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基于作物水分亏缺指数分析北方旱作地区春玉米干旱时空变化
引用本文:张晓芳,张勃,马尚谦,黄浩,陈杰,周婧.基于作物水分亏缺指数分析北方旱作地区春玉米干旱时空变化[J].中国农业气象,2022,43(9):749.
作者姓名:张晓芳  张勃  马尚谦  黄浩  陈杰  周婧
作者单位:1.西北师范大学地理与环境科学学院,兰州 730070;2.中国农业大学资源与环境学院,北京 100083
基金项目:国家自然科学基金项目(41561024)
摘    要:采用1961-2018年北方旱作区156个气象站日值气候数据和1991?2013年春玉米生育期资料,依据干燥度指数将北方旱作地区划分为4个亚区。基于作物水分亏缺指数(CWDI),通过分析各亚区水分供需状况、CWDI年际变化、干旱站次比和频率,揭示了中国北方旱作区春玉米干旱时空变化特征。结果表明:(1)Ⅰ区(湿润地区)和Ⅱ区(半湿润地区)水分供需平衡,Ⅲ区(半干旱地区)和Ⅳ区(干旱地区)水分供需不平衡。Ⅰ区降水量均方差达到70mm,CWDI年际间变化幅度最大,20世纪70年代CWDI在播种?出苗期和出苗?拔节期均为剧烈波动期;Ⅱ区CWDI在20世纪80年代春玉米乳熟?成熟期波动幅度较大;Ⅲ区在抽雄?乳熟期CWDI以1.06个百分点?10a?1(P<0.05)速率增加,年际间干旱有扩大趋势;Ⅳ区年际间CWDI变化不大。(2)1961?2018年抽雄?乳熟期干旱站次比以2.58个百分点?10a?1(P<0.05)的速率显著递增,其中发生特旱的平均站次比为14.95%,其余4个生育阶段无显著变化。抽雄?乳熟期发生旱级最重且干旱范围有显著扩大趋势。(3)在空间分布上,干旱等级和频率均呈现明显的东西向分布。黑龙江东部和西南部、吉林辽宁西部(Ⅰ区和Ⅱ区)发生轻旱频率达到3a两遇以上;张承地区(Ⅲ区)在播种?出苗期中旱频率5a一遇以上;毛乌素沙地(Ⅳ区)在拔节?抽雄期特旱频率达到3a两遇,以特旱威胁拔节?抽雄期为主。

关 键 词:作物水分亏缺指数(CWDI)  春玉米  中国北方  旱作农业  时空变化  
收稿时间:2021-11-10

Analysis on Temporal and Spatial Variation of Spring Maize Drought in Dry Farming Area of Northern China Based on Crop Water Deficit Index
ZHANG Xiao-fang,ZHANG Bo,MA Shang-qian,HUANG Hao,CHEN Jie,ZHOU Jing.Analysis on Temporal and Spatial Variation of Spring Maize Drought in Dry Farming Area of Northern China Based on Crop Water Deficit Index[J].Chinese Journal of Agrometeorology,2022,43(9):749.
Authors:ZHANG Xiao-fang  ZHANG Bo  MA Shang-qian  HUANG Hao  CHEN Jie  ZHOU Jing
Institution:1. College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China; 2. College of Resources and Environmental Sciences, China Agricultural University, Beijing 100083
Abstract:Based on the daily climate data of 156 meteorological stations in the dry farming area of northern China during 1961-2018 and the data of the spring maize growth period during 1991?2013, the dryland area in northern China was divided into four sub-areas according to the aridity index. Based on the crop water deficit index (CWDI), the temporal and spatial characteristics of spring maize drought in dryland area of northern China were revealed by analyzing water supply and demand, inter-annual variation of CWDI, drought station ratio, and frequency. The results showed that :(1) the water supply and demand in Ⅰ(humid area)and Ⅱ(sub-humid area)areas were balanced, while the water supply and demand in Ⅲ(sub-arid area)and Ⅳ(arid area)areas were unbalanced. The mean square error of precipitation in area Ⅰ was up to 70mm, and the interannual variation range of CWDI was the largest. In the 1970s, CWDI floated sharply in the sowing-seedling and seedling-jointing stages. CWDI in area II fluctuated greatly during the tasseling-milking stage in the 1980s. In area Ⅲ, CWDI increased at the rate of 1.06pp?10y?1(P<0.05) during the tasseling-milking stage, and the interannual drought tended to expand. There was little interannual change in area Ⅳ. (2) During 1961?2018 the ratio of drought stations in the tasseling-milking stage increased significantly at the rate of 2.58pp?10y?1(P<0.05), and the average of extra severe drought was 14.95%. There was no significant change in the other four growth stages. During the tasseling-milking stage, the drought level was the most severe, and the drought range was significantly expanded. (3) In terms of spatial distribution, drought grade and frequency showed an obvious east-west distribution. The frequency of slight drought in eastern and southwestern Heilongjiang province, western Jilin, and western Liaoning province (area Ⅰ and Ⅱ) is more than twice in three years. In the Zhangcheng district (the cities of Zhangjiakou and Chengde in Hebei, area Ⅲ), the frequency of moderate drought during the stage of sowing-seedling reached more than once in 5 years. In Mu Us Desert (it is located between Yulin, Shaanxi province and the Ordos, Inner Mongolia Autonomous Region, area Ⅳ), the frequency of extra severe drought in the jointing-tasseling stage reached twice in 3 years, and the period of jointing-tasseling was mainly threatened by extra severe drought.
Keywords:Crop water deficit index (CWDI)  Spring maize  Northern China  Dry farming  Spatial-temporal variation  
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