Scaling and clustering effects of extreme precipitation distributions

Qiang Zhang*, Yu Zhou, Vijay P. Singh, Jianfeng Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

One of the impacts of climate change and human activities on the hydrological cycle is the change in the precipitation structure. Closely related to the precipitation structure are two characteristics: the volume (m) of wet periods (WPs) and the time interval between WPs or waiting time (t). Using daily precipitation data for a period of 1960–2005 from 590 rain gauge stations in China, these two characteristics are analyzed, involving scaling and clustering of precipitation episodes. Our findings indicate that m and t follow similar probability distribution curves, implying that precipitation processes are controlled by similar underlying thermo-dynamics. Analysis of conditional probability distributions shows a significant dependence of m and t on their previous values of similar volumes, and the dependence tends to be stronger when m is larger or t is longer. It indicates that a higher probability can be expected when high-intensity precipitation is followed by precipitation episodes with similar precipitation intensity and longer waiting time between WPs is followed by the waiting time of similar duration. This result indicates the clustering of extreme precipitation episodes and severe droughts or floods are apt to occur in groups.
Original languageEnglish
Pages (from-to)187-194
Number of pages8
JournalJournal of Hydrology
Volume454-455
Early online date18 Jun 2012
DOIs
Publication statusPublished - 6 Aug 2012

User-Defined Keywords

  • Scaling properties
  • Conditional probability dependence
  • Clustering effects
  • Precipitation distribution

Fingerprint

Dive into the research topics of 'Scaling and clustering effects of extreme precipitation distributions'. Together they form a unique fingerprint.

Cite this