Suning Liu

By Yao Wang, Suning Liu, Zhaoqiang Zhou and Haiyun Shi

Analyzing the hydrological sequence from the non-stationary characteristics can better understand the responses of changes in extreme rainfall to climate change. Taking the plain area in the middle and lower reaches of the Yangtze River basin (MLRYRB) as the study area, this study adopted a set of extreme rainfall indices and used the Bernaola-Galvan Segmentation Algorithm (BGSA) method to test the non-stationarity of extreme rainfall events. The General Pareto Distribution (GPD) was used to fit extreme rainfall and was calculated to select the optimal threshold of extreme rainfall. In addition, the cross-wavelet technique was used to explore the correlations of extreme rainfall with El Niño-Southern Oscillation (ENSO) and Western Pacific Subtropical High (WPSH) events. The results showed that: (1) extreme rainfall under different thresholds had different non-stationary characteristics; (2) the GPD distribution could well fit the extreme rainfall in the MLRYRB, and 40–60 mm was considered as the suitable optimal threshold by comparing the uncertainty of the return period; and (3) ENSO and WPSH had significant periodic effects on extreme rainfall in the MLRYRB. These findings highlighted the significance of non-stationary assumptions in hydrological frequency analysis, which were of great importance for hydrological forecasting and water conservancy project management.

Part of the book: Flood Risk in a Climate Change Context