Study on the Response of Flood in Wuzhou Station of Xijiang River to Extreme Climate Change

Based on meteorological and hydrological data, the extreme climate change in Xijiang River Basin in Guangxi was statistically analyzed, and the spatial distribution and temporal evolution characteristics of annual extreme maximum temperature, annual extreme minimum temperature and annual extreme maximum rainfall 1d and 7d were emphatically analyzed. The correlation between extreme weather factors of each station and the annual maximum discharge of Wuzhou Hydrological Station was analyzed by SPSS statistical software, and the extreme weather of Xijiang River Basin in Guangxi in 20 15 and 2020 was predicted by using the sequential grey prediction method. Finally, using BP neural network method, an evaluation model of extreme climate change on flood-"extreme temperature, rainfall-flood prediction model" is established to study the response of flood in Wuzhou hydrological station to extreme climate change. The main conclusions are as follows:

1) According to the extreme temperature, extreme rainfall and flood data of Wuzhou Hydrological Station in Xijiang River Basin 1958-2007, and taking the corresponding extreme climate factors that significantly affect the annual maximum flow of Wuzhou Hydrological Station as input, an artificial neural network model is established: the qualified rate of "annual extreme temperature, annual maximum rainfall 1d- Wuzhou BP model" is 90. The qualified rate of "annual extreme temperature, annual maximum rainfall 7d- Wuzhou annual maximum flow" is 70%, and the qualified rate of "annual maximum flow-annual maximum water level BP neural network model" is 100%.

2) Pearson type ⅲ distribution is most suitable for fitting the annual maximum flow series; Lognormal distribution is the best method to fit the annual maximum water level series.

3) Under the influence of extreme climate change in the future, the maximum discharge of Wuzhou Hydrological Station will be reduced to varying degrees, and the annual maximum water level will drop.

4) For the maximum discharge in the same year, due to the changes of annual extreme maximum temperature, annual extreme minimum temperature and annual maximum rainfall 1d, the frequency decreases and the return period increases; However, due to the changes of annual extreme maximum temperature, annual extreme minimum temperature and annual maximum 7-day rainfall, its frequency becomes larger and the return period becomes shorter. For the same frequency and return period, the corresponding annual maximum discharge decreases after being influenced by annual extreme maximum temperature, annual extreme minimum temperature and annual maximum rainfall1d. Affected by the changes of annual extreme maximum temperature, annual extreme minimum temperature and annual maximum 7-day rainfall, the corresponding annual maximum flow increases.