common in under-developed cities, developed countries like Canada experience 1,516 CSOs in different locations, including 130 municipalities (Our Living Waters, 2018). Table 3-2 outlines the volume of CSOs in Canadian provinces based on a municipal report from 2017. Only two provinces (Newfoundland and Labrador and Prince Edward Island) and a territory (Yukon) did not report any CSOs (Statistics Canada, 2019). Table 3-2. Volume of combined sewer flows in Canadian provinces (2017) Province Volume (millions m3) % Quebec 54 33 British Columbia 40 24 Nova Scotia 24 15 Ontario 21 13 New Brunswick 14 8 Manitoba 7 4 Alberta 5 3 The surface runoff generated through the stormwater drainage system includes approximately 30-50% of the total rainwater volume. Both stormwater discharges and CSOs experience high flows during or immediately following wet weather. The concentration of pollutants in the snowpack is high during snowmelts, which are released into storm sewers (Government of Canada, 2013b). Flows can be mixed with sewage if storm sewers flood, leading to greater infrastructure damage. Following this scenario, the infiltration properties of the soil are reduced. As a result, during heavy rainfall events, stormwater runoff accumulates further (McBean et al., 2015), leading to increased peak inflow and infiltration into existing collection and pumping systems. These unfavourable situations might contribute to system backups and the discharge of untreated sewage to receiving water bodies, disturbing the source water quality (The Regional Municipality of York, 2016). Globally, 37% of waste is disposed of in landfills (The World Bank, 2019). Specifically, during heavy rainfall events, leachate produced by water or liquid wastes from landfills could contaminate soil, groundwater, and surface water resources (Fatta et al., 1999). Groundwater contamination is observed primarily during rainy seasons because of a high rate of leachate infiltration, percolation, 26 and migration (Oyiboka, 2014). Landfills are commonly placed over aquifers close to surface waters, where permeable soil can contaminate the groundwater through leachate transmission. In addition, threats of contamination intrusion in groundwater continue if landfills are not capped with impermeable materials such as clay, synthetic liners, or leachate collection systems (USEPA, 2015). Stormwater-drinking water In regions with a high population density, large portions of fertile vegetative porous surfaces are converted into impervious surfaces through various construction projects (i.e., roads, driveways, and parking lots). These impervious surfaces contribute to decreased land area availability for stormwater runoff infiltration, resulting in adverse effects on drinking water sources. The main effects include: i) reduction in surface drinking water systems, and ii) reduction in the availability of groundwater sources (Konard and Booth, 2005) and groundwater recharge rates. The chances of flood and erosion are high as impervious surfaces cause an increased surface runoff volume and rate. In addition, impervious surfaces increase surface temperatures causing increased water temperatures (Ground Water Protection Council, 2019). Climate change has increased extreme weather events, including heat waves, high intensity, and frequent torrential rainfall (USEPA, 2016). These rainfall events directly impact the source water quality through direct contaminants transport via surface and groundwater runoff (Rodriguez and Delpla, 2017). These contaminants include, but are not limited to, total suspended solids, nutrients, organic pollutants, particulate matter, and trace materials (i.e., zinc, copper, lead) from various sources (pavement, driveway) and make their way into drinking water sources (Hamel et al., 2013). With climate change, an overall reduction in precipitation, increased temperature, and increases in water demands cause decreases in water supplies (Ibatullin et al., 2009). A temperature rise may also lead to an increased frequency of high rainfall events, resulting in low natural water storage. Subsequently, the risks of urban flooding and water scarcity will seriously threaten drinking water and wastewater services (Howard et al., 2016). Further, climate change will disrupt frequency, strength, spatial range, and snowfall duration. As a result, snowmelt can create problems in hydrology and water quality. A considerable quantity of snowmelt discharge to water bodies is particularly observed during spring (Kim et al., 2015). The combination of snowmelt and heavy rainfall can result in flooding, damaging water infrastructure 27 and contaminating source waters (Credit Valley Conservation, 2016a), decreasing the reliability of water supplies. In addition, climate warming might increase the snowmelt rate and spring runoff (Rodriguez and Delpla, 2017), which can deteriorate source water quality. A study conducted by a group of researchers in Quebec (Canada) concluded that the deterioration in water quality in one of their rivers was closely linked with spring snowmelt that preceded river peak flow and storms (Jallif et al., 2015). As a result, the source water quality decreased due to contaminants intrusion leading to a decline in drinking water treatment efficiency (Rodriguez and Delpla, 2017). Contaminated source water requires additional water treatments (McBean et al., 2015), incurring larger costs. After heavy rainfall, runoff gathered from stormwater has been associated with more than 50% of waterborne disease outbreaks since 1948 (Curriero et al., 2001). One of the most tragic incidents resulting from microbial contamination of drinking water was the Walkerton E. coli outbreak in May 2000 in Ontario, Canada, killing six people and sickening more than 2,000 people. The incident was a result of incessant rainfall that swept away manure from surrounding agricultural land into drinking water wells (Brown and Hussain, 2003). Stormwater is significant in transporting contaminants (i.e., debris, chemicals like heavy metals (notably lead and zinc) on roadside dust, oil, grease, road salt, and sediments) from land surfaces to receiving water bodies. Some contaminants remain for a longer time in the aquatic environment affecting the aquatic ecosystem (Ground Water Protection Council, 2019). Typically, wet soils have higher hydraulic conductivities; therefore, fertilizers seep rapidly into soils, especially during heavy rainfall. This increases bypass flow and allows fertilizers to pass more easily into drains, soil layers, and surface waters. Therefore, it is important to consider land use changes in the hydrological system to ensure better surface runoff and water quality (Singh & Tiwari, 2019). 3.5 One water approach As discussed in Chapter 1, OWA is an innovative concept that has been slowly gaining attention from water professionals across the globe. An overview The overview comprises OWA definition and scope, guiding principles, and existing approaches. 28 3.5.1.1 Definition and scope OWA is a modern paradigm that considers all forms of urban waters as one entity. It studies the close interconnectedness among UWS service components for better management and reliability of available water resources, thereby meeting the needs of both communities and ecosystems (Water Research Foundation, 2017). The OWA also aims to resolve the general misconception that water is abundant and readily available in the environment. (OWCA, 2010). In other words, OWA is an integration process involving drinking water, wastewater, and stormwater and considers no water used or available in the environment as waste. It views all water sources and their by-products as a very important resource that can potentially reduce water demands and conserve water sources. The water sources are confined to surface water and groundwater and locally available water in the form of rain, which can be used for rainwater harvesting and reuse. The reused water is instrumental in meeting additional water-supply requirements and enhancing and restoring the natural environment. From the lens of OWA, wastewater should not be viewed as a harmful product but should be considered to be a useful recoverable resource that could benefit the environment. The organic materials in wastewater can be recovered and used for energy recovery and producing soil conditioning products, while nutrients like phosphorus and nitrogen can be extracted and reused (Daigger, 2011). For example, DC Water Resource Recovery (USA) produced 10 megawatts of electricity from a wastewater treatment plant, which reduced one-third of the electricity consumption of the Blue Plains Plant. Such innovations can recover resources while reducing CO2 emissions and dependence on fossil fuels (US Water Alliance, 2016). Green water infrastructure, a vital component of OWA, significantly reduces the water footprint, stormwater runoff, and urban flooding. The application of green infrastructure also assists in improving the livability of a community based on recreation, amenity, green growth and microclimate (Li and Bergen, 2018). OWA promotes thinking of water as a single system and recognizes that all urban water flows are potentially useful resources. It explores the mutual relationship and close interconnectedness between surface water, groundwater, wastewater, and stormwater and their overall impacts on flooding, water quality, wetlands, and waterbodies. Figure 3-3 shows a water management triangle, depicting how each urban water service component relies on one another. 29 This triangle is analogous to the soil texture triangle (i.e., a triangle that shows the classification of 12 soil textures based on sand, silt, and clay percentages), where the numbers at each corner of the triangle show the percentage distribution of UWS service components about a particular water system. For example, on the right side of the triangle, if the Drinking water wastewater (DWWW) percentage is 70%, it refers to a water system that emphasizes 70% drinking water and the remaining 30% wastewater. The triangle conveys two essential pieces of information: 1) it represents the current UWM, where municipal water systems focus on either one or two UWS service components (e.g. drinking water-wastewater or wastewater-stormwater), and 2) it depicts the need for a holistic approach in the form of OWA, where all three UWS service components are interconnected to promote greater water sustainability. Figure 3-3. Water management triangle 30 Stakeholder engagement is a key component of OWA. Complex challenges, such as climate change, can be minimized by proposing optimal solutions with partnerships between two or more water stakeholders. For example, through a partnership, the vulnerability of water and sanitary infrastructure in erosion-prone areas stretching over two or more municipalities can be solved by developing new rainfall intensity curves to better address climate change impacts (The Regional Municipality of York, 2016). Through an engagement process, the OWA concept attempts to bring players from different silos into one common ground. The purpose of engagement is to identify stakeholders with similar interests to attain a common goal by effectively utilizing the resources available through the interconnectedness of the urban water cycle. The stakeholders may include different departments within one utility, such as water supply, water treatment, stormwater management, city planning, urban designing, and transportation. Apart from these stakeholders, the engagement process involves the active participation of elected officials, governing bodies, regulators, technical advisors, private sectors, and the community (Water Research Foundation, 2017). 3.5.1.2 Guiding principles and benefits The guiding principles of OWA are listed below . Principle 1: A mindset that all water has value: All forms of water, including drinking water, wastewater, stormwater, and source water of different types, have intrinsic value. Professionals require a shift in mindset to think beyond their area of expertise (e.g., drinking water quality) to holistic water management. Principle 2: A focus on achieving multiple benefits: OWA focuses on obtaining several benefits from water. For example, wastewater can be used for recovering nutrients, energy, and obviously as potable or reclaimed water. Principle 3: A systems approach: OWA views water as a system with watershed-scale thinking and action. A systems approach will help to control UWS components in a sustainable, reliable, and resilient manner. For example, a system-wide water balance at the local or regional level allows stakeholders to understand water quantity and quality for all end users. Principle 4: Fit-for-purpose water use: OWA uses the water of the appropriate quality for the right use, which is referred to as fit-for-purpose water use. This approach reduces the overuse of resources and costs (Chhipi-shrestha et al., 2017a; Chhipi-shrestha et al., 2017b). Based on this 31 principle, OWA uses a lower level of treatment for a good quality water source, whereas advanced treatment facilities are used to treat highly polluted water, including emerging contaminants. . Principle 5: Flexible and adaptive infrastructure: OWA is based on green, flexible, and adaptive infrastructure. Water infrastructures are flexible and adaptive to future changes, including climate change impacts from drought and floods. In addition, green infrastructures, such as soil, vegetation, and other natural systems, are used as much as possible. Such infrastructures increase resilience and reliability. Principle 6: Stakeholder engagement: Multiple stakeholders from all levels are engaged in partnerships for mutual progress. Water agencies collaborate with other agencies and the public to identify effective solutions. By applying the above guiding principles, OWA can achieve the following benefits (US Water Alliance, 2016; Water Research Foundation, 2017; Howe and Mukheibir, 2015; Cardone and Carol, 2018): a) Promoting the interrelationship between drinking water, wastewater, and stormwater results in highly coordinated and integrated water management and leads to holistic, sustainable, resilient, and reliable water systems. b) Emphasizing stakeholder engagement from many sectors (both private and government) and considering public interests in water-related decision-making. c) Considering all by-products of water use as useful resources, which can be recovered (e.g., energy generation through waste and nutrient recycling) and reused (e.g., toilet flushing, car washing) for various potable and non-potable uses to meet increasing water demands. d) Exploring innovations by implementing modern technologies (e.g., using LIDs to manage stormwater) to enhance water management and encourage integrated decision-making between water management, urban design, and landscape architecture. e) Recommending government authorities pave a path to enhanced water management strategies by introducing new water policies, laws, and regulations contributing to overall water security, sustainability, reliability, and resiliency. 3.5.1.3 Existing approaches and connections with OWA Over the years, urban water planners and water scientists have invented different names such as net-zero water, zero waste, net-positive water, greywater recycling, wastewater reclamation, and stormwater harvesting. These terminologies should not be confused with the OWA definition and 32 scope. Although these strategies are considered OWA components, they do not define the concept explicitly. For instance, net-zero water only focuses on reducing freshwater resource consumption and returning the same water back to the watershed so that both groundwater and surface water resources are not depleted in quality and quantity over time (Chhipi-shrestha et al., 2018; USEPA, 2016). To define the concept of the water integration process in addition to total water management and whole water, the following terms are used: integrated water resource management, water-sensitive urban design, integrated watershed management, adaptive management, and integrated UWM. All these terms from various disciplines and study areas strongly advocate the need for each UWS component to be managed holistically to achieve the best results for a sustainable, resilient, and reliable community. The terms, such as water-sensitive urban design and integrated UWM, are widely used in business, engineering, and mathematics, whereas integrated water management is used to a greater degree in earth and agricultural sciences. This wide variation in terms can be attributed to spatial context (i.e., different regions requiring different water management strategies) and individual experience (i.e., the field of study that the authors belong to) (Furlong et al., 2015). The most common terms are explained and compared to OWA in Table 3-3. 33 Table 3-3. Existing water management strategies in contrast to OWA SN Existing approaches Definition Contrast to OWA 1 Net-zero water  Aim to reduce freshwater resources consumption and return the same water to the watershed such that both groundwater and surface water resources are not depleted in quality and quantity over some time (Chhipi-shrestha et al., 2018) Focuses on reducing freshwater withdrawal (Principle 1) 2 Zero waste  Popular concept that regards no product as a “Waste”  Acknowledges that after the end-of-life phase of a specific product, the same product can either be reused, repaired or remanufactured into a new secondary product (Zaman, 2014). Focuses on reducing waste (Principle 1 & 2) 3 Net-positive water  Relies more on using water through captured precipitation or natural closed-loop water systems and/or by recycling the used project water, and must be purified as per the requirement without using chemicals for the project’s water needs (excluding the potable uses) (International Living Future Institute, 2016) Focuses on generating net water in specific system (Principle 1 & 2) 4 Greywater recycling  Recycling of greywater, i.e., the wastewater generated via domestic water uses such as hand washing, taking showers, and doing laundries Focusses on treatment & reuse of greywater (Principle 1) 5 Wastewater reclamation  Treatment of wastewater, including stormwater to reuse the treated water. Focusses on treatment & reuse of wastewater (Principle 1 & 2) 6 Stormwater harvesting  Collection and treatment of stormwater for reuse and/or groundwater recharge Focusses on treatment and reuse of stormwater (Principle 1 & 2) 7 Integrated Water Resource Management (IWRM)  Holistic medium of water management that primarily focuses on four aspects: i) natural aspect that takes into account the entire hydrological cycle; ii) human aspect that takes into consideration of all the relevant stakeholders that are directly or indirectly related to water resources; iii) spatial variation including distribution and level of water resources management (e.g., at user level, institutional); and Although interchangeably used, OWA has a focus on resilience and reliability 34 SN Existing approaches Definition Contrast to OWA iv) temporal variation takes water availability, demand, and infrastructure into consideration (Savenije and Zaag, 2008). 8 Integrated Watershed Management (IWM)  Systematic approach of managing all water resources available in the drainage basin by taking social, economic, political, and institutional factors into consideration to achieve specific objectives of a particular region (Katusiime and Schütt, 2020). Although interchangeably used, IWM focusses on watershed and land uses 9 Adaptive water management (AWM)  A holistic approach helps achieve urban water sustainability by addressing complex water problems, including water shortages, flooding, and managing and restoring aquatic ecosystems.  The experimental approach of managing water resources is based on a learning by doing theme and involves stakeholders’ participation from multi-disciplinary backgrounds (Edalat and Abdi, 2015 ; Peat et al., 2017). Although interchangeably used, AWM is specifically an experimental approach 10 Water sensitive urban design (WSUD)  Considered as an essential component of IUWM (Sharma et al. 2016).  Initially, WSUD’s concept was more related with stormwater quality management but now the trend has been shifted to the holistic management of all the three UWS service components (Wong, 2015) Although interchangeably used, OWA has a focus on resilience and reliability 11 Integrated Urban Water Management (IUWM)  Integrates various components of UWM including water use and reuse, water conservation strategies; land use and energy, interlinkage between storage, distribution, treatment, recycling, and disposal, involvement of stakeholders in decision making, and adopting newer legislation and policies (Furlong et al., 2017). IUWM and OWA have the same concepts for cities 35 State-of-the-practice Different countries have followed water management practices similar to the OWA in some cities. Globally, different institutions focus on various elements of UWM, contributing to the One Water paradigm, as in Table 3-4. The following subsection details the current practices of the OWA across various countries in the world. 3.5.2.1 United States In the US, institutions such as the Water Research Foundation and the US Water Alliance, and cities such as Los Angeles, Denver, Richmond, Philadelphia, and San Francisco, have advocated for the OWA concept for sustainable water supplies. The City of Los Angeles, in partnership with the Department of Water and Power, developed a framework called One Water LA. The framework is based on water reuse, stormwater capture, community assets, green infrastructure, and extensive stakeholder engagement (Howe and Mukheibir, 2015). Interestingly, the city had already experienced some success from their collaboration with stakeholders. For example, they have identified climate-resilient trees for landscape projects, regional water planning, and code changes allowing recycled water to be used in concrete. The city of Philadelphia has practised OWA by developing a model for green infrastructure. The 25-year project has concentrated on improving the health of water resources through a land-based approach via stormwater management. More than 1,100 green stormwater projects have been implemented since 2011. Stormwater roadside trenches, green roads, rain barrels, pervious pavement, and rain gardens have been constructed across the city. The initiative has already seen great results, as the city was able to keep 1.5 billion gallons of polluted water from contaminating source waters in 2016 alone (US Water Alliance, 2016). The city of Denver has implemented the OWA through water collection, recycling, treatment, and distribution and has assisted with future water demands (Water Research Foundation, 2017). The city of Richmond (US) plans to create a watershed-based permit to collectively manage stormwater and wastewater through stakeholder outreach, partnerships, and better communication among collaborating water stakeholders (Water Research Foundation, 2017). 36 Table 3-4. Institutions definition on OWA Institutions OWA depends on Sources Water Research Australia  Bold leadership  Planning and collaboration  Culture, knowledge, and capacity  Citizen and stakeholder engagement  Economics and finance  Regulation and legislation Howe and Mukheibir 2015 Credit Valley Conservation  Legislation and Governance  Integrated Risk Assessment  Innovative Green Infrastructure Technology  Integrated Financial Planning  Asset Management  Watershed Approach Credit Valley Conservation 2016 Regional municipality of York Region  Water conservation and reuse  Inflow and infiltration reduction;  Integration of growth and renewal projects  Climate change and mitigation strategies  Energy optimization and renewable energy  Financial sustainability York Region 2016 US Water Alliance  Reliable and resilient water utilities  Thriving cities  Sustainable agriculture  Competitive business and industry  Social and economic inclusion  Healthy waterways US Water Alliance 2016 Water Research Foundation  Climate change adaptation plan  Stormwater and Green Infrastructure Management Plan  Infrastructure Resilience Plan  Drinking Water Supply Reliability Plan Water Research Foundation 2017 37 The San Francisco Public Utilities Commission (SFPUC) introduced the OneWaterSF program in 2016, focussing on water and energy resource management that aims for a balanced community and caters to ecosystem needs. The SFPUC approach for OneWater revolves around strong collaboration with internal and external partners, such that technology and innovation are utilized to optimize finite resources to make water and energy systems more sustainable and resilient. In 2012, based on this principle, SFPUC built one of the first buildings in the US and the first in California with the onsite treatment of blackwater and greywater suitable for toilet flushing. SFPUC Living Machine has been able to reduce individual water consumption from 12 gallons per day (gpd) to 5 gpd, while recycling 5,000 gallons of water each week day (San Francisco Water Power Sewer, 2019). Feingold et al. (2018) assessed the challenges of water, waste, and climate change across the six US cities using the City Blueprint Approach, composed of three indicator assessments. The authors concluded that the integrated approach has the potential to bridge the gap between science, policy, and practice, which can assist in overcoming the challenges related to UWM. Another example includes the water management strategy that has been adopted by the Orange County Water District (OCWD) in California. They have established the world’s largest water purification system for potable water uses (indirect), which supplies water to 19 cities and various water agencies that pump 85% of their water. The County has also recommended several water conservation strategies and promote water efficiency uses to cope against water systems challenges largely after California experience thee severe five-year drought between 2012-2016 (OCWD 2023). 3.5.2.2 Australia The concept of IUWM dates back to the early 1990s in Australia, with major cities like Sydney and Melbourne backing the idea. Mukheibir et al. (2015) point out seven key factors as the drivers of One Water, 1) flood protection and drainage; 2) environmental pollution management; 3) resilience to current changes; 4) efficient use of resources; 5) reliable, secure, and clean water supply; 6) fulfilling city needs; and 7) human health protection. Although the City of Sydney (CoS) did not explicitly use the term OWA, its approach to strengthening OWA is closely associated with reducing water demand and using the current infrastructure sustainably. The CoS aims to reduce water demands through effective water efficiency programs and to reduce pollutant discharge into source water via stormwater run-off as a part of its Decentralised Water Master Plan to achieve the goals of Sustainable Sydney 2030 38 (City of Sydney, 2012). The Decentralised Water Master Plan includes water efficiency, stormwater infrastructure improvement, water-sensitive urban design, and a decentralised non-potable water network (Healey et al., 2012). > 50% of the city’s water infrastructure was older than 70 years in 2012 (City of Sydney, 2012). To better assist the sustainability of water infrastructure, the city planned to replace the potable water supply with recycled water, thereby extending the operational life of the existing infrastructure. In addition, to support the stormwater harvesting and water recycling programs, the city increased the tree canopy cover around the city to reduce the heat island effect (City of Sydney, 2012; Mukheibir and Currie, 2016). With the city’s largest stormwater harvesting program at Sydney Park, the CoS planned to meet 30% of water demands through local water reuse, to reduce suspended solids by 50%, and to reduce nutrient discharge into receiving water bodies by 15%. The CoS, in partnership with Green Square, came up with a stormwater drainage system to mitigate flood risks. The system aims to capture stormwater, purify it on site, and supply water to consumers for clothes washing, toilet flushing, and park irrigation (Mukheibir and Currie, 2016). South Australia envisioned the concept of OWA by reimagining its water resources to tackle the drought crisis. For this purpose, they organized programs to raise awareness, identify specific areas of water concerns, utilize research and tools to make decisions, and implement capacity building to achieve the goals (Water Research Foundation, 2017). The city of Melbourne has four action levels: regenerative water services, basin-connected cities, water-sensitive urban design, and water-wise communities to manage water resources sustainably (IWA, 2020). Mitchell (2006) reviewed the Australian experience of IUWM by examining fifteen case studies at different locations across Australia. Based on the findings, the author indicated a substantial reduction in water demands by using rainwater and stormwater for various non-potable uses (i.e., toilet flushing, gardening, and landscaping) and, in some cases even for potable use (i.e., rainwater for hot water tanks). The study concluded that it is possible to implement practical IUWM approaches that are both technically achievable and acceptable to the water stakeholders. Furlong et al. (2017) reviewed the concept of IUWM in infrastructure planning by analyzing nine case studies from Melbourne. In their study, the authors considered various factors related to recycled water, stormwater harvesting, rainwater harvesting, type of treatment plants, sewer mining, and greywater. Based on their review, the authors concluded that there was a discrepancy in plans and factors considered by various institutions for IUWM, while some IUWM factors’ performance was 39 not satisfactory compared to conventional water supplies regarding energy use. Guthrie et al. (2017) categorized nine case studies from Australia’s IUWM plans into two scope descriptors, which include three width scope descriptors (i.e., service considered, people impacted, and geographical size) and three depth descriptors (i.e., technical detail, approval authority, and intended outcomes). In their study, the authors considered sewage, drainage and waterways, potable water, and fit-for-purpose as four water services. The main outcome of this study was that all the plans stressed the need for a collaborative approach and extensive stakeholder engagement to achieve the goals of IUWM. 3.5.2.3 Singapore In Singapore, most water consumption is related to non-domestic use (commerce and industry) as 430 million gallons of water is consumed daily, which is more than half of the total daily demand. Apart from being a small country, it also lies in a rain shadow region, because of which the country receives less rainfall. Given that the country is grappling with population and economic growth, OWA practices could be significant. Currently, two-thirds of Singapore serves as a catchment area and watershed that captures rainfall, later used as potable water. Because Singapore considers water a reusable resource, reclaimed water is also used for drinking. Despite being geographically small and severely water-stressed, Singapore is well-equipped with advanced technologies. As a result, wise water use, integration, circularity, and continuous research have helped Singapore to overcome water-related difficulties exceptionally well, making them a leader in the OWA (Ng and Teo, 2020). Singapore’s national water agency [Public Utility Board (PUB)] has devised strong plans to implement OWA policies in four ways, including programs for water desalination, potable water use, collection of rainwater, and imported water. In addition, Singapore considers public interests a primary pillar for water enhancement and management. The visionary leadership of Singapore, which aims to transform the country into a city of Gardens and Water by actively engaging people, is a great example of its adoption of OWA practices. This is conducted via the introduction of super trees, which allow people to gather rainwater and solar energy. Another example includes establishing a pumping station that acts as a water education centre by providing additional green space (Cardone and Carol, 2018; Public Utility Board, 2020). 40 3.5.2.4 Canada The concept of OWA has not yet been fully implemented in Canada. Very few municipalities and water governing bodies have devised plans, and its practical implementation in Canadian municipalities has not been undertaken to date. However, the regional municipality of York and Credit Valley Conservation in Ontario, the City of Vancouver and the Okanagan Basin Water Board in BC have supported the concept of OWA and are developing plans to achieve these goals. In 2016, York region, in collaboration with government partners, came up with a master plan through the “Made-in-York OWA,” primarily focusing on two key objectives: i) Build an economical, flexible plan for growth up to and beyond 2041 for water and wastewater infrastructure; and ii) Build a cohesive and durable strategy to attain urban water and wastewater services sustainability. The regional municipality stresses integrating social, environmental, and financial sustainability principles to achieve the OWA goals. York Region’s long-term water conservation strategy has significantly reduced water demand and increased water savings, resulting in efficient use of infrastructure, lower energy consumption, and fewer GHG emissions. Since the York region has a two-tier municipal system, the respective local body oversees stormwater services for municipalities (The Regional Municipality of York, 2016). Credit Valley Conservation, a water conservation authority, collaborated with different universities and developed a risk management framework based on the OWA principles. This framework aimed to understand the impact/interaction of each infrastructural component of UWS. Further, the framework emphasized understanding the vulnerability of infrastructure to risks, such as climate change and ageing infrastructure (Credit Valley Conservation, 2016a). The Okanagan Basin Water Board, a water governance body in the Okanagan region of BC, has consistently supported the concept of OWA. In 2006, the board organized a conference inviting water stakeholders from various water communities, including BC and the United States, focusing on “One Watershed-One Water.” The conference focused on six aspects of water management, which included effective communication with stakeholders, water governance, future water demand, water quality issues, implementation of water plans, and continuation of water forums (OBWB, 2008). Okanagan Basin Water Board is working on a “One Valley One Water” theme to strengthen collaboration and partnership among the water stakeholders in the Okanagan region to manage water resources in the valley (OBWB, 2019). The city of Vancouver has recently started 41 prioritising the concept of OWA by establishing a collaborative relationship with various departments in their organization to make water-related decisions benefiting the community, environment, and consumers. This plan will partially be achieved with the city focusing on programs that assist in preserving water sources, data analysis (i.e., flow and quality of water) of buried water infrastructure, and building green rainwater infrastructure (City of Vancouver, 2020c). 3.5.2.5 European Union and the United Kingdom The member countries of the European Union (EU), as well as the United Kingdom (UK), follow the Water Framework Directive (WFD). In 2000, WFD became an EU law (Watson 2014) that aims to protect and enhance aquatic ecosystems, leading to a holistic approach to ensure sustainable water use (Carvalho et al., 2019). The WFD combines economic and ecological perspectives on water and includes principles of integrated river basin management within a legal framework (European Commission, 2012). The primary objective of WFD is to achieve the best water quality for both surface and groundwater by 2027 among all European countries (Watson, 2014). The implementation of WFD holds great significance for supporting integrated water management at the basin level, which is achieved through a comprehensive involvement of water stakeholders, the administrative sector, and the public in river basin planning. Stakeholder engagement is an important component of implementing the WFD, given the role of relevant stakeholders and the general public in decision-making (Jager et al., 2016). The interaction between different sectors, such as agriculture, fisheries, hydropower, and flood protection, strengthens the partnership. The member countries undertook both “basic measures,” which cover water legislation, and “supplementary measures”, which include removing barriers to enhance fish retention measures and flood plain function to support the river basin management program (European Commission, 2015). However, these measures were fully implemented by a few member countries, with only 20% of the WFD basic measures and 10% of the supplementary measures completed by the end of 2015 (Carvalho et al., 2019). In Amsterdam (Netherlands), the OWA concept was realized through the sustainability of integrated water resource management. The integration process was studied using the City Blueprint approach. Initiated in 2006, the Blueprint brought all three components of UWSs under a single roof. It was composed of 24 indicators focusing on eight categories: drinking water, water 42 quality, water security, sanitation, infrastructure, climate robustness, biodiversity and attractiveness, and water governance, including public participation. The city justified this novel approach as it proved to be highly beneficial in terms of UWM. The city was regarded as the best-performing city among 30 cities assessed across the Netherlands. This achievement can be attributed to many factors, such as the city’s long-term vision, integration of UWS service components, and stakeholder engagement (Van Leeuwen and Sjerps, 2015). The OWA concept is expected to be implemented in the UK and in many EU member nations by focusing on water-sensitive urban design. In the UK, plans for water-sensitive urban design have been drafted, although no communities have been designed to explicitly follow the concept as far as the publicly accessible literature indicates. Looking at the model Australia has designed using the same principle, the UK has great potential for success. This community can be built in the UK and EU member nations focusing on three main applications: 1) cope with both water scarcity and excess problems in an integrated manner; 2) manage water cycle and potential opportunities in a local manner; and 3) manage water synergistically, including ecosystem, environment, urban services, design, and planning processes (Ashley et al., 2013). Further, the concept focuses more on partnership and stakeholder engagement starting from the beginning of the project. The engagement process raises awareness and informs stakeholders from multiple disciplines about the benefits of the concept that will facilitate the sustainable success of water-sensitive urban design features (Urrutiaguer et al., 2010). Apart from the cities mentioned above across the US, Canada, Singapore, UK and EU member nations, some other firms and cities are slowly embracing initiatives to better manage urban water in their respective communities. Challenges in adopting OWA Although the OWA concept has the potential to build sustainable, resilient, and reliable UWS, its challenges in the present context cannot be understated. 3.5.3.1 Institutions, policy, and legislation Conventionally, water system purveyors work independently to meet regulatory compliance (US Water Alliance, 2016). Institutional issues cover a broader spectrum of challenges related to government, diverse cultures and capacities, and public participation (Mukheibir et al., 2014). These challenges exist at strategic, tactical, and operational levels that discourage different 43 institutions/organizations from working in an integrated fashion, collating different activities within a single organization, and embracing water leaders’ vision in optimizing green-grey infrastructure and resource recovery (Mukheibir et al., 2015). The intricate structure of guidelines and regulations that exists separately for drinking water, wastewater, and stormwater present a significant challenge for UWM. Federal, provincial/state, and municipal governments bear different responsibilities, and the absence of integrated policies and regulatory structures across water cycles is regarded as a major institutional challenge (Credit Valley Conservation, 2016b). For example, in Canada, the responsibility for drinking water is shared by all three levels of government; however, the Federal-Provincial- Territorial committee on drinking water develops the water quality guidelines published by Health Canada. In most cases, municipal governments are responsible for supplying water to consumers; thus, they are more aware of ongoing water quality issues in different communities (Government of Canada, 2008). However, without the representation of the municipal government in decision-making, the established guidelines may be less effective. In addition, Canada does not have national guidelines on water reuse and reclamation, apart from domestic reclaimed water for use in toilets and urinal flushing (Health Canada, 2009). Only two Canadian provinces, Alberta and BC, have guidelines for water reuse (Exall et al., 2004). This national-level discrepancy between water reclamation and reuse legislation in the Canadian provinces certainly impedes a water management paradigm shift like OWA. Institutions are required to develop, implement, and evaluate OWA processes. As OWA is a relatively recent concept, the existing policies and legislation must change. To adopt these new policies, the current government lacks a) awareness about the benefits of OWA and b) adequate training in implementing the OWA concept. Both of these situations present significant challenges. Hence, legal fragmentation acts as a barrier to UWM, a challenge that must be dealt with to embrace the OWA concept. 3.5.3.2 Stakeholder engagement Water is a fragmented sector where different departments within a utility bear multiple responsibilities for managing interconnected water activities. Because water is a social issue, urban water managers or professionals should not be solely responsible for solving water problems. Year after year, water issues are becoming more interlinked with different development issues and various social, economic, environmental, and political elements at national and international 44 levels. To ensure sustainable water supplies, stakeholder feedback from multiple professional backgrounds is needed (Biswas, 2001). The engagement process should be emphasized and prioritized in managing urban water services because it is an essential element of the OWA concept. Thus, siloed water management systems require greater integration of drinking water, wastewater, and stormwater (Sapkota et al., 2015). 3.5.3.3 Financial stability Innovation in current practices will incur costs for the water sector. Water reuse has various costs and benefits at different sub-system levels compared to when combined at a much larger scale. For example, the need for a separate distribution network for potable and reclaimed water for non-potable uses presents a significant cost barrier. Additionally, a substantial amount of energy is required to convey water for its respective uses (Daigger, 2011). Water providers appreciate lower pumping costs, a decrease in GHG emissions from water systems, and other gains, but these may be difficult to measure (Venkatesh, 2012). Although water recycling reduced water use at different levels in Germany, the unexpected results of increased deterioration in pipes due to lower flows and larger concentrations of solids in the waste stream presented a problem. Short-term problems from decreased water consumption need to be balanced with long-term benefits. (Kiparsky et al., 2013). Developing a collaborative financial partnership among water stakeholders is necessary to implement the OWA. As previously mentioned, the fragmented nature of the water sector makes it difficult to bring all players into one common zone. Usually, different governments, non-governmental organizations, and private sectors are involved in water management. Since their relevant budgets and cost calculations are disjointed, strong collaboration is required to access their financial activities collectively (ACTEON, 2010). As a result, creating a strong collaborative financial partnership is challenging. 3.5.3.4 Technical capability The conventional data capturing, storage, and sharing approach is difficult, although attempts have been made to meet SDGs on water and sanitation (i.e., SDG goal 6). A report published by the United Nations in 2018 states that 60% of countries across the globe lack data availability on more than four global SDG indicators. Successful implementation of OWA principles relies on data sharing, management, and analysis, which involves greater coordination and cooperation among relevant stakeholders. 45 Integrated water resource management, an important indicator under SDG goal 6, has been implemented by only 48% of member countries (United Nations, 2018). Another report jointly prepared by the World Health Organization and the United Nations Children’s Emergency Fund indicated that only 35% of countries could report on safely managed water (WHO and UNICEF, 2017). These statistics suggest a major knowledge gap in data availability, sharing, and collection. In addition, artificial intelligence in current water utilities is not very popular. Artificial intelligence must be integrated into water systems to promote better data analysis and utilisation. With access to artificial intelligence in water systems, operator decision-making may improve, minimising energy costs, conserving water resources, and better organising staff members (IWA, 2019a). Despite heavy initial capital investments, municipalities should consider the long-term benefits of artificial intelligence. 3.5.3.5 Awareness and education While OWA is an ideal concept for managing water resources sustainably, it is difficult for water system practitioners to understand this concept because it is significantly different from the current UWM system. Indeed, water managers might face the following challenges in accepting OWA: difficulties in understanding the concept, lack of knowledge on implementing it, and limited expertise in using the concept. To implement the concept, capacity building is vital as it helps to enhance the understanding of a particular community's scope and environmental potential (Elfithril et al., 2019). Such capacity development needs to be improved at different levels, i.e., individual, organizational, environmental, and national (UNDP, 2009). Training and education programs, public water forums, facilities establishment, communication, and modular and material development programs must be organized to raise awareness of the OWA. In addition, various efforts should be made to market the concept by creating an environment that advocates for organizational change, establishing a legal framework, effective data sharing among relevant water stakeholders, and implementing research and development projects (Elfithril et al., 2019). Most importantly, significant changes are expected around the government’s response and managerial spectrum when water and non-water stakeholders meet and agree on a common platform to resolve urban water issues. 46 3.6 Way forward for OWA implementation Based on the review conducted in this Chapter, Figure 3-4 shows important elements essential in successfully implementing OWA in a One Water community. Figure 3-4. Element contributing to the OWA concept Proper scope and definition The scope and definition of OWA vary with regions based on their conditions and requirements. These conditions depend on climate and geography, availability of water resources, and different environmental indicators. The broader picture of OWA can be drawn based on an individual region’s expectation in achieving OWA goals. These goals should focus on building water supplies' reliability, resiliency, and sustainability (Water Research Foundation, 2017). In the Okanagan Valley (Canada), where 55% of water is currently used for agricultural purposes (Okanagan Basin Water Board, 2015), OWA defined this valley must include sustainable agriculture as one of its primary scopes. This highlights the importance of the agriculture sector in UWM that can assist relevant water stakeholders in the Okanagan Valley to sustainably utilize available water resources, thereby reducing pressures on freshwater supplies. Another example includes growing water scarcity concerns in Sydney, Australia, particularly with recent wildfires 47 and drought conditions. In Sydney, OWA can play a significant role in mitigating water challenges. Various strategies such as water efficiency, water pressure management, water recycling, leak detection programs, water-wise guidelines, and commissioning of desalination plants have been introduced to overcome the key water-related challenges (Sydney Water, 2020). The Salinas Valley (US) has fertile lands suitable for agriculture, but a high water demand has resulted in excessive water pumping and salt intrusion in the agricultural land. The region required a careful water management strategy where the scope of OWA would fit well. Hence, to overcome this problem, the Monterey Regional Water Pollution Control Agency, in collaboration with local farmers, started the agricultural reuse irrigation project in the US. As a result, recycled water currently irrigates 12,000 acres of farmland, and 60% of water is recycled yearly (US Water Alliance, 2016). Strong leadership Strong leadership is critical for the smooth running of initiatives, particularly new ones. The relatively new OWA concept requires a bold leader to drive planning and push the implementation of the One Water initiative. Water stakeholders can start this initiative at different levels, including government, non-government, and private organizations. A strong and dedicated team should be set up to implement strategies and manage related activities concerning OWA. At the government level, policymakers need to realize the benefits of OWA and plan programs accordingly. OWA leaders must be dedicated to community engagement in planning, establishing policies, and decision-making. Stakeholder engagement Stakeholders from diverse backgrounds and multiple levels should be included and engaged in making decisions on various water-related issues. This decision-making process involves a greater account of public needs, assets, and perspectives in designing and implementing water policies and projects (OECD, 2015). For example, energy and transportation are two sectors that rely heavily on water, and both sectors have a large water footprint. As a result, these sectors need to practise OWA principles by building collaborations with water utilities for effective UWM (US Water Alliance, 2016). The benefits of collaboration are illustrated in the following example. The Dow Chemical Company (USA) experienced a shortage in its water supplies. Hence, they collaborated with the City of Lake Jackson. The city transmitted its reclaimed water to Dow’s raw 48 freshwater canal, which helped Dow meet the company’s water needs. This result is expected to reduce Dow’s freshwater supplies by 1.3 billion gallons annually, sufficient to supply water in a community of 30,000 people (US Water Alliance, 2016). The feasibility of any new engineering project depends on the expertise of engineers; however, to implement the project successfully, stakeholders across different departments should understand the mutual benefits of the project deliverables. Since OWA is a relatively recent paradigm shift in the current UWM, stakeholder engagement has a bigger role in disseminating integral elements of this concept. The engagement process may be carried out by organizing public forums, campaigns, and workshops, where stakeholders are informed about the importance, need, and vision of OWA. These discussions should seek to understand stakeholders’ perspectives and reflect their recommendations for implementing OWA principles. Policies and legislation Globally, no OWA regulations have been established. Strong and consistent policies, legislation, and guidelines for regulatory bodies and utilities covering different states/provinces are imperative. These policies should focus on developing water quality guidelines for reclaimed water use (wastewater) and stormwater reuse, resource recovery and their potential benefits, GHG emission reduction programs, and low impact on aquatic environments. The water quality guidelines should be developed with greater attention to microbiological water quality, aesthetic values, and energy consumption, as consumers are more concerned about those parameters. The developed policies and guidelines should consider water utilities of varying sizes (i.e., small, medium, and large) to establish uniform regulations. Guidelines should be standardized to promote the attainment of OWA goals. The current local ordinances and guidelines should encourage OWA, while utilities need to generate enough regulatory flexibility. Governments should plan for various incentive programs to encourage water utilities to adopt OWA as this will help reduce the water footprint, promote water conservation, and build a sustainable water system. Economics and finance A strong financial foundation is necessary to initiate any development work. Governments should provide a separate budget in their national plan to invest in maximizing urban water supplies by supporting innovative ideas such as OWA. It is important to include the private sector in the mainstream, attract them to invest, and develop lucrative frameworks to market the concept. The 49 framework should benefit the water industry, urban planners, and consumers. The revenues generated through citizens via business and commercial applications should be wisely utilized to implement strategies such as green infrastructures and LID, which are pivotal to attaining OWA goals. Sufficient funds should be allocated for infrastructure investment and upgrades. A new neighbourhood should be built using the OWA concept. Ultimately, the OWA should be planned and designed to be financially viable via technical innovation. Technical innovation Technical innovation is critical for practically implementing the principles of OWA, particularly for designing with a system approach, developing flexible and adaptive infrastructure, applying methods for achieving multiple benefits from different forms of water, and implementing fit-for-purpose water use techniques. The examples of technical innovation include but are not limited to: i) Analyzing the interaction among different UWS service components. This will allow urban water managers to understand better the interrelationship between drinking water, wastewater, and stormwater; harvesting with LID techniques; resource recovery strategies; and investment in technologies that promote decisions between water management, urban design, and landscape architecture (Mukheibir et al., 2014). ii) Establishing sustainable UWS via high-tech computerized applications, including big data and AI. AI ensures better infrastructure planning, asset monitoring, contaminant detection, and process optimization and control (IWA, 2019b; Hu et al., 2022). For example, in Philadelphia (US), the Philadelphia Water Department uses cutting-edge computer modelling technology to facilitate the partnership between upstream dischargers and industrial and municipal intakes (Couillard et al., 2015). iii) Sharing data between water utilities and comparing performance trends within individual utilities to those of different utilities of similar size based on population and number of connections. The enhanced access to real-time water data helps to conserve water resources and encourages innovations (IWA, 2019b). iv) Creating a web-based tool will offer a unique opportunity for UWSs to store, analyze, and share data analytics and performance trends. The web portal will assist urban water managers in understanding existing water challenges and making necessary interventions based on performance trends. 