作 者：Dai, MengHuang, ShengzhiHuang, QiangLeng, GuoyongGuo, YiWang, LuFang, WeiLi, PeiZheng, Xudong

作者机构：State Key Laboratory of Eco-hydraulics in Northwest Arid Region of ChinaXi'an University of Technology Xi'an710048 ChinaEnvironmental Change InstituteUniversity of Oxford OxfordOX1 3QY United Kingdom

出 版 物：《Agricultural Water Management》

年 卷 期：2020年第231卷

核心收录：

中图分类：S2[农业科学-农业工程]

学科分类：08[工学]0828[工学-农业工程]

基 金：This study was jointly funded by the National Key Research and Development Program of China (grant number 2017YFC0405900 )the Key laboratory research projects of the education department of Shaanxi province (grant number 17JS104 )the National Natural Science Foundation of China (grant number 51709221 )the Planning Project of Science and Technology of Water Resources of Shaanxi (grant numbers 2015slkj-27 and 2017slkj-19 )and the Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin (China Institute of Water Resources and Hydropower Researchgrant number IWHR-SKL-KF201803 ).

主 题：Risk assessmentAgricultural robotsAgricultureDroughtDynamicsGemsRiversWatershedsAgricultural damage dataAgricultural droughtCopula functionsDynamic risksJoint return periodPearl River basinPearl River deltaStandardized precipitation index

摘 要：Assessment of agricultural drought risk is significant for risk division and management. Nevertheless, the drought risk dynamic evolution characteristics have not been revealed. To this end, the agricultural drought conditions are characterized by the standardized precipitation index (SPI), and the time scale of SPI is determined based on agricultural damage data. The joint return periods of various drought severities and durations under different agricultural drought scenarios are calculated by using copula functions. Moreover, drought risk factors (resilience, vulnerability, and exposure) are also used to characterize drought risk. Subsequently, based on the moving window, the joint return period and risk factors in each window are calculated, and agricultural drought dynamics are explored. The Pearl River Basin (PRB) is selected as a case study. Results indicated that: (1) the 4-month most appropriate timescale for the SPI in characterizing agricultural drought based on agricultural damage data in the PRB; (2) risk factors method is more suitable than joint return period in assessing agricultural drought risk; (3) most of the PRB exhibit a significant increasing agricultural drought risk, while the drought risk of the Pearl River Delta has a decreased trend within the past 50 years. Generally, this study show new insights into agricultural drought risk assessment, thus promoting local agricultural drought preparedness and mitigation. © 2020 Elsevier B.V.