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Critical zone response to perturbation: from mountain building to wildfire

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Critical zone response to perturbation: from mountain building to wildfire

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Content CRITICAL ZONE RESPONSE TO PERTURBATION: FROM MOUNTAIN BUILDING TO WILDFIRE by Abra Catherine Atwood A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (GEOLOGICAL SCIENCES) August 2023 Copyright 2023 Abra Catherine Atwood ii Dedication For my father, the first geologist in my life iii Acknowledgments My advisor, Dr. Josh West: Working with you has been a privilege. You have allowed me to explore a breadth of scientific topics, allowing me to move from geomorphologist to hydrologist over the course of six years and that is reflected in this dissertation. I appreciate the depth of feedback and discussions we have engaged in, your encouragement and clear-headedness at moments of crisis and your work to make me a stronger scientist and writer. My committee, Dr. Marin Clark, Dr. Doug Hammond, Dr. Bill Deverell: Thank you for pushing my work to a higher level and your guidance throughout my dissertation. My many Nepal collaborators: This work has been a massive collaboration in the field and the lab. Thank you for allowing me to engage in these complex scientific endeavors and pushing me to try new techniques and think deeper about my science. My San Gabriel Mountains collaborators: This work was some of the most rewarding of my short scientific career, in large part to the diverse array of expertise brought by you all. Thanks especially to Maddie Hille for being the best co-first author I could ask for as well as an excellent field partner. The residents of the Melamchi Valley: Thank you for allowing me to spend so much time in your incredible landscape and for trying to help guide me to groundwater springs with my limited Nepali. My family and friends: Thank you for helping- voluntarily or not- with various field work and presentations, who spent hours talking me about my work and hours distracting me from my work. This is especially true of my siblings, Bruce, Caroline and Caleb and my mom, Lorraine. Finally, my husband, Tom Callahan: Words cannot express the gratitude I have for you and your support in all aspects of our lives throughout the past six years. Thank you for being my editor and your willingness to discuss my research– to have a partner so willing to engage in my work is a true dream. iv Table Of Contents DEDICATION .................................................................................................................................................... II ACKNOWLEDGMENTS .................................................................................................................................. III LIST OF TABLES .............................................................................................................................................. VI LIST OF FIGURES ........................................................................................................................................... VII ABSTRACT ...................................................................................................................................................... VIII CHAPTER 1: INTRODUCTION ........................................................................................................................ 1 1.1 CRITICAL ZONE SCIENCE ................................................................................................................................................ 1 1.2 SCOPE AND ORGANIZATION .......................................................................................................................................... 4 1.3 REFERENCES ..................................................................................................................................................................... 8 CHAPTER 2: EVALUATION OF HIGH-RESOLUTION DEMS FROM SATELLITE IMAGERY FOR GEOMORPHIC APPLICATIONS: A CASE STUDY USING THE SETSM ALGORITHM ......................... 13 2.1 ABSTRACT ........................................................................................................................................................................ 13 2.2 INTRODUCTION .............................................................................................................................................................. 14 2.3 METHODS ....................................................................................................................................................................... 18 2.4 RESULTS ........................................................................................................................................................................... 26 2.5 DISCUSSION ..................................................................................................................................................................... 35 2.6 CONCLUSIONS ................................................................................................................................................................. 38 2.7 REFERENCES ................................................................................................................................................................... 40 CHAPTER 3: SHIFTS IN THE LANDSCAPE: DOCUMENTING THE TRANSITION FROM KINETICALLY LIMITED TO KINETICALLY CONTROLLED WEATHERING IN THE HIGH HIMALAYA, NEPAL .......................................................................................................................................... 48 3.1 INTRODUCTION .............................................................................................................................................................. 48 3.2 SITE DESCRIPTION ......................................................................................................................................................... 51 3.3 METHODS ........................................................................................................................................................................ 53 3.4 RESULTS ........................................................................................................................................................................... 55 3.5 DISCUSSION ..................................................................................................................................................................... 59 3.6 REFERENCES ................................................................................................................................................................... 69 CHAPTER 4: MOUNTAIN BUILDING AND THE DEEP CRITICAL ZONE: FRACTURING AND MINERAL WEATHERING IN A BOREHOLE FROM THE HIGH HIMALAYA OF CENTRAL NEPAL ................................................................................................................................................................ 73 4.1 INTRODUCTION .............................................................................................................................................................. 74 4.2 SITE DESCRIPTION ......................................................................................................................................................... 76 4.3 METHODS ........................................................................................................................................................................ 81 4.4 RESULTS ........................................................................................................................................................................... 87 4.5 DISCUSSION .................................................................................................................................................................. 102 4.6 CONCLUSION ................................................................................................................................................................ 108 4.7 REFERENCES ................................................................................................................................................................ 109 CHAPTER 5: EFFECTS OF CRITICAL ZONE AND TOPOGRAPHIC CHANGES ON MOUNTAIN GROUNDWATER RESIDENCE TIMES ........................................................................................................114 5.1 INTRODUCTION ........................................................................................................................................................... 114 5.2 METHODS ..................................................................................................................................................................... 120 5.3 RESULTS ........................................................................................................................................................................ 124 5.4 DISCUSSION ................................................................................................................................................................. 131 5.5 CONCLUSION ................................................................................................................................................................ 135 5.6 REFERENCES ................................................................................................................................................................ 136 v CHAPTER 6: IMPORTANT ROLE OF SUBSURFACE MOISTURE IN HYDROLOGICAL RESPONSE TO STORMS AFTER WILDFIRE IN SOUTHERN CALIFORNIA, USA ....................................................141 6.1 INTRODUCTION ........................................................................................................................................................... 141 6.2 RESULTS ........................................................................................................................................................................ 146 6.3 DISCUSSION .................................................................................................................................................................. 152 6.4 MATERIALS AND METHODS ...................................................................................................................................... 157 6.5 REFERENCES ................................................................................................................................................................ 162 APPENDIX A: SUPPLEMENTAL MATERIAL OF CHAPTER 1 ................................................................ 169 APPENDIX B: SUPPLEMENTAL MATERIAL FOR CHAPTER 6 ............................................................. 174 vi LIST OF TABLES Table 2.1. Details of image pairs used in this study.. ..................................................................................................................... 18 Table 2.2. LiDAR datasets from OpenTopography ..................................................................................................................... 20 Table 2.3. Elevation and slope metrics and error analysis relative to benchmark LiDAR DEMs ........................................ 27 Table 2.4. Xinmo landslide statistics from this and previous studies ......................................................................................... 35 Table 3.1 Details and XRF results from soil samples. CIA = chemical index of alteration. .................................................. 65 Table 3.2 XRF results from four detailed weathering profiles. ................................................................................................... 67 Table 3.3 Subcatchment properties. Latitude and longitude refer to location of catchment outlet. ..................................... 68 Table 4.1 Weathering degree classification during core logging. ................................................................................................. 82 Table 4.2 Petrographic thin section analysis of borehole samples. ............................................................................................. 92 Table 4.3 Geochemical results from XRF. Elemental contents reported in weight percent. ................................................. 96 Table 4.4 Microfracture mapping data from thin section subsections from the FracPaq package .................................... 101 Table 5.1 Parameters from groundwater springs, SF6 and CFC .............................................................................................. 125 Table 5.2 Measured noble gas concentrations and calculated excess air (EA) from noble gas samples. ........................... 126 Table 5.3 Groundwater residence times derived from multiple mixing models ................................................................. 128 Table 5.4 Best mixing model and mean age for each sample in 2018 and 2019 .................................................................... 130 vii LIST OF FIGURES Figure 2.1. Satellite images from Google Earth of the locations used in this study:. ............................................................... 22 Figure 2.2. SETSM derived 2m DEMs overlying hillshades used in this study.. ..................................................................... 23 Figure 2.3. Kernel density estimate (KDE) plots of elevation distributions and elevation differences with RMSE .......... 26 Figure 2.4. Hillshade maps of a small section of the Coyote Mountains, along the base of the mountains. ....................... 28 Figure 2.5. Scatterplots comparing relationship between elevation RMSE (left) and slope RMSE (right) .......................... 29 Figure 2.6. Subsection of WorldView-1 imagery used to produce Wainiha Valley SETSM DEM. ...................................... 30 Figure 2.7. KDE plots of slope distributions, slope differences, and quantile-quantile plots of slope distribution. .......... 31 Figure 2.8. Slope maps from the Carrizo Plains region ................................................................................................................ 32 Figure 2.9. Hillshades derived from LiDAR of each location. .................................................................................................... 33 Figure 2.10 Volume change between pre and post landslide DEMs .......................................................................................... 34 Figure 3.1. Hillshade from SETSM 2m DEM of the Melamchi Valley with sample locations. ............................................. 50 Figure 3.2 Temperature, precipitation and (U-Th)/He low temperature thermochronology cooling ages ......................... 51 Figure 3.3 Chemical Index of alteration (CIA) values for weathering profiles ......................................................................... 56 Figure 3.4 Curvature and slope from 100m spaced transects along ridgeline ......................................................................... 57 Figure 3.5 Curvature profiles along the Melamchi Valley eastern ridgeline .............................................................................. 58 Figure 3.6 Hypotheses of the effects of climate and tectonics on chemical weathering intensity and regolith .................. 60 Figure 3.7 Comparison of subcatchment properties representative of different landscape domains ................................... 61 Figure 3.8 Synthesis figure of ridgeline transitions. ....................................................................................................................... 63 Figure 4.1 Satellite optical imagery, shaded relief map showing elevation and geologic map ................................................ 77 Figure 4.2 Photos ofthe drill site and drill apparatus at the Tallathok site and the core ......................................................... 80 Figure 4.3 Illustrations from Delvigne (2005) of weathering textures across degrees feldspar, garnet, and biotite ........... 83 Figure 4.4 Borehole log of strength, porosity, and weathering degree ....................................................................................... 88 Figure 4.5 Shear wave profiles at the Melamchi ridge-top drill site and 3 km north along the same ridge ......................... 90 Figure 4.6 Thin section images in plane-polarized light of changes in weathering textures from schist to gneiss ............. 91 Figure 4.7 Individual mineral content (biotite, quartz, feldspar and alteration mineral) with depth ..................................... 94 Figure 4.8 Geochemical results from XRF analysis. .................................................................................................................... 97 Figure 4.9 Examples of blue epoxied thin section image subsections and corresponding fracture map ............................. 98 Figure 4.10 Rose diagrams of fracture orientation from three subsections of each sample. .................................................. 99 Figure 4.11 Boxplots of fracture connectivity, density, trace number and intensity with depth ......................................... 100 Figure 4.12 Chemical Index of Alteration (CIA) with depth and CIA vs Fe 2O 3 by rock type .......................................... 104 Figure 4.13 Schematic figure of changes in fracture and weathering with depth and changing lithology. ........................ 106 Figure 5.1 Satellite imagery denoting Melamchi Khola outline and groundwater spring location ...................................... 116 Figure 5.2 Examples of two groundwater springs sampling sites. ........................................................................................... 117 Figure 5.3 CFC and SF 6 atmospheric concentrations and 3 H precipitation concentrations since 1940.. .......................... 119 Figure 5.4 δ 18 O plotted against elevation for each spring site, colored by topographic position in the valley ............... 124 Figure 5.5 Cross plots of groundwater spring SF 6 and CFC-12 concentrations from 2018 and 2019 .............................. 129 Figure 5.6 Cross plots of SF 6 and CFC-12 derived ages using different lumped parameter models. ................................ 131 Figure 5.7 Histograms of mean modeled ages from 2018 and 2019. ...................................................................................... 132 Figure 5.8 Groundwater mean ages versus latitude for all samples ......................................................................................... 133 Figure 6.1 Map of the 2020 Bobcat Fire in the San Gabriel Mountains ................................................................................. 145 Figure 6.2 Times series of 15-minute binned precipitation and 𝛿 18 O from precipitation and streamfloq ........................ 147 Figure 6.3 Conceptual diagrams of the water budget ................................................................................................................ 148 Figure 6.4 Timelapse electrical resistivity images at unburned and burned catchments. ..................................................... 149 Figure 6.5 Boxplots of 𝛿 18 O measurements from rainfall and stream during storm events.. ............................................. 152 viii Abstract The critical zone, the region from the trees to the base of the groundwater, is where key life supporting processes such as soil development and groundwater storage occur. Such processes can be fundamentally altered by perturbations both on short and long times scales and understanding critical zone response to these disturbances is important for climate modeling, understanding natural hazards, and resource availability. In this dissertation, I investigate how mountain building events, specifically the uplift of the Nepal Himalaya, affect topography and subsequently soil development, deep weathering and critical zone architecture, and ground water residence times in the Melamchi Valley. I then look at how a more modern perturbation, wildfire, changes subsurface water storage and its connection to stormflow in the Mediterranean climate of the San Gabriel Mountains in Southern California. Mountain building imposes extreme change on the landscape, bringing fresh rock rapidly up to the surface to weather and erode, developing steep channels and hillslopes, and creating its own orographic precipitation patterns. I first demonstrate the utility of using high-resolution stereophotogrammetric digital elevation models (DEMs) for geomorphic analysis in environments such as large-scale mountain terrain that normally lack such high-resolution data. I find that 2m DEMs derived from the SETSM algorithm are significantly more accurate than other widely available products such as 30m SRTM DEMs and allow for detailed landscape analysis generally limited to regions with Light Detection and Ranging (LiDAR). I then utilize a SETSM 2m DEM to analyze topographic change along an exhumation gradient where I determined zones of increased channel and hillslope steepness that correspond with a chemical weathering shift, indicative of a transient environment where chemical weathering is controlled by extremely rapid erosion. To investigate weathering at depth, I use an 80m borehole that reveals multiple weathering profiles ix through schist and augen gneiss. Using petrography, geochemistry and microfracture mapping, I find that in this highly fractured region, there is a lithologic control on regolith development, and widespread fracturing plays a secondary role. Finally, I look at how groundwater residence times change across these landscape and weathering gradients, using environmental tracers. CFC-12 SF 6, and 3 H indicate residence times from <1-35 years in groundwater springs. In springs situated along the valley bottom and on hillslopes, residence times decrease with increasing topographic steepness, while springs situated along the ridgeline do not change residence times, indicating the dominance in topography on transit times in this zone. Wildfire affects the critical zone on much shorter timescales (1s to 10s of years), but has a profound impact during that timescale, with the loss of vegetation leading to major shifts hydrologically and geomorphically. Here, I utilize a paired-catchment (burned and unburned) approach to understand how subsurface water storage changed and connected to stormflow in ephemeral catchments after the 2020 Bobcat Fire in Southern California’s San Gabriel Mountains. Timelapse electrical resistivity imaging revealed a larger reservoir of subsurface moisture in burned catchments that persisted between water years due to reduced evapotranspiration. Paired precipitation and streamflow water isotopes indicate that this subsurface reservoir contributes to stormflow in equal proportion in both catchments, despite significant differences in discharge. This work creates a more nuanced conceptual model for elevated discharge postfire, revealing a greater role for subsurface water in stormflow than previously considered. 1 Chapter 1 “Water, stories, the body, all the things we do, are mediums that hide and show what’s hidden. Study them, and enjoy being washed with a secret we sometimes know, and then not.” -Rumi, thirteenth century Introduction 1.1 Critical Zone science Rumi, a thirteenth century poet, provides an apt description for how water both conceals and reveals information about landscapes, a gift and a frustration that is familiar to any earth scientist. As rain falls, it can disappear, infiltrating into the subsurface, breaking down rock into soil and regolith through chemical and physical weathering processes. Or it can flow overland, revealing new features of the landscape and leading to floods, mudslides, and debris flows. These processes erode and shape mountains, carve valleys, provide nutrient rich soils for crops, cause natural hazards, and determine water availability. We refer to the zone where this happens as the critical zone (CZ) of Earth’s surface, the life- bearing region from the tree tops to the bottom of the groundwater table that is often referred to as the skin of the earth (Brantley et al., 2006; Anderson et al., 2007; Riebe et al., 2017). The use of CZ as a research term came into prominence in the early 2000s, but it stems from the fields of geomorphology, hydrology, and ecology. Past CZ research has focused on understanding over what timescales rock breaks down to regolith and soil, and the factors that control this weathering and erosion (Heimsath et al., 1997; Riebe et al., 2003; Dixon et al., 2009), how groundwater flow determines deep critical zone architecture (Maher, 2011; Rempe and Dietrich, 2014; Anderson et al., 2 2019), and how vegetation affects these processes (Dietrich and Perron, 2006; Roering et al., 2010; Adams et al., 2014; Brantley et al., 2017). The inherently interdisciplinary approach of CZ science has utilized a wide swath of techniques across earth science disciplines to understand this system, including near surface geophysics (St Clair et al., 2015; Riebe et al., 2017; Rempe and Dietrich, 2018; Flinchum et al., 2018; Kotikian et al., 2019; Holbrook et al., 2019), water isotope analysis and hydrochemistry (Maher and Druhan, 2014; Ackerer et al., 2020; Tune et al., 2020), remote sensing (Mackey and Roering, 2011; Perron and Royden, 2013; Gabet et al., 2021), geochronology (Riebe et al., 2003, 2004; Dixon et al., 2009; Heimsath et al., 2012) and soil and rock geochemistry (Riebe et al., 2003; Anderson et al., 2007; Brantley et al., 2011, 2013; Buss et al., 2013). This thesis aims to further understand how the structure of the critical zone, and its interaction with fluids, determine its response to perturbation, which is critical for understanding soil, water, and biogeochemistry in a world of changing climate and land use. I focus on two major perturbations to the CZ that occur on different temporal scales: mountain building and wildfire. 1.1.1 Perturbation 1: Mountain Building Mountain building occurs on the million-year timescale via rock uplift in active orogeneses, bringing highly fractured, highly weatherable bedrock to the surface. The development of the critical zone is dependent upon 1) mineral and water residence times (White et al., 1998; Anderson et al., 2002; Riebe et al., 2004; Maher and Druhan, 2014), 2) the reactivity of the minerals (Fletcher et al., 2006; Bazilevskaya et al., 2013; Buss et al., 2017), and 3) the reaction kinetics and related climatic factors, namely temperature and precipitation (Lasaga et al., 1994; West et al., 2005; White, 2008; Adams et al., 2020). The unique features of mountain environments create countervailing forces. Steep slopes speed up erosion and fluid transport, which decreases the mineral and water residence times and leads to shallower weathering profiles and critical zone architecture. On the other hand, 3 orographic precipitation and fresh reactive minerals speed up weathering of bedrock into clays, slowing groundwater flow and creating deeper critical zone architecture. A recent explosion of high-resolution topography, specifically from LiDAR, has led to the ability to better characterize steep landscapes, focusing on parameters such as curvature, channel steepness and slope distributions (Dibiase et al., 2012; Perron and Royden, 2013; Mudd, 2017; Gabet et al., 2021). But given the challenges of collecting LiDAR data over large parts of the globe, especially mountain ranges, these data are not widely available. To make up for this shortcoming, algorithms to derive high-resolution DEMs via stereophotogrammetry of high-resolution satellite images have emerged, such as Ames Stereo Pipeline, MicMac and SETSM (Moratto et al., 2010; Rupnik et al., 2017; Noh and Howat, 2017). However, the ability of those algorithms to accurately characterize terrestrial geomorphic processes has been relatively unknown. This lack of information limits their application in locations such as the Himalaya or other remote mountain ranges that would benefit the most from high-resolution elevation data. Additionally, the challenges of accessing such remote locations, despite their outsized importance globally, has limited studies of mountain groundwater transit times and deep critical zone architecture (Rademacher et al., 2001; Calmels et al., 2011; Manning et al., 2012; Doyle et al., 2015; Frisbee et al., 2017; Carroll et al., 2020; Thiros et al., 2023). Mountains are considered the “water towers of the world,” providing a significant portion of water to downstream communities (Viviroli et al., 2007; Immerzeel et al., 2020). The ability of these water towers to buffer against a changing climate depends in part on how rapidly groundwater passes through their fractured rock aquifers. If transit times are long, groundwater will be less affected by rapid changes to precipitation patterns. But if transit times are short, groundwater supplies will be affected quickly by climate change. These transit times are influenced by both the topography and the CZ architecture that develops after a tectonic perturbation, emphasizing the interdisciplinary nature of such studies. 4 1.1.2 Perturbation 2: Wildfire By contrast, wildfires are a much more rapid change. Wildfires occur on the day-month timescale, instantly changing the CZ through the loss of vegetation, leading to more precipitation throughfall, decreased surface roughness, decreased slope stability from rooting, and subsequent increases in debris flows and flooding (Moody and Martin, 2001; Cannon and Gartner, 2005; Shakesby and Doerr, 2006; Onda et al., 2008; Moody and Ebel, 2014; Hoch et al., 2021). Wildfires increase in frequency and intensity as a function of a changing climate (Westerling et al., 2006; Flannigan et al., 2009), and researchers have been working to understand how their alteration of the CZ changes local hydrology (Bart, 2016; Hallema et al., 2017; Wine et al., 2018). A paradigm in the post-fire hydrologic response has arisen: rapid overland flow dominates during storms due to the development of hydrophobic layers and decreased surface roughness (Shakesby and Doerr, 2006; Onda et al., 2008; Moody and Martin, 2015; Hoch et al., 2021), but increased baseflow has also been observed, suggesting increased subsurface water content due to removal of vegetation and evapotranspiration (Jung et al., 2009; Bart and Tague, 2017; Giambastiani et al., 2018). Our understanding of how such opposing processes occurs has remained limited. 1.2 Scope and Organization This thesis reflects the multidisciplinary nature of CZ science, with Chapters 2 and 3 utilizing remote sensing to evaluate changing land surfaces, Chapters 5 and 6 tracking subsurface water movement through the CZ with groundwater transit time tracers, and Chapters 3 and 4 furthering our understanding of the chemical breakdown of rock via water and its effect on CZ architecture. In Chapter 2, I quantified the utility of high-resolution (2m) digital elevation models (DEMs) produced by the Surface Extraction from TIN Space-search Minimization (SETSM) algorithm to address key geomorphic problems, enable terrain analyses, and contribute to rapid response after major landslides. I compared elevation and its derivative products (slope and curvature) at a variety 5 of terrestrial environments to a benchmark LiDAR DEM as well as two globally available lower resolution DEMs to test the global applicability of satellite-derived high-resolution DEMs. I found that SETSM DEMs performed noticeably better across the range of vegetation and topographic gradients than the lower resolution DEMs but found systemic biases relative to the LiDAR DEMs in vegetated regions. Moreover, noise in the initial SETSM elevation data is amplified with every subsequent derivative, significantly decreasing quality. Finally, we evaluated the potential use of SETSM products for change detection. Applying DEM differencing to a major landslide, I found volume and sediment thickness from SETSM DEMs were similar to volumes and thicknesses from other studies. Overall, I found that DEMs derived from satellite image stereo-photogrammetry can markedly improve on lower resolution global elevation data for terrain analysis and can open possibilities for change detection, but that care needs to be taken in their application especially in regions with significant vegetation. In Chapter 3, I utilized a SETSM-derived high-resolution DEM, combined with soil geochemistry, to quantify changes in topography and soil weathering across an exhumation gradient in the Melamchi Khola, Nepal in the High Himalaya. The Melamchi Khola experienced pulses of uplift from 5 Ma in the south to 0.9 Ma in the north based on low-temperature thermochronology (Murray et al., 2018; Medwedeff, 2022). This gradient provides a natural experiment to understand how mountain building shapes critical zone structure and landscape change. I found that soil geochemistry exhibits a pronounced change that corresponds to increased channel (K sn) and hillslope steepness, where soils are much less weathered and regolith is shallower in steeper terrain indicating an erosional control on soil development. I also found the break in the relationships between K sn, hillslope steepness and ridgetop curvature, topographic indicators of “threshold”, rapidly eroding landscapes. These characteristics correspond to a step change from a “kinetically limited” regime to a “kinetically controlled” regime (Dixon et al., 2012). 6 In Chapter 4, I explored deep critical zone architecture and weathering at the southern end of the same Melamchi Khola using an 80m deep borehole. I utilized detailed geochemistry, borehole logging, near-surface geophysics, and petrography to quantify relationships between fracturing, lithology and regolith development. I uncovered a doublet weathering profile, where the overlying schist is characterized by a typical weathering profile from soil to regolith to bedrock from 0-7m followed by a return to regolith at a lithologic boundary with augen gneiss. In the gneiss, regolith persists to 24m, followed by heterogenous weathering and core recovery. Detailed petrologic analysis revealed heavy weathering in feldspars and differing fracture patterns in the schist and gneiss. These results highlight the dominance of lithology in CZ architecture development, specifically the importance of the highly weatherable feldspars. Fracture availability in such tectonically and topographically stressed environments plays a secondary role, leading to deep weathering in the gneiss, likely through rapid advective flow. This work highlights the complexity of CZ development in such active orogens, as compared to simpler environments with singular lithologic profiles. In Chapter 5, I analyzed how groundwater transit times are affected by the changes in topography and CZ architecture in the Melamchi Khola highlighted in Chapters 3 and 4. Prior to this work, no direct quantification of transit times in Nepal Himalayan groundwater springs existed, despite their importance to valley residents and downstream communities. We used environmental transit time tracers (SF 6, CFC-12 and 3 H/ 3 He tritium) in 23 springs located throughout the valley to quantify changes in water residence times. I used recharge elevations derived from water isotopes and lumped parameter models (LPMs) to determine groundwater ages. Generally, transit times ranged from 1-30 years. I found a decrease in transit times in the north, where hillslopes are steep and chemical weathering is limited (Chapter 3). Longer transit times in the southern end correspond to deep regolith and clay-infilled fractures seen in the borehole in Chapter 4. 7 In Chapter 6, I again used water transit tracers, in addition to near surface geophysics, this time to understand post-wildfire changes to catchment hydrology in the San Gabriel Mountains, CA, USA. We monitored three catchments, two burned and one relatively unburned, for almost two complete water years (October 2020-June 2022) after the Bobcat Fire, which burned from September-October 2020. We measured changes in electrical resistivity perpendicular to channels near catchment outlets after every major storm and analyzed paired precipitation-streamflow water isotopes during storms in two of the catchments, one burned and one unburned. We found decreases in resistivity in all catchments, but deepest and most prolonged in the burned catchments indicating significant subsurface water additions during storms. Water isotope data revealed similar proportions of subsurface and surface water contributions in the burned and unburned catchments, despite significantly (up to 6x) higher discharge in the burned catchments. These findings suggest that stormflow is not controlled solely by overland surface flow, but also previously unrecognized subsurface contributions. 8 1.3 References Ackerer J., Steefel C., Liu F., Bart R., Safeeq M., O’Geen A., Hunsaker C. and Bales R. (2020) Determining How Critical Zone Structure Constrains Hydrogeochemical Behavior of Watersheds: Learning From an Elevation Gradient in California’s Sierra Nevada. Frontiers in Water 2, 1–19. Adams B. A., Whipple K. X., Forte A. M., Heimsath A. M. and Hodges K. V. (2020) Climate controls on erosion in tectonically active landscapes. Science Advances 6. Adams H. R., Barnard H. R. and Loomis A. K. (2014) Topography alters tree growth–climate relationships in a semi-arid forested catchment. Ecosphere 5, 1–16. Anderson R. S., Rajaram H. and Anderson S. P. (2019) Climate driven coevolution of weathering profiles and hillslope topography generates dramatic differences in critical zone architecture. Hydrological Processes 33, 4–19. Anderson S. P., von Blanckenburg F. and White A. F. (2007) Physical and chemical controls on the critical zone. Elements 3, 315–319. Anderson S. P., Dietrich W. E. and Brimhall G. H. (2002) Weathering profiles, mass-balance analysis, and rates of solute loss: Linkages between weathering and erosion in a small, steep catchment. Bulletin of the Geological Society of America 114, 1143–1158. Bart R. R. (2016) A regional estimate of postfire streamflow change in California. Water Resources Research 52, 1465–1478. Bart R. R. and Tague C. L. (2017) The impact of wildfire on baseflow recession rates in California. Hydrological Processes 31, 1662–1673. Bazilevskaya E., Lebedeva M., Pavich M., Rother G., Parkinson D. Y., Cole D. and Brantley S. L. 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E., Schulz M. S., Vivit D. V., Stonestrom D. A., Larsen M., Murphy S. F. and Eberl D. (1998) Chemical weathering in a tropical watershed, Luquillo Mountains, Puerto Rico: I. Long-term versus short-term weathering fluxes. Geochimica et Cosmochimica Acta 62, 209–226. Wine M. L., Makhnin O. and Cadol D. (2018) Nonlinear Long-Term Large Watershed Hydrologic Response to Wildfire and Climatic Dynamics Locally Increases Water Yields. Earth’s Future 6, 997–1006. 13 Chapter 2 Evaluation of high-resolution DEMs from satellite imagery for geomorphic applications: A case study using the SETSM algorithm * Atwood, A. and West, A. J. 2.1 Abstract High-resolution digital elevation models (DEMs) have revolutionized research in geomorphology by allowing for detailed quantitative analysis of Earth’s surface. Satellite stereo images offer the promise of expanding the availability of high-resolution DEMs over broad areas, but rigorous evaluation of the scientific application of these datasets remains limited. In this study, we consider DEMs built using stereo pairs of high-resolution (0.5m) satellite imagery and the open-source DEM extraction algorithm. We selected locations across a range of landscapes to evaluate the application of these DEMs to geomorphic problems, with particular attention to hillslope analyses where high spatial resolution has been shown to be important for revealing topographic signatures of tectonic and environmental processes. We compared the quality of SETSM 2m DEMs to LiDAR-derived DEMs and the widely available SRTM-30m and ALOS-30m DEMs by comparing the elevation data and derivative products (e.g., slope, aspect, and curvature). We also evaluated the fidelity of each dataset in retrieving the transition from hillslopes to colluvial channels. We found that SETSM DEMs performed noticeably better than SRTM and ALOS DEMs, but with systematic biases relative to LiDAR DEMs in regions with vegetation. Moreover, noise in the initial SETSM elevation data is amplified with every subsequent derivative, significantly decreasing quality. Finally, we evaluated the * This chapter has been published as Atwood, A., & West, A. J. (2022). Evaluation of high-resolution DEMs from satellite imagery for geomorphic applications: A case study using the SETSM algorithm. Earth Surface Processes and Landforms, 47(3), 706-722. Atwood contributed project design, DEM construction, GIS analyses and manuscript authorship. 14 potential use of SETSM products for change detection. Applying DEM differencing to a major landslide, we found volume and sediment thickness from SETSM DEMs were similar to volumes and thicknesses from other studies. This example illustrates the capabilities of SETSM and other satellite-based stereo-photogrammetry for contributing to rapid response after natural disasters. Overall, we conclude that DEMs derived from satellite image stereo-photogrammetry can markedly improve on lower resolution global elevation data for terrain analysis and can open possibilities for change detection, but that care needs to be taken in their application especially in regions with significant vegetation. 2.2 Introduction 2.2.1 Background and Motivation The availability of high-resolution topographic data has transformed the study of Earth’s surface (Crosby et al., 2020; Passalacqua et al., 2015; Tarolli, 2014). Digital elevation models (DEMs) provide the basis for determining key landscape metrics such as slope angles, curvature, and river channel steepness. Information derived from DEMs has enabled quantitative analysis of geomorphic, tectonic, and environmental processes ranging from erosion and fault movement to flooding and delta evolution, and more. Increasing DEM resolution has expanded the frontiers of such analyses. In particular, high resolution elevation models from Light Detection and Ranging (LiDAR), typically with horizontal spacing of 1m or lower, reveal geomorphic signals in hillslopes that are not apparent from lower-resolution DEMs, such as the widely available datasets with 10- 30m horizontal spacing (Dibiase et al., 2012; Godard et al., 2020; Grieve et al., 2016). As a consequence, high resolution topographic data are critical for decoding the signature of tectonic and environmental processes in hillslope topography (Hurst et al., 2013; Milodowski et al., 2015; Mudd, 2017), as well as for identifying the location of channel heads (Clubb et al., 2014) and the related transition between fluvially and colluvially dominated geomorphic regimes (Montgomery and 15 Buffington, 1997). Equally, high resolution DEMs enable unparalleled understanding of active landscape change, including but not limited to landslide occurrence and susceptibility (Mackey and Roering, 2011), river sediment erosion and deposition (Wheaton et al., 2010), and fault motion (Johnson et al., 2014). Despite the importance of high-resolution topographic data for these applications, the expense and difficulty of procuring LiDAR datasets limits their availability. Moreover, differential studies of major catastrophic events (landslides, floods, or faulting) using LiDAR are challenging given that pre-event datasets often do not exist. Therefore, many large-scale studies rely on freely available DEMs with global or near-global coverage, such as SRTM-30m (NASA JPL, 2020; Rabus et al., 2003), ASTER-90m (Yamaguchi et al., 1998), and more recently ALOS-30m (Tadono et al., 2016). However, the limited resolution and missing data in these products can cause artifacts and in some cases lead to under- or over-estimating landscape metrics such as slope angles (Deng et al., 2007; Wang et al., 2012; Zhang and Montgomery, 1994). More generally, many features simply are not identifiable at the 30m or lower horizontal resolution of these datasets. Some higher resolution DEMs are available over large areas, including the TanDEM-X WorldDEM (Esch et al., 2012) and ALOS 5m DEM (Tadono et al., 2014), but these have limited release or high costs. Thus, there remains a need for low-cost, high-resolution DEMs that are available over large areas, and that can be acquired rapidly in response to natural disasters and other event-scale phenomena. High-resolution optical satellite imagery collected in stereo offers one solution to this problem, potentially filling the gap between the low resolution of existing global DEMs and high- resolution LiDAR data collected over relatively small areas (Mudd, 2020) . Photogrammetry techniques have now been optimized for satellite images, and some commercially available DEMs, such as from the Pleiades satellite (e.g., Nasir et al., 2015), are produced this way. A number of studies have compared satellite-derived DEMs to benchmark datasets, reporting values such as 16 mean elevation differences and variances (Boulton and Stokes, 2018; Deng et al., 2007; Nasir et al., 2015; Purinton and Bookhagen, 2017; Vaze et al., 2010; Wessel et al., 2018; Woodrow et al., 2016). While such straightforward comparisons are an important step towards validating these satellite- derived products, less work has evaluated how well the resulting datasets perform in geomorphic applications — for example, whether the unique insights gained into hillslope form using LiDAR data can also be retrieved from satellite photogrammetry. One widely recognized shortcoming is that DEMs from photogrammetry are derived from digital surface models (DSMs), so that the elevation value (z-value) includes the bare earth surface plus any vegetation or man-made features (the reflective surface). In contrast, bare-earth DEMs from LiDAR are derived from digital terrain models (DTMs), which exclude vegetation. The incorporation of vegetation in DSMs can lead to systemic errors, clearly evident at the scale of individual trees, for example. Yet it remains unknown whether photogrammetry-derived DSMs can nonetheless provide sufficiently accurate representation of hillslopes to capture landscape-scale features (e.g., differences in slope angle distributions or hillslope curvature across landscapes) that are not evident in coarser DEMs (e.g., SRTM, ALOS). This study evaluates the products of one open-source DEM extraction algorithm, Surface Extraction from TIN Space-search Minimization (SETSM). This code was developed by Noh and Howat (2017); it has been used in the ArcticDEM project (Morin et al., 2016) and for the Reference Elevation Model of Antarctica, or REMA (Howat et al., 2019), with plans for world-wide implementation (Polar Geospatial Center, 2021). Though existing SETSM-derived elevation products are freely available, and the open-source code can be applied to generate new custom DEMs for any region given suitable optical satellite imagery, studies evaluating derivative datasets that have the potential to address key problems in geomorphology and hydrology remain lacking. This gap reflects the generally limited understanding of how DEMs generated from stereo satellite 17 photos perform for terrain analysis applications. These DEMs include those produced using other codes such as MicMac, PCI Geomatica, Ames Stereo Pipeline (ASP), and from commercially available satellite imagery, e.g., from Pleaides imagery (Han et al., 2020; Rupnik et al., 2017; Shean et al., 2016). 2.2.2 SETSM SETSM builds high-resolution elevation datasets by identifying common features using stereo auto-correlation techniques from two overlapping high-resolution (~0.5m pixel size) photogrammetric images. The code is optimized for imagery from the WorldView-1 and -2 satellites but in principle can be applied for other stereo-satellite images. SETSM is fully automated, requiring only the stereo pair of images and corresponding metadata (e.g., WorldView XML files). It utilizes the rational polynomial coefficients (RPCs) from the satellite sensor to determine the absolute accuracy of the DEM. For WorldView imagery, the RPCs are provided in the imagery XML files (Noh and Howat, 2015) removing the need for a priori information, “seed” DEMs, or ground control points — meaning DEMs can be constructed for almost any location where imagery is available. Noh and Howat (2017) provide a more detailed description of the algorithm. SETSM outputs a DEM at the user-specified resolution, currently to as low as 0.5m using WorldView satellite imagery. Other user-specified options include tile size, projection, use of a seed DEM and surface filtering. A local surface filter (LSF) tool is available as part of SETSM and applies a moving quadratic polynomial over a moving computational kernel, where kernel size determines the range of pixels over which a surface is fit in order to preserve DEM features (Noh and Howat, 2019). Co- registration of SETSM outputs is needed to place the output into a reference frame to compare with other spatial data, as discussed below. This study evaluates the application of SETSM-derived DEMs, using the unfiltered and filtered versions, in the context of metrics used to address major questions in geomorphology, 18 focusing on those for which DEM spatial resolution is particularly important — specifically hillslope analysis (slope and curvature) and delineation of headwater channels (e.g., hillslope to colluvial to fluvial transitions). Various environments, from arid to vegetated, are considered. In addition, the application of SETSM is explored for change detection associated with a large, deadly landslide event, asking whether this tool could be deployed effectively in change detection and particularly in a rapid-response mode. 2.3 Methods 2.3.1 Image procurement The core SETSM workflow is automated, and user input is focused on the steps before and after DEM generation (Appendix A Figure 1). A crucial step is obtaining appropriate high-resolution imagery. Imagery used here was acquired between 2009 and 2017 by WorldView satellites, for which the automated SETSM workflow is optimized (see details of images used in this study in Table 2.1). Initial screening of imagery is crucial for high quality elevation models. Only images with little (<20%) to no cloud coverage were used; no cloud coverage is clearly preferable, but not all locations currently have stereo imagery available with no cloud For highest accuracy, imagery with <30° off- Table 2.1. Details of image pairs used in this study. WV-01: WorldView-1; WV-02: WorldView-2. Stereo Type Off Nadir Angle 1 (°) Off Nadir Angle 2(°) Convergence Angle (°) Satellite Date of Acquisition Time of Acquisition Carrizo Plains, CA In track 31.909 19.33 40.22 WV-01 30-Oct-2012 19:03:15 Carrizo Plains, CA (2) In track 11.87 31.84 33.95 WV-02 01-Apr-2019 19:06:22 Coyote Mountains, CA In track 15.04 15.91 31.25 WV-01 21-Jul-2016 21:35:34 Boulder Creek, CO In track 23.77 33.52 34.75 WV-01 18 Sept-2009 18:19:02 Wainiha Valley, HI In track 10.58 31.99 39 WV-01 21-Jan-2012 21:36:45 Xinmo Post-Slide In track 22.89 27.66 38.5 WV-02 17-Jul-2017 4:14:53 Xinmo Pre-Slide (A) Cross track 19.38 na WV-02 12-Sep-2013 4:04:45 Xinmo Pre-Slide (B) Cross track na 26.29866 WV-02 06-Dec-2012 4:23:48 19 nadir angle and convergence angles between 30-60° were used when available (Becker et al., 2015; Shean et al., 2016). Small convergence angles can cause degradation to the DEM, which is amplified in low contrast surfaces (Noh and Howat, 2019). SETSM can work with either in-track stereo imagery (two images of one region taken from the same satellite close in time, typically within 45-90 seconds of one another) or cross-track stereo imagery (images of the same region taken at different times and thus different satellite positions). In- track imagery is optimal, because it minimizes unwanted differences between images that can complicate matching (e.g., due to diurnal and seasonal changes in light conditions, seasonal change in vegetation and snow cover, etc.). However, in-track imagery is not always available, and cross- track imagery can be used in those cases. A comprehensive evaluation of differences between DEMs generated from in-track vs. cross-track image pairs would be a worthwhile endeavor but is beyond the scope of this study, considering the complexity and potential variability in cross-track product quality (e.g., depending on differences in many of the factors noted above between images in each cross-track pair). However, the landslide change detection case study presented here uses cross-track imagery for the pre-event DEM, reflecting typical lack of in-track imagery in historical archives. 