摘要
Sichuan Basin is located in southwestern China and affected by a complex water vapor (WV) sources. Here, the spatial and temporal patterns of precipitation and extreme events are investigated by six indices of World Meteorology Organization Commission, including annual precipitation total (AP), maximum daily precipitation (Maxld), intensity of rainfall over 1 mm/d (IR1), maximum and mean consecutive dry days (Max CDD, Mean CDD) and coefficient of variance. Based on 24 daily precipitation time series from 1951 to 2o11, Mann-Kendall test is employed to quantify the significant level of these indices, from which the classification of precipitation change and its spatial patterns are obtained. Meanwhile, the probability distributions of these indices are identified by L-moment analysis and the Goodness-of-fit test, and the corresponding values are calculated by theoretical model at different return periods. The results reveal that the western basin displays normal drought: less AP and precipitation intensity while longer drought. The southern basin shows normal increase: larger AP and precipitation intensity but shorter CDD. However, in hilly region of the central basin and the transition zone between basin and mountains, precipitation changes abnormally: increasing both drought (one or both of Mean CDD and MaxCDD) and precipitation intensity (one or both of Maxld and trend of AP is. Probability IR1) no matter what the distribution models also demonstrate the complex patterns: a negative correlation between Maxld and Max CDD in the west (R2≥0.61) while a positive correlation in the east (R2≥0.41) at all return periods. These patterns are induced by the changes in WV sources and the layout of local terrain. The increase of WV in summer and decrease in spring leads to the heavier rainfall and longer drought respectively. The large heat island effect of the basin contributes to a lower temperature in transition zones and more precipitation in the downwind area. These results are helpful in reevaluating the risk regionally and making better decisions on water resources management and disaster prevention.
基金
funded by open funding of Guizhou Provincial Key Laboratory of Public Big Data(Guizhou University, Grant No.2017BDKFJJ021)
Special Science and Technology Funding of Guizhou Province Water Resources Department (KT201707)
Guizhou Province Science and Technology Joint Founding (LH [2017]7617)
China Postdoctoral Science Foundation (Grant No.2016M5 92671)