50 v) Digitalizing urban water services reduces water loss and enhances efficiency, water conservation, and customer service. For example, developing an algorithm-based leak detection technique ensures early response to unfavourable events, such as pipe leaks, bursts, water pressure variation, and quality incidents. 3.7 Conclusions UWS service components (drinking water, wastewater, and stormwater) are highly interlinked; thus, water managers advocate for the OWA to build sustainable, resilient, and reliable UWS. Water-related socio-economic and environmental processes are interconnected elements of UWS. Currently, urban water system service components are managed in silos, which have roots in the historical development of individual systems for “different” water types. This siloed water management is both conventional and unsustainable. The modern shift towards OWA will open avenues in UWM, contributing to reduced water footprints, better stakeholder engagement, and more connected leadership. Thus, supporting water resiliency and reliability. The scope of OWA varies from region to region. Countries such as Australia, Canada, the Netherlands, Singapore, and the United States, have practised OWA in a few of their cities, and the results have been positive in UWM. However, adopting OWA into the current system also brings challenges that add financial burdens, changes in working administration, and the need for consistent regulations across states for water reclamation and stormwater reuse. The findings proposed six guiding principles and emphasized the need for stakeholder engagement, strong leadership, financial support from the government, and technical innovations to implement the OWA successfully. 51 Development of Key Performance Indicators A version of this chapter has been published in the Urban Water Journal as an article titled “Key Performance Indicators for Small and Medium-Sized Urban Water Systems in a semi-arid Region: A Case Study in the Okanagan Valley, Canada”. 4.1 Introduction A performance is defined as a measure that estimates how well UWSs can accomplish its objectives in terms of providing safe water supplies to consumers (Alegre, 1999); treating wastewater for potential potable and non-potable uses (López-ruiz et al., 2021); and managing stormwater against floods and restore natural landscape in an urban environment by promoting the use of green infrastructure (Goonetilleke et al., 2017; Pokhrel et al., 2022). UWSs are differentiated based on their on population size. For example, Health Canada defines a water system that serves a population of less than 5,000 as small (Health Canada, 2013). The United States Environment Protection Agency (USEPA) terms the water system that serves a population between 3,300 – 100,000 as medium (USEPA, 2009). In this study, SMWSs serve a population between 5,000 – 35,000 (Canadian Infrastructure Report Card, 2019). The performance evaluation becomes more critical for SMWSs because these systems continuously wrestle with insufficient funds, inadequate infrastructure, limited human resources, enforcement of stringent regulations (Pokhrel et al., 2020a), and disoriented water governance structure (Mcfarlane and Harris, 2018) . To overcome above challenges, adequate planning and management of UWSs should be ensured. Performance assessment using PIs is a commonly applied management tool. PIs are "measures of the efficiency and effectiveness of the water utilities concerning specific aspects of the utility's activity and of the system's behaviour"(Alegre, 1999). Some of these PIs are more important and serve specific objectives, termed Key Performance Indicators (KPIs). These KPIs are generally classified under various performance criteria. Common criteria include but are not limited to environment, finance, infrastructure, staff, social, and operating and monitoring aspects of water systems. Few SMWSs rely on the KPIs developed for LWSs to gauge their performance, which is not ideal. A practical approach is required to measure SMWSs performance as the challenges LWSs face differ from SMWS. However, the same stringent rules and regulations have to be followed by 52 SMWSs in maintaining their water quality (Pokhrel et al., 2020b). The KPIs developed for LWSs have much deeper focus on UWS as they have more resources (e.g., human, finance, and technical) to conduct detailed performance assessments. For example, SMWSs are not concerned with monitoring unregulated disinfection-by products as a part of their water compliance assessment (Mian et al., 2018); GHG emissions from pumps and treatment plants are not tracked as SMWSs do not have required resources; financial indicators such as capital expenditure, the economic real rate of return, credit rating, interest net profit are not given a higher preference in assessment as these indicators required detailed economic analyses. Because SMWSs are struggling to recruit seasoned operators and full-time employees, experiencing water quality issues more frequently (compared to LWSs) that cause advisories to be placed, dealing with revenues collection issues due to smaller population size, and consuming more water to meet increasing agriculture water demands (e.g., Okanagan Valley Canada). New KPIs must be developed to meet the unique challenges faced by SMWSs (Mcfarlane and Harris, 2018). Typically, the KPIs identification process involves a mix of qualitative and quantitative methods. The former method is applied by conducting workshops and surveys, whereas the latter includes adopting a suitable MCDA technique. For example, the Delphi is a widely used survey technique to reach a consensus on a particular topic. It contains four main characteristics: anonymity, iteration, controlled feedback and statistical “group response” (Heiko 2012). Although, this method has been under scrutiny because of significant variance occurring and lack of clarity in following specific steps; it is still considered as a reliable method when appropriate tools are used to evaluate the methodology. Hence, when using a Delphi technique, it is suggested to clearly identify the research problem, select right experts in the field, provide detail and controlled feedback, conduct iterative discussions, determine and analyze consensual criteria, and establish stable results (Nasa et al.,2021). MCDA techniques, including TOPSIS, PROMETHEE, ELECTRE, and VIKOR, frequently use quantitative methods to identify indicators. PROMETHEE has a big family and comprises of eight methods, including PROMETHEE I-VI, PROMETHEE-GAIA, and PROMETHEE GDSS. PROMETHEE is based on the outranking relationship and is widely used to solve the problems when different priority orders are established for a selected “n” number of criteria and alternatives (da Cunha et al., 2022). Among various methods, PROMETHEE II is considered a better option in indicators identification process because 1) It provides a complete ranking of alternatives (i.e., ranks are provided to each 53 alternative instead of a partial ranking, where only two options are comparable); 2) It is based on preference function, which assists decision-makers to identify their preference between pairs of alternatives for each criterion; and 3) The rank is based on a net outranking flow, suggesting that higher is the value of an indicator, better is the alternative (Abdullah et al., 2019). The KPIs developed in this study are generic and suitable for measuring the global drinking water, wastewater, and stormwater performance of SMWSs. A case study was conducted in the Okanagan Valley to understand the effectiveness of the developed KPIs. The valley is located in a semi-arid region. With changing climate and increasing population, it will be fair to estimate that the Valley might face water scarcity in the near future. The Valley is considered as the study area because of various reasons: 1) All the water utilities across the region are small-medium sized, which justifies developed KPIs suitability, relevancy, and reliability; 2) The region has a high water consumption rate but low freshwater availability, which requires specific KPIs to gauge required performance (OBWB, 2021); 3) Rapid growth of population in the Central Okanagan region because of which relevant KPIs are needed to keep track of system performance; 4) The water demand in the region is mixed for urban and agriculture; thus, the KPIs selection should consider agriculture water use too (RDNO, 2019); 5) The reoccurring flooding in recent years (2017 & 2018) has shifted wider attention towards innovative stormwater management (City of Kelowna, 2021a). To the authors’ best knowledge, this is one of the very few studies that established KPIs for all three water systems through a perspective of SMWSs in a single study. The main objective of this chapter is to engage municipalities, water utilities, and stakeholders across the Okanagan Valley and identify the KPIs for different UWS services. The chapter further attempts to bring municipal representatives to a common table and devise a systematic strategy to develop a uniform matrix to gauge utility performance. The proposed KPIs will assist SMWS decision-makers in comparing the performance of similar water system operations, identifying solutions, and establishing action needs and planning for the overall enhancement of UWS performance. 4.2 Methods Figure 4-1 shows a detailed method adopted to identify the KPIs for drinking water, wastewater, and stormwater. Three steps were used for the identification purpose, which are described as follows: 54 Figure 4-1. Methods adopted for the KPIs identification Screening In this phase, a preliminary list of indicators comprising drinking water, wastewater, and stormwater, was selected from various institutional reports and academic papers. Initially, a total of 486 PIs were identified. To reduce the number of PIs, further literature search was conducted, and academic expert opinions were sought to identify the PIs critical in assessing UWS performance in small and medium-sized communities. As a result, 246 PIs were removed because of the following reasons: a) 486 PIs itself is a huge number, and it is not practical to assess UWS performance using all the PIs; b) All PIs did not depict characteristics of SMWSs that were specific to semi-arid regions; c) The removed PIs mainly focused on LWS, which were established to fulfil 55 the objectives of particular institutions; d) Some PIs were developed specifically for the developing countries (e.g., water price subsidy for low-income family, provision of sanitation and hygiene, and billing collection period), which was not within the scope of the current assessment process. Therefore, a total of 240 PIs (Drinking water (90), wastewater (78), and stormwater (72)) were short-listed for further selection. The shortlisted PIs are broadly classified into six performance criteria: environmental, financial, infrastructure, operational and monitoring, social and institutional, and staff for each UWS service component (Chhipi-Shrestha et al., 2017). Rating A questionnaire (Appendix A) was divided into two parts. Part A (Table A-1) was designed to rate the short-listed PIs under each criterion. Respondents were supposed to assign relevant scores based on Table 2. The definition and unit for each PI were also distributed as a reference, which would assist the respondents to rate the PIs accordingly. Part B (Table A-2) was prepared to determine weights for the selection criteria. The weights are important characteristics to identify because they are used to perform analysis based on the importance level of each selection criterion. AHP was used to determine weights. Before the questionnaire distribution, ethics approval (H20-02646) was granted by the Behavioural Research Ethics Board, The University of British Columbia, Okanagan Campus. The short-listed PIs were rated using four decision criteria, i.e., accuracy, comparability, clarity, and relevancy. This method of selection criteria has been previously used by (Chhipi-Shrestha et al., 2017) and (Haider et al., 2015). Table 4-1 outlines the definition for each of the decision criteria. These decision criteria used a linguistic scale, and corresponding scores were assigned. Two criteria, relevancy and clarity, were rated based on a five-point Likert scale. Table 4-1. Definition of the selection criteria Selection Criteria Definition Relevancy How much an indicator is relevant to measure the performance of small and medium-sized water systems (SMWS)? Clarity How much an indicator is clear in its meaning and can be interpreted well to both public and utility technical staff? Accuracy How much an indicator can be accurately / precisely measured? Those indicators which can be measured with a calibrating device are known to be accurately measured Comparability How much the indicator can be compared with existing indicators available at national and international levels? 56 Ten water utility teams were consulted to rate these criteria for all three UWS service components. Each team comprise municipal managers, engineers, and water technicians. The number of members in each team ranged between 3-5. Hence, a total of 30-50 individuals’ expertise knowledge was utilized to finalize the rating. The remaining two criteria (accuracy and comparability) were rated using a three-point Likert scale. Past literature was reviewed to rate these two criteria because much information about these criteria is readily available in the published work. Table A-3 shows the proposed linguistic scale, definition, and score assigned for the decision criteria. Identification To identify the KPIs from the list of PIs, a Delphi technique and MCDA were employed. The Delphi technique was applied by conducting workshops between September 2020 and March 2021. Three workshops were conducted to finalize the KPIs identification process. The authors’ team facilitated the first two workshops, and the last was conducted by the two representatives from the water utility team. In this workshop, the authors’ team presented generic information to the water utility’s team about objectives, the medium of questionnaire distribution, and the procedure for rating the PIs. The questionnaire was distributed to the water utility team via email. After receiving the survey results, data were analyzed using the MCDA technique, and 120 PIs were shortlisted for further analysis. In this Chapter, PROMETHEE–II, a non-compensatory method, was used as the suitable MCDA method for data analysis. The following steps were followed to identify the KPIs. Step 1: Develop a decision matrix A decision matrix was developed as PI under each performance criterion vs decision criteria. For example, if there were eight PIs under the environment criterion for drinking water, each of these PIs formed a decision matrix against each decision criterion (i.e., relevancy, accuracy, reliability, and comparability) (Alhumid et al., 2019). The same procedure was adopted for the remaining drinking water, wastewater, and stormwater criteria. Step 2: Normalize the decision matrix for each decision criterion The value of PIs in the above-shown decision matrix was normalized for each decision criterion. Normalization was carried out using the following formula for each of the decision criteria: 57 𝑇𝑖𝑗 = 𝑋𝑖𝑗 − 𝑚𝑖𝑛𝑖𝑗[max (𝑥𝑖𝑗) − min(𝑥𝑖𝑗)] (4-1) where Xij = performance indicator value, minij = minimum PI value; max(ij) = maximum PI value Step 3: Calculate the difference between each PI against all the PIs This step is composed of three sub-steps. First, the difference between each PI was calculated with all other PIs for each selection criterion. Second, the same procedure is applied to calculate the difference between the remaining PIs with each other across all three remaining decision criteria (i.e., comparability, accuracy, and clarity). Third, similar to the first sub-step, the difference is calculated for the remaining selection criteria (i.e., finance, infrastructure, staff, operation and monitoring, and social and institutional) across four decision criteria such that it becomes convenient to calculate the preference function value in the next step. Step 4: Determine the preference function value The preference function value was calculated based on two conditions. If the difference between the two PIs was less than or equal to zero, 0 was identified as the desired value. However, if their difference was greater than zero, the difference value is used as the preference function value. Mathematically, the following expression was used to illustrate the explanation as follows: 𝑃𝑗(𝑐, 𝑑) = 0 𝑖𝑓 𝑃𝑐𝑗 ≤ 𝑃𝑑𝑗 [(𝑃𝐼1 − 𝑃𝐼2)] ≤ 0 (4-2) 𝑃𝑗(𝑐, 𝑑) = (𝑃𝑐𝑗 − 𝑃𝑑𝑗) 𝑖𝑓 𝑃𝑐𝑗 > 𝑃𝑑𝑗 [(𝑃𝐼1 − 𝑃𝐼2)] > 0 (4-3) Step 5: Compute the aggregate preference value To calculate the aggregate preference value [π (c,d)], the weights (which were calculated based on AHP) were multiplied with each preference function value for each PI under all the criteria. These values were summed across the rows. The obtained value was then divided by the sum of all the weights across four criteria. The following equation depicts the aggregate preference value. [π (c, d)] =∑ 𝑤𝑗𝑃𝑗𝑛𝑗=1 (𝑐, 𝑑)∑ 𝑤𝑗𝑛𝑗=1 (4-4) Step 6: Calculate the leaving and entering outranking flow 58 A pairwise comparison was done for each PI based on the calculated preference function value in step 5. The leaving flow is the average value of the aggregated preference function value across rows divided by the difference of the total number of PIs with 1. The entering flow is the average value of the aggregated preference function value across columns divided by the difference of the total number of PIs with 1. The following equations (4 & 5) present the description for leaving and entering flow in a mathematical term. 𝛾+ = 1𝑎 − 1∑ 𝜋(𝑐, 𝑑)𝑚𝑓=1 (4-5) 𝛾− = 1𝑎 − 1∑ 𝜋(𝑐, 𝑑)𝑚𝑓=1 (4-6) Step 7: Determine the net outranking low The net outranking flow was determined by taking the difference between the leaving flow and entering flow, which is represented as follows: 𝛾(𝑙) = γ + (l) = a − (l) (4-7) The higher is the value of 𝛾 (𝑙), the higher will be the rank for the corresponding PI (Sapkota, Arora, Malano, Sharma, & Moglia, 2018). Following the seven steps mentioned above, the ranks were determined for each PI for all six criteria. The results discovered from the data analysis in the second workshop were presented to the water utility team. Most team members expressed concerns over the list related to data availability and units of a few KPIs. Based on the teams’ feedback, necessary revisions were made. The final workshop focused on the following questions: a) Is the KPI relevant for assessing water systems performance? b) Is the KPI measurable and the suggested units appropriate? c) Is data readily available for the KPI? and d) Is the KPI useful in comparison with other utilities? Due to the time limitation, only drinking water KPIs were discussed. The discussion led to the removal of the fourteen KPIs. Based on the consensus reached by the group on the drinking water KPIs, wastewater (8) and stormwater (5) KPIs were also removed. The final list of KPIs was sent to the water utility team for approval. All the team members were fully satisfied with the list, and 96 KPIs were identified to measure the performance of SMWSs across the Okanagan Valley. 59 4.3 Result and discussion The results of PROMETHEE II for the selection of drinking water, wastewater, and stormwater KPIs are elucidated in the following sections. The discussion is strictly limited to the selected KPIs in this chapter. It is important to note that all 96 KPIs cannot be explained in the text because of space limitations. In the upcoming section, only the higher-ranked and the lower-ranked KPIs have been explained to justify the identification process. Table 4-2 shows the drinking water, wastewater, and stormwater KPIs and their units. The detailed definition of these KPIs is available in Table A-4 – Table A-6. The following sections reflect the importance of each KPI identification in gauging water system performance. Table 4-2. Developed Urban Water System KPIs KPI Unit Drinking water I. Environment 1. Residential water use* (IDE1) % 2. Source water quality (IDE2) % 3. Annual water production (IDE3) m3/year/connection 4. Agricultural water use* (IDE4) % 5. Annual agriculture water use per hectare (IDE5) m3/hectares 6. Groundwater distributed (IDE6) % 7. Treatment energy intensity (IDE7) kwh/ m3 8. Industrial, commercial, and institutional (ICI) water use (IDE8) % 9. Treated surface water distributed (IDE9) % 10. Non-potable water distributed* (IDE10) % 11. Maximum Daily Demand: Average daily demand(IDE11) Unitless 12. Alternative source water (IDE12) Unitless II. Financial 1. Water price* (IDF1) $/m3 $/ m3 -ha –year 2. Operation and maintenance cost for raw water withdrawal from the source to the treatment plant* (IDF2) $ /m3 3. Operational and maintenance cost of water treatment (IDF3) $ /m3 4. Operation and maintenance cost for water distribution (IDF4) $/ km 5. Debt service ratio (%) (IDF5) % 6. Residential and ICI Service revenue generated (Agriculture revenue is excluded)* ( IDF6) $/100 connections III. Infrastructure 1. Total main length (IDI1) km / 100 connections 2. Reported pipe failures (IDI2) Number / 100 km 3. Water leakage (IDI3) % 4. Total mains replaced/renewed/rehabilitated (IDI4) % 5. New mains installed* (IDI5) % 60 KPI Unit 6. Average Annual Life Cycle Investment (AALCI)* (IDI6) $ per connection IV. Operation and monitoring 1. Compliance with microbiological tests* (IDO1) % 2. Compliance with physical and chemical tests* (IDO2) % 3. Monitoring (Compliance) of THMs and HAAs*(IDO3) % 4. Frequency of hydrant inspection (IDO4) % 5. Compliance to leak detection program* (IDO5) Unitless V. Staff 1. Full-time operators (FTO) (IDST1) Number of FTO / 100 km main length 2. Distribution of the workforce by age (IDST2) Number of certified operators / 100 connections 3. Experience of operators (IDST3) Number of years per total number of operators 4. Absentees due to accidents (IDST4 Days/operators/year 5. Overtime hours (IDST5) Number of overtime hours per total number of operators VI. Social and Institutional 1. Water quality complaints (IDS1) Number of complaints / 100 connections 2. Growth within Service Area (IDS1) % 3. Boil water notice (BWN) (IDS1) % 4. Water quality advisory (WQA)* (IDS1) % 5. Emergency response readiness (IDS1) Unitless Wastewater I. Environment 1. Wastewater treatment plant capacity (IWE1) % 2. Wastewater treatment energy intensity (IWE2) kWh/ m3 3. Volume of wastewater spills (IWE3) m3 / 100 km 4. Biosolids reuse (IWE4) % per year 5. Sludge production (IWE5) kg/ 100wastewater connections 6. Wastewater generated per person (IWE6) m3/person/day II. Financial 1. Operation and maintenance cost of sewer mains (IWF1) $ / km 2. Operational and maintenance cost of wastewater treatment (IWF2) $/m3 3. Wastewater price * (IWF3) $/m3 4. Debt service ratio (%) (IWF4) % 5. Service revenue generated (IWF5) $/100 connections III. Infrastructure 1. Sewer blockage (IWI1) # of blockages / 100 km 2. Average Annual Life Cycle Investment (AALCI)* (IWI2) $ per wastewater connection 3. Reported sewer failures (IWI3) Failures/100 km 4. Lift station failures* (IWI4) % of Pump-hours /year 5. Total number of wastewater mains that are replaced, renewed, and rehabilitated per year (IWI5) % 6. New wastewater mains installed* (IWI6) % 7. Age of the wastewater treatment plant (IWI7) years IV. Operation and Monitoring 1. Pumps inspection (IWO1) % 2. Sewer mains inspection (IWO2) % 3. Inspection of manhole chambers (IWO3) % 4. Combined sewer overflow (CSO) control (IWO4) % 61 KPI Unit 5. Compliance with microbiological tests (IWO5) % 6. Compliance with physical and chemical tests (IWO6) % V. Staff 1. Full-time operators (FTO) (IWST1) Number of FTO / 100 km main length 2. Distribution of the workforce by age (IWST2) Number of certified operators / 100 km main length 3. Experience of operators (IWST3) years per operator VI. Social and institutional 1. Wastewater Complaints (IWS1) Number of complaints/ 100 service connections 2. Growth within the service area (IWS2) % 3. Emergency response readiness (IWS3) Unitless Stormwater I. Environment 1. Annual snowfall* (ISE1) cms/ year 2. Impervious surface* (ISE2) % 3. Stormwater reuse (ISE3) % 4. Disposed stormwater quality to the water body (ISE4) % 5. Stormwater treated (ISE5) m3 II. Finance 1. Operation and maintenance cost of stormwater mains (ISF1) $ / km 2. Operational and maintenance cost of stormwater treatment (ISF2) $/m3 3. Stormwater service price (ISF3) $/m3 4. Debt service ratio (%)(ISF4) % III. Infrastructure 1. Flood-affected properties (ISI1) #of properties affected / 100 storm sewer connection 2. Reported storm main failure (ISI2) # per 100 km 3. Storm main blockages (ISI3) # per 100 km 4. Number of constructed wetlands (ISI4) # per year 5. % Surface flooding (ISI5) Number/100 km 6. Total storm main length (ISI6) km / 100 connections IV. Operation and Monitoring 1. Pumping station inspection (ISO1) % 2. Gully pots inspection (ISO2) % 3. Gully pots cleaning (ISO3) % 4. Annual rainy days (ISO4) # per year V. Staff 1. Full-time operators (FTO) (ISST1) Number of FTO / 100 km main length 2. Distribution of the workforce by age (ISST2) # of operators / 100 stormwater connections 3. Experience of operators (ISST3) Number per total number of operators VI. Social and institutional 1. Water quality complaints (ISS1) # of complaints / 100 connections 2. Growth within the service area (ISS2) % of new houses in an area in a year 3. Swimming advisories* (ISS3) % of days with swimming advisories in a year 4. LID implementation* (ISS4) Number / km 5. Inclusion of risk management in the water master plan (ISS5) Unitless Note: * refers to the newly developed KPIs that have been developed in the present study. 62 Drinking water Thirty-nine KPIs were identified for drinking water services. Environment: Residential water use was the top-ranked KPI. It is an important KPI that assesses present water needs. Based on current water uses, urban water managers can predict the future water demands required to accommodate the increasing population. In addition, it informs utility managers about the current water licensing limitations and recommends them to think about alternate water sources in case of water shortages {Formatting Citation}. The average Canadian water consumption is 329 liters per person per day (lpd), and 490 lpd in British Columbia (OBWB, 2015). Okanagan Valley consumes 1,032 lpd water (OBWB, 2021). This high water consumption rate highlights the need to use residential water properly. Source water quality is the second KPI. This KPI might be an important indicator because water quality assessment at the source might affect the treatment methods and disinfectants employed (Pokhrel et al., 2020a). Annual water production is the third KPI. It is an important indicator as it measures the total water produced in a year to satisfy customer demands. The increasing demand is met by adopting water conservation strategies, defined land-use patterns for agriculture, and population densification (City of Kelowna, 2019). Another KPI is agricultural water use. The agriculture sector accounts for the maximum water consumption in the Okanagan Valley. In addition, the valley is known for its fruits and wines, which consume large volumes of water to ensure healthy and productive orchards. For example, 10 - 45 million cubic meters of water were used for agriculture purposes in April – September (OBWB, 2021). Alternative source water is the last KPI listed under the environment criterion. In arid and semi-arid regions, this KPI plays a significant role, which is why the utility managers have to expand their water sources and establish a collaborative relationship with other utilities to build sustainable, resilient, and reliable water systems (Stokes-draut et al., 2017). Financial: Water price is ranked highest. Drinking water infrastructure is expensive; water managers require strong financial backup and economic viability to enhance system performance (Nauges & Whittington, 2017). Utilities require sufficient financial resources in reserves to build new water infrastructure and tackle water-related challenges. Unfortunately, in SMWS, consumer revenues are not always sufficient, especially those generated from water pricing (Nauges & Whittington, 2017; Boukhari et al., 2020). Three KPIs are identified related to operation and maintenance (O&M) costs. These include O&M costs related to water withdrawal, treatment, and 63 distribution. Water abstraction cost is an important drinking water KPI. Abundant freshwater supplies across Canada are also responsible for large water withdrawn. The Canadian water systems drew around 38 million m3 of water from different sources, including rivers, lakes, groundwater, and the ocean in 2013 (Government of Canada, 2017). Drinking water treatment is an important step to ensure a safe and healthy lifestyle. An ample cost is incurred during the water treatment, which is O&M cost (i.e., pumping, energy, and equipment cost). According to a survey conducted in the small community (population < 1,000 for their study) in Alberta Canada, the authors concluded that i) O&M cost increases with decrease in community population and ii) O&M costs are higher in those communities that used surface water as the primary source for potable uses compared to groundwater (Janzen et al., 2016). The debt service ratio is one of the lowest-ranked KPIs. It is specifically relevant to the water utilities built on loans or borrowed from federal and provincial governments to meet consumer demands (Haider et al., 2014). Infrastructure: The total main length is the first KPI. Pipe failures are the second KPI. These failures are observed in small utilities twice as compared in large utilities. Large systems are equipped with strong financial resources, which result in robust pipe installation methods and better asset management strategies, contributing to fewer failures (Folkman, 2018). In addition, SMSWs are not provided with enough resources. Because of this, these failures are not tracked. Hence, this KPI was selected to improve pipe integrity performance so that the utilities could take necessary corrective actions based on the data availability (Shirzad et al., 2020). The third KPI is water leakage. Globally, the loss of treated water through buried infrastructure is a serious problem. Large municipalities install sensors to identify leaks, while some use a convolution neural network (Shukla & Piratla, 2020). However, in SMWSs, alternate plans are welcomed because of resource constraints. In the present study, the utility representatives recommended using the AWWA-approved leak detection software to record leaks in the water systems and take precautionary actions to resolve the problem. The number of mains replaced, renewed, and rehabilitated is another KPI. According to the Canadian Infrastructure Report Card, more than 20% of linear (i.e., local and transmission pipes) and non-linear drinking water assets (i.e., treatment facilities, pumping stations, and reservoirs) did not meet the condition of good and very good criteria (Canadian Infrastructure Report Card, 2019). Hence, this KPI was identified to keep a record of necessary asset management actions that need to be taken by the utilities to extend water system longevity and enhance system performance. 64 Operation and monitoring: The first three KPIs are related to the compliance of microbiological, physical, and chemical, trihalomethanes (THMs), and Haloaceticacids (HAAs) tests in the distributed water. In BC, water utilities are required to follow the water quality guidelines set by the health authorities. E.coli and total coliforms are the primary indicators of microbiological water quality. The presence of these organisms in drinking water results in the spread of water-borne diseases, such as diarrhoea and emesis (Wen et al., 2020). The non-compliant issues immensely impact drinking water performance because 100% compliance is expected specifically for the microbiological parameters as they directly affect human health. In this study, the compliance of physical and chemical tests was limited to four parameters: turbidity, temperature, pH, and free residual chlorine. The turbid water can compromise the water quality integrity because it reduces the disinfecting potential of the disinfectants. High temperature leads to a low dissolved oxygen concentration because there might be increased pathogens, nutrients, and algal blooms in source waters. As a result, source water impacts the DN, which disrupts the overall drinking water performance (Eck and Saito, 2018; USEPA, 2021a). THMs and HAAs are exposed via drinking, bathing, or breathing in steam when showering. Usually, the concentrations of DBPs are found at an elevated level in small water systems, impacting drinking water performance. Although there are no immediate health complications related to these DBPs, long-term exposure might result in the risks of cancer (Chowdhury, 2018). Compliance with leak detection is the last identified KPI. In SMWSs, leak detection programs are not conducted because they depend on government support and resource pooling to improve water efficiency. This KPI was identified with a view that utilities will appeal government for funding and install leak detection instruments at different points throughout the DN. The leak detection technology will be pivotal in reducing water losses. Staff: A number of full-time operators (FTOs) were the first KPI. In small water systems, one operator is shouldered with multiple responsibilities, including maintenance of water systems, events planning, and public works (Kot et al., 2011). This situation suggests that maintaining a sufficient number of operators is a challenge. The distribution of the workforce by age is ranked second. Because it is important to maintain a balance between experienced and new hires numbers as the utilities do not want the populace to the contingent with only the trainees, workforce distribution is a vital KPI to be identified. The third KPI is the experience of operators. Operators’ experience is the most critical factor in enhancing drinking water performance, particularly in 65 SMWSs. Water treatment and drinking water management highly rely on the judgment and decision of operators, which are dependent upon the operators' experience, knowledge, and perception (Scheili et al., 2016). Overtime hours is the last KPI. This KPI was identified because operators deal with irregular working hours, as they could be called anytime (day or night) with short notice. It is important to assess the water system's performance with operators’ roles given a top priority (Pokhrel et al., 2020a). Social and institution: Water quality complaints is the first KPI. In SMWSs, most complaints are related to aesthetic problems, color, taste, and odor of chlorine, and non-compliance with water quality guidelines (Minnes and Vodden, 2017). To measure the water system’s performance, customer complaints must be addressed efficiently and promptly. Growth within a service area is the second KPI. Drinking water connections must be added, and necessary infrastructure must be built to accommodate the changing population. The third and fourth KPIs are interrelated, which include boil water notice (BWN) and water quality advisory. In small Canadian drinking water systems (population < 5,000) 98.5% of BWNs were issued between 2010 and 2012, while 88.5% of notices were placed during the same period in the systems serving a population less than 1,000 (Health Canada, 2015). Emergency response readiness is the fifth KPI. Because SMWSs lack sufficient staff, resources, and funds, these plans are developed minimally. That is why this KPI is provided with the least rank. Wastewater Thirty KPIs were selected for wastewater. Environment: Wastewater treatment plant capacity is the first KPI. The wastewater capacity of a treatment plant is an important indicator as it provides useful information about the actual volume of wastewater treated at the different treatment plant units. Sometimes, the design capacity of treatment plants can be compromised as they might receive more wastewater (e.g., because of increased sewer flow due to heavy rainfall) than their actual capacity. As a result, the treatment plant can be flooded, and it might not be able to treat all wastewater received at the plant in a day (Sojobi & Zayed, 2022). Wastewater treatment energy intensity is the second KPI. The treatment facilities have a high energy consumption that ranges between 1-3% of the total electricity required by a country (Capodaglio and Olsson, 2020). Over 20% of the total electricity required by public utilities is used to operate the treatment plants at a municipal level (Kenway et al., 2019). It is clear that efficiently using recovered energy during the treatment process (e.g., production of biogas 66 from biosolids) will not only reduce energy footprint but also allow the reuse of wastewater resources. Sludge production and wastewater generated per person are the bottom two KPIs. Sludge generation during treatment is unavoidable and needs to be handled, disposed of, and treated properly. This KPI was identified to measure wastewater performance because it brings challenges of sludge management, which are related to treatment cost, contaminants concerns, sewage issues, and public acceptance (Joo et al., 2015). The last KPI is wastewater generated per person. In Canada, between 2013 and 2017, a large volume of wastewater (anywhere between 5,689 – 6,074 million m3) was discharged into municipal sewer systems. Out of this, 4.4% (based on 2017) of municipal sewage was discharged into source water with no treatment (Government of Canada, 2020). Financial: The first two KPIs are related to the O&M cost of sewer mains and wastewater treatment. O&M cost is an important indicator to measure wastewater system performance because it assists municipalities in making necessary interventions based on the funds available in their respective utilities. O&M costs concern replacement, rehabilitation, and repair costs primarily. For example, in 2017, the City of Kelowna spent $300,000 on replacement and repair cost that treats 70,000 m3/day of wastewater (City of Kelowna, 2017a). The O&M cost of wastewater treatment depends on the treatment level, which implies that advanced treatment methods incur large costs. Service revenues generated is the last KPI. The revenues generated from wastewater are important for carrying out the upgrade activities and enhancing system performance. Some SMWSs might not have advanced treatment facilities at their utilities. Hence, adequate government funding and revenues generated from consumers will assist managers in building the necessary infrastructure. Infrastructure: Sewer blockages is the first KPI. These blockages are a common type of operational failure occurring in the sewer mains, leading to loss of services and flooding. Identifying it as an important KPI will assist municipal managers in shifting their attention to building various models that can predict sewer blockages (Altarabsheh et al., 2019). Average annual life cycle investment is the second KPI. According to the CoK asset management plan in 2017, the wastewater infrastructure is depreciating at $10.9 million per year, while only $3.7 million is separated for the renewal for the upcoming ten years (City of Kelowna, 2017b). The reason behind selecting this KPI may be attributed to understanding the status of their utility regarding funds available in their jurisdiction. Reported sewer failure is the third KPI. The pipes installed for the wastewater conveyance are subjected to various stresses because of inefficient 67 design and construction, ageing infrastructure, and combined sewer flow or traffic loads. These stresses lead to sewer failure incidents, such as infiltration and exfiltration, corrosion, pipe breaks and cracks, and deformations (Damvergis, 2014). The age of the wastewater treatment plant is the last KPI. Ageing infrastructure is a key problem for utilities. The selection of this KPI is justified because continuous monitoring of wastewater assets will ensure that the upgrade activities are conducted both efficiently and effectively based on consumer needs. Operation and monitoring: Pumps inspection is the first KPI. A failure in the pump may delay the plant operations and result in large energy consumption, which may hinder the overall performance of the treatment plants (C. Lin & Lin, 2019). Sewer mains inspection is the second KPI. Usually, inspection is conducted by conventional closed-circuit television. Based on the protocols established by the provinces, condition grades are provided to the sewer, which helps asset managers plan future renewal actions and strategies and allocate necessary funds (Khazraeializadeh et al., 2014) to enhance wastewater system performance. Two KPIs in this criterion are associated with compliance of microbiological (E.coli and faecal coliform) and physical (turbidity and temperature) and chemical tests [biochemical oxygen demand (BOD) and chemical oxygen demand (COD)]. For these parameters to comply, the corresponding values should fall within the range outlined in the provinces' regulations and standards. A study conducted in the Upper Santa Cruz watershed in Arizona indicated that 13-15% of sample concentration exceeded 800 colony-forming units (cfu) per 100 ml of faecal coliform after heavy rainfall. The exceedance shows the non-compliance rate and decline in performance. (Sanders et al., 2013). Staff: These KPIs are similar to that of drinking water. FTO is the first KPI. In SMWSs, a limited number of operators are employed to carry out wastewater operations. It is, therefore, important to track the number of FTOs depending on the treatment plant's size and recruit operators per the utility needs. The distribution of the workforce by age is the second KPI. This KPI vastly impacts the wastewater system performance as the age group is directly proportional to managing wastewater. The more the diverse age group, the wastewater issues will be looked at from a different perspective, which might lead to extensive solutions. The experience of operators is the third KPI. Vastly experienced operators should share their knowledge with new operators so that 68 no complications arise when performing relevant works at treatment plants, pumping stations, and other locations in the wastewater system. Social and institutional: Wastewater complaints is the first KPI. These complaints are primarily related to odour generated from wastewater treatment plants. The odour generated from the treatment plants will also adversely affect the quality of life (Lebrero et al., 2011). For example, it has been reported that 15% of housing cost is reduced in the locality where strong odours from the treatment plants have occurred. Growth within the service area is the second KPI. To suffice increasing demands, wastewater connections must be increased to treat and manage wastewater adequately. This KPI is also important regarding the utilities plan to introduce separate systems for wastewater and stormwater conveyance. Emergency response readiness is the third KPI. These plans include but are not limited to algal bloom response, phosphorus and nitrogen management in source water, the sudden interruption of wastewater treatment plants, and an efficient public notification system in case of water contamination or failures. Stormwater Twenty-seven KPIs were selected for stormwater. Environment: Annual snowfall is the first KPI. It is much of a context indicator, which might be why this KPI has not been reported in the past literature. However, as the KPI characterizes the climatic context, the current study advocates that the performance of stormwater systems is interpreted based on snow accumulation. The snowmelts from snowpack carry various contaminants such as chloride, heavy metals, and trace organics as runoff to surface water, hindering the water quality performance (Galfi et al., 2017). Impervious surface is the second KPI. Due to rapid urbanization and industrialization, a large portion of the previous land is converted into impervious surfaces (e.g., roads, driveways, and parking lots), which reduces the groundwater recharge rate and increases the stormwater volume (Elimers and McDonald, 2015). Another KPI is stormwater reuse. Stormwater should be reused based on a concept of ‘fit-for-purpose. That means water of varying quality can be used for various non-potable uses (Goonetilleke et al., 2017). The reuse of stormwater will also reduce pressure on stormwater infrastructure, thereby reducing the cost of water treatment and enhancing performance. Financial: The first two KPIs are related to the O&M cost of stormwater mains and treatment. O&M cost of stormwater mains involves the total cost required to replace, rehabilitate, and repair the mains. These KPIs have been previously used to assess stormwater system performance. For 69 example, 40-60% of stormwater infrastructure falls under good and very good criteria. Given the burning issues of climate change, the stormwater infrastructure is in a critical condition because a large sum might be spent to rehabilitate and replace the existing assets(Canadian Infrastructure Report Card, 2019). The treatment cost related to stormwater implies treating runoff collected from several sites, whereas treating stormwater gathered from the roof of the house for various potable and non-potable uses. For this purpose, the cost is incurred in hard infrastructures, such as curbs, gutters, and pipes (USEPA, 2007). The debt service ratio is the last KPI. This KPI was identified if the utilities had certain debts related to interest expenses, loan payments, and principal repayment debt instruments for stormwater management. Given the financial and capacity constraints, SMWSs use their resources vastly for drinking water and wastewater, while a low priority is set towards stormwater assets. Infrastructure: Flood-affected properties is the first KPI. Several Canadian provinces have been witnessing floods at different times, which disrupts the functionality of stormwater systems. For example, in Calgary, the spring of 2013 caused excess water from the Bow River (watershed area for stormwater collection) to accumulate on the impervious surfaces, which affected 3,000 buildings and almost caused a property loss worth $ 6 billion (The Canadian Encylopedia, 2015). Reported storm main failures is the second KPI. These failures are caused by runoff exceeding the storm main capacity, flow velocity exceeding the velocity for erosion control, and minimum flow velocity being less than the velocity for deposition control (Gouri & Srinivas, 2015). Constructed wetlands (CW) is another KPI. CWs play a significant role in stormwater management as they help treat stormwater and attenuate pollutants collected and transported by runoff to the water sources. In addition, CWs reduce the peak flow during the flood and act as storage, because of which pressure on water supplies is reduced (Lucas et al., 2015). Total storm main length is the last KPI. The main length is an important parameter in determining stormwater performance. The length of stormwater mains should be designed in such a way that it allows easy passage of stormwater flow. The mains should be able to accommodate both heavy rainfall and excess snow melting. Operation and monitoring: The first two KPIs are related to the inspection. Pumps should be designed in such a way that they can easily draw water during the instantaneous stormwater flow. During heavy rainfall, the flow velocity might be high and in the case of combined sewer, CSO (Government of Ontario, 2019) might be disturbing stormwater performance. The pumps should 70 be inspected for machinery failure, wear, and tear. Inspecting gully pots is important because solids and debris accumulation in pipes may result in storm mains blockage. Another KPI is gully pot cleaning. Langeveld et al. (2016) found that cleaning gully pots every 2 months compared to annually increases the removal efficiency by a factor of approximately close to 3. Staff: Three KPIs are identified in this criterion. FTO is the first KPI. FTOs are responsible for responding to storm main breaks, failures (e.g., pipe leakage), and response to customer complaints. Distribution of the workforce by age is the second KPI. Many SMWSs have difficulty in hiring separate operators for stormwater systems. These systems use water and wastewater operators to carry out their operational functions. The experience of operators is the third KPI. Experienced operators always play a significant role in stormwater management. Unfortunately, many SMWSs struggle to keep their operators working in a particular utility for longer. Specifically, operators in smaller communities tend to change their work once they gain sufficient experience because of better opportunities in LWSs. Social and institutional: Stormwater complaints is the first KPI. These complaints (basement flooding, overflow of water in the pipes, and heavy snow melting) disturb stormwater system performance integrity (City of Toronto, 2021). A swimming advisory is another KPI. It is an advisory administered when bacterial levels are higher in the source water (e.g., lake water used for swimming). According to the Guidelines for Canadian Recreational Water Quality, E.coli in recreational water should not exceed 200 per 100 mm sample to maintain water system performance (City of Kelowna, 2021b). Including risk management in the water master plan is the last KPI. This KPI is identified to gather information about water utility inclusion of various risk management plans (flood control, source water protection, and minimization of blockages) about stormwater in enhancing water system performance. 