Science, Faculty of
Earth, Ocean and Atmospheric Sciences, Department of
DSpace
UBCV
Donaldson, Cassidy
Master of Science - MSc
University of British Columbia
The Salish Sea is a biologically productive coastal sea that is home to a human population of over 8.9 million residents shared between the USA and Canada, and supports high but threatened ecosystem diversity and species richness. It is an estuarine system with freshwater inputs from numerous rivers that influence ocean dynamics, biogeochemistry, and ecosystem processes. While previous studies have examined circulation and salinity patterns of freshwater, primarily focusing on the Fraser River in the Strait of Georgia or Puget Rivers in Puget Sound, the relative influence of different riverine sources on basins other than the one they directly feed into is unclear. This study first evaluates the performance of the SalishSeaCast model at capturing salinity patterns in small river plume regions for 11 key rivers using two different model versions, finding that the newer version with daily estimates of river flow improves the model's performance. Then, passive model dye tracers are used to examine the distribution of freshwater from river inputs in the Salish Sea from those 11 rivers using the new model version, with emphasis on examining inter-basin transport between the Strait of Georgia and Puget Sound. The study utilizes a physics-only version of SalishSeaCast, a 3-D ocean model built on the NEMO framework with half-kilometer horizontal grid resolution. Model results indicate that while the Fraser River has the largest influence on riverine freshwater compared to any other individual river, smaller rivers are non-negligible in relative magnitude and their influences are present throughout the Salish Sea. The findings suggest that smaller rivers play a role in salinity distributions and freshwater content throughout the Salish Sea, highlighting the need to consider their contributions in model development and analyses across Salish Sea basins. By advancing the understanding of freshwater dynamics in the Salish Sea, this study provides insights for future research on estuarine circulation, ecosystem impacts, and climate change resilience.
Modeling Freshwater from a Subset ofRivers Throughout the Salish Sea Using DyeTracersbyCassidy DonaldsonB.Sc. Physics and Oceanography, The University of British Columbia, 2022A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Oceanography)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)April 2025© Cassidy Donaldson 2025The following individuals certify that they have read, and recommend to the Faculty of Graduate and Post-doctoral Studies for acceptance, the thesis entitled:Modeling Freshwater from a Subset of Rivers Throughout the Salish Sea Using Dye Tracerssubmitted by Cassidy Donaldson in partial fulfillment of the requirements for the degree of MASTER OFSCIENCE in Oceanography.Examining Committee:Susan Allen, Professor, Earth, Ocean, and Atmospheric Sciences, The University of British ColumbiaSupervisorDebby Ianson, Professor, Earth and Ocean Sciences, The University of VictoriaSupervisory Committee MemberGregory Lawrence, Professor, Civil Engineering, The University of British ColumbiaSupervisory Committee MemberStephanie Waterman, Professor, Earth, Ocean, and Atmospheric Sciences, The University of British ColumbiaAdditional ExamineriiAbstractThe Salish Sea is a biologically productive coastal sea that is home to a human population of over 8.9 millionresidents shared between the USA and Canada, and supports high but threatened ecosystem diversity andspecies richness. It is an estuarine system with freshwater inputs from numerous rivers that influence oceandynamics, biogeochemistry, and ecosystem processes. While previous studies have examined circulationand salinity patterns of freshwater, primarily focusing on the Fraser River in the Strait of Georgia or PugetRivers in Puget Sound, the relative influence of different riverine sources on basins other than the onethey directly feed into is unclear. This study first evaluates the performance of the SalishSeaCast model atcapturing salinity patterns in small river plume regions for 11 key rivers using two different model versions,finding that the newer version with daily estimates of river flow improves the model’s performance. Then,passive model dye tracers are used to examine the distribution of freshwater from river inputs in the SalishSea from those 11 rivers using the new model version, with emphasis on examining inter-basin transportbetween the Strait of Georgia and Puget Sound. The study utilizes a physics-only version of SalishSeaCast,a 3-D ocean model built on the NEMO framework with half-kilometer horizontal grid resolution. Modelresults indicate that while the Fraser River has the largest influence on riverine freshwater compared to anyother individual river, smaller rivers are non-negligible in relative magnitude and their influences are presentthroughout the Salish Sea. The findings suggest that smaller rivers play a role in salinity distributions andfreshwater content throughout the Salish Sea, highlighting the need to consider their contributions in modeldevelopment and analyses across Salish Sea basins. By advancing the understanding of freshwater dynamicsin the Salish Sea, this study provides insights for future research on estuarine circulation, ecosystem impacts,and climate change resilience.iiLay SummaryThe Salish Sea region, whose waters span Washington State in the USA and British Columbia in Canada,is home to many humans and different species of plants, animals, and other organisms. It is importantto understand the behavior of the ocean here, especially because a growing number of its residents arethreatened by the changes it is currently undergoing. Freshwater from rivers is a key component of theSalish Sea, and is the main focus of this thesis. I used a computer model called SalishSeaCast to quantify andtrack freshwater throughout the region, finding that even small rivers are significant in magnitude throughoutthe system and that waters on different sides of the USA-Canada border are interconnected. I also foundthat our new method of including the rivers in the model improved the model performance in representingsalinity around river mouths. This work shows how important rivers are, and provides a foundation forfuture research and model development.iiiPrefaceThis thesis is the original work of the author, Cassidy Donaldson. Supervision was provided by SusanAllen, who gave guidance on model configuration, interpretation of results, and thesis writing. ChatGTP-4was used sparingly as a supplemental search engine to discover peer-reviewed research, and for editorialadjustments to sentence structure and flow. This work is currently unpublished, but sections pertaining tothe dye tracers analysis are undergoing preparation for future publication.ivTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiiList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiDedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 The Salish Sea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1.1 Regional Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1.2 Importance of Rivers to the Salish Sea . . . . . . . . . . . . . . . . . . . . . . . . 31.2 Estuarine Exchange Flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.2.1 The Classical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.2.2 Exchange Flow Dynamics in the Salish Sea . . . . . . . . . . . . . . . . . . . . . . 51.3 Riverine Biogeochemical Influences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.3.1 Nutrients and Phytoplankton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.3.2 Hypoxia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.3.3 DIC and TA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.4 Freshwater Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.4.1 Other Work Utilizing Model Dye . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.4.2 Application to the Salish Sea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81.5 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.1 Model Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.2 SRP Model Evaluations of Salinity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.3 Freshwater Dye Tracers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14vTable of Contents3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.1 SRP Model Evaluations of Salinity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.1.1 All Rivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.1.2 Large Rivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183.1.3 Puget Sound Rivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183.1.4 East Coast Vancouver Island Rivers . . . . . . . . . . . . . . . . . . . . . . . . . . 203.1.5 Small Vancouver Island Rivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203.2 Freshwater Dye Tracers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203.2.1 Fraser River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203.2.2 Puget Sound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294.1 SRP Model Evaluations of Salinity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294.1.1 Skagit River Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294.1.2 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304.2 Freshwater Dye Tracers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314.2.1 Freshwater Exchange in Puget Sound . . . . . . . . . . . . . . . . . . . . . . . . . 324.3 Streamflows in a Changing Climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354.3.1 Rainfall Rivers: East Coast of Vancouver Island . . . . . . . . . . . . . . . . . . . 364.3.2 Glacial Rivers: West Coast of BC Mainland . . . . . . . . . . . . . . . . . . . . . 374.3.3 Mixed Rivers: Puget Sound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374.3.4 The Fraser River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384.3.5 Overall Impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43AppendicesA Coastal Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52A.1 Motivation and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52A.2 Clustering of T, S, and Aragonite Saturation . . . . . . . . . . . . . . . . . . . . . . . . . . 54A.2.1 C1: Summer Freshwater-Influenced Waters . . . . . . . . . . . . . . . . . . . . . . 55A.2.2 C2: Spring Bloom Waters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55A.2.3 C3: Winter Pacific Water Dominated Waters . . . . . . . . . . . . . . . . . . . . . 55A.2.4 C4: Summer and Fall Mixed Pacific Waters . . . . . . . . . . . . . . . . . . . . . . 55A.2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55B Supporting Tables and Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57viTable of ContentsC Complete SRP Evaluation Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59C.1 Nooksack River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59C.2 Skagit River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62C.3 Snohomish River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65C.4 Nisqually River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68C.5 Elwha River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71C.6 Cowichan River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74C.7 Nanaimo River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77C.8 Puntledge River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80C.9 Salmon River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83C.10 Homathko River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86C.11 Squamish River . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89viiList of Tables2.1 The number of observations used for model evaluations in each SRP subregion. . . . . . . . 142.2 Each of the model rivers tracked in this study and its corresponding SalishSeaCast modelwatershed, model hydrograph signal(s), classification type, and mean model flow. . . . . . . 153.1 The maximum, minimum, mean, and mean absolute value of all evaluation metrics. . . . . . 17B.1 Values of all evaluation metrics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57viiiList of Figures1.1 Map of the Salish Sea and Puget Sound. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.1 Daily model river hydrographs, 2019-2023. . . . . . . . . . . . . . . . . . . . . . . . . . . 112.2 Comparison of Snohomish River flow between model v201905 and model v202111. . . . . . 122.3 Boxes for SRPs and SS subregions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.1 Salinity evaluation metrics for the top 10 m of each SRP subregion. . . . . . . . . . . . . . 193.2 SSC Bathymetry, Fraser River volume, and concentration Fraser water. . . . . . . . . . . . . 213.3 Daily Fraser River FWC by region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.4 Cross-sections of Fraser River concentration near boundaries. . . . . . . . . . . . . . . . . . 233.5 Puget Sound freshwater content. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243.6 Daily time series of river FWC in Puget Sound and hydrographs. . . . . . . . . . . . . . . . 263.7 Concentration of significant rivers in PS with Temp and Salinity TS. . . . . . . . . . . . . . 284.1 Salinity and bathymetry with observations for the Skagit River. . . . . . . . . . . . . . . . . 304.2 Elwha model salinities and observation data. . . . . . . . . . . . . . . . . . . . . . . . . . . 314.3 Daily Snohomish River FWC by region. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324.4 Mean Fraser River Concentration along the Admiralty Inlet thalweg. . . . . . . . . . . . . . 344.5 Schematic of waters and mixing through Admiralty Inlet. . . . . . . . . . . . . . . . . . . . 354.6 Rainfall to Rainfall and Fraser River ratio. . . . . . . . . . . . . . . . . . . . . . . . . . . . 374.7 Summary of predicted watershed changes and their oceanographic impacts. . . . . . . . . . 39A.1 Spatial and temporal distributions for five intertidal zone clusters based on model salinity,temperature, and ΩA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54A.2 Box and whisker plots showing the salinity, temperature, and aragonite saturation state ΩAfor the five clusters of the intertidal zone. . . . . . . . . . . . . . . . . . . . . . . . . . . . 56A.3 Property-property plots of temperature and salinity, showing clusters and ΩA . . . . . . . . . 56B.1 Average depth of Fraser River FWC with FWC and Pearson R Correlation. . . . . . . . . . 58C.1 Nooksack model bathymetries and observation data. . . . . . . . . . . . . . . . . . . . . . . 59C.2 Nooksack temporal data distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59C.3 Nooksack model hydrographs, 2019-2023 climatology. . . . . . . . . . . . . . . . . . . . . 60C.4 Nooksack evaluation metrics by season for both models. . . . . . . . . . . . . . . . . . . . 61C.5 Nooksack model vs observations scatter plots by season for both models. . . . . . . . . . . 61C.6 Skagit model bathymetries and observation data. . . . . . . . . . . . . . . . . . . . . . . . 62ixList of FiguresC.7 Skagit temporal data distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62C.8 Skagit model hydrographs, 2019-2023 climatology. . . . . . . . . . . . . . . . . . . . . . . 63C.9 Skagit evaluation metrics by season for both models. . . . . . . . . . . . . . . . . . . . . . 64C.10 Skagit model vs observations scatter plots by season for both models. . . . . . . . . . . . . 64C.11 Snohomish model bathymetries and observation data. . . . . . . . . . . . . . . . . . . . . . 65C.12 Snohomish temporal data distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65C.13 Snohomish model hydrographs, 2019-2023 climatology. . . . . . . . . . . . . . . . . . . . 66C.14 Snohomish evaluation metrics by season for both models. . . . . . . . . . . . . . . . . . . . 67C.15 Snohomish model vs observations scatter plots by season for both models. . . . . . . . . . . 67C.16 Nisqually model bathymetries and observation data. . . . . . . . . . . . . . . . . . . . . . . 68C.17 Nisqually temporal data distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68C.18 Nisqually model hydrographs, 2019-2023 climatology. . . . . . . . . . . . . . . . . . . . . 69C.19 Nisqually evaluation metrics by season for both models. . . . . . . . . . . . . . . . . . . . . 70C.20 Nisqually model vs observations scatter plots by season for both models. . . . . . . . . . . . 70C.21 Elwha model bathymetries and observation data. . . . . . . . . . . . . . . . . . . . . . . . 71C.22 Elwha temporal data distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71C.23 Elwha model hydrographs, 2019-2023 climatology. . . . . . . . . . . . . . . . . . . . . . . 72C.24 Elwha evaluation metrics by season for both models. . . . . . . . . . . . . . . . . . . . . . 73C.25 Elwha model vs observations scatter plots by season for both models. . . . . . . . . . . . . 73C.26 Cowichan model bathymetries and observation data. . . . . . . . . . . . . . . . . . . . . . . 74C.27 Cowichan temporal data distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74C.28 Cowichan model hydrographs, 2019-2023 climatology. . . . . . . . . . . . . . . . . . . . . 75C.29 Cowichan evaluation metrics by season for both models. . . . . . . . . . . . . . . . . . . . 76C.30 Cowichan model vs observations scatter plots by season for both models. . . . . . . . . . . 76C.31 Nanaimo model bathymetries and observation data. . . . . . . . . . . . . . . . . . . . . . . 77C.32 Nanaimo temporal data distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77C.33 Nanaimo model hydrographs, 2019-2023 climatology. . . . . . . . . . . . . . . . . . . . . 78C.34 Nanaimo evaluation metrics by season for both models. . . . . . . . . . . . . . . . . . . . . 79C.35 Nanaimo model vs observations scatter plots by season for both models. . . . . . . . . . . . 79C.36 Puntledge model bathymetries and observation data. . . . . . . . . . . . . . . . . . . . . . . 80C.37 Puntledge temporal data distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80C.38 Puntledge model hydrographs, 2019-2023 climatology. . . . . . . . . . . . . . . . . . . . . 81C.39 Puntledge evaluation metrics by season for both models. . . . . . . . . . . . . . . . . . . . 82C.40 Puntledge model vs observations scatter plots by season for both models. . . . . . . . . . . 82C.41 Salmon model bathymetries and observation data. . . . . . . . . . . . . . . . . . . . . . . . 83C.42 Salmon temporal data distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83C.43 Salmon model hydrographs, 2019-2023 climatology. . . . . . . . . . . . . . . . . . . . . . 84C.44 Salmon evaluation metrics by season for both models. . . . . . . . . . . . . . . . . . . . . . 85C.45 Salmon model vs observations scatter plots by season for both models. . . . . . . . . . . . . 85C.46 Homathko model bathymetries and observation data. . . . . . . . . . . . . . . . . . . . . . 86C.47 Homathko temporal data distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86xList of FiguresC.48 Homathko model hydrographs, 2019-2023 climatology. . . . . . . . . . . . . . . . . . . . . 