Arts, Faculty of
Geography, Department of
DSpace
UBCV
Turley, Mike
Doctor of Philosophy - PhD
University of British Columbia
It is becoming increasingly important to understand how mountain regions will respond to accelerated glacier retreat and land use changes. Long-term records that capture the inherent variability of these systems and document past landscape responses and recovery from glaciation are essential for contextualizing contemporary changes and enabling future predictions. Sediment storage landforms are useful archives of past landscape response and are important for linking onsite erosion to sediment yield at the outlet. This thesis employs a spatially and temporally nested approach to provide a long-term record of sediment dynamics and yield while linking insights across different scales. In particular, we investigate Holocene sediment dynamics, focusing on the controls, variability, and interactions of sediment supply, storage, and yield in glacierized mountain catchments. First, we construct a contemporary bed material sediment budget for the 94 km² Fitzsimmons Creek watershed using field surveys, net-change lidar analysis, and geomorphic mapping. Active channel, floodplain, and landslide sources each contributed roughly one-third of the total sediment yield at the outlet. Historical sediment yield, quantified through field surveys and air photo analysis, varied by a factor of 17 over 76 years, driven primarily by a large landslide-flood event and gravel extraction activities. Second, we explore the inter- and intra-catchment variability in sediment dynamics for tributaries of the 875 km² Green River watershed throughout the Holocene. We approach this through geomorphic mapping, geochronology, postglacial erosion and storage estimates, and longitudinal analyses of channel and valley characteristics. We propose a conceptual model linking geology, glacial history, and postglacial history as controls on sediment dynamics, explaining the observed longitudinal patterns in postglacial mountain watersheds. Finally, we synthesize the Holocene history of the 3,765 km² Lillooet River watershed focusing on controls on sediment dynamics, sediment supply, and yield. In line with our conceptual model, we demonstrate how topographic steps result in a unique hypsometry of sediment storage and fragment the sediment cascade. Our findings reveal that specific sediment yield varies by more than five orders of magnitude across space and time within the same watershed, underscoring the importance of considering landscape organization and its’ control on sediment dynamics.
Late Quaternary controls on sediment dynamics and yield in paraglacial landscapes: A spatially and temporally nested approach by Mike Turley BSc, Utah State University, 2018 MSc, University of British Columbia, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Doctor of Philosophy in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Geography) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) February 2025 © Mike Turley, 2025 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Late Quaternary controls on sediment dynamics and yield in paraglacial landscapes: A spatially and temporally nested approach submitted by Mike Turley in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Geography Examining Committee: Marwan Hassan, Professor, Geography, UBC Supervisor Olav Lian, Professor, Geoscience, University of the Fraser Valley Supervisory Committee Member Andre Zimmermann, Adjunct Professor, Geography, UBC Supervisory Committee Member Andrew Martindale, Professor, Anthropology, UBC University Examiner John Innes, Professor, Forestry, UBC University Examiner Additional Supervisory Committee Members: Ian McKendry, Professor Emeritus, Geography, UBC Supervisory Committee Member iii Abstract It is becoming increasingly important to understand how mountain regions will respond to accelerated glacier retreat and land use changes. Long-term records that capture the inherent variability of these systems and document past landscape responses and recovery from glaciation are essential for contextualizing contemporary changes and enabling future predictions. Sediment storage landforms are useful archives of past landscape response and are important for linking onsite erosion to sediment yield at the outlet. This thesis employs a spatially and temporally nested approach to provide a long-term record of sediment dynamics and yield while linking insights across different scales. In particular, we investigate Holocene sediment dynamics, focusing on the controls, variability, and interactions of sediment supply, storage, and yield in glacierized mountain catchments. First, we construct a contemporary bed material sediment budget for the 94 km2 Fitzsimmons Creek watershed using field surveys, net-change lidar analysis, and geomorphic mapping. Active channel, floodplain, and landslide sources each contributed roughly one-third of the total sediment yield at the outlet. Historical sediment yield, quantified through field surveys and air photo analysis, varied by a factor of 17 over 76 years, driven primarily by a large landslide-flood event and gravel extraction activities. Second, we explore the inter- and intra-catchment variability in sediment dynamics for tributaries of the 875 km2 Green River watershed throughout the Holocene. We approach this through geomorphic mapping, geochronology, postglacial erosion and storage estimates, and longitudinal analyses of channel and valley characteristics. We propose a conceptual model linking geology, glacial history, and postglacial history as controls on sediment dynamics, explaining the observed longitudinal patterns in postglacial mountain watersheds. Finally, we synthesize the Holocene history of the 3,765 km2 Lillooet River watershed focusing on controls on sediment dynamics, sediment supply, and yield. In line with our conceptual model, we demonstrate how topographic steps result in a unique hypsometry of sediment storage and fragment the sediment cascade. Our findings reveal that specific sediment yield varies by more than five orders of magnitude across space and time within the same watershed, underscoring the importance of considering landscape organization and its’ control on sediment dynamics. iv Lay Summary Mountain regions are increasingly affected by glacier retreat and land use change, making it more important than ever to understand how these environments will respond. This research examines landscape response, as measured in the initial movement, accumulation, and flow of sediment through a landscape, over several different spatial and temporal scales. We show that over the last 76 years, the amount of coarse sediment passing through a small river system varied by up to 17 times, primarily as a result of a large landslide-flood event and gravel extraction. We then looked at what controls landscape response over the last ~11,000 years and found that geology, glacial history, and events such as landslides that occurred after the glaciers retreated were important. Finally, measuring the amount of sediment passing through a large river system in different places and over different periods resulted in values that varied by more than 100,000 times. v Preface This thesis is original work completed by Mike Turley under the guidance of the supervisory committee. Mike Turley was responsible for research design and conceptualization, data collection, analysis, interpretation, and writing. The majority of the optical dating laboratory work and data analysis was completed by Mike Turley, with training and supervision provided by Olav Lian and Maria Schaarschmidt. This work was completed in the Luminescence Dating Laboratory in the Department of Geoscience at the University of the Fraser Valley. This thesis includes one published manuscript, and two additional research chapters that are planned for submission. A version of Chapter 2 is published in the Journal of Geophysical Research: Earth Surface (Turley et al., 2024), with co-authors Marwan A. Hassan, Andre Zimmermann, and Olav Lian who supervised the work. Andre Zimmermann was also involved with data collection for this Chapter. Fitzsimmons Creek Lidar data was generously provided by the Resort Municipality of Whistler. Turley, M., Hassan, M. A., Zimmermann, A., & Lian, O. (2024). Sediment source partitioning and budgeting over historical timescales in a glacierized, mountain catchment. Journal of Geophysical Research: Earth Surface, 129, e2024JF007819. https://doi.org/10.1029/2024JF007819. vi Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ......................................................................................................................... vi List of Tables ..................................................................................................................................x List of Figures ............................................................................................................................... xi List of Abbreviations .................................................................................................................. xii Acknowledgements .................................................................................................................... xiii Chapter 1 - Introduction ...............................................................................................................1 1.1 Background ..................................................................................................................... 2 1.1.1 Quaternary History of BC ....................................................................................... 2 1.1.2 Conceptual Approach: Sediment Disconnectivity and Storage Controlled by Landscape Organization.......................................................................................................... 4 1.2 Research Objectives and Thesis Structure ...................................................................... 7 Chapter 2 - Sediment source partitioning and budgeting over historical timescales in a glacierized, mountain catchment ................................................................................................10 2.1 Introduction ................................................................................................................... 10 2.2 Study Site ...................................................................................................................... 13 2.2.1 Watershed Characteristics ..................................................................................... 13 2.2.2 The Fitzsimmons Creek Landslide and 1991 Event ............................................. 15 2.2.3 Previous Sediment Yield Estimates ...................................................................... 16 2.3 Data & Methods ............................................................................................................ 16 2.3.1 Geomorphic Mapping ........................................................................................... 16 2.3.2 Sediment Budgeting (Bed Material) ..................................................................... 17 2.3.3 Characterizing Potential Sediment Yield Controls ............................................... 22 2.4 Results ........................................................................................................................... 23 2.4.1 Historical Sediment Yield at the Delta ................................................................. 23 2.4.2 Sediment Budgeting: Spatial Patterns ................................................................... 24 2.4.3 Evaluating Potential Sediment Yield Controls ..................................................... 28 2.5 Discussion ..................................................................................................................... 30 vii 2.5.1 Closing the Budget ................................................................................................ 30 2.5.2 Sediment Source Partitioning ............................................................................... 33 2.5.3 Interannual Variability .......................................................................................... 34 2.6 Implications................................................................................................................... 35 2.7 Conclusions ................................................................................................................... 37 Chapter 3 - Controls on the dissimilarity between (post)glacial sediment dynamics of adjacent mountain watersheds ...................................................................................................38 3.1 Introduction ................................................................................................................... 38 3.2 Study Site ...................................................................................................................... 41 3.2.1 Geology & Topography ........................................................................................ 41 3.2.2 Hydrology & Climate ........................................................................................... 44 3.3 Data & Methods ............................................................................................................ 44 3.3.1 Geomorphic Mapping ........................................................................................... 44 3.3.2 Coupling, Confinement, & Connectivity .............................................................. 45 3.3.3 Geochronology ...................................................................................................... 46 3.3.4 Long Profile Analysis ........................................................................................... 46 3.3.5 Postglacial Erosion and Storage ............................................................................ 48 3.3.6 Sediment Yield Data ............................................................................................. 48 3.4 Results ........................................................................................................................... 49 3.4.1 Geomorphic Mapping ........................................................................................... 49 3.4.2 Channel, Valley, and Connectivity Characteristics .............................................. 52 3.4.3 Sediment Yield/Budgeting .................................................................................... 55 3.5 Discussion ..................................................................................................................... 60 3.5.1 Longitudinal Patterns of Landscape Characteristics ............................................. 60 3.5.2 Controls on Sediment Dynamics .......................................................................... 62 3.5.3 Conceptual Model ................................................................................................. 66 3.6 Implications................................................................................................................... 68 3.7 Conclusion .................................................................................................................... 70 Chapter 4 - Investigating postglacial sediment supply, storage, and yield in a large glaciovolcanic watershed .............................................................................................................72 4.1 Introduction ................................................................................................................... 72 4.2 Study Site ...................................................................................................................... 74 viii 4.2.1 Geology and Volcanics ......................................................................................... 75 4.2.2 Late Quaternary History ....................................................................................... 76 4.2.3 Lillooet River ........................................................................................................ 76 4.3 Data & Methods ............................................................................................................ 77 4.3.1 Geomorphic Mapping ........................................................................................... 77 4.3.2 Spatial Analysis of Sediment Storage ................................................................... 77 4.3.3 Geochronology ...................................................................................................... 78 4.3.4 Connectivity Analyses .......................................................................................... 84 4.3.5 Sediment Budgeting .............................................................................................. 85 4.4 Results ........................................................................................................................... 86 4.4.1 Spatial Distribution of Sediment Storage ............................................................. 86 4.4.2 Catchment-Scale Connectivity Estimates ............................................................. 88 4.4.3 Sediment Storage Timescales ............................................................................... 90 4.4.4 Sediment Budget Updates ..................................................................................... 94 4.5 Discussion ..................................................................................................................... 94 4.5.1 Holocene History of Lillooet River and Its Implications...................................... 94 4.5.2 Controls on Spatial Storage Patterns................................................................... 101 4.5.3 Scale Variations of (Post)Glacial Sediment Yield .............................................. 102 4.7 Conclusion ........................................................................................................................ 106 Chapter 5 - Contributions and Future Research Opportunities ...........................................108 5.1 Summary of Contributions .......................................................................................... 109 5.1.1 Closure of a detailed bed material sediment budget for a glacierized, mountain catchment ............................................................................................................................ 109 5.1.2 Geology, glacial history, and postglacial history control sediment dynamics and yield at a range of temporal scales ...................................................................................... 110 5.1.3 Sediment dynamics and yield are highly variable in space and time ................. 112 5.1.4 Challenges and applicability of optical dating techniques in the Lillooet River watershed ............................................................................................................................ 114 5.1.5 Timescales of sediment storage and Holocene history of the Lillooet River watershed ............................................................................................................................ 115 5.2 Future Research Opportunities ................................................................................... 116 5.2.1 Measuring bed material contributions from glacial and proglacial sources in the Fitzsimmons Cr. watershed ................................................................................................. 116 5.2.2 Comparative detailed sediment budgeting in the Green River watershed .......... 116 5.2.3 Update the Lillooet River watershed Holocene sediment budget ....................... 117 5.2.4 Further exploration of the scale- and geographic-dependence of SSY and its’ implications ......................................................................................................................... 117 References ...................................................................................................................................119 ix Appendices ..................................................................................................................................138 Appendix A ............................................................................................................................. 138 A.1 Fitzsimmons Creek Sediment Budgeting............................................................ 138 A.2 Fitzsimmons Creek Hydrometric Analysis ......................................................... 145 Appendix B ............................................................................................................................. 146 B.1 Green River subwatersheds geomorphic mapping results .................................. 146 B.2 Green River subwatersheds: longitudinal characteristics ................................... 149 Appendix C ............................................................................................................................. 151 C.1 Lillooet River watershed catchment characteristics ........................................... 151 C.2 Geochronology & Sediment Yield Data ............................................................. 153 C.3 Connectivity Results ........................................................................................... 167 x List of Tables Table 2.1: Historical bed material yield for Fitzsimmons Creek .................................................. 24 Table 3.1: Catchment characteristics for Green River watershed and its tributaries. ................... 43 Table 3.2: Geochronology data pertaining to the postglacial history of the Green River watershed. ..................................................................................................................................... 47 Table 3.3: Summary longitudinal characteristics .......................................................................... 53 Table 4.1: SAR procedure for fading measurements .................................................................... 84 Table 4.2: C-factors used in the calculation of the weighting factor ............................................ 85 Table 4.3: Optical ages from the Lillooet River watershed .......................................................... 93 xi List of Figures Figure 1.1: Conceptual model of lateral connectivity and channel characteristics ......................... 7 Figure 2.1: Fitzsimmons Creek watershed.................................................................................... 14 Figure 2.2: Key datasets used in calculating the sediment budget components. .......................... 19 Figure 2.3: Historical bed material yield and gravel removal ...................................................... 25 Figure 2.4: Example geomorphic mapping and percent geomorphic coverage ............................ 27 Figure 2.5: Sediment budget components plotted by landform .................................................... 28 Figure 2.6: Historical record of sediment yield and its’ potential controls. ................................. 29 Figure 2.7: Annual bed material sediment budget ........................................................................ 32 Figure 3.1: Green River watershed and its tributaries. ................................................................. 42 Figure 3.2: Geomorphic and surficial geology map of the Soo River watershed ......................... 50 Figure 3.3: Summary longitudinal characteristics of Soo River watershed.................................. 54 Figure 3.4: Longitudinal profile plots ........................................................................................... 56 Figure 3.5: Estimated postglacial erosion and deposition from gully-cone systems. ................... 57 Figure 3.6: Rutherford Cr. fan stratigraphic section and optical dating sampling location .......... 59 Figure 3.7: Geologic controls and surface expression in the Green River watershed .................. 64 Figure 3.8: Conceptual model of landscape controls on sediment dynamics ............................... 68 Figure 4.1: Map of the Lillooet watershed above Lillooet Lake. ................................................. 75 Figure 4.2: Single-aliquot regenerative-dose (SAR) protocol ...................................................... 82 Figure 4.3: Luminescence dating summary for sample MT-lil-02. .............................................. 83 Figure 4.4: Spatial distribution of alluvial storage areas .............................................................. 87 Figure 4.5: Spatial distribution of known large landslides ........................................................... 88 Figure 4.6: Catchment scale sediment supply, storage, and connectivity estimates. .................... 89 Figure 4.7: Holocene climate and controls on sediment dynamics and yield. .............................. 98 Figure 4.8: Holocene landslides and volcanic events ................................................................... 99 Figure 4.9: Holocene sediment yield estimates .......................................................................... 100 Figure 4.10: Valley step-controlled sediment storage hypsometry. ........................................... 102 Figure 4.11: Specific sediment yield (SSY) estimates................................................................ 103 xii List of Abbreviations ADCP Acoustic Doppler Current Profiler BC British Columbia BP Before Present Cal yr BP Calendar Years Before Present CAM Central Age Model DEM Digital Elevation Model DoD DEM of Difference ECA Effective Catchment Area FMA First Millennium Advance FVFP Fraser Valley Fire Period IC Index of Connectivity IQR Interquartile Range IRSL Infrared Stimulated Luminescence KF Potassium Feldspar LDAC Luminescence Dose and Age Calculator LGM Last Glacial Maximum LIA Little Ice Age Lidar Light Detection and Ranging LMT Lithium Metatungstate MAD Mean Annual Discharge MAM Minimum Age Model MASL Meters Above Sea Level MMVC Mount Meager Volcanic Complex MWP Medieval Warm Period OD Overdispersion OSL Optically Stimulated Luminescence RKM River Kilometer RWP Roman Warm Period SAR Single-Aliquot Regenerative-Dose SDR Sediment Delivery Ratio SSY Specific Sediment Yield SY Sediment Yield TL Thermoluminescence TRIM Terrain Resource Information Management UAV Uncrewed Aerial Vehicle VKM Valley Kilometer xiii Acknowledgements I would first like to thank my partner and best friend, McKelle. She inspired me to dream big and then was my biggest support when that meant six years of graduate school. I extend my deepest gratitude to the Geoscience Department at Utah State University, which instilled in me a passion for scientific research and provided the foundation necessary to pursue that passion. I am particularly indebted to my undergraduate thesis supervisor, Joel Pederson, who introduced me to geomorphic mapping and fundamentals of geochronology, ultimately inspiring me to pursue geomorphology as a career. To the many other individuals in that department who were incredibly motivating to me including Carol, Tammy, Alexis, Tom, Dennis, Suzanne, Jim, and Dave, thank you. I appreciate my friends and colleagues at UBC geography for making me feel at home almost immediately. I have had so many great conversations with lab mates that exposed me to new ideas, shaped how I approach science, and inspired me as a person – too many to list here. I would especially like to thank Porter Abbey, David Puhl, Conor McDowell, Kevin Pierce, and Dave Reid, who through conversations, helped shape this thesis. Many of you were also a huge help with fieldwork including Tianqi Xing, Porter Abbey, David Puhl, Sol Leader-Cole, Yunlong Lei, and Suman Bhattacharyya. Thank you to the UBC Geography Department staff for their logistical support over the years, and for keeping me on track. I greatly appreciate everyone in the Luminescence Dating Laboratory in the Department of Geoscience at the University of the Fraser Valley. They all brightened my day while working in a dark lab. In particular, Maria Schaarschmidt was always willing to take the time to train me in the lab and answer my many questions. I have been extremely lucky to have great thesis committee members over the years. Olav Slaymaker changed the way I view landscapes, taught effective communication, and got me excited about sediment budgets. Andre Zimmermann always keeps me on my toes by asking the best questions and motivating the direct application of concepts. Olav Lian matches my passion for looking into the past using geochronology methods and is always generous with his time and resources. Marwan, thank you for your supervision, kindness, and trust over the last six years. I hope I kept my promise to challenge you when you agreed to keep me on as a PhD student. You have taught me about much more than just geomorphology. xiv I thank the University of British Columbia for supporting me with a Four-Year Fellowship. This work took place on the traditional, ancestral, and unceded territory of the Xʷməθkwəy̓əm (Musqueam), Sḵwx̱wú7mesh (Squamish), St’át’imc, and Líl̓wat First Nations who have lived here since time immemorial. Western science has a lot to learn from their stewardship.1 Chapter 1 - Introduction In the recent geologic past, numerous glaciations have sculpted and reorganized approximately one-third of Earth’s total land area, including much of British Columbia (Clague, 2017). Glaciers are effective agents of erosion, producing large volumes of eroded material while carving broad U-shaped valleys and sharpening mountain peaks. Following glacier retreat, fluvial and mass wasting (hillslope) processes become dominant, redistributing glacial sediments and leading to elevated sediment yields during the paraglacial period (Church & Ryder, 1972). However, postglacial landscapes also exhibit a high degree of disconnectivity between hillslope sediment sources and river networks, with large volumes of sediment temporarily stored in valley margin and valley bottom reservoirs (Turley & Hassan, 2023). The reworking of these sediment stores can disproportionately cause a downstream channel response compared to upland sediment sources due to shorter transport distances and increased connectivity between sediment sources and the channel network (Scott & Collins, 2021). In many British Columbian watersheds, this results in a downstream increase in specific sediment yield (SSY) in contrast to unglaciated basins (Church & Slaymaker, 1989). Increased sediment in rivers may lead to more frequent and intense flooding, channel migration, water quality issues, and reservoir filling, all of which may put human lives in danger and prove expensive if not well understood. Today, climate change is causing the remaining high-mountain glaciers to retreat (Moore et al., 2009) and is increasing the occurrence of mass wasting events (e.g., Jakob & Owen, 2021). This will likely further increase the volume of sediment delivered to the channel network and exacerbate hazards. Understanding the past landscape response and degree of recovery from glaciation will put modern conditions (e.g., climate change, land-use change, etc.) in context, inform land management decisions, and help communities plan for a safer and more sustainable future. While research in paraglacial landscapes has advanced significantly (e.g., Ballantyne, 2002; McColl, 2012; Carravick & Tweed, 2021), there is a need to apply new methodologies to explore watershed-scale sediment dynamics in mountainous regions with a complex history (Slaymaker & Embleton-Hamann, 2018; Turley et al., 2021). Past studies have focused on the short-term evolution of paraglacial systems (e.g., Lane et al., 2017; Mancini & Lane, 2020), while quantitative, long-term descriptions remain limited. 2 The primary objective of this research is to better understand Holocene sediment dynamics in glacierized mountain landscapes, such as those in British Columbia (BC). The Lillooet Basin in BC (~3,765 km2) is an excellent location for this study. The basin has large paraglacial sediment stores that record the history of adjustment, and there is a wealth of research to provide background information including preliminary sediment budgeting, landslide records, glacial history, geochronology data, and both short and long-term sediment yield records at multiple spatial scales (e.g., Jordan & Slaymaker, 1991; Desloges & Gilbert, 1994; Friele et al., 2008; Schiefer et al., 2010). 1.1 Background Here we provide a brief overview of the Quaternary history of BC, as pertaining to sediment dynamics and yield (Section 1.1.1), and the conceptual approach applied in this study (Section 1.1.2). 1.1.1 Quaternary History of BC This section provides a brief summary of the Quaternary history of BC relevant to the proposed study. Tectonism and associated volcanism, glaciations, sedimentation patterns, and the most recent paraglacial period are discussed with an emphasis on southwestern BC. 1.1.1.1 Tectonics and Volcanism The present tectonic regime in Southwestern BC is a result of motion of the Pacific, Juan de Fuca, and American plates (Clague, 1989). A convergent boundary exists just off the coast of Southwestern BC that has led to the accretion of numerous terranes (with a North-Northwest structural trend) and associated mountain building during Mesozoic and Cenozoic time. Much of the Coast Range in BC is underlain by the Coast Crystalline (Plutonic) Complex composed of granodiorites, diorites, granites, and other metamorphosed rocks (Green et al., 1988; Armstrong, 1990). Subduction of the Juan de Fuca plate underneath the American plate also led to crustal melting and the formation of volcanic belts approximately 250 km inland (e.g., Garibaldi volcanic belt; Green et al., 1988). The volcanic fields are composed of stratovolcanoes built by lava flows and pyroclastic deposits (e.g., Mount Meager) and isolated basaltic flows or 3 pyroclastic cones (Green et al., 1988). Throughout the Quaternary, rivers of ice have scoured and shaped mountain valleys, as will be discussed in the following section. 1.1.1.2 Glaciation Our understanding of BC’s Quaternary history is biased by a temporal filtering of events, where the evidence of past events and glaciations has been largely eroded and overwritten by subsequent glaciations, such that only patchy stratigraphical evidence remains (Clague & Ward, 2011). However, it is clear that throughout the Quaternary, the British Columbian landscape was shaped by intense tectonic activity, and the repeated growth and decay of the Cordilleran Ice Sheet (Clague, 1986). Clague (1986, 2017) summarizes the general characteristics of Quaternary glaciations as follows: (i) During nonglacial periods, glaciers were restricted to high mountain areas, similar to today. (ii) Climatic cooling led to the expansion of alpine and valley glaciers, which at times overtopped ridges and coalesced to form the expansive Cordilleran Ice Sheet, covering much of modern-day British Columbia. (iii) Glaciations terminated with rapid downwasting and complex frontal retreat at the margins, and “top-down” wasting and stagnation in the low-lying interior, as a result of climatic warming. Eight of these cycles have occurred in the last 800 ka, each lasting about 100 ka. Ryder et al. (1991) note the asymmetry in the growth and decay of the Cordilleran Ice Sheet during the last glacial cycle (Fraser Glaciation). Ice expansion began ~29 ka and reached the glacial maxima ~14-14.5 ka. Deglaciation occurred rapidly and was largely completed by ~11.5 ka. 1.1.1.3 Sedimentation and Stratigraphy Most Quaternary sedimentary units in British Columbia are glacially sourced marine, proglacial, till, or lacustrine deposits. The nature and patterns of sedimentation have been called episodic, spasmodic, and diachronous (Clague, 1986). The stratigraphic record suggests long intervals of nondeposition, and erosion punctuated by brief periods of rapid deposition (during ice sheet growth and decay) with the spatial patterns controlled by physiography. The most rapid rates of sedimentation commonly occurred at the ice margins, which shifted over time. Sedimentation patterns following glacier retreat are further discussed in the next section with an emphasis on the most recent glacial to interglacial transition. 4 1.1.1.4 Postglacial / Paraglaciation Glaciers can have long-lasting effects on many different system characteristics including sediment yield, landscape form, and landscape disconnectivity. This phenomenon is discussed and explored in the paraglacial literature. The term paraglacial was first employed by Ryder (1971a, 1971b) in reference to misfit alluvial fans in BC. Church and Ryder (1972) expanded on this work and described a paraglacial sedimentation curve in which sediment yields are elevated during and immediately following deglaciation with yields rapidly declining back to “normal” subaerial values. In the broadest sense, the term paraglacial refers to “nonglacial earth-surface processes, sediment accumulations, landforms, land systems and landscapes that are directly conditioned by glaciation and deglaciation” (Ballantyne, 2002). Church and Slaymaker (1989) explored specific sediment yield patterns in BC and found sediment yield increases with basin area up to 3 x 104 km2. This effect is a result of the dominance of secondary remobilization of glacially sourced sediment along river valleys over primary denudation (Church & Slaymaker, 1989; Owens & Slaymaker, 1992). To this day, the sediment yield of large basins is influenced by glaciers that retreated more than 10 ka. This contrasts with previous conceptual models (based on disturbed agricultural basins) that suggested specific sediment yield decreases with increasing basin area (Walling, 1983). 1.1.2 Conceptual Approach: Sediment Disconnectivity and Storage Controlled by Landscape Organization It has long been recognized that only a small fraction of the sediment eroded within a basin will reach the outlet within a specified period (Walling, 1983). Linking onsite erosion to sediment yield (SY) at the basin outlet remains an important, but difficult challenge today. Contemporary sediment yields rarely if ever reflect primary denudation rates due to gains or losses of sediment from storage effects (Owens & Slaymaker, 1992). Therefore, approaching this problem requires greater understanding of sediment delivery processes, the efficiency of sediment transfer pathways, the spatiotemporal patterns of sediment storage, and the effect of scale. 