Location: Location not imported yet.Title: Improving hydrologic predictions of a catchment model via assimilation of surface soil moisture) Author
Submitted to: Advances in Water Resources
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/26/2011
Publication Date: 4/1/2011
Publication URL: http://handle.nal.usda.gov/10113/60039
Citation: Chen, F., Crow, W.T., Starks, P.J., Moriasi, D.N. 2011. Improving hydrologic predictions of a catchment model via assimilation of surface soil moisture. Advances in Water Resources. 34:526-536. Interpretive Summary: Data assimilation is a mathematic process by which information gleaned from independent sources is optimally merged to yield the best single estimate of an unknown variable. In the hydrologic sciences such techniques are commonly applied to minimize errors in soil moisture, streamflow and land surface evaporation estimates which are, in turn, used for agricultural applications including: crop yield forecasting, the optimization of fertilizer application, water quality monitoring and irrigation scheduling. However, there is currently a large gap between the types of hydrologic models which data assimilation techniques have been successfully applied and the types of models that are actually used for agricultural applications (and key ARS customers). This manuscript attempts to close this gap by developing a data assimilation system to integrate remotely-sensed surface soil moisture retrievals in a commonly-used ARS hydrologic model – the Soil Water Assessment Tool (SWAT) – within the Fort Cobb basin of Oklahoma. Results in this analysis highlight aspects of the SWAT model which must be modified before it can fully leverage available information in remotely-sensed surface soil moisture retrievals.
Technical Abstract: This paper examines the potential for improving Soil and Water Assessment Tool (SWAT) hydrologic predictions within the 341 km2 Cobb Creek Watershed in southwestern Oklahoma through the assimilation of surface soil moisture observations using an Ensemble Kalman filter (EnKF). In a series of synthetic twin experiment, assimilating surface soil moisture is shown to effectively update SWAT upper-layer soil moisture predictions and provide moderate improvement to the lower layer soil moisture and evapotranspiration estimates. However, low amounts of SWAT-predicted vertical coupling results in limited updating of deep soil moisture, regardless of the SWAT parameterization chosen for root-water extraction. Likewise, a real data assimilation experiment using ground-based soil moisture observations has only limited success in updating upper-layer soil moisture and is generally unsuccessful in enhancing SWAT stream flow predictions. Comparisons against ground-based observations suggest that SWAT significantly under-predicts the magnitude of vertical soil water coupling at the site, and this lack of predicted coupling impedes the ability of the EnKF to effectively update deep soil moisture, groundwater flow and surface runoff. Poor model calibration also contributes to the failure of EnKF to improve streamflow prediction as the EnKF is unable to correct the existing biases in SWAT-predicted sub-surface stream flow components.