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SNOWCLIM: High Resolution Snow Model and Data for the Western United States (Extended Abstract)
Submitted by ecourtri on Mon, 02/07/2022 - 11:15
|Title||SNOWCLIM: High Resolution Snow Model and Data for the Western United States (Extended Abstract)|
|Publication Type||Conference Proceedings|
|Year of Conference||2021|
|Authors||Lute, A.C., Abatzaglou John, and Link Timothy|
|Conference Name||88th Annual Western Snow Conference|
|Conference Location||Bozeman, MT|
|Keywords||climate change, gridded data, snow modeling|
Seasonal snowpack shapes the climatic, hydrologic, ecological, economic, and cultural characteristics of many global regions. In many mountain regions, recent decades have seen less precipitation falling as snow, lower peak snow water equivalent (SWE), shorter snow duration, and earlier snowmelt runoff as a result of anthropogenic climate change (Knowles et al., 2006; Mote et al., 2018; Choi et al., 2010; Fritze et al., 2011). These developments are expected to continue in the coming decades, resulting in substantial declines (>50%) in seasonal snowpack for areas such as the western US and significant impacts to human and natural systems (Fyfe et al., 2017; Huss et al., 2017).
Understanding these changes and their implications often requires snow models and modeled snow data products (hereafter snow data) that satisfy at least one of several criteria. These criteria might include that the data is a) simulated with physics-based representations of energy and mass transfer processes, b) spatially continuous, c) high spatial resolution, d) large extent, e) multivariate, and f) multitemporal. There are two major hurdles to the development of a snow dataset that meets these criteria: computational cost and appropriate forcing data.