Multi-objective groundwater management strategy under uncertainties for sustainable control of saltwater intrusion: Solution for an island country in the South Pacific. (15th March 2019)

• Record Type: Journal Article
• Authors: Lal, Alvin; Datta, Bithin
• Is Part Of: Journal of environmental management. Volume 234(2019)
• Page Start: 115
• Page End: 130
• Publication Date: 2019-03-15
• Subjects: Groundwater -- Simulation-optimization -- Saltwater intrusion -- Pacific island -- Aquifer parameter uncertainty -- Support vector machine regression; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2018.12.054
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: To date, simulation-optimization (S/O) based groundwater management models have delivered optimal saltwater intrusion management strategies for coastal aquifer systems. At times, however, uncertainties in the numerical simulation model due to uncertain aquifer parameters are not incorporated into the management model. The present study explicitly incorporated aquifer parameter uncertainty into a multi-objective management model for the optimal design of groundwater pumping strategies from the unconfined Bonriki aquifer situated in a small Pacific island country. The objective of the multi-objective management model was to maximise pumping from production wells and minimize pumping from the barrier wells (hydraulic barriers) to ensure that the water quality at different monitoring locations (MLs) were within pre-specified sustainable limits. To achieve the targeted management goal, a coupled flow and transport numerical simulation model of the Bonriki aquifer was developed using the FEMWATER numerical code. The developed three-dimensional numerical model was calibrated and validated using limited available hydrological data. To achieve computational efficiency and feasibility of the management model, the numerical simulation model in the S/O model was replaced with ensembles of Support Vector Machine Regression (SVMR) surrogate models. Each SVMR standalone surrogate model in the ensemble was constructed using datasets from different numerical simulation models with different hydraulic conductivity and porosity values. These ensemble SVMR models were coupled to the multi-objective genetic algorithm optimization model to solve the Bonriki aquifer management problem. The executed optimization model presented a Pareto-front with 600 non-dominated optimal trade-off pumping solutions. The reliability of the management model established after validation of the optimal solution results suggests that the implemented constraints of the optimization problem were satisfied, i.e., the salinity concentrations at respective MLs were within the pre-specified limits. Overall, the results from this study indicated that the developed management model has the potential to address groundwater salinity problems in small island countries. Graphical abstract: Image 1 Highlights: A groundwater numerical simulation model is developed for the Bonriki aquifer system. Ensemble models are developed to incorporate aquifer parameter uncertainty. The ensemble models are linked to the multi-objective genetic algorithm optimization model. Optimal groundwater pumping patterns are presented based on the optimal Pareto-front.