A real-time groundwater management model using data assimilation

This study develops a groundwater management model for real-time operation of an aquifer system. A groundwater flow model is allied with a nudging data assimilation algorithm that reduces the forecast error, minimizes the risk of system failure, and improves management strategies. The nudging algorithm treats the unknown private pumping as an additional sink term in the groundwater flow equation and provides a consistently physical interpretation for the identification of pumping rates. The system response due to pumping and injection is represented by a response matrix that is generated by the influence coefficient method. The response matrix (with a much smaller dimension) is used as a reduced model and is embedded directly in the management model as a part of the constraint set. Additionally, the influence coefficient method is utilized to include the nudging effect in the reduced model. The management model optimizes the monthly operation for 12 months into the future and determines the optimal strategy using the information provided by nudging. The management model is updated at the beginning of each month when new head observations and pumping data become available. We also discuss the utility, accuracy, and efficiency of the proposed management model for real-time operation.