Noah P. Molotch

In a new application of GRACE and GRACE‐FO satellite observations, we estimate basin‐scale net groundwater recharge rates for the combined Sacramento, San Joaquin, and Tulare basins encompassing California's Central Valley aquifer. Net recharge is calculated as the temporal derivative of groundwater storage anomalies and compared with precipitation and snow water equivalent over a fixed study area. Net positive recharge is rare, occurring in only six water years since 2004, and is consistently associated with years of above‐average Sierra Nevada snowpack. Across the observational record, net positive recharge is observed only in years when snow water equivalent exceeds approximately 21 km ³ (17 MAF; ∼121% of the 20‐year mean value), with the largest recharge event coinciding with the exceptionally wet winter of water year 2023. These results suggest that above‐average snowpack conditions, rather than rainfall alone, are an important enabling condition for basin‐scale groundwater recharge under a warming climate.

Earth's snow cover strongly influences the climate system and represents an important resource for agricultural, industrial, and domestic water use. The last decade of snow‐focused research has improved our understanding of snow across scales. These efforts have culminated in new snow measurement instruments and methods, operational models for tracking snowpack evolution and forecasting snowmelt, multi‐year and international snow and remote sensing field campaigns, and satellite mission proposals to measure snowpack water resources from space, with two submitted to NASA's Earth Explorer AO and the Environment and Climate Change Canada Terrestrial Snow Mass Mission moving closer to a launch opportunity. Yet, shortcomings in each snowpack observation system still exist, including uncertainty in product performance, mission proposal advancement, and synergies across methods. The snow community aims to navigate next actionable steps toward improved and global‐scale snow monitoring for climate and human purposes. Building from recent advances in snow research and operations and carrying momentum from the conclusion of the NASA SnowEx field campaigns, NASA's Terrestrial Hydrology Program (THP) sponsored a Community Snow Meeting in August 2024 in Boulder, Colorado, USA, with 200 total in‐person and virtual attendees. Meeting objectives were to outline existing and ongoing snowpack monitoring techniques and identify knowledge gaps and recommended next steps for the snow community. We broadly summarize the state of numerous snow science sub‐disciplines and share the insights and takeaways from the Community Snow Meeting, focused largely but not exclusively on NASA opportunities, and intended to support ongoing and future pathways toward the next decade of snow research and development.

Earth's snow cover strongly influences the climate system and represents an important resource for agricultural, industrial, and domestic water use. The last decade of snow-focused research has improved our understanding of snow across scales. These efforts have culminated in new snow measurement instruments and methods, operational models for tracking snowpack evolution and forecasting snowmelt, multi-year and international snow and remote sensing field campaigns, and satellite mission proposals to measure snowpack water resources from space, with two recently submitted to NASA's Earth Explorer AO and the CSA Terrestrial Snow Mass Mission moving closer to a launch opportunity. Yet, shortcomings in each snowpack observation system still exist, including uncertainty in product performance, mission proposal advancement, and synergies across methods. The snow community aims to support and navigate next actionable steps toward improved and global-scale snow monitoring for climate and human purposes. Building from recent advances in snow research and operations and carrying momentum from the conclusion of the NASA SnowEx field campaigns, NASA's Terrestrial Hydrology Program sponsored a Community Snow Meeting in August 2024 in Boulder, Colorado, USA, with 200 total in-person and virtual meeting attendees. The meeting objectives were to outline existing and ongoing snowpack monitoring techniques, identify remaining knowledge gaps, and recommended next steps for the snow community. Here, we broadly summarize the state of numerous snow science sub-disciplines and share the collective insights and takeaways from the NASA Community Snow Meeting, intended to support ongoing and future pathways toward the next decade of snow science research and development.

