Prioritization of Water Allocation for Adaptation to Climate Change Using Multi-Criteria Decision Making (MCDM)

• Published in: Water resources management 2019-08, Vol.33 (10), p.3401-3416
• Main Authors: Golfam, Parvin, Ashofteh, Parisa-Sadat, Rajaee, Taher, Chu, Xuefeng
• Format: Article
• Language: English
• Subjects: Adaptation; Agricultural management; Agriculture; Analytic hierarchy process; Atmospheric Sciences; Civil Engineering; Climate adaptation; Climate change; Climate change adaptation; Decision analysis; Decision making; Demand; Earth and Environmental Science; Earth Sciences; Environment; Geotechnical Engineering & Applied Earth Sciences; Hierarchies; Hydrogeology; Hydrology/Water Resources; Multiple criteria decision making; Multiple criterion; Reduction; Sensitivity analysis; Uncertainty; Uncertainty analysis; Water allocation; Water demand; Water resources; Weight
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-019-02307-7

The complex nature of water resources and the related uncertainty cause decision making to be difficult in practice. In this study, two multi-criteria decision making (MCDM) methods, Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), were applied to determine the best scenario adapting to climate change in agriculture for the Gharanghu basin in Northwest Iran for a 30-year period (2040-2069). Reservoir efficiency indexes were used as evaluation criteria. Specifically, the preference of each criterion relative to the other criteria was determined based on experts’ opinions. Five management scenarios were considered, involving reductions in agricultural water demand by 5, 10, 15, 20, and 25%, respectively. By applying the AHP approach, the consolidated weight of each criterion was calculated; the best adaptation scenario to climate change was determined; the inconsistency rate was calculated; and sensitivity analysis was also performed. The AHP results showed that the fifth scenario (25% demand reduction) with a weight of 33.5% was the best one for agricultural water demand management. The results obtained from the TOPSIS model indicated that the third scenario (15% demand reduction) with a weight of 20.8% was the best management scenario for agriculture in the period of climate change. Thus, estimation of uncertainty related to climate change is critical to choosing the best alternative using the MCDM models. Uncertainty analysis helps address the questions about whether the management scenarios are sustainable under unforeseen changes, and whether they are an ideal response to critical conditions of climate change.