Predicting the Onset of Snowmelt using Hydrologic Models and Microwave Satellite Observations
Tedesco, M.1; Kostas, A.2; Lettenmaier, D.2; Reichle, R.3; Loew, A.4
1NASA/UMBC; 2Environmental Engineering, University of Washington, Seattle, WA; 3Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD; 4University of Munich

Snow plays an important role in the hydrologic cycle, through its effects on water storage and the land surface energy balance. Streamflow from mountainous river basins, such as those in the western United States, is dominated by snowmelt (accounting for about 70-90%). Consequently, the need to accurately predict snow characteristics, such as the onset of snowmelt, becomes apparent. Surface observations are unable to capture fully the considerable spatial and temporal variability in snow properties over large areas. For this reason, large scale strategies for observing snow properties have relied mostly on remote sensing. Passive and active microwave satellite observations can be used to identify the melt state of snowpacks, by observing melt and refreeze cycles. Complementary information about snow properties can be derived from physically-based hydrologic models, which represent the effects of topography, soil, and vegetation on snow ablation processes.

We show results regarding the comparison of between onset of snowmelt derived from spaceborne passive microwave observations and hydrology models at large scale over ~ 50 selected locations of the Northern hemisphere. We simulated the onset of snowmelt, in the Colorado River basin for the period between 2003-2006, using a suite of hydrologic models, and then compared the model predictions with microwave-based estimates. The hydrologic models include the Variable Infiltration Capacity (VIC) model, the ESCIMO model, the CLSM model as well as SNTHERM. Additionally, we evaluated the potential of merging predictions from the models and satellite observations, through comparisons with surface snow (SNOTEL) and streamflow measurements. This study aims at supporting the development and refinement of prediction systems in the context of streamflow forecasting.


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