JWSS - Journal of Water and Soil Science

Desertification is known as a major crisis in arid regions of Isfahan province. This study aimed to assess the performance of three main desertification models including MEDALUS, MICD and FAO-UNEP for mapping desertification severity in the hotspot of Jarghuyeh region, eastern Isfahan. Different desertification indicators and their related indices were chosen based on the characteristics of the region and fieldwork, and spatially mapped in 27 geomorphologic facies. The desertification severity maps were classified based on the classification scheme for each model in ArcGIS 10 environment, and then comparison of the models and selection of the best one were achieved using IDRISI Tiga 16.03 software. The results of all three models showed that more than 95% of the region can be classified as severe desertification but due to the differences in the number of desertification classes and also indicators and indices only 45% of desertification severity was observed to be similar across the models. Results indicated that the MEDALUS model due to its flexibility to accept new indicators and indices, GIS-based characteristics, and use of geometric mean of indicators in desertification mapping seems to be a suitable model for studying desertification severity in the region. According to this model, 85% and 15% of the area are classified as very severe and severe class of desertification, respectively, which indicates that the rate of desertification is very high and immediate management programs are needed to slow down the desertification process in the region.

Appropriate criteria and methods are required to assess desertification potential in various ecosystems. This paper aimed to assess desertification levels in Segzi plain located in east part of Isfahan, with a focus on soil quality criteria used in MEDALUS model. Bayesian Belief Networks (BBNs) were also used to convert MEDALUS model into a predictive, cause and effects model. Soil samples were collected from 17 soil profiles in all land units and some of their characteristics such as texture, soluble sodium and chlorine, organic material, Sodium Absorption Ratio (SAR), Electrical Conductivity (EC) and CaSo4 of all soil samples were determined in soil laboratory. The effects of measured soil quality indicators on desertification intensity levels were assessed using sensitivity and scenario analysis in BBNs. Results showed that the used integrated method can appropriately accommodate uncertainty in the desertification assessments approaches created as a result of the influence of different soil characteristics on desertification. According to the results of MEDALUS model, 28.28 % and 71.72 % of the study area were classified as poor and moderate areas in terms of soil quality respectively. Sensitivity analysis by both models showed that soil organic matter, SAR and EC were identified as the most important edaphic variables responsible for desertification in the study area. Evaluating the effects of various management practices on these variables can assist managers to achieve sound management strategies for controlling desertification.
 

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.