Dynamic interactions between vegetation and land use in semi-arid Morocco: Using a Markov process for modeling rangelands under climate change

Integrated scientific assessments of semi-arid agroecosystems with mathematical models are challenging because of computational constraints. These constraints arise from exponentially increasing decision options due to dynamic interactions between the biophysical states of rangeland vegetation and farsighted decisions taken by pastoral stakeholders. This study applies a methodology that integrates these interactions in a computationally feasible manner. We equip a dynamic land use decision model with a detailed representation of biophysical processes by using a Markov chain meta-model of EPIC (Environmental Policy Impact Calculator). Using separate Markov chains for different weather scenarios, we investigate the economic and ecological impacts of droughts on rangeland management in southern Morocco. The drought simulations (2 years with 33% less precipitation) show a decrease in profits from pastoralism by up to 57%. Pastoral land use of the rangeland in our model increases surface runoff by 20%, doubles infiltration, and thus influences irrigation agriculture. The economic and ecological impacts of drought in our simulation go substantially beyond its meteorological time horizon.

Research highlights
► Coupling biophysical vegetation dynamics to farsighted land use decisions using Markov chains.
► Economic and ecological impacts of drought go substantially beyond its meteorological time horizon.
► Intense grazing of rangelands enhances water availability for down-stream irrigation agriculture.