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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 04 Mar 2019

Research article | 04 Mar 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Dual state/rainfall correction via soil moisture assimilation for improved streamflow simulation: Evaluation of a large-scale implementation with SMAP satellite data

Yixin Mao1, Wade T. Crow2, and Bart Nijssen1 Yixin Mao et al.
  • 1Department of Civil and Environmental Engineering, University of Washington, Seattle, WA
  • 2Hydrology and Remote Sensing Laboratory, Agricultural Research Service, USDA, Beltsville, MD

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.

Yixin Mao et al.
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Yixin Mao et al.
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