Regional agricultural drought risk assessment based on attribute interval identification: a study from Zhengzhou, China

Abstract

Agricultural drought risk assessment is helpful in quantitatively understanding agricultural drought and scientifically guiding 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 were 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 Zhengzhou City being in a moderate drought is 79.5%, and the probability of being in a severe drought 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 provides 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. HIGHLIGHTS The probability corresponding to the agricultural drought risk level should be considered.; Interval values are used instead of fixed values to quantify the evaluation indicators.; Gray correlation is introduced to construct an interval attribute recognition and evaluation model.; The construction of an evaluation index system is very important.; The dynamic changes of indicator values should be taken seriously.;

Keywords

• agricultural drought risk assessment
• attribute interval recognition
• probabilistic analysis
• qualitative analysis