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Automated snow station networks provide critical hydrologic data. Whether point observations represent snowpack at larger areas is an enduring question. Leveraging the recent proliferation of airborne lidar snow depth data, we revisit the question of snow station representativeness at multiple scales surrounding 111 stations in Colorado and California (USA) from 2021–2023 (n=476 total samples). In about 50 % of cases, station depths were at least 10 cm higher than areal-mean snow depth (from lidar) at 0.5 to 4 km scales. The nearest 50 m lidar pixels had lower bias and were more often representative of the areal-mean snow depth than coincident stations. The closest 3 m lidar pixel often agreed with station snow depth to within 10 cm, suggesting differences between station snow depth and the nearest 50 m lidar pixel result from highly localized conditions and not the measurement method. Representativeness decreased as scale increased up to ∼6 km, mainly explained by the elevation of a site relative to the larger area. Relative values of vegetation and southness did not have significant impacts on site representativeness. The sign of bias at individual snow stations is temporally consistent, suggesting the relationship between station depth and that of the surrounding area may be predictable. Improving understanding of snow station representativeness could allow for more accurate validation of modeled and remotely sensed data. [ABSTRACT FROM AUTHOR]

Estimating snow mass in the mountains remains a major challenge for remote-sensing methods. Airborne lidar can retrieve snow depth, and some promising results have recently been obtained from spaceborne platforms, yet density estimates are required to convert snow depth to snow water equivalent (SWE). However, the retrieval of snow bulk density remains unsolved, and limited data are available to evaluate model estimates of density in mountainous terrain. Toward the goal of landscape-scale retrievals of snow density, we estimated bulk density and length-scale variability by combining ground-penetrating radar (GPR) two-way travel-time observations and airborne-lidar snow depths collected during the mid-winter NASA SnowEx 2020 campaign at Grand Mesa, Colorado, USA. Key advancements of our approach include an automated layer-picking method that leverages the GPR reflection coherence and the distributed lidar–GPR-retrieved bulk density with machine learning. The root-mean-square error between the distributed estimates and in situ observations is 11 cm for depth, 27 kgm-3 for density, and 46 mm for SWE. The median relative uncertainty in distributed SWE is 13 %. Interactions between wind, terrain, and vegetation display corroborated controls on bulk density that show model and observation agreement. Knowledge of the spatial patterns and predictors of density is critical for the accurate assessment of SWE and essential snow research applications. The spatially continuous snow density and SWE estimated over approximately 16 km2 may serve as necessary calibration and validation for stepping prospective remote-sensing techniques toward broad-scale SWE retrieval. [ABSTRACT FROM AUTHOR]

Automated snow station networks provide critical hydrologic data. Whether point observations represent snowpack at larger areas is an enduring question. Leveraging the recent proliferation of airborne Lidar snow depth data, we revisit the question of snow station representativeness at multiple scales surrounding 111 stations in Colorado and California (U.S.A.) from 2021–2023 (n= 476 total samples). In about 50 % of cases, station depths were at least 10 cm higher than areal-mean snow depth (from Lidar) at 0.5 to 4 km scales. The nearest 50 m Lidar pixels had lower bias and were more often representative than coincident stations. The closest 3 m Lidar pixel often agreed (within 10 cm) with station snow depth, suggesting differences between station snow depth and the nearest 50 m Lidar pixel result from highly localized conditions, not the measurement method. Representativeness decreased as scale increased up to 6 km, mainly explained by the elevation of a site relative to the larger area. The bias direction at individual snow stations is temporally consistent, suggesting the relationship between station depth and that of the surrounding area may be predictable. Improving understanding of snow station representativeness could allow for more accurate validation of modelled and remotely sensed data. [ABSTRACT FROM AUTHOR]

