Abstract
Abstract. Soil moisture (SM) measurements contain information about both pre-storm hydrologic states and within-storm rainfall estimates, both of which are required inputs for event-based streamflow simulations. In this study, an existing dual state/rainfall correction system is extended and implemented in the 605 000 km2 Arkansas–Red River basin with a semi-distributed land surface model. The 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 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 due primarily to the improved baseline quality of the new GPM satellite product. In addition, rainfall correction is poorer in regions with dense biomass due to lower SMAP quality. Nevertheless, SMAP-based dual state/rainfall correction is shown to generally improve streamflow estimates, as shown by comparisons with streamflow observations across eight Arkansas–Red River sub-basins. However, more substantial streamflow correction is limited by significant systematic errors present in model-based streamflow estimates that are uncorrectable via standard data assimilation techniques aimed solely at zero-mean random errors. These findings suggest that more substantial streamflow correction will likely require better quality SM observations as well as future research efforts aimed at reducing systematic errors in hydrologic systems.
Highlights
Accurate streamflow simulation is important for water resources management applications such as flood control and drought monitoring
We extended the ensemble Kalman filter (EnKF) version of Soil Moisture Analysis Rainfall Tool (SMART) introduced by Crow et al (2011) to an ensemble Kalman smoother (EnKS), in which the Antecedent Precipitation Index (API) state is updated at time steps when Soil Moisture Active Passive (SMAP) is available and updated during measurement gaps
The EnKF implementation results in less r improvement than the EnKS implementation, which confirms the benefit of applying SMART using a smoothing approach
Summary
Accurate streamflow simulation is important for water resources management applications such as flood control and drought monitoring. Reliable streamflow simulation requires accurate estimates of pre-storm soil moisture (SM) that control the partitioning of infiltration and surface runoff during rainfall events, as well as longer-memory subsurface flow (Freeze and Harlan, 1969; Western et al, 2002; Aubert et al, 2003). SM measurements contain information about both antecedent hydrologic states and within-storm rainfall events. Other studies have explored the use of SM measurements to back-calculate within-storm rainfall or to correct existing rainfall time series products (e.g., Crow et al, 2011; Chen et al, 2012; Brocca et al, 2013, 2014, 2016; Koster et al, 2016)
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