10B.2 Airborne Snow Depth Retrieval for Improved Hydrological Modeling in the Black Hills of South Dakota

Wednesday, 15 January 2020: 10:45 AM
253A (Boston Convention and Exhibition Center)
Joshua K. Roundy, Univ. of Kansas, Lawrence, KS; and Y. Zhang and E. Arnold

Due to an increasing population and the continued effects of human-induced climate change, the impacts of extremes are likely to intensify. But there is still hope for a more resilient society capable of adapting to change that more efficiently uses the vital resources within the energy-water-food systems. This requires the ability to monitor and predict changes in the water cycle and the impact of these changes on vital water resources. 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 snow melt to enable effective water management. While great strides have been made in developing and implementing seasonal prediction of snow melt, 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, we will use the University of Kansas’s (KU) Snow Radar, which has been previously used to measure snow accumulation in polar regions, on board KU’s Cessna C-172 over a drainage basin located in the Black Hills of South Dakota. The spatial fields of snow depth will then be used for parameter estimation, sensitivity analysis and data assimilation to understand the influence of measured snow depth on the snow processes within the WRF-Hydro framework using the Noah-MP model. This will then inform the development of new methods for assimilating spatial fields of snow depth within land surface models for the improvement of seasonal streamflow prediction. Although these remotely sensed measurements will be collected in February of 2020 and 2021, we will present the data collection plan as well as initial modeling results compared to SNOTEL sites and USGS streamflow gauges for the study area. Anticipated challenges and possible collaboration will also be discussed.

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