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Spatio-Temporal Characteristics and Trend Prediction of Extreme Precipitation—Taking the Dongjiang River Basin as an Example
The intricate interplay between human activities and climate change has resulted in a rise in the occurrence of extreme precipitation worldwide, which has attracted extensive attention. However, there has been limited dissemination of accurate prediction of extreme precipitation based on analysis of spatio-temporal characteristics of such events. In this study, the intra-annual distribution of extreme precipitation was subjected to scrutiny via an analysis of precipitation concentration degree (PCD) and precipitation concentration period (PCP), while also investigating the spatio-temporal trends of the annual precipitation, maximum daily precipitation, maximum 5-day precipitation, and extreme precipitation (defined as daily precipitation exceeding the 99th percentile of the total precipitation). Furthermore, subsequently, conducting simulation, verification, and prediction of extreme precipitation was achieved through the application of a back-propagation artificial neural network (BP-ANN). This study employed the data of the daily precipitation in the Dongjiang River Basin from 1979 to 2022, a time period which was of sufficient length to reflect the latest changes in precipitation patterns. The results demonstrated spatio-temporal differences between precipitation levels in the upper and lower reaches of the Dongjiang River Basin, that is, the PCD of the lower reach was higher and the PCP of the lower reach came half a month later compared with the upper reach. Moreover, the extreme precipitation indices increased from northeast to southwest, with the characteristics of lower-reach precipitation being more extreme and periodic. It was predicted that the total precipitation in 2023 would decrease, while the extreme precipitation would increase. The qualification rate of forecasting extreme precipitation ranged from 27% to 72%.
Spatio-Temporal Characteristics and Trend Prediction of Extreme Precipitation—Taking the Dongjiang River Basin as an Example
The intricate interplay between human activities and climate change has resulted in a rise in the occurrence of extreme precipitation worldwide, which has attracted extensive attention. However, there has been limited dissemination of accurate prediction of extreme precipitation based on analysis of spatio-temporal characteristics of such events. In this study, the intra-annual distribution of extreme precipitation was subjected to scrutiny via an analysis of precipitation concentration degree (PCD) and precipitation concentration period (PCP), while also investigating the spatio-temporal trends of the annual precipitation, maximum daily precipitation, maximum 5-day precipitation, and extreme precipitation (defined as daily precipitation exceeding the 99th percentile of the total precipitation). Furthermore, subsequently, conducting simulation, verification, and prediction of extreme precipitation was achieved through the application of a back-propagation artificial neural network (BP-ANN). This study employed the data of the daily precipitation in the Dongjiang River Basin from 1979 to 2022, a time period which was of sufficient length to reflect the latest changes in precipitation patterns. The results demonstrated spatio-temporal differences between precipitation levels in the upper and lower reaches of the Dongjiang River Basin, that is, the PCD of the lower reach was higher and the PCP of the lower reach came half a month later compared with the upper reach. Moreover, the extreme precipitation indices increased from northeast to southwest, with the characteristics of lower-reach precipitation being more extreme and periodic. It was predicted that the total precipitation in 2023 would decrease, while the extreme precipitation would increase. The qualification rate of forecasting extreme precipitation ranged from 27% to 72%.
Spatio-Temporal Characteristics and Trend Prediction of Extreme Precipitation—Taking the Dongjiang River Basin as an Example
Ningning Li (author) / Xiaohong Chen (author) / Jing Qiu (author) / Wenhui Li (author) / Bikui Zhao (author)
2023
Article (Journal)
Electronic Resource
Unknown
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