| The mechanism of flood forecasting is a complex process, which involves precipitation, drainage-basin characteristics, land use/cover types, and runoff discharge. Because of the complexity of flood forecasting, hydrological models and statistical models need to be developed for flood frequency analysis, river runoff prediction, and flood forecasting. This dissertation investigates the performance of Log-Pearson III (LP3), Generalized Extreme Value (GEV), Power Law (PL), and Generalized Pareto (GP) models for flood frequency analysis. It suggests that there are no significant differences of the LP3, GEV, PL and GP models for less than approximate 10-year return period flood frequency analysis in the ORM area. And there are no significant differences of the LP3, GEV, PL and GP models for greater than approximate 10-year return period flood frequency analysis in the ORM area. In addition, these frequency analysis models and other hydrological models are applied for flood forecasting in the ORM area, including Soil Conservation Service (SCS) Curve Number model for river runoff prediction, Concentration-Area (CA) fractal model for flood threshold selection and singularity fractal model for flood characteristics description.However, the application of these flood forecasting models requires the efficient management of large spatial and temporal datasets, involving data acquisition, storage, processing, analysis and display of model results. Difficulty in linking data, analysis tools, and models is one of the barriers to be overcome in developing an integrated flood forecasting system. The current revolution in technology and the online availability of spatial data facilitate Canadians' need for information sharing in support of decision making. This need has resulted in studies demonstrating the suitability of the web as a medium for implementation of flood forecasting. Web-based Spatial Decision Support Services (WSDSS) provides comprehensive support for information retrieval and model analysis and extensive visualization functions for decision-making support and information services. This dissertation examines the current state of the art and future prospects of hydrological models and statistical models for flood frequency analysis and flood forecasting and develops a prototype WSDSS that integrates models, analytical tools, databases, graphical user interfaces, and spatial decision support services to help the public and decision makers to easily access flood and flood-threatened information. Flood WSDSS helps to mitigate flood disasters through river runoff prediction, flood forecasting, and flood information (flood discharge, water level and flood frequency) dissemination. The ultimate aim of this system is to improve access to flood model results by the public and decision makers. |
