Parameter Uncertainty Reduction for SWAT Using Grace, Streamflow, and Groundwater Table Data for Lower Missouri River Basin1. (9th January 2013)

• Record Type: Journal Article
• Authors: Qiao, Lei; Herrmann, Robert B.; Pan, Zaitao
• Is Part Of: Journal of the American Water Resources Association. Volume 49:Number 2(2013:Apr.)
• Page Start: 343
• Page End: 358
• Publication Date: 2013-01-09
• Subjects: Water-supply -- Periodicals; Hydrology -- Periodicals; Water resources development -- Periodicals; Water resources development -- Environmental aspects -- Periodicals; 333.9100973
• DOI: 10.1111/jawr.12021
• Languages: English
• ISSNs: 1093-474X

Abstract

<abstract abstract-type="main" xml:lang="en"> <title> <x xml:space="preserve">Abstract</x> </title> <p> <bold>Abstract: </bold> This study incorporates the newly available Gravity Recovery and Climate Experiment (GRACE) water storage data and water table data from well logs to reduce parameter uncertainty in Soil and Water Assessment Tool (SWAT) calibration using a SUFI2 (sequential uncertainty fitting) framework for the Lower Missouri River Basin. Model evaluations are performed in multiple stages using a multiobjective function consisting of multisite streamflow and GRACE water storage data as well as a groundwater component. Results show that (1) a model calibrated with both streamflow and GRACE data simultaneously can maintain the water balance for the whole basin, but may improperly partition surface flow and base flow. Additional inclusion of the groundwater constraint can significantly improve the model performance in groundwater hydrological processes. In our case, the estimation of specific yield of shallow aquifers has been increased to 10<sup>−2</sup> from previous much underestimated level (<10<sup>−3</sup>). (2) The daily streamflow data are needed to confine the parameters related to water flow in channels such as the Manning's coefficient, which are less sensitive to the monthly simulations. (3) Parameters are nonuniformly sensitive for different goal variables, and thus, proper specification of a prior distribution of parameters may be the key factor for global optimization algorithms to obtain stable and realistic model performance.</p> </abstract>