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Theoretical and Applied Climatology

, Volume 98, Issue 1–2, pp 89–99 | Cite as

Observed changes of drought/wetness episodes in the Pearl River basin, China, using the standardized precipitation index and aridity index

  • Qiang Zhang
  • Chong-Yu Xu
  • Zengxin Zhang
Original Paper

Abstract

Monthly precipitation data of 42 rain stations over the Pearl River basin for 1960–2005 were analyzed to classify anomalously wet and dry conditions by using the standardized precipitation index (SPI) and aridity index (I) for the rainy season (April–September) and winter (December–February). Trends of the number of wet and dry months decided by SPI were detected with Mann-Kendall technique. Furthermore, we also investigated possible causes behind wet and dry variations by analyzing NCAR/NCEP reanalysis dataset. The results indicate that: (1) the Pearl River basin tends to be dryer in the rainy season and comes to be wetter in winter. However, different wetting and drying properties can be identified across the basin: west parts of the basin tend to be dryer; and southeast parts tend to be wetter; (2) the Pearl River basin is dominated by dry tendency in the rainy season and is further substantiated by aridity index (I) variations; and (3) water vapor flux, moisture content changes in the rainy season and winter indicate different influences of moisture changes on wet and dry conditions across the Pearl River basin. Increasing moisture content gives rise to an increasing number of wet months in winter. However, no fixed relationships can be observed between moisture content changes and number of wet months in the rainy season, indicating that more than one factor can influence the dry or wet conditions of the study region. The results of this paper will be helpful for basin-scale water resource management under the changing climate.

Keywords

Rainy Season Standardize Precipitation Index Western Pacific Subtropical High Aridity Index Meteorological Drought 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CUHK405308), by a Direct Grant from the Faculty of Social Science, The Chinese University of Hong Kong (Project No.: 4450183), National Natural Science Foundation of China (Grant No.: 40701015), and the Programme of Introducing Talents of Discipline to Universities—the 111 Project of Hohai University. Cordial thanks should be extended to two anonymous reviewers and the managing editor, Prof. Dr. Hartmut Grassl, for their invaluable comments and suggestions which greatly improved the quality of this paper.

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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Institute of Space and Earth Information ScienceThe Chinese University of Hong KongShatinChina
  2. 2.State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and LimnologyChinese Academy of SciencesNanjingChina
  3. 3.Department of GeosciencesUniversity of OsloOsloNorway
  4. 4.Jiangsu Key Laboratory of Forestry Ecological EngineeringNanjing Forestry UniversityNanjingChina
  5. 5.Nanjing Institute of Geography and LimnologyChinese Academy of ScienceNanjingChina

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