Spatio‐temporal variation of dryness/wetness across the Pearl River basin, China, and relation to climate indices Academic Article uri icon


  • © 2017 Royal Meteorological Society Correlations between wet/dry variations and El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian Ocean Dipole (IOD), and Pacific Decadal Oscillation (PDO) across the Pearl River basin were analysed at annual and seasonal time scales using the Pearson correlation and moving correlation coefficient. The basin was divided into homogeneous climate zones using the fuzzy C-means method. The influences of ENSO, NAO, IOD, and PDO on annual and seasonal wet/dry variations and impacts of warm and cold episodes of these indices on Standardized Precipitation Index-based dry and wet conditions were investigated. Furthermore, the mechanisms of the influences were tried to be analysed from the perspective of moisture transmission. The results indicated that: (1) IOD, NAO, and ENSO are the principle factors driving annual, wet, and dry seasonal precipitation variations, respectively. Moreover, opposite correlations can be identified between precipitation and IOD, NAO, and ENSO in the same/previous year; (2) Correlations between precipitation variations at different time scales and climate indices are strongly stable. Furthermore, climate indices tend to have more significant and stable impacts on precipitation variations during the dry season than on annual precipitation and precipitation variations during the wet season; (3) There exist different moisture convergence and divergence phenomena in the Pearl River basin under anomalous year of climate indices, so the annual, wet, and dry seasonal precipitation characteristic will be affected; (4) ENSO exhibits remarkable impacts on the variations of dry conditions; and meanwhile ENSO and IOD have significant impacts on the variations of wet conditions. In general, the cold phase of climate indices tends to trigger a higher risk of droughts, and the warm phase of climate indices tends to cause higher rates of extreme wet conditions. Taking different climate indices as predictors, this study may help forecast precipitation variations at different time scales.

author list (cited authors)

  • Huang, C., Zhang, Q., Singh, V. P., Gu, X., & Shi, P.

citation count

  • 10

publication date

  • August 2017