Stochastic mathematical models to balance human and environmental water needs and select the best conservation policy for drought-prone river basins Article

Farzaneh, MA, Rezapour, S, Fovargue, R et al. (2021). Stochastic mathematical models to balance human and environmental water needs and select the best conservation policy for drought-prone river basins . JOURNAL OF CLEANER PRODUCTION, 291 10.1016/j.jclepro.2020.125230

cited authors

  • Farzaneh, MA; Rezapour, S; Fovargue, R; Neeson, TM

abstract

  • In this paper, a multi-objective, multi-period, and stochastic mathematical model is developed to identify drought-resilient strategies for balancing human and environmental water needs given uncertainty about future water availability. The model makes sustainable water withdrawal/distribution decisions for the interconnected water reservoirs of a river network through balancing societal and environmental water needs. These decisions are restricted by the storage capacities of reservoirs and fluctuations of water availability over the planning horizon. The model relies on interval estimation of stochastic hydrological factors, which may be drawn from historical data or future climate projections. The model finds a compromise between drought resilience and satisfaction of water needs through a reliability level that represents the risk attitude of water decision-makers. Finally, the model is extended to include water conservation options. The extended model determines the spatial and temporal prioritization of water reservoirs for water conservation. The proposed models are tested on the Red River of the south-central United States. We quantify tradeoffs between human and environmental water needs and identify water sustainability strategies that are resilient to stochastic drought events. Our model is applicable to drought-prone river basins around the world where water managers seek to balance environmental and human water needs while remaining resilient to unexpected fluctuations in water availability.

publication date

  • April 1, 2021

published in

Digital Object Identifier (DOI)

volume

  • 291