Dual state/rainfall correction via soil moisture assimilation for improved streamflow simulation: Evaluation of a large-scale implementation with SMAP satellite data
Abstract. Soil moisture (SM) measurements contain information about both pre-storm hydrologic states and within-storm rainfall estimates, both are essential for accurate streamflow simulation. In this study, an existing dual state/rainfall correction system is extended and implemented in a large basin with a semi-distributed land surface model. The latest Soil Moisture Active Passive (SMAP) satellite surface SM retrievals are assimilated to simultaneously correct antecedent SM states in the model and rainfall estimates from the latest Global Precipitation Measurement (GPM) mission. While the GPM rainfall is corrected slightly to moderately, especially for larger events, the correction is smaller than that reported in past studies because of the improved baseline quality of the new GPM satellite product. The streamflow is corrected slightly to moderately via dual correction across 8 Arkansas-Red sub-basins. The correction is larger at sub-basins with poorer GPM rainfall and poorer open-loop streamflow simulations. Overall, although the dual data assimilation scheme is able to nudge streamflow simulations in the correct direction, it corrects only a relatively small portion of the total streamflow error. Systematic modeling error accounts for a larger portion of the overall streamflow error, which is uncorrectable by standard data assimilation techniques. These findings suggest that we may be reaching a point of diminishing returns for applying data assimilation approaches to correct random errors in streamflow simulations. More substantial streamflow correction would rely on future research efforts aimed at reducing the systematic error and developing higher-quality satellite rainfall products.