Articles | Volume 12, issue 5
Nat. Hazards Earth Syst. Sci., 12, 1353–1365, 2012
Nat. Hazards Earth Syst. Sci., 12, 1353–1365, 2012

Research article 09 May 2012

Research article | 09 May 2012

Changes in annual maximum number of consecutive dry and wet days during 1961–2008 in Xinjiang, China

Y. Zhang1,2,5, F. Jiang1, W. Wei1, M. Liu1,5, W. Wang3, L. Bai1, X. Li4, and S. Wang1,2 Y. Zhang et al.
  • 1Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, 818 South Beijing Road Urumqi, Xinjiang 830011, China
  • 2Graduate School, Chinese Academy of Sciences, Beijing 100049, China
  • 3Xinjiang Bureau of Meteorology, Urumqi, Xinjiang 830011, China
  • 4Tianshan Snow Avalanche Research Station, Chinese Academy of Sciences, Urumqi, Xinjiang 830000, China
  • 5Department of Mathematics, Xinjiang University, Urumqi, 830046, China

Abstract. Extreme precipitation events are major causes of severe floods and droughts worldwide. Therefore, scientific understanding of changing properties of extreme precipitation events is of great scientific and practical merit in the development of human mitigation of natural hazards, such as floods and droughts. Wetness and dryness variations during 1961–2008 in Xinjiang, a region of northwest China characterised by an arid climate, are thoroughly investigated using two extreme precipitation indices. These are annual maximum consecutive dry days (CDD) and annual maximum consecutive wet days (CWD), based on a daily precipitation dataset extracted from 51 meteorological stations across Xinjiang. As a result, we present spatial distributions of mean annual CDD and mean annual CWD and their trends within the study period. The results indicate that: (1) CDD maximize in the Taklimakan and Turban basins of southeast Xinjiang, while minima are found in the Tianshan Mountains and the Ili river valley of northwest Xinjiang. On the contrary, the longest CWD are observed in northwest Xinjiang and the shortest in the southeast part of the region. (2) On an annual basis, CWD temporal variability shows statistically positive trends and a rate of increase of 0.1d/10a. CDD temporal variability shows statistically negative trends and a rate of decrease of 1.7d/10a. (3) Goodness-of-fit analysis for three candidate probability distribution functions, generalised Pareto distribution (GPD), generalised extreme value (GEV) and Gumbel, in terms of probability behaviours of CDD and CWD, indicates that the GEV can well depict changes of CDD and CWD. (4) The CDD and CWD better describe wet and dry conditions than precipitation in the Xinjiang. The results pave the way for scientific evaluation of dryness/wetness variability under the influence of changing climate over the Xinjiang region.