The utility of a hybrid GEOMOD-Markov Chain model of land-use change in the context of highly water-demanding agriculture in a semi-arid region

Abstract

Land change simulation for highly water-demanding crops may prove a valuable tool to guide integrated land-and-water management in semi-arid regions facing water scarcity. We explored this premise by mapping and modelling past (1985–2015) and future (2015–2030) orchard development relative to water resources and other factors in Iran. We employed a hybrid GEOMOD-Markov Chain model whereby both the spatial allocation and quantity of orchard development were simulated. By 2030, orchard cover is projected to increase by 20% of its 2015 area, straining limited water resources. To gauge the accuracy of our projection of orchard gain to 2030, we assessed a comparable simulation of orchard gain for 2000–2015 according to the various components the Figure of Merit (FOM) metric. Misses, Hits and False Alarms of simulated orchard gain accounted for 1.84%, 0.45% and 0.74% of the study area respectively over 2000–2015 at a 200-m spatial resolution, for which the FOM was appreciable (15%) given the limited extent of simulated orchard gain and actual orchard cover across the study region (1.2% and 5.3%). With respect to orchard gain, spatial allocation error was more than land-change quantity error at 200-m resolution, at 1.48% and 1.10% of the study area, respectively. Predicting the location of agricultural change remains a priority and challenge for model utility, given scant agricultural footprints in semi-arid regions and their large draw on limited water resources. Results also indicate the importance of incorporating dynamic water availability and demand over the course of agricultural expansion, including shifts in the location preference amongst farmers. The integration of dynamic, agent-based models within our GEOMOD-Markov Chain framework is therefore methodologically appealing, but would adversely increase complexity for policymakers.