Implementation of Surface Soil Moisture Data Assimilation with Watershed Scale Hydrological Model (SWAT)

The objective of this study is to investigate the effect of surface soil moisture data assimilation on hydrological responses at the catchment scale through a synthetic experiment. Especially, using intentionally limited rainfall input, we investigate how soil moisture update through surface soil moisture assimilation may compensate for errors in the hydrologic prediction due to the inaccurate rainfall. In addition, this study also aims to investigate the effects of spatially varying input such as landuse and soil type on data assimilation results. The Ensemble Kalman Filter (EnKF) is applied to assimilate surface (5 cm) soil moisture into a watershed scale, semi-distributed hydrologic model, the Soil and Water Assessment Tool (SWAT). Results show that daily assimilation of surface soil moisture for each HRU improves model predictions especially reducing errors in surface and profile soil moisture estimation. Almost all hydrological processes associated with soil moisture are also improved with decreased Root Mean Square Error values through the EnKF scenario. The EnKF does not produce as much of an improvement in streamflow predictions as compared to soil moisture estimates in the presence of large precipitation errors and the limitations of the infiltration-runoff model mechanism. Distributed errors of the soil water content also show the benefit of surface soil moisture assimilation and the influences of spatially varying inputs such as soil and landuse types. Thus, soil moisture update through data assimilation can be a supplementary way to overcome the errors created by inaccurate rainfall. Even though this synthetic study shows the potential of remotely sensed surface soil moisture measurements for applications of watershed scale water resources management, future studies are planned that focus on the use of real-time observational data.