87C.49 Homathko evaluation metrics by season for both models. . . . . . . . . . . . . . . . . . . . 88C.50 Homathko model vs observations scatter plots by season for both models. . . . . . . . . . . 88C.51 Squamish model bathymetries and observation data. . . . . . . . . . . . . . . . . . . . . . . 89C.52 Squamish temporal data distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89C.53 Squamish model hydrographs, 2019-2023 climatology. . . . . . . . . . . . . . . . . . . . . 90C.54 Squamish evaluation metrics by season for both models. . . . . . . . . . . . . . . . . . . . 91C.55 Squamish model vs observations scatter plots by season for both models. . . . . . . . . . . . 91xiAcknowledgmentsI am incredibly grateful to the many people who have supported me throughout my oceanography journey.First and foremost, I want to thank my supervisor, Susan, for your guidance, patience, and confidence in me.From the beginning in undergrad, you’ve encouraged me to think critically and push myself. Your passionfor research, dedication to students, and thoughtful mentorship have shaped me in ways I will carry forward.I’m especially grateful for how you’ve created such a positive and supportive experience. From the ZwiftRide Ons to every challenge you’ve helped me tackle, your influence has been invaluable.I also want to sincerely thank my committee members. Debby, thank you for your insightful feedbackand for encouraging me to think about the broader context of my work. Your reminder to evaluate theconnections between information has been incredibly helpful. Greg, thank you for your encouragementand enthusiasm, and attention to detail in your feedback. You made the environmental fluid dynamicsclass engaging and rewarding. A special thank you to Stephanie, who affirmed my decision to pursueoceanography back when I was still deciding my major. Your enthusiasm for the field was contagious, andI’m grateful for the role you played in guiding me down this path.The MOAD group has been a source of both technical guidance and personal support. I’m especiallygrateful to Karyn and Raisha for your advice, and to Doug for your incredible software support. Yourpatience and expertise have taught me so much. Camryn, I can’t imagine going through this without you.From day one, we faced every challenge together, and while we may no longer sit side by side, I know ourfriendship (forged in the fires of Fall 2022) will last a lifetime.I’m also deeply grateful to my past supervisors and colleagues. Carla and Tom, thank you for pushingme outside my comfort zone and showing me how much I could learn in a short time. To Jonathan and theteam at ECCC, thank you for affirming that I was capable of pursuing graduate studies.To my friends from undergrad, EOAS, triathlon, and volleyball: thank you for making my time inVancouver unforgettable. Whether it was Dene Room 634, D&D in IKB, physics labs filled with sillyvoices, the EOAS Gala, trying to understand Spanish in Chile, Healthy Noodle House dinners, 7 p.m. swimpractices, “recovery” rides on River Road, step-stool dinnertimes, or Chaldecott volleyball sessions, yourfriendship made the hard days easier and the good days even better.To my family: thank you for shaping me into who I am today and for supporting me, not because youexpected me to take this path, but because you backed me when I chose it. I’m proud of myself thanks toyou. To Blake’s family, thank you for welcoming me with open arms and offering your support in ways bothbig and small. To my partner, Blake: thank you for everything. For your unwavering support, for makingsure I ate and drank when I was too focused to remember, for reminding me to make time for the things Ienjoy, and for staying up late so I wouldn’t have to write alone. I am so grateful to be a team.Finally, I acknowledge UBC for the funding that made this work possible. Through the STAIR grant,I had the opportunity to collaborate with ecologists in the Zoology department, reinforcing my belief inmaking science more accessible and interdisciplinary.xiiDedicationIn loving memory of Katherine Helvey Donaldson: rocket scientist, do-good human, and my extraordinarymom.xiiiChapter 1IntroductionEstuaries are inherently tied to life on Earth; they are some of the most biologically productive naturalsystems on the planet (Dame, 2008), and about a quarter of the human population lives within 50 km of acoastline (Cosby et al., 2024) with the majority living near estuaries (Millennium Ecosystem Assessment,2005). The Salish Sea (Figure 1.1) is an estuarine system whose waters span Washington State in the USAand British Columbia in Canada. It is a biologically productive environment that historically supports high(but potentially threatened in present day) ecosystem diversity and species richness (Sobocinski et al., 2022;Hollarsmith et al., 2022; Quinn and Losee, 2022) and is also home to more than 8.9 million people splitabout evenly between the US and Canada (Sobocinski et al., 2022). These growing populations rely on theSalish Sea for commercial fishing, transportation, and recreation, and so have a vested interest in preservingand protecting the region.1.1 The Salish Sea1.1.1 Regional OverviewThe Salish Sea is comprised of multiple semi-enclosed basins and waterways that, while interconnected, canbe divided to better understand the sub-processes that govern the Salish Sea (Figure 1.1). Exchange betweenthe Pacific Ocean (PO) and the Salish Sea mainly occurs through Juan de Fuca Strait (JdF), with a minorityoccurring through Johnstone Strait (Khangaonkar et al., 2017; Pawlowicz et al., 2019).In JdF, deep water flows in and surface water flows out primarily based on the strength of the densitygradient (LeBlond, 1983). This Pacific inflow follows complex dynamics with reflux/efflux (inflow mixedinto the outflow/outflow mixed into the inflow) at the sill-side of JdF (MacCready et al., 2021), and longtimescale means find that anywhere between 70% (Pawlowicz et al., 2019) and 34-51% (Beutel and Allen,2024) of this inflow continues into the next landward regions of the Salish Sea (the Strait of Georgia andPuget Sound). The estuarine structure in JdF consists of three layers: a fast outflow layer at the surface, andtwo slower inflow layers below (Thomson et al., 2007). There is another flow pattern called the transientregime that occurs slightly less than half of the time in winter and 10% of the time in summer, meaningthat the estuarine regime is still dominant on average (Thomson et al., 2007; Beutel, 2023). There are twobranches on the pathway to continue further into the Salish Sea: through the Haro Region into the Straitof Georgia, which receives about 80% of the inflow, or into Puget Sound, which receives the other 20%(Pawlowicz et al., 2019; Beutel and Allen, 2024).In the Haro Region, this deep and salty inflow water flows over shallow, rough bathymetry which hasthe effect of creating a region of enhanced vertical mixing between JdF and the Strait of Georgia (SoG)(Waldichuk, 1957; Cokelet and Stewart, 1985), and adds a backtracking component to the flow of bothexchange layers (MacCready et al., 2021). While there are multiple channels through the San Juan and Gulf11.1. The Salish SeaIslands (Haro Strait, Rosario Strait, and San Juan Channel), Haro Strait is the deepest and most significantchannel of two-layered exchange flow (LeBlond, 1983; Stang and Allen, 2024).Figure 1.1: Map of the Salish Sea and model domain area. Panel A shows the full model domain in thegrey box with surrounding geography, while panel B shows a cutout of Puget Sound. Important populationcenters (1) Vancouver, (2) Victoria, (3) Seattle, and (4) Tacoma are labeled. The colored contours showthe surface (0.5 m depth) salinity for June 15th, 2020. The black dotted line shows the international borderbetween the United States and Canada. The orange star represents the station at which profiles are examinedby depth in Section 3.2. Map is drawn based on code provided by Ben Moore-Maley.After moving through the Haro Region, the inflow reaches the SoG, which is the largest of the SalishSea’s basins. It is approximately 200 km long by 30 km across, and reaches depths near 400 m with anaverage depth close to 150 m. Water in the SoG occupies three main water masses, each with a different res-idence time: deep water below 200 m, surface waters down to 50 m, and intermediate waters between them(Pawlowicz et al., 2007). The deep waters experience seasonal renewal in the late summertime, spurred bylow tidal mixing in the Haro Region and upwelling of dense Pacific Ocean water off the coast of Washing-ton and Vancouver Island (Masson, 2002; Thomson et al., 2020; Masoud and Pawlowicz, 2022; Allen et al.,2025b). Intermediate waters upwell seasonally based on winds (and the strength of stratification betweenthe intermediate layer and surface layer) (Moore-Maley and Allen, 2022). In addition to these three mainwater masses, in the southern SoG there is a shallow plume from the Fraser River. The Fraser River is thelargest source of runoff for the Salish Sea, and its mouth is in the SoG next to the city of Vancouver (Figure1.1).Puget Sound (PS) is the other branch of this inflow pathway (Figure 1.1), though its estuarine exchange21.1. The Salish Seais driven by the sum of runoff from other rivers within PS as opposed to the Fraser River. PS is connectedto JdF through Admiralty Inlet and Deception Pass (which is much narrower and shallower than AdmiraltyInlet) (Thomson, 1981). The main length of Puget Sound is about 160 km, with widths on the scale of tensof kilometers. The cities of Seattle and Tacoma are on the coast of the PS basin. It is a large and complexglacial fjord, meaning that it generally has deep bathymetry and has a sill at its entrance (Admiralty Sill)that creates enhanced vertical mixing and reflux/efflux dynamics (Ebbesmeyer and Barnes, 1980). PugetSound has a similar structure of brackish surface water and intermediate water comprised of a mixture offreshwater and inflow from the Pacific Ocean (Thomson, 1981; Yang and Khangaonkar, 2010). Year-to-yearvariability in transport into and out of Puget Sound is affected by river discharge (Babson et al., 2006). Thestrength of stratification and reliance on tidal movement over sills to drive enhanced vertical mixing meansthat hypoxia events are significant in PS (Pelletier et al., 2014; Khangaonkar et al., 2018; MacCready et al.,2021).Other basins and waterways within the Salish Sea include the glacial fjords along the west coast ofthe British Columbian Mainland, and Johnstone Strait. The significant glacial fjords include Howe Sound,Jervis Inlet, Toba Inlet, and Bute Inlet. Each of these fjords experiences estuarine exchange over a sill,where the maximum depth of inflow is modulated by the sill at its entrance. Waters below the sill depthmay remain stagnant until renewal by seasonal processes (Pickard, 1975; Bianucci et al., 2024). JohnstoneStrait is significant to the inflow of bottom waters in the northern Strait of Georgia. The narrow passagesand shallow sills present throughout the length of Johnstone Strait create fast currents and high amounts ofvertical mixing (Thomson, 1981; Foreman et al., 2012).1.1.2 Importance of Rivers to the Salish SeaAll of these basins are influenced by the freshwater entering the Salish Sea, whether that be from the FraserRiver or smaller rivers. Due to the significant amount of streamflow input into the Salish Sea, the consid-eration of the influence of rivers on the waters of the Salish Sea extends upstream to their watersheds andanthropogenic influences. For example, if all nutrient and wastewater pollution inputs via rivers and outfallswere removed in Puget Sound, hypoxia exposure is estimated to decrease by 11.2% (Khangaonkar and Yun,2023). River flows act as a link between the terrestrial and marine environment.The Salish Sea has a surface area of 17,800 km2, but the combined drainage area of the watersheds(dubbed the Salish Sea Bioregion (Flower, 2021)) spans 320,000 km2 (Sobocinski et al., 2022). Thesewatersheds have different typical yearly streamflows, called hydrographs. Efforts to model these rivers (e.g.Morrison et al. (2012) and Halverson and Fleming (2015)) draw on these hydrographs and classify them bytheir streamflow sources: pluvial (rainfall), nivial (snowmelt), glacial, or mixed-type. This classification isimportant because a large number of the rivers that drain into the Salish Sea are ungauged. The large-scalewatersheds that directly drain into the Salish Sea are: Juan de Fuca, Puget, Skagit, Fraser, Howe, Jervis,Toba, Bute, Northeast Vancouver Island, and Southeast Vancouver Island (Morrison et al., 2012). Thesewatersheds span a large amount of geography, geology, and climactic zones and encompass all of rainfall,snowmelt, and glacial streamflow sources.The largest river in the Salish Sea, the Fraser River, is primarily driven by snowmelt sources and is fedby the largest drainage basin on the British Columbian coast (Morrison et al., 2012). A freshwater plumedominates the surface of the southern SoG during its high-flow period (freshet) (Pawlowicz et al., 2007).31.2. Estuarine Exchange FlowsThe freshet typically occurs from May to July and increases the stratification of the surface layer of the SoG(LeBlond, 1983). The Fraser River is particularly susceptible to industrial and wastewater pollutants dueto the distribution of population centers and factories along it. It is also susceptible to climate change, andit is predicted that the freshet will decrease in total volume and shift earlier in the year (Islam et al., 2019;Morrison et al., 2002). The maximum water temperature near the mouth is predicted to warm more than0.1 ◦C in the summer each decade between the years 2000 and 2100 (Ferrari et al., 2007; Morrison et al.,2002). This temperature increase may degrade the spawning success of salmon returning to the Fraser Riverestuary (Morrison et al., 2002).1.2 Estuarine Exchange Flows1.2.1 The Classical ModelBiological productivity in estuaries is governed by the abundance of light and nutrients, which in turn aregoverned by the physical dynamics of the estuary. Freshwater impacts the strength of stratification, andtherefore the availability of both light and nutrients (Underwood and Kromkamp, 1999). The river at thehead of an estuary may contribute nutrients depending on the location and season, but the key process thatdetermines available nutrient concentration in the Salish Sea is estuarine exchange (Sutton et al., 2013). Insystems where the river is not a main nutrient source, freshwater stabilizes the water column by increasingthe vertical salinity gradient, which increases light exposure in the mixed layer but decreases the upwardmixing of nutrients (Underwood and Kromkamp, 1999).In the classical model of an enclosed estuary, as turbulent river water flows out through the estuary,seawater is entrained into the surface layer as it is transported away from the mouth of the river. Thistheoretical two-layer model can be described mathematically by the Knudsen Relations (Knudsen, 1900),utilizing conservation of volume and salt. The volume flowing out (water from the river + water in thetop layer) is equal to the water flowing in (water in the bottom layer). At the same time, the mass ofsalt entering the estuary in the bottom layer is equal to the mass of salt exiting in the bottom layer. Thiscirculation encompasses a classic density-driven estuarine exchange especially appropriate for describingshallow, straight, and bathymetrically smooth estuaries (Hansen and Rattray, 1965).While this two-layer model provides a foundational understanding of the dynamics of estuaries, the de-tails of their circulation can vary significantly based on factors such as tidal forcing, river discharge, andbasin bathymetry. In the Delaware Bay estuary, which has a unique “channel-shoal” geometry, the exchangeflow is classically density driven during neap tides but is driven by tidal dispersion during spring tides(Geyer et al., 2020). Foundational literature submits three different classifications for estuaries: highly-stratified (where river flow dominates over tidal flow), partially-mixed (river flow is low compared to tidalflow and stratification increases toward the mouth), and well-mixed (tidal velocities are sufficient in the spe-cific geometry to homogeneously vertically mix the water column) (Svendsen, 1986). These flow behaviorsare further diversified with the considerations of sills, such as in fjords, and steeper slopes of continentalshelves that force inflows upward in the water column. Both of these features may introduce more turbulentmixing processes, which in turn impacts stratification, which modulates the availability of nutrients. Even insome well-mixed estuarine systems, seasonal stratification may occur due to vertical temperature gradientswhere a strong salinity gradient is absent (Pein et al., 2021). One of the most well-known estuaries in North41.2. Estuarine Exchange FlowsAmerica is Chesapeake Bay in Maryland, which is a relatively shallow formation that faces hypoxia dueto stratification and upstream nutrient loading that leads to eutrophication (Officer et al., 1984; Cerco andNoel, 2007). While Chesepeake Bay and the Salish Sea are both estuarine systems, the long flat and shallowgeometry of Chesepeake Bay compared with the variable depth and multiple sills in the Salish Sea yielddifferent behaviors.1.2.2 Exchange Flow Dynamics in the Salish SeaIn the Salish Sea, flushing and residence times have been studied using both models and observations.Flushing time refers to the time it takes to replace a pre-defined amount of water within a water mass,while residence time refers to the length of time that a water parcel remains in a water mass (Matso, 2018;Premathilake and Khangaonkar, 2022). With regard to impact on nutrient concentrations, in systems thatare rapidly flushed or that have low residence times, biological processes will not have enough contacttime with new nutrients to see a significant impact (Balls, 1994). On the other hand, the inverse is truefor large estuaries with longer flushing/residence times, where biological processes become much moreimportant to the fate of estuarine nutrients (Balls, 1994). Residence times in the SoG have been found tovary seasonally, with longer residence times occurring in the winter compared to summer. In the deep watermass, residence times range from 1-3 years in summer to 65 days in winter (Pawlowicz et al., 2007). Inthe surface water mass, residence times range from 40-80 days compared to 10-40 days (Pawlowicz et al.,2007). The residence time of the intermediate water mass was found to be 160 days in both summer andwinter, and the residence time of the Fraser River plume was 1 day for both seasons (Pawlowicz et al., 2007).Premathilake and Khangaonkar (2022) use a Lagrangian modeling approach to find the residence timeand an Eularian dye tracer approach to find flushing time in multiple basins of the Salish Sea, finding thatthe flushing time is generally longer than the residence time. There