5 In viewing the landscape as a complex system, disconnectivity can be described as the dominant, but inefficient state of the system in transferring matter and energy within and between system components. Connectivity then refers to a state of efficient transfer. Recently, several authors have suggested that (dis)connectivity provides the necessary framework to tackle some of the biggest problems in geomorphology (for recent examples, see: Najafi et al., 2021; Singh et al., 2021) including understanding sediment yields following deglaciation (Carravick & Tweed, 2021). They also note that recognizing disconnectivity, sediment sinks, and stores is the most notable deficiency in recent work (Hooke & Souza, 2021). Disconnectivity is a function of the spatial and temporal scale of measure, generally increasing with area, and decreasing with time. At most spatiotemporal scales of interest, landscapes are inefficient at transferring sediment, and disconnectivity dominates (Turley & Hassan, 2023). The issue of transferring knowledge between different scales, and the scale linkage problem remains a key challenge in geomorphology (Slaymaker, 2006; Wohl et al., 2018). Most of the established techniques for measuring sediment dynamics and yield were developed for, or are only applicable at, a certain scale (e.g., geomorphic cell, plot, hillslope, reach, small basin, large basin), and up- or down-scaling direct measurements remains difficult (Keesstra et al., 2018; Poeppl & Parsons, 2018). Because we as scientists categorize and compartmentalize the landscape and the numerical models we use to describe it, more work is needed to better model transition zones and linkages between different landscape compartments (Wohl et al., 2018). Fortunately, the rapid improvement and increased availability of high-resolution topographic data alongside GIS routines is making spatially and temporally detailed investigations possible at larger scales (Heckmann et al., 2018; Keesstra et al., 2018). Additionally, (dis)connectivity within an open systems framework provides useful tools and indices applicable across spatial and temporal scales (Heckmann et al., 2018). Wohl et al. (2018) further argue that (dis)connectivity is an emergent property of the system. For example, as the spatial area increases, individually efficient parts of the landscape within an inefficient network structure can still result in disconnectivity and low sediment yield. Slaymaker (2006) provides a useful synethesis on the effect of scale and emergent properties on sediment dynamics. At the catchment scale, sediment disconnectivity is generally influenced by the spatial arrangement and presence of disconnecting landforms, bio-geomorphometric characteristics (i.e., 6 vegetation, slope), network structure, thresholds, negative feedbacks, and the magnitude-frequency distributions of the dominant processes of sediment detachment, transport, and deposition (Turley & Hassan, 2023). Disconnectivity is essential in understanding how and why landscapes retain matter and energy and remain in a state of transience or disequilibrium. Brierley (2010) suggests that landscapes and sediment stores that reflect past climatic conditions are examples of climatic memory and note that persistence of this memory is dependent on the sensitivity/resilience and the degree of connectivity within the system. Fryirs et al. (2007a) classifies forms of disconnectivity into buffers, barriers or blankets based on whether they prevent sediment from entering the channel, disrupt sediment moving along the channel, or prevent vertical reworking of sediment through smothering, respectively. Sources of disconnectivity act to delay, disperse or disrupt the signal transfer. Recent studies have identified sources of disconnectivity that are common within glaciated landscapes and note that their effective timescales range between the individual event and tens of thousands of years (Turley & Hassan, 2023; Hassan et al., 2023). Building on previous work in glaciated landscapes (e.g., Brardinoni & Hassan, 2006; Hassan et al., 2019; Reid et al., 2021), Hassan et al. (2023) propose a conceptual model of lateral connectivity in mountainous, postglacial basins (Figure 1.1). The model includes a reference unglaciated landscape and three different postglacial landscapes that vary according to topographic features, lateral connectivity, and channel characteristics as a result of glacial history. Glacial and paraglacial landforms are considered primary controls on sediment connectivity through the intermediaries of valley width and channel confinement. In doing so, the model emphasizes the high inter- and intra-catchment spatial heterogeneity that can arise from glacial history alone. 7 In summary, linking sediment supply to sediment yield measured at the outlet requires an understanding of the efficiency of sediment transfers, and the spatiotemporal patterns of sediment storage. Sediment storage is the physical manifestation of sediment disconnectivity within the landscape (Turley & Hassan, 2023), and moderates sediment yield. Hassan et al. (2023) present a working conceptual model that explains how glacial landscape configuration controls valley geometry, lateral connectivity, and channel characteristics. This model and a disconnectivity framework can be used to investigate long term sediment dynamics and yield in complex glacial landscapes. 1.2 Research Objectives and Thesis Structure The primary objective of this research is to better understand Holocene sediment dynamics in glacierized mountain landscapes by addressing questions related to sediment supply, storage, and Figure 1.1: Conceptual model of lateral connectivity and channel characteristics in mountainous postglacial basins (modified from Hassan et al., 2023). Panel (a) presents an unglaciated basin for comparison, while panels (b-d) present three types of postglacial landscapes with varying topographic, lateral connectivity, and channel characteristics according to glacial history. 8 yield including their (i) identifiable patterns within the landscape, (ii) primary controls, and (iii) resulting spatiotemporal variability. To meet this objective, this thesis is divided into three primary research chapters each designed to answer specific research questions at a given scale. Additionally, this thesis was designed to have a spatially and temporally nested structure, with each chapter broadening the scale of analysis to facilitate discussions of scale effects. In Chapter 2, we investigate the historical bed material yield and spatiotemporal patterns of sediment dynamics in the 94 km2 Fitzsimmons Creek watershed. In particular we pose the following research questions. 1. How has sediment yield at the outlet varied throughout the historical record, and what controlled these variations? 2. Can detailed sediment budget measurements adequately explain sediment yield measured at the outlet? 3. What are the primary sources of sediment, and their relative contribution to the bed material yield at the outlet? To address these questions, we first quantify the historical bed material yield using field surveys and historical air photo analysis. Potential sediment yield controls are then identified through comparisons with a hydro-meteorological analysis. Finally, we construct a detailed sediment budget for the watershed using a multi-method measurement approach paired with geomorphic mapping. In Chapter 3, we explore the (dis)similarity of landscape characteristics and sediment connectivity patterns in adjacent tributaries of the 875 km2 Green River watershed and identify regional controls on sediment dynamics and yield throughout the Holocene. In particular we pose the following research questions. 1. Do landscape/channel characteristics and sediment connectivity vary along the mainstem channels in a predictable way? 2. What are the primary controls on sediment dynamics in postglacial landscapes? To address these questions, we first compile summary catchment characteristics for the adjacent subwatersheds to identify dissimilarities. We then map the surficial geology, estimate postglacial erosion and storage volumes, and quantify hillslope-channel coupling, channel confinement, and 9 sediment connectivity along the mainstem channels. These data are used to identify longitudinal patterns and ultimately construct a conceptual model describing the primary controls on sediment dynamics. In Chapter 4, we investigate the spatiotemporal patterns of sediment storage and yield within the 3,765 km2 Lillooet River watershed throughout the Holocene. In particular we pose the following research questions. 1. Where is sediment being stored within the system, and for how long? 2. What controls the patterns of sediment storage and yield? 3. How variable is sediment yield in time and space? To address these questions, we map sediment storage landforms and synthesize all available geochronology data, to which we add additional luminescence and radiocarbon dates. We then calculate sediment connectivity for the entire watershed to explain sediment transfer patterns. We also contribute additional sediment yield estimates. Finally, we synthesize previously published records of sediment yield controls (i.e., climate, fire frequency, glacier advances), sediment supply events (i.e., large landslides, volcanic eruptions), and specific sediment yield for the entire Holocene. Finally, Chapter 5 provides a summary of contributions and broader implications of this research and identifies future research opportunities. The key themes of the thesis identified in this chapter are discussed at a range of spatial and temporal scales based on findings from all three research chapters. 10 Chapter 2 - Sediment source partitioning and budgeting over historical timescales in a glacierized, mountain catchment 2.1 Introduction Managing and living with geohazards is especially challenging in mountain landscapes. Steep topography, glacier retreat, and extreme temperature and precipitation patterns can result in rapid landscape change and high sediment yields. In turn, increased sediment in rivers may lead to more frequent and intense flooding, channel migration, water quality issues, and reservoir filling, all of which may put human lives in danger and prove expensive if not well understood. With a growing human presence in mountain environments (Ehrlich et al., 2021), risk is amplified, and hazard management is increasingly complex. Therefore, it is important to gain a better understanding of the factors that control the amount of sediment in rivers, and how the amount varies. Humans are not only affected by the landscape, but they also actively alter it, and in so doing alter sediment dynamics and yield. For example, watersheds that have been ‘disturbed’ through clear-cut logging (Roberts & Church, 1986) often have higher sediment yields. Glaciers are also effective agents of landscape change (Koppes & Montgomery, 2009) shaping the landscape and producing large volumes of eroded materials, which can lead to elevated sediment yields following glacier retreat (i.e., paraglacial period; Church & Ryder, 1972; Zhang et al., 2022). When glacial sediment stores are readily accessible to the river, they can disproportionately cause a downstream response (Scott & Collins, 2021). In many glaciated watersheds, this results in a downstream increase in specific sediment yield in contrast to unglaciated watersheds (Church & Slaymaker, 1989). As a result of climate change, mountain communities may experience earlier spring snowmelt and a shrinking snowpack that dramatically reduces summer low flows (Kang et al., 2016), and intensified rainfall and rain-on-snow events that leads to an increase in flooding (Dethier et al., 2020), mass wasting (Jakob & Owen, 2021), and sedimentation (Cache et al., 2023). Because sediment yield, at the outlet is the result of complex sediment dynamics that absorb, shred, or amplify tectonic, climatic, and anthropogenic forcings (Jerolmack & Paola, 2010; Tofelde et al., 2021), we cannot rely on point-based measurements alone. This is particularly true 11 for bed material, which is more likely to enter storage. Few systematic catchment-scale studies exist that partition SY measurements by sediment source and grain size. It can therefore be challenging to estimate the relative contribution of sediment sources, and attribute sediment yield at the outlet to particular events or transfer processes. Therefore, more research is needed that investigates sediment dynamics over multiple scales (spatial and temporal) to allow for cross-scale analysis (Ke & Zhang, 2023). Spatially explicit methodologies need to be applied to explore watershed-scale sediment dynamics in mountainous regions with a complex history (Slaymaker & Embleton-Hamann, 2018; Turley et al., 2021). Sediment budgeting is a valuable framework for understanding complex dynamics in glacierized, mountainous catchments that have been modified by human development (Slaymaker, 2003). A sediment budget is a quantitative description of sediment sources, sinks, pathways, and transfer processes (Dietrich et al., 1982; Reid & Dunne, 1996). As a conceptual framework, sediment budgeting links disparate parts of the system, facilitating interpretation of complex system dynamics, and providing a practical management tool (Slaymaker, 2003; Walling & Collins, 2008). A complete budget incorporates clastic (i.e., bedload, suspended load), solute, and nutrient components, as well as the relevant processes of between-component transfer (Slaymaker, 2004). However, such detailed studies are usually not possible due to time and financial limitations. Many methods exist to quantify different components of the budget including a hydro-climatological approach supplemented by lake-based sediment core proxies (suspended load; e.g., Desloges & Gilbert, 1994; Menounos et al., 2006; Schiefer et al., 2006, 2010; Heideman et al., 2018), geochemical methods (dissolved load; e.g., Slaymaker et al., 2003), and morphological methods (bed material load) including reconstructing paleosurfaces and differencing erosion (e.g., Brooks, 1994; Tunnicliffe et al., 2012), mapping and simplified geometric calculations of storage and erosion volumes (e.g., Campbell & Church, 2003; Tunnicliffe & Church, 2011), and net change analysis from repeat topographic (often lidar) surveys (e.g., Anderson & Pitlick, 2014; Savi et al., 2023). Sediment budgeting comes with several challenges and limitations. First, the conclusions drawn from sediment flux measurements are influenced by the temporal and spatial scale over which these fluxes are measured (Slaymaker, 2006), as well as the frequency of surveys (Lindsay & Ashmore, 2002; Saletti et al., 2015). Sediment yields are highly episodic meaning that short-term 12 measurements often underpredict the average sediment yield (Kirchner et al., 2001). Conversely, if short-term measurements capture a large event, the average sediment yield may be significantly overpredicted. Surveyed volumes can also be negatively biased as a result of compensating volumes of erosion and deposition between surveys (Lindsay & Ashmore, 2002). The requirement of additional considerations with increasing spatiotemporal scale, known as emergent properties, complicates transferring knowledge between scales (Slaymaker, 2006). This becomes problematic considering that detailed process-based sediment budgets aren’t usually constructed for catchments larger than a few tens of square kilometers in size, and for periods longer than a few decades. Traditionally, different research disciplines use measurement strategies appropriate to only specific spatiotemporal scales, further complicating cross-scale comparisons (Hinderer, 2012). Second, bed material sediment budgets, which refer to the sediment that forms the bed and lower banks of a river (Church, 2006), become rarer as basin size increases. Among other reasons this is due to measurement challenges. Additionally, because the bedload to suspended load ratio is highly variable, it can be challenging to infer the bed material load from other components of the budget (Schiefer et al., 2010). In this paper, we seek to address several of the aforementioned challenges and limitations. In doing so we quantify and describe the bed material yield and spatiotemporal patterns of sediment dynamics for a glacierized, mountainous catchment in British Columbia. In particular we pose the following research questions. 1. How has sediment yield at the outlet varied throughout the historical record, and what controlled these variations? 2. Can detailed sediment budget measurements adequately explain sediment yield measured at the outlet? 3. What are the primary sources of sediment, and their relative contribution to the bed material yield at the outlet? To address these questions, we leverage a suite of techniques to measure and quantify sediment transfers at several locations throughout the watershed to better constrain the spatial and temporal patterns. First, we quantify the historical bed material yield (1947-2023) using field surveys and historical air photo analysis. Second, we utilize high-resolution, multi-temporal lidar data (2014-2018) and detailed geomorphic mapping to perform net change analysis by landform 13 along the middle and lower channel, and identify sediment sources, sinks, and transfer pathways. These values are further constrained by estimates of sediment passing through a run-of-river facility. Third, we resolve the historical SY and detailed sediment budgeting results and discuss spatiotemporal sediment dynamics, sediment source partitioning, and potential control on sediment yield. Finally, we discuss the implications of this research in the context of informing sediment management strategies in the area. We selected the Fitzsimmons Creek watershed for this study based on the availability of high-resolution topographic data and published measures of historical suspended and dissolved load components, and because it is a seemingly closed system with respect to bed material. 2.2 Study Site The Fitzsimmons Creek watershed, located in Southwest BC, Canada covers approximately 94 km2 of mountainous terrain. By the late 19th century, the region was occupied by settlers for trapping, fishing, and mining. Today the region has more than 15,000 inhabitants, many of whom live on the Fitzsimmons Creek fan, which is actively aggrading, leading to sediment and flood management challenges (Figure 2.1). In response to this issue, intermittent gravel removal began in the 1970’s, and it has been ongoing on an annual basis since the 1990’s from the lower reaches of the creek. While gravel removal is a common flood management strategy in channels with fixed banks and high sediment loads, there are also concerns of negatively impacting fish and aquatic species habitat. 2.2.1 Watershed Characteristics Elevations in the Fitzsimmons Creek watershed range from 635 to 2,684 meters above sea level. Three primary bedrock geologies exist within the watershed (Monger & Journeay, 1994). The Fitzsimmons Range along the southern edge of the watershed is primarily composed of marine sedimentary and volcanic rocks of the Gambier Group. The valley bottom is characterized by andesitic volcanic rocks of the Whistler Pendant, while the Blackcomb Range and upper watershed is primarily composed of intrusive quartz diorite. These lithologies are separated by faults striking nearly parallel to the valley. Repeated glaciations throughout the Pleistocene deepened and widened the valley, and carved cirques, horns, and aretes at high elevations. 14 Today, approximately 7% of the watershed is glaciated, down from ~12% during the Little Ice Age, which locally occurred between the early 1700’s AD to the 1920’s AD (Osborn et al., 2007). As more expansive glaciers advanced and retreated in the past, glacial sediments were also deposited throughout much of the watershed, particularly in the valley bottom. Glacial Figure 2.1: Fitzsimmons Creek watershed. Inset maps are of the (a) delta, (b) canyon reach with landslides, and (c) run-of-river intake headpond mentioned in the text. The numbering of landslides in panel (b) corresponds to the numbering in Figure 2.4 and Figure 2.5. 15 sediments along the valley bottom are primarily till, however, along the lower valley there are glacio-lacustrine deposits likely deposited at a time when the main trunk glacier in the Green River valley impounded glacial meltwater from the Fitzsimmons Creek valley (Blais-Stevens, 2008). There are two representative weather stations at different elevations in the area (Figure 2.1). At 658 masl, the mean annual precipitation is 1,260 mm (~30% snowfall) and mean monthly temperature ranges from 16.7˚C in August to -2.8˚C in December (Environment Canada station 1048898). At 1835 masl, the mean annual precipitation is 1,730 mm (~68% snowfall) and mean monthly temperature ranges from 14.5˚C in August to -8.5˚C in December (Environment Canada station 1108906). Bio-geoclimatic zones within the watershed include Coastal Western Hemlock within the valley bottom, Mountain Hemlock & Engelmann Spruce at mid-elevations, and Coastal Mountain-Heather Alpine at high elevations (Salkeld et al., 2014). Fitzsimmons Creek emanates from the Fitzsimmons glacier and flows for ~19.7 kilometers before spilling into Green Lake (Figure 2.1). However, for consistency, all measurements are mapped onto the valley centerline (~17.5 km in length), divided into 100-meter increments, and denoted as valley kilometers (VKM). Along its length, Fitzsimmons Cr. flows through glacial forefield (VKM 0-6), broad valley (VKM 6-10.5), canyon (VKM 10.5-13.5), fan (VKM 13.5 – 17) and delta (VKM 17-17.58) reaches. The creek has a primarily wandering, gravel-bed planform, with a boulder to cobble step-pool reach within the canyon zone, and discrete confined single-thread reaches. At the delta front, the bed material load is composed of coarse sand to fine gravel. Fitzsimmons Creek discharge is characterized by a mixed hydrologic regime, with the two dominant flood-generating mechanisms being spring snowmelt and fall or early winter rain or rain-on-snow events (Water Survey of Canada Station 08MG026). Over the 29 years of record, the mean annual discharge was approximately 4 m3 s-1, and the annual instantaneous peak discharges range from ~15 to 70 m3 s-1. 2.2.2 The Fitzsimmons Creek Landslide and 1991 Event An approximately 1 x 106 m3 slow-moving mass of soil and till material, later named the Fitzsimmons Creek landslide (FCLS), was first reported in the 1970’s along the lower reaches of Fitzsimmons Creek within the canyon reach (Figure 2.1; Golder Associates Ltd., 1993). In August 1991, the FCLS slid several meters and momentarily blocked the channel following five 16 days of unseasonably heavy rain. During this event, the debris flood was up to 4 to 5 meters thick, river discharge reached ~100 m3 s-1 (highest in record), many bridges were destroyed, and an estimated 128,000 m3 of sediment was deposited on the fan. Following the event (in 1992) remediation efforts included the removal of ~128,000 m3 of gravel and ~1,000 trees from the fan zone (Nichols, 1992). In part, the 1991 event also motivated the construction of a debris barrier at approximately VKM 13.5, and annual gravel removal from the Fitzsimmons fan to maintain freeboard (Figure 2.1). Chronic landsliding occurs within the canyon reach due to channel incision through glacial valley fill deposits resulting in steep, unstable slopes downstream of a knickpoint (VKM 10.5). 2.2.3 Previous Sediment Yield Estimates Previous researchers, who partitioned the total sediment load based on the mechanism of transport, have quantified the bedload, suspended load, and dissolved load fractions within the Fitzsimmons Creek watershed, primarily using sedimentation patterns in Green Lake as a proxy (Menounos et al., 2006; Schiefer et al., 2006; Schiefer et al., 2010). They found that the bedload to suspended load ratio varied by more than a factor of 3 over historical timescales (from 80 to 270). They also found the total yield to be comprised of 35% suspended load, 50% bedload, and 15% dissolved load over historical timescales (Schiefer et al., 2010). These values suggest that the proportion of bedload in this catchment is relatively high. Suspended sediment yield was found to closely relate to glacier retreat, flood events, and landsliding (Schiefer et al., 2006). 2.3 Data & Methods The datasets and methods employed in this study range from geomorphic mapping and surveys collected in the field, to net change analysis on available lidar data. Brief descriptions are provided in the following paragraphs, while detailed descriptions can be found in the supplementary materials. 2.3.1 Geomorphic Mapping Geomorphic mapping was completed for the entire Fitzsimmons watershed at a 1:8,000 scale based on field observations and a desktop analysis using available elevation (i.e., 1-meter lidar 17 for lower ~67 km2, 25-meter DEM for upper 27 km2), and imagery data. Mapping is based on a simplification of the BC Terrain Classification System (Howes & Kenk, 1997) and generally includes surficial materials (indicative of formative processes; e.g., fluvial, colluvial, glacial, etc.) and surface expression (e.g., fan, cone, blanket, veneer, terrace, etc.). Specific units mapped include anthropogenic sediments (sediments and landforms reworked by humans), active channel, floodplain, organics, terraces, alluvial fans, deltas, debris cones, colluvial blankets/veneers, talus, till blankets/veneers, ice contact sediments, glaciers, and bedrock. Additionally, linear features were mapped including cirques, moraines, confining margins, flood protection structures, and rivers. This mapping builds on work from others, especially a 1:50,000 surficial geology map by Blais-Stevens (2008) along the main Green River valley (lower Fitzsimmons watershed; VKM 10 to outlet). Bedrock was manually delineated, and then specific rock types were labeled based on provincially available geology data (Cui et al., 2017). In this study, geomorphic mapping provides a basis for quantitative sediment budgeting. 2.3.2 Sediment Budgeting (Bed Material) The sediment budget is based on diverse datasets and methodologies, including air photo analysis, an aerial drone survey, bathymetric surveys using an echo sounder and Acoustic Doppler Current Profiler (ADCP), and net change analysis using multi-temporal lidar data. These methods and data are complemented by direct field observations. Through these various techniques we quantify the volume of bed material as defined by Church (2006), in this case constituting sediment of sizes > 1mm in diameter. In the following paragraphs, we describe the methods employed in this study grouped by the location of sediment yield measurements. 2.3.2.1 The Outlet (Delta) Sediment yield at the outlet was calculated based on an air photo analysis to quantify progradation area between consecutive air photos, and a combination of bathymetric and GPR surveys to quantify the receiving basin depth and thus the volume of sediment deposited. Air photo analysis was completed using Agisoft Metashape software on historical air photos retrieved from the University of British Columbia Geographic Information Centre for the years 1947, 1964, 1974, 1977, 1980, 1982, 1989, 1990, 1994, 1999, 2005, 2006, 2016. The historical 18 air photos were georeferenced in ArcGIS Pro using identifiable fixed points in the landscape, such as the BC Rail bridge crossing at Fitzsimmons Creek. Aerial imagery was also collected in 2023 using a DJI drone. The drone images were stitched together and georeferenced in Agisoft Metashape, using ground control points collected on an EMLID RS2 GNSS RTK unit (Figure 2.2a). After compiling all of the available imagery, the delta topsets were carefully delineated, and the progradation area between consecutive images was calculated. The depth of the receiving basin and sediment accumulation was calculated using several different datasets. Pelpola and Hickin (2004) calculated 1-meter paleolake contours (i.e., depth of the receiving basin in front of and below the 1999 delta) based on GPR and bathymetric surveys. Using photogrammetry software and the drone imagery taken in 2023, we produced a 25cm resolution digital surface model (DSM), which we then classified as vegetation, ground surface, and water surface to produce a bare-earth raster of the 2023 delta surface (Figure 2.2a). Sedimentation depths were calculated by subtracting the paleolake surface from the 2023 surveyed surface. Additionally, we collected multibeam bathymetric data of the delta front and proximal receiving basin using an ADCP unit pulled behind a canoe. A sound velocity cast was not performed which increases the measurement error. However, because we are only using these measurements to estimate sedimentation in the proximal basin (past the delta front; Figure 2.2a), and not for the sediment yield directly, we believe these limitations are acceptable. The multibeam data were converted to ASCII files using WinRiver II software and then checked against manually measured water surface elevations for quality control. Finally, VMT software was used to produce vertical depths. The delta progradation volume was then calculated by multiplying the progradation area by the depth of sedimentation. Only volumes are presented in this text for consistency. However, volume may be converted to mass using an average bulk density of 1.60 ± 0.10 x 103 kg m-3 reported by Pelpola and Hickin (2004) for this delta. 2.3.2.2 Lower Watershed (VKM 3 to Outlet) Sediment budgeting was completed for Fitzsimmons Creek based on net change analysis using 1-meter lidar data from 2014 and 2018. The datasets were first co-registered using a terrain 19 Figure 2.2: Key datasets used in calculating the sediment budget components. Panels show 2023 composite bathymetric and DSM data (a), and DoD results centered on landslide 1 (b) and a location with prominent bank erosion (c). Flow direction is from right to left in panels (b) and (c). matching technique described by Anderson and Pitlick (2014). The random error uncertainty (𝑈𝑟𝑒) was calculated following equation 1, where 𝜎𝑟𝑒 is the standard deviation of unresolved errors between the two datasets, and 𝐴 is the area. 𝑈𝑟𝑒 = 𝜎𝑟𝑒√𝐴 20 Systematic uncertainty (Usys) was then calculated following equation 2, where ∆𝑒𝑙𝑒𝑣̅̅ ̅̅ ̅̅ ̅̅ is the unresolved mean elevation differences between the datasets. 𝑈𝑠𝑦𝑠 = ∆𝑒𝑙𝑒𝑣̅̅ ̅̅ ̅̅ ̅̅ ∗ 𝐴. A limit of detection (LoD), defined here as the 2.5% and 97.5% cumulative alignment differences was applied for gross measurements, but not for net change analysis (Anderson, 2019). See the supplementary materials for a more detailed description of the lidar analysis. The 2014 and 2018 lidar datasets both cover approximately the lower three quarters of the watershed, from VKM 3 to the outlet. The analysis was restricted to active areas, primarily on the valley floor, to avoid including large areas of insignificant change and unnecessarily increasing the uncertainty. Gross erosion/deposition and net change were calculated every 100 meters along the valley centerline differentiated by the source geomorphic units. The geomorphic units in this analysis include active channel, floodplain, delta, and landslides (Figure 2.2). Annual gravel removal estimates from the alluvial fan (VKM 13.5-17), which have occurred since 1992, supplement the delta SY and net change analysis results. 2.3.2.3 Mid- and Upper-Valley Estimates At VKM 9.2 there is a run-of-river intake headpond that regularly fills with coarse sand to cobble-sized bed material (Figure 2.1c). This sediment is “flushed” or “sluiced” past the gate, with assistance from an excavator as needed. However, no sediment is removed from the creek in this process. During all flow conditions, fine sediment is transported over the weir and through the Coanda screen where it is washed through the penstock and into the tailrace before entering the tailrace pond and finally rejoining Fitzsimmons Creek. Additionally, a fraction of the coarse material passing through the headpond (as large as cobble-sized sediment) is transported over the weir during high flows and when the sediment wedge has aggraded to the top of the Coanda, effectively reducing the trapping efficiency. The headpond is therefore a leaky sediment trap and volumetric flushing measurements underestimate the sediment yield. This structure presents a unique opportunity to estimate the minimum sediment yield past this point (Micheletti & Lane, 2016). To do so, we compiled the recent flush history, and estimated the volume of sediment excavated each time. During on-site communication, it was determined that the excavation occurs in the same manner and over approximately the same area and depth each time. 21 2.3.2.4 Uncertainty Each of the datasets and methods in this study has its own assumptions and uncertainties. DEM-based uncertainty was estimated for each of the sediment budget components following methods outlined in Anderson and Pitlick (2014; see supplementary materials for details). In addition to the DEM-based uncertainty, we estimate the sediment yield at the delta to have an uncertainty of ± 20%. This value is similar to, but slightly more conservative than estimates by Pelpola and Hickin (2004) and is meant to account for errors and uncertainties in the air photo georeferencing, delta topset delineation, and resolution differences between the paleolake and 2023 surface estimates. We estimate the gravel removal volume uncertainties to also be ± 20%, accounting for truck count to volume discrepancies. Often 2 or 3 independent gravel removal estimations were completed and were found to be within this value. We would expect our sediment yield estimates at the headpond and upper valley to have uncertainties on the order of ± 50%. This largely accounts for the interannual variability in flushing events as well as flushing area and depth. We also acknowledge that comparing volumetric measurements of sediments with different bulk densities will introduce additional uncertainty. However, we expect this uncertainty to fall within the range discussed above. Finally, in the next section, we estimate the bed material fraction of sediment sources, which will contribute uncertainty to our volumetric estimates. 2.3.2.5 Bed Material Corrections The grain size distribution, and subsequently the proportion of sediment that will participate in bedload transport and morphological change, of material supplied to the channel varies dramatically depending on the source. Given that there are no significant contemporary sediment storage reservoirs along Fitzsimmons Creek, and that the material deposited in the delta is primarily fine sand and coarser, we would expect the intermediate storage potential of silt and clay to be low, and for this fraction to be deposited directly in Green Lake as annual varve couplets (Schiefer et al., 2010). We therefore consider it reasonable to exclude the expected silt and clay fraction from sediment sources in order to close the sediment budget. Based on the surficial mapping and sediment source areas considered in this study, sediment sources include reworked fluvial sediments (within the active channel and floodplain), and till 22 (supplied from bank erosion and landsliding). In an unpublished geotechnical investigation of the FCLS (a primary sediment source, Figure 2.1), grain size distribution data from boreholes and bulk sediment samples suggest that 30% or more (up to 86%) of the till material (including the large clasts) is silt sized or finer (Golder Associates Ltd., 1993). While diamictons are inherently heterogenous making it difficult to define a representative grain size distribution, we believe that these estimates are reasonable based on other studies (Vorren, 1977). 2.3.3 Characterizing Potential Sediment Yield Controls Because local weather records only encompass the 1978-2023 period, decadal changes in the regional climate were approximated using the mean annual value of the Pacific decadal oscillation (PDO) index obtained from the National Oceanic and Atmospheric Administration. PDO has been shown to influence the storm track for the region and correlate with regional temperature and precipitation patterns (Anderson & Konrad, 2019; Whitfield et al., 2010). In southwest British Columbia, negative PDO phases are linked to cooler, wetter conditions, positive glacier mass balance, and higher streamflow, while positive PDO phases correlate with warmer, drier conditions, negative glacier mass balance, and lower streamflow (Whitfield et al., 2010). Hydrometric data for Fitzsimmons Creek are limited to the 1994-2023 (29-year) period and are subject to an unstable bed and rating curve due to high sediment transport. We therefore extend the record using a reliable, long-term record from the nearby Lillooet River (Water Survey of Canada Station 08MG005; 1914-2023) as a proxy. Although there is a large difference in watershed area (Lillooet watershed is 2,150 km2) the discharge records are strongly correlated (Spearman’s rank statistic = 0.93, p < 0.0001; Schiefer et al., 2006; Figure A. 6). Maximum daily discharge measurements are normalized by drainage area to facilitate direct comparisons. Additionally, we calculate the total annual streamflow volume above the 10% exceedance interval (Lillooet River: 740 m3 s-1; Fitzsimmons Creek: 24 m3 s-1) which integrates both streamflow intensity and frequency. Historical glacier terminus positions (1920-2002) and their rates of change were obtained from Osborn et al. (2007) and extended to 2023, using Overlord Glacier as a representative example. 23 We also assess the impact of land use changes such as timber harvesting, urbanization, and flood management practices. Potentially disruptive development and land use changes have been ongoing since the Pacific Great Eastern Railway first reached the valley in 1914, making it a hub for mining and logging. Timber harvest data from the BC data catalogue shows that harvesting occurred mainly in valley bottom areas and did not leave a riparian buffer. In 1975, construction began on the new town center on the Fitzsimmons Creek alluvial fan. At this time, and over the following decade a training wall and dikes were constructed to manage flooding. Later, between 2008-2010 a run-of-river intake structure (VKM 9.2) and debris barrier (VKM 13.5) were constructed. 2.4 Results 2.4.1 Historical Sediment Yield at the Delta Deposition of bed material load occurs in a Gilbert type delta as the Fitzsimmons Creek distributary channels flow into Green Lake. Deposition primarily occurs directly at the mouth of these channels, rather than across the entire delta, resulting in the development of progradation lobes. This can be seen in the historical aerial imagery (Figure A. 5). Our delta sediment yield estimates were compared with those of Schiefer et al. (2010) for the overlapping period (1947-2006) and found to be within 1%. We therefore have confidence in the consistency and comparability of the record. On average, 430 m2 of land has been added to the delta each year since 1947 (Table 2.1). The average (weighted) annual SY as measured at the delta is ~7,030 m3 yr-1. However, SY fluctuates considerably over time, ranging from 2,720 to 14,458 m3 yr-1 when measured approximately every decade. Annual SY is expected to be even more variable. Additionally, since 1992, an average of ~12,500 m3 of gravel is removed annually just upstream of the delta to maintain freeboard (Figure 2.3). Adding the gravel removed and the sediment accumulated in the delta together results in an annual SY of ~12,300 m3 yr-1 over the last 76 years. This is likely a minimum estimate given that periodic gravel removal occurred prior to 1992 with little documentation. Total sediment yield (SY at delta and gravel removal) varied by a factor of ~17. If we exclude the 1990-1999 period as an outlier, the remaining SY values fall within two standard deviations of each other, where one standard deviation is ~45% of the 24 weighted mean. However, SY during the 1990’s, stands out at nearly nine standard deviations above the mean. Table 2.1: Historical bed material yield for Fitzsimmons Creek (this study). Start End Area Annual SY (yr) (yr) (m2) (m3 yr-1) 1947 1964 9,649 9,399 1964 1974 5,733 9,723 1974 1977 721 4,220 1977 1980 473 2,904 1980 1982 1,132 9,178 1982 1989 1,763 4,340 1989 1990 172 2,720 1990 1994 3,434 13,910 1994 1999 4,551 14,458 1999 2005 1,285 3,227 2005 2006 343 5,456 2006 2016 1,830 2,869 2016 2023 1,498 3,254 Historical Average: 7,030 2.4.2 Sediment Budgeting: Spatial Patterns 2.4.2.1 Conceptual Understanding of Sediment Budget Components Our conceptual understanding of the sediment sources, sinks, pathways, and transfer processes within the Fitzsimmons Creek watershed comes primarily from geomorphic mapping, and fieldwork observations. The primary sources of sediment within the watershed include bedrock, and glaciogenic sediment. Bedrock is exposed or near the surface for one third of the watershed area, while glacial sediments (i.e., ice-contact, till) cover approximately 44% of the watershed area. Secondary sources of sediment include hillslope (i.e., colluvium, talus, landslides), valley margin (i.e., cones, fans), and valley bottom (i.e., active channel, floodplain, terraces) landforms. These secondary sources of sediment cumulatively cover ~15% of the watershed by area. The primary processes that deliver hillslope sediments to the channel include mass wasting processes (e.g., debris flows, rockfalls, snow avalanches), and bank erosion. This is evident in the numerous debris cones within the watershed, many of which have been eroded at their toes (Figure 2.4). Valley fill till and glaciolacustrine sediments are an important source of sediment 25 Figure 2.3: Historical bed material yield and gravel removal from Fitzsimmons Creek (1947-2023). The 1930-1947 period is only estimated or assumed as noted in Schiefer et al. (2010) and is excluded from averages. within the canyon reach of the watershed (VKM 10.5-13.5), where the channel has incised into these sediments creating unstable hillslopes prone to mass wasting. 