2.3.2 DEM generation, co-registration, and filtering Prior to DEM construction, images were preprocessed using Ames Stereo Pipeline’s tool, wv_correct, to adjust for camera jitter and striping artifacts (Shean et al., 2016). Images were then scaled from a floating point raster to a 16-bit integer raster (required by SETSM) using gdal_translate. SETSM DEM processing in this study was done at the University of Southern California (USC) Department of Earth Sciences, on a Linux cluster using compute nodes with 24 cores. Typical 2m DEM production with this setup took 5-6 hours for an area of ~200-250km 2 (depending on image overlap). We focus our analysis on SETSM DEMs produced at 2m horizontal resolution, since this resolution appears to optimize the quantitative interpretation of geomorphic 20 metrics from SETSM products. SETSM DEMs built at 0.5m resolution produced significant noise, and those at 8m had notably lower quality due to their lower resolution (Appendix A Figure 2). An unsmoothed and smoothed (LSF applied) DEM was produced for each location, and metrics derived from the filtered DEMs were compared with those from unfiltered SETSM DEMs. Other DEMs were acquired from online repositories. LiDAR data (Table 2.2) were obtained from OpenTopography (a publicly available repository of elevation datasets) and are used as the benchmark for comparison. All LiDAR datasets used in this study are airborne collected, last returns were used and converted into a raster using inverse distance weighting (IDW). While LiDAR provides the highest standard of elevation data currently, it also can contain artifacts from collection and production; these are not considered here. Average vegetation canopy height was found by differencing the DTM and DSM derived from LiDAR point clouds for each study region, using the average and maximum values respectively (using DTMs and DSMs from OpenTopography). ALOS- 30m DEMs were provided by the Earth Observation Research Center (EORC) at the Japan Aerospace Exploration Agency. The NASADEM Merged DEM Global 1 arc second SRTM datasets were obtained using the USGS Earth Explorer (NASA JPL, 2020). LiDAR data Point Density (pts/m 2 ) Dataset Year Collected DOI Open Topography ID Point spacing (m) Carrizo Plains, CA 2.98 B4 LiDAR Project 2005 https://doi.org/10.5069/G97P8W9T OT.032006.32611.1 0.5792844464 Wainiha Valley, HI 4.58 NCALM- Hawaii, Kauai 2009 https://doi.org/10.5069/G91V5BWJ OT.052012.32604.1 0.4672693135 Boulder Creek, CO 11.33 Boulder Creek Critical Zone Observatory August 2010 LiDAR Survey 2010 https://doi.org/10.5069/G93R0QR0 OT.032012.26913.1 0.2970879555 Coyote Mountains, CA 4.61 EarthScope Southern & Eastern California Lidar Project 2007 https://doi.org/10.5069/G9G44N6Q OT.122009.32611.1 0.4657464328 Table 2.2. LiDAR datasets from OpenTopography 21 After DEM production and acquisition, DEMs were co-registered using the Nuth and Kääb (2011) methodology in Python. Co-registration of SETSM or other photogrammetry-derived DEM is critical to ensure vertical accuracy (Noh and Howat, 2017) and to remove any potential aspect dependencies. Code specifically designed for post-processing of SETSM-derived DEMs (https://github.com/PolarGeospatialCenter/setsm_postprocessing_python) is available but proved challenging to implement and was not used in this study. 2.3.3 Comparing geomorphically-relevant metrics between DEMs 2.3.1. Study site locations for comparison of geomorphic metrics Study site locations were chosen based on LiDAR availability as well as to encompass a variety of geomorphic and environmental regimes. Figure 2.1 shows Google Earth views and Figure 2.2 shows SETSM DEMs overlaying hillshades of each location. General characteristics of each site are described here: Boulder Creek, Colorado, USA: Boulder Creek drains from the Rocky Mountains west of Boulder, Colorado. The climate is semi-arid, and land cover is composed dominantly of forest, shrub and alpine environments (Murphy, 2006). The study area is located in the foothills where the Rocky Mountains transition to urbanized plains, at elevations between 1,800 and 2,400m. The diversity of this region allows us to evaluate DEM quality across different landscapes and slope steepness. 22 Carrizo Plains, California, USA: The Carrizo Plains are located in semi-arid to arid environments at the southern end of the California central valley, with few trees or other large vegetation. The study site is located along the San Andreas fault trace and contains small arroyos that have been displaced by the fault. This region has lower relief and gentler slope angles than the other sites in this study. Figure 2.1. Satellite images from Google Earth of the locations used in this study: A) Coyote Mountains, CA; B) Carrizo Plains, CA; C) Boulder Creek, CO and D) Wainiha Valley, HI. White bounding boxes denote sections used in this study. These images show the difference in both terrain (from mountainous to gently sloping) and environment (from arid to tropical). The table describes the main features of the study areas (vegetation height derived from OpenTopography LiDAR data as described in the main text). 23 Coyote Mountains, California, USA: The Coyote Mountains are located in southern California in the arid to semi-arid Colorado Desert, with few trees. The study site includes two regions, one along the southeastern end of the range and one along the more mountainous northwestern edge. The mountains were uplifted by the Elsinore fault which is visible in geomorphic markers along the range front. The region is dominated by large alluvial fans eroding from the mountains. This region has a variety of topographic features, including both mountains and gently sloping alluvial fans. Wainiha Valley, Kauai, Hawaii: The Wainiha Valley on Kaua’i is a rugged, forested, tropical valley on the northern edge of the island. The island has a strong rainfall gradient, and the valley receives an average annual rainfall that ranges from 3m at the mouth to 9m at Mt. Waileale, the highest point on the island (Ferrier et al., 2013). Extremely steep-sided valley walls and a low gradient main stem river characterize the valley. 4431000 4428000 469000 472000 2300 2200 2100 2000 1900 1800 Elevation (m) Boulder Creek 500 450 400 350 300 3633500 3630500 579000 585000 Elevation (m) Fig. 4 Coyote Mountains 2454000 2446000 439000 446000 1200 1000 800 600 400 200 Elevation (m) Fig. 6 Fig. 4 Wainiha Valley 3910000 3905000 241000 244000 730 720 710 700 690 680 670 660 650 Elevation (m) Fig. 8 Carrizo Plains UTM northing (m) UTM easting (m) UTM Zone 11 UTM Zone 4 UTM Zone 13 UTM Zone 10 Figure 2 Figure 2.2. SETSM derived 2m DEMs overlying hillshades used in this study. Inset boxes denote subsections of the DEMs used for more detailed analysis with the corresponding figure number. 24 A summary of the sites from this study and some of their key characteristics are presented in Figure 2.1. 2.3.2. Geomorphic metrics Hillslope metrics, including first- and second-order derivatives of elevation, such as slope, aspect, and curvature, are fundamental tools in geomorphology. They are used for hydrological modeling (Beven, 2010; Zhang and Montgomery, 1994), to explore the complex relationship between tectonically driven uplift and climatically driven erosion (Burbank et al., 1996; Larsen and Montgomery, 2012), in natural hazard analysis (Booth et al., 2009; Carrara, 1983; Dai and Lee, 2002), for groundwater studies (Detty and McGuire, 2010), and in many other applications. Increased DEM resolution has been shown to increase the accuracy of hillslope metrics in numerous prior studies (Chang and Tsai, 1991; Evans, 1980; Grieve et al., 2016; Purinton and Bookhagen, 2017; Thompson et al., 2001; Vaze et al., 2010; Walker and Willgoose, 1999). Slope angles and curvature relationships were calculated using TopoToolbox (Schwanghart and Scherler, 2014). Error analysis was done by differencing the sample DEM from the benchmark LiDAR DEM using Nearest Neighbor interpolation; a three-sigma threshold was used to remove outliers, and the mean error, standard deviation and root mean square error (RMSE) are reported (Table 2.2). 2.3.4 Evaluating landslide volume with pre- and post-DEMs from SETSM Change detection is a quickly growing field enabled by increasing access to high-resolution datasets. It provides crucial information for assessing landslides (Martha et al., 2010; Tsutsui et al., 2007; Ventura et al., 2011) as well as a variety of other geomorphic processes, including soil erosion (e.g. Rieke-Zapp & Nearing, 2005), river channel evolution (Milan et al., 2007) and determining earthquake deformation and fault geometry (Zhou et al., 2016; Zinke et al., 2019). However, the lack of high-resolution pre-event datasets is often a significant barrier to such studies, as is the limited 25 spatial coverage of LiDAR and unmanned aerial vehicle (UAV) studies. ArcticDEM has been used in change detection studies for glacier mass balance (Belart et al., 2017; Zheng et al., 2018), and the large trove of WorldView and other high-resolution satellite images (many in-track stereo, or suitable for cross-track analysis) makes it possible to construct pre-event SETSM DEMs for areas and times when LiDAR or UAV data are non-existent (Barnhart et al., 2019; Gold et al., 2019). Moreover, even where LiDAR data may be available, post-event field surveys can be challenging to complete quickly, whereas satellite imagery can be tasked in rapid response mode, with derived DEMs helping in immediate assessment and recovery efforts. In order to test the capability of SETSM DEMs for change detection and rapid response use, we used the 2017 Xinmo landslide as a case study. At 5:00 am on June 24, 2017 a major landslide occurred in Xinmo Village, Sichuan Province, China, triggered by heavy rainfall, steep topography, and a deep seated slide interface (Meng et al., 2018). This catastrophic event buried the village of Xinmo and resulted in 10 fatalities. Unmanned aerial vehicle (UAV) surveys were used in prior studies to determine the size, movement, and cause of the landslide (Fan et al., 2017a, 2017b), and to create a post-sliding DEM. In four studies with published volume estimates for the Xinmo landslides, three utilized DEM differencing methods to estimate scarp depth and mobilized volume, as well as total mobilized volume. The fourth study (Meng et al. 2018) did not publish methods for their estimates. 26 This locale represents a non-ideal but fairly typical case for procuring satellite stereo-DEMs, especially in a potential rapid-response mode. High quality imagery in this region is limited because of significant cloud coverage. Therefore, a pre-event SETSM DEM was constructed from cross- track images from 2012 and 2013. A post-event DEM was constructed from the available in-track stereo imagery, collected July 17, 2017, less than a month after the event. We defined the scarp/initiation area from differencing pre- and post-event the DEMs. The scarp region defined in this study correlated in area to the one defined by Fan, X., et al. (2017), and the analysis here followed their method in which the volume of the scarp material and overall mobilized material were calculated using the cut/fill tool in ArcGIS. 2.4 Results 2.4.1 Topographic metrics Elevation: When compared to the LiDAR elevation data, both smoothed and unsmoothed SETSM DEMs for all four sites in this study have smaller error distributions and lower RMSE than Figure 2.3. Kernel density estimate (KDE) plots of elevation distributions and elevation differences with RMSE for Wainiha Valley (top left), Carrizo Plains (top right), Coyote Mountains (bottom left) and Boulder Creek (bottom right). In arid and semi-arid locations, like the Carrizo Plains, Coyote Mountains and even Boulder Creek, the distributions are almost identical for SETSM 2m and LiDAR datasets, while SRTM varies somewhat and ALOS is most distinct, especially in Boulder Creek. In the vegetated Wainiha Valley, SETSM performs worse than in the arid regions; however, some of the difference compared to the LiDAR is due to artifacts from shadows in extremely steep mountainous terrain (in addition to effects of vegetation). 27 both SRTM and ALOS DEMs (Figure 2.3 and Table 2.3). SETSM DEMs perform particularly well in Mean Elevation (m) Mean Elevation Difference and SD (m) Elevation Difference RMSE (m) Mean Slope (degree) [5 th , 95 th percentiles] Slope Quantiles (degree) Mean Slope Difference and SD (degree) Slope Difference RMSE (degree) Boulder Creek SETSM-2m 1932 0.62±3.3 3.39 22.3 6.3, 41.9 0.70±10.7 10.76 SETSM smoothed 1932 0.74 ±3.3 3.34 22.7 4.7, 43.5 -0.76±9.5 9.51 SRTM 1932 0.02±6.1 6.09 17.2 5.5, 32.1 -5.7±8.6 10.34 ALOS 1933 2.41±6.7 6.93 18.6 6.7, 32.9 -3.88±7.9 8.80 LiDAR 1932 23.1 6.2, 41.6 Wainiha Valley SETSM-2m 614 2.4±14.7 (0.5*) 14.9 (4.3*) 38.85 11.5, 64.4 -2.6±11.2 11.5 SETSM smoothed 614 2.6± 14.6 (0.3*) 14.8 (4.1*) 36.81 10.3, 60.9 -3.0±10.9 11.3 SRTM 611 2.3±16.7 16.8 31.25 8.0, 55.3 -10.6±11.9 16 ALOS 611 2.9±17.7 17.9 31.45 8.1, 53.2 -8.3±12.2 14.8 LiDAR 608 39.98 12.2, 64.3 Coyote Mountain SETSM-2m 372 -0.09±0.8 0.8 17.24 2.6, 37.3 -1.1±5.5 5.6 SETSM smoothed 371 -0.11±0.8 0.8 15.9 2.3, 36.4 -2.3±5.4 5.9 SRTM 373 0.26±4.32 4.33 10 1.5, 24.9 -8.25±9.6 12.68 LiDAR 372 18.37 2.7, 39.5 Carrizo Plains SETSM-2m 688 0.009±0.26 0.26 5.66 1.3, 15.8 0.50±2.35 2.4 SETSM smoothed 688 0.007±0.23 0.22 4.66 1.43, 13.18 -0.37±1.72 1.76 SRTM 688 -0.7±0.1.6 1.74 3.33 1.1, 6.7 -1.16±3.24 3.44 ALOS 688 -0.01±0.99 0.99 3.4 1.3, 7.1 -1.3±3.01 3.28 LiDAR 688 5.4 1.3, 16.8 * denotes elevation differences on low-slope portion of Wainiha Valley DEM to differentiate vegetation error from shadow anomalies. Table 2.3. Elevation and slope metrics and error analysis relative to benchmark LiDAR DEMs 28 arid environments without significant vegetation, such as the Carrizo Plains and Coyote Mountains, where error distribution and RMSE are the lowest. These values are comparable to the error found by Noh and Howatt (2015) for SETSM-produced DEMs in the Arctic. Across elevation profiles at the Carrizo Plains and Coyote Mountains, SETSM and LiDAR are both able to capture small-scale features that cannot be identified in the SRTM data (e.g., Figure 2.4 for Coyote Mountains, where fault offsets are much more clearly defined by SETSM). Generally, the smoothed SETSM DEMs perform slightly better than unsmoothed DEMs. In forested environments, such as the Wainiha Valley and Boulder Creek, both the error distribution and the RMSE are higher for the SETSM DEMs when compared to the largely unvegetated sites of the Carrizo Plains and Coyote Mountains, indicating that the presence of Figure 2.4. (A) Hillshade maps of a small section of the Coyote Mountains, along the base of the mountains. The LiDAR dataset clearly distinguishes features such as arroyos and alluvial fans. These features are also apparent in the SETSM 2m with a slight decrease in distinguishable features that is further decreased in SETSM 2m smoothed, while the SRTM-30m has almost no distinguishable topographic features at this scale. Elevation transects of the DEMs parallel and perpendicular to the mountains show SETSM and LiDAR are able to pick up small elevation differences. SETSM smoothed and unsmoothed DEMs are indistinguishable. (B) Elevation transects of DEMs in the vegetated Wainiha Valley show SETSM performs better than ALOS and SRTM, but differences compared to LiDAR in valleys reflect errors from vegetation. 29 vegetation has a noticeable effect on SETSM DEM quality. The increased RMSE scales with increased tree height, as calculated by differencing the bare earth LiDAR DEM from the LiDAR DSM (Figure 2.5). Yet despite the complications from vegetation, the SETSM DEMs performed noticeably better than the 30m-resolution alternatives (SRTM, ALOS) even at Boulder Creek and Wainiha Valley. Another major challenge for photogrammetric DEMs is shadowed mountainous terrain, due to loss of contrast. While SETSM performed relatively well in the study sites with moderately steep mountains, SETSM proved unable to resolve terrain in the extremely steep (>60°) headwaters in Wainiha Valley, resulting in overall high mean elevation differences and RMSE (Table 2.3). Figure 2.6 shows the two ortho images used in SETSM DEM creation, where shadows are apparent in the lower left-hand and upper right-hand corners. These correspond to locations of major difference between the LiDAR and SETSM DEMs as well as the steepest slopes derived from the LiDAR DEM. Slope: Figure 2.7 shows distributions of slope angles, the differences in slope angles between the sample DEM and LiDAR, and Q-Q plots of each sample distribution versus LiDAR. Generally, SETSM DEMs are more capable than ALOS and SRTM at capturing steep slopes, one of the most Figure 5 1 2 3 4 2 4 6 8 10 Elevation RMSE Slope RMSE Vegetation Height (m) Figure 2.5. Scatterplots comparing relationship between elevation RMSE (left) and slope RMSE (right) with LiDAR derived vegetation heights with R- squared values. Both elevation and slope RMSE show a very strong correlation to vegetation height. 30 sensitive parameters to DEM resolution (Deng et al., 2007). This effect is apparent in the slope distributions as well as the Q-Q plots; the Q-Q plots show that while all distributions are skewed towards lower slopes compared to the LiDAR, the SETSM slopes are significantly less skewed. The ability to capture high slope angles at higher DEM resolutions is generally due to small scale ruggedness in terrain, apparent in the slope maps, e.g., along the steep river channels of the Carrizo Plains (Figure 2.8). Because they do a better job of capturing steeper slopes than SRTM or ALOS, the SETSM DEMs yield mean slope values close to those from LiDAR, while those from SRTM and ALOS are offset (Table 2.3). However, for all but the Coyote Mountains, the RMSE of slope angles is similar for SETSM and SRTM (Table 2.3), and the distributions of slope angle differences shows a range in error that is similar between SETSM and SRTM (Figure 2.7). As with the error in elevation data, the Ortho Image 1 Ortho Image 2 20 -20 0 -10 10 LiDAR-SETSM Elevation Difference Elevation Difference (m) 80 40 60 20 Slope (degrees) LiDAR Slope Figure 6 Figure 2.6. Subsection of WorldView-1 imagery (copyright 2012 Digital Globe, Inc.) used to produce Wainiha Valley SETSM DEM, with LiDAR-SETSM elevation difference map and LiDAR slope map of the DEM subsection. Locations of significant difference (dark blue) correspond with differently shadowed regions in the ortho images as well as areas of extreme slopes. 31 RMSE of SETSM-derived slope angles increases with increasing average vegetation height (Figure 2.5). This relationship suggests that, for Wainiha Valley and Boulder Creek, the high RMSE of slope angles may be related, at least in part, to noise associated with vegetation. The Carrizo Plains SETSM DEM also exhibits high RMSE for slope angles, despite the absence of vegetation. At this site, SETSM-derived slope distributions are slightly biased towards higher slope angles compared to all of the other DEMs. Map view comparisons show that the higher slopes from SETSM for this region appear to correlate with anomalous surface roughness. This roughness is likely due to high frequency noise associated with the optical image-matching technique (Milledge et al., 2009), creating some artificially steeper slopes, especially in flatter areas such as alluvial fans (e.g. Figure 2.8) Figure 2.7. KDE plots of slope angle distributions, slope differences between sample and LiDAR DEMs, and quantile-quantile plots of slope distributions for Wainiha Valley (top left), Carrizo Plains (top right), Coyote Mountains (bottom left) and Boulder Creek (bottom right). SRTM and ALOS datasets appear to systematically underestimate slopes across all environments. SETSM 2m distributions are generally closer to LiDAR distributions than the SRTM or ALOS, although SETSM-derived slopes do not follow LiDAR as accurately as elevation distributions — especially for the Carrizo Plains, where the SETSM DEM systematically overestimates slopes, largely due to artifactual roughness on the alluvial fan surfaces (Figure 2.8). There artifacts are removed in the SETSM 2m smoothed dataset. 32 (Noh and Howat, 2019). The LSF-smoothed SETSM DEM removes most of this high frequency noise, although it also smooths over some of the highest resolution features (Figure 2.8). This is also Figure 2.8. Slope maps from the Carrizo Plains region derived from LiDAR, two unsmoothed SETSM DEMs, a smoothed SETSM DEM and SRTM. Note that SETSM slope maps are able to accurately capture most of the detailed geomorphic features such as the small channels, while the SRTM is generally lower resolution. However, both unsmoothed SETSM maps show small scale roughness, with anomalously high slopes present in the lowest slope areas of the region. These artifacts persisted despite producing SETSM DEMs using two different pairs of in-track stereo satellite images (Table 2.1). The smoothed SETSM DEM does not have these roughness artifacts. 33 apparent in the slope distribution as well as the QQ plots, where SETSM overestimates slopes relative to LiDAR at the lowest values (0-10), while the smoothed SETSM does not. (Figure 2.7). Curvature: Figure 2.9a shows curvature maps from Wainiha Valley, Boulder Creek and the Coyote Mountains. In the vegetated Wainiha Valley and Boulder Creek, SETSM curvature maps contain significant artifacts that are not present in the LiDAR maps. However, in the sparsely vegetated Coyote Mountains, the SETSM map does not contain these artifacts. This result suggests that SETSM’s utility for higher derivative products (curvature, topographic roughness), might be limited to regions with sparse vegetation. The plots in Figure 2.9b show that curvature distributions across the DEMs are broadest for the LiDAR and SETSM DEMs, and significantly smaller in ALOS and SRTM. This observation is similar to the results from Grieve et al. (2016), who showed that increasing grid resolution allows for increased distribution of curvature values. However, in this case the distributions at Wainiha Valley and Boulder Creek appear to be correlated with noise in the Figure 2.9. a: Column 1: Hillshades derived from LiDAR of each location. Columns 2-5: Plan curvature maps produced using TopoToolbox plan curvature function. Plan curvature values above 0 indicate a concave feature (such as a ridge line) and values below 0 indicate a convex feature (such as a valley) b: Violin plots of distributions of plan curvature at each location from each DEM. 34 data, rather than meaningful differences in values. The curvature distributions from the Coyote Mountains are presumably more meaningful. 2.4.2 Change Detection: Landslide volume analysis Delineation of the scarp area for the Xinmo landslide are shown in Figure 2.10b, using ortho imagery, and the spatial distribution of the depth of erosion and deposition is shown in Figure 2.10b. The scarp depth found is similar to those reported by Fan, X., et al. (2017) using UAV-based imagery from after the landslide. Total scarp volumes from the Cut/Fill estimate from the pre- and post-event SETSM DEMs (Figure 2.10a) are presented and compared to estimates from other published studies in Table 2.4. Figure 10 Net Loss Net Gain Volume Change 0 0.25 0.5 km Elevation Change (m) >25 15 0 -15 -30 <-30 Xinmo, China 372000 375000 3548000 3550000 UTM Zone 48 SETSM smoothed scarp a b Figure 2.10 (Left) Volume change between pre and post landslide DEMs, derived using the Cut Fill tool in ArcGIS. (Right) Elevation change between pre- and post-landslide DEMS. Average elevation change at the source area was 45m. Elevation change distribution is very similar to that from X. Fan et al. (2017). 35 Fan, J., et al., 2017 Meng et al., 2018 Ouyang et al., 2017) Fan, X., et al., 2017 This study: SETSM / SETSM smoothed Initiation/Scarp mobilized volume (1e 6 m 3 ) -------- 3.6 2.875 4.46 3.00 / 3.06 Average scarp elevation difference (m) -------- 23 ------- 46 46.1 / 46 Total mobilized volume (1e 6 m 3 ) 7.70 ± 1.46 ~8 5.08 8.76 (eroded) 13±1 (deposited) 7.79 / 7.49 Table 2.4. Xinmo landslide statistics from this and previous studies 2.4.3 SETSM mismatches During production, the SETSM algorithm provides a corresponding “match tag” file for every DEM produced. This is a binary file where pixels are labeled 1 for a satisfactory match and 0 for a mismatch during autocorrelation between the two images in an image pair. Howat et al. (2019) recommends using these match tag files to filter out erroneous surfaces from clouds or opaque shadows. In our study areas, mismatch levels vary widely, from 1,183 out of 1,300,202 (0.0009%) at the Carrizo Plains to 1,004,474 out of 8,313,016 (12%) at Boulder Creek. Arid environments generally show lower mismatches, while vegetation appears to increase mismatches. The identified mismatches do not appear to correspond with increased difference in elevation between the LiDAR- and SETSM-derived DEMs (Appendix A Figure 4). At each location, the mismatch plots have similar distributions to the general elevation differences, with correspondingly similar means. Thus, these mismatch files are not capable of identifying locations of high error, such as the shadow anomalies in the Wainiha Valley DEMs. 2.5 Discussion 2.5.1 Applicability of SETSM to hillslope analyses When compared to widely available 30m elevation data, SETSM 2m DEMs provide elevation data that is more comparable to high-resolution LiDAR elevation data, at least in most of the cases studied here. SRTM-30m and ALOS-30m fail to pick up topographic features that are 36 evident in both of the higher resolution datasets. In the arid environments studied here, elevation differences between SETSM and LiDAR were generally <1m and in some cases <0.5m. However, the RMSE of elevation increased with increasing vegetation, as may be expected of DSMs derived from photogrammetry. Even in these cases, SETSM performed better than SRTM and ALOS. Therefore, SETSM DEMs provide a useful bridge between very high-resolution elevation data that is difficult to acquire and more accessible, lower resolution data. However, caution needs to be used when SETSM (or another stereophotogrammetry algorithm) is applied to regions with extreme (>60°) slopes where shadows from the surrounding terrain can compromise the quality of the DEM (Figure 2.6), as is the case Wainiha Valley. The SETSM match tag files do not show significantly higher mismatches in these areas, making it challenging to identify these anomalies without sufficiently high-resolution benchmark datasets for comparison. The quality of the SETSM-derived data decreases with increasing derivative products, especially in highly vegetated environments. SETSM is capable of capturing steeper slopes and has lower mean differences versus LiDAR when compared to SRTM and ALOS DEMs. However, for the vegetated and low-relief arid sites studied here, slope angles from SETSM do not significantly improve on SRTM and ALOS when considering the RMSE and distribution of differences in slope angles. In contrast, SETSM slope angles showed lower RMSE than the SRTM equivalent at the Coyote Mountains. The overall high RMSE of SETSM-derived slope angles for many localities is likely due to noise from vegetation as well as high frequency noise from imagery matching in low- relief settings such as the Carrizo Plains. While this high frequency noise can be mitigated using the smoothed SETSM DEM, some resolution is lost. Other approaches to evaluating slope and aspect error in DEMs, such as utilizing the quality metric proposed by Smith et al., (2019), which looks at the impact of truncation error and propagated elevation error, would be useful for future analysis but beyond the scope of this paper. 37 The discrepancies between SETSM and LiDAR are accentuated in second order derivatives, such as curvature (Figure 2.9) and topographic roughness (Appendix A Figure 3), with application to vegetated regions such as Wainiha Valley and Boulder Creek resulting in significant artifacts. While SETSM DEMs might be appropriate for second order derivative analysis in arid environments with limited vegetation and significant relief, or when larger neighborhood sizes are used and small-scale noise in the DEM is averaged out, our results suggest that extra caution is needed before using these datasets in vegetated or low-relief unvegetated terrain. Significant high frequency noise in photogrammetric DEMs has been observed and addressed in previous studies (Milledge et al., 2009; Noh and Howat, 2019; Walker and Willgoose, 1999). Noise amplification in derivative products, similar to that we find for the SETSM DEMs, also has been documented (Florinsky, 2002). While this noise is mitigated in part by SETSM’s smoothing algorithm, the reduced resolution of the DEM makes these less useful. Despite the high frequency noise and high RMSE on slope values, the slope angle distributions from the SETSM DEMs pick up key features that are seen in LiDAR but not captured by the lower-resolution SRTM and ALOS. These results indicate that SETSM and other photogrammetric DEMs offer promise for identifying topographic signatures in hillslopes, such as those recording tectonic processes. These signatures often are not readily evident in coarser data such as SRTM (DiBiase et al., 2012; Godard et al., 2020). For example, slope angle distributions from SETSM pick up skewness not identifiable in SRTM data, even in vegetated regions (e.g., Wainiha Valley and Boulder Creek in Figure 2.7). Overall, the results from the analyses in this study illustrate that photogrammetric DEMs could open possibilities for studying the hillslope record of tectonics, or other environmental forcing, across scales not currently practical with LiDAR, especially in locations lacking vegetation. 38 2.5.2 Application of SETSM for change detection/landslide volume analysis The potential for SETSM and similar photogrammetric techniques is also notable in creating DEMs to evaluate landscape change before and after major events such as landslides or earthquakes, or for change detection in river evolution. In the Xinmo landslide case, SETSM made it possible to create both pre- and post-event DEMs, map the landslide as well as its scarp, and calculate volume and elevations changes that were comparable to other published work on the event. This example, along with past work using ArcticDEM to look at glacial and lava flow mass balance changes (Barr et al., 2018; Cisek and Blauvelt, 2018; Dai and Howat, 2017), shows that SETSM DEMs can be used effectively in change detection scenarios — even in regions with vegetation. However, the errors introduced into SETSM by water demand extra caution for application to problems such as channel evolution. The potential availability of satellite imagery soon after natural disasters makes this an appealing potential application. For example, following a large landslide such as that at Xinmo, it could be possible to task satellite imagery rapidly and develop sediment thickness maps such as those in Figure 2.10 before on-ground surveys can be completed. Such thickness information could help guide rescue efforts in cases such as Xinmo where a village was buried. 2.6 Conclusions This study evaluated the performance of 2m SETSM DEMs for geomorphic applications that demand high spatial resolution, in comparison to LiDAR-, SRTM- and ALOS-derived DEMs. To first order, SETSM DEMs retrieved features that were not identifiable in SRTM and ALOS DEMs, and in many cases, the performance of SETSM DEMs was comparable to LiDAR DEMs. However, noise from vegetation or optical image-matching that appears minimal in the elevation data of SETSM DEMs can become amplified in derivative products, especially second order derivatives like roughness or curvature, rendering them much less accurate. Other artifacts can occur 39 from the presence of water or cloud coverage, the latter of which is especially prevalent in tropical environments, where cloud-free stereo-imagery might not be currently available. With these caveats in mind, SETSM DEMs nonetheless are promising for identifying key topographic signatures, such as slope angle distributions and channel heads, that cannot be captured by SRTM or similar data, and in regions and over scales were acquiring LiDAR data is currently impractical. Another promising use of SETSM is the production of pre- and post-event DEMs for natural disasters. The example from the Xinmo landslide shows that this technique works well for a large landslide and may have promise for other events, especially given the ease of acquiring images in their immediate aftermath (e.g., as opposed to UAV imagery or LiDAR). However, change detection with photogrammetric DEMs is limited to terrestrial or cryospheric landforms, as artifacts from water obscure changes over rivers or lakes. 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Joshua West, Dimitrios Zekkos, Marin Kristen Clark, Deepak Chamlagain 3.1 Introduction Mountain building is a major perturbation to a landscape as it alters geomorphic and hydrologic processes, creates its own climate through orographic precipitation and brings fresh rock to the Earth’s surface. Uplift tends to drive a landscape out of balance, with erosion responding to restore steady state via steepening channels (Kirby et al., 2003; Ouimet et al., 2009; Dibiase et al., 2012; Perron and Royden, 2013), heightened weathering rates and exports (Riebe et al., 2001, 2004; West et al., 2005; Godard et al., 2014; Hilton and West, 2020), and shifts to different erosional processes, such as landsliding (Dibiase et al., 2012; Li et al., 2014; Roback et al., 2018; Wolff et al., 2022). Soil production and development, critical to supporting life, are both effected by these changing processes (Heimsath et al., 1997; Ferrier and Kirchner, 2008; Dixon et al., 2012; Dixon and Riebe, 2014; Ferrier et al., 2016). Relationships between exhumation, weathering and soil development have been studied extensively (Burke et al., 2007; DiBiase et al., 2010; Rasmussen et al., 2011; Heimsath et al., 2012; Dixon et al., 2012; Dixon and Riebe, 2014), guided by geomorphic rules such as the soil production function (Gilbert, 1877; Dietrich et al., 1995; Heimsath et al., 1997) and mass transfer equations (Garrels and Mackenzie, 1967). These works, as well as small catchment studies, have revealed two dominant weathering regimes: transport-limited and kinetically-limited. In a transport-limited landscape, the mineral residence time in the weathering zones (ie saprolite and soils) is longer than the reaction time and there are sufficient reactants, such that rocks become completely leached of their weatherable cations and total weathering fluxes are limited by erosion. Meanwhile, in kinetic-limited settings, mineral residence time is shorter than the time it takes for complete reaction, so * Atwood contributed sample collection and field work, laboratory analyses, GIS analyses and manuscript authorship 49 minerals remain relatively unweathered before they are removed by physical erosion. This difference can be driven by the kinetics that drive weathering reaction such as temperature and precipitation. At high physical erosion rates, exhumation can limit chemical weathering through increased erosion, which removes fresh material faster than it can weather, known as kinetically controlled weathering (Dixon et al., 2012). The transition from kinetically limited to kinetically controlled regimes remains relatively poorly understood, despite the fact that they generally occur in steep, mountainous environments, which are hotspots for weathering and sediment transport. Major advances in geomorphology have come via the advent of high resolution (<1m resolution) LiDAR-derived digital elevation models (DEMs). These DEMs have been used to quantify small-scale topographic features, such as knickpoints and hilltop curvature, that have allowed researchers to better understand landscape response to erosion and uplift (Kirby et al., 2003; Wobus et al., 2006; Miller et al., 2013; Perron and Royden, 2013; Clubb et al., 2014; Mudd, 2017; Gabet et al., 2021). However, LiDAR datasets are unavailable globally, limiting these analyses. Stereophotogrammetric DEMs from high resolution (0.5m) satellite imagery are newly becoming globally available through various algorithms such as Ames Stereo Pipeline (ASP) (Moratto et al., 2010) and Surface Extraction from TIN based Search-space Minimization (SETSM) (Noh and Howat, 2017). These DEMs provide an exciting opportunity but have drawbacks including high resolution noise which can dominate elevation derivatives (specifically second derivatives such as curvature) and shadows from clouds and steep topography (Florinsky, 2002; Milledge et al., 2009; Atwood and West, 2022). Therefore, different methods of understanding hilltop curvature need to be explored to better understand ridge morphology. Here we utilize a combination of soil geochemistry and GIS analysis from 2m photogrammetrically derived DEMs in the Melamchi Khola, in the Nepal Himalaya. The Melamchi Khola is an ideal setting, as recent work has revealed a strong exhumation gradient from south to north, relatively limited lithologic variation and strong orographic precipitation and temperature patterns. By exploiting these gradients, we use this 50 valley as a natural experimental setting to study the relationship between soil development and chemical weathering, climate, exhumation, and topography. Figure 3.1. (a) Hillshade from SETSM 2m DEM of the Melamchi Valley with sample locations (orange) and ridgeline (black line). (b) Satellite imagery (ESRI World Imagery) with subcatchments outlined and (c) High resolution bedrock geology map (Duvadi, 2015). 51 3.2 Site Description 3.2.1 Geologic and Tectonic Setting The Melamchi Khola is a north-south trending valley 50 km northeast of Kathmandu with a dramatic topographic gradient characteristic of the frontal Himalaya. The Himalaya are the archetype of continental- continental collision zones and mountain building and here, the Main Central Himalayan Thrust is mapped ~15km south of the southern end of the valley. Recent work has indicated that this region has seen periods of rapid uplift over the past 5 million years. Low temperature thermochronology using apatite and zircon (U- Th)/He and AFT cooling ages across the valley show a distinctive trend of rapid exhumation followed by slower exhumation (Figure 3.2b). This rapid exhumation occurred 5 million years ago in the southernmost end of the valley and is still occurring in the northern end (Murray et al., 2018; Medwedeff, 2022). The Melamchi Khola is underlain by the High Himalaya Crystalline Series, dominantly interlayered augen gneiss and mica-garnet schist with some quartzite groups (Figure 3.1c; Duvadi et al., 2005). Rock Figure 3.2 (a) Temperature and precipitation patterns across the Melamchi Valley. (b) (U-Th)/He low temperature thermochronology cooling ages from bedrock apatite and zircon grains and near surface seismic velocities at at 30m from Medwedeff (2022). 52 strength varies with topographic position, consistent with prior work on the effects of topographic stress (Medwedeff et al., 2022, St Clair et al., 2015). Bedrock channels are the strongest where erosion of material exposes intact bedrock while ridges are the weakest, due to high degree and depth of weathered rock (Medwedeff et al., 2022). Ridgeline seismic velocities and GSI values are consistently low (200-400m/s and 15-40 respectively) – representative of highly weathered and fractured rock before increasing to 750-1000m/s and 60-80, at approximately treeline (Figure 3.2b; Medwedeff et al., 2022). 3.2.2 Topographic Description The Melamchi Khola is a moderately sized watershed with an area is 342km 2 , an average slope of 31° and a relief of 500m in the southern end and 1800m the north. The pronounced topographic change from gentler, lower relief slopes in the south to steep, high relief in the north is commonly referred to as the Physiographic Transition (PT2) (Wobus et al., 2005). Monsoonal landslides occur annually, transporting material from ridges onto hillslopes and then into rivers. Additionally, the M7.8 Gorkha earthquake in April 2015 caused thousands of coseismic landslides in the valley. Landsliding increased significantly northward, creating a strong landslide density gradient (Roback et al., 2018). Typical major factors do not appear to control this gradient: lithology remains relatively constant throughout the valley, the region had little variety in ground shaking, and slopes approach regional “threshold” levels while landslide density continues to increase (Quackenbush et al., in prep). Recent research shows that while these landslides were significant, they were not well connected to river systems and minimal sediment has been exported into the river system from these landslides (Goff et al 2023; Chen et al. 2023). 3.2.3 Climate and Vegetation The Melamchi Khola shows a strong orographic climate pattern in precipitation, ranging from 2400 mm/year in the southern end to 3400 mm/year in the mid-northern end (Bookhagen and Burbank, 2010). In the southern end, precipitation falls entirely as rain while in the northern end it falls as a mix of rain and snow. Extreme events during the monsoon are more intense in the mid-northern end near Ama Yangri Peak (Figure 3.2) and then decrease in intensity northward (Hille, 2022). Temperature also changes dramatically across the valley, driven by elevation changes (Figure 3.2a). Subcatchment average mean annual temperatures 53 (MAT) from 1981-2010 ranges from 18.7°C in the southern end to -3°C in the northern peaks (Karger et al., 2017). Vegetation ranges from Sal forest below 1000m to chir pine forests (1000-2000m) to oak and rhododendron dominated broadleaf forest (2000-3800m) to alpine meadow at higher elevations (above 3800m) (Paudel et al., 2012; Ghimire et al., 2019). Significant portions of the lower Melamchi valley are human impacted through town and road development as well as agricultural cultivation (Paudel et al., 2012; Ghimire et al., 2019) although this decreases significantly to the north. Tree line in the Melamchi Valley is at ~3800m. Satellite imagery (Figure 3.1b) show changes from cropland to forest to alpine grasses from south to north in the valley. 3.3 Methods 3.3.1 Field and Laboratory Methods Soil and weathering profile samples were collected through multiple field campaigns in October 2017, October 2018, and April 2022. Soil is defined as the mobile layer of physically transported material where regolith is defined as altered bedrock that retains an in situ relict rock structure (foliation, banding) (Dixon et al., 2012). We sampled nine soil pits and four weathering profiles along the eastern Melamchi ridgeline (Figure 3.1a, Figure 3.2a). Samples were collected dominantly along the main ridgeline, taking care to avoid locations where soil creep or erosion could deposit material. SP-5 and SP-6 were collected from spur ridges off the main ridgeline due to accessibility. Soil pits were dug using a trowel through regolith and samples were taken in every soil layer (if available) as well as regolith. Higher resolution weathering profiles were cut back 20cm into roadcuts to avoid recent surficial weathering and sampled to depths between 2-4.25m, depending on site characteristics. Samples were collected every 10-20cm in each weathering profile. Soils were dried and subsampled for bulk chemical analysis. In total we analyzed 23 soils samples from the nine soil pits and 81 samples from the weathering profiles for elemental composition. Elemental composition was determined using x-ray fluorescence (XRF). Samples were powdered and sieved to >200 um using a Spex 8000 mixer/mill. 8 grams of sample and 2 grams (20% by weight) 54 cellulose binder were combined in a mortar and pestle and pressed for 3 minutes under 10 tons of pressure in a 32mm diameter pressing die. Samples were analyzed at the Los Angeles Natural History Museum on an XGT-7200 bench top x-ray fluorescence using a 1.2mm beam and 30kV. Eight sites were selected in a gridded pattern, analyzed for 120 seconds and spectra from each site was averaged to determine elemental composition. Analyses are reported with standard deviation of spectra. USGS Cody Shale was used as a reference material and 5% deviation was recorded. 3.3.2 Chemical Index of Alteration (CIA) Chemical Index of Alteration (CIA) is a weathering index for feldspar-rich rocks that does not rely on knowing the underlying parent composition as other indices such as tau values do (Nesbitt and Young, 1982). CIA relies on Al as the nonmobile element and uses the mobility of Na, Ca and K which are dominant in feldspar rich rocks and mobile during weathering: 𝐶𝐼𝐴 = 100×) !" ! # " !" ! # " $%&#$'& ! #$( ! # * Eq. 3.1 Fresh granite generally falls between 45-55% and higher values represent more intense weathering. 3.3.3 Topographic analysis methodology Topographic analyses were done using a smoothed 2m DTM produced using the SETSM algorithm using WorldView 2 imagery from 2017. One cloud feature was masked out and filled with a 2m DTM produced using ASP using Pleiades imagery from 2021. Land class rasters were downloaded from the International Centre for Integrated Mountain Development (ICIMOD) (FRTC, 2022). Temperature rasters come from CHELSA MAT 1981-2010 (Karger et al., 2017). Transects of 25 and 100m along the ridgeline every 100m were constructed at varying widths perpendicular to the ridgeline using SAGA’s “Profiles from Lines” tool and converted to points. Raster attributes were extracted along each profile. Average ridge topography was determined by binning transects in 2.5km bins by location along individual transects. Mean slope at each transect location was calculated by averaging values across each transect. Curvature was calculated from each transect using a polyfit line for 55 second derivative of elevation because the 2m smoothed SETSM DTM contains too much high resolution variability to accurately calculate curvature across the landscape (Atwood and West, 2022). Average subcatchment curvatures were calculated by binning 100m transects along the length of the ridgeline covered by each subcatchment. Slope rasters were constructed using Topotoolbox (Schwanghart and Scherler, 2014). K sn values were calculated in Topotoolbox a threshold upstream area of 1 km 2 . In order to determine the appropriate threshold theta value, we compared mn values derived from the following methods: 1) using a slope-area method (mn=0.16), 2) Topotoolbox’s mnoptimvar() function (mn =0.25), 3) using Topotoolbox’s chiplot() function and 4) the standard reference theta (mn=0.45) (Perron and Royden, 2013). In final analyses, the standard reference theta was used. 3.4 Results 3.4.1 Chemical Weathering Weathering Profiles: Soil properties and CIA values in more detailed weathering profiles show a distinct decrease in weathering degree along the ridgeline, from south to north (Figure 3.3). WP-5, the southernmost site that sites south of the Melamchi Valley (Figure 3.1a), is the most weathered. CIA values range from 82 in the near surface, highly weathered zone with no structure before a decrease to 75 at the saprolite boundary at 0.9m (Figure 3.3b). Values remain consistent in the mid to low 70s until the base of the profile despite a lithologic change at approximately 2m depth (Figure 3.3b). WP-1 and WP-3 are at similar latitudes, but WP-3 is a hillslope site off the ridge (on a spur ridge) (Figure 3.1a). While they broadly show similar surficial weathering, they have different weathering patterns with depth. WP-1 is variably weathered with depth, with zones of higher weathering (mid 70s) throughout the profile and interlayered zones of saprolite with structure and zones of no structure present throughout the first two meters (Figure 3.3c). WP-3, while similar at the surface to WP-1, is less weathered at depth and does not show the same heterogeneity (Figure 3.3d). The saprolite boundary at 1.1m is apparent in both the visual profile and a marked change in CIA values from 70 to 62. WP-4, the northernmost profile, has the least weathering, with a very limited soil horizon and CIA values ranging from 50-62 (Figure 3.3e). 