Web-based Framework for Multi-Objective Optimization of Managed Aquifer Recharge

The increasing water scarcity around the world has led to a widespread interest in the implementation of managed aquifer recharge (MAR) systems, which offer the potential for storing surface water underground for future use or for environmental benefits. MAR has been proven to be an effective approach in addressing problems related to spatial and temporal water shortages and mitigation of climate change impacts on global water resources. Nevertheless, when designing MAR systems, competing objectives must be balanced, such as optimizing recharge efficiency while reducing operational costs. To address these trade-offs and aid decision-making, this study aims to develop a novel framework for a multi-objective optimization of MAR systems. The paper introduces the first design steps and the general structure of a framework that integrates the capabilities of the existing web-based groundwater modelling platform INOWAS (www.inowas.com) with a hybrid evolutionary algorithm. The framework effectively explores complex solution spaces by combining groundwater models setup on the INOWAS platform using tools from MODFLOW family (MODFLOW-2005, MT3DMS, SEAWAT) with global search capabilities (using Genetic algorithm) and local refining methods (using Simplex algorithm). This allows the simulation of specific MAR challenges such as, for example, optimization of recharge wells’ location by maximizing the removal of total dissolved solids (TDS) at recovery wells, the water recovery efficiency, the recharge rate and the overall economic feasibility, etc. Solutions are expressed as pareto fronts, which represent a set of optimal trade-off solutions that are non-dominated to each other but are superior to the rest of solutions in the search space.  To achieve a tool which eventually provides a robust framework for planners, engineers, and policymakers to design and manage MAR systems effectively, typical MAR scenarios will be defined to identify and classify boundary conditions and limiting factors together with the objectives to be optimized.