An aspect of hydrologic sensitivity to climate change in snow dominated systems that has been moderately explored relates to the role of snowfall and snowpack accumulation in storing water throughout cold season months and releasing this water to terrestrial systems during warmer months when potential evapotranspiration (PET) is relatively elevated. Importanlty, previous studies have not been able to document an explicit mechanism linking precipitation type to runoff production; yet several studies have noted that a sensitivity does exist. Given that a shift from snowfall to rainfall is inevitable as the climate warms, this fundamental question as to how streamflow responds to a change in precipitation type is extremely timely and important for water resource management. Hence, one would expect that climate-related shifts toward earlier snowmelt or a shift from snowfall to rainfall would lead to an increased misalignment between the seasonality of surface water input (i.e. rainfall and snowmelt) and PET, and thus act to decrease annual partitioning to evapotranspiration and increase annual streamflow. This shift from snowfall to rainfall, and it's associated influence on hydrologic partitioning will be refered to below as the 'energy-water-misalignment perspective'. A second hypothesis, that is counter to the energy-water-misalignment perspective, has documented an increase in runoff partitioning with increased snowfall fraction which relates to a theorized greater effiency of soil-water drainage from snowmelt versus rainfall due to the high rate and duration of snowmelt; hereafter referred to as the 'snowmelt-rate perspective'. In pursuit of this line of inquiry, this presentation will cover recent modeling and observation-based experiments that reveal the importance of both of the aforementioned runoff response perspectives. The combination of these modeling and observation-based studies confirm that the energy-water misalignment perspective and the snowmelt-rate perspective both influence hydrologic partitioning sensitivity to precipitation type. The resultant streamflow sensitivity to precipitation type and climate warming is therefore complex with the sign of the partitioning sensitivity being governed by the relative importance of the two paradoxical runoff response perspectives presented herein.

Patterns of alpine plant productivity are extremely variable in space and time. Complex topography drives variations in temperature, wind, and solar radiation. Surface and subsurface flow paths route water between landscape patches. Redistribution of snow creates scour zones and deep drifts, which drives variation in water availability and growing season length. Hence, the distribution of snow likely plays a central role in patterns of alpine plant productivity. Given that these processes operate at sub‐1 m to sub‐10 m spatial scales and are dynamic across daily to weekly time scales, historical studies using manual survey techniques have not afforded a comprehensive assessment of the influence of snow distribution on plant productivity. To address this knowledge gap, we used weekly estimates of normalised difference vegetation index (NDVI), snow extent, and peak snow depth, acquired from drone surveys at 25 cm resolution. We derived six snowpack‐related and topographic variables that may influence vegetation productivity and analysed these with respect to the timing and magnitude of peak productivity. Peak NDVI and peak NDVI timing were most highly correlated with maximum snow depth, and snow‐off‐date. We observed up to a ~30% reduction in peak NDVI for areas with deeper and later snow cover, and a ~11‐day delay in the timing of peak NDVI in association with later snow‐off‐date. Our findings leverage a novel approach to quantify the importance of snow distribution in driving alpine vegetation productivity and provide a space for time proxy of potential changes in a warmer, lower snow future.

In the montane western United States, where the majority of downstream water resources are derived from snowmelt, a warming climate threatens the timing and amount of future water availability. It is expected that the fraction of precipitation falling as snow will continue decreasing and the timing of snowmelt will continue shifting earlier in the year with unknown impacts on partitioning between evapotranspiration and streamflow. To assess this, we employ a Snow Storage Index (SSI) to represent the annual temporal phase difference between daily precipitation and daily modeled surface water inputs (SWI, the sum of rainfall and snowmelt), weighted by the respective amounts. We coupled the SSI metric with a Budyko‐based framework to determine the effect of snow water storage on relative hydrologic partitioning across snow‐influenced watersheds in the western U.S. Greater snow water storage was positively correlated with greater hydrologic partitioning to streamflow, particularly in the North Cascades/Cascades (r²: 0.62), Blue Mountains (r²: 0.56), Canadian Rockies (r²: 0.55), Idaho Batholith, (r²: 0.48), and Columbia Mountains/Northern Rockies (r²: 0.45). The weekly SWI:P ratio was an equally strong predictor for hydrologic partitioning, particularly in mid‐spring (e.g., March/April) in the same mountainous areas (r²: 0.62–0.74, across the same eco‐regions). The retention of snow water storage and subsequent release of stored water in summer months resulted in increased hydrologic partitioning to streamflow. If SSI decreases with future warming, the volume of water partitioned streamflow will decrease non‐uniformly across the western U.S. with substantial implications for ecosystems and agricultural, industrial, and domestic water supplies.

Mountain snowpacks act as natural water towers, storing winter precipitation until summer months when downstream water demand is greatest. We introduce a Snow Storage Index (SSI), representing the temporal phase difference between daily precipitation and surface water inputs—sum of rainfall and snowmelt into terrestrial systems—weighted by relative magnitudes. Different from snow water equivalent or snow fraction, the SSI represents the degree to which the snowpack delays the timing and magnitude of surface water inputs relative to precipitation, a fundamental component of how snow water storage influences the hydrologic cycle. In western North America, annual SSI has decreased (p < 0.05) from 1950–2013 in over 25% of mountainous areas, as a result of substantially earlier snowmelt and rainfall in spring months, with additional declines in winter precipitation. The SSI and associated trends offer a new perspective on hydrologic sensitivity to climate change which have broad implications for water resources and ecosystems.