is a lack of consistency in literature con-cerning the definitions of residence time and flushing time (Matso, 2018) which can be seen in MacCreadyet al. (2021) and Premathilake and Khangaonkar (2022), where Premathilake and Khangaonkar (2022)’scomputational definition of flushing time is almost identical to MacCready et al. (2021)’s definition of res-idence time, both using a passive dye tracer concentration. Equation 1.1 shows this definition, where C(t)represents tracer concentration, C0 represents initial concentration, t represents time, and τ represents eitherthe residence time or flushing time. When t = τ , the spatially averaged concentration is 1/e of the initial con-centration. On the observational side, surface drifter observations in the the SoG and JdF estimate residencetimes that are shorter than other methods (Pawlowicz et al., 2019). Additional processes along the inflow andoutflow pathways of the Salish Sea complicate the simple model of exchange flow and residence/flushingtimes (Pawlowicz et al., 2019; MacCready et al., 2021; Premathilake and Khangaonkar, 2022).C(t) =C0e−t/τ (1.1)Exchange flow is strongest at the JdF boundary, and decreases going landward (MacCready et al., 2021;Sutherland et al., 2011), meaning that it is modified but not completely interrupted along the complexbathymetry of the Salish Sea. However, there are additional sub-dynamics accompanying the mean ex-change flow. Bathymetric features and sills act as mixing regions, where the inflow and outflow layersundergo enhanced vertical mixing processes. This mixing causes some of the inflow to be mixed up into theoutflow (efflux), and some of the outflow to be mixed down into the inflow (reflux), such that a portion of51.3. Riverine Biogeochemical Influenceseach exchange flow returns to its source (Cokelet and Stewart, 1985; MacCready et al., 2021). For the SoG,the main mixing region is dubbed the Haro Region between JdF and the SoG, while the sill-based mixingregion between JdF and PS is Admiralty Inlet. In the Haro Region, the reflux and efflux have lower volumetransport and salt flux and are therefore less significant dynamics than the exchange flow (Stang and Allen,2024). Admiralty Inlet likely experiences a relatively higher amount of reflux/efflux, with a large amountof freshwater originating within Puget Sound being refluxed back into the intermediate waters (Ebbesmeyerand Barnes, 1980). Admiralty Inlet has been studied recently for its total transport (MacCready et al., 2021;Khangaonkar et al., 2017) with MacCready et al. (2021) finding more exchange flow via use of the TotalExchange Flow (TEF) method which accounts for salt flux due to tidal pumping.Discussions on the exhcnage flow dynamics of the Salish Sea are still ongoing within the scientific com-munity. The exchange flow of the Salish Sea has been explained by baroclinic pressure gradients historically,but the influence of tidal pumping is non-negligible (MacCready and Geyer, 2024; Allen et al., 2025b). Ad-ditionally, exchange flow in the inner basins of the Salish Sea is not directly proportional to the freshwateroutflow due to the complexity of the other drivers such as offshore density and tidal pumping (Pawlowiczet al., 2007; MacCready et al., 2021). However, density differences through the Haro Region are the maindrivers of water transport (Stang and Allen, 2024). Mixing on average throughout the Salish Sea is directlycorrelated with river flow and does not exhibit a spring-neap tidal cycle (Broatch and MacCready, 2022).1.3 Riverine Biogeochemical Influences1.3.1 Nutrients and PhytoplanktonNutrients in the Salish Sea are primarily determined by the properties of the Pacific inflow waters, withlocal sub-basin effects stemming from nutrient loading in rivers (especially in Puget Sound, where riverflow is more concentrated along the coastline relative to the surface area of the basin) (Sutton et al., 2013;Khangaonkar et al., 2012). Khangaonkar et al. (2018) estimated that over a year, only about 8% of the totalnitrate in the photic zone is from land-based sources over the whole Salish Sea. Nutrients are availablein the photic zone throughout the wintertime, but the spring bloom (and subsequent consumption of thesenutrients) generally occurs between late February and mid-April in the SoG when the mixed-layer depthbecomes stable enough for phytoplankton to receive the requisite amount of light for population growth(Collins et al., 2009). This date is largely modulated by wind; the Fraser River flow increases stratification,but simultaneously may increase the strength of estuarine exchange flow as well, balancing its influence(Collins et al., 2009). In the summertime, when light is abundant but nutrients in the surface layer have beenused up by the spring bloom, a secondary bloom occurs when wind drives upwelling (Moore-Maley andAllen, 2022). Fraser River flow may influence nutrient availability in surface waters; historically low FraserRiver flow in July 2023 was linked to stronger vertical mixing of nitrate and an exceptionally strong summerdiatom bloom in the Strait of Georgia (Allen, 2024), suggesting a link between low river flow, decreasedstratification, and this exceptional bloom.1.3.2 HypoxiaHypoxia (low dissolved oxygen) concerns primarily impact Puget Sound in the Salish Sea. The inflow to theSoG is well-oxygenated by the vertical mixing processes occurring in the Haro Region (Ianson et al., 2016),61.4. Freshwater Trackingand only experiences local seasonal hypoxia predominantly in Saanich Inlet, a well-known inlet with lowoxygen below the sill depth (Herlinveaux, 1962). Low-oxygen zones in Puget Sound reoccur annually insub-basins like Hood Canal that facilitate stratification and experience nutrient loading from wastewater andrivers (Pelletier et al., 2014; Khangaonkar et al., 2018; MacCready et al., 2021). Stratification and nutrientloading are both impacted by freshwater influences, thereby tying river flows to hypoxia.1.3.3 DIC and TARivers can impact dissolved inorganic carbon (DIC) and total alkalinity (TA) by either acting as a source oras a dilution agent. In the SoG, DIC is typically lower in the summer than in the winter due to the combi-nation of DIC consumption by phytoplankton and the dilution effect of the Fraser River freshet (Jarnı´kova´et al., 2022; Ianson et al., 2016). The Fraser River is a moderate, seasonally variable source of TA (Moore-Maley et al., 2018). In Puget Sound, rivers are found to generally have both low DIC and TA relative toseawater and act as diluting agents (Bianucci et al., 2018). On local scales in shallow bays and inlets withinPuget Sound, this freshwater dilution was found to increase resiliency to DIC and TA changes in the Pa-cific inflow (Bianucci et al., 2018). DIC:TA ratios differ by river, and even moderate differences betweenthese ratios may have significant impacts on resulting estuarine (salinity < 20 g kg−1) carbonate chemistry(Moore-Maley et al., 2018).1.4 Freshwater Tracking1.4.1 Other Work Utilizing Model DyeTracking riverine freshwater can be used as a method to understand the influence of river inputs on and withina system. Other modeling studies have used dye tracers (also called “passive dye tracers” due to their lack ofinfluence on model dynamics) to study freshwater in ocean systems. Tilburg et al. (2007) used both in-situand model dye to examine transport near the Delaware River plume during an upwelling event by injectingit into the halocline, finding that the model dye accurately reproduced a previously unobserved bifurcatedtransport pattern. Feddersen et al. (2016) also used both in-situ and model dye to track the freshwater plumeout of the New River inlet, again finding good agreement between the two. True (2018) sought to examinemodeled dye as a tool to establish prohibitive zones for shellfish harvesting based on concentrations of bothin-situ and model dye from an upstream wastewater treatment plant in the Royal/Cousins River estuary,finding that the model dye sufficiently established a prohibitive zone similar to that established by the U.S.Food and Drug Administration’s in-situ study. Because these experiments were on a smaller local scale, thein-situ experimentation was an option. However, there are some scenarios in which the research questionsout-scope in-situ dye studies and have required the use of modeled dye.In the Salish Sea, model dye tracers have been used to estimate flushing/residence times and exchangeprocesses. Khangaonkar et al. (2017) examined the influence of exchange through Johnstone Strait on circu-lation in Puget Sound by simulating the evolution of model dye in one “normal” scenario and one in whichthe strait was blocked off. Both MacCready et al. (2021) and Premathilake and Khangaonkar (2022) useddye tracers to assess the residence/flushing times across different basins in the Salish Sea. Beyond the Sal-ish Sea, dye tracers have been used to quantify freshwater sources and transport pathways on larger spatialscales. Studies in the Arctic Ocean, such as Jahn et al. (2010) and Zhang et al. (2021), used model dyes to71.4. Freshwater Trackingdistinguish freshwater contributions from various sources and under different freshwater accumulation sce-narios, highlighting the evolving freshwater budget of the region. Similar methods have also been applied inlimnological studies, as demonstrated by Masunaga et al. (2025), who used dye tracers to estimate residencetimes and mixing processes in a connected two-basin lake system. Studies outside of the Salish Sea withmethodologies most closely aligned with this thesis are those of Newton et al. (2008) and Weiss-Gibbonset al. (2024), which focus on freshwater circulation in the Arctic Ocean. Newton et al. (2008) incorporateddye tracers proportional to river flow to explore surface circulation patterns and freshwater storage changesbetween 1979 and 1993. In the discussion, it was also suggested that one way to improve model dynam-ics near river mouths would be to improve forcing datasets, which is one of the large differences betweenSalishSeaCast model version 201905 (v201905) and model version 202111 (v202111). Building on this,Weiss-Gibbons et al. (2024) assessed the Arctic Ocean’s sensitivity to changing runoff by implementingdifferent runoff forcings, offering insights into how climate change influences freshwater distribution in theArctic Ocean. Altogether, these studies both within and outside of the Salish Sea illustrate the versatility ofmodel dye tracers in capturing freshwater dynamics and provide a foundation for the present work.1.4.2 Application to the Salish SeaTracking freshwater from the rivers in the Salish Sea advances understanding of the complexities of theestuarine system. Freshwater dynamics from rivers in the Salish Sea have been studied using observationalmethods such as river end-member tracers (Simpson et al., 2022), in-situ dye (Hodgins et al., 1998), remote-sensing (Pawlowicz et al., 2017), and surface drifters (Kastner et al., 2018). Freshwater dynamics in generalhave been studies using Lagrangian particle tracking models as well (Eg. Osadchiev and Zavialov (2013)).However, there are limitations to these studies that modeled dye tracking can address by giving a direct,quantifiable volume of tracer in every grid cell of the model domain. Salinity is sometimes used to inferthe influence of the Fraser River due to its dominance in low-salinity water in the Salish Sea. However, forsmaller rivers, salinity is not a good proxy for their influence as their waters become more diluted outsideof their river plumes. Dye tracers can be used to quantify the freshwater content from individual rivers evenwhen they are diluted by both oceanic waters and other freshwater sources. This method is beneficial todetermine the circulation, transport, and mixing within the system, and illustrates how different freshwatersources contribute to it. Isolating and tracking the water from individual rivers also helps to anticipatethe impacts of potential anthropogenic influences, such as nutrient loading, wastewater and stormwaterpollution, or changes in flow patterns due to water use or climate change. Running model dye experimentsallows for more flexibility in picking cyclically diverse scenarios (i.e. wind or tidal conditions) and allowsfor streamflow/runoff to be modified to represent different climactic predictions. The scale in space andtime of model dye experiments can be much greater than that of observational campaigns, and model dye isable to more closely represent a freshwater proxy than Lagrangian particles because it can follow the samemixing schemes as salinity. These advantages make model dye tracking the optimal choice to address theresearch questions of this thesis.81.5. Research Questions1.5 Research QuestionsThe complexity of the Salish Sea’s estuarine dynamics and its divergence from simplified theory drives theneed for working models of the system in order to help study its physics. There are multiple 3-dimensionalnumerical ocean model frameworks that have been implemented, including the Finite Volume CommunityOcean Model (FVCOM) (Chen et al., 2003), the Regional Ocean Modeling System (ROMS) (Moore et al.,2011), and Nucleus for European Modelling of the Ocean (NEMO) (Madec et al., 2023). Some of thepopular Salish Sea models referenced in this work include LiveOcean (MacCready et al., 2021), the SalishSea Modeling Center’s Salish Sea Model (Khangaonkar et al., 2012), and SalishSeaCast (Soontiens et al.,2016; Soontiens and Allen, 2017).This thesis seeks to characterize and track freshwater from both the Fraser River and the smaller riversin the Salish Sea Bioregion in order to understand individual rivers’ involvement in general circulation,inter-basin exchange, and freshwater storage. This goal is achieved using a 3-D numerical ocean model,SalishSeaCast, to track freshwater from rivers directly using modeled numerical dye. This Eularian approachallows for the dye to volumetrically trace water from the river it is assigned to. The research questions thatguides this work are as follows:1. How well does the new SalishSeaCast model version (v202111), with the introduction of a newmethod of river forcing, capture the salinity patterns near small river mouths compared to an olderversion (v201905)?2. How does water from the Fraser River distribute itself throughout the Salish Sea? What regionscontain the most Fraser River water? How do these distibutions vary seasonally? What is the depth-wise distribution in specific regions? How is it involved in overall circulation and freshwater storage?3. What are the major sources of freshwater in Puget Sound, and what is their depth-wise distribution?(a) How does freshwater in Puget Sound vary seasonally? How much freshwater in the intermediatewaters of Puget Sound is from externally-sourced rivers vs internal rivers?(b) What do the significant sources of freshwater tell us about the connectivity between Puget Soundand the Strait of Georgia?4. How are changes in streamflow due to climate change going to be translated to the Salish Sea?In Chapter 2, the SalishSeaCast model will be explained and the methods of analysis will be recordedfor both the evaluation of the model’s performance and the dye tracing experiment. In Chapter 3, resultswill be presented that address research questions 1-3a. Chapter 4 will be a discussion of these results, andwill seek to address research questions 3b and 4. Appendix A briefly explores the temperature, salinity,and ΩA (aragonite saturation state, related to the DIC:TA system) of the intertidal zone of the Salish Sea incollaboration with the Harley Lab in the UBC Department of Zoology.9Chapter 2MethodsThis work focuses on both the evaluation and analysis of freshwater in SalishSeaCast, a 3D numerical modelwith physics, biology, and chemistry. This study evaluated the model in small river plume (SRP) regions fora previous version (v201905) and a newer version (v202111). Detailed early model descriptions of Salish-SeaCast can be found in Soontiens et al. (2016) and in Soontiens and Allen (2017). Updates incorporatedinto model v201905 are described in Olson et al. (2020) and in Jarnı´kova´ et al. (2022), and updates formodel v202111 are described in Stang and Allen (2024). A physics only model version 202111 was used inthis study to track modeled dye from a selected subset of rivers and analyze salinity and temperature fields.2.1 Model DescriptionSalishSeaCast is a 3-D coupled biological-chemical-physical model built on the Nucleus for European Mod-elling of the Ocean (NEMO), and both version 201905 and version 202111 use NEMO version 3.6 (Madecand NEMO Team, 2016). The model domain covers the entire Salish Sea, including Juan de Fuca Strait, theStrait of Georgia, Puget Sound, and Johnstone Strait (Figure 1.1). This domain is divided into 398 by 898by 40 model grid cells, rotated 29 degrees counterclockwise to optimize model dimensions with a horizontalresolution of about half a kilometer and a variable vertical resolution that ranges from 1 m in the surfaceto a maximum of 27 m at the deepest parts of the model (440 m) (Soontiens et al., 2016). The resolutionaround and within the arms of the Fraser River is compressed such that the horizontal resolution is 250 min the model x-direction (Olson et al., 2020). Bathymetry was set using the Canadian Hydrographic Servicedataset (Olson et al., 2020), with depths between 0 and 4 m set to 4 m and a smoothing technique applied tothe entire bathymetry (Soontiens et al., 2016). A partial step representation of the bottom boundary is used(Soontiens and Allen, 2017). Vertical mixing is included using the generic length-scale framework (Umlaufand Burchard (2003); Soontiens and Allen (2017)) and horizontal eddy diffusivity and viscosity are set to1.5 m2s−1 (Olson et al., 2020).The open boundaries in Johnstone Strait and Juan de Fuca Strait are forced with eight tidal constituents(Soontiens and Allen, 2017). In Juan de Fuca Strait, temperature and salinity fields are forced by LiveOceanmodel outputs (MacCready et al., 2021) while the boundary in Johnstone Strait is forced by a climatology(Olson et al., 2020; Dosser et al., 2021). Atmospheric forcing for momentum, heat fluxes, and precipitationare from the High Resolution Deterministic Prediction System (HRDPS) from Environment Canada (Mil-brandt et al., 2016; Soontiens and Allen, 2017). In model version 201905, freshwater input for the 150+included rivers (except the Fraser) is forced using a monthly parametrization of river flow based on the workof Morrison et al. (2012) providing a method to estimate freshwater runoff in Salish Sea regional water-sheds based on precipitation data from 1970-2012 (Soontiens et al., 2016). This method will be referred toas monthly climatologies. In contrast, the rivers in model version 202111 are forced with daily flows (Fig-102.1. Model Descriptionure 2.1), estimated by rescaling available gauge data by the watersheds described by Morrison et al. (2012)allowing for daily fluctuating flow values and year-to-year variability in river flows (Stang and Allen, 2024)(Figure 2.2). In both