2.4.2.2 Surveys and Yield at the Headpond The recent flushing history for the run-of-river intake headpond on Fitzsimmons Creek suggests that flushing occurs on a semi-annual to annual basis (on average 1.5 times each year). During the sluicing event in October 2023 observations of the excavation area, depth, and sediment size were taken. Before sluicing, sediment had accumulated in the headpond to the elevation of the Coanda. The excavation occurs over approximately 1,800 m2, removing sediment to a depth of 26 about 1.25 ± 0.5 meters below the October 2023 pre-flush aggradation surface. At this depth the excavator hits a coarser and more indurated layer that likely represents the lower limit of the active channel scour depth. Given that sediment is flushed on average 1.5 times each year, the estimated volume of sediment trapped in the headpond is 3,375 ± 1,350 m3 yr-1. Given that the headpond is a leaky sediment trap, the trapped volume represents a minimum estimate of the sediment transported past this point (VKM 9.2) each year. 2.4.2.3 Multitemporal Lidar Analysis Figure 2.5 illustrates the patterns of gross erosion, deposition, and net change along the Fitzsimmons Creek valley centerline every 100 meters. The volumes have been annualized to be directly comparable to other volumetric measurements. The system appears to be net erosional from approximately VKM 3 (beginning of lidar coverage) to VKM 17 near the outlet. Looking at the net volume of sediment sourced from each reach we see a downstream increase in supply (Glacial Forefield: 0.4 x 103 m3 yr-1 km-1; Broad Valley: 1.65 x 103 m3 yr-1 km-1; Canyon: 2.41 x 103 m3 yr-1 km-1with landslides supplying: 1.85 x 103 m3 yr-1 km-1) up until the fan and delta, where the channel is net aggradational (Fan: -1.63 x 103 m3 yr-1 km-1; Delta: -7.22 x 103 m3 yr-1 km-1). Two locations stand out as particularly active. First, just downstream of VKM 7, extensive bank erosion occurred, mobilizing ~1,250 m3 yr-1 of sediment. However, this was compensated by ~550 m3 yr-1 of deposition in the channel immediately adjacent (Figure 2.2c). Second, landslides along the canyon reach (VKM 10.5-12.5) together mobilized up to 7,980 m3 yr-1. However, about two thirds of this sediment was deposited at the base of the slope, or temporarily stored on the floodplain or in the active channel (Figure 2.2b). Table A. 1 provides a summary of the sediment budgeting results, combining values from the multi-temporal lidar net change analysis, sediment yield at the delta, gravel removal, and headpond estimates. Calculating a simple sediment delivery ratio (net change / gross erosion) based on values from Table A. 2 results in moderately high values (0.57-0.73) from the upper watershed to the fan, where a large proportion of the sediment is removed, and the ratio is reduced to 0.34. This value is further reduced to 0.23 on the delta. 27 Figure 2.4: Example geomorphic mapping and percent geomorphic coverage (total watershed). Note: each inset map is displayed at a different scale for illustration, while all mapping was completed at an approximate 1:8,000 scale (scale shown in c). Landslide numbering in panel (c) corresponds to numbering in Figure 2.1 & Figure 2.5. 28 2.4.3 Evaluating Potential Sediment Yield Controls Regional climate was approximated using an 11-year running average and cumulative departure of the annual PDO index (Figure 2.6). The PDO cumulative departure negatively correlates with glacier mass balance. Key shifts in climate and glacier advance/retreat occurred in 1925, 1947, 1978, and 1998, starting from a cold PDO phase in 1920. Positive PDO years correspond to nival floods, while negative PDO years correspond to pluvial floods, with exceptions in 1975, 1991, and 2021. Flood frequency on the Lillooet River has increased over the last half century, with nine of the ten largest floods occurring post-1968. Landslide activity, although not extensively documented, often coincides with flood events (e.g., 1991, 2003). Figure 2.5: Sediment budget components plotted by landform every 100m along the valley centerline for the period 2014-2018. Uncertainty bars (vertical black bars) are plotted for gross erosion/deposition for each landform. 29 Relating hydro-meteorological events to bed material yield at the delta is complex. Elevated yields between 1990-1999 are linked to the 1991 flood and landslide. Outside this period, positive PDO phases (warm and dry) correlate with lower sediment yields, while negative PDO phases (cool and wet) correlate with higher yields. Glacier advance/retreat shows only a tentative correlation with sediment yield, which weakens post-1991. Flood events alone do not fully Figure 2.6: Historical record of sediment yield and its’ potential controls. Panels include timber harvesting (a), bed material yield and gravel removal (b), maximum daily discharge for Lillooet River in grey and Fitzsimmons Creek in blue (c), glacier advance/retreat rates (d), and the annual PDO index (e). Engineering reports (e.g., Golder, 1993) and anecdotal evidence suggest heavy rain, landslides, and/or flooding occurred on Fitzsimmons Creek for each of the Lillooet River floods dated in panel (c). 30 explain sediment yield patterns; five of the ten largest floods on the Lillooet River occurred between 1975 and 1990, a period of low sediment yield. Human disturbances further complicate these correlations. Intense timber harvesting between 1947-1950 may partly explain the increase in sediment yield beginning in 1947. Development on the fan, including berms, dikes, and possible undocumented gravel extraction in the late 1970s and 1980s, may account for lower sediment yields during that time. 2.5 Discussion In the following paragraphs, we describe the bed material sediment yield and spatiotemporal patterns of sediment dynamics for the Fitzsimmons Creek watershed, a glacierized, mountainous catchment. We do so by first presenting methods for closing the 2014-2018 sediment budget. Although the measurement period is short, we expect the 2014-2018 budget to be somewhat representative of an “average” period given that there were no extreme precipitation or discharge events during this time, and that gravel removal volumes and SY at the outlet roughly match historical averages. However, we acknowledge the explanatory limitations given spatiotemporal variability. We then discuss sediment source partitioning (spatial patterns), and the interannual variability in sediment yield (temporal patterns) and potential controls. 2.5.1 Closing the Budget Bed material SY measured at the delta and detailed sediment budgeting results are remarkably similar, which lends support for the accuracy of both types of measurements (Figure 2.7). While general agreement exists between the measurement techniques, an estimated 7.85 ± 9.99 x 103 m3 (~23% of total erosion) more erosion occurred than deposition between the sediment sources and the delta front. In the following paragraphs we discuss possible explanations for this discrepancy. We expect that a combination of these scenarios (and perhaps others) explains the excess sediment in the sediment budget. 2.5.1.1 Cumulative Uncertainty The first thing that should be considered is the cumulative uncertainty. In propagating the uncertainty of individual measurements, we end up with 9.99 x 103 m3 of uncertainty at the 31 outlet. This alone could explain all of the excess sediment. The highest proportion of uncertainty comes from active channel and floodplain sources (DEM-based uncertainty), each accounting for ~30% of the total, due to the relatively large area of analysis. Gravel removal estimates are the next highest source of uncertainty, explaining ~20% of the total. Finally, delta sedimentation and mass movement sources each account for ~10% of the total uncertainty. 2.5.1.2 Proximal Basin Sedimentation While we do not expect any of the bed material load fraction to be transported past the delta front by Fitzsimmons Creek initially, some redistribution is likely. Schiefer (2006) discusses Green Lake sedimentation in detail and suggests several possible redistribution processes. First, they note sand-beach expansion around the delta as a result of longshore wind and wave action. They also note that delta foreset deposits are reworked and redistributed further into the lake through slumping and turbidity flow processes. Lake sediment cores collected several hundred meters past the delta front (north and northwest) revealed coarse, graded sediment interpreted as turbidite deposits. Additionally, GPR surveys collected by Pelpola & Hickin (2004) suggest coarser grains are present in the bottomset beds. We estimate that since 1999, deposition in the mini-basin proximal to the delta front averaged 18.58 x 103 m3 of sediment annually (Table A. 1; Figure 2.2a). Approximately 42% of this volume would need to be redistributed bed material load (sand and coarser) to explain all of the excess sediment in the budget (Figure 2.7). 2.5.1.3 Sediment Attrition Sediment attrition may lead to a “loss” of bed material in the downstream direction (Attal and Lavé, 2009). This can be roughly approximated assuming bed material is transported ~360 m yr-1 (Beechie, 2001; Avg. channel width = 18m), and a mass loss of 0.2-10% km-1 given the mix of resistant quartz diorite and more friable volcanic lithologies in the watershed (Attal and Lavé, 2009). Applying these values to estimates of gross erosion, sediment attrition could explain anywhere between 1% and 60% of the excess sediment in the budget. Three additional considerations suggest a lower attrition rate is more reasonable, (i) the bed material transport rate is primarily derived from short-term tracer studies that may underestimate burial times, (ii) the rates of mass loss are calculated for clasts between 20-80 mm in diameter, whereas bed material 32 in Fitzsimmons Creek can be as large as boulders, and (iii) Schiefer et al. (2010) note a bedload to suspended load ratio of up to 270% (150% historically) at the outlet. We therefore expect the effect of sediment attrition on our sediment budget to be minor. Figure 2.7: Annual bed material sediment budget (2014-2018) for Fitzsimmons Creek including percent contributions from different sources. 33 2.5.1.4 Additional Storage or Removal In the lower portion of the watershed, Fitzsimmons Creek flows through a relatively narrow canyon (VKM 10.5-13.5) and then is laterally restricted by levees and has fixed/reinforced banks along the fan (VKM 13.5-17) limiting sediment storage outside of the active channel. Knowing this, and having DoD coverage for the entire lower watershed, we can assume that all major sediment storage areas are accounted for in the analysis. However, a small, but unknown amount of gravel removal occurs at the BC Rail bridge just upstream of the Fitzsimmons delta to maintain freeboard (Figure 2.1a). Additionally, the tailrace pond is used as a water source for snowmaking for the local ski resort. Sediment slowly accumulates in this pond and must be dredged every few years to maintain storage capacity. Nevertheless, we expect this sediment to be primarily < 1mm because coarser sediment is screened out at the intake, making it unlikely that it would affect the bed material load sediment budget. 2.5.2 Sediment Source Partitioning In quantifying the importance of different sediment sources during the 2014-2018 period, several things stand out. First, active channel, floodplain, and mass movement sediment sources each contributed about one third of the total eroded sediment (Figure 2.7). Beginning in the headwaters, net erosion of material on the valley bottom, in particular from active channel and floodplain sources, resulted in a bed material transport rate that steadily increased moving downstream. Mass movements became important in the canyon reach (VKM 10.5-12.5; Figure 2.4 & Figure 2.5) where the channel has incised into and destabilized glaciogenic sediments at a hanging valley step, equating to ~80% of the total sediment sourced from this reach. These glaciogenic sediments are important for sediment yield because they are proximal to the outlet, highly accessible to the channel, unconsolidated, and high in volume (Scott & Collins, 2021). As point sources of sediment, these mass movements resulted in an abrupt increase in bed material supplied to the river. Downstream, on the alluvial fan and delta, Fitzsimmons Creek becomes net depositional. While headwater sources could not be measured explicitly using the data available, 34 sediment budget results, corroborated by flushing estimates from the run-of-river headpond, suggest sediment contributions from glacial and proglacial areas were relatively minor. Second, over the last three decades, twice as much sediment was removed for flood management than was deposited at the delta. This highlights the important role humans play in modifying sediment dynamics and yield. Third, erosion of debris cones, terraces and hillslope sediment sources was minimal during the 2014-2018 period. However, sediment delivery from debris flows and the subsequent erosion of debris cones is likely important over longer time periods. This is evident in the large volume of sediment stored in cones at the hillslope-valley bottom transition, and their eroded distal margins. Disconnected gully-cone systems in the broad valley reach, and chronic landsliding at the hanging valley step indicate that glacial landscape configuration and valley geometry are important factors controlling sediment supply in this watershed (Reid et al., 2021). While active channel, floodplain, and mass movement sources contributed approximately equal volumes of sediment during a period with average conditions, this proportion will vary from year to year, particularly during large sediment transport events. During the 1991 event, approximately 5 times more sediment was eroded from the FCLS than is supplied during an average year (based on our budget), however, this only accounted for ~15% of the sediment deposited on the fan during that event (Golder Associates Ltd., 1993). 2.5.3 Interannual Variability Previous researchers have highlighted the interannual variability in sediment yield at the Fitzsimmons delta (Pelpola & Hickin, 2004; Schiefer et al., 2010). Our reanalysis and additional measurements support this finding, with SY varying by up to a factor of ~17. The high variability in SY measured over a 76-year period provides a unique opportunity to explore potential sediment yield controls. Because there are numerous factors controlling SY in this watershed, identifying controlling factors or even correlations is a challenging task. However, humans have had the single biggest impact on bed material SY over the last three decades. Not only has gravel removal outpaced delta sedimentation by a factor of 2, but dikes confine the river at high discharges preventing overbank sedimentation and likely leading to increased sediment transport. The 1991 flood and 35 landslide event also produced a significant and recognizable increase in SY that stands out as the largest in the historical record (Figure 2.3). The event resulted in elevated bed material yields for the entire 1990-1999 period, during which SY was nine standard deviations above the mean. The 1991 event was also the event of record with regards to a 3,000-year dataset of suspended SY (Menounos et al., 2006). This event was likely exceptional only because it involved high streamflows and significant sediment inputs near the catchment outlet. In contrast to the suspended SY record (Menounos et al., 2006; Schiefer et al., 2006), no other individual flood or landslide events appeared to control the bed material yield. This is probably the result of the coarser temporal resolution of the bed material record, and longer transport times for bed material. Excluding the 1990-1999 period, cool and wet periods tend to result in higher freshet flows and relatively high SY, while warm and dry periods tend to result in rainfall dominated events and relatively low SY. The opposite observations were made in the nearby Nooksack River (~125 km south) after shifting the record to account for downstream propagating channel adjustments (Anderson & Konrad, 2019). Although lagged correlations are not statistically supported given the coarse temporal resolution, a 20 to 30-year lag time in Fitzsimmons Creek is reasonable (Schiefer et al., 2010) and would result in high SY during warm and dry periods similar to the Nooksack River. The bed material SY response to glacier advance and retreat is tenuous at best. It appears that over a distance of just 20 km through a relatively confined valley bottom with little long-term storage potential, the SY signal gets shredded by complex sediment dynamics and other forcings (Jerolmack & Paola, 2010; Tofelde et al., 2021). 2.6 Implications One of the strengths of this study is that we apply a suite of complimentary methods to constrain both sediment yield at the outlet and the sediment budget along the river system, ultimately resolving the two measures. Here, we briefly discuss the implications of these methods regarding sediment and flood management (Church et al., 2001; Slaymaker, 2003; Walling & Collins, 2008). We provide Fitzsimmons Creek as an example, which we believe is applicable to many locations globally. 36 Nearly 15,000 people have settled on the Fitzsimmons Creek alluvial fan in the last 50 years. The location of the residents, and the creek’s high sediment supply, makes sediment and flood management challenging. In response, many of the creek’s banks are fixed/reinforced to prevent bank erosion, and training berms were constructed to prevent avulsion. Additionally, this management approach has necessitated annual gravel removals from the fan to maintain flood conveyance (Pinter & Heine, 2005). Although this is a common situation and flood management approach for gravel bed rivers globally (Church et al., 2001), this practice raises concerns about the impact to fish habitat in this fish-bearing reach. The historical SY measured at the delta can be used to estimate the average annual sediment yield and its’ variability and therefore inform gravel removal volumes. Sediment yield measurements can also be incredibly useful for understanding basin denudation rates and system change over centuries to millennia (e.g., Heideman et al., 2018) providing context to short term measurements (Kirchner et al., 2001). Long term SY patterns can also be related to hydro-meteorological events and trends and potential impacts of human disturbance to better understand system behavior. However, measuring SY at a single point is a ‘black box’ approach that ignores internal sediment dynamics, and is therefore limited in describing complex landscapes where the sediment delivery ratio is low, such as Fitzsimmons Creek (Sediment delivery ratio, “SDR” < 0.3). Detailed sediment budgeting, on the other hand, provides information on the relative importance of sediment sources, sinks, and transfer processes, their spatial variability, and estimates how much sediment is passing through any given point along the channel. In the Fitzsimmons Cr. watershed, net degradation within the valley bottom (VKM 0-13.5) contributed two thirds of the sediment reaching the outlet, while localized mass wasting of glaciogenic valley fill sediments proximal to the outlet (VKM 10.5-13.5) supplied the remaining one third. These conclusions rest on geomorphic mapping, which is an important but underutilized tool. From a flood management perspective, this information can be used to inform the ideal timing and location of sediment removal to maximize benefits while reducing the impact on fish and invertebrate habitat, or evaluate alternative management practices such as slope stabilization, implementation of check dams, etc. 37 2.7 Conclusions In this study, we use a suite of techniques to measure and quantify sediment transfers at several locations within a glacierized mountain catchment to better constrain the spatial and temporal patterns. In particular, we quantify the historical bed material yield at the outlet using field surveys and historical air photo analysis. The annual sediment yield (measured approximately every decade) varied by up to a factor of 17 over the 76-year record. Humans have had the single largest influence on bed material yield over the last three decades through gravel extraction and diking. An extreme flood and landslide event in 1991 produced the highest SY in the record, resulting in elevated sediment yields for at least a nine-year period. The effects of other potential sediment yield controls and flood events are less clear. It appears that over a distance of just 20 km, through a relatively confined valley bottom with little long-term storage potential, the SY signal gets shredded by complex sediment dynamics and masked by simultaneous forcings. We then utilize high-resolution, multi-temporal lidar data and detailed geomorphic mapping to perform net change analysis by landform along the middle and lower channel, identify sediment sources, sinks, and transfer pathways, and construct a detailed sediment budget. These values are further constrained by estimates of sediment passing through a run-of-river facility. Net degradation within the valley bottom (VKM 0-13.5) contributed two thirds of the sediment reaching the outlet, while localized mass wasting of glaciogenic valley fill sediments proximal to the outlet (VKM 10.5-13.5) supplied the remaining one third. While glacial and proglacial sources were not measured directly, sediment budgeting results suggest they were low. A minor discrepancy between the historical SY and detailed sediment budgeting results can be resolved using one or more of the following explanations, (i) accounting for cumulative uncertainty from all methods, (ii) allowing for minor transport of bed material past the delta front, and (iii) sediment attrition. Finally, we discuss the implications of this research in the context of informing sediment management strategies in the area. Historical SY estimates and detailed sediment budgeting are complimentary and provide useful information for flood and sediment management. Explicitly measuring bed material contributions from glacial and proglacial sources seems like a logical and important next step. Discussions of scale would also benefit from exploring the postglacial history of this watershed. 38 Chapter 3 - Controls on the dissimilarity between (post)glacial sediment dynamics of adjacent mountain watersheds 3.1 Introduction Quantifying sediment dynamics and yield is a complex and challenging task. Part of this challenge comes from high variability in both space and time, with sediment yield from glaciated catchments spanning 5 orders of magnitude (Carravick & Tweed, 2021). Despite these challenges, understanding sediment dynamics and yield remains a key task for geomorphologists and engineers. The volume of sediment in rivers has implications for water storage and abstraction, reservoirs and hydro-power facilities, water quality and pollutants, biogeochemical and nutrient cycling, river channel stability, riverine habitat, and landscape evolution (Carravick & Tweed, 2021). Understanding sediment dynamics is a particularly crucial task in (de)glacial landscapes, which are known to dominate global sediment yield (Hallet et al., 1996). There is a growing body of work dedicated to identifying landscape controls on catchment sediment yield, from which several global and regional controls have emerged. In analyzing a global dataset of erosion rates, Koppes and Montgomery (2009) found that tectonic uplift controls the rates of both fluvial and glacial erosion over millennial and longer timescales. In a desert environment, lithologic variability and tectonic uplift similarly influence the rate of channel incision (Wohl et al., 1994). Additionally, many studies have noted that friable lithologies, such as sedimentary and volcanic bedrock, are associated with high sediment yields (Mueller & Pitlick, 2013; Bywater-Reyes et al., 2017). Climate is another key control on sediment dynamics. In glacierized catchments, climate influences sediment yield by controlling the glacier thermal regime (Koppes et al., 2015). Independent of percent ice cover and ice flux, mean annual temperatures above 0˚C result in increased meltwater accessing the bed and elevated sediment yields (Koppes et al., 2015). Precipitation is also positively correlated with weathering and subsequent sediment transport (Porder et al., 2007). Climate further exerts control on sediment yield through the intermediary of vegetation and fire dynamics. For example, Diabiase and Lamb (2013) found that increased sediment yields following wildfires are driven by transient storage and release of sediment upslope of vegetation dams. 39 Under the umbrella of anthropogenic disturbance, landscape modifications by humans such as mining (e.g., James et al., 2022), forestry (e.g., Roberts & Church, 1986), agriculture (e.g., Hassan et al., 2017), and flood control measures (e.g., dikes, dams) have also been documented to alter sediment yield. In analyzing a global dataset, Koppes and Montgomery (2009) found that the highest rates of erosion were associated with a transient response to disturbances such as industrial agriculture, climate change, and volcanic eruptions (Koppes & Montgomery, 2009). Syvitski and Milliman (2007) ran a near-global model to assess the relative importance of geology, humans, and climate on sediment yield from large basins. Their model attributed 65% of the between-river variation in sediment yield to geology, 16% to human impacts, and 14% to climate. Similarly, in a study with over 80 drainage basins, Mueller and Pitlick (2013) determined that relative sediment supply was dominated by lithology, while factors such as drainage density, relief, and mean basin slope showed little to no influence. In a recent study, sediment yield variations over more than half a century in the upper Yangtze River were explained primarily by dam construction (~50-75%), followed by changes in precipitation (~10-25%), and finally soil conservation practices that reduced sediment supply (~5-20%; Duan et al., 2024). While geology, climate, and anthropogenic disturbances have been shown to control sediment supply, transport, and subsequently yield, the role of landscape configuration must also be considered. Landscape organization imposes a particular network structure (i.e., spatial structure of source-to-sink pathways), which in turn influences the complexity, stability, connectivity, sensitivity, and synchroneity of sediment cascades (Heckmann et al., 2015; Schmitt et al., 2016; Gran & Czuba, 2017). As the spatial and temporal scale increases, network structure and storage become increasingly more important in modulating sediment signals (Gran & Czuba, 2017). Glaciation results in distinct topographic features, landscape configuration patterns, sediment dynamics, and yield (see Chapter 1 for a detailed overview). Hassan et al. (2023) propose a conceptual model of glacially induced longitudinal trends in sediment connectivity and channel characteristics. The model includes three different types of postglacial landscapes that vary based on glacial history while assuming homogenous geology. In doing so, the model emphasizes the high intra-catchment spatial heterogeneity that can arise from glacial history alone. For example, in high-relief, previously glaciated basins, the channel long profile is often complex and stepped, 40 resulting in alternating coupled/confined and decoupled/unconfined reaches based on valley bottom slope and width. This is in alignment with past work on process domains which stated that glacial history imposes the local channel gradient and degree of coupling (Brardinoni & Hassan, 2006) which in turn determines the local channel morphology (Brardinoni & Hassan, 2007). Additionally, glacial valley geometry and landforms can result in sediment disconnectivity (e.g., broad u-shaped valleys, cirques, hanging valleys, breached divides, lateral moraines, tarns; Hassan et al., 2023). In contrast, in unglaciated or small glaciated basins without stepped profiles, downstream trends are uninterrupted resulting in steadily decreasing slope, increasing valley width, and decreasing lateral and longitudinal connectivity (Hassan et al., 2023; Turley et al., 2021; Turley & Hassan, 2023). While our general understanding of sediment dynamics in (de)glaciated landscapes has greatly improved, significant challenges remain. For example, adjacent basins with similar glacial history, geology, and climate are often still characterized by high inter- and intra-variability in geomorphological functioning (Carravick & Rushmer, 2009; Carravick & Tweed, 2021). This dissimilarity in sediment dynamics and yield between adjacent and seemingly similar watersheds hampers our ability to extrapolate and compare between systems. Further work is needed that explores landscape controls at varying scales in order to explain this heterogeneity. There are two main objectives of this paper, (i) explore the (dis)similarity of landscape characteristics and sediment connectivity patterns in adjacent watersheds, and (ii) identify the regional controls on sediment dynamics and yield and evaluate their ability to describe inter- and intra-catchment patterns. In so doing we ask the following questions, 1. Do landscape/channel characteristics and sediment connectivity vary along the mainstem channels in a predictable way? a. If so, do they follow the conceptual model proposed by Hassan et al. (2023)? 2. What are the primary controls on sediment dynamics in postglacial landscapes? a. Which aspects of the contemporary system can be traced back to geology, glacial history, or the particular trajectory of postglacial adjustment? b. Do the primary controls fully explain the patterns of observed landscape characteristics? 41 We approach these questions by first compiling summary catchment characteristics and data for adjacent watersheds (i.e., Fitzsimmons Cr., Rutherford Cr., and Soo River) in southwest British Columbia. This landscape is characterized by complex, heterogeneous geology and was shaped by past glaciations. Additionally, at the catchment scale, Fitzsimmons Cr., Rutherford Cr., and Soo River have similar geology, glacial history, and climate while sediment dynamics vary dramatically. This allows us to explore the role of intra-catchment variations in sediment dynamics in determining sediment yield at the outlet. We then map the surficial geology, estimate postglacial erosion and storage volumes, and quantify hillslope-channel coupling, channel confinement, and sediment connectivity along the mainstem channels. This data is used to identify longitudinal patterns which are tentatively associated with geologic, glacial, and postglacial controls. 3.2 Study Site The Green River watershed, located in southwestern BC, Canada covers approximately 875 km2 of mountainous, glacierized terrain. The Green River flows Northeast from Alta Lake to the Lillooet River and its major tributaries include the Soo River (290 km2) and Rutherford Creek (182 km2) which flow from West to East, and Fitzsimmons Creek (94 km2) which flows Northwest, each of which emanate from glaciers (Figure 3.1; Table 3.1). General characteristics of the Green River watershed are discussed in the following sections, while numerical summaries for all watersheds can be found in Table 3.1. 3.2.1 Geology & Topography Within the watershed, the topographic relief exceeds 2,500 meters, with elevations ranging from 197 to 2,880 meters above sea level. Four primary bedrock geologies exist within the watershed (Monger & Journeay, 1994). Roughly 70% of the watershed is underlain by Late Jurassic to Early Cretaceous intrusive granodioritic rocks primarily from the Pemberton Diorite Complex. Approximately one quarter of the watershed is underlain by andesitic and volcaniclastic rocks from the Neogene to Early Cretaceous Periods including the Whistler Pendant. These volcanic 42 rocks occur primarily along a narrow (1-6 km) band stretching N-NW from Fissile Peak to upper Rutherford. Less than 10% of the watershed is underlain by marine sedimentary rocks. These include the Early Cretaceous Gambier Group along the SW edge and catchment outlet. Finally, only 1% of the watershed (located in the upper Soo) is underlain by Mesozoic age orthogneiss metamorphic rocks. Approximately 230 km of faults have been mapped at the surface generally along strike of the Fitzsimmons Cr., Soo River, and Green River valleys. Figure 3.1: Green River watershed and its tributaries. Includes Environment Canada weather stations and hydrometric stations, and luminescence sampling locations. 43 Table 3.1: Catchment characteristics for Green River watershed and its tributaries. Characteristics Fitzsimmons Creek Rutherford Creek Soo River Green River Catchment Area (km2) 94 182 290 875 Shape - Gravelius Coefficient (GC)1 1.55 1.83 2.10 1.79 Lithology Intrusive (%) 47 79 61 69 Metamorphic (%) 0 0 3 1 Sedimentary (%) 12 0 13 8 Volcanic (%) 41 21 22 23 Topography Minimum Elevation (masl) 635 331 343 197 Maximum Elevation (masl) 2684 2569 2560 2880 Mean Elevation (masl) 1613 1506 1404 1416 Relief (m) 2049 2238 2217 2683 Hypsometric Integral 0.477 0.525 0.478 0.454 Valley Length (km) 17.58 25.75 39.52 35.46 Hydrology Mainstem River Length (km) 19.69 28.60 47.83 42.862 Mainstem Channel Width (m) 18.16 25.84 45.46 36 Drainage Density (km/km2) 1.29 1.78 2.22 1.54 Mean Annual Discharge (m3 s-1)3 3.92 11.6 19.0 48.0 Max. Daily Discharge Recorded (m3 s-1)3 36.7 204.0 184.0 402.0 Glacier Coverage - Modern (%) 7.0 11.4 13.9 9.2 Climate4 Mean Annual Precipitation (mm yr-1) Whistler - 658 (masl) 1262 (~30% snow) Whistler Roundhouse - 1835 (masl) 1523 (~70% snow) Callaghan Valley – 884 (masl) 1886 Pemberton Airport CS – 204 (masl) 834 (~20% snow) Monthly Mean Temperature (˚C) Whistler Station Max of 16.7 in Aug Min of -2.8 in Dec Whistler Roundhouse Station Max of 10.8 in Aug Min of -5.3 in Dec Callaghan Valley Station Max of 16.1 in Aug Min of -2.4 in Dec Pemberton Airport CS Station Max of 19.75 in Jul Min of -2.4 in Jan Notes: 1. Measure of the compactness of a watershed, where a circle is equal to 1 (Sassolas-Serrayet et al., 2018). 2. Includes Alta Cr., Alta Lake, and Green Lake. 3. Water Survey of Canada hydrometric stations include Fitzsimmons: 08MG026 (1993-2022), Rutherford: 08MG006 (1914-1948), Soo: 08MG007 (1915-1951), Green: 08MG003 (1913-1951). Record length in parentheses. 4. Environment Canada weather stations include Whistler: 1048898, Whistler Roundhouse: 1108906, Callaghan Valley: 1101300, Pemberton Airport: 1086082. 44 3.2.2 Hydrology & Climate Repeated glaciations throughout the Pleistocene have shaped the Green River watershed, creating broad, U-shaped valleys with stepped profiles, cirques, horns, aretes, and over-steepened hillslopes. Today, approximately 9% of the watershed is glaciated. Four local weather stations highlight temperature and precipitation gradients in the watershed (Table 3.1). In general, the monthly mean maximum temperature increases to the northeast, while both maximum and minimum temperatures decrease at higher elevations. The mean annual precipitation and percentage falling as snow is highest in the southwest and at high elevations and decreases to the northeast. Unfortunately, only limited hydrometric data is available, but a cursory analysis indicates mean annual discharges of approximately 4, 12, 19, and 48 m3 s-1 for Fitzsimmons Cr., Rutherford Cr., Soo River, and Green River, respectively. These rivers are dominated by a mixed hydrologic regime, with spring snowmelt and fall rain or rain-on-snow events as the two dominant flood-generating mechanisms. The mainstem river lengths, as noted in kilometers down valley of their glaciers (VKM), are approximately 20, 29, 48, and 42 km for the Fitzsimmons, Rutherford, Soo, and Green Rivers, respectively. The overall drainage density, based on the BC freshwater atlas (GeoBC, 2024), is rather low at 1.54 km/km2. Ponton (1972) offers as explanation that much of the runoff occurs as interflow or in unconcentrated flow over bedrock. This seems likely given the high percentage of exposed bedrock as discussed later in section 3.4.1. 3.3 Data & Methods 3.3.1 Geomorphic Mapping Geomorphic mapping was completed for the entire Green River watershed (~875 km2) at an approximate 1:10,000 scale based on field observations and a desktop analysis using available elevation (i.e., 1-meter lidar for Fitzsimmons and Green valleys, 25-meter DEM for the remainder of the watershed) and imagery (i.e., orthophotos, historical aerial photos) data. Please note that due to the size of the catchment many areas required mapping the geomorphology based on surface expression alone, and therefore should be interpreted carefully. That being said, fieldwork was conducted in all subwatersheds and included more than 900 individual surficial geology observations, >1,500 photos, aerial (UAV – uncrewed aerial vehicle) and bathymetric 45 (Acoustic doppler current profiler – ADCP) surveys, and sediment cores from Green Lake. In general, mapping categories included surficial materials and surface expression (e.g., alluvial fan, debris cone, till veneer, etc.), and linear features (e.g., rivers, moraines, cirques, confining margins, etc.). A more detailed overview of the geomorphic mapping process and criteria is outlined in (Turley et al., 2024; Chapter 2). 3.3.2 Coupling, Confinement, & Connectivity Channel sections were grouped into distinct zones according to their morphological characteristics, coupling, confinement, and connectivity. Channel width, valley bottom width, and channel sinuosity were measured every 100 meters based on detailed geomorphic mapping. The channel and valley width measurements were then used to calculate the degree of hillslope channel coupling based on the empirical classification system of Whiting and Bradley (1993). This method determines the probability that hillslope sediment will reach the channel assuming a debris flow runout of 25 meters and has been shown to be a relatively robust measure (Turley et al., 2021). The hillslopes are then assumed to be coupled to the channel when the probability is greater than 50%, and decoupled when lower. Confining margins were mapped where the active channel abuts a landform other than floodplain. Where this occurred, the confining landform was noted, and a length ratio was calculated (confinement length / total channel length) following the method proposed by Fryirs et al. (2016) and further explored in O’Brien et al. (2019). Confinement ratios were calculated every 100 meters on both sides of the channel producing a range of values from 0% (no contact) to 100% (continuous contact). Value breaks used here follow O’Brien et al. (2019) and include 0-10% (laterally unconfined), 10%-50% (partly confined – planform controlled), 50%-85% (partly confined – margin confined), and 85%-100% (confined). Additionally, confinement can be subdivided into valley bottom, valley margin, and anthropogenic confinement categories based on the confining landform. Finally, constriction occurs where the channel is confined on both sides but is otherwise calculated in the same manner. Structural connectivity was estimated based on the effective catchment area (ECA; Fryirs et al., 2007b). The ECA method explicitly accounts for the spatial distribution of sediment delivery and lack thereof by delineating the area within a catchment that has the potential to directly 46 contribute sediment to the specified outlet at a given scale (Fryirs et al., 2007b). Most applications of the method are based on the simple assumption that the slope gradient controls sediment delivery. Here, we apply a slope threshold of 8%, noting the possibility of overprediction caused by the coarse resolution DEM (25-meter; Turley et al., 2021). 