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Title: Assimilating Remotely Sensed Surface Soil Moisture into SWAT using Ensemble Kalman Filter

item CHEN, FAN - Science Systems, Inc
item Crow, Wade
item Starks, Patrick
item Moriasi, Daniel

Submitted to: Watershed Management Conference Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 8/12/2010
Publication Date: 8/25/2010
Citation: Chen, F., Crow, W.T., Starks, P.J., Moriasi, D.N. 2010. Assimilating remotely sensed surface soil moisture into SWAT using Ensemble Kalman Filter. In: Proceedings of the Watershed Management Conference, August 23-27, 2010, Madison, Wisconsin. 2010 CDROM.

Interpretive Summary:

Technical Abstract: In this study, a 1-D Ensemble Kalman Filter has been used to update the soil moisture states of the Soil and Water Assessment Tool (SWAT) model. Experiments were conducted for the Cobb Creek Watershed in southeastern Oklahoma for 2006-2008. Assimilation of in situ data proved limited success in the top layers only, due to the weak surface and root-zone coupling of SWAT. Negative impact on stream flow and especially storm-scale runoff result from degraded deep-layer soil moisture prediction. This indicates that although the model is calibrated for stream flow, the parameters are not tuned to represent the realistic physical coupling of the system.Therefore the improved surface soil moisture cannot lead to better runoff and flow estimates. Synthetic twin study proved the potential of assimilating remotely sensed surface soil moisture in improving SWAT hydrologic predictions. (root-zone soil moisture, evapotranpiration, surface runoff and stream flow) with surface soil moisture assimilation. However, perturbations could result in systematic bias in wet/dry regimes and lead to biases in related model fluxes. The mechanisms causing such biases and the methods to correct them need to be further investigated.