Regional agricultural drought risk assessment based on attribute interval identification: a study from Zhengzhou, China
Author(s) -
Huihui Hao,
Hanyu Zhu,
Fuqiang Wang
Publication year - 2022
Publication title -
water science and technology water supply
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.318
H-Index - 39
eISSN - 1607-0798
pISSN - 1606-9749
DOI - 10.2166/ws.2022.177
Subject(s) - agriculture , grey relational analysis , risk assessment , vulnerability (computing) , risk management , identification (biology) , environmental resource management , risk analysis (engineering) , water resource management , environmental science , geography , business , computer science , statistics , mathematics , ecology , computer security , archaeology , finance , biology
Agricultural drought risk assessment is helpful to quantitatively understand agricultural drought and scientifically guide disaster prevention and mitigation. Therefore, according to the characteristics of attribute uncertainty and index weight subjectivity in agricultural drought risk assessment, an attribute interval identification model combined with grey relational analysis was established to evaluate agricultural drought risk. Firstly, the agricultural drought risk evaluation index system was established from four aspects: disaster, exposure, vulnerability and resistance. Then, the objective weights of the indicators are calculated using the grey relational method. Finally, the agricultural drought risk in Zhengzhou was evaluated by qualitative analysis and probabilistic analysis. Qualitative analysis results showed that the agricultural drought risk in Zhengzhou is at the level of moderate drought. The probability analysis showed that the probability of a serious drought disaster in Zhengzhou is 79.5%, and the probability of a serious drought disaster is 20.5%. In addition, the superiority of the attribute interval identification model in agricultural drought risk assessment was further verified by comparative analysis. This research provided a new method for regional agricultural drought risk assessment. Furthermore, it can provide support for management departments to further understand the regional drought risk level and improve the efficiency of drought risk management.
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