Assimilation of citizen science data in snowpack modeling using a new snow data set: Community Snow Observations

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.

Spatial Patterns of Snow Distribution for Improved Earth System Modelling in the Arctic

“…However, we observed a decrease in SWE at the top of Kougarok, where snow is removed completely from the upper windswept top (Assini and Young 2012;Shook and Gray 1996;Homan and Kane 2015). For the next phase of our work, we are considering more advanced wind functions (Winstral et al, 2002), and implementing a physically-based wind model for comparison and testing against our statistical models (Crumley et al, 2021;Liston, 2004). 570 TPI in our model was found to be the third most important variable, indicating the importance of coarsescale features in the sub-Arctic landscapes of the Seward Peninsula.…”

Spatial patterns of snow distribution in the sub-Arctic

“…Furthermore, there are likely relationships between TPI and NDVI that should be investigated, including at smaller spatial scales. We are also considering the application of more advanced wind functions (Winstral et al, 2002) in our models and implementing a physically based wind model for comparison and testing against our statistical models (Crumley et al, 2021;Liston, 2004).…”

Stuck in the Wild—The Hydrology of the Teklanika River (Alaska) in the Summer of 1992

“…In order to model snowpack processes and runoff across the Teklanika watershed, we used a collection of process-based models that have been used widely in high-latitude locations dominated by snow and ice, including Alaska (Beamer et al, 2016(Beamer et al, , 2017Crumley et al, 2019Crumley et al, , 2021. Only a brief summary is presented here, with readers directed to the original citations for additional details.…”

“…Direct calibration of the Teklanika watershed is complicated by the fact that the period for which streamflow is available does not overlap with the period of record of the CFS reanalysis product. Therefore, for this study, model parameters were selected based on previous comparable studies (Beamer et al, 2016(Beamer et al, , 2017Crumley et al, 2019Crumley et al, , 2021 in Alaska. Calibration for those studies was based on glacier mass balance data, streamflow data, and snow telemetry data.…”