The simulation of concentration values and use of such data for history-matching is often impeded by the computation time of groundwater transport models based on the resolution of the advection-dispersion equation. This is unfortunate because such data are often rich in information and the prediction of concentration values is of great interest for decision making. Particle tracking can be used as an efficient alternative under a series of simplifying assumptions, which are often reasonable at groundwater sinks (wells and drains). Our approach consists of seeding particles around a sink and tracking particles backward, up to the source boundary condition, such as a contaminated stream. This particle tracