Abstract
Abstract. 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.
Highlights
The importance of snow in ecosystem function, in both human and natural systems, and in water resource management in western North America cannot be overstated (Bales et al, 2006; Mankin et al, 2015; Viviroli et al, 2007)
The empirical cumulative distribution function (ECDF) and histogram analysis from the precipitation adjustment factor experiment show model improvements when there was broad underestimation of snow depths in the NoAssim case in WY2017 and broad overestimation in WY2018. These results demonstrate that using Community Snow Observations (CSO) measurements for assimilation can improve model performance when the available weather forcing data set has known biases, but when those biases have been decreased, the improvements become less clear, they vary from year to year, and are less consistent between spatial and temporal results
We use a new snow data set collected by participants in the Community Snow Observations (CSO) project in coastal Alaska to improve snow depth and snow water equivalent (SWE) outputs from a snow process model
Summary
The importance of snow in ecosystem function, in both human and natural systems, and in water resource management in western North America cannot be overstated (Bales et al, 2006; Mankin et al, 2015; Viviroli et al, 2007). Similar national in situ snow observational networks exist in Europe, like the MeteoSwiss and Météo-France programs that include snow depth, snowfall, and SWE data sets. Snow course information is collected by state programs such as the California Cooperative Snow Surveys in the USA and, in the case of Canada, by provincial programs such as the British Columbia Snow Survey. These in situ snow observations provide critical information on snow conditions and snow distribution worldwide, but vast areas of snowpack remain unsampled
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