model versions, the Fraser River is forced by the sum of gauged data gathered at Hopeand other downstream sources to obtain a full Fraser River flow, and is the only river in the model with anextended upstream arm (Soontiens and Allen, 2017; Stang and Allen, 2024) (Figure 1.1). To improve thespatial distribution of the Fraser River plume in comparison with observations, the coastline was changedfrom the mean sea level isobath in v201905 to the 2 m isobath in v202111 (Stang and Allen, 2024). Mixingwas reduced in model v202111 by a new parameterization of surface wave turbulence (Moore-Maley, 2022;Stang and Allen, 2024).Figure 2.1: Daily time series of model river hydrographs from 2019-2023. Rivers have been grouped byhydrograph type as they are defined in Table 2.2. Note that the Skagit and Snohomish Rivers share a modelwatershed and have similar flow amounts, so their flows appear aligned in time.112.2. SRP Model Evaluations of SalinityFigure 2.2: Daily time series of model Snohomish River hydrographs from 2019-2023 for the old (v201905)and new (v202111) model versions. The new version introduces daily and inter-annual variability to therepresentation of flow.2.2 SRP Model Evaluations of SalinityEstimates of river flow cannot be directly evaluated for all rivers as there are often no flow observations tocompare to, so a regional analysis of small river plumes (SRPs) in the domain of SalishSeaCast was usedto evaluate the impacts of changes to river forcing on the performance of the model. Salinity observationsare chosen to compare to the model because they are most likely to represent the river within its plumeand every river inputs 0 salinity water. Prior evaluations (Allen et al., 2025a) between the old and newmodel versions focused on the entire model domain, but this evaluation is more focused. Salinity data wereobtained for the Salish Sea for 2013-2018 from the Department of Fisheries and Oceans (DFO) hydrographicsurveys (Department of Fisheries and Oceans, Canada, 2018; Masson, 2006; Masson and Pen˜a, 2009), thePacific Salmon Foundation (PSF) Citizen Science program (Pacific Salmon Foundation, 2018), onboardferry monitoring from Ocean Networks Canada (ONC) (Ocean Networks Canada, 2023), and monitoringprograms from the Hakai Institute (Hakai Institute and Pocock, 2019) as described in Olson et al. (2020).These data were supplemented with data from 2014-2022 from ONC’s monitoring stations and additionalferry data (Ocean Networks Canada, 2023), from 2015-2018 from the Washington Department of Ecology(Washington State Department of Ecology, 2018), from 2007-2023 from BC Lightstations (Fisheries andOceans Canada and Lu, 2023), the IOS CTD (Institute of Ocean Sciences et al., 2023b) and Rosette bottledata (Institute of Ocean Sciences et al., 2023a), and the Hakai Institute monitoring program (Hakai Institute,2023a,b). These data were assimilated into a single dataset of observations to perform the following model-observation comparisons.Evaluations were performed on subsets of observations in the top 10 m of the water column in specificsmall river plume (SRP) subregions. This depth cutoff was chosen to include observations in and near theriver plumes and haloclines for all SRPs on average. Each SRP subregion is a 44 by 44 grid cell box (22 kmby 19.72 km, 433.84 km2) centered on the main river mouth as defined in the model (Figure 2.3a). This122.2. SRP Model Evaluations of SalinityFigure 2.3: Boxes showing the small river plume regions and the Salish Sea subregions with SalishSeaCastmodel bathymetry. Panel A shows the river mouths as points and the small river plume subregions in black,used in model evaluations. Panel B shows the subregions of the Salish Sea used for analyzing the spatialdistribution of freshwater.box size was picked to encompass the small river plume, which is likely to reach scales of 20 km for thelargest rivers in the subset (Osadchiev and Yankovsky, 2022), and the surrounding surface waters. The sizeof the small river plume will be variable, but the size of the SRP subregion is kept constant to compare astandardized area of influence across subregions and seasons. Within these subregions, observations werematched with the location of the grid cell nearest their latitude, longitude, and depth, and then the observedvalues were compared with the model values for this location at the nearest matching observation and modeltimes. Eleven subregions were chosen based on the dye tracers study as explained in Section 2.3, and thenumber of observations per subregion is given in Table 2.1.Statistical evaluation of model salinity was performed using standard methods of Bias and root-meansquared-error (RMSE), and the following additional methods. The Willmott skill score (Willmott, 1981) wascalculated from Equation 2.1 where xM represents modeled values and xO represents observed values, andranges from a low (bad) skill score of 0 to a high (good) skill score of 1. The Slope Deviation and Interceptare 1-m and b respectively, where m and b represent the slope and y-intercept of the line of best fit throughobserved values on the x-axis and modeled values on the y-axis, y = mx + b. The Systematic Error wascalculated to capture the amount of repeatable deviation of the model’s predictions from the observationsusing Equation 2.2. The Non-Systematic error was calculated to quantify the amount of unpredictable132.3. Freshwater Dye Tracerserror that comes from natural variability not captured by the model using Equation 2.3. Finally, the SpreadDeviation of the model was calculated as in Equation 2.4 to show the ability of the model to capture thespread of the observations such that the closer the spread deviation is to zero, the closer the two variancesare to each other. In equations 2.1 - 2.4, an overbar represents the mean over all samples.SRPSubregionNumber ofObservationsNooksack 822Skagit 806Snohomish 812Nisqually 6615Elwha 655Cowichan 12264Nanaimo 401Puntledge 1573Salmon 439Homathko 2504Squamish 271Table 2.1: The number of observations used for model evaluations in each SRP subregion. Data distributionsare shown for each SRP in Appendix C.Willmott =∑(xM− xO)2∑(|xM− xO|+ |xO− xO|)2 (2.1)Systematic =√(xP− xO)2 (2.2)Non−Systematic =√(xM− xP)2 (2.3)Spread Deviation = 1− (xM− xM)2(xO− xO)2(2.4)2.3 Freshwater Dye TracersNEMO v3.6 supports the addition of user-specified tracers to the model through building different config-urations (Madec and NEMO Team, 2016). In this study, a passive dye tracer was added to each river inproportion to the volume of river flow at the grid cells where it enters the model. The dye follows the sameadvection and mixing processes as salinity, and thus serves as a diagnostic tool for tracking the distributionof riverine freshwater. Unlike actual freshwater input, the dye does not affect stratification or circulation,and does not feed back into the model dynamics. It is important to note that this dye tracer does not replacefreshwater when added to the model.The simulated dye tracers used in this study were added to rivers by defining 13 different tracers: 11 fora subset of small rivers throughout the Salish Sea, one for the Fraser River, and one that was added to all theremaining rivers as a catch-all tracer. The 11 rivers in the subset were chosen to encompass a wide range142.3. Freshwater Dye Tracersof flow magnitudes, river types (Eaton et al., 2010; Halverson and Fleming, 2015), and spatial distributionthroughout the Salish Sea and its different watersheds (Morrison et al., 2012) (Table 2.2). Since the dyetracers were added proportionally to the volume of the flow of river water, they represent a quantitativemeasurement of the volume of water from each river that exists in each box. The dye was added to a“spun-up” model with just physics starting on January 1st, 2012 and allowed run for 7 years to reach anapproximately mean steady state (steady between years) within the Salish Sea before analyzing the modeloutput than spans 2019-2023. Output starting on January 1st, 2012 was used to calculate the residence time(T) and steady state volume (V) of each river by fitting Equation 2.5 to the total amount of dye volume inthe model (v(t)).v(t) = V∗ (1− e−t/T) (2.5)River Subset MetadataBased on the hydrographs within the SalishSeaCast modelRiver SSC Watershed SSC Hydro Signals Type Mean Model Flow(2019-2023) [m3s−1]Nooksack Fraser Rain Rainfall 18Skagit Skagit Rain/Snow Mixed 199Snohomish Skagit Rain/Snow Mixed 281Nisqually Puget Rain/Snow Mixed 78Elwha Juan de Fuca Rain/Snow Rainfall 53Cowichan East Van Isl South Rain/Snow Rainfall 29Nanaimo East Van Isl South Rain/Snow Rainfall 24Puntledge East Van Isl South Rain/Snow Rainfall 37Salmon East Van Isl North Rain/Snow Mixed 78Homathko Bute Glacial Glacial 308Squamish Howe Glacial/Snow/Rain Glacial 349Fraser Fraser Snow/Glacial/Rain Snowmelt 3480Table 2.2: Each of the model rivers tracked in this study and its corresponding SalishSeaCast model wa-tershed, model hydrograph signal(s), classification type, and mean model flow. For the hydrographs withmultiple signals, the primary signal is bolded while the secondary and tertiary signals are greyed out. Thetype classification does not refer to the river’s only source, but the dominant signal(s) in the SalishSeaCastmodel hydrograph. Note that the mean flow of the Fraser River is an order of magnitude greater than theother tracked rivers.The amount of Fraser River water was analyzed by regions of the Salish Sea (Figure 2.3b) which weredefined by regional geography and physical processes. Puget Sound was assigned its own box and includedall water inland of Admiralty Inlet and Deception Pass. Juan de Fuca Strait was split into an Eastern anda Western section such that there was one box closer to the Strait of Georgia and another box closer to thePacific Ocean, excluding the model grid boxes right on this boundary. Next, a box called “Mixing Region”was defined to encompass the San Juan Islands, as this region has been deemed important in the exchangeof deep water between the Strait of Georgia and Juan de Fuca Strait (LeBlond, 1983) and in the tidal mixingof surface water with deep water Allen et al. (2025b). Within the Strait of Georgia, four boxes were defined:Strait of Georgia North, Strait of Georgia Central, Fraser River Plume, and Strait of Georgia South. TheFraser River Plume box was set to cover the approximate extent of the Fraser River Plume throughout the152.3. Freshwater Dye Tracersyear in model v202111. On the coast of the British Columbian mainland, a box called “Inlets” was definedaround both Toba and Bute Inlets. A final box was defined called “Johnstone” to encompass the channels ofJohnstone Strait in the northeastern region of the Salish Sea.Riverine freshwater was examined through the lens of two different calculations. The concentration offreshwater from one river in a cell is calculated as Volume River Dye / Total Volume of Cell and is reportedas a proportion. The freshwater content (FWC) in meters from one river is congruent to the freshwatercontent value used in observational studies (Eg. Aagaard and Carmack (1989)) to describe the amount offreshwater in a water column that would need to be mixed with water of a reference salinity to achievethe observed average salinity. However, in the modeled case, the freshwater content is calculated from onespecific river at a time by directly summing the volume of dyed freshwater in each model grid cell over acolumn of water and dividing by the horizontal area of the column.16Chapter 3Results3.1 SRP Model Evaluations of SalinityWhile previous work showed that changes from v201905 to v202111 improved all metrics over the entiredomain and top 5m of the water column (Allen et al., 2025a), this work highlights results both on averageand in specific SRP subregions. The mean results of this more focused evaluation are shown in Table 3.1.The observed and modeled salinities over all relevant observations and both models range from 0 g kg−1 toabout 35 g kg−1, so metrics for all regions can be put into perspective using this range.Model Worst Best Mean Abs. Meanv201905 1.37 -0.05 0.61 -0.18Bias [g kg−1]v202111 -1.11 -0.02 0.45 -0.16v201905 4.89 0.29 2.14RMSE [g kg−1]v202111 4.76 0.31 2.05v201905 0.65 0.95 0.80Willmott Skill Scorev202111 0.73 0.96 0.84v201905 -0.88 0.06 0.36 0.10Slope Deviationv202111 -0.51 0.10 0.27 -0.01v201905 -26.49 0.23 10.13 2.41Intercept [g kg−1]v202111 -15.78 2.15 7.96 -0.26v201905 3.01 0.20 1.13Systematic [g kg−1]v202111 1.93 0.24 0.88v201905 4.17 0.18 1.69Non-Systematic [g kg−1]v202111 5.24 0.17 1.86v201905 -1.54 0.10 0.55 0.06Spread Deviationv202111 -1.08 0.18 0.45 -0.08Table 3.1: The worst, best, mean absolute value, and mean across the eleven SRP subregions for allevaluation metrics and both model versions. For all metrics except for Non-Systematic Error, the metricsimproved from the old model version to the new one. Note that the Willmott Skill Score is the only metricfor which an increase in absolute value constitutes an improvement in the model.3.1.1 All RiversThe mean absolute value for all metrics except for the Non-Systematic Error (NSE) improved, and theworsened NSE was due to the Skagit SRP (Table 3.1). Both model v201905 and model v202111 hadslightly fresh model biases. The models had average RMSE values over 2 g kg−1, with the larger rivers inthe model exhibiting much larger RMSE values closer to 4 g kg−1. The WSS indicated high skill in bothmodels. The Slope Deviation and Intercept indicated that both models were more likely to be too salty atlow salinities and/or fresh at higher salinities (homogenizing-skew), but also points to the general lack of173.1. SRP Model Evaluations of Salinitylow salinity observations throughout the selected SRPs (Section C). The Systematic Error (SE) went fromabove 1 g kg−1 to below this value. The NSE increased due to the Skagit SRP increasing from 1.85 g kg−1to 5.24 g kg−1 with all other SRPs improving somewhere between 0.01 g kg−1 and 0.51 g kg−1. Modelv201905 had slightly too little variance, while model v202111 had slightly too much variance on average.Results varied for each SRP within the model (Figure 3.1). All evaluation metrics improved for theCowichan and Nanaimo SRPs, though their larger Systematic Error and large negative Spread Deviationsleave room for improvement. The Elwha and Homathko SRPs both improved in all metrics except Bias,though the Homathko SRP had some of the worst values for RMSE, SE, and NSE, and conclusions fromthe Elwha SRP are unsubstantial due to the data’s spatial distribution in the subregion (Section 4.1.2). TheNisqually SRP worsened in both Bias and SE, but the SE only changed by 0.01 g kg−1. The Skagit SRPworsened significantly in both RMSE and NSE (Table B.1). The Nooksack and Squamish SRPs both sawmixed results, worsening only in Slope Dev. & Intercept, SE, and Spread Dev. and both trended saltierin Bias, towards a more homogenizing-skew in Slope Dev. & Intercept, and toward less model variance.The Snohomish SRP improved in RMSE, WSS, and NSE, and was a moderate contributor to poor RMSEand NSE values when compared across all SRPs. The Puntledge SRP improved only in NSE and SpreadDev. and only by 0.01 g kg−1 and 0.01, but none of the worsened metrics were significantly worse thanthe mean of the SRPs. Finally, the Salmon SRP only improved in NSE, but also had metrics either aroundor better than the average across all SRPs in both models, with the best scores for RMSE, SE, and NSE inmodel v202111. The improved means across all metrics (excluding the NSE due to the Skagit SRP) andthe improvements within each river (or consistently good scores for the Salmon SRP) both point to overallimprovement in the behavior of the model in salinity around SRPs in model v202111.3.1.2 Large RiversThe high RMSE and NSE values for the Skagit, Homathko, Squamish, and Snohomish SRPs may be furtherimproved with investigations into bathymetry alterations directly surrounding the river mouth, as was donefor the Fraser River (Allen et al., 2025a). The higher RMSE values for these larger river SRPs in bothmodel versions relative to the rest of the SRPs may also be an artifact of their river scale: the model is ableto resolve more of the larger plumes, and therefore there is more chance for observations to be spatiallymisaligned for larger rivers in general (Section C.2, C.10, C.11, C.3).3.1.3 Puget Sound RiversThe three SRPs in Puget Sound (Skagit, Snohomish, and Nisqually) all skewed fresh at low salinities and/orsalty at high salinities (amplifying-skew Slope Dev. & Intercept) in conjunction with fresh Biases in modelv202111, suggesting too much freshwater in the model. They also all change from positive to negativeSpread Dev. (from not enough to too much model variance), which could be due to the variability in eachhydrograph. The Skagit SRP has too much variance model mostly in winter and spring (Section C.2),pointing to issues with the rainfall component of the hydrograph. Conversely, the Snohomish SRP hastoo much model variance in the summer (Section C.3), the highest flow due to snowmelt portion of thehydrograph. The Nisqually SRP has uniform bias across all seasons, but too much model variance in thewinter and somewhat too much in the spring and summer (Section C.4).183.1. SRP Model Evaluations of SalinityFigure 3.1: Salinity evaluation metrics for the top 10 m of each SRP subregion. From a-h, the subplots showBias, RMSE, Willmott Skill Score, Slope Deviation, Intercept, Systematic Error, Non-Systematic Error, andSpread Deviation.193.2. Freshwater Dye Tracers3.1.4 East Coast Vancouver Island RiversThe Cowichan and Nanaimo SRPs both belong to small rivers on the East Coast of Vancouver Island withmid-range RMSE values, SE values that improved from high to mid-range, mid-range NSE values, andlarge amplifying-skew Slope Dev. & Intercept and large negative Spread Deviations (extra model variance)in model v202111. The Cowichan SRP has a uniform fresh model Bias across all seasons, and a uniformnegative Spread Dev. in spring, summer, and fall with winter having a smaller magnitude (Section C.6). TheNanaimo SRP has large Spread Dev. across all seasons, but the wintertime Spread Dev. is three times thatof the other seasons and also has the freshest model Bias (Section C.7). The Nanaimo SRP is consideredwith the caveat that the inclusion of ferry data tells a different story with much better Spread Dev. valuesand worse RMSE, WSS, and NSE. It was evaluated without ferry data for this study to maintain a morestandardized observation source between all SRPs.3.1.5 Small Vancouver Island RiversThe Puntledge and the Salmon SRPs had generally good metrics compared to the mean, but also becameslightly worse from model v201905 to model 202111. Both went from salty to saltier in model Bias andhigher homogenizing-skew based on the Slope Dev. & Intercept. They also both have too little variance inthe model. Both rivers are rainfall dominated, located on the Northeastern side of Vancouver Island. Noseasonal issues are obvious for these two rivers (Section C.8 and C.9).3.2 Freshwater Dye Tracers3.2.1 Fraser RiverThe Fraser River was found to represent 3.6% of all water in the Salish Sea based on its steady state volume(84×109 m3), and explained 70% of the variance of all surface water at 0.5m depth during the study periodbased on a preliminary Principal Component Analysis study. Freshwater from the Fraser River was presentin all regions of the Salish Sea. The depth-summed volume of Fraser River water closely followed thebathymetry in the Strait of Georgia, the San Juan Islands, and Puget Sound, with the deepest parts of theseregions having the largest amounts of Fraser freshwater. In the Juan de Fuca Strait, there was less FraserRiver water by both volume and mean concentration than in the Strait of Georgia and San Juan Islands.