Determining and forecasting drought susceptibility in southwestern Iran using multi-criteria decision-making (MCDM) coupled with CA-Markov model

Forecasting drought and determining relevant data to predict drought are an important topic for decision-makers and planners. It is critical to predicting drought in the south of Fars province, an important agricultural center in Iran located in arid and semi-arid climates. The purpose of this study was to generate a drought map in 2019 using 12 parameters: altitude, aridity index, erosion, groundwater depth, land use, PET (Potential evapotranspiration), precipitation days, precipitation, slope, soil texture, soil salinity, and distance to river, and predict drought maps in 2030 and 2040 using the cellular automata (CA)-Markov model spatially. The fuzzy method was first used to homogenize the data. Next, by evaluating each parameter, the weight of each parameter was calculated using the analytic hierarchy process (AHP), and a map of drought-prone areas was generated. The results of the fuzzy-AHP method showed that the eastern and southeastern regions of the study area were prone to drought. The four most predictive parameters in causing drought, i.e., aridity index, PET, precipitation, and soil texture, were selected using the Best search method and were then chosen as the input to determine drought mapping using the fuzzy and AHP methods. Finally, the CA-Markov model was used to predict future drought maps, and the results showed that in 2030 and 2040 the drought situation in the east and south of the study area would intensify. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

Forecasting drought using various parameters and spatial models is crucial for decision-makers, especially in arid regions like the south of Fars province in Iran. This study utilized fuzzy and AHP methods to identify drought-prone areas and selected key predictive parameters to map drought. The predictions indicate an intensification of drought in the east and south of the study area by 2030 and 2040.