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Managed aquifer recharge has become a standard water resources management practice to promote the development of locally sustainable water supplies and combat water scarcity. However, installation of injection wells for replenishment purposes in urban areas with complex hydrogeology faces many challenges, such as limited land availability, potential impacts on municipal production wells and known subsurface contamination plumes, and complex spatially variable hydraulic connections between aquifer units. To assess the feasibility and cost‐effectiveness of injecting advanced treated water (ATW) into a complex urban aquifer system, a Simulation‐Optimization (SO) model was developed to automate a systematic search for the most cost‐effective locations to install new wells for injecting various quantities of ATW, if feasible. The generalized workflow presented here uses an existing MODFLOW groundwater model—along with advanced optimization routines that are publicly available—to flexibly accommodate a multiobjective function, complex constraints, and specific project requirements. The model successfully placed wells for injection of 1 to 4 MGD of ATW in aquifers underlying the study area. The injection well placement was primarily constrained by avoiding excessive impact on environmental sites with underlying groundwater plumes. The largest costs were for well installation and piping to the wells from the existing ATW pipes. This workflow is readily adaptable to other sites with different complexities, decision variables, or constraints. [ABSTRACT FROM AUTHOR]

MODFLOW 6 is the latest in a line of six "core" versions of MODFLOW released by the U.S. Geological Survey. The MODFLOW 6 architecture supports incorporation of additional hydrologic processes, in addition to groundwater flow, and allows interaction between processes. The architecture supports multiple model instances and multiple types of models within a single simulation, a flexible approach to formulating and solving the equations that represent hydrologic processes, and recent advances in interoperability, which allow MODFLOW to be accessed and controlled by external programs. The present version of MODFLOW 6 consolidates popular capabilities available in MODFLOW variants, such as the unstructured grid support in MODFLOW‐USG, the Newton‐Raphson formulation in MODFLOW‐NWT, and the support for partitioned stress boundaries in MODFLOW‐CDSS. The flexible multi‐model capability allows users to configure MODFLOW 6 simulations to represent the local‐grid refinement (LGR) capabilities available in MODFLOW‐LGR, the multi‐species transport capabilities in MT3DMS, and the coupled variable‐density capabilities available in SEAWAT. This paper provides a new, holistic and integrated overview of simulation capabilities made possible by the MODFLOW 6 architecture, and describes how ongoing and future development can take advantage of the program architecture to integrate new capabilities in a way that is minimally invasive and automatically compatible with the existing MODFLOW 6 code. [ABSTRACT FROM AUTHOR]

The performance of time series models is assessed using synthetic head series simulated with a numerical model that solves Richards' equation for variably saturated flow. Heads were simulated in a homogeneous unconfined aquifer between two parallel canals; measured daily precipitation and potential evaporation are specified at the land surface and root water uptake is simulated. The head response to a precipitation event is nonlinear and depends on the saturation degree and rainfall before and after the precipitation event while evaporation reduction occurs during summers. Synthetic series were generated for 27 years and three different soil types; the unsaturated zone thickness varies between 0 and >5 m. The synthetic head series were simulated with a linear and nonlinear time series model. Performance of a linear time series model with four parameters, using a scaled Gamma response, gave R2 values ranging from 0.67 to 0.96. The nonlinear time series model with five parameters simulates recharge using a root zone reservoir after which the head response to recharge is simulated with a scaled Gamma response function. The nonlinear time series model was able to simulate all synthetic head series very well with R2 values above 0.9 for almost all models. The head response of the nonlinear model to a single precipitation event compares well to the response of the variably saturated groundwater model. The provided scripts may be used to simulate synthetic head series for other climates or for systems with additional complexity to assess the performance of other data‐driven models. [ABSTRACT FROM AUTHOR]

Abstract: A groundwater flow model is typically used to provide the flow field for conducting groundwater solute transport simulations. The advection term of the mass conserved formulation for groundwater transport assumes that the flow field is perfectly balanced and that all water flowing into a numerical grid cell is exactly balanced by outflows after accounting for sources/sinks or internal storage. However, in many complicated regional or site‐scale models, there may be localized flow balance errors that may be difficult to eliminate through tighter flow convergence tolerances due to simulation time constraints or numerical limits on convergence tolerances. Thus, if water is erroneously gained or lost within a grid cell during the flow computation, the solutes within it will also be numerically affected in the associated transport simulation. Transport solutions neglect this error in groundwater flow as the transport equations that are solved assume no error in flow. This flow imbalance error can however have consequences on the transport solution ranging from unnoticeable errors in the resulting concentrations to spurious oscillations that can grow in time and hinder further solution. An approach has been suggested here, to explicitly handle these flow imbalances during mass conserved advective transport computations and report them in the corresponding transport mass balance output, as corrections that are needed to handle errors originating in the flow solution. Example problems are provided to explain the concepts and demonstrate the impacts. [ABSTRACT FROM AUTHOR]