Implementation of surface soil moisture data assimilation with watershed scale distributed hydrological model

SummaryThis paper aims to investigate how surface soil moisture data assimilation affects each hydrologic process and how spatially varying inputs affect the potential capability of surface soil moisture assimilation at the watershed scale. The Ensemble Kalman Filter (EnKF) is coupled with a watershed scale, semi-distributed hydrologic model, the Soil and Water Assessment Tool (SWAT), to assimilate surface (5 cm) soil moisture. By intentionally setting inaccurate precipitation with open loop and EnKF scenarios in a synthetic experiment, the capability of surface soil moisture assimilation to compensate for the precipitation errors were examined. 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 (RMSE) values through the EnKF scenario. The EnKF does not produce as much a significant 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 necessary that focus on the use of real-time observational data.

► The effect of soil moisture data assimilation on hydrologic processes is investigated.
► Assimilation of surface soil moisture improves profile soil moisture predictions.
► Different landuse and soil types affect the capability of soil moisture data assimilation.
► EnKF does not produce a significant improvement in streamflow predictions.
► The success of data assimilation is dependent on accurate rainfall and model characteristics.