4.4 Conclusions In this chapter, KPIs were identified for SMWSs and a case study was conducted in the Okanagan water utilities. For this purpose, a questionnaire was distributed to water utilities across the Okanagan Valley and KPIs were identified by combining the Delphi technique and PROMETHEE II methods. The identification process was conducted against six performance criteria. These KPIs are developed by eliciting comprehensive feedback from 30-50 water utility representatives in the Okanagan Valley across four decision criteria, relevancy, clarity, accuracy, and comparability. Several workshops with the municipal representatives at different time intervals justify the 71 rigorous exercise and in-depth discussions that were carried out. In total, 96 KPIs were identified. Environment criterion has a maximum KPIs (12) for drinking water, whereas the infrastructure criterion has the most KPIs for wastewater (7) and stormwater (6). Based on stakeholders’ feedback, “agriculture water use”, “water price”, “average annual life cycle investment”, “swimming advisories”, and “low impact development implementation” are critical for the Okanagan utilities. One of the limitations of this chapter is data unavailability as SMWSs managers do not keep detail track of their records due to the limited resource capacity. In addition, KPIs identification process can be further strengthened by conducting large stakeholder engagement workshops with more participation from different municipal representatives. The identification process will pave the way to conduct both self and competitive benchmarking of SMWSs, as discussed in the next chapter. Benchmarking is critical for continuous performance improvement of water systems which will allow urban water managers and decision-makers to make necessary interventions in enhancing overall system performance. The identified KPIs can also be used to develop asset management programs such as conducting replacement, rehabilitation, and renewal activities, promoting regular monitoring of water systems assets, and keeping track of operational and maintenance costs that are integral in achieving urban water sustainability, resiliency, and reliability. Inclusion of life cycle assessment (LCA) and life cycle cost (LCC) indicators are also important to increase indicators robustness in measuring sustainable water system performance. These assessments are not considered in this research as it is out of scope and the participating utilities did not have adequate information to share about LCA and LCC data. 72 Performance Assessment Model for Urban Water Systems A version of this chapter has been submitted to the Urban Water journal entitled with “Performance Assessment Model for Small and Medium-Sized Urban Water Systems: Development and Implementation”. 5.1 Introduction Performance assessment starts with identifying KPIs (as discussed in Chapter 4) across their suitable performance criteria and collecting relevant data for each KPI that determines an index value to assess performance. It is important for operational, tactical, and strategic decision-making, which is defined as “an approach that evaluates efficiency or effectiveness of a process or activity using KPIs (Alegre et al., 2014). In the last two decades, performance assessment has been an integral component of UWM (Alegre et al., 2017). While there are various methods (e.g. total factor productivity indices monitoring urban water infrastructure, and production frontiers) of performance assessment established for water systems, a PI method is considered as the most prominent one because it requires less sophisticated data (Haider et al., 2014), facilitates establishment of comparisons and benchmarking, enhances water services quality, assists water utilities to assess UWS performance with reduced costs, and builds a foundation in developing and implementing public water policies (Berg, 2020). Several studies have been conducted to assess the performance of LWSs. Nevertheless, very few studies have related to the performance assessment of SMWSs. Chhipi-Shrestha et al. (2017) identified 38 sustainability indicators for SMWSs at technical, environmental, economic, social, and institutional levels. Although these indicators depict important characteristics of UWS components, the study did not develop a performance assessment framework to calculate the performance index to determine the overall water system performance (Chhipi-Shrestha et al., 2017). Pokhrel et al. (2020) assess the performance of 66 small drinking water systems across three categories of local bodies (regional districts, municipalities, and improvement districts) in BC. For all types of local bodies, the performance level was found to be maximum for operators’ capabilities, followed by water quality issues, treatment and disinfection, infrastructure and funding, and operational characteristics performance criteria (Pokhrel et al., 2020). An interdisciplinary study led in Newfoundland and Labrador indicated that small drinking water 73 systems' performance was heavily impacted due to insufficient human, social, intuitional, and financial constraints in tackling water-related challenges (Minnes and Vodden., 2017). Collivignarelli et al. (2021) evaluated the performance of medium-sized wastewater systems based on sewer systems and treatment plant services that are provided to 15,638 consumers. Fifty-nine PIs across six criteria and 11 KPIs across two criteria were established to assess sewer and treatment plant performance. PIs scores were calculated and compared into three classes (i.e., under satisfactory, moderately satisfactory, and satisfactory) to determine performance indices (Collivignarelli et al., 2021). Korgmaa et al. (2019) evaluated the performance of 245 small wastewater treatment plants based on two criteria; total evaluation of complexity (represents a total number of treatment processes used and their advancement in use) and total evaluation of performance (represents essential factors such as influent quality, equipment functionality that impacts treatment process). A few studies have been conducted to determine stormwater system performance in SMWSs. Bhimanadhuni (2015) developed a performance index consisting of 50 PIs based on a weighted sum method to determine the performance of stormwater mains. The result suggested that the water system’s performance was primarily impacted by the pipe’s structural condition and subsequently by pipe blockages and capacity, highlighting the importance of the hydraulic properties of stormwater mains. Appendix B (Table B-1) shows the detailed list of common PIs that are developed for drinking water, wastewater, and stormwater till date in the past literature. Based on the above-mentioned past studies, it is clear that KPIs for SMWSs performance assessment has been overlooked in the literature as a limited number of works have been conducted in this regard. Various MCDA techniques (e.g., weighted sum, AHP, Fuzzy interferences) have been applied to conduct SMWSs performance assessment. Among them, TOPSIS is considered a reliable technique. It is an established MCDA technique that is specifically employed to identify specific solution or an index value from a pool of alternatives and criteria. It is based on the principle that the most ideal alternative should have the shortest distance from the positive ideal solution, while maintain the farthest distance from the negative ideal solution (Jahanshahloo, et al., 2006). For example, if a performance scale has a value ranging between 0-100, the most ideal solution will have the value of 100. Hence, when employing TOPSIS, the alternative maintaining the most proximity relation to 100 is considered as the best alternative. In addition, TOPSIS has 74 the following advantages : a) It is a simple, rational and easy technique to follow, b) It provides a numerical value that identifies both positive and negative ideal solutions and thus helps in performance assessment, and c) It is an intuitive and clear logic method that characterizes human choice (Vega et al., 2014). Each of the above studies provides valuable insights to improve UWS performance at the individual level focusing on a specific aspect of a water system. None covers the comprehensive characteristics surrounding holistic attributes of UWS. The current study is a novel undertaking because i) A performance assessment model (PAM) has been developed in a single study that evaluates the performance of each UWS service component at different criteria and indicator levels. a); ii) Inter-utility benchmarking of SMWS is conducted that identifies strengths and weaknesses of water systems based on performance scores; and iii) Different intervention types are proposed in the study, which can be applied to any water systems with slight modifications based on individual system’s characteristics and adaptability. The proposed model is implemented in five drinking water, three wastewater, and two stormwater municipal utilities across the Okanagan Valley in British Columbia, Canada. The Valley is considered as the study area because of the following reasons: 1) All water systems across the Valley are small and medium-sized; 2) A Performance assessment matrix is not available at the utility level to estimate and compare existing UWS performance; 3) Necessity of an assessment model that is simple to use yet it provides a robust performance assessment of water systems; 4) A need of a model to evaluate the performance of water systems in the Valley located in a semi-arid region. Developed PAM is generic in nature. The implementation of PAM in the global arena can be attributed to its collaborative approach of developing KPIs by various municipal professionals, wider inclusion of important performance criteria and KPIs in gauging system performance, and qualitative performance measurement (e.g., high, medium, low) from the quantified data based on a scale. The successful application of PAM will provide a platform for urban water managers to identify utilities’ shortcomings and fill existing performance gaps, implement necessary interventions, and optimise available resources in providing efficient and effective water services to consumers. 75 5.2 Methods PAM was developed by obtaining information from various SMWSs and analysing their changing performance against similar water systems. Like a typical model, the PAM comprises three units (input, process, and output) as outlined in Figure 5-1. Figure 5-1. Methodology adopted for the study At the input level, necessary data were collected from different water utilities and were compiled. Process is the second stage, where the major part of the model building takes place and data are analyzed. Output is the last stage of the model, which involves the calculation of the performance index for each UWS service component, and necessary interventions were recommended based on the performance level. The robustness of the model is strengthened based on the quality and 76 quantity of input generated from the maximum participation of the wider water stakeholders. The five-step process is described in detail below. Step1: Identification of KPIs The model comprises six performance criteria. The KPIs developed in Chapter 4 were used for the performance assessment of each UWS service component. Step2: Aggregation of KPIs and criteria TOPSIS was employed to perform aggregation and it has two main assumptions: a) Quantitative sales are used to represent criteria scores; b) monotonic relationship is established for the same criteria (Corrente and Tasiou ,2023). The entire analysis was broken down into six stages, which are as follows: i) Normalization The KPI values were presented in different units. To maintain uniformity in the calculation process, these values were normalized into two criteria, beneficial and non-beneficial (Equations 1 and 2). Minimization and maximization normalization technique was used. The former criterion is used when a high KPI score is desired, and later is used when a low KPI score is warranted. 𝑋𝑖𝑗 − 𝑋𝑚𝑖𝑛𝑋𝑚𝑎𝑥 − 𝑋𝑚𝑖𝑛 (5-1) 𝑋𝑚𝑎𝑥 − 𝑋𝑖𝑗𝑋𝑚𝑎𝑥 − 𝑋𝑚𝑖𝑛 (5-2) where Xij = KPI value; Xmin = minimum KPI value; Xmax = maximum KPI value ii) Aggregation of KPIs The second stage is aggregation. This is done for each criterion. The normalized KPI value is called the performance score. These scores were aggregated using Ordered Weighted Average (OWA) method (Sadiq & Tesfamariam, 2007; Kaur et al., 2021). The OWA method was used because it provides flexibility in determining minimum and maximum values for efficient decision-making (Kaur et al., 2021). In this process, OWA weights were calculated for each KPI. To calculate the weights, first, mean and standard deviation were computed by the following equation: 77 𝜇𝑛 =1𝑛𝑛(1 + 𝑛)2= 𝛼(1 + 𝑛) (5-3) 𝜎𝑛 = √1𝑛∑(𝑖 − 𝜇𝑛)2𝑛𝑖=1 (5-4) where µn = mean; ϭ n = standard deviation; n = number of KPIs for each criterion; α = fractile representing the actual location of the maximum weight, which corresponds to 50th percentile. In this calculation, α is taken as 0.5 as it is the most commonly assigned value for fractile determination. Second, weights were assigned to each KPI using the following equation 𝑤𝑖 = 1𝜎𝑛√2𝑝𝑖𝑒−[𝑖−𝜇𝑛)22𝑒2 (5-5) Third, these weights were normalized by 𝑊𝑁 = 𝑤𝑘∑ 𝑤𝑘𝑛𝑘=1 (5-6) The performance score (e.g., C1, C2, C3 ...Cn) of each KPIs are arranged in descending order and they are then multiplied by the corresponding weights (WN1, WN2 ...WNn) to get an aggregated score for each criterion, which is known as an aggregated performance score. Mathematically, the aggregation process is represented as the following. 𝐴 = 𝐶1 ∗ 𝑊𝑁1 + 𝐶2 ∗ 𝑊𝑁2 + 𝐶𝑛 ∗ 𝑊𝑁𝑛 (5-7) where A = performance score iii) Assigning criteria weights A decision matrix is formed against utilities and aggregated criteria. This decision matrix is then converted into a weighted decision matrix. Stakeholders provided weights to each criterion when identifying the KPIs. AHP was used to compute the weights for each criterion. iv) Positive and negative ideal solution A positive-ideal solution is defined as the maximum performance score generated for each criterion, whereas the negative-ideal solution is the minimum performance score generated 78 for the same criterion. These scores are determined from the weighted decision matrix table outlined in the above stage. In this study, all the criteria are maximized. Therefore, the positive ideal solution is given by Vj+ and minimized by Vj-. However, it is important to note that not always the maximum values belong to PIS. If a KPI desires a lower value, then the minimum KPI belongs to a positive ideal solution in such a situation. For example, water price is a financial KPI, whose lower price is considered good. In this case, the positive ideal solution is the minimum value, and the negative ideal solution is the maximum value. Mathematically, these values are represented as: 𝑉𝑗+= max (𝑈𝑛) (5-8) 𝑉𝑗−= max (𝑈𝑛) (5-9) v) Euclidean distance A Euclidean distance measures the distance of each criterion from the PIS and NIS as outlined in the following formula below: 𝑆𝑖+ = √∑(𝑉𝑖𝑗 − 𝑉𝑗+)2𝑚𝑗=1 (5-10) 𝑆𝑖− = √∑(𝑉𝑖𝑗 − 𝑉𝑗−)2𝑚𝑗=1 (5-11) where Si+ = distance from the PIS and Si- = distance from the NIS vi) Overall aggregation and performance index The performance index is calculated by multiplying the performance value by 100. This multiplication is carried out because performance value is a ratio and can be converted into an index by multiplying with 100. The performance value of each utility is determined when the Euclidean distance of the negative solution is divided by the sum of Si + and Si . It is represented by the following equation. 𝑃 =𝑆𝑖−(𝑆𝑖+ + 𝑆𝑖−) (5-12) 79 𝑷 =𝑺𝒊−(𝑺𝒊+ + 𝑺𝒊−) ∗ 𝟏𝟎𝟎 (5-13) Step 3: Performance assessment categories After calculating the performance index value in step 2 (vi), the water system’s performance is assessed based on a performance scale. This scale comprises five divisions, whose values range between 0 -100, as shown in Figure 5-2. Figure 5-2. Performance Scale The performance level (PL) is categorized as “very low”, “low”, “medium”, “high”, and “very high”. Based on this PL, the performance index can be interpreted in three ways i) Type I - the PL is poor and requires immediate major interventions to enhance the water system’s performance; ii) Type II - PL is fair but requires either major or minor interventions, which is dependent upon intimateness between PL and performance index and iii) Type III - PL is good and depending on water system proximity to the index value in the scale, slight interventions are recommended in achieving maximum (close to 100%) index value in years ahead. If the PL is over 90 %, water systems must maintain a similar PL. Step 4: Handling missing data An imputation technique was employed to fill in the missing data. In the form of a univariate imputation, penalty method was used, where the lowest KPI value in a particular criterion across the sample data was used to replace the missing data. K-nearest neighbourhood (KNN) method was employed as a multivariate imputation. In this method, missing data is determined by obtaining the mean of data points closest to it based on similar data available in a decision matrix. This is computed by calculating the Euclidean distance as follows (Emmanuel et al., 2021): 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 (𝑎, 𝑏) = √(𝑊𝑒𝑖𝑔ℎ𝑡𝑠) ∗ (𝑑𝑖1 − 𝑑𝑖2) (5-14) where (𝑑𝑖1 − 𝑑𝑖2) = square distance from the present coordinates 80 𝑤ℎ𝑒𝑟𝑒 𝑤𝑒𝑖𝑔ℎ𝑡 𝑖𝑠 𝑑𝑒𝑡𝑒𝑟𝑚𝑖𝑛𝑒𝑑 𝑎𝑠 𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑜𝑜𝑟𝑑𝑖𝑛𝑎𝑡𝑒𝑠𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑟𝑒𝑠𝑒𝑛𝑡 𝑐𝑜𝑜𝑟𝑑𝑖𝑛𝑎𝑡𝑒𝑠 (5-15) It is important to note that none of the utilities provided a complete set of data, which is why missing data were filled with the relevant techniques as described in step (iv). After assigning specific values to each KPI for all the utilities, data were aggregated, and performance index values were calculated. Step 5: Application of the developed model This is the output step. Data were collected for the identified KPIs of each water system between the years 2018- 2020 from ten primary Okanagan utilities. Three different spreadsheets (as outlined in Appendix B, Tables B-3, B-4, and B-5) were prepared with detailed formulae for each KPI. Respondents (e.g., municipal managers, engineers, technicians) were required to provide corresponding value to each data point and calculate the KPIs value based on the provided formulae. The first and second authors visited the respective offices for some utilities to collect data. The data collection process began in April 2021 and ended in December 2021, and which took a long time for the utilities to provide the data. 5.3 Results and discussion Five drinking water utilities, three wastewater utilities, and two stormwater utilities provided their data for performance assessment. Although the sample size is considerably small, important conclusions have been discovered in this Chapter which will be discussed in the following sub-sections. There are numerous studies available in the literature, where a fewer sample size has provided valuable insights to the specific body of knowledge. Scheili et al. (2016) studied the impact of human operational factors on drinking water quality in small water systems by conducting 21 individual interviews with operators across two Canadian regions. Despite the sample size was low, the authors were able to conclude that operators experience and certification and training programs had major impact in operators’ performance (Scheili et al., 2016). Dobbin (2019) conducted 27 semi-structured interviews with drinking water stakeholders to understand the role of participatory groundwater governance in California (US) to protect source water. The authors found that short-terms inventions are required to protect public health, while staff, financial, and technical capacities need to strengthen to build strong collaborative governance (Dobbin 2019). Saleem et al. (2021) identified PIs for aquatic centers in Canada by conducting 81 surveys with only five representatives. However, the authors were successful in identifying 81 PIs across seven criteria for their assessment to meet aquatic centres sustainability objectives. Drinking water Table 4-3 (Chapter 4) shows drinking water KPIs. The overall performance level (PL) of five drinking water utilities is represented in Figure 5-3. Figure 5-3. Overall performance level of drinking water The corresponding performance scores are given in Figure 5.4 using KNN method and Appendix C (Figure C-1) employing penalty method. . KNN and penalty methods showed that utilities A and C have a “high” overall PL with their relative performance index values (70 and 69) and (65 and 62), respectively. In the environment criterion, excluding utility “B”, the utilities had a high PL based on the KNN method. In the penalty method, utilities “A”, “B”, and “D” retained the same PL, while “C” and “E” decreased their PL to the medium. Residential water uses (IDE1 & IDE2) for both seasons, agriculture water use (IDE6), and industries, commerce and institution (ICI) water use (IDE10) had similar results, including both methods for their respective KPIs except IDE2, where utility, “D” had 63% performance score compared to 1.5% in the penalty method. Utility E had the minimum 703465475569596233210 10 20 30 40 50 60 70 80ABCDEPerformance IndexUtilityPenalty KNN82 agriculture water use, followed by “A”,”B”, “C”, and “D”. Apart from utility “E”, the rest of the utilities had alternate water use (IDE14) plans in their jurisdiction. In case of emergencies, such as sudden contamination of source water (e.g., runoff transmitted from roads into source water), failure of treatment plants (e.g., dysfunction of a UV technology), and restrictions on water use (e.g., decrease in water levels due to extreme heat), these plans being placed adds benefits to a utility. Energy-efficient water treatment leads to less water consumption, which is also important in reducing greenhouse gas emissions. Two utilities (“A” and “B”) demonstrated a high PL in terms of treatment energy intensity (IDE10) with performance scores of 65 and 75%, respectively. Figure 5-4. Performance scores of drinking water criteria using KNN method Individual utility performance is elucidated in the following sections. 5.3.1.1 Utility A The overall PL of utility “A” lies in a “high” region based on both methods. Out of six criteria, Operating and monitoring (O&M) criterion fall under a “very high” region. Above 80% performance score attained by each KPI must be attributed to the O&M’s outstanding 7759686469585529575654845550472696555769343656684107957710 10 20 30 40 50 60 70 80 90ABCDEPerformance score (%)UtilityOperation & Monitoring Social Staff Infrastructure Finance Environment83 performance. Only two KPIs (temperature IDO4 and Trihalomethane IDO7) have 88% and 84% compliance rates, respectively. A majority (67%) of criteria, environment, finance, social, and infrastructure, are located in a “medium” region, whereas only staff criterion falls in a low region. Among them, the infrastructure (54% and 46% based on methods I and II) criterion has the lowest aggregated performance score. This is because utility A did not place replacement (IDI4), renewal (IDI5), and rehabilitation (IDI6) programs, which means either pipe mains were in excellent condition or the utility did not have enough resources to conduct these programs. Based on the performance score (2%) gained by the utility for reported pipe failures (IDI2), the later cause seems to be more applicable. In Newfoundland and Labrador, a similar situation was observed (Minnes and Vodden, 2017). Pipe failures are common infrastructure issues due to extreme weather conditions, corrosion, abrupt change in water velocity, and contaminants clogging (Sarin et al., 2004). However, only 29% of small municipalities (population less than 5,000 in their study) across Canada have such a plan in their jurisdiction, while the % climbs up to 78% in large municipalities (Canadian Infrastructure Report Card, 2019 ). Baring IDS2 and emergency response readiness (IDS5), all the KPIs have performance scores above 99% in social criterion, which suggests that utility “A” has improved water services related to water complaints and advisories. 5.3.1.2 Utility B Based on the KNN method, the overall PL of Utility “B” is “low”, whereas the penalty method shows that the utility lies in a “medium” region. The aggregated performance scores for individual criteria using both methods provided the same result, which means the utility is not impacted by the type of methods employed or their limitations, as discussed in the methodology section of this Chapter. At the individual criterion level, both methods depict that infrastructure and social criteria lie in a “very high” region, Environment & Finance are located in a “medium” region, and Staff & Operation and monitoring fall under a “very low” region. The O&M criterion has the worst aggregated performance score (10%). Since the score is extremely low, it has also impacted the utility's overall performance index. This is because individual KPIs had the worst performance score, with 21% being the highest rate achieved by IDO1. A low water compliance rate means the utility has struggled to maintain its water quality values to an acceptable standard. IDO2 (0.4%) and IDO7 (1%) score the worst, adding extra 84 pressure specifically because they are commonly measured biological and chemical parameters. Among all the utilities, agricultural water price (IDF2) was the least. Low price of agricultural water may be attributed to its abundance availability and limited consumption and proper distribution. The operational cost for water withdrawal (IDF3) was minimum, with the performance score inching closer to 100%, while the costs for treatment (IDF4) and distribution (IDF5) plummeted to just over 60%. This score justifies the fact that the water price was on the higher side for this utility. The utility might be using advanced treatment methods to treat water, while large distribution and transmission mains might have been used to connect the service lines, incurring higher costs. 5.3.1.3 Utility C Regardless of the methods employed, the overall PL of utility “C” falls under a “high” level, with their performance indices corresponding to 65 and 62. This difference in the index is seen because the performance scores for two criteria (Environment & O&M) are decreased by 14 % and 11%, respectively, using the penalty method. Excluding these criteria, the remaining criteria provide the same score for both methods. Defying the usual performance trend of other utilities, utility “C” stands out the most regarding staff criterion, with its PL lying in the “high” region. Among all the utilities, utility “C” has the maximum number of full-time operators and an all-around balance maintained of operators’ numbers across the varying age group. The performance score of IDST1 is 99%, which justifies that operators are used in a propionate manner. This situation can be further strengthened with IDST8 since extra working hours among operators are considered 2nd best among all the five utilities. No accidents occurred in this utility because of which absentees due to accidents (IDST7) KPI has a 100% performance score, which specifies that employee safety has been given the topmost priority in the utility. Performance of O&M criterion lies in a “high” region; however, six KPIs did not experience a 100% compliance rate. The utility expressed its interest in conducting leak detection programs. This interest can be justified by the low-performance score (33%) of water leakage KPI (IDI3), which means that the utility experiences a considerable number of leakages in its water system and wants to enhance its performance by introducing suitable leak detection programs. 85 The PL of finance falls under a “low” region with their aggregated performance score of only 29%. Apart from two KPIs, debt service ratio (IDF6) and water price (IDF1), the remaining KPIs have very poor performance scores that range between 0.07% – 4%. Residential and ICI service revenues generated (IDF7) have the lowest score, which signifies the minimum ICI water used by this utility. This is evident by IDE10 in the environment criterion, as utility C records the least ICI water use. The utility maintained a low drinking water price; however, the agriculture water price was the most expensive among all the utilities. Utility B has a higher drinking water price and lower agricultural water price, while the water prices are opposite for utility C. The results indicate that utility C might have large pipe networks covering a wider area to deliver water in agriculture, which incurs high costs. In addition, some utilities also treat their agriculture water to the drinking water standards, which means more cost is required to maintain its quality compliance. 5.3.1.4 Utility D Based on the KNN method, the overall PL of Utility “D” is “medium”, whereas the penalty method indicates that the utility lies in the “low” region. Excluding staff criterion, there is a varying difference in the performance level regions for all the criteria. The aggregated performance scores always decrease from KNN to penalty, either from high to medium (Environment and Social) or from medium to low (Finance, Infrastructure, and O&M). Environment criterion falls under a “high” level based on KNN, while an aggregated score of 60 for the penalty method puts the same criterion in a “medium” level. The source water is free of turbidity (IDE3) and nitrate (IDE4) contaminants, which means the utility has good source water conditions. The treatment energy intensity (IDE9) was minimum, with a 93% score. In this utility, water quality complaints (IDS1) were minimum, with a 94% score, only 2nd to utility “A” among all the participating utilities. Water quality advisory (IDS4) was introduced at a low frequency. However, this finding contradicts with boil water notice (IDS3) KPI because the utility has the 2nd highest score (40%) in this KPI. The introduction of IDS3 means that the water system was occasionally disturbed for various reasons, such as water quality failures, water contamination at the source and distribution network, and insufficient water treatment and disinfection (Lui, 2015). Although new mains installed had the highest score (94%), the remaining KPIs under the infrastructure criterion had tentatively low scores. Since the size of the utility is small, the total length of the water main is also small, which indicates that the utility might encounter problems in 86 future, specifically with the increasing population and lifestyle. The utility could not provide relevant data for those KPIs because missing input values completed the decision matrix for the aggregated performance scores. The case was similar for the financial criterion when only two KPIs related to water price data were provided. Lack of data availability in SMWSs has been a constant problem that has been evident in several studies conducted by authors across the globe (Pokhrel et al., 2020; Minnes and Vodden, 2017; Kot et al., 2011). 5.3.1.5 Utility E According to the KNN method, the overall PL of utility “E” is “medium”, while the performance decreases to a “low” region when the penalty method is employed with corresponding performance indices values at 55 and 21, respectively. The most notable difference in PL is observed for the social criterion. Based on the KNN method, the aggregated performance score is 66%, while the score decreases to 12% for the penalty method. Excluding IDS3, which has the lowest performance score (15%), no data was provided for the remaining KPIs. Similarly, none of the data related to infrastructure criteria was provided. The least difference in the aggregated performance score was recorded for the environment criterion at 69% and 55%, respectively. Three KPIs- annual water production (IDE5), agriculture water use (IDE6), and annual agriculture water use (IDE7) have consistent performance scores over 90% and are rated the best among the five utilities. The debt service ratio (IDF6) has a performance score of 30% (2nd worst among all the utilities), indicating that the utility has considerably high debt. Funding the SMSWs has always been a common issue. Conversely, the revenues generated from residential and ICI uses (IDF7) are comparatively higher, which is also justified by relatively higher water uses for ICI purposes (IDE10). Table 5-1 represents the inter-utility comparison of the notable drinking water KPIs. It summarizes the major reasons for each utility's specific drinking water performance. 87 Table 5-1. Inter-utility performance of drinking water Performance Criteria Utility Performance Indicator Score (%) Reasons for low / high scores Staff i) A, B, D & E ii) D & E iii) B, D & E i) Overtime hours ii) Distribution of workforce iii) Experience of operators i) 3 - 4 ii) 0 – 3 iii) 0.1- 2 i) Operators may be on call 24*7. In SMWS, operators shoulder multiple responsibilities, including monitoring and recording water quality parameters, responding to customer complaints and preparing budget plans (Pokhrel et al., 2020). ii) Operators of varying age groups are unavailable, which might hinder the smooth knowledge transition (Kot et al., 2011). iii) Operators are not highly experienced in most utilities, which is a common problem in SMWSs (Schieli et al., 2017). Baring utility “A”, which has a 94% score. Infrastructure i) B ii) B i) Total mains replaced, renewed, and rehabilitated iii) Pipe failures and water leakage i) 100 ii) 95 & 92 i) Utility periodically conducts maintenance programs, which is imperative in enhancing mains longevity and durability. ii) Utility consistently placed necessary equipment in detecting leaks and failures at different locations. The utility uses American Water Works Association Water Audit (AWWAWA)’s free software, which calculates the actual volume of water lost instead of the percentage loss. This scenario allows utility managers to take timely necessary actions to prevent deteriorating pipe conditions (Naik and Glickfeld, 2017). Operation & Monitoring i) B ii) B & C iii) A i) Compliance of turbidity ii) Compliance of total coliforms iii) Compliance of turbidity, E.coli, total coliform, pH, residual chlorine, Haloaceticacids i) 12 ii) 0.4 & 27 iii) 100 i) Freshet during the spring season, accumulation of debris swept away by erosion and flooding into source water may lead to turbid source water conditions (Chhipi-Shrestha et al., 2018 & Pokhrel et al., 2020). ii) High total coliform contamination means environmental contamination (e.g., soil or vegetation mixed with drinking water because of pipe decaying) in the distributed water. iii) 100 % score refers to the maintenance of excellent performance level for a majority of water quality indicators Environment i) D ii) E i). Treated groundwater distributed ii) Residential water use (winter & summer) i) 94 iii) 38 & 2 i) Distributes groundwater as a primary drinking water source. The contamination is relatively less because it is naturally available and does not get exposed to external environments (Gungor-demirci et al., 2019; Mian et al., 2021). ii) Highest water usage in both seasons. The trend has to decline because the present water license volume in the Okanagan 88 Performance Criteria Utility Performance Indicator Score (%) Reasons for low / high scores iii) A & E iii) Agriculture water use iii) 70 & 91 Valley might not meet increasing water demands (OBWB, 2021). iii) Less agricultural water use means that water has been efficiently utilized as per the land size, which is a good sign. Because of expanding agricultural land base across the Valley, there is a significant change in elevated water withdrawals (OBWB 2021). Finance i) C & A ii) B & A iii) B, D & E i) Agriculture water price ii) Water price iii) Debt service ratio i) 0.4 & 62 ii) 44 & 64 iii) 13- 29 i) Typically, agriculture water is not metered. Customers might be using more water than required, leading them to pay more even when the agricultural water price is high (Viaggi et al., 2020). ii) High water prices may be related to the utilities’ budget being heavily used in fixing water leaks & pipe failures or introducing bigger changes (e.g., establishment of a new water treatment plant) such that revenues generated were insufficient in balancing operation and maintenance costs. iii) High debt ratio suggests that the utilities had either undergone a big project in the recent past, where ample funds were invested, or they are completing a large project, which demands maximum funds. Social i) E & D ii) C i). Boil water notice (BWN) ii) Water quality complaints i) 15 & 40 ii) 9 i) The introduction of BWNs reveals that the water system was occasionally disturbed for various reasons, such as water quality failures, water contamination at the distribution network, and insufficient water treatment and disinfection (Lui, 2015). ii) High water quality complaints are recorded due to colour, poor aesthetic, turbidity, and abrupt water contamination (i.e. mixing of sewage water) Note: Score refers to performance scores of the KPIs 89 Wastewater Wastewater KPIs are presented in Section 4.3 (Chapter 4). Figure 5-5 shows the overall PL of three utilities. Based on both KNN and penalty methods, the results indicated that PL was “high” for utility “G”, “medium” for utility “F”, and “low” for utility “H”. The most consistent aggregated performance scores have been detected across two utilities (“F” and “G”) for both environment (53% each) and social and institution (42% and 52%) criteria, implying both methods for the missing data. Utility “H” shows a varying difference in PL for both environment and operation and monitoring (O&M) criteria with a combination of medium and high and high and very high, respectively, following KNN and penalty methods. Regarding the O&M criterion, all three utilities showed a “high’ PL, with Utility “H” demonstrating a “very high” PL when method II was employed. Across all physical (turbidity and temperature), biological (E. coli and faecal coliform), and chemical [biochemical oxygen demand (BOD) and chemical oxygen demand (COD)] parameters, utility “G” had 100% compliant results. No data was provided for E. coli and COD tests. Utility H provided data only for two parameters (turbidity and BOD) for which the performance score was 95% and 100%, respectively. One of the reasons utilities “F” and “H” not providing complete data set can be their lack of sampling or utilities not conducting tests for the respective parameters. This, however, raises a serious question over utilities monitoring mechanisms as important wastewater quality parameters are ignored. Past studies have shown that monitoring the frequency of important water quality parameters is minimum for small communities (Pokhrel et al., 2020; Minnes and Vodden, 2017). Regarding the O&M criterion, all three utilities showed a “high’ PL, with Utility “H” demonstrating a “very high” PL when method II was employed. Across all physical (turbidity and temperature), biological (E. coli and faecal coliform), and chemical [biochemical oxygen demand (BOD) and chemical oxygen demand (COD)] parameters, utility “G” had 100% compliant results. No data was provided for E.coli and COD tests. Utility H provided data only for two parameters (turbidity and BOD) for which the performance score was 95% and 100%, respectively. One of the reasons utilities “F” and “H” not providing complete data set can be their lack of sampling or utilities not conducting tests for the respective parameters. This, however, raises a serious question over utilities monitoring mechanisms as important wastewater quality parameters are ignored. Past 90 studies have shown that monitoring the frequency of important water quality parameters is minimum for small communities (Pokhrel et al., 2020; Minnes and Vodden, 2017). Figure 5-5. Overall performance level of wastewater Inspection of maintenance hole chambers (IWO3) was observed in an extremely ow frequency in utility “G” (performance score ~ 8%), while utility “F” (65%) and H (83%) inspected the chambers in between appropriate intervals. The inspection is considered as important KPI because it allows operators to clean or remove debris in the sewer pipes, adjust and change pipes alignment, and stop the escape of foul gas to the outside environment. Figure 5-6 shows the performance scores for each utility across six criteria using KNN method. Appendix C (Figure C-2) shows the performance scores employing penalty method. None of the utilities reported a loss of wastewater (IWE2) through exfiltration, wastewater main failures, or any other mediums resulting in 100% minimization of wastewater loss. All three utilities had a high wastewater capacity (IWE1), which means wastewater is channelized properly and treated adequately. 5563364962370 10 20 30 40 50 60 70FGHPerformance IndexUtilityPenalty KNN91 Figure 5-6. Performance scores of wastewater criteria using KNN method 5.3.2.1 Utility F The overall PL of utility “F” lies in a “medium” region based on both methods. The aggregated performance score lied in a “very high” region for social criterion with a score of 91%. The utility had almost no complaints related to wastewater, while the service area's growth seems minimal. The utility performance also lied in a “high” region for infrastructure criterion. The utility showed a “low” performance for the finance criterion. Excluding operational and maintenance costs of water treatment (IWF2), the remaining KPIs lie in “medium” or “very low” PL. The worst performance was evident in in-service revenue generated (IWF5). The lack of revenue generation indicates that the utility’s wastewater price might not be sufficient to maintain O&M costs. It is, therefore, important to efficiently utilize the by-products generated from wastewater treatment so that additional revenues generated from them can maintain a balance in O&M costs (The World Bank, 2020). 5342593694307138714834189190376878790 10 20 30 40 50 60 70 80 90FGHPerformance score (%)UtilityOperation & Monitoring Social Staff Infrastructure Finance Environment92 Similarly, the utility had higher debts with only a 0.5% performance score. This situation occurred because the utility might have been upgrading its infrastructure, which requires large capital investments. In addition, the utility might have been already coping with debts in previous years, due to which the initial amount has been compounded, incurring more debts. Since the debt ratio is a critical factor in measuring a utility's financial sustainability, the high debt results in a possibility of lower growth, which might lead the utility into bankruptcy. Indebted utilities will experience losses yearly, making it difficult for them to invest in alternate water uses programs to meet urban water sustainability goals (E. Santos, 2021). 5.3.2.2 Utility G Utility “G” overall PL lies in a “high” region with only a 1% difference over their aggregated performance scores for both methods. The utility has a “very high” PL for two criteria, finance and social. Excluding IWF5 (89%), the remaining KPIs have performance scores above 90%. Although the wastewater price was the least among the three utilities, revenue collection was the maximum. This result indicates that the utility has managed its finances efficiently, satisfying customers' and the administration's needs. The utility seems to have adequately maintained a balance between its O&M costs and ongoing investments and cleared previous debt because the debt-service ratio appears to be very low. Similar to utility “F”, very few complaints were registered and the growth within the service area has not been rampant. Emergency response plans have been well-placed and executed by the utility mangers. The utility demonstrated a “low” performance regarding infrastructure and staff criteria. It has the highest number of reported sewer failure (IWI3) incidents with a performance score of 5%. Ageing infrastructure, sewer backup and blockages, and combined sewer overflow are the primary causes behind such poor performance. For example, the US accounted for 23,000 -75,000 sanity sewer overflow in 2021, which does not include sewer blockages. These overflows might contaminate drinking water sources because of mixing with human sewage, industrial waste, and pollutants. All these activities raise a serious public health concern (USEPA, 2021c). The annual life cycle investment (IWI2) was low, meaning the deprecation of assets, including its replacement and service life values, is poor. In addition, the age of the wastewater plant was high. In the United States, SMWSs face challenges with aging wastewater infrastructure, resulting in unfavourable conditions in constructing and operating wastewater treatment plants (WWTP). It is 93 estimated that 80% of the WWTPs will be constructed in small-medium sized communities, providing services to 1.1 million people, underscoring the importance of these plants being constructed in SMWSs (USEPA, 2016; Moussavi et al., 2021). The results suggest that infrastructure must be upgraded, including wastewater treatment plants, sewer mains, and lift stations. 5.3.2.3 Utility H The overall PL of utility “H” is “low” with performance indices 36 and 37 for methods I and II, respectively. Two criteria, staff and finance, are the chief factors behind the utility’s poor performance. More specifically, the PL of staff criterion lies in a “very low” region with an aggregated performance score of 18%. Although the utility has four full-time operators (IWST5), the workforce distribution (IWST1-IWST4) is very uneven. Employing method 1(poor, PL), the PL decreases to “very poor” when method II is implemented for the finance criterion. The result is unsurprising because, baring IWF5, the remaining KPIs have performance scores below 20%. Wastewater price (IWF3) and O&M cost of sewer mains (IWF1) are amongst the KPIs, with the lowest performance scores, resulting in higher costs. Characteristics of wastewater effluent discharge, type of treatment processes employed, energy consumption, and location of WWTPs are the various factors that impact O&M cost (Wendland & Ozoguz, 2005). Albeit IWF3 is on the higher side, the revenues collected from the wastewater utility is the highest. This might be because there is less unaccountable water, decreased customer complaints, and proper maintenance of wastewater service lines. Increased IWF1 refers to adequate expenses spent on maintenance activities such as routine inspection of wastewater assets and equipment and regular operational activities related to flushing and pipe laying. This statement is further justified because no sewer failures were reported, while sewer blockages were witnessed in a very small quantity. Table 5-2 represents the inter-utility comparison of the notable wastewater KPIs. It summarizes the major reasons for having specific wastewater performance for each utility. 94 Table 5-2. Inter-utility performance of wastewater Performance Criteria Utility Performance Indicator Score (%) range Reasons for low / high scores Environment i) F, G & H i) Biosolids reuse i) 100 (for all the three utilities) i) Biosolids have been utilized in all the utilities for various purposes, including concentrated nutrient recovery, onsite recovery leading to reduced costs and energy consumption, and fertilisers using crop productivity (Diaz-elsayed et al., 2019). Operation & Monitoring i) F i). Compliance of chemical tests i) 15 i) Higher BOD concentration levels in the wastewater effluent can be attributed to increased water temperature. High BOD means that more organic matter is present in the source water body leading to greater dissolved oxygen consumption by bacteria. Depletion of dissolved oxygen forms an unsuitable environment for aquatic organisms' survival (Chapra and Camacho, 2021). Finance i) F, G & H i). Service revenues generated i) 0.05, 89, 99 i) Increased levels of non-revenue water, old customers’ database, a large number of unpaid bills, and irregular or haphazard bills distribution might be the contributing factors for a lacking performance for utility “F”. On the other hand, utilities “G” & “H” have “very high” performance, which suggests that they collect enough revenues from the customers to support O&M costs. Infrastructure i) G, F & H ii) F, G, & H i) Sewer blockages ii) Total wastewater mains renewed i) 10, 87, 98 ii) 0 (for all the three utilities) i) The passing of fat, oil and grease (FOGs) via the drain and clogging of materials that do not pass over mains (e.g., plastics, solid debris, and tree rots) are key sources responsible for creating blockages in utility “G”, deteriorating its performance. On the other hand, utilities “F” & “H” have high performance, which means they have adequate flushing programs, sufficient awareness regarding FOGs, less infiltration and inflows, and sufficient wastewater capacity (USEPA, 2021b). ii) No renewal activities have been conducted in all three utilities, which suggests that their water mains commissioning has been relatively new. Staff i) F, G & H i). Distribution of workforce by age i) 50, 32, 10 i) For utility “H”, the performance score is the lowest as they have operators aged only between 20-30 years, which means they lack sufficient experience in dealing with wastewater-related issues. In addition, it also reflects the utility’s inability to hire/retain seasoned operators who could transfer their knowledge and expertise to the young operators. As a result, a smooth transition in responsibility is not warranted. This argument is justified because the “experience of operators” KPI has the lowest 95 Performance Criteria Utility Performance Indicator Score (%) range Reasons for low / high scores (20%) score among all the three utilities. The other two utilities also do not have a good performance concerning this KPI, which indicates that all the utilities, directly or indirectly, are struggling to maintain a mix of youth and seasoned operators. Social i) F, G, H i) Emergency response readiness i) 67 (for all the three utilities) i) Emergency response readiness plans were fairly implemented in all three utilities. These plans were executed to combat issues related to eutrophication, phosphorus and nitrogen contamination, sudden equipment failure of treatment units, and natural calamities Note: Score refers to performance scores of the KPIs 96 Stormwater Stormwater KPIs are shown in Table 4-3 (Chapter 4). The overall performance level (PL) of two stormwater utilities is represented in Figure 5-7. The result clearly shows that all utilities do not collect stormwater data. Thirteen percent of data were not given by these utilities. Limited data collection and incomplete data set for stormwater suggest that SMWSs have not been given much importance to stormwater. This might be because urban water managers at SMWSs consider both wastewater and stormwater as one component, inadequate resources, and pseudo assumptions about stormwater data do not improve UWS performance. Figure 5-7. Overall performance level of stormwater Employing both methods, KNN and penalty; the overall PL was the same. Utilities “I” and “J” have ‘medium” PL with their relative performance index values (54 and 46), respectively.. The corresponding performance scores are given in Figure 5.8 using KNN method and Appendix C (Figure C-3) employing penalty method The PL lies in a “low” region for financial criterion for both utilities. Out of five KPIs, Utility “I” provided data for three KPIs. It includes “O&M costs for stormwater mains (ISF1), O&M cost for stormwater treatment (ISF2), and cost of stormwater 5446IJPerformance IndexUtility 97 treated per person (ISF3), while utility “J” provided data only for the first two KPIs. Apart from ISF3, which has 100% performance score, utility “I” score was less than 19% for ISF1 and ISF2 respectively. This suggests that high cost has been incurred to operate and maintain stormwater mains and treat stormwater. Figure 5-8. Performance scores of stormwater criteria using KNN method 5.3.3.1 Utility I Both methods provide the same aggregated performance scores for four criteria (O&M, staff, environment, and social). However, their performance level in the performance scale is located in “very high”, “medium”, and “very low” regions. O&M criterion had an 86% performance score, which is largely attributed to its three KPIs [Pumping station inspection (ISO1), Gully pots inspection (ISO2), and gully pots cleaning (ISO3)]. Achieving 100% compliance on pump inspection suggests that pumps efficiently pump a large volume of stormwater flow occurring simultaneously (Government of Ontario, 2019). 44420236651465178886180 10 20 30 40 50 60 70 80 90 100IJPerformance score (%)UtilityOperation & Monitoring Social Staff Infrastructure Finance Environment 98 All the KPIs in the social criterion had their performance scores ranging between 0 -28%, while the scores ranged between 0-8% for the environment criterion. The utility faced stormwater complaints and imposed swimming advisories. Due to increasing population growth and urbanization, there was limited pervious land spanning across the region. This situation reduces the groundwater recharge rate and increases the stormwater volume (Elimers and McDonald, 2014), reflected in their performance score (8%). Annual snowfall was on the higher end, decreasing utilities' environmental aggregated performance score to 8%. Snowmelts also contaminate drinking water sources due to the mixing of chloride, metals, and trance organics as runoff (Galfi et al., 2017). 