56 Additional ridgeline weathering profiles (WP-8 and WP-9) were collected from 15km and 20km along the ridge. Field analysis shows deeper weathering, similar to WP-1, which corresponds higher soil CIA values in these areas (Figure 3.3c). Soil Pits: CIA values in soil pits show a distinct change in alteration degree at 25km along the ridge (Figure 3.3c). South of 25km, both soil samples and regolith are heavily weathered with CIA values generally between 80-85 regardless of soil depth. North of 25km, CIA values drop to between 60-70, except for one anomalous saprolite sample (MRS-9) that has a CIA value of 85. The decrease in CIA does not correspond with a decrease in regolith depth, which happens further south along the ridge (Figure 3.4d). Depth to Figure 3.3 a) Chemical Index of alteration (CIA) values for three ridgeline (WP-1, WP-4 and WP-5) and one hillslope (WP- 3) profiles show intense weathering at depth in southern end of valley (WP-1, WP-5) and less intense in mid-north (WP-4). (b-e) Weathering profiles pictures, annotated weathering changes and CIA values at each sampling location. Note that changes in CIA values are correlated to physical changes in all sites where CIA decreases at locations where saprolite with relict structure appeared. WP-5 and WP4 (b and e) also show soil strength values from pocket penetrometer measurements. These strength values do not appear to correlate with CIA values. 57 regolith decreases from 35cm to 13cm between 0-17km and remains steady around 13cm from 17-38km before decreasing to 4cm at the northernmost site (SP-5) (Figure 3.4d). Figure 3.4 a) Curvature and slope from 100m spaced transects along ridgeline. Slope was extracted from 25m and 100m length transects and curvature is calculated from 100m transects binned every 2.5km. b) Subcatchment (from subcatchments in Figure 3.1b) slope and normalized channel steepness (K sn) both increase until slope reaches a threshold value (37.5 degrees). c) Chemical Index of Alteration (CIA) values from soil profiles show a step change at 25km, where values are generally high (80+) from 0-25km and lower (60-70) from 25-50km. d) Regolith is deeper (20-37cm) from 0-15km, decreasing to 10-15cm after 15km. 58 3.4.2 Topographic Analyses Ridge morphology: 2.5km binned curvature profiles are relatively gentle in the southern end of the ridge and generally decrease from 0 to 27.5km, near Yangri Peak (Figure 3.3). The average curvature of these profiles decreases from 0-30km from -0.015 to -0.05 (Figure 3.4a). North of Yangri peak, 2.5km binned curvature profiles steepen northward with average curvatures decreasing from -0.06 to -0.1 (Figure 3.5, Figure 3.4a). The curvature shows a brief, apparent increase at 42.5-45km (Figure 3.5, Figure 3.4a), in recently glaciated terrain (Figure 3.1b). Slopes derived from both 25m and 100m ridgeline transects have a moderate increase from 0-30km from the Melamchi river’s confluence with the Indriwati River and then steepen from 30-40 km before decreasing at the far north of the valley, following a similar pattern to curvature (Figure 3.4a). Maximum mean slopes for the 25 and 100m transects are 42.1° and 47.2° respectively, while minimum mean slopes are 12° and 17.5° respectively. Subcatchment properties: Mean subcatchment slope increases from south to north, with a minimum mean catchment slope of 24.6 degrees to an apparent threshold maximum catchment slope of 37.5 degrees in catchments between 30-42km, decreasing slightly in the northernmost catchment (Figure 3.4b). K sn values increase from 81 to 304.8, with the highest K sn values between 27.5-40 km along the ridge, before decreasing Figure 3.5 Curvature profiles along the Melamchi Valley eastern ridgeline from 100m transects averaged over 2.5km bins from 2m SETSM DEM. Curvature from 0-25km (dark (blue-green) colors) is gentler compared to curvature from 25-47.5km (light (yellow-red) colors), which is generally steeper. 59 to the north (Figure 3.4b), where formerly glaciated valleys are filled with till (Chen et al 2023). Mean ridgeline curvature averaged along the sections of ridgeline above a catchment follows the same trend as mean ridgeline curvature from 2.5km binning in Figure 3.4a. 3.5 Discussion Hypothesized effects of climatic and tectonic gradients on chemical weathering: The strong temperature, precipitation and erosional south-north gradients present in the Melamchi Valley make it an ideal natural laboratory for understand controls on chemical weathering. Temperature has long been considered a global control on weathering, as it has an exponential effect on reaction rate kinetics. Global compilations have indicated that the chemical index of alteration (CIA), a measurement of weathering intensity and mean annual temperature (MAT) scale linearly, as does regolith depth (Deng et al. 2022); as temperature rises, reaction rates increase leading to a higher weathering intensity (Figure 3.3). Precipitation has a similar effect, as precipitation drives removal of mobile elements in soils and saprolite, leading increased CIA (Figure 3.3; (Schaller and Ehlers, 2022)). Exhumation can also drive changes in chemical weathering, although this scales nonlinearly- due to the relationship between supply and kinetically limited weathering regimes. Initially regolith depth will be high, but then rapidly decrease with a linear increase in exhumation and then remain low nonlinearly (Ferrier and Kirchner, 2008). Weathering intensity shows a similar but less immediate trend, as weathering will remain high at first and then decrease nonlinearly (Ferrier and Kirchner, 2008). These trends are illustrated in Figure 3.3. These three drivers of chemical weathering thus result in different patterns of regolith depth and chemical weathering intensity, allowing us to determine what the dominant driver is in this actively tectonic orogen. 60 Topographic signals of rapid erosion: When landscapes respond to rapid uplift, it has been posited that the initial response is by rivers, which begin to steepen (Montgomery and Buffington, 1997; Kirby et al., 2003; Duvall et al., 2004; Ouimet et al., 2009). As rivers steepen, the hillslopes around them respond by steepening and finally this signal propagates upward to the ridgelines (Hurst et al., 2013; Mudd, 2017; Gabet et al., 2021). Figure 3.6 Hypotheses of the effects of climate and tectonics on chemical weathering intensity and regolith depth along the eastern ridge of the Melamchi. 61 However, at the highest erosional regimes landscapes can reach a “threshold”, where slopes reach a Figure 3.7 Comparison of subcatchment properties representative of different landscape domains, with higher K sn representing steepening channels, higher slope representing steepening hillslopes and lower curvature representing steepening ridgelines. Generally, hillslopes and channels (e) show a strong connection, except when hillslopes reach threshold slopes (generally north of 25km) (f). Ridgelines are generally less correlated to both hillslopes and channels ((a) and (c)) but this lack of connection is stronger in the northern end of the catchment ((b) and (d)). The lack of relation between these landscapes is indicative of transient landscapes where tectonic uplift has not propagated to ridgelines yet. 62 maximum mean steepness, as landscapes adjust to the oversteepening of the river channels and reach a maximum slope stability (Burbank et al., 1996; Montgomery et al., 2001, Ouimet et al. 2009).. These regions often correlate with non-linear increases in landslide rates as well. (Burbank et al., 1996). Therefore, as landscapes that have adjusted to exhumation, we expect to see strong relationships between channels, hillslopes and ridgeline features until threshold landscapes are reached. When we compare channel steepness (mean K sn), hillslope steepness (mean slope) and ridgeline steepness (mean curvature) for subcatchments, we find several important things. First, it appears that subcatchments reach a mean threshold slope of 37.5 degrees, where slopes do not steepen past that (Figure 3.4b, Figure 3.6e, f). Secondly, broadly we see the strongest connections between channels and hillslopes, then between slope and ridgeline and the weakest between channels and ridgelines (Figures 3.6a, 3.6c, 3.6e). Between channels and hillslopes, we see a decoupling when we reach the threshold slope value, where channels begin to steepen more than hillslopes in the steepest sections of the landscape (Figure 3.6e). In the southern sections of the valley (south of 25km), there is scatter but we see some relation between steepening hillslopes and ridges as well as steepening channels and ridges (Figure 3.6b, Figure 3.6d). There is a relatively strong relationship between channels and hillslopes (Figure 3.6f). However, north of 25km, we see no correlation between ridgeline curvature with channels or hillslopes and a poor correlation between channels and hillslopes (Figure 3.6b, 3.6d and 3.6f). This lack of channel and hillslope correlation is likely because many of these catchments are at a threshold slope and thus cannot continue to steepen with increasing channel steepness. In this section, it also appears that channels and hillslopes are generally steeper than expected based on the curvature- despite lower curvature values. Curvature values in this steepest zone appear to cluster (Figure 3.5a, 3.6), which could indicate a ridgetop curvature threshold at highest erosion rates. While past work looking at curvature in soil-mantled landscapes have noted scaling relationships between erosion and ridgetop curvature, such work has been limited in threshold landscapes where landscapes dominate erosional processes (Gabet, et al., 2021). Chemical weathering drivers: Three zones appear along the Melamchi ridgeline (Figure 3.7): 1) 0-15 km: ridge line curvature decreases, K sn increases rapidly, regolith is deeper and CIA values are high (Figure 3.4). 2) 63 15-25 km: regolith is shallow but remains highly weathered, K sn is relatively stable and curvature is variable— here we posit that minerals have long enough residence times and fast enough kinetics to weather, but higher erosion leads to shallower regolith (Hurst et al., 2013; Mudd, 2017; Gabet et al., 2021). 3) 25 km- 40 km: CIA values decrease suddenly and regolith remains shallow (Figure 3.4c, 3.4d). These patterns are reflected in the deeper weathering profiles, where we see higher weathering pervade at depth in sites south of 25 km. In the two southernmost ridge sites (WP-1 and WP-5), weathering at the bottom of the pits (~4.25m) is higher than surface weathering at the northernmost site (WP-4) (Figure 3.3a). Therefore, not only is the weathering higher in the south, but it is also deeper and more pervasive regardless of lithologic heterogeneity (see WP-5). The correlation between the weathering shift, shallower regolith, steepened channels and hillslopes and youngest bedrock cooling ages in Zone 3 (Figure 3.4, Figure 3.7, Figure 3.2b) suggests that rapid erosion is moving material downslope before it can fully weather, indicative of a kinetic controlled environment (Dixon et al., 2012). Globally, temperature has been posited to be the main control on chemical weathering rates (Deng et al., 2022; Brantley et al., 2023), While precipitation increases orographically in this area, providing sufficient reactive waters, mean annual temperatures are lower at higher elevation, slowing down weathering reactions (Figure 3.2a) (Hille, 2022). This likely explains the weathering and regolith patterns in Figure 3.8 Synthesis figure of ridgeline transitions. Zone 1 (blue) and Zone 2 (green) have generally increase channel steepness and slopes from south to north, as well as decreasing regolith depth. These zones are where the vast majority of inhabitants in the Melamchi Valley live. Zone 3 (orange) occurs at the chemical weathering shift, where chemical weathering decreases, threshold slopes occur, channels steepen and ridgeline concavity is steeper, but lower than predicted based on slopes and channels steepness. This zone is had the highest landslide density during the Gorkha earthquake as well as during recent monsoons. 64 Zones 1 and 2, where regolith depth is decreasing with increasing erosion, but surface weathering remains due to these changing climate factors. In Zone 3, however, if temperature was the main control, the expected weathering change would be a linear decrease with decreasing temperatures not a step change function as seen in our soil profiles (Figure 3.4d). Thus, increased erosion appears to be the main driver of chemical weathering shifts in the Melamchi. Further work utilizing 10 Be to determine soil production and weathering rates could help elucidate these relationships. This zone of kinetically limited weathering collocates with the regions of highest density landslides, both tectonically driven (from the 2015 Gorkha Earthquake (Roback et al., 2018)) and monsoon driven (Hille, 2022). Such an erosional regime shift has been previously noted to limit chemical weathering despite the high erosion rates (Dixon et al., 2012; Larsen et al., 2014). Summary Mountain building drives changes to soil development, erosional regimes and chemical weathering and thus is a driving force in Critical Zone architecture development. In the Melamchi Valley, we document the transition from hillslope-channel-ridgeline connection to disconnection due to tectonic perturbations. At the same time, we see a shift in chemical weathering intensity which corresponds with an apparent shift in erosional processes, where both higher monsoonal and co-seismic landslides occur. The decline seen in regolith depth and weathering intensity combined with increased erosion documents a shift from a kinetically limited landscape to a kinetically controlled landscape, an important shift for landscape modelling. Further work incorporating weathering and soil production rates will help further our understanding if such declines in chemical weathering are linked to declines in chemical weathering rates as well. 65 SAMPLE ID Latitude Longitude Elevation (m) Depth (cm) Regolith Depth (cm) MAT (ºC) Elemental Composition (all reported in weight %) CIA Average Sample CIA Na2O MgO Al2O3 SiO2 So3 K2O CaO TiO2 Fe2O3 ZrO2 MRS-1 27.8470 85.5712 1299 10 36 18.45 0.07 1.57 18.72 62.21 0.08 3.43 0.29 1.41 12.05 0.08 83.18 83.35 MRS-1 27.8470 85.5712 1299 30 36 18.45 0.05 1.47 20.37 59.19 0.07 3.37 0.22 1.48 13.58 0.07 84.83 MRS-1 27.8470 85.5712 1299 41 36 18.45 0.05 1.31 19.34 61.33 0.05 3.81 0.37 1.35 12.23 0.07 82.05 MRS-2 27.8541 85.5666 1431 12 20 17.75 0.01 0.92 17.02 55.65 0.04 2.51 0.13 1.22 22.37 0.07 86.50 83.20 MRS-2 27.8541 85.5666 1431 30 20 17.75 0.07 1.86 14.49 63.72 0.05 3.44 0.13 1.20 14.87 0.09 79.91 MRS-5 27.8982 85.5792 2020 18 28 14.45 0.12 1.01 14.79 70.11 0.12 2.04 0.19 1.00 10.48 0.06 86.30 86.30 MRS-6 27.9440 85.5973 2553 7 12 10.45 0.14 0.69 12.68 76.18 0.22 1.99 0.11 0.63 7.29 0.04 85.04 84.75 MRS-6 27.9440 85.5973 2553 19 12 10.45 0.04 0.51 10.26 82.64 0.09 1.74 0.11 0.37 4.17 0.03 84.46 MRS-7 27.9778 85.5827 3104 12 17 8.15 0.19 2.44 18.69 55.01 0.29 2.58 0.26 1.80 18.57 0.07 86.04 85.79 MRS-7 27.9778 85.5827 3104 27 17 8.15 0.17 2.19 14.15 67.56 0.12 2.00 0.23 1.15 12.34 0.07 85.54 MRS-8 27.9926 85.5721 3219 3 11 6.95 1.68 0.12 10.74 80.66 0.08 4.72 0.53 0.12 1.29 0.02 60.78 63.82 MRS-8 27.9926 85.5721 3219 17 11 6.95 1.61 0.24 12.92 78.12 0.08 4.17 0.63 0.13 2.05 0.01 66.87 SP-8 28.0211 85.5687 3423 7.5 15 5.35 0.80 1.93 15.91 61.60 0.71 2.64 2.02 1.35 12.83 0.10 74.46 66.44 SP-8 28.0211 85.5687 3423 15 15 5.35 1.95 0.86 11.03 74.73 0.04 1.32 4.58 0.64 4.74 0.08 58.42 MRS-9 28.0476 85.5668 3885 6 10 3.95 0.79 1.49 12.62 66.79 0.40 2.85 1.82 1.37 11.73 0.08 69.82 74.31 MRS-9 28.0476 85.5668 3885 10 10 3.95 0.91 1.20 11.18 72.18 0.13 3.06 1.34 0.95 8.93 0.06 67.79 MRS-9 28.0476 85.5668 3885 14 10 3.95 0.18 2.32 15.72 63.26 0.16 2.26 0.27 1.33 14.37 0.08 85.33 SP-7 28.0496 85.5678 3983 8 16 3.95 1.00 1.11 15.29 63.37 0.55 3.29 1.42 1.41 12.40 0.09 72.80 69.26 SP-7 28.0496 85.5678 3983 16 16 3.95 1.47 1.10 11.92 71.60 0.05 3.60 1.15 0.60 8.40 0.05 65.72 SP-6 28.0736 85.5742 4146 6.5 13 2.05 1.16 1.26 14.18 63.73 0.66 3.11 1.22 1.45 13.03 0.07 72.10 66.54 SP-6 28.0736 85.5742 4146 13 13 2.05 2.26 0.74 13.12 71.62 0.04 5.22 0.92 0.68 5.28 0.04 60.98 SP-5 28.1051 85.5774 4173 2 4.5 0.35 0.89 0.49 13.34 71.88 0.86 4.63 0.90 1.35 5.45 0.10 67.52 65.03 SP-5 28.1051 85.5774 4173 4.5 4.5 0.35 0.61 0.16 9.19 83.47 0.05 4.30 0.60 0.22 1.25 0.02 62.53 Table 3.1 Details and XRF results from soil samples. CIA = chemical index of alteration. 66 WEATHERING PROFILE ID Depth (m) Elemental Composition (all reported in weight %) Chemical Index Of Alteration Na2O MgO Al2O3 SiO2 SO3 K2O CaO TiO2 Fe2O3 SrO ZrO2 WP-5 0.1 1.31 2.34 16.55 64.40 0.07 2.56 0.41 1.05 11.17 0.01 0.07 79.47 WP-5 0.2 1.12 2.20 16.26 65.15 0.05 2.74 0.34 1.10 10.91 0.01 0.06 79.47 WP-5 0.3 0.64 2.31 16.43 64.74 0.06 3.07 0.30 1.11 11.21 0.01 0.06 80.40 WP-5 0.4 0.87 2.63 16.69 63.70 0.07 2.69 0.34 1.15 11.70 0.01 0.07 81.06 WP-5 0.5 0.90 2.23 16.65 63.67 0.06 2.94 0.33 1.15 11.94 0.01 0.06 79.96 WP-5 0.6 0.85 2.16 15.86 65.23 0.05 2.72 0.33 1.10 11.58 0.01 0.06 80.31 WP-5 0.7 0.62 2.36 17.51 62.78 0.05 2.98 0.33 1.17 12.06 0.01 0.07 81.66 WP-5 0.8 1.02 2.18 16.52 64.48 0.04 2.73 0.35 1.11 11.44 0.01 0.06 80.11 WP-5 0.9 2.34 2.31 13.61 66.93 0.02 1.89 0.35 1.00 11.44 0.01 0.05 74.81 WP-5 1.1 2.22 1.66 14.23 68.89 0.03 1.67 0.46 0.94 9.77 0.01 0.06 76.58 WP-5 1.3 2.33 2.47 15.25 65.01 0.02 2.18 0.39 1.10 11.11 0.01 0.05 75.66 WP-5 1.5 2.34 2.31 13.61 66.93 0.02 1.89 0.35 1.00 11.44 0.01 0.05 74.81 WP-5 1.7 2.59 2.40 13.39 67.56 0.03 1.67 0.39 0.99 10.86 0.01 0.05 74.24 WP-5 1.9 2.02 2.50 13.68 67.91 0.02 1.65 0.32 1.08 10.66 0.01 0.08 77.40 WP-5 2.1 2.41 1.99 13.67 68.68 0.02 1.55 0.38 0.98 10.20 0.01 0.05 75.90 WP-5 2.3 2.29 1.78 14.91 67.08 0.02 1.83 0.43 0.95 10.59 0.01 0.06 76.60 WP-5 2.5 1.69 1.49 14.75 68.34 0.03 1.99 0.45 1.01 10.11 0.00 0.08 78.13 WP-5 2.7 2.11 1.69 14.46 68.29 0.03 1.91 0.47 0.94 10.00 0.01 0.06 76.32 WP-5 2.9 1.96 1.75 15.32 65.80 0.02 2.24 0.49 1.04 11.25 0.01 0.07 76.58 WP-5 3.1 1.71 1.89 14.56 67.38 0.03 2.53 0.39 1.02 10.36 0.00 0.06 75.85 WP-5 3.3 1.48 2.00 15.27 66.41 0.03 3.01 0.32 1.02 10.33 0.00 0.06 76.06 WP-5 3.5 1.44 1.58 13.75 69.10 0.02 2.68 0.31 0.98 10.02 0.00 0.06 75.69 WP-5 3.7 1.61 1.93 14.53 67.74 0.03 2.81 0.33 0.96 9.93 0.01 0.05 75.41 WP-5 3.9 2.30 1.66 14.08 68.18 0.03 1.95 0.54 0.96 10.21 0.01 0.06 74.62 WP-5 4.1 2.94 1.64 13.68 68.54 0.03 1.32 0.76 0.88 10.14 0.01 0.05 73.19 WP-5 4.3 3.47 1.82 13.61 68.37 0.02 1.07 0.83 0.88 9.86 0.01 0.05 71.73 WP-5 4.5 3.24 1.59 14.12 67.65 0.03 1.40 0.77 0.93 10.20 0.01 0.05 72.32 WP-1 0 2.11 19.01 50.03 0.14 7.06 0.58 2.02 18.52 0.03 0.29 71.33 WP-1 0.25 1.89 18.58 52.93 0.12 7.14 0.44 1.67 16.83 0.02 0.22 71.03 WP-1 0.5 1.81 19.96 50.27 0.05 7.75 0.74 1.71 17.44 0.02 0.03 70.18 WP-1 0.75 1.85 21.25 47.27 0.07 8.14 0.31 1.88 18.98 0.01 0.03 71.54 WP-1 1 2.07 22.42 47.27 0.05 7.12 0.61 1.76 17.94 0.02 0.54 74.36 WP-1 1.25 2.39 20.53 46.42 0.05 6.90 0.54 2.10 20.90 0.01 0.01 73.40 WP-1 1.5 2.22 17.84 53.10 0.04 6.42 0.26 1.65 18.15 0.02 0.08 72.76 WP-1 1.75 2.23 18.09 53.54 0.05 7.19 0.30 1.77 16.38 0.01 0.27 70.72 WP-1 2 2.22 17.98 55.56 0.04 7.43 0.49 1.55 14.45 0.03 0.03 69.40 WP-1 2.25 2.30 17.99 51.52 0.04 7.62 1.69 1.86 16.62 0.04 0.14 65.91 WP-1 2.5 2.56 18.01 55.36 0.03 6.90 1.79 1.53 13.53 0.05 0.03 67.45 WP-1 2.75 2.28 16.70 58.36 0.06 7.03 1.57 1.57 12.08 0.05 0.09 66.01 WP-1 3 1.94 15.89 61.68 0.05 5.67 0.54 1.43 12.44 0.05 0.12 71.93 WP-1 3.25 1.72 15.62 64.72 0.05 3.98 1.42 1.41 10.56 0.05 0.25 74.31 WP-1 3.5 2.21 16.16 58.64 0.05 5.42 2.51 1.81 12.85 0.08 0.12 67.09 WP-1 3.75 1.79 15.34 62.78 0.07 4.28 2.12 1.50 10.39 0.06 1.56 70.57 WP-1 4 2.58 16.36 58.05 0.06 5.38 0.89 1.65 14.63 0.04 0.16 72.30 67 WP-1 4.25 2.37 17.95 53.77 0.03 7.07 1.32 1.64 15.59 0.05 0.03 68.17 WP-3 0.1 0.08 1.90 16.88 57.82 0.06 5.48 0.85 1.75 14.61 0.03 0.41 72.44 WP-3 0.2 0.30 2.26 17.35 55.93 0.04 6.16 0.90 1.77 14.95 0.03 0.10 70.21 WP-3 0.3 0.34 1.93 16.70 58.37 0.03 6.34 0.75 1.69 13.53 0.03 0.04 69.24 WP-3 0.4 0.19 2.46 16.85 55.96 0.05 6.60 0.82 1.80 14.84 0.02 0.22 68.91 WP-3 0.5 0.24 2.29 19.44 54.86 0.05 5.60 0.75 1.85 14.46 0.06 0.09 74.68 WP-3 0.6 0.29 3.03 18.97 49.78 0.06 6.74 0.83 2.21 17.51 0.04 0.31 70.74 WP-3 0.7 0.16 3.02 19.45 47.33 0.03 6.93 1.42 4.03 16.92 0.04 0.44 69.57 WP-3 0.8 0.59 2.35 17.64 56.04 0.06 7.66 0.57 1.79 12.93 0.03 0.16 66.63 WP-3 1 0.41 2.03 17.27 59.26 0.05 4.87 2.10 1.79 11.91 0.05 0.09 70.06 WP-3 1.2 0.97 2.31 17.35 55.43 0.06 6.52 2.94 1.77 12.42 0.09 0.04 62.46 WP-3 1.4 0.28 2.39 15.64 58.37 0.06 8.13 1.79 1.69 11.13 0.08 0.10 60.54 WP-3 1.6 0.39 2.64 16.11 58.56 0.04 6.22 2.20 1.53 11.80 0.07 0.11 64.68 WP-3 1.8 1.14 1.95 15.55 60.67 0.04 5.05 2.62 1.53 10.99 0.08 0.26 63.83 WP-3 2 1.20 2.67 15.72 56.34 0.05 6.47 2.42 1.83 12.67 0.11 0.35 60.91 WP-3 2.2 0.61 2.42 14.33 58.70 0.04 6.43 1.49 1.93 13.67 0.10 0.13 62.70 WP-3 2.4 1.17 2.19 14.75 58.12 0.04 5.98 3.33 1.64 12.37 0.12 0.06 58.46 WP-3 2.6 1.13 2.75 14.89 58.36 0.05 5.25 2.56 2.24 12.29 0.10 0.23 62.48 WP-4 0.1 1.13 0.44 13.31 69.90 0.13 8.22 0.90 0.46 5.28 0.02 0.06 56.50 WP-4 0.2 0.56 0.77 17.66 61.44 0.11 9.47 0.82 0.51 8.49 0.02 0.03 61.95 WP-4 0.3 0.58 0.65 18.37 61.96 0.09 9.25 0.99 0.68 7.26 0.02 0.01 62.93 WP-4 0.4 1.14 0.22 14.74 66.94 0.06 11.82 1.04 0.23 3.50 0.01 0.14 51.29 WP-4 0.5 2.28 0.26 16.31 61.31 0.09 11.01 1.60 0.31 6.49 0.02 0.13 52.27 WP-4 0.6 0.83 0.42 18.98 56.47 0.17 11.40 1.73 0.63 9.19 0.04 0.03 57.63 WP-4 0.7 1.24 0.22 17.06 66.14 0.08 9.22 1.30 0.29 4.27 0.02 0.01 59.22 WP-4 0.8 1.62 0.10 11.62 74.49 0.07 8.04 0.84 0.10 2.91 0.02 0.07 52.54 WP-4 0.9 2.64 0.13 13.26 70.64 0.07 7.99 1.64 0.21 3.19 0.03 0.12 51.95 WP-4 1 2.62 0.09 13.76 70.29 0.04 8.69 1.54 0.22 2.63 0.02 0.03 51.71 WP-4 1.1 1.93 0.32 12.94 71.38 0.05 8.14 1.50 0.25 3.26 0.02 0.05 52.79 WP-4 1.2 1.39 0.15 14.60 67.45 0.06 11.33 1.07 0.27 3.52 0.02 0.03 51.43 WP-4 1.3 1.75 0.20 15.44 66.90 0.04 10.49 1.13 0.28 3.62 0.01 0.01 53.61 WP-4 1.4 1.92 0.21 13.47 69.27 0.07 9.50 1.02 0.31 4.02 0.01 0.06 51.98 WP-4 1.5 1.18 0.11 11.25 73.95 0.06 9.37 0.71 0.20 2.96 0.02 0.02 49.97 WP-4 1.6 1.08 0.05 11.26 75.37 0.08 8.07 0.84 0.22 2.83 0.01 0.02 52.98 WP-4 1.7 1.03 0.18 14.56 70.32 0.06 9.00 0.84 0.22 3.55 0.01 0.01 57.24 WP-4 1.8 1.54 0.27 14.04 69.46 0.05 9.49 0.82 0.24 3.98 0.01 0.02 54.24 WP-4 1.9 1.20 0.18 16.34 67.50 0.05 8.31 0.95 0.31 4.96 0.01 0.02 60.95 Table 3.2 XRF results from four detailed weathering profiles. 68 Latitude Longitude Mean Slope (º) Mode Slope (º) Slope SD (º) Catchment Area (m 2 ) Mean Ridge Curvature Mean Ksn 27.835863 85.551070 29.68 16.30 11.41 1011909 na 135.93 27.865066 85.555546 28.94 26.01 11.71 1129918 -0.0223 80.90 27.874103 85.559988 24.61 16.63 11.20 3456059 -0.0207 98.90 27.896073 85.566293 26.88 16.98 12.60 5024125 -0.0293 144.22 27.913911 85.566232 29.91 21.01 11.05 1701348 na 143.37 27.920585 85.562353 37.80 28.64 13.46 1089650 na 126.22 27.921906 85.577583 29.14 18.23 11.87 4336859 -0.0486 172.51 27.932738 85.584783 31.39 27.48 12.44 4086565 -0.0449 176.33 27.938797 85.540243 21.86 16.82 9.83 1526280 na 148.66 27.944932 85.580293 30.07 19.88 12.40 5525983 -0.0334 185.14 27.955242 85.526277 28.24 14.83 10.93 1197228 na 263.11 27.961176 85.570623 33.39 34.50 13.25 8090584 -0.0272 193.19 27.979633 85.563310 32.86 29.33 13.05 7592923 -0.0511 186.82 27.998137 85.555011 29.17 27.09 12.81 2113436 -0.0149 210.22 28.007272 85.555352 37.20 37.78 12.65 3466699 -0.0386 231.11 28.021118 85.555931 34.69 22.80 12.47 3911371 -0.0343 247.52 28.029126 85.518384 29.68 16.30 11.41 1616133 na 135.93 28.029898 85.549362 37.48 38.00 12.36 1969054 -0.0521 208.76 28.046915 85.557273 37.00 33.96 10.91 4173708 -0.0755 304.80 28.074569 85.558345 37.50 33.63 16.48 2255550 -0.0581 276.37 28.132775 85.541075 34.55 16.71 16.37 13893098 -0.0671 157.32 Table 3.3 Subcatchment properties. 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Tectonophysics 834. 73 Chapter 4 Mountain building and the deep critical zone: fracturing and mineral weathering in a borehole from the High Himalaya of central Nepal * Atwood, A, West, A.J., Zekkos, D., Clark, M.K., Chamlagain, D., Budhathoki, S. Abstract Rock weathering plays a central role in soil formation, the evolution of bedrock landscapes, and the regulation of the geologic carbon cycle. Interactions between mineral availability, groundwater flow and permeability control subsurface weathering and resulting critical zone structure. Rapid exhumation dramatically affects these factors through fracture development and fresh mineral supply, yet little detailed work on deep critical zone architecture has been done in active orogens. This study documents deep weathering in the rapidly exhuming Melamchi Valley, in the High Himalaya of central Nepal. We present data from an 80m borehole drilled through layered garnet- mica schist and augen gneiss, including geochemical and thin section analyses of recovered core, associated interpretations of weathering degree, and results from geotechnical strength tests. Core recovery varied significantly, reflecting widespread macro-fracturing. Thin section observations indicate that biotite oxidation is pervasive, resulting in intramineral microfracturing and increased porosity in the rock mass. However, we find that the garnet-mica schist, which is near the surface and contains abundant biotite, is overall less weathered in thin section and has greater core recovery than the immediately underlying augen gneiss. This lithological difference in weathering conditions is also apparent in near-surface seismic velocity profiles. We attribute the increase in weathering intensity at depth to the presence of feldspar causing greater mineral dissolution and clay formation * Atwood contributed lab and thin section analyses and manuscript authorship. This chapter will be an article submitted for publication to Geochimica et Cosmochimica Acta 74 in the augen gneiss than the overlying garnet-mica schist, which lacks feldspar. We find abundant fractures even in the deepest sections of our core, despite limited weathering of this portion of the gneiss, suggesting that inherited fractures are sufficient to provide fluid access to drive weathering even without additional fracturing driven by biotite oxidation. This study reveals how the heterogeneous composition and pervasive fracturing of rock in active orogens can produce more complex critical zone architecture than expected from models based on straightforward downward propagation of weathering fronts. 4.1 Introduction The formation of regolith — the package of soil, saprolite and weathered rock that makes up the near surface — controls the production of soil (Heimsath et al., 1997), the availability of rock- derived nutrients (Vitousek and Farrington, 1997), the transport and storage of groundwater (Holbrook et al., 2014), and the global carbon cycle via chemical weathering and physical erosion (Walker et al., 1981; Raymo and Ruddiman, 1992). The architecture of regolith not only influences soil and water resources but can also determine when and where natural hazards occur, especially landsliding, rock fall, and other mass wasting processes (Gallen et al., 2015). Regolith thus sustains terrestrial life and is intertwined with human society, making understanding of its development broadly important (Riebe et al., 2017). Over the past two decades, the critical zone perspective, which focuses on integrated study of the zone at Earth’s surface from treetops to the bottom of the groundwater table, has widened the understanding of how regolith forms and evolves (Anderson et al., 2017). Recent studies on the role of physical and chemical weathering, and their coupling, have emphasized the importance of processes at depths well below the soil-saprolite transition in setting the pace and character of regolith development. Lithology, fracture availability from topographic stresses and tectonics, 75 climate, and groundwater drainage have all been posited as controls on the thickness of regolith (Rasmussen et al., 2011; Brantley et al., 2011, 2013; Bazilevskaya et al., 2013; Rempe and Dietrich, 2014; St Clair et al., 2015; Gu et al., 2020). These controls can be highly interdependent, such as topographic location and groundwater drainage via advection (Bazilevskaya et al., 2013). Lithologic and mineralogical roles have received particular attention. Prior studies have hypothesized that biotite oxidation and resulting micro-fracturing may control deep weathering by increasing permeability (Buss et al., 2008, 2013; Goodfellow et al., 2016), and higher biotite content has been linked to greater weathering of bedrock (Isherwood and Street, 1976). Initiation of weathering via biotite oxidation has been noted in tonalite, charnokite, granites and granodiorite (Isherwood and Street, 1976; Rebertus et al., 1986; Buss et al., 2008; Shen et al., 2019). Biotite oxidation and expansion from reactive O 2-rich waters can, in turn, promote plagioclase dissolution (Buss et al., 2008). Fracturing due to physical processes such topographic stresses and tectonic activity has also been shown to control regolith thickness and weathering intensity, especially along ridgelines where topographic stress opens fractures (Slim et al., 2015; St Clair et al., 2015). These fractures permit reactive, O 2-rich waters to reach deeper into the subsurface and accelerate the propagation of weathering fronts (Anderson et al., 2007; Bazilevskaya et al., 2013). One of the remaining challenges is to identify the links between the physical and chemical weathering processes involved in regolith formation, especially as manifest in tectonically active mountains, which are considered hotspots of weathering and sediment transport (Milliman and Syvitski, 1992; West et al., 2005), are critical to global biogeochemical cycles (Hilton et al., 2008; Torres et al., 2016; Hilton and West, 2020), and act as the “water towers” of the world (Viviroli et al., 2007; Immerzeel et al., 2020). Deep weathering of high relief, rapidly eroding mountain terrains 76 has remained relatively understudied, and it remains unclear if observations and models developed in other parts of the world can easily translate to these settings. For example, although the link between biotite oxidation and micro-fracturing has been described, this process may or may not be important where fracture density is already high due to significant tectonic and topographic stresses acting upon rocks (Gu et al., 2020). Here, we present results from a deep critical zone study at the foothills of the Himalaya in central Nepal, in the Melamchi Khola valley, where we investigate the drivers of regolith development in actively exhuming terrain. The Himalaya are the archetype of continent-continent collision zones, providing a compelling locale for studying regolith formation associated with mountain building. We drilled an 80m borehole in High Himalayan Crystalline Series through schist and gneiss, providing a unique opportunity to further understand the role of lithology in the initiation and progression of weathering from depth to the surface. We find two distinctive weathering profiles defined by contrasting processes operating in an overlying schist zone and an underlying gneiss. The differences between these zones reveal (1) the role of coupling of biotite oxidation and feldspar dissolution in setting the pace of weathering, and (2) a decoupling of weathering from fracturing, since fractures are widely available throughout the core but weathering decreases significantly with depth. 4.2 Site Description 4.2.1 Geologic, Tectonic, and Topographic Setting Our borehole site is located on the eastern ridgeline of the Melamchi Khola, which is a north-south trending valley 50 km northeast of Kathmandu (Figure 4.1a). The watershed of the Melamchi Khola covers an area of 1,145km 2 with an average slope angle of 31°. The valley is characterized by a dramatic topographic gradient typical of the central Himalaya, with gentler, lower relief slopes in the south (typical relief of ~500m) and steep, high relief in the north (typical relief of 77 ~1800m). This shift is commonly referred to as the Physiographic Transition, or PT2 (Hodges et al., 2001; Wobus et al., 2005). Our drill site is located at the southern end of the Melamchi ridge, in the town of Tallathok (~3 km north of the town of Melamchi Bazaar) — approximately 10-20 km south of PT2 (Wobus et al., 2005). The elevation at the ridge-top drill site is 1420m asl, where the river is located at 825m asl, reflecting ~600m relief (Figure 4.1b). The average slope angle in nearby sub- catchments of the Melamchi Khola is 19-22°. Figure 4.1 (a) Satellite optical imagery (ESRI World Imagery), (b) shaded relief map showing elevation (based on SRTM 30m data), and (c) geologic map (Duvadi et al., 2005) of the Melamchi Valley, showing borehole site (yellow star) in each panel. (d) Ridgeline elevation profile at borehole site. 78 Unlike much of the central Himalaya, the physiographic transition in the Melamchi Khola does not coincide with the major structural and lithological change across the Main Central Thrust (MCT). The MCT runs to the south of the Melamchi Khola, and the valley itself is underlain entirely by the High Himalaya Crystalline Series rocks, predominantly interlayered augen gneiss and mica- garnet schist with some quartzite groups (Figure 4.1c). The borehole site was drilled in the Dhad Khola formation, which is a porphyroblastic augen gneiss with thin bands of quartzite and schist (Duvadi et al., 2005). Nearby structural features include a mapped anticline ~1 km to the north and a syncline ~1 km to the south of the drill site. Rock outcrops in this location were highly weathered with a geological strength index (GSI) of 40, and average 1-D shear wave velocities at the site over 18m depth were 180 m/s, indicative of a highly weathered rock mass (Medwedeff et al., 2022). These GSI and shear wave velocities are typical of the southern end of the Melamchi ridge. In the Melamchi valley as a whole, rock strength varies with topographic position, consistent with prior work on the effects of topographic stress of rock properties (St Clair et al., 2015). Bedrock channels are the strongest where erosion of material exposes intact bedrock, while ridges are the weakest, likely due to high degree of weathering and greater depth of weathered rock (Medwedeff et al., 2022). Centennial-to-millennial timescale erosion rates in the Middle Hills of central Nepal are typically ~0.25 mm/yr in areas analogous to our drill site (West et al., 2015). North of PT2, erosion rates increase to 1-3 mm/yr (Godard et al., 2012). Widespread landsliding during monsoon storms, and during the 2015 7.8M Gorkha earthquake, occurred in the central and northern parts of the valley (Roback et al., 2018). However, landsliding tends to be limited in extent and generally shallow — and less important as a contribution to erosion — in the lower-relief Middle Hills (West et al., 2015) typical of the region where our drill site is found. 79 Low temperature thermochronology using apatite and zircon (U-Th)/He and apatite fission track cooling ages across the Melamchi valley suggest a longer-term history of rapid exhumation followed by slower exhumation, resulting from a ramp/flat geometry in the Main Himalayan Thrust where rock mass rapidly passed over a ramp and then has been slowly conveyed across the flat (Murray et al., 2018). The rapid exhumation occurred 5 million years ago in the southernmost end of the Melamchi valley and is still occurring in the northern end. The drill site is located in a region where rapid exhumation occurred ~3.5 million years ago (Medwedeff, 2022). 4.2.2 Climate and Vegetation The Melamchi Khola is characterized by a strong orographic pattern in mean annual precipitation, ranging from 1127 mm/year at the location of the borehole in the southern end to 2978 mm/year at the northern end of the valley (Hille, 2022). Precipitation falls as rain in the south (including at the borehole site) and a mix of rain and snow in the north. Precipitation in Nepal has a strong monsoonal seasonality, with most falling from early June through September. Mean annual temperature (MAT) ranges from 5 and 33°C in the valley, driven by elevation changes (Shrestha et al., 2017), with MAT at the borehole of ~25°C. The vegetation in the valley is dominated by Sal forest below 1000m, transitioning first to chir pine forests (1000-2000m) and then to oak and rhododendron dominated broadleaf forest at mid elevations (2000-4000m) and eventually to alpine meadow at higher elevations, above 4000m (Paudel et al., 2012; Ghimire et al., 2019). The drill site is in former chir pine (Pinus roxburghii) forest which generally has little or no understory and can grow in nutrient deficient soils. However, significant portions of the lower Melamchi valley, including around the drill site, are human impacted through town and road development as well as agricultural cultivation (Paudel et al., 2012; Ghimire et al., 2019). The borehole location is surrounded by a mix of remnant forest, human dwellings, and khet terraces used for irrigated agriculture (predominantly for growing rice). The drill 80 site itself was cleared, lowered by ~2m, and levelled prior to our drilling (in preparation for potential future housing development). We sampled the top ~2m of the original ridge regolith profile from an exposed face immediately adjacent to the drill site itself (Figure 4.2a, soil chemistry currently in sample prep). Figure 4.2 Photos of (a) the drill site and drill apparatus at the Tallathok site, looking north up the Melamchi Khola valley (note that depth 0 of the core was ~2m below the original ridgetop surface because the top section had been cleared prior to drilling) and (b) the core from 0-5m (schist), 21-25 (gneiss, limited recovery) and 61-65 (gneiss, high recovery). 81 4.3 Methods 4.3.1 Drilling We drilled an 80m borehole with a 76mm diameter working with East Management Engineering Services (EMES), a local contractor, using a Koken rotary platform drill, a triple tube core barrel, and a diamond bit. The borehole was cased during drilling to prevent wall collapse. The top ~0-3m and the interval from ~7-21m were too weak to drill with the triple tube apparatus and were instead augered with standard penetration tests (SPTs) every ~1 m to characterize material and collect representative samples. SPTs are used to determine the strength of weathered bedrock and saprolite, where a hollow stem auger is driven into the ground by blows from a slide hammer. Once the auger has been driven into the ground by a fixed distance (1m in our case), the number of blows per depth penetrated is recorded as a proxy for strength. During coring, we recorded percent recovery, descriptions of rock type, weathering degree and orientation of fractures when possible. We also calculated the rock quality designation (RQD), defined as the number of pieces greater than 10cm over the course of a single core run (Abzalov, 2016): RQD index (%) = 100 × Σ ("#$%&' )* +),# -.#+#/ 0 1.31 4) (6)&78 8#$%&' )* +),# ,9$) (Eq 4.1) Rocks with greater than 75% RQD are defined as good-excellent, while 25-75% RQD are poor-fair. Weathering degree was documented during logging using the classification scheme from the Geological Society of London (Anon, 1970) as shown in Table 4.1. Fracturing, aperture and roughness were also documented when appropriate. Whole rock analyses for point load testing (PLT), unconfined compressive strength (UCS) and porosity were performed by EMES in Kathmandu, Nepal. 82 4.3.2 Near surface seismology 1D shear wave velocity profiles were collected with Geometrics ES300 and Geode seismographs using 4.5 Hz vertical component geophones laid out in a 115 ft linear array with 5ft spacing and a 20 ft source offset. A 4.5 kg sledgehammer was struck against a 5 cm thick plastic plate to generate stress waves. The profiles were analyzed using Surface Wave Inversion and Profiling (Pasquet and Bodet, 2017). Further details on collection and analysis of seismic profiles (site VS 50 and VS 51) can be found in Medwedeff et al. (2022). 4.3.3 Petrographic analysis Microscopic analyses were performed on standard-thickness (30 μm) thin sections cut and mounted by Wagner Petrographic. Thin sections from SPT, surface material, and highly weathered rocks were clear epoxy impregnated before mounting. Mineralogy from thin section was determined using point-count methodology. Optical porosity was measured using the jPOR digital image analysis technique on blue epoxy-impregnated thin sections adopting a threshold color value of 59 Term Description Grade Fresh No visible sign of rock material weathering, perhaps slight discoloration on major discontinuities. I Slightly Weathered Discoloration indicates weathering of rock material and discontinuity surfaces. All the rock material may be discolored by weathering and may be somewhat weaker externally than its fresh condition. II Moderately weathered Less than half of the rock material is decomposed and/or disintegrated to a soil. Fresh or discolored rock is present either as continuous framework or as corestones. III Highly weathered More than half of the rock material is decomposed and/or disintegrated to a soil. Fresh or discolored rock is present either as a discontinuous framework or as corestones. IV Completely weathered All rock material is decomposed and/or disintegrated to soil. The original mass structure is still largely intact. V Residual soil All rock material is converted to soil. The mass structure and material fabric are destroyed. There is a large change in volume, but the soil has not been significantly transported. VI Table 4.1 Weathering degree classification during core logging. 83 (Grieve et al., 2016). Porosity samples were only taken from cored rocks (not from SPT samples) due to concerns about compaction during SPT sample collection. Thin section analysis of weathered material is an underutilized way to determine weathering degree and chemical alteration patterns, especially when parent material is challenging to determine. Stoops et al. (1979) developed a weathering degree scale using weathering textures and alteration patterns seen in thin section. This weathering degree scale allows one to determine the individual Figure 4.3 Illustrations from Delvigne (2005) of weathering textures across degrees (0-4) for (a) feldspar, (b) garnet, and (c) biotite. Bottom panels show thin section photographs of minerals from our Melamchi/Tallathok borehole samples, annotated with relevant weathering degree values. Samples are from (d) 3.6 m depth, characteristic of the lightly weathered schist, and (e) 15 m depth, characteristic of the weathered gneiss. 84 weathering degree of each mineral within a thin section sample and compare that to other samples without needing to rely on a parent material. Weathering degree (from 0-4) was determined for each mineral present in selected thin sections from our core based on the Stoops weathering alteration scales (Figure 4.3, full descriptions found in Delvigne, 1998). Alteration minerals that did not have a clearly identifiable primary mineral were assigned the highest alteration degree (4). Full thin section weathering degree (WD sample) was calculated by the summation of each individual mineral degree (WD i) normalized by the percentage of mineral present in the sample (%min i): 𝑊𝐷 :;<=>? =∑ %<AB ! × DE ! 311 <ABF <ABA (Eq 4.2) 4.3.4 Geochemical analyses Elemental composition of samples was determined using x-ray fluorescence (XRF). Samples were powdered and sieved to >200 um using a Spex 8000 mixer-mill. 2.5 grams of sample and 0.5 grams (20% by weight) cellulose binder were combined in a mortar and pestle and pressed for 3 minutes under 5 tons of pressure in a 15mm diameter pressing die. Samples were analyzed at the Los Angeles County Museum of Natural History on an XGT-7200 bench top x-ray fluorescence system using a 1.2 mm beam and 30 keV energy. Eight sites on each pressed pellet were selected in a gridded pattern and analyzed for 120 seconds, and spectra from each site were averaged to determine elemental composition. Results are reported with standard deviation of spectra from each pellet. Chemical index of alteration (CIA) One quantitative metric of chemical weathering is the Chemical Index of Alteration (CIA) (Nesbitt and Young, 1982), which is especially useful for feldspar-bearing rocks with heterogenous 85 parent material because it 1) does not rely on a fixed parent material composition, instead using Al as an immobile element and 2) looks at the mobility of Na, Ca and K which are dominant in feldspars weathering to clays, such as kaolinite. CIA is defined as: 𝐶𝐼𝐴 =100×8 G> " H # G> " H # IJ;HIK; " HIL " H 9 (Eq 4.3) Fresh granite generally falls between 45-55%, and higher values represent more intense weathering. We note that CIA is not sensitive to Mg content, and therefore only weakly depends on biotite content which can vary considerably in layered meta-sedimentary rocks such as those of the Melamchi Khola. Tau and chemical depletion factor (CDF) Elemental changes throughout a profile can also be evaluated for weathering-mobile elements by comparing the content in a given sample to a parent rock composition using chemically less-mobile elements (Zr, Ti, etc). In this approach, the parent rock is assumed to be the chemical starting point for the profile, and the parent material is assumed to be relatively homogenous. Here we have assumed three parent materials, based on field observations of rock type (schist to gneiss transition) and an apparent chemical composition and rock texture change at 35m depth. We define the parent material as the least chemically depleted (lowest CIA) samples within each zone, taking the average composition of samples from 4.5-6m for the schist parent material and 34.5-35m for the initial gneiss parent material. Equation 4 shows the weathering-related mass loss in fractional terms, relative to the amount of the parent material or protolith, calculated as τ x, which is the “mass-transfer coefficient” for element X (Brimhall and Dietrich, 1987; Anderson et al., 2002): 86 𝜏 M = N $%&'&(!') O %*+&(!') O $%&'&(!') N %*+&(!') −1 (Eq 4.4) In this equation, I represents the immobile element (we used Ti), X represents the mobile element and the subscript refers to the parent (protolith) or weathered (regolith) material. Another complementary way to look at the overall chemical loss is the chemical depletion factor (CDF), which compares the immobile elemental concentration in the parent material relative to the weathered material (Riebe, 2017): 𝐶𝐷𝐹 =1− N $%&'&(!') N %*+&(!') (Eq 4.5) Assuming that Ti is chemically immobile and that the parent material is representative, a τ Ti, X, value of -0.5 would indicate a 50% depletion of that element in the weathered material. Similarly, a CDF value of 0.5 would indicate 50% of the weathered sample’s mass was lost chemically relative to the parent material. 4.3.5 Microfracture mapping Thin section fracture mapping was done with imagery taken at 2.5x zoom on a Axio Cam High Res Color using a Zeiss Axio Imager M2m microscope. Six rock samples spaced throughout the borehole were evaluated. Six subsampled boxes (500 pixels x 500 pixels) were evenly distributed across each thin section and fractures were mapped by hand in Adobe Illustrator following the methods outline in the FracPaq manual (Healy et al., 2017). Each subsection was imported into FracPaq and analyzed for fracture characteristics including number and lengths of traces and segments as well as fracture density and connectivity. Fracture connectivity was determined by the number of nodes that terminated at another fracture (y nodes) or crossed another fracture (x nodes) 87 divided by the number of nodes that did not intersect (I nodes). Node buffer (distance that i nodes count as y nodes) was defined at 0.01µm. 4.4 Results 4.4.1 Drilling and core logging The drilled borehole revealed an initial zone ranging from saprolitic schist to lightly weathered but heavily fractured schist (0-6m), followed by a zone of gneissic saprolite (6-21m) and then alternating zones of highly fractured gneiss within disaggregated regolith (21-80m) (Figure 4.4, Figure 4.2b). Sample recovery — calculated for each 1m of drilling run — varied from 0% (representing regolith recovered only as sand or mud or drilled via auger/SPT) to 80-100%. Recovered rock varied from intact to fractured. In the upper zone of the borehole with schistose parent rock (0-6m depth), SPT results show a rapid increase in blowcounts from saprolite to bedrock from 0-3m, indicating increasing strength with depth (Figure 4.4). From 3-6 m, coring was done with triple tube. Recovery varied from 20-50%, meaning that 50-80% of the volume contained void space (fractures) or regolith. The recovered schist was highly fractured. SPT tests resumed starting at 7 m due to low rock strength. Blowcounts remained low (40- 50) from 7-13 m, apart from at 10 m where they increased to 100 (the maximum blowcount tested). Below 13 m, blowcounts were 100 for the remaining saprolite portion to 21m, at which point triple tube coring resumed. Samples were recovered from SPT tests from 7-21 m and used for petrographic analysis; these samples indicated a transition from the overlying schist to gneissic parent material around 7 m depth. During triple tube coring from 21-80 m, recovery varied significantly. Throughout most of this section, recovery was 10-60%, with a large zone of no recovery from 40-50 m and a zone of significantly higher (80-100%) recovery from 60-68 m. Rock 88 Figure 4.4 Borehole log of strength, porosity, and weathering degree. Rock type, weathering degree from rock mass and fracturing (a). Strength indicators (b-e) show varying strength with depth. Triple tube recovery (%) varies throughout the core with significant portions of no recovery (b). Results from SPT and RQD show an increase in strength from surface to 7m in the schist, with a decrease in strength and return to regolith at the schist/gneiss transition at ~7m followed by varying strength through the gneiss (c-d). Point load and UCS results show an increase in strength of recovered rock at ~64m (e). Porosity from thin section and whole rock samples varies throughout, decreasing at 64 m (f). Weathering indicators from whole rock chemistry (g) and thin section analysis (h) agree. Overall we document minimal weathering in the schist cap (3-7m), followed by return of regolith and significant weathering in gneiss, with heterogeneous but generally decreasing weathering with depth. 