3.3.3 Geochronology This section outlines the geochronology data available for the Green River watershed that can be used to understand the postglacial sediment dynamics and yield of the basin. The data includes ages of rock avalanches, rockslides, debris flow and alluvial fans, lake sediments, floodplain sediments, and river terraces (Table 3.2). The ages vary from immediately postglacial (~11 ka) to modern and are based on terrestrial cosmogenic nuclide (TCN), radiocarbon, tephrochronology, varve chronology, and optical dating methodologies. A detailed description of the optical dating process is provided in Chapter 4. Postglacial terraces and fans were targeted for sampling. Terraces record changes in base level, sediment supply, and climate, and applicable Infrared stimulated luminescence (IRSL) dating techniques are well documented (Rittenour, 2008). Fans are important valley margin storage landforms and can similarly be used as archives of climate variability and associated sediment dynamics (Woor et al., 2023). Sampling locations are noted in Figure 3.1, while stratigraphic sections and descriptions for each sample can be found in Appendix C . All sample preparations and analyses were conducted at the University of the Fraser Valley’s Luminescence Laboratory (see Chapter 4 for details). 3.3.4 Long Profile Analysis River long profiles were constructed by extracting elevations from freely available BC TRIM DEM datasets (GeoBC, 2024) every 25 meters along the mainstem channels. Knickpoints were then identified by first calculating the gradient index (Hack SL index) every kilometer (Hack, 1973) and then normalizing this value by the gradient index of the entire profile to identify anomalies (Seeber & Gornitz, 1983). Normalized gradient index values ≥ 2 and ≥ 10 indicate steep (2nd order anomaly) and very steep (1st order anomaly) channel reaches, respectively. 47 Table 3.2: Geochronology data pertaining to the postglacial history of the Green River watershed. Location Date Method Source Rock Avalanches / Slides Mystery Creek Rock Avalanche 2200-3600 BP 36CL – TCN Blais-Stevens et al. (2011) Soo River Rockslide >7600 BP (up to 11 ka) 14C, Tephrochronology Cordilleran Geosciences (2018) Cougar Mountain Rock Avalanche 1986 AD Historical Blais-Stevens (2008) Suicide Hill Rock Avalanche ~11-12 ka Stratigraphic Position; Geomorphology Blais-Stevens (2008) Blackcomb Glacier Rock Avalanche < 12 ka Stratigraphic Position P. Friele, Personal Communication (2024) Debris Flow Fans Several Mt. Currie (3) & Rutherford (5) Fans Many (47): 1946-2019 Historical Imagery, lidar differencing, fieldwork Zubrycky et al. (2021) Floodplain Sediment Cores Soo Wetland >7600 BP (up to 11 ka) 14C, Tephrochronology Cordilleran Geosciences (2018) Lake Sediment Cores Green Lake Last 3 ka Varve Chronology, 14C, 137Cs Menounos et al. (2006) Alluvial Fan & Terraces Fitzsimmons Terrace 6.06 ± 0.74 ka IRSL1 This Study Soo Terrace3 199.49 ± 116.63 ka2 IRSL1 This Study Rutherford Fan 38.08 ± 13.59 ka2 IRSL1 This Study Notes: 1. Infrared stimulated luminescence of feldspar. Age estimates are based on a minimum age model (MAM). 2. Sediments sampled were likely not fully reset and so appear spuriously old. Discussed in more detail in Ch. 4. 48 3.3.5 Postglacial Erosion and Storage Relative volumes of erosion from gully systems and channel incision, as well as sediment storage in floodplains, fans, cones, and deltas are used as context for discussing and comparing catchment sediment dynamics. In the Fitzsimmons Creek watershed, 1-meter lidar data coverage made detailed volumetric measurements possible. Here, postglacial erosion and deposition volumes were estimated for valley fills and gully-cone systems by manually reconstructing pre- erosion/deposition surfaces and then differencing reconstructed surfaces from modern surfaces (2018, 1-meter lidar), similar to the method described by Tunnicliffe and Church (2011). Because only coarse resolution elevation data (25-meter) are available for the Rutherford and Soo River watersheds, no attempt was made to quantify volumes for gully-cone systems. Instead, fan/cone volumes are approximated by their area, a common practice supported by empirical evidence (Giles, 2010). Because we are primarily interested in relative volumes, and the same method was applied to each watershed, we believe this approximation is acceptable. Additionally, bulk density corrections of the estimated volumes were not completed, due to data and time limitations. Floodplain storage is estimated by multiplying area by an average depth estimate. In the Soo River watershed, floodplain sediment cores from the lower wetland/floodplain (wetland 2) indicate the wetland is more than 30 meters deep in places (Cordilleran Geoscience, 2008). We therefore apply a conservative average depth of 16 meters. In the remaining floodplains, average depths are estimated based on floodplain area and river long profile geometries. Landslide volumes and ages are compiled from previous reports (Mystery Cr. – Blais-Stevens et al., 2011; Soo River– Cordilleran Geosciences, 2018; Cougar Mtn. and Suicide Hill – P. Friele, personal communication, 2024). The postglacial period is assumed to be 11 ka to present (Friele & Clague, 2002). Whenever possible, ages are provided for the sediment storage landforms to help constrain their effect on the postglacial sediment cascade (see section 3.3.3 for more details). 3.3.6 Sediment Yield Data A detailed sediment budget and the historical sediment yield (76 years) were calculated for the Fitzsimmons Creek watershed (Turley et al., 2024; Chapter 2). Key points relevant to this 49 chapter are summarized below. The 76-year (1947-2023) average Fitzsimmons Creek historical bed material yield was approximately 12,300 m3 yr-1 (130 m3 km-2 yr-1; Turley et al., 2024). During an overlapping measurement period (1930-2006) Schiefer et al. (2010) estimated that the total yield was comprised of roughly 50% bedload, 35% suspended load, and 15% dissolved load, suggesting a total sediment yield of approximately 21,400 m3 yr-1 (228 m3 km-2 yr-1). Long term (3000-year) lake-based suspended sediment yield estimates for the Green Lake catchment area, of which Fitzsimmons Creek is the principal inflow and source of sediment, are comparable to historical yields at approximately 7,164 ± 1,885 m3 yr-1 (76 ± 20 m3 km-2 yr-1; assuming a bulk density of 1,723 kg m-3; Menounos et al., 2006). It is important to note, however, that these long-term estimates are based on very few sediment cores making comparisons tenuous at best. No detailed estimates of sediment yield are available for the Rutherford or Soo River watersheds. However, the contemporary “bed material” sediment yield of the Soo River is expected to be greater than 4000 m3 yr-1 (15 m3 km-2 yr-1) based on flushing volumes at the hydroelectric dam (S. Croft, personal communication, June 20, 2022). These are minimum estimates given that a majority of the flow is allowed to bypass the intake channel and most of the suspended and dissolved load components remain in transport. 3.4 Results 3.4.1 Geomorphic Mapping 3.4.1.1 Bedrock and Glacial Landforms Figure 3.2 shows an example geomorphic map of the Soo River watershed, while maps for Fitzsimmons Creek and Rutherford Creek can be found in Appendix B (Figure B. 1; Figure B. 2). In general, bedrock is exposed at or near the surface, primarily at high elevations and steep slopes, for approximately one third (Fitzsimmons Cr.) to one half (Rutherford Cr.) of the watersheds by area (Figure 3.2b). Contemporary glacier coverage ranges from 7% (Fitzsimmons Cr.) to 12% (Soo River). Glaciers play an important role in the hydrology of all basins. Little Ice Age moraines and till blankets commonly extend ~1 km past the termini of modern glaciers. Till also covers the majority of the mid-watershed area in each of the watersheds. These glacial deposits generally form veneers on hillslopes while the valley floors are blanketed with thick glaciogenic sediments. Ice contact (i.e., glaciolacustrine, etc.) sediments and glaciofluvial 50 terraces are commonly found in the lower portions of the watersheds near their respective confluence with the main Green River valley. In total, glaciogenic sediments cover between 19% (Rutherford Cr.) and 44% (Fitzsimmons Cr.) of the watersheds by area. Figure 3.2: Geomorphic and surficial geology map of the Soo River watershed, displayed here at 1:200,000 scale (panel a), and the percent geomorphic coverage of Fitzsimmons Cr., Rutherford Cr., and Soo River watersheds (panel b). 51 3.4.1.2 Colluvial Landforms Because each of the watersheds have high local relief, colluvial processes also play an important role in sediment production and transport. Snow avalanches, debris flows, and gullying occur throughout the watersheds, but are more concentrated in the mid-watershed reaches. These processes mobilize weathered bedrock, colluvium, or in many cases, till. The colluvial material is transported downslope and deposited in debris cones at the valley margin. Apart from gully systems, colluvium can be found blanketing lower slopes in the upper watersheds and in steep tributary valleys. Talus can be found at the base of steep slopes and in reverse scarps. Nearly 1000 discrete talus fields/aprons were mapped, of which ~40% are in Rutherford Creek watershed. Landslides of note include Fitzsimmons Creek landslide (see section 2.2.2), and Soo River, Cougar Mtn., Mystery Creek, Suicide Hill, and Blackcomb Glacier rock avalanches. Interestingly, all of the rock avalanches occurred in intrusive bedrock. Previous authors have described the preferential planes of weakness (i.e., lineaments and joint sets) responsible for the Mystery Creek landslide (Nichol et al., 2002; Blais-Stevens et al., 2011) and note that talus is commonly found in reverse scarps. Cordilleran Geoscience (2008) also traced a single lineament through the headscarps of the Soo River and Cougar Mtn. rock avalanches (Figure 3.7). It seems that topple deformation of glacially over-steepened, intrusive bedrock is a common initiation mechanism. The Fitzsimmons Creek landslide as well as several other unnamed landslides along Fitzsimmons and Rutherford creeks are the result of channel incision into, and subsequent destabilization and slumping of, glaciogenic valley fill sediments. In total, colluvial sediment covers between ~11% (Fitzsimmons & Soo) and ~17% (Rutherford) of the watersheds by area. 3.4.1.3 Alluvial Landforms Alluvial landforms include active channels, floodplains, wetlands, terraces, deltas, and alluvial fans. By far, floodplains/wetlands account for the greatest proportion of alluvial landforms by area. This is particularly true in the Soo River watershed which has three major floodplains/wetlands (Figure 3.2). Despite this, alluvial sediments only cover ~4% (Fitzsimmons) to ~5.5% (Soo) of the watersheds by area. 52 3.4.2 Channel, Valley, and Connectivity Characteristics Average zonal characteristics are summarized for each subwatershed in Table 3.3 and briefly described in the text, while a longitudinal summary of the Soo River watershed is provided in Figure 3.3 (see Appendix B for Fitzsimmons and Rutherford Cr. watersheds). In general, valley bottom width, coupling, confinement, and connectivity metrics are correlated. Valley bottom width tends to increase in the downstream direction but narrows again where the channel has incised at hanging valley steps (e.g., zone 4) or valley margin landforms are extensive (e.g., Rutherford and Soo zones 2b). Where the valley bottom is sufficiently wide and low gradient, channels become low energy and sinuous (e.g., Soo zone 3c; Figure 3.3). Anthropogenic structures such as dikes and levees can artificially increase channel confinement (e.g., Fitzsimmons zones 5a and 5b), while connectivity and coupling remain low. Overall, channel confinement across the watersheds averages 51%, with the majority of this confinement attributed to valley margin constraints (80%), followed by valley bottom margin constraints (17%), and a minor portion due to anthropogenic factors (3%). The influence of bedrock on valley characteristics is notable, with the Kruskal-Wallis test revealing significant differences in valley bottom width between lithologies (p << 0.005). Performing a Wilcoxin post-hoc test with a Bonferroni correction for multiple hypothesis testing confirmed that valley bottom widths underlain by volcanic bedrock are significantly different compared to reaches underlain by sedimentary or intrusive bedrock. These analyses underscore the complex interplay between valley bottom width, slope, confinement, and coupling across different watersheds, highlighting significant inter- and intra-catchment variability. 53 Table 3.3: Summary longitudinal characteristics by zone for the Fitzsimmons, Rutherford, and Soo watersheds. Zone VKM Slope (%) VB Width (m) Percent Coupled3 Confinement Proportion1,2 (%) Percent ECA Description Fitzsimmons Creek 1 0-2.5 11.5 25 100 52 (R) 8 Glacial Forefield 2 2.5-6 7.0 75 54 65 (Dc, Tb) 49 Colluvial 3 6-10.5 3.0 125 22 50 (At, Dc) 35 Broad Valley 4 10.5-13.5 8.0 50 77 97 (Cs, HD) 91 Canyon 5a 13.5-17 1.0 1065 0 30 (HD) 27 Fan 5b 17-17.5 <1.0 1300 0 36 (HD) 13 Delta Total: 6.5 321 53 57 (Cs, Dc, Tb) 40 - Rutherford Creek 1 0-3 25.0 15 100 100 (Tb) 25 Glacial Forefield 2a 3-9 7.5 20 98 99 (Cs, Tb) 60 Colluvial 3 9-12.5 2.5 570 0 11 (Af) 3 Wetland 2b 12.5-18 2.5 140 45 65 (Dc, Cs) 32 Debris Cone 4 18-23 5.0 40 98 43 (Cs) 46 Canyon 5 23-26 5.0 500 3 66 (Aft) 34 Paraglacial Fan Total: 6.0 175 69 63 (Cs, Tb, Dc) 37 - Soo River 1 0-2 17.0 45 100 99 (R) 13 Glacial Forefield 3a 2-4 2.5 85 100 100 (R) 17 Hanging Valley 2a 4-10 5.0 120 50 39 (R) 45 Colluvial 3b 10-17 1.0 620 0 3 (Af) 11 Wetland 1 2b 17-21.5 2.5 50 93 86 (Tb, Dc) 20 Debris Cone 3c 21.5-30 0.5 940 0 2 (Tb) 4 Wetland 2 2c 30-34 2.5 145 60 71 (MM) 14 Rock Avalanche 4 34-40 2.0 165 52 86 (Gh) 57 Canyon Total: 3.0 380 56 42 (R, Gh, Tb) 24 - All Average: 4.5 305 60 51 30 - Notes: 1. May be categorized as follows, Unconfined: 0-10%, Partially Confined: 10-85%, Confined: 85-100%. 2. Primary confining landform(s) in parenthesis following the percentage. 3. Percent of hillslopes within zone that are coupled to channel. 54 Figure 3.3: Summary longitudinal characteristics of Soo River watershed. Characteristics include (a) channel and valley slope, (b) channel sinuosity, (c) confinement/constriction proportion, (d) hillslope-channel coupling, and (e) effective catchment area. 55 3.4.3 Sediment Yield/Budgeting In this section, key sediment budgeting results are presented for each of the subwatersheds to facilitate relative comparisons. Figure 3.4 presents longitudinal profile plots for Fitzsimmons Creek, Rutherford Creek, Soo River, and Green River along with major storage and channel incision along the mainstems. Postglacial sediment budget and yield estimates are speculative, and are assumed to be minimum estimates, not accounting for glacial and diffusive sources of sediment. However, even order of magnitude and relative estimates are beneficial for comparing catchments and identifying patterns. 3.4.3.1 Fitzsimmons Creek From VKM 2-12, prominent gully-cone systems record postglacial hillslope activity and sediment supply (Figure 3.4a; Figure 3.5). Postglacial gully erosion is estimated to be 26 x 106 m3, while debris cone deposition is 8.7 x 106 m3 resulting in a net yield of ~17.3 x 106 m3 (SDR = 0.67; ~17 m3 km-2 yr-1). SDR values for unique gully-cone systems range from nearly 0 to 1, with a mean of 0.67 and a median of 0.74. Area-to-volume relationships for debris cones and gullies are poorly defined due to few gullies with an area between 1-2 x 105 m2, and high scatter for cones (Figure 3.5cd). A traditional power law fit between cone area and volume resulted in the functional relation, 𝑉𝑜𝑙𝑢𝑚𝑒𝑐𝑜𝑛𝑒 = 1.99 𝐴𝑟𝑒𝑎 𝑐𝑜𝑛𝑒1.31 with an R2 of 0.76. The linear relation between gully area and volume is, 𝑉𝑜𝑙𝑢𝑚𝑒𝑔𝑢𝑙𝑙𝑦 = 3.61𝐴𝑟𝑒𝑎𝑔𝑢𝑙𝑙𝑦1.51 with an R2 of 0.75. Additionally, lower Fitzsimmons Creek and many of its tributaries have incised into the glaciogenic valley fill. This incision has mobilized an estimated 23.6 x 106 m3 (~23 m3 km-2 yr-1) of sediment, of which 23 x 106 m3 comes from the mainstem (Figure 3.4a). A luminescence age from the terrace just downstream of the knickpoint dates to 6.06 ± 0.74 ka (minimum age model), suggesting that channel incision into the hanging valley step has been ongoing throughout at least the latter half of the Holocene (Figure C. 11). All discrete yield estimates result in a net total of 40.9 x 106 m3 (~40 m3 km-2 yr-1) reaching the Fitzsimmons Cr. outlet. 56 Figure 3.4: Longitudinal profile plots for (a) Fitzsimmons Cr., (b) Rutherford Cr., (c) Soo River, and (d) Green River. Plots include Hack SL Index and major storage and channel incision along mainstem. 57 Figure 3.5: Estimated postglacial erosion and deposition from gully-cone systems along Fitzsimmons Creek. (a) Volumetric estimates of gully-cone systems organized by distance down valley, (b) example gully-cone system along Fitzsimmons Creek at ~VKM 9, (c) volume-to-area relationship for gullies, and (d) volume-to-area relationship for cones. 3.4.3.2 Rutherford Creek Rutherford Creek has one major wetland/floodplain in the mid valley reach that stretches ~5 km in length (VKM 9-14). If we assume that, on average, the accumulated thickness of the sediment is 10 meters, then an estimated 22 x 106 m3 (11 m3 km-2 yr-1) of sediment is stored in the wetland (Figure 3.4). From VKM 10-20 active debris cones and alluvial fans deliver sediment to the valley floor. The areal coverage of these fans and cones is approximately equal to those in the Fitzsimmons Cr. watershed, resulting in a lower density for the catchment. However, the gully-cone systems in Rutherford Cr. have been more active in recent decades, likely as a result of high local relief (up to 1,800 meters; Zubrycky et al., 2021). Along the lower reaches of Rutherford Creek (VKM 18.5-24.5), the occasional glacial surface stands between 45-65 meters above the modern channel. These surfaces may represent kame terraces deposited along the ice margin more than 12 ka. However, based on stratigraphic evidence we believe they are remnants of Rutherford Cr. outwash sediments that were later incised during the early postglacial period 58 leaving behind fill terraces. An optical dating sample was collected near the base of the outwash fan (~30m above the modern channel, believed to be immediately postglacial in age), but unfortunately exhibited signs of partial bleaching (e.g., overdispersion of 41 ± 5 %) and yielded an older age than seems reasonable (Figure 3.6; see Chapter 4 and Appendix C for additional IRSL information). Incision into glaciofluvial valley fill would amount to an approximate 82 x 106 m3 (41 m3 km-2 yr-1) of sediment having been exported during the postglacial period. At the downstream end, and approximately 30m lower than the glaciofluvial surface, Rutherford Creek has built an impressive fan believed to be early postglacial in age, judging by the fact that it directly overlies glaciolacustrine sediments. The contemporary channel is incised approximately 15m into this inset fan surface, suggesting at least ~26 x 106 m3 (13 m3 km-2 yr-1) of sediment is stored in the fan. While the uppermost surface is clearly a fill terrace, it is somewhat unclear whether the lower fan surface is a nested fill terrace or a cut terrace. Unfortunately, gravel mining is slowly erasing the stratigraphic evidence. 3.4.3.3 Soo River Debris cones and alluvial fans are found from VKM 4-25, but at a much lower density than in Fitzsimmons and Rutherford watersheds. However, the fans and cones in Soo River watershed are much larger in area and likely store a large volume of sediment. They are also known to be very active in recent decades. One hanging valley and two major wetlands exist along the mainstem Soo River valley, each trapping sediment during the postglacial period (Figure 3.2a & Figure 3.4c). Floodplain sediment storage increases in area in the down-valley direction ranging from just over 0.2 x 106 m2 to nearly 8 x 106 m2 in area. The hanging valley is the result of glacial valley over deepening with a bedrock sill at VKM 4 creating the step. Base level for wetland 1 is controlled by the Torrent Creek debris fan, which pins the Soo River against the opposing valley wall. Wetland 1 is estimated to be an average of 10 meters in depth. A large rock avalanche mapped by Cordilleran Geoscience (2018) apparently dammed the Soo River valley at VKM 30-34 and led to the formation of a ~9 km wetland (wetland 2) up valley (Figure 3.2a; Figure 3.4c). The existence of Mazama tephra (7682–7584 cal. yr BP; Egan et al., 2015) in the lower sections of sediment cores taken from the floodplain, as well as stratigraphy showing that the rock avalanche ripped up the surface of glaciolacustrine sediment that overlies till suggests 59 Figure 3.6: Rutherford Cr. fan stratigraphic section and optical dating sampling location (star symbols). 60 the landslide is immediately postglacial in age. The landslide is roughly estimated to be 100 x 106 m3 in volume (Cordilleran Geoscience, 2018). Wetland 2 is more than 30 meters deep in places and is estimated to have an average depth of 16 meters (Cordilleran Geoscience, 2018). The resulting sediment storage estimates for the hanging valley, and wetlands 2 and 3 are 1, 46, and 121 x 106 m3, respectively. The total volume of sediment estimated to be in long term storage in the valley bottom is at least 168 x 106 m3 (53 m3 km-2 yr-1). The large volume of sediment in storage along the mainstem channel accounts for the relative lack of sediment at the outlet in comparison to the other watersheds. 3.5 Discussion 3.5.1 Longitudinal Patterns of Landscape Characteristics In many ways, the longitudinal patterns of landscape characteristics observed in this study reflect those typical of mountainous, glaciated basins with complex long profiles, as described by Hassan et al. (2023). In each of the subwatersheds, the mainstem channel flows through the following zones, 1. Steep and narrow glacial forefield 2. Moderately steep and narrow colluvial or debris cone 3. Low gradient and broad valley wetland or hanging valley 4. Moderately steep and narrow canyon (valley step) 5. Low gradient fan or delta in an expansive valley bottom Where the long profile is stepped, zones 2 and 3 repeat (Table 3.3). The stepped profile also results in the repetition of the typical downstream sequence of channel types (i.e. Cascade, step-pool, rapid, riffle-pool) seen in simple concave-up channels (Montgomery & Buffington, 1997; Brardinoni & Hassan, 2007). The degree of coupling, confinement, and connectivity, associated with this typical sequence, fluctuates according to valley bottom width and slope, which is largely imposed by glacial macroforms (Brardinoni & Hassan, 2009). In low gradient reaches with a wide valley bottom, hillslopes are decoupled from the channel (i.e., lateral disconnectivity), the channel is unconfined, and longitudinal connectivity is reduced. Where the valley bottom is steep and narrow, the channel is confined, and both lateral (coupling) and longitudinal connectivity are high. 61 There are, however, several exceptions. First, channel confinement departs from valley bottom width trends where there are relatively narrow or discontinuous floodplain pockets (e.g., Soo zone 2a, Rutherford zone 4). Over longer timescales, lateral migration of the channel will result in partial confinement in these locations. Second, anthropogenic modifications such as dikes and levees (e.g., Fitzsimmons Cr. zones 5a & 5b) artificially increase channel confinement, while run-of-river intake structures (e.g., VKM 9.2 - Fitzsimmons Cr.; VKM 16.5 - Rutherford Cr.; VKM 32.5 - Soo River) reduce the longitudinal connectivity of the channel. Third, connectivity estimates, based on the effective catchment area method, depart from the trends that would be expected when only considering mainstem characteristics. In practice, connectivity is more variable than confinement and coupling, because it accounts for topographic characteristics outside of the mainstem valley. Hillslope and tributary features such as cirques, latero-frontal moraines, hanging valleys, breached divides, etc., act as sources of disconnectivity and complicate general trends (Turley et al., 2023). Finally, postglacial channel characteristics deviate from those inherited from previous glaciations depending on the degree of adjustment. Following glacier retreat, nonglacial processes begin to reshape the landscape. In particular, channel incision typically occurs at hanging valley steps due to increased stream power (Bizzi & Lerner, 2015), resulting in increased channel confinement and sediment mobilization at the step. Postglacial valley margin landforms, such as debris cones, alluvial fans, and terraces, slowly increase channel confinement, particularly in broad valley reaches, and can eventually lead to enhanced hillslope-channel coupling. Additionally, large rock avalanches can block the entire valley bottom and impose a new local base level which causes upstream sediment aggradation and wetland formation. While the subwatersheds in this study share many longitudinal characteristics, there are substantial differences as well. The biggest difference is the size, slope, and number of broad valley reaches. For example, in Fitzsimmons Creek, there is a single broad valley zone, it has a maximum width of 250 meters and a slope of 3%. As a result, sediment is transported through and past this reach with relatively little long-term storage. On the other hand, Soo River watershed has three broad valley reaches, the largest of which is nearly 2,500 meters wide, 9 km long, and has a slope of 0.5%. As a result, there is a significant amount of long-term sediment storage and little to no coarse sediment from upstream (270 km2) reaches the outlet. The 62 importance of valley morphology and large within-valley sediment traps were also emphasized by Brooks (1994). In this study, the largest tributary contributed the smallest portion of reworked glacial sediments due to substantial within-valley storage. 3.5.2 Controls on Sediment Dynamics At the catchment scale, the three subwatersheds have very similar geology, glacial history and climate (Table 3.1). Despite these shared characteristics and history, the three subwatersheds appear to differ greatly in their contemporary and postglacial sediment dynamics and yield. Key differences include, (i) significantly greater sediment storage per km2 and subsequently low SSY in the Soo River watershed, (ii) varying percent geomorphic coverage and importance of sediment source type, and (iii) the celerity of (post)glacial adjustment. In the following paragraphs, we discuss these differences and others in the context of controls on sediment dynamics and yield. Before identifying and evaluating potential controls on sediment yield, it is important to first consider the role of basin size. Based on trends identified by Church and Slaymaker (1989) in glaciated basins, specific sediment yield should increase with increasing basin size as a result of late stage paraglacial adjustments. In contrast to this, relative (albeit qualitative) SSY estimates suggest that (post)glacial SSY in the Soo River has been much lower than in the Rutherford and Fitzsimmons Creek watersheds. We therefore turn to specific intra-catchment variability in geology, glacial history, and (post)glacial adjustment to explain the observed dissimilarities. It is important to note that the Sḵwx̱wú7mesh (Squamish), St’át’imc, and Líl̓wat First Nations have lived in this region since time immemorial. However, the potential effects that their land stewardship practices may have had on sediment storage and yield were not directly considered in this study. Glacial controls have been the focus of several recent studies (Hassan et al., 2019; Reid et al., 2021; Hassan et al., 2023; Turley & Hassan, 2023). In the Green River watershed, valley geometry (i.e., width, slope) and landscape configuration is fairly typical of a “large, high-relief glacial basin with a valley step” as discussed in Hassan et al. (2023; Figure 1.1). Each of the subwatersheds, have glacially induced valley steps both in the headwaters and near the catchment outlet as a result of tributary-mainstem ice flow convergence (Figure 3.4). These 63 valley steps are particularly important in their control on sediment dynamics resulting in repeated longitudinal patterns (discussed further in Section 3.5.1). However, glacial history alone fails to fully explain intra- and inter-catchment patterns. We therefore also consider the geologic and (post)glacial history. Geologic controls on sediment dynamics and yield include lithology, topographic relief, and structures such as faults, lineaments, and joint sets. Valley alignment appears to be controlled by fault orientation and differential erosion of rock types (Figure 3.7; northwest-southeast structural and topographic grain – Monger & Journeay, 1994). All major valleys follow along strike of faults, except for Rutherford Creek where none are present. Additionally, Fitzsimmons Creek is oriented with erodible volcanic rock along the valley bottom, and more resistant lithologies making up the ridges. The spatial distribution of volcanic rock also seems to control valley geometry. Valley sections underlain by volcanic rock are on average four times less steep and four times wider, than valley sections underlain by other rock types (Figure 3.7). Abrupt decreases in valley slope in the upper Rutherford and Soo River watersheds may also be associated with a transition from more resistant lithologies to volcanic bedrock. Additionally, there appears to be a lithologic and structural control on the spatial distribution of rock avalanches, with topple deformation occurring in glacially over-steepened intrusive bedrock along defined joint sets (Nichol et al., 2002; Cordilleran Geoscience, 2018; Blais-Stevens et al., 2011). However, at this scale, landsliding is ultimately stochastic, and the exact timing, location, and size of these landslides cannot be resolved based on known variables (Dadson & Church, 2005). Finally, colluvial processes are more important in Rutherford Cr. as a result of greater local relief (i.e., up to 1800 m) rugged exposed bedrock, and prominent gully systems. While the geologic and glacial history of the landscape appear to influence the particular postglacial response, it has long been known that attempting to extrapolate into the past or future based on modern, measurable conditions and variables can be both problematic and challenging (Schumm, 1991). Several challenges include, (i) equifinality, where valley width appears to be controlled by both ice flow convergence and the spatial distribution of volcanic bedrock, (ii) multiplicity, where landslide occurrence is the result of both glacial and geologic controls, and (iii) complexity, which is illustrated by the reactivation of glacial sources and dissimilarity in the paraglacial response of basins. 64 Keeping these challenges in mind, we explore the particular postglacial response of each subwatershed in order to explain contemporary sediment dynamics and yield. Valley steps, which are characterized by alternating steep/narrow and flat/wide valley bottoms, result in large sediment sinks in the hanging valley which greatly reduces downstream sediment transport. Valley steps are typical of glaciated landscapes (Hassan et al., 2023) but can also be the result of Figure 3.7: Geologic controls and surface expression in the Green River watershed (a), and plot of lithology vs. valley bottom width (b). 65 stochastic landsliding which dams the river, sets a new baselevel, and results in backwatering and up valley sedimentation. Examples of such landsliding in the Green River watershed include Soo River, Mystery Creek, Suicide Hill, and Blackcomb Glacier rock avalanches (Table 3.2; Figure 3.4). This has also been noted as an important mechanism in the Himalayas (e.g., Walsh et al., 2012) and many other locations globally (Hewitt et al., 2008; Fan et al., 2020). Landslide dams can persist for thousands of years, interrupting the sediment cascade and controlling sediment yield until the river is able to incise through the material and adjust its profile (Cossart & Fort, 2008). The effects of the Soo River landslide have persisted throughout the Holocene. By narrowing the valley bottom, valley margin landforms increase channel confinement in otherwise unconfined reaches (~15% of total confinement in Green River watershed) and increase the likelihood of hillslope-channel coupling. Postglacial fluvial adjustment to irregularities in the longitudinal profile typically result in incision at hanging valley steps. The celerity of knickpoint migration and channel response will depend on the erodibility of the material making up the channel bed (e.g., bedrock, glaciogenic sediments). Incision at the lowermost hanging valley step has progressed rather quickly in the Rutherford and Fitzsimmons Cr. catchments, which have incised between 40-65 meters into glaciogenic sediments over a distance of 6-7 kilometers. In contrast, channel incision has progressed slowly in the lower Soo River (i.e., ~4 meters for approximately 4.5 kilometers), which is armored by rock avalanche debris and talus blocks that can exceed 4 meters in diameter. Channel incision increases confinement and can also destabilize valley fill material resulting in landsliding. As a result, bank erosion and slumping are common in the lower Rutherford and Fitzsimmons Creek watersheds, significantly increasing the SY near the outlet. However, sediment delivery from adjacent hillslopes remains low, due to the buffering effect of the terraced valley fill. The rapidity of the paraglacial response varies based on catchments size and landscape (dis)connectivity. Sediment storage, which is generally related to basin size, results in a slower and more complex response where late-stage reactivation of intermediate storage becomes important (Church & Slaymaker, 1989). The relation between sediment storage and basin size holds true for the Green River watershed. There is a positive relationship between specific sediment storage (m3 km-2 yr-1) and basin size. However, alluvial landforms (i.e., paraglacial fan) 66 are only important sources of sediment in the Rutherford Cr. watershed, while Fitzsimmons Cr. and Soo River watersheds are still reworking primary glaciogenic sediments. Together the spatial variability in geologic controls, glacial landscape configuration, and complex (post)glacial response explains the intra- and inter-variability in contemporary sediment dynamics and yield. Because these different controls are acting on the system simultaneously, signals may be amplified, dampened, shredded, or destroyed (Jerolmack & Paola, 2010) and a consideration of all primary controls is required to explain the observed patterns. 3.5.3 Conceptual Model To synthesize our analysis of the longitudinal patterns and controls on sediment dynamics and yield in (post)glacial landscapes, we propose a conceptual model that includes geology, glacial history, and postglacial history of landscapes (Figure 3.8). This model builds on the framework recently proposed by Hassan et al. (2023), which focused solely on glacial controls while holding all other factors constant. Our model similarly emphasizes the importance of glacial processes in shaping landscapes and generating spatial variability in channel characteristics and connectivity. However, the model we present here differs from previous models in that it (i) incorporates the interplay between geology and postglacial history alongside glacial history, (ii) expands on how geology and glacial history influence the particular postglacial response, and (iii) incorporates stochastic landsliding. This model aims to supplement Hassan et al.’s (2023) work by providing a more comprehensive understanding of the key controls on sediment dynamics and yield. It helps explain variability within and between neighboring (de)glaciated mountain catchments. Figure 3.8 illustrates this model as applied to a high-relief (post)glacial catchment with a complex long profile, such as the nearly 900 km2 Green River watershed and its’ subwatersheds analyzed in this study. However, the controls discussed here are also applicable to other glaciated valley landsystems (e.g., panels b and d from Hassan et al., 2023). Geology plays a foundational role in shaping valley alignment, which is determined primarily by structural features such as faults, and to a lesser extent, lithology. Lithology also influences valley width, with more erodible rock types, such as volcanic rocks, resulting in wider valley bottoms. Additionally, lithology, joint sets, and lineaments control the occurrence of large 67 (post)glacial rock avalanches, where intrusive rock is susceptible and prone to topple deformation. Glaciers, operating on the geologic template, produce unique landforms and reorganize the landscape. Typical glacial landforms include cirques, aretes, horns, tarns, hanging valleys, wide and low gradient u-shaped valleys, and over-steepened hillslopes. These contribute to landscape heterogeneity and disconnectivity as described in Hassan et al. (2023). A key aspect is the formation of valley steps and complex profiles created by glacier flow convergence. These steps result in the repetition of typical longitudinal sequences of channel types and connectivity patterns (Brardinoni & Hassan, 2007; Hassan et al., 2023). While geology and glacial history condition the postglacial response, complexity in fluvial systems (Schumm, 1991) and stochastic processes (e.g., landsliding) introduce variability. Glacially over-steepened intrusive bedrock, with joint sets and lineaments, is prone to large rock avalanches. These landslides can temporarily dam entire valleys and reset the typical longitudinal sequences, similar to the effects of glacial valley steps. However, landsliding is stochastic at this scale. Ultimately, stepped long profiles result in increased valley bottom storage, decreased connectivity along the mainstem, and reduced sediment yield at the catchment outlet. Valley margin paraglacial landforms (e.g., debris cones) add confinement and can lead to increased hillslope-channel coupling, especially in broad valley reaches. Along the mainstem, glacial meltwater channels form outwash plains (i.e., sandurs), and paraglacial sedimentation can build impressive fans at the channel outlet. As the mainstem continues to adjust to glacier retreat and sediment yield decreases, channel incision is likely to occur, especially at hanging valley steps. Channel incision can lead to increased confinement, destabilization and landsliding of glaciogenic valley fill sediments (where present), and elevated sediment yields (Scott & Collins, 2021; Church & Slaymaker, 1989; Turley et al., 2024). 68 3.6 Implications Geology, glacial history and the particular postglacial response impart heterogeneity to the otherwise continuous downstream trends. Landscape heterogeneity is important in several Figure 3.8: Conceptual model of landscape controls on sediment dynamics and system functioning in (post)glacial landscapes. 