Application of Compromise Programming Method and Fuzzy-Spatial Indicators for Assessment of Water Allocation Scenarios, (Case Study; Aras Basin)

In the context of integrated water resources management, assessment of water allocation scenarios is very important and complex. There are different and sometimes conflicting indicators in water resources management that have different values in different areas. Regarding this, evaluation of allocation scenarios involves performing the spatial multi-criteria analysis. The aim of this study was to evaluate water resources allocation scenarios using a spatial decision support system. Therefore, the compromise programming method with the economic, social and environmental indicators has been implemented in the Aras basin. In the first step, the indicators were considered as lumped and distributed form with equal weight. In the second step, the sensitivity of the compromise programming method was analyzed changing one of the indicators weight, while maintaining the other indicators constant. In step three, group and fuzzy decision making approach was used to determine the weight of the indicators. Then, scenarios 1 to 5 ranked fifth, third, second, first and fourth respectively. The results of this study showed implementing spatial distribution of indicators influence both scores and rankings of the water resources allocation scenarios. So that the Spearman correlation coefficient of the rankings, caused by application of lumped and distributed indicators, was calculated to be 0.6. Also, application of the compromise programming method, group-fuzzy weight and distributed indicators leads to a change in ranking and reduce correlation coefficient up to 0.2. Regarding the effect of two parameters, including the type of indicators and the group-fuzzy weight of indicators, on the scenarios ranking results, a significant uncertainty in the process of assessing scenarios could be occurred if the proposed parameters would not be considered. Therefore, it is essential to consider the spatial distribution of the values and the group-fuzzy decision-making should be used to determine the weight of evaluation indices.