Conjunctive Water Management under Multiple Uncertainties: A Case Study of the Amu Darya River Basin, Central Asia

With population growth, climate volatility, and economic expansion, the conjunctive management of surface–groundwater (SGW) faces great challenges. In this study, a hybrid factorial optimization programming (HFOP) method is developed through integrating factorial analysis, interval linear programming, flexible fuzzy programming, and two-stage stochastic programming into a general framework. HFOP can effectively reflect the multiple uncertainties and quantitatively identify the effects of multiple factors. Then, a HFOP-SGW model is formulated for the middle reaches of the Amu Darya River Basin, where 125 scenarios are analyzed. Some of the major findings are: (i) the improvement of surface-water transport efficiency and the proper use of groundwater can effectively alleviate regional water shortage; (ii) agricultural users have a high risk of water scarcity for all states, especially under a low-flow level; (iii) uncertainties of water-flow levels and risk-reverse attitudes of decision makers have significant impacts on the system’s benefits and water-allocation scheme; and (iv) the surface-water-transmission loss rate and risk perceptions of decision makers are the main factors affecting the system’s benefit’s and water-allocation scheme. These findings can help decision makers obtain desired water-allocation strategies to respond to the variations in water availability.