Spatiotemporal complexities increase the uncertainty of the results of various desertification assessment models. We integrated the Mediterranean desertification and land‐use (MEDALUS) model with Bayesian networks (BNs) to develop a novel method for desertification assessment in central Iran. We first derived the current desertification status of the study area from empirical observations of actual, current desertification conditions and mapped the obtained status based on the criteria of the MEDALUS model. We then utilized BNs to produce three separate causal models for the soil, groundwater, and wind erosion indicators of the MEDALUS model. We then integrated all the related indicators and indices into a desertification assessment BNs model. After the reevaluation and sensitivity analysis of the model, we compared its results with those of the MEDALUS model. The obtained results highlighted soil condition and wind erosion as the main determinants of desertification in the study area. Considering the ability of BNs to accommodate uncertainty in the assessment process, these models can assist relevant authorities in making informed decisions. They can also be applied as decision support tools for adaptive management in fragile ecosystems of arid areas.

This paper aimed to assess the severity of desertification in Segzi plain located in the eastern part of Isfahan city, focusing on groundwater quality criteria used in MEDALUS model. Bayesian Belief networks (BBNs) were also used to convert MEDALUS model into a predictive, cause and effects model. Different techniques such as Kriging and IDW were applied to water quality data of 12 groundwater wells to map continuous variations of the CL, SAR, EC, TDS, pH and decline in water table indices in GIS environment. The effects of measured water quality indicators on desertification severity levels were assessed using sensitivity and scenario analysis in BBNs model. According to the results of the MEDALUS, the desertification of the study area was classified as severe class due to its low quality of groundwater. Sensitivity analysis by the both models showed that decline in waater table, water chloride content and electrical conductivity were the most important parameters responsible for desertification in the region from ground water condition standpoint. The determination coefficient between the outputs of the MEDALUS and BBNs models (R2>0.63) indicated that the results of both models were significantly correlated (α=5 %). These results indicate that the application of BBNs model in desertification assessment can appropriately accommodate the uncertainty of desertification methods and can help managers to make better decision for upcoming land management projects.

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