| This study focuses on the development of a physically based spatially distributed hydrologic model coupling an existing land surface model with a surface flow routing and a subsurface flow routing models. Incorporating detailed information on climate, soil, vegetation, and digital elevation, the hydrologic model was applied in the Monongahela River basin (13,875 km 2 areal extent), a subbasin of the Ohio River basin located on the western slopes of the Appalachian Mountains, for the assessment and simulation of extreme hydrologic regimes of the 1988 dry year and the 1993 wet year. One unique challenge in modeling the hydrologic regime of the Monongahela River basin is the complex spatial variability of the soil-terrain-hydrogeology system.; The hydrologic model was driven by atmospheric forcing data produced by the National Center for Atmospheric Research. Since the research objective was to investigate the model behavior for wet and dry hydrologic regimes, no model calibration was carried out. In the evaluation of model simulations, several issues relevant to hydrologic and regional climate modeling were considered. First, effects of spatial variability and spatial scale on the hydrologic response were examined for different basins. More specifically, sensitivity of physical parameterizations of land processes with respect to the type of climate forcing (dry year of 1988 vs. wet year of 1993), and the characteristics of hydrologic model response (1-km vs. 5-km) as a function of model resolution were investigated through analyses of the simulation results for the two hydrologic regimes. Further, strong and weak links in coupled land-atmosphere modeling with respect to reproducing hydrologic regimes were identified by investigating the impact of important state variables of precipitation and air temperature on simulation results. Finally, the value and utility of regional climate model outputs in the simulation of distinct hydrologic regimes, and the reliability of regional climate forecasts for water resources impact studies were assessed.; A sensitivity analysis of the hydrologic model to selected model parameters using the fractional factorial design method showed that the model sensitivity is different for the two hydrologic regimes tested. (Abstract shortened by UMI.)... |