89 that was recovered by coring from 21-80m was highly fractured. RQD showed generally very poor to poor rock quality (0-39%) with only one sample (67m) deemed as fair (63%). Weathering degree from logging reflected recovery patterns (Figure 4.4). From 3-6 m the schist rock was slightly weathered (grade II). From 7-33 m, recovered rock was generally highly weathered (grade IV) with some moderately weathered zones from 24-26 m. From 37-80 m, weathering degree varied from slightly to highly weathered, with more weathered rock mass generally co-located with areas of less recovery. No rock was considered “fresh” (grade I). High degrees of fracturing were persistent throughout all recovered rock, and fractures had generally close (60-200 mm) to very close (20-60 mm) spacing. No filling was observed, although fillings might have been washed out during core recovery. Roughness between discontinuities was dominantly grade 3 (4-6), with some grade 4 (6-8), indicative of smoother profiles. The observed depth of the water table during drilling (before drilling commenced for the day) was 18.95-19.5m depth. Though an attempt was made to case the completed hole with PVC, water level monitoring did not prove possible. 4.4.2 Near surface seismology The 1D shear wave profile at the drill site (Figure 4.5a) shows lower velocities (140-200 m/s) at the surface followed by a jump to higher velocities (400-700 m/s) at 5-7 m. Below 7 m, velocities decrease back to 140-300 m/s and remain low to the resolvable depth of ~20 m. A separate 1-D shear wave profile (Figure 4.5b) along the ridge 3 km north of the borehole site from Medwedeff et 90 al. (2022) shows a similar jump and decrease in velocity in the first 5-10 m, indicating that this horizon is present in other sections of the ridgeline. 4.4.3 Strength tests Overall rock strength from both point load and unconfined compressive strength tests is low (Figure 4.4). Both axial and diametrical PLT remained relatively constant at ~0.2 Mpa until ~65 m depth, when both increased to ~0.5 Mpa at 80m. UCS was more variable and ranged from 2.8 to 13.5 Mpa throughout the core. 4.4.4 Petrographic analyses Thin section mineral percentages, and individual mineral and thin section weathering degrees, are presented in Table 4.2 for samples between 3.5-50m. Petrographic analyses were Figure 4.5 Shear wave profiles at the Melamchi ridge-top drill site and 3 km north along the same ridge. The 1D profile at the drill site shows a higher shear wave velocity zone corresponding with the location of the lightly weathered schist cap, followed by very low velocity below. The same low-velocity zone appears 3 km north of the drill site along the ridge, suggesting that it is a feature across the ridgeline (Medwedeff et al., 2022). 91 unavailable above 3.5m. The schist had the lowest thin section weathering degree average of 1.18±0.1, the saprolitic gneiss had the highest average of 2.53±0.5, and the weathered gneiss had an average of 1.8±0.8, indicative of its elevated but variable weathering throughout (Figure 4.3). Similarly, individual minerals show significantly lower weathering degrees in the schist than in any of Figure 4.6 Thin section images in plane-polarized light of changes in weathering textures from schist (a) to gneiss (b-f). Main mineralogy and associated textures and weathering features are annotated. 92 Depth (m) Quartz % Quartz WD Muscovite % Muscovite WD Biotite % Biotite WD Feldspar % Feldspar (inferred) % Feldspar WD Garnet % Garnet WD Alteration Mineral % Thin Section WD 3.82 45 1 40 1 10 1 0 0 0 5 3 0 1.1 4.63 55 1 15 1 20 1 0 0 0 10 4 0 1.3 6 60 1 20 1 15 1 0 0 0 5 4 0 1.15 7 55 1 25 1 15 1 0 0 0 5 4 0 1.15 8 65 1 4 2 30 4 0 0 0 1 4 0 1.97 9 30 0 5 1 35 4 0 25 4 2 4 4 2.69 11.1 30 3 7 1 30 4 0 5 4 3 4 25 3.49 12.15 30 1 3 1 35 3 0 5 4 2 4 15 2.26 13 30 0 5 1 35 4 0 25 4 0 na 5 2.65 15.22 40 2 10 2 25 3 10 10 3 1 4 5 2.69 17.7 40 2 5 2 25 3 10 15 3 1 4 5 2.79 19.8 35 1 5 2 30 3 20 2 3 0 na 8 2.35 20.8 30 1 5 2 20 3 5 0 2 0 4 40 2.7 22.58 40 0 2 2 25 4 33 0 2 0 na 0 1.7 24.95 43 2 7 2 15 3 10 0 2 0 na 25 2.65 26.64 45 2 3 1 25 3 20 0 2 0 na 7 2.36 27.86 45 1 7 2 13 3 20 0 4 0 na 15 2.38 28.88 40 1 15 1 20 3 15 0 2 0 na 10 1.85 30.9 40 2 10 1 20 3 10 0 4 0 na 20 2.7 31.54 55 0 10 1 20 4 15 0 1 0 na 1 1.09 32.7 50 0 15 1 20 4 15 0 1 0 na 1 1.14 33.79 35 2 4 1 7 4 13 0 4 0 na 40 3.14 35.9 38 1 2 2 15 4 45 0 2 0 na 0 1.92 36.95 47 0 3 2 5 4 45 0 4 0 na 0 2.06 37.93 70 0 25 1 1 1 4 0 2 1 1 0 0.35 38.92 60 1 15 1 5 3 15 0 3 0 na 5 1.55 39.48 70 1 10 2 3 4 10 0 3 0 na 7 1.6 39.88 60 1 10 2 10 4 15 0 3 0 na 5 1.85 40.85 85 1 7 1 3 3 5 0 2 0 na 1 1.15 43.94 45 1 7 1 3 3 45 0 4 0 na 0 2.41 46.69 45 0 5 1 10 3 40 0 0 0 na 0 0.35 50.95 60 0 3 1 7 3 30 0 4 0 na 0 1.44 Table 4.2 Petrographic thin section analysis of borehole samples. WD = weathering degree. Thin section WD calculated using Eq 4.2. Inferred feldspar % based on identifiable relict structures of primary feldspar that are now psuedomorphs. Alteration mineral % indicates no identifiable primary mineral grain. the deeper gneiss material (Figure 4.6). Detailed petrographic descriptions for each portion of the core follow 93 Schist (3.5-7m): The samples of relatively unweathered schist reveal the parent material in this part of the borehole to be a medium-grained and garnet-rich schist with a porphyrblastic texture (Figure 4.6a). Its main constituents are quartz, muscovite, biotite and Fe-rich garnet (almandine) with some accessory apatite. Quartz and muscovite constitute the largest crystals in the rocks with porphyrblastic garnets. In all schist samples, garnet alters to ferruginous weathering products, often along grain cleavage or microfractures as well as grain surface edges. Biotite shows minor chloritization around grain edges, with feather-like texture. Quartz and muscovite are unaltered. Saprolitic gneiss (7-24m): The saprolitic gneiss is a coarse grained augen gneiss. Its main constituents are quartz, biotite, muscovite, and weathered products from feldspars. Some accessory almandine garnet is present. All samples are heavily weathered, generally decreasing in intensity of alteration with depth (Figure 4.6b-d). In all samples, limited primary feldspars remain, with most altered completely to clay psuedomorphs. Biotite shows significant vermiculite weathering throughout, with indicative titanium oxide dots present in the pseudomorphs, feathered textures and expansion between cleavage planes. Garnets are generally altered to ferruginous weathering products, often along grain cleavage or microfractures as well as grain surface edges. Quartz and muscovite show some edge and intramineral fracture dissolution textures throughout. The sample at 20.8 m contains a section with a fine-grained matrix of clay minerals and weathered biotite, likely fault gouge, that is present in samples up to 35m depth. Weathered gneiss (24-50m): The weathered gneiss is a coarse-grained augen gneiss. Its main constituents are quartz, feldspar, biotite, muscovite, with some accessory garnet (almandine), similar to the overlying saprolitic gneiss but with less weathering. Biotite content decreases with depth (Figure 4.6a), while feldspar and quartz contents vary throughout. Feldspar below 40 m shows myrmekite textures. Intramineral fractures are present in quartz and feldspars. 94 4.4.5 Geochemical analyses Figure 4.7a and Table 4.3 show the absolute concentration of major rock forming elements in relation to Ti, which is used here as a weathering-conservative normalizing element. The horizontal line represents the approximate contact between the schist and gneiss. The schist (0-7 m) contains generally lower concentrations of Na and Ca than the gneiss. At the surface (0-2 m), schist samples are slightly lower in Fe and K and slightly higher Ti and Al. The gneiss (7-50 m depth) contains lower Ca and Na which remain very low in concentration until 20 m and then increase with depth. Al and Ti have slightly higher concentrations from 7-20 m and then decrease with depth. There Figure 4.7 Individual mineral content (biotite, quartz, feldspar and alteration mineral) with depth, shaded by the individual mineral weathering degree from thin section observations. Biotite content decreases linearly with depth in the gneiss, but the weathering degree of biotite varies and shows no clear relationship with depth. Tan shaded box covering upper ~7m indicates schist layer. 95 Depth Na2O MgO Al2O3 SiO2 SO3 K2O CaO TiO2 Fe2O3 CIA Lithology τNa τMg τAl τSi τS τK τCa τFe CDF 0 0.05 2.14 17.74 56.79 0.07 5.81 0.17 1.83 14.99 74.65 schist -0.80 0.00 0.18 -0.26 0.04 -0.37 0.05 -0.31 0.22 0.25 0.17 1.36 13.67 58.59 0.05 6.34 0.25 1.55 17.77 66.92 schist -0.14 -0.25 0.07 -0.10 -0.12 -0.19 0.86 -0.04 0.08 1 0.26 1.60 12.82 58.65 0.05 6.58 0.17 1.16 18.36 64.67 schist 0.77 0.18 0.34 0.21 0.17 0.12 0.71 0.32 -0.24 1.45 0.15 1.45 14.17 57.02 0.05 7.53 0.16 1.37 17.88 64.41 schist -0.15 -0.10 0.26 -0.01 -0.03 0.09 0.34 0.09 -0.05 2 0.20 1.86 13.43 57.70 0.03 8.41 0.14 1.42 16.54 60.56 schist 0.13 0.12 0.15 -0.03 -0.43 0.18 0.12 -0.02 -0.01 2.45 0.25 1.56 13.26 58.96 0.04 8.06 0.19 1.23 16.06 60.95 schist 0.63 0.10 0.32 0.15 -0.15 0.31 0.76 0.10 -0.17 3.82 0.31 1.76 12.69 60.29 0.07 6.94 0.17 1.32 16.07 63.11 schist 0.87 0.14 0.17 0.09 0.62 0.04 0.49 0.02 -0.09 4.63 0.25 1.72 12.52 58.25 0.05 7.79 0.16 1.45 17.57 60.42 schist 6 0.10 1.63 11.06 61.82 0.05 6.66 0.09 1.42 16.68 61.76 schist 7 0.49 1.52 10.48 57.45 0.03 5.93 0.14 1.21 22.42 61.50 schist 7.45 0.10 1.98 15.67 59.82 0.06 5.18 0.10 1.81 15.02 74.42 gneiss -0.95 -0.25 -0.02 -0.31 0.16 -0.18 -0.98 -0.06 0.27 8 0.33 2.36 11.42 61.95 0.06 5.70 0.15 1.57 16.25 64.89 gneiss -0.82 0.03 -0.18 -0.17 0.23 0.03 -0.97 0.17 0.16 8.45 0.14 2.51 19.18 49.65 0.06 7.57 0.14 1.82 18.49 70.96 gneiss -0.94 -0.06 0.19 -0.43 0.10 0.19 -0.98 0.15 0.28 9 0.27 2.11 17.26 54.33 0.06 6.34 0.15 1.78 17.45 71.86 gneiss -0.87 -0.19 0.09 -0.36 0.16 0.01 -0.98 0.11 0.27 9.45 0.13 2.72 18.64 51.29 0.06 6.37 0.14 1.74 18.69 73.76 gneiss -0.94 0.07 0.21 -0.38 0.25 0.04 -0.98 0.21 0.25 10.29 0.04 1.14 14.99 71.04 0.05 4.20 0.13 0.72 7.21 77.43 gneiss -0.95 0.07 1.34 1.05 1.37 0.65 -0.95 0.12 -0.81 11.1 0.19 1.81 16.90 59.17 0.04 5.09 0.12 1.36 15.13 75.78 gneiss -0.88 -0.10 0.40 -0.09 0.00 0.06 -0.97 0.25 0.04 11.4 0.12 2.50 17.28 51.38 0.08 6.82 0.13 1.82 19.61 70.95 gneiss -0.94 -0.07 0.07 -0.41 0.48 0.07 -0.98 0.22 0.28 12.15 0.15 2.60 17.47 50.51 0.04 6.87 0.20 1.67 20.19 70.74 gneiss -0.92 0.06 0.18 -0.37 -0.25 0.17 -0.96 0.36 0.22 12.4 0.32 2.28 18.48 48.85 0.05 7.78 0.16 1.65 20.02 69.12 gneiss -0.83 -0.06 0.27 -0.38 0.06 0.34 -0.97 0.37 0.21 13 0.20 1.64 16.65 59.62 0.06 6.22 0.25 1.61 13.34 71.39 gneiss -0.89 -0.30 0.17 -0.23 0.19 0.10 -0.95 -0.06 0.19 14.29 0.45 2.21 16.02 57.64 0.03 6.95 1.43 1.68 13.20 64.48 gneiss -0.77 -0.10 0.08 -0.28 -0.29 0.18 -0.75 -0.11 0.22 15.22 0.23 2.47 16.71 55.87 0.10 6.27 1.19 1.61 15.32 68.50 gneiss -0.88 0.05 0.18 -0.27 1.22 0.11 -0.78 0.08 0.18 15.4 0.28 2.87 18.15 52.02 0.04 8.26 0.86 1.75 15.48 65.87 gneiss -0.86 0.12 0.17 -0.38 -0.20 0.34 -0.85 0.00 0.25 16.29 0.10 2.52 16.62 55.90 0.05 6.05 0.44 1.83 16.27 71.63 gneiss -0.95 -0.06 0.03 -0.36 -0.14 -0.06 -0.93 0.01 0.28 17.26 0.17 2.53 16.75 55.81 0.07 7.24 0.66 1.51 14.62 67.48 gneiss -0.90 0.14 0.25 -0.23 0.55 0.36 -0.87 0.09 0.13 17.7 0.09 2.70 17.66 52.51 0.05 6.50 0.82 1.77 17.64 70.45 gneiss -0.96 0.04 0.13 -0.38 0.07 0.05 -0.86 0.13 0.26 18.45 0.32 3.00 15.55 54.38 0.06 6.39 1.77 2.24 15.94 64.68 gneiss -0.88 -0.09 -0.22 -0.49 -0.08 -0.19 -0.76 -0.20 0.42 19.24 0.18 2.48 14.77 54.81 0.04 6.08 1.91 1.83 16.68 64.38 gneiss -0.91 -0.07 -0.09 -0.37 -0.29 -0.05 -0.69 0.03 0.28 20.55 0.72 2.67 13.28 59.05 0.03 5.78 2.05 1.66 14.31 60.85 gneiss -0.63 0.09 -0.10 -0.26 -0.31 -0.01 -0.63 -0.03 0.21 20.8 0.39 2.45 14.37 57.46 0.07 5.87 2.59 1.75 14.76 61.88 gneiss -0.81 -0.05 -0.07 -0.31 0.40 -0.04 -0.56 -0.05 0.25 21.55 0.91 2.40 12.80 60.26 0.04 5.77 3.07 1.49 12.65 56.77 gneiss -0.47 0.10 -0.03 -0.15 0.01 0.10 -0.39 -0.04 0.12 22.55 0.81 2.02 11.80 60.32 0.06 6.01 3.49 1.66 13.51 53.36 gneiss -0.58 -0.17 -0.20 -0.24 0.22 0.03 -0.37 -0.08 0.21 22.98 1.17 1.99 12.56 60.89 0.05 4.82 3.96 1.60 12.55 55.80 gneiss -0.37 -0.15 -0.11 -0.20 0.18 -0.14 -0.26 -0.11 0.18 23.5 1.57 1.14 11.12 68.22 0.04 4.56 3.64 0.97 8.46 53.23 gneiss 0.40 -0.20 0.29 0.47 0.33 0.33 0.11 -0.02 -0.35 24.08 1.12 2.20 14.08 58.14 0.04 6.85 2.36 1.70 13.20 57.71 gneiss -0.43 -0.12 -0.07 -0.29 -0.26 0.14 -0.59 -0.13 0.23 24.74 0.87 1.72 12.61 62.64 0.04 5.62 2.74 1.39 12.04 57.73 gneiss -0.46 -0.16 0.02 -0.06 -0.10 0.15 -0.41 -0.02 0.06 24.82 0.88 2.36 11.94 61.01 0.04 6.32 2.07 1.61 13.43 56.30 gneiss -0.53 0.00 -0.16 -0.21 -0.13 0.12 -0.62 -0.06 0.19 24.95 1.18 1.73 13.14 62.81 0.06 4.88 3.53 1.33 10.99 57.81 gneiss -0.23 -0.11 0.12 -0.01 0.56 0.05 -0.21 -0.06 0.01 26.63 1.45 2.30 12.56 61.35 0.05 4.48 3.70 1.49 12.29 56.59 gneiss -0.16 0.05 -0.05 -0.14 0.24 -0.14 -0.26 -0.07 0.12 26.9 1.40 1.50 11.58 64.90 0.04 4.86 3.79 1.26 10.32 53.55 gneiss -0.04 -0.19 0.04 0.08 0.16 0.10 -0.11 -0.08 -0.04 96 27.78 1.13 2.23 11.99 60.14 0.03 5.50 3.11 1.53 14.06 55.20 gneiss -0.36 -0.01 -0.12 -0.18 -0.25 0.02 -0.40 0.03 0.15 27.86 1.41 2.52 14.42 55.94 0.04 6.12 3.60 1.52 14.13 56.46 gneiss -0.20 0.13 0.07 -0.23 -0.13 0.14 -0.30 0.05 0.14 29.25 1.13 1.89 13.51 61.49 0.04 5.31 3.43 1.45 11.40 57.80 gneiss -0.33 -0.12 0.05 -0.12 -0.09 0.04 -0.30 -0.12 0.10 30.85 1.03 2.26 13.64 59.35 0.03 5.53 2.63 1.55 13.54 59.75 gneiss -0.43 0.00 -0.01 -0.20 -0.28 0.01 -0.50 -0.01 0.15 30.9 0.81 2.20 13.85 60.20 0.03 5.36 2.73 1.44 13.05 60.87 gneiss -0.51 0.04 0.09 -0.12 -0.25 0.06 -0.44 0.02 0.09 31.54 1.34 2.10 14.34 58.55 0.03 5.92 3.04 1.50 12.76 58.19 gneiss -0.23 -0.05 0.08 -0.18 -0.23 0.12 -0.40 -0.04 0.13 32.45 0.81 2.34 12.80 60.48 0.04 5.97 2.71 1.56 13.00 57.42 gneiss -0.55 0.02 -0.07 -0.19 -0.01 0.09 -0.48 -0.06 0.16 32.7 0.82 2.20 12.73 59.63 0.04 6.34 2.53 1.59 13.66 56.79 gneiss -0.55 -0.06 -0.10 -0.22 -0.06 0.13 -0.53 -0.03 0.18 32.87 1.24 2.49 12.26 61.01 0.04 5.28 2.94 1.40 13.00 56.46 gneiss -0.23 0.21 -0.01 -0.09 0.05 0.07 -0.38 0.05 0.07 33.79 0.64 2.67 13.59 59.74 0.04 5.74 3.15 1.41 12.63 58.80 gneiss -0.61 0.29 0.09 -0.11 -0.06 0.16 -0.33 0.01 0.07 33.9 0.83 2.53 12.47 60.67 0.06 5.48 3.36 1.55 12.77 56.31 gneiss -0.54 0.11 -0.09 -0.18 0.26 0.01 -0.36 -0.07 0.15 34.47 1.36 1.90 11.47 64.88 0.04 3.62 4.75 1.21 10.45 54.11 gneiss 34.7 1.68 1.95 11.73 60.41 0.04 5.59 4.06 1.41 12.76 50.88 gneiss 36.95 2.85 0.36 10.18 77.07 0.03 1.93 3.00 0.43 3.83 56.67 gneiss 37.3 0.66 0.25 7.33 82.08 0.03 4.86 0.55 0.41 3.54 54.71 gneiss 38.2 0.86 0.29 9.48 78.00 0.04 5.42 1.54 0.38 3.67 54.81 gneiss 38.92 0.58 0.40 10.16 73.36 0.04 8.64 1.00 0.57 4.91 49.85 gneiss 39.48 0.92 0.36 9.09 73.39 0.03 9.60 0.88 0.51 4.78 44.35 gneiss 39.88 1.50 0.56 11.21 69.58 0.03 10.86 1.72 0.53 3.76 44.32 gneiss 40.95 1.64 0.04 11.16 73.09 0.04 11.66 1.31 0.15 0.74 43.30 gneiss 41.67 1.25 0.72 10.13 73.96 0.03 5.85 1.75 0.65 5.42 53.36 gneiss 42.15 1.53 0.70 9.28 74.44 0.02 6.87 1.62 0.56 4.73 48.07 gneiss 43.92 1.15 0.60 10.50 72.85 0.03 7.66 1.46 0.60 4.80 50.53 gneiss 44.5 1.39 0.51 9.48 74.29 0.03 6.87 1.42 0.65 5.12 49.47 gneiss 45.5 0.90 0.61 10.34 71.58 0.04 6.90 1.87 0.78 6.47 51.67 gneiss Table 4.3 Geochemical results from XRF. Elemental contents reported in weight percent. CIA = Chemical Index of Alteration (Eq 4.3). τ values normalized to Ti (Eq 4.4). CDF = Chemical Depletion Fraction (Eq. 4.5) 97 is a significant change in relative concentrations of the major elements at 35m depth, indicating a change in rock type, with increased Si and decreased Mg, Ca, Fe and Ti. Figure 4.8 Geochemical results from XRF analysis. (a) Elemental composition in weight percent of recovered core and SPT samples. (b) Tau values relative to Ti content, and chemical depletion fraction (CDF) for schist and gneiss. The schist shows some depletion in weathering-mobile elements in the top ~1.5m. The gneiss shows depletion in Na and Ca, and elevated CDF, from 7-24m. 98 In the very near surface (0-2m), the schist is slightly depleted in Si, Fe and K (τ Ti, X < 0), and these samples also show elevated CDF ranging up to 21% (Figure 4.7b). With greater depth in the schist, τ Ti, X values are scattered and generally show limited depletion by weathering, and the CDF is scattered but overall low, with average mass loss of only 3%. In the gneiss, τ Ti, X values vary with depth. Na and Ca show clear depletion from 7-20m, which correlates with higher CDF values reflecting average mass loss of 14% (with a maximum of 42%). τ Ti, Ca and τ Ti, Na then become less depleted from 21-24m. In the schist, CIA values are 75% at the surface and then decrease rapidly to about 62% until 7 m (Figure 4.8a). At the schist/gneiss transition, the CIA value increases again to 75% and stays in the mid to low 70s until 20m where it begins to decrease steadily to the values in the mid 50s. At 38 m, this then decreases to the mid 40s, co- located with the potential change in rock type. 4.4.6 Porosity Porosities from whole rock analysis and blue epoxied thin sections show varying but generally decreasing trends. The analyzed samples represent the materials that are expected to have Figure 4.9 Examples of blue epoxied thin section image subsections used for mapping (left) and corresponding fracture map (right). Fractures in gneiss dominantly follow feldspar fracturing and dissolution (samples from 22.6m and 50m), while schist is more linear (sample from 2m). 99 the lowest porosity in the core, as highly weathered samples (including SPT samples) were unable to be recovered for porosity measurements. Porosity in thin section samples (n=4) varies from 3.7% to 8.1%, while in whole rock samples (n=14) values vary from 1.4% to 15.23%. In the one sample where both thin section and whole rock porosity were measured together, these values agree well (5.4% from thin section, 6.1% from whole rock). There is a change in porosity at about 60 m; average porosity from 20-60m is 6.8%, while average porosity from 60-80 m is 2.4%. 4.4.7 Micro-fracturing Thin section fracture characteristics are presented in Table 4.4. Examples of subsampled sites within each thin section are presented in Figure 4.9. Fractures in the schist dominantly run parallel to schistosity with the second mode running perpendicular, predominantly in quartz grains (Figure 4.9, Figure 4.10). In contrast, the gneiss shows no dominant mode of fractures in any sample, with fractures frequently even distributed at every angle (Figure 4.10). These fractures often occur as intra-grain fractures in feldspar and feldspar grains. Evidence of biotite expansion and fracturing is apparent in all samples, where fractures propagate from the tip of biotite grains into quartz and feldspar grains. Fracture intensity, density and connectivity are all lowest in the schist (Table 4.4, Figure 4.11). Gneiss samples show increasing fracture Figure 4.10 Rose diagrams of fracture orientation from three subsections of each sample. The schist shows two modes, the dominant one parallel with schistosity, the second one perpendicular. The gneiss samples show no dominant mode throughout a sample. 100 connectivity with depth, while intensity, density and trace numbers remain consistent throughout all samples. Figure 4.11 Boxplots of fracture connectivity, density, trace number and intensity with depth. Boxplots consist of six subsections per sample (Table 4.4) and are colored by weathering degree of the overall thin section (Table 4.2). 101 Depth (m) Site Traces Segments Nodes I nodes Y nodes X nodes Intensity (1/m) Density (1/m 2 ) Connectivity 2 1 242 533 775 474 8 39 136857.3347 59057720716 0.099156118 2 2 134 366 500 256 8 31 102204.7795 40397632263 0.15234375 2 3 224 558 782 438 5 67 147353.9253 61760872462 0.164383562 2 4 131 381 512 253 5 24 116453.5135 42218082384 0.114624506 2 5 220 554 774 429 8 68 169405.9612 61149489450 0.177156177 2 6 227 521 748 439 11 41 125354.3667 57587426671 0.118451025 22 1 382 977 1359 715 49 137 221519.6722 1.17718E+11 0.26013986 22 2 344 932 1276 641 45 189 225061.9771 1.12216E+11 0.365054602 22 3 482 1159 1641 883 82 128 246106.9782 1.39586E+11 0.237825595 22 4 315 440 755 613 13 135 145057.0783 53031407166 0.241435563 22 5 236 294 530 459 8 73 108817.115 35495362977 0.176470588 22 6 575 872 1447 1100 51 236 238473.3955 1.05506E+11 0.260909091 36 1 292 800 1092 550 32 126 223618.1713 1.28271E+11 0.287272727 36 2 199 567 766 377 22 96 158442.2561 90678278321 0.312997347 36 3 412 1209 1621 759 67 181 319578.5803 1.94378E+11 0.326745718 36 4 283 924 1207 527 38 85 218380.1935 1.48002E+11 0.233396584 36 5 374 1068 1442 700 47 165 289378.9982 1.71037E+11 0.302857143 36 6 372 924 1296 693 51 97 228596.9727 1.47687E+11 0.213564214 46 1 605 1805 2410 1076 140 311 423302.1897 2.70884E+11 0.419144981 46 2 489 1292 1781 914 75 230 237654.1393 1.94237E+11 0.333698031 46 3 389 1040 1429 726 51 46 287461.1616 1.56599E+11 0.133608815 46 4 701 2160 2861 1254 159 337 441994.6285 3.24756E+11 0.39553429 46 5 1117 3556 4673 1978 283 775 678301.7935 5.32654E+11 0.534883721 46 6 512 1406 1918 941 80 294 333933.2567 2.10639E+11 0.397449522 50 1 625 739 1364 1207 40 408 275170.1222 84985578040 0.371168186 50 2 438 1193 1631 802 76 238 263122.6742 1.37273E+11 0.391521197 50 3 494 1177 1671 932 58 327 277164.0851 1.35308E+11 0.413090129 50 4 250 696 946 472 25 101 144640.1171 80749308855 0.266949153 50 5 507 1250 1757 948 110 307 304432.0192 1.43576E+11 0.439873418 50 6 461 1179 1640 843 76 316 335506.6719 1.35911E+11 0.465005931 Table 4.4 Microfracture mapping data from thin section subsections from the FracPaq package 102 4.5 Discussion 4.5.1 Heterogenous weathering with depth Canonical models for regolith formation describe unweathered rock at depth transitioning upward first to weathered rock and saprolite and then to overlying soil (Anderson et al., 2007; Braun et al., 2016). This progressive change is typically accompanied by one or more weathering fronts, characterized by discrete changes in elemental content and associated mineral abundance (Lichtner, 1988; Brantley and White, 2009; Phillips et al., 2019). These fronts are often but not always step-like, with their location and shape determined by multiple interacting factors including rates of fluid flow, weathering reactions, and erosion (e.g., Lebedeva et al., 2007; Maher, 2011). The driving mechanisms for weathering front advance remain debated and may vary from one location to another, with possibilities including advection of reactive fluids from above (Brantley et al., 2017) and drainage of fluids from below (Rempe and Dietrich, 2014). Irrespective of mechanism, at a given location, the regolith profile is generally expected to progress from less weathered rock at depth to more weathered saprolite and soil towards the surface. Our results from the Tallathok core reveal a more complex subsurface underlying an actively exhuming mountain range than is presumed in these typical regolith models. Perhaps most notably, we effectively see a “doublet” weathering profile defined by the schist and gneiss layers in our borehole. The initial schist layer shows a typical weathering profile from the surface to ~7 m depth. Chemical indices of weathering degree (CIA, CDF and tau values) all decrease with depth within this zone, while strength indices including SPT values increase (Figure 4.6, Figure 4.3). The transition to gneiss at ~7 m depth leads to a sudden return to weaker and more weathered regolith, characterized by low SPT values and no triple tube recovery until 24 m depth. The gneiss remains relatively weak from 24-80 m, with RQD, uniaxial and uncompressive strength all remaining low until about 65m, where they begin to increase. Whole rock porosity follows a similar trend, decreasing at about 65 m 103 (Figure 4.3). Chemical weathering indices (CIA, CDF and tau values) all show significant depletion from 7 to 21 m depth. From 21 to 24 m, all values show a slow decline in depletion, indicative of the edge of a weathering front (Figure 4.7, Figure 4.8). In effect, we see a first weathering profile developed in the upper 7 m of the borehole defined by the schist parent, and this profile is then “reset” to near-surface weathering condition around 7 m at the transition to gneiss parent material, progressing again from there with depth towards less weathering. This transition is also reflected in the seismic profile at the borehole and at a site 3 km north along the Melamchi ridge (Figure 4.4), suggesting that the heterogeneous weathering we observe is not isolated to the drilling location. The implication is that weathering front development in layered rock such as that of the High Himalaya may be generally heterogenous with depth. Such rocks are typical of much of the Earth’s surface, including sedimentary and meta- sedimentary rocks, as well as basalts where similar heterogenous weathering has been documented in layered formations based on seismic velocity profiles (Von Voigtlander et al., 2018). Modeling of weathering front propagation and regolith development in these settings may need to account for vertical heterogeneity to accurately represent hydrologic, geochemical, and erosional processes. 4.5.2 Lithologic control and coupled biotite-feldspar weathering So how and why do the different weathering processes at work in the schist and gneiss produce the “doublet” weathering profile that we observe within a single core? Biotite expansion and oxidation has been noted as a main way to initiate weathering in a wide range of lithologies (Murphy et al., 1998; Buss et al., 2008; Brantley and White, 2009; Goodfellow et al., 2016). As oxygen from pore water diffuses into fresh rock, it oxidizes the Fe(II) into Fe (III) within biotite, causing an expansion of the (001) d-spacing from 10 Å to 10.5 Å (Murphy et al., 1998; Dong et al., 1998; Buss et al., 2008; Shen et al., 2019). This expansion can build up enough elastic strain to promote micro-fracturing in the rock, allowing O 2- and CO 2-rich waters to enter and dissolve the 104 available weatherable minerals. Throughout our core, biotite content is similar in the schist and gneiss, yet weathering differs dramatically between the two — indicating that biotite oxidation alone cannot explain our observations. The presence of feldspar in the gneiss but not the schist appears to be the key to the different weathering behavior of these two distinct layers in our core. Buss et al. (2008) noted that the right conditions can produce a biotite expansion-feldspar dissolution feedback whereby feldspar dissolution allows fluid penetration, facilitating biotite oxidation, and in turn enabling greater feldspar dissolution. The intense weathering in our gneiss samples can be attributed to such coupling of biotite and feldspar weathering, with feldspar grains in our samples showing extensive dissolution and replacement textures. Feldspar dissolution could allow for penetration of O 2-rich water and Figure 4.12 (a) Chemical Index of Alteration (CIA) decreases from the surface to 7m and then increases again in the gneiss. Changes in CIA mirror changes in triple tube recovery (gold bars). Quartz content (blue dots) is highest in layers that are followed by no recovery. (b) CIA vs Fe 2O 3 by rock type. Grey area indicates typical unweathered CIA values. 105 promote the oxidation/dissolution feedback. The result is intense weathering of the gneiss where it first appears at ~7 m depth, including the replacement of feldspar and biotite by clay alteration minerals. In the overlying schist, without feldspars or other readily dissolvable minerals, biotite oxidation is much slower and weathering is confined to oxidation of garnet and minor oxidation along mineral grain edges in biotite. Our chemical data corroborate the importance of biotite oxidation in driving weathering in our core, along with the complexity and dependence on coupling with feldspar dissolution. Across the entire core, CIA generally correlates positively with Fe 2O 3 content (Figure 4.12). However, schist samples have lower CIA values than gneiss samples with similar Fe 2O 3 content, providing further evidence that, while biotite oxidation is a key part of weathering in these rocks, the feldspar dissolution feedback plays a critical role. 4.5.3 Coupling and decoupling of fracturing and weathering The role of fracturing in facilitating weathering at depth is widely recognized in many settings (Fletcher et al., 2006; Buss et al., 2008; St Clair et al., 2015; Gu et al., 2020), and we expect an important role for fractures as conduits for fluid flow and weathering in high-relief, tectonically active settings such as the Himalaya. Interestingly, fractures seem to play an important role at our borehole site, but with complexity that we did not initially expect. The difference in fracture patterns between the schist and the underlying gneiss is consistent with fracture availability controlling fluid penetration and reaction. In the schist, fractures dominantly occur parallel to schistosity, rather than associated with a network of dissolution features (Figures 9, 10, 12), suggesting that the low connectivity and density of fracturing is due to lack of feldspars. The limited fracture availability means reactive fluids do not easily permeate into the rock. In contrast, in the gneiss, fractures show no dominant mode and are often associated with intra- grain feldspar fracturing. This high fracture availability can allow for advection of O 2- and CO 2-rich 106 waters to move rapidly to the highly reactive feldspars, promoting dissolution and clay replacement. Differences in the availability of pathways for fluid flow can produce differences in regolith depth (Bazilevskaya et al., 2013), potentially explaining the deeper weathering profile we see in the gneiss (which is intensely weathered over tens of meters) compared to the schist (which is only lightly weathered already at ~5 m depth) (Figure 4.13). Counterintuitively, within the gneiss zone, fractures become more prevalent (higher fracture density) and more connected with depth (Figure 4.11). We attribute this increase in fracture density and connectivity to a combination of 1) high baseline fracture density throughout the core due to tectonic history and topographic stress and 2) the infilling of those fractures due to clay and FeO- rich alteration minerals in the more weathered rocks nearer to the surface (Figure 4.13). In other settings, fracture availability has been associated with more intense weathering (Gu et al., 2020), but Figure 4.13 Schematic figure of changes in fracture and weathering with depth and changing lithology. Representative fracture patterns were taken from microfracture mapping samples. Infilled fractures represented by brown lines. 107 in deeper sections of our core, weathering indices (CIA, tau values, thin section weathering degree) decrease even as fractures are widely available. Thus, in the lower core, weathering is likely limited by the amount of O 2 that can reach the lower borehole rather than by available fluid flow paths. As weathering occurs, rocks with higher Fe 2O 3 content will consume that available O 2 rapidly (Bazilevskaya et al., 2013; Brantley et al., 2013). Here, highest Fe 2O 3 percentage is closest to the surface where biotite content is greater and oxidation of biotite is pervasive in thin section (Figure 4.12). Biotite is much less oxidized at depth in thin section despite the continued availability of fractures (Figure 4.6f). Where weathering does occur at depth, it is generally located in a network of intragrain fractures in feldspars (Figure 4.9) and the degree of weathering in biotite and feldspars are not well correlated (Figure 4.7). Thus, while we do see that biotite oxidation contributes to microfracturing in the augen gneiss, it appears less important in driving weathering than has been identified in other settings (Buss et al., 2008; Isherwood and Street, 1976; Goodfellow et al., 2016), which we attribute to the more intense pre-existing fracture network in this steep, tectonically active setting. One lingering question is the processes occurring in the gneiss in sections of the core with no recovery at depth (40-50 m, 53-55 m, 67-71 m). We are unable to determine weathering intensity in these zones where the only recoverable material has likely been washed of clays and other fine grains. Interestingly, at boundaries above these zones of no recovery, strength indices increase and whole rock porosity decreases (Figure 4.4d-f). Above 40 m, we also see a marked increase in quartz content (Figure 4.8a). These zones of high quartz content might be more resistive to fracturing and weathering (reflected in decreased porosity and increased strength, respectively), which we speculate might concentrate fluids and their flow in adjacent regions with more dissolvable minerals, leading to intense weathering. 108 4.6 Conclusion Geochemical, petrologic, and geophysical results from an 80-m borehole in schist and gneiss in the High Himalaya of Nepal reveal how lithology and fracture availability work together to control regolith development. We observed a “doublet” weathering profile where the overlying schist, containing no feldspar, has a relatively shallow weathering profile and more intact rock at shallow depth, whereas saprolite and intense weathering reappears in the deeper, feldspar-rich gneiss. Feldspars in gneiss are highly weathered and have high intra-grain fracturing, allowing for fluids to permeate and dissolve these reactive minerals. Deeper in the core, we observe a decoupling of fracture availability and chemical weathering in the gneiss, where microfractures, inherited from topographic and tectonic stresses, remain available but chemical weathering intensity decreases. Thus, in this setting, fracture availability does not limit weathering depth; rather, the ability of CO 2- and O 2-rich waters to reach such depths prior to reacting is likely the limiting step. This work shows how mountain building and recent exhumation, coupled with the resultant lithologic heterogeneity and pervasive fracturing, can result in more complex critical zone architecture development than simple downward propagation of weathering fronts. 109 4.7 References Abzalov M. (2016) Applied Mining Geology., Springer International Publishing, Cham. Anderson S. P., von Blanckenburg F. and White A. F. (2007) Physical and chemical controls on the critical zone. Elements 3, 315–319. Anderson S. P., Dietrich W. E. and Brimhall G. H. 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Wobus C., Heimsath A., Whipple K. and Hodges K. (2005) Active out-of-sequence thrust faulting in the central Nepalese Himalaya. Nature 434, 1008–11. 114 Chapter 5 “The entire hydrologic cycle from atmosphere to ocean and back is a marathon line of nearly unabridged hydrogen bonds, a continual flow of awareness. To touch water, especially water out of a spring or seep, is to return to each origin, meeting the rains and the snowmelts and the cold interior of the planet…” – Craig Childs from The Secret Knowledge of Water Effects of critical zone and topographic changes on mountain groundwater residence times * Atwood, A., Hosono, T., Kagabu, M., Ide, K., Clark, M.K., Zekkos, D., Chamlagain, D.,Tiwari, S. West, A. J. 5.1 Introduction Mountainous terrain plays a critical role both as a driver of weathering processes and as a water source. Areas of high denudation transport solutes at high rates, thus modulating Earth’s climate on geologic timescales (Chamberlain, 1899). Mountains are also considered the “water towers” of the world, providing the majority of water to downstream communities, as well as local residents (Viviroli et al., 2007; Immerzeel et al., 2020). Yet mountain groundwaters are rarely considered when discussing mountain water resources and water-rock interactions, in part because very limited data about groundwater exist from hard to access mountainous terrains (Immerzeel et al., 2020). Groundwater is often a dynamic system in a hillslope, with multiple different flow paths based on subsurface permeability and fracturing. Understanding whether groundwater is transported over long- or short-timespans is crucial to be able to determine how well a groundwater system will buffer against a changing environment. Groundwater with short transit times react to changes in the atmosphere, such as temperature or pollution, on an * Atwood contributed field design and sample collection, mixing model and noble gas modelling analysis and manuscript authorship. 115 annual cycle, suggesting minimal buffering (Luce et al. 2014, Hare, et al. 2020). Meanwhile, longer groundwater transit times are unaffected by surface conditions on an annual level, indicating the potential to buffer against climatic changes in the short term (Kurylyk et al. 2015). Understanding this ability to buffer based on transit time is critical both for water resource planning and for determining the ecological impacts of a warming climate for a watershed (Immerzeel et al., 2010; Kurylyk et al., 2015; Hare et al., 2021). But despite that importance, the links between topography and transit times are still not well understood in steep terrain (McGuire et al., 2005; Maher, 2011; Carroll et al., 2020; Manning et al., 2020). Groundwater springs have been used to understand chemical weathering since the seminal work by Garrels and Mackenzie (1967), but robust transit time estimates were not possible until recent developments in geochemical tracers. Despite the importance of groundwater transit times, the only study in the Nepal High Himalaya comes from remotely sensed precipitation and river data (Andermann et al., 2012), making work in this region even more critical. The impacts of a changing climate are already felt in the Melamchi Valley springs. Groundwater springs in the valley were extensively mapped and cataloged by 116 Ghimire et al. (2019). Springs are societally critical, utilized by residents for drinking and washing (Figure 5.2; (Shrestha et al., 2019)). Residents have noted that these springs are already affected by climate change, and that women, who are generally charged with water collection, face an increased burden because of the increasing distances they have to travel to find water as springs dry up (Shrestha et al., 2019; Chapagain et al., 2019). Recent work indicates that mountain groundwater can be significantly older than conventionally thought, which has major implications for groundwater-derived weathering rates (Rademacher et al., 2001; Maher, 2011; Manning et al., 2012; Frisbee et al., 2013; Maher and Druhan, 2014; Jasechko et al., 2016), as well as the time scale on which groundwater will be affected by a warming climate (Immerzeel et al., 2010; Kløve et al., 2014; Briggs et al., 2018). Geochemical groundwater transit time tracers techniques have developed significantly since the mid-1990s, specifically with increased reliance on the anthropogenic gases of CFCs, Figure 5.1 (a) Satellite imagery denoting Melamchi Khola outline and groundwater spring location (b) slope map derived from 2m SETSM DEM (c) hillshade and DEM with catchment boundary and (d) DEM of subset of springs, note the complexity of the terrain in the environment. a b c d 117 SF 6 as well as the 3 H/ 3 He system (Schlosser et al., 1988; Busenberg and Plummer, 1992; Solomon et al., 1993; Cook et al., 1995; Cook and Solomon, 1997). These gases are globally well mixed, generally non-reactive, and have distinctive changes in inputs over time, making them useful groundwater transit time tracers. Here, we present results from 22 springs across the valley using the environmental tracers CFC-12, SF 6 and 3 H. We use a lumped parameter model in tandem with geochemically derived recharge parameters to quantify mean residence times. Multi-tracer analysis indicates that the majority of springs are best represented with an exponential mixing model rather than a simple piston-flow model, indicating the presence of older groundwater. Mean groundwater transit times for those springs range from <1 to 35 years, with springs located in the valley bottom and hillslopes exhibiting decreasing transit times to the north with steepening slopes. The short residence times results suggest groundwater transit in this mountain environment is rapid and vulnerable to changing temperature and precipitation patterns. Figure 5.2 Examples of two groundwater springs sampling sites. MKS-11 exits out of former river terrace near the valley bottom in the southern end. MKS-13 exits out of bedrock near the ridgeline in the center of the valley. Both groundwater sites are utilized by residents for drinking water and washing (note the bamboo chute used as a spout at MKS-11 and tin roof to protect water in MKS-13). 118 5.1.1 Site locality The Melamchi Khola is a moderately sized watershed with an area of 1,145km 2 , an average slope of 31°, and a relief of 500m in the southern end and 1800m in the north. It is a north-south trending valley located 50km northeast of Kathmandu, Nepal (Figure 5.1), that traverses the foothills to the High Himalaya. The valley is broadly underlain by the High Himalayan Crystalline series rock, which is dominated by interbedded gneiss and schist, with some quartzite and calcareous rich units (Duvadi et al., 2005). Low temperature thermochronology has documented a gradient in the timing of tectonic uplift: bedrock from the southern end of the valley indicates rapid cooling ~3.5 Ma ago while samples from the northern end indicate cooling as recently as ~0.9 Ma ago. That uplift gradient corresponds to a chemical weathering gradient in soils and at depth (Chapters 3 and 4), with deeper, more pervasive weathering occurring south of 27.95 degrees latitude. The Melamchi Khola shows a strong orographic precipitation pattern ranging from 1127 mm/year in the southern end to 2978 mm/year in the mid-northern end (Hille, 2022). In the southern end, precipitation falls entirely as rain, while in the northern end it falls as a mix of rain and snow. Extreme events during the monsoon are more intense in the mid- northern end than the southern end and then decrease in intensity northward (Hille, 2022). Temperature also changes dramatically across the valley, due to the changes in elevation. Subcatchment averaged mean annual temperatures (MAT) from 1981-2010 range from 18.7°C in the southern end to -3°C in the northern peaks (Karger et al., 2017). 5.1.2 Geochemical tracers 3 H/ 3 He: 3 H atmospheric concentration peaked in the late 1950s and early 1960s during the nuclear bomb testing era, leading to the frequent use of 3 H as a groundwater tracer (Figure 5.3; Nir, 1964; Egboka et al., 1983; Busenberg and Plummer, 1992; Gleeson et al., 2016). 3 H 119 entering the groundwater system, radioactively decays, producing 3 He, a noble gas. Thus, dissolved 3 He concentrations increase in groundwater with time. The half-life of 3 H is 12.43 years, making it useful for dating younger groundwaters. Other sources, such as neutron capture by 6 Li, resulting in fission to form 3 He and 4 He, are minor and can be estimated based on increases in 4 He.. Tritiogenic 3 He ( 3 He trit) values can be isolated using a mass balance equation (Schlosser et al., 1988). The following equation is used to calculate groundwater age from 3 H/ 3 He trit where t is the estimated groundwater age, 𝜆 is the 3 H decay constant, and 3 H and 3 He trit are measured in tritium units (TU): 𝑡 =𝜆 P3 ln @ Q? '%!' # Q # +1B Eq 5.1 CFCs and SF 6: Atmospheric concentrations of the environmental tracers CFC-11, CFC-12, CFC-13, and SF 6 increased exponentially beginning in the 1950s. Since the implementation Figure 5.3 CFC and SF 6 atmospheric concentrations and 3 H precipitation concentrations since 1940. Based on data from the U.S. Geological Survey (http://water.usgs.gov/lab/software/air_curve/). 3 H in precipitation SF6 120 of the Montreal Protocol in 1992, CFC concentrations have plateaued and begun to decline slightly, while SF 6 concentrations have continued to increase exponentially (Figure 5.3). These gases are considered globally well mixed, making them useful worldwide. The historical atmospheric concentration of these gases is preserved in groundwater, dependent on salinity, temperature, and pressure at the site of recharge (Busenberg and Plummer, 1992). SF 6 and CFCs are both considered conservative tracers in aerobic systems but can be consumed during microbial activity in anaerobic environments (Oster, 1994). There has been some evidence of point-source contamination for CFCs (Busenberg and Plummer, 1992) as well as terrigenous sources for SF 6 (Busenberg and Plummer, 2000; Chambers et al., 2019). 5.2 Methods 5.2.1 Field sampling Sample collection to determine groundwater transit times was conducted at 22 springs across the Melamchi Valley in 2018 and resampling was conducted at a subset of 12 springs in 2019. Thirty-four CFC and SF 6 samples (22 from 2018 and 12 from 2019), six 3 H and 3 He/ 4 He samples (2018), 12 noble gas samples (2019) and 36 water isotope samples were collected over the course of the sampling period. Temperature, pH, dissolved oxygen (DO), and conductivity were measured using an Extech DO700 probe during each sampling campaign. Groundwater transit samples were taken at the spring outlet. Tubing was inserted into the bedrock to ensure minimal atmospheric mixing. SF 6 samples and CFC samples were collected unfiltered in two 500ml and three 125ml glass bottles with metal lined caps respectively, through Tygon tubing in metal buckets, using the displacement method to create a water barrier to prevent atmospheric contamination (Oster, 1994). Tritium samples were collected through plastic tubing in 1L glass bottles. Noble gas and 3 H/ 3 He samples 121 were collected through plastic tubing into copper tubing. Copper tubes were flushed with 1L of groundwater ensuring no visible bubbles were passing through, tapped to dislodge any bubbles, and crimped closed at both ends (Hunt, 2015). Water isotope samples were filtered using 0.20 µm nylon syringe filters and collected in 12ml glass vials. Sample bottles were all rinsed with sample water three times before collection. 5.2.2 Groundwater analysis CFC and SF 6 samples were measured by gas chromatography using a closed system purge and trap gas chromatography with an electron capture detector (GC-8A; Shimadzu Corp., Japan) at the University of Kumamoto (2018 samples) and Nagasaki University (2019 samples). The analytical uncertainties of CFC-11 and CFC-12 measurements is 3% while the analytical uncertainty of SF 6 measurements is less than 5%. Tracer concnetrations are reported as the gas mixing ratio (in pptv) in equilibrium with the measured sample concentration, taking into account recharge temperature, elevation, and assuming negligent excess air. 3 He/ 4 He and tritium samples were analyzed on a helium isotope mass spectrometer in 2018 at Woods Hole Oceanographic Institute. Samples were measured to an accuracy of better than 0.15% in 3 He/ 4 He ratio using WOCE-standard techniques. Tritium measurements are accurate to 0.005 TU and 1%. Noble gas (He, Ne, Ar, Kr and Xe) samples (2019) were reextracted using a gas flux extraction line, then transferred to the Mass Analyzers Products – Model 215-50 Magnetic Sector Mass Spectrometer for determination of 3 He and 4 He. All other noble gases were determined using a Stanford 50 Research SRS – Model RGA 300 quadrupole mass spectrometer at University of Utah Noble Gas Laboratory with a measurement uncertainty of ± 1% to 5% of value. 122 Samples were analyzed for stable isotopes of water (δ 18 O and δ 2 H), with results reported here using permille notation relative to the Vienna Standard Mean Ocean Water standard. Water isotope samples were analyzed on a Picarro L2130i Cavity Ring Down Spectrometer (Chapman University). The internal error of isotope measurements on the Picarro is 0.1‰ or better for δ 18 O and 2‰ or better for δ 2 H. 5.2.3 Tracer-based age calculations Lumped parameter models (LPMs) are often used to calculate groundwater ages derived from the measure environmental tracers (Maloszewski and Zuber, 1996). LPMs allow for multiple age distribution models including piston-flow, exponential mixing, and bimodal mixing. A piston-flow model (PFM) assumes uniform flow paths and no mixing with older waters. An exponential mixing model (EMM) corresponds to a total mixing of a distribution flow paths, from infinite age to zero-age water. The EMM is characterized by the mean of this distribution, which corresponds to the mean groundwater age. It is defined by the following equation: Cs ti = Cs (ti-1) + [1/t EMM] x [Cin ti-Cs ti-1], where Cs ti is the concentration of tracer in the mixture at time ti, Cs (ti-1) is the concentration in the mixture at the previous time step, Cin ti is the concentration in the new recharge added to the mixture in the time step and t EMM is the mean groundwater age. Those two mixing models are the simplest and most frequently used in environmental tracer studies (Maloszewski and Zuber, 1996; Cook and Böhlke, 2000; Manning et al., 2012; Thiros et al., 2023). The exponential piston model (EPM) incorporates a section of the flow path that can be described by the EMM followed by a section than can be described as a piston model. Finally, a binary mixing model is used to describe flow that 123 mixes a pre-modern old fraction (older than 1950) with a modern young fraction (<1 year). In this study, we used the USGS TracerLPM workbook, a commonly used LPM that utilizes local concentrations of 3 H to model groundwater ages (CONCEPTUALIZATION, 2012). 