Characteristics of dry-wet abrupt alternation events in the middle and lower reaches of the Yangtze River Basin and the relationship with ENSO

Abstract

© 2018, Institute of Geographic Science and Natural Resources Research (IGSNRR), Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature. During recent decades, more frequent flood-drought alternations have been seen in China as a result of global climate change and intensive human activities, which have significant implications on water and food security. To better identify the characteristics of flood-drought alternations, we proposed a modified dry-wet abrupt alternation index (DWAAI) and applied the new method in the middle and lower reaches of the Yangtze River Basin (YRB-ML) to analyze the long-term spatio-temporal characteristics of dry-wet abrupt alternation (DWAA) events based on the daily precipitation observations at 75 rainfall stations in summer from 1960 to 2015. We found that the DWAA events have been spreading in the study area with higher frequency and intensity since 1960. In particular, the DWAA events mainly occurred in May and June in the northwest of the YRB-ML, including Hanjiang River Basin, the middle reaches of the YRB, north of Dongting Lake and northwest of Poyang Lake. In addition, we also analyzed the impact of El Niño Southern Oscillation (ENSO) on DWAA events in the YRB-ML. The results showed that around 41.04% of DWAA events occurred during the declining stages of La Niña or within the subsequent 8 months after La Niña, which implies that La Niña events could be predictive signals of DWAA events. Besides, significant negative correlations have been found between the modified DWAAI values of all the rainfall stations and the sea surface temperature anomalies in the Nino3.4 region within the 6 months prior to the DWAA events, particularly for the Poyang Lake watershed and the middle reaches of the YRB. This study has significant implications on the flood and drought control and water resources management in the YRB-ML under the challenge of future climate change.