Modeling and Optimization of Seawater Intrusion Barriers in Southern California Coastal Plain

A five-layered confined-unconfined flow and transport models are developed and calibrated for the Alamitos seawater intrusion barrier in Southern California. The conceptual model is based on the geological structure of the coastal aquifer system, and the key parameters in the flow and transport models are calibrated using field measurements of hydraulic conductivity as well as head and concentration observations. Because of the abundance of point measurements of hydraulic conductivity, the heterogeneous and random hydraulic conductivity field for each of the five aquifers is estimated by the proposed geostatiscal method of natural-neighbor-kriging (NNK). The longitudinal and transverse dispersivities in the transport model are estimated by an inverse procedure that minimizes the least-squares error for concentration (LSE-CON). The minimum LSE-CON is achieved near 50 ft (15.2 m) and 5 ft (1.52 m) for the longitudinal and transverse dispersivities, respectively. The calibrated simulation model is linked with two optimization models to investigate alternatives for enhancing seawater intrusion barrier operations for the Alamitos Barrier Project in Los Angeles. Two types of management problems are analyzed the optimal scheduling problem (OSP) and the optimal well location problem (OWLP). The objective of the OSP is to minimize the total injected water subject to constraints on the state variables: hydraulic head and chloride concentration at target locations. Two OSP formulations are considered, a pure hydraulic gradient formulation, and a combined hydraulic and transport formulation. Optimization results suggest that algorithm performance is best when the number of decision variables can be limited to approximately ten wells. Next, a genetic algorithm is linked with the calibrated simulation model to determine the locations of new injection wells that maximize the marginal increase in head targets along the barrier. Parallel processing is also employed to improve algorithm efficiency.

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