5.3.3.2 Utility J Similar to utility “I”, both methods provide the same aggregated performance scores for three criteria (O&M, staff, environment. and social). However, the location in the performance scale is “very low” for O&M and staff criteria, “medium” for the environment criterion, and “very high” for the social criterion. The workforce distribution by age group is variable, and their performance scores vary between 2 - 41%. The results suggest that the utility did not spend enough resources on hiring staff, because of which the stormwater system’s overall performance level is at the most declining state. The utility has an 18% aggregated performance score for O&M criterion. This is because ISO1 (0%), ISO2, (11%) and ISO3 (0.5%) have very low performances, which indicates that utility works around pumps and gully pots cleaning has not been effective. Although the mains have not been cleaned periodically, there seem to have no storm main blockages (ISI3), which is shown by the ISI3 performance score of 99% under the infrastructure criterion. The performance score of storm main failures (ISI2) is 99%, indicating that failures are very rare. The aggregated performance score of social criteria is 88%, and largely this “very high” PL must be credited to the utility’s fewer stormwater complaints registered, strong risk management plans implemented, and no swimming advisories being placed. No advisory means E.coli in the water body did not exceed 200 per 100 mm sample as outlined by the Guidelines for Canadian Recreational Water Quality (City of Kelowna, 2021). Table 5-3 represents the inter-utility comparison of the notable stormwater KPIs. It summarizes the major reasons for each utility's specific stormwater performance. 99 Table 5-3. Inter-utility performance of stormwater Performance Criteria Utility Performance Indicator Score (%) Reasons for low / high scores Finance i) I & J ii) I & J i) Stormwater treated per person ii) O&M cost for stormwater i)100, none ii) 13 & 71 i) Stormwater has been treated completely by the utility “I”. This result positively impacts stormwater management as very few water systems treat their stormwater. Treated stormwater can be used for various non-potable uses. ii). High O&M costs for stormwater mains suggest that the utility “I” spends excessively on maintaining its mains, while its revenues are insufficient to conduct regular operation and monitoring activities. On the other hand, utility “J” has relatively low O&M costs, indicating that it is well equipped to maintain its regular O&M costs. Operation and monitoring i) I & J ii) I & J iii) I & J i) Pumping station inspection ii) Gully pots inspection iii) Gully pots cleaning i) 100, 0 ii) 100, 12 iii) 100, 0.05 Utility “I” conducts its maintenance program periodically and has been efficient in achieving 100% compliance results for both inspection and cleanliness. Increased gully pot cleaning frequency (annually to every two months) improves the removing efficiency of gully pots to factor 3 (Langeveld et al., 2016). Proper maintenance of these stormwater components means that they have a long service life, which allows the utility not to go through frequent replacement or rehabilitation programs. Thus, it helps in increasing the overall age of the stormwater infrastructure. While utility “J” does not conduct any programs rigorously, it has a very low performance for all three KPIs. Social i) I ii) J & I i) Stormwater quality complaints ii) LID Implementation i) 28 ii) 0 & 4 i) Combined sewer overflow, source water contamination due to increased runoff, snowpack melting, and basement flooding are the contributing factors to the registration of stormwater complaints (City of Toronto, 2021). ii) The implementation of LID programs has been very limited in utility “I”, where as “J” did not conduct any such programs. Lack of feasible study and municipal standards and plans, insufficient inter-departmental coordination, lack of leadership, and inadequate resources to overcome operational and maintenance challenges are barriers to successfully implementing LDI programs (Podolsky, 2012). 100 Performance Criteria Utility Performance Indicator Score (%) Reasons for low / high scores Staff i) I & J i). Full time operators (FTO) i). 84 & 0 i) Utility “J” do not have FTOs (stormwater) and recruits only a few part-timers, because of which the experience of these operators is less. Environmental Operators Certification Program (EOCP) has listed only four types of operators, and a separate position for stormwater operators is not mentioned (EOCP, 2022). This discrepancy can also be one of the reasons why no FTOs are available in utility J as the guideline does not mandate it. In addition, SMWSs also struggle to recruit the desired number of operators as planned (Pokhrel et al., 2020). On the other hand, utility “I” has a good number of FTOs, which is also why their performance is comparatively better than “J”. Infrastructure i) I & J ii) I & J i). Total storm main length ii) % surface flooding i) 80 & 9 ii) 100 & 40 i) Based on the performance score, utility “J” appears to have more combined sewer mains rather than a separate one for stormwater. With increasing flood incidents and rapid urbanization, the utility needs to construct separate mains for proper water channelization. ii) Heavy rainfall and quick snow melts are the major contributing factors to surface flooding. In some cases, floods lead to the overflow of sewer and stormwater into the ground, which might disturb the integrity of the drinking water source. These incidents certainly cause a decline in the performance level. Environment i) I & J ii) I & J i) Impervious surface ii) Annual snowfall i) 9 & 91 ii) 9 & 87 i) Increased impervious surface decreases the performance as permeable land surfaces get converted into areas for buildings construction, due to which stormwater does not infiltrate properly into the ground, disturbing the overall groundwater recharge capacity (Pokhrel et al., 2021). ii) Heavy snowfall followed by a quick snow melting can contaminate the drinking water source as the pollutants gathered from storm runoff contain harmful chemical contaminants that are detrimental to the health of the source water. As a result, stormwater system performance declines. Note: Score refers to performance scores of the KPIs 101 Interventions required for urban water systems Different types of interventions are identified after evaluating water systems using the developed PAM, which is listed in Table 5-4. Table 5-4. Types of interventions Utility Service Intervention Type Performance Criteria for Intervention A DW III Staff and infrastructure B II Staff and operation and monitoring C III Infrastructure and social D I Staff and operation and monitoring E I Staff and social F WW II Finance and staff G III Staff and infrastructure H I Finance and staff I SW II Environment and finance J II Finance and staff Two performance criteria are listed, which require the most attention when implementing interventions in their respective utilities. The intervention type (step 3: Development of the PAM) is recommended based on utilities performance index values and their corresponding level in the performance scale. Utilities might implement different intervention types but to the same criterion. For instance, both utility “A” and “D” have to perform interventions for the staff criterion, where “A” undergoes the minor and “D” undergoes the major intervention. In such a scenario, utility “A” might have to hire a few operators compared to utility “D” to suffice the human resources in enhancing water system performance. Another example could be utility “A” witnessing fewer workplace incidents than utility “D”. In this case, both utilities will be recommended to follow the work safe BC protocols or a distinguished safety organization that the utility is following. However, utility “D” would want to develop more stringent policies and adopt better cautions in minimizing accidents since their intervention type is more severe. Appendix D details the various interventions employed for drinking water, wastewater, and stormwater. 5.4 Conclusions PAM was developed and successfully applied to five drinking water, three wastewater, and two stormwater utilities in the Okanagan Valley, Canada. The findings manifest the practicality of the model to strengthen urban water sustainability. The effectiveness of TOPSIS in calculating the performance indices indicates the method’s reliability in determining the overall UWSs performance. In the performance scale, none of the utilities recorded their performance in a “very high” region, which suggests that interventions ranging from a minimum to maximum degree are 102 certainly required to enhance UWS performance and promote sustainability. In drinking water, utilities (“A” and “C”) completely outranked utilities (“B”, “D”, and “E”), with the former's performance level (PL) located in a “high” region. Both these utilities had better alternate source water plans, the least residential (winter) and agricultural water use per hectare, and higher compliance rate for E.coli, residual chlorine, turbidity and pH parameters leading to their high performance. In wastewater, the utilities had varying PL. Utility “G” had a “high” PL primarily because of the least wastewater price, low O&M costs for sewer mains and wastewater treatment, fewer registered wastewater complaints and well-structured emergency response readiness plans. In stormwater, the PL lay in the “medium” region for both utilities. The performance score of the environment criterion was “very low” for the utility “I”. This was mainly because of the increased population that led to the conversion of pervious surfaces into impervious and no stormwater reuse. Utility “J” had a minimum number of stormwater complaints registered and swimming advisories placed, whilst both utilities did not implement LID techniques in their regions. One of the primary limitations of this chapter is that SMWSs provided inadequate KPIs data to the research team. Although, imputation techniques were applied to incorporate missing data, real KPIs data would have resulted in gauging water system performance in a better manner. Specifically, stormwater data is available at a minimum level, which raises questions over utilities’ database in keeping and maintaining stormwater records. Increased municipal participation in this benchmarking study would have generated better comparisons against water systems performance and contributed in effectively implementing interventions. As a result, it would have also allowed the findings gathered from this study to be compared against various national and international benchmarking studies, such as NWWBI, AWWA, and IWA; providing excellent validation to the present study. Future research must be oriented towards gauging uncertainties with limited data and encouraging utility managers to provide maximum data (e.g., daily data points) for a more robust analysis. In addition, data related to spatial and temporal variations can be considered and with available information, performance can be evaluated more frequently, which will assist utility managers in making immediate interventions. 103 Conceptual Model of One Water Community A version of this chapter is currently under review in the Journal of Cleaner Production entitled “Conceptual Model of One Water Community: A Path to Move Forward”. 6.1 Introduction The DPSIR Driver-Pressure-State-Impact-Response) framework has recently been popular among researchers and scientists to understand various water-related problems (Song and Frostell 2012; Vannevel 2018; Gari et al., 2018; Kaur et al., 2020). The framework is useful in identifying solutions comprising various environmental and lifecycle-related issues surrounding UWSs. Considering it an effective tool, the DPSIR framework has been widely used to keep top-level management informed about various strategies that can be used to manage water resources effectively. The framework comprises five states. Drivers (e.g., population growth and urbanization) include human-oriented activities and processes. Pressure (increasing water demands, urban flooding, pipe failures, and water losses) negatively impacts the environment. The state (ageing infrastructure, segregated UWM) refers to the present status of water infrastructure and assets. Impact (freshwater resources depletion, water footprint) measures the various environmental, social, and economic changes. Response (OWA) is the specific actions taken to reduce the pressure and impacts (Kaur et al., 2020). Globally, freshwater consumption has been increasing at 1% annually since 1980. As a result, it is predicted that water use will increase by 20-30% by 2050. Agriculture, industry, energy, residential, and municipalities are and will also be the key contributors responsible for increasing water demands (Burek et al., 2016). Because of exponentially escalating climate change incidents, water resource availability is at the cusp of depletion in already water-stressed regions. Canada, which comprises 7% of total renewable freshwater resources (Government of Canada, 2018), is also under pressure to manage its urban water supplies, specifically in its semi-arid regions (e.g., Okanagan Valley, BC). In the last five years, the Valley witnessed unfavourable events related to acute floods, forest fires, heat waves, and cold winter months. Because of the extremely hot weather experienced by the British Columbians during the summer of 2021, the provincial government increased the drought level to 4 in the Valley, asking consumers to reduce their water use (Provincial Government of British Columbia, 2021). 104 The consequences mentioned above suggest that water needs to be used wisely since it is a finite resource. Urban water managers and consumers need to comprehend the value of each water drop, which is possible by implementing OWA goals in the One Water community. For this purpose, reducing potable water use (RPWU) is regarded as a stepping stone. RPWU not only assists in areas with water shortage problems but also paves the way for policymakers not to build additional dams or extract water from water sources to accommodate the increasing population. In addition, RPWU decreases wastewater generated, consumes minimum energy for water treatment. As a result, greenhouse gases emission is also reduced from treatment plants, thus contributing to enhance urban water sustainability. (Australian Government, 2020). Several conceptual models have been developed to decrease potable water consumption whilst building sustainable, reliable, and resilient UWSs. Sapkota et al. (2015) also developed an integrated model by combining a centralized water system with alternative water supplies (e.g., recycled water, precipitation). The authors indicated that such a hybrid combination uses 17% less potable water than conventional centralized systems (Sapkota et al., 2015). Zeisl et al. (2018) developed a conceptual urban water balance model and applied it in Melbourne using scenarios, including water-saving appliances and decentralized water storage techniques. The authors found that traditional rainwater reuse met a 25% reduction in potable water demand, whereas 60% of this demand is reduced by integrating rainwater and greywater recycling (Zeisl et al., 2018). Cureau and Ghisi (2019) proposed four strategies to enhance potable water savings, which include using dual-flush toilets, rainwater harvesting, greywater reuse, and combining these three strategies. The authors found that greywater reuse was the best method to save water to meet non-potable water demands, while potable water savings ranged between 1.7% and 50.5% (Cureau & Ghisi, 2019). Basupi (2021) developed an integrated water-saving scheme model comprising water-saving appliances, rainwater harvesting systems, and water-reuse methods. The author indicated a close relationship between drinking water, sanitary, and water-saving schemes, which significantly minimise potable water demands and build sustainable UWSs (Basupi, 2021). Optimization techniques are critical to increasing water systems’ efficiency and effectiveness, which can guide the decision-maker in the early planning stages. Porse et al. (2017) developed a network flow model based on optimization principles to observe the reliance of Los Angles water systems on imported water. The findings suggested a combination of optimal conditions, including 105 increased stormwater capture and aquifer recharge rate, augmented water reuse strategies, and groundwater reallocation rights. Mcclymont et al. (2020) developed an optimization model based on sustainability and resiliency to implement low-impact development strategies considering water quality and quantity as two optimization objectives and improved quality of life index to estimate optimal engineering strategy, including biodiversity and amenity. The results indicated that green swales and bioretention tanks are important in promoting water quality resilience, whereas green roofs are significant in gaining flood resilience. Bakhtiari et al. (2020) developed a risk-based optimization model to determine optimum annual water allocation to cope with potable water demand uncertainties and water resources availability in achieving sustainable urban water development. Saleem et al. (2022) employed a Taguchi-based optimization approach to optimize the aquatic centre’s operational performance in terms of performance-to-cost ratio. The authors suggested that maintaining a medium level pH, temperature, free chlorine, humidity level, and the number of working hours were critical in optimizing aquatic centre performance. Taguchi’s experimental design indicates the minimum number of trials that are conducted with appropriate factors and levels to determine the best possible outcome for a given condition (Meena et al., 2018). Dr. Genichi Taguchi first developed this method to enhance the productivity of an engineering task (Tagauchi, 1993). Taguchi's reliability over other optimization methods ( e.g., response surface method, central composite design) can be attributed to a) it is a robust optimization technique that involves multiple factors and provides comprehensive quantitative information from a minimum trial (Pundir et al., 2018); and b) It is simple, efficient, and systematic method to optimize performance across diverse engineering systems. In UWM, potable water conservation has become a major concern due to its limited availability, increasing water demands, and climate change incidents. Based on the authors’ best knowledge, no comprehensive, optimized models exist to reduce potable water use from a multi-dimensional facet. Therefore, this chapter aims to develop an optimization model (using OWA principles) to assess and build sustainable, resilient, and reliable UWSs. Integrating each UWS component (drinking water, wastewater, and storm water) into a single model is the need of the current time to devise more realistic and applicable water management intervention strategies. 106 6.2 Methods Figure 6-1 shows the methodology adopted to develop a Taguchi-based optimized model that identifies the most optimal condition to reduce potable water use. The details regarding model development are discussed in the following sub-sections. Figure 6-1. Methodology adopted for the current study DPSIR framework The conceptual model will be built based on a DPSIR framework. In this chapter, the response derived from the framework will focus on five key interventions: water-saving appliances (WSA), reclaimed water use (RWU), low-impact development (LID), resource recovery (RR), and good governance (GG). These interventions have been identified by the comprehensive literature 107 review, where the scope of OWA is defined more at a strategic level. Therefore, the scope is limited to water conservation strategies, resource recovery platforms, stakeholder engagement activities, and building strong water governance. The interventions are based on six guiding principles of OWA proposed by Pokhrel et al. (2021). These principles integrate all three UWS components into a single system, making OWA achieve multiple benefits, which are essential in building the One Water community of greater sustainability, resiliency, and reliability. Principle 1 states, “A mindset that all has value, " is related to all five interventions. Table 6-1 shows the relationship between interventions and guiding principles. Table 6-1. Interventions and OWA guiding principles Intervention Guiding principle (Number) Relation between intervention & guiding principle Water saving appliances Systems approach (3) Installation of water-efficient devices in households and commercial buildings saves gallons of water per day. Reclaimed water use Fit-for-purpose water use (4) Non-potable reclaimed water does not require very high-quality potable water. Resource recovery Achieve multiple benefits (2) Resource recovery from wastewater, such as energy helps produce heat and electricity and reduces GHG emissions. Low impact development Flexible and adaptive infrastructure (5) LID adds greenery to the community, reduces and retains runoff, and recharges groundwater. Decreased contaminant levels in source water will improve overall source water quality. Good governance Stakeholder engagement (6) Participation of the public in water-related decision- making and the inclusion of diverse stakeholders to enhance collaboration will assist decision-makers in managing UWS more effectively and efficiently. Note: Numbers in parenthesis represent guiding principle number 6.2.2 Data collection and simulation Data on percentage reduction in potable water use was collected for all the parameters via various case studies, conference proceedings, book chapters, and reports compiled by various organizations, including USEPA, Statistics Canada. Appendix E shows the data collected from thee literature for the most of the parameters. Because of space limitation, all data values have not been added. “Potable water”, “percentage reduction”, “water saving appliances”, “water sense labelled”, “urinals”, “low-performance shower head”, “dual flush toilets”, “cloth washers”, “dishwashers”, “reclaimed water use”, “agriculture”, “landscaping and industries”, “soil and weather-based sensors”, “energy reduction”, “GHG emission”, “treatment plants”,” resource recovery”, “low impact development”, “good governance”, “public participation”, “collaboration”, “bioswales”, “bioretention”, and “green roof” were the keywords used for the 108 literature search.. Over 100 publications were compiled, out of which 90% of the literature was published after 2015. Priority was placed on accessing up-to-date and high-quality literature as sources of information; thus, a large volume of literature selected was published after 2015 and beyond. Fifty-one of these articles were derived from peer-reviewed journals and the remaining literature included reports, web sources, book chapters, and conference proceedings. Most articles were collected from peer-reviewed journals, including Water Research, Journal of Environmental Management, Urban Water Journal, Environmental Management, Water Policy, Science of Total Environment, Sustainability, Journal of Hydrology, and Environmental Reviews. @Risk software was used to calculate 10th, 50th, and 90th percentiles (Palisade, 2023). These percentiles refer to the corresponding percentage of potable water use reduction at a specific rate. For example, if a low-performance shower reduces 47% of potable water demand at the 90th percentile. It means that at a 90% confidence interval, 47% of potable water use is reduced. Iterations were run 10,000 times and simulated twice to receive percentile values and distributions. Table 2 represents each intervention and its corresponding parameters. Table 6-2. Interventions and parameters Intervention Parameters Water saving appliances Low-performance shower head, urinals, dual flush toilets, dishwashers, cloth washers Reclaimed water use Agriculture, landscaping and industries, soil and weather-based sensors Resource recovery Energy reduction/saving, GHG emissions reduction from treatment plants Low Impact Development `Bioswales, Bioretention, Green roofs Good governance Collaboration and public awareness The following points outline a short description of the identification of each intervention in reducing potable water use. Water-saving appliances: In the United States, using WaterSense-labeled products (e.g., use of efficient urinals, showerheads, bathroom faucets) can help an average family conserve 13,000 gallons of potable water annually. Around 30% of potable water demand is reduced by replacing the old method of household irrigation with WaterSense labelled irrigation controller (USEPA, 2022b). The use of Water Efficiency Labelling and Standards devices (e.g., taps, showers, and cloth washers) in Australian households saved 112 gigalitres (GL) of water per year in 2017. Based on this rate, it is predicted that potable water might be saved up to 185 GL/year and 231 GL/year in 2026 and 2036, respectively, across Australia (Institute for Sustainable Fututres, 2018). 109 Reclaimed water use: It plays a significant role in reducing potable water consumption. It is believed that households using reclaimed water in toilet flushing can save up to 30% of potable water consumption (District of Schelt, 2017). Parks connected to reclaimed water supplies in the form of recycled water reduce potable water usage significantly. One of the studies conducted in California (USA) showed that each unit of recycled water reduces 0.817 units of potable water consumption (Maier et al., 2022). Resource recovery: Fifty-three percent of total energy consumption in a typical wastewater treatment plant can be compensated by the energy recovered from a combination of an anaerobic digester and combined heat and power plant (Hao et al.,2015). Around 6.5% of self-sufficiency in energy can be produced from the wastewater treatment plant (having a design capacity of 30,000 m3/day), when the plant is properly retrofitted using electrical (photovoltaics and hydropower) and thermal (heat pump) devices (Chae & Kang, 2013). Both water treatments and pumping require a considerable amount of energy. Typical drinking water and wastewater treatment plants consume 30-40% of total municipal energy use (USEPA, 2022a), which is why reducing potable water consumption significantly reduces energy consumption. Similarly, leakages and water flow rates in the pipe must be properly managed as lesser these leaks, less water is lost, thereby reducing water consumption. Low Impact Development: Implementing LID strategies in reducing stormwater runoff is a popular engineering design approach adopted globally. In managing stormwater quality and quantity, this manmade development that depends on natural phenomena holds much importance (Hager et al., 2019). Runoff from stormwater is composed of several pollutants and chemical contaminants, quickly mixed with surface water sources, degrading aquatic habitats. LID practices (e.g., bioswales, bioretention) reduce and retain runoff, contributing to the passage of minimum runoff into surface water sources and allowing restoration of natural habitat for flora and fauna. The increasing population has prompted urban developers to convert a majority of permeable land into impermeable surfaces, because runoff cannot easily penetrate the ground (Pokhrel et al., 2021). LID strategies (e.g., permeable pavement) enable rainfall to be retained more onsite, allowing runoff to pass through the ground, filtered by soil, enhancing groundwater table level, and contributing to increasing groundwater recharge rate (USEPA, 2012). Thus, the use of LID strategies assists urban water managers in increasing their water availability source. These sources 110 can be used for other non-potable applications, which ultimately helps in increasing water sources for drinking purposes. Good governance: In 2016, the Regional Municipality of York collaborated with neighbouring local municipalities to reduce the inflow and infiltration rate on private property and new developments. By 2031, the municipality plans to reduce 40 million litres of inflow and infiltration per day (The Regional Municipality of York, 2016). As a result, groundwater, considered an important water source for potable supplies, gets conserved. Stormwater can be retained and used for other non-potable uses. Sewer systems become more efficient as they treat water based on the design capacity and do not need to accommodate extra water volume due to inflow and infiltration. Another example includes the collaboration of Dow Chemical Company (US) with the City of Jackson in meeting their freshwater supplies. The city supplied their reclaimed water to the company’s freshwater canal, helping them meet their water demands. Because of this collaboration, it is assumed that 1.3 billion gallons of water are saved annually, sufficient to provide potable water supplies to 30,000 people (US Water Alliance, 2016). 6.2.3 Taguchi method Taguchi method involves five steps, which are described as follows: a) Select factors and levels: Five controllable factors and four corresponding levels were identified. These factors are the same five interventions mentioned in the above DPSIR framework. The factors were selected based on their ability to reduce potable water use (RPWU), which is considered a response variable, contributing to increased water system performance. Four levels were selected based on the percentile values for each factor. b) Define orthogonal array: An orthogonal array (OA) uses fractional experiment designs, which are dependent on a number of factors and levels. The array is important in reducing the number of trials to determine the optimum condition for the reduction of potable water use. Equation 6-1 shows the formula used to determine the least number of trials in an OA. 𝑁 = (𝐿 − 1) ∗ (𝐹 + 1) (6-1) where N = minimum number of trials, L = number of levels, F = number of factors In the present study, an 𝐿 16 (45) OA is selected corresponding to five factors and four levels, which forms sixteen trials. 111 c) Compute response variable value: An aggregated value is determined for each trial to compute the response variable value. The Ordered weighted averaging method was employed to calculate the aggregated value. The same technique was also used for performance index calculation, as discussed in Chapter 5. The response variable determines the most optimal condition based on the S/N ratio. d) Determine Signal to noise (S/N) ratio: S/N ratio refers to the ratio of the mean (i.e., desired value) to the standard deviation (i.e., undesired value) that is determined for each trial. It is dependent on the characteristics of the response variable. There are three types of SN ratios, which are listed in Equations 6-2 to 6-4 𝐻𝑖𝑔ℎ𝑒𝑟 − 𝑡ℎ𝑒 − 𝑏𝑒𝑡𝑡𝑒𝑟 = −10 𝑙𝑜𝑔 ∗ [1𝑛∑1𝑧2𝑛𝑘=1 ] (6-2) 𝑁𝑜𝑚𝑖𝑛𝑎𝑙 − 𝑡ℎ𝑒 − 𝑏𝑒𝑡𝑡𝑒𝑟 = −10 ∗ log (𝑧2) (6-3) 𝐿𝑜𝑤𝑒𝑟 − 𝑡ℎ𝑒 − 𝑏𝑒𝑡𝑡𝑒𝑟 = −10 𝑙𝑜𝑔 ∗ [1𝑛∑ 𝑧2𝑛𝑘=1 ] (6-4) where, n is the total number of runs conducted under each trial, z is the aggregated value for the specific trial. Since the maximum water reduction potential is the optimal value for this study, a higher-the-better S/N ratio is desired. Aggregating the highest five S/N ratios corresponding to each factor provides the optimal combination to reduce potable water use. e) Prediction and confirmation: After identifying the optimal strategy, the next step is to predict the response using the most optimal factors. For this purpose, confirmation tests were conducted. The predicted response value at the most optimal situation is determined as: 𝑍𝑜𝑝𝑡 = 𝑚 + ∑[𝑚(𝑎, 𝑏) − 𝑚]𝑘𝑖=1 (6-5) Where, 𝑍𝑜𝑝𝑡= predicted response value optimally, m = mean over 16 trials, m ab = mean value under the optimal conditions. A confirmation test is performed to validate the results by comparing the experimental value (Ev) with the predicted value (Pv). For the results to validate, Ev should always be greater than the Pv. 112 6.2.4 Statistical tests and modelling: ANOVA is conducted to determine the control factors' contribution to the response variable. It is also useful to asses a better understanding of whether or not the results obtained are justified and whether the trials performed are within the controlled conditions (Razmi and Ghasemi-fasaei, 2018). A regression model was developed to test the overall model effectiveness based on the coefficient of regression (R2) values. These values were determined across three quartiles (10th, 50th, and 90th). 6.3 Results and discussion The results obtained through Taguchi analysis, confirmation tests, analysis of variance, and a regression model are elucidated in the following sections. 6.3.1 Simulation compilation Table 6 - 3 shows the results of various parameters at three different percentiles and identifies a unique distribution type. Table 6-3. Results of percentiles calculation % Interventions and parameters 10th 50th 90th Distribution Type I. Water Saving Appliances (WSA) Low performance shower heads (WSA1) 22.98 32.29 46.89 Extvalue Urinals (WSA2) 59.25 78.31 91.45 Extvalue Min Dual flush toilets (WSA3) 8.23 28.44 41.32 Extvalue Min Standard dishwashers (WSA4) 27.65 32.18 48.75 Laplace Compact dishwashers (WSA5) 44.95 48.71 52.47 Pareto Clothes washers (WSA1) 7.98 35.7 63.42 Uniform II. Reclaimed Water Use Agriculture (RWU1) 9.83 54.0 98.16 Uniform Landscaping & industries (RWU2) 9.63 14.06 49.6 Pareto Soil moisture & weather-based sensors (RWU3) 22.19 34.99 47.79 Uniform III. Resource Recovery Energy reduction / saving (RR1) 16.33 36.25 56.16 Laplace GHG emissions reduction from treatment plants (RR2) 15.5 22.49 62.34 Pareto IV. Low Impact Development Bioswales (LID1) 62.69 85.76 99 Extvale Min Bioretention (LID2) 19.99 45.49 71 Uniform Green roof (LID3) 17 39 61 Uniform V. Good Governance Collaboration (GG1) 8.92 50.18 81.44 Normal Public Awareness (GG2 ) 29.69 60.49 91.29 Uniform All the components of WSA are water sense labelled, which means that these components meet the USEPA’s guidelines and standards regarding reducing water footprints Components having 113 this label indicate that they save water in a measurable quantity at a national level and achieve water efficiency using different technologies (USEPA, 2021c). Water reduction potential at the 90th percentile showed that using water sense labelled urinals decreased potable water use by 91%, the highest in this category. In comparison, the lowest was 47% for low-performance shower heads. The highest water reduction at both 50th (78%) and 10th (59%) percentiles were showed by urinals, while dual flush toilets (28 % and 8 %) showed the lowest savings. Urinals with WaterSense labelled use less than 0.5 gallons per water flow and save anywhere between 0.5-4.5 gallons per flush. It is believed that using efficient types of urinals can save commercial buildings about 26,000 gallons of water annually (USEPA, 2017). Reclaimed water in agriculture reduces potable water consumption by 98% at the 90th and 54% at 50th percentile, respectively. Across four US cities (Florida, Hawai, California, and Idaho), 8-92% of reclaimed water was used for agriculture (Sheikh et al., 2018). In Vernon (Canada), ninety-eight percent of reclaimed water is used to irrigate local golf courses, soccer fields, and agricultural lands (City of Vernon, 2021). Soil moisture and weather-based sensors contribute to 35% and 22% of water reductions at the 50th and 10th percentiles. A household replacing their clock-based controller for irrigation purposes with WaterSense-labeled soil moisture sensors can decrease their water use by up to 30%, equivalent to saving 1,000 gallons of potable water yearly (USEPA, 2021c). About 20-50% of potable water is reduced by installing weather-based controllers into small and medium-sized irrigation systems. These controllers have innovative characteristics such as making adjustments to the irrigation schedule based on weather reports, providing optimal water based on the nature of the landscape, determining actual water use by equipping with web-based technologies, and detecting rain, leaks, and service line break (Able Irrigation, 2022). Greenhouse gas (GHGs) emissions from the treatment plants can be reduced by up to 62% at the 90th percentile, while more than 16% of energy can be recovered from these plants at the 10th and 50th percentiles. Along with biogas recovery from the anaerobic digester, GHG emissions can be reduced by 14.5% from the two wastewater treatment plants (Yerushalmi et al., 2013). Another study conducted by (Gingerich & Mauter, 2018) found that treatment plants having a capacity of 5 MGD can reduce GHG emissions by 25%, while the emissions reduction potential soars up to 84% from biogas-fueled heat and electricity production with the design capacity greater than 5 MGD (Gingerich & Mauter, 2018). 114 Bioswales help reduce runoff at a higher percentage, a maximum up to 99% at the 90th percentile, and improve water quality. Similarly, green roofs could reduce runoff by between 16-61%. Xiao et al. (2017) assessed the effectiveness of two bioswales in California (US) that was constructed eight years ago. The authors found that bioswales reduced surface runoff by 99.4% and reduced chemical contaminants (nitrogen, phosphate, and total organic carbon loading) by above 99% (Xiao et al., 2017). In Virginia (US), bioswales reduced 56% runoff during one year, with an annual rainfall intensity of 1,404.1 mm (Hayes et al., 2021). Talebi et al. (2019) investigated the runoff reduction potential of green roofs over two types of vegetation across six Canadian cities. The authors found that low-water use plants reduce runoff between 17 – 50%, whereas the best performance for the high water use plants reduced the runoff by between 52 -62%. Based on the city, Regina had the best runoff reduction and Halifax had the worst (Talebi et al., 2019). Water being more of a subject of social interest, collaboration and public awareness play a significant role in reducing potable water use. The highest reduction is observed at the 90th percentile, with the water reduction lying between 80-92%. (Hurlimann & Dolnicar, 2016) conducted an online survey in 2012 across nine countries to understand the public acceptance of recycled water (i.e., water has been treated to the highest quality), rainwater, and desalinated water for both potable and non-potable uses. The results indicated that 75-88% of the public were convinced about using recycled water for non-drinking purposes (i.e., washing cloths, cleaning, toilet flushing, and garden watering), whereas 28-62% of the public opined to using the same water for potable consumption (i.e., drinking, cooking, and washing body) (Hurlimann and Dolnicar, 2016). Another study conducted in Spain showed that 54 – 71% of people agreed to use recycled water (i.e., water collected from sewage system undergo intensive water treatment before its distribution) for different non-potable uses (i.e., washing clothes, cleaning kitchen and bathroom, washing car, and garden watering), whereas 19-34% people expressed their interest to use the same form of water for potable applications (drinking, cooking, showering, brushing, and feeding pets) (López-ruiz et al., 2021). The increasing percentage of public acceptance of using water other than highly treated water for drinking states that the public is willing to adopt alternate water sources to fulfil their water demands, thus reducing potable water supplies. 6.3.2 Signal-to-noise ratio The signal-to-noise ratio (S/N) of each controllable factor was calculated to maximize the reduction potential of potable water use. Table 6-4 represents the S/N ratios for trials at three 115 different percentiles. Higher is the S/N ratio; there exists a minimum variation difference between the expected output and measured value. Table 6-4. Signal-to-noise ratios for L16 trials Factors Response Results WSA RWU ERR LID GG RPWU S/N ratio 90th percentile 40 40 50 50 70 73.83 37.36 40 60 70 70 85 84.05 38.49 40 80 80 90 90 88.98 38.98 40 99 90 95 95 94.30 39.49 60 40 70 90 95 85.98 38.68 60 60 50 95 90 83.88 38.47 60 80 90 50 85 89.22 39.00 60 99 80 70 70 88.49 38.93 80 40 80 95 85 86.64 38.75 80 60 90 90 70 91.59 39.23 80 80 50 70 95 86.77 38.76 80 99 70 50 90 90.52 39.13 95 40 90 70 90 92.43 39.31 95 60 80 50 95 90.90 39.17 95 80 70 95 70 92.12 39.28 95 99 50 90 85 96.87 39.72 50th percentile 20 15 10 30 20 58.26 35.30 20 25 20 50 40 64.59 36.20 20 45 30 70 60 71.24 37.05 20 55 40 90 80 76.95 37.72 40 15 20 70 80 71.10 37.03 40 25 10 90 60 72.73 37.23 40 45 40 30 40 70.32 36.94 40 55 30 50 20 69.66 36.85 60 15 30 90 40 76.37 37.65 60 25 40 70 20 74.58 37.45 60 45 10 50 80 72.42 37.19 60 55 20 30 60 73.37 37.31 80 15 40 50 60 76.45 37.66 80 25 30 30 80 76.48 37.67 80 45 20 90 20 78.63 37.91 80 55 10 70 40 62.31 35.89 10th Percentile 5 8 10 15 7 54.48 34.72 5 13 15 35 20 57.52 35.19 5 17 20 50 25 61.62 35.79 5 25 25 70 35 66.49 36.45 25 8 15 50 35 63.18 36.01 25 13 10 70 25 65.42 36.31 25 17 25 15 20 61.85 35.82 25 25 20 35 7 60.82 35.68 50 8 20 70 20 69.79 36.87 50 13 25 50 7 66.57 36.46 50 17 10 35 35 64.61 36.20 50 25 15 15 25 64.97 36.25 60 8 25 35 25 71.47 37.08 116 Factors Response Results WSA RWU ERR LID GG RPWU S/N ratio 60 13 20 15 35 67.33 36.56 60 17 15 70 7 69.88 36.88 60 25 10 50 20 67.28 36.55 Figure 6-2 shows the mean S/N ratio graph obtained for the RPWU at the 90th percentile. The highest mean S/N ratio obtained for RPWU are WSA at 95%, RWU at 99%, RR and LID at 90% each, and GG at 95%. The corresponding percentages reflect the controllable factors' potential to reduce potable water use. Among all the parameters of the controllable factors, an optimum parameter is identified for each factor based on the above percentage proximity to the nearest parameter, as identified in Table 3. For example, 95% potable water reduction under the WSA category reflects the selection of “Urinals” as the optimum parameter. Similarly, the use of reclaimed water for agriculture purposes, GHG reductions from treatment plants, use of bioswales as LID, and increased public awareness for establishing good governance are the most optimum parameters to achieve a high reduction in potable water use (RPWU). Hence, the optimum combination at the 90th percentile in the order of prioritization to achieve RPWU is given as follows: RPWU 90 = Urinals + Agriculture use + GHG emissions reduction + Bioswales + Public Awareness Various studies show that combining one or more strategies is useful in reducing potable water use. (Cureau and Ghisi, 2019) indicated that a combination of three strategies: replacement of traditional toilets with dual flush systems, greywater reuse, and rainwater harvesting have a strong potential to achieve potable water savings. The authors found that up to 85% of water savings are possible in public and commercial buildings, while more than 30% of savings can be achieved in single and multi-family residential buildings (Cureau & Ghisi, 2019). Potable water savings of up to 79% are feasible when water-saving appliances, greywater reuse, and rainwater harvesting are installed in commercial buildings (Proença & Ghisi, 2013). 117 Figure 6-2. S/N ratio of RPWU at 90th percentile Note: WSA: Water saving appliances; RWU: Reclaimed water use; RR: Resource Recovery, LID: Low impact Development; GG: Good governance; RPWU: Reduce potable water use The same procedure was applied to obtain the graphs for mean S-N ratios at the 50th and 10th percentiles, as outlined in Figures 6-3 and 6-4. At the 50th percentile, the highest mean S/N ratio obtained for RPWU are LID at 90%, RR at 40%, GG at 80%, WSA at 60%, and RWU at 45%. At the 10th percentile, S/N ratio was found to be the highest for WSA at 60% followed by LID at 70%, and RR, GG, and RWU at 25% each. The optimum combinations for both percentiles based on the order of prioritization are as follows: RPWU 50 = Bioswales + Energy reduction + Public awareness + Urinals + Soil moisture & weather-based sensors RPWU 10 = Urinals + Bioswales + Energy reduction + Public awareness + Soil moisture & weather -based sensors 118 Figure 6-3. S/N ratio of RPWU at the 50th percentile Note: A: Water saving appliances; B: Reclaimed water use; C: Resource Recovery, D: Low impact Development; E: Good governance; RPWU: Reduce potable water use Figure 6-4. S/N ratio of RPWU at the 10th percentile 119 Note: A: Water saving appliances; B: Reclaimed water use; C: Resource Recovery, D: Low impact Development; E: Good governance; RPWU: Reduce potable water use 6.3.3 Confirmation tests A confirmation test was conducted across three different percentiles to predict the value for the RPWU. For this purpose, the most optimum condition was used and the S/N ratios of the highest levels were selected to determine the predicted value using the Minitab software. Table 6-5 presents the results of confirmation tests along with their respective errors and improvement in S/N ratio. It was noted that the predicted and experimental values for RPWU were close to each other. The difference between experimental and initial values improves the response variable results. The experimental value was calculated manually by introducing the new response value determined from the optimum condition established for the prediction. This value was introduced into the S/N ratio formula as provided in equation 6-2. The experimental values were greater than the predicted values at all three quartiles, which successfully justified the optimum condition attained via an objective maximisation function. Errors were less than 25% between experimental and predicted values, suggesting that the proposed model can predict the response with considerable accuracy, thus validating the optimization model. Table 6-5. Confirmation test results for the response variable Percentile Initial (dB) Prediction (dB) Experiment (dB) Error (%) Improvement (dB) 90 37.36 40.43 51.70 21.79 14.34 50 35.30 38.88 50.2 22.5 14.9 10 34.72 37.73 49.2 23.31 14.48 6.3.4 Analysis of variance Table 6-6 shows the ANOVA results. Based on the percentage contributions, the result indicates that all five controllable factors influence the response variable (RPWU) at three different percentile levels. At the 90th percentile, RWU is considered as the most influential factor for RPWU followed by WSA, RR, LID, and GG. RPWU is significantly influenced by RR at the 50th percentile, followed by WSA, LID, GG, and RWU. WSA is the most influential factor at the 10th percentile, whereas LID, ERR, GG, and RWU have respective influences on RPWU. Both p-values and F-values also support the influential impacts on RPWU. For all the controllable factors, the p-values are less than 0.05. Similarly, F-values are greater than 1, which means that these factors have larger variances compared to the variance of errors. It indicates that all the controllable factors have major impacts on the response variable (Korake and Jadhao, 2021). Errors across the factors are less than 120 50%, which suggests that trials were conducted under controlled circumstances (Madan and Wasewar, 2017). Table 6-6. ANOVA results Percentile Factors DF Adj SS Adj MS F-Value P-Value % Contribution 90 WSA 1 124.03 124.028 40.77 0.000 29.42 RWU 1 126.17 126.167 41.47 0.000 29.93 RR 1 81.01 81.008 26.63 0.000 19.21 LID 1 39.71 39.713 13.05 0.005 9.42 GG 1 20.18 20.184 6.63 0.028 4.78 Error 10 30.42 3.042 7.21 Total 15 421.52 50 WSA 1 82.86 82.86 6.09 0.033 17.00 RWU 1 6.495 6.495 1.08 0.452 1.33 RR 1 134.68 134.68 9.91 0.010 27.63 LID 1 70.05 70.05 5.15 0.047 14.37 GG 1 57.22 57.22 4.21 0.067 11.74 Error 10 135.96 135.96 27.90 Total 15 487.27 10 WSA 1 185.65 185.65 149.87 0.000 58.90 RWU 1 5.176 5.176 2.01 0.314 1.64 RR 1 28.53 28.53 23.04 0.001 9.05 LID 1 67.59 67.59 54.57 0.000 21.44 GG 1 15.84 15.84 12.79 0.005 5.02 Error 10 12.38 3.92 Total 15 315.19 6.3.5 Modelling A predictive regression model has been developed using a linear regression analysis for the response variable RPWU. The equations determined from the regression analysis at three different percentiles are represented below from Equation 6-6 to Equation 6-8. 𝑅𝑃𝑊𝑈90 = 42.43 + (0.3143 ∗ 𝑊𝑆𝐴) + (0.1275 ∗ 𝑅𝑊𝑈) + (0.1521 ∗ 𝑅𝑅)+ (0.0885 ∗ 𝐿𝐼𝐷 + (0.1201 ∗ 𝐺𝐺) (6-6) 𝑅𝑃𝑊𝑈50 = 49.78 + (0.1018 ∗ 𝑊𝑆𝐴) + (0.111 ∗ 𝑅𝑊𝑈) + (0.2595 ∗ 𝑅𝑅)+ (0.0936 ∗ 𝐿𝐼𝐷) + (0.0846 ∗ 𝐺𝐺) (6-7) 𝑅𝑃𝑊𝑈10 = 48.11 + (0.1584 ∗ 𝑊𝑆𝐴) + (0.0169 ∗ 𝑅𝑊𝑈) + (0.2389 ∗ 𝑅𝑅)+ (0.1020 ∗ 𝐿𝐼𝐷 + (0.0987 ∗ 𝐺𝐺) (6-8) The model's effectiveness is determined by comparing the values for the coefficient of determination (R2). A model is considered effective if its R2 value is closer to 1 (i.e., 100%). The 121 R2 values were 93%, 72%, and 97% at 90th, 50th, and 10th percentiles. These values indicate that new observations were determined with 93%, 72 %, and 97% variability. Higher R2 values at the 90th and 10th percentiles indicate a good fit between dependent (controllable factors) and independent variables (response). Normality tests were conducted, and it was concluded that data were normally distributed, except there was an outlier at the 50th percentile. Appendix F shows the results of normality tests across three percentiles. 6.4 Conclusions A conceptual model of One Water Community was developed using a Taguchi optimisation method. The community is built on six OWA guiding principles that aim to reduce potable water use (RPWU). In achieving OWA goals, the community relies on five key interventions (i.e., control factors) and sixteen corresponding parameters. Each parameter was simulated twice by running 10,000 iterations to compute the percentage reduction of potable water use at the 90th, 50th, and 10th percentiles. The S/N ratio for 16 trials was generated to identify the most optimum condition. The results of the model revealed that the optimum combination of the parameters to obtain the maximum reduction potential of potable water use include water sense labelled urinals (WSA2), reclaimed water for agriculture uses (RWU1), emissions reduction from treatment plants (RR2), bioswales (LID1), and public awareness (GG2) at the 90th percentile. The entire optimal combination order, including two parameters, was different at the 50th percentile as LID1, followed by energy savings from treatment plants (RR1), GG2, WSA2, and RWU3 (soil & weather-based sensors) were considered as the most fitted combination in achieving the best performance of RPWU. Similar to the 50th percentile, the 10th percentile includes the same set of parameters but follows a different order as WSA2 followed by LID1, RR1, GG2, and RWU3. The contribution of each control factor (CF) to the response variable was analyzed using ANOVA. Reclaimed water use (RWU) was the most influential factor, followed by water-saving appliances (WSA), resource recovery (RR), LID, and good governance (GG) at the 90th percentile. A close relationship was observed between experimental and predicted results across three quartiles. The errors were less than 25% between experimental and predicted values of the S/N ratio. RPWU was improved by 14 dB to the initial operational setting. A linear regression model was developed for RPWU as a function of CF. The coefficient of determination (R2) values were 93%, 72%, and 97% at the 90th, 50th, and 10th percentiles. 122 In this chapter, the proposed model has some limitations depending on where it is implemented based on its temporal and spatial variations. This is because of the type of interventions that have been identified in building the model. For example, The City of Red Deer (Alberta) has developed a comprehensive conservation, efficiency, and productivity (CEP) plan. This plan encourages its customers the use of low flush toilets and waterless urinals, promotes water-efficient landscaping, employs LIDs in their community plan, and builds collaborative relationships with various agencies to enhance CEP in their jurisdictions (City of Red Deer, 2016). The developed model will produce better output for the city because it practices at least three control factors (i.e., WSA, LID, and good government) rigorously. On contrary, when the model is applied in areas that does not depict any relationship with control factors, the model output is null. The developed model adopts an integrated approach, which means additional uncertainty can arise because of the existing siloed based UWM system and modifications that integration process brings into water governance (e.g., social and institutional changes) structure. The current model is developed based on the data points generated from a comprehensive literature search. While the model outcomes have laid a strong foundation, it has also paved a way for a continuous progression in achieving long-term OWA goals. Future studies must be oriented towards building a model with real-time data input and inclusion of large number of parameters for the control factors. 123 Identification and Prioritization of One Water Approach Indicators A version of this chapter has been published in the Sustainable Production and Consumption journal entitled “Integrated Performance Assessment of Urban Water Systems: Identification and Prioritization of One Water Approach Indicators”. 