Prediction of Land Use Change and its Hydrological Effects Using Markov Chain Model and SWAT Model

Access to current and future water resources is one of the concerned problems for managers and policymakers around the world. Because of the communication between water resources and land use, these two topics had come together in different researches. Scenarios designed in regional land planning provide the basis for analyzing the existing opportunities and making the right decisions for managing these natural resources. In this research, a combination of the Markov Chain model and multilayer perceptron network (MLP) were used for predicting the land-use changes in Sarab watershed  and the SWAT model was used for hydrological modeling of the watershed area. Using the land use map in 2015, soil map, digital elevation model and meteorological data during the period (1987-2015), the hydrological model of the area is formed and also calibrated. According to the land-use changes in the past (1987-2015), three scenarios defined and three land use maps have been predicted for 2030 by modeling the land-use changes and calculating the conversion probability matrix using the Markov chain model. The watershed hydrological response to the first scenario with the title of conversion of grassland to the irrigated agriculture was observed an increase of 0.7% of the annual average run-off and a 4% decrease in the river flow. In the second and the third scenarios, the surface run-off has been increased by 1% and 2.5% respectively by conversion of the grassland to the rain-fed agriculture and bare lands. Flow changes in these two scenarios show an increase of 1.8%. According to the results of this research, grazing and conversion grassland to bare lands will have the greatest impact on underground water resources in the Sarab basin. Furthermore, the expansion of irrigated agriculture lands, by increasing the harvesting of surface water and underground water resources will result in a significant reduction of these resources.