不同空间尺度农业旱涝灾害气象因子致灾阈值的确定——以淮河上游地区为例

Agricultural industry is one of the most sensitive industries that affected by climate change, especially prone to be affected by drought and waterlogging and cause serious losses, so that it’s of great importance to carry out the early warning research of drought and waterlogging disasters in agriculture. Taking the Huaihe River basin, which is an important agricultural area in China, as study area, taking the winter wheat and summer maize as research object, the following works will be carried out: 1) Based on the crop drought and waterlogging control test, agricultural drought and waterlogging inducing factors is recognized and the range of the disaster-inducing threshold of drought and waterlogging is determined. 2) With the help of point- surface scale conversion tools like the SWIM hydrological model and the geostatistics tools, the crop damage data during 1971-2014 are used to establish the quantitative relationships between agricultural drought and waterlogging inducing factors-soil moisture-crop damage data. 3) Through the comparison of quantitative relationships at different scale, the leading meteorological disaster-inducing factors and its contribution to the crop damage under different disaster-inducing environment of sub basins in the upper Huai River Basin are recognized. Taking the typical disaster data and the wild investigated damage data as the basis of validation and calibration, the disaster-inducing threshold of agricultural drought and waterlogging at different scales is finally achieved. The research mainly explores the difference and variation characteristics of disaster-inducing threshold of meteorological factors at point-surface scale, providing theoretic basis and technology support for coping mechanism to the agricultural drought and waterlogging at different scales.

农业是受气候变化影响最为敏感的产业之一，极易受旱涝灾害影响而造成严重损失，开展农业旱涝灾害研究意义重大。本项目以中国重要农业产地淮河上游地区为研究区，选择冬小麦和夏玉米两种农作物为研究对象，1）在农作物旱涝控制试验基础上，识别农业旱涝灾害气象致灾因子，初步圈定点尺度农业旱涝灾害气象致灾因子的致灾阈值范围；2）借助SWIM水文模型和地统计学等点—面尺度转换工具，结合1971—2014年农作物灾损数据，建立点和面尺度农业旱涝灾害的气象致灾因子—土壤墒情—农作物灾损的定量关系集合；3）通过上述定量关系比较，明晰研究区各子流域不同孕灾环境下气象致灾因子及其对农作物灾损的作用强度，以典型灾情和野外调研灾损数据为率定和验证依据，最终确定不同空间尺度农业旱涝灾害气象因子的致灾阈值。项目探索气象因子的致灾阈值在点—面尺度上的差异和推绎规律，为阐明农业旱涝灾害在不同尺度上应对机制提供理论依据和技术支持。

农业是受气候变化影响最为敏感的产业之一，极易受旱涝灾害影响而造成严重损失，开展农业旱涝灾害研究意义重大。项目以中国重要农业产地淮河上游地区为研究区，选择冬小麦和夏玉米两种农作物为研究对象，尝试确定不同空间尺度农业旱涝灾害气象因子的致灾阈值。.1、淮河流域农业旱涝灾害事件时空演变特征。淮河流域干旱事件发生频次由5年一次变为3.9年一次，洪涝事件发生频次由3.7年一次变为3.6年一次。淮河流域干旱事件发生频率较高地区经历了“淮河中上游地区→除流域西北、东南部以外地区→沂沭泗河及流域内河南省西北部地区”的空间变化过程，而洪涝事件发生频率较高地区则经历了“中下游及沂沭泗河地区→除流域内河南省北部以外地区→淮河上游地区”的空间变化过程。.2、冬小麦旱涝灾害气象因子致灾阈值。利用淮河上游地区1961－2015年13个气象站逐日降水数据，计算冬小麦生长关键期（2、3、4月份）1个月尺度及3个月尺度SPI值，结合冬小麦产量数据，确定生长关键期对产量灾损率影响时段，在此基础上分析降水量变化及发生旱涝灾害空间分布，建立SPI值与产量灾损率之间的定量关系。结果表明：1）生长关键期（SPI_All）和4月份（SPI4）SPI值均与产量灾损率相关性程度高，分别通过0.01和0.05置信度检验；两个时段降水量变化普遍呈现减少趋势，且旱涝灾害空间分布存在一定的差异；2）SPI_All值和SPI4值分别趋于-2.23和-1.82时，达到决定发生旱灾时产量灾损的阈值；3）SPI_All值和SPI4值与产量灾损率相关系数分别为0.94和0.82，构建两个时段SPI值与产量灾损率之间的预测模型，确定涝灾阈值。.3、夏玉米旱涝灾害气象因子致灾阈值。结合降水概率分位数法和最优概率分布模型确定夏玉米水分关键期内干旱致灾降水阈值。结果表明：研究区内各站点夏玉米生长水分关键期内降水序列的最优概率分布模型差异明显，设定的夏玉米干旱致灾降水阈值地区差异较大，但各级降水阈值空间分布特征基本相同，均呈现北部至南部逐渐增大的现象，基于得到的站点降水阈值，利用泰森多边形法确定整个研究区夏玉米生长水分关键期内干旱致灾降水阈值，分别是：轻旱：73≤R<91mm，中旱：54≤R<73mm，重旱：37≤R<54mm，特旱：R<37mm。