Optimization tools for environmental water decisions: A review of strengths, weaknesses, and opportunities to improve adoption. (October 2016) - Horne, Avril; Szemis, Joanna M.; Kaur, Simranjit; Webb, J. Angus; Stewardson, Michael J.; Costa, Alysson; Boland, Natashia
Abstract: Public investment in river restoration through environmental watering has increased substantially in recent years. To sustain public support for such investment, management of environmental water must achieve the best possible outcomes in a transparent and defensible manner. The current management of environmental water relies on the ability of managers to estimate the impacts of their decisions under complex scenarios, often with multiple interdependent decisions that span over different spatial and temporal scales. Optimization modeling has been widely used in other forms of conservation management and an increasing body of literature documents the development of optimization models that could be used to improve environmental water decisions. This paper reviews this disparate research, showing that there are a range of different questions addressed using this modeling approach and that the representation of environmental outcomes varies. Future work must focus on improved adoption through engagement with end users and stakeholders during model development. Highlights: Environmental water managers currently rely mainly on their experience to make complex decisions to best use limited volumes of water to achieve maximum benefit for the environment. The development of optimization tools to assist environmental water management is a growing area of research with a sharp increase in the number of publications in the last ten years. Considerable challenges remain concerning how best to represent the complex physical and environmental systems as both meaningful and solvable optimization problems; particularly the representation of environmental outcomes and objective functions. Benefits of optimization modeling are both in the improved understanding of the system being analysed gained through the model development phase, and in the model outputs providing information to assist decision making. Realizing these benefits requires end-user engagement. However, to date there has limited uptake of these models in practice.