基于多指数安徽省强降水灾害近45年的时空演变
孙莉娟1,4, 陈金华2, 徐 阳2, 黄 进3

(1.安徽省人工影响天气办公室, 合肥 230031; 2.安徽省农村综合经济信息中心(安徽省农业气象中心), 合肥 230031; 3.南京信息工程大学 应用气象学院, 南京 210044; 4.淮河流域气象中心, 安徽 合肥 230031)

强降水; 多指数; 安徽省; 主成分分析

Spatial and Temporal Evolution of Heavy Rainfall Disasters in Anhui Province in Recent 45 Years Based on Multi-indictors
SUN Lijuan1,4, CHEN Jinhua2, XU Yang2, HUANG Jin3

(1.Anhui Weather Modification Office, Hefei 230031, China; 2.Rural Comprehensive Economic Information Center of Anhui Province/Anhui Agrometeorological Center, Hefei 230031, China; 3.School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China; 4.Huaihe River Basin Meteorological Center, Hefei 230031,China)

heavy rainfalls; multi-indictors; Anhui Province; principal component analysis

备注

强降水事件的频发给人类社会造成了巨大危害,研究其时空变化特征具有重要的现实意义。依托安徽省77个气象站点1973—2017年逐日降水资料及全省粮食产量数据,运用降水指标群评估了研究区强降水灾害的时空演变格局。结果 表明:(1)基于各站点11个降水指数的多年均值,主成分分析表明安徽省强降水的极值和持续性呈现出显著的南北梯度,高值区域主要集中在南部地区;(2)各站点不同类型降水指数与全省水灾受灾总面积的相关分析表明极端雨天总降水量(P95)是表征雨涝灾害最有效的指标;(3)基于主成分分析,安徽省可以划分为6个呈现不同P95变化情形的子区域,分别为中南部、中北部、南部、西北角、东北角、最北端,其中大部分区域2003年后P95增加态势较为强烈;(4)全省夏、秋量总产量对中北部P95的年际异常更为敏感,特别是近45年来中北部P95的增加趋势给夏粮产量带来了0.72%的减产。研究结果对评估气候变化对安徽省粮食生产安全的可能影响具有参考意义。

The frequent occurrence of heavy rainfalls has caused great harm to human society, and it is important to explore its temporal and spatial characteristics. By using the daily precipitation data of 77 meteorological stations and provincial grain yield records in Anhui province during 1973—2017, the spatio-temporal variability of heavy rainfalls disasters were evaluated with precipitation indicator groups. The main results are as follows.(1)Based on the multi-annual average of 11 precipitation indices of each station, the principal component analysis(PCA)showed that the extreme and persistence of heavy rainfalls in Anhui Province presented a notable north-south gradient, and the high values mainly concentrated in the south area.(2)The correlation analysis between precipitation indices of each station and provincial total area affected by floods indicated that the total precipitation of extreme rainy days(P95)was the most effective indicator for waterlogging disasters.(3)By using PCA, Anhui could be divided into six sub-regions with different temporal variations in P95 such as south-central area, north-central area, south area, northwest corner, northeast corner, northernmost area, and most of Anhui had been dominated by the strong increase of P95 since 2003.(4)The provincial summer and autumn grain yield was more sensitive to the inter-annual anomalies of P95 in north-central area, especially the increasing trend of P95 in north-central area during recent 45 years brought the decrease of summer grain yield by 0.72%. The study results can be used as a reference for assessing the possible impact of climate change on food production security in Anhui Province.