| 农业干旱业务化监测研究进展与展望 |
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| 引用本文： | 刘伟琦,马绍休,宫毓来,冯坤,梁林昊.农业干旱业务化监测研究进展与展望[J].中国沙漠,2023,43(1):197-211. |
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| 作者姓名： | 刘伟琦 马绍休 宫毓来 冯坤 梁林昊 |
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| 作者单位： | 1.中国科学院西北生态环境资源研究院 沙漠与沙漠化重点实验室，甘肃 兰州 730000;2.中国科学院大学，北京 100049 |
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| 基金项目： | 国家重点研发计划项目(2017YFE0119100);中国科学院“百人计划”项目(Y729G01001) |
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| 摘 要： | 业务化农业干旱监测系统是农业干旱监测和预测以及农业风险评价和防范的有力工具，为了更好地促进农业干旱业务化监测的发展，系统回顾了基于气象变量、土壤湿度、植被状态和多变量等4类常用干旱指数，详细分析了美国、中国、欧洲和联合国粮食及农业组织等业务化农业干旱监测系统的特征，讨论了业务化农业干旱监测系统中存在的问题：如数据的质量及融合不稳定、综合干旱指数的构建不确定、监测的时间分辨率有待提高、缺乏考虑水文条件以及作物的生长过程等影响的问题。展望了未来农业干旱业务化监测，应从利用多源数据监测干旱、构建综合指标时需考虑区域时空差异及不同指标间的累积性和滞后性、加强机器及深度学习在综合指标构建中的作用、发展日时间尺度监测干旱以应对骤旱事件的发生、强化作物生长过程模型和先进的技术手段在干旱监测中的作用等方面深入发展。
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| 关 键 词： | 农业干旱 业务化监测系统 干旱指数 干旱监测 |
| 收稿时间： | 2022-08-19 |
| 修稿时间： | 2022-09-25 |
Research progress and perspective for operationalization of agricultural drought monitoring |
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| Weiqi Liu,Shaoxiu Ma,Yulai Gong,Kun Feng,Linhao Liang.Research progress and perspective for operationalization of agricultural drought monitoring[J].Journal of Desert Research,2023,43(1):197-211. |
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| Authors: | Weiqi Liu Shaoxiu Ma Yulai Gong Kun Feng Linhao Liang |
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| Institution: | 1.Key Laboratory of Desert and Desertification，Northwest Institute of Eco-Environment and Resources，Chinese Academy of Sciences，Lanzhou 730000，China;2.University of Chinese Academy of Sciences，Beijing 100049，China |
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| Abstract: | The operational agricultural drought monitoring system is a powerful tool for monitoring and predicting agricultural drought， as well as agricultural risk assessment and prevention. In order to better promote the development of the operationalization of agricultural drought monitoring， we systematically reviewed four types of commonly used drought indices based on meteorological variables， soil moisture， vegetation status and multivariate， and analyzed the characteristics of operational agricultural drought monitoring systems in the United States， China， Europe and the United Nations Food and Agriculture Organization， etc. in detail， and discussed the problems existing in operational agricultural drought monitoring systems. For example， the quality and fusion of data are unstable， the construction of the comprehensive drought index is uncertain， the temporal resolution of monitoring needs to be improved， and the hydrological conditions and the growth process of crops are not considered. Looking forward to the future， the operational agricultural drought monitoring system should develop further from using multi-source data to monitor drought， considering regional spatial and temporal differences and the accumulation and lag between different indicators when constructing comprehensive indicators， strengthening the role of machine and deep learning in the construction of comprehensive indices， developing daily time-scale monitoring of drought to deal with the occurrence of flash drought events， strengthening the role of crop growth process models and advanced technical means in drought monitoring. |
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| Keywords: | agricultural drought operational monitoring systems drought index drought monitoring |
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