69 different ways. Topographic complexity and process-form disequilibrium can result in high sediment yields (e.g., paraglacial sedimentation) and/or significant sediment storage and disconnectivity (Turley et al., 2023). Buffers, barriers, and blankets impede sediment transfer, significantly reduce the area of the catchment that contributes sediment to the outlet, and in so doing also reduces sediment yield (Fryirs et al., 2007a). These sources of disconnectivity range in size from valley steps to individual logjams (Hassan et al., 2024). Because sediment inputs are spatially restricted and sources of disconnectivity are widespread, river channels are often characterized by discontinuity in longitudinal grain size distributions and may be segmented into discrete sedimentary links (Davey & Lapointe, 2007). Longitudinal variations in valley bottom width and gradient have an effect on landscape resilience and response to extreme events. Broad valley reaches (i.e., floodplain/wetland; also referred to as river beads) dissipate energy and store excess water and sediment, which has been shown to increase the resiliency of river systems (Wohl et al., 2018; Wohl et al., 2024). Valley bottom width and channel confinement have also been linked to channel width response during flood events. Reid et al. (2021) showed that glacial history influences the distribution of sediment availability, which in turn controlled the channel width response to floods. In a study by Davidson et al., (2024), channel widening was greatest during an extreme event where the channel was confined by glaciofluvial terraces (i.e., valley bottom confinement). In contrast, channel widening was negatively correlated with valley margin confinement during an extreme event in a non-glacial landscape (Scorpio & Comiti, 2024). Confinement is therefore an important factor influencing the channel response to flood events but may have an opposing effect depending on the erodibility of the confining landform. Based on the primary confining landforms in different zones in our study, confinement within alluvial reaches (i.e., floodplain/wetland, fan) and reaches incised into glaciogenic sediments (i.e., canyon) is likely to enhance changes in channel width and increase lateral sediment inputs from the banks during extreme events. These correlations may be weak during large events, and reversed for colluvial reaches where the confining margin is difficult to erode. 70 3.7 Conclusion This study examined the controls on sediment dynamics and yield of mountainous, (post)glacial watersheds in order to explain the observed intra- and inter-variability in system functioning. Initial observations suggested that adjacent watersheds, which share similar geologic, climatic, and glacial histories, exhibit distinct spatial patterns of connectivity, sediment storage, and contemporary sediment yield. The apparent dissimilarity of these basins presented an opportunity to test available conceptual models and evaluate potential controls. We approached this through detailed geomorphic mapping, geochronology, estimates of (post)glacial erosion and storage, and longitudinal analyses of channel and valley characteristics and connectivity. While many of the observed longitudinal patterns align with those typical of mountainous, glaciated basins with complex long profiles (Hassan et al., 2023), glacial history alone could not fully explain the variability and complexity seen within and between catchments. At least for the watersheds in this study, which together cover 875 km2, it was important to also consider the role of geology, and the particular postglacial response. To synthesize our findings, we propose a conceptual model that includes geology, glacial history, and postglacial history of landscapes, building on the framework recently presented by Hassan et al. (2023) for (de)glacial landscapes. Our revised conceptual model is presented for a high-relief (post)glacial catchment with a complex long profile but is applicable to other glaciated valley landsystems. In this model, geology influences valley alignment through the presence of faults, and to a lesser extent, lithology. Lithology also influences valley width, with more erodible rock types resulting in wider valley bottoms. Additionally, lithology, joint sets, and lineaments influence landslide susceptibility. Glaciers, operating on the geologic template, reorganize the landscape and produce unique landform assemblages and connectivity patterns. Typical glacial landforms contribute to landscape heterogeneity and disconnectivity. In particular, ice flow convergence leads to the formation of confluence steps, valley widening and over deepening, setting the stage for future sediment storage in wetlands and floodplains. Valley steps can also be produced by postglacial processes. Glacially over-steepened intrusive bedrock, with joint sets and lineaments, is prone to large rock avalanches. These landslides can dam entire valleys and lead to significant sediment storage upstream and irregularities in the river longitudinal profile. Stepped long profiles result in the repetition of typical longitudinal sequences of channel types, 71 characteristics, and connectivity patterns. This added complexity can increase sediment storage, enhance system resilience, and reduce sediment yield at the catchment outlet. However, postglacial incision into hanging valley steps can lead to destabilization of valley fill sediments, landsliding, and significantly increase sediment yield. When this occurs near the catchment outlet, as is the case for Fitzsimmons and Rutherford Creeks, this signal can dominate the contemporary sediment yield. The results of this study underscore the complexity and variability of mountainous (post)glacial systems, and the challenges in extrapolating results even between adjacent watersheds with shared histories. However, careful consideration of controlling factors (i.e., geology, glacial history, postglacial history) can explain the observed patterns. Three avenues for future work seem both possible and particularly fruitful. First, the collection of additional geochronological data could be used to resolve the specific timing of postglacial adjustments in the Green River watershed. This could then be related back to the observed spatial patterns in this study. Second, this study provides a foundation for more detailed sediment budget measurements in the Rutherford Cr. and Soo River watersheds. This data could then be compared to the detailed measurements for Fitzsimmons Creek (Chapter 2) for a more robust inter-catchment comparison. And third, rigorous and quantitative testing of the proposed conceptual model (Figure 3.8) could be achieved through numerical modelling of sediment storage in over deepened valleys and alternating narrow-wide valley bottom reaches, and systematic mapping and modelling of conditions leading to landsliding. 72 Chapter 4 - Investigating postglacial sediment supply, storage, and yield in a large glaciovolcanic watershed 4.1 Introduction Our current understanding of sediment dynamics and yield in glaciated terrain comes primarily from work on paraglacial sedimentation and glacial landscape configuration. Following glacier retreat, nonglacial processes redistribute glacial sediments, resulting in elevated sediment yields during the paraglacial period (Church & Ryder, 1972). The reworking of highly accessible and connected glacial and paraglacial sediment stores can disproportionately contribute to sediment fluxes (Scott & Collins, 2021), leading to a downstream increase in specific sediment yield in contrast to unglaciated basins (Church & Slaymaker, 1989). Hassan et al. (2023) propose a conceptual model of glacially induced longitudinal trends in sediment connectivity and channel characteristics. The model illustrates three different types of postglacial landscapes distinguished by their glacial histories. While glacial history results in high spatial heterogeneity within catchments, the resulting channel, landform, and connectivity characteristics exhibit predictable patterns driven by landscape organization. Glacial history often results in complex and stepped river long profiles which result in alternating coupled/confined and decoupled/unconfined reaches, primarily controlled by valley bottom width and slope (Brardinoni & Hassan, 2006). This landscape organization then determines longitudinal and local patterns of sediment supply from hillslopes (Hassan et al., 2019), grain size distributions, stream power, bed material transport rates (Dell'Agnese et al., 2015), and channel morphology (Brardinoni & Hassan, 2007). Time since deglaciation has also been linked to riparian forest establishment and fish habitat (Cowie et al., 2014). While our general understanding of postglacial landscapes has improved significantly, important questions remain. For example, the role of sediment storage and disconnectivity in modulating sediment yield is poorly understood and often ignored. In most cases, only a small proportion of the sediment eroded within a catchment will reach the outlet within a specified period (Walling, 1983). As a result, linking onsite erosion to sediment yield at the outlet requires explicit consideration of sediment storage. In other words, contemporary sediment yields rarely if ever 73 reflect primary denudation rates due to gains and losses of sediment from storage effects (Owens & Slaymaker, 1992). Sediment storage is the physical manifestation of inefficient sediment transfer within the landscape (i.e., disconnectivity; Turley & Hassan, 2023) and explains both how and why landscapes can remain in a state of transience or disequilibrium. In this way, landscapes can retain a “memory”, both in their morphology and in sediment storage landforms, of past conditions that can persist for millennia (Brierley, 2010). Postglacial landscapes typically exhibit a high degree of disconnectivity between hillslope sediment sources and river networks, resulting in large volumes of sediment temporarily stored in valley margin and valley bottom reservoirs (Turley & Hassan, 2023). These storage landforms are important archives of the landscape response to changing conditions (e.g., Bridgland & Westaway, 2014; Mather et al., 2017; Woor et al., 2023). While external drivers such as climate, geology, and human activities are commonly linked to sediment yield variability, the role of sediment storage within catchments remains underexplored, warranting closer investigation. More studies are needed that combine principles of geomorphology and sedimentology to explore sediment dynamics, and in particular the role of sediment storage, at varying scales (Hinderer, 2012). Hooke and Souza (2021) note that recognizing disconnectivity and sediment storage is a notable deficiency in recent work. Another key challenge is the pronounced spatial and temporal variability of sediment yield. Sediment dynamics are episodic, spasmodic, and diachronous, as seen in the stratigraphic record of British Columbia (Clague, 1986). Variability occurs across scales. At a regional scale, Church and Slaymaker (1989) document suspended fluvial sediment yields that vary by at least four orders of magnitude across British Columbia. A global dataset of sediment yield from glaciated catchments reports values that span five orders of magnitude (Carravick & Tweed, 2021). Comparisons between basins are complicated by the fact that sediment yield is often correlated with drainage area, and the nature of this correlation varies from region to region (Church, 2017). These factors limit our ability to extrapolate results from one location to another, and from one period to another. Additional work is needed that systematically explores sediment dynamics and yield at varying scales. The primary objective of this chapter is to investigate and quantify the spatial and temporal patterns of sediment supply, storage, and yield in a mountainous, (post)glacial landscape 74 throughout the Holocene. The Lillooet Basin in BC (~3,765 km2) is an excellent location for this study. The basin has large paraglacial sediment stores that record the history of adjustment, and there is a wealth of research to provide background information including preliminary sediment budgeting, landslide records, glacial history, geochronology data, and both short and long-term sediment yield records at multiple spatial scales (e.g., Jordan & Slaymaker, 1991; Desloges & Gilbert, 1994; Friele et al., 2008; Schiefer et al., 2010). The scale of this investigation varies from areas less than 1 km2 to the entire watershed (3,765 km2), and from individual events to the Holocene period (~10.5 ka). In particular we pose the following research questions. 1. Where is sediment being stored within the system, and for how long? 2. What controls the patterns of sediment storage and yield? 3. How variable is sediment yield in time and space? To address these questions, we first map sediment storage landforms (e.g., floodplains, alluvial fans, debris cones, etc.) for the entire Lillooet River watershed. We then compile and synthesize all the available geochronology data for this watershed, to which we contribute additional optical, radiocarbon, and relative ages. This data is used to discuss the spatial distribution and timescales of sediment storage and identify primary controls on these patterns. Sediment storage is then linked to landscape response using measures of structural connectivity and a wealth of sediment yield data measured at different spatiotemporal scales throughout the Holocene. 4.2 Study Site The Lillooet River watershed (~3,765 km2) is located in Southwest British Columbia within the southern Coast Mountains (Figure 4.1). The basin is characterized by steep mountainous terrain with roughly half of the basin above timberline, up to 2800 meters of local relief, and peaks reaching 3000 meters in elevation (Jordan and Slaymaker, 1991; Friele et al. 2005). Because most precipitation comes from the North Pacific Ocean in the southwest, the structural orientation of the watershed imposes a strong, rain shadow effect on the watershed resulting in dramatically decreased temperature, precipitation, streamflow, and glacierization from west to east across the basin (Slaymaker et al., 2017). Today, approximately 12% of the watershed is 75 occupied by icefields and glaciers. Summary statistics for the Lillooet River watershed and its’ major tributaries can be found in the Table C. 1. 4.2.1 Geology and Volcanics Most of the basin is underlain by resistant plutonic rocks (i.e., granite, granodiorite, diorite) of the Coast Crystalline Complex with a strong northwest-southeast structural and topographic grain (Monger & Journeay, 1994). The Mount Meager Volcanic Complex (MMVC), centered Figure 4.1: Map of the Lillooet watershed above Lillooet Lake. Positions along the Lillooet River measured as kilometers down valley (along valley centerline) from the 2024 terminus of Lillooet Glacier. 76 around heavily glaciated and hydrothermally altered Mount Meager, is arguably the most unstable and landslide-prone mountain massif in Canada (Friele et al., 2008). The MMVC only comprises approximately 2% by area of the basin lithology (e.g., andesite, dacite, rhyolite), but is subject to catastrophic landslides that can travel tens of kilometers down valley and disproportionately affect down valley sedimentation (Friele et al. 2005). The last known eruption occurred ~2400 years ago and spread ash as far east as Alberta. Hickson (1994) notes that the MMVC is prone to edifice collapse, large debris flows and rock avalanches, associated river damming, and floods and hyper-concentrated flows produced by catastrophic failure release of landslide-dammed lakes. Evidence of these events has been identified past the catchment outlet in lake sediment cores (Desloges and Gilbert, 1994). The relative importance of volcanism as a contributor to the sediment yield is further emphasized in the revised paraglacial models presented by Jordan and Slaymaker (1991), and Friele and Clague (2009). 4.2.2 Late Quaternary History The last glacial period was marked by ice expansion from 29-14.5 ka, and then glacier retreat from 14-11.5 ka (Ryder et al., 1991). Alpine glaciers dominated most of this period, while the Cordilleran Ice Sheet only existed from about 19 ka to 13.5 ka (Ryder et al., 1991). By ~11.5 ka glaciers were restricted to high-mountain areas similar to today, and paraglacial processes dominated (Clague, 1986). During this time, the Kakila Cr. fan extended across the valley and dammed the Lillooet River, forming the 75-km-long Lillooet Lake (Friele et al., 2005). Since that time (~10.5 ka), the Lillooet River has advanced its delta about 50 km at an average rate of 6 m yr-1, while the floodplain has aggraded at a rate of 4.4 mm yr-1 (Friele et al., 2005). 4.2.3 Lillooet River The Lillooet River is fed by the Lillooet Glacier (VKM 0), and flows ~105 km before emptying into Lillooet Lake near the town of Pemberton. The river progresses from a braided, cobble bed channel in its upper reaches (~VKM 0-59) to a wandering to meandering cobble-pebble bed channel (~VKM 59-72), to a heavily engineered sand to pebble gravel reach (previously irregular meandering; ~VKM 72-90), and finally to a moderately sinuous, sand bed channel (~VKM 90-105; Friele et al. 2005). The river flows through an aggrading floodplain that is 1-2 km wide 77 bounded on either side by steep slopes and paraglacial fans. Major tributaries to the Lillooet include Meager Cr. (VKM 31), Ryan River (VKM 84), Green River (VKM 95; Mean Annual Discharge (MAD): 48 m3 s-1), and Birkenhead River (joins at Lillooet Lake; MAD 24 m3 s-1). Gilbert et al. (2006) notes a MAD of Lillooet River of 126 m3 s-1, and nival and glacial melt peaks of 400-600 m3 s-1 occurring in June and July, respectively. Ten large floods with a maximum daily discharge greater than 750 m3 s-1 have occurred in the last 60 years (Water Survey of Canada Station: 08MG005) with a maximum instantaneous discharge of 1490 m3 s-1 in 2003. Heideman (2013) notes that the magnitude of the floods appears to be increasing over time, which considered with a rapidly growing population in the valley has resulted in increased vulnerability and risk to flooding. 4.3 Data & Methods 4.3.1 Geomorphic Mapping The procedures and results of the detailed geomorphic mapping of Fitzsimmons Creek and Green River watersheds are discussed in Chapter 2 & Chapter 3. In this chapter, select geomorphic features were mapped for the entire Lillooet River watershed (~3,765 km2) at an approximate 1:10,000 scale following the same procedures as for the Green River. High resolution lidar data, available for portions of Lillooet River, Birkenhead River, Meager Cr., and Boulder Cr. valleys was particularly useful. Features mapped for the entire Lillooet River watershed include glaciers, lakes, active channels, floodplains, alluvial fans, debris cones, known large rock avalanches/slides, debris flow tracks, and latero-frontal moraines. The distinction between fans and cones is made based on slope gradient and profile concavity (e.g., fan-cone threshold = 26%; Howes & Kenk, 1997). 4.3.2 Spatial Analysis of Sediment Storage The spatial distribution of features representing sediment supply (e.g., known large rock avalanches/slides, debris flow tracks) and storage (e.g., active channel, floodplain, fans, cones) were analyzed in two different ways. First, the surficial coverage (km2) of each feature class was summarized every kilometer along the channel systems (all watersheds) based on the non-overlapping contributing area for that 1-kilometer stretch of channel. This results in a 78 longitudinal representation of sediment storage that can be directly linked to valley geometry and catchment characteristics. Second, the total surficial coverage (km2) of each feature class was calculated for all watersheds. These values were then normalized by subtracting the expected contribution based on the Lillooet River watershed average (feature coverage km2 / catchment area km2) and then dividing by the expected contribution. The resulting values range from -1 to 1, and represent contribution anomalies, where negative values indicate underrepresentation and positive values indicate overrepresentation of the feature in that subwatershed. While approximating sediment storage by a given landforms surficial coverage (km2) is far from ideal, it has been found to provide reasonable estimates for fans/cones (Giles, 2010). It is, however, important to note that this will bias the results in the main Lillooet River valley which has been net aggradational throughout the Holocene, resulting in interfingering of landslide, alluvial fan, and floodplain deposits. Approximating floodplain storage volume by area is more problematic, and so volumetric estimates are also provided whenever possible. 4.3.3 Geochronology Multiple chronometric methods were leveraged and employed in this study to constrain the activity of landscape components throughout the Holocene. A substantial body of geochronological data is available for this watershed, including floodplain sediment cores, varve chronologies, and ages derived from radiocarbon, terrestrial cosmogenic nuclide, tephrochronology, and dendrochronology dating methods. Existing data were supplemented with relative dating techniques paired with known ages and marker beds (Section 4.3.3.1), to approximate the age and activity of landforms. Additionally, absolute dating methods were employed (Section 4.3.3.2), with an emphasis on optical dating (Section 4.3.3.3), a technique not previously applied in this watershed. It is important to note here that while the geochronology of this watershed is relatively well understood, the record is still highly fragmented, suffering from data censoring of small events (Jakob, 1996), and temporal filtering. 4.3.3.1 Tephrochronology The tephra horizon (i.e., Bridge River ash) created by the most recent eruption (2,360 cal yr BP; Clague et al., 1995) at Mt. Meager is perhaps the single most useful and identifiable age marker 79 in the Lillooet River watershed. In the upper watershed, airfall pumice is equally distinctive and often several meters thick. Post eruption fluvial reworking has resulted in distinctive deposits where the inclusion of pumice clasts has reduced over time. Due to the characteristic modern burial depths, Mazama tephra (7,630 cal yr BP; Egan et al., 2015) was not directly noted in this study, but informs studies cited in this text. 4.3.3.2 Radiocarbon Dating Radiocarbon (14C) dating has been proven to be particularly useful in the Lillooet River watershed given the abundance of organic material. When the organic material is found in growth position, the radiocarbon age will correspond closely to the depositional age of the surrounding sedimentary deposit. However, when the organic material is detrital, radiocarbon dating can only provide a maximum age. The sample was collected with a trowel, placed in a clean plastic bag, and then dried in an 80˚ C oven. The sample was sent to A.E. Lalonde AMS Laboratory for processing and analysis. The age was calibrated using OxCal v.4.2.4 (Bronk Ramsey, 2009) and the IntCal 20 calibration dataset (Reimer et al. 2020). 4.3.3.3 Luminescence Dating While radiocarbon has been extensively employed in this watershed, there are several factors that warrant attempting to apply optical dating techniques. Optical dating does not require the presence of organic material, is applicable for quartz and feldspar minerals, and can directly date the time that has elapsed since these minerals were last exposed to light which is usually consistent with the formation of landforms. However, it is important to note that luminescence dating is inherently experimental, comprising a suite of techniques selectively applied based on the nature and characteristics of the sample (Lian and Roberts, 2006). In this study, luminescence dating samples were collected from fans and terraces expected to be postglacial in age by inserting opaque tubes into cleaned section faces (see stratigraphic sections in Appendix C. Given that the Lillooet River watershed is high energy and primarily net aggradational (Jordan & Slaymaker, 1991), there were limited exposures suitable for this method. Sampling focused on fan and terrace materials that have undergone subsequent incision either from the contributing channel or erosion at the toe from mainstem channels. Samples were 80 only collected where the sedimentology suggested a low energy, fluvially sorted depositional environment, and at least 20 cm away from bounding units (Table C. 2). Sample preparation and equivalent dose measurements were completed at the University of the Fraser Valley luminescence dating laboratory. The sample tubes were opened and processed under dim orange light, and ~5 cm of sediment was removed from either end of the sample to reduce the likelihood of including light-contaminated grains in the measurements. Water content and environmental dose rate samples were taken from this discarded sediment. Wet sieving was used to isolate fine sand grains between 180-250 μm, which were then treated with HCL to remove carbonate minerals. Next, a lithium metatungstate (LMT) solution was used to separate heavy minerals (> 2.7 g cm-3), quartz (2.62-2.7 g cm-3), and potassium feldspar (2.58-2.60 g cm-3) grains by adjusting the density of the LMT solution using deionized water or evaporation and allowing mineral separation via settling. Samples were then etched using a 10% hydrofluoric acid solution to remove the α – effected portion of the grain surfaces and clean the outside of the grains, followed by an HCL rinse. Finally, aliquots consisting of approximately 50-100 grains, were mounted to the center of 10 mm diameter aluminum discs using silicone spray. Quartz and Feldspar, the two most common minerals used in luminescence dating, each have their own advantages and limitations (Wintle, 2008). Quartz is generally the mineral of choice due to its luminescence signal being more rapidly bleached (i.e., fast component), and because it does not suffer from anomalous fading (Lian & Roberts, 2006). Quartz and feldspar grains contain structural defects and impurities that can act as traps for free electrons produced by ionizing radiation. In the dating process it is important to isolate thermally stable traps, which can reliably store electrons over timescales of interest, from thermally unstable traps which lose signal over time (Lian & Roberts, 2006). Additionally, to avoid inherited ages of sediments, it is important to sample only the most light-sensitive traps which are easily reset when exposed to sunlight. These sensitive traps result in the fast component of a dose-response curve. However, in many regions, especially those that are glacierized and volcanic, the quartz grains available for dating are eroded directly from bedrock or have not been sufficiently sensitized (i.e., subjected to multiple cycles of erosion and deposition) making their fast component dim or virtually non-existent, and them unreliable chronometers (Preusser et al., 2009; Tanski et al., 2024). When this is the case, potassium feldspar (KF) minerals are utilized for dating. Significant drawbacks of 81 using KF in luminescence dating include difficulty in correcting for changes in luminescence sensitivity within the SAR protocol (Wallinga et al., 2000), and anomalous fading which will be discussed later (Lian & Roberts, 2006). Today, ~12% of the Lillooet River watershed is covered in glaciers (Table C. 1), while virtually the entire region was covered in a vast ice sheet within the last ~13.5 ka (Ryder et al., 1991). Furthermore, much of the bedrock is volcanic, from the Mount Meager Volcanic Complex and Garibaldi Volcanics, suggesting that quartz minerals may not display a fast component. Pilot measurements on quartz samples confirmed this suspicion, as they lacked a clear fast component. Therefore, KF was chosen for the remaining measurements. To determine the environmental dose rate, or the rate at which the sample was exposed to ionizing radiation while in situ, a small portion of each sample was dried, milled, and sent to the Bureau Veritas Laboratories in Mississauga, Ontario for neutron activation analysis. All of the samples consisted of well sorted fine sands and the as-collected water content was used in the dose-rate calculations, with a ± 10% uncertainty to account for moisture fluctuations over time. Dose rates were calculated in Luminescence Dose and Age Calculator (LDAC) software (Liang et al., 2019). The contribution from cosmic ray radiation was estimated using present burial depths, elevation, and geographic location and the relationship of Prescott and Hutton (1994). Dose rate variables are presented in Table C. 3. Samples were measured using a Risø TL/OSL DA-20 reader/irradiator equipped with a calibrated 90Sr/90Y source delivering β radiation to samples at a rate of ~4.4 Gy/min. KF grains were stimulated with 130 mW/cm2 infrared (IR) light (~880 nm) and violet (~400 nm) luminescence emissions were detected using a 9235QB photomultiplier tube fitted with a blue filter pack with Schott BG-39 and Corning 7-59 optical filters. The single-aliquot regenerative-dose (SAR) protocol on KF grains was employed (Wallinga et al., 2000) given the noted difficulties with quartz in this region. The procedure is illustrated in Figure 4.2 with standard SAR protocols adjusted based on pilot experiments. Standard SAR quality control checks were applied, which included assuring recuperation signals were less than 5%, recycling ratios were less than 10%, maximum test dose errors were less than 10%, and the test dose (Ti) signal was greater than 3 sigma above the background signal. A final test was performed to detect statistical outliers in the accepted equivalent dose (De) values based on the 82 interquartile range (IQR) method (i.e., Q1 – IQR x 1.5; Q3 + IQR x 1.5; Medialdea et al., 2014). Additionally, dose recovery tests were performed using known laboratory doses in place of the natural dose and running a SAR protocol to attempt recovery of the known dose. These tests resulted in given dose to recovered dose ratios that ranged between 1.02 ± 0.01 and 1.12 ± 0.02 indicating that the SAR protocol is applicable for dating KF in the study region (Table C. 4). For each sample, 35 or more aliquots were measured, and those that passed the quality controls checks were used to determine the De values. Partial bleaching during transport, post depositional mixing, and dose rate heterogeneity will produce scatter in De values measured from a single sample. Reducing the complex distribution to a single representative value requires the careful application of an age model. Because the samples collected in this study are from sediments deposited by turbid glacial river systems, partial bleaching might be expected and a minimum age model (MAM) is likely applicable (Galbraith et al., 1999; Rittenour, 2008). All results are presented with the application of both the central age model (CAM) and MAM. A Figure 4.2: Single-aliquot regenerative-dose (SAR) protocol based on Wallinga et al. (2000), with specific values adjusted based on pilot experiments. 83 representative example is presented in Figure 4.3. The modelled De values were then divided by the estimated environmental dose rate to produce uncorrected optical ages for each sample. Potassium feldspar is susceptible to anomalous fading (i.e., signal loss over time), which results in ages that are too young, unless corrected for (Huntley & Lamothe, 2001). In this study, the correction method outlined by Huntley and Lamothe (2001) was applied. Ten aliquots from each sample were tested for anomalous fading based on the modified SAR procedure discussed by Auclair et al. (2003) and outlined in Table 4.1. The fading rate for each aliquot is then determined from the slope of a fitted line on the resulting fading rate plot (e.g., Figure 4.3c), and then all accepted sample aliquots are analyzed using the CAM to provide a weighted mean fading rate for each sample. Using this average fading rate, corrections were made using the ‘Luminescence’ package by Kreutzer et al. (2023) within R statistical software (v4.2.1, R Core Team 2021). Figure 4.3: Luminescence dating summary for sample MT-lil-02. The panels include, (a) shinedown and dose response curve, (b) abanico plot of De values, and (c) fading plot. Panels (a) and (c) are based on representative aliquots, while panel (b) is based on all accepted aliquots. Plotted using the ‘Luminescence’ package (Kreutzer et al., 2023) within R statistical software (v4.2.1, R Core Team 2021) See Appendix C for all other samples. 84 Table 4.1: SAR procedure for fading measurements, performed for each aliquot. The pause time in step 9 is increased with each consecutive measurement. Step Fading measurements Measurement 1 Regenerative dose 2 Preheat (160ºC, 10s) 3 Stimulation (IR LEDs, 50ºC, 100 s) Li 4 Test dose (3 Gy) 5 Preheat (160ºC, 10s) 6 Stimulation (IR LEDs, 50ºC, 100 s) Ti 7 Regenerative dose 8 Preheat (160ºC, 10s) 9 Pause (10min, 1h, 10h, 5-10d) 10 Stimulation (IR LEDs, 50ºC, 100 s) Li 11 Test dose (3 Gy) 12 Preheat (160ºC, 10s) 13 Stimulation (IR LEDs, 50ºC, 100 s) Ti 14 Return to step 1 4.3.4 Connectivity Analyses In this chapter, structural connectivity was estimated using the index of connectivity (IC) method first proposed by Borselli et al. (2008). In this method, topographic characteristics and flow pathways are derived from a DEM and then paired with a weighting factor that represents surface roughness, flow impedance, and soil erodibility estimated using the C-factor of USLE-RUSLE models (Table 4.2). Land cover classes used in this study are based on the classification of Landsat 5 and Landsat 7 ortho-images at a 1:250,000 scale (GeoBC, 2021). While there are many different published methodologies for producing the weighting raster (e.g., DEM-based roughness - Cavalli et al., 2013; Manning’s n – Persichillo et al., 2018; etc.), land cover was found to produce results that best reflect field observations. The input surface and weighting rasters both had a final resolution of 25-meters, which was considered acceptable given the size of the catchment. All calculations were completed within R statistical software (v4.2.1, R Core Team 2021), using the R package R_IC (Baggio et al., 2022) described by Martini et al., (2022), and modified to consider lakes as sediment sinks. 85 Table 4.2: C-factors used in the calculation of the weighting factor in the index of connectivity. Values are primarily based on Borselli et al. (2008). Land Cover C-factor Exposed rock and soil 0.9 Grassland 0.15 Agricultural areas 0.1 Developed (green urban areas) 0.05 Shrubland and herbaceous vegetation 0.04 Broadleaf forest (mixed forest and riparian vegetation) 0.006 Coniferous forest 0.001 Wetlands 0.001 Permanent snow/Ice NA 4.3.5 Sediment Budgeting Extensive sediment budget and yield data are available for the Lillooet River watershed, coming from numerous publications, theses, and this study. The most relevant sediment yield data are presented in Table C. 5, while Jordan and Slaymaker (1991) present an overview sediment budget for the watershed. These data were collected using different techniques and instrumentation and so have varying uncertainties (which were not always reported in the original study). Whenever possible these uncertainties are reported based on the original data source or estimated in this study. The most important updates to this budget come from two studies. First, Friele et al. (2008) compiled a dataset of all known landslide events from the MMVC and then constructed a frequency-magnitude diagram from which they estimate an average denudation rate. The estimated denudation rate is about 3000 m3 km-2 yr-1 (Friele & Clague, 2009), which equates to 3,600-4,800 Mg km-2 yr-1 accounting for a range of bulk densities (1.2 – 1.6 Mg m-3). Second, in the Silt Lake catchment (upper Lillooet River), Schiefer and Gilbert (2008) estimated the SSY to be ~ 1,000 Mg km-2 yr-1 based on sediment trap data during a 10-day measurement period. They also provide a bathymetric contour map, a bulk density estimate (1.2 Mg m-3), and an estimate of lake trapping efficiency. This data was used alongside 3-meter resolution PSScene orthoimages (Planet Labs PBC, 2024) to quantify delta 86 progradation rates from 2005-2024. Finally, progradation areas were converted to minimum volumes using the 2005 bathymetric survey, and then mass based on the bulk density. 4.4 Results 4.4.1 Spatial Distribution of Sediment Storage Sediment storage and colluvial sediment supply is unevenly distributed within the Lillooet River watershed as seen in Figure 4.4 & Figure 4.5. The most obvious pattern that arises from these figures is the high density of large rock avalanches/slides, lahars, and debris flows flanking the MMVC. This results in an equally high density of debris cone storage, and an increase in downstream channel width (proportional to storage area) and floodplain storage area in Meager Cr. and Lillooet River. The contemporary channel system has not yet fully recovered from the 2010 Capricorn Cr. landslide. Landslides from the MMVC are typically the result of hydrothermal alteration of pyroclastic rock and can be highly mobile traveling down valley as far as Lillooet Lake. These large events are well documented and range in volume between 104 – 109 m3 (e.g., Friele et al., 2004; Friele et al., 2008). Friele et al. (2008) note that volcanic and landslide material from the MMVC has frequently blocked the upper Lillooet River. A second population of known large landslides occur in granitic rock, are generally smaller in volume, less frequent, and less mobile (Slaymaker et al., 2017; P. Friele, Personal Communication, 2024). Several of these occur in the lower Green River watershed (Blais-Stevens, 2008) and dammed the associated river valleys resulting in significant up valley sediment storage (Chapter 3). Debris flow tracks, shown in Figure 4.5b, are abundant and occur in every subwatershed. The second pattern that is evident in Figure 4.4 & Figure 4.5 is that sediment storage (i.e., floodplain, fans, cones) is concentrated in mid-valley reaches, and at the outlets. This is particularly prevalent for alluvial fans which are more restricted and do not occur in the upper reaches of the subwatersheds. Debris cones follow a similar pattern but are much more prevalent and extend far up valley of most subwatersheds. Floodplain storage is greatest within the main Lillooet River valley, with an estimated 15 km3 deposited during the Holocene (Figure 4.6a; Friele et al., 2005). For reference this is ~90 times more sediment than is stored in floodplains within the Soo River watershed (Chapter 3). Floodplains extend all the way up to subalpine 87 environments in some subwatersheds, while others (e.g., Petersen Cr., Sockeye Cr., North Cr.) lack floodplain storage almost entirely. In comparing outlet fan area of the subwatersheds, some interesting patterns arise. Watersheds with relatively large outlet fans for their area include Meager Creek, Ryan River, Rutherford Creek, and North Creek, all of which are notably active. Watersheds with relatively small outlet Figure 4.4: Spatial distribution of alluvial storage areas in the Lillooet Watershed. Panels include (a) active channel, (b) floodplain, (c) alluvial fans, and (d) debris cones. Storage areas are summarized every kilometer along the channel systems based on the non-overlapping contributing area for that 1-kilometer stretch (e.g., storage areas mapped along Ryan River does not include storage areas within Petersen Creek). Note that because active channel width is directly proportional to the area, a storage area of 0.5 km2 = 500 m wide channel, etc. 88 fans for their area include Green River, Birkenhead River, Soo River, Taillefer Creek, and Fish Creek, which all have large sediment sinks (i.e., lakes, valley steps) that likely intercepts much of the sediment. 