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Snowmelt is a critical water resource in the Sierra Nevada impactingpopulations in California and Nevada. In this region, forest managersuse treatments like selective thinning to encourage resilient ecosystemsbut rarely prioritize snowpack retention due to a lack of simplerecommendations and the importance of other management objectives likewildfire mitigation and wildlife habitat. We use light detection andranging (lidar) data collected over multiple snow accumulation seasonsin the Sagehen Creek Basin, central Sierra Nevada in California, USA, toinvestigate how snowpack accumulation and ablation are affected byforest structure metrics at coarse, stand-scales (e.g., fraction ofvegetation, or fVEG) and fine, tree-scales (e.g., a modified leaf areaindex, and the ratio of gap-width to average tree height). Using a newlydeveloped lidar point cloud filtering method and an “open-areareference” approach, we show that for each 10% decrease in fVEG thereis a ~30% increase in snow accumulation and a~15% decrease in ablation rate at the Sagehen fieldsite. To understand variability around these relationships, we use arandom forest analysis to demonstrate that areas with fVEG greater than~30% have the greatest potential increased accumulationresponse after forest removal. This spatial information allows us toassess the utility of completed and planned forest restorationstrategies in targeting areas with the highest potential snowpackresponse. Our new lidar processing methods and reference-based approachare easily transferrable to other areas where they could improvedecision support and increase water availability from landscape-scaleforest restoration projects. 

Geologic features (e.g., fractures and alluvial fans) can play an important role in the locations and volumes of groundwater discharge and degree of groundwater-surface water (GW-SW) interactions. However, the role of these features in controlling GW-SW dynamics and streamflow generation processes are not well constrained. GW-SW interactions and streamflow generation processes are further complicated by variability in precipitation inputs from summer and fall monsoon rains, as well as declines in snowpack and changing melt dynamics driven by warming temperatures. Using high spatial and temporal resolution radon and water stable isotope sampling and a 1D groundwater flux model, we evaluated how groundwater contributions and GW-SW interactions varied along a stream reach impacted by fractures (fractured-zone) and downstream of the fractured hillslope (non- fractured zone) in Coal Creek, a Colorado River headwater stream affected by summer monsoons. During early summer, groundwater contributions from the fractured zone were high, but declined throughout the summer. Groundwater contributions from the non-fractured zone were constant throughout the summer and became proportionally more important later in the summer. We hypothesize that groundwater in the non-fractured zone is dominantly sourced from a high-storage alluvial fan at the base of a tributary that is connected to Coal Creek throughout the summer and provides consistent groundwater influx. Water isotope data revealed that Coal Creek responds quickly to incoming precipitation early in the summer, and summer precipitation becomes more important for streamflow generation later in the summer. We quantified the change in catchment dynamic storage and found it negatively related to stream water isotope values, and positively related to modeled groundwater discharge and the ratio of fractured zone to non-fractured zone groundwater. We interpret these relationships as declining hydrologic connectivity throughout the summer leading to late summer streamflow supported predominantly by shallow flow paths, with variable response to drying from geologic features based on their storage. As groundwater becomes more important for sustaining summer flows, quantifying local geologic controls on groundwater inputs and their response to variable moisture conditions may become critical for accurate predictions of streamflow. 

High elevation mountain watersheds are undergoing rapid warming and declining snow fractions worldwide, causing earlier and quicker snowmelt. Understanding how this hydrologic shift affects subsurface flow paths, biogeochemical reactions, and solute export has been challenging due to the entanglement of hydrological and biogeochemical processes. Coal Creek, a high-elevation catchment (2,700 3,700 m, 53 km2) in Colorado, is experiencing a higher rate of warming than surrounding low-lying areas. This warming corresponds with dynamic and increased responses from biogenic solutes and dissolved organic carbon (DOC), whereas the behavior of geogenic solutes and dissolved inorganic carbon (DIC) has remained relatively unchanged. DOC has experienced the largest concentration increase (>3x), with annual average flow weighted concentrations positively correlated to average annual temperature. This suggests temperature is the main driver of increasing DOC levels. Although DOC and DIC response to warming is influenced by many drivers, the relative contribution of each remains unknown. DOC and DIC were analyzed to incorporate both carbon component products of soil respiration (DOC and CO2) and to represent high solute concentrations transported by shallow (DOC) versus deep (DIC) subsurface flow. The contrasting behavior of these carbon solutes indicates climate change and warming are driving changes in organic matter decomposition and soil respiration. Modeling results from the process-based model HBV-BioRT show increased temperatures cause earlier snowmelt and streamflow generation and lower peak discharge. As stream flow generation occurs earlier, so do DOC flushing and DIC dilution events. Additionally, post-snowmelt periods show greater DOC production and concentrations under warming scenarios. Results indicated increased production of DOC in post-snowmelt periods. DOC is then flushed out by earlier snowmelt partitioned through the shallow soil zone. Most process-based studies lack a watershed-scale understanding of carbon transformation and flow path alterations. This work demonstrates complex hydrologic and biogeochemical coupling at the watershed scale to illustrate how water flow paths and chemistry are responding to a changing climate in highelevation mountain watersheds. 

Abstract Summer streamflow predictions are critical for managing water resources; however, warming‐induced shifts from snow to rain regimes impact low‐flow predictive models. Additionally, reductions in snowpack drive earlier peak flows and lower summer flows across the western United States increasing reliance on groundwater for maintaining summer streamflow. However, it remains poorly understood how groundwater contributions vary interannually. We quantify recession limb groundwater (RLGW), defined as the proportional groundwater contribution to the stream during the period between peak stream flow and low flow, to predict summer low flows across three diverse western US watersheds. We ask (a) how do snow and rain dynamics influence interannual variations of RLGW contributions and summer low flows?; (b) which watershed attributes impact the effectiveness of RLGW as a predictor of summer low flows? Linear models reveal that RLGW is a strong predictor of low flows across all sites and drastically improves low‐flow prediction compared to snow metrics at a rain‐dominated site. Results suggest that strength of RLGW control on summer low flows may be mediated by subsurface storage. Subsurface storage can be divided into dynamic (i.e., variability saturated) and deep (i.e., permanently saturated) components, and we hypothesize that interannual variability in dynamic storage contribution to streamflow drives RLGW variability. In systems with a higher proportion of dynamic storage, RLGW is a better predictor of summer low flow because the stream is more responsive to dynamic storage contributions compared to deep‐storage‐dominated systems. Overall, including RLGW improved low‐flow prediction across diverse watersheds.