7.1 Introduction As mentioned in previous chapters and discussions, conventional water systems face increasing populations, ageing infrastructure, and climate uncertainties. Therefore, a paradigm shift towards IUWM is the need for time, which is underpinned by the OWA concept. The overview, definition, state-of-the-practice, and need of OWA in enhancing UWS performance are discussed in Chapter 3. IUWS is the most popular and common term used in the urban system. Applying an IUWS can protect water resources and improve water quality; reduce urban water resource risks; integrate water collection, distribution, treatment, and storage systems; and establish coordination between players across water, land use, and energy (Global Water Partnership, 2013). Besides, IUWS also develops a close interconnection with other urban energy components, the environment, and society. For example, the nexus of water and environment depicts the impact of urban water infrastructure on the environmental ecosystem (e.g., flora and fauna habitat). The partnership between the utility and the public justifies the relationship between water and society in building a sustainable urban water community (Ugarelli & Rostum, 2012). The IUWS concept has emerged recently, and several studies have conducted IUWS performances. Behzadian and Kapelan (2015) IUWS performance using 13 KPIs spanning over five intervention strategies. The findings showed that rainwater harvesting and greywater reuse options were the best interventions to manage increasing urban water demands. Bouziotas et al. (2019) assessed the integrated performance of decentralized UWS comprising four interventions (water saving appliances, rainwater harvesting, greywater recycling, and sustainable urban design) using six KPIs. The authors found that 32-58% of urban water demand is reduced using water-saving appliances, and a 42% reduction is achieved in wastewater when treated grey water is used for various non-potable applications. Landa-Casigno et al. (2020) assessed IUWS performance for centralized and decentralized water reuse strategies that constitute KPIs across the water-energy-pollution nexus. The results indicated that decentralized water use strategies improve IUWS 124 performance in water conservation, reduce emissions from GHGs, and minimise eutrophication potential in receiving water bodies (Landa-cansigno et al., 2020). Furthermore, studies have individually assessed water system performance using sustainable, resilient, and reliable indicators. Zhalmurziyeva et al. (2021) developed 28 sustainability indicators across four dimensions (environmental, economic, social, and technological) to assess the performance of urban water infrastructure. The findings suggested that high-ranked indicators were related to water quality (physical and chemical), affordable water price, availability of water meters, and water infrastructure age. Mcmillian et al. (2020) identified four main criteria (validity, reliability, time and space continuance, and feasibility) across social and institutional indicators to assess sustainability and resiliency characteristics of UWSs. The authors indicated that social and institutional indicators form a strong foundation to assess UWS performance, which can assist policymakers in developing robust indicators for water distribution (Mcmillan et al., 2020). Saikia et al. (2022) developed a planning tool named City Water Resilience Framework to assess UWS performance across four criteria (infrastructure and ecosystems, health and wellbeing, planning and finance, and leadership and strategy) and identify gaps in building resilient water systems. The authors stressed that water governance characteristics, bold leadership, sustainable planning, and collaboration between diverse stakeholders are essential parameters in enhancing urban water resilience. MCDA techniques are widely used in the water sector to prioritize indicator selection (e.g., alternatives) and determine performance index values based on the decision-makers preferences (Gebre & Cattrysse, 2021). The existing literature showed that different MCDA techniques had been used widely to identify indicators for the effective management of urban water resources. Radmehr et al. (2022) employed Fuzzy TOPSIS (F-TOPSIS) method to rank water management alternatives in managing agricultural water for irrigation and drainage networks by considering sustainability criteria (Radmehr et al., 2022). Saleem et al. (2021) identified and prioritized 63 indicators to evaluate aquatic centres' performance against three decision criteria (understandability, measurability, and importance) using Fuzzy AHP (F-AHP) and fuzzy mean clustering methods (Saleem et al., 2021). Birgani and Yazdandoost (2018) developed an integrated framework based on the combined MCDA method of AHP, entropy, and TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) comprising of economical, social, technical, 125 and environmental to achieve urban flood resilience. TheThe framework provides essential information on urban drainage plans and acts as a decision tool to attain sustainable and resilient urban drainage management (Birgani & Yazdandoost, 2018). AHP is a suitable technique in analyzing problems that involves hierarchy-based structure, whereas Analytic Network Process (ANP) analyzes problems of the network-based structure. One of the problems with AHP is its subjective evaluation that results into vagueness and uncertainty in the pairwise comparison. Hence, Fuzzy sets are used to overcome those uncertainties (Saleem et al., 2021) and are also considered as the model for decision-makers judgement. The addition of the fuzzy logic provides decision-makers to use the fuzzy numbers instead of a crisp value. Fuzzification is the reliable process that converts crisp values into a set of fuzzy numbers. Each fuzzy number set is accompanied by the membership function. A membership function is defined as a curve that shows how the membership values vary between 0 and 1. These functions are developed from basic functions including, Gaussian distribution function, sigmoid curve, cubic polynomial curves, and quadratic. Triangular membership function uses triangular fuzzy numbers, which are the representation of a centre-based triplet approach. These numbers are the representation of the linguistic values and are simple and effective representation of fuzzy numbers (Rahim et al., 2021). F-AHP is the improved MCDA technique based on AHP that uses fuzzy numbers to address vagueness / uncertainties that occurred based on decision-makers preferences into a score when considering different selection alternatives in prioritization and identification process (Khademi and Kourosh, 2010). F-AHP applicability and reliability in prioritizing indicators must be attributed to the following factors: a) It provides decision-makers a reasonable interval (i.e., interval judgements instead of a rigid judgement) to gauge their preferences (Afolayan et al., 2020); b) Both qualitative and quantitative data can be added into a decision model (Percin, 2008). Fuzzy TOPSIS integrates the fuzzy theory in the conventional TOPSIS method. It is based on the concept of distance measurement, where the best alternative has the shortest distance from the fuzzy positive ideal solution. F-TOPSIS is used in determining the impact of various decision criteria in identifying indicators. This method uses the linguistic variables instead of precise data to obtain the desired values, while alternatives can be arranged based on the decision makers preferences (Issa et al., 2022). Unlike, other Fuzzy MCDA methods (e.g., F-AHP), F-TOPSIS 126 does not change the final ranking / prioritization order when new selection criteria and alternatives are added into the identification process. In fact, it is considered as a reliable method when the number of alternatives are increased as the consistency in the decision makers’ preference also increases. As a result, it provides consistent preference order and it deals with imprecision and subjectivity of a problem statement in an efficient manner (Rahim et al., 2021). Based on the above discussions, three important conclusions can be derived: a) a limited number of performance assessment studies are conducted for IUWS; b) indicators that are developed to assess IUWS performance are more inclined towards water-saving strategies, proposing alternate water sources, and reducing GHG emissions all at individual levels; and c) individual indicators are developed in achieving urban water sustainability, resiliency, and reliability Hence, there is a clear gap in assessing IUWS performance from a multi-dimensional aspect that integrates environment, economic, infrastructure, institution, and social components of UWS. This chapter proposes a new pragmatic one water indicator-based method to assess IUWS performance using a novel set of OWA indicators. Furthermore, all developed OWA were assessed through the lens of sustainability, resiliency, and reliability to better rate water systems performance. The robustness of indicators in assessing IUWS performance can be justified based on their linkage with long-term sustainable development goals, generalizability, and demonstration of a holistic IUWM system. 7.2 Methods A One Water indicator-based method was developed, which comprises five performance objectives, i.e., i) enhance environmental health, ii) achieve improved urban water assets performance, iii) strengthen financial situation and technical advancement, iv) promote good governance, and v) enhance community perception and participation. Figure 7-1 shows the methodology adopted in the study. 127 Figure 7-1 Methodology adopted for OWA Identification These performance objectives are identified because of the following reasons: i) They depict a close relationship with six OWA guiding principles (Pokhrel et al., 2022); ii) They include an essential component of the OWA definition proposed by various institutions in achieving One Water goals (US Water Alliance, 2016; Water Research Foundation, 2017); and iii) They demonstrate a holistic approach in managing UWSs by considering amalgamation of environment, infrastructure, finance and technical, institutional, and social characteristics, which are essential in 128 enhancing UWS performance (Pokhrel et al., 2022). The proposed method is demonstrated using the data from 35 municipal water utilities in British Columbia. 7.2.1 Content analysis A content analysis was conducted to identify OWA indicators. Journal articles, municipal reports, conference proceedings, and book chapters were important resources to complete the screening process. The focus of the search was oriented more towards the past literature about various terms such as IUWM, integrated water resource management, integrated watershed management, adaptive water management, total water management, whole water, and water-sensitive urban design. Some part of this literature was also discussed in Chapter 3. All these terms depict the importance of managing each UWS service component in an integrated manner such that urban water can be managed holistically (Furlong et al., 2015; Pokhrel et al., 2021). 7.2.2 Data collection Twenty-nine indicators were identified as the final OWA indicators. In addition to the content analysis performed in the above step, academic experts' and municipal representatives’ opinions were also sought for indicator identification. For this purpose, a questionnaire was distributed to British Columbia, Canada municipalities. Appendix G (Table G-1) shows the questionnaire. Respondents were asked to input a suitable performance score to these indicators based on three decision criteria – sustainability, resiliency, and reliability. When supplying a score to a specific indicator, respondents were asked to rate indicators by keeping a generic question in their mind – (How much does the indicator contribute to increasing sustainability, reliability, and resiliency performance of UWSs?). Respondents were then required to rate these indicators on a linguistic scale of 1, 3, and 5. Table 7-1 shows the linguistic scale for rating OWA indicators and their detailed explanation. Previous studies also categorised some OWA indicators as separate drinking water, wastewater, and stormwater indicators. The primary difference between individual and OWA indicators relies on the fact that- OWA indicators focus more on the integration process, represent OWA components, and possess at least one of the following four characteristics: a) Demonstrates interconnectedness between either any two or three urban water services (e.g., water management triangle) (Pokhrel et al., 2022); 129 b) Reflects relationship with global sustainability indicators to meet urban water sustainability goals (United Nations, 2022); c) Overcomes infrastructure challenges to meet increasing urban water demands; and d) Represents the holistic approach of UWM in building One Water Community. The One Water Community is built based on five OWA components: technical innovation, financial backup, stakeholder engagement, strong leadership, and proper scope definition (Pokhrel et al., 2022). Table 7-1. Linguistic scale for rating decision criteria Scale Score Sustainability Resiliency Reliability Low 1 Does not improve / promote environmental, economic, and social impacts of UWS Introduction of the intervention may enhance resilience in a low degree. No change in UWS Medium 3 Improves / Promotes environment, economic, and social impacts of UWS moderately Introduction of the intervention may enhance resilience to a medium degree. Improves one or two of the three characteristics (continuity, predictability, and functionality) of UWS High 5 Improves / Promotes environment, economic, and social impacts of UWS significantly Introduction of the intervention may enhance resilience in a high degree. Improves all three characteristics of UWS 7.2.3 OWA indicators identification In this Chapter, OWA indicators are identified using F-AHP. The normal F-AHP assumption states that all possible criteria / alternatives are not dependent on each other. In practice, there always exists the relationship between criteria, which are pretty complex and shows interdependencies. In this Chapter, it does not require specific assumption rather the analysis is based on linear programming. The assumption of fuzzy weights is based on three levels, including alternatives, criteria, and goal (Sobandi et al., 2018). The following points describe the ranking procedure of OWA. 7.2.3.1 Develop fuzzy pairwise decision matrix A fuzzy pairwise decision matrix is developed for three decision criteria (DC). Respondents were asked to assign the relative importance between the DCs. The rating scheme is outlined in Table 7-2 (Kaur et al.,2021b). Each numerical value (crisp number) was converted into TFN to assign the degree of importance between two same criteria or diagonal entries, equal importance value is considered. The inter-correspondent criteria value is inverted using Equation 7-1. 130 Table 7-2. Rating schemes for fuzzy numbers Numerical value Triangular fuzzy number Degree of importance 1 1,2,3 Equally important 2 2,3,4 Less important 3 3,4,5 Moderately important 4 4,5,6 Strongly important 5 5,6,7 Extremely important (𝑎, 𝑏, 𝑐)−1 = (1𝑐,1𝑏,1𝑎) (7-1) 7.2.3.2 Calculate weights Fuzzy Geometric mean (FGM) was computed to calculate weights. It was calculated for each row of the decision matrix using equations (7-2) to obtain FGM value. Fuzzy weights for each decision criterion are calculated using Equation (7-3) and represented as a fuzzy weight matrix. 𝑆𝑖 = (𝑎~𝑖1 ⊗ 𝑎~𝑖2 ⊗ 𝑎~𝑖𝑛)^(1𝑛) (7-2) 𝑡𝑖 = 𝑆𝑖 ⊗ (𝑆1 ⊕ 𝑆2 … 𝑆𝑛)−1 (7-3) Final fuzzy weights (ACi) were calculated based on the average value generated from each respondent’s fuzzy weight matrix (i.e., three fuzzy weights value for each decision criteria) as given in equation (7-4) 𝐴𝐶𝑖 =1𝑁𝑆𝑖 ⊗ (𝑆1 ⊕ 𝑆2 … 𝑆𝑛)−1 (7-4) where N represents number of decision-makers 7.2.3.3 Compute aggregated scores The final fuzzy weights (ACi ) were then aggregated with the fuzzy scores (Bi) given by the respondents to determine three fuzzy weights (Di) for each KPI as represented in equation (7-5). 𝐷𝑖 = 𝐴𝐶𝑖 ⊗ (𝐵𝑖1 ⊕ 𝐵𝑖2 ⊕ … . 𝐵𝑖𝑛) (7-5) A centroid method was employed to generate a crisp value for each DC. These values were then normalized. Since three decision criteria are available for each KPI, the summation of DC normalized values provides the aggregated score for each KPI. Based on the aggregated scores generated for KPIs, ranks are assigned (i.e., higher is the normalized value, higher is the rank). The prioritization scale is classified into five levels as shown in Figure 7-2. 131 Figure 7-2. Prioritization scale 7.2.4 Decision criteria scores 7.2.4.1 Develop fuzzy decision matrix F-TOPSIS was used to understand the impact of decision criteria. A decision matrix was established between three decision criteria and 29 KPIs. Respondents’ assigned scores are based on the linguistic values outlined in Table 7-2 (above). Later these scores were converted into TFNs (d, e, f). Since there were 35 respondents, 35 decision matrices were formed. A combined decision matrix was established to facilitate further analysis using the following equation. 𝑑𝑖𝑗 = min(𝑑𝑘𝑖𝑗) , 𝑒𝑖𝑗 =1𝑘∑ 𝑒𝑖𝑗𝑘 ,𝑘𝑘=1𝑓𝑖𝑗 = max (𝑓𝑖𝑘𝑗) (7-6) 132 The decision matrix is then normalized using equation ℎ𝑖𝑗 = (𝑑𝑖𝑗𝑓𝑗∗ ,𝑒𝑖𝑗𝑓𝑗∗ ,𝑓𝑖𝑗𝑓𝑗∗) 𝑎𝑛𝑑 𝑓𝑗∗ = max (𝑓𝑖𝑗) (7-7) 7.2.4.2 Compute weighted normalized matrix In this case, equal fuzzy weights (xj) are assigned to each KPI. Using the following equation, these weights are then multiplied with the normalized decision matrix obtained in the above step. 𝑡𝑖𝑗~ = ℎ𝑖𝑗~ ∗ 𝑥𝑗 (7-8) The final calculation provides the normalized fuzzy decision matrix 7.2.4.3 Calculate fuzzy positive and negative ideal solution Based on the fuzzy normalized weighted decision matrix formed in the above step, Fuzzy Positive Ideal Solution (FPIS) is determined based on the maximum value of the f component in the TFN (in case if all the f values are same, the maximum value for e component is sought). In contrast, the Fuzzy Negative Ideal Solution is determined based on the minimum value of the‘d’ component. Mathematically, FPIS and FNIS are represented in equations (7-4) and (7-5). 𝐿∗ = (𝑦1∗, 𝑦2∗, … … . 𝑦𝑛∗), where 𝑦∗𝑗 = max (𝑦𝑖𝑗3) (7-9) 𝐿− = (𝑦1−, 𝑦2−, … … . 𝑦𝑛−), where 𝑦−𝑗 = min(𝑦𝑖𝑗3) (7-10) 7.2.4.4 Determine the distance from FPIS and FNIS Each decision criterion distance was determined from FPIS and FNIS by using equation (7-6). Equations (7-7) and (7-8) are the summation of KPI values for each decision criterion. 𝑑(𝑜, 𝑝) = √13[(𝑎1 − 𝑎2)2 + (𝑏1 − 𝑏2)2 + (𝑐1 − 𝑐2)2] (7-11) 𝑑𝑖∗ = ∑ 𝑡𝑖𝑗~𝑛𝑗=1𝑦𝑗∗ (7-12) 𝑑𝑖− ∑ 𝑡𝑖𝑗~𝑛𝑗=1𝑦𝑗− (7-13) The closeness coefficient (Ccf) for each decision criterion is determined using equation 7-9. 𝐶𝑐𝑓 =𝑑𝑖−𝑑𝑖− + 𝑑𝑖∗ (7-14) The Ccf value is normalized to obtain the final performance score for each decision criterion as outlined in equation 7-10. 133 𝑃 =𝐶𝑐𝑓∑ 𝐶𝑐𝑓 (7-15) Converting the normalized value into percentage provides the percentage impact of decision criteria in identifying the OWA indictors. % = 𝐶𝑐𝑓 ∗ 100 (7-16) 7.2.5 Statistical analysis Mean, standard deviation, coefficient of variance, and correlation analysis were conducted for statistical analyses purpose. Spearman correlation was used to identify the relationship between sustainability, resiliency, and reliability decision criteria for all 29 OWA indicators. This type of correlation was preferred over Pearson correlation because i) Spearman Correlation is applicable for the categorical and ordinal data and ii) Spearman Correlation is the most appropriate method to determine the correlation coefficient values for the Likert scale based (i.e., ranked) data. Table G-2 (Appendix G) represents the correlation coefficient interpretation (Pokhrel et al., 2020). 7.3 Results and discussions Thirty-five respondents responded to the questionnaire. Table 7-3 outlines OWA indicators and their corresponding definitions and ranks. Five performance objectives are presented in this study. Each performance objective consists of a series of OWA indicators, which will be elucidated in the following subsections: 134 Table 7-3. OWA indicators I. Enhance environment health Indicators Definition Relation to OWA characteristics Rank Wastewater reuse (I1) Use treated wastewater for non-potable uses (e.g., toilet flushing, car washing, and landscaping, industrial, commercial, and institutional uses) and/or potable use a, b 24 Stormwater reuse (I2) Collect, treat, and reuse stormwater runoff (rain and snow). E.g., collect stormwater in detention ponds or reservoirs and treat it for non-potable uses (such as new construction, retrofits, and landscaping) or direct / indirect potable use a, b 22 Fit-for-purpose treatment (I3) Supply of appropriate quality of water (i.e., treated, partially treated, untreated) to specific water uses. E.g., supply of partially treated water for agriculture services. a, b 20 Efficient pump and SCADA system (I4) Install efficient water pumps for water treatment and SCADA system for water operation monitoring c 5 Energy recovery from the wastewater treatment plant (WWTP) (I5) Recover energy from WWTP (e.g., biogas) to supply heat and electricity. The energy recovered can be used to support daily plant operation and save electricity consumption. a, d 23 Nutrients recovery from wastewater (I6) Recover energy from WWTP, such as phosphorus and nitrogen from the wastewater, which can later be used as fertilizers to increase crop productivity. d 28 Biosolids reuse (I7) Use recycled sludge as fertilizers to increase crop productivity. The remaining sludge during the energy recovery process can be utilized as fertilizers. c, d 27 GHGs emissions from the treatment plant (I8) Estimate GHGs generated by WWTP (e.g., from wastewater processes) a, b 29 Natural hazard control (I9) Implement policy to control natural hazard (e.g., floods, heat waves) in a way that is affordable, accessible, and protect consumers from the catastrophic events a, d 6 II. Achieve improved urban water assets performance Implementation of Low Impact Development (I10) Introduce low impact development techniques such as bioswales, biofilters, permeable pavement, and green roof to manage stormwater to manage storm runoff and decrease water pollutants into source water bodies. a, c, d 14 Water leakage reduction (I11) Install leak detection equipment such as acoustic instrument; flow meters, conduct continuous auditing program, which records leak daily over time. a, c 2 Dual distribution system (I12) Establish separate transmission and distribution mains for potable and non-potable uses a, c 16 Rapid recovery from natural calamities (I13) Adequate infrastructure and human resources to recover from various natural calamities, such as floods, droughts, forest fire, and heat waves b, c 1 (Adaptive infrastructure) Availability of resources to meet next 50 years demand (I14) Accessibility of necessary resources, including infrastructure, finance, staff at present to meet current and changing population demand. c, d 4 135 Note: The different color coding divides the indicators based on the prioritization scale (Figure 7-2). Relation to OWA characteristics refer to at least one out of four attributes that the indicator must have to become OWA indicator as outline in Section 7.2.2. III. Strengthen financial situation and technical advancement Average annual lifecycle investment (I15) Optimize the assets depreciation costs that are owned by the municipalities based on the assets’ life and replacement costs c, d 7 Proper water price (I16) Maintain a price that consumers can pay, while generating sufficient revenues to meet operational and maintenance cost of utilities d 10 Availability of emergency funds (I17) Strengthen the utility’s financial circumstance with extra funds available that may be used in emergency situations d 9 Water saving appliances (I18) Install devices in residential and non-residential. buildings that are water efficient (e.g., performance shower heads, low flushing toilets, waterless urinals) a, d 19 Energy efficient devices (I19) Install energy star certified air purifiers, clothes dryers, clothes washers, dishwashers, freezers, and refrigerators a, d 26 Web based tool (I20) Develop an online platform that assist to store, share, and analyze data with changing performance trends a, c, d 25 Smart infrastructure monitoring (I21) Ensure reliable infrastructure planning, asset monitoring, contaminants identification, and process optimization and control. b, d 12 IV. Promote good governance Collaboration across diverse disciplines (I22) Encourage stakeholder engagement and coordination across organizations, departments, and governmental bodies to plan and implement water smart strategies b, d 13 Government funding in water sensitive practices (I23) Recommend government to separate budget to implement cutting edge technology in designing urban water systems and invest in upgrading infrastructure. a, d 11 Emergency response plans (I24) Devise plans to combat sudden contamination of cynotoxins (hydro carbons, algal blooms, lead), occurrence of floods and landslides through an authorized public notification system, and water security issues. a, d 15 Strengthen Sustainable Development Goals (I25) Enhance urban water system performance in a way that it not only supports SDGs but also advocate for an effective implementation b, d 18 V. Enhance community perception and participation Public willingness to pay (I26) Encourage public to pay more to water utilities by clearly citing the benefits of improved water infrastructure, which comes with an appropriate cost. a, d 8 Public willingness to change behavior (I27) Increase public awareness on water reuse, conservation strategies that are beneficial to water and associated resources, which will increase willingness to change behavior. a, c, d 3 Public engagement in decision-making (128) Conduct water forums in the communities and encourage active public participation while making important decision for water related activities b, d 21 Involvement of indigenous communities (129) Encourage indigenous communities participation and recognize their values and interests in water governance arrangements and decision-making b, d 17 136 7.3.1 Enhance environmental health This performance objective consists of nine indicators. Out of which, seven indicators have “low” and very “low’ ranks. Efficient pumps and SCADA system has a “very high” rank. Installation of efficient pumps in water and wastewater utilities results in minimizing energy costs and improving energy efficiency. For this purpose, pumps must be sized adequately such that the speed of the pump matches with operating flow conditions. Reduce GHG emissions from treatment plants has the lowest rank. One of the reasons for this might be attributed to lower carbon emissions (1.3 %) generated from wastewater treatment plants (WWTPs) compared to other sectors, such as transportation (16%), buildings (17.5%), and agriculture and land use (18%) (Ritchie & Roser, 2022). However, various direct (CH4, CO2, N2O) and indirect emissions (electricity and chemicals) generated from WWTPs not only hinder plants' capability to achieve carbon neutrality but it also impedes in achieving wastewater system sustainability (Lanqing Li et al., 2022). In drinking water infrastructure, electricity is the biggest source of GHG emissions, which is consumed during pumping (especially to the high rise buildings or if the source water is far from the treatment plant), and when employing advanced treatment processes, such as ozonation and activated carbon filtration (Smith et al., 2021). Despite nutrients recovery from wastewater received the 2nd lowest rank, one cannot undermine benefits of recycled nutrients in increasing crop productivity. In addition, nutrients recovery reduced the demand for conventional fossil-based fertilizers and prevented phosphorus and nitrogen depletion. As a result the recovery process assists in conserving energy and water sources (Mo & Zhang, 2013). Although, energy recovery from wastewater plants has a low rank (23), it is believed that potential energy recovered from wastewater and its by-products produce more energy than consumed for wastewater treatment (Shizas et al., 2005; Fraia et al., 2018). For example, a combination of solar energy and biogas generated from anaerobic digestion produces enough self-sufficient electric energy to meet plant’s electricity needs (Bustamante & Liao, 2017). In this way, electricity saved for wastewater treatment can be utilized for drinking water or stormwater treatment, reflecting how energy saving for one UWS service component can be a valuable resource for others two. Three indicators, Fit-for-purpose treatment (20), wastewater reuse (22), and stormwater reuse (24) have low ranks. Albeit these indicators play a significant role in reducing water footprint and strengthening IUWM practice, it was surprising to observe these indicators were given a low rank. Some possible explanations for this could be: a) people concerns 137 over various health risks associated with water reuse, b) people perception in conserving water but their disagreement using stormwater for potable and non-potable uses and c) Individual beliefs that are based on cultural and community norms, where water reuse has no additional values, and d) public unconvinced about cost-benefit reasons in building new infrastructure in promoting water reuse and overall UWS sustainability, resiliency, and reliability (Mankad et al., 2019). 7.3.2 Achieve improved urban water assets performance This performance objective has overall the best rank among five objectives. Three indicators have a “very high” rank, while two have “medium” ranks. The top two ranked indicators are rapid recovery from natural calamities and water leakage reduction. Disasters including, floods, drought, and landslides, can disturb UWS functioning as they invite severe water service disruptions such as, pipe leakage, water infrastructure failure, and cross-contamination. Engineers need to develop tools and software to determine UWS resilience to natural disasters. Water Network Tool for Resilience is one such tool capable of creating multiple unfavourable scenarios and estimating the water system’s resilience in overcoming weather-related challenges. The software can assist utilities in evaluating potential damages caused by hazards and precaution strategies, prioritising action plans, recommending efficient repair strategies, and suggesting best practices for operation and maintenance (Klise et al., 2017). Water leakage is a serious problem that utilities face in terms of attaining urban water sustainability and resiliency. The use of smart water meters and end-use sensing devices plays an important role in leak detection. Installation of smart water meters can replace manual operators on filed as data are automatically generated and stored in the computer. It might require operators’ attention only in case of technical issues arising in the computer. Similarly, sensing devices are employed to reveal leaks by showing water flow when all the household fixtures are turned off (Mutchek & Williams, 2014). Adaptive infrastructure is ranked 4th. Adaptability is an integral component of resiliency. Unlike conventional systems, adaptive infrastructure focuses on diverse and flexible strategies spanning across multitude areas of social, technical, and economic realm of UWM. It is based on integration of systems, organizations, and diverse stakeholders. When building a new infrastructure to meet increasing water demands, more than focusing on technical choices, attention needs to shift towards strategic decisions to respond to the changing conditions that require coordination between stakeholders and their shared experiences (Leigh & Lee, 2019). 138 7.3.3 Strengthen financial situation and technical advancement Seven indicators are included under the third performance objective. Three indicators have “high” rank, one indicator each has “low” and “medium” rank, and two indicators have “very low” rank. Availability of emergency funds is ranked 9th. Emergency fund is required when water systems experience unfavourable situations all of a sudden. For example, during COVID-19 pandemic, freshwater across many regions around the globe witnessed SARS-CoV-2 in their source waters. The virus was also seen in untreated and inadequately treated wastewater and grey water (Girón-navarro et al. 2021; Lapolla et al. 2020). The United States Department of Agriculture has a provision to provide grant up to $500,000 for rural communities to help them maintain their drinking water quality and $150,000 to undertake replacement and repairment activities during emergencies (USEPA, 2022d). Proper water price is ranked 10th. Affordable water price is important because it provides utility managers with enough income to keep operational and maintenance activities ongoing, fosters sustainable and efficient water uses, and renders equitable access to urban water services. Thus, maintaining proper water price helps utility to promote urban water affordability, efficiency, equity, and sustainability (García-rubio et al., 2015). It was surprising to observe that three indicators, Water saving appliances (19), energy-efficient devices (26), and web-based tool (25) have their ranks spread out between “low” and “very low” levels. In regards to adopting water-saving appliances, there may be barriers, including but are not limited to consumers’ hesitation about devices effectiveness, unavailability of government incentives when practicing water-saving approaches, and lack of right set of skills to implement conservation practices, which might have resulted the indicator a lower rank (Addo et al.,2018). Similarly, adopting energy-efficient devices also bring similar challenges with itself such as, i) consumers might not be fully aware about the exact benefits of energy savings, which is why they might invest less in these devices (Gillingham & Palmer, 2014); ii) consumers might not have enough money to invest (Schleich et al., 2016) iii) market is not convinced to provide loans for energy efficiency investments because of risks associated with higher transaction costs, credits, asymmetric information, and uncertainty around actual energy saving vs expected savings (Palmer et al., 2012). One reason for the low rank of the web-based tool can be attributed to a lack of people's awareness of the tool's importance in analyzing, sharing, and storing data, which are critical in comprehending existing water-related challenges. All three indicators play a significant role in achieving OWA goals. However, as the concept is relatively new, people might have 139 difficulties understanding the approach, lack of sound knowledge about its implementation, and insufficient resources to practice OWA (Pokhrel et al., 2022). 7.3.4 Promote good governance Four indicators are listed under this performance objective. Only one indicator was able to secure a “high” rank, while two indicators achieved a “medium” rank, and the last indicator claimed a “low” rank. Government funding in water-sensitive practices is ranked 11th, whereas Collaboration across diverse disciplines is ranked 13th. Both these indicators are closely related to each other as large projects usually consist of several stakeholders. For example, the city of Cedar Rapids (USA) collaborated with 15 partners and applied for five-year funding to combat the issues related to flooding, nitrate contamination in drinking water, and poor water quality. The Saginaw Bay Watershed at Michigan (US) collaborated with local partners on a $5 million project to improve watershed status by introducing programs, such as dam removals, wildlife and natural vegetation restoration, and contaminated soils remedy (US Water Alliance, 2016). The Department of Water Resources in California has recently released a $193 million grant program for the local municipalities to build water infrastructure and resilience projects. The program will focus on various water sensitive practices and infrastructure upgrade, including wastewater treatment, water desalination, water conservation and groundwater recharge (California Department of Water Resources, 2022). Strengthen Sustainable Development Goals (SDGs) is ranked 18th (low), which is unusual considering SDGs immense emphasis on providing clean water and sanitation (SDG 6) and building sustainable cities and communities (SDG 11) by 2030. One reason that utilities did not consider SDG as an important indicator is their lack of understanding of various global indicators developed to achieve SDGs, particularly SDG 6. Some indicators have very vague definitions and it might have been difficult for urban water managers to understand their essence. For example, indicator 6.5 focused on implementing integrated water resource management at all levels. While this indicator is a good measure of sustainability, it can be interpreted in different ways (e.g., does the integration process covers all three waters, is it more inclined towards land management) and might not cover areas that the indicator was originally intended to be developed. In addition, the utilities might be struggling to see the benefits gained after implementing SDGs. For this purpose, 140 urban water managers are required to create a link between SDGs and its benefits to the general public must be communicated effectively (Bhaduri et al., 2016). 7.3.5 Enhance community perception and participation Four indicators are identified under this performance objective. Two indicators succeeded in achieving a “high” rank while remaining each indicator achieved “medium” and “low” ranks. Public willingness to change behaviour (3rd) and Public willingness to pay (8th) depict the significance of social components in identifying the OWA indicators. The former indicator is related to individual behaviour regarding potable water savings to accommodate the ever-increasing water stress issues by using alternate water sources to fulfil non-potable demands. According to the survey conducted in Spain, the authors found that 62% and 72% of people are willing to use recycled water (i.e., treated wastewater before mixing into water sources) and rainwater (i.e., water that goes basic treatment) for various non-potable water uses (López-ruiz et al., 2021). More than 50% of people agree that using alternate sources is a positive sign from the perspective of water conservation that aims to enhance urban water sustainability. A study in Athens (Greece) revealed that only 38% of the public are willing to pay more for improved water services (Drimili et al., 2019). Another study in Mettu (Ethopia) found that 69% public were in favour to pay additional costs for improved water quality. Public willingness differs from one location to other. Satisfaction over water services, affordable water price, age and level of education, and family income are the key factors in determining public interest to pay more for water systems (Singh, 2020). Public engagement in decision-making has a low rank (21st), which might be justified because some public discussions can be very time-consuming with no concrete outputs, costly to host large gatherings, more opinions resulting in chaotic situations, and biased opinions sharing (Irvin & Stansbury, 2004). However, there is a counterintuitive opinion about the importance of engaging public in decision-making as it cannot be undermined specifically with the increasing cases of water insecurity across the globe in enhancing the sustainability of water resources. Public engagement is a powerful tool that assist in building sustainable and resilient water management strategies focusing on environmental, engineering, and ecological principles (Golladay et al., 2021). 141 7.4 Decision Criteria The impact of decision criteria is elucidated in the following sections. Sustainability (58%) criterion followed by resiliency (24%) and reliability (19%) is the order of impact in identifying OWA indicators. 7.4.1 Sustainability The overall sustainability score is 0.764. In achieving sustainable water systems, the contribution of IUWM cannot be undermined as several cities across the globe have embraced this concept as an opportunity to enhance their UWSs. For example, the city of Melbourne (Australia) practised triple bottom line of urban water sustainability by considering rainwater tanks, an onsite water treatment plant, separate stormwater mains, and direct sewer diversion to IUWM (Corbett, 2012). Ten respondents gave the highest performance score to five indicators (I10, I23 I25, I26, and I27) and seven respondents gave the same score to eight indicators (I1, I2, I5, I6, I7, I12, I14, I29). Three respondents gave the highest score to three indicators (I4, I17, and I24). Although, I10 (Low Impact Development) has the highest score in this criterion, which 10 respondents give, only four respondents gave the same score in resiliency and reliability criteria because of which it has a “medium” overall rank. In the path of achieving sustainable development, LID plays an integral role as it promotes effective stormwater management, which not only helps in reducing and retaining urban runoff but it also helps in improving water quality(Liu et al.,2021). In addition, LID also assists in restoring aquatic habitat, enhancing groundwater recharge rate, add greenery to the urban neighbourhood, decreasing urban heat island effect, reducing climate change impacts, and saving energy (USEPA, 2012). 7.4.2 Resiliency The overall resiliency score is 0.250. Transitioning traditional water systems more into resilient systems, integration is paramount. For example, integrating nature-based solutions (e.g., sustainable drainage systems) with various UWS components helps manage water quality, quantity, amenity, and biodiversity (Mcclymont et al., 2020). Twelve respondents gave the highest performance score to three indicators (I9, I13, I14) for the resiliency criterion. Similar responses were also given to these indicators for sustainability and reliability criteria because of which their overall ranks were 6th, 1st, and 4th. Natural hazard management (I9) is an important indicator that helps improve urban water resiliency. This can be 142 done by implementing various LID techniques in a community because water managers can enjoy numerous benefits including reduced urban flooding, prevention from water logging, and increasing the water table level contributing to improved groundwater systems. In addition, an early notification system that warns consumers about the catastrophic events must be established so that necessary actions could be taken prior to the unfavourable situations (Lin et al.,2018). The eight respondents supported the highest impact of I24 (Emergency response plans) in enhancing urban water resiliency. The respondents’ number decreased for reliability (5) and sustainability (2) criteria because of which it has received overall rank of 15. In UWS, emergencies occur because of main failures, chemical spills, water leakage, distribution main breaks, and contamination intrusions. In order to gain resiliency, urban water managers must be well prepared to tackle these unfavourable situations in advance. For this purpose, managers must conduct periodic assessment of water systems in terms of monitoring water quality indicators, carrying out water infrastructure maintenance activities, and getting their staff rehearsed to respond emergency situations (USEPA, 2022c). 7.4.3 Reliability The overall reliability score is 0.310. Reliable UWS plays a significant role in attaining OWA goals. The UWS reliability is closely related to achieving greater urban water resilience, which leads to more sustainable solution for better UWM. To provide reliable water services, it is important to tackle water system threats and their impact on the overall system performance, which have broadly environmental, economical, and social consequences highlighting the triple bottom line of UWM (Butler et al., 2016). Seven respondents gave the highest performance score to Smart infrastructure monitoring (I21) under reliability and resiliency criteria, while five respondents gave the same rating under sustainability criterion. To provide reliable water services, it is important to monitor water quality parameters using internet of things and artificial intelligence, which is also considered as cost-effective, improving water system efficiency. A web-based system needs to be designed that contains sensors to monitor various physical, chemical, and microbiological parameters and provide urban water managers real-time data. In this way, operators can make necessary interventions as soon as they observe abnormality in their water systems (Pasika & Gandla, 2020). 143 7.5 Statistical analysis The Standard Deviation (SD) values ranged between 1.34 - 1.60 and the coefficient of variance was less than 1 for all the performance objectives against three decision criteria (Table 7-4). This result indicated that performance scores for the OWA indicators had less variability in their data sets. Among the least SD values, both sustainability and resiliency criteria had the highest SD for the asset performance objective, which means that the scores were relatively deviating more away from the mean. Reliability’s SD was greater for financial and technical advancement. The mean was the highest for the participation and perception objective under sustainability. The least mean was obtained for the environmental health objective under reliability, which indicates that the majority of indicators under this category were provided “low” scores. Table 7-4. Performance scores variability PO SD Mean CoV Sustainability P1 1.51 2.75 0.548 P2 1.57 2.90 0.541 P3 1.50 2.75 0.545 P4 1.55 2.91 0.531 P5 1.46 3.14 0.466 Resiliency P1 1.35 2.12 0.638 P2 1.59 2.93 0.542 P3 1.44 2.46 0.584 P4 1.48 2.64 0.560 P5 1.45 2.49 0.583 Reliability P1 1.37 2.01 0.679 P2 1.44 2.92 0.494 P3 1.47 2.56 0.575 P4 1.43 2.40 0.595 P5 1.45 2.53 0.573 Note: PO – Performance objective, SD – Standard deviation, CoV: Coefficient of variance Figure 7-3 shows the Spearman correlation for five performance objectives against three decision-pairs (i.e., sustainability-resiliency, reliability- sustainability, and reliability-resiliency). The finding suggested p-values were less than 0.05 and average – very good correlation exist for all five performance objectives. The highest correlation (r = 0.854) was observed for the reliability-resilience decision pair under governance objective, which indicates that collaboration and government involvement in decision-making plays an integral role in attaining one water goals. 144 Figure 7-3. Spearman correlation of five performance objectives 145 Six out of seven indicators under the third performance objective showed that no correlation exists between any two pairs of decision criteria. Average annual lifecycle investment is the only the indicator that accepted the null hypothesis for all three decision-pairs. Four indicators (I1, I4, I5, I6) for the environmental health objective represented that no statistically significant relationship were established between sustainability – resiliency pair. Two of these indicators (I4 & I5) also displayed statistically insignificant relationship between sustainability-reliability criteria. The top-ranked indicator (I9) exhibited a “good” correlation for all three decision-pair, whereas apart from resiliency-reliability (poor), the 2nd ranked (I11) indicator also demonstrated a “good” correlation. Although I27 received a third rank, the correlation coefficient values were less than 0.2 for three decision-pairs, establishing a “very poor” correlation. Strengthen Sustainable Development Goals (I25), resiliency-reliability is the only pair that demonstrated a “Very good” relation (r = 0.84) despite having a “low” overall rank of 18th. Implementation of LIDs has a “medium” rank but it has the best correlation coefficient values for their decision pairs. Water-saving appliances (I18) and energy efficient devices (I19) indicators received “low” and “very low” ranks, which is also justified by their “very poor” and “average” correlation that was established across the decision-pairs. Altogether nine indicators accepted the null hypothesis, which means that at least one decision pair did not show the correlation between them. Among them 67% indicators achieved an overall “low” or “very low” ranks. This percentage correspond to 50% of total low ranked indicators (i.e., 6 out of 12) derived from the study. The results suggest that for a high ranked OWA indicator, it must pose good scores for all three decision-criteria. This finding also validates the assumption that an OWA-based community must demonstrate strong characteristics for urban water sustainability, resiliency, and reliability. Table G-3 shows the correlation data for the OWA indicators. 7.6 Conclusions This chapter presents OWA indicators for defining IUWS performance assessment across multi-dimensional facets to enhance UWM. Development of OWA indicators demonstrates unique water systems characteristics that showcase UWS service components relationship, global sustainability attributes, infrastructure features in accommodating increasing water services demands, and holistic building of One Water Community. 