4.4.2 Catchment-Scale Connectivity Estimates Connectivity analysis for the Lillooet River watershed suggests that Meager Cr. has the highest connectivity of any subwatershed when considering the channel (lateral connectivity) or the catchment outlet (lateral + longitudinal connectivity) as the target (Figure 4.6b). In contrast, Green and Birkenhead River watersheds (and their subwatersheds) have relatively low connectivity, especially when considering the outlets as targets. The low connectivity is a result of many disconnecting lakes (both) and significant valley steps (Green River). The smaller tributaries in the upper Lillooet River watershed (i.e., Boulder, South, Salal, Manatee, North, and Miller Creeks) generally have higher connectivity values, which is likely related to watershed size, high hypsometric integrals, high topographic relief, and relatively little storage. Proglacial Figure 4.5: Spatial distribution of known large landslides (a), and all debris flow tracks (b). Variables are summarized every kilometer along the channel systems based on the non-overlapping contributing area for that 1-kilometer stretch (e.g., variables mapped along Ryan River are non-inclusive of those mapped along Petersen Creek). 89 Figure 4.6: Catchment scale sediment supply, storage, and connectivity estimates. In panel (a), sediment supply and storage are represented as contribution anomalies (normalized between -1 to 1) considering the entire Lillooet River watershed. Features include: df – debris flow tracks, MM – mass movements, Dc – debris cones, Af – alluvial fans, Ap – floodplains, Ac – active channels, L – lakes. In panel (b), connectivity is estimated by applying the index of connectivity method (IC; Borselli et al., 2008) considering the channels (no transparency) and the outlets (partial transparency) as targets, and lakes as sinks. The percent disconnected areas at the bottom of panel (b) are the result of lakes disconnecting the upstream areas and should be considered along with the connectivity boxplots. See Appendix C for a detailed connectivity map of the watershed. 90 lakes are present in all subwatersheds. However, they vary in size and effectiveness as disconnectivity features. Proglacial sediment trapping appears highest in the upper Lillooet River, Ryan River, Green River, Rutherford Creek, Miller Creek, and South Creek watersheds. 4.4.3 Sediment Storage Timescales Discussing sediment storage timescales in a watershed as large as the Lillooet River (3,765 km2) requires extensive, well-distributed geochronology data across space, time, and storage landforms. This study utilizes several published datasets of geochronology data, integrates relative dating techniques and geomorphic mapping, and adds new optical dating and radiocarbon dates. There is evidence of elevated early Holocene sedimentation as glaciers retreated, with meltwater streams depositing significant sediment in outwash plains and paraglacial fans, such as those at Rutherford and Blackwater Creeks. These fans were later incised, likely due to reduced sediment supply. Unfortunately, an optical age from near the base of the Rutherford Cr. glaciofluvial outwash fan exhibited signs of partial bleaching and a relatively high fading rate which resulted in an unreasonable age (Table 4.3; Figure C. 12). Partial bleaching is likely the result of high turbidity of the glacial meltwater channel, while the high fading rate may be due to the prevalence of volcanic feldspar sources. Volcanic feldspars have a more disordered structure due to rapid cooling, which is more prone to quantum-mechanical tunneling (Huntley & Lamothe, 2001). Glacially formed valley steps likely began storing sediment immediately following glacier retreat. Many have slopes < 3% indicating they have acted as effective sediment traps throughout the Holocene. Friele et al. (2005) documented the valley fill architecture of the Lillooet Valley based on floodplain sediment cores. They suggest that the valley was occupied by a 75-kilometer-long fjord lake that gradually filled in throughout the Holocene, resulting in sediment deposition on the order of 15 km3. It is thought that the Lillooet Valley has been net aggradational throughout the entire Holocene (Jordan & Slaymaker, 1991). It is challenging to accurately estimate the relative activity of the many tributary fans within the Lillooet Valley. Based on the paraglacial sedimentation model, we would expect significant fan-building activity immediately following glacier retreat. However, because the Lillooet Valley has 91 been net aggradational, the contemporary surface expression of the tributary fans likely best represents sedimentation from the latter part of the Holocene, while earlier fan sediments interfinger with Lillooet River floodplain material in the subsurface. That being said, a sample from Perkins Cr. yielded a corrected age of 9.46 ± 1.49 ka based on the minimum age model (Table 4.3; Figure C. 7) indicating this fan may have had a similar extent as today during the early Holocene. Slaymaker et al. (2017) document a barrier-defended terrace that abuts debris cones just across the river from the Perkins Cr. fan. The base of this terrace dates to ~7 ka, providing a minimum age for the landslide that formed the terrace as well as significant debris cone development. Optical dating samples were also collected from the base of exposures on South and Silva Cr. fans (Figure C. 4; Figure C. 6). The Silva Cr. sample suffered from high recuperation rates (> 10%), a weak fast signal component, and was ultimately rejected. The South Cr. sample resulted in a corrected age of 3.88 ± 0.40 ka based on the minimum age model. There are several possible explanations for the relatively young age of this sample. First, as previously discussed, floodplain aggradation may affectively bury or at least limit exposure of older fan material. Second, fan development competes against erosion from the Lillooet River as it migrates laterally across the floodplain. It is likely that particularly intense erosion took place during the many documented dam breach and outburst flood events (Friele et al., 2008). Additional samples were collected (i.e., Figure C. 4; Figure C. 5) but not processed due to significant methodological challenges including the unreliability of quartz as a chronometer, and anomalously high fading rates. The majority of the tributary fans and debris cones within the Lillooet Valley have been active throughout the historical period (Jordan, 1994; Jakob, 1996; Bovis & Jakob, 1999; Zubrycky et al., 2021). Additionally, a radiocarbon sample (MT-lil-09) was collected from a large detrital stump overlying lacustrine sediments on the Owl Creek fan (Appendix C ). This sample (lab # UOC-20626, Supplementary Materials) has an uncalibrated 14C age of 81 ± 11 yr BP, and when calibrated has a 95.4% probability of being in the range of 254 cal yr BP to present. Large landslides are well-documented in this watershed and are primarily discussed in relation to their influence on sediment yield, and their spatial distribution. However, it is also important to note that a significant proportion of the landslide material is stored proximally to the source area 92 where it may remain for millennia (Figure 4.5a). These deposits may also temporarily dam river valleys resulting in long term sediment storage up valley. Green and Birkenhead Lakes have acted as persistent sediment sinks throughout the entire Holocene, trapping the majority of sediment supplied from about 400 km2. Smaller lakes may store significant volumes of sediment but operate over shorter timescales controlled by infilling rates. 93 Table 4.3: Optical ages from the Lillooet River watershed (this study). See Table C. 2 for sample locations and data required for calculations. Lab # Age Model n for dating OD De Dose Rate Uncorrected Age n for fading g value Corrected Age used(total) % Gy Gy/ka ka used(total) % / decade ka MT-lil-02 CAM 36(40) 41 ± 5 26.65 ± 1.84 1.65 ± 0.08 18.14 ± 1.47 10(10) 7.47 ± 0.60 66.32 ± 24.95 MAM 16.31 ± 1.38 11.10 ± 1.04 38.08 ± 13.59 MT-lil-05 CAM NA NA NA 1.77 ± 0.08 NA 10(10) 5.94 ± 0.72 NA MAM MT-lil-08 CAM 33(55) 14 ± 2 64.62 ± 1.71 1.52 ± 0.08 49.98 ± 2.65 10(10) 7.48 ± 0.66 212.99 ± 110.76 MAM 61.08 ± 3.08 47.24 ± 3.22 199.49 ± 116.63 MT-lil-10 CAM 27(52) 55 ± 8 9.60 ± 1.03 2.3 ± 0.09 4.51 ± 0.50 10(10) 8.37 ± 0.28 19.58 ± 3.87 MAM 5.17 ± 0.42 2.43 ± 0.21 9.46 ± 1.49 MT-lil-13 CAM 28(32) 20 ± 3 7.80 ± 0.31 3.16 ± 0.11 2.61 ± 0.13 10(10) 5.01 ± 0.34 4.57 ± 0.34 MAM 6.65 ± 0.50 2.23 ± 0.18 3.88 ± 0.40 MT-lil-15 CAM 25(44) 46 ± 7 12.36 ± 1.16 1.61 ± 0.09 8.95 ± 0.96 9(10) 2.80 ± 0.22 11.96 ± 1.32 MAM 6.33 ± 0.67 4.58 ± 0.53 6.06 ± 0.74 1. OD – overdispersion; CAM – central age model; MAM – minimum age model; De – equivalent dose; g value – fading rate94 4.4.4 Sediment Budget Updates Between 2005-2024, the Silt Lake delta front has advanced at an average rate of 23 meters each year. During this 19-year period, the lake has trapped more than 1.5 million m3 of sediment, corresponding to an average annual sediment yield of more than 8 x 104 m3 yr-1 (> 960 Mg km-2 yr-1). Importantly, this volume does not include the portion of sediment deposited in Silt Lake past the Oct. 2024 delta front position, and so is likely a significant underestimate. The actual sediment yield is also likely more than double this value considering that the lake has an estimated trapping efficiency of 50-70% (Schiefer et al., 2008). These values corroborate results from Schiefer et al. (2008) who estimated that the SSY might exceed 103 Mg km-2 yr-1. Minimum estimates of sediment delivered to the Silt Lake delta over the last two decades suggest that the lake will be completely filled in and removed as a sediment trap within the next 60 years. Interestingly, the volume of sediment trapped in Silt Lake is proportional to at least 12% of the fine sediment delivered to Lillooet Lake (369-year avg. of 213 ± 38 Mg km−2 yr−1; Heideman et al., 2018), despite being less than 3% of the contributing area. Given the lakes trapping efficiency, one could reasonably assume that the Upper Lillooet River watershed is capable of supplying as much as 25% of the fine sediment deposited in Lillooet Lake. 4.5 Discussion 4.5.1 Holocene History of Lillooet River and Its Implications The geomorphology of the Lillooet River watershed is relatively well understood, thanks in large part to a 40+ year research program beginning in the early 1970’s. Several key findings of this research program are outlined in Slaymaker et al. (2017) along with many other relevant studies. In this section, we synthesize data from the Lillooet River watershed (or more generally SW BC or Western Canada when necessary), that relates to the variability in sediment yield, sediment supply, and their potential controls throughout the Holocene. To our knowledge, no systematic discussions of the Holocene History of Lillooet River watershed exist that include this range of sediment yield-related variables. The purpose of this discussion is to provide context for shorter term SY measurements, sediment supply events, and sediment storage within this watershed. It is important to note that the Sḵwx̱wú7mesh (Squamish), St’át’imc, and Líl̓wat First Nations have lived in this region since time immemorial. While several of their legends and cultural histories 95 anecdotally match western scientific findings presented in this section, the impact of their land stewardship practices on sediment storage and yield is likely minor and was not directly considered in this study. Deglaciation in the Lillooet River watershed would not have occurred everywhere at the same rate, but there is significant evidence that glaciers were no more extensive than present-day by 11-10 ka (Souch, 1989; Ryder et al., 1991; Menounos et al. 2009). The final stages of glacier retreat occurred during a period of rapid climate warming which also led to increased fire frequency in the Northern Coast Mountains (Hallet et al., 2003; Figure 4.7). The onset of the paraglacial period (Church & Ryder, 1972) likely resulted in increased sediment yields as glacial material was quickly reworked. Evidence of this includes the Rutherford Cr. and Blackwater Cr. paraglacial fans. However, SY records from the Lillooet River watershed are poorly resolved during that period, and revegetation may have occurred rapidly. A few large granitic rock avalanches occurred during and shortly after glacier retreat (Figure 4.8), potentially due to slope debuttressing. The beginning of the Early Holocene Xerothermic Interval was characterized by warm and dry conditions, during which glacier cover reached a Holocene minimum (Menounos et al., 2009), and fire frequency was high (Hallet et al., 2003). Later in this period, there is some evidence that the global 8,200 cal yr BP cold event may have reduced fire frequency in SW BC, which remained suppressed due to moister conditions until about 6.5 ka. A minor glacier advance may have also occurred during this time (Menounos, 2009). Many of the large known granitic rock avalanches also occurred during the early Holocene (e.g., Salal Cr., Soo River. Upper Lillooet). Because these events supply a mixture of finer-grained and coarse blocky material, they play a dual role in catchment SY, both supplying significant sediment to the river systems and often damming the river valleys causing significant up valley sediment storage and overall decreased SY (Chapter 3). A large rock avalanche/debris flow is also documented from the MMVC during this period (i.e., 7,900 cal yr BP; Friele & Clague, 2004) which would have traveled tens of kilometers down the Lillooet River Valley and significantly increased the SY at the delta. Apart from sediment supplied from catastrophic landsliding, limited evidence suggests sediment yield was low during this period (Menounos, 2002; Souch, 1989; Figure 4.9). 96 By the mid-Holocene (6-7 ka) until about 3.5 ka, the climate became more variable, resulting in fluctuations in fire frequency (Hallet et al., 2003) and Garibaldi Phase glacier advances (Koch et al., 2007 & 2009). Three large rock avalanche/debris flow events occurred from the MMVC, again traveling down the Lillooet River Valley producing valley fill deposits several meters thick (Friele et al., 2005). Of these landslides, the 4,400 cal yr BP event is estimated to have resulted in a delta progradation rate of 105 m yr-1, in comparison to the 6 m yr-1 Holocene average (Friele et al., 2005). During this time, the South Cr. fan was active, and Fitzsimmons Cr. (and likely other tributary channels) was actively incising into the hanging valley step (Table 4.3). The Green Lake clastic index constructed by Menounos (2002) suggests that SY increased during this period, which was associated with the glacier advance, while SY in the subalpine Goat Meadows watershed was at a minimum (Souch, 1989). From 3.5 ka to present, superimposed on a general cooling trend, three cycles of cooling and warming have occurred, with matching fluctuations in fire frequency and glacier extent. Periods of glacier advances include the Tiedemann advance (~3.5 – 2.8 ka), First Millennial Advance (~1.7 – 1.3 ka), and the Little Ice Age (~0.9 – 0.1 ka). Subsequent periods of glacier retreat include the Fraser Valley Fire Period – Roman Warm Period (~2.4 – 1.7 ka), the Medieval Warm Period (~1 – 0.6 ka), and present day anthropogenic global warming. Menounos et al. (2009) note episodic but successively greater glacier expansion throughout the Holocene, likely driven by decreased summer insolation, culminating in the Little Ice Age advance. Sediment yield in the Goat Meadows watershed reaches its highest rate beginning at ~2.4 ka to present (Souch, 1989). The Green River clastic index is similarly high during the late Holocene, and even generally follows the minor glacier advance/retreat patterns discussed above (Menounos, 2002). During this period, sedimentation in the Lillooet River valley is dominated by the most recent volcanic eruption at ~2,360 cal yr BP (Clague et al., 1995; Stasiuk et al., 1996) and a precursor rock avalanche/debris flow from Job Creek (Friele et al., 2005). A pulse delta progradation rate during this period is estimated to be 150 m yr-1 (Friele et al. 2005). These events likely represent the largest single-period input of sediment into the Lillooet River valley throughout the Holocene. Interestingly, the Mystery Cr. landslide also occurred during this same time period but was composed of course fragments that dammed the Green River and reduced the downstream SY for some time (Blais-Stevens, 2008). 97 The historical period has been characterized by increased human settlement and development. Historical river straightening and training has led to increased coarse sediment transport in the lower reaches of Lillooet River (Weatherly & Jakob, 2014). When considering more frequent extreme rainfall events, the straightened/narrowed channel will likely lead to an elevated flood risk (Weatherly & Jakob, 2014). A shift in precipitation towards more rainfall at the expense of snowfall will also likely lead to increased sediment transport, and up to a 300% increase in shallow landsliding (Jakob & Owen, 2021). However, Slaymaker et al. (2017) hypothesize that sediment yields will decrease over the next century as glaciers continue to retreat, slopes revegetate, and proglacial lakes form. While this may be the case for catchment-wide sediment supply and decadal to century timescales, the Lillooet River SY seems to be most controlled by catastrophic events over millennial timescales. 98 Figure 4.7: Holocene climate and controls on sediment dynamics and yield. In panel (a), climate is summarized from Souch (1989), Reyes & Clague (2004), and Reyes et al. (2006). In panel (b), fire frequency is based on Hallet et al. (2003). Abbreviations include: FVFP – Fraser Valley Fire Period, RWP – Roman Warm Period, MWP – Medieval Warm Period, LIA – Little Ice Age. In panel (c), glacier advances are summarized from Koch et al. (2007 & 2009), Reyes & Clague (2004), Menounos et al. (2009), and Koehler & Smith (2011). 99 Figure 4.8: Holocene landslides and volcanic events in the Lillooet River watershed. Landslides occurring from the Mount Meager Volcanic Complex (MMVC) are summarized in Friele et al. (2008), and Roberti et al. (2018). Volumes of the Bridge River Volcanic Assemblage are discussed in Stasiuk et al. (1996), while Clague et al. (1995) and Egan et al. (2015) constrain the ages of the Bridge River, and Mt. Mazama eruptions, respectively. Granitic landslide events are summarized from Blais-Stevens et al. (2011), Cordilleran Geoscience (2018), Slaymaker et al. (2017), and personal communications with P. Friele (2024). All known landslides from the MMVC are included, while only large granitic rock avalanches (> 1 x 106 m3) are included. 100 Figure 4.9: Holocene sediment yield estimates from the Lillooet River watershed. In panel (a), Lillooet Lake progradation rate estimates are taken from Friele et al. (2005), Slaymaker et al. (2017), and KWL (2002). In panel (b) the Green Lake clastic index is a proxy for relative sediment yield taken from Menounos (2002), while the Goat Meadows sediment yield is taken from Souch (1989) updated based on a new age estimate for Mazama ash (Egan et al. (2015). 101 4.5.2 Controls on Spatial Storage Patterns Topographic steps in the valley long profiles present the greatest control on the spatial patterns of sediment storage in the Lillooet River watershed. This pattern was discussed for the Green River watershed in Chapter 3 and is further corroborated by mapping results from the entire Lillooet River watershed (Figure 4.10). Furthermore, there appears to be three elevation ranges in which valley steps and significant sediment storage occur. At ~250 masl (~200-350 masl), the largest peak in sediment storage results from base level set by Lillooet Lake. Storage at this elevation far exceeds other elevations, with approximately 15 km3 of sediment in the main Lillooet River valley (Friele et al., 2005), and significant outlet fans from many of the major tributaries. At ~650 masl, a second peak in sediment storage occurs. This coincides with a valley step in the upper Lillooet River, formed by the 2,360 cal yr BP eruption of Mt. Meager (Clague et al., 1995), and valleys steps in the primary tributaries (i.e., Green, Birkenhead, and Ryan Rivers). Large lakes within the Green and Birkenhead River catchments also occur at this elevation and would accentuate this pattern if included. These large tributaries also set base level for their subwatersheds which often have significant outlet fans. The final peak in sediment storage with respect to elevation occurs at ~1,250 masl. At this elevation several of the larger tributaries have a second valley step (e.g., Birkenhead, and Ryan Rivers), and primary valley steps occur within many of the smaller tributary watersheds (e.g., Miller, Boulder, and Manatee Creeks). The hypsometry of sediment storage is such that sediment supplied from erosion of hillslopes and valley steps enters storage within a short vertical distance, resulting in a highly fragmented sediment cascade (Turley & Hassan, 2023). This is in alignment with results from Turley and Hassan (2023) who found that mobilized sediment often must pass through two or more sources of disconnectivity before reaching the channel network. These results are supported by similar observations along the upper Rhone Valley, where Otto et al. (2008) identified a toposequence with three valleys steps that act as storage zones. The configuration of channel junctions has also been found to moderate sediment transfer (Tunnicliffe & Church, 2011). Geilhausen et al. (2012) similarly found that material from paraglacial slope adjustment was quickly stored in valley margin and valley bottom reservoirs, from which reworking occurred much slower. Valley steps create a unique hypsometry of sediment storage that ultimately controls the spatial patterns of storage and yield within the watershed. 102 Figure 4.10: Valley step-controlled sediment storage hypsometry. Longitudinal profiles of Lillooet River and its major tributaries (a), and sediment storage area (km2) plotted by elevation (b). Sediment storage includes active channels, floodplains, fans, and cones. Climate also appears to control the spatial patterns of sediment storage, although to a lesser degree. Following the decrease in temperature, precipitation, streamflow, and glacierization from west to east, storage is also lower in the northeast (e.g., Sockeye and Taillefer Cr. watersheds). A relative lack of sediment storage can be the result of decreased supply and/or efficient throughput of sediment (connectivity). The Birkenhead River tributaries are consistently more disconnected, suggesting that sediment storage patterns may be the result of a lack of supply. This is further supported by Gilbert (1973), who concluded that the suspended sediment yield from the Birkenhead River was only ~1% of that from the Lillooet and Green Rivers. Linking structural connectivity estimates to observations of sediment storage (or a lack thereof) can therefore provide a first order estimate of geomorphic activity and relative sediment yield integrated over the timescale of storage. 4.5.3 Scale Variations of (Post)Glacial Sediment Yield Specific sediment yield data from the Lillooet River watershed exhibits high spatiotemporal variability, with long-term averages varying by more than five orders of magnitude. The data available suggest that SSY rapidly increases with increasing area up to ~100 km2 after which it levels off or even decreases slightly (Figure 4.11a). This doesn’t completely follow the trend reported by Church and Slaymaker (1989) from fluvial suspended sediment across British 103 Figure 4.11: Specific sediment yield (SSY) estimates from within the Lillooet River watershed. SSY is plotted by area (a) and time (b). Black vertical bars represent uncertainty, while colored horizontal lines represent the time period over which the value is averaged. Refer to Table C. 5 for a list of data sources and limitations. 104 Columbia. There are two reasons this is likely the case. First, the data points around 100 km2 come from disturbed landscapes (i.e., volcanic – landslide prone, retreating glacial). And second, the main Lillooet River valley is net aggradational suggesting SY decreases downstream (and with increasing area). Because all of the data reported in Figure 4.11 are from the same watershed, this also emphasizes the challenge of transferring knowledge between scales, and the need to acknowledge emergent properties at increasing scales (Slaymaker, 2006). Improvements can be made to traditional extrapolation of SY between scales if there is sufficient data to construct a regional or watershed scaling relation (Church, 2017). Such a relation for the Lillooet River watershed would need to account for several factors. First, Owens and Slaymaker (1992) found that in small (<1 km2) subalpine to alpine watersheds, SSY increased with decreasing elevation as a result of poor connectivity and sediment storage near ridge tops. These watersheds also had SY values several orders of magnitude below those for larger catchments. Second, SSY dramatically increases for larger catchments (up to approximately 100 km2) as a result of channel incision and sediment mobilization at hanging valley steps (Turley et al., 2024), para- and proglacial sedimentation (Schiefer & Gilbert, 2008), and frequent large landslides (Friele et al., 2009). Finally, the main Lillooet River valley is net aggradational resulting in a possible slight decrease in SSY at the largest scale. In the nearby Chilliwack River valley, Tunnicliffe and Church (2011) note a decrease in SSY with catchment area for tributaries smaller than 10 km2 as a result of sediment storage at the slope base. This pattern is not present in the available suspended load SSY data for the Lillooet River watershed, which shows an approximate 1:1 increase for nested watersheds at this scale (i.e., Gallie Pond, Central Creek, Miller Creek). These patterns may differ due to differences in timescale, the size fraction of sediment being measured, or both. Additionally, our data suggest sediment storage plays the largest role at the scale of valley steps, although this may be complicated by sediment recruitment from existing storage landforms (section 4.4.1). Not only is the spatial scale important, but also landscape heterogeneity. Spatially variable topographic characteristics, sediment connectivity and sediment storage patterns result in the juxtaposition of geomorphically very active and inactive landscapes (Slaymaker et al., 2017). This is illustrated by comparing very active landscapes such as the MMVC (SSY: ~3,600 - 4,800 Mg km-2 yr-1; Friele et al., 2009) or Silt Lake watershed (SSY: ~1,000 Mg km-2 yr-1; Schiefer & 105 Gilbert, 2008; section 4.4.4), to inactive and disconnected landscapes such as the Birkenhead River watershed (SSY: <1% of Lillooet-Green; Gilbert, 1973). Additionally, about 40% of the Lillooet River watershed is at least partially disconnected due to lakes with an additional 12% under ice cover. The spatial pattern of sediment storage, namely increased storage at valley steps which typically occur at three different elevation zones (~250, ~650, and ~1250 masl), further illustrates disconnectivity and a down system reduction in SY at different scales (Figure 4.10). Sediment mobilized in headwater catchments is typically intercepted at 1 or more valley steps before reaching a trunk stream. In the Lillooet River watershed, the temporal variability in SY is highly influenced by exceptional events. This is evident in small subwatersheds such as Fitzsimmons Creek. The SSY (bed material) from Fitzsimmons Creek following the 1991 flood event was much higher than any recorded from Lillooet Lake in the last several centuries (Turley et al., 2024; Figure 4.11b), and remained exceptional in comparison to an approximately 3,000-year record for Green Lake (Menounos et al., 2006). At a much larger spatial scale, several extremely large rock avalanche and debris flow events originating from the MMVC are documented to have significantly increased the sediment yield of the lower Lillooet River throughout the Holocene (Friele & Clague, 2004; Friele et al., 2005; Figure 4.8). Friele et al. (2005) document at least four valley-wide debris flow deposits ranging from 2-8 meters thick and extending as far down valley as present-day Lillooet Lake. Pulse (50-year) delta progradation rates associated with these events reach 150 m yr-1, in comparison to the 6 m yr-1 long-term average (Friele et al., 2005; Figure 4.9a). The SSY at the catchment outlet is unknown for these individual events. However, colluvial supply from individual events may have reached 109 m3 (Friele et al., 2008). Friele et al. (2008) compiled all known landslides from the MMVC and constructed a frequency-magnitude diagram from which a Holocene-averaged colluvial sediment yield of 3,000 m3 km-2 yr-1 is derived (~3,600 – 4,800 Mg km-2 yr-1 based on a range of bulk densities). While this value is comparable to short-term bed material SSY from a small watershed draining the flanks of a stratovolcano to the south (SSY: 900-6,250 m3 km-2 yr-1; Turley & Hassan, 2023), it is ~17-23 times higher than the ~400-year average SSY measured from Lillooet Lake (Heideman et al., 2018). Clearly, in the Lillooet River watershed, SSY is highly variable in both space and time at all possible scales throughout the Holocene. 106 Perhaps the most important implication of these results is the acknowledgement that the chosen spatial and temporal scale of SSY measurements will dictate the outcome. Furthermore, measured SSY values may have little relevance or explanatory power even for nearby locations or for different spatiotemporal scales. This acknowledgement, however, should not be discouraging for SY studies. Instead, this emphasizes the need for carefully considering landscape organization and condition, landscape characteristics, relevant processes and their frequency-magnitude distributions, emergent properties of scale, and the benefit of constructing a regional sediment yield scaling relation if data permits. 4.7 Conclusion In this study, we use geomorphic mapping, sediment budgeting, connectivity analyses, and geochronological data to investigate the spatial distribution and timescales of sediment storage and yield within the Lillooet River watershed. We first provide context by synthesizing the Holocene history of the watershed, including a longitudinal discussion of climate and other controls on sediment dynamics and yield, sediment supply based on a large landslide inventory, and sediment yield at the full range of spatial scales (< 1 km2 to 3,765 km2). Deglaciation was diachronous but was likely complete by approximately 10.5 ka during a period of rapid climate amelioration. The onset of the paraglacial period likely resulted in elevated sediment yields but is poorly constrained within this watershed. The early- to mid-Holocene was characterized by warm and dry conditions, minimum ice cover, and relatively low sediment yields. Within the Lillooet Valley, this pattern was interrupted several times by extremely large (up to 109 m3), and mobile landslides as well as a volcanic eruption from the Mount Meager Volcanic Complex which resulted in delta progradation rates up to 25 times higher than the Holocene average. During the late Holocene and historical period, sediment yields were relatively high likely as a result of episodic and successively greater cycles of glacier expansion and retreat, and anthropogenic modifications. This history provides context for individual sediment yield measurements and allows linkage between sediment storage and yield. Our spatial analysis of storage revealed that topographic steps in the valley long profiles present the greatest control on the spatial patterns of sediment storage within the Lillooet River watershed. These topographic steps tend to occur at three 107 elevation ranges (~250, ~650, ~1,250 masl) resulting in a unique hypsometry of sediment storage and a vertically and spatially fragmented sediment cascade. The resulting juxtaposition of connected and highly active landscapes and disconnected relatively inactive landscapes subsequently results in SSY which varies by more than five orders of magnitude within the same watershed. Our results emphasize the complexity of sediment dynamics and yield in glaciated landscapes. It is therefore necessary to carefully consider landscape organization and its’ control on sediment (dis)connectivity and storage patterns, and the resulting inherent spatiotemporal variability in sediment dynamics and yield. Future studies should continue to explore the links between sediment storage, yield, and landscape evolution across a range of spatial and temporal scales. Quantifying the trapping efficiency of different sources of disconnectivity and the sediment residence time of storage landforms would lead to a deeper understanding of the role sediment storage plays in determining landscape evolution and SSY. 108 Chapter 5 - Contributions and Future Research Opportunities The primary objective of this thesis was to advance understanding of Holocene sediment dynamics, with a focus on the controls, variability, and interactions of sediment supply, storage, and yield in glacierized mountain catchments. This work was motivated by a need for greater understanding of how (post)glacial landscapes respond to changing conditions, such as accelerated glacier retreat and land use changes, particularly as mountain regions become increasingly populated. While studies of contemporary and historical sediment dynamics offer valuable insights, examining past landscape responses and the extent of recovery from glaciation provides essential context for interpreting these changes. To this end, this thesis was designed to have a spatially and temporally nested structure, with each chapter broadening the scale of analysis to facilitate discussions of scale effects alongside the core research questions. Chapter 2 focused on the 94 km2 Fitzsimmons Creek watershed, where we constructed a detailed bed material sediment budget, related this to the historical sediment yield measured at the outlet, and resolved the two measures. We also discussed potential sediment yield controls at this scale. The study area and topic of Chapter 3 was initially motivated by an observed dissimilarity in sediment dynamics between adjacent basins. In this chapter, the study area was expanded to the 875 km2 Green River watershed, which includes Fitzsimmons Creek, and we investigated (i) longitudinal patterns of valley geometry, channel characteristics, and sediment connectivity, and then (ii) identified primary controls on sediment dynamics in (post)glacial landscapes. Finally, Chapter 4 investigated the 3,765 km2 Lillooet River watershed, which includes Green River, and answered questions related to (i) the Holocene history and primary sediment yield controls, (ii) the spatial and temporal patterns of sediment storage and their role in determining landscape response following glacier retreat, and (iii) the spatiotemporal variability in sediment yield. The following sections provide a summary of primary contributions, and the broader implications arising from this thesis (section 5.1), as well as opportunities for future research (section 5.2). 109 5.1 Summary of Contributions 5.1.1 Closure of a detailed bed material sediment budget for a glacierized, mountain catchment In Chapter 2, we developed a detailed bed material sediment budget for the 94 km2 glacierized and mountainous Fitzsimmons Creek watershed. Constructing bed material budgets for dynamic mountain watersheds poses significant challenges due to access limitations and measurement complexities. Consequently, such budgets are rare for watersheds larger than a few tens of square kilometers, and it is even more uncommon to find sediment budgets partitioned by sediment sources and grain size. The sediment budget presented in Chapter 2 is unique in that we applied a suite of complimentary methods to constrain both sediment yield at the outlet and the sediment budget along the river system, ultimately resolving the two measures. Methods employed include lidar net change analysis, run-of-river headpond volumetric flushing estimates, synthesis of gravel extraction reports, drone surveys, and repeat bathymetric surveys paired with an historical air photo analysis. In several instances, multiple and independent measurements were taken of individual components of the sediment budget, allowing for increased certainty of the findings. In closing the sediment budget, several explanations of a minor discrepancy between the point-based sediment yield estimate at the catchment outlet and the detailed sediment budgeting along the river system are proposed, including (a) accounting for cumulative uncertainty from all methods, (b) allowing for minor transport of bed material past the delta front, and (c) sediment attrition. Cumulative uncertainty alone was enough to resolve the two measures. Ultimately, closure of the budget increases confidence in all measurements and is a key strength of this study. Sediment budgeting results, paired with geomorphic mapping, revealed that landslide, active channel, and floodplain sources each contributed about one-third of the total sediment supply. Net degradation occurred along the entire length of the valley bottom upstream of the fan-delta, resulting in a progressive downstream increase in sediment yield. This trend was punctuated by chronic landsliding near the outlet, driven by postglacial incision through glaciogenic sediments at a hanging valley step. These results contribute to our general understanding of sediment dynamics and yield in glacierized mountain catchments. In comparing these results to a hydro-meteorological analysis, 110 we were able to show that over a distance of just 20 kilometers, through a relatively confined valley bottom with little long‐term storage potential, the SY signal was shredded by complex sediment dynamics and masked by simultaneous forcings. Consequently, point-based SY measurements are useful for quantifying interannual variability which can be related to hydro-meteorological variables and other forcings, estimating basin denudation rates and system-wide change, but provide limited insight into internal system functioning. On the other hand, spatially variable sediment dynamics and system functioning can be characterized through careful sediment budgeting. However, this process is data intensive, likely requiring a multi-method approach. Researchers and practitioners need to be cognizant of these strengths and limitations when designing studies. 5.1.2 Geology, glacial history, and postglacial history control sediment dynamics and yield at a range of temporal scales A primary theme that emerges from this thesis is the dominant role of geology, glacial history, and postglacial history in controlling sediment dynamics and yield at a range of spatial and temporal scales. Exploration of this topic was motivated by an observation that adjacent basins with similar glacial history, geology, and climate are often still characterized by high inter- and intra-catchment variability in geomorphological functioning. This dissimilarity in sediment dynamics and yield limits the applicability of existing conceptual models and complicates comparisons between systems. To address this, we tested existing conceptual models and examined landscape controls at multiple scales in order to explain the observed heterogeneity (primarily Chapter 3). Recent studies have emphasized the importance of glacial controls on sediment dynamics (Hassan et al., 2019; Reid et al., 2021; Turley & Hassan, 2023). Most recently Hassan et al. (2023) presented a conceptual model of lateral connectivity and channel characteristics in mountainous postglacial basins. Many of the conclusions in this thesis support and extend such work, by contributing a large dataset encompassing 3,765 km2 of glacially sculpted terrain, with over 500 km of channel, more than 6,000 mapped landforms, and assessments of hillslope channel coupling, sediment connectivity (lateral and longitudinal), and channel confinement. 