|HAN, EUNJIN - Purdue University|
|MERWADE, VENKATESH - Purdue University|
Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/20/2011
Publication Date: 1/1/2012
Citation: Han, E., Merwade, V., Heathman, G.C. 2012. Implementation of surface soil moisture data assimilation with watershed scale distributed hydrological model. Journal of Hydrology. 416-417:98-117.
Interpretive Summary: In this study, a synthetic experiment was conducted to investigate how surface soil moisture assimilation affects hydrological processes in the Upper Cedar Creek Watershed using the SWAT hydrologic model and the Ensemble Kalman Filter (EnKF). This study compared three scenarios: 1) a true scenario with no errors in model, precipitation and soil moisture observations; 2) an open loop scenario with limited precipitation information; and 3) the EnKF scenario with the same imperfect information as the open loop but assimilating observed surface (~5cm) soil moisture every day using the EnKF data assimilation technique. Soil moisture update through the EnKF improved surface and profile soil moisture estimations compared to the open loop. In addition, the EnkF improved predictions of other subsequent hydrological variables with reduced errors even though the magnitude of the improvements varied according to different variables and rainfall accuracy. However, for real world applications, further studies are required to answer the following questions. 1) How can we best use remotely sensed soil moisture observations in coarse resolution in time and space for a watershed scale hydrologic model? This question will lead to more studies on developing temporal and spatial downscaling methods. 2) How can we determine the uncertainties in precipitation, models and observations? Various approaches have been presented in previous studies with the development of data assimilation techniques, some of which being mentioned in this paper. Although this study demonstrates the potential of remotely sensed surface soil moisture measurements and data assimilation for applications of watershed scale water resources management, future studies using actual observed data are necessary to effectively transfer the science to practical applications.
Technical Abstract: This paper aims to investigate how surface soil moisture data assimilation affects each hydrologic process and how spatially varying inputs affect the potential capability of surface soil moisture assimilation at the watershed scale. The Ensemble Kalman Filter (EnKF) is coupled with a watershed scale, semi-distributed hydrologic model, the Soil and Water Assessment Tool (SWAT), to assimilate surface (5 cm) soil moisture. By intentionally setting inaccurate precipitation with open loop and EnKF scenarios in a synthetic experiment, the capability of surface soil moisture assimilation to compensate for the precipitation errors were examined. Results show that daily assimilation of surface soil moisture for each Hydrologic Response Unit (HRU) improves model predictions especially reducing errors in surface and profile soil moisture estimation. Almost all hydrologic processes associated with soil moisture are also improved with decreased Root Mean Square Error (RMSE) values through the EnKF scenario. The EnKF does not produce as much a significant improvement in streamflow predictions as compared to soil moisture estimates in the presence of large precipitation errors and the limitations of the infiltration-runoff model mechanism. Distributed errors of the soil water content also show the benefit of surface soil moisture assimilation and the influences of spatially varying inputs such as soil and landuse types. Thus, soil moisture update through data assimilation can be a supplementary way to overcome the errors created by inaccurate rainfall. Even though this synthetic study shows the potential of remotely sensed surface soil moisture measurements for applications of watershed scale water resources management, future studies are necessary that focus on the use of real-time observational data.