| Global hydrologic modeling is advancing in response to the needs of global change studies, water conflict resolution, global hazard forecasting, and more. There remain many challenges limiting continued advancement. This dissertation describes research addressing two of the challenges: (1) accuracy of satellite rainfall data and (2) quantifying factors influencing the rainfall-runoff process in global hydrologic models.;The assessment of satellite rainfall data accuracy is accomplished by comparing the 3B42 satellite rainfall product from NASA's Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) to rain gage observations in semiarid to humid climatic regions. Although TMPA matches well with rain gage observation at all locations, TMPA was slightly underestimated for semiarid regions and overestimated for humid regions. The relative magnitude of TMPA was smaller for urbanized watersheds and higher intensity events. Based on the analysis of TMPA accuracy by season and the correlation with temperature and relative humidity, TMPA was concluded to be more accurate for convective rainfall events.;To study the influence of land slope, land use/cover, and drainage size on the global rainfall-runoff process, a new global runoff model was developed implementing the Curve Number (CN) approach. The land slope was found to have a significant influence. The simulated runoff was consistently overestimated for flat river-basins, but underestimated for steep river-basins. In addition, river basins with greater human impact were found to have rainfall-runoff relationships more sensitive to slope.;The last phase of the dissertation research involved the development of a new relationship to incorporate a slope effect into the global runoff model. Land-slope effect was accounted for in the model using a land-slope correction computed through a trendline analysis of simulated and observed runoff. The correction was found to provide improved runoff volume estimates in more than 40% of the river basins. Overall, the mean absolute error of the runoff estimate was reduced by 33%. |