111 In Chapter 2, we demonstrated that glacial landscape configuration, particularly erosion at a hanging valley step, controls the contemporary and historical spatial distribution of sediment supply and yield within Fitzsimmons Creek watershed (94 km2). Chapter 3 expanded this analysis, revealing a consistent and predictable longitudinal pattern of channel and valley characteristics that repeats at valley steps, largely as a result of glacial landscape configuration. However, we found that glacial history alone failed to fully explain inter- and intra-catchment patterns, and explicit consideration of the geologic and postglacial history was also necessary. We synthesized these controls based on our large dataset and published literature. Geologic controls on sediment dynamics and yield include lithology, topographic relief, and structures such as faults, lineaments, and joint sets. Within the Green River watershed (Chapter 3) valley alignment and geometry is controlled by fault orientations and differential erosion of rock types. Valley sections underlain by volcanic lithologies are on average four times less steep and four times wider than valley sections underlain by other rock types. Additionally, there are two populations of rock avalanches, which are controlled by lithologic and structural characteristics. Rock avalanches in glacially over-steepened intrusive (e.g., granite) bedrock typically occur along defined joint sets. These are distinct from rock avalanches and debris flows that originate from the Mount Meager Volcanic Complex, where failures occur in hydrothermally altered volcanic bedrock. Granitic rock avalanches are typically an order of magnitude smaller in volume and are composed of blocky debris that is not easily reworked by the channel (Slaymaker et al., 2017). These landslides are documented to frequently dam river systems and set a new local base level, which may persist for thousands of years and reduce the SSY (Chapter 3; Chapter 4). In contrast, volcanic landslides are typically more mobile, more easily reworked by river systems, form transient dams that can last decades to centuries, and can subsequently rapidly increase the SSY (Chapter 4). The particular postglacial response is variable as a result of the stochasticity of landsliding and the complexity of fluvial systems. In Chapter 4 we demonstrated that topographic steps in the valley long profiles present the greatest control on the spatial patterns of postglacial sediment storage. These valley steps typically occur at three elevation ranges (~250, ~650, ~1,250 masl), resulting in a unique hypsometry of sediment storage. Sediment supplied from erosion of hillslopes and valley steps enters storage within a short vertical and longitudinal distance, 112 resulting in a highly fragmented sediment cascade. In Chapter 3, we presented an updated conceptual model that synthesizes how geology, glacial history, and postglacial history control the spatial variability of sediment dynamics and yield. This conceptual model was able to explain the observed dissimilarity of landscape characteristics and sediment connectivity patterns making it a valuable framework for understanding sediment dynamics in (post)glacial mountain catchments. 5.1.3 Sediment dynamics and yield are highly variable in space and time This thesis was intentionally structured to include studies that are both spatially and temporally nested, enabling a clear illustration of the scale-dependent nature of sediment dynamics and yield. In particular, Fitzsimmons Cr. (Chapter 2) is a tributary of the Green River (Chapter 3), which is a tributary of the Lillooet River (Chapter 4). Chapter 2 was concerned with the contemporary and historical period, while Chapter 3 and Chapter 4 were concerned with the entire Holocene. Specific sediment yield was found to vary by five orders of magnitude as a function of the spatial scale (Chapter 4; < 1 km2 to 3850 km2). These results are particularly intriguing and important given that they all originate from the Lillooet River watershed. Small (< 1 km2) alpine to subalpine tributary watersheds have SSY values as low as ~0.01 Mg km-2 yr-1, coming from low gradient ridge tops overridden by the Cordilleran Ice Sheet that inefficiently transfer sediment. As the catchment area increases there is an approximate equal magnitude increase in SSY, at least up to catchments ~100 km2 in area. We provided bed material SSY data for Fitzsimmons Creek (Chapter 2; 94 km2) over a 76-year period and Silt Lake (Chapter 4; 101 km2; minimum estimates) over a 19-year period. The Fitzsimmons Creek bed material yield varied by a factor of 17, and landslide contributions varied by at least a factor of 5 during the historical period (Chapter 2). At Silt Lake, located in the headwaters of the Lillooet River, SSY exceeded 1,000 Mg km-2 yr-1 and is plausibly twice that value (Chapter 4; Schiefer & Gilbert, 2008). This represents the highest fluvial SSY measured in the Lillooet River watershed. However, in synthesizing the published literature for this watershed we note that the colluvial SSY from the Mount Meager Volcanic Complex is estimated to average between 3,600 and 4,800 Mg km-2 yr-1 throughout the Holocene (Chapter 4; Friele et al., 113 2009). This is approximately 1.6 x 105 times higher than the lowest long term SSY measured in this watershed. The sharp increase in SSY with area up to ~100 km2 was attributed to channel incision at hanging valley steps (Chapter 2), para- and proglacial sedimentation (Chapter 4), and frequent large landslides (Chapter 4). The relation between SSY and area is remarkably consistent when considering measurements from a single year and those averaged over millennia. For watershed areas greater than ~100 km2, SSY levels off or decreases slightly due to sediment storage at hanging valley steps and within the main Lillooet Valley (Chapter 4). The temporal variability in SSY is also pronounced. Chapter 4 showed that SSY typically varies by ~1 or 2 orders of magnitude at individual locations over time, depending on the site and measurement period. To illustrate an extreme example of this, we might consider the colluvial SSY estimated from Job Creek during one of the recorded rock avalanche events which could have exceeded 108 Mg km-2 yr-1, compared to a value of 102 Mg km-2 yr-1 based on a ‘typical’ year from the MMVC (Chapter 4; Friele et al. 2008). Additionally, our temporal analysis of SSY throughout the Holocene suggested a peak immediately following rapid glacier retreat (~10.5 ka), a minimum during the mid-Holocene, and an increase during the late Holocene, particularly in the historical period. The primary implication of these results is the juxtaposition of connected and highly active landscapes and disconnected, inactive landscapes within the same watershed. This results in a strong scale- and geographic-dependance of SSY measurements, such that SSY values may have little relevance or explanatory power even for nearby locations or for different spatiotemporal scales. This has been discussed by previous researchers (e.g., Slaymaker, 2006; Kirchner et al., 2001). However, we believe that illustrating this phenomenon with measurements from a single watershed was particularly convincing. Additionally, our results illustrated the importance of considering emergent properties (additional considerations with increasing spatiotemporal scale; Slaymaker, 2006), and the utility of constructing a regional sediment yield scaling relation (Church, 2017). 114 5.1.4 Challenges and applicability of optical dating techniques in the Lillooet River watershed The Lillooet River watershed was selected as the study area for this thesis, in part, due to the wealth of pre-existing geochronological and sediment yield data. However, the vast majority of this data pertains to landslides, with a smaller subset coming from lake and floodplain sediments (Chapter 4). There was comparatively less age control for fluvial terraces and alluvial fans. Additionally, most existing dates came from radiocarbon dating, supplemented by terrestrial cosmogenic nuclide dating, tephrochronology, lichenometry, and dendrochronology. To address these gaps, we elected to supplement the existing data with optical dating of fluvially deposited sediments on terraces and fans. Optical dating offers distinct advantages, including the ubiquitous presence of quartz and feldspar (the target minerals) and the ability to directly date the time of sediment deposition. For these reasons, optical dating was a great candidate for filling in key gaps in the age control of sediment storage landforms. However, optical dating is inherently experimental, and the particular methods applied must be adapted based on the location, depositional process(es) and environment, and characteristics of the sediment. The lessons learned through this study contribute valuable insights for future applications of optical dating in similar environments. The high-energy, net-aggradational nature of the Lillooet River watershed posed significant challenges for optical dating, as suitable exposures were limited. Ideally samples would have been collected to document the onset of fan building, time of fan abandonment (if not presently active), and timing of channel incision recorded in river terraces. These ideal samples would also come from depositional environments where the material was sufficiently bleached (e.g., aeolian dune caps). However, site access limitations and the nature of the landscape meant only two terraces were sampled. Most alluvial fans and debris cones are presently active and have not been incised by the channel system. The Rutherford Creek paraglacial fan is an exception, and a luminescence sample was taken from the base of an abandoned surface. This sample documented the onset of sedimentation during the paraglacial period. The remaining samples documented fan development but were not limiting ages. All samples came from low energy fluvial deposits where the chance of insufficient light exposure may have been an issue. 115 In this study, we found that quartz lacked a fast signal component, likely due to the volcanic origin of the quartz and insufficient sensitization of the grains, making it an unreliable chronometer. In contrast, most potassium feldspar samples exhibited strong luminescence signals, with strong shine-down and dose-response curves. From these samples, a sufficient number of aliquots passed the quality control checks, and dose recovery tests. However, anomalous fading rates were relatively high for most samples in comparison to those measured from the nearby Fraser River (Gingerich, 2021). And, in the case of two samples, the fading rates were too high to allow for corrections to be made. Optical dating results from this thesis suggest that the method is viable for dating fluvially deposited terrace and fan material but is not without limitations. Additional refinements to the approach might include the application of single grain dating to better capture a population of well-bleached grains, and refinement of anomalous fading measurements and calculations. Additionally, several tributary channels of the Lillooet River have incised into hanging valley steps along their lower reaches. Accessing these reaches and targeting fluvially deposited terrace material could better constrain postglacial adjustment within the basin. 5.1.5 Timescales of sediment storage and Holocene history of the Lillooet River watershed In Chapter 4, we synthesized sediment supply, storage, and yield data for the Lillooet River watershed, to which we contributed detailed geomorphic mapping, sediment yield, and geochronology data. The resulting dataset and systematic discussion of the Holocene history of the Lillooet River watershed was unique because of the watershed size (3,765 km2), relative completeness of the record in space and time, and range of variables studied. In this dataset, we detailed storage landforms that were active only during the early paraglacial period, those that are highly active today, and those that have been sediment sinks throughout the entire Holocene. A critical finding from this work was the large number of features and landforms that have been sediment sinks throughout the entire Holocene. This includes most of the valley steps and large lakes within the watershed, which alone significantly fragment the sediment cascade. From the synthesis of variables throughout the Holocene we were able to show correlations between potential sediment yield controls (i.e., climate, fire frequency, glacier advances) and 116 sediment yield at a range of spatial scales. Our discussion of sediment supply was limited to large landslide events. Large granitic rock avalanches appear to have occurred more frequently during the early Holocene, potentially due to slope debuttressing. However, landsliding is ultimately stochastic and imparts high variability in the sediment yield record. These results support the revised paraglacial sedimentation models presented by Jordan and Slaymaker (1991) and Friele and Clague (2009), offering nuanced insights into sediment dynamics and storage processes. 5.2 Future Research Opportunities 5.2.1 Measuring bed material contributions from glacial and proglacial sources in the Fitzsimmons Cr. watershed An intriguing and potentially significant finding from the bed material sediment budget for Fitzsimmons Cr. (Chapter 2), was the observation that contemporary glacial and proglacial sources appear to contribute relatively little coarse sediment. However, these sources were not measured directly but were instead inferred based on comparisons between lidar net change beginning 3 kilometers down valley from the Fitzsimmons Glacier, and volumetric flushing estimates from a run-of-river headpond 9.2 kilometers down valley. Direct measurements could corroborate these findings, which appear to be contradictory to contributions from many other proglacial areas (e.g., Chapter 4; Turley et al., 2023). 5.2.2 Comparative detailed sediment budgeting in the Green River watershed The detailed geomorphic mapping and estimates of postglacial storage in the Green River watershed (Chapter 3) present an opportunity to construct detailed sediment budgets for the Rutherford Cr. and Soo River watersheds, enabling meaningful comparisons to Fitzsimmons Creek (Chapter 2). Such comparative analyses could provide additional critical insights into the variability of sediment dynamics across adjacent catchments with shared geological and glacial histories. This could be approached in two different ways. First, a Holocene sediment budget could be constructed for each watershed through the collection of additional geochronological data, and conversion of sediment storage areas to volumes. Second, contemporary sediment budgets could be completed for the Rutherford and Soo River watersheds, paired with the 117 geomorphic mapping data, and then interpreted through the lens of the geological, glacial, and postglacial controls on sediment dynamics presented in Chapter 3. 5.2.3 Update the Lillooet River watershed Holocene sediment budget The data collected and presented in this thesis provide an opportunity to significantly update the Holocene sediment budget for the Lillooet River watershed originally proposed by Jordan and Slaymaker (1991). First, the sediment yield from glaciers could be revised based on additional data from the Silt Lake catchment (Chapter 4). Our results indicate that previous estimates of glacier contributions may be significantly underestimated. Data collected for Chapter 4 included updated glacier coverage, newly formed proglacial lakes, and estimates of sediment connectivity that could be used alongside the SY measurements to calculate a more robust glacier contribution value. Second, the synthesis of the landslide record from Friele et al. (2008) and others provides a better estimate of the colluvial sediment supply, particularly from the Mount Meager Volcanic Complex. These findings suggest that the landslide contribution was also significantly underestimated. And third, this thesis contributes a large dataset of over 6,000 mapped storage landforms. These mapped areas could be converted into volumetric estimates using geometric calculations or relations derived from depth-to-bedrock measurements obtained using ground penetrating radar or seismic methods. Quantifying the trapping efficiency of different sources of disconnectivity and the sediment residence time of storage landforms would also lead to a deeper understanding of the role sediment storage plays in determining landscape evolution and SSY. Future updates to the sediment budget should include additional consideration of the uncertainties, and errors associated with each measurement. The instrumentation, measurement techniques, and analyses presented in the literature cited in this study represent standard or even state-of-the-art practices at the time of their publication (i.e., 1960s to present). However, significant advances have been made over the past 80 years and will bias direct comparisons. 5.2.4 Further exploration of the scale- and geographic-dependence of SSY and its’ implications Results of this thesis highlighted the strong scale- and geographic-dependence of SSY measurements, a finding which is applicable globally. It is often imperative to make predictions 118 about expected sediment yields in response to changing conditions, such as land use and climate change. Findings in this thesis suggest that reliable predictions would require long-term sediment yield studies that capture system response times, estimate the expected variability, and provide context to short-term measurements and predictions. Additionally, data limitations often necessitate extrapolating SSY measurements in space. Our results indicate that this approach presents significant challenges. Therefore, further research is needed to inform best practices and mitigate potential issues. 119 References Albers S. (2017). tidyhydat: Extract and tidy Canadian hydrometric data. The Journal of Open Source Software, 2(20). doi:10.21105/joss.00511. Anderson, S., & Pitlick, J. (2014). Using repeat lidar to estimate sediment transport in a steep stream. Journal of Geophysical Research: Earth Surface, 621–643. https://doi.org/10.1002/2013JF002933. Antoniazza, G., & Lane, S. N. (2021). Sediment yield over glacial cycles: A conceptual model. Progress in Physical Geography, 1–24. https://doi.org/10.1177/0309133321997292. Armstrong, J. E. (1990). Vancouver Geology. In Geological Association of Canada: Cordilleran Section. Auclair, M., Lamothe, M., & Huot, S. (2003). Measurement of anomalous fading for feldspar IRSL using SAR. Radiation Measurements, 37(4–5), 487–492. https://doi.org/10.1016/S1350-4487(03)00018-0. Baggio, Tommaso., Martini, Lorenzo., & Torresani, Loris. (2022). R_IC_v1.0 (1.0). Zenodo. https://doi.org/10.5281/zenodo.6566013. Bailey, R. M., & Arnold, L. J. (2006). Statistical modelling of single grain quartz De distributions and an assessment of procedures for estimating burial dose. Quaternary Science Reviews, 25(19–20), 2475–2502. https://doi.org/10.1016/j.quascirev.2005.09.012. Ballantyne, C. K. (2002). Paraglacial Geomorphology. Quaternary Science Reviews, 21, 1935–2017. https://doi.org/10.1016/B978-0-444-53643-3.00089-3. Bizzi, S., & Lerner, D. N. (2015). The Use of Stream Power as an Indicator of Channel Sensitivity to Erosion and Deposition Processes. River Research and Applications, 31(1), 16–27. https://doi.org/10.1002/rra.2717. Blais-Stevens, A. (2008). Surficial geology and landslide inventory of the upper Sea to Sky corridor, British Columbia; Geological Survey of Canada, Open File 5324, scale 1:50,000. Blais-Stevens, A., Hermanns, R. L., & Jermyn, C. (2011). A 36Cl age determination for Mystery Creek rock avalanche and its implications in the context of hazard assessment, British Columbia, Canada. Landslides, 8(4), 407–416. https://doi.org/10.1007/s10346-011-0261-0. 120 Blum, M. D., & Roberts, H. H. (2009). Drowning of the mississippi delta due to insufficient sediment supply and global sea-level rise. Nature Geoscience, 2(7), 488-491. https://doi.org/10.1038/ngeo553. Borselli, L., Cassi, P., & Torri, D. (2008). Prolegomena to sediment and flow connectivity in the landscape: A GIS and field numerical assessment. Catena, 75(3), 268–277. https://doi.org/10.1016/j.catena.2008.07.006. Bovis, M. J., & Jakob, M. (1999). The role of debris supply conditions in predicting debris flow activity. Earth Surface Processes and Landforms, 24(11), 1039–1054. https://doi.org/10.1002/(SICI)1096-9837(199910)24:11<1039::AID-ESP29>3.0.CO;2-U. Brardinoni, F., & Hassan, M. A. (2006). Glacial erosion, evolution of river long profiles, and the organization of process domains in mountain drainage basins of coastal British Columbia. Journal of Geophysical Research, 111(F1), F01013. https://doi.org/10.1029/2005JF000358. Brardinoni, F., & Hassan, M. A. (2007). Glacially induced organization of channel-reach morphology in mountain streams. Journal of Geophysical Research: Earth Surface. https://doi.org/10.1029/2006JF000741. Brardinoni, F., Hassan, M. A., Rollerson, T., & Maynard, D. (2009). Colluvial sediment dynamics in mountain drainage basins. Earth and Planetary Science Letters, 284(3–4), 310–319. https://doi.org/10.1016/j.epsl.2009.05.002. Bridgland, D. R., & Westaway, R. (2014). Quaternary fluvial archives and landscape evolution: A global synthesis. Proceedings of the Geologists’ Association, 125(5–6), 600–629. https://doi.org/10.1016/j.pgeola.2014.10.009. Brierley, G. J. (2010). Landscape memory: The imprint of the past on contemporary landscape forms and processes. Area, 42(1), 76–85. https://doi.org/10.1111/j.1475-4762.2009.00900.x. Bronk, Ramsey C. (2009). Bayesian analysis of radiocarbon dates. Radiocarbon 51: 337–360. Brooks, G. R. (1994). The Fluvial Reworking of Late Pleistocene Drift, Squamish River Drainage Basin, Southwestern British Colombia. Géographie Physique et Quaternaire, 48(1), 51–68. Bywater-Reyes, S., Segura, C., & Bladon, K. D. (2017). Geology and geomorphology control suspended sediment yield and modulate increases following timber harvest in temperate headwater streams. Journal of Hydrology, 548, 74–83. https://doi.org/10.1016/j.jhydrol.2017.03.048. 121 Cache, T., Ramirez, J. A., Molnar, P., Ruiz-Villanueva, V., & Peleg, N. (2023). Increased erosion in a pre-Alpine region contrasts with a future decrease in precipitation and snowmelt. Geomorphology, 436(November 2022), 108782. https://doi.org/10.1016/j.geomorph.2023.108782. Campbell, D., & Church, M. (2003). Reconnaissance sediment budgets for Lynn Valley, British Columbia: Holocene and contemporary time scale. Canadian Journal of Earth Sciences, 40(5), 701–713. https://doi.org/10.1139/e03-012. Carrivick, J. L., & Rushmer, E. L. (2009). Inter- And intra-catchment variations in proglacial Geomorphology: An example from Franz Josef glacier and fox glacier, New Zealand. Arctic, Antarctic, and Alpine Research, 41(1), 18–36. https://doi.org/10.1657/1523-0430-41.1.18. Carrivick, J. L., & Tweed, F. S. (2021). Deglaciation controls on sediment yield: Towards capturing spatio-temporal variability. Earth-Science Reviews. https://doi.org/10.1016/j.earscirev.2021.103809. Cavalli, M., Trevisani, S., Comiti, F., & Marchi, L. (2013). Geomorphometric assessment of spatial sediment connectivity in small Alpine catchments. Geomorphology, 188, 31–41. https://doi.org/10.1016/j.geomorph.2012.05.007. Church, M. (2006). Bed material transport and the morphology of alluvial river channels. Annual Review of Earth and Planetary Sciences, 34, 325–354. https://doi.org/10.1146/annurev.earth.33.092203.122721. Church, M. (2017). Interpreting sediment yield scaling. Earth Surface Processes and Landforms, 42(12), 1895–1898. https://doi.org/10.1002/esp.4165. Church, M., & Ryder, J. M. (1972). Paraglacial Sedimentation: A Consideration of Fluvial Processes Conditioned by Glaciation. Geological Society of America Bulletin, 83, 3059–3072. Church, M., & Slaymaker, O. (1989). Disequilibrium of Holocene sediment yield in glaciated British Columbia. Nature, 337(6206), 452–454. https://doi.org/10.1038/337452a0. Church, M., Ham, D. and Weatherly, H. (2001). Gravel management in lower Fraser River. Department of Geography, The University of British Columbia. Report prepared for The City of Chilliwack, 110pp. Available online at www.geog.ubc.ca/fraserriver. Clague, J. J. (1986). The Quaternary stratigraphic record of British Columbia - evidence for episodic sedimentation and erosion controlled by glaciation. Canadian Journal of Earth Sciences, 23(6), 885–894. https://doi.org/10.1139/e86-090. 122 Clague, J. J. (1989). Quaternary geology of the Canadian Cordillera. In Quaternary geology of Canada and Greenland (pp. 15–96). https://doi.org/10.1130/dnag-gna-k1.15. Clague, J. J. (2017). Deglaciation of the Cordillera of Western Canada at the end of the Pleistocene. Geographical Research Letters, 43(2), 449–466. https://publicaciones.unirioja.es/ojs/index.php/cig/article/view/3232/2907. Clague, J. J., & Ward, B. (2011). Pleistocene Glaciation of British Columbia. In Developments in Quaternary Science (1st ed., Vol. 15, pp. 563–573). Elsevier Inc. https://doi.org/10.1016/B978-0-444-53447-7.00044-1. Clague, J. J., Evans, S. G., Rampton, V. N., & Woodsworth, G. J. (1995). Improved age estimates for the White River and Bridge River tephras, western Canada. Canadian Journal of Earth Sciences, 32(8), 1172–1179. https://doi.org/10.1139/e95-096. Cordilleran Geoscience. (2018). Geochronology of the Soo River Rockslide near Whistler, BC: a minimum age constraint based on analysis of drill cores collected from backwatered sediments. For Andree Blais-Stevens, Geological Survey of Canada. Cossart, E., & Fort, M. (2008). Consequences of landslide dams on alpine river valleys: Examples and typology from the French Southern Alps. Norsk Geografisk Tidsskrift, 62(2), 75–88. https://doi.org/10.1080/00291950802094882. Cowie, N. M., Moore, R. D., & Hassan, M. A. (2014). Effects of glacial retreat on proglacial streams and riparian zones in the Coast and North Cascade Mountains. Earth Surface Processes and Landforms. https://doi.org/10.1002/esp.3453. Croft, S. (2022). Personal Communication. Cui, Y., Miller, D., Schiarizza, P., and Diakow, L.J. (2017). British Columbia digital geology. British Columbia Ministry of Energy, Mines and Petroleum Resources, British Columbia Geological Survey Open File 2017-8, 9p. Dadson, S. J., & Church, M. (2005). Postglacial topographic evolution of glaciated valleys: A stochastic landscape evolution model. Earth Surface Processes and Landforms, 30(11), 1387–1403. https://doi.org/10.1002/esp.1199. Davey, C., & Lapointe, M. (2007). Sedimentary links and the spatial organization of Atlantic salmon (Salmo salar) spawning habitat in a Canadian Shield river. Geomorphology, 83(1–2), 82–96. https://doi.org/10.1016/j.geomorph.2006.06.011. Davidson, S. L., Marin-Esteve, B., & Eaton, B. (2024). What controls river widening? Comparing large and extreme flood events. Earth Surface Processes and Landforms, April, 1–17. https://doi.org/10.1002/esp.5875. 123 Dell’Agnese, A., Brardinoni, F., Toro, M., Mao, L., Engel, M., & Comiti, F. (2015). Bedload transport in a formerly glaciated mountain catchment constrained by particle tracking. Earth Surface Dynamics, 3(4), 527–542. https://doi.org/10.5194/esurf-3-527-2015. Desloges, J. R., & Gilbert, R. (1994). Sediment source and hydroclimatic inferences from glacial lake sediments: the postglacial sedimentary record of Lillooet Lake, British Columbia. Journal of Hydrology, 159(1–4), 375–393. https://doi.org/10.1016/0022-1694(94)90268-2. Dethier, E. N., Sartain, S. L., Renshaw, C. E., & Magilligan, F. J. (2020). Spatially coherent regional changes in seasonal extreme streamflow events in the United States and Canada since 1950. Science Advances, 6(49), 1–8. https://doi.org/10.1126/sciadv.aba5939. Dibiase, R. A., & Lamb, M. P. (2013). Vegetation and wildfire controls on sediment yield in bedrock landscapes. Geophysical Research Letters, 40(6), 1093–1097. https://doi.org/10.1002/grl.50277. Dietrich, W. E., Dunne, T., Humphrey, N. F., & Reid, L. M. (1982). Construction of sediment budgets for drainage basins. General Technical Report PNW-141. In F. J. Swanson, R. J. Janda, T. Dunne, & D. N. Swanston (Eds.), Sediment budgets and routing in forested drainage basins (pp. 5–23). Pacific Northwest Forest and Range Experiment Station. Duan, L., Tian, Y., Zhang, S., Wang, X., Li, C., & Chen, M. (2024). Statistical studies of the characteristics and key influencing factors of sediment changes in the upper Yangtze River over the past 60 years. Geomorphology. https://doi.org/10.1016/j.ijbiomac.2024.133617. Egan, J., Staff, R., & Blackford, J. (2015). A high-precision age estimate of the Holocene Plinian eruption of Mount Mazama, Oregon, USA. Holocene, 25(7), 1054–1067. https://doi.org/10.1177/0959683615576230. Ehrlich, D., Melchiorri, M., & Capitani, C. (2021). Population trends and urbanisation in mountain ranges of the world. Land, 10(3), 1–19. https://doi.org/10.3390/land10030255. Fan, X., Dufresne, A., Siva Subramanian, S., Strom, A., Hermanns, R., Tacconi Stefanelli, C., Hewitt, K., Yunus, A. P., Dunning, S., Capra, L., Geertsema, M., Miller, B., Casagli, N., Jansen, J. D., & Xu, Q. (2020). The formation and impact of landslide dams – State of the art. Earth-Science Reviews, 203, 103116. https://doi.org/10.1016/j.earscirev.2020.103116. Friele, P. (2024). Personal Communication. Friele, P. A., & Clague, J. J. (2002). Readvance of glaciers in the British Columbia Coast Mountains at the end of the last glaciation. Quaternary International, 87(1), 45–58. https://doi.org/10.1016/S1040-6182(01)00061-1. 124 Friele, P. A., & Clague, J. J. (2004). Large Holocene landslides from Pylon Peak, southwestern British Columbia. Canadian Journal of Earth Sciences, 41(2), 165–182. https://doi.org/10.1139/e03-089. Friele, P. A., & Clague, J. J. (2009). Paraglacial geomorphology of Quaternary volcanic landscapes in the southern Coast Mountains, British Columbia. Geological Society, London, Special Publication, 320, 219–233. https://doi.org/10.1144/SP320.14. Friele, P. A., Clague, J. J., Simpson, K., & Stasiuk, M. (2005). Impact of a Quaternary volcano on Holocene sedimentation in Lillooet River valley, British Columbia. Sedimentary Geology, 176(3–4), 305–322. https://doi.org/10.1016/j.sedgeo.2005.01.011. Friele, P. A., Jakob, M., & Clague, J. J. (2008). Hazard and risk from large landslides from Mount Meager volcano, British Columbia, Canada. Georisk, 2(1), 48–64. https://doi.org/10.1080/17499510801958711. Fryirs, K. A., Brierley, G. J., Preston, N. J., & Kasai, M. (2007a). Buffers, barriers and blankets: The (dis)connectivity of catchment-scale sediment cascades. Catena, 70(1), 49–67. https://doi.org/10.1016/j.catena.2006.07.007. Fryirs, K. A., Brierley, G. J., Preston, N. J., & Spencer, J. (2007b). Catchment-scale (dis)connectivity in sediment flux in the upper Hunter catchment, New South Wales, Australia. Geomorphology, 84(3–4), 297–316. https://doi.org/10.1016/j.geomorph.2006.01.044. Fryirs, K. A., Wheaton, J. M., & Brierley, G. J. (2016). An approach for measuring confinement and assessing the influence of valley setting on river forms and processes. Earth Surface Processes and Landforms, 41(5), 701–710. https://doi.org/10.1002/esp.3893. Galbraith, R. F., Roberts, R. G., Laslett, G. M., Yoshida, H., & Olley, J. M. (1999). Optical dating of single and multiple grains of quartz from Jinmium rock shelter, northern Australia: Part I, experimental design and statistical models. Archaeometry, 41(2), 339–364. https://doi.org/10.1111/j.1475-4754.1999.tb00987.x. Geilhausen, M., Otto, J. C., & Schrott, L. (2012). Spatial distribution of sediment storage types in two glacier landsystems (Pasterze & Obersulzbachkees, Hohe Tauern, Austria). Journal of Maps, 8(3), 242–259. https://doi.org/10.1080/17445647.2012.708540. GeoBC. (2021). GeoBase 250K Land Cover, Circa 2000 – Vector. BC Data Catalogue. Accessed Sept. 2024. https://catalogue.data.gov.bc.ca/dataset/exposed-land-1-250-000-geobase-land-cover. GeoBC. (2024). Freshwater Atlas Rivers [Data file]. BC Data Catalogue. Accessed Sept. 2023. https://catalogue.data.gov.bc.ca/dataset/f7dac054-efbf-402f-ab62-6fc4b32a619e. 125 Gilbert, R. (1973). Observations of lacustrine sedimentation at Lillooet Lake, British Columbia. University of British Columbia. Gilbert, R., Crookshanks, S., Hodder, K. R., Spagnol, J., & Stull, R. B. (2006). The record of an extreme flood in the sediments of montane Lillooet Lake, British Columbia: Implications for paleoenvironmental assessment. Journal of Paleolimnology, 35(4), 737–745. https://doi.org/10.1007/s10933-005-5152-8. Giles, P. T. (2010). Investigating the use of alluvial fan volume to represent fan size in morphometric studies. Geomorphology, 121(3–4), 317–328. https://doi.org/10.1016/j.geomorph.2010.05.001. Gingerich, T. (2021). The nature and timing of postglacial valley fill incision, Fraser River, Big Bar to Watson Bar, British Columbia. (Unpublished Thesis). Simon Fraser University. Golder Associates Ltd. (1993). Geotechnical investigation and stability analysis, Fitzsimmons Creek slide, Whistler, B.C. Submitted to Resort Municipality of Whistler. Document No. 912-1484, 170 p. Government of British Columbia, GeoBC Branch. (2024, May 1). Digital Elevation Model for British Columbia - CDED - 1:250,000. Natural Resources Canada, Federal Geospatial Platform. https://osdp-psdo.canada.ca/dp/en/search/metadata/NRCAN-FGP-1-7b4fef7e-7cae-4379-97b8-62b03e9ac83d Gran, K. B., & Czuba, J. A. (2017). Sediment pulse evolution and the role of network structure. Geomorphology, 277, 17–30. https://doi.org/10.1016/j.geomorph.2015.12.015. Green, N. L., Armstrong, R. L., Harakal, J. E., Souther, J. G., & Read, P. B. (1988). Eruptive history and K-Ar geochronology of the late Cenozoic Garibaldi volcanic belt, southwestern British Columbia. Bulletin of the Geological Society of America, 100(4), 563–579. https://doi.org/10.1130/0016-7606(1988)100<0563:EHAKAG>2.3.CO;2. Hack, J. T. (1973). Stream-profile analysis and stream-gradient index. Journal of Research of the U.S. Geological Survey, 1(4), 421–429. Hallet, B., Hunter, L., & Bogen, J. (1996). Rates of erosion and sediment evacuation by glaciers: A review of field data and their implications. Global and Planetary Change, 12, 213–235. https://doi.org/10.1029/2004JF000189. Hallett, D. J., Lepofsky, D. S., Mathewes, R. W., & Lertzman, K. P. (2003). 11 000 Years of fire history and climate in the mountain hemlock rain forests of southwestern British Columbia based on sedimentary charcoal. Canadian Journal of Forest Research, 33(2), 292–312. https://doi.org/10.1139/x02-177. 126 Hassan, M. A., Bird, S., Reid, D. A., Ferrer-Boix, C., Hogan, D., Brardinoni, F., & Chartrand, S. M. (2019). Variable hillslope-channel coupling and channel characteristics of forested mountain streams in glaciated landscapes. Earth Surface Processes and Landforms, 16. https://doi.org/10.1002/esp.4527. Hassan, M. A., McDowell, C., Bird, S., Reid, D. A., Turley, M., & Hogan, D. (2024). A conceptual model on the influence of logjam formation on longitudinal and lateral sediment dynamics in forested streams. Geomorphology, 458(109260). https://doi.org/10.1016/j.geomorph.2024.109260. Hassan, M. A., Roberge, L., Church, M., More, M., Donner, S. D., Leach, J., & Ali, K. F. (2017). What are the contemporary sources of sediment in the Mississippi River? Geophysical Research Letters, 44(17), 8919–8924. https://doi.org/10.1002/2017GL074046. Hassan, M. A., Turley, M., Reid, D. A., Bird, S., & Abbey, P. (2023). Influence of lateral connectivity on channel characteristics in (post)glacial landscapes. In S. Dutta & V. Chembolu (Eds.), Recent Development in River Corridor Management: Select Proceedings of RCRM 2022 (pp. 3–29). Heckmann, T., Schwanghart, W., & Phillips, J. D. (2015). Graph theory-Recent developments of its application in geomorphology. Geomorphology, 243, 130–146. https://doi.org/10.1016/j.geomorph.2014.12.024. Heideman, M. (2013). Flood hazard and risk in Lillooet River valley, British Columbia. (Unpublished Thesis). Simon Fraser University. Heideman, M., Menounos, B., & Clague, J. J. (2018). A multi-century estimate of suspended sediment yield from Lillooet Lake, southern Coast Mountains, Canada. Canadian Journal of Earth Sciences, 32, 18–32. Hewitt, K., Clague, J. J., & Orwin, J. F. (2008). Legacies of catastrophic rock slope failures in mountain landscapes. Earth-Science Reviews, 87(1–2), 1–38. https://doi.org/10.1016/j.earscirev.2007.10.002. Hickson, C.J., (1994). Character of volcanism, volcanic hazards, and risk, northern end of the Cascade magmatic arc, British Columbia and Washington State. In: J.W.H. Monger, ed. Geology and geological hazards of the Vancouver region, Southwestern British Columbia. Geological Survey of Canada, Bulletin 481, 231-250. Hinderer, M. (2012). From gullies to mountain belts: A review of sediment budgets at various scales. Sedimentary Geology, 280, 21–59. https://doi.org/10.1016/j.sedgeo.2012.03.009. Holm, K., Bovis, M., & Jakob, M. (2004). The landslide response of alpine basins to post-Little Ice Age glacial thinning and retreat in southwestern British Columbia. Geomorphology, 57(3–4), 201–216. https://doi.org/10.1016/S0169-555X(03)00103-X. 127 Hooke, J. M., & Souza, J. O. P. (2021). Challenges of mapping, modelling and quantifying sediment connectivity. Earth Science Reviews. https://doi.org/10.1016/j.earscirev.2021.103847. Howes, D.E. and Kenk, E. (1997). Terrain classification system for British Columbia (Version 2). B.C. Ministry of Environment, Recreational Fisheries Branch, and B.C. Ministry of Crown Lands, Surveys and Resource Mapping Branch, Victoria, B.C. Huntley, D. J., & Lamothe, M. (2001). Ubiquity of anomalous fading in K-feldspars and the measurement and correction for it in optical dating. Canadian Journal of Earth Sciences, 38(7), 1093–1106. https://doi.org/10.1139/cjes-38-7-1093. Jakob, M. (1996). Morphometric and Geotechnical Controls of Debris Flow Frequency and Magnitude in Southwestern British Columbia. (Unpublished Thesis). University of British Columbia. Jakob, M., & Owen, T. (2021). Projected effects of climate change on shallow landslides, North Shore Mountains, Vancouver, Canada. Geomorphology, 393, 107921. https://doi.org/10.1016/j.geomorph.2021.107921. James, L. A., Pfeiffer, A. W., & Hung, C.-L. J. (2022). Long-term sediment storage and flux in a small, catastrophically aggraded mountain catchment. Anthropocene, 37, 100313. Jerolmack, D. J., & Paola, C. (2010). Shredding of environmental signals by sediment transport. Geophysical Research Letters, 37(19), 1–5. https://doi.org/10.1029/2010GL044638. Jones, P. S. A. (1982). Sediment movement in a sub-alpine basin in the Coast Mountains of British Columbia. (Unpublished Thesis). University of British Columbia. http://eprints.uanl.mx/5481/1/1020149995.PDF. Jordan, P. (1994). Debris Flows in the Southern Coast Mountains, British Columbia: Dynamic Behaviour and Physical Properties. (Unpublished Thesis). University of British Columbia, October, 1–271. https://circle.ubc.ca/handle/2429/8796. Jordan, P., & Slaymaker, O. (1991). Holocene Sediment Production in Lillooet River Basin, British Colombia: A Sediment Budget Approach. Géographie Physique et Quaternaire, 45(1), 45–57. https://doi.org/10.7202/032844ar. Kang, D. H., Gao, H., Shi, X., Islam, S. U., & Déry, S. J. (2016). Impacts of a Rapidly Declining Mountain Snowpack on Streamflow Timing in Canada’s Fraser River Basin. Scientific Reports, 6(January), 1–8. https://doi.org/10.1038/srep19299. Ke, Q., & Zhang, K. (2023). Scale issues in runoff and sediment delivery (SIRSD): A systematic review and bibliometric analysis. Earth Science Reviews. https://doi.org/10.1016/j.earscirev.2024.104729. 