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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 The Sierra Nevada has experienced unprecedented wildfires and reduced snowmelt runoff in recent decades, due partially to anthropogenic climate change and over a century of fire suppression. To address these challenges, public land agencies are planning forest restoration treatments, which have the potential to both increase water availability and reduce the likelihood of uncontrollable wildfires. However, the impact of forest restoration on snowpack is site specific and not well understood across gradients of climate and topography. To improve our understanding of how forest restoration might impact snowpack across diverse conditions in the central Sierra Nevada, we run the high‐resolution (1 m) energy and mass balance Snow Physics and Lidar Mapping (SnowPALM) model across five 23–75 km²subdomains in the region where forest thinning is planned or recently completed. We conduct two virtual thinning experiments by removing all trees shorter than 10 or 20 m tall and rerunning SnowPALM to calculate the change in meltwater input. Our results indicate heterogeneous responses to thinning due to differences in climate and wind across our five central Sierra Nevada subdomains. We also predict the largest increases in snow retention when thinning forests with tall (7–20 m) and dense (40–70% canopy cover) trees, highlighting the importance of pre‐thinning vegetation structure. We develop a decision support tool using a random forests model to determine which regions would most benefit from thinning. In many locations, we expect major forest restoration to increase snow accumulation, while other areas with short and sparse canopies, as well as sunny and windy climates, are more likely to see decreased snowpack following thinning. Our decision support tool provides stand‐scale (30 m) information to land managers across the central Sierra Nevada region to best take advantage of climate and existing forest structure to obtain the greatest snowpack benefits from forest restoration. 

Abstract Predicting winter flooding is critical to protecting people and securing water resources in California’s Sierra Nevada. Rain-on-snow (ROS) events are a common cause of widespread flooding and are expected to increase in both frequency and magnitude with anthropogenic climate change in this region. ROS flood severity depends on terrestrial water input (TWI), the sum of rain and snowmelt that reaches the land surface. However, an incomplete understanding of the processes that control the flow and refreezing of liquid water in the snowpack limits flood prediction by operational and research models. We examine how antecedent snowpack conditions alter TWI during 71 ROS events between water years 1981 and 2019. Observations across a 500-m elevation gradient from the Independence Creek catchment were input into SNOWPACK, a one-dimensional, physically based snow model, initiated with the Richards equation and calibrated with collocated snow pillow observations. We compare observed “historical” and “scenario” ROS events, where we hold meteorologic conditions constant but vary snowpack conditions. Snowpack variables include cold content, snow density, liquid water content, and snow water equivalent. Results indicate that historical events with TWI > rain are associated with the largest observed streamflows. A multiple linear regression analysis of scenario events suggests that TWI is sensitive to interactions between snow density and cold content, with denser (>0.30 g cm⁻³) and colder (<−0.3 MJ of cold content) snowpacks retaining >50 mm of TWI. These results highlight the importance of hydraulic limitations in dense snowpacks and energy limitations in warm snowpacks for retaining liquid water that would otherwise be available as TWI for flooding. Significance StatementThe purpose of this study is to understand how the snowpack modulates quantities of water that reach the land surface during rain-on-snow (ROS) events. While the amount of near-term storm rainfall is reasonably predicted by meteorologists, major floods associated with ROS are more difficult to predict and are expected to increase in frequency. Our key findings are that liquid water inputs to the land surface vary with snowpack characteristics, and although many hydrologic models incorporate snowpack cold content and density to some degree, the complexity of ROS events justifies the need for additional observations to improve operational forecasting model results. Our findings suggest additional comparisons between existing forecasting models and those that physically represent the snowpack, as well as field-based observations of cold content and density and liquid water content, would be useful follow-up investigations. 

The SUMup database is a compilation of surface mass balance (SMB), subsurface temperature and density measurements from the Greenland and Antarctic ice sheets. This 2023 release contains 4 490 442 data points: 1 778 540 SMB measurements, 2 706 413 density measurements and 5 489 subsurface temperature measurements. This is respectively 1 477 132, 420 825 and 4 715 additional observations of SMB, density and temperature compared to the 2022 release. This new release provides not only snow accumulation on ice sheets, like its predecessors, but all types of SMB measurements, including from ablation areas. On the other hand, snow depth on sea ice is discontinued, but can still be found in the previous releases. The data files are provided in both CSV and NetCDF format and contain, for each measurement, the following metadata: latitude, longitude, elevation, timestamp, method, reference of the data source and, when applicable, the name of the measurement group it belongs to (core name for SMB, profile name for density, station name for temperature). Data users are encouraged to cite all the original data sources that are being used. Issues about this release as well as suggestions of datasets to be added in next releases can be done on a dedicated user forum: https://github.com/SUMup-database/SUMup-data-suggestion/issues. Example scripts to use the SUMup 2023 files are made available on our script repository: https://github.com/SUMup-database/SUMup-example-scripts. 

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.