Disturbance of mountain forests, which supply critical streamflow to downstream water users, remains an active area of research (NRC, 2008) with substantial implications on large-scale water availability and carbon budgets (Williams et al., 2016(Williams et al., , 2022Zhang et al., 2017). Recent insect outbreaks have caused widespread forest mortality in snow-dominated basins in the Western US (Goeking & Tarboton, 2020;Meddens et al., 2012). Bark beetles, in particular spruce beetle (Dendroctonus rufipennis) and mountain pine beetle (Dendroctonus ponderosae), have affected approximately 58.8 million acres of coniferous forest in the region since 2000 (USDA Forest Service, 2020). Bark beetle population growth over the last several decades has been attributed to increased reproduction rates (Mitton & Ferrenberg, 2012) and warmer air temperatures (Pettit et al., 2020). Bark beetles and associated secondary fungal infections restrict water uptake and kill host trees over the course of a growing season (Frank et al., 2014;Hubbard et al., 2013). Dead host trees drop their needles over one to 3 years, but in contrast to trees affected by logging and stand-replacing fire, they can remain standing for many years (Rhodes et al., 2020).

Context Forest gaps affect snowmelt timing and amount because canopies are key controls over snowpack dynamics and interact with topography. Overlying canopy can decrease snowmelt by intercepting snowfall, but it can also reduce ablation rates from increasing shading. Changes in forest structure and canopy gaps, may therefore affect the amount, timing, and duration of snowmelt and potentially forest response to different water limitations. Objectives We test how the higher energy-input edges of gaps (‘warm edges’) differ from the lower energy-input edges of gaps (‘cool edges’) with respect to snow depth, snowmelt timing, and tree growth in a snow-dominated forest in the Western US. Methods We use multiple dates of LiDAR-based measurement to assess springtime snow depths in warm and cool gap edges in Sagehen Creek Basin, CA. Then we use paired tree sampling and ring width chronologies to ascertain moisture sensitivity of trees adjacent to warm and cool gap edges. Results Pre-ablation snow depths in cool gap edges exceeded those in warm gap edges by 9% to 18% (; the effect size depended on elevation and aspect. Snow also persisted longer in cool edges than in warm edges. Growth variations in warm-edge-adjacent trees were more correlated with interannual variations in snow depth those of cool edge trees, although neither had strong correlations. Conclusions These findings suggest that forest structures that maximize cool edge area may benefit snow depth and persistence leading to cool-edge trees that are less sensitive to interannual hydroclimatic variability than warm edge trees, despite this effect being small relative to other controls over growth.

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.

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