Assimilating remotely sensed snow observations into a macroscale hydrologic model

Kostas Andreadis, Marketa McGuire^a, and Dennis P. Lettenmaier
Department of Civil and Environmental Engineering
University of Washington

Accurate forecasting of snow properties is important for effective water resources management, especially in mountainous areas like the western United States. Current model-based approaches to hydrologic forecasting are hindered by model biases and input data uncertainties. Remote sensing offers an opportunity for observing snow properties, like areal extent and water equivalent, over larger areas. Data assimilation provides the framework for optimally merging information from remotely sensed observations and hydrologic model predictions. Direct insertion and an ensemble Kalman filter (enKF) were used to assimilate remotely sensed snow observations into the Variable Infiltration Capacity (VIC) macroscale hydrologic model over the Snake River basin. A preliminary assessment that utilized the MODIS snow covered extent (SCE) product, and the snow water equivalent (SWE) product from the Advanced Microwave Scanning Radiometer (AMSR-E, flown on board the NASA Aqua satellite) into the VIC model was conducted for the winter of 2004. The effect of assimilation of the SCE and SWE products on observed reservoir inflows, and (to a more limited extent) reservoir storage volume forecasts were evaluated. While assimilation of the MODIS SCE data resulted in some forecast improvements, especially for relatively short lead forecasts in spring, the results were less encouraging for the AMSR SWE product (for which comparisons were made with surface SWE observations from the SNOTEL station network rather than river discharge). The lack of improvement in model predictions appears to reflect biases in the AMSR-E SWE product that result from saturation of the SWE estimates for deep mountain snowpacks.

^anow at Golder Associates, Redmond, WA

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