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ARS Home » Midwest Area » West Lafayette, Indiana » National Soil Erosion Research Laboratory » Research » Publications at this Location » Publication #254669
Title: Surface Soil Moisture Assimilation with SWAT

Author
item HAN, EUNJIN - Purdue University
item Heathman, Gary
item MERWADE, VENKATESH - Purdue University

Submitted to: Annual International SWAT Conference
Publication Type: Proceedings
Publication Acceptance Date: 5/20/2010
Publication Date: 8/4/2010
Citation: Han, E., Heathman, G.C., Merwade, V. 2010. Surface Soil Moisture Assimilation with SWAT [abstract]. 2010 International Soil Water and Assessment Tool (SWAT) Conference, August 4-6, 2010, Seoul, South Korea. CD ROM.

Interpretive Summary: Improved estimates of soil moisture are critical in the areas of hydrologic, agricultural and environmental modeling because of its role in governing the transfer of energy between the land surface and the atmosphere. Due to current satellite technology, remotely sensed observations of surface soil moisture (5cm) have become increasingly available and often applied in various environmental studies. Certain studies have demonstrated that assimilating observed surface soil moisture into a hydrologic model results in improved predictions of profile soil water content. In this study, the USDA, Agricultural Research Service, Soil Water and Assessment Tool (SWAT) was used to estimate profile soil moisture to better understand how surface soil moisture data assimilation affects various hydrologic processes at the watershed scale. Data assimilation has been traditionally used in meteorology to improve weather forecasting and more recently in an effort to improve predictions of soil moisture status in the soil profile. This approach is based on the assumption that assimilating (or updating) surface soil moisture with measured data, such as remotely sensed data, the hydrologic model would improve estimates of soil water content in the entire profile. We determined how the updated soil water condition with surface measured soil moisture influences model predictions of profile soil water content, runoff and streamflow. Model evaluations were conducted by using time series graphs and standard statistical measures including the correlation coefficient (R), mean bias error (MBE), and root mean square error (RMSE).

Technical Abstract: Soil moisture is one of the most critical state variables in hydrologic modeling. Certain studies have demonstrated that assimilating observed surface soil moisture into a hydrologic model results in improved predictions of profile soil water content. With the Soil and Water Assessment Tool (SWAT), however, there is a lack of investigative research as to how the spatial variability of inputs affect the potential capability of data assimilation techniques, especially the assimilation of remotely sensed surface soil moisture data. Therefore, a synthetic experiment is performed to better understand how soil moisture data assimilation affects various hydrologic processes in the model at the watershed scale. The study area for this work is the Upper Cedar Creek Watershed (UCCW) which is located in the St. Joseph Watershed in northeastern Indiana. In the UCCW, the USDA, Agricultural Research Service National Soil Erosion Research Laboratory (NSERL) maintains a hydrometerological network where five years of precipitation and soil moisture data are available. The model is first run with rainfall data from the National Climatic Data Center (NCDC) and the NSERL raingauge network to represent the “true” state. Subsequently, the model is run for the same time period with an intentionally poor set of initial conditions and “limited” forcing data. Instead of using all available rainfall data from data sources, simulation was performed using only the NCDC data. These “limited” inputs are to represent our imperfect knowledge of the true hydrologic processes. By limiting precipitation input, which is the driving force of soil moisture and streamflow, while keeping other model parameters unchanged, we determined how the updated soil water condition with surface measured soil moisture influences model predictions of profile soil water content, runoff and streamflow. Model evaluations were conducted by using time series graphs and standard statistical measures including the correlation coefficient (R), mean bias error (MBE), and root mean square error (RMSE).