Optimization-Driven Mathematical Models for Sustainable Water Resource Management under Uncertainty

Abstract

Climate variability and augmentation of water scarcity by the high demand are a matter of great concern to the management of water resources in a sustainable way. The conservative deterministic models are inclined to disregard the uncertainty around inflows, demand and policy dynamics and therefore yield sub-optimal solutions. The paper presents mathematical optimisation-driven mathematical models that integrate stochastic programming, robust optimization and multi-objective decision-making to create a balance between economic efficiency, on one hand, and social equity on the other hand and ecological sustainability. The framework is tested on semi-arid river basin, both synthetic and real-world data. Findings indicate up to 18 percent gains in supply reliability, 12 percent in water losses and resiliency to extreme hydrological events relative to deterministic baselines. The results show the promising nature of optimization-based models, which is a generalizable, dynamic decision-making tool in uncertain situations of sustainable water management.