No abstract

Trend detection in hydro-climatological time series is a prime task under the context of climate changes. Rainfall is a key component of the water cycle and its variability can profoundly influence agriculture, ecosystems and water resource management. In this paper, an innovative trend analysis (ITA) method with a significant test is used for rainfall trend detection at 14 stations in the Yangtze River Delta (YRD) during 1961 to 2016. The trends are separately evaluated for low (<10th percentile), medium (10th-90th percentile) and high (>90th percentile) rainfall at the annual and seasonal scales. The slope and significance of the rainfall trends derived from the ITA method are compared with those from the classical trend analysis methods Theil-Sen approach and Mann-Kendall test, respectively. The ITA shows significant increasing trends at the 99% confidence level in annual rainfall at all stations. While the same significant increasing trends are identified for summer and winter, decreasing trends dominate in spring and autumn. Contrasting trends are found for extreme rainfall with strong increasing trends in high rainfall in summer and winter and decreasing trends in low rainfall in spring and autumn. The results of the ITA confirmed by the classical trend analysis methods call for more attention to the risk management of flood in extreme seasons and drought in transitional seasons across the YRD region.