146 A One Water indicator-based method was developed in five steps. First, twenty-nine indicators across five performance objectives were identified through a literature review and expert opinions. Second, thirty-five municipal representatives rated the indicators based on three decision criteria (sustainability, resiliency, and reliability). Third, F-TOPSIS was used to determine the impact of each decision criterion. The results indicated that the sustainability criterion (58%) had the highest influence, followed by resiliency (24%), and reliability (19%). One of the reasons behind the high impact of sustainability criterion can be attributed to the 66% of indicators given a full score by more than six respondents compared to 24% resilient indicators and 21% reliability indicators for as many respondents. Fourth, F-AHP was employed to prioritize the indicators. The indicators across infrastructure (I4, I11, I13, I14), followed by financial and technical (I15, I16, I17), social (I26, I27), institutional (I23), and environment (I9) components of IUWS play a significant role in achieving the higher rank of OWA indicators. On the contrary, seven out of eight indicators representing environment characteristics of IUWS had lower ranks (I1, I2, I3, I5, I6, I7, I8). Statistical analyses were also conducted to discern variability in performance scores and determine the relationship between decision criteria in the indicators identification process. In this chapter, there are limitations related to the low participation, decision criteria scores, absence of spatial and temporal indicators, and data related to monitoring water quality parameters. Out of 162 BC municipalities, only 35 participated. Provided that the respondent numbers increased, it would have assisted in the decision-making process for the OWA indicators identification. The decision criteria scores are based on the linguistic scale. In order to provide more accurate scores for sustainability, resiliency, and reliability criteria, mathematical formula and numerical values would aid better results. The inclusion of spatial and temporal factors would have increased indicators robustness in measuring IUWS performance. Industrial water consumptions have also not been directly connected with OWA indicators. This is because, indicators such as wastewater reuse and fit-for-the-purpose advocate the water reuse practices and using right water for right purposes. The overall goal is to reduce the potable water uses, thus promoting the long-term water sustainability and resiliency. Future studies must be oriented towards measuring IUWS performance by employing real time data in achieving OWA goals. Based on the specific utility needs, more number of indicators can be introduced and identification scope could be extended to municipalities outside BC. As a result, 147 it will provided water managers across Canada an opportunity to participate in the study that has wider application to their respective municipalities in enhancing water conservation, productivity, and consumption needs. Overall, it would have enabled urban water managers to understand their utilities actual performance in building One Water Community. In addition, the water quality monitoring and sampling specific indicators can also be included under OWA considerations because these indicators directly impact the human health and users’ perspectives. Since OWA is a holistic concept, inclusion of users’ perception is critical to bring positive water management changes to enhance better quality of life. 148 Implementation of One Water Approach in Urban Water Systems A version of this chapter is currently under review in the Utilities Policy journal entitled with “ Incorporating Design Thinking in the Implementation of One Water Approach for Urban Water Management” 8.1 Introduction OWA is regarded as a new concept and an emerging shift in UWM, which is why the implementation of this concept is not fully mature. This concept, as mentioned, has been widely advocated and practised in many countries, while some Canadian municipalities are embracing it at the basic level. Recently, different organizations and water providers have claimed to benefit from implementing the OWA strategy in managing their water resources and systems (Pokhrel et al., 2022). The implementation of OWA is directly related to IUWM practices, which align with the United Nations SDG 6. The global leaders and decision-makers in the water sector have rightly identified the path for the future water, where integrated management will have a primary role not only in meeting increasing water service demands but also using economy and infrastructure in the right direction for the holistic management of UWSs (Nieuwenhuis et al., 2022). The concept of integration is popular at UWM. Thuy et al. (2020) developed a Sponge City model by integrating four modules, including MIKE URBAN model, sustainable urban drainage system tool, life cycle assessment, and multi-criteria analysis and considering various indicators (social, urban water, ecosystem service, climate, and economic) as model variables. The model implementation can predict multiple benefits of UWM and dynamics of hydrological processes, determine economic values, and identify the suitable plan to manage UWSs effectively. Sletto et al. (2019) implemented a sustainable UWM concept for stormwater management by bringing different players across local communities, local authorities, and civil society organizations to enhance drainage services in Santo Domingo Norte, Dominican Republic (Sletto et al., 2019). Vergara-araya et al. (2020) integrated water, waste, and energy to design the integrated system by generating electricity and heat by combining blackwater with municipal wastes and separating it with wastewater. The integration was implemented in Chile, reducing freshwater resources by 30% and generating electricity and heat at 19 % and 51%, respectively (Vergara-araya et al., 2020). Using existing frameworks for the implementation of IUWM is a common practice. Mattas et al. (2014) used a DPSIR framework to identify the main causes of water resource degradation and 149 optimize the water system for integrated groundwater resource management for managing resources sustainably (Mattas et al., 2014). Cole et al. (2018) built and used a Collaborative risk-informed triple bottom line MCDA planning framework to overcome barriers of IUWM in decision-making by building partnerships, promoting participation, and maintaining transparency ., and eg (Cole et al., 2018). Saikia et al. (2022) employed a city water resilience framework to assess UWS performance across four criteria (infrastructure and ecosystems, health and well-being, planning and finance, and leadership and strategy) and understand how urban water responds to shocks and stresses, identify interactions between system components, and determine stakeholders’ and water governance processes (Saikia et al., 2022). A significant number of studies have been conducted to implement IUWM practices. However, no studies have focused in determining the integrated index (known as the OWA index in this study) that provides numerical and tangible value to rate water system performance. The integrated management system is a wide knowledge gap in water systems. Among studies that are available in the literature, OWA is a unique integration process that has not been implemented in the municipalities to discern stakeholders’ perceptions about their feasibility in evaluating IUWM performance. Hence, this study employs Design Thinking (DT) to implement OWA in the existing UWSs. The selection of this technique is attributed to its limited application in the UWM sector and justifies the benefits of using it in managing UWSs holistically. DT is a human-centered approach that is based on the iterative problem-solving technique. A big part of this approach lies in the stakeholders’ opinions that integrates participants’ reasoning and mindsets (Liedtka, 2015). The entire technique is divided into five stages and defined as the following: a) empathize: Deals with understanding a larger spectrum of problems that surround urban population related to a specific design challenge; b) define: provides clarity to the problems identified in empathize stage and narrows down these issues so that the problem statement is well defined; c) ideate: provides a plausible set of solutions from a wider perspective to overcome the problem identified; d) experimentation: builds a medium that assists designers to reach one step closer to identifying their best solution as a prototype; and e) test: examines the prototype and solicit user feedback in understanding their perceptions in adopting solutions to overcome problems investigated in the earlier stage. For this purpose, various MCDA techniques are employed. One of the most common MCDA techniques is VIKOR that is used to determine the 150 rank from a set of criteria using initial weights. This method is reliable when solving a discrete decision-making problem with variable units (i.e., non-commensurable) and multiple conflicting alternatives. It is based on the ranking and identifying criteria from the pool of alternatives and determines the potential solution of a problem with the conflicting criteria. Thus, it assists decision-makers to make a final solution (Zeleny, 1975). It is based on the assumption of “closeness” to the positive ideal solution (Sayadi et al.,2009) and is developed from the Lp-metric, which is employed as an aggregating function in the programming methods (Zeleny, 1975). The suitability of VIKOR in testing the prototype is because i) It measures the closeness to the ideal solution based on the multi-ranking index method (Sayadi et al., 2009); ii) It provides the solution that is accepted by the maximum decision-makers as it renders “group utility” of the majority and minimum individual opportunities regretted; iii) Evaluation of alternatives based on the established performance matrix; and iv) Direct and indirect influence of the decision-makers in the final performance assessment (Opricovic & Tzeng, 2007). Various design thinking (DT) techniques are available in the literature, and their discussion slightly varies. However, the gist of the DT remains the same. For example, Glen et al. (2015) used a six- step process to include a design thinking project in their business curriculum (Glen et al., 2015). The findings suggested that DT significantly designed a student-focused curriculum that fostered student engagement and built their confidence in solving complex issues. Shapira et al. (2017) employed DT to contribute to their sustainable strategic development program based on Maxwell’s interactive model for qualitative research, involving main methods, sub-methods, and three prototype development steps (Shapira et al., 2017). The authors found that the prototype implementation assisted the design team in attaining strategic and sustainable results. Andalusia and Bermejo-mart (2020) used DT to promote household engagement in achieving efficient water use, developing a web-based tool capable of providing updates regarding water use and production and keeping track of system performance (Andalusia & Bermejo-mart, 2020). The novelty of this chapter is based on the following criteria:1) Based on the OWA indicators established in Chapter 7, corresponding utility data will be collected for each indicator under a specific performance objective; 2) Development of an OWA index that is essential in estimating current utility status in achieving long term One Water goals, and 3) Determination of percentage improvement that utilities will require to make in moving towards building One Water community; 151 3) The findings from this chapter will open new avenues for policy-makers in improving the holistic performance of IUWS. 8.2 Methods The current study uses the DT toolkit proposed by the IDEO, “Design Thinking for Educators” because it is the most widely used and easy-to-interpret method. In addition, this tool kit has not yet been used for the effective UWM in the literature. The tool kit divides the entire process into five phases (IDEO, 2012), which are listed as follows. 8.2.1 Empathize It is the 1st step of DT, where key problems surrounding UWSs were identified. Increasing urban water service demands, changing weather patterns, and sprawling urban communities are serious challenges that cause hindrances in the current UWM 8.2.2 Define It is the 2nd step of DT, where a well-defined problem surrounding UWSs are investigated. These problems include but are not limited to poor environmental practices, ageing infrastructure with the decline in assets service life, financial constraints and equitable technical advancements, weak water governance structure, and insufficient public participation in decision-making. 8.2.3 Ideation In the third step, integrating UWSs into a single component, replacing conventional UWM strategy and engaging relevant stakeholders in decision-making were considered potential solutions to manage UWSs holistically. 8.2.4 Experimentation Adopting OWA in the existing UWSs combines the ideas generated in step 3. The implementation is directly linked with assessing integrated UWS performance, identifying systems’ weaknesses, and comprehending its significance from a policy-making perspective so that decision-makers can make well-informed decisions for building the One Water ideal community. The community serves as a prototype. For this purpose, a first set of questionnaires were designed for the experimentation of the ideal One Water community. The same set of questionnaire (Table D-1) as discussed in Chapter 7 was used for this purpose. 8.2.5 Test An ideal One Water community was characterized based on the questionnaire responses from the experimentation phase. The ideal One Water community served as a prototype to be tested. The 152 2nd questionnaire (Appendix H) was developed and distributed to various BC municipalities to test the prototype. Upon receiving responses, the percentage improvement required to reach the ideal One Water community was determined by developing an OWA index. The questions in the questionnaire contain multiple sub-questions related to a specific indicator. Following steps need to be completed in completing the testing process. 8.2.5.1 Step 1: Formulation of a decision matrix A decision matrix is formulated comprising utilities and indicators. Performance scores are inserted for each utility. The best (Y+) and worst (Y-) scores are identified across indicators, which can be represented as follows: 𝑌𝑖+ = 𝑘𝑚𝑎𝑥𝑌𝑖𝑘 (8-1) 𝑌𝑖− = 𝑘𝑚𝑖𝑛𝑌𝑖𝑘 (8-2) 8.2.5.2 Step 2: Determination of weight The combined method of AHP and Criteria Importance Through Inter-criteria Correlation (CITIC) were used to determine weights. AHP and CITIC are the combinations of the subjective (ws) and objective weighting (wo) techniques. A hierarchical structure is built and expert opinions are elicited to provide a pairwise comparison of the indicators for the AHP. Appendix I shows the examples of calculation for AHP and one MCDA method. For the CITIC, performance score values are normalized, followed by the standard deviation calculation. A correlation matrix is formed between the indicators. The indicators were developed to serve as criteria, which measure the conflict created by individual criterion. The criterion information is determined, which helps determine each indicator’s weight value. The final weights are determined from the combination of subjective and objective weights as follows: 𝑤𝑘 =𝑤𝑘 𝑠 𝑤𝑘𝑜∑ 𝑤𝑘 𝑠 𝑤𝑘𝑜26𝑘=1 (8-3) where k = 1, 2,…26 8.2.5.3 Step 3: Computation of the group utility value and regret value Both group utility (Sk) and individual regret values (Rk) are calculated using the following equation 153 𝑆𝑘 = ∑ 𝑤𝑘(𝑋𝑖+ − 𝑋𝑖𝑘)(𝑋𝑖+ + 𝑋𝑖_)𝑛𝑖=1 (8-4) 𝑅𝑘 = 𝑚𝑎𝑥𝑖 𝑤𝑘(𝑋𝑖+ − 𝑋𝑖𝑘)(𝑋𝑖+ − 𝑋𝑖−) (8-5) Calculate the group utility value Sk and the individual regret value Rk 8.2.5.4 Step 4: Determination of the electric value The electric value (Qk) is given by the following equation 𝑄𝑘 = 𝜗𝑆𝑘 − 𝑆−𝑆∗ − 𝑆−+ (1 − 𝜗)𝑅𝑘− − 𝑅−𝑅+ − 𝑅− (8-6) where S- = min k Sk ; S* = max k Sk ; R - = min k Rk ; and R* = max k R k ; 𝜗 = 0.5 8.2.5.5 Step 5: Calculation of OWA Index OWA index (IOWA) is calculated by multiplying the index value (Iv) by 100. The multiplication can be attributed to the Iv ranging between 0 – 1. Iv is taken as the difference with 1 as shown by equation 7. OWA index is represented out of 100 and is calculated for each utility performance. Higher is the IOWA value, means higher is the rank, resulting in better utility performance in achieving OWA target. In addition, the utility’s percentage improvement is calculated to understand how much the utility needs to improve (in terms of percentage) in meeting OWA goals. Following are the equations used in this step. 𝐼𝑦 = (1 − 𝑄) (8-7) 𝐼𝑂𝑊𝐴 =(1 − 𝑄)(𝑀𝑎𝑥 𝑣𝑎𝑙𝑢𝑒)∗ 100 (8-8) %𝑖𝑚𝑝𝑟𝑜𝑣𝑒𝑚𝑒𝑛𝑡 = (1 − 𝐼𝑦) ∗ 100 (8-9) where % improvement refers to the minimum percentage improvement that the utility has to make in achieving OWA goals. Using the above steps (1 – 5), the indicator index value (Iiv) was computed. The only change was in formulating a decision matrix, where the column represented OWA indicators, and row referred to utilities. 154 8.3 Results and discussion Twelve participants responded to the questionnaire. Figure 8-1 shows the OWA index value and the corresponding percentage improvement required to achieve OWA goals for the participating utilities. Utilities are represented by the symbol (U) and a number at the subscript. Figure 8-1 OWA Index Values U11 achieved the perfect performance score (5) for three indicators I3, I7, and I22, while 16 indicators had moderate scores (i.e., at or above 3). The main contributors to the utility’s high index must be their well-documented and executed emergency plans, rigorous practice of fit-for-purpose treatment, and nutrients recovery from wastewater. It is only one of two utilities that recovered nutrients from wastewater, significantly preventing phosphorus and nitrogen depletion when the treated effluent is disposed into source waters. I1, I3, and I15 were three indicators receiving a perfect score, whereas 14 indicators had a moderate score for U5. Similar to U11, the utility recovered nutrients from their wastewater, which were later used as fertilizers to increase crop productivity. In addition, they also had the most efficient pumps and SCADA system, along with a robust database management system that kept track of their utilities’ performance trends. 0102030405060708090100U1 U2 U3 U4 U5 U6 U7 U8 U9 U10 U11 U12Index value and % improvement requriedUtilityOWA INDEX Improvement to OWA 155 U9 third highest index value can be credited to its perfect score of I4, I14, and I22, whilst 14 indicators had the moderate score. In this utility, drinking and sewer water prices were affordable and successfully generated sufficient revenues to fulfill operational and maintenance costs. U6, U1, and U3 were the three bottom-ranked indicators as they had the most number (20, 12, and 23) of low score indicators (i.e., score less than 3). I2, I3, I4, I7, I9, I12, I16, I17, and I23 were the common indicators to all three utilities that had low scores. Among them, I2, I3, and I4 has the worst scores, which was < 1. The findings clearly suggest that these utilities do not recover energy from their treatment plants, nutrients from wastewater, and do not reuse biosolids. I17 was another indicator that not only had the worst scores for the poor-performing utilities, but apart from U9 and U10, the remaining utilities had scores below 3. Figure 8-2 shows the indicator index value (Iiv). Based on the index value, I22, I12, I21, I15, and I24 were the top five ranked indicators, while I3, I4, I2, I14 and I8 were the bottom five. The findings suggested that most of these utilities had enough human and economic resources to tackle against the unfavourable situations (e.g., floods, heat waves, and droughts). It was encouraging to observe that utilities believed collaboration played an essential role in effective UWM. Many utilities built strong collaborative relationships with other departments and share information, which means that these utilities were not siloed. Considering conventional water management focuses only on their departments, the participating utilities’ collaborative nature must be considered a welcoming step. Utilities had also prioritized the inclusion of indigenous communities in decision-making, which was another positive finding in embracing diversity and allowing equitable water management. Many utilities did not have enough emergency funds in their account, which is a sign of a big worry considering BC experiencing unpredictable weather circumstances in recent years. 156 Figure 8-2 Indicators index value The sub-sections 8.3.1 – 8.3.5 detail the performance of each indicator, which are classified based on performance objectives in achieving OWA goals. 8.3.1 Enhance environmental health Seven indicators are included under this performance objective. Performance scores (PS) of each indicator is represented as a radar diagram (Figure 8-3). Efficient pump and SCADA system (I1) is the first indicator. Utility 5 (U5) is only the system that achieved a full PS for this indicator. One of the reasons behind such a high PS must be attributed to the utilities’ provision of monitoring pumping equipment continuously and conducting periodic replacement of older pumps. These utilities had also installed SCADA system, which constantly monitors their water quality parameters, optimize pressure, detect failures, and provide them with real time data. Five utilities had their PS ranged between 3 and 3.5. Pumps at these utilities were operating at a moderate efficiency, decreasing their energy costs. SCADA system enables optimization of pumping stations reliability and effectiveness, lessen energy consumption, and reduction of water loss. These activities lead to less water pumping, leading to minimum energy consumption and corresponding costs (Temido et.al., 2014). 0102030405060708090100I1 I2 I3 I4 I5 I6 I7 I8 I9 I10 I11 I12 I13 I14 I15 I16 I17 I18 I19 I20 I21 I22 I23 I24 I25 I26Indicators IndexOWA Indicators 157 Energy recovery from the wastewater treatment plants (WWTPs, I2) is the 2nd indicator. U8 is the only system that had a PS above 4, while U9 and U10 had their scores close to the boarder at 4. Although eight respondents believed in recovering energy from treatment plants, only five recovered energy. None of the utilities generated sufficient energy that could meet plant’s efficiency needs. Globally, as there has been an attempt to achieve carbon neutrality and maximum energy savings, which is why WWTPs should be designed in a way that it can generate maximum energy and also contribute to resource recovery. The recovered energy can be used in producing methane gas, which plays an integral role in reducing fuel consumption for the effective operation of treatment plants (Maktabifard, 2018). Nutrients recovery from wastewater (I3) is the 3rd indicator. Two utilities (U5 and U11) received a full PS for the indicator, while the majority of the utilities had a very poor (below 2) PS. Capacity gap, lack of education and awareness, increase operation and implementation costs, narrow applicability and insufficient financial drivers might be the contributing factors for such poor performance (Shaddel et al., 2019). Additionally, these utilities might not have experienced issues related to phosphorus and nitrogen depletion in their source waters, which is why they might not have considered the importance of the nutrients’ recovery. Biosolids reuse (I4) is the 4th indicator. Only three utilities had the PS over 3 and four utilities did not respond to the question. U9 is only the utility with a full PS. This is because U9 not only recovered biosolids, but it also sold them for commercial purposes. For example, Metro Vancouver (a Canadian municipality) used biosolids to produce a fertilizer that is essential in plant growth, improve soil’s ability to hold water, and create soil that absorbs methane gas and greenhouse gas (GHGs) emissions (Metro Vancouver, 2022). Reduced GHG emissions from treatment plants (I5) is the fifth indicator. Eight respondents believed that reducing GHG emissions from treatment plants is important. Despite this, seven utilities do not calculate their GHG emissions. Considering eight respondents moderately agreed that their treatment plants have excellent operational conditions, these utilities might not have tracked their GHGs productions. Another reason could be the presence of limited human resources. This can be verified as one respondent quoted that “We can calculate our system’s emissions via Urban System’s GHG calculator, but presently we are short of water efficiency coordinator, who is responsible for performing such type of calculation”. 158 Natural hazard management (I6) is the sixth indicator. Ten utilities had their PS below 2.5, suggesting that most systems did not consider natural hazards a serious threat. The score also indicated that neither utilities had a proper document outlining robust solutions to tackle catastrophic situations nor formed policies to mitigate hazards with minimum risks to consumers. Further, none of these utilities conducted risk assessments periodically. Paterson & Charles (2019) researched to study the perception of community-based responses in dealing with natural hazards. The authors found consumers were most concerned with securing basic human needs at the time of hazard. At the same time, there was a lack of concrete multi-hazard response plans at the municipal level to cope against natural disasters. Such initiatives were lacking primarily because of financial constraints (Paterson & Charles, 2019). Fit-for-purpose treatment (I7) is the 7th indicator. Eight utilities had their PS equal to and above 3. U11 (5) had the highest score for this indicator, whereas U1 (2), U3 (2), and U6 (1.80) had the lowest scores. Ten utilities supplied highly treated water (i.e., of the drinking water standard) for potable and non-potable applications, while only two utilities supplied reclaimed water for non-potable uses. The result clearly shows that water reuse is still not a common practice in most of the utilities. Instead, advanced treated water is used to meet different urban water uses that do not require the highest quality water. Freshwater abundance, public perception, and sufficient water licensing capacity are a few notable causes that might have discouraged utilities from practising water reuse. However, there is a greater possibility that the trend might change soon because various issues related to increasing population, urbanization, and climate change incidents have added extra pressure on freshwater supplies, specifically in the semi-arid regions. As a result, water reuse can be a potential solution to reduce potable water uses. Six utilities treated their wastewater to the potable water standards before disposing it into receiving water bodies, while only one utility disposed of its wastewater with zero treatment into source water 159 Note: U1, U2…U12 represents utility participants Figure 8-3 Indicator performance scores under objective 1 160 8.3.2 Achieve improved urban water assets performance Five indicators fall under this performance objective and their corresponding scores are represented in Figure 8-4. Nine utilities had their PS above 3 for the 8th indicator, Implementation of Low Impact Development (I8). Out of these, three utilities had their PS between 4–4.2. These utilities had placed LID in their respective regions to manage storm runoff. Pavers, swales, storm ponds, exfiltration galleries, infiltration bulges, tree trenches, green roofs, and rainwater harvesting were the LID techniques used by the utilities. Five respondents believed that LIDs play an instrumental role in controlling floods. However, the same number of respondents did not agree that they are important in improving source water quality. This finding was a bit surprising because LIDs significantly reduce pollutant content in a runoff. For example, when Seo et al. (2017) employed various combinations of LID practices (e.g., rain gardens, bioswales, bioretention cells), nitrate (NO3) was reduced between 23- 32% and total phosphorus concentrations decreased between 10 -23% (Seo et al., 2017). Eight respondents emphasized the need for LIDs in managing stormwater. Six respondents moderately agreed that they could not install LIDs in their regions because of financial barriers. In comparison, three respondents did not want to place LIDs despite not having financial barriers. None of the utilities had a PS above 3.5 for the water leakage reduction (I9) indicator. Utilities (6) not installing leak detection instruments at different locations in distribution networks, absence of real-time sensors at the water mains point of entry, and utilities (10) not keeping track of customer records in installing leak detection instruments in their houses are primary causes behind their low performance. Water leakage is a huge problem. In the US alone, 17% of water is lost yearly because of leakages (Rupiper et al., 2022). This situation is further aggravated as tracking water leaks is not regulated and mandated in different countries. For example, in the US, only seven states mandate water loss audits, while no states regulate water loss levels (Sayers et al., 2016). Many North American utilities randomize exact percentages for unaccounted water to the public and do not use a proper system (e.g., American Water Audit software) to track leak quantities. Utilities and consumers have failed to understand the actual value of water as later are ready to pay 10,000 times more for bottled water instead of using highly treated tap water (Municipal Sewer and Water, 2022). In addition, no utilities encouraged their customers to install moisture-detecting sensors, which are essential in determining leaks at different points (e.g., bathroom sinks, basement pipes, washing machines, refrigerators) in the households. 161 Provision of Dual distribution system (I10) is the least favoured option to supply water. Eight utilities did not have separate mains to distribute potable and reclaimed water, while two utilities used different mains to supply two types of water. It was interesting to observe that most (8) utilities are not planning to build separate mains for water distribution. Large capital costs in building a new distribution network, small demand for reclaimed water uses, and less population might be the possible factors against establishing a dual distribution network system (Rasoulkhani et al., 2019). More than half of the participating utilities had their PS below 3 for the Adaptive infrastructure (I11) indicator. Ageing infrastructure is the main reason for the low score as ten respondents agreed that their water systems could not function properly with the same efficiency and effectiveness in the next 50 years. One of the reasons behind it is continuing population growth and increased water service demands. The latest Canadian Infrastructure report card suggests that 16% drinking water, 21% wastewater, and 16% stormwater assets are in a “poor” and “very poor” state. This means that government must take plausible interventions to improve asset condition sooner, which will require heavy investment (Canadian Infrastructure Report Card, 2019). In addition, eight respondents agreed that constant increase in water demands is a potential problem moving forward, which is why decision-makers must manage water systems in an integrated manner. Even countries such as Canada, which has 7% of world’s renewable freshwater resources, are at a great risk of facing water shortages especially in their semi-arid regions (Government of Canada, 2013a). Eight utilities had their PS at 3 or above for the rapid recovery from natural calamities (I12) indicator. Recently, the global community has been experiencing floods, heat waves, and other weather related incidents more frequently because of the climate change events. Despite this fact, it was encouraging to observe that most utilities (11) were well-equipped with resources to tackle unfavourable situations. Four utilities had no financial constraints in dealing with unintended weather events, while five utilities were at the average state. Five utilities also provided alternate source water supplies in case of water contamination and drought events to suffice consumer needs. 162 Note: U1,, U2…U12 represents utility participants Figure 8-4 Indicators performance scores under objective 2 163 8.3.3 Strengthen financial situation and technical advancement Performance scores of indicators under this objective are represented in Figure 8-5. Average annual lifecycle investment (I13) is the thirteenth indicator. Seven utilities mentioned that they do not collect enough revenues to conduct replacement work for water infrastructure. A similar result was observed when the lifecycle costs of four countries (Kenya, Ethiopia, Cambodia, and the US) were compared. The authors indicated a large gap between distributed water cost and revenues collected, ranging between $1 to $17 per customer annually. The capital investment in the operation and maintenance of water systems was not enough to meet the water services level in all four countries (Libey et al., 2020). Respondents also believed that the service life of both linear and non-linear assets is ageing. This finding resonates with the adaptive infrastructure indicator, where respondents believed utility managers were worried about their assets to fulfil increased water service demands. Water price (I14) includes drinking water and sewer prices. Five utilities stated that revenues generated from drinking water were insufficient to bear their O&M costs, and other expenses related to water abstraction, distribution, treatment, and disinfection. Two utilities agreed that the wastewater price was affordable, while four utilities completely disagreed that revenues generated from wastewater services were enough to counter day-to-day activities at their facilities. One reason behind this could be utilities, not recovering biosolids, which can be sold for agricultural purposes to augment crop productivity, thus adding economic value to the wastewater utility. Wastewater revenues are extremely important as they can be used for multiple applications, including building resource recovery facilities, biogas production, and generating heat and electricity (The World Bank, 2020b). Ten respondents emphasized the need for a web-based tool (I15) as an important indicator to keep track of changing water system performance over time. The tool will not only serve as a robust database management system that will keep a record of water systems performance but it will also assist utility managers in introducing necessary interventions in improving declining performance. The tool is also of paramount significance because nine out of 12 utilities do not have such device placed currently in their utility. Keeping up-to-date information on performance trends will also help utility managers secure government funding, provided that water system is not performing well because of a lack of essential resources availability (Pokhrel et al., 2022). Although, the tool was installed in their utility, two utilities made one interesting observation: they did not have adequate staff trained to use the device for data sharing. Three respondents moderately agreed to 164 their former utility participants. Thus, the result suggests that short staffing and restrained resources are still major problems in a few Canadian utilities specifically in SMWSs. Nine utilities had their PS at 3 or below, indicating that water-saving appliances (WSA, I16) were still not common in households. This is because most of the utilities (10) did not award rebates to the customers for installing WSA in their houses. In addition, they did not keep a record of households that installed WSA, indicating that utilities have not prioritized water-sensitive practices at the household level. This is further justified as only five utilities have encouraged their customers to adopt WSA. Common barriers associated with low WSA adoption include a lack of consumers’ belief in WSA applicability, inadequate knowledge about water conservation practices, and unavailability of incentive provisions for households installing efficient devices (Addo et al., 2018). The situation was even worst for the energy-saving devices (ESD, I17) as ten utilities had their PS at 2.50 or less. None of the utilities provided rebates to customers for installing ESD. Despite the installation of WSA and ESD being given the least priority, it was surprising to observe that more than nine utilities suggested that installing these devices in their households was beneficial from the perspective of utilizing their water resources efficiently and effectively. One respondent quoted that “WSA is a part of the building permit process but there are still households that have not upgraded as they were built before new rules”, suggesting that this utility is mandating new houses to install WSA. Availability of emergency funds (I18) is the 18th indicator. None of the utilities received government funds to tackle emergencies directly. One participant responded “If it is an emergency, the provincial government will give us an emergency number to cover certain costs but it generally does not cover our infrastructure repair costs. The result clearly indicates that there is a greater need for a bigger collaboration at federal and provincial levels in a way that they come up with robust strategic planning and structured policies in assisting utilities that require funds in conducting repair and replacement activities. In addition, nine utilities agreed that although they can request additional grants from the government, bureaucracy causes a delay in the approval process. Ten utilities collected enough revenues to tackle minor emergencies (e.g., pump failure, presence of E.coli in distributed water), which is a positive sign as they do not need to depend on the government for financial assistance. 165 Note: U1,, U2…U12 represents utility participants Figure 8-5 Indicators performance scores under objective 3 166 8.3.4 Promote good governance Indicators’ performance scores are shown in Figure 8-6. Government funding in water-sensitive practices (I19) is the 19th indicator. A majority (7) of utilities had their PS at 2.5 or below, suggesting that they do not receive adequate funding from the government to implement water- sensitive practices. Nine utilities had their response settled between “agree” to “moderately agree” that they were eligible to receive government loans at a lower interest rate to conduct innovative projects that would optimize and maximize water resources. For example, the Canadian government provides funds for various programs, such as Eco Action community (supports financially to reduce harmful contaminants in freshwater to protect ecosystem health), climate action and awareness fund, ecological gifts program, and Great Lake Protection Initiative. All these programs are oriented towards building sustainable urban water communities (Government of Canada, 2022). Regarding infrastructure upgrading, eight utilities confirmed that they do not receive funds from the government. Supporting this argument, one respondent quoted “The government does not fund rehabilitation programs, they fund treatment and other “hot item” tickets such as flooding. We apply for lots of things and complete projects when we get the finds and sometimes when we do not “. It is hard to receive government aid despite utilities (8) encourage their customers to practice water sensitive practices in their households. The alarming problem of distributing government fund must be tackled immediately to enhance UWS performance. Strengthening Sustainable Development Goals (SDGs) is the 20th indicator (I20). Each of the four utilities was either aware or unaware of the SDGs; rather the focus was shifted more towards managing their water resources. Ten utilities confirmed that their response lay between “agree” (3) and “moderately agree” (7) when asked about their knowledge surrounding SDG 6 (i.e., providing clean and safe water and sanitation services) and the efforts they were making in meeting those goals. Eight utilities moderately agreed that they practised IUWM through a partnership with other municipalities, recycling wastewater for non-potable use, and reusing non-potable water to fulfil agriculture water demands. Based on the responses received, it is fair to conclude that utility did not emphasize much on the SDGs goals with IUWM still being a slow-growing concept. This maybe because historically Canada is known for its abundant water resources and practising IUWM has been overlooked as its people and utility managers might be sharing the opinion that it is still a long way before the country faces serious water availability threats. 167 Collaboration is shown through data sharing, collective discussion building, informed decision-making, and collaboratively working. Two utilities agreed that they built strong collaboration between diverse disciplines (I21) to promote effective UWM. Three more utilities followed a similar trait as they had their PS at or above 4 for the same indicator. These utilities had intra-utility data access (i.e., drinking water utilities had wastewater and stormwater data accessibility and vice-versa) and were able to share their data with any relevant water stakeholders when needed. In addition, six utilities entirely kept their low-level management staff informed about their upper management decisions, while five utilities shared some of their decisions. Four utilities did not hold inter-departmental discussions when they were making important decisions such as, keeping wastewater and stormwater utility about the commissioning of a new water treatment plant by drinking water facility. Two utilities revealed that they worked individually. The result supports the assumption of a siloed-based water system, indicating that integration of UWS service components is still distant thinking for the most utilities. Six utilities participated in the public-private partnership projects, including asset management investment plans, climate change readiness assessments, and benchmarking and performance assessment studies. This is an inspiring indication given that fact that public utilities are building partnerships with various sectors (e.g., industries, academic institutions) in welcoming new strategic projects for the continuous performance improvement of their systems. Consequently, it is fair to conclude that collaboration promotes a shared vision, comprehends diverse perspectives, and builds a generic mental model of informed changes that is essential in achieving strategic priorities (Brown et al., 2016). Eight utilities had a PS at and above 3 for Emergency response plans (ERP, I22). Eleven utilities responses lay between “agree” (4) and “moderately agree” (7) when inquired if they had devised a proper master plan to overcome emergencies. Only three utilities trained and had their workforce rehearsed in successfully implementing ERP, while eight of them were still in the intermediate phase of conducting similar programs. For the effective implementation of ERP, eight utilities build a systematic chain of command the line of authority to inform about emergencies. Four utilities built an early notification systems to keep their customers informed about any unfavourable activities taking place at their utility. 168 Note: U1,, U2…U12 represents utility participants Figure 8-6 Indicators performance scores under objective 4 169 8.3.5 Enhance community perception and participation Indicator scores are represented graphically in Figure 8-7. Note: U1,, U2…U12 represents utility participants Figure 8-7 Indicators performance scores under objective 5 Public engagement in decision-making (I23) has the least PS (< 3) for the majority (10) of utilities across all the indicators. Most utilities do not conduct public water forums, while very few conduct 2-3 engagements annually. These forums inform the public about their water systems’ present status or information about new programs within their jurisdiction. None of the utility organized forums regarding the IUWS, which means that the public is not yet informed about the benefits of 170 IUWM. On average, 30 participants attended these forums as reported by three utilities. In two utilities, public participation was represented indirectly as these utilities were found quoting, “The public is represented by elected officials on the board, who oversee all aspects of the water utility. In the past, we have had stakeholder committees when appropriate (i.e. for master plan). We also have two watershed stakeholder groups that provide input into watershed decision-making”. Another respondent said, “We do not use discrete utility forums to engage the public. They are run through land use and boarder planning process”. As public engagement is low, utilities (7) do not obtain public feedback on alternatives and decision put forwarded by the utilities. Similarly, there is a lack of public surveys conducted before and after decision-making, which is justified by only one utility conducting such type of survey. The exclusion of the public in decision-related activities demonstrates a wide gap created between public and utility managers, which is one of the short-comings of the existing water management systems. Only two utilities had a perfect PS (5) regarding the inclusion of indigenous community members in decision-making (I24). Most utilities do not adapt indigenous knowledge about their environment when making water-related decisions. This is because most state water governance systems do not identify indigenous water rights and responsibilities, while few governments support the “free, prior, and informed consent” of indigenous community members in decision-making. For example, in BC, the province’s decision to use groundwater and aquifers aggressively and distribute licenses without assessing cumulative effects on aquatic habitats and interactions with surface waters is considered a violation of indigenous rights (Curran, 2019). It is a serious problem in the existing system as utilities must acclimatize to indigenous settings since they are also part of the same community. There is a serious disparity in the Canadian water systems as they have been unable to bridge the wider gap between indigenous and non-indigenous people. On the contrary, ten utilities “agreed” (6) and “moderately agreed” (4) that indigenous rights, culture, and ethics were recognized before making any water-related decisions. Similarly, five utilities provided equal consideration to indigenous and non-indigenous opinions. The final two indicators are public willingness to pay (I25) and public willingness to change behavior (I26). To facilitate better water infrastructure provision and improved urban water services, a utility must have enough funds to generate either through revenues collected from 171 customers or additional financial aid supported by the government. Highlighting its importance, five respondents strongly believed that they must separately introduce stormwater prices to manage stormwater systems, while four respondents partially agreed with the former’s justification. Accommodating increasing water demands, reclaimed water use to fulfil non-potable demands is a common practice adopted by several water-scarce countries. Unfortunately, in Canada, the use of reclaimed water is still not practised to a large extent, which is evident by utilities' (10) unwillingness to conduct surveys to inquire about the public acceptance of reclaimed water applications. However, utilities encourage (strongly – fair) the public to build rain barrels in their households to accumulate excess rainwater. Watering plants and gardens consume large water volumes, which is why plants must be watered efficiently. Utilities (8) seem to have understood its intrinsic value as they strongly recommend household owners water their garden during fixed hours. 8.4 Conclusions This chapter conducts a performance assessment of twelve BC municipalities through the lens of OWA. A design thinking approach was used in implementing the OWA concept in the existing urban water system. The assessment is based on developing an OWA index by aggregating performance scores of 26 indicators across five performance objectives. Radar diagrams were used to represent the performance scores graphically. U11, U5, and U9 are three top-ranked utilities based on the OWA index. U11 highest index value must be attributed to its well-documented and executed emergency response plans, efficient water use based on their purpose (e.g., use of reclaimed water for non-potable supplies), and recovery of nutrients from wastewater important to increase crop productivity. Similarly, U5 performance was on a higher end because they were facilitated with the most efficient pumps and SCADA system and established a systematic database management system, which was instrumental in keeping utilities performance records checked. U9 acknowledged the use of biosolids in their region as they could generate electricity based on methane gas production and sell biosolids for economic gains. Considering indicator index values, I22, I12, I21, I15, and I24 were the best-performing indicators with high-performance scores for most utilities. Utilities collaborative relationship was fostered by providing access and sharing relevant data, holding inter-departmental discussions, keeping low-level management staff informed about decision-making, and working holistically for effective UWM. The siloed-based practice was less common, 172 which is a good indication that utilities are slowly transitioning from conventional water management. Many utilities also promoted equitable water management practices as indigenous communities’ opinions were given priority in decision-making. Considering the findings gathered from the current Chapter as a pilot investigation, the participation of 12 BC utilities clearly justifies the knowledge gap among the utility managers in understanding the OWA concept and discerning its importance in gauging water systems performance. However, the limited responses that have been gathered should not undermined the valuable conclusions that were derived from this Chapter. Since the participants were asked to record their responses using a linguistic scale and later converted into numerical values, the indicators used in the Chapter lacked mathematical formula. As a result, the OWA index value is based on the qualitative data, and decision-makers preference, which can sometimes be biased. The findings derived from this study also add a great value in terms of policy-making towards practicing integration in water systems, making needful interventions, and promoting collaboration across diverse areas. Future studies must be built in using these results as strong foundation for decision-makers to establish integrated water system policies that are instrumental in enhancing IUWS performance. The sample size should be extended to wider utilities, which is essential in understanding how the existing municipalities are prioritizing the need of integrated approach in improving their overall water systems performance. 173 Chapter 9 Conclusions and Recommendations 9.1 Overview OWA concept integrates three separate UWS service components (drinking water, wastewater, and stormwater) into a single entity. It recognizes all urban water flows as potentially useful resources, fosters stakeholder engagement, and focuses on revising institutional policies of segregated government structure. A paradigm change in the current siloed-based UWM is the need for time to overcome challenges in terms of continuous population growth, climate change, and increased service demands. OWA concept is more suitable for SMSWs as they are constantly surrounded by additional challenges, i.e., inadequate resources, insufficient funds, and the absence of advanced water treatment facilities. In this research, a framework has been proposed to assess the holistic performance of UWS through the lens of OWA. The study started with extensive workshops with the primary municipal representatives across the Okanagan Valley to identify conventional KPIs for SMWSs. After identifying the gap for an integrated system, a conceptual model of the One Water community was developed. Furthermore, OWA indicators were established to evaluate IUWS performance. An OWA index was developed based on the information gathered for these indicators, which were later used to implement IWA in the existing UWSs effectively. Implementing the OWA concept is of paramount significance as it allows urban water managers to understand their systems’ performance in an integrated manner and make necessary interventions to enhance their overall performance. Adopting this framework can guide the utilities in evaluating their and other utilities’ performance. Besides, it provides the basis to conduct self and competitive assessments. The decision-makers can use the lessons from this research in strategic planning to effectively implement the OWA concept in an urban community. 9.2 Summary and conclusions A summary of the results from chapters 3–8 and the main conclusions are given below:  In Chapter 3, a comprehensive literature review was conducted for OWA (definition and scope), existing practices, challenges, and strategies on its effective implementation in the current UWSs. In addition, the interconnection between drinking water-wastewater-stormwater was studied thoroughly, while historical and current UWM trends were also 174 discussed expansively. Six guiding principles were proposed. The findings emphasized the need of stakeholder engagement, strong leadership, financial backup and technical innovations for the successful OWA implementation.  In Chapter 4, 96 KPIs were identified across six performance criteria using Delphi technique and PROMETHEE II MCDA method. These KPIs were identified separately for drinking water, wastewater, and stormwater systems of SMWSs in semi-arid regions. Multiple stakeholder engagements were conducted. Agriculture water use, water price, average annual life cycle investment, swimming advisories and LID were some notable KPIs unique to the Valley.  In Chapter 5, a performance index was determined using a TOPSIS method by aggregating KPIs identified in Chapter 4. The developed model was successfully applied to five drinking water, three wastewater, and two stormwater utilities in the Okanagan Valley. The results indicated that only two drinking water and one wastewater utility had been rated as “high” performance. Operation and monitoring criterion had the lowest performance score for the remaining drinking water utilities, while the financial criterion contributed to the low performance for wastewater. None of the utilities in stormwater performance was rated as “high”.  In Chapter 6, a One Water Community conceptual model was developed using Taguchi-based optimization to reduce potable water use (RPWU). L16 orthogonal arrays were employed as an experimental design plan and S/N ratio for 16 trials were generated. Besides, confirmation tests validated the model by predicting the S/N ratio at the optimum condition. The findings indicated that WaterSense labelled urinals, reclaimed water for agriculture purposes, emissions reductions from treatment plants, bioswales, and increased public awareness were the most optimum parameters in achieving the highest potable water reduction.  In Chapter 7, OWA indicators were developed and prioritized for IUWS by eliciting expert opinions. Twenty-nine indicators were established against three decision criteria F-AHP was employed to prioritize OWA indicators. In contrast, F-TOPSIS was used to understand the impact of each decision criterion, i.e., sustainability, resiliency, and reliability. Rapid recovery from natural calamities was the top-ranked indicator, followed by water leakage reduction and public willingness to change behaviour, whilst GHG emissions from treatment plants is the bottom-ranked. 