5.2.4 Recharge parameters from water isotopes and dissolved noble gases CFC-12 and SF 6 groundwater age calculations are sensitive to various recharge parameters, including 1) the solubility of gases at the time of recharge, which is determined by temperature, salinity, and pressure, and 2) the inclusion of “excess air”, which is related to the entrapment and dissolution of air bubbles during recharge. To attempt to account for these parameters, we utilized water isotope and noble gas samples. Water isotopes from springs were used to derive recharge elevations (Z) utilizing the linear relationship between elevation and isotopic composition of precipitation (Poage and Chamberlain, 2001). For ridgetop springs, we assumed that recharge elevation was local. For hillslope and valley springs, we derived the recharge elevation from the relationship between elevation and isotopic composition of the ridgetop springs (Jefferson et al., 2006). For springs where the calculated recharge elevation was lower than the elevation of the spring (n=3), we assumed that the recharge elevation was the same as the elevation of that spring. We then used recharge elevation (Z, in m) derived from water isotopes as a proxy for total pressure using the empirical lapse rate (Cook and Herczeg, 2000): 𝑃 =81− 1.11RS×T UVV.3S 9 S.USR3 Eq 5.2 Dissolved noble gases are a valuable tool for calculating some of these parameters, including temperature and excess air, because they are chemically inert and have no clear subsurface sources (Aeschbach-Hertig et al., 1999). Concentrations in groundwater are controlled by solubility equilibrium at the time of recharge and, assuming negligible salinity 124 (as these are freshwater springs (Kipfer et al., 2002)), noble gas concentration is a function of recharge temperature and total pressure. Temperature and excess air (EA) values for the four springs with noble gas samples were derived from each gas sample using the least squares minimization software PANGA (Jung and Aeschbach, 2018). The unfractionated air (UA) model of excess air formation was used (Kipfer et al., 2002; Manning et al., 2012). 5.3 Results 5.3.1 Recharge Parameters The strong linear relationship between the isotopic composition and elevation of ridgetop springs (r 2 =0.94) supports the use of water isotopes to extrapolate recharge elevations from the hillslope and valley springs (Figure 5.4). Results from those springs and the subsequent barometric pressures from Eq. 5.2 are presented in Table 5.1. Figure 5.4 δ 18 O plotted against elevation for each spring site, colored by topographic position in the valley. Linear regression of ridgetop samples shows an expected strong correlation, as ridgetop springs should recharge from elevations similar to their location. Recharge elevation for mid-hillslope and valley bottom springs was determined based on this elevation/ δ 18 O relationship from the ridgetop springs (Jefferson et al., 2006). 125 Sample ID Elevation (m) DO (mg/L) Temp (C) Recharge elevation (m) 2018 SF6 (pptv) 2019 SF6 (pptv) 2018 CFC 12 (pptv) 2019 CFC 12 (pptv) 2018 3He (TU) 2018 3H (TU) MKS-1 1433 6.57 20.1 1524 12.00 7.31 468.22 519.56 2.202 MKS-2 1085 6.08 23.1 1108 11.07 505.49 MKS-3 1152 3.86 23.4 1198 4.83 656.93 1.45 2.568 MKS-4 2521 1.97 13.6 2582 13.57 9.25 523.07 474.39 MKS-5 2430 6.01 14 2430 10.33 10.44 515.25 406.27 MKS-6 2123 3.24 14.8 2123 5.27 423.80 MKS-7 1807 6.1 17.7 2108 7.07 526.91 MKS-8 1429 6.29 19.3 2040 1.76 540.07 0.577 MKS-9 1212 3.81 22 1893 11.00 14.02 543.88 524.46 MKS-10 930 5.8 24.5 650 5.55 11.52 483.14 536.05 MKS-11 1083 4.91 22.3 1108 8.70 5.40 439.69 457.60 -0.0589 4.281 MKS-12 1478 6.13 17.5 2161 MKS-13 2034 2.4 15.6 2298 12.39 540.54 MKS-14 2474 1.5 12.5 2945 7.92 MKS-15 2001 6.76 16.2 2745 10.03 8.92 556.95 505.24 0.167 3.431 MKS-16 1946 3.99 17.6 2598 9.62 567.96 MKS-17 2162 6.67 14.2 2903 14.97 MKS-18 2262 6.43 15.1 2508 9.11 10.64 509.05 0.4 3.692 MKS-19 2162 5.48 13.7 2582 8.05 530.33 0.476 3.451 MKS-20 1903 3.92 17.1 2250 9.05 572.74 MKS-21 3005 8.4 10.6 3256 8.38 9.67 478.91 503.19 MKS-22 3128 8.04 9.4 3093 5.19 10.94 526.87 490.11 MKS-23 3378 8.27 5.8 3378 6.22 483.85 MKS-24 849 4.94 24.3 1166 4.01 14.56 399.30 437.74 Table 5.1 Parameters from groundwater springs, SF6 and CFC reported as the calculated mixing ratio in equilibrium with the measured sample concentration, taking into account recharge temperature, elevation, and negligent excess air. TU=tritium unit. 126 Dissolved noble gas concentrations and results from the PANGA modeling are presented in Table 5.2. All samples shown have a negative EA value, indicating that some degassing may have occurred during sampling. Thus, recharge temperature is assumed to be the groundwater temperature. For future samples, dissolved noble gases will be resampled to determine EA. CFC and SF 6 samples were collected separately from the noble gas samples. There do not appear to be any degassing issues with the CFC and SF 6 samples. They exhibit high SF 6 concentrations, which are the most sensitive to degassing (Gooddy et al., 2006). 5.3.2 Limitations The current work has two major limitations. First, the initial noble gas sampling campaign had challenges during sampling, likely leading to degassing. This lack of data means that we cannot quantify the excess air fraction in these samples, potentially leading to a bias to young calculated ages. Consequently, the reported ages should be lower limits. Additional sampling to quantify noble gas-derived recharge Sample ID He (cm 3 STP/g) Ne (cm 3 STP/g) Ar (cm 3 STP/g) Kr (cm 3 STP/g) Xe (cm 3 STP/g) EA [ccSTP/g] mean EA [ccSTP/g] SD Chi Squared Probability (%) MKS-1 3.8221E-08 1.4498E-07 0.00023508 5.5715E-08 7.744E-09 -0.000343654 0.00052268 1.1816546 55.3868881 MKS-11 3.4882E-08 1.4614E-07 0.00025328 5.7448E-08 8.2991E-09 -0.001095246 0.00054008 0.7850028 67.53654 MKS-15 2.891E-08 1.1141E-07 0.00019652 4.6927E-08 6.3101E-09 -0.000596353 0.00038686 MKS-22 3.1221E-08 1.3522E-07 0.00026583 6.5585E-08 9.4489E-09 -0.000392435 0.00047541 0.20072728 90.4508442 Table 5.2 Measured noble gas concentrations and calculated excess air (EA) from noble gas samples. Chi-squared and probability indicate uncertainty of fit using noble gas parameters in PANGA. 127 parameters will allow us to further calculate uncertainty in transit times, as well as determine 4 He derived from terrigenous sources, which is indicative of extremely old groundwater (Solomon et al., 1996). Second, we currently lack pre-monsoon data. Pre-monsoon SF 6, CFC and 3 H samples were going to be collected in April 2020, along with additional noble gas samples. Because of the COVID-19 pandemic and permitting issues, that sampling was delayed until April 2023. 5.3.3 Age models (singular tracers) SF 6, CFC-12, and 3 H/ 3 He trit concentrations and recharge parameters are presented in Table 5.1. In several cases, SF 6 concentrations are higher than the modern atmospheric concentration at the time of sampling, suggesting contamination from terrigenic sources, which has been observed in sedimentary and granitic terrain (von Rohden et al., 2010; Chambers et al., 2019). Modeled transit times range from 1 to 33 years for SF 6 using a PFM and <1 to 98 years using an EMM (Table 5.3). Generally, the PFM and EMM transit times agree for transit times less than 25 years, while EMM transit times over 25 years are generally older than PFM transit times, which is typical (Manning et al., 2012). EPM tracer ages range from <1 to 51 years. CFC-12 modeled transit times range from <1 to 34 years using a PFM and <1 to 31.56 using an EMM. In that case, EMM ages are systematically younger than PFM ages. EPM ages range from 8 to 34 years. 3 H/ 3 He modeled ages range from <1 to 12 years 128 Table 5.3 Groundwater residence times derived from piston flow models (PFM), exponential mixing models (EMM) and exponential piston model (EPM) NA means non-applicaple because concentration did not yield a reliable age. Sample ID October 2018 SF6 October 2018 CFC-12 October 2019 SF6 October 2019 CFC-12 PFM age (yr) EM age (yr) EPM age (yr) PFM age (yr) EM age (yr) EPM age (yr) PFM age (yr) EM age (yr) EPM age (yr) PFM age (yr) EM age (yr) EPM age (yr) MKS-1 NA NA NA 30.00 19.22 24.00 9.00 9.60 9.08 3.00 9.00 NA MKS-2 NA NA NA 27.00 12.79 18.00 - - - - - - MKS-3 18.00 22.89 18.99 NA NA NA - - - - - - MKS-4 NA NA NA 0.00 10.90 16.00 2.00 2.23 2.22 30.00 17.90 23 MKS-5 NA NA NA 26.00 0.51 15.00 NA NA NA 34.00 28.47 32 MKS-6 16.00 19.30 17.00 32.00 25.04 29.00 - - - - - - MKS-7 10.00 10.52 10.00 27.00 12.10 17.00 - - - - - - MKS-8 33.00 98.72 51.00 28.00 13.38 18.00 - - - - - - MKS-9 NA NA NA 28.00 13.55 18.00 NA NA NA 26.00 6.36 12 MKS-10 13.00 16.15 15.00 28.00 13.45 18.00 NA NA NA 18.00 6.36 9.72 MKS-11 3.00 3.13 3.10 31.00 22.86 26.00 16.00 20.13 17.00 31.00 20.46 25 MKS-13 NA NA NA 28.00 15.37 20.00 - - - - - - MKS-14 9.00 9.42 9.00 NA NA NA - - - - - - MKS-15 1.00 1.48 1.47 27.00 12.03 17.00 3.00 3.40 3.36 28.00 12.69 17 MKS-16 2.00 2.45 2.43 25.00 4.79 13.00 - - - - - - MKS-18 3.00 2.56 2.56 NA NA NA NA NA NA 0.00 11.90 16 MKS-19 7.00 6.99 6.98 27.00 12.87 18.00 - - - - - - MKS-20 3.00 3.16 3.13 17.00 6.03 9.19 - - - - - - MKS-21 5.00 5.11 5.00 30.00 19.04 24.00 1.00 0.83 0.84 28.00 13.09 18 MKS-22 16.00 19.83 18.00 24.00 6.04 8.00 NA NA NA 29.00 15.40 8 MKS-23 12.00 13.50 13.00 29.00 16.13 20.00 - - - - - - MKS-24 22.00 32.92 24.00 34.00 31.56 34.00 NA NA NA 32.00 23.48 28 129 5.3.3 Multi-tracer cross plots and mixing models Direct comparison of age tracer concentrations allows us to assess the plausibility of different mixing models (Plummer et al., 2001; Manning et al., 2012; Blumstock et al., 2015). Cross plots of calibrated SF 6 and CFC-12 gas concentrations from spring samples are shown in Figure 5.5 as well as concentrations derived from PFM, EMM, and EPM based on historical CFC-12 and SF 6 atmospheric mixing ratios. Many of the samples show the strongest agreement with an EMM, while some agree better with a PFM (Figure 5.5). Several samples do not plot on any line, which is often the case in multi-tracer studies. The samples that do not plot along these mixing lines could be consistent with a range of mixing models and components. Such differences are often the result of some bimodal mixing, in which an older fraction of water mixes with a younger fraction of water. Melamchi Khola samples dominantly agree with an EMM model, which indicates an older groundwater influence. Some mixing of older and younger water is consistent with results from a number of other Figure 5.5 Cross plots of groundwater spring SF 6 and CFC-12 concentrations from 2018 (a) and 2019 (b) with mixing model concentrations of PFM, EMM, EPM and a binary mix shown. 130 mountain groundwater studies (Rademacher et al., 2001; Manning et al., 2012; Frisbee et al., 2013, 2017; Jasechko et al., 2016). Unsurprisingly, groundwater ages from SF 6 and CFC concentrations derived from an EMM agree the best with one another (Figure 5.6a). Generally, the PFM and EPM models generally produce systemically younger groundwater ages when using SF 6 instead of CFC-12 (Figure 5.6b and c). Based on these cross plots, we determined the best mean age for each spring at different years (Table 5.4). The rest of the analysis will be based on this best mean age. Sample ID 2018 Mixing Model 2018 Mean Age 2019 Mixing Model 2019 Mean Age MKS-1 EMM (CFC) 19.22 EMM (SF6) 9.6 MKS-2 EMM (CFC) 12.79 MKS-3 EMM (SF6) 22.89 MKS-4 EMM (CFC) 10.9 MKS-5 EMM (CFC) 0.51 MKS-6 EMM (SF6+CFC) 20.96 MKS-7 EMM (SF6+CFC) 10.51 MKS-8 PFM (SF6+CFC) 32.55 MKS-9 EMM (CFC) 13.55 MKS-10 EMM (SF6+CFC) 16.15 EMM (CFC) 6.36 MKS-11 EMM (SF6+CFC) 3.13 EMM (SF6+CFC) 20.13 MKS-13 EMM (CFC) 15.38 MKS-14 EMM (SF6) 9.45 MKS-15 EMM (SF6+CFC) 1.48 EMM (SF6+CFC) 3.38 MKS-16 EMM (SF6+CFC) 2.45 MKS-18 EMM (SF6) 2.56 EMM (CFC) 11.9 MKS-19 EMM (SF6+CFC) 6.99 MKS-20 EMM (SF6+CFC) 3.16 MKS-21 EMM (SF6+CFC) 5.11 EMM (SF6+CFC) 0.84 MKS-22 EMM (SF6+CFC) 19.82 EMM (CFC) 15.4 MKS-23 EMM (SF6+CFC) 13.51 MKS-24 EMM (SF6+CFC) 32 EMM (CFC) 32 Table 5.4 Best mixing model and mean age for each sample in 2018 and 2019 131 5.4 Discussion 5.4.1 Transit time variations As a whole, SF 6 modeled ages reflect younger transit times than CFCs (Table 5.3). This disagreement between tracers can be attributed to CFC-12 degradation due to microbial activity, SF 6 contamination, degassing during collection, and incorrect residence time distribution model assumptions. CFC-12 degradation will often bias mean residence times older, while SF 6 contamination will bias ages younger. Systematic degassing would affect SF6 age more than CFC age. The prevalence of SF 6 contamination in samples indicates that SF 6 derived transit times reflect the minimum mean residence times. Given that, our mean modeled ages range from <1-35 years (Figure 5.7), and are broadly similar to other mountain groundwater residence times derived from environmental tracers (Manning et al., 2012; Thiros et al., 2023). These are significantly longer than the 45 days modeled by Andermann et al. (2012) from precipitation and discharge patterns in the High Himalaya. The large discrepancy between the results produced by the various methods suggests that different sampling methods reflect distinct aspects of residence time distributions. Our year to decadal length transit times are likely more reflective of deeper flow paths, while the 45 day Figure 5.6 Cross plots of SF 6 and CFC-12 derived ages using different lumped parameter models. 132 groundwater storage is likely more reflective of shallower pathways, such as soil and vadose zone storage (Manning et al., 2012; Hale et al., 2016; Somers and McKenzie, 2020). Our multi-year sampling of 2018 and 2019 reveals both similarities and differences in springs across the valley. For MKS-15, MKS-19, MKS-21, MKS-22, and MKS-24, the 2018 mean age is within ten years of the 2019 mean age, despite differences in which mixing model is suitable for each year. For MKS-1 and MKS-10, the 2019 mean age is more than 10 years older than the 2018 mean age, whereas for MKS-11 the 2019 mean age is more than 10 years younger than the 2018 mean age (Figure 5.8). The general similarity in ages between years likely reflects relative stability in flow path contribution to springs. Figure 5.7 Histograms of mean modeled ages from 2018 (left) and 2019 (right). 133 5.4.2 Controls on transit times According to Darcy’s Law, the most simplistic groundwater flow model, flow rate is dependent on groundwater head gradient (dh/dL) and hydraulic conductivity of the media (k). Although mountain groundwater systems are far more complex than an idealized flow experiment, they are also dependent on the topographic gradient (equivalent to dh/dL) and subsurface permeability and porosity to varying extents (McGuire et al., 2005; Hale et al., 2016; Somers and McKenzie, 2020). The Melamchi Khola has both a strong topographic gradient and a strong weathering gradient (see Chapters 3 and 4). The northern end of the valley exhibits steeper hillslopes, decreased weathering, and more open fractures as Figure 5.8 (a) Groundwater mean ages versus latitude for all samples, (b-d) groundwater mean ages versus latitude based on topographic location along the hillslope.ca 134 compared to the southern end. Because of the increasing dh/dL and likely increase in k values, we would expect younger residence times in the northern end of the valley. Yet overall residence times do not appear to change with latitude across the valley (Figure 5.8a). However, when we classify springs by topographic location we see that springs at the valley bottom and along hillslopes show decreasing residence times as latitude increases, (Figure 5.8b, c), while ridgetop spring residence times stay relatively consistent (Figure 5.8d). Flow path lengths also likely vary across the valley and affect residence times, with open fractures in mountainous environments often leading to longer flow paths and therefore longer residence times (Jasecho et al., 2016; Manning et al. 2013, Frisbee et al., 2014). Our decreasing residence times to the north as well as in the valley bottom suggest that this relationship might be more complex, as we see our shortest residence times here. Future work incorporating isotope chemistry and hydrologic modeling will help elucidate these complexities. This relationship between spring topographic position, residence time, and latitude suggests that topographic gradient exerts more of a control than subsurface permeability on the residence times in the khola. If permeability were the dominant control, we would expect residence times to decrease in all topographic positions since weathering is very limited at the near surface in the northern regions (Chapter 3). However, in our study area, we see decreasing residence times only at valley and hillslope springs. Because those locations are more effected by steepening hillslopes, our data suggest that topographic gradient is the driving factor in residence times. Future work will include modeling to further explore the interaction between topography and subsurface permeability and refine our understanding of the relative importance of those two factors on transit times. 135 5.4.3 Vulnerability of Melamchi Khola springs to climate change Our results, which show short residence times, especially in northern valley and hillslope springs, suggest that high mountain groundwater will respond rapidly to changing climate. Thus, springs in the Melamchi Khola will be sensitive to the effects of climate change rather than acting as buffers. As precipitation changes, local women, who are responsible for water collection, are already walking further to find available water when nearby springs dry up (Shrestha et al., 2019). Furthermore, groundwater with shorter transit times will also respond to atmospheric warming, potentially affecting stream and river biota sensitive to temperature increases such as fish (Hare et al., 2021). In the Himalaya, where groundwater can make up to 66% of streamflow (Andermann et al., 2012), this has significant implications for fisheries along the Melamchi and other Himalayan valleys. 5.5 Conclusion Groundwater plays an essential role in mountain environments as a contributor to local streamflow and to downstream water resources. In the Melamchi Khola, we see that groundwater transit times based on environmental tracers range from <1 to 35 years. Those transit times decrease in valley and hillslope springs as topographic gradient and hydraulic conductivity increase. 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Chapter 6 Important role of subsurface moisture in hydrological response to storms after wildfire in southern California, USA * Atwood, A., Hille, M., Clark, M.K., Rengers, F., Ntaralagiannis, D., Townsend, K., West, A. J. Abstract Wildfire alters the hydrologic cycle, with important implications for water supply and hazards including flooding and debris flows. In this study we used a combination of electrical resistivity and stable water isotope analyses to investigate the hydrologic response during storms of three catchments: one unburned and two burned during the 2020 Bobcat Fire in the San Gabriel Mountains, California, USA. Electrical resistivity imaging shows that in the burned catchments, rainfall infiltrated into the weathered bedrock and persisted. Stormflow isotope data indicate that the amount of mixing of surface and subsurface water during storms was similar in all catchments, despite higher streamflow post-fire. Therefore, both surface runoff and infiltration likely increased in tandem. These results suggest that the hydrologic response to storms in post-fire environments is more dynamic and involves more surface-subsurface exchange than previously conceptualized, which has important implications for vegetation regrowth and post-fire landslide hazards for years following wildfire. 6.1 Introduction Wildfire can profoundly change landscapes, most obviously by its effect on vegetation, but also by altering hydrologic and geomorphologic processes. Wildfire frequency and size are expected to increase as global climate change affects seasonal temperature and precipitation intensity * This chapter is published Nature Communications. Atwood helped conceptualize the project, led the water isotope, hydraulic conductivity, precipitation and discharge collection and analyses and co-wrote the article with Hille. 142 extremes 1–4 . More frequent and intense fires could exacerbate floods and debris flows, increase erosion, and imperil water resources 5–7 . One of the commonly observed hydrologic effects of wildfire is an increase in storm streamflow from burned areas relative to unburned areas 8–11 . In southern California, this hydrologic response has been primarily attributed to infiltration-excess surface runoff due to high rainfall rates coupled with changes in surface properties, including but not limited to hyper-dry conditions immediately after a fire 12 , soil compaction from rainwater impact 13 , sealing from ash clogging 14 , soil water repellency 15 , and a decrease in surface roughness 16 . In association, and particularly in climates dominated by convective storms, the probability of runoff- generated debris flows increases dramatically in the immediate years following a wildfire 17,18 , with these debris flows linked to changes in surface water transport. Set against this long-standing paradigm of enhanced post-fire stormflow, recent work has indicated that the hydrologic effects of fires may be more complex, involving changes in both surface and subsurface water. The creation of macropores from burned vegetation 19,20 , combined with decreased evapotranspiration following vegetation mortality, may increase groundwater storage 21–23 . In some cases, the hydrologic regime can change for years after a wildfire, leading to increased baseflow 20,24–26 and aquifer recharge and desalinization 27–29 . These post-fire changes in groundwater systems observed at the annual timescale are, at least superficially, inconsistent with the expectation of increased surface runoff observed at the storm timescale. In this respect, the relationship between subsurface and surface hydrology during post-fire storms remains unclear, as does the magnitude and timescale over which the two seemingly paradoxical processes of increased surface runoff and increased groundwater storage operate 26,30 . Disentangling these aspects of the hydrologic response to wildfire, specifically during storms, is important for understanding the effect of fires on water storage, erosion, and the potential for debris flows and landslide initiation. In this 143 work, we test whether a dynamic reservoir of subsurface moisture plays a more important role in post-wildfire storm streamflow than is often presumed in current conceptual models. The San Gabriel Mountains, located in Los Angeles and San Bernardino Counties of southern California, USA, are an important locality for understanding post-wildfire hydrology and its effects on natural hazards and water resources 31 . Fire has long played a critical role in southern California and the San Gabriel Mountains and has modified the landscape through its effect on sediment mobility and vegetation 32,33 . Indigenous peoples of southern California used fire as a resource management technique for myriad reasons, including to increase food and material supplies and promote growth of new vegetation 34 ; during this time fires are thought to have been limited by fuel load or environmental conditions rather than suppression 35 . Currently, however, fires in the American West and elsewhere around the world are suppressed using modern equipment and techniques, leading to greater fuel loads that increase the size and intensity of fires 35 . In the San Gabriel Mountains, post-wildfire debris flows and shallow landslides represent a substantial hazard to the surrounding urban communities, while severe droughts over the past two decades highlight the importance of understanding water resource availability in this region. Numerous studies in this area have indicated post-fire streamflow variability 24,36–38 , and documented long-term wildfire effects including debris flows and shallow landsliding 39–41 . This is a region where increased streamflow post- fire is frequently attributed to reduced infiltration 40,41 , but some work has also connected increased streamflow responses to increased groundwater storage 36 . In this study, we explore post-fire hydrological dynamics in the bedrock landscapes of the San Gabriel Mountains. We characterize how subsurface water storage changes post-fire, and how these changes affect streamflow and its sources 42 using a unique combination of time-lapse electrical resistivity imaging (ERI) and water stable isotope (δ 2 H, δ 18 O) data from storm events over two water years. Combining these methods allows us to investigate the connections between increased 144 overland flow and increased groundwater storage. Our study is based on paired catchments, following a long-established and widely adopted approach in hydrology (e.g., Bates, 1921 43 ; Bosch and Hewlett, 1982 44 ). We focus on adjacent burned (Louise, ~0.07 km 2 ; Thelma, ~0.06 km 2 ) and mostly unburned (Henry; ~0.10 km 2 ) catchments in the San Gabriel Mountains, following the September-December 2020 Bobcat Fire (Figure 6.1). These study catchments were selected because they have broadly similar lithology, aspect, and slope steepness, while contrasting in burn intensity (Figure 6.1; Appendix B Figure S1; Appendix B Table S1). We find that both surface runoff and Figure 6.1 (a) Map of the 2020 Bobcat Fire in the San Gabriel Mountains with soil burn severity (USDA Forest Service, 2020), showing the location of the study catchments. (b) Oblique photo looking south at burned (pink) and unburned (blue) catchments (photo courtesy of A.J. West). (c) Soil burn severity map of the study catchments. (d) Pre-fire imagery annotated with study catchments, rain gauges and geophysical surveys. Pre-fire imagery was obtained by Pléiades ©CNES (2020), Distribution AIRBUSDS, sourced via SkyWatch Space Applications Inc. Lidar is from the USGS 3D Elevation Program (3DEP). 145 subsurface water storage are greater in the burned catchments than the unburned catchment within the two water years following the Bobcat Fire, and we posit that increased post-fire streamflow is a result of dynamic connections between subsurface and surface water. Our findings add to the conceptual understanding of the hydrologic response to storms in post-fire, ravel-dominated small Figure 6.2 (a) Times series of 15-minute binned precipitation and 𝛿18O from precipitation and streamflow samples during storms. Stream and precipitation samples from 10 March 2021, 14 December 2021, and 28 March 2022 are similar, indicating that runoff during that event was likely generated in large part from overland flow. 15 March 2021 stream samples in both catchments and 23-24 December 2021 stream samples in the unburned catchment show a distinct signature from co-temporal precipitation. (b) Total precipitation record in 15-min binned intervals (grey bars) and cumulative precipitation (blue line) for the two water years and ERI surveys (red lines). Approximate periods of streamflow are indicated by green and orange bars based on trail camera and field observations. Extended baseflow occurred in the burned catchment after the large storms in December 2021. (c) 30-min values for 14 and 23 December 2021 showing different streamflow response. The 14 December 2021 stream response shows fluctuating amounts of new water in streamflow, whereas 23 December 2021 shows a lower fraction new water, especially in the burned catchment, indicating higher groundwater contributions. 146 catchments and have implications for the timescale of vegetation recovery, water resource availability, and natural hazards such as debris flows and flooding. 6.2 Results 6.2.1 Precipitation data, streamflow, and hydraulic conductivity Nine storm events occurred during our study period during December 2020-March 2022, with maximum rainfall intensities ranging from 7 to 20 mm/hr. Water year 1 (WY1) (October 2020- September 2021) was drier, recording ~18 cm cumulative precipitation, whereas WY2 (October 2021-September 2022) recorded ~40 cm, the majority during three major events in December 2021 (Figure 6.2b). Average annual precipitation from 1990-2020 in this location was ~70 cm 45 , with notable cyclicity, although this changes periodically based on El Niño/La Niña cycles. Figure 6.3 Conceptual diagrams of the water budget in (a) a typical unburned catchment, (b) a burned catchment dominated by overland flow due to hydrophobic layer or decreased surface roughness and (c) a burned catchment with increased infiltration and surface runoff. (d) Boxplots and swarm plots of field- saturated soil hydraulic conductivity (K fs) values from the unburned and burned catchments compared to rainfall intensity, where the points represent data values, the box represents the quartiles with the middle line indicating the median value, and the whiskers represent the data distribution excluding outliers. (e) Inset of (c) showing potential rapid infiltration points in burned catchments as well as photographs from the Louise catchment (left photo shows water flowing out of fractured bedrock from 4 February 2022; photos courtesy of A. Atwood). Diagrams after Jung et al., 2009 36 . 147 Median values of soil surface field-saturated hydraulic conductivity (K fs) measured post- rainfall were similar in all three catchments (Figure 6.3d; Appendix B Table S2). Rainfall intensity during storms rarely exceeded the median K fs values (Appendix B Figure S2), yet always exceeded the minimum measured K fs during storms where streamflow was generated. The burned catchments (Thelma, Louise) had more variable K fs (significant at the 5% level, based on an F-test; Table S3) than the unburned catchment, indicating substantial spatial variability in potential infiltration-excess surface runoff and infiltration. 6.2.2 Erosion and the subsurface characteristics The storms in December 2021 resulted in substantial erosion and channel lowering (~2 m of channel incision at one of the burned catchments, Louise; Appendix B Figure S3). A major drawback to studying post-fire catchments is the difficulty of collecting streamflow data due to flashy, debris-filled streamflow and instrument loss, which prevented us from collecting stage height records. Nonetheless, other observations constrained streamflow dynamics for the burned-unburned pair of Louise and Henry (see Methods). Streamflow duration was broadly similar in the two catchments, except for the prolonged flow after the December 2021 storms in the burned catchment (Figure 2b). The more pronounced difference was that the burned catchment exhibited greater streamflow during each event (Appendix B Figure S4). Calculated maximum streamflow from the December 2021 storms, inferred from channel geometry and high-flow markers near the channel outlet (Appendix B Figure S5), were 3.3 m 3 /s in the burned catchment and 0.51 m 3 /s in the unburned: ~ 6x higher in the burned catchment. 6.2.3 Electrical Resistivity Imaging ERI results allowed us to compare shallow groundwater retention patterns in burned and unburned catchments, and between WY1 and WY2. In WY1, a pulse of decreased resistivity. followed the first large rainstorm in all three catchments (Appendix B Figure S6), indicating addition 148 of water to the subsurface. In the burned catchments, this water persisted within both the shallow Figure 6.4 Timelapse electrical resistivity images at unburned (Henry) and burned (Louise and Thelma) catchments from (a) a seasonal perspective and (b) an interstorm perspective. Seasonal timelapse images are in reference to the baseline December 2020 surveys. Interstorm comparisons are labelled. Both catchments showed seasonal fluctuations of subsurface water over the course of WY1 and WY2. The unburned catchment showed shallow surface drying with resistivity exceeding early season values by the beginning of WY2 (iv, x), consistent with patterns in evapotranspiration and seasonal water fluctuations after a drier-than-average year. The burned catchments showed little to no change in resistivity during WY1, indicating persistent water in the subsurface, but near surface drying towards the late season of WY2 (xiv), which may be explained by vegetation regrowth and an increase in ET (Figures S9, S10). 149 and deeper subsurface (between ~1 and 7 m depth) for the remainder of WY1, even after a ~2- month period of little to no rainfall (Figure 6.4ii-iii, xi-xii; Figure 6.2b). Minor changes in drying and wetting at the very near surface were observed, but the subsurface remained remarkably static in the burned catchments prior to June 2021. In the unburned catchment, water addition at the near surface (1-2 m depth) steadily dried out, and by June 2021 conditions appeared similar to those observed immediately post-fire (Figure 6.4i, x). After the dry summer months and at the start of WY2, the subsurface of the burned catchments had dried out but did not return to the immediate post-fire dryness evident at the beginning of WY1, and moisture was still evident in the near surface (between ~1-5m) (Figure 6.4v- vi). By comparison, the unburned catchment appeared drier at the beginning of WY2 than it did at the start of WY1, which we posit may be related to a notably low rainfall water year coupled with the effects of evapotranspiration. Following the December and January storms of WY2, we observed a deeper wetting front within all three catchments (Figure 6.4vii-ix) when compared to WY1, which persisted through the remainder of WY2 in both burned and unburned catchments. Over the course of the water year, near-surface drying co-located with vegetation regrowth in the burned catchments was evident more than in WY1 (Figure 6.4xiv). Even so, in the unburned catchment, the lateral extent of drying of the near surface exceeded that of one of the burned catchments (Figure 6.4xiii). 6.2.3 Storm water stable isotopes Precipitation stable isotopes: One burned catchment (Louise) and one unburned catchment (Henry) were sampled for analysis of the stable isotope (H and O isotope) composition of rain and stream water isotopes. Precipitation 𝛿 18 O values (n=126), which were similar between catchments, were heaviest at the beginning of WY2 and became progressively lighter into January before becoming heavier again in 150 March, following the expected sinusoidal seasonal isotope pattern 46 (Figures 4, 5). Precipitation 𝛿 18 O composition fell along a local meteoric water line, based on 𝛿D and 𝛿 18 O, with a slope of 8.3207 and intercept of 19.657 (Appendix B Figure S7). Precipitation-amount weighted average isotope compositions are presented in Appendix B Table S4. Rainwater 𝛿 18 O values also showed intra-storm variability (~2-5‰) (Figures 4, 5). The 10 March 2021 storm, which was the storm with the least total rainfall, had the highest variability in rainwater 𝛿 18 O. However, trends in 𝛿 18 O values with time through storms were not always the same. In the 25 October 2021 storm, rainfall 𝛿 18 O became progressively lighter with increasing precipitation, whereas in the 14 December 2021 storm, 𝛿 18 O was initially lighter and then heavier as the precipitation increased. We saw some variability in 𝛿 18 O values between catchments (largest on 10 March 2020), but most storms had similar 𝛿 18 O values. The observed variability with time and between catchments is consistent with convective storms and spatial heterogeneity over steep mountainous topography such as that of the San Gabriel Mountains 47 . Streamflow stable isotopes: Streamflow 𝛿 18 O values (n=142) were generally less variable than precipitation 𝛿 18 O values during storms and were similar between catchments (Figures 4i, 5). However, during individual storms, we found that streamflow 𝛿 18 O values could differ from contemporaneous precipitation 𝛿 18 O values (Figure 6.5). For example, on 10 March 2021, 25 October 2021, 14 December 2021, and 28 March 2022, the streamflow and precipitation samples had similar 𝛿 18 O distributions based on overlapping mean and quartile distributions for storm totals, whereas the 15 March 2021 and 23 December 2021 streamflow samples, as well as a streamflow sample collected after the 30 December 2021 storm, had offset distributions from rainfall (Figure 6.5). Intra-storm 𝛿 18 O time series also reflected this pattern, where streamflow on 10 March 2021 and 14 December 2021 closely followed 151 changes in precipitation 𝛿 18 O, whereas streamflow on 15 March 2021 and 23 December 2021 did not (Figure 2). 25 October 2021 and 29 March 2022 storms show offset precipitation and streamflow 𝛿 18 O values, suggesting a delayed streamflow response to rainfall. Streamflow isotopes at the start and end of the 14 December 2021 storm deviated from precipitation values during times of lighter rainfall. These differences were also reflected in calculated D-excess values (Appendix B Figure S7). New (20-30min) fraction of streamflow: Streamflow generated by overland flow is expected to reflect the most recent rainfall isotope signature. The fraction of “new” water (recent precipitation) in streamflow, which can also be thought of as the fraction from overland flow, (F new; following Kirchner, 2019 48 , Eq. 8) is a hydrograph separation technique that does not require a stationary end member and therefore can be used with changing rainfall signatures during storms. “New” in this context is defined by Figure 6.5 Boxplots of 𝛿 18 O measurements from rainfall and stream during storm events. White horizontal lines indicate precipitation weighted averages of each rainfall event (see Supplementary Text S3 for methodology), where the box represents the quartiles with the middle line indicating the median value, and the whiskers represent the data distribution excluding outliers. No time series sampling occurred during 30 December 2021 storm, but integrated rains samples were collected and streamflow in the burned catchment from the extended baseflow period was collected on 4 January 2022. There was no streamflow in the unburned catchment to sample at that time. 152 sampling frequency. Here, we sampled every 20-30 minutes, which was the highest possible frequency for sampling given field conditions. F new values were calculated for 14 and 23 December 2021 storms when continuous precipitation and streamflow samples were collected to calculate F new at the single-storm timescale (Figure 2c). Both catchments showed similar F new values during the 14 December 2021 storm, with changing F new values between 0-75% new water and the highest values recorded during highest precipitation time periods. During the 23 December 2021 storm, the burned catchment showed low F new values, while the unburned catchment had a more variable F new signature (Figure 2c). In the burned and unburned catchments during both December 2021 storms, the calculated fraction of new water in streamflow samples correlated with rainfall intensities that matched or exceeded median K fs (Appendix B Figure S8). 6.3 Discussion Timelapse ERI revealed seasonal-scale resistivity changes resulting from subsurface water accumulation and retention in the burned watersheds, indicating greater groundwater storage in this dry, bedrock environment post-fire compared with the unburned watershed (Figure 6.4). Streamflow observations (Figures S4, S5) and water isotope data (Figure 6.2) indicate that this subsurface water reservoir is dynamic and contributes to surface flow, which challenges the longstanding paradigm that in southern California, post-fire storm streamflow originates solely from infiltration-excess overland flow. In unburned environments, streamflow during storms is typically comprised of a mixture of rainfall and stored water, where “stored” water is any water within the catchment subsurface that becomes mobilized during storms 49–51 . In Mediterranean climates such as the San Gabriel Mountains, between 22% and 100% of streamflow can be attributed to rainfall 50 , depending in part on antecedent rainfall and soil moisture conditions. Isotopic similarities in streamflow between the unburned catchment and the sampled burned catchment in our study show that the same 153 proportions of stored water and rainfall are driving streamflow in the two catchments (Figures 6.4i, 6.5), despite greater observed streamflow in the burned catchment during storms (Appendix B Appendix B Figure S4). Moreover, storms following an extended dry period (~2 months) show higher proportions of surface runoff contributing to streamflow, whereas streamflow in closely spaced storms (within ~1 week) shows a relatively higher proportion of subsurface flow (Figure 6.5; Figure 6.2c). This observation is consistent with transient changes in subsurface storage between storms and may also reflect increased soil-water repellency and enhanced infiltration-excess surface runoff due to a reduction in soil moisture between storms (Figure 6.2c). These observations pose an apparent conundrum: how can storm streamflow be increased in the burned watershed, yet comprise a similar mixture of rainfall and stored water as in the unburned watershed? Rainfall intensities exceeded minimum K fs values in both catchments during storms that generated streamflow (Figure 6.3d; Appendix B Figure S2), indicating that infiltration-excess overland flow was contributing to streamflow in both catchments (see further discussion in the Supplement). Yet our data indicate that a substantial portion of precipitation also infiltrated into the subsurface and, importantly, that some of this stored water was then mobilized back as streamflow during storms (Figure 6.3c). We suggest that the streamflow response in the burned catchment reflects significantly enhanced subsurface exchange relative to the unburned. Our resistivity data are consistent with this interpretation of the isotopic data, with the deeper wetting front within the burned catchment indicative of overall greater water content in the subsurface compared to the unburned catchment. This subsurface water provides a larger reservoir stored in the burned catchment that can be mobilized during storms. These observations potentially reconcile differences in the study catchments’ relative streamflow with the similar proportions of subsurface and surface water contributions to streamflow that we observe in the isotope data. 154 Although both the unburned and burned catchments experienced fluctuations in subsurface water following storms, the burned catchments exhibited more pronounced decreases in resistivity at greater depths and perhaps most importantly, showed persistent subsurface moisture over the duration of the study period, similar to observations after fire in pine forests in Texas, USA 27 . In the burned catchments in our study, water persisted at the near surface between WYs, but towards the end of WY2, drying was co-located with observed vegetation regrowth. In the unburned catchment, persistent water storage was not evident, and instead resistivity in the near surface progressively increased as the wet season tapered and substantial drying occurred over the summer months prior to WY2, indicating drying that is consistent with observations of evapotranspiration (ET) from remote sensing data (Figures S9, S10). Changes in ET over time align with patterns in resistivity; although all three catchments showed similar ET prior to the Bobcat Fire, the unburned catchment had higher ET during WY1 when vegetation regrowth in the burned catchments was limited. In WY2, ET rates rebounded in the burned catchments, indicating substantial increase in vegetation growth that is consistent with near-surface changes in resistivity in WY2 (Figure 6.4xiv). We observe higher discharge in the burned catchments than the unburned catchment in WY2 despite this increase in ET, which attribute to the subsurface that persisted from WY1 to WY2 contributing to streamflow. These broad patterns in changing resistivity and ET indicate substantial, relatively deep, and lasting changes in subsurface water in the burned catchments. This change resulted in greater volumes of streamflow mobilized from the subsurface during storms, generating higher streamflow as a mixture of stored water and rainfall similar to unburned settings (Figures 6.2a and c). The differences we observe in resistivity between the burned and unburned catchments may reflect the importance of vegetation on water storage and transport in the San Gabriel Mountains. In this highly fractured and dry environment, the groundwater table is likely meters below the depth 155 limit of the ERI surveys. Our observations document changes in the water reservoirs of fractured bedrock that fluctuate over weeks to months with the addition of water through rain infiltration and vegetation modulation — a reservoir that is increasingly recognized for its hydrologic importance 52,53 . In the unburned catchment, we attribute seasonally fluctuating subsurface moisture to deeply rooted chaparral plants that “hold'' shallow water nearer to the surface 54 (Figure 6.3; Figure 6.4iv, x, xiii). In comparison, deeper and persistent water the burned catchments may be explained by rapid infiltration of rainwater through flow paths following preferential finger flow or created by burned-out root systems or fractures exposed from the removal of the organic duff layer 19,20,27 (Figure 6.3e), as well as substantial loss of reduction in evapotranspiration during WY1 55,56 . In addition, channel erosion may have allowed subsurface water to rejoin the surface water flow via exfiltration of channel banks. An important implication of our observations is that a deep reservoir of water plays a role in hydrology at different timescales, from storms to seasonal and multi-year processes. Enhanced surface runoff is not the only mechanism driving increased discharge after wildfire, and the proportions of new and subsurface water in streamflow appear to be influenced by antecedent storms. We find increased subsurface storage immediately following a single storm as well as prolonged streamflow after substantial rainfall (Figure 6.2b), consistent with observations of long- term hydrological effects of wildfire that include increased baseflow and aquifer recharge 25,27,28,36,57–60 . This recharge may contribute to water resources but potentially at the cost of water quality through delivery of fire-associated contaminants including organics and heavy metals 61–64 . Greater subsurface water storage and exchange may also contribute to rock moisture storage, which is shown to buffer forests against drought conditions 52 . In addition to affecting streamflow and groundwater resources, the persistent and dynamic subsurface water storage we observe may have important implications for post-fire landscape 156 evolution. Surface runoff is critical for the triggering of debris flows in the months following fires 33,65,66 , and wet sediment may be more easily mobilized than dry sediment 67 . Our results suggest that debris flow predictions may be improved by considering spatially heterogeneous infiltration (e.g., McGuire et al., 2018 66 ) and subsurface contributions to streamflow, particularly during rapidly sequential storms at the beginning of the wet season when the likelihood of a debris flow occurring is considered highest 39 . The accumulation of subsurface moisture via high infiltration pathways may also contribute to shallow landsliding through excessive pore-water pressures several years post-fire, as documented by the shift from runoff-generated debris flows to “infiltration-generated debris flows” in the 2-4 years following wildfire 39–41,68,69 . Shallow water storage in our study site appears to facilitate rapid vegetation regrowth, affecting post-fire ecology and potentially stabilizing hillslopes in the process 29,70 . However, it is important to acknowledge that the first-year triggering conditions for post-wildfire runoff-generated debris flows in southern California are closely tied to bursts of high intensity rainfall, and, unlike shallow landslides, debris flow initiation is poorly correlated with soil water content 33,71 . Observations in this study and others (e.g., McGuire et al., 2018 66 ) showing spatially heterogeneous infiltration can account for the fact that water can both infiltrate deeply in high-infiltration areas and runoff in low-infiltration areas. Our work demonstrates that following a wildfire, dynamic subsurface reservoirs can increase streamflow and provide a water resource for vegetation regrowth. As wildfire frequency increases in southern California and other locations, it will become increasingly important to consider subsurface hydrology in the context of post-fire cascading hazards (e.g., shallow landslides, flooding) and ecosystem recovery. 157 6.4 Materials and Methods 6.4.1 Study area We identified three first-order catchments within the perimeter of the 2020 Bobcat Fire in the San Gabriel Mountains (Figure 6.1; Louise, Thelma, and Henry). All are underlain by Precambrian gneiss and Cretaceous quartz diorite of the San Gabriel Mountains range 72 and have similar slope angles, catchment areas, and long-term rainfall volume and intensity (Appendix B; Appendix B Figure S1; Appendix B Table S1). All catchments have ephemeral streams that generate streamflow only during heavy precipitation events. During the Bobcat Fire of 2020, Louise and Thelma (burned) were moderately to severely burned over the entire area while 23.6% of Henry (unburned) was burned at moderate to high severity, with the rest unburned (Figure 6.1). The comparison of these catchments allows us to study the role of differing vegetation and burn severity, while controlling for other major factors expected to influence hydrology and erosion. 