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

Abstract

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.