(Figure 3.2a,b,c).203.2. Freshwater Dye TracersFigure 3.2: The bathymetry of SalishSeaCast, depth-summed volume of Fraser River water, and depth-averaged concentration of Fraser River water. Panel A shows the Seafloor Depth in meters for the modeldomain. Panel B shows the depth-summed volume of Fraser River water in cubic meters. Note the corre-lation between the deepest parts of the model in Panel A and the highest volumes of Fraser River water inPanel B. Panel C shows the depth-averaged concentration of Fraser River water in m3 Fraser River waterper m3 total grid cell volume. Note the correlation between the highest concentrations in Panel C and theshallowest regions of Panel A that are near to the mouth of the Fraser River and in the Gulf Islands.Regional analysis of Fraser River freshwater content (FWC) showed the co-trends of bathymetry andhigher FWC, as well as seasonal and sub-seasonal trends. The Strait of Georgia - Central (SoGC) had thedeepest average bathymetry and the largest mean Fraser FWC. Within the Strait of Georgia boxes (SaS,SoGN, SoGC, FRP, and SoGS), the freshwater content followed the order of the bathymetric depth with theexception of the Fraser River Plume due to the plume itself in the surface waters (Figure 3.3). This depthdistribution is further indicated by the high magnitude of seasonal variability in Fraser FWC. The SaS andthe SoGC both have much lower seasonal and daily variability compared to the other regions in the Straitof Georgia. In the strait, most of the new water is surface water. For regions in and adjacent to Juan deFuca Strait (MR, JdFE, JdFW, and PS), it was found that the relative amount of Fraser FWC was in lessagreement with the bathymetric depth than within the Strait (Figure 3.3). The Mixing Region (MR), whichis the shallowest in the region, had the highest amount of Fraser River content in Juan de Fuca Strait, likelydue to the surface water from the Fraser River Plume. The seasonal and daily fluctuations in Fraser FWCfor FRP, SoGS, and MR are strongly in-phase. Puget Sound (PS) had out of phase seasonal fluctuations, andits daily signal was an order of magnitude less than all other regions. Regions to the north of the San Juanand Gulf Islands had higher Fraser FWC than Puget Sound and the Juan de Fuca Strait. The Inlets regionhad the highest amount of Fraser FWC, pointing to storage of Fraser River water in the deep inlets along thecoast of the British Columbia Mainland (Figure 3.3). These inlets are deeper in real life than in the model,and might store even more deep-mixed Fraser FWC behind their sills. The average depth of the Fraser Riverfreshwater throughout the regions of the Salish Sea was generally deeper in regions with deeper average213.2. Freshwater Dye Tracersbathymetry. All regions except for Puget Sound varied similarly in season, with shallowing of the averagedepth aligned with the Freshet of the Fraser River (Figure B.1).Figure 3.3: Time series of Fraser River Freshwater Content in each region of the Salish Sea. Panel A showsa map of the regions. Panel B shows the FWC colored by region, and the average depth for each region ofthe Salish Sea in the legend.Transects across the Salish Sea in the Juan de Fuca Strait, Johnstone Strait, Admiralty Inlet (PugetSound), and Deception Pass (Puget Sound) (Figure 3.4a,b,c,d) show that although there is a higher FraserFWC in the Johnstone region than in the Juan de Fuca West region, there are much higher concentrations ofFraser River water near the JdFW-Pacific Ocean boundary than the Johnstone-Pacific Ocean boundary in themodel (Figure 3.4e,f). The Fraser River water that is contained in the Johnstone region is not transported tothe Pacific Ocean to the same extend that the Fraser River water in the Juan de Fuca Strait is. A bias towardthe northern coastline of the Juan de Fuca Strait is seen as well, with the highest concentrations near 0.025cubic meters/cubic meter extending to about 40 m depth on the northern side and concentrations closer to0.015 cubic meters/cubic meter on the southern side only extending 5-10 meters deep. The concentration ofFraser River water that enters Puget Sound from both Admiralty Inlet and Deception Pass is near uniformthrough the depth of each transect, and that the depth distribution of Fraser River water in deeper parts ofPuget Sound is constrained by the bathymetry through these cross-sections (Figure 3.4g,h).223.2. Freshwater Dye TracersFigure 3.4: The concentration of Fraser River water across four transects in the model domain. Panel Ashows a transect through Juan de Fuca Strait, panel B shows a transect through Johnstone Strait, panel Cshows a transect through Admirality Inlet, and panel D shows a transect through Deception Pass. Panel Eshows the concentration of Fraser River water through Juan de Fuca Strait, panel F shows the concentrationthrough Johnstone Strait, panel G shows the concentration through Admiralty Inlet, and panel H shows theconcentration through Deception Pass. The colorbar is shared between panels E-H such that the concentra-tions can be directly compared between panels. The total volume of Fraser River water within the surfacearea of each transect is shown in text above E-H.3.2.2 Puget SoundThe Fraser River, as shown in Section 3.2.1, has less influence on Puget Sound compared to other parts of theSalish Sea. Within Puget Sound, the freshwater content was found to be comprised of mainly Fraser River(1.7 m), Snohomish River (1.1 m), Nisqually River (0.6 m), Skagit River (0.6 m), Squamish River (0.2 m),and the Homathko River (0.1 m) (Figure 3.5). The rest of the river tracers contributed less than 0.10 m/0.4%each to the total amount of freshwater and combined accounted for 1.1% of the average freshwater contentfor all years. The Other Rivers tracer had the highest impact, accounting for about half (47.9%) of thefreshwater in the region, and includes all of the un-traced model rivers both within and outside of PugetSound. This dominance is expected, since it includes the sum of many smaller rivers. These rivers are fedby multiples types of sources (rainfall, mixed, snowmelt, and glacial).233.2. Freshwater Dye TracersFigure 3.5: The freshwater concent in Puget Sound only from all the traced rivers for 2019-2023. The barsare stacked such that the most significant sourced of freshwater are on top, and decrease descending downthe bar. The legend is in the same order. Rivers after the Homathko River do not contribute a significantamount to the total freshwater in Puget Sound. The “Other” Rivers tracker is largest since it encompassesthe sum of many rivers.External RiversThe Fraser, Squamish, and Homathko Rivers all originate from outside of Puget Sound and are snowmeltor glacial sourced rivers. This source is reflected in their yearly FWC in Puget Sound, with each having asingle peak and trough (Figure 3.6a,i,k). Their distance from the mouth of Puget Sound also means that theFWC signals experience little small-scale variation, as that variation is damped in transit though the Straitof Georgia and the San Juan Islands.The Fraser River (Figure 3.6a,b), the closest and largest of the external rivers, peaks in FWC in PugetSound in September while the middle of freshet occurs in June. Markedly lower Fraser FWC peak signals(2019 and 2023) co-occur with lower freshet peaks that diminish faster than the mean. The timing of the lowand high Fraser FWC peaks is not significantly different between the five years, nor is the onset of freshetvisibly distinct from the mean. The Fraser FWC can be subdivided by year into two groups: 2019 and 2023being low flow years, and 2020, 2021, and 2022 being high flow years. Although each year in the group hasa different starting point, the maximum amount of Fraser FWC is similar within these groups. Examiningthe hydrograph, the low flow years have different peak flows of freshet but both are much shorter than thehigh flow years, in which freshet lasts almost a month longer.The Squamish River (Figure 3.6i,j) is the second closest of the external rivers and has the second highestmean flow of the traced rivers. The Squamish FWC peak occurs in September, later than the Fraser’s, andtapers off slower than the Fraser FWC. The peak Squamish flow occurs around July, though the uptick inflow begins around the same time as the Fraser River in April while the hydrograph takes much longer onaverage to taper off than the Fraser, likely what is reflected in the slower FWC decay.The Homathko River (Figure 3.6k,l) is the furthest and has the third highest flow of the external rivers.243.2. Freshwater Dye TracersIts FWC peak occurs even later, in late December, while its hydrograph is only slightly different than theFraser and Squamish Rivers’. Flow begins increasing in May, and the rate of decay is between that of theFraser and the Squamish. The amplitude range of the Homathko FWC is smaller than that of the SquamishFWC, which is smaller than the Fraser FWC.Both the Squamish and Homathko rivers have exceptional lows in FWC in 2020 and relatively lowpeaks, while the Fraser River has a low trough but a high peak flow. In contrast, the Homathko causesexceptional FWC highs in both 2019 and 2023, which are overall low years for the Fraser River FWC. In2021, the Homathko FWC peaks low while the Fraser FWC is high in both trough and peak.Internal RiversThe Snohomish River, Nisqually River, and Skagit River all originate from within Puget Sound. All threerivers exhibit mixed behavior in their hydrographs, but the Snohomish and the Skagit Rivers have largersnowmelt portions than the Nisqually River. More small-scale variation is present in the FWC of the internalrivers, but does not reflect the same scale of signals as the hydrograph.The Snohomish (3.6c,d) and the Skagit (3.6g,h) Rivers are forced by the same watershed within themodel and so exhibit the same behaviors in their hydrographs on different scales. The hydrographs of bothrivers show a rainy season from October-March and a snowmelt season starting in April and peaking inJune. This hydrograph is reflected as two peaks in FWC, the first being in February (rainfall) and the secondin July (snowmelt). The lowest amount of FWC from both rivers occurs in October, between the end ofthe snowmelt-forced river flow and the beginning of the rainfall-forced river flow. The difference in FWCbetween these two rivers is in the relative amplitudes of the rainfall/snowmelt peaks; the Snohomish FWChas a slightly higher rainfall peak than snowmelt peak, while the Skagit River’s peaks are approximatelyeven with each other. This difference is likely due to the different locations of the two river mouths.The Nisqually River’s (3.6e,f) hydrograph shows a rainy season from November to March and a smallersnowmelt freshet starting in April and peaking in late May. In the FWC, there are two visible peaks but theyare much less resolved than for the Snohomish and Skagit FWCs because there is little decay between therainfall FWC peak and the snowmelt FWC peak in the mean. In individual years, one is usually higher thanthe other (rainfall is higher in 2020 and 2021).The year 2019 is low throughout for the Nisqually FWC, while the Snohomish and Skagit FWCs expe-rience snowmelt peaks that are of similar amplitude to other years but peak more than a month earlier thanother years. Both the Snohomish and Skagit FWCs have low/no rainfall peak in 2023, which in turn leads toa slightly lower snowmelt peak that also occurs half a month earlier than other years. The Nisqually FWCin 2023 is lower than the mean throughout, but does not show such extreme shifts in timing.253.2. Freshwater Dye TracersFigure 3.6: Daily time series of river FWC in Puget Sound in meters is shown in the first column, andcorresponding river daily hydrographs in the second column. The years 2019-2023 are shown in varyingcolor and linestyles, while the mean is shown in the solid black line. For panels A-H, the years 2019 and2023 are low FWC and flow years. In panels I-L, the standout low year is 2020. The hydrograph of eachriver is the main driver of the shape of its FWC signal, regardless of whether it originates inside or outsideof Puget Sound.263.2. Freshwater Dye TracersRivers by DepthA monthly time series for the top 100 m of the water column at a point south of Whidbey Island, which isat a main junction in Puget Sound (Figure 1.1b), shows the same general trends that Figure 3.6 shows, withthe addition of depth-wise nuances that inform about dominating processes. The arrival times of the Fraser,Squamish, and Homathko rivers are not only staggered, but also arrive stronger and earlier at depths below25 m (Figure 3.7a,e,f). The Fraser River water concentration is near-uniform between 10 m and 100 m at thisstation, implying vertical mixing processes occurring between the Admiralty Inlet transect shown in Figure3.4g and the point shown in Figure 1.1b. In the fall, concentrations of the Fraser River reach 0.025 m3/m3 inthe top 25 m and greater than 0.030 m3/m3 below 25 m at this station, which represents water that is directlyinfluential to both Seattle and Tacoma (the 1st and 3rd most populated cities on the Salish Sea Coastline).The Squamish and Homathko Rivers have similar effects at lower concentrations, but all three represent theinfluence of water from the Strait of Georgia (from Canada) on Puget Sound (in the United States).The Rivers within Puget Sound (Snohomish, Nisqually, and Skagit) have a much stronger influence onthe water column shallower than 10 m. The Nisqually River extends deeper (Figure 3.7c) due to verticalmixing caused by Tacoma Narrows, which pinches the only path between the mouth of the Nisqually Riverand the rest of Puget Sound. The Nisqually River also maintains a lower but more constant presence inthe surface water throughout each year, while the Snohomish and Skagit Rivers are more staccato (Figure3.7b,d). The Nisqually and Fraser Rivers show similar low-concentrations in 2019 and 2023, as do theSnohomish and Skagit to a lesser extent.Contours of higher salinity (Figure 3.7g) coincide with the timing and depth-shape of the highest con-centrations of Fraser and Squamish river water, indicating they have been well-mixed with saltier water fromthe Pacific Ocean before or while entering Puget Sound. The Homathko River, appearing later in the year atthe station, coincided with freshening taking place at depth in the water column. The more direct effects ofthe Snohomish and Skagit rivers are seen in the contours of salinity lower than 25 g kg−1, which correspondalmost exactly in time and depth to the higher concentration contours of these rivers. The leading-edge ofthe contour lines for 8.5 ◦C temperature (Figure 3.7h) tilt with a negative slope, meaning that the surfacewarms before deeper waters. Neither this nor the warm contours of temperature coincide well with riverconcentrations. Slightly cooler water with depth, as well as slightly fresher water below the top 10 m of thewater column, are due to freshwater that originated inside of Puget Sound mixed below the surface layer.As an example, the extreme rainfall event of February of 2020 is observed in Figure 3.7 as high surface con-centrations of internal rivers, low surface concentrations of external rivers due to displacement, and lowersalinity in the surface and a strong gradient from 10 m to 100 m. Overall, external rivers have a greaterinfluence on the deeper waters of Puget Sound, and arrive well-mixed with water from the Pacific Oceanthat enters the Salish Sea via estuarine circulation. The internal rivers to Puget Sound have a more directeffect on the surface above 10 m, freshening the water as they mix into the water column.273.2. Freshwater Dye TracersFigure 3.7: Time series with depth at a station south of Whidbey Island (Figure 1.1b). Panels A-F showthe concentrations for six rivers; note the color scale changes for each panel. Contours are drawn for panelsA, E, and F around 80% of each river’s maximum concentration value and panels B, C, and D around 60%of each river’s maximum concentration value. Panels G and H show salinity and temperature, with salinitycontours at 25 g kg−1 (white) and 30.4 g kg−1 (black) and temperature contours at 8.5 ◦C (white) and 15 ◦C(black). 