6.4.2 Precipitation data, soil infiltrometry and streamflow estimates The climate of the San Gabriel Mountains is Mediterranean, with most precipitation falling in winter months and only sporadic rain in summer, often with months of uninterrupted dry weather 73 . We installed three tipping bucket precipitation gauges in December 2020. One of the gauges was placed near the catchment mouth of Louise (burned), the second at the top of the ridge on the south side of Louise (burned) and Henry (unburned), and the third on the opposite and southern side of the ridge at Thelma (burned; Figure 6.1) to capture local-scale spatial variability and accurate timing of storm arrivals. Gauges showed similar rainfall values during WY1 (Supplementary Information); the gauge data from the catchment mouth of Louise (burned) is presented in this study. Field-saturated hydraulic conductivity (K fs) measurements of the catchments were made in April and August 2021 (post-rainfall), using a Meter Environmental minidisk portable tension infiltrometer with a suction of 1 cm, with multiple (~20) measurements made at each site (Appendix 158 B Table S2). The volume of water infiltrated was recorded as a function of time and converted to K fs using the differential linearization method 74 . For detailed methodology, refer to Wall et al., 2020 75 . Note that K fs was not measured in the upper reaches of the watersheds due to accessibility. A portion of Henry burned at moderate to high severity (~23%), which may have influenced surface runoff contributions to streamflow, but is not accounted for in the K fs data. Time lapse trail cameras were installed at the base of each catchment to capture the length of potential stream flow or erosional processes, such as debris flows. The ephemeral nature of the catchments, substantial sediment mobility during storms, and high risk of post-fire debris flows prevented installation of stream gauges to monitor streamflow. Maximum streamflow estimates are based on channel profiles collected in March 2022. Channel profiles were collected using an RTK Septentrio Global Navigation Satellite Systems (GNSS) instrument (Appendix B Figure S12), and cross-sectional area and the wetted perimeter were calculated using channel geometry (Appendix B Figure S5). One profile was collected above small concrete walls at the mouth of each catchment (Appendix B Figure S5) and the Froude number is 1 assuming critical flow. Thus: 𝑉 = G𝑔𝑅 Eq. 6.1 where 𝑉 = velocity and 𝑅 = hydraulic radius of the measured channel cross-section, and streamflow (𝑄) is calculated using the cross-sectional area of the channel 𝐴: 𝑄 =𝑉𝐴 Eq. 6.2 159 6.4.3 Electrical resistivity imaging We measured apparent resistivity over 42-, 46-, and 46-m survey lengths perpendicular to the catchment channels to capture water fluctuation in both hillslopes and channels at Louise (burned), Thelma (burned), and Henry (unburned), respectively (Figures 1, 4; Appendix B Table S5; Appendix B Figure S11). We used a Syscal Pro electrical resistivity meter from Iris Instruments and a combined Dipole-Dipole and Wenner-Schlumberger array with 2-m spacing for better signal-to- noise ratio and lateral and vertical resolution 76 . Reciprocal measurements were also made at 1/3 density for robust error quantification to improve model results. We inverted two-dimensional (2D) resistivity models using ResIPy 77 , an open-source inversion software 78 . For time-lapse models, we calculated changes between surveys as a percent difference in resistivity, which reflects the addition or removal of water. Small-scale lateral variability in resistivity was not distinguishable between potential fractured bedrock outcrops that limit groundwater flow and inversion artifacts or noise from data overfitting 79 . Large-scale lateral variability may be attributed to unmapped bedrock contacts or compositional differences in the San Gabriel Mountains Precambrian gneiss 72 . 6.4.4 Precipitation and streamflow collection and stable isotope analysis Water samples were collected from the study catchments during streamflow episodes to determine 𝛿D (D/H, ‰), 𝛿 18 O ( 18 O/ 16 O, ‰) and deuterium (D) excess (defined as Dxs = δD − 8 × δ 18 O, in ‰), all reported relative to Vienna Standard Mean Ocean Water (V-SMOW). Stable isotope analysis was done on Picarro L2130i cavity ringdown spectrometers at Chapman University in Orange, California, and University of Southern California in Los Angeles, California. The internal error of isotope measurements on the Chapman spectrometer was 0.1 ‰ or better for 𝛿 18 O and 2‰ or better for δD. The standard deviation of an independent quality control standard used for analysis at University of Southern California was ≤0.2‰ 𝛿 18 O and ≤ 2‰ for δD. Precipitation (n=126) and streamflow (n=142) samples were collected at high frequency (sub-hourly) during the storms on 10 160 and 15 March 2021, 25 October 2021, and 14, 23 and 24 December 2021. We did not sample during storms in December 2020 and January 2021 due to significant debris flow risk and associated road closures. Precipitation samples were collected in open containers at channel outlets and transferred to 12 mL glass exetainers. During the 14 December, 23 December, 28 December 2021, and 28 March 2022 storms, buckets with mineral oil were used to collect integrated rain samples (n=8) for the duration of the storm. Although variations in isotope composition due to elevation changes across the catchment are expected, past studies in this region indicate only 0.5‰ change per 500-m elevation change 80 , which is consistent with data collected from integrated rain samples on the ridge and base of the burned catchment during the 28 March 2022 storm. Streamflow grab samples were collected directly into 10-mL glass exetainers, filtered using 0.2-m nylon syringe membrane and stored at 5°C until analysis. 6.4.5 F new calculations New fraction water (F new) from Kirchner (2019) 48 is a type of hydrograph separation based on correlations between tracer fluctuations in streamflow and end members, thus allowing for endmembers to change with each timestep and estimation of the amount of new water between one timestep and the next. In environmental regimes like post-fire areas where runoff is assumed to rapidly contribute to streamflow, we would expect to see high F new values relative to an unburned area. F new was calculated using Equation 8 from Kirchner (2019) 48 : 𝐹 B?WF = @ J ,- PJ ,-./ J 0*1- PJ ,-./ B, Eq. 6.3 where C Qj is the 𝛿 18 O value of the stream water from the current timestep, C Qj-1 is the 𝛿 18 O value of the stream water in the previous timestep, F newj is the fraction of new water in the stream water in the 161 current timestep, and C newj is the 𝛿 18 O value of the new precipitation that fell during the current timestep. Data Availability The Hydroshare data repository associated with this study is available at http://www.hydroshare.org/resource/670e332937c148eb94178f0e4e18cdd7. 162 6.5 References 1. Donat, M. G., Lowry, A. L., Alexander, L. V., O’gorman, P. A. & Maher, N. More extreme precipitation in the world’s dry and wet regions. (2016) doi:10.1038/NCLIMATE2941. 2. Mann, M. L. et al. Incorporating Anthropogenic Influences into Fire Probability Models: Effects of Human Activity and Climate Change on Fire Activity in California. PLOS ONE 11, e0153589 (2016). 3. 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Schubert, G.) 233–259 (Elsevier, 2015). doi:10.1016/B978-0-444-53802-4.00192- 5. 80. Wicks, L. Hydrogeologic and Geochemical Investigation of Robust Spring Discharge at Wingate Ranch, Eastern San Gabriel Mountains, California. MS dissertation, California State Polytechnic University, Pomona 92 (2014). 168 169 Appendix A Supplemental Figure 1. Flowchart of workflow to build SETSM DEMs. The steps in blue require user input, while the steps in purple are automated steps in the SETSM algorithm. Match overlapping images from stereo pair Submit each pair for processing in SETSM at desired DEM resolution SETSM extracts elevation data using stereo images and corresponding Rational Polynomial Coefficients through a coarse-fine resolution process, Vertical Line Locus to constrain the sarch space and matching points to find optimal height A Triangulated Irregular Network is derived at each resolution. User Input SETSM DEM is created at desired resolution from final TIN using neighborhood smoothing and Inverse Distance Weighting interpolation If in-track is unavailable: Cross-track stereo imagery (taken of the same location from different times) Obtain cloud-free WorldView 1, 2 stereo imagery Ideal: In-track stereo imagery (taken by the same satellite 45-90 seconds apart) Co-register using Nuth and Kaab (2011) co-registration methodology Supplemental Figure 1. Flowchart of work!ow to build SETSM DEMs. "e steps in blue require user input, while the steps in purple are automated steps in the SETSM algorithm. 170 Supplemental Figure 2. Comparison of SETSM DEMs produced at different resolutions. Distributions of aspect (top panel) and slope (bottom panel) from LiDAR and SETSM 0.5, 2 and 8m DEMs at Coyote Mountains. For both metrics SETSM-2m performs the closest to LiDAR, with the 8m DEM underestimating slope and the 0.5m DEM overestimating due to small scale noise. 171 Supplemental Figure 3. Aspect distributions. Distribution of aspect in DEM from SETSM, ALOS, SRTM and LiDAR DEMs at each study site. Note that, in all cases the SETSM-2m performs the closest to LiDAR. 172 Supplemental Figure 4. Topographic Position Index (TPI) maps of Boulder Creek, CO from SETSM, SRTM and LiDAR DEMs. TPI was calculated using different neighborhood sizes (100m, 30m and 5m). All three produced similar TPI maps at 100m resolution, but both SETSM and SRTM have significantly more noise at a 30m resolution. At 5m resolution, LiDAR still shows a variety of features that are indistinguishable in the noise of the SETSM map. 173 Supplemental Figure 5. Utility of SETSM matchtag files in identifying elevation differences. SETSM 2m elevation difference from LiDAR distributions at locations classified as “matches” and “mismatches” from SETSM’s matchtag file, produced with each DEM. “Mismatch” sites have very similar distributions as the “match” differences. This suggests that “matchtag” files provided by SETSM do not necessarily enable the user to locate or flag the largest elevation differences in a DEM. Supplemental Figure 5. SETSM 2m elevation di!erence from LiDAR distributions at locations classi"ed as “matches” and “mismatches” from SETSM’s matchtag "le, produced with each DEM. “Mismatch” sites have very similar distributions as the “match” di!erences. #is suggests that “matchtag” "les provided by SETSM do not necessarily enable the user to locate or $ag the largest elevation di!erences in a DEM. Mismatch Match Comparison of “Match” and “Mismatch” Points from SETSM Matchtag !le 174 Appendix B Supplementary Information for Chapter 6: Important role of subsurface moisture in hydrological response to wildfire in southern California, USA Text S1-S2 discusses precipitation data collected over the study period as well as observations of erosion as they relate to USGS debris flow hazard probabilities. Text S3 discusses D-excess calculations and results and the methodology behind our weighted precipitation isotope values for individual storm events. Text S4-S5 covers the methodology behind our resistivity field surveys and inversion process, as well as error considerations. Text S6 discusses the methodology and findings from evapotranspiration (ET) estimates both pre- and post-fire in all three catchments. Text S1. Precipitation data We collected rain gauge data from December 2020 through May 2022 from three gauges, although all three recorded similar amounts and thus rain gauge #3 is the only one reported. Rainfall over the course of WY1(December 2020 -June 2021) totaled about 18.78 cm at rain gauge #1, 18.06 cm at rain gauge #2 and 17.06 cm at rain gauge #3: about 25% of the average annual rainfall total for southern California (~70cm according to PRISM); WY2 was significantly wetter, ~47cm but still below average. Five-minute rainfall amounts recorded by installed rain gauges. Maximum 15-minute rainfall intensities during each recorded storm event did not exceed USGS debris flow intensity thresholds (see Figure S3 and S4). Maximum 15-minute rainfall intensities did exceed K fs values during all storms that generated streamflow (Figure S12). Text S2. Erosion observations Trail camera footage and field observations showed no signs of a debris flow event in any catchment during 2021. However, significant sediment evacuation and rilling were observed in early February 2021 following the first two rainstorm events of the season. Up to a meter of sediment was removed from the burned catchments (Louise and Thelma), exposing scoured bedrock. Debris dams and 175 splash were also observed in early February. Minor sediment movement was observed in the channel of the unburnt catchment (Henry), but no significant patterns were recorded on the hillslopes, which are densely vegetated. In 2022, during the three December storms, Louise experienced significant flow events (Figure S7) that resulted in >2 meters of sediment evacuation in the channel. However, no geomorphic evidence (levees, deposits) of debris flows were present, although much smaller nearby catchments did show minor debris flow deposits. No significant erosion was observed in Henry over the same time period. The elevation of each electrode at each survey site was measured after each large storm that resulted in significant erosion and topographic change in the catchment channels. We used a RTK Septentrio GPS instrument. The accuracy of relative elevation in the same GPS survey is on the order of mm (Septentrio Reference Guide, 2016). We observed minor-scale surface erosion changes in Henry and Thelma in early February 2021, and a general smoothing of the hillslope surface in Louise with about 0.5m change in elevation. After the14 December 2021 rainstorm and debris flow event in Louise, significant topographic change was evident in the GPS survey in the channel. By February 2022, the steep channel walls had relaxed and the hillslopes and channel returned to a smooth V-shape. Text S3. D-excess and amount weighted precipitation isotope average values for storms The stable isotope ratios of water (D/H and 18 O/ 16 O) of water provide information about water sources, offering a tool to trace surface-subsurface connectivity and changes in the source of discharge over time. Deuterium excess (Dxs) (defined as Dxs=δD−8×δ 18 O, in ‰)) represents the 176 offset from the meteoric water line and variations are controlled by the kinetics of evaporation. Dxs is especially helpful when variations in 𝛿 18 O isotopes are small. Plots of Dxs vs δ 18 O (Figure S2) support the δ 18 O results, where 10 March, 25 October and 14 December storms show overlap between streamflow and precipitation; while 15 March and 23 and 24 December streamflow show offset from precipitation. Amount weighted precipitation isotope average values for each storm were calculated to create end members for each storm event for 𝛿 18 O and d-excess. Precipitation amounts were binned by 30 minute intervals and 30 minute average isotope and d-excess values were calculated, using a 7 minute offset relative to collection time to reflect an average rainfall time for samples, rather than the time of collection. Amount weighted averages for precipitation isotopes for each storm (𝛿𝑠𝑡𝑜𝑟𝑚) were then calculated using the equation (following (3)): 𝛿𝑠𝑡𝑜𝑟𝑚 = P :XYZ< ?B[ :XYZ< :X;ZX 𝛿30𝑚𝑖𝑛× 𝑃 30𝑚𝑖𝑛 𝑃𝑠𝑡𝑜𝑟𝑚 𝑡𝑜𝑡𝑎𝑙 where 𝛿30𝑚𝑖𝑛 were the average isotopic values every fifteen minutes, starting from initial rainfall, P 30min is the binned 30 minute amount of rain and P storm total is the total amount of rain that fell during the storm event. Text S4. Electrical resistivity imaging (ERI) data processing and inversion parameters Across the SGM study catchments, geologic properties remain relatively constant both laterally within each catchment and with depth, and starting conditions are highly resistive. Therefore, ERI is an ideal method for observing shallow subsurface water storage (4, 5) that are typically too deep to 177 monitor with soil moisture sensors. Further, the short-term deployment of the resistivity array enables surveys within the steep and unstable terrain of our study catchments, which would otherwise be challenging to instrument for soil moisture. Resistivity survey data was downloaded from the instrument and filtered for outlier data points in Prosys II software from Iris Instruments. We removed negative apparent resistivity values and erroneously high resistivity data points in both normal and reciprocal surveys such that >90% of the data was still used in each inversion model. Topography was added during the inversion process. Inversion models were produced in ResIPy open-source software (6) with a regularized inversion with linear filtering, a model-to-data error (RMSE) tolerance value between 1 and 1.5 and weight parameters a and b, which were derived from a power-law error model where absolute reciprocal error is a function of resistance, which gives a better representation of the subsurface and a well- constrained model of resistivity (7–9). A fine scale triangular mesh was used to accommodate topography. Time-lapse inversion models were also produced in Resipy without reciprocal measurements. Here, the ratio of new data to the first dataset is taken and multiplied by a forward model with homogeneous subsurface resistivity (7), and a sensitivity matrix in calculating percent change in resistivity (4). Text S5. Electrical resistivity imaging error considerations. A highly resistive environment like the soils, regolith, and fractured bedrock of the San Gabriel Mountains lends itself to tracking moisture contrasts in resistivity (10) but presents similarly high contact resistance between the electrodes and the shallow ground surface. Low contact resistance is desirable for strong electrical conductance and a consistent electrical current in the subsurface; high 178 contact resistances can lead to erroneous data points and collection error (11). A typical threshold for good contact resistance for surface data is 20 ohm (11) although this varies with environmental and geological factors. In the driest months of our study period (early December and March onward), we encountered contact resistances regularly ranging between 100-500 ohm. To reduce contact resistance between the electrode probes and the dry, gravelly near-surface, we used a combination of bentonite clay cat litter and water packed around each electrode, which reduced contact resistances to <60 ohm in 2021 and <400 ohm in 2022. We used ~50 ohm as our acceptable threshold where practical, primarily because of time constraints and efficiency. The highest contact resistances consistently registered at electrodes located in materials with extensive air space (e.g., ravel cones or gravelly soils). Reciprocal measurements are the standard quantification of data collection error for ERI (7, 9). Separate reciprocal surveys with ~⅓ data density were paired with normal surveys and individual power-law error models built for each weighted inversion. Absolute reciprocal error is modeled as a function of resistivity in 20-pt bins of increasing resistance. R-squared values of the error models range between 0.767-0.972 (Table S2), and the output is used to calculate inversion weight parameters a and b. Environmental factors such as temperature and salinity can strongly affect resistivity (12) especially when calculating soil water content. Here, we do not attempt to calculate soil water content or water volume because we do not have the necessary soil temperature or porosity data to do so, and the uncertainties of this approach are large in a locally-variable environment like the shallow subsurface within the San Gabriel Mountains (13–15). Instead, we simply interpret a reduction in resistivity as 179 an addition of water to the subsurface under the assumption that very little water was present in the early Fall. Text S6. Evapotranspiration (ET) estimates 70m resolution ECOSTRESS ET data derived from PT-JPL algorithm (ECO3ETPTJPL) were downloaded from October 1 st 2019-October 1 st 2022 using the Land Processes Distributed Active Archive Center (LP DAAC) Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) (18). ECOSTRESS has been used in past postfire paired catchment studies to quantify ET in Southern California (19). ECOSTRESS rasters were processed in Python using rasterstats zonal_stats() function to calculate the median ET values for each catchment. ET rasters were screened for QA flags as well as sites where ET was three standard deviations greater. In total, 114 rasters were used in this study. Daily ET measurements in W/m 2 are presented in Figure S10. Annual ET measurements are presented in Figure S11. ECOSTRESS ET shows similar annual ET values in all three catchments in pre-fire conditions. In WY1 there is a reduction in ET in the unburned site, likely because WY1 was drier. Greater reductions are seen in both burned catchments in WY1. WY1 annual ET is ~2x higher in the unburned catchment than the burned catchments, but in WY2 all catchments show similar ET amounts (SFY). The difference in ET between the catchments corroborate the large increase in discharge and subsurface storage. Given that these are all ephemeral catchments, ET likely dominates the water budgets with limited water exiting as discharge. Therefore, if ET is only half of what it would normally be, then the amount available to go into subsurface storage and discharge would increase 180 by significantly more proportionally, since they make up much smaller fractions of the overall budget. While a complete water budget calculation is unavailable due to lack of discharge measurements, it is clear that such a drop in ET as seen from this data could lead to 5-6x greater discharge as well as increased subsurface storage. Other postfire studies in Southern California have found ET made up as much as 90% of the water budget in the unburned catchment (994 mm/year ET; 102 mm/year runoff) and also saw larger changes in storage in the burned catchment and higher discharge (19). Figure S1. Catchment boundary conditions including vegetation index (NDVI), slope angle and bedrock geology (16). Louise and Henry were more heavily vegetated pre-fire (August 2020 NDVI: ~7000) with chaparral-type deciduous shrubbery, manzanita and oak trees ((17); MODIS reflectance data). Thelma was less densely vegetated with a pre-fire NDVI index of ~6300 and fewer large oak trees. 181 Figure S2. Dual isotope plot (dD and d 18 O) and D-excess vs d 18 O. D-excess vs d 18 O monthly plots show rainfall isotope values weighted by concurrent precipitation amount. 182 Figure S3. Rainfall intensities compared to USGS debris flow thresholds at rain gauge #2 for WY1 (2020-2021). 183 Figure S4. Rainfall intensities compared to USGS debris flow thresholds at rain gauge #2 for WY2 (2021-2022). 184 Figure S5. Photo comparison of catchments before and after storms in WY1 and WY2. 185 Figure S6. Stream flow comparisons between burned (Louise) and unburned (Henry) during different storm events. Camera broke during the 14 December storm. 186 Figure S7. Stream profiles (orange and green lines) using an RTK Septentrio GPS instrument for maximum discharge estimation for December 2021 storms. Blue indicates estimated cross-sectional area of streams. 187 Figure S8. Resistivity inversion models at Henry, Louise and Thelma between December 2020 and June 2021. 188 Figure S9. Resistivity inversion models at Henry, Louise and Thelma between October 2021 and May 2022. 189 Figure S10. Daily ET values from ECOSTRESS data colored by catchment. All catchments have similar pre-fire ET while in WY1 (postfire), the unburned catchment shows higher ET during most days captured by ECOSTRESS. This increase is less pronounced during WY2, when vegetation recovery in the burned catchments increased. 190 Figure S11. Annual ET over each catchment during the pre-fire WY, WY1 and WY2. Figure S12. Comparison of K fs in Louise and Henry with rainfall rates over the study period. 191 Figure S13. Plot comparing rainfall intensity/median K fs values to the fraction of new water during December 2021 storms. New water fraction roughly correlates with rainfall intensities that exceed median K fs values in each catchment, although more scatter in F new is observed in the burned catchment when rainfall intensities do not exceed median K fs, which is consistent with more variable post-fire K fs. Catchment Percent of catchment unburned or burned at low severity (%) Percent of catchment burned at moderate severity (%) Percent of catchment burned at high severity (%) Pre-fire NDVI (MODIS reflectance data; August 2020 area- averaged) Lidar- derived slope (avg; degrees) Average Ksn values (normalized channel steepness) Area (km 2 ) Thelma 0 63.6 36.4 6304 39.44 29.0 0.0594 Louise 0 27.0 73 7080 39.59 29.8 0.0652 Henry 76.2 18.2 5.6 6973 39.48 33.0 0.1011 Table S1. Catchment boundary conditions, averaged over catchment area. 192 Survey Date Contact Resistance range (ohm) % data used after filtering R-squared value of reciprocal error model Inversion RMS misfit Henry December 12, 2020 8 to 58 97.08 0.891 1.71 February 2, 2021 5.57 to 13.49 96.35 0.952 1.03 February 9, 2021 5.54 to 26.79 96.29 0.873 0.98 February 16, 2021 3.96 to 13.93 96.53 0.934 1 February 23, 2021 4.79 to 14.07 96.71 0.927 1.34 March 1, 2021 4.94 to 16.55 96.35 0.938 1 March 12, 2021 2.98 to 12.2 96.41 0.942 1.77 April 24, 2021 8.99 to 67.27 96.47 0.885 1.2 June 4, 2021 5 to 50 96.71 0.767 1.16 October 23, 2021 103.72 to 421.63 96.11 0.838 1.06 October 26, 2021 - 93.16 0.970 1.17 December 21, 2021 3 to 19 96.02 0.952 1.44 January 4, 2022 4 to 15 95.63 0.851 1.49 March 11, 2022 4.76 to 30.51 95.92 0.895 1.05 May 18, 2022 5.62 to 49.73 96.17 0.871 1.01 Louise December 11, 2020 13 to 60 93.43 0.887 1.18 February 2, 2021 2.57 to 16.41 94.65 0.874 0.86 February 9, 4.48 to 21.75 95.21 0.921 1.14 193 2021 February 16, 2021 5.54 to 27.19 95.78 0.895 1 February 23, 2021 4.52 to 15.9 95.21 0.915 1.1 March 2, 2021 3.67 to 17.07 95.13 0.903 1.97 March 12, 2021 2 to 16 93.92 0.877 1.11 April 24, 2021 2 to 24.5 94.73 0.932 1.01 June 2, 2021 2 to 50 95.13 0.779 1 October 23, 2021 20 to 300 93.23 0.806 2.15 October 26, 2021 2.28 to 17.8 96.67 0.970 1.17 December 21, 2021 1.18 to 12 95.54 0.887 1.29 January 4, 2022 1.33 to 12 96.67 0.893 1.50 March 11, 2022 1.59 to 16.46 95.65 0.891 1.35 May 18, 2022 2.12 to 59.58 95.82 0.863 1.01 Thelma December 13, 2020 10 to 60 96.65 0.877 1.33 February 1, 2021 3.43 to 20.36 97.26 0.972 1.00 February 9, 2021 5.39 to 21.21 95.44 0.924 1.06 February 16, 2021 4.36 to 17.34 97.32 0.946 1.00 February 23, 2021 5.98 to 17.12 97.26 0.961 1.04 March 2, 2021 6.63 to 18.65 96.04 0.956 1.03 March 12, 2021 2.5 to 14.3 96.17 0.971 1.00 April 24, 2021 5.8 to 18.44 96.84 0.924 1.00 June 5, 2021 5 to 50 97.14 0.805 1.09 194 October 26, 2021 4 to 14 97.62 0.884 1.40 March 11, 2022 5.93 to 22.59 97.14 0.845 1.22 Table S2. Resistivity survey and model metrics at Henry, Louise and Thelma. Sample Name Date Time Sample Type Location Storm Rain (cm) 𝛿18O SD 𝛿18O 𝛿D SD 𝛿D HR-1 3/10/21 3:21 Rain Henry storm 1 0.22 -7.536 0.023 -39.499 0.099 HR-2 3/10/21 3:48 Rain Henry storm 1 0.12 -8.815 0.018 -50.709 0.031 HR-3 3/10/21 4:17 Rain Henry storm 1 0.1 -8.566 0.024 -47.762 0.104 HR-4 3/10/21 5:06 Rain Henry storm 1 0.1 -7.986 0.032 -43.364 0.017 HR-5 3/10/21 5:36 Rain Henry storm 1 0.2 -8.986 0.013 -50.543 0.005 HR-6 3/10/21 6:03 Rain Henry storm 1 0.08 -9.312 0.006 -54.532 0.029 HR-7 3/10/21 6:34 Rain Henry storm 1 0.02 -9.561 0.033 -56.372 0.024 HR-8 3/10/21 7:06 Rain Henry storm 1 0.02 -8.996 0.009 -52.476 0.029 HR-9 3/10/21 11:20 Rain Henry storm 1 0.02 -8.000 0.025 -44.524 0.168 HR-10 3/10/21 14:03 Rain Henry storm 1 0.02 -6.096 0.044 -33.389 0.100 HR-11 3/10/21 14:30 Rain Henry storm 1 0.16 -5.539 0.036 -26.227 0.132 HR-12 3/10/21 14:53 Rain Henry storm 1 0.02 -5.584 0.016 -24.875 0.087 HR-13 3/10/21 17:47 Rain Henry storm 1 0.12 -7.242 0.012 -33.723 0.079 HR-14 3/10/21 23:10 Rain Henry storm 1 0.22 -7.570 0.079 -38.743 0.320 HR-15 3/10/21 23:48 Rain Henry storm 1 0.14 -9.832 0.044 -55.569 0.082 LR-1 3/10/21 3:06 Rain Louise storm 1 0.22 -14.333 0.036 -101.908 0.149 LR-2 3/10/21 3:36 Rain Louise storm 1 0.2 -4.559 0.009 -36.740 0.192 LR-3 3/10/21 4:03 Rain Louise storm 1 0.1 -13.116 0.017 -90.697 0.218 LR-4 3/10/21 4:33 Rain Louise storm 1 0.06 -7.628 0.008 -48.110 0.138 LR-5 3/10/21 5:24 Rain Louise storm 1 0.22 -7.514 0.018 -47.047 0.006 LR-6 3/10/21 5:50 Rain Louise storm 1 0.1 -13.159 0.016 -90.924 0.131 LR-7 3/10/21 6:15 Rain Louise storm 1 0.04 -9.340 0.023 -59.943 0.082 LR-8 3/10/21 6:48 Rain Louise storm 1 0.2 -7.659 0.028 -47.880 0.045 195 LR-9 3/10/21 11:38 Rain Louise storm 1 0.02 -8.165 0.009 -51.975 0.008 LR-10 3/10/21 14:17 Rain Louise storm 1 0.06 -7.718 0.019 -48.044 0.033 LR-11 3/10/21 14:42 Rain Louise storm 1 0.12 -14.239 0.005 -99.808 0.141 LR-12 3/10/21 17:28 Rain Louise storm 1 0.12 -8.719 0.009 -54.978 0.124 LR-13 3/10/21 22:56 Rain Louise storm 1 0.16 -10.570 0.020 -77.838 0.368 LR-14 3/10/21 23:31 Rain Louise storm 1 0.16 -7.501 0.023 -48.086 0.033 HS-1 3/10/21 23:14 Stream Henry storm 1 -7.085 0.020 -34.937 0.121 HS-2 3/10/21 23:48 Stream Henry storm 1 -7.671 0.010 -42.617 0.033 LS-1 3/10/21 3:06 Stream Louise storm 1 -7.111 0.030 -38.149 0.072 LS-2 3/10/21 5:24 Stream Louise storm 1 -7.969 0.010 -44.313 0.050 LS-3 3/10/21 22:52 Stream Louise storm 1 -7.232 0.027 -39.332 0.076 LS-4 3/10/21 23:31 Stream Louise storm 1 -7.816 0.062 -45.630 0.217 LR-15 3/11/21 0:03 Rain Louise storm 1 0.06 -13.257 0.020 -92.688 0.080 HR-16 3/11/21 0:07 Rain Henry storm 1 0.08 -6.454 0.024 -32.949 0.088 HR-17 3/11/21 0:22 Rain Henry storm 1 0.06 -6.743 0.049 -36.036 0.118 LR-23 3/15/21 7:45 Rain Louise storm 2 0.04 -12.052 0.007 -84.700 0.166 LR-24 3/15/21 8:15 Rain Louise storm 2 0.04 -10.003 0.008 -65.637 0.086 LR-25 3/15/21 8:35 Rain Louise storm 2 0.06 -7.745 0.022 -48.454 0.104 LR-26 3/15/21 8:45 Rain Louise storm 2 0.04 -5.003 0.012 -24.028 0.122 LR-27 3/15/21 9:00 Rain Louise storm 2 0.06 -4.759 0.079 -23.067 0.289 LR-28 3/15/21 9:20 Rain Louise storm 2 0.08 -5.242 0.029 -26.014 0.061 LR-16 3/15/21 11:10 Rain Louise storm 2 0.04 -7.798 0.013 -49.094 0.068 LR-17 3/15/21 11:26 Rain Louise storm 2 0.12 -7.705 0.025 -48.599 0.149 LR-18 3/15/21 11:41 Rain Louise storm 2 0.1 -7.767 0.012 -48.702 0.040 LR-19 3/15/21 11:51 Rain Louise storm 2 0.1 -7.565 0.006 -47.916 0.088 LR-20 3/15/21 12:00 Rain Louise storm 2 0.12 -7.542 0.017 -47.905 0.090 LR-21 3/15/21 16:10 Rain Louise storm 2 0.08 -7.632 0.028 -48.139 0.110 LR-22 3/15/21 16:46 Rain Louise storm 2 0.2 -6.816 0.014 -39.112 0.101 HS-3 3/15/21 11:57 Stream Henry storm 2 -5.690 0.022 -27.485 0.122 HS-4 3/15/21 12:30 Stream Henry storm 2 -5.983 0.015 -31.125 0.025 HS-5 3/15/21 12:42 Stream Henry storm 2 -6.083 0.034 -30.749 0.188 HS-6 3/15/21 12:50 Stream Henry storm 2 -6.467 0.006 -34.598 0.113 HS-7 3/15/21 13:17 Stream Henry storm 2 -7.061 0.012 -39.700 0.105 HS-8 3/15/21 13:50 Stream Henry storm 2 -7.070 0.016 -39.814 0.095 HS-9 3/15/21 16:18 Stream Henry storm 2 -5.994 0.022 -31.105 0.027 HS-10 3/15/21 16:40 Stream Henry storm 2 -5.384 0.016 -24.388 0.057 HS-11 3/15/21 17:00 Stream Henry storm 2 -5.435 0.009 -24.585 0.049 HS-12 3/15/21 17:20 Stream Henry storm 2 -6.617 0.030 -36.013 0.062 196 LS-5 3/15/21 11:47 Stream Louise storm 2 -5.859 0.083 -27.833 0.262 LS-6 3/15/21 12:02 Stream Louise storm 2 -5.679 0.022 -29.318 0.064 LS-7 3/15/21 12:18 Stream Louise storm 2 -6.316 0.023 -33.354 0.029 LS-8 3/15/21 12:38 Stream Louise storm 2 -6.329 0.020 -34.844 0.131 LS-9 3/15/21 13:00 Stream Louise storm 2 -6.710 0.003 -38.694 0.081 LS-10 3/15/21 13:30 Stream Louise storm 2 -6.902 0.014 -39.629 0.012 LS-11 3/15/21 16:27 Stream Louise storm 2 -5.610 0.029 -27.232 0.081 LS-12 3/15/21 16:50 Stream Louise storm 2 -5.008 0.052 -23.564 0.155 LS-13 3/15/21 17:07 Stream Louise storm 2 -5.522 0.026 -27.837 0.094 LS-14 3/15/21 17:30 Stream Louise storm 2 -6.320 0.002 -34.716 0.052 OLR-1 10/25/21 8:30 Rain Louise storm 3 0.02 -1.405 0.019 2.936 0.132 OLR-2 10/25/21 9:30 Rain Louise storm 3 0.08 -1.390 0.019 4.100 0.108 OLR-3 10/25/21 10:30 Rain Louise storm 3 0.12 -2.015 0.039 2.460 0.062 OLR-4 10/25/21 11:30 Rain Louise storm 3 0.16 -2.204 0.020 1.313 0.037 OLR-5 10/25/21 12:10 Rain Louise storm 3 0.28 -2.487 0.020 -0.945 0.085 OLR-6 10/25/21 13:00 Rain Louise storm 3 0.38 -2.488 0.020 -1.519 0.041 OLR-7 10/25/21 13:30 Rain Louise storm 3 0.38 -3.254 0.020 -7.390 0.019 OLR-8 10/25/21 14:00 Rain Louise storm 3 0.52 -3.664 0.031 -10.461 0.049 OLR-9 10/25/21 14:20 Rain Louise storm 3 0.42 -3.888 0.029 -13.350 0.073 OLR-10 10/25/21 14:35 Rain Louise storm 3 0.14 -4.051 0.037 -14.748 0.021 OLR-11 10/25/21 14:50 Rain Louise storm 3 0.26 -4.525 0.010 -18.303 0.171 OLR-12 10/25/21 15:05 Rain Louise storm 3 0.18 -4.553 0.005 -18.603 0.019 OLR-13 10/25/21 15:20 Rain Louise storm 3 0.08 -4.359 0.016 -22.531 0.025 OLR-14 10/25/21 15:35 Rain Louise storm 3 0.04 -4.797 0.027 -22.843 0.051 OLR-15 10/25/21 16:07 Rain Louise storm 3 0.06 -5.909 0.024 -35.118 0.073 OLR-16 10/25/21 16:30 Rain Louise storm 3 0.08 -6.344 0.029 -37.449 0.040 OLR-17 10/25/21 16:47 Rain Louise storm 3 0.02 -3.850 0.022 -22.426 0.043 OLS-4 10/25/21 15:03 Stream Louise storm 3 -4.100 0.040 -15.570 0.041 OLS-5 10/25/21 15:18 Stream Louise storm 3 -3.925 0.030 -14.816 0.141 OLS-7 10/25/21 16:15 Stream Louise storm 3 -3.869 0.032 -16.716 0.067 OHS-3 10/25/21 14:46 Stream Henry storm 3 -3.077 0.064 -10.203 0.075 OHS-4 10/25/21 15:01 Stream Henry storm 3 -2.894 0.024 -9.810 0.054 OHS-5 10/25/21 15:16 Stream Henry storm 3 -2.745 0.022 -9.368 0.015 OHS-6 10/25/21 15:31 Stream Henry storm 3 -3.338 0.025 -10.916 0.099 OHS-7 10/25/21 15:46 Stream Henry storm 3 -3.287 0.021 -11.053 0.140 OHS-8 10/25/21 16:01 Stream Henry storm 3 -3.328 0.013 -11.842 0.054 HIR-1 12/14/21 5:15 Integrated Rain Rain Gauge storm 4 -5.879 0.022 -19.655 0.037 HIR-2 12/14/21 9:15 Integrated Rain Rain Gauge storm 4 -6.803 0.042 -29.547 0.078 197 HIR-3 12/14/21 13:15 Integrated Rain Rain Gauge storm 4 -6.537 0.035 -28.354 0.023 DLR-1 12/14/21 5:15 Rain Louise storm 4 0.14 -7.158 0.028 -30.987 0.054 DLR-2 12/14/21 5:33 Rain Louise storm 4 0.28 -7.664 0.026 -36.228 0.046 DLR-3 12/14/21 5:48 Rain Louise storm 4 0.2 -7.059 0.037 -30.820 0.045 DLR-4 12/14/21 6:08 Rain Louise storm 4 0.36 -7.071 0.025 -30.611 0.024 DLR-5 12/14/21 6:28 Rain Louise storm 4 0.24 -7.784 0.008 -36.038 0.033 DLR-6 12/14/21 6:45 Rain Louise storm 4 0.2 -7.722 0.024 -35.609 0.046 DLR-7 12/14/21 7:05 Rain Louise storm 4 0.22 -7.390 0.018 -35.254 0.120 DLR-8 12/14/21 7:25 Rain Louise storm 4 0.4 -7.919 0.022 -38.453 0.079 DLR-9 12/14/21 7:45 Rain Louise storm 4 0.44 -7.713 0.018 -37.356 0.090 DLR-10 12/14/21 8:05 Rain Louise storm 4 0.4 -7.703 0.006 -38.647 0.078 DLR-11 12/14/21 8:25 Rain Louise storm 4 0.54 -7.327 0.010 -35.988 0.050 DLR-12 12/14/21 8:45 Rain Louise storm 4 0.48 -6.441 0.004 -29.115 0.061 DLR-13 12/14/21 9:05 Rain Louise storm 4 0.6 -5.779 0.037 -23.535 0.054 DLR-14 12/14/21 9:25 Rain Louise storm 4 0.44 -5.561 0.026 -22.737 0.055 DLR-15 12/14/21 9:45 Rain Louise storm 4 0.36 -5.455 0.020 -22.764 0.048 DLR-16 12/14/21 10:10 Rain Louise storm 4 0.3 -5.416 0.038 -22.445 0.055 DLR-17 12/14/21 10:30 Rain Louise storm 4 0.24 -5.704 0.008 -25.670 0.038 DLR-18 12/14/21 10:50 Rain Louise storm 4 0.26 -5.442 0.028 -26.768 0.009 DLR-19 12/14/21 11:10 Rain Louise storm 4 0.16 -4.820 0.038 -20.334 0.087 DLR-20 12/14/21 11:30 Rain Louise storm 4 0.34 -5.069 0.050 -20.863 0.038 DLR-21 12/14/21 11:55 Rain Louise storm 4 0.44 -4.863 0.026 -18.439 0.018 DLR-22 12/14/21 12:20 Rain Louise storm 4 0.16 -4.553 0.048 -17.018 0.061 DLR-23 12/14/21 12:40 Rain Louise storm 4 0.18 -4.206 0.008 -13.007 0.037 DLR-24 12/14/21 13:00 Rain Louise storm 4 0.24 -3.796 0.019 -15.589 0.107 DHR-1 12/14/21 6:00 Rain Henry storm 4 0.46 -7.083 0.062 -31.087 0.130 DHR-2 12/14/21 6:30 Rain Henry storm 4 0.4 -7.478 0.014 -34.264 0.067 DHR-3 12/14/21 7:00 Rain Henry storm 4 0.32 -7.749 0.008 -36.271 0.076 DHR-4 12/14/21 7:30 Rain Henry storm 4 0.6 -7.850 0.020 -37.808 0.065 DHR-5 12/14/21 8:00 Rain Henry storm 4 0.66 -7.850 0.007 -39.385 0.042 DHR-6 12/14/21 8:30 Rain Henry storm 4 0.74 -7.479 0.026 -36.943 0.109 DHR-7 12/14/21 9:00 Rain Henry storm 4 0.8 -6.471 0.055 -29.012 0.026 DHR-8 12/14/21 9:30 Rain Henry storm 4 0.72 -5.737 0.016 -23.727 0.137 DHR-9 12/14/21 10:00 Rain Henry storm 4 0.42 -5.632 0.046 -24.410 0.059 DHR-10 12/14/21 10:30 Rain Henry storm 4 0.38 -5.887 0.026 -26.477 0.087 DHR-11 12/14/21 10:40 Rain Henry storm 4 0.24 -6.436 0.003 -31.027 0.053 DHR-12 12/14/21 11:00 Rain Henry storm 4 0.08 -5.495 0.035 -27.548 0.110 DHR-13 12/14/21 11:30 Rain Henry storm 4 0.42 -4.384 0.023 -15.656 0.134 198 DHR-14 12/14/21 12:00 Rain Henry storm 4 0.3 -5.354 0.037 -22.147 0.071 DHR-15 12/14/21 12:30 Rain Henry storm 4 0.44 -5.186 0.026 -21.444 0.059 DHR-16 12/14/21 13:00 Rain Henry storm 4 0.32 -4.662 0.011 -18.539 0.043 DLS-1 12/14/21 5:00 Stream Louise storm 4 -6.294 0.071 -24.590 0.099 DLS-2 12/14/21 5:05 Stream Louise storm 4 -6.392 0.014 -25.358 0.154 DLS-3 12/14/21 5:15 Stream Louise storm 4 -6.451 0.050 -26.666 0.106 DLS-4 12/14/21 5:33 Stream Louise storm 4 -6.442 0.008 -27.616 0.071 DLS-5 12/14/21 5:48 Stream Louise storm 4 -6.440 0.020 -27.872 0.063 DLS-6 12/14/21 6:08 Stream Louise storm 4 -6.561 0.023 -28.065 0.035 DLS-7 12/14/21 6:28 Stream Louise storm 4 -7.142 0.037 -32.011 0.092 DLS-8 12/14/21 6:45 Stream Louise storm 4 -7.240 0.031 -32.846 0.020 DLS-9 12/14/21 7:05 Stream Louise storm 4 -7.221 0.012 -33.169 0.071 DLS-10 12/14/21 7:25 Stream Louise storm 4 -7.454 0.011 -35.501 0.050 DLS-11 12/14/21 7:45 Stream Louise storm 4 -7.447 0.017 -35.974 0.024 DLS-12 12/14/21 8:05 Stream Louise storm 4 -7.621 0.015 -37.584 0.055 DLS-13 12/14/21 8:25 Stream Louise storm 4 -7.574 0.008 -36.934 0.085 DLS-14 12/14/21 8:45 Stream Louise storm 4 -7.153 0.040 -33.744 0.108 DLS-15 12/14/21 9:05 Stream Louise storm 4 -6.760 0.017 -31.271 0.089 DLS-16 12/14/21 9:25 Stream Louise storm 4 -6.495 0.036 -29.974 0.156 DLS-17 12/14/21 9:45 Stream Louise storm 4 -6.451 0.043 -29.093 0.049 DLS-18 12/14/21 10:10 Stream Louise storm 4 -6.401 0.019 -29.362 0.142 DLS-19 12/14/21 10:30 Stream Louise storm 4 -5.952 0.026 -27.154 0.047 DLS-20 12/14/21 10:37 Stream Louise storm 4 -6.371 0.029 -29.859 0.144 DLS-21 12/14/21 10:50 Stream Louise storm 4 -6.371 0.012 -30.060 0.099 DLS-22 12/14/21 11:10 Stream Louise storm 4 -6.424 0.097 -29.491 0.214 DLS-23 12/14/21 11:30 Stream Louise storm 4 -6.197 0.011 -28.708 0.167 DLS-24 12/14/21 11:55 Stream Louise storm 4 -6.000 0.037 -27.265 0.122 DLS-25 12/14/21 12:20 Stream Louise storm 4 -5.960 0.034 -26.945 0.085 DLS-26 12/14/21 12:40 Stream Louise storm 4 -6.009 0.026 -27.335 0.070 DLS-27 12/14/21 13:00 Stream Louise storm 4 -6.102 0.033 -28.520 0.049 DLS-28 12/14/21 13:20 Stream Louise storm 4 -6.284 0.040 -29.449 0.159 DHS-1 12/14/21 5:30 Stream Henry storm 4 -5.706 0.029 -25.153 0.210 DHS-2 12/14/21 6:00 Stream Henry storm 4 -5.687 0.009 -24.862 0.045 DHS-3 12/14/21 6:30 Stream Henry storm 4 -6.664 0.010 -30.609 0.028 DHS-4 12/14/21 7:00 Stream Henry storm 4 -7.121 0.036 -33.140 0.014 DHS-5 12/14/21 7:30 Stream Henry storm 4 -7.275 0.038 -34.660 0.116 DHS-6 12/14/21 8:00 Stream Henry storm 4 -7.590 0.039 -37.188 0.106 DHS-7 12/14/21 8:30 Stream Henry storm 4 -7.235 0.020 -35.205 0.064 199 DHS-8 12/14/21 9:00 Stream Henry storm 4 -6.857 0.040 -32.181 0.053 DHS-9 12/14/21 9:30 Stream Henry storm 4 -6.536 0.038 -29.917 0.018 DHS-10 12/14/21 10:00 Stream Henry storm 4 -6.406 0.054 -29.165 0.076 DHS-11 12/14/21 10:30 Stream Henry storm 4 -6.253 0.030 -28.707 0.152 DHS-12 12/14/21 10:40 Stream Henry storm 4 -6.267 0.034 -29.707 0.047 DHS-13 12/14/21 11:00 Stream Henry storm 4 -6.333 0.020 -29.923 0.069 DHS-14 12/14/21 11:30 Stream Henry storm 4 -6.321 0.045 -29.181 0.128 DHS-15 12/14/21 12:00 Stream Henry storm 4 -5.910 0.044 -25.980 0.065 DHS-16 12/14/21 12:30 Stream Henry storm 4 -6.122 0.053 -28.047 0.169 DHS-17 12/14/21 13:00 Stream Henry storm 4 -6.140 0.005 -28.144 0.023 GIR-3 12/23/21 11:00 Integrated Rain Rain Gauge storm 5 -7.104 0.025 -43.511 0.074 GIR-4 12/23/21 14:00 Integrated Rain Rain Gauge storm 5 -8.536 0.049 -54.625 0.043 DLR-50 12/23/21 11:38 Rain Louise storm 5 0.06 -11.222 0.037 -72.709 0.109 DLR-51 12/23/21 12:00 Rain Louise storm 5 0.08 -11.199 0.012 -71.516 0.077 DLR-52 12/23/21 12:18 Rain Louise storm 5 0.2 -11.222 0.034 -72.028 0.041 DLR-53 12/23/21 12:37 Rain Louise storm 5 0.1 -10.796 0.025 -69.205 0.112 DLR-54 12/23/21 12:58 Rain Louise storm 5 0.18 -11.133 0.008 -70.946 0.071 DLR-55 12/23/21 13:16 Rain Louise storm 5 0.18 -10.941 0.062 -69.448 0.444 DLR-56 12/23/21 13:35 Rain Louise storm 5 0.22 -11.030 0.023 -68.786 0.121 DLR-57 12/23/21 13:53 Rain Louise storm 5 0.24 -11.447 0.013 -72.844 0.058 DHR-50 12/23/21 12:05 Rain Henry storm 5 0.16 -10.144 0.017 -66.774 0.104 DHR-51 12/23/21 12:27 Rain Henry storm 5 0.14 -9.869 0.013 -64.524 0.027 DHR-52 12/23/21 12:47 Rain Henry storm 5 0.18 -9.895 0.019 -64.711 0.108 DHR-53 12/23/21 13:07 Rain Henry storm 5 0.18 -9.697 0.033 -62.036 0.011 DHR-54 12/23/21 13:25 Rain Henry storm 5 0.16 -9.884 0.037 -63.261 0.100 DHR-55 12/23/21 13:45 Rain Henry storm 5 0.3 -10.155 0.022 -64.738 0.020 DLS-50 12/23/21 11:15 Stream Louise storm 5 -10.376 0.025 -67.398 0.076 DLS-51 12/23/21 11:37 Stream Louise storm 5 -10.009 0.013 -64.177 0.039 DLS-52 12/23/21 11:55 Stream Louise storm 5 -10.148 0.046 -64.109 0.027 DLS-53 12/23/21 12:16 Stream Louise storm 5 -10.193 0.063 -64.959 0.048 DLS-54 12/23/21 12:35 Stream Louise storm 5 -10.220 0.010 -65.394 0.071 DLS-55 12/23/21 12:56 Stream Louise storm 5 -10.555 0.024 -66.392 0.044 DLS-56 12/23/21 13:15 Stream Louise storm 5 -10.271 0.030 -64.048 0.118 DLS-57 12/23/21 13:34 Stream Louise storm 5 -10.056 0.003 -62.413 0.044 DLS-58 12/23/21 13:53 Stream Louise storm 5 -10.248 0.027 -63.590 0.074 DHS-50 12/23/21 11:25 Stream Henry storm 5 -9.936 0.017 -65.111 0.136 DHS-51 12/23/21 11:46 Stream Henry storm 5 -10.204 0.003 -65.440 0.049 DHS-52 12/23/21 12:05 Stream Henry storm 5 -9.986 0.033 -64.341 0.061 200 DHS-53 12/23/21 12:26 Stream Henry storm 5 -10.261 0.028 -64.261 0.025 DHS-54 12/23/21 12:47 Stream Henry storm 5 -10.232 0.017 -64.231 0.084 DHS-55 12/23/21 13:06 Stream Henry storm 5 -9.949 0.049 -61.371 0.067 DHS-56 12/23/21 13:24 Stream Henry storm 5 -9.762 0.014 -59.930 0.043 DHS-57 12/23/21 13:45 Stream Henry storm 5 -9.900 0.009 -60.386 0.058 GIR-5 12/24/21 6:01 Integrated Rain Rain Gauge storm 5 -9.703 0.021 -63.217 0.066 DGR-50 12/24/21 7:11 Rain Rain Gauge storm 5 0.04 -7.106 0.029 -36.127 0.121 DGR-51 12/24/21 7:30 Rain Rain Gauge storm 5 -6.802 0.033 -37.280 0.079 DLS-59 12/24/21 5:35 Stream Louise storm 5 -8.553 0.020 -49.473 0.121 DLS-60 12/24/21 6:05 Stream Louise storm 5 -8.331 0.018 -48.511 0.057 DLS-61 12/24/21 6:31 Stream Louise storm 5 -8.422 0.015 -48.754 0.111 DLS-62 12/24/21 6:50 Stream Louise storm 5 -8.417 0.030 -48.225 0.052 DLS-63 12/24/21 7:13 Stream Louise storm 5 -8.212 0.027 -46.862 0.090 DLS-64 12/24/21 7:35 Stream Louise storm 5 -8.186 0.012 -46.605 0.076 DLS-65 12/24/21 7:54 Stream Louise storm 5 -8.450 0.027 -48.246 0.104 DLS-66 12/24/21 8:15 Stream Louise storm 5 -8.384 0.019 -48.300 0.146 DHS-58 12/24/21 5:47 Stream Henry storm 5 -8.770 0.018 -50.866 0.064 DHS-59 12/24/21 6:20 Stream Henry storm 5 -8.787 0.028 -50.603 0.128 DHS-60 12/24/21 6:41 Stream Henry storm 5 -8.609 0.017 -49.988 0.066 DHS-61 12/24/21 7:03 Stream Henry storm 5 -8.455 0.037 -48.766 0.082 DHS-62 12/24/21 7:23 Stream Henry storm 5 -8.195 0.022 -45.704 0.140 DHS-63 12/24/21 7:45 Stream Henry storm 5 -8.462 0.001 -47.894 0.037 DHS-64 12/24/21 8:03 Stream Henry storm 5 -8.643 0.039 -49.394 0.107 GIR-6 12/28/21 13:00 Integrated Rain Rain Gauge -5.885 0.016 -24.134 0.096 DLS-67 12/28/21 13:00 Stream Louise -7.938 0.037 -44.779 0.009 GIR-7 ¼/22 13:00 Integrated Rain Rain Gauge storm 6 -8.364 0.030 -45.304 0.061 JLS-1 ¼/22 13:00 Stream Louise storm 6 -8.162 0.027 -47.096 0.126 MLR-3 3/28/22 10:00 Rain Louise storm 7 0.02 -3.720 0.026 -13.630 0.100 MLR-4 3/28/22 10:30 Rain Louise storm 7 0.12 -4.228 0.054 -14.741 0.137 MLR-5 3/28/22 11:00 Rain Louise storm 7 0.14 -5.306 0.039 -21.715 0.058 MLR-6 3/28/22 11:30 Rain Louise storm 7 0.22 -6.196 0.037 -27.930 0.070 MLR-7 3/28/22 12:00 Rain Louise storm 7 0.26 -6.141 0.021 -26.623 0.046 MLR-8 3/28/22 12:30 Rain Louise storm 7 0.18 -5.861 0.013 -24.438 0.024 MLR-9 3/28/22 13:00 Rain Louise storm 7 0.5 -7.111 0.023 -35.197 0.063 MLR-10 3/28/22 13:30 Rain Louise storm 7 0.44 -7.794 0.097 -41.819 0.883 MLR-11 3/28/22 14:00 Rain Louise storm 7 0.22 -7.541 0.020 -43.250 0.027 MLR-12 3/28/22 14:30 Rain Louise storm 7 0.02 -7.137 0.022 -40.147 0.114 MHR-1 3/28/22 10:05 Rain Henry storm 7 0.02 -6.809 0.037 -38.843 0.065 201 MHR-2 3/28/22 10:30 Rain Henry storm 7 0.12 -4.006 0.014 -14.678 0.106 MHR-3 3/28/22 11:00 Rain Henry storm 7 0.14 -4.453 0.014 -15.445 0.237 MLS-1 3/28/22 12:30 Stream Louise storm 7 -5.871 0.044 -25.514 0.096 MLS-2 3/28/22 13:00 Stream Louise storm 7 -6.865 0.036 -33.324 0.015 MLS-3 3/28/22 13:30 Stream Louise storm 7 -7.281 0.033 -37.870 0.066 MLS-4 3/28/22 14:00 Stream Louise storm 7 -7.269 0.033 -38.742 0.006 MLS-5 3/28/22 14:30 Stream Louise storm 7 -7.117 0.028 -38.263 0.045 MLS-6 3/28/22 15:00 Stream Louise storm 7 -7.131 0.010 -37.558 0.103 MHS-1 3/28/22 12:40 Stream Henry storm 7 -6.081 0.042 -26.931 0.053 MHS-2 3/28/22 13:03 Stream Henry storm 7 -6.244 0.024 -30.633 0.068 MHS-3 3/28/22 13:33 Stream Henry storm 7 -6.893 0.030 -34.514 0.099 MHS-4 3/28/22 14:03 Stream Henry storm 7 -6.961 0.035 -34.666 0.005 MHS-5 3/28/22 14:33 Stream Henry storm 7 -6.949 0.032 -34.645 0.146 MHS-6 3/28/22 15:00 Stream Henry storm 7 -6.987 0.024 -34.781 0.048 MGIR-1 3/28/22 16:00 Integrated Rain Rain Gauge storm 7 -6.578 0.007 -32.191 0.070 MRIR-1 3/28/22 16:00 Integrated Rain Ridge storm 7 -6.931 0.041 -34.310 0.053 MHIR-1 3/28/22 16:00 Integrated Rain storm 7 -6.744 0.020 -32.997 0.004 Table S3. Isotopic data collected during storm events from precipitation and streamflow samples, concurrent rainfall amounts and storm identification. 202 Sample ID Catchment Collection Date Kfs (mm/hr) LMD1 Louise 26 February 2021 0 LMD3 Louise 26 February 2021 0 LMD5 Louise 26 February 2021 2.98 LMD6 Louise 26 February 2021 11.96 LMD7 Louise 26 February 2021 8.33 LMD8 Louise 26 February 2021 5.74 LMD9 Louise 26 February 2021 21.86 LMD10 Louise 26 February 2021 20.97 LMD11 Louise 31 August 2021 0.97 LMD12 Louise 31 August 2021 106.49 LMD13 Louise 31 August 2021 90.87 LMD14 Louise 31 August 2021 34.6 LMD15 Louise 31 August 2021 17.93 LMD16 Louise 31 August 2021 48.62 LMD17 Louise 31 August 2021 40.49 LMD18 Louise 31 August 2021 45.21 LMD19 Louise 31 August 2021 20.37 LMD20 Louise 31 August 2021 21.51 LMD21 Louise 31 August 2021 62.96 HMD1 Henry 31 August 2021 2.29 HMD2 Henry 31 August 2021 14.85 HMD4 Henry 31 August 2021 24.54 HMD5 Henry 31 August 2021 43.6 HMD6 Henry 31 August 2021 8.35 HMD7 Henry 31 August 2021 10.64 HMD9 Henry 31 August 2021 248.92 HMD10 Henry 31 August 2021 0 HMD11 Henry 31 August 2021 31.75 HMD12 Henry 31 August 2021 0 HMD13 Henry 31 August 2021 15.43 HMD14 Henry 31 August 2021 33.64 HMD15 Henry 31 August 2021 20.98 HMD16 Henry 31 August 2021 19.16 HMD17 Henry 31 August 2021 47.84 HMD18 Henry 31 August 2021 14.72 HMD20 Henry 31 August 2021 0 HMD21 Henry 31 August 2021 20.81 MD1 Thelma 25 February 2021 11.45 MD2 Thelma 25 February 2021 39.84 MD3 Thelma 25 February 2021 28.37 MD4 Thelma 25 February 2021 11.15 MD5 Thelma 25 February 2021 12.66 203 MD6 Thelma 25 February 2021 15.07 MD7 Thelma 25 February 2021 49.04 MD8 Thelma 25 February 2021 25.88 MD9 Thelma 25 February 2021 55.61 MD10 Thelma 25 February 2021 11.26 MD11 Thelma 3 April 2021 27.61 MD12 Thelma 3 April 2021 8.3 MD14 Thelma 3 April 2021 101.54 MD15 Thelma 3 April 2021 48.76 MD16 Thelma 3 April 2021 36.18 MD17 Thelma 3 April 2021 17.5 MD18 Thelma 3 April 2021 54.54 MD19 Thelma 3 April 2021 39.04 MD20 Thelma 3 April 2021 96.96 MD21 Thelma 3 April 2021 11.73 E11 Thelma 25 February 2021 17.31 E15 Thelma 25 February 2021 1.68 Table S4. Field-saturated hydraulic conductivity (K fs) values from Henry, Louise and Thelma. Zero values indicate no infiltration after 45 minutes. Catchment Variance Catchment Comparison P-value Thelma 688.3 Louise vs Henry 0.0026 Louise 871.7 Thelma vs Henry 0.0083 Henry 202.0 Louise vs Thelma 0.2937 Table S5. Statistical assessment of variance between K fs values measured in Henry, Louise and Thelma 204 SI References 1. W. Dansgaard, Stable isotopes in precipitation. Tellus 16, 436–468 (1964). 2. M. G. Sklash, R. N. Farvolden, P. Fritz, Erratum: A conceptual model of watershed response to rainfall, developed through the use of oxygen-18 as a natural tracer. Can. J. Earth Sci. 13, 715–715 (1976). 3. K. Rozanski, L. Araguás-Araguás, R. Gonfiantini, Isotopic Patterns in Modern Global Precipitation. 1–36 (1993). 4. W. R. Whalley, et al., Methods to estimate changes in soil water for phenotyping root activity in the field. Plant Soil 415, 407–422 (2017). 5. M. Kotikian, A. D. Parsekian, G. Paige, A. Carey, Observing Heterogeneous Unsaturated Flow at the Hillslope Scale Using Time-Lapse Electrical Resistivity Tomography. Vadose Zo. J. 18, 1–16 (2019). 6. G. Blanchy, et al., Time-lapse geophysical assessment of agricultural practices on soil moisture dynamics. Vadose Zo. J. 19 (2020). 7. L. Slater, A. . Binley, W. Daily, R. Johnson, Cross-hole electrical imaging of a controlled saline tracer injection. J. Appl. Geophys. 44 (2000). 8. A. Binley, A. Kemna, “DC Resistivity and Induced Polarization Methods” in Hydrogeophysics, (Springer Netherlands, 2005) https:/doi.org/10.1007/1-4020-3102-5_5. 9. J. Koestel, A. Kemna, M. Javaux, A. Binley, H. Vereecken, Quantitative imaging of solute transport in an unsaturated and undisturbed soil monolith with 3-D ERT and TDR. Water Resour. Res. 44 (2008). 10. W. Nijland, M. van der Meijde, E. A. Addink, S. M. de Jong, Detection of soil moisture and vegetation water abstraction in a mediterranean natural area using electrical resistivity tomography. Catena 81, 209–216 (2010). 11. F. D. Day-Lewis, C. D. Johnson, K. Singha, J. W. Lane, “BEST PRACTICES IN ELECTRICAL RESISTIVITY IMAGING: DATA COLLECTION AND PROCESSING, AND APPLICATION TO DATA FROM CORINNA, MAINE An Administrative Report for EPA Region 1.” 12. A. M. Carey, G. B. Paige, B. J. Carr, M. Dogan, Forward modeling to investigate inversion artifacts resulting from time-lapse electrical resistivity tomography during rainfall simulations. J. Appl. Geophys. 145, 39–49 (2017). 13. P. Brunet, R. Clément, C. Bouvier, Monitoring soil water content and deficit using Electrical Resistivity Tomography (ERT) - A case study in the Cevennes area, France. J. Hydrol. 380, 146–153 (2010). 14. D. Michot, et al., Spatial and temporal monitoring of soil water content with an irrigated corn crop cover using surface electrical resistivity tomography. Water Resour. Res. 39 (2003). 15. T. C. J. Yeh, et al., A geostatistically based inverse model for electrical resistivity surveys and its applications to vadose zone hydrology. Water Resour. Res. 38, 14-1-14–13 (2002). 16. T. W. J. Dibblee, Geologic map of the Glendora quadrangle. Dibblee Geol. Found. Map, St. Barbar. California, DF- 89 (2002). 17. C. E. Conrad, Common shrubs of chaparral and associated ecosystems of southern California. Pacific Southwest For. Range Experiement Stn., 1–92 (1987). 18. J. B. Fisher, et al., ECOSTRESS: NASA’s Next Generation Mission to Measure Evapotranspiration From the International Space Station. Water Resources Research, 56 (2020). 19. B. A. Wilder, A. M. Kinoshita, Incorporating ECOSTRESS evapotranspiration in a paired catchment water balance analysis after the 2018 Holy Fire in California. Catena, 215 (2022).

