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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. 

Abstract Bark beetles have impacted over 58 million acres of coniferous forest in the Western US since 2000. Most beetle impacted forests are in snow dominated, water limited headwater basins, which generate a disproportionate fraction of water supplies. Previous studies show mixed impacts of bark beetle outbreaks on streamflow with the potential to cause increased or decreased flows, but these studies either predate long‐term snowpack data, are model‐based, or examine only mountain pine beetle outbreaks. Ours is the first study to use an empirical, climate‐normalized paired catchment approach to quantify streamflow response to spruce beetle kill. Using 27 years of climate and streamflow observations from southwest Colorado, we show that in three of the six beetle impacted study basins, annual climate‐normalized streamflow increased by 22%–37% for at least three to 6 years after the beetle outbreak. Impacted basins exhibited no decreased flows and flows in unimpacted control basins remained unchanged. Among impacted basins, no single basin characteristic clearly explained variation of streamflow response. Higher runoff ratios during snowmelt contribute anywhere from 9% to 64% of streamflow increases, implying the importance of both snow and growing season processes in driving streamflow increases. These findings show variable, sometimes substantial streamflow increases in critical water supply basins following beetle kill in subalpine spruce forests, and contrast with evidence of unchanged or decreased streamflow following beetle kill in lower elevation pine forests in colder northern Colorado basins, highlighting the importance of climate and forest composition in refining hydrologic predictions following mountain forest disturbances. 

Abstract Understanding the severity and extent of near surface critical zone (CZ) disturbances and their ecosystem response is a pressing concern in the face of increasing human and natural disturbances. Predicting disturbance severity and recovery in a changing climate requires comprehensive understanding of ecosystem feedbacks among vegetation and the surrounding environment, including climate, hydrology, geomorphology, and biogeochemistry. Field surveys and satellite remote sensing have limited ability to effectively capture the spatial and temporal variability of disturbance and CZ properties. Technological advances in remote sensing using new sensors and new platforms have improved observations of changes in vegetation canopy structure and productivity; however, integrating measures of forest disturbance from various sensing platforms is complex. By connecting the potential for remote sensing technologies to observe different CZ disturbance vectors, we show that lower severity disturbance and slower vegetation recovery are more difficult to quantify. Case studies in montane forests from the western United States highlight new opportunities, including evaluating post‐disturbance forest recovery at multiple scales, shedding light on understory vegetation regrowth, detecting specific physiological responses, and refining ecohydrological modeling. Learning from regional CZ disturbance case studies, we propose future directions to synthesize fragmented findings with (a) new data analysis using new or existing sensors, (b) data fusion across multiple sensors and platforms, (c) increasing the value of ground‐based observations, (d) disturbance modeling, and (e) synthesis to improve understanding of disturbance.