Submitted to: Hydrology and Earth System Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/15/2008
Publication Date: 1/9/2009
Publication URL: http://handle.nal.usda.gov/10113/40950
Citation: Crow, W.T., Ryu, D. 2009. A new data assimilation approach for improving hydrologic prediction using remotely-sensed soil moisture retrievals. Hydrology and Earth Systems Sciences. 13:1-16. Interpretive Summary: A commonly-cited application for remotely-sensed soil moisture retrievals is the enhancement of hydrologic stream flow forecasts used for water resource management decision support and flood forecasting. However, to date, efforts to definitively demonstrate the value of remotely-sensed soil moisture estimates for these types of hydrologic applications have been largely unsuccessful. This paper describes potential reasons for this lack of success (to date) and develops a new strategy for assimilating soil moisture retrievals into a hydrologic model. Preliminary testing of this new strategy indicates that it is more efficient at improving stream flow forecasts made by a hydrologic model than the data assimilation techniques used in past studies. Consequently, the paper provides a potentially valuable algorithm for maximizing the benefit of remote sensing observations for water resource applications (e.g. flood forecasting) within agricultural regions.
Technical Abstract: A number of recent studies have focused on enhancing hydrologic prediction via the assimilation of remotely-sensed surface soil moisture retrievals into a hydrologic model. The majority of these approaches have viewed the problem purely from a state or parameter estimation perspective in which remotely-sensed soil moisture estimates are assimilated to improve the characterization of pre-storm soil moisture conditions in a hydrologic model, and consequently, its simulation of runoff response to subsequent rainfall. However, recent work has demonstrated that soil moisture retrievals can also be used to filter errors present in satellite-based rainfall accumulation products. This result implies that soil moisture retrievals have potential benefit for characterizing both antecedent moisture conditions (required to estimate sub-surface flow intensities and subsequent surface runoff efficiencies) and storm-scale rainfall totals (required to estimate the total surface runoff volume). In response, this work presents a new sequential data assimilation system that exploits remotely-sensed surface soil moisture retrievals to simultaneously improve estimates of both pre-storm soil moisture conditions and storm-scale rainfall accumulations. Preliminary testing of the system, via a synthetic twin data assimilation experiment based on the Sacramento hydrologic model and data collected from the Model Parameterization Experiment (MOPEX), demonstrates that the new approach is more efficient at improving stream flow predictions than data assimilation techniques focusing exclusively on the constraint of antecedent soil moisture conditions.