Abstract (if available)

Abstract The critical zone, the region from the trees to the base of the groundwater, is where key life supporting processes such as soil development and groundwater storage occur. Such processes can be fundamentally altered by perturbations both on short and long times scales and understanding critical zone response to these disturbances is important for climate modeling, understanding natural hazards, and resource availability. In this dissertation, I investigate how mountain building events, specifically the uplift of the Nepal Himalaya, affect topography and subsequently soil development, deep weathering and critical zone architecture, and groundwater residence times in the Melamchi Valley. I then look at how a more modern perturbation, wildfire, changes subsurface water storage and its connection to stormflow in the Mediterranean climate of the San Gabriel Mountains in Southern California.

Mountain building imposes extreme change on the landscape, bringing fresh rock up to the surface rapidly to weather and erode, developing steep channels and hillslopes and creating its own orographic precipitation patterns. I first demonstrate the utility of using high-resolution stereophotogrammetric DEMs for geomorphic analysis in environments such as large-scale mountain terrain that normally lack such high-resolution data. I find that 2m DEMs derived from the SETSM algorithm are significantly more accurate than other widely available products such as 30m SRTM DEMs and allow for detailed landscape analysis generally limited to regions with LiDAR. I then utilize a SETSM 2m DEM to analyze topographic change along an exhumation gradient where I determined zones of increased channel and hillslope steepness corresponding to a chemical weathering shift, indicative of a transient environment where chemical weathering is controlled by extremely rapid erosion. To investigate weathering at depth, I use an 80m borehole that reveals multiple weathering profiles through schist and augen gneiss. Using petrography, geochemistry and microfracture mapping, I find that in this highly fracture region, there is a lithologic control on regolith development and widespread fracturing play a secondary role. Finally, I look at how groundwater residence times changed across these landscape and weathering gradients using environmental tracers. CFC-12 SF6, and 3H derived residence times indicate residence times from <1-35 years in groundwater springs, with residence times decreasing with increasing topographic steepness in springs situated along the valley bottom and on hillslopes.

