A new paper on the use of data assimilation to improve streamflow estimates was published earlier this year in the Journal of Hydrometeorology. The paper described the development of a framework to diagnose bottlenecks in data assimilation of satellite soil moisture in hydrological models for the purpose of improving simulated streamflow. Yixin Mao, who recently completed her Ph.D. in the Computational Hydrology group and has started a new position as Data Scientist at Salesforce, Inc. in San Francisco, was the lead author.
The goal of the paper was to explore how (and if) data assimilation of soil moisture states might improve simulated streamflow. It was part of a broader, multiyear collaboration between the Computational Hydrology group and Wade Crow, a Research Scientist at the United States Department of Agriculture (USDA) Agricultural Research Service (ARS) Hydrology and Remote Sensing Laboratory, located in Beltsville, MD. The collaboration, and study that resulted, comprised the bulk of Yixin’s doctoral research. Although data assimilation of satellite soil moisture has been used before to improve streamflow estimates, these past studies have shown mixed results. Some studies pointed to improvements in streamflow estimates, while others pointed to little improvement or even degraded streamflow estimates. The point of the broader study was to design a model framework that would quantitatively diagnose what factors contributed both to improvements and degradation in streamflow simulations.
To accomplish this, the authors set up a diagnostic model framework so that they could determine which factors were contributing to errors in hydrologic simulations, quantify those errors, and discern the extent to which data assimilation of soil moisture might correct the errors. The crux of the experiments tested the benefit of assimilating soil moisture measurements from the Soil Moisture Active Passive (SMAP) satellite into the Variable Infiltration Capacity (VIC) model in the Arkansas-Red river basin, and VIC-modeled runoff was then routed using the RVIC streamflow routing model and compared to USGS streamflow observations. They conducted a series of synthetic experiments to complement the experiment with SMAP, termed the “real data” experiment.
The authors found that approximately 60% of errors in runoff in the basin came from precipitation forcings rather than soil moisture states. In addition, systematic model errors (due to model structure and model parameters) dominated much of the remaining error and these errors cannot be “fixed” through data assimilation alone. They found that runoff with a slower response time was highly dependent on moisture in the bottom soil layer, but that the assimilated satellite surface soil moisture did not contain sufficient information about the deeper layer to significantly improve overall streamflow performance. These results highlight that correcting soil moisture states based on surface soil moisture alone is insufficient to significantly improve streamflow simulations. To achieve significant improvements in simulated streamflow, according to Yixin, future research efforts should focus more on precipitation forcing errors and model representations of runoff rather than the development of increasingly sophisticated data assimilation techniques for soil moisture states.
Citation: Mao, Y., W. T. Crow, and B. Nijssen, 2018: A framework for diagnosing factors degrading the streamflow performance of a soil moisture data assimilation system. Journal of Hydrometeorology, doi:10.1175/JHM-D-18-0115.1.
Funding and acknowledgements: This work was supported in part by NASA Terrestrial Hydrology Program Award NNX16AC50G to the University of Washington and NASA Terrestrial Hydrology Program Award 13-THP13-0022 to the United States Department of Agriculture, Agricultural Research Service. Yixin Mao also received a Pathfinder Fellowship by CUAHSI with support from the National Science Foundation (NSF) Cooperative Agreement EAR-1338606. The VIC model used in the study is available at https://github.com/UW-Hydro/VIC. Specifically, we used VIC version 5.0.1 with a modification to the calculation of drainage between soil layers (https://github.com/UW-Hydro/VIC/releases/tag/Mao_etal_stateDA_May2018). The DA code used in this study is available at https://github.com/UW-Hydro/dual_DA_SMAP.
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