Dual state/rainfall correction via soil moisture assimilation for improved streamflow simulation: evaluation of a large-scale implementation with Soil Moisture Active Passive (SMAP) satellite data

Authors: MAO, Y. - University Of Washington; Crow, Wade; NIJSSEN, B. - University Of Washington

Submitted to: Hydrology and Earth System Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/8/2019
Publication Date: 2/17/2020
Citation: Mao, Y., Crow, W.T., Nijssen, B. 2020. Dual state/rainfall correction via soil moisture assimilation for improved streamflow simulation: evaluation of a large-scale implementation with Soil Moisture Active Passive (SMAP) satellite data. Hydrology and Earth System Sciences. 24/615-631. https://doi.org/10.5194/hess-24-615-2020.
DOI: https://doi.org/10.5194/hess-24-615-2020

Interpretive Summary: The estimation of stream flow using rainfall-runoff models is a valuable tool for water resource monitoring in agricultural watersheds. Here we describe a new approach for improving such modelling via the application of soil moisture remote sensing. Our method is based on utilizing remotely sensed surface soil moisture retrievals to simultaneously update both pre-storm soil moisture values and within-storm precipitation totals. Such dual use allows the retrievals to simultaneously address the two largest sources of uncertainty in rainfall-runoff modelling. While specific challenges are highlighted in the paper, preliminary results suggest that this approach will eventually yield improved stream flow predictions for water resource applications in agricultural watersheds.

Technical Abstract: Soil moisture (SM) measurements contain information about both pre-storm hydrologic states and within-storm rainfall estimates, both of which are essential for accurate streamflow simulation. In this study, an existing dual state/rainfall correction system is extended and implemented in a regional-scale 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 generation GPM satellite product. The streamflow is corrected slightly to moderately via dual correction across 8 Arkansas-Red subbasins, with larger correction at subbasins with poorer GPM rainfall and baseline streamflow simulation, and smaller correction at subbasins with better GPM rainfall and baseline streamflow quality. Overall, although the dual data assimilation scheme is able to nudge streamflow simulations toward the correct direction, it corrects only a relatively small portion of the total streamflow error. Systematic modeling error accounts for a larger relative portion of the overall streamflow error, which are uncorrectable by standard data assimilation techniques. These findings suggest that we may be approaching the limit of using SM data assimilation to correct random errors in streamflow simulations. More substantial streamflow correction would rely on future research efforts aimed at reducing systematic error and developing higher-quality satellite rainfall products.