175  In Chapter 8, a DT approach was adopted to implement OWA across 12 utilities. It indicated that IUWM practices are required for effective UWM, which is underpinned by embracing OWA. An ideal One Water community was proposed by developing the OWA index. Only four utilities had an OWA index value above 80, while the same number had a value less than 50. The results suggested that most utilities prepared proper documentation and executed emergency response plans well, promoted inter-departmental collaboration, and practised equitable water management strategies, which were vital in achieving One Water goals. Based on the above summary, it is clear that the contributions made in the respective Chapters have close connection with the overall objective of this research. The KPIs identification process in assessing SMWSs performance were critical in developing performance index for individual water systems, including drinking water, wastewater, and stormwater. As a result, it allowed municipalities to share their data in conducting benchmarking to understand the strength and weaknesses of water systems across the Okanagan Valley. Considering the highest performance index values at highs of 60 and many indicators showing interdependencies among UWS service components, the findings prompted this research to conduct a rigorous literature review on the integrated approach to understand the benefits of adopting OWA in the existing municipal systems. Hence, an interdependency relationship between UWS components were studied and global OWA practices were reviewed to comprehend the situation of integrated approaches in managing water services. As a result six guiding principles were proposed, which were critical in developing a One Water community. This community included five key inter-related interventions and sixteen parameters which laid the foundation in understanding the role of reduction in potable water uses in building sustainable, resilient, and reliable water systems. The results further emphasized the combination of various water management strategies at different percentiles, which can later be used by the decision-makers in adopting a reliable strategy based on their utility’s scope and needs. Unlike conventional community, where water systems are managed in silos, the OWA community followed an integrated path. Following this trend, it was important to develop new set of indicators that measure IUWS performance. Hence, OWA indicators were developed and prioritized that evaluated the integrated performance based on three decision criteria (sustainability, resiliency, and reliability). Finally, these indicators were used to assess the existing municipal performance of water systems to understand their current status in achieving long term OWA goals. 176 The entire research also contributed in promoting collaboration between stakeholders across various municipalities in BC. It brought urban water managers, engineers, operators, technicians, academic researchers into a single table to develop effective water management strategies that can be easily implemented in the municipal systems to manage their water services. The research also highlighted the role of data collection in conducting studies and difficulties that are endured specifically among small and medium-size utilities because of resource constraints. However, the sample size should not be considered as a single factor in analyzing the overall findings viewing the notable contributions that have been made throughout this study. Specifically, the OWA implementation is a pilot study but the results gathered from this research will narrow the knowledge gap and encourage water managers to implement the concept in their systems, more importantly because of its holistic nature. Considering the existing water systems challenges globally, the recently concluded UN water conference also stressed on building reliable collaboration between diverse sectors. This is a testimony that the world leaders are focusing the need of integrated approach, which is why this research is a timely study that was conducted to show the path for decision-makers in managing their water systems in achieving long term sustainability, resiliency, and reliability. 9.3 Originality and contributions The three broad contributions of this research are listed below:  Development of performance indicators, assessment, and benchmarking approach: Ninety-six KPIs were established for three UWS components – drinking water, wastewater, and stormwater of SMWSs based on intensive engagement. Besides, a robust performance assessment method was developed, which can be used by utility managers to evaluate the performance of urban water services. The developed approach facilitates data sharing between utilities, enabling them to compare their performance with others and implement necessary interventions.  Development of Conceptual One Water Model: A conceptual model of One Water Community was developed employing six guiding principles across five interventions along with sixteen parameters. The model can identify the optimal combination of strategies for reducing potable water uses and predict water reduction potentials across three percentiles using a regression model. 177  Development of integrated performance assessment approach using OWA concepts: Twenty-nine OWA indicators were established to evaluate utility’s integrated performance. Performance was determined based on the calculation of sustainability, resiliency, and reliability scores. These novel indicators reflect essential characteristics of water systems (i.e., global sustainability attributes, infrastructure features in accommodating increasing water services demands, and holistic building of One Water Community). For the successful OWA implementation, detailed information on each indicator was discerned for a specific utility to develop the OWA index. The index could determine current utility status in achieving OWA goals and percentage improvement in reaching an ideal scenario (i.e., One Water community). 9.4 Limitations The limitations of this research are as follows:  Limited data availability: Limited data was available regarding indicators to perform performance assessment and benchmarking. Expert judgements were sought throughout the research, and the cases of occasional biases might have impacted the final output. Besides, the participating utilities were of small and medium-sized because of which they had inadequate data inventory and limited resources.  Limited participation: Ten utilities participated in the benchmarking study and 12 utilities were involved in obtaining OWA index values from their existing UWSs. The research would have immensely benefitted had the participation been extended to more water utilities both within BC and beyond.  Limited information on OWA performance: Limited information about IUWS performance was available in the literature. The present study identified a lack of an integrated water system performance assessment combining sustainability, resiliency, and reliability factors. Therefore, significant efforts were required to initiate a rigorous process for developing OWA indicators. 9.5 Future research directions The following recommendations are made for future research:  Improving KPIs: Based on the geographic location, scope, and time-variant variables, both conventional and OWA KPIs can be modified to meet the desired expectations of the utilities. Indicators related to spatial and temporal variations can be a resourceful addition to the current 178 list. Additionally, utilities can remove certain indicators considering that it has minimum importance to their respective water systems.  Developing a web-based portal: Keeping a real-time check on performance trends of UWS components across diverse performance criteria and indicators provide utilities with opportunities to continuously keep track of their existing performance and make immediate interventions based on resources available. For this purpose, a web-based tool can be developed, where utilities can enter their performance parameters over time. The tool will be not only able to keep past track records but also be capable of predicting future performance trends based on previous years’ data.  Building a generalizable conceptual model: The existing optimization model constitutes five controllable factors and 16 corresponding parameters. Future research should be conducted by introducing additional factors and parameters so that overarching analyses can be performed. These factors may also be modified based on a particular utility’s specific characteristics and requirements. In addition, utilities must collect real-time data to build a more robust model and validate its practicability and reliability with similar utilities within and beyond BC.  Deriving OWA-based implementation strategies: Quantitative data must be collected to determine the index value by developing formulas for each OWA indicator. As a result, it helps for the effective OWA implementation since the number scale represents performance objectively compared to the conversion of qualitative scale into quantitative. OWA indicators used in the study should be disseminated nationally and internally among academic, institutional, municipal, and industrial stakeholders. This condition will benefit utilities in developing a robust indexing system, which can be widely used in measuring IUWS performance more strongly. 179 References Abdalla, F., & Khalil, R. (2018). Potential effects of groundwater and surface water contamination in an urban area , Qus City , Upper Egypt. Journal of African Earth Sciences, 141, 164–178. https://doi.org/10.1016/j.jafrearsci.2018.02.016 Abdullah, L., Chan, W., & Afshari, A. (2019). Application of PROMETHEE method for green supplier selection : a comparative result based on preference functions. 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Conceptual Urban Water Balance Model for Water Policy Testing : An Approach for Large Scale Investigation. https://doi.org/10.3390/su10030716 Zeleny, M. (1975). Multiple criteria decision making Kyoto. 220 Appendices Appendix A Table A-1. Questionnaire indicators selection Selection of Performance Indicators for Urban Water Service Components Job title ___________________________________ The main purpose of this study is to identify the performance indicators (PI) for each service component of urban water system, which includes drinking water, wastewater, and stormwater. These components are composed of seven performance criteria: environment, financial, infrastructure, operational and monitoring, organizational, staff, and social. The questionnaire is composed of 9 pages, remaining pages are included only for reference purpose. We highly appreciate you taking your valuable time and efforts in participating in this study. Instructions: Part A: Identification of Indicators 1. Please supply a suitable score to each PI (For reference, the definition is provided in the appendix section of this questionnaire) based on a scale of 1-5 against two selection criteria, relevancy and clarity. 2. The definition for the selection criteria is provided in the following table: Selection Criteria Definition Relevancy How much an indicator is relevant to measure performance of small and medium sized water systems (SMWS)? Performance is defined as a function of effectiveness (how well the indicator is capable to estimate the desired/end result), reliability (how well the indicator’s consistency and truthfulness can be measured), and cost (is measuring the indicator cost efficient?). Clarity How much an indicator is clear in its meaning and can be interpreted well to both public and utility technical staff (engineers, managers, operators, and technicians)? Accuracy How much an indicator can be accurately / precisely measured? Those indicators which can be measured with a calibrating device are known to be accurately measured 221 Selection Criteria Definition Comparability How much the indicator can be compared with existing indicators available at national and international levels? 3. The definition of scales and their corresponding scores are provided in the table below: Scale Score Relevancy Clarity Very Low 1 Not relevant to performance and SMWS Difficult to understand for everyone Moderately Low 2 Slightly relevant to performance and SMWS Understandable only to utility managers Average 3 Average relevant to performance and SMWS Understandable to both utility managers & operators Moderately High 4 Highly relevant to performance and slightly average relevant to SMWS. Understandable to only staff High 5 Highly relevant to performance and SMWS Understandable to both staff and public Drinking water Performance Criteria Performance Indicator Relevancy Clarity Environment Availability of water resources Source water quality Alternative source water Volume of water drawn from surface water Volume of water drawn from groundwater Volume of water supplied to residential customers Volume of water supplied to commerce, industries, and institutions Volume of water supplied for agriculture purposes Water demand (daily normal hours) Water demand (peak flow) Actual maximum daily production (treated water) Distance to the treatment plant from the source water Total energy consumption (to treat 1m3 of water) Total Pumping energy consumption Total GHG emissions Chemicals and disinfectants use for water treatment Financial Revenue water Non-revenue water Water price Total cost incurred in the utility system (including capital expenditures) Total operational and maintenance cost for a utility system Total operating and maintenance cost per 100 km of a pipe Water abstraction cost Water distribution cost Economic real rate of return Debt service ratio Liquidity ratio Availability of emergency funds Annual funds received from federal / provincial governments Research and development expenditure Annual insurance amount 222 Performance Criteria Performance Indicator Relevancy Clarity Infrastructure Raw water storage capacity Treated water storage capacity Treatment plant capacity Age of the treatment plant Age of the distribution network Pumping utilization Total number of pumps per 1,000 people Metering level Total main length Total mains replaced / renewed / rehabilitated Total number of main failures Total number of leaks (real losses) Other losses (apparent losses) Disruption of water service (planned) Disruption of water service (Unplanned) Presence of sensors Organizational Water quality compliance Achievement of water demand reduction targets Boil water advisory Policy on water charges subsidy for poverty stricken households Inclusion of risk management in the water master plan Emergency response readiness training Preparedness for uncertainties / back-up plans Possibility of fund transfer to different water system under a single utility Operational & monitoring Cleaning of the storage tank Mains cleaning Hydrants inspected Pumps inspected Treatment plants inspected Distribution networks inspected Cross connection Aesthetic water quality tests Microbiological tests Physical and chemical water quality tests Tests for leakage Information on water age Presence of a SCADA system Social Population coverage Water quality complaints Other complaints apart from water quality Average number of calls to customer service representative Average talk time minutes First call resolution Number of legal actions taken for the non-payment of bills Number of new houses requiring water services per year Number of people affected due to water outage Number of people affected due to water quality failure Number of people affected due to water borne diseases 223 Performance Criteria Performance Indicator Relevancy Clarity Staff Number of full time operators Number of part time operators Number of qualified / trained operators Experience of operators Overtime work / hours Total number of full time employees Employee turnover Absentees due to accidents Absentees due to illness Distribution of the workforce by age Retirement eligibility Wastewater Performance Criteria Performance Indicator Relevancy Clarity Environment Biosolids reuse Sludge production in the wastewater treatment plant (WWTP) Sludge going to the landfill Wastewater generated per person Nutrients recovery GHG emissions generated through the wastewater treatment plant Volume of the treated wastewater outflow from the WWTP per day Volume of wastewater losses and spills Energy consumption to treat 1 m3 of wastewater Volume of wastewater treated during preliminary treatment Volume of wastewater treated during primary treatment Volume of wastewater treated during secondary treatment Volume of wastewater treated during tertiary treatment Financial Treatment unit revenue generated Service revenue generated Wastewater price Total cost incurred in the utility system (also include capital expenditures) Total operating and maintenance cost for a wastewater system Operating and maintenance cost to treat 1m3 of wastewater per day Operating and maintenance cost per 100 km of sewer pipe Operating and maintenance cost for pump Energy purchased to treat wastewater Debt service ratio Liquidity ratio Availability of emergency funds for wastewater Annual fund received by federal/ provincial governments for wastewater Research and development expenditure Annual insurance amount Infrastructure Sewer failure Sewer main length (separate from stormwater pipes) Sewer main length (combined with stormwater pipes) Sewer blockage Sewer leakage Sewer replacement Sewer rehabilitate 224 Performance Criteria Performance Indicator Relevancy Clarity Pump failures Age of the wastewater treatment plant Age of sewer mains Joints repair Organizational Wastewater quality compliance Number of regulated tests out of compliance Policy on wastewater charges subsidy for poverty stricken household Inclusion of risk management in the wastewater master plan Emergency response readiness training Preparedness for uncertainties / back-up plans Possibility of fund transfer to multiple WW system under a single utility Policy on wastewater reuse Operational & monitoring Frequency of combined sewer overflow Combined sewer overflow control Sewer mains inspection Pumps inspected Flow meters calibration inspection Inspection of pumping stations Inspection of manhole chambers Microbiological tests Physical and chemical wastewater quality tests Test for leakages Social Population coverage to sewer systems Complaints related to wastewaters Population coverage to the wastewater treatment plant Average number of calls to customer service representative Average talk time minutes First call resolution Number of legal actions taken for the non-payment of bills Number of new houses requiring wastewater services per year Number of people affected due to the wastewater quality failure Average response time to empty septic tanks Staff Number of full time operators Number of part time operators Number of qualified / trained operators Experience of operators Overtime work / hours Total number of full time employees Employee turnover Absentees due to accidents Absentees due to illness Distribution of the workforce by age Retirement eligibility 225 Stormwater Performance Criteria Performance Indicator Relevancy Clarity Environment Stormwater reuse Disposed stormwater quality to the water body Design storm Average precipitation intensity Average velocity of flow in stormwater mains Total runoff Infiltration rate Exfiltration rate Peak flow Groundwater recharge rate Average cms of snow in a year Volume of melting of snowpack into sewer/storm mains Impervious surface Nutrients level in the surface water Financial Economic loss caused due to flood damages Stormwater price Total cost incurred in the utility system (also include capital expenditures) Total operating and maintenance cost for a stormwater system Operating and maintenance cost per 100 km of stormwater pipe Energy purchased to treat wastewater Debt service ratio Liquidity ratio Availability of emergency funds for stormwater Annual fund received by federal/ provincial governments for wastewater Research and development expenditure Annual insurance amount Infrastructure Storm main failure Storm main length Storm main blockage Storm main leakage Storm main replacement and rehabilitate Surface flooding Flooding of properties from storm sewer in dry weather Flooding of properties from storm sewer in wet weather Record of extreme events other than floods Number of constructed wetlands Number of detention and retention ponds Inclusion of climate change impacts in design Organizational Swimming advisories Use of low impact development / green infrastructure Inclusion of risk management in the stormwater master plan Emergency response readiness training Preparedness for uncertainties / back-up plans Presence of guidelines and standards Operational & monitoring Frequency of rainfall Frequency of snow melting Runoff frequency 226 Performance Criteria Performance Indicator Relevancy Clarity Gully pots inspection Gully pots cleaning Pumping station inspection Number of samples tested at the downstream of stormwater catchment Microbiological tests Physical and chemical wastewater quality tests Social Population coverage to stormwater mains Complaints related to stormwater Average number of calls to customer service representative Average talk time minutes First call resolution Number of legal actions taken for the non-payment of bills Number of new houses requiring stormwater services per year Number of people affected due to the storm mains failure Staff Number of full time operators Number of part time operators Number of qualified / trained operators Experience of operators Overtime work / hours Total number of full time employees Employee turnover Absentees due to accidents Absentees due to illness Distribution of the workforce by age Retirement eligibility Table A-2 Weights determination Please supply the numbers 1-5 to represent the following ratings 1 = Equally important 2 = More important 3 = Weakly important 4 = Strongly important 5 = Very strongly important 6 = Absolutely important  Please fill the ‘More’ column if column 1 is equal or more important than column 2  Please fill the ‘Less’ column if column 1 is less important than column 2 in the following Tables 1 and 2 For example: If ‘Environment’ criterion is ‘weakly more important’ compared to ‘Infrastructure’ criterion then, Colum1 More Less Column 2 Environment 3 Compared to Infrastructure If ‘Environment’ criterion is ‘absolutely less important’ compared to ‘Infrastructure’ criterion then, Colum1 More Less Column 2 Environment 6 Compared to Infrastructure 227 Comparison for the determination of the weights of performance criteria Colum1 More Less Column 2 Environment ‘Environment’ criterion is compared to “Financial’ criterion ‘Environment’ criterion is compared to ‘Infrastructure’ criterion ‘Environment’ criterion is compared to ‘Organizational’ criterion ‘Environment’ criterion is compared to ‘ Operational and monitoring’ criterion ‘Environment’ criterion is compared to “Social” criterion ‘Environment’ criterion is compared to “ Staff” criterion Financial ‘Financial’ criterion is compared to ‘Infrastructure’ criterion ‘Financial’ criterion is compared to ‘Organizational’ criterion ‘Financial’ criterion is compared to ‘ Operational and monitoring’ criterion ‘Financial’ criterion is compared to “Social” criterion ‘Financial’ criterion is compared to “ Staff” criterion Infrastructure ‘Infrastructure’ criterion is compared to ‘Organizational’ criterion ‘Infrastructure’ criterion is compared to ‘ Operational and monitoring’ criterion ‘Infrastructure’ criterion is compared to “Social” criterion ‘Infrastructure’ criterion is compared to “ Staff” criterion Organizational ‘Organizational’ criterion is compared to ‘ Operational and monitoring’ criterion ‘Organizational’ criterion is compared to “Social” criterion ‘Organizational’ criterion is compared to “ Staff” criterion Operational and monitoring ‘Operational and monitoring’ criterion is compared to “Social” criterion ‘Operational and monitoring’ criterion is compared to “ Staff” criterion Social ‘Social criterion is Compared to “Staff criterion” Comparison for the determination of the criteria weight Colum1 More Less Column 2 Relevancy Relevancy is compared to clarity compared to accuracy compared to comparability Clarity Clarity is compared to accuracy compared to comparability Accuracy Accuracy is compared to comparability 228 Table A-3 Proposed linguistic scale Scale Score Relevancy Clarity Accuracy Comparability Very Low 1 Not relevant to performance and SMWS Difficult to understand for everyone Either qualitative or have estimated values Not used for performance assessment Moderately Low 2 Slightly relevant to performance and SMWS Understandable only to utility managers N/A N/A Average 3 Average relevant to performance and SMWS Understandable to both utility managers & operators High variability in nature and requires more observations Used by AWWA and IWA but not by NWWBI for performance assessment Moderately High 4 Highly relevant to performance and slightly average relevant to SMWS. Understandable to only staff N/A N/A High 5 Highly relevant to performance and SMWS Understandable to both staff and public Specific value or annual measurement to collect relevant data Used by NWWBI for the performance assessment Note: N/A refers to two criteria accuracy and comparability were measured in three-point Likert scale of 1-3-5. 229 Table A-4 Drinking water KPIs definition KPI Definition Unit I. Environment 1. Residential water use % of water consumed by residential sector excluding multi-family residence units. The Winter season refers to months between December and February. The summer season refers to months between June and August. % 2. Source water quality % of non-compliant raw water tests for turbidity, total organic carbon, and nitrate. % 3. Annual water production A total volume of treated water (including all the treatment plants) is produced in a day per connection. Treatment refers to the final treatment of water before its distribution to consumers. This may include either one or all the following treatment processes. Conventional treatment (coagulation, sedimentation, flocculation, and filtration) and disinfection (chlorine, UV, or any other disinfectants). The number of connections employed in the formula should be calculated separately for agriculture, residential, and industrial, commercial, and institutional (ICI) uses. m3/year/connection 4. Agricultural water use % of water supplied to agricultural (e.g., irrigation,) activities. Please supply two different values for supplied agriculture water (potable and non-potable). Non-potable refers to untreated water. *Supplied water refers to metered water if applicable otherwise measured at intake. Consumers include connections in residential, ICI, and agriculture uses. % 5. Annual agriculture water use per hectare Total annual water use to irrigate one-hectare land. m3/hectares 6. Groundwater distributed % of total treated water supplied (metered) from the groundwater sources such as deep wells, shallow wells. % 7. Treatment energy intensity Average energy consumed to treat 1 m3 of water. kwh/ m3 8. Industrial, commercial, and institutional (ICI) water use % of the treated water supplied (metered) to industrial commercial, and institutional (ICI) customers including hospitals, hotels, factories, universities, parks, and industries. % 9. Treated surface water distributed % of total treated water supplied from the surface water sources such as rivers, lakes, streams. % 10. Non-potable water distributed % of non-potable water supplied (metered) out of the total water supplied by the system. % 11. Maximum Daily Demand: Average daily demand The ratio of the highest daily water demand in a year to the average daily demand in the year. Inflow to demand means all water taken in (potable and non-potable) and measured before distribution to customers. Unitless 12. Alternative source water Qualitative – Does your utility has alternative source water, which can be used when your usual water source is contaminated, water Unitless 230 KPI Definition Unit quality advisory in effect, or cleaning of transmission or distribution mains? II. Financial 1. Water price Price of 1m3 of drinking water (potable). Price of 1m3 of agriculture water. Please supply the total volume of water required to irrigate one-hectare land in a year. For example, 5,500 m3 of water is used to irrigate one-hectare land in a year. $/m3 $/ m3 -ha -year 2. Operation and maintenance cost for raw water withdrawal from the source to the treatment plant The total cost incurred to withdraw 1m3 of water from the sources (e.g., river, lake, wells) in a year from source to the treatment plant. $ /m3 3. Operational and maintenance cost of water treatment The total cost incurred to treat 1m3 of water in the year. $ /m3 4. Operation and maintenance cost for water distribution The total cost incurred to distribute 1m3 of water in the year per total length of distribution mains. The water is distributed to single-family detached houses, agriculture, industries, commerce, and intuitions. $/ km 5. Debt service ratio (%) Measures the total amount of cash available to pay the ongoing /present debt obligations. The financial debt service includes interest expenses, loan payments, and principal repayment debt instruments. % 6. Residential and ICI Service revenue generated (Agriculture revenue is excluded) These revenues include domestic, institutional, commercial, and industrial service revenues. $/100 connections III. Infrastructure 1. Total main length The total length of water mains including, raw water collection, transmission, and distribution mains per 100 connections. The water mains also include non-potable mains. km / 100 connections 2. Reported pipe failures The total number of reported pipe failures (leakage, contaminant intrusion, and cross-connection) occurred in the water mains per 100 km. This does not include private main failures. Number / 100 km 3. Water leakage % of water leaked in a year. Leaks refer to an unintended crack in a transmission or distribution valve, pipes, hydrant, appurtenance, and service connection that continuously results in water loss although does not disrupt water service. Real losses include leakage on transmission and distribution lines, leakage and overflows at drinking water storage tanks, service connection up to the point of customer metering. % 4. Total mains replaced/rene% of mains replaced /renewed/rehabilitated in a year. % 231 KPI Definition Unit wed/rehabilitated 5. New mains installed % of new mains installed in a year. % 6. Average Annual Life Cycle Investment (AALCI) Includes the total of each asset’s annual depreciation in the drinking water utility that is based on the asset replacement cost and service life per total volume of drinking water produced. $ per connection IV. Operation and monitoring 1. Compliance with microbiological tests % of non-complaint microbiological tests conducted as required by the standards in a year. Microbiological tests refer to the monitoring frequency of microbiological parameters, including E.coli and fecal coliform. % 2. Compliance with physical and chemical tests % of non-complaint physical and chemical tests conducted as required by the standards in a year. Physical tests refer to the monitoring frequency of physical parameters, which include turbidity and temperature. Chemical tests refer to the monitoring frequency of chemical parameters, which include pH and free residual chlorine. % 3. Monitoring (Compliance) of THMs and HAAs % of non-complaint THMs and HAAs tests conducted as required by the standards in a year. % 4. Frequency of hydrant inspection % of hydrants inspected and maintained in a year. % 5. Compliance to leak detection program The leak detection program includes i) detection of the leak based on human observation. ii) installation of leak identifying instruments (i.e., acoustic leak detection) iii) installation of flow meters and /or pressure gauges to study the pressure differences and identify leaks iv) Continuous auditing program, which records leaks daily over time Unitless V. Staff 1. Full-time operators (FTO) Number of FTOs recruited in the utility system per total length of water mains. Number of FTO / 100 km main length 2. Distribution of the workforce by age Division of certified operators by age per 100 connections. Number of certified operators / 100 connections 3. Experience of operators The average number of years of experience of operators per total number of operators. Number of years per total number of operators 4. Absentees due to accidents The average number of days per operator is absent in a year- due to accidents encountered at the workstation. Days/operators/year 5. Overtime hours The number of extra hours worked by operators other than regular working hours in a year per total number of operators. Number of overtime hours per total number of operators VI. Social and Institutional 1. Water quality complaints Water quality complaints (e.g., elevated colors, turbidity, residual chlorine concentration taste, and odour of chlorine) registered by customers per 100 connections. Number of complaints / 100 connections 232 KPI Definition Unit 2. Growth within Service Area % of new residential, ICI, and agriculture connections installed in a year. % 3. Boil water notice (BWN) % of household days in a year with BWN. It is issued when the public health risk posed by the water is significant and it is recommended to bring the tap water to a rolling boil for at least 1 minute before consumption. Household days refers to the total number of households in which WQA were placed * the number of days that these houses had WQA placed. % 4. Water quality advisory (WQA) % of household days in a year with WQA. It is issued when there is a certain risk with drinking water but does not have enough evidence to put stringent measures such as BWN, do not consume. % 5. Emergency response readiness Degree of the emergency response readiness is mentioned in the Planning document. The emergency response plans include plans related to turbidity response, BWA risks, contaminant intrusion in treated source water, dysfunction of the treatment unit (e.g., UV system not working), public notification (e.g., media releases via newspaper, social media, and websites), and impacts due to natural calamities such as earthquake, floods. Unitless Table A-5 KPIs wastewater KPI Definition Unit I. Environment 1. Utilization of wastewater treatment capacity % of wastewater treated by a treatment plant per day. If there are multiple treatment plants, the total volume of water treated from these treatment plants must be added. Wastewater entering the treatment units refers to the primary treatment unit (e.g., screening). % 2. Wastewater treatment energy intensity Energy consumed to treat 1 m3 of water. kWh/ m3 3. Volume of wastewater spills The total volume of wastewater spills from the sewer mains in the year per 100 km pipe length. Wastewater losses and spills may occur due to leakage, pipe bursts, cross-connection, seepage through soils. The spills on the treatment plant are not considered. m3 / 100 km 4. Biosolids reuse Biosolids are recycled sludge and that has gone through a treatment process. In other words, Biosolids are nutrient-rich organic materials generated through the treatment of domestic sewage in the wastewater treatment facility. % per year 5. Sludge production The total mass of sludge produced by the WWTP in a year per 100 wastewater connections. Use a dry mass of sludge. kg/ 100wastewater connections 6. Wastewater generated per person The total volume of wastewater generated per person per day. m3/person/day II. Financial 233 KPI Definition Unit 1. Operation and maintenance cost of sewer mains The total cost incurred to replace, rehabilitate, and repair sewer mains per total length of sewer mains. $ / km 2. Operational and maintenance cost of wastewater treatment The total cost incurred to treat 1m3 of wastewater in the year. Treatment refers to the complete treatment of wastewater (i.e., either it is used for non-potable uses or disposed of in source water) from screening to disinfection. $/m3 3. Wastewater price Price of 1m3 of wastewater. $/m3 4. Debt service ratio (%) It measures the total amount of cash available to pay the ongoing /present debt obligations. The financial debt service includes interest expenses, loan payments, and principle repayment debt instruments. % 5. Service revenue generated These revenues include domestic, institutional, commercial, and industrial service revenues. $/100 connections III. Infrastructure 1. Sewer blockage The number of sewer blockages occurring in both separate and combined sewers per 100 km of sewer mains in a year. # of blockages / 100 km 2. Average Annual Life Cycle Investment (AALCI) Includes the total of each asset’s annual depreciation in the wastewater utility that is based on the assets replacement cost and service life per number of wastewater connections $ per wastewater connection 3. Reported sewer failures The number of failures occurring per 100 km of sewer mains in a year. These failures are related to main failure, wastewater quality failures, blockages, leakages. Failures/100 km 4. Lift station failures % of hours that experienced pump failures by all the pumps in the wastewater utility in a year. % of Pump-hours /year 5. Total number of wastewater mains that are replaced, renewed, and rehabilitated per year % of sewer mains replaced /renewed/rehabilitated in a year. % 6. New wastewater mains installed % of new wastewater mains installed in a year. % 7. Age of the wastewater treatment plant The number of years that have passed since the wastewater treatment plant was first commissioned. years IV. Operation and Monitoring 1. Pumps inspection % of pumps inspected in a year. % 2. Sewer mains inspection % of sewer mains inspected in a year. % 3. Inspection of manhole chambers Frequency of manhole chambers inspection. % 4. Combined sewer overflow (CSO) control % of the total length of sewer mains that have devices to monitor CSO in the year. To control CSO, various monitoring equipment for sewage flows and overflows should be installed at different locations in the sewer system. % 5. Compliance with microbiological tests % of microbiological tests conducted as required by the standards in a year. % 234 KPI Definition Unit Microbiological tests refer to the monitoring frequency of microbiological parameters of the raw wastewater at the treatment plant, which includes E.coli and fecal coliform. 6. Compliance with physical and chemical tests % of physical and chemical tests conducted as required by the standards in a year. Physical tests refer to the monitoring frequency of physical parameters of raw wastewater at the treatment plant, which includes temperature and turbidity. Chemical tests refer to the monitoring frequency of chemical parameters of raw wastewater at the treatment plant, which includes biological oxygen demand and chemical oxygen demand. % V. Staff 1. Full-time operators (FTO) Number of FTOs recruited in the utility system per total length of wastewater mains. Number of FTO / 100 km main length 2. Distribution of the workforce by age Division of operators by age per 100 connections. Number of certified operators / 100 wastewater connections 3. Experience of operators The average number of years of experience of operators per total number of operators. years per operator VI. Social and institutional 1. Wastewater Complaints The number of wastewater complaints recorded in a year per total wastewater connections. Number of complaints/ 100 service connections 2. Growth within the service area % of new wastewater connections installed in a year. % 3. Emergency response readiness Degree of the emergency response readiness is mentioned in the Planning document. The emergency response plans include plans related to algal blooms (eutrophication), phosphorus and nitrogen contamination, in source water, dysfunction of the wastewater treatment unit (e.g., sudden failure of the aeration tank), public notification (e.g., media releases via newspaper, social media, and websites), and impacts due to natural calamities such as earthquake, floods. Unitless Table A-6 Stormwater KPIs KPI Definition Unit I. Environment 1. Annual snowfall Average snowfall in a year. cms/ year 2. Impervious surface % of impervious surfaces that include or are covered with water-resistant materials including pavement, asphalt such that the water does not penetrate underground surfaces. % 3. Stormwater reuse A total volume of stormwater use for potable / non-potable usages. % 4. Disposed stormwater quality to the water body % of raw water quality parameters including turbidity, color, E.coli, phosphorous, and nitrogen that are non-compliant % 235 KPI Definition Unit 5. Stormwater treated % volume of stormwater treated % II. Financial 1. Operation and maintenance cost of stormwater mains The total cost incurred to replace, rehabilitate, and repair stormwater mains per total length of stormwater mains. $ / km 2. Operational and maintenance cost of stormwater treatment The total cost incurred to treat 1m3 of stormwater in the year. $/m3 3. Stormwater service price Cost for managing 1 m3 of stormwater. $/m3 4. Debt service ratio (%) Measures the total amount of cash available to pay the ongoing /present debt obligations. The financial debt service includes interest expenses, loan payments, and principal repayment debt instruments. % III. Infrastructure 1. Flood-affected properties % of properties affected due to flooding per year. Wet weather refers to the weather when rainfall occurs in a high frequency. Flooding occurs due to breakage of sewer mains during wet weather. #of properties affected / 100 storm sewer connection 2. Reported storm main failure Number of failures occurring per 100 km sewer mains # per 100 km 3. Storm main blockages The number of blockages occurred per 100 km of storm mains. # per 100 km 4. Number of constructed wetlands # of artificial wetlands constructed for retention and detention and overall stormwater management. A wetland is an area that contains a large volume of water, which is stored either permanently or temporarily. The water covers the soil and is present during varying periods or throughout the year. # per year 5. % Surface flooding Surface flooding occurs when the quantity of rainwater and stormwater that fall into the ground do not drain away in the existing drainage system but rather accumulate over the ground instead. The incidents recorded include several storm main failures occurrence, combined sewer overflow, and several times (after each rainfall) the water is accumulated in the ground in a year. % of surface flooding per total storm main length. Number/100 km 6. Total storm main length The total length of stormwater mains per 100 stormwater connections. km / 100 connections IV. Operation and Monitoring 1. Pumping station inspection % of pumps inspected and maintained in a year. % 2. Gully pots inspection Frequency of gully pots inspection and maintenance. % 3. Gully pots cleaning Frequency of gully pots cleaning. % 4. Annual rainy days An average number of days receiving rainfall in a year. # per year V. Staff 1. Full-time operators (FTO) Number of FTOs recruited in the utility system per total length of stormwater mains. Number of FTO / 100 km main length 236 KPI Definition Unit 2. Distribution of the workforce by age Division of certified operators by age per 100 connections. # of operators / 100 stormwater connections 3. Experience of operators An average number of years of experience of operators per total number of operators. Number per total number of operators V1. Social and institutional 1. Water quality complaints Complaints related to polluted source water due to surface runoff or contaminants related to snowpack and snow melting registered by customers per 100 connections. # of complaints / 100 connections 2. Growth within the service area % of new stormwater connections installed in a year. % of new houses in an area in a year 3. Swimming advisories % of swimming advisories occurred in a year. A warning imposed by the local governments to stop or prevent swimming in water sources (e.g., river, lake), if more than one waterbody, take an average. % of days with swimming advisories in a year 4. LID implementation Qualitative – Does your utility implement LID or Green Infrastructure (GI) to manage stormwater? Number / km 5. Inclusion of risk management in the water master plan Qualitative – To what degree is the risk management plan included in your master plan stormwater? The degree of the plan is broadly categorized into three levels (i.e., low, medium, and high). Unitless 237 Appendix B Table B-1. Urban water service component KPIs Criteria (number in parenthesis) Number of PIs Reference Size of system Drinking Water Water resources, personnel, physical, operational, quality of service, and economics and finance (6) 170 (Alegre et al., 2017) Large Organizational development, business operations, customer relations, and water operation (4) 90 (AWWA, 2019) Large Ensuring adequate capacity, protecting the environment, providing reliable and sustainable infrastructure, protecting public health and safety, providing safe and productive workplace, satisfied and informed customers, and meeting service requirements with economic efficiency (7) 50 (NWWBI, 2018) Large Water resources and environment, personnel and staffing, physical and asset, operational, water quality and public health, quality of service, financial and economic (6) 109 (Haider et al., 2014) Small-medium Wastewater Environment, Personnel, Physical, Operational, Quality of Services, Economic and financial (6) 132 (Matos et al., 2002) Large Wastewater compliance, operating and maintenance costs, and energy consumption (3) 20 (AWWA, 2019) Large Environment, finance, service requirement, safe working environment, customer relations, and capacity development (6) 43 (NWWBI 2018) Large Water resources, asset, finance and economic, customer, environment, and pricing (6) 64 (Government of Australia, 2019) Large Stormwater Existence, effectiveness, freedom of action, security, adaptability, and co-existence (6) 7 (Sunderberg 2004) Large Building physics and services, landscape quality, urban climate, biodiversity, groundwater, surface water, direct costs, and resource use 24 (Matzinger et al., 2014) Large Protect the environment, Obtain adequate and sustainable funding, meet service requirements with economic efficiency, ensure adequate capacity to protect life and property, provide a safe and productive workplace, and have satisfied and informed customers (6) 11 (NWWBI 2018) Large Cultural , environmental , social, and economic (4) 23 (Cunningham et al. 2016) Large Protection of public health and safety (4), protection of economic development (3), Protection of surface receiving waters quality (2), Protection of the natural hydrologic cycle (3), Infrastructural sustainability of SWS (7), Sustainable use of resources (3), provision of ecological benefits (1), and provision of amenity benefits (2). 