Improving Flood Prediction Through the Assimilation of AMSR-E Soil Moisture Retrievals into a Hydrologic Model

Knowledge of antecedent soil moisture conditions provides a key source of predictability for short-term streamflow forecasting. Such knowledge can potentially be retrieved from passive microwave instruments aboard spaceborne satellites. In this study, the marginal benefit of assimilating spaceborne soil moisture retrievals into a hydrologic model for improved streamflow and flood prediction is explored. Surface soil moisture data from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) is assimilated into the Noah land surface model within the Land Information System (LIS) using the ensemble Kalman filter (EnKF). The assimilation is performed at six medium-scale (103 to 104 km2) basins in the United States Southern Great Plains and the Noah-predicted streamflow (derived with and without the assimilation of AMSRE-E soil moisture) is compared with the observed discharge data at the outlet of each basin. Results suggest the potential for improving flood forecasting through the assimilation of remotely sensed soil moisture data into a hydrologic model. Discussion on the performance of the assimilation will be presented in the context of known differences existing between Noah and AMSR-E soil moisture climatologies and variations in the accuracy of soil moisture retrievals among the study basins.