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基于SPEI和MI分析陕西省干旱特征及趋势变化
引用本文:丁怡博,徐家屯,李亮,蔡焕杰,孙亚楠. 基于SPEI和MI分析陕西省干旱特征及趋势变化[J]. 中国农业科学, 2019, 52(23): 4296-4308. DOI: 10.3864/j.issn.0578-1752.2019.23.010
作者姓名:丁怡博  徐家屯  李亮  蔡焕杰  孙亚楠
作者单位:1 西北农林科技大学旱区农业水土工程教育部重点实验室,陕西杨凌7121002 西北农林科技大学中国旱区节水农业研究院,陕西杨凌7121003 西北农林科技大学水利与建筑工程学院,陕西杨凌 712100
基金项目:国家重点研发项目(2016YFC0400201);国家自然科学基金项目(51879223)
摘    要:【目的】目前干旱研究多为基于历史干旱事件分析成因与变化趋势,而结合过去与未来长时间序列数据更能揭示干旱变化特点。寻找在基于CMIP5模型输出未来气象数据时模拟干旱指数方法并探究陕西省过去与未来干旱变化特点,为陕西省未来农业水资源管理提供依据。【方法】根据陕西省18个气象站历史数据以及CMIP5模式输出未来气象数据,比较了3种模型模拟参考作物蒸发蒸腾量(ET0),并基于参考作物蒸发蒸腾量(ET0)和降水数据计算标准降水蒸发指数(SPEI)和相对湿润指数(MI)反映干旱程度,比较过去(1958—2018年)与未来(2019—2100年)干旱的时空变化特点。【结果】多元线性回归模型(Multiple Linear Regression, MLR)能较准确的模拟参考作物蒸发蒸腾量(ET0)(RMSE=0.457 mm·d -1);在RCP2.6和RCP8.5情景下未来干旱指数呈现上升趋势,在RCP8.5情景下,21世纪40年代存在干旱指数的突变年份;陕西省未来干旱程度降低,年内干旱分布更加不均匀;未来时期夏玉米生长季干旱程度减小,冬小麦生长季干旱程度增加。【结论】在不同RCP情景下,未来干旱变化特征存在差异,相同RCP情景下,SPEI和MI反映的干旱特征变化基本一致,但部分时段存在变化差异。为有效应对气候变化对旱作作物产量造成的负面影响,应当增强土壤蓄水保墒能力,尤其加强冬小麦生长季的抗旱工作。

关 键 词:标准降水蒸发指数(SPEI)  相对湿润指数(MI)  蒸发蒸腾量(ET0  多元回归  神经网络  典型浓度路径(RCP)  趋势检验(Mann-Kendall)  陕西省  
收稿时间:2019-03-22

Analysis of Drought Characteristics and Its Trend Change in Shaanxi Province Based on SPEI and MI
DING YiBo,XU JiaTun,LI Liang,CAI HuanJie,SUN YaNan. Analysis of Drought Characteristics and Its Trend Change in Shaanxi Province Based on SPEI and MI[J]. Scientia Agricultura Sinica, 2019, 52(23): 4296-4308. DOI: 10.3864/j.issn.0578-1752.2019.23.010
Authors:DING YiBo  XU JiaTun  LI Liang  CAI HuanJie  SUN YaNan
Affiliation:1 Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest A&F University, Yangling 712100, Shaanxi;2 Institute of Water Saving Agriculture in Arid Areas of China, Northwest A&F University, Yangling 712100, Shaanxi;3 College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, Shaanxi;
Abstract:【Objective】 At present, most drought studies were based on historical drought events to analyze the causes and trends. This paper sought to simulate the drought index method when outputting future meteorological data based on CMIP5 model, and explored the characteristics of past and future drought changes in Shaanxi Province, which could provide a basis for the future management of agricultural water resources in Shaanxi Province. 【Method】Based on the historical data of 18 meteorological stations in Shaanxi Province and CMIP5 model, the future meteorological data were output. The reference crop evapotranspiration (ET0) was simulated by comparing three kinds of models. The standard precipitation evaporation index (SPEI) and relative moisture index (MI) were calculated based on the reference crop ET0 and precipitation data to reflect the drought degree. The spatial and temporal characteristics of drought in the past (1958-2017) and in the future (2018-2100) were compared.【Result】Multiple linear regression (MLR) simulation could accurately predict the reference crop ET0 (RMSE=0.457 mm·d -1). In the RCP2.6 and RCP8.5 scenarios, the future drought index showed an upward trend. Under the RCP8.5 scenario, there was a sudden change in the drought index in the 1940s. The degree of drought would decrease in the future of Shaanxi Province, and the distribution of drought would be more uneven during the year. In the future, the degree of drought would decrease during summer maize growth season, and the degree of drought would increase during winter wheat growth season.【Conclusion】The characteristics and extent of drought change were different under different RCP scenarios. The changes in drought characteristics reflected by SPEI and MI were basically the same, but there were differences in the changes in some time periods. In order to effectively cope with the negative impact of climate change on dry crop yields, it was necessary to enhance soil water storage and conservation capacity, especially to strengthen drought resistance during the winter wheat growing season.
Keywords:Standard Precipitation Evaporation Index (SPEI)  Relative Moisture Index (MI)  evapotranspiration(ET0)  multiple regression  neural network  Representative Concentration Pathways (RCP)  Mann-Kendall trend test  Shaanxi Province  
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