28Chapter 4Discussion4.1 SRP Model Evaluations of SalinityThe evaluations of model v201905 and v202111 around the small river plumes provides more local insightsfor the improvement of future iterations of the model. The overall improvement from model v201905to model v202111 is promising for the change to the daily rivers method, and highlights areas of furtherimprovement in both the hydrographs and other dynamics as described in Section 3.1. However, there arevariations in the level of information the model evaluations provide about each river.4.1.1 Skagit River CaseImprovements can be made especially for a case like the Skagit River. Section 3.1.2 highlights that RMSEand Non-Systematic Error may be improved for the large rivers in this study by investigating the bathymetrysurrounding the river mouth. The Skagit River is unique within this subset due to its exceptionally highand worsened RMSE and NSE between model versions in tandem with a notable improvement in the Bias.The mouth of the Skagit River sits inside of a bathymetric feature that is mostly enclosed (Figure 4.1). Inv201905, the coastline on which the river mouth exists extends further inland and there is a flow-throughchannel to the south that connects the plume to a more southern part of Puget Sound. In v202111, thecoastline follows a deeper line of topography, effectively removing a large amount of shallow water from themodel, and the flow-through is closed to the rest of Puget Sound. Although the RMSE and NSE worsened,this change in bathymetry moved the sample point closer to the river’s mouth, which helps to explain theimprovement in Bias from too salty to slightly too fresh in the winter, spring, and fall. Higher winter flow inthe model v202111 Skagit hydrograph also offset the large winter salty bias in v201905. Additional figuresfor the Skagit River can be found in Section C.2.294.1. SRP Model Evaluations of SalinityFigure 4.1: Model version 201905 and model version 202111 within the Skagit River box as defined in Sec-tion 2.2. A and B show a snapshot of surface salinity (0.5 m depth) for June, 2022. In this month, v201905better reproduces the observed salinity at this station. C and D show the seafloor depth. In A-D, the blackstar represents the mouth of the Skagit River and the red circle represents the station where observationsused for evaluations were located. The station represents 806 data points. Colorbars are consistent betweenmodel versions.4.1.2 LimitationsStatistical analysis for each river within the subset is a beneficial starting place, but conclusions about in-dividual rivers need to be taken with caveats. The takeaways for each SRP sub-region are limited by thequality and proximity of the observational data used. More thorough visualizations of the data and statis-tics for each river are presented in Appendix C, but do not belong in the main text of this work for scopepurposes. However, a note about the Elwha River can be used as an example for the kind of considerationsmade when using this data.The Elwha River subregion was found to improve; however, an examination of the observational datafound that few of the points used in this evaluation were likely to be within the plume of the Elwha (Figure4.2), and instead captured the behavior of the surrounding waters within Juan de Fuca Strait. The meanmodel position of the plume over the entire study period did not overlap with the locations of the observa-tions. In addition to the issues with the spatial distribution of points, the temporal distribution of observationsfor the Elwha subregion was sparse, with the months of peak Elwha flow being underrepresented or not rep-304.2. Freshwater Dye Tracersresented at all. All other SRP evaluations in this work are more robust than the Elwha’s based on thesecriteria, but few have optimal coverage in both space and time.Figure 4.2: Model version 201905 and model version 202111 within the Elwha River box as defined inSection 2.2. Both A and B show snapshots of surface salinity (0.5 m depth) for June, 2022. The black starrepresents the mouth of the Elwha River and the red circle represents the stations where observations usedfor evaluations were located. The stations represent 655 data points between them. Colorbars are consistentbetween model versions. The plume from the Elwha River is clearly not reaching the observation stations.There is further analysis that could be done to diagnose and fine-tune individual SRPs. One example,relevant especially to the Skagit River, would be a methodical “grid-shift” analysis. This study would informif the location of the plume is correct by comparing the evaluation of a matched observation “station” andgrid cell pairing with evaluations of the same observation data paired with grid cells that neighbor theoriginal matched location. A similar study could be done in time to investigate if the hydrograph forcingproduces more accurate model results when shifted in time.Evaluations for all SRPs were performed on salinity, but the same study on temperature could begin toinform the implementation of a different method for setting river temperature in SalishSeaCast. Currently,each river is set to the same temperature as the Fraser River. Other evaluations of SalishSeaCast for modelv202111 could be linked with river temperature in the surface waters, especially, oxygen, nutrients, andzooplankton (Allen et al., 2025a).4.2 Freshwater Dye TracersThe dynamics of freshwater both influence and inform about circulation patterns in the Salish Sea. Thedistribution of the Fraser River in the model shows the significance of the Fraser River and highlights theamount of mixing within the region. Fraser River water is not only significant in the surface, but accumu-lates at depth due to its incorporation into the deep water arriving from the Pacific Ocean. Additionally,accumulation of Fraser water dye in certain areas implies the renewal of deep water, which is significant inregions of deep water storage like fjords or the central Strait of Georgia basin.314.2. Freshwater Dye Tracers4.2.1 Freshwater Exchange in Puget SoundIf the Fraser River can be taken as a proxy for Strait of Georgia water entering PS, then conversely a riverlike the Snohomish can be taken as a proxy for Puget Sound surface water entering the rest of the SalishSea (Figure 4.3). The flow of the Fraser River is an order of magnitude greater than that of the SnohomishRiver, and this is reflected in the scale of the Snohomish freshwater content. Except for Puget Sound, allregions in the Salish Sea fall in the range between 10 and 45 cm of water from the Snohomish River. Likethe Fraser River, the largest and most persistent amount of Snohomish River water occurs in the Inletsand Central Strait of Georgia regions, meaning that surface water from Puget Sound is transported northand incorporated into intermediate and deep water masses. In the Inlets region, the seasonal increases inSnohomish and Fraser River water likely represent renewal of the deep water in the deep glacial fjords due tothe longer timescale of their variation. The absolute amount of freshwater at depth in these regions is likelylarger than represented in these results, as the model bathymetry cuts off full depth of fjords like Toba, Bute,and Jervis at 400 m while they extend to near 700 m in reality. The ubiquity of both Snohomish River andFraser River water throughout the Salish Sea highlights not only the known exchange flow within basins viasurface and deep exchange pathways (Khangaonkar et al., 2017), but also the sub-regional significance ofriver water and the inter-basin exchange.Figure 4.3: Time series of Snohomish River Freshwater Content in each region of the Salish Sea. PanelA shows a map of the regions. Panel B shows the FWC colored by region, and the average depth for eachregion of the Salish Sea in the legend.The focus on Puget Sound and the Fraser River in this work highlights the exchange between the Straitof Georgia, the main Canadian basin, and Puget Sound, the main US basin. The Salish Sea is an estuary,with Fraser River water flowing out on the surface and Pacific Ocean water flowing in at depth through both324.2. Freshwater Dye Tracersof Juan de Fuca Strait and Johnstone Strait. However, Puget Sound’s estuarine flow is driven by the outflowof the rivers internal to its basin, not the Fraser River.The concentration of Fraser River water along the thalweg into Puget Sound through the Admiralty Inlet(Figure 4.4a) shows that on the JdF side of the inlet, the surface water above 30 m is comprised of FraserRiver water mixed with Strait of Georgia Water. It sits on top of water that contains no river dye, and istherefore from an external inflow source (the Pacific Ocean via Juan de Fuca Strait). Most of the inflowthrough Johnstone Strait upwells and flows back out of the Salish Sea in the surface layer, therefore haslittle impact on Puget Sound (Khangaonkar et al., 2017).Contours along the concentration of Fraser water show that after the first bump in the sill, the incomingFraser River and Strait of Georgia water wedge is forced to dive below the outgoing Puget Sound freshwater(Figure 4.4b). This plot can be used to infer the mixing and transport processes taking place betweenJdF/SoG and PS. At the mouth of Admiralty Inlet, Fraser River + SoG Mixed Surface water mixes withPacific Ocean (PO) water, and starts to sink due to the increasing density of the wedge via the addition ofsalty PO water compared with the relatively fresh Puget Sound Freshwater and Puget Sound IntermediateWater. At the front of the wedge, the combined Fraser, SoG, and PO water mixes into the Puget SoundIntermediate Water. To balance this inflow, Puget Sound Surface Water flows outward, entraining PugetSound Intermediate Water and some of the top of the sinking wedge along the way (Figure 4.5). Tracingrivers more clearly shows the involvement of the Fraser River in the inflow to Puget Sound, but it should benoted that this structure has been shown before in observed or modeled salinity in Yang and Khangaonkar(2010), Leonov and Kawase (2009), and Lavelle et al. (1991).334.2. Freshwater Dye TracersFigure 4.4: The 5-year (2019-2023) mean concentration of Fraser River water along the Admiralty Inletthalweg by depth. The path of the thalweg is shown in panel A. The horizontal axis of panel B movedfrom north to south along the thalweg. Three contours are drawn at irregular intervals (0.0210 m3/m3,0.0225 m3/m3, and 0.0230 m3/m3) to better illustrate the Pacific Ocean water/Puget Sound surface fresh-water, the Fraser + SoG Mixed water wedge, and the mixing occurring between the wedge and the PugetSound intermediate water.This process explains the relative freshness of Puget Sound intermediate waters as well as the amountof Fraser River water present in Puget Sound. Based on the findings of (Stang and Allen, 2024), the bulk ofthis Fraser River water from SoG to PS likely comes through Rosario Strait, which has primarily southwardflow at all depths. Puget Sound water that makes it into the regions north of the San Juan and Gulf Islandslikely gets mixed into deeper water before it travels north, since the main northward currents through thesechannels are at depth (Stang and Allen, 2024).344.3. Streamflows in a Changing ClimateFigure 4.5: Schematic illustrating known structure of transport over the Admiralty Sill with additionalinformation about the Fraser River water from Figure 4.5. The four water types identified are Pacific Ocean(PO) water, Fraser River + SoG Mixed Surface (FR+SoG) water, Puget Sound surface freshwater, and PugetSound intermediate water. Colored arrows represent mean direction of flow, while dark circular arrowsrepresent a mixing process. The gray boxes explain the mixing processes.The exchange between the Strait of Georgia and Puget Sound is non-negligible. Not only does the FraserRiver impact the waters in Puget Sound, but the rivers in Puget Sound impact the rest of the Salish Sea in theform of a non-Fraser originating freshwater source. This means that contaminants in the Strait of Georgia inthe surface water above 50m, especially near the Fraser River, may enter Puget Sound and become part ofthe Puget Sound Intermediate Water (which will eventually upwell in PS and renew in the deep basins, butfreshwater is the main vector of outward flow). An example of this is Vancouver Sewage: Metro-Vancouverreleases treated wastewater directly into the Fraser River. Similarly, pollution in the surface of Puget Soundwill likely reach the rest of the Salish Sea depending on the properties of the pollutants or rate of decay.4.3 Streamflows in a Changing ClimateDirect pollution is one type of human impact, but wide-spread anthropogenic climate change will also haveimpacts on the Salish Sea. With the previous hydrograph classifications into rainfall, glacial, and mixedcategories (Table 2.2), the impact of climate change on processes that impact streamflow based on sourcecan be generalized to the hydrograph type.The different major components of hydrograph outflow (precipitation, snowmelt, glacial melt) will re-act differently to changes in climate. In the Pacific Northwest, it is predicted that annual precipitation willremain similar, but that winter precipitation will increase while summer precipitation will decrease and in-354.3. Streamflows in a Changing Climatetense rainfall events will become more common (Snover et al., 2013). Additionally, an average increase intemperature is predicted for all seasons (Snover et al., 2013). Snowpack-melt streamflow sources will haveless flow as the snowpack is less able to accumulate, and the freshet will occur earlier as rising overall tem-perature drives melting season earlier in the year (Snover et al., 2019). Glacial contributions to streamflowmay either increase due to increased temperatures in early season, or decrease due to diminishing glacialvolume to draw from. In British Columbia, glaciers have already shrunk such that glacial streamflow con-tributions are decreasing and this is expected to continue (Bolch et al., 2010). On an even longer timescale,as the climate warms, some streamflows that are mixed rainfall-snowmelt source will transition to a morerainfall-dominant regime as higher temperatures inhibit the accumulation of snowpack and will instead re-alize precipitation more immediately (Islam et al., 2019). Since river types align with the model watershed,using the set of rivers as representative for their watershed can lead to regional predictions (Figure 4.7a).4.3.1 Rainfall Rivers: East Coast of Vancouver IslandThe rainfall rivers tracked in the model are the Nooksack (Fraser watershed), Elwha (Juan de Fuca water-shed), Cowichan, Nanaimo, and Puntledge (East Vancouver Island South watershed) Rivers. It should benoted that the Nooksack River likely fits into the “Mixed” category based on observed data from the UnitedStates Geological Survey, but its modeled hydrograph in v202111 is strictly rainfall. Especially along thecoast of Vancouver Island and Juan de Fuca Strait, the changes in these rivers are likely to mostly impact thesurface layer of the Salish Sea, contributing more freshwater in the winter and less in the summer. Changesin their behavior will mean that Juan de Fuca Strait and the East Coast of Vancouver Island regions willcontribute more surface freshwater in the winter and less in the summer. The spring diatom bloom is un-likely to be affected by the increase of freshwater from rain in the wintertime, as increased vertical mixingof stored nutrients in the springtime is its main catalyst. However, the increase of freshwater in the wintermay increase the Salish Sea’s estuarine exchange. Comparing the flow from the tracked rainfall rivers tothe flow of the Fraser shows that they accounted for 13% of the rainfall and Fraser combined flow in win-ter months from 2019-2023 (Figure 4.6). This amount doesn’t include other rivers along the coast such asthe Chemainus, Englishman, or Tsolum Rivers. Even small increases for each river will sum to grow theinfluence of rainfall rivers vs the Fraser in wintertime.364.3. Streamflows in a Changing ClimateFigure 4.6: Time series of the ratio of rainfall river flow to rainfall and Fraser River combined flow. Yellowpoints represent the summer, when rainfall is low and Fraser River flow is extremely high. The blue pointsrepresent the winter, when flow from rainfall rivers makes up a larger percentage of the total freshwaterinput in the Salish Sea.4.3.2 Glacial Rivers: West Coast of BC MainlandThe Homathko River and the Squamish River are the glacial rivers tracked in the model, and glacial stream-flow rivers dominate their watershed on the West Coast of the British Columbia mainland. Decreasing glacialflow will cause increasingly dry summers, and increasingly weaker salinity gradients along this coastlinecompared to historical behavior. Weak and predominately northwesterly winds (winds blowing to the south-east) in summer drive upwelling along the mainland coast of British Columbia (Moore-Maley and Allen,2022). Weaker salinity gradients in mid to late summer will allow for more upward mixing of nutrient-richwater from the Pacific Ocean during the summer.The largest of these glacial rivers are often at the head of or along the coast of a fjord (Eg. the SquamishRiver is at the head of Howe Sound, and the Homathko River is at the head of Bute Inlet). Reduced flowwill lead to less estuarine exchange within these fjords.4.3.3 Mixed Rivers: Puget SoundThe mixed-type rivers tracked in the model are the Skagit, Snohomish (Skagit watershed), Nisqually (Pugetwatershed), and Salmon (East Vancouver Island North watershed) Rivers. The Salmon River has a smallersnowmelt component of its hydrograph compared to the rainfall component. The rainfall component of thishydrograph will have the same impacts as the rivers further south along the coast.The other mixed-type rivers originate in Puget Sound, and contribute to the Puget Sound Freshwater(PSFW). Increased freshwater in the winter months will increase estuarine exchange flow in both directionsin addition to increasing stratification. In the summer, less rainfall, in addition to less snowmelt streamflow,will weaken exchange and stratification. This weakened summer exchange flow leaves Puget Sound moresusceptible to impacts from point-source or river-sourced pollution. The increase in stratification and ex-change will occur earlier in the spring season. The results in Section 3.2.2 for 2019 and 2023 may reflectwhat is expected to occur under climate change during the summer months, with the caveat that they showed374.3. Streamflows in a Changing Climateless flow from rainfall during the winter. The change in timing may also impact the transport of distant exter-nal rivers into Puget Sound, like the Squamish and the Homathko. While it is generally known that summerstreamflow will begin earlier for both Puget Sound and external rivers fed by melting, the amount and rateof shift of these hydrographs compared to each other is uncertain.Rivers in Puget Sound have been found to play a significant role in buffering the carbonate system(Bianucci et al., 2018). Reduction in the amount of freshwater especially in late summer will leave localecosystems more vulnerable to the negatives impacts of higher ocean acidity.4.3.4 The Fraser RiverThe single snowmelt classified river explored in this research is the Fraser River. Since this river dominatesthe freshwater of the Salish Sea, the changes in Fraser flow will have impacts on multiple scales. Earlierand reduced Fraser River freshet will change both the timing and intensity of the off-shelf transport of deepwater into the Salish Sea. The sources of inflow to the Salish Sea are seasonally dependent, with mostcoming from the north or offshore in the summer versus the south in the winter (Beutel and Allen, 2024).This dependency has implications for the chemical properties of the inflow into the Salish Sea. Less bulkFraser flow may also lessen the deep transport into the Salish Sea through Juan de Fuca Strait, but it has beensuggested that the differences in the densest water entering the SoG will remain similar due to a projectedincrease in offshore upwelling of dense waters, which increases the density difference and drives exchange(Allen et al. (2025b);Thomson et al. (2020)).Changes in the Fraser River freshet will influence productivity in the Strait of Georgia. An analysis of ahistoric low flow in July of 2023 suggested that stronger surface mixing and a summer diatom bloom werecaused by the decreased salinity gradient in the surface waters of the Salish Sea (Allen, 2024). The freshetbeginning earlier also may inhibit upwelling of additional nutrients, even while the inhibition is weaker dueto the lower freshet volume.The Fraser River is moderately alkaline and DIC rich, with seasonally varying DIC:TA ratios (Moore-Maley et al., 2018). Changes in the Fraser River freshet may impact the timing and magnitude of carbonatechemistry processes occurring within the salinity gradients of the Salish Sea estuary (Simpson et al., 2022).4.3.5 Overall ImpactsSince all types of rivers are predicted to have less flow in the summer, the summertime estuarine exchangein the Salish Sea will slow down, bringing fewer external nutrient sources. Conversely in the winter, whenestuarine exchange is normally low, as rainfall streamflow increases during this season and eventually somesnow accumulation turns into rain, estuarine exchange has the potential to increase slightly. This increasewould mean that more of the nutrient-rich waters from the Pacific Ocean arrive earlier. The impacts of thisdepend on the off-shelf sources of water into the Salish Sea.384.3. Streamflows in a Changing ClimateFigure 4.7: Summary of the predicted changes to each watershed under climate change (left) and theexpected impacts within the Salish Sea (right). Ovals represent the approximate watershed. On the right-hand side, the Fraser River Freshwater impacts are expected to be observed throughout the Salish Sea. Thecontents of this figure are discussed in detail in Section 4.3. 