基于相关分析的淮河流域暴雨灾害风险评估

以淮河流域暴雨灾害为研究对象,从致灾因子的危险性和承灾体的易损性两方面出发,选择降水量、径流量、河网、高程、坡度、人口、经济等评价指标,通过相关分析法计算各指标权重,加权求和法得到淮河流域暴雨灾害的危险图、易损图及综合风险图,利用洪涝淹没面积数据验证暴雨灾害综合风险评估结果.结果表明:1)淮河流域暴雨灾害危险性较高的地区主要分布在河南南部、安徽北部以及江苏东部;2)淮河流域易损性较高的地区主要分布在河南中北部、安徽中部、山东南部及江苏东北部;3)淮河流域暴雨灾害高风险区集中在河南南部、安徽北部,以及江苏东部;4)洪涝淹没区隶属中高风险等级的面积占总面积的75.24%,与实际情况吻合,风险评估精度较高.     

洞庭湖植被对降水的响应

We analyzed the Normalized Difference Vegetation Index (NDVI) from satellite images and precipitation data from meteorological stations from 1998 to 2007 in the Dongting Lake wetland watershed to better understand the eco-hydrological effect of atmospheric precipitation and its relationship with vegetation. First, we analyzed its general spatio-temporal distribution using its mean, standard deviation and linear trend. Then, we used the Empirical Orthogonal Functions (EOF) method to decompose the NDVl and precipitation data into spatial and temporal modes. We selected four leading modes based on North and Scree test rules and analyzed the synchronous seasonal and inter-annual variability between the vegetation index and precipitation, distinguishing time-lagged correlations between EOF modes with the correlative degree analysis method. According to our detailed analyses, the vegetation index and precipitation exhibit a prominent correlation in spatial distribution and seasonal variation. At the 90% confidence level, the time lag is around 110 to 140 days,which matches well with the seasonal variation.