Evaluating two multi-model simulation–optimization approaches for managing groundwater contaminant plumes

Abstract Addressing conceptual model uncertainty using multi-model approaches has become an important topic in groundwater hydrology. Model uncertainty is of practical importance when designing a pump-and-treat system for groundwater remediation, as inappropriate consideration of model uncertainty may ultimately lead to ineffective system designs. This study introduces two multi-model approaches for addressing model uncertainty at a site contaminated by chlorinated hydrocarbons and BTEX compounds. We use a multi-objective simulation–optimization method to design a multiple-well pump-and-treat system to contain contaminant plumes within the site perimeter, while minimizing the pumping rate. The design parameters include the coordinates of the pumping well locations and pumping rates. We consider four groundwater models to address uncertainty in conceptualizing site geology, boundary condition, and recharge. Using two multi-model approaches, we evaluate different design concepts. The first approach is a model selection approach that identifies the critical models, which particularly govern the design process, within the multi-model ensemble. The second approach is a model aggregation approach that identifies system designs that meet the remediation objectives for all models of the ensemble. Results show that the model aggregation approach is a more conservative option, which results in reliable designs at the expense of higher pumping rates. This study evaluates these two approaches within the context of multi-model simulation–optimization, providing insights on multi-model simulation–optimization in groundwater management.