39Chapter 5Conclusions5.1 SummaryThe use of daily river forcing in SalishSeaCast improves the performance of the model both overall andon average in small river plume regions. Additionally, the results from dye tracing of eleven key riversthroughout the Salish Sea highlight the scale of the connectivity between its basins.With the exception of the Skagit River, the mean value for all salinity evaluation metrics improved frommodel v201905 to model v202111. In model v202111, the mean Bias was -0.16 g kg−1 (fresh), the meanRMSE was 2.05 g kg−1, the mean Willmott Skill Score was 0.84, the mean Systematic Error was 0.88g kg−1, the mean Non-Systematic Error was 1.86 g kg−1, and the mean Spread Deviation was -0.08 (excessmodel variance). Trends in evaluation metrics tended to group together either by model watershed or riverscale. While there were some limitations due to data sparsity, the small river plume (SRP) evaluationsindicate a general improvement in how the model implements rivers and highlights regions in which furtherdevelopment can be implemented.Freshwater from the Fraser River is present in the plume and surface waters, but is also stored in thedeep basins of the SoG and the fjords on the British Columbia mainland coastline. Fraser River waterin all regions of the Salish Sea undergoes a yearly seasonal cycle, with more Fraser Freshwater Content(FWC) concurrent with the freshet and rainy seasons and less FWC with the dry months. In each region,there exists a deeper, baseline storage of Fraser River FWC and a shallow and more variable amount nearthe surface. Transects of mean concentration for 2019-2023 across Juan de Fuca Strait, Johnstone Strait,Admiralty Inlet, and Deception Pass show Fraser River water primarily in surface waters in Juan de Fuca,Admiralty, and Deception. Johnstone Strait and Deception Pass have similar sums of Fraser River water ontheir cross sections despite the cross-sectional area of Johnstone being much larger, leading to a much lowerconcentration distributed across Johnstone Strait.The mean freshwater content in Puget Sound has contributions from rivers originating both inside andoutside of the basin. The Fraser River, Squamish River, and Homathko River are all present in significantamounts in Puget Sound (1.7 m, 0.2 m, and 0.1 m FWC) and originate in the Strait of Georgia basin. Theserivers are all snowmelt or glacially sourced, and their FWC signals throughout the year are phase-shiftedfrom their peak flow corresponding to their distance from Puget Sound (likely due to both hydrographtiming and water transit time through the SoG). They arrive at the entrance of Admiralty Inlet in the surfacelayer from the SoG, and are mixed with Pacific Ocean water over Admiralty Sill to fill the intermediatewater of Puget Sound. From inside Puget Sound, the Snohomish River, Nisqually River, and Skagit Riversare significant in FWC (1.1 m, 0.6 m, and 0.6 m FWC respectively), outweighing the Fraser River whensummed. They are mixed source rivers, and their yearly FWC shows more small-scale variation than theexternal rivers, but not on the same daily-scale as in their hydrographs. Their depth-wise distribution in Puget405.2. Future WorkSound is mostly concentrated to the top 5 m of the water column, but full-column increases in concentrationco-occur with peaks in surface concentration in a monthly time series, suggesting vertical mixing responsetime within a month (likely occurring as reflux at the Admiralty Sill and vertical mixing at Tacoma Narrows).Freshwater from the Snohomish River was most prevalent within Puget Sound (ranging from 0.7 m to1.5 m), but had FWC values between 0.1 m - 0.4 m thoughout the rest of the Salish Sea regions. SnohomishRiver water is stored deeper in the water column in the SoG, co-occurring with deeper bathymetry similar tothe Fraser River. In the mixing region and Juan de Fuca, the daily variation in Snohomish FWC indicates thatthe Snohomish water is mostly in the surface or is in a region of intense vertical mixing. This distribution ofSnohomish FWC throughout the Salish Sea indicates non-negligible connectivity between surface water inPuget Sound and the Strait of Georgia.The impacts of future changes to streamflows under climate change can be inferred based on the behaviorof rivers sorted by hydrograph type. Tracked rainfall rivers on the east coast of Vancouver Island accountedfor 13% of the rainfall river and Fraser River combined flow in winter months and are likely to increase theircontribution to wintertime freshwater. Glacial rivers on the west coast of the B.C. Mainland will contributeless summer freshwater, leading to weaker vertical salinity gradients and potentially more nutrient-rich watermixing into the surface. Mixed source rivers within Puget Sound will contribute more freshwater in wintermonths, increasing exchange flow and stratification; the inverse is predicted for summer months. The FraserRiver’s earlier and lower freshet in the spring will likely inhibit earlier local wind-driven upwelling, but atthe same time this inhibition will be weaker due to the lower amount of freshwater.This work expanded the understanding of model river forcing and freshwater dynamics in the Salish Sea,which are both crucial for the use of modeling tools in the study of anthropogenic impacts on the Salish Seavia rivers. Additionally, the evaluation of the daily river forcing supports its use for other modeled coastalocean systems that lack gauged river data.5.2 Future WorkBoth river modeling methods and freshwater dynamics in the Salish Sea should be investigated further. A“grid-shift” experiment for small river plumes is proposed in Section 4.1.2 that would help to fine-tune howthe river mouths and surrounding bathymetry are implemented in the model. Additionally, the same SRPanalysis on just model v202111 could be performed on temperature instead of salinity to evaluate how wellthe temperature of the Fraser River represents the rest of the rivers in the domain. Then, one SRP with poorperformance could be selected and the model run with realistic temperature estimations and the sensitivityof the SRP to these changes evaluated.The richness of the dye tracer dataset produced for this work lends itself to more exploratory analysis.One approach that builds directly on this research would be to group freshwater dyes by hydrograph type orwatershed and do the same FWC analysis as before, with more focus on seasonality. An addition to the FWCanalysis could be to, instead of calculating FWC for the entire water column, calculate it in depth rangesrepresenting approximate water masses in each basin. This analysis would show the phase lags betweendynamics in the surface, intermediate, and deep waters of the SoG as an example. It would also lend itself tothe evaluation of dry years and wet years with respect to the Fraser River or other types of rivers, and answerquestions about how these river streamflow anomalies propagate to different water masses throughout the415.2. Future WorkSalish Sea.Finally, the scope of this work could be significantly expanded by including the biogeochemical com-ponent of the SalishSeaCast model on top of the physics-only results in this research. The impacts ofnutrient-loading or DIC content in specific rivers could be evaluated under different flow scenarios that oc-cur within the 2019-2023 dataset. An alternative to this if the biogeochemical properties of the rivers arenow well known is to use a dataset of observations for the properties of interest and correlate them with dyeconcentrations of each river. 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Urban seas as hotspots of stress in the Anthropocene ocean: The Salish Sea example.Elementa: Science of the Anthropocene, 10(1):00055.Soontiens, N. and Allen, S. E. (2017). Modelling sensitivities to mixing and advection in a sill-basin estu-arine system. Ocean Modelling, 112:17–32.Soontiens, N., Allen, S. E., Latornell, D., Le Soue¨f, K., Machuca, I., Paquin, J.-P., Lu, Y.,Thompson, K., and Korabel, V. (2016). Storm Surges in the Strait of Georgia Simulatedwith a Regional Model. Atmosphere-Ocean, 54(1):1–21. Publisher: Taylor & Francis eprint:https://doi.org/10.1080/07055900.2015.1108899.Stang, C. and Allen, S. E. (2024). Seasonably variable estuarine exchange through inter-connected channelsin the Salish Sea. ESS Open Archive.Sutherland, D. A., MacCready, P., Banas, N. S., and Smedstad, L. F. (2011). A Model Study of the Salish SeaEstuarine Circulation. Journal of Physical Oceanography. Section: Journal of Physical Oceanography.Sutton, J. N., Johannessen, S. C., and Macdonald, R. W. (2013). A nitrogen budget for the Strait of Geor-gia, British Columbia, with emphasis on particulate nitrogen and dissolved inorganic nitrogen. Biogeo-sciences, 10(11):7179–7194. Publisher: Copernicus GmbH.Svendsen, H. (1986). Mixing and Exchange Processes in Estuaries, Fjords and Shelf Waters. In Skreslet, S.,editor, The Role of Freshwater Outflow in Coastal Marine Ecosystems, pages 13–45, Berlin, Heidelberg.Springer.Thomson, R. E. (1981). Oceanography of the British Columbia coast. Number 56 in Canadian specialpublication of fisheries and aquatic sciences. Dept. of Fisheries and Oceans, Ottawa.Thomson, R. E., Kulikov, E. A., Spear, D. J., Johannessen, S. C., and Peter Wills, W. (2020). ARole for Gravity Currents in Cross-Sill Estuarine Exchange and Subsurface Inflow to the South-ern Strait of Georgia. Journal of Geophysical Research: Oceans, 125(4):e2019JC015374. eprint:https://onlinelibrary.wiley.com/doi/pdf/10.1029/2019JC015374.Thomson, R. E., Miha´ly, S. F., and Kulikov, E. A. (2007). Estuarine versus transient flowregimes in Juan de Fuca Strait. Journal of Geophysical Research: Oceans, 112(C9). eprint:https://onlinelibrary.wiley.com/doi/pdf/10.1029/2006JC003925.Tilburg, C. E., Houghton, R. W., and Garvine, R. W. (2007). Mixing of a dye tracer in the Delaware plume:Comparison of observations and simulations. Journal of Geophysical Research: Oceans, 112(C12).eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2006JC003928.True, E. D. (2018). Using a Numerical Model to Track the Discharge of a Wastewater Treatment Plant in aTidal Estuary. Water, Air, & Soil Pollution, 229(8):267.Umlauf, L. and Burchard, H. (2003). A generic length-scale equation for geophysical turbulence models.Journal of Marine Research, 61(2).50Underwood, G. J. C. and Kromkamp, J. (1999). Primary Production by Phytoplankton and Microphyto-benthos in Estuaries. In Nedwell, D. B. and Raffaelli, D. G., editors, Advances in Ecological Research,volume 29 of Estuaries, pages 93–153. Academic Press.Waldichuk, M. (1957). Physical Oceanography of the Strait of Georgia, British Columbia. Journal of theFisheries Research Board of Canada, 14(3):321–486. Publisher: NRC Research Press.Washington State Department of Ecology (2018). Long-term marine water column monitoring 1999-present.Weiss-Gibbons, T., Tefs, A., Hu, X., Stadnyk, T., and Myers, P. G. (2024). Sensitivity of Sim-ulated Arctic Ocean Salinity and Strait Transport to Interannually Variable Hydrologic ModelBased Runoff. Journal of Geophysical Research: Oceans, 129(10):e2023JC020536. eprint:https://onlinelibrary.wiley.com/doi/pdf/10.1029/2023JC020536.Willmott, C. J. (1981). ON THE VALIDATION OF MODELS. Physical Geography. Publisher: Taylor &Francis Group.Yang, Z. and Khangaonkar, T. (2010). Multi-scale modeling of Puget Sound using an unstructured-gridcoastal ocean model: from tide flats to estuaries and coastal waters. Ocean Dynamics, 60(6):1621–1637.Zhang, J., Weijer, W., Steele, M., Cheng, W., Verma, T., and Veneziani, M. (2021). Labrador Sea fresheninglinked to Beaufort Gyre freshwater release. Nature Communications, 12(1):1229. Publisher: NaturePublishing Group.51Appendix ACoastal ClusteringSection A.1 of this appendix was adapted from a collaborative report for the STAIR program at UBC,co-authored by Cassidy Donaldson and Sophie Johnston and reviewed by Susan Allen and Chris Harley.Paragraph two is based on information provided by Sophie to describe the ecological background of theSalish Sea and the significance of SalishSeaCast to the Harley Lab.A.1 Motivation and MethodsOceanography is an interdisciplinary science that builds on understanding the interplay between biology,chemistry, physics, geology, and other branches of science. However, oceanographers can enhance theimpact of their work by collaborating directly with other fields of science. For this project, I collaboratedwith Sophie Johnston of the Harley Lab in the Zoology Department at UBC, which centers research oncoastal marine ecology and the impacts of climate change. Sophie is a PhD student whose research focuseson the impact of increased thermal variability and ocean acidification on marine biodiversity in the SalishSea.Sophie’s project investigates the responses of marine organisms to extreme conditions using in-situexperimentation methodologies. This can make it difficult to determine the sites to use for studies with-out robust knowledge of the physical oceanographic properties at those sites. SalishSeaCast will assistin the Harley Lab’s ability to select sites for intertidal experiments by providing missing physical infor-mation without the necessity of a research cruise to measure water properties directly. It may also helpthem explain unexpected results from past studies. As an example, sites surveyed by Harley Lab mem-bers in 2018 showed anomalous drops in Pisaster sea star populations and associated increases in musseldensity. The model showed an anomalous salinity for that year that lab members believe is related to thebehavior of the Pisaster population. The purpose of this collaboration is to assist in site selection for fieldexperiments occurring in the summer of 2025, which will explore the impacts of corrosive (defined as lowpH/undersaturated ΩA) conditions on invertebrates and algal communities in the Salish Sea. Additionally,field measurements of salinity, temperature, and pH will be taken at selected sites to evaluate the near-shoreaccuracy of SalishSeaCast.A full bio-chemical-physical version of the SalishSeaCast model was used to obtain mean daily andmean monthly values spanning the years 2016-2023 for salinity, temperature, pH, and (using the mocsypackage, Orr et al. (2018)) the Aragonite saturation state (ΩA). A coastal 5 m isobath was found from themodel bathymetry to mark the transition between land and water along all coastlines in the Salish Sea.To represent the intertidal zone, the near surface (0.5 m) values at the first grid cell on the water side ofthis isobath were used (the spatial distribution of data can be seen in Figure A.1a). Using the 5 m isobathensured values were oceanward of any shallow sand banks. A dataset of monthly climatologies for salinity,52A.1. Motivation and Methodstemperature, and ΩA were created and then clustered using KMeans in the scikit-learn package, (Pedregosaet al., 2011) with equal weight on all three parameters. K-Means is an unsupervised machine learningalgorithm that groups data into N groups based on their distances from data centroids, and absent of pre-defined categories. The user defines N, and the algorithm produces natural groupings. Five different clusterswere found that represented different physical environments in the intertidal zone occurring throughout bothspace and time. Additionally, to give historical context to proposed ecological study sites, daily time seriesof the same parameters were provided based on the corresponding closest model grid cell. While these timeseries are specific to Sophie’s work, the coastal clusters can be continually used by the Harley Lab to assistin appropriate site selection for hypothesis testing in future studies.53A.2. Clustering of T, S, and Aragonite SaturationA.2 Clustering of T, S, and Aragonite SaturationFigure A.1: Spatial and temporal distributions for five intertidal zone clusters based on monthly climatolo-gies of model salinity, temperature, and ΩA from 2016-2023. Panel A shows the number of months of of theyear where each cluster occurs for each grid cell of the model, ranging from 0 occurrences to 12. Panels Band C show the monthly time series of the number of grid cells where each cluster occurs. C0 is placed onits own axis in panel B due to its low variation and low relative occurrence.The five clusters, C0-C4, show distinct combinations of salinity, temperature, and ΩA (Figure A.2). C0is fresh, has a wide temperature range, and generally low ΩA (undersaturated). It solely represents waterwithin the arms of the Fraser River that are included within the model domain (Figure A.1a), and so willnot be discussed in detail. Further reference to “low salinity” water is relative to clusters C1-C4, which aregenerally at least 25 g kg−1 and so salinities within the 25-27.5 g kg−1 range are considered low relative tothe 30 g kg−1 saltiest waters.54A.2. Clustering of T, S, and Aragonite SaturationA.2.1 C1: Summer Freshwater-Influenced WatersC1 is fresh, warm, and generally has high ΩA values well above 1.0 (Figure A.2). It appears in the glacialfjords (mostly Bute Inlet, Jervis Inlet, and Howe Sound) and to a lesser extent in Skagit Bay and the southernbasin of Puget Sound near Olympia, Washington (Figure A.1a). It is in-season from May - September(Figure A.1c), which aligns with the timing of glacial and snowmelt freshet. The water column is stratifiedand influenced by freshwater dilution and summertime primary productivity when C1 appears.A.2.2 C2: Spring Bloom WatersC2 is moderately fresh, cold, and has high ΩA values well above 1.0 (Figure A.2). It appears across much ofthe Salish Sea, with exceptions in Johnstone Strait, the Haro region, and parts of Juan de Fuca Strait (FigureA.1a). It is in season in the spring, specifically in April and May (Figure A.1b), which is well-aligned withDIC drawdown effects from the spring diatom bloom as well as the beginnings of dilution effects fromfreshet freshwater inputs. This is likely nutrient-rich Pacific water that has mixed vertically, and is mixedwell enough with freshwater to feel the dilution effects unlike Johnstone, Haro, and JdF.A.2.3 C3: Winter Pacific Water Dominated WatersC3 is salty, cold, and has the lowest ΩA values of the four clusters (Figure A.2). It appears throughout theSalish Sea, especially in Johnstone Strait and Juan de Fuca Strait (Figure A.1a). It