Multi-objective management models for optimal and sustainable use of coastal aquifers
Conference Contribution ResearchOnline@JCUAbstract
Determining the sustainable rates of groundwater extraction from coastal aquifers is a challenging groundwater management problem. Overexploitation of coastal aquifers owing to ever-increasing demands results in the saltwater intrusion and eventual contamination of these valuable sources of freshwater. Saltwater intrusion is a slow process and it is very difficult, if not impossible, to remediate saltwater intruded aquifers. Hence carefully planned strategies of management are required for the sustainable use of coastal aquifers. In this study, a linked simulationoptimization model is developed for the management of coastal aquifers. The potential applicability of the management model is illustrated by applying the model to a small coastal aquifer study area to determine the optimal pumping rates at different locations and times. Salinity intrusion management models are used to prescribe management strategies for the sustainable use of coastal aquifers by controlling salt water intrusion. Developing an optimal management model involves integrating a groundwater flow and transport simulation model within an optimization framework. Flow and transport equations for salinity intrusion are coupled together by the density variation occurring during the mixing process, requiring simultaneous solution of both the equations. The numerical model for the density dependent flow and transport simulation would be computationally expensive, especially when used in a simulation-optimization framework. In this study, trained and tested surrogate models based on Genetic Programming are used as approximators for the numerical simulation model for simulating flow and transport process in the aquifer. A threedimensional, density dependent flow and transport simulation model FEMWATER is used to simulate the aquifer processes. The input-output patterns generated using the simulation model is then used to train and test the Genetic Programming based surrogate models. The surrogate models are linked to an optimal decision model to evolve multi-objective optimal management strategies for the aquifer. The use of trained and tested surrogate models ensure that the evolved optimal strategies are based on the physical processes in the aquifer, while substantially reducing the computational burden involved in directly linking the numerical simulation model. Two objectives of management are considered in this study. The first objective is to maximize the total beneficial pumping of fresh water from the coastal aquifer. The second objective is to minimize the pumping from a set of barrier wells near the coast which pump out saltwater to hydraulically control saltwater intrusion. The two objectives are conflicting to each other; hence a Multi-Objective Genetic Algorithm is used to solve the optimization model. The management model provides a Pareto-optimal set of solutions specifying the optimal rates of pumping. The developed management strategy ensures that the salinity levels are maintained below the permissible maximum salt concentrations for the intended use of the withdrawn water. The obtained optimal solutions are also validated by simulating the aquifer responses corresponding to the optimal solution using the numerical simulation model.
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Groundwater 2010
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5
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Canberra, ACT, Australia
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Australia
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