The period of disrupted human activity caused by the COVID‐19 pandemic, coined the “anthropause,” altered the nature of interactions between humans and ecosystems. It is uncertain how the anthropause has changed ecosystem states, functions, and feedback to human systems through shifts in ecosystem services. Here, we used an existing disturbance framework to propose new investigation pathways for coordinated studies of distributed, long‐term social‐ecological research to capture effects of the anthropause. Although it is still too early to comprehensively evaluate effects due to pandemic‐related delays in data availability and ecological response lags, we detail three case studies that show how long‐term data can be used to document and interpret changes in air and water quality and wildlife populations and behavior coinciding with the anthropause. These early findings may guide interpretations of effects of the anthropause as it interacts with other ongoing environmental changes in the future, particularly highlighting the importance of long‐term data in separating disturbance impacts from natural variation and long‐term trends. Effects of this global disturbance have local to global effects on ecosystems with feedback to social systems that may be detectable at spatial scales captured by nationally to globally distributed research networks.

Anthropogenic global warming caused by increased atmospheric carbon forcing is expected to cause a decrease in peak snow water equivalent (SWE), shift the timing of snowmelt to earlier in the year, and lead to slower melt rates in the mountains of the Western United States. High-elevation forests in mountainous terrain represent a critical carbon sink. Understanding the ecohydrology of subalpine forests is crucial for assessing the health of these sinks. The Niwot Ridge Long Term Ecological Research station, located at 3000 m amsl in the southern Rocky Mountains of Colorado, receives just over 1 m of annual precipitation mostly as snow, supporting a persistent seasonal snowpack in alpine and subalpine ecosystems. Previous studies show that longer growing season length is correlated with shallower snowpack, earlier spring onset and reduced net CO2 uptake. Co-located sensors provide over 20 years of continuous SWE and eddy covariance (EC) data, allowing for robust direct comparison of snow and carbon phenomena in a high-elevation catchment. Linear regression and time series analysis was performed on snowmelt, meteorological, phenological and ecosystem productivity variables. Peak productivity is correlated with peak SWE (R2=0.54) and further correlated with snowmelt disappearance (R2=0.38) and the timing of spring growth onset (R2=0.30). Timing of both peak productivity and spring growth onset are correlated with snowmelt and meteorological variables. A multivariable regression of meteorological variables, timing of spring growth onset, a temporal trend, and snowmelt rate and explains 94% of interannual variability in the timing of peak forest productivity. These results develop support and introduce new evidence for the existing studies of Niwot Ridge ecohydrology. Future work will investigate the meteorological and hydrological record extending back to 1979 and the long-term trends in snowmelt and forest productivity.

Anthropogenic global warming caused by increased atmospheric carbon forcing is expected to cause a decrease in peak snow water equivalent (SWE), shift the timing of snowmelt to earlier in the year, and lead to slower melt rates in the mountains of the Western United States. High-elevation forests in mountainous terrain represent a critical carbon sink. Understanding the ecohydrology of subalpine forests is crucial for assessing the health of these sinks. The Niwot Ridge Long Term Ecological Research station, located at 3000 m amsl in the southern Rocky Mountains of Colorado, receives just over 1 m of annual precipitation mostly as snow, supporting a persistent seasonal snowpack in alpine and subalpine ecosystems. Previous studies show that longer growing season length is correlated with shallower snowpack, earlier spring onset and reduced net CO2 uptake. Co-located sensors provide over 20 years of continuous SWE and eddy covariance (EC) data, allowing for robust direct comparison of snow and carbon phenomena in a high-elevation catchment. Linear regression and time series analysis was performed on snowmelt, meteorological, phenological and ecosystem productivity variables. Peak productivity is correlated with peak SWE (R2=0.54) and further correlated with snowmelt disappearance (R2=0.38) and the timing of spring growth onset (R2=0.30). Timing of both peak productivity and spring growth onset are correlated with snowmelt and meteorological variables. A multivariable regression of meteorological variables, timing of spring growth onset, a temporal trend, and snowmelt rate and explains 94% of interannual variability in the timing of peak forest productivity. These results develop support and introduce new evidence for the existing studies of Niwot Ridge ecohydrology. Future work will investigate the meteorological and hydrological record extending back to 1979 and the long-term trends in snowmelt and forest productivity.