Use of Radar Estimated Precipitation for Flood Forecasting

Abstract

Flooding is one of the deadliest natural hazards in the world. Forecasting floods in advance can significantly reduce the socio-economic impacts. An accurate and reliable flood forecasting system is heavily dependent on the input precipitation data. Real-time, spatially, and temporally continuous Radar Quantitative Precipitation Estimates (QPEs) is useful precipitation information source. This research aims to investigate the efficacy of American and Canadian weather radar QPEs on hydrological model calibration and validation for flood forecasting in urban and semi-urban watersheds in Canada. A comprehensive review was conducted on the weather Radar network and its’ hydrological applications, challenges, and potential future research in Canada. First, radar QPEs were evaluated to verify the reliability and accuracy as precipitation input for hydrometeorological models. Then, the radar-gauge merging techniques were assessed to select the best method for urban flood forecasting applications. After that, merged Radar QPEs were used as precipitation input for the hydrological models to assess the impact of radar QPEs on hydrological model calibration and validation. Finally, a framework was developed by integrating hydrological and hydraulic models to produce flood forecasts and inundation maps in urbanized watersheds. Results indicated that dual-polarized radar QPEs could be effectively used as a source of precipitation input to hydrological models. The radar-gauge merging enhances both the accuracy and reliability of Radar QPEs, and therefore, the accuracy of streamflow simulation is also improved. Since flood forecasting agencies usually use hydrological models calibrated and validated using gauge data, it is recommended to use bias-corrected Radar QPEs to run existing hydrological models to simulate streamflow to produce flood extent maps. The hydrological and hydraulic models could be integrated into one framework using bias-corrected Radar QPEs to develop a successful flood forecasting system.