As one of the largest super-saline lakes in the world, Lake Urmia in northwestern Iran has been facing severe drying in recent years. Drought and rapid expansion of agricultural activities are considered to be the main driving factors for the shrinking of the lake. To address this problem, an analysis of the spatiotemporal dynamics of land use/land cover (LULC) is important. This research implemented a multi-source satellite image analysis through support vector machine (SVM) for mapping LULC distributions for the years 2000, 2010, and 2020. Cellular automata (CA)–Markov was prepared for modeling the future landscape changes for 2030 and 2040. In the last step, the water requirement of agriculture in the catchment area of the Urmia Lake was simulated through the NETWAT model. Through the employed future LULC modeling, it was found that the areas covered by irrigated agriculture and gardens will grow from 1,450 and 395 km2 to 3,600 and 1,650 km2 in 2040, respectively, as deduced from the changes that occurred from 2000 to 2020. This will increase the water requirement of agriculture from 1,500 billion cubic meters in 2000 to more than 4,100 billion cubic meters in 2040.

  • LULC modeling is implemented through CA–Markov model to predict future LULC for 2030 and 2040.

  • The NETWAT model was used to simulate the water requirement of agriculture.

  • A significant increase in the areas covered by agriculture activities were identified.

  • A 200% increase in water requirement of agriculture was observed in the period of 40 years (2000–2040).

land change modeler, land use/land cover change, Markov–cellular automata, support vector machine, Urmia Lake
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