Department of Range and Watershed Management, College of Agriculture and Natural Resources of Darab, Shiraz University, Shiraz, Iran.
Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran.
Sci Total Environ. 2021 Aug 10;781:146703. doi: 10.1016/j.scitotenv.2021.146703. Epub 2021 Mar 25.
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.
预测干旱并确定相关数据来预测干旱是决策者和规划者的一个重要议题。预测伊朗干旱和半干旱气候下重要农业中心法尔斯省南部的干旱情况至关重要。本研究的目的是使用 12 个参数（海拔、干燥指数、侵蚀、地下水深度、土地利用、潜在蒸散量、降水天数、降水、坡度、土壤质地、土壤盐分和距河流的距离）生成 2019 年干旱图，并使用元胞自动机（CA）-马尔可夫模型在空间上预测 2030 年和 2040 年的干旱图。首先使用模糊方法对数据进行均匀化处理。然后，通过评估每个参数，使用层次分析法（AHP）计算每个参数的权重，并生成易旱区地图。模糊-AHP 方法的结果表明，研究区的东部和东南部容易发生干旱。使用最佳搜索方法选择导致干旱的四个最具预测性的参数（干燥指数、潜在蒸散量、降水和土壤质地），然后选择它们作为输入，使用模糊和 AHP 方法确定干旱图。最后，使用 CA-Markov 模型预测未来干旱图，结果表明 2030 年和 2040 年研究区东部和南部的干旱情况将加剧。
