Keywords
Integrated hydrologic model, SWAT-MODFLOW, Sensitivity, Uncertainty, Parameter estimation
Start Date
27-6-2018 2:00 PM
End Date
27-6-2018 3:20 PM
Abstract
Integrated water resource management in river basins is often accomplished using complex coupled groundwater/surface water hydrologic models. Around 30 such models, each with a distinct modeling code and solute strategy, have been developed and applied to around 40 regions worldwide, with model domains ranging from 100 to 100,000 km2. These coupled flow models, however, have not been used extensively and when used, focus on specific and practical management questions rather than addressing appropriate methodology e.g. quantifying parameter sensitivity and uncertainty, and estimating land surface and hydrogeologic parameters. Thus, they are limited in the extent to which they can be applied worldwide. This presentation provides methods for determining the influence of hydrologic factors on groundwater and surface water resources in complex integrated hydrologic models. These methods include sensitivity analysis, uncertainty analysis, and parameter estimation for coupled groundwater/surface water models. Methodology will be presented using the newly developed SWAT-MODFLOW code. Parameter estimation and uncertainty analysis are performed using PEST (Parameter ESTimation tool), with new pre-processing and post-processing algorithms developed to modify and jointly assess land surface hydrologic parameters (SWAT model) and hydrogeological parameters (MODFLOW). This presentation also demonstrates the use of sensitivity analysis with respect to other model factors such as model stresses (rainfall frequency and intensity, groundwater pumping magnitude and intensity), and to determine the optimal spatio-temporal discretization of the coupled flow model. These methods are applied to a 471 km2 regional site in the Middle Bosque River Watershed in Texas-Gulf region of central Texas and will provide generic results that likely are transferable to other watersheds.
Methodology for quantifying model factor sensitivity, uncertainty, and estimation for integrated groundwater/surface water hydrologic models
Integrated water resource management in river basins is often accomplished using complex coupled groundwater/surface water hydrologic models. Around 30 such models, each with a distinct modeling code and solute strategy, have been developed and applied to around 40 regions worldwide, with model domains ranging from 100 to 100,000 km2. These coupled flow models, however, have not been used extensively and when used, focus on specific and practical management questions rather than addressing appropriate methodology e.g. quantifying parameter sensitivity and uncertainty, and estimating land surface and hydrogeologic parameters. Thus, they are limited in the extent to which they can be applied worldwide. This presentation provides methods for determining the influence of hydrologic factors on groundwater and surface water resources in complex integrated hydrologic models. These methods include sensitivity analysis, uncertainty analysis, and parameter estimation for coupled groundwater/surface water models. Methodology will be presented using the newly developed SWAT-MODFLOW code. Parameter estimation and uncertainty analysis are performed using PEST (Parameter ESTimation tool), with new pre-processing and post-processing algorithms developed to modify and jointly assess land surface hydrologic parameters (SWAT model) and hydrogeological parameters (MODFLOW). This presentation also demonstrates the use of sensitivity analysis with respect to other model factors such as model stresses (rainfall frequency and intensity, groundwater pumping magnitude and intensity), and to determine the optimal spatio-temporal discretization of the coupled flow model. These methods are applied to a 471 km2 regional site in the Middle Bosque River Watershed in Texas-Gulf region of central Texas and will provide generic results that likely are transferable to other watersheds.
Stream and Session
E3: Complexity, Sensitivity, and Uncertainty Issues in Integrated Environmental Models