摘要
干旱脆弱性评价作为干旱风险评估和灾损评估的重要环节,在保障国家粮食安全和农业可持续发展中具有重大意义。该文以中国5大玉米种植区为研究区域,以其中241个主要玉米种植城市为基本单元,采用扩展傅里叶幅度检验法选取出2个敏感参数(作物冠层形成后到衰老之前的作物系数和参考收获指数),并在此基础上对AquaCrop作物模型进行逐市的参数标定。利用参数标定后的模型对不同灌溉条件下玉米受到的水分胁迫及相应情景下的产量进行模拟计算,分别建立了5个玉米种植区对应的干旱脆弱性曲线。结果表明:5个区域的脆弱性曲线拟合结果均为S形曲线,当干旱强度指标达到0.2附近时,产量损失率开始迅速增加;当干旱强度指标达到0.6左右时,产量损失率接近最大值。拟合函数的决定系数R^2分别在0.47~0.98之间,曲线拟合结果较好,在中国区域性玉米干旱脆弱性研究与干旱风险评估领域具有一定的理论与应用价值。
Drought disaster assessment has become increasingly significant in ensuring national food security and sustainable agricultural development. Vulnerability assessment plays a significant role in disaster research area and vulnerability curve is one of the common quantitative evaluation methods in the field of vulnerability research. In this paper, using the AquaCrop model that has been calibrated city by city, we simulated the response of maize yield to different water stress and then constructed drought vulnerability curves for 5 maize planting regions in China: the north spring maize planting region, the Huang-Huai-Hai summer maize planting region, the southwest mountain maize planting region, the south hilly maize planting region and the northwest irrigated maize planting region. In this research, firstly, 2 of 36 main crop parameters of maize were selected as sensitive parameters based on a global sensitivity analysis method, Extended Fourier Amplitude Sensitivity Test. Then, AquaCrop model was calibrated city by city in 241 maize-growing cities and used to simulate the maize yield under different irrigation scenarios. Finally, we built drought vulnerability curves of 5 main maize plating regions with an improved drought hazard index construction method, which used an average value of daily drought hazard indexes instead of the commom accumulate value, thus we raised comparability of drought hazard index between different maize planting regions and took extreme drought situation into account. The results showed that: 1) The 2 most sensitive parameters to maize yield in the Aquacrop model were the crop coefficient when canopy growth was complete but prior to senescence and the reference harvest index. We finally obtain 241 groups of parameters for the 241 maize planting cities after finishing model calibration and according to the result of validation, the accuracy of the model calibration was satisfactory(R^2=0.67). 2) All the 5 vulnerability curves followed an "S" shape. And we found that when the drought hazard index reached 0.2, the yield loss rate began to increase rapidly;and when it reached 0.6, the yield loss rate approached the maximum value. The R^2 of the fitted functions in 5 maize planting regions were 0.93, 0.86, 0.47, 0.70, 0.98, respectively. The northwest irrigated maize planting region had the highest R^2 and the southwest mountain maize planting region had the lowest. The drought situation was more serious in the northwest irrigated maize planting region, followed by the north spring maize planting region, the Huang-Huai-Hai summer maize planting region, the south hilly maize planting region and the southwest mountain maize planting region. The research enriched case studies of the AquaCrop model and vulnerability curve construction, quantitatively explored the spatial and temporal differences in drought effects on maize yield in China and enhanced the researches on yield loss prediction. It provides valulble information for the study of drought hazard vulnerability of maize in China and has a certain practical value in the field of drought risk assessment.
作者
徐昆
朱秀芳
刘莹
侯陈瑶
Xu Kun;Zhu Xiufang;Liu Ying;Hou Chenyao(Institute of Remote Sensing Science and Engineering,Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China;State Key Laboratory of Earth Surface Processes and Resource Ecology,Beijing Normal University,Beijing 100875,China;Key Laboratory of Environmental Change and Natural Disaster,Ministry of Education,Beijing Normal University,Beijing 100875,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2020年第1期154-161,共8页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家重点研发计划（2019YFAO606900）
国家自然科学基金青年基金项目（41401479）
