Prediction of river basin hydrological response to extreme meteorological events is a primary concern in areas with frequent flooding, landslides, and debris flows. Natural hydrogeological disasters in many regions lead to extensive property damage, impact on societal activities, and loss of life. Hydrologists have a long history of assessing and predicting hydrologic hazards through the combined use of field observations, monitoring networks, remote sensing, and numerical modeling. Nevertheless, the integration of field data and computer models has yet to result in prediction systems that capture space-time interactions between meteorological forcing, land surface characteristics, and the internal hydrological response in river basins. Capabilities for assessing hydrologic extreme events are greatly enhanced via the use of geospatial data sets describing watershed properties such as topography, channel structure, soils, vegetation, and geological features. Recent advances in managing, processing, and visualizing cartographic data with geographic information systems (GIS) have enabled their direct use in spatially distributed hydrological models. In a distributed model application, geospatial data sets can be used to establish the model domain, specify boundary and initial conditions, determine the spatial variation of parameter values, and provide the spatial model forcing. By representing a watershed through a set of discrete elements, distributed models simulate water, energy, and mass transport in a landscape and provide estimates of the spatial pattern of hydrologic states, fluxes, and pathways.