80 (L. F. Santos, Cardoso, Galvão, & Cardoso, 2022)(L Large Sustainability Index composed of resiliency, reliability, and vulnerability indicators 3 Azari and Tabesh 2022 Large Table B-2. Drinking water 238 Environment Residential water use (winter season, i.e, December - February) [Total average volume (m3) of water consumed by all single family residential connections during winter season] / [Total average volume (m3) of water distributed to all types of connections during this season] * 100 Residential water use (summer season, i.e., June - August) [Total average volume (m3) of water consumed by all single family residential connections during summer season] / [Total average volume (m3) of water distributed to all types of connections during this season] * 100 Source water quality (Number of non-compliant raw water turbidity tests in a year) / (Total number of raw water turbidity tests conducted in a year) * 100 Source water quality (Number of non-compliant raw water total organic carbon tests in a year) / (Total number of raw water organic carbon tests conducted in a year) * 100 Source water quality (Number of non-compliant raw water nitrate tests in a year) / (Total number of raw water nitrate tests conducted in a year) * 100 Annual water production [Total volume (m3) of treated water produced per year] / [Total number of residential and ICI (industrial, commercial, and institutional) connections] Agricultural water use [(Total volume (m3 ) of water supplied for agriculture purposes per year] / [Total volume (m3) of distributed water to all consumers per year] * 100 Annual agriculture water use per hectare [Total annual volume (m3) of water supplied to agriculture customers] / (Hectares of currently irrigated land) Treated ground water distributed [Total volume (m3) of groundwater distributed in a year ] / [Total volume (m3) of water distributed to all consumers in a year] * 100 Treatment energy intensity (Total amount of energy consumed by water treatment in a year) / (Total volume of water treated in a year) Industries, commerce, and institutions (ICI) water use [Total volume (m3) of water supplied for ICI purpose per year] / [Total volume (m3) of distributed water to all consumers per year)] * 100 Treated surface water treated [Total volume (m3) of surface water distributed in a year] / [Total volume (m3) of distributed water to all consumers in a year] * 100 Non-potable water distributed [Total volume (m3) of non-potable water distributed in a year] / [Total volume (m3) of distributed water to all consumers in a year] * 100 Maximum Daily Demand: Average daily demand (Max Day Inflow to demand) / (Average Day Inflow to demand) Alternative source water Yes / No Finance Water price (only residental houses) (Total price of annual water distributed to a household in a year ) / (Total volume (m3) of water distributed to a household in a year i.e., 33 m3) Water price (only residental houses) (Total price of annual water distributed to a household in a year ) / (Total volume (m3) of water distributed to a household in a year i.e., 44 m3) Water price (only residential houses) (Total price of annual water distributed to a household in a year ) / (Total volume (m3) of water distributed to a household in a year i.e., 59 m3) Water price (only residential houses) (Total price of annual water distributed to a household in a year ) / (Total volume (m3) of water distributed to a household in a year i.e., 129 m3) 239 Agriculture water price (Total price of annual water distributed to agriculture customers in a year) / [Total volume (m3) of water required to irrigate one hectare land in a year] Operation and maintenance cost for raw water withdrawal from the source to the treatment plant [Cost ($) for operation and maintenance to withdraw total volume of raw water from the source to treatment plant in a year] / [Total volume (m3) of water withdrawn in a year] Operational and maintenance cost of water treatment [Operation and maintenance cost ($) of water treatment in year ] / [ Total volume (m3) of water treated in a year ] Operation and maintenance cost for water distribution [Cost ($) for operation and maintenance the year ] / [Total length (km) of water distribution mains ] Debt service ratio [(Cash income) / (Financial debt service) per year * 100] Residential and ICI Service revenue generated (Agriculture revenues is excluded) [Total amount ($) of revenues generated from the domestic and ICI services in a year] / (Total connections) * 100 Infrastructure Total main length (Total length of water mains) / (Total number of connections) *100 Reported pipe failures (Number of water main and service failures occurred in a year) / (Total mains length) *100 Water leakage Please record the values from the American Water Works Association (AWWA) audit software. Total mains replaced (Length of mains replaced in a year) / (Total mains length) * 100 Total mains renewed (Length of water mains renewed in a year) / (Total mains length) * 100 Total mains rehabilitated (Length of water mains rehabilitated in a year) / (Total mains length) * 100 New mains installed (Length of new water mains installed in a year) / (Total mains length) * 100 Average Annual Life Cycle Investment (AALCI) (Sum of depreciation on the asset replacement cost and service life of the drinking water infrastructure) / (Total number of drinking water connections) Staff Full time operators (FTO) (Total number of FTO) / [Total length (km) of water mains ] * 100 Distribution of the workforce by age Number of operators aged between 20-30) / (Total connections) * 100 Distribution of the workforce by age Number of operators aged between 31-50) / (Total connections) * 100 Distribution of the workforce by age Number of operators aged between 51-65) / (Total connections) * 100 Distribution of the workforce by age Number of operators aged 65+) / (Total connections) * 100 Experience of operators (Total number of years experience of all the operators ) / (Total number of operators) Absentees due to accidents (Number of absent days due to accidents for operators per year) / (Total number of operators*total working days in a year) * 100 Overtime hours (Total number of overtime hours for operators in a year) / (Total number of operators for that year) Social and institutional Water quality complaints [(Total number of water quality complaints in a year) / (Number of service connections) * 100] Growth within service area (Number of new residential, ICI, and agriculture connections installed in a year) / (Total number of available connections in a year) * 100 Boil water notice (BWN) (Number of household-days in a year that BWN are in effect) / (365* total number of household-days) * 100 240 Water quality advisory (WQA) (Number of household-days in a year that WQA are in effect) / (365 * total number of household-days) * 100 Emergency response readiness No plans = Low; Plans being formulated but not implemented at the moment = Medium; Plans currently implemented = High; Plans are updated on a regular schedule and utility staff undertake mock emergency scenario training = Very high Operation and monitoring Compliance of microbiological tests (municipal) [(Number of non-compliant E.coli tests in the distributed water in a year) / (Total number of E.coli tests conducted in a year) * 100 Compliance of microbiological tests (irrigation) [(Number of non-compliant E.coli tests in the distributed water in a year) / (Total number of E.coli tests conducted in a year) * 100 Compliance of microbiological tests (municipal) [(Number of non-compliant fecal coliform tests in the distributed water in a year) / (Total number of fecal coliform tests conducted in a year) * 100 Compliance of microbiological tests (irrigation) [(Number of non-compliant fecal coliform tests in the distributed water in a year) / (Total number of fecal coliform tests conducted in a year) * 100 Compliance of physical tests [(Number of non-compliant turbidity tests in the distributed water in a year) / ( Total number turbidity tests conducted in a year) * 100 Compliance of physical tests [(Number of non-compliant temperature tests in the distributed water in a year) / ( Total number of temperature tests conducted in a year) * 100 Compliance of chemical tests [(Number of non-compliant pH tests in the distributed water in a year) / ( Total number of pH tests conducted in a year) * 100 Compliance of chemical tests [(Number of non-compliant free residual chlorine tests in the distributed water in a year) / ( Total number of free residual chlorine tests conducted in a year) * 100 Monitoring (Compliance) of disinfection by-products ( THMs) (Number of non-compliant THMs tests in the distributed water in a year) / ( Total number of THMs tests in the year) * 100 Monitoring (Compliance) of disinfection by-products ( HAAs) (Number of non-compliant HAAs tests in a year) / ( Total number of HAAs tests conducted in a year) * 100 Frequency of hydrants inspection (Total number of hydrants inspected and maintained in a year) / (Total number of hydrants) * 100 Compliance to leak detection program Does the utility have water main leak detection program? (Yes, No, currently working, interested in) Table B-3. Wastewater Environment Wastewater capacity [Volume (m3) of wastewater entering treatment units per day] / [Volume (m3) of wastewater received by the wastewater treatment facility per day] * 100 Wastewater treatment energy intensity (Total amount of energy consumed by wastewater treatment in a year ) / (Total volume of wastewater treated per year) Volume of wastewater loss [Volume (m3) of wastewater loss from the sewer mains in the year] / (Total length of sewer mains) * 100 Biosolids reuse (Amount of biosolids reused in the year) / (Total biosolids generated in the year (m^3) ) * 100 Sludge production [(Mass of sludge (kg) produced in the WWTP per year] / [Total number of wastewater connection] * 100 241 Wastewater generated per person [Total volume (m3) of wastewater received per day] / [Total population] Finance Operation and maintenance cost of sewer mains [Cost ($) for operation and maintenance of sewer mains in a year] / (Total length of sewer mains) Operational and maintenance cost of wastewater treatment [Operation and maintenance cost ($) of wastewater treatment in year ] / [ Total volume (m3) of wastewater treated in a year ] Wastewater price (Total price of annual wastewater generated from households in a year) / (Total volume (m3) of wastewater generated in a year) Debt service ratio (%) [(Cash income) / (Financial debt service) per year * 100] Service revenue generated (Total amount ($) of revenues generated from the wastewater services) /(Wastewater connections) *100 Infrastructure Sewer blockages (Total number of sewer blockages that occurred both in separate and combined sewer per year) / (Total sewer main length ) * 100 Average Annual Life Cycle Investment (AALCI) (Sum of depreciation on the asset replacement cost and service life of the wastewater infrastructure) /( Total number of wastewater connections) Reported sewer failures (Total number of reported failures in a year) / Total length of sewer)* 100 Lift station failures (Total number of hours failed for all lift stations in the year) / (Total number of lift stations*total annual operating hours) * 100 Total wastewater mains replaced (Length of sewer mains replaced in a year) / (Total sewer mains length) * 100 Total wastewater mains renewed (Length of sewer mains renewed in a year) / (Total sewer mains length) * 100 Total wastewater mains rehabilitated (Length of sewer mains rehabilitated in a year) / (Total sewer mains length) * 100 New wastewater mains installed (Length of new water mains installed in a year) / (Total mains length) * 100 Age of the wastewater treatment plant Number of years the wastewater treatment plant was commissioned Operation and Monitoring Pumps inspection (Total number of pumps inspected in a year) / (Total number of pumps) * 100 Sewer mains inspection [(Length (km) of sewer mains inspected in a year] / (Total length of sewer mains) *100 Inspection of manhole chambers (Total number of manhole chambers inspected in a year) / (Total number of manhole chambers) * 100 Combined sewer overflow (CSO) control [(Total length (km) covered by the device/s to monitor CSO)] / [Total length of sewer mains] * 100 Compliance of microbiological tests [(Number of non-compliant E.coli tests in wastewater effluent in a year) / (Total number of E.coli tests conducted in a year) * 100 Compliance of microbiological tests [(Number of non-compliant fecal coliform tests in wastewater effluent in a year) / (Total number of fecal coliform tests conducted in a year) * 100 242 Compliance of physical tests [(Number of non-compliant turbidity tests in wastewater effluent in a year) / ( Total number turbidity tests conducted in a year) * 100 Compliance of physical tests [(Number of non-compliant temperature tests in wastewater effluent in a year) / ( Total number temperature tests conducted in a year) * 100 Compliance of chemical tests (Number of non-compliant BOD tests during biological / secondary treatment stage in a year) / (Total number of BOD tests conducted in a year) * 100 Compliance of chemical tests (Number of non-compliant COD tests during biological / secondary treatment stage in a year) / (Total number of COD tests conducted in a year) * 100 Staff Distribution of the workforce by age Number of operators aged between 20-30) / (Total wastewater connections) * 100 Distribution of the workforce by age Number of operators aged between 31-50) / (Total wastewater connections) * 100 Distribution of the workforce by age Number of operators aged between 51-65) / (Total wastewater connections) * 100 Distribution of the workforce by age Number of operators aged 65+) / (Total wastewater connections) * 100 Full time operators (FTO) (Total number of FTO) / [Total length (km) of wastewater mains ] * 100 Experience of operators (Total number of years experience of all the operators ) / (Total number of operators) Social and institutional Wastewater Complaints [(Total number of wastewater quality complaints registered in a year) / (Total number of wastewater connections) * 100] Growth within service area (Number of new wastewater connections installed in a year) / (Total number of available connections in a year) * 100 Emergency response readiness No Plans = Low ; Plans being formulated but not implemented at the moment = Medium ; Plans currently implemented = High; Plans Are Updated on a Regular Schedule and Utility Staff Undertake Mock Emergency Scenario Training = Very High Table B-4. Stormwater Environment Annual snowfall Average snow (cms) experienced in your area in a year Impervious surface [Total area (km2) of impervious surfaces in the region] / [Total land area (km2) of that region] *100 Stormwater reuse [Volume of stormwater reused (m3) in a year] / [Total volume (m3) of stormwater in a year] * 100 Disposed stormwater quality to the water body (Number of non-compliant raw water quality tests in a year) / (Total number of raw water samples taken in a year) * 100 Finance Operation and maintenance cost of stormwater mains [Cost ($) for operation and maintenance in a year ] / (Total length of stormwater mains) Operational and maintenance cost of stormwater treatment [Operation and maintenance cost ($) of stormwater treatment in year ] / [ Total volume (m3) of stormwater treated in a year ] 243 Stormwater treated per person [Total volume (m3) of stormwater treated in ] day] / [Total volume of stormwater collected]*100 Stormwater service price (Total price of stormwater based in a year for residential and ICI uses) / (Total volume (m3) of stormwater generated in a year) Debt service ratio (%) [(Cash income) / (Financial debt service) per year * 100] Infrastructure Flood affected properties (Total number of properties affected by the storm sewer flooding during the dry and wet weather in a year) / (Total number of properties connected to the storm sewer connections in a year) * 100 Reported storm main failure Total number of reported storm main failures occurred in a year per 100 km of storm mains Storm main blockages Total number of storm main blockages occurred per 100 km of storm mains in a year Number of constructed wetlands (Number of constructed wetlands) / (Total area of a city in a year) % Surface flooding (Total number of surface flooding incidents recorded in a year) / (Total storm main length) * 100 Total storm main length (Total length of stormwater mains) / (Total number of connections) *100 Operation and monitoring Pumping station inspection (Total number of pumps inspected in a year) / (Total number of pumps) * 100 Gully pots inspection (Total number of gully pots inspected in a year) / (Total number of gully pots) * 100 Gully pots cleaning (Total number of gully pots cleaned in a year) / (Total number of gully pots) * 100 Annual rainy days Number of days that received rainfall in your area / region in a year Staff Full time operators (FTO) (Total number of FTO) / [Total length (km) of stormwater mains ] * 100 Distribution of the workforce by age Number of operators aged between 20-30) / (Total wastewater connections) * 100 Distribution of the workforce by age Number of operators aged between 31-50) / (Total wastewater connections) * 100 Distribution of the workforce by age Number of operators aged between 51-65) / (Total wastewater connections) * 100 Distribution of the workforce by age Number of operators aged 65+) / (Total wastewater connections) * 100 Experience of operators (Total number of years experience of all the operators ) / (Total number of operators) Institutional and social Stormwater quality complaints [(Total number of stormwater complaints in a year) / (Total number of stormwater connections) * 100] Growth within service area (Number of new stormwater connections installed in a year) / (Total number of available connections in a year) * 100 Swimming advisories (Number of days with swimming advisories in a year) / (Total number of summer days in a year) * 100 LID implementation (Number of low impact development technologies implemented to manage stormwater) / (Total area covered by the LDI) Inclusion of risk management in the water master plan No Plans = Low; Plans being formulated but not implemented at the moment = medium; Plans currently implemented = high 244 Appendix C Figure C-1. Performance scores of drinking water criteria using penalty method 6959546055575529333046855537262696554769343471285106840350 10 20 30 40 50 60 70 80 90ABCDEPerformance score (%)UtilityOperation & Monitoring Social Staff Infrastructure Finance Environment 245 Figure C-2. Performance scores of wastewater criteria using penalty method 5342593694307138714834189190376878790 10 20 30 40 50 60 70 80 90FGHPerformance score (%)UtilityOperation & Monitoring Social Staff Infrastructure Finance Environment 246 Figure C-3. Performance scores of stormwater criteria using penalty method 44436257767465178886180 10 20 30 40 50 60 70 80 90 100IJPerformance score (%)UtilityOperation & Monitoring Social Staff Infrastructure Finance Environment 247 Appendix D Interventions Drinking water a) While all utilities had problems related to workforce management, three (“B”, “D”, and “E”) of them also struggled to keep the number absentees to a minimal. First, it recommendation is to distribute staff of varying age groups across utilities, such that novice operators get opportunities to learn from experienced operators maintaining a balance between youth and experience. Second, in order to maintain operators’ safety, they must undergo rigorous safety training, and the utility must ensure that they follow Work Safe BC protocols or guidelines of their designated safety-maintaining jurisdiction. b) Excluding utility “B”, all experienced many pipe failures, so it is recommended to constantly monitor pipes throughout the distribution mains and apply periodic coatings. These actions will allow utilities to take necessary interventions, which minimize overall failure chances. Apart from utility “A”, none of the utilities reported their annual average lifecycle investment value. Hence, utilities are suggested to devise robust asset management plans to keep track of their ageing infrastructure and conduct replacement, repair, and rehabilitation plans effectively. Summer water consumption is relatively high in utilities “A”, “C” and “E”. Hence, these utilities must enforce effective water conservation programs to increase water efficiency and productivity. The use of water-saving appliances and energy-efficient devices must be encouraged. One way to do so is to provide a rebate to customers installing water- and energy-saving devices in their residents. c) All utilities struggled to generate enough revenue. Hence, greater attention must be shifted towards reducing non-revenue water. Conducting efficient leak detection programs and installing leaks identifying instruments at different locations across distribution and transmission mains are a few effective ways to minimize these incidents. Meanwhile, revenues can also be increased by making consumers understand that sufficient investment is essential in enhancing overall water system performance specifically considering increased water service demands. Continuous attempts must be made to secure government funds for building new infrastructure and replacing aged water assets. Wastewater 248 a) Wastewater price is a concern for utilities “F” and “H”. Hence, affordable water rates must be employed based on customers’ economic conditions (e.g., subsidy in rates for seniors and economically backward groups). Both these utilities faced challenges in maintaining adequate O&M costs for wastewater treatment. Initiatives to gather funds from the government in building resource recovery facilities must be emphasized so that energy can be saved and produce electricity, which can reduce utility O&M costs. b) Utilities “F” and “G” faced problems in hiring full time operators. It is recommended that utility separate budget that can be invested in providing training and education to operators. In this way, they gain advanced certification, which will qualify them more working full-time for a utility. c) All utilities experienced problems in controlling combined sewer overflows. Avoiding these will require utilities to establish separate mains for wastewater and stormwater passage. The separate system will share their water-holding capacity resulting in a low chance of sewage being mixed into drinking water sources. As a result, it will also reduce wastewater complaints. Stormwater a) Both utility did not provide most of the data related to environment, finance, and infrastructure criteria. Hence, relevant data must be collected regularly to keep track of stormwater performance. Timely data collection will help utility managers to identify gaps and plan necessary programs to overcome shortcomings. b) None of the utilities reused stormwater. Coping with recent climate change incidents and continuous population growth, treated wastewater and stormwater will reduce pressure on potable water supplies. Therefore, the government should focus on establishing guidelines regarding reclaimed water for non-potable uses. . c) Both utilities did not implement LID technologies effectively. Utilities are recommended to choose a suitable LID option based on their geographic region, which are essential in managing floods, absorbing pollutants, and preventing source water contamination. d) Full-time stormwater operators must be recruited to manage stormwater systems. These operators should receive the necessary training and certification on stormwater issues. 249 Appendix E Table E-1 Potable water reduction data Location Runoff reduction / retention References Green roofs Calgary 61 (Talebi et al. 2019) Regina 53 (Talebi et al. 2019) Calgary 47 (Talebi et al. 2019) Regina 37 (Talebi et al. 2019) Halifax 27 (Talebi et al. 2019) Halifax 17 (Talebi et al. 2019) Toronto 48 (Talebi et al. 2019) Toronto 32 (Talebi et al. 2019) Toronto 60 (Hill et al. 2017) Calgary 67 (Sims et al. 2016) London 48 (Sims et al. 2016) Halifax 34 (Sims et al. 2016) Sheffield, UK 50.2 Stovin et al . 2012 Sheffield, UK 59 Stovin et al. 2013 Northwest & Central Italy 68 Fioretti et al. 2010 Genoa, Italy 68 Genecco et al. 2011 Georgia, USA 78 Carter&Rasmuseen 2006 Michigan, US 50 Morgon et al. 2013 Michingan, US 80.8 Getter et al. 2007 Auckland, NZ 56 Fassman-Beck et al 2013 Lund, Sweden 63 Bengtsson 2005 Leuven, Beligum 76 Mentens et al. 2006 Neubrandenburg, Germany 77 Koehler 2005 Seoul, Korea 68 Shafique et al. 2017 Bioswales Location Runoff reduction / retention References New York 44 Shetty et al., (2018) New York 20 Shetty et al., (2018) New York 75 Shetty et al., (2018) New York 46 Shetty et al., (2018) New York 43 Shetty et al., (2018) New York 30 Shetty et al., (2018) New York 26 Shetty et al., (2018) New York 36 Shetty et al., (2018) Virgina 56 (Hayes et al. 2021) Davis California 99.4 (Xiao et al. 2017) China 98.25 (Jiang et al. 2017) China 77.65 (Jiang et al. 2017) Bioretention Location Runoff reduction / retention References Virgina 71 (Hayes et al., 2021) Ursuline College 59 (Winston et al., 2016) Holden Arboretum, Ohio 42 (Winston, et al. 2016) Holden Arboretum, Ohio 36 (Winston, et al. 2016) North Carolina 30 (Luell 2011) 250 North Carolina 20 (Luell 2011) Cincinnati 50 (Alikhani et al. 2020) Maryland, US 18 (Thompson et al., 2022) Sao Carlos, Brazil 70 (de Macedo et al., 2019) Parking lot, NC, US 69 (Brown et al. 2012) University, China, 47.80 (Jia et al. 2015) Agriculture Location % reclaimed water use Reference Vernon 98 (City of Vernon 2021) Israel 50 (Tal 2016) Centre for Interactive Research on Sus, UBC 100 (Design Build Network 2021) Tunisia 20 (Mahjoub et al. 2009) Florida 8 (Sheikh et al., 2018) Hawai 29 (Sheikh et al., 2018) California 30 (Sheikh et al., 2018) Idaho 92 (Sheikh et al., 2018) Israel 92 (Sheikh et al., 2018) China 29 (Wang et al., 2017) Australia 66 (Sun et al., 2011) Landscaping Location % water saving Reference China 34 (Wang et al., 2017) Cary, NC, US 15 (Lee et al., 2021) Australia 14 (Sun et al. 2011) Japan 27 (Sun et al. 2011) China 23 (Wang et al. 2017) Australia 9 (Sun et al. 2011) Soil and weather based sensors Location % water saving Reference USA 30 USEPA 2021 USA 20 Able Irrigation 2022 USA 21 Able Irrigation 2022 USA 22 Able Irrigation 2022 USA 23 Able Irrigation 2022 USA 24 Able Irrigation 2022 USA 25 Able Irrigation 2022 USA 26 Able Irrigation 2022 USA 27 Able Irrigation 2022 USA 28 Able Irrigation 2022 USA 29 Able Irrigation 2022 USA 30 Able Irrigation 2022 USA 31 Able Irrigation 2022 USA 32 Able Irrigation 2022 USA 33 Able Irrigation 2022 USA 34 Able Irrigation 2022 USA 35 Able Irrigation 2022 USA 36 Able Irrigation 2022 USA 37 Able Irrigation 2022 USA 38 Able Irrigation 2022 USA 39 Able Irrigation 2022 USA 40 Able Irrigation 2022 USA 41 Able Irrigation 2022 USA 42 Able Irrigation 2022 251 Appendix F USA 43 Able Irrigation 2022 USA 44 Able Irrigation 2022 USA 45 Able Irrigation 2022 USA 46 Able Irrigation 2022 USA 47 Able Irrigation 2022 USA 48 Able Irrigation 2022 USA 49 Able Irrigation 2022 USA 50 Able Irrigation 2022 Collaboration Location % water saving Reference Dow Chemical company US 1.3 billion gallons (US Water Alliance 2016) Dow Chemical company US 50% (US Water Alliance 2016) Sweden 50% (Aqualia 2022) Salamanca, Spain 48% (Aqualia 2022) York, Ontario, Canada 20 million liters per day (6%) (The Regional Municipality of York 2016) York Ontario, Canada 40 million liters per day (12%) (The Regional Municipality of York 2016) Singapore 50 (Allen et al. 2011) USA 25 (USEPA 2013) Public awareness Location % water saving Reference Australia 92%, 36% (Dolnicar et al., 2010) China 70% (Zhu et al. 2017) Italy 50-55% (Michetti et al. 2019) Spain 58% (López-ruiz et al. 2021) Spain 29% (López-ruiz et al. 2021) Spain 78% (López-ruiz et al. 2021) Spain 65% (López-ruiz et al. 2021) Nine locations 84% (Hurlimann and Dolnicar 2016) Nine locations 88% (Hurlimann and Dolnicar 2016) Nine locations 43% (Hurlimann and Dolnicar 2016) Nine locations 53% (Hurlimann and Dolnicar 2016) Resource recovery Location Energy reduction / saving Reference Yongin, Korea 6.5% (Chae and Kang 2013) China 53% (Hao et al., 2015) Netherlands 60% (Graaff et al. 2010) India 555-1068 kWh/tonne (Singh, et al. 2020) India 315-608 kWh/tonne (Singh et al. 2020) USA 30 – 50% (Keicher et al., 2008) USA 30-40% (Crawford and Sandino 2010) 252 Figure F-1. Normality test at 90th percentile Figure F-2. Normal probability plot at 50th percentile 253 Figure F-3. Normal probability plot at 10th percentile Appendix G Table G-1. Identification of One Water Approach Indicators 254 In this study, One Water Approach (OWA) is defined as a systematic approach of urban water management that aims to build reliable, resilient, and sustainable water system. Five performance objectives are rated based on three criteria, reliability, resiliency, and sustainability. Table 1 shows the rating definitions and Table 2 outlines the linguistic scale. Table G-1.1: Definition for the selection criteria Selection Criteria Definition Reliability Measures continuity (e.g., clean water available throughout the day), predictability (e.g., intermittent supplies available at regular intervals), and functionality (e.g., occurrence of failures, pressure fluctuations) of acceptable quality of characteristics of an UWS. Continuity refers to continuous and regular supply of clean water. Predictability refers to supply of clean water at a regular intervals. Functionality refers to supply of clean water without any types of disturbance. How much does the indicator contribute to increase the reliability of UWS? Resiliency Measures the capacity of an UWS to withstand external disruptive forces and recover quickly if damage occurs. How much does the indicator contribute to increase resilient capacity of the UWS? Sustainability Measures UWS capacity in meeting the current needs of water without comprising the needs for the future, while ensuring proper utilization of water resources. Sustainability can be characterized in terms of environmental, economic, and social impacts due to water use. How much does the indicator contribute to increase urban water sustainability? Note: UWS= Urban water system Table G-1.2: Linguistic scale Scale Score Reliability Resilience Sustainability Low 1 No change in UWS Introduction of the intervention may enhance resilience in a low degree. Does not improve / promote environment, economic, and social aspect of UWS Medium 3 Improves one or two of the three characteristics (continuity, predictability, and functionality) of UWS Introduction of the intervention may enhance resilience in a medium degree. Improves / Promotes environment, economic, and social aspect of UWS moderately High 5 Improves all three characteristics of UWS Introduction of the intervention may enhance resilience in a high degree. Improves / Promotes environment, economic, and social aspect of UWS significantly UWM= Urban water management Note: Based on your experience as a water utility professional, please input suitable scores to the indicators on pages 2, 3, & 4 against the three rating criteria. The questions (definition, red ink in Table 1) should be kept in mind when assigning the scores defined in Table 2. For example, if an indicator “A” increases reliability with “high”, resiliency with “medium”, and sustainability with “low” degrees for an UWS; the scores of 5, 3, and 1 must be assigned respectively. 255 Table G-1.3: OWA Indicators I. Enhance environmental health Indicators Definition Reliability Resiliency Sustainability Wastewater reuse Use treated wastewater for non-potable uses (e.g., toilet flushing, car washing, and landscaping, industrial, commercial, and institutional uses) and/or potable use Stormwater reuse Collect, treat, and reuse stormwater runoff (rain and snow). E.g., collect stormwater in detention ponds or reservoirs and treat it for non-potable uses ( such as new construction, retrofits, and landscaping) or direct / indirect potable use Fit-for-purpose treatment* Supply of appropriate quality of water (i.e., treated, partially treated, untreated) to specific water uses. E.g., supply of partially treated water for agriculture services. Efficient pump and SCADA system Install efficient water pumps for water treatment and SCADA system for water operation monitoring Energy recovery from the wastewater treatment plant (WWTP) Recover energy from WWTP (e.g., biogas) to supply heat and electricity. The energy recovered can be used to support daily plant operation and save electricity consumption. Nutrients recovery from wastewater Recover energy from WWTP, such as phosphorus and nitrogen from the wastewater, which can later be used as fertilizers to increase crop productivity. Biosolids reuse Use recycled sludge as fertilizers to increase crop productivity. The remaining sludge during the energy recovery process can be utilized as fertilizers. GHGs emissions from wastewater treatment plants Estimate GHGs generated by WWTP (e.g. from wastewater processes) Natural hazard control Implement policy to control natural hazard (e.g., floods, heat waves) in a way that is affordable, accessible, and protect consumers from the catastrophic events 256 III. Strengthen financial situation and technical advancement Indicators Definition Reliability Resiliency Sustainability Average annual lifecycle investment Optimize the assets depreciation costs that are owned by the municipalities based on the assets’ life and replacement costs Proper water price Maintain a price that consumers can pay, while generating sufficient revenues to meet operational and maintenance cost of utilities Availability of emergency funds Strengthen the utility’s financial circumstance with extra funds available that may be used in emergency situations Water saving appliances Install devices in residential and non-residential. buildings that are water efficient (e.g., performance shower heads, low flushing toilets, waterless urinals) Energy efficient devices Install energy star certified air purifiers, clothes dryers, clothes washers, dishwashers, freezers, and refrigerators Web based tool Develop an online platform that assist to store, share, and analyze data with changing performance trends Smart infrastructure monitoring Ensure reliable infrastructure planning, asset monitoring, contaminants identification, and process optimization and control. II. Achieve improved urban water assets performance Indicators Definition Reliability Resiliency Sustainability Implementation of Low Impact Development Introduce low impact development techniques such as bioswales, biofilters, permeable pavement, and green roof to manage stormwater to manage storm runoff and decrease water pollutants into source water bodies. Water leakage reduction Install leak detection equipment such as acoustic instrument; flow meters, conduct continuous auditing program, which records leak daily over time. Dual distribution system Establish separate transmission and distribution mains for potable and non-potable uses Rapid recovery from natural calamities Adequate infrastructure and human resources to recover from various natural calamities, such as floods, droughts, forest fire, and heat waves (Adaptive infrastructure) Availability of resources to meet next 50 years demand Accessibility of necessary resources, including infrastructure, finance, staff at present to meet current and changing population demand. 257 IV. Promote good governance Indicators Definition Reliability Resiliency Sustainability Collaboration across diverse disciplines Encourage stakeholder engagement and coordination across organizations, departments, and governmental bodies to plan and implement water smart strategies Government funding in water sensitive practices Recommend government to separate budget to implement cutting edge technology in designing urban water systems and invest in upgrading infrastructure. Emergency response plans Devise plans to combat sudden contamination of cynotoxins (hydro carbons, algal blooms, lead), occurrence of floods and landslides through an authorized public notification system, and water security issues. Strengthen Sustainable Development Goals Enhance urban water system performance in a way that it not only supports SDGs but also advocate for an effective implementation V. Enhance community perception and participation Indicators Definition Reliability Resiliency Sustainability Public willingness to pay Encourage public to pay more to water utilities by clearly citing the benefits of improved water infrastructure, which comes with an appropriate cost. Public willingness to change behaviour Increase public awareness on water reuse, conservation strategies that are beneficial to water and associated resources, which will increase willingness to change behaviour. Public engagement in decision-making Conduct water forums in the communities and encourage active public participation while making important decision for water related activities Involvement of indigenous communities Encourage indigenous communities participation and recognize their values and interests in water governance arrangements and decision-making 258 Table G-2. Correlation coefficient interpretation Correlation coefficient range Categorization level a < 0.2 Very poor 0.2 < a < 0.4 Poor 0.4 < a < 0.6 Average 0.6 < a < 0.8 Good 0.8 < a < 1 Very good 259 Table G-3. Spearman correlation values for OWA Indicators Decision Criteria 1 Decision Criteria 2 Correlation P-Value Wastewater reuse Sustainability Resiliency 0.335 0.076* Sustainability Reliability 0.439 0.017 Resiliency Reliability 0.370 0.048 Stormwater use Sustainability Resiliency 0.388 0.038 Sustainability Reliability 0.618 0.000 Resiliency Reliability 0.552 0.002 Fit-for-purpose treatment Sustainability Resiliency 0.650 0.000 Sustainability Reliability 0.720 0.000 Resiliency Reliability 0.547 0.002 Efficient pump and SCADA system Sustainability Resiliency 0.303 0.110* Sustainability Reliability 0.282 0.138* Resiliency Reliability 0.554 0.002 Energy recovery from the wastewater treatment plant Sustainability Resiliency 0.209 0.277* Sustainability Reliability 0.189 0.325* Resiliency Reliability 0.706 0.000 Nutrients recovery from wastewater Sustainability Resiliency 0.220 0.252* Sustainability Reliability 0.437 0.018 Resiliency Reliability 0.504 0.005 Biosolids reuse Sustainability Resiliency 0.382 0.041 Sustainability Reliability 0.555 0.002 Resiliency Reliability 0.582 0.001 Reduced GHGs emissions from treatment plants Sustainability Resiliency 0.599 0.001 Sustainability Reliability 0.392 0.035 Resiliency Reliability 0.543 0.002 Natural hazard management Sustainability Resiliency 0.630 0.000 Sustainability Reliability 0.694 0.000 Resiliency Reliability 0.615 0.001 Implementation of Low Impact Development Sustainability Resiliency 0.739 0.000 Sustainability Reliability 0.653 0.000 Resiliency Reliability 0.709 0.000 Water leakage reduction Sustainability Resiliency 0.626 0.000 Sustainability Reliability 0.618 0.001 Resiliency Reliability 0.329 0.081* Dual distribution system Sustainability Resiliency 0.479 0.009 Sustainability Reliability 0.395 0.034 Resiliency Reliability 0.505 0.005 Rapid recovery from natural calamities 260 Sustainability Resiliency 0.553 0.002 Sustainability Reliability 0.344 0.068 Resiliency Reliability 0.403 0.030 Adaptive infrastructure Sustainability Resiliency 0.520 0.004 Sustainability Reliability 0.587 0.001 Resiliency Reliability 0.704 0.000 Average annual lifecycle investment Sustainability Resiliency 0.226 0.240* Sustainability Reliability 0.240 0.210* Resiliency Reliability 0.303 0.110* Proper water price Sustainability Resiliency 0.448 0.015 Sustainability Reliability 0.439 0.041 Resiliency Reliability 0.417 0.025 Availability of emergency funds Sustainability Resiliency 0.510 0.005 Sustainability Reliability 0.349 0.064* Resiliency Reliability 0.461 0.012 Water saving appliances Sustainability Resiliency 0.453 0.014 Sustainability Reliability 0.287 0.131* Resiliency Reliability 0.285 0.133* Energy efficient devices Sustainability Resiliency 0.459 0.012 Sustainability Reliability 0.206 0.284* Resiliency Reliability 0.407 0.029 Web-based tool Sustainability Resiliency 0.346 0.066* Sustainability Reliability 0.609 0.000 Resiliency Reliability 0.403 0.030 Smart infrastructure monitoring Sustainability Resiliency 0.581 0.001 Sustainability Reliability 0.462 0.012 Resiliency Reliability 0.495 0.006 Collaboration across diverse disciplines Sustainability Resiliency 0.559 0.002 Sustainability Reliability 0.624 0.000 Resiliency Reliability 0.557 0.002 Government funding in water sensitive practices Sustainability Resiliency 0.646 0.000 Sustainability Reliability 0.448 0.015 Resiliency Reliability 0.635 0.000 Emergency response plans Sustainability Resiliency 0.506 0.005 Sustainability Reliability 0.706 0.000 Resiliency Reliability 0.458 0.012 Strengthen Sustainable Development Goals Sustainability Resiliency 0.503 0.005 Sustainability Reliability 0.555 0.002 Resiliency Reliability 0.846 0.000 Public willingness to pay Sustainability Resiliency 0.465 0.011 Sustainability Reliability 0.375 0.045 Resiliency Reliability 0.573 0.001 261 Public willingness to change behaviour Sustainability Resiliency 0.423 0.022 Sustainability Reliability 0.413 0.026 Resiliency Reliability 0.448 0.015 Public engagement in decision-making Sustainability Resiliency 0.585 0.001 Sustainability Reliability 0.468 0.011 Resiliency Reliability 0.658 0.000 Involvement of indigenous communities Sustainability Resiliency 0.676 0.000 Sustainability Reliability 0.547 0.002 Resiliency Reliability 0.722 0.000 262 Appendix H Integrated Urban Water System Performance Assessment Please answer the questions below in regards to your own municipal water system (drinking water, wastewater, and stormwater system). The topic question is the OWA indicator and sub questions are asked to derive valuable information regarding the specific indicator. Respondents are requested to click into one of the options for each question. They are suggested to provide answers based on their expert knowledge regarding the subject matter. To confirm that I received your consent to participating in this study, please insert your signature below. 1. Collaboration between diverse disciplines Agree Moderately Agree Disagree Our utility not only has access but also share drinking water data to wastewater and stormwater utility and vice-versa (if-needed) Our utility holds inter-departmental discussion with a fixed agenda (e.g., keeping wastewater and stormwater counterparts informed about drinking water management plans to commission a new water treatment plant) We keep low level management staff informed about the top management officials shared goals and upcoming plans All our utility systems (drinking water, wastewater, and stormwater) work individually and independently Number of public-private partnership projects (e.g., a benchmarking project between municipalities and academic institutions) in the last five years 2. Public engagement in decision-making Zero Between 1-6 Between 7-12 More than 12 Average number of water forums (separate for drinking water, wastewater, stormwater) conducted in a year Average number of integrated (i.e., integration of drinking water, wastewater, and stormwater systems) water forums conducted in a year Average number of participants in water forums (both separate and integrated) Please rate your utility’s public engagement in decision-making 263 Low Medium High Our utility keeps public informed about the objective of the engagement, problems, and potential solutions We obtain public feedback on alternatives and decisions We involve public directly (e.g., via representative) in decision-making and ensure that their concerns are head, understood, and considered We conduct surveys before and after decision-making to check public satisfaction towards end goals 3. Emergency Response Plans Agree Moderately Agree Disagree Our utility has developed a master plan that documents comprehensive strategies to tackle emergency situations We train and get our workforce rehearsed to implement ERP We build a chain of command and line of authority to inform about emergency situations We have established early notification system to keep consumers informed about emergency 4. Please rate your utility’s inclusion of indigenous community members in water-related decision-making Agree Moderately Agree Disagree Adaptation of indigenous knowledge about their surrounding environment Recognition and respect of indigenous rights (inherent and treaty), culture, ethics, and values Equal consideration of indigenous opinions similar to non-indigenous people such that former opinions are not undermined 5. Government funding in water sensitive practices Agree Moderately Agree Disagree Our utility receives loan at a lower interest rate from the government to strengthen infrastructure upgrade activities to support water sensitive practices Our utility receives enough fund from government to upgrade infrastructure (e.g., pipe rehabilitation programs, installation of low impact development techniques) Government may provide additional funds to our utility if we encourage consumers to practice water sensitive approaches such as installation of water saving appliances, energy efficient devices, periodic watering of plants in their households 264 6. Strengthen sustainable development goals (SDGs) Agree Moderately Agree Disagree Our utility is unaware of SDGs and we are concerned only about managing own water resources We are aware of SDGs particularly goal 6 of providing clean water and sanitation to consumer and are constantly making efforts to meet SDG goals We practice integrated urban water management in our utility 7. Implementation of Low impact development Agree Moderately Agree Disagree Our utility has placed LIDs at different locations to manage storm runoff We believe LIDs play significant role in controlling floods because our excess water gets infiltrated into ground to recharge groundwater supplies We introduced LIDs in our region because our source water quality has improved to some extent due to decreased pollutants level in receiving water bodies We do not need LID in our region as conventional means (i.e., rapid passage of stormwater through storm-drains) to manage stormwater works just fine for us Because of financial constraints, we are unable to install LID technologies into our region despite of us having a great interest towards it Type of LID installed in your region 8. Water leakage reduction Agree Moderately Agree Disagree Our utility has installed leak detection instruments at different locations throughout distribution mains and constantly monitor change in pressure flowrate, and temperature using SCADA system We install real time sensors (e.g., ultrasonic wave sensor) at the water’s main point of entry into households to detect leaks We encourage our customer to install moisture detecting sensors, which can be placed at various locations in households, including underneath kitchen and bathroom sinks, basement pipes, washing machines & refrigerators We do not keep track whether or not costumers install leak detection instruments in their households 9. Dual distribution system Agree Moderately Agree Disagree 265 Our utility has separate mains to distribute potable and non-potable water supplies (primarily for agriculture and landscaping) We are planning to build separate mains for potable and non-potable uses Dual distribution system does not interest our utility as it incurs higher capital, operational and maintenance costs, and consumes high energy 10. Adaptive infrastructure Agree Moderately Agree Disagree Our utility has sufficient resources to meet increasing water demands for the next 50 years Currently our drinking water, wastewater, and stormwater mains are not aging and can function effectively for the next 50 years We do not see increasing water demands as potential problem in near future as our utility has abundance water availability 11. Average annual life cycle investment Agree Moderately Agree Disagree Our replacement cost of linear assets (transmission and distribution mains) and non-linear assets (treatment plants, storage tanks, and reservoirs) will be covered from the annual revenues (i.e., the total operating revenues is higher than the total operating expenses) Our return period of initial investment on linear and non-linear assets is higher than the depreciation rate The service life of current water asset is high and our utility is not concerned about average annual life cycle investment for the next 50 years. 12. Proper water price Agree Moderately Agree Disagree Our water price is affordable and revenues generated from customers’ is sufficient to cover utility operational and maintenance costs Our sewer price s affordable and revenues generated from customers’ is sufficient to cover utility operational and maintenance costs We do not charge additional price to manage stormwater systems 13. Web-based tool Agree Moderately Agree Disagree We believe that a tool is necessary to keep track of water systems’ performance Our utility is already equipped with a robust database management system; therefore using a new tool to measure performance is pointless 266 Our utility does not have sufficient staff to use the tool and share water systems’ performance trends with other utilities 14. Water saving appliances Agree Moderately Agree Disagree Our utility encourages customers to install water-saving appliances in their households We award rebates to customers who install water-saving appliances in their households We do not keep track whether or not customers are using water-saving appliances We do not see the benefits of using water-saving appliances What percentage of households in your region uses water-saving appliances? 15. Energy efficient devices Agree Moderately Agree Disagree Our utility encourages customers to install energy-efficient devices in their households We award rebates to customers who install energy efficient devices in their households We do not keep track whether or not customers are using energy efficient devices We do not see the benefits of using energy efficient devices What percentage of households in your region uses energy efficient devices? 16. Availability of emergency funds Agree Moderately Agree Disagree Our utility receives annual funds from the government to tackle emergency situations In case of severe emergency (e.g., flood disrupting treatment facility functioning, lead contamination in drinking water) Our utility can request provincial and federal government to provide immediate grants We can request for grants but the bureaucracy at government level causes delay in the approval process Our utility collects enough revenues and we have sufficient funds to tackle minor emergency situations (e.g., pump failure, minor acts of vandalism, and 267 positive E.coli and coliform tests in distributed drinking water) 17. Rapid recovery from natural calamities Agree Moderately Agree Disagree Our utility has enough human resources to tackle unfavourable situations (e.g., clearing of overflowed flood water at pumping stations, monitoring affected areas and seeking immediate assistance from government) We have adequate financial resource to handle unintended weather events and fix any mechanical failures that might occur in distribution mains, treatment plants, and pump stations. We have alternate source water supplies in case of sudden water contamination We have enough water supplies for our consumers in case of drought events and increased heat wave incidents 18. Public willingness to pay Agree Moderately Agree Disagree Our drinking water and sewer price must be increased at least by 50 cents per unit water consumed to improve overall system performance We must separately introduce stormwater price to properly manage stormwater systems We inform our customers the benefits of integrated water systems in supporting utility long term plans of integrated urban water management We think public does not want to pay more as majority of customers believe that water is abundantly present in Canada and there is no point in paying extra bucks in buying something that is freely available 19. Public willingness to change behaviour Agree Moderately Agree Disagree Our utility conducts awareness programs to keep public informed about the benefits of water reuses We conduct survey to inquire public acceptance to use reclaimed water and stormwater for non-potable uses We encourage household owners to use rain barrels to store excess rainwater and later use them for gardening We encourage household owners to water garden during the fixed hours and turn off the sprinklers when it is raining 268 20. Efficient pimps and SCADA system Agree Moderately Agree Disagree Our utility periodically replace older pumps and continuously monitor them All our pumps are efficient, which is why energy costs is reduced and energy efficiency is improved Our SCADA system monitors all the water quality parameters and provide real time data Installation of SCADA in our distribution system and treatment plant has reduced the operating costs 21. Energy recovery from the wastewater treatment plants Agree Moderately Agree Disagree Energy that is recovered from our treatment plants is self-sufficient to meet plant’s efficiency needs No energy is recovered from our treatment plants We do not think energy recovery is important as it produces GHG emissions 22. Nutrients recovery from wastewater Agree Moderately Agree Disagree Our utility recovers nutrients from wastewater to prevent nitrogen and phosphorus depletion We believe that nutrients recovered from wastewater improve effluent water quality to be disposed into receiving water bodies We use nutrients recovered from wastewater as fertilizers in agriculture to increase crop productivity 23. Biosolids reuse Agree Moderately Agree Disagree Our utility recovers biosolids because it improves soil quality and increase crop production We sell biosolids to industries for commercial purposes Our anaerobic digester uses biosolids to produce methane, which is later used to generate electricity We believe that biosolids helps to prevent GHG emissions as soil stores carbo and delay release of CO2 from decaying organic materials 24. Reduced GHG emissions from treatment plants Agree Moderately Agree Disagree Our utility does not consider GHGs reduction from treatment plants important because building, transportation, and other sectors emit more GHGs than water systems Our treatment plants have excellent operational conditions and improved configuration of water treatment processes, which assist in GHG reductions We calculate total GHG emitted from our treatment systems on a regular basis 269 25. Natural hazard management Agree Moderately Agree Disagree Our utility has a standard document that identified potential solutions to overcome natural hazard occurrences We conduct risk assessment of our utility systems on a periodic basis We have placed policies to mitigate natural hazard effects that are affordable and easily accessible to consumers 26. Fit-for-purpose treatment Agree Moderately Agree Disagree Our utility distributes reclaimed water for non-potable uses (e.g., toilet flushing, irrigation, landscaping, and cloth washing) We supply highly treated water for both potable and non-potable uses We supply raw water (i.e., untreated water) to meet agricultural needs Our wastewater is disposed into water sources without any treatment Our wastewater is treated to the potable water standards and disposed into receiving water bodies We do not treat our stormwater. The primary goal of our stormwater management is to properly channel stormwater into storm drains 270 Appendix I AHP Calculation 1. Create a pair-wise decision matrix (obtained based on the expert opinion) Sustainability Resiliency Reliability Sustainability 1 5 2 Resiliency 1/5 1 3 Reliability 1/2 1/3 1 (Considering Sustainability is 5 times important than resiliency, reliability is two times less important than sustainability, resiliency is three times more important than reliability (this assumption varies according to the respondents). 2. Sum the pair-wise decision matrix Sustainability Resiliency Reliability Sustainability 1 5 2 Resiliency 1/5 1 3 Reliability 1/2 1/3 1 Sum 1.7 6.33 6 3. Normalize the decision matrix – divide each element by the sum of columns Sustainability Resiliency Reliability Sustainability (1) /(1.7) = 0.588 (5)/(6.33) = 0.789 (2)/(6) = 0.333 Resiliency (1/5)/(1.7) = 0.117 (1)/(6.33)= 0.157 (3)/(6) =0.5 Reliability (1/2)/(1.7) = 0.294 (1/3) /6.33 = 0.052 (1)/(6) =0.167 4. Calculate the criteria weights = average all the element across the row Sustainability Resiliency Reliability Criteria weights Sustainability 0.588 0.789 0.333 0.570 Resiliency 0.117 0.157 0.500 0.258 Reliability 0.294 0.052 0.167 0.171 (e.g., [(0.588+0.789+0.333) = 0.570)] 5. Calculate the consistency – take the same pair-wise matrix, which is not normalized, and multiply by the criteria weights 0.570 0.258 0.171 Sustainability Resiliency Reliability Sustainability (1)*(0.570) = 0.570 (5)*(0.258) = 1.29 (2)*(0.171) = 0.342 Resiliency (1/5)*(0.570) = 0.114 (1)*(0.258) = 0.258 (3) * (0.171)= 0.513 Reliability (1/2)*(0.570) = 0.285 (1/3)*(0.258) = 0.086 (1)*(0.171)= 0.171 6. Calculate the weighted sum value – add all the values across row Sustainability Resiliency Reliability Weighted sum value Sustainability 0.570 1.29 0.342 2.202 Resiliency 0.114 0.258 0.513 0.885 271 Reliability 0.285 0.086 0.171 0.542 7. Calculate the ratio of weighted sum value and criteria weights Sustainability Resiliency Reliability Ratio Sustainability 0.570 1.29 0.342 (2.202/0.570) = 3.863 Resiliency 0.114 0.258 0.513 (0.885/0.258) = 3.430 Reliability 0.285 0.086 0.171 (0.542/0.171) = 3.169 8. Calculate λmax - take the average of ratio calculated in step 7 λmax =3.487 9. Calculate the consistency Index λ𝑚𝑎𝑥−𝑛𝑛−1 = 3.487−33−1= 0.243 Since we have three criteria, n = 3 9. Calculate consistency ratio = (CI)/(RI) = 0.243 / 0.58 = 0.420 (RI = Random index, n=3, RI = 0.58). The value of RI can be found in the literature. Since the consistency ratio is greater than 0.10, we can assume that our matrix is not consistent. This means that we have to conduct analysis again with more participation. FUZZY AHP 1. Create a decision matrix Decision criteria Sustainability Resiliency Reliability Sustainability 1 5 4 Resiliency 1/5 1 2 Reliability 1/4 1/2 1 2. Convert the value above into fuzzy number (Here, triangular fuzzy numbers have been used. Literature can be sought to find the values for fuzzy numbers) Decision criteria Sustainability Resiliency Reliability Sustainability (1,2,3) (5,6,7) (4,5,6) Resiliency (1/7, 1/6, 1/5) (1,2,3) (2,3,4) Reliability (1/6, 1/5, 1/4) (1/4, 1/3, 1/2 ) (1,2,3) 3. Calculate fuzzy geometric mean Decision criteria Sustainability Resiliency Reliability Sustainability (1*5*4)^(1/3) (2*6*5)^(1/3) (3*7*6)^(1/3) Resiliency (1/7*1*2)^(1/3) (1/6*2*3)^(1/3) (1/5*3*4)^(1/3) Reliability (1/6*1/4*1)^(1/3) (1/5*1/3*2)^(1/3) (1/4*1/2*3)^(1/3) 272 Decision criteria Sustainability Resiliency Reliability Sustainability 2.714 3.915 5.013 Resiliency 0.659 1.000 1.339 Reliability 0.347 0.511 0.721 Sum 3.720 5.426 7.073 4. Calculate inverse of the fuzzy decision matrix Decision criteria Sustainability Resiliency Reliability Sustainability (1/7.073) = 0.141 (1/5.426) = 0.18 (1/3.720) = 0.269 5. Calculate Fuzzy weight Decision criteria Sustainability Resiliency Reliability Sustainability (2.714/0.141) = 0.384 (3.915/0.18)=0.722 (5.013/0.269)=1.348 Resiliency (0.659/0.141) = 0.093 (1.000/ 0.18)= 0.184 1.339/0.269)=0.360 Reliability 0.347)/(0.141) = 0.049 (0.511/0.18) = 0.094 0.721/0.269)=0.194 6. Compute fuzzy scores of performance indicators Decision criteria KPI 1 (Fuzzy scores) Sustainability (0,0.25, 0.5) Resiliency (0.25,0.5, 0.75) Reliability (0.5,0.75,1) The above fuzzy score value is an example. In our study, there are 29 indicators for OWA, the scores need to be calculated for each indicator 7. Multiply fuzzy weight with fuzzy scores of performance indicators Decision criteria KPI 1 (Fuzzy scores) Sustainability (0*0.384, 0.25*0.722, 0.5* 1.348) = (0,0.180,0.674) Resiliency (0.25*0.093, 0.5*0.184, 0.75*0.360) = (0.023, 0.092, 0.27) Reliability (0.5*0.049, 0.75*0.094, 1*0.194)= (0.0245, 0.0705, 0.914) 8. Use the centroid method to calculate the crisp value = average the fuzzy score for each decision criterion in step 7 Decision criteria KPI 1 (Fuzzy scores) Sustainability 0.284 Resiliency 0.128 Reliability 0.336
Thesis/Dissertation
eng
Civil Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Sadiq, Rehan
Hewage, Kasun
A path towards "one water" community : performance assessment and benchmarking
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