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长江中下游地区一季稻高温热害风险评估与区划 被引量:9

Risk assessment and regionalization of heat injury for single-season rice in Middle and Lower Yangtze River
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摘要 为探究1961-2013年长江中下游地区一季稻高温热害风险分布,根据一季稻抽穗开花期高温热害等级指标,统计逐站逐年各强度高温热害发生频次,利用Arc GIS分析高温热害空间分布,采用Morlet小波分析高温热害周期规律;设定抽穗开花期有无高温热害2种天气方案,使用ORYZA2000模型提取灾损率,进而根据灾害风险评估模型完成风险区划。结果表明:一季稻抽穗开花期高温热害分布面积为轻度>中度>重度,轻度、中度、重度高温热害分布面积最大省份均为湖北省;年际尺度28年为1961-2013年一季稻抽穗开花期高温热害频次的第一主周期。高温热害灾损率高值区为江苏中西部、浙江中部、安徽东南部、湖南西南部。高温热害风险高值区为湖北中南部及东北部、安徽中西部、湖南中北部。高温热害及其灾损率的空间分布不完全一致。 To explore the risk distribution of heat injury for single-season rice in Middle and Lower Yangtze River from 1961 to 2013, based on the indicators of single rice heat injury grade during flowering, the frequency of the damage with different intensities of each station and each year was counted up, the horizontal distribution of heat injury was analyzed by ArcGIS, and the periodic trend was analyzed by Morlet wavelet. Two weather programs were set, which were with and without heat injury during flowering, and the risk regionalization of heat injury were achieved using the model of damage risk e-valuation based on the damage rate of heat injury ex-tracted by ORYZA2000 model. Mild heat injury cov-ered the largest area, and severe heat injury, the smallest area. Hubei province presented the largest ar-eas for all three injury grades of heat. The first major period of heat injury during flowering from 1961 to 2013 were shown as 28 years. The areas with high damage rate of heat injury were midwestern Jiangsu province, central Zhe-jiang, southeastern Anhui, and southwestern Hunan. The areas with high risk of heat injury were south-central and northeastern Hubei, midwestern Anhui, north-central Hunan. The spatial distributions of heat injury was not con-sisten with its damage rate.
出处 《江苏农业学报》 CSCD 北大核心 2015年第5期1053-1059,共7页 Jiangsu Journal of Agricultural Sciences
基金 "农业应对气候变化"项目(132020010406 1411145000994) 江苏省普通高校研究生科研创新计划项目(CXZZ12_0503) 公益性行业(气象)科研专项(GYHY201506018)
关键词 高温热害 ORYZA2000 灾损率 风险评估模型 heat injury ORYZA2000 damage rate risk assessment model
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