A Simulation-Optimization Model for ASR Operational Planning Subject to Probabilistic Supply and Water Quality Constraints

Aquifer Storage and Recovery (ASR) systems address one of the main challenges being faced by water managers; to store sufficient water during periods of high demand and having an appropriate storage for water during periods of low demand.  ASR systems utilize wells to inject water into an aquifer, which can be extracted at a later time.  The main challenges associated with the operation of an ASR system are avoiding degradation of native groundwater quality and appropriate operation.  This work expands on a previous study which focused on providing an operational planning tool for ASR systems under supply side uncertainty.  The transport of a conservative dissolved solute was simulated using the modular 3-D transport model (MT3D) and its results abstracted using an artificial neural network (ANN).  A simulation-optimization of ASR operations under stochastic supplies including water quality constraints was then carried out to obtain long term operational policy of ASR operations in the Corpus Christi region considering both quantity and quality issues.  The resulting ASR long term operational policy suggests that water should be stored during the months of January to May; most water is to be extracted during the months of July and August to meet regional demands while meeting water quality constraints.  The percentage of injected water extracted to native groundwater extracted was found to be ~95%.  The case study showed the ASR system to be sustainable with ~65% reliability. This work provides a tool for water managers for selecting appropriate operational policies that consider both the stochastic nature of precipitation as well as the impacts of water quality in a conjunctive manner.