Improved Hydrological Modeling in the Black Hills of South Dakota from Airborne Snow Depth Retrievals

Abstract:

Due to an increasing population and the continued effects of human-induced climate change, the impacts of water cycle extremes are likely to intensify. A particular threated part of the water cycle is the seasonal generation of streamflow from winter snowpack that many communities have come to rely on. Under a changing climate, there is a greater urgency to understand the physical processes of snow accumulation and melting and mimic these processes in land surface models in order to predict the seasonal streamflow from snowmelt to enable effective water management. While great strides have been made in developing and implementing seasonal prediction of snowmelt, the lack of spatially continuous measurements makes it difficult to validate and correctly model the snowpack heterogeneity. To overcome this limitation and improve representation of snow heterogeneity in land surface models, the University of Kansas’s (KU) Snow Radar, on board KU’s Cessna C-172, was used to remotely sense snow depth over a drainage basin located in the Black Hills of South Dakota. This presentation will focus on using these spatial estimates of snow depth for parameter estimation and sensitivity analysis to understand on the snow processes within the WRF-Hydro framework using the Noah-MP model. The model and remotely sensed measurements will be assessed on their consistency with in-situ measurements and their ability to predict seasonal streamflow during the 2020 water year. Limitations of the remotely sensed data and modeling framework will be discussed as well as plans for collecting additional snow depth measurements in 2021.