Statistical spatiotemporal analysis of hydro-morphological processes in China during 1950-2015

Nan Wang, Weiming Cheng*, L. Lombardo, Junnan Xiong, Liang Guo

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)
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hydro-morphological processes (HMPs) are some of the most destructive natural disasters. Understanding the spatiotemporal characteristics of HMPs across China is important for enabling better disaster estimation and prevention at the national scale. However, few studies have focused on the spatiotemporal HMP characterization under various geomorphic settings in China, which may shed light on their regional/national evolution. To bridge this research gap, we analysed the longest HMP time series available in China, which including 47,483 HMP records, to detect spatiotemporal patterns. Specifically, we run several tests namely, Mann–Kendall test, wavelet analysis, Monthly Frequency, and Index of Dispersion, with the objective of detecting the temporal evolution, trends, period, and clustering of HMPs in six geomorphological regions (geomorphic-regions): Eastern Plain, South Eastern Mountain (SEM), North Central Plateau, North Western Basin, South Western Mountain (SWM), and Tibetan Plateau. Our results show that in the last decades have been associated with a marked increase in HMPs and this should be accounted for, especially in those areas where we have retrieved high spatiotemporal clustering (e.g., SEM, SWM). Besides, the main periodicity of HMPs is approximately 12–25 years for most of China since the 1980s, which showed analogous patterns with precipitation anomalies. This study provides a preliminary reference for revealing the spatiotemporal characteristics of HMPs in the context of climate change; therefore, the information provided can be crucial to plan engineering applications with specific return period.
Original languageEnglish
Pages (from-to)2377-2397
Number of pages21
JournalStochastic environmental research and risk assessment
Early online date19 Apr 2021
Publication statusPublished - Aug 2022


  • Hydro-morphological processes
  • Spatiotemporal distribution
  • Periodicity
  • Clustering
  • 22/1 OA procedure


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