128 Keesstra, S., Nunes, J. P., Saco, P., Parsons, T., Poeppl, R. E., Masselink, R., & Cerdà, A. (2018). The way forward: Can connectivity be useful to design better measuring and modelling schemes for water and sediment dynamics? Science of the Total Environment, 644, 1557–1572. https://doi.org/10.1016/j.scitotenv.2018.06.342. Kerr Wood Leidal Associates Ltd (KWL). (2002). Engineering study for Lillooet River corridor. KWL File No. 713.002, Burnaby, BC. Kirchner, J. W., Finkel, R. C., Riebe, C. S., Granger, D. E., Clayton, J. L., King, J. G., & Megahan, W. F. (2001). Mountain erosion over 10 yr, 10 k.y., and 10 m.y. time scales. Geology, 29(7), 591–594. https://doi.org/10.1130/0091-7613(2001)029<0591:MEOYKY>2.0.CO;2. Koch, J., Clague, J. J., & Osborn, G. D. (2007). Glacier fluctuations during the past millennium in Garibaldi Provincial Park, southern Coast Mountains, British Columbia. Canadian Journal of Earth Sciences, 44(9), 1215–1233. https://doi.org/10.1139/E07-019. Koch, J., Menounos, B., & Clague, J. J. (2009). Glacier change in Garibaldi Provincial Park, southern Coast Mountains, British Columbia, since the Little Ice Age. Global and Planetary Change, 66(3–4), 161–178. https://doi.org/10.1016/j.gloplacha.2008.11.006. Koehler, L., & Smith, D. J. (2011). Late Holocene glacial activity in Manatee valley, southern coast mountains, British Columbia, Canada. Canadian Journal of Earth Sciences, 48(3), 603–618. https://doi.org/10.1139/E10-087. Koppes, M., Hallet, B., Rignot, E., Mouginot, J., Wellner, J. S., & Boldt, K. (2015). Observed latitudinal variations in erosion as a function of glacier dynamics. Nature, 526(7571), 100–103. https://doi.org/10.1038/nature15385. Koppes, M. N., & Montgomery, D. R. (2009). The relative efficacy of fluvial and glacial erosion over modern to orogenic timescales. Nature Geoscience, 2(9), 644–647. https://doi.org/10.1038/ngeo616. Kreutzer, S., Burow, C., Dietze, M., Fuchs, M., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Philippe, A., Riedesel, S., Autze,n M., Mittelstrass, D., Gray, H., Galharret, J. (2023). Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.9.23, https://CRAN.R-project.org/package=Luminescence. Lane, S. N., Bakker, M., Gabbud, C., Micheletti, N., & Saugy, J.-N. (2017). Sediment export, transient landscape response and catchment-scale connectivity following rapid climate warming and Alpine glacier recession. 210–227. https://doi.org/10.1016/j.geomorph.2016.02.015. 129 Lian, O. B., & Roberts, R. G. (2006). Dating the Quaternary: progress in luminescence dating of sediments. Quaternary Science Reviews, 25(19–20), 2449–2468. https://doi.org/10.1016/j.quascirev.2005.11.013. Liang, P., Forman, S.L. (2019). LDAC: An Excel-based program for luminescence equivalent dose and burial age calculations. Ancient TL 37 (2), 21-40. Lindsay, J. B., & Ashmore, P. E. (2002). The effects of survey frequency on estimates of scour and fill in braided river model. Earth Surface Processes and Landforms, 27(1), 27–43. https://doi.org/10.1002/esp.282 Mancini, D., & Lane, S. N. (2020). Changes in sediment connectivity following glacial debuttressing in an Alpine valley system. Geomorphology, 352. https://doi.org/10.1016/j.geomorph.2019.106987. Martini, L., Baggio, T., Torresani, L., Crema, S., & Cavalli, M. (2022). R_IC: A novel and versatile implementation of the index of connectivity in R. Environmental Modelling and Software, 155(June), 105446. https://doi.org/10.1016/j.envsoft.2022.105446. Mather, A. E., Stokes, M., & Whitfield, E. (2017). River terraces and alluvial fans: The case for an integrated Quaternary fluvial archive. Quaternary Science Reviews, 166, 74–90. https://doi.org/10.1016/j.quascirev.2016.09.022. McColl, S. T. (2012). Paraglacial rock-slope stability. Geomorphology, 153–154, 1–16. https://doi.org/10.1016/j.geomorph.2012.02.015. Medialdea, A., Thomsen, K. J., Murray, A. S., & Benito, G. (2014). Reliability of equivalent-dose determination and age-models in the OSL dating of historical and modern palaeoflood sediments. Quaternary Geochronology, 22, 11–24. https://doi.org/10.1016/j.quageo.2014.01.004. Menounos, B. (2002). Climate, Fine-Sediment Transport Linkages, Coast Mountains, British Columbia. (Unpublished Thesis). University of British Columbia. Menounos, B. (2006). Anomalous early 20th century sedimentation in proglacial Green Lake, British Columbia, Canada. Can. J. Earth Sci. 43, 671–678. Menounos, B., Osborn, G., Clague, J. J., & Luckman, B. H. (2009). Latest Pleistocene and Holocene glacier fluctuations in western Canada. Quaternary Science Reviews, 28(21–22), 2049–2074. https://doi.org/10.1016/j.quascirev.2008.10.018. Menounos, B., Schiefer, E., & Slaymaker, O. (2006). Nested temporal suspended sediment yields, Green Lake Basin, British Columbia, Canada. Geomorphology, 79(1–2), 114–129. https://doi.org/10.1016/j.geomorph.2005.09.020. 130 Micheletti, N., & Lane, S. N. (2016). Water yield and sediment export in small, partially glaciated Alpine watersheds in a warming climate. Water Resources Research, 52(51), 5974–5997. https://doi.org/10.1002/2016WR018977. Monger, J.W.H., Journeay, J.M. (1994). Guide to the geology and tectonic evolution of the Southern Coast Mountains. Geol. Surv. Can. Open File, 2490, Vancouver, Canada. Montgomery, D. R., & Buffington, J. M. (1997). Channel-reach morphology in mountain drainage basins. Bulletin of the Geological Society of America, 109(5), 596–611. https://doi.org/10.1130/0016-7606(1997)109<0596:CRMIMD>2.3.CO;2. Moore, R. D., Fleming, S. W., Menounos, B., Wheate, R., Fountain, A., Stahl, K., Holm, K., & Jakob, M. (2009). Glacier change in western North America: influences on hydrology, geomorphic hazards and water quality. Hydrological Processes, 23(1), 42–61. https://doi.org/10.1002/hyp. Mueller, E. R., & Pitlick, J. (2013). Sediment supply and channel morphology in mountain river systems: 1. Relative importance of lithology, topography, and climate. Journal of Geophysical Research: Earth Surface, 118(4), 2325–2342. https://doi.org/10.1002/2013JF002843. Murray, A. S., & Wintle, A. G. (2000). Luminescence dating of quartz using an improved single-aliquot regenerative-dose protocol. Radiation Measurements, 32(1), 57–73. https://doi.org/10.1016/S1350-4487(99)00253-X. Najafi, S., Dragovich, D., Heckmann, T., & Sadeghi, S. H. (2021). Sediment connectivity concepts and approaches. Catena, 196, 104880. https://doi.org/10.1016/j.catena.2020.104880. Nichol, S. L., Hungr, O., & Evans, S. G. (2002). Large-scale brittle and ductile toppling of rock slopes. Canadian Geotechnical Journal, 39(4), 773–788. https://doi.org/10.1139/t02-027 Nichols, R. W. (1992). A design brief on the floodplain mapping study, Whistler Area. Province of British Columbia Ministry of Environment, Lands and Parks, Water Management Division. File: 35100-30/119-4671. O’Brien, G. R., Wheaton, J. M., Fryirs, K. A., Macfarlane, W. W., Brierley, G. J., Whitehead, K., Gilbert, J., & Volk, C. (2019). Mapping Valley Bottom Confinement at the Network Scale. Earth Surface Processes and Landforms, esp.4615. https://doi.org/10.1002/esp.4615. Osborn, G., Menounos, B., Koch, J., Clague, J. J., & Vallis, V. (2007). Multi-proxy record of Holocene glacial history of the Spearhead and Fitzsimmons ranges, southern Coast Mountains, British Columbia. Quaternary Science Reviews, 26(3–4), 479–493. https://doi.org/10.1016/j.quascirev.2006.09.003. 131 Otto, J.-C., Goetz, J., & Schrott, L. (2008). Sediment storage in Alpine sedimentary systems-quantification and scaling issues. IAHS Publication, 325, 258–265. Owens, P. N. (1990). Lake sediment-based sediment yields and erosion rates in the Coast Mountains, British Columbia (T). University of British Columbia. Retrieved from https://open.library.ubc.ca/collections/ubctheses/831/items/1.0098534. Owens, P.N. and Slaymaker, O. (1992). Late Holocene sediment yields in small alpine and sub-alpine drainage basins. International Association of Hydrological Sciences, Publication 209, 147–154. Pelpola, C. P., & Hickin, E. J. (2004). Long-term bed load transport rate based on aerial-photo and ground penetrating radar surveys of fan-delta growth, Coast Mountains, British Columbia. Geomorphology, 57(3–4), 169–181. https://doi.org/10.1016/S0169-555X(03)00101-6. Persichillo, M. G., Bordoni, M., Cavalli, M., Crema, S., & Meisina, C. (2018). The role of human activities on sediment connectivity of shallow landslides. Catena, 160(September 2017), 261–274. https://doi.org/10.1016/j.catena.2017.09.025. Pinter, N., & Heine, R. A. (2005). Hydrodynamic and morphodynamic response to river engineering documented by fixed-discharge analysis, Lower Missouri River, USA. Journal of Hydrology, 302(1–4), 70–91. https://doi.org/10.1016/j.jhydrol.2004.06.039. Planet Labs PBC. (2024). Planet Application Program Interface: In Space for Life on Earth. Planet. https://api.planet.com. Poeppl, R. E., & Parsons, A. J. (2018). The geomorphic cell: a basis for studying connectivity. Earth Surface Processes and Landforms, 43(5), 1155–1159. https://doi.org/10.1002/esp.4300. Ponton, J. R. (1972). Hydraulic geometry of Green and Birkenhead Rivers: Southwestern Coast Mountains, British Columbia. (Unpublished Thesis). University of British Columbia. Porder, S., Hilley, G. E., & Chadwick, O. A. (2007). Chemical weathering, mass loss, and dust inputs across a climate by time matrix in the Hawaiian Islands. Earth and Planetary Science Letters, 258(3–4), 414–427. https://doi.org/10.1016/j.epsl.2007.03.047. Prescott, J.R., & Hutton, J.T. (1994). Cosmic ray contributions to dose rates for luminescence and ESR dating: Large depths and long-term time variations. Radiation Measurements, 23: 497–500. Preusser, F., Chithambo, M. L., Götte, T., Martini, M., Ramseyer, K., Sendezera, E. J., Susino, G. J., & Wintle, A. G. (2009). Quartz as a natural luminescence dosimeter. Earth-Science Reviews, 97, 188–214. https://doi.org/10.1016/j.earscirev.2009.09.006. 132 R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Reid, D. A., Hassan, M. A., & McCleary, R. J. (2021). Glacial landscape configuration influences channel response to flooding. Earth Surface Processes and Landforms. https://doi.org/10.1002/esp.5240. Reid, L. M., & Dunne, T. (1996). Rapid evaluation of sediment budgets. GeoEcology Paperbacks. Reimer, P.J., Austin, W.E.N., Bard, E., Bayless, A., Blackwell, P.G., Bronk, Ramsey C., Butzin, M., Cheng, H., Edwards, R.L., Friedrich, M., Grootes, P.M., Guilderson, T.P., Hajdas, I., Heaton, T.J., Hogg, A.G., Hughen, K.A., Kromer, B., Manning, S.W., Muscheler, R., Palmer, J.G., Pearson, C., van der Plicht , J., Reimer, R.W., Richards, D.A., Scott, E.M., Southon, R.J., Turney, C.S.M., Wacker, L., Adolphi, F., Büntgen, U., Capano, M., Fagrni, S.M., Fogtmann-Schulz, A., Friedrich, R., Köhler, P., Kudsk, S., Miyake, F., Olsen, J., Reinig, F., Sakamoto, M., Sookdeo, A., Talamo, S. (2020). The Intcal20 northern hemisphere radiocarbon age calibration curve (0-55 cal kBP). Radiocarbon 62(4):725-757. Rempel, L. L., & Church, M. (2009). Physical and ecological response to disturbance by gravel mining in a large alluvial river. Canadian Journal of Fisheries and Aquatic Sciences, 66(1), 52–71. https://doi.org/10.1139/F08-184 Reyes, A. V., & Clague, J. J. (2004). Stratigraphic evidence for multiple Holocene advances of Lillooet Glacier, southern Coast Mountains, British Columbia. Canadian Journal of Earth Sciences, 41(8), 903–918. https://doi.org/10.1139/E04-039. Reyes, A. V., Wiles, G. C., Smith, D. J., Barclay, D. J., Allen, S., Jackson, S., Larocque, S., Laxton, S., Lewis, D., Calkin, P. E., & Clague, J. J. (2006). Expansion of alpine glaciers in Pacific North America in the first millennium A.D. Geology, 34(1), 57–60. https://doi.org/10.1130/G21902.1. Rittenour, T. M. (2008). Luminescence dating of fluvial deposits: Applications to geomorphic, palaeoseismic and archaeological research. Boreas, 37(4), 613–635. https://doi.org/10.1111/j.1502-3885.2008.00056.x. Roberti, G., Ward, B., van Wyk de Vries, B., Falorni, G., Menounos, B., Friele, P. A., Williams-Jones, G., Clague, J. J., Perotti, L., Giardino, M., Baldeon, G., & Freschi, S. (2018). Landslides and glacier retreat at Mt. Meager volcano: hazard and risk challenges. Geohazards 7, June, 9. Roberts, R. G., & Church, M. (1986). The sediment budget in severely disturbed watersheds, Queen Charlotte Ranges, British Columbia. Canadian Journal of Forest Research, 16, 1092–1106. 133 Ryder, J. M. (1971a). Some Aspects of the Morphometry of Paraglacial Alluvial Fans in South-central British Columbia. Canadian Journal of Earth Sciences, 8(10), 1252–1264. https://doi.org/10.1139/e71-114. Ryder, J. M. (1971b). The Stratigraphy and Morphology of Para-glacial Alluvial Fans in South-central British Columbia. Canadian Journal of Earth Sciences, 8(2), 279–298. https://doi.org/10.1139/e71-027. Ryder, J. M., Fulton, R. J., & Clague, J. J. (1991). The Cordilleran Ice Sheet and the Glacial Geomorphology of Southern and Central British Colombia. Géographie Physique et Quaternaire, 45(3). Saletti, M., Molnar, P., Zimmermann, A., Hassan, M. A., & Church, M. (2015). Temporal variability and memory in sediment transport in an experimental step-pool channel. Water Resources Research, 51(11), 9325–9337. https://doi.org/10.1002/2015WR016929. Salkeld, T., Walton, A., Mackenzie, W. (2014). British Columbia biogeoclimatic ecosystem classification. British Columbia Forest Analysis and Inventory Branch. https://catalogue.data.gov.bc.ca/dataset/fda19189-6cb4-4712-992f-500acadae749 Sassolas-Serrayet, T., Cattin, R. & Ferry, M. (2018). The shape of watersheds. Nat Commun 9, 3791. https://doi.org/10.1038/s41467-018-06210-4. Savi, S., Buter, A., Heckmann, T., Theule, J., Mao, L., & Comiti, F. (2023). Multi-temporal analysis of morphological changes in an Alpine proglacial area and their effect on sediment transfer. Catena, 220(PB), 106701. https://doi.org/10.1016/j.catena.2022.106701 Schiefer, E. (2006). Contemporary sedimentation rates and depositional structures in a montane lake basin, southern Coast Mountains, British Columbia, Canada. Earth Surface Processes and Landforms, 31, 1311–1324. https://doi.org/10.1002/esp. Schiefer, E., & Gilbert, R. (2008). Proglacial sediment trapping in recently formed Silt Lake, Upper Lillooet Valley, Coast Mountains, British Columbia. Earth Surface Processes and Landforms, 33, 1542–1556. https://doi.org/10.1002/esp. Schiefer, E., Hassan, M. A., Menounos, B., Pelpola, C. P., & Slaymaker, O. (2010). Interdecadal patterns of total sediment yield from a montane catchment, southern Coast Mountains, British Columbia, Canada. Geomorphology, 118(1–2), 207–212. https://doi.org/10.1016/j.geomorph.2010.01.001. Schiefer, E., Menounos, B., Slaymaker, O. (2006). Extreme sediment delivery events recorded in the contemporary sediment record of a montane lake, southern Coast Mountains, British Columbia. Can. J. Earth Sci. 43, 1777–1790. 134 Schmitt, R. J. P., Bizzi, S., & Castelletti, A. (2016). Tracking multiple sediment cascades at the river network scale identifies controls and emerging patterns of sediment connectivity. Water Resources Research, 52(5), 3941–3965. https://doi.org/10.1002/2015WR018097. Schumm, S. A. (1991). To interpret the earth: Ten ways to be wrong. Cambridge University Press. 133p. Scorpio, V., & Comiti, F. (2024). Channel changes during and after extreme floods in two catchments of the Northern Apennines (Italy). Geomorphology. https://doi.org/10.1016/j.geomorph.2024.109355. Scott, D. N., & Collins, B. D. (2021). Frequent Mass Movements from Glacial and Lahar Terraces, Controlled by Both Hillslope Characteristics and Fluvial Erosion, are an Important Sediment Source to Puget Sound Rivers. Water Resources Research. https://doi.org/10.1029/2020wr028389. Seeber, L., & Gornitz, V. (1983). River profiles along the Himalayan Arc as indicators of active tectonics. Tectonophysics, 92, 335–367. Singh, M., Sinha, R., & Tandon, S. K. (2021). Geomorphic connectivity and its application for understanding landscape complexities: a focus on the hydro‐geomorphic systems of India. Earth Surface Processes and Landforms, 46(1), 110–130. https://doi.org/10.1002/esp.4945. Slaymaker, O. (2003). The sediment budget as conceptual framework and management tool. Hydrobiologia, 494, 71–82. Slaymaker, O. (2004). Mass balances of sediments, solutes and nutrients: The need for greater integration. Journal of Coastal Research, SPEC. ISS. 43, 109–123. Slaymaker, O. (2006). Towards the identification of scaling relations in drainage basin sediment budgets. Geomorphology, 80(1–2), 8–19. https://doi.org/10.1016/j.geomorph.2005.09.004 Slaymaker, O., & Embleton-Hamann, C. (2018). Advances in global mountain geomorphology. Geomorphology, 308, 230–264. https://doi.org/10.1016/j.geomorph.2018.02.016. Slaymaker, O., Clague, J.J., Gilbert, R., Friele, P.A., Jordan, P., Menounos, B., Schiefer, E. (2017) Lillooet-Harrison Drainage Basin: Variable landscapes within the Coast Mountains. In: Landscapes and Landforms of Western Canada. Editor: O. Slaymaker. https://doi.org/10.1007/978-3-319-44595-3. Slaymaker, O., & Gilbert, R. (1972) Geomorphic processes and land use changes in the Coast Mountains of British Columbia: a case study. In P. Macar and A. Pissart, eds., Processus Periglaciaires Etudies sur le Terrain. Universite de Liege, 269-279. 135 Slaymaker, O., Souch, C., Menounos, B., & Filippelli, G. (2003). Advances in Holocene mountain geomorphology inspired by sediment budget methodology. Geomorphology, 55(1–4), 305–316. https://doi.org/10.1016/S0169-555X(03)00146-6 Souch, C. (1989). Holocene sedimentary environments of the Goat Meadows watershed, southern Coast Mountains, British Columbia. Geographie Physique et Quaternaire, 43(1), 77–85. Souch, C., & Slaymaker, O. (1986). Temporal variability of sediment yield using accumulations in small ponds. Physical Geography, 7(2), 140–153. https://doi.org/10.1080/02723646.1985.10642286. Stasiuk, M. V., Russell, J. K., James K., & Hickson, C. J. (1996). Distribution, nature, and origins of the 2400 BP eruption products of Mount Meager, British Columbia: linkages between magma chemistry and eruption behaviour. In Geological Survey of Canada Bulletin (Vol. 486). Geological Survey of Canada. Syvitski, J. P. M., & Milliman, J. D. (2007). Geology, geography, and humans battle for dominance over the delivery of fluvial sediment to the coastal ocean. Journal of Geology, 115(1), 1–19. https://doi.org/10.1086/509246. Tanski, N. M., Rittenour, T. M., Pavano, F., Pazzaglia, F., Mills, J., Corbett, L. B., & Bierman, P. (2024). Quartz luminescence sensitivity enhanced by residence time in the critical zone. Quaternary Geochronology. https://doi.org/10.1016/j.quageo.2024.101613. Tofelde, S., Bernhardt, A., Guerit, L., & Romans, B. W. (2021). Times associated with source-to-sink propagation of environmental signals during landscape transience. Frontiers in Earth Science, 9, 1–26. https://doi.org/10.3389/feart.2021.628315 Tunnicliffe, J. F., & Church, M. (2011). Scale variation of post-glacial sediment yield in Chilliwack Valley, British Columbia. Earth Surface Processes and Landforms, 36(2), 229–243. Tunnicliffe, J., Church, M., Clague, J. J., & Feathers, J. K. (2012). Postglacial sediment budget of Chilliwack Valley, British Columbia. Earth Surface Processes and Landforms, 37(12), 1243–1262. https://doi.org/10.1002/esp.3229 Turley, M., & Hassan, M. A. (2023). Spatial patterns of disconnectivity explain catchment-scale sediment dynamics and transfer efficiencies. Journal of Geophysical Research: Earth Surface, 128, e2023JF007111. https://doi. org/10.1029/2023JF007111. Turley, M., Hassan, M. A., & Slaymaker, O. (2021). Quantifying sediment connectivity: Moving toward a holistic assessment through a mixed methods approach. Earth Surface Processes and Landforms, 46(12), 2501–2519. https://doi.org/10.1002/esp.5191. 136 Turley, M., Hassan, M. A., & Zimmermann, A. (2024). Datasets for sediment source partitioning and budgeting: Fitzsimmons Creek Watershed, BC, Canada. [Dataset]. Figshare. https://doi.org/10.6084/m9.figshare.25754655.v1. Turley, M., Hassan, M. A., Zimmermann, A., & Lian, O. (2024). Sediment source partitioning and budgeting over historical timescales in a glacierized, mountain catchment. Journal of Geophysical Research: Earth Surface, 129, e2024JF007819. https://doi.org/10.1029/ 2024JF007819. Vorren, T.O. (1977), Grain-size distribution and grain-size parameters of different till types on Hardangervidda, south Norway. Boreas, 6: 219-227. https://doi.org/10.1111/j.1502-3885.1977.tb00351.x. Walling, D. E. (1983). The Sediment Delivery Problem. Journal of Hydrology, 65, 209–237. Walling, D. E., & Collins, A. L. (2008). The catchment sediment budget as a management tool. Environmental Science and Policy, 11(2), 136–143. https://doi.org/10.1016/j.envsci.2007.10.004. Wallinga, J., Murray, A., & Wintle, A. (2000). Single-aliquot regenerative-dose (SAR) protocol applied to coarse-grain feldspar. Radiation Measurements, 32(5), 529–533. https://doi.org/10.1016/S1350-4487(00)00091-3. Walsh, L. S., Martin, A. J., Ojha, T. P., & Fedenczuk, T. (2012). Correlations of fluvial knickzones with landslide dams, lithologic contacts, and faults in the southwestern Annapurna Range, central Nepalese Himalaya. Journal of Geophysical Research: Earth Surface, 117(1), 1–24. https://doi.org/10.1029/2011JF001984. Weatherly, H., & Jakob, M. (2014). Geomorphic response of Lillooet River, British Columbia, to meander cutoffs and base level lowering. Geomorphology, 217, 48–60. https://doi.org/10.1016/j.geomorph.2014.04.002. Wheaton, J. M., Fryirs, K. A., Brierley, G. J., Bangen, S. G., Bouwes, N., & O’Brien, G. (2015). Geomorphic mapping and taxonomy of fluvial landforms. Geomorphology, 248(November 2017), 273–295. https://doi.org/10.1016/j.geomorph.2015.07.010. Whiting, P. J., & Bradley, J. B. (1993). A Process-Based Classification System for Headwater Streams. Earth Surface Processes and Landforms, 18, 603–612. Wintle, A. G. (2008). Luminescence dating: Where it has been and where it is going. Boreas, 37(4), 471–482. https://doi.org/10.1111/j.1502-3885.2008.00059.x. Wohl, E. E., Brierley, G. J., Cadol, D., Coulthard, T. J., Covino, T., Fryirs, K. A., Grant, G. E., Hilton, R. G., Lane, S. N., Magilligan, F. J., Meitzen, K. M., Passalacqua, P., Poeppl, R. E., Rathburn, S. L., & Sklar, L. S. (2018a). Connectivity as an emergent property of 137 geomorphic systems. Earth Surface Processes and Landforms. https://doi.org/10.1002/esp.4434. Wohl, E. E., Fryirs, K. A., Grabowski, R. C., Morrison, R. R., & Sear, D. (2024). Enhancing the natural absorbing capacity of rivers to restore their resilience. BioScience, 0(0), 1–15. Wohl, E.E., Greenbaum, N., Schick, A.P. and Baker, V.R. (1994). Controls on bedrock channel incision along Nahal Paran, Israel. Earth Surf. Process. Landforms, 19: 1-13. https://doi.org/10.1002/esp.3290190102. Wohl, E. E., Lininger, K. B., & Scott, D. N. (2018b). River beads as a conceptual framework for building carbon storage and resilience to extreme climate events into river management. Biogeochemistry, 141(3), 365–383. https://doi.org/10.1007/s10533-017-0397-7. Woor, S., Thomas, D. S. G., Durcan, J. A., Burrough, S. L., & Parton, A. (2023). The aggradation of alluvial fans in response to monsoon variability over the last 400 ka in the Hajar Mountains, south-east Arabia. Quaternary Science Reviews, 322(October), 108384. https://doi.org/10.1016/j.quascirev.2023.108384. Zhang, T., Li, D., East, A. E., Walling, D. E., Lane, S., Overeem, I., Beylich, A. A., Koppes, M., & Lu, X. (2022). Warming-driven erosion and sediment transport in cold regions. Nature Reviews Earth and Environment, 3(12), 832–851. https://doi.org/10.1038/s43017-022-00362-0. Zubrycky, S., Mitchell, A., McDougall, S., Strouth, A., Clague, J. J., & Menounos, B. (2021). Exploring new methods to analyse spatial impact distributions on debris‐flow fans using data from south‐western British Columbia. Earth Surface Processes and Landforms, 46(12), 2395–2413. https://doi.org/10.1002/esp.5184. 138 Appendices Appendix A A.1 Fitzsimmons Creek Sediment Budgeting Lidar co-registration The 2014 and 2018 lidar datasets were received as full-feature point cloud datasets and processed in ArcGIS Pro to create 1-meter grid bare-earth DEMs using only the ground-return points. The remainder of the analyses were performed using R statistical software (v4.2.1, R Core Team 2021). To perform the coreferencing, we performed the steps below, also outlined in Figure A. 1. Initial Steps 1. Ensure all DEMs are projected to the same horizontal (NAD 1983 UTM Zone 10N) and vertical (CGVD 1928) datums. 2. Snap and resample the 2014 dataset to the 2018 grid. Since we only needed relative values, and the 2018 dataset was more rigorously referenced with ground control points, it was chosen as the baseline. 3. Create a preliminary DoD by subtracting the 2018 DEM from the 2014 DEM. This ensures that deposition is represented by positive values. 4. Identify geomorphically stable locations covered by both datasets. In this case, multiple locations were selected (including HWY 99, and several low-gradient hillslopes) that were spatially distributed within the dataset for a total of ~1.5 km2. Horizontal Datum Correction 1. Perform a terrain-matching technique by plotting apparent change as a function of slope aspect (Anderson & Pitlick, 2014). Systematic errors will result in a sinusoidal graph, with peaks and troughs indicating the direction and magnitude of the offset (Figure A. 2). 2. Adjust for horizontal offsets based on the previous step. In this case, we identified offsets on the order of ~0.28m. 139 Vertical Datum Correction 1. Create a histogram of DoD values within the stable areas. 2. Use the mean apparent change from the histogram to estimate offset direction and magnitude. In this case, we identified a vertical offset of ~0.11m. Figure A. 1: Conceptualization of lidar misalignment and coreferencing methods. 140 Figure A. 2: Graph of apparent change by aspect based on point measurements from the assumed stable areas. Figure A. 3: Histograms of apparent change before coreferencing (a,b) and after (c,d). 141 Lidar Uncertainty The random error and systematic error uncertainty was estimated for the DoD measurements following methods outlined by Anderson & Pitlick (2014) and Turley & Hassan (2023). These methods are summarized in the following steps. 1. Resample the corrected DoD using the same areas identified as stable from the coreferencing steps. 2. The random error uncertainty (Ure) was estimated by calculating the standard deviation of unresolved errors (σre) between the 2014 and 2018 stable areas. In our case, σre was 0.14 m, which was conservatively increased to 0.3 m. Random error uncertainty was estimated as, 𝑈𝑟𝑒 = 𝜎𝑟𝑒√𝐴 where A is the area of interest in m2. 3. The systematic uncertainty (Usys) was estimated by calculating the unresolved mean elevation differences (∆𝑒𝑙𝑒𝑣̅̅ ̅̅ ̅̅ ̅̅ ) between the 2014 and 2018 stable surface. In our case, ∆𝑒𝑙𝑒𝑣̅̅ ̅̅ ̅̅ ̅̅ was approximately 0.05 m. The systematic uncertainty was then estimated using the equation, 𝑈𝑠𝑦𝑠 = ∆𝑒𝑙𝑒𝑣̅̅ ̅̅ ̅̅ ̅̅ ∗ 𝐴. Lidar limit of detection (LoD) Even after elevation datasets are carefully coreferenced, there will still be some errors and random noise within the resulting DoD. These errors may be the result of alignment, data precision, and/or resolution. To account for this, we calculated the limit of detectable change, or LoD, with a statistical confidence of 95% (Figure A. 4). This method is summarized in the following steps. 1. Resample the corrected DoD using the same areas identified as stable from the coreferencing steps. In our case, more than 1.5 million points were sampled, representing an area of ~1.5 km2. 2. Plot the cumulative percentage of apparent change and calculate the 2.5 and 97.5 percentiles. These represent the lower and upper detection limits, which in our case, were -0.355 and 0.290 m, respectively. 142 Table A. 1: Key information for the 2014 and 2018 lidar datasets covering Fitzsimmons Creek. Date Collected Operator All-Return Point Density (pt/m2) Ground-Return Point Density (pt/m2) Coverage Data Source Summer 2014 GeoSYS 9.22 1.97 VKM 3-Outlet Not publicly available Fall 2018 GeoSYS 28.82 7.94 VKM 3- Outlet Not publicly available Figure A. 4: Limit of detection of the corrected 2014-2018 DoD. The LoD is defined as the 2.5% and 97.5% cumulative alignment differences. 143 Figure A. 5: Fitzsimmons Creek delta progradation delineated on select aerial photographs from 1947-2023. See the dataset by Turley et al. (2024) for additional imagery. 144 Table A. 2: Fitzsimmons Creek annual bed material sediment budget summarized by process and zone. All measurements represent volumes of sediment with units x103 m3 yr-1 calculated for the 2014-2018 period. Zone (VKM) Process Source Input Volume Input Uncertainty Output Volume Output Uncertainty Cumulative Net Change Cumulative Net Uncertainty 0-2.5 Fluvial Area-Based Estimate NA NA NA NA -1.00 0.50 2.5-6 Fluvial Area-Based Estimate + lidar -2.40 0.38 0.92 0.29 -2.49 1.14 6-10.5 Fluvial Lidar -10.90 1.68 3.44 0.92 -9.95 3.99 10.5-13.5 Fluvial Lidar -6.08 0.97 4.38 0.58 -11.66 5.37 Colluvial Lidar -7.98 0.62 2.44 0.16 -17.19 6.29 13.5-17 Fluvial Lidar -5.10 0.64 1.10 0.27 -21.19 7.08 Anthropogenic Reports NA NA 9.73 1.95 -11.46 9.03 17-17.5 Fluvial (vertical) Lidar -0.16 0.01 0.71 0.18 -10.91 9.38 17.5+ Fluvial (progradation) Historical Air Photos NA NA 3.06 0.61 -7.85 9.99 Green Lake Proximal Basin Fluvial/Colluvial Bathymetry NA NA 18.58 3.72 NA NA 145 A.2 Fitzsimmons Creek Hydrometric Analysis Hydrometric data were accessed and analyzed for two Water Survey of Canada stations (Fitzsimmons Creek, 08MG026; Lillooet River, 08MG005) using R statistical software (V4.2.1; R Core Team, 2021) and the tidyhydat package (Albers, 2017). During the overlapping period (1994-2023), the discharge records display a near linear relationship and are strongly correlated as indicated by the Spearman Rank order statistic (Figure A. 6). Figure A. 6: Scatterplot comparison of the maximum daily discharge record for Lillooet River and Fitzsimmons Creek (1994-2023). 146 Appendix B B.1 Green River subwatersheds geomorphic mapping results This section includes geomorphic mapping results for the Fitzsimmons and Rutherford Creek watersheds (primarily associated with Chapter 3). 147 Figure B. 1: Geomorphic map of the Fitzsimmons Creek watershed. Displayed here at approximately 1:75,000. 148 Figure B. 2: Geomorphic map of the Rutherford Creek watershed. Displayed here at approximately 1:115,000. 149 B.2 Green River subwatersheds: longitudinal characteristics This section includes plots of summary longitudinal characteristics for the Fitzsimmons and Rutherford Creek watersheds (Chapter 3). Figure B. 3: Summary longitudinal characteristics of Fitzsimmons Creek. Characteristics include (a) channel and valley slope, (b) channel sinuosity, (c) confinement/constriction proportion, (d) hillslope-channel coupling, and (e) effective catchment area. 150 Figure B. 4: Summary longitudinal characteristics of Rutherford Creek. Characteristics include (a) channel and valley slope, (b) channel sinuosity, (c) confinement/constriction proportion, (d) hillslope-channel coupling, and (e) effective catchment area. 151 Appendix C C.1 Lillooet River watershed catchment characteristics This section includes a table of catchment characteristics for the Lillooet River watershed and its tributaries (primarily associated with Chapter 4).152 Table C. 1: Catchment characteristics for Lillooet River watershed and its major tributaries. Watershed Area (km2) Elevation (m) Hypsometric Integral Modern Glacier Cover River Length (km) Min Max Mean Relief (%) Lillooet River Green River Soo R. 290 343 2560 1404 2217 0.478 13.9 47.83 Rutherford Cr. 182 331 2569 1506 2238 0.525 11.4 28.60 Fitzsimmons Cr. 94 635 2684 1613 2049 0.477 7.0 19.69 Total 875 197 2880 1416 2683 0.454 9.2 42.86 Birkenhead River Taillefer Cr. Sockeye Cr. 99 632 2498 1783 1866 0.616 1.5 19.24 Phelix Cr. 81 632 2502 1577 1870 0.505 1.2 4.85 Total 227 625 2502 1587 1877 0.512 0.0 1.87 Total 672 199 2586 1437 2387 0.519 0.8 56.06 Ryan River Petersen Cr. 70 702 2501 1689 1799 0.548 15.2 11.54 Total 416 220 2586 1555 2366 0.564 17.0 53.34 Meager Cr. Fish Cr. 90 646 2742 1683 2096 0.495 16.6 8.95 Total 279 399 2812 1603 2413 0.499 14.3 26.64 Boulder Cr. 132 426 2797 1845 2371 0.598 22.9 19.25 South Cr. 122 270 2752 1723 2482 0.585 25.6 12.10 Salal Cr. 101 668 2695 1838 2027 0.577 17.3 19.29 Manatee Cr. 98 741 2842 1850 2101 0.528 29.3 12.50 North Cr. 80 288 2783 1748 2495 0.585 10.8 12.66 Miller Cr. 76 220 2576 1586 2356 0.58 20.7 16.63 Total 3765 193 2992 1485 2799 0.461 12.2 106.95 153 C.2 Geochronology & Sediment Yield Data This section includes stratigraphic sections and optical dating sampling locations, optical dating results, and summary sediment yield data for the Lillooet River watershed. Stratigraphic Sections and Sampling Locations Figure C. 1: Fitzsimmons Creek terrace stratigraphic section and optical dating sampling location. 154 Figure C. 2: Soo River terrace stratigraphic section and optically dating sampling location. 155 Figure C. 3: Owl Creek fan stratigraphic section and radiocarbon sampling location.156 Figure C. 4: South Creek fan stratigraphic section and optical dating sampling location. 157 Figure C. 5: Lillooet River floodplain stratigraphic section and optical dating sampling location. Black arrow is pointing to ripple marks. 158 Figure C. 6: Silva Creek fan stratigraphic section and optical dating sampling location. 159 Figure C. 7: Perkins Creek fan stratigraphic section and optical dating sampling location. 160 Luminescence Dating: Data and Results Table C. 2 Luminescence dating sampling notes. Sample Landform Name Latitude (˚N) Longitude (˚W) Elevation (masl) Burial Depth (cm) Unit Thickness (cm) Depth in Unit (cm) Description MT-lil-02 Rutherford Fan 50.26902 -122.87365 396 ~1,800 45 22 Fine upper to medium laminated sands MT-lil-05 Silva Cr. Fan 50.59826 -123.36287 376 136 39 15 Fine upper to medium cross bedded sands coarsening downward MT-lil-08 Soo Terrace 50.25255 -122.86249 363 ~500 40 20 Laminated sands MT-lil-10 Perkins Cr. Fan 50.59472 -123.36698 381 250 40 20 Moderately sorted, ripple laminated and cross bedded sands (fine upper to coarse lower) MT-lil-13 South Cr. Fan 50.54827 -123.19306 296 244 120 85 Medium lower to fine upper cross bedded sands, occasional charcoal fragment MT-lil-15 Fitzsimmons Terrace 50.08781 -122.92894 964 80 35 18 Laminated sands with minor bioturbation 161 Table C. 3 Primary data required for optical dose rate calculations (see also Table C. 1). Table C. 4 Dose recovery test results. Sample ID Radionuclide Contents Dose Rate MT-lil-## U (ppm) Th (ppm) K (%) Rb (ppm) Water Content (%) Cosmic (mGy/yr) Feldspar (mGy/yr) Total (mGy/yr) MT-lil-02 0.72 ± 0.09 1.94 ± 0.06 0.91 ± 0.05 26.0 ± 1.1 7.599 ± 0.760 0.183 ± 0.018 1.470 ± 0.061 1.653 ± 0.079 MT-lil-05 1.16 ± 0.07 2.43 ± 0.06 0.89 ± 0.06 22.3 ± 1.2 5.560 ± 0.556 0.192 ± 0.019 1.579 ± 0.063 1.771 ± 0.082 MT-lil-08 0.75 ± 0.10 1.70 ± 0.06 0.69 ± 0.05 15.6 ± 1.0 1.730 ± 0.173 0.230 ± 0.023 1.293 ± 0.059 1.523 ± 0.082 MT-lil-10 1.61 ± 0.08 3.95 ± 0.10 1.18 ± 0.06 27.5 ± 1.3 1.801 ± 0.180 0.170 ± 0.017 2.128 ± 0.069 2.298 ± 0.086 MT-lil-13 3.39 ± 0.11 8.42 ± 0.21 1.69 ± 0.09 49.8 ± 1.6 18.511 ± 1.851 0.168 ± 0.017 2.989 ± 0.092 3.157 ± 0.109 MT-lil-15 1.00 ± 0.08 2.16 ± 0.06 0.78 ± 0.07 25.4 ± 1.4 15.112 ± 1.511 0.229 ± 0.023 1.381 ± 0.069 1.610 ± 0.092 Sample ID Aliquots Used Given Dose Overdispersion Dose Recovery Ratio MT-lil-## Total(used) Gy % Given Dose/Recovered Dose MT-lil-02 10(10) 33.2 0 1.02 ± 0.01 MT-lil-05 9(10) 5.5 0 1.12 ± 0.02 MT-lil-08 9(10) 62.7 0 1.02 ± 0.02 MT-lil-10 10(10) 14.8 4 ± 2 1.05 ± 0.02 MT-lil-13 10(10) 10.9 9 ± 2 1.04 ± 0.03 MT-lil-15 10(10) 14.6 0 ± 14 1.04 ± 0.01 162 Figure C. 8: Optical dating summary for sample MT-lil-08. The panels include, (a) shinedown and dose response curve, (b) abanico plot of De values, and (c) fading plot. Panels (a) and (c) are based on representative aliquots, while panel (b) is based on all accepted aliquots. Figure C. 9: Optical dating summary for sample MT-lil-10. The panels include, (a) shinedown and dose response curve, (b) abanico plot of De values, and (c) fading plot. Panels (a) and (c) are based on representative aliquots, while panel (b) is based on all accepted aliquots. 163 Figure C. 10: Optical dating summary for sample MT-lil-13. The panels include, (a) shinedown and dose response curve, (b) abanico plot of De values, and (c) fading plot. Panels (a) and (c) are based on representative aliquots, while panel (b) is based on all accepted aliquots. Figure C. 11: Optical dating summary for sample MT-lil-15. The panels include, (a) shinedown and dose response curve, (b) abanico plot of De values, and (c) fading plot. Panels (a) and (c) are based on representative aliquots, while panel (b) is based on all accepted aliquots. 164 Figure C. 12: Sample IRSL fading rate curves. 165 Lillooet River watershed sediment yield data Table C. 5: Sediment yield data from the Lillooet River watershed. Watershed SY Component Method Date Range Reference Notes Ash Lake Suspended Lake cores 2360 BP – 1989 AD Owens & Slaymaker (1992) Owens (1990) • SY corrected for outflow losses Aeolian Bulk collector 1981 AD Jones (1982) • Based on plot-scale measurements Gallie Pond Suspended Lake cores 10510 BP – 1989 AD Souch (1989) Owens & Slaymaker (1992) • Adjustment to long-term rate based on more recent Mazama tephra age control (Egan et al., 2015) Aeolian Bulk collector 1981 AD Jones (1982) • Based on plot-scale measurements Total Mixed 1981 AD Jones (1982) • Plot-scale measurements using Gerlach troughs, splash troughs, bulk collectors, tracer particles, standpipes, etc. Middle Lake Suspended Lake cores 2360 BP – 1989 AD Owens & Slaymaker (1992) • SY corrected for outflow losses Aeolian Bulk collector 1981 AD Jones (1982) • Based on plot-scale measurements Central Cr. Suspended Fluvial Unknown Souch & Slaymaker (1986) • Original source unavailable Miller Cr. Suspended Fluvial Unknown Souch & Slaymaker (1986) • Original source unavailable MMVC Colluvial Landslide Frequency-Magnitude Holocene Friele et al. (2008) Friele & Clague (2009) • Based on non-eruptive triggered landslides • First order estimate Fitzsimmons Cr. Dissolved Fluvial specific conductance 1930-2006 AD Schiefer et al. (2010) • Assumed to vary primarily based on discharge (conductance varied little) Bed Material Bathymetric surveys and air photos 1947-2024 AD Pelpola & Hickin (2004) Schiefer et al. (2010) Turley et al. (2024) • Approximately decadal estimates Silt Lake Bed Material Delta progradation & bathymetry 2005-2024 AD Schiefer & Gilbert (2008) This study • Minimum estimate only • Lake trapping efficiency notes as 50-70% 166 • Estimate does not include lake sedimentation past the delta front Green Lake Suspended Lake cores 3000 BP - 2000 AD; 1930-2006 AD Menounos (2002) Menounos et al. (2006) Schiefer et al. (2010) • Long record is based only on 4 sediment cores • Increased uncertainty before 1700 cal yr BP Birkenhead Lake Suspended Lake cores 1937-1997 AD Menounos (2002) • Averaged sedimentation from recovered core Lillooet Lake Suspended Lake cores 1179-2004 AD Slaymaker & Gilbert (1972) Gilbert (1973) Desloges & Gilbert (1994) Heideman et al. (2018) • The record from 1179-1628 AD is only based on 2 cores which were not representative Bedload Delta progradation & bathymetry 1864-1988 AD Desloges & Gilbert (1994) • Bulk density is unknown 167 C.3 Connectivity Results This section includes results from the application of the Index of Connectivity for the entire Lillooet River watershed. Figure C. 13: Connectivity map of the Lillooet River watershed based on the index of connectivity method with major tributaries as targets (Borselli et al., 2008).
Thesis/Dissertation
eng
Geography
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Hassan, Marwan A.
Late Quaternary controls on sediment dynamics and yield in paraglacial landscapes : a spatially and temporally nested approach
Text