is dominantly in seasonduring the winter months (Figure A.1b). This cluster likely represents Pacific Ocean water that is much lessdiluted and experiences no DIC drawdown due to the season it occurs in.A.2.4 C4: Summer and Fall Mixed Pacific WatersC4 is salty, moderately cold, and has a lower ΩA (Figure A.2). It is present throughout the Salish Sea, butespecially in Johnstone Strait, the Haro Region, and Juan de Fuca Strait (Figure A.1a). It is an approximatespatial inverse of C1 in these more dominant regions. It is present somewhat throughout the summer, butdominates in the fall (Figure A.1b). This cluster likely represents Pacific Ocean water that has been mixedto the surface during the summer and fall, which in contract to C1 has not seen strong dilution effects orDIC drawdown.A.2.5 SummaryThe clusters most energetically favorable to CaCO3 shell-forming organisms are C1 and C2, while C2 andC4 are less favorable. ΩA appears somewhat correlated with temperature, with lower temperature clustershaving lowerΩA and vice versa. However, it is unlikely to be the main the controlling mechanism; it is likelydue to the freshwater dilution effect and/or the drawdown of DIC by phytoplankton primary productionduring warm months (Ianson et al., 2016; Moore-Maley et al., 2016; Simpson et al., 2024). Property-property plots reconfirm that temperature is not the main control, as ΩA is not shown to vary directly withtemperature (Figure A.3b). Figure A.3a also shows that C2 and C4 have a large overlap in their temperatureand salinity ranges, but are distinct by their ΩA ranges. C1 and C3 are shown to be most distinct fromeach other in everything but salinity, which is supported by the almost inverse time-dominance of these55A.2. Clustering of T, S, and Aragonite Saturationclusters (Figure A.1b). C1 and C2 are generally fresher spring and summer clusters with higher ΩA, whileC3 and C4 are more saline and have lower ΩA values in the fall and winter. C1 and C2 likely have morefreshwater influenced based on the dominant volume of flow throughout the Salish Sea occurring in thespring and summer, while C3 and C4 are more influenced by relatively DIC-rich Pacific Ocean waters,which is reflected in the ΩA values of all four clusters.Figure A.2: Box and whisker plots showing the salinity, temperature, and aragonite saturation state ΩA forthe five clusters of the monthly climatology of parameters in the intertidal zone throughout the Salish Sea.For each box, the internal black line represents the median value, the filled box the inner two quartiles, thewhiskers the minimum and maximum values, and the circles outlier values. C0, represented by the purpleboxes, is a cluster that solely represents the arms of the Fraser River included in the model domain (FigureA.1). For the four other clusters, temperature has a large effect on the ΩA saturation state.Figure A.3: Property-property plots of temperature and salinity, showing clusters and ΩA. The data repre-sented here are the climatology values of each month for every coastal grid cell in the study. Panel A showspoints colored by cluster, ranging from 0-4. Panel B shows points colored by ΩA, ranging from 0 to 2.56Appendix BSupporting Tables and FiguresTable B.1: Values for the evaluation metrics for both model versions and for all SRP Subregions. The valuefor model v202111 is highlighted in green if its absolute value is less than that for the corresponding modelv201905 metric. Yellow indicates that there was no significant change between model versions.57Appendix B. Supporting Tables and FiguresFigure B.1: Time series from 2019-2023 of both the FWC of each Salish Sea subregion (colored lines) andits calculated average depth (black dotted lines). The Pearson Correlation Coefficient is calculated; all butthe Puget Sound are positive, suggesting that for most of these regions the dominant source of variabilityis in the surface, whereas in Puget Sound it is in the intermediate waters. In the Juan de Fuca boxes, theremoval of FWC in spring shallows the average depth and the addition of FWC in summer does the same.It deepens in the fall/winter while the total FWC remains relatively constant or slightly higher.58Appendix CComplete SRP Evaluation FiguresC.1 Nooksack RiverFigure C.1: Bathymetry for model version 201905 (left) and model version 202111 (right) within theNooksack River box as defined in Section 2.2. The black star represents the mouth of the river and thered circle represents the station(s) where observations used for evaluations were located. The station(s)represent 822 data points.Figure C.2: Monthly total proportion of observations matched with model data for evaluations within theNooksack SRP subregion. The value of 112 ≈ 0.083, so the ideal distribution would show all months at thisvalue.59C.1. Nooksack RiverFigure C.3: Model hydrographs for the Nooksack River, 2019-2023 climatology. Both hydrographs areshown (top) and their difference (bottom). This shows the transition from monthly climatologies (v201905)to daily rivers (v202111).60C.1. Nooksack RiverFigure C.4: Seasonal evaluation metrics for the Nooksack River. Each panel shows a different metric, barcolors represent seasons, dotted bars represent model v201905, and plain bars represent model v202111.Figure C.5: Scatter plots of observed vs modeled salinity for model v201905 and model v202111 forthe Nooksack River. Points are colored by season. The black line is the ideal one-to-one line of perfectagreement, while the red line is the line of best fit through all the points.61C.2. Skagit RiverC.2 Skagit RiverFigure C.6: Bathymetry for model version 201905 (left) and model version 202111 (right) within theSkagit River box as defined in Section 2.2. The black star represents the mouth of the river and the red circlerepresents the station(s) where observations used for evaluations were located. The station(s) represent 806data points.Figure C.7: Monthly total proportion of observations matched with model data for evaluations within theSkagit SRP subregion. The value of 112 ≈ 0.083, so the ideal distribution would show all months at thisvalue.62C.2. Skagit RiverFigure C.8: Model hydrographs for the Skagit River, 2019-2023 climatology. Both hydrographs are shown(top) and their difference (bottom). This shows the transition from monthly climatologies (v201905) to dailyrivers (v202111).63C.2. Skagit RiverFigure C.9: Seasonal evaluation metrics for the Skagit River. Each panel shows a different metric, barcolors represent seasons, dotted bars represent model v201905, and plain bars represent model v202111.Figure C.10: Scatter plots of observed vs modeled salinity for model v201905 and model v202111 for theSkagit River. Points are colored by season. The black line is the ideal one-to-one line of perfect agreement,while the red line is the line of best fit through all the points.64C.3. Snohomish RiverC.3 Snohomish RiverFigure C.11: Bathymetry for model version 201905 (left) and model version 202111 (right) within theSnohomish River box as defined in Section 2.2. The black star represents the mouth of the river and thered circle represents the station(s) where observations used for evaluations were located. The station(s)represent 812 data points.Figure C.12: Monthly total proportion of observations matched with model data for evaluations within theSnohomish SRP subregion. The value of 112 ≈ 0.083, so the ideal distribution would show all months at thisvalue.65C.3. Snohomish RiverFigure C.13: Model hydrographs for the Snohomish River, 2019-2023 climatology. Both hydrographs areshown (top) and their difference (bottom). This shows the transition from monthly climatologies (v201905)to daily rivers (v202111).66C.3. Snohomish RiverFigure C.14: Seasonal evaluation metrics for the Snohomish River. Each panel shows a different metric,bar colors represent seasons, dotted bars represent model v201905, and plain bars represent model v202111.Figure C.15: Scatter plots of observed vs modeled salinity for model v201905 and model v202111 forthe Snohomish River. Points are colored by season. The black line is the ideal one-to-one line of perfectagreement, while the red line is the line of best fit through all the points.67C.4. Nisqually RiverC.4 Nisqually RiverFigure C.16: Bathymetry for model version 201905 (left) and model version 202111 (right) within theNisqually River box as defined in Section 2.2. The black star represents the mouth of the river and the redcircle represents the station(s) where observations used for evaluations were located. The station(s) represent6615 data points.Figure C.17: Monthly total proportion of observations matched with model data for evaluations within theNisqually SRP subregion. The value of 112 ≈ 0.083, so the ideal distribution would show all months at thisvalue.68C.4. Nisqually RiverFigure C.18: Model hydrographs for the Nisqually River, 2019-2023 climatology. Both hydrographs areshown (top) and their difference (bottom). This shows the transition from monthly climatologies (v201905)to daily rivers (v202111).69C.4. Nisqually RiverFigure C.19: Seasonal evaluation metrics for the Nisqually River. Each panel shows a different metric, barcolors represent seasons, dotted bars represent model v201905, and plain bars represent model v202111.Figure C.20: Scatter plots of observed vs modeled salinity for model v201905 and model v202111 forthe Nisqually River. Points are colored by season. The black line is the ideal one-to-one line of perfectagreement, while the red line is the line of best fit through all the points.70C.5. Elwha RiverC.5 Elwha RiverFigure C.21: Bathymetry for model version 201905 (left) and model version 202111 (right) within theElwha River box as defined in Section 2.2. The black star represents the mouth of the river and the red circlerepresents the station(s) where observations used for evaluations were located. The station(s) represent 655data points.Figure C.22: Monthly total proportion of observations matched with model data for evaluations within theElwha SRP subregion. The value of 112 ≈ 0.083, so the ideal distribution would show all months at thisvalue.71C.5. Elwha RiverFigure C.23: Model hydrographs for the Elwha River, 2019-2023 climatology. Both hydrographs are shown(top) and their difference (bottom). This shows the transition from monthly climatologies (v201905) to dailyrivers (v202111).72C.5. Elwha RiverFigure C.24: Seasonal evaluation metrics for the Elwha River. Each panel shows a different metric, barcolors represent seasons, dotted bars represent model v201905, and plain bars represent model v202111.Figure C.25: Scatter plots of observed vs modeled salinity for model v201905 and model v202111 for theElwha River. Points are colored by season. The black line is the ideal one-to-one line of perfect agreement,while the red line is the line of best fit through all the points.73C.6. Cowichan RiverC.6 Cowichan RiverFigure C.26: Bathymetry for model version 201905 (left) and model version 202111 (right) within theCowichan River box as defined in Section 2.2. The black star represents the mouth of the river and thered circle represents the station(s) where observations used for evaluations were located. The station(s)represent 12264 data points.Figure C.27: Monthly total proportion of observations matched with model data for evaluations within theCowichan SRP subregion. The value of 112 ≈ 0.083, so the ideal distribution would show all months at thisvalue.74C.6. Cowichan RiverFigure C.28: Model hydrographs for the Cowichan River, 2019-2023 climatology. Both hydrographs areshown (top) and their difference (bottom). This shows the transition from monthly climatologies (v201905)to daily rivers (v202111).75C.6. Cowichan RiverFigure C.29: Seasonal evaluation metrics for the Cowichan River. Each panel shows a different metric, barcolors represent seasons, dotted bars represent model v201905, and plain bars represent model v202111.Figure C.30: Scatter plots of observed vs modeled salinity for model v201905 and model v202111 forthe Cowichan River. Points are colored by season. The black line is the ideal one-to-one line of perfectagreement, while the red line is the line of best fit through all the points.76C.7. Nanaimo RiverC.7 Nanaimo RiverFigure C.31: Bathymetry for model version 201905 (left) and model version 202111 (right) within theNanaimo River box as defined in Section 2.2. The black star represents the mouth of the river and the redcircle represents the station(s) where observations used for evaluations were located. The station(s) represent401 data points.Figure C.32: Monthly total proportion of observations matched with model data for evaluations within theNanaimo SRP subregion. The value of 112 ≈ 0.083, so the ideal distribution would show all months at thisvalue.77C.7. Nanaimo RiverFigure C.33: Model hydrographs for the Nanaimo River, 2019-2023 climatology. Both hydrographs areshown (top) and their difference (bottom). This shows the transition from monthly climatologies (v201905)to daily rivers (v202111).78C.7. Nanaimo RiverFigure C.34: Seasonal evaluation metrics for the Nanaimo River. Each panel shows a different metric, barcolors represent seasons, dotted bars represent model v201905, and plain bars represent model v202111.Figure C.35: Scatter plots of observed vs modeled salinity for model v201905 and model v202111 forthe Nanaimo River. Points are colored by season. The black line is the ideal one-to-one line of perfectagreement, while the red line is the line of best fit through all the points.79C.8. Puntledge RiverC.8 Puntledge RiverFigure C.36: Bathymetry for model version 201905 (left) and model version 202111 (right) within thePuntledge River box as defined in Section 2.2. The black star represents the mouth of the river and thered circle represents the station(s) where observations used for evaluations were located. The station(s)represent 1573 data points.Figure C.37: Monthly total proportion of observations matched with model data for evaluations within thePuntledge SRP subregion. The value of 112 ≈ 0.083, so the ideal distribution would show all months at thisvalue.80C.8. Puntledge RiverFigure C.38: Model hydrographs for the Puntledge River, 2019-2023 climatology. Both hydrographs areshown (top) and their difference (bottom). This shows the transition from monthly climatologies (v201905)to daily rivers (v202111).81C.8. Puntledge RiverFigure C.39: Seasonal evaluation metrics for the Puntledge River. Each panel shows a different metric, barcolors represent seasons, dotted bars represent model v201905, and plain bars represent model v202111.Figure C.40: Scatter plots of observed vs modeled salinity for model v201905 and model v202111 forthe Puntledge River. Points are colored by season. The black line is the ideal one-to-one line of perfectagreement, while the red line is the line of best fit through all the points.82C.9. Salmon RiverC.9 Salmon RiverFigure C.41: Bathymetry for model version 201905 (left) and model version 202111 (right) within theSalmon River box as defined in Section 2.2. The black star represents the mouth of the river and the redcircle represents the station(s) where observations used for evaluations were located. The station(s) represent439 data points.Figure C.42: Monthly total proportion of observations matched with model data for evaluations within theSalmon SRP subregion. The value of 112 ≈ 0.083, so the ideal distribution would show all months at thisvalue.83C.9. Salmon RiverFigure C.43: Model hydrographs for the Salmon River, 2019-2023 climatology. Both hydrographs areshown (top) and their difference (bottom). This shows the transition from monthly climatologies (v201905)to daily rivers (v202111).84C.9. Salmon RiverFigure C.44: Seasonal evaluation metrics for the Salmon River. Each panel shows a different metric, barcolors represent seasons, dotted bars represent model v201905, and plain bars represent model v202111.Figure C.45: Scatter plots of observed vs modeled salinity for model v201905 and model v202111 for theSalmon River. Points are colored by season. The black line is the ideal one-to-one line of perfect agreement,while the red line is the line of best fit through all the points.85C.10. Homathko RiverC.10 Homathko RiverFigure C.46: Bathymetry for model version 201905 (left) and model version 202111 (right) within theHomathko River box as defined in Section 2.2. The black star represents the mouth of the river and thered circle represents the station(s) where observations used for evaluations were located. The station(s)represent 2504 data points.Figure C.47: Monthly total proportion of observations matched with model data for evaluations within theHomathko SRP subregion. The value of 112 ≈ 0.083, so the ideal distribution would show all months at thisvalue.86C.10. Homathko RiverFigure C.48: Model hydrographs for the Homathko River, 2019-2023 climatology. Both hydrographs areshown (top) and their difference (bottom). This shows the transition from monthly climatologies (v201905)to daily rivers (v202111).87C.10. Homathko RiverFigure C.49: Seasonal evaluation metrics for the Homathko River. Each panel shows a different metric, barcolors represent seasons, dotted bars represent model v201905, and plain bars represent model v202111.Figure C.50: Scatter plots of observed vs modeled salinity for model v201905 and model v202111 forthe Homathko River. Points are colored by season. The black line is the ideal one-to-one line of perfectagreement, while the red line is the line of best fit through all the points.88C.11. Squamish RiverC.11 Squamish RiverFigure C.51: Bathymetry for model version 201905 (left) and model version 202111 (right) within theSquamish River box as defined in Section 2.2. The black star represents the mouth of the river and the redcircle represents the station(s) where observations used for evaluations were located. The station(s) represent271 data points.Figure C.52: Monthly total proportion of observations matched with model data for evaluations within theSquamish SRP subregion. The value of 112 ≈ 0.083, so the ideal distribution would show all months at thisvalue.89C.11. Squamish RiverFigure C.53: Model hydrographs for the Squamish River, 2019-2023 climatology. Both hydrographs areshown (top) and their difference (bottom). This shows the transition from monthly climatologies (v201905)to daily rivers (v202111).90C.11. Squamish RiverFigure C.54: Seasonal evaluation metrics for the Squamish River. Each panel shows a different metric, barcolors represent seasons, dotted bars represent model v201905, and plain bars represent model v202111.Figure C.55: Scatter plots of observed vs modeled salinity for model v201905 and model v202111 forthe Squamish River. Points are colored by season. The black line is the ideal one-to-one line of perfectagreement, while the red line is the line of best fit through all the points.91
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Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Allen, Susan Elizabeth
Modeling freshwater from a subset of rivers throughout the Salish Sea using dye tracers
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