We present meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains (USA) over three consecutive water years with different amounts of snow water equivalent (SWE) accumulation: A year with above average SWE (2019), a year with average SWE (2020), and a year with below average SWE (2021). This data set is distinguished by its emphasis on paired open‐forest sites in a continental snow climate. Approximately once a month during February–May, we collected data from 15 to 20 snow pits and took 8 to 19 snow depth transects. Our sampling sites were in open and adjacent forested areas at 3,100 m and in a lower elevation aspen (3,035 m) and higher elevation conifer stand (3,395 m). In total, we recorded 270 individual snow pit density and temperature profiles and over 4,000 snow depth measurements. These data are complimented by continuous meteorological measurements from two weather stations: One in the open and one in the adjacent forest. Meteorology data—including incoming shortwave and longwave radiation, outgoing shortwave radiation, relative humidity, wind speed, snow depth, and air and infrared surface temperature—were quality controlled and the forcing data were gap‐filled. These data are available to download from Bonner, Smyth, et al. (2022) at https://doi.org/10.5281/zenodo.6618553, at three levels of processing, including a level with downscaled, adjusted precipitation based on data assimilation using observed snow depth and a process‐based snow model. We demonstrate the utility of these data with a modeling experiment that explores open‐forest differences and identifies opportunities for improvements in model representation. Key Points: We describe a detailed, multiyear snowpack and meteorological data set collected in a paired forest‐open setting in a continental climateMeteorological observations are processed for quality and continuity and supplemented with downscaled and assimilated precipitationThe data set can support snow model evaluations and ongoing community investigations on snow‐forest processes and mountain hydrometeorology [ABSTRACT FROM AUTHOR]

Snow duration in post‐fire forests is influenced by neighbourhoods of trees, snags, and deadwood. We used annually resolved, spatially explicit tree and tree mortality data collected in an old‐growth, mixed‐conifer forest in the Sierra Nevada, California, that burned at low to moderate severity to calculate 10 tree neighbourhood metrics for neighbourhoods up to 40 m from snow depth and snow disappearance sampling points. We developed two linear mixed models, predicting snow disappearance timing as a function of tree neighbourhood, litter density, and simulated incoming solar radiation, and two multiple regression models explaining variation in snow depth as a function of tree neighbourhood. Higher densities of post‐fire large‐diameter snags within 10 m of a sampling point were related to higher snow depth (indicating reduced snow interception). Higher densities of large‐diameter trees within 5 m and larger amounts of litter were associated with shorter snow duration (indicating increased longwave radiation emittance and accelerated snow albedo decay). However, live trees with diameters >60 cm within 10 m of a snow disappearance sampling point were associated with a longer‐lasting spring snowpack. This suggests that, despite the local effects of canopy interception and emitted longwave radiation from boles of large trees, shading from their canopies may prolong snow duration over a larger area. Therefore, conservation of widely spaced, large‐diameter trees is important in old‐growth forests because they are resistant to fire and can enhance the seasonal duration of snowmelt. [ABSTRACT FROM AUTHOR]

Intermittent snow depth observations can be leveraged with data assimilation (DA) to improve model estimates of snow water equivalent (SWE) at the point scale. A key consideration for scaling assimilation to the basin scale is its performance at forested locations—where canopy‐snow interactions affect snow accumulation and melt, yet are difficult to model and parameterize. We implement a particle filter (PF) technique to assimilate intermittent snow depth observations into the Flexible Snow Model, and validate model outputs against snow density and SWE measurements across adjacent forest and open sites, at two locations with different climates and forest structures. Assimilation reduces snow depth error by 70%–90%, density error by 5%–30%, and SWE error by 50%–70% at forest locations (relative to controls). The PF simulates the seasonal evolution of the snowpack under forest canopy, including where interception losses decrease SWE in the forest during accumulation, and shading reduces melt during the ablation season (relative to open sites). Model outputs are sensitive to canopy‐related parameters, but DA reduces the range in snow depth and SWE estimates resulting from variations or uncertainties in these parameters by over 50%. These results demonstrate that the importance of accurately measuring, estimating, or calibrating canopy‐related parameters is reduced when snow depth observations are assimilated. Finally, we assimilate remotely sensed snow depth observations at 50 m resolution across the East River Basin, Colorado (1,000 km2). Across elevations, the PF increases estimated precipitation at forested sites by ∼15% relative to open sites, likely compensating for excessive sublimation of intercepted snow. Key Points: Data assimilation improves modeled snow depth, density, and SWE at forested sites in two contrasting climates and over multiple yearsAssimilation reduces apparent model output sensitivity to forest canopy parameters that are difficult to measure or calibrateAssimilation increases estimated precipitation in the forest relative to the open, likely due to excessive sublimation of intercepted snow [ABSTRACT FROM AUTHOR]

Snow is a fundamental component of global and regional water budgets, particularly in mountainous areas and regions downstream that rely on snowmelt for water resources. Land surface models (LSMs) are commonly used to develop spatially distributed estimates of snow water equivalent (SWE) and runoff. However, LSMs are limited by uncertainties in model physics and parameters, among other factors. In this study, we describe the use of model calibration tools to improve snow simulations within the Noah-MP LSM as the first step in an observing system simulation experiment (OSSE). Noah-MP is calibrated against the University of Arizona (UA) SWE product over a western Colorado domain. With spatially varying calibrated parameters, we run calibrated and default Noah-MP simulations for water years 2010–20. By evaluating both simulations against the UA dataset, we show that calibration decreases domain averaged temporal RMSE and bias for snow depth from 0.15 to 0.13 m and from −0.036 to −0.0023 m, respectively, and improves the timing of snow ablation. Increased snow simulation performance also improves estimates of model-simulated runoff in four of six study basins, though only one has statistically significant improvement. Spatially distributed Noah-MP snow parameters perform better than default uniform values. We demonstrate that calibrating variables related to snow albedo calculations and rain–snow partitioning, among other processes, is a necessary step for creating a nature run that reasonably approximates true snow conditions for the OSSEs. Additionally, the inclusion of a snowfall scaling term can address biases in precipitation from meteorological forcing datasets, further improving the utility of LSMs for generating reliable spatiotemporal estimates of snow. [ABSTRACT FROM AUTHOR]

Snow depth observations can be leveraged with data assimilation (DA) to improve estimation of snow density and snow water equivalent (SWE). A key consideration for mission and campaign design is how snow depth retrieval characteristics (including observation timing/frequency and sampling error) influence SWE accuracy and uncertainty in a DA framework. To quantify these effects, we implement a particle filter (PF) assimilation technique to assimilate depth and validate this approach against observed snow density and SWE at 49 snow telemetry sites across 9 years. We sample from continuous in situ snow depth records to test a range of measurement timing and sampling error scenarios representative of remote sensing capabilities. Assimilation reduces density bias by over 40% and SWE bias by over 70% across climate zones and in both wet and dry years. There is little incremental benefit to SWE accuracy when assimilating more than one depth observation near peak accumulation. SWE estimates are less sensitive to observation timing than sampling error. Alternatively, more frequent depth observations improve melt‐out date timing and reduce SWE uncertainty, a key consideration when evaluating the operational utility of DA. In matching depth observations, the PF mostly acts to increase model precipitation inputs, while not systematically shifting other parameter values or forcings across the climate zones represented with the study sites. This demonstrates that precipitation is the largest source of model error. With DA, density errors are still nontrivial (above 10%), illuminating the need for further improvements to modeled density to estimate SWE within specified error limits. Plain Language Summary: The amount of water stored in seasonal snowpack (snow water equivalent or "SWE") is an important variable for water management yet is currently difficult to measure in mountainous areas. One technique is to measure snow depth from airborne or satellite platforms and use that depth to guide a model that simulates snow density and SWE with a technique called data assimilation. We show that assimilation reduces errors in modeled SWE relative to control model runs, even when using a single depth observation to guide the model. However, more depth observations are helpful to reduce model uncertainty. Key Points: Data assimilation improves modeled snow depth, density, and SWE across a range of climates and wet/dry seasons in the western United StatesAssimilating one snow depth observation near maximum accumulation yields the majority of improvements to estimated snow density and SWEAssimilating frequent snow depth observations yields the lowest SWE uncertainty and smallest melt‐out date errors [ABSTRACT FROM AUTHOR]