Wildfire affects the critical zone on much shorter timescales (1s to 10s of years), but have a profound impact during that period, with the loss of vegetation leading to major shifts hydrologically and geomorphically. Here, we utilized a paired-catchment (burned and unburned) approach to understand how subsurface water storage changed and connected to stormflow in ephemeral catchments after the 2020 Bobcat Fire in the San Gabriel Mountains in Southern California. Timelapse electrical resistivity imaging revealed a larger reservoir of subsurface moisture in the burned catchments that persisted between water years due to lack of evapotranspiration. Paired precipitation and streamflow water isotopes indicate that that subsurface reservoir contributes to stormflow in equal proportion in both catchments despite significant differences in discharge. This work creates a more nuanced conceptual model of elevated discharge postfire, revealing a greater role for subsurface water in stormflow.

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Asset Metadata

Creator Atwood, Abra Catherine (author)

Core Title Critical zone response to perturbation: from mountain building to wildfire

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Geological Sciences

Degree Conferral Date 2023-08

Publication Date 01/06/2025

Defense Date 06/14/2023

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag critical zone,disturbance geology,Geochemistry,Hydrology,OAI-PMH Harvest

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor West, A. Joshua (committee chair), Clark, Marin (committee member), Deverell, William (committee member), Hammond, Doug (committee member)

Creator Email aatwood@usc.edu,abraatwood@gmail.com

Unique identifier UC113262961

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Legacy Identifier etd-AtwoodAbra-12036

Document Type Dissertation

Format theses (aat)

Rights Atwood, Abra Catherine

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