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This work introduces a novel snow metric, snow water storage (SwS), defined as the integrated area under the snow water equivalent (SWE) curve (units: length-time, e.g., m d). Unlike other widely used snow metrics that capture snow variables at a single point in time (e.g., maximum SWE) or describe temporal snow characteristics (e.g., length of snow season), SwS is applicable at numerous spatial and temporal scales. This flexibility in the SwS metric enables us to characterize the inherent reservoir function of snowpacks and quantify how this function has changed in recent decades. In this research, changes in the SwS metric are evaluated at point, gridded and aggregated scales across the conterminous United States (hereafter US), with a particular focus on 16 mountainous Environmental Protection Agency (EPA) Level III Ecoregions (ER3s). These ER3s account for 72 % of the annual SwS (SwS A) in the US, despite these ER3s only covering 16 % of the US land area. Since 1982, spatially variable changes in SwS A have been observed across the US with notable decreasing SwS A trends in the western US and in the 16 mountainous ER3s. All mountainous ER3 (except for the Northeastern Highlands in New England) exhibit decreasing trends in SwS A resulting in a 22 % overall decline in SwS A across mountainous ER3s. The peak monthly SwS (SwS M) occurs in March at all spatial scales, while the greatest percentage loss of SwS M occurs early in the snow season, particularly in November. Unsurprisingly, the highest elevations contribute most to SwS A in all mountain ranges, but the specific elevations that have experienced loss or gain in SwS A over the 39-year study period vary between mountain ranges. Comparisons of SwS with other snow metrics underscore the utility of SwS, providing insights into the natural reservoir function of snowpacks, irrespective of SWE curve variability or type (e.g., ephemeral, mountain, permanent). As we anticipate a future marked by increased climate variability and greater variability in mountain snowpacks, the spatial and temporal flexibility of snow metrics such as SwS may become increasingly valuable for monitoring and predicting snow water resources. [ABSTRACT FROM AUTHOR]

A physically based snowpack evolution and redistribution model was used to test the effectiveness of assimilating crowd-sourced snow depth measurements collected by citizen scientists. The Community Snow Observations (CSO; https://communitysnowobs.org/ , last access: 11 August 2021) project gathers, stores, and distributes measurements of snow depth recorded by recreational users and snow professionals in high mountain environments. These citizen science measurements are valuable since they come from terrain that is relatively undersampled and can offer in situ snow information in locations where snow information is sparse or nonexistent. The present study investigates (1) the improvements to model performance when citizen science measurements are assimilated, and (2) the number of measurements necessary to obtain those improvements. Model performance is assessed by comparing time series of observed (snow pillow) and modeled snow water equivalent values, by comparing spatially distributed maps of observed (remotely sensed) and modeled snow depth, and by comparing fieldwork results from within the study area. The results demonstrate that few citizen science measurements are needed to obtain improvements in model performance, and these improvements are found in 62 % to 78 % of the ensemble simulations, depending on the model year. Model estimations of total water volume from a subregion of the study area also demonstrate improvements in accuracy after CSO measurements have been assimilated. These results suggest that even modest measurement efforts by citizen scientists have the potential to improve efforts to model snowpack processes in high mountain environments, with implications for water resource management and process-based snow modeling. [ABSTRACT FROM AUTHOR]

In this study, we examine the effectiveness of incorporating citizen science snow depth measurements into the seasonal snow model chain through data assimilation. We also introduce the Community Snow Observations dataset, a citizen science based snow depth measuring campaign. Improvements to model performance are characterized and evaluated using remote sensing datasets, fieldwork, and SNOTEL datasets. After citizen science snow depth measurements were incorporated, improvements to model performance were found in 62 % to 78 % of the simulations, depending on model year. The results suggest that modest measurements from citizen scientists have the potential to improve efforts to model snowpack processes in high mountain environments, with implications for water resource management and process-based snow modeling. [ABSTRACT FROM AUTHOR]

ABSTRACT: While global climate models (GCMs) are useful for simulating climatic responses to perturbations in the Earth's climate system, there are many instances where higher spatial resolution information is necessary. In all instances, interpretation of interpolated or downscaled GCMs must be done cautiously because each method has its own set of assumptions and potential disadvantages. Here, we present an update to the Global Climate Data (GCD) package, which enables the package to efficiently bias correct and interpolate precipitation and air temperature output from GCM simulations to very high spatial resolutions using the delta change method. While the delta change method is relatively simple, it has previously been shown to enhance the physical representation of interpolated climate time‐series compared to directly interpolating the gridded climate time‐series to a higher spatial resolution. The bias correction methods programmed into the GCD package are univariate empirical quantile mapping (QM) and bivariate empirical joint bias correction (JBC). The skill of QM and JBC for improving GCM simulations processed with the delta change method is evaluated through comparing the cumulative distribution functions (CDFs) of the interpolated GCM simulations to the CDFs of Global Historical Climatology Network (GHCN) station observations for three test regions: Oregon (in the USA), the Alps (spanning several countries in Europe), and the Ganges Delta (in India and Bangladesh). We also assess the representation of precipitation and mean temperature joint probability distributions relative to those present in GHCN station observations. Overall, GCM simulations that are bias corrected with QM prior to being input to the delta change method perform best under our analysis. [ABSTRACT FROM AUTHOR]

ABSTRACT Monthly total precipitation and mean temperature climate surfaces, gridded to 30-arcseconds (≈1 km at the equator) and available for all global land areas, are presented. These datasets are generated with a Delta downscaling method, using the 30-arcsecond WorldClim climatologies to scale monthly anomaly grids. For monthly mean temperature, the anomalies are constructed from both the Climate Research Unit ( CRU) and Willmott & Matsuura (W&M) 0.5 degree time-series datasets, whereas for monthly precipitation Global Precipitation Climatology Centre ( GPCC) data are also used. The 0.5 degree anomalies are then interpolated to the 30-arcsecond resolution. Use of piecewise cubic Hermite interpolating polynomials (PCHIP) to interpolate the anomaly grids results in more physically representative Delta downscaled surfaces, compared to bilinear and cubic spline interpolation. The Delta downscaled products are compared to Global Historical Climatology Network ( GHCN) station records for six test regions distributed globally. In this analysis, the Delta grids produced using the W&M time-series dataset perform better than grids produced using GPCC or CRU. Using Oregon, USA as a test region, the Delta downscaled datasets are compared to the Parameter-elevation Regressions on Independent Slopes Model ( PRISM) datasets. For monthly precipitation, PRISM performs better than each of the three Delta downscaled datasets, but for mean temperature both Delta downscaled datasets outperform PRISM. Through computing the Pearson product-moment correlation coefficient between GHCN station delineated errors in the WorldClim climatologies and the Delta downscaled W&M data, it is shown that performance of the Delta grids corresponds strongly to performance of the reference climatologies. Therefore, future improvement of the 30-arcsecond Delta grids described in this article is strongly tied to advances in the high-resolution climatological data for all global land surfaces. The Delta downscaled datasets discussed herein are open-source and freely distributed at . [ABSTRACT FROM AUTHOR]