Flood Warning And Forecasting In Small And Medium-sized Watershed Using Distributed Hydrological Model And Multi-source Precipitation

Due to the limited capacities of monitoring,forecasting and warning,flash flood has been recognized as one of the most extensive disasters in China that leads to high mortality.In addition,global warming and national urbanization may further increase people's exposure risk and vulnerability to flash floods in China.Thus,development of the flash floods forecasting and warning is urgently necessary.Reliable warning systems must depend upon the accurate real-time rainfall estimation,quantitative rainfall forecast with high spatial temporal resolution,and effective warning/forecasting methods.Aims to solving the difficulties in the early warning of flash floods,this thesis proposed a new method to determine the rainfall threshold for flood warning and then applied this method to the montanous areas in China,and tested applicability of the GPM satellite precipitation as well as the quantitative precipitation forecast for flood forecasting.This study implements a geomorphology-based hydrological model(GBHM)in four small mountainous catchments in different climate regions over China.Then it proposes a method to determine the rainfall threshold for flood warning by using frequency analysis and binary classification based on long-term hydrological simulation by GBHM.It conducts a comprehensive evaluation of the rainfall threshold and finds that the proposed method produces reasonably accurate flash flood warnings in the study catchments.Furthermore,a national modeling system with resolution of 0.01 degree has been built to simulate the runoff generation and soil water dynamic processes based on GBHM,and the SCS dimensionless unit hydrograph method has been used for flow routing in 1849 selected small catchments over the mountainous region of China.It has simulated the runoff processes and the soil moisture dynamics from 2003 to 2014,based on the simulated results the rainfall thresholds for flash flood warning are derived for the 1849 study catchments.To improve the flood forecasting in small and medium-sized catchments,this thesis applies the latest near real time GPM precipitation in Xiangjiang River Basin.Statistically,near real time IMERG products show high accuracy.IMERG precipitation products have strong timeliness,high spatial-temporal resolution,good accuracy in hydrological simulation,thus is valuable for real-time flood forecasting in small and medium-sized catchments.In addition,it proposes a deep neural network composed of CNN and LSTM to downscale the ECMWF precipitation.Results show considerable improvement of precipitation accuracy compared to the ECMWF-Interim reanalysis and benchmark downscaling approaches including quantile mapping and SVM,etc.The proposed method can provide more accurate precipitation prediction at lead time from 1 day up to 2 weeks.The superiority decreases along forecast lead time,but its improvement of systematic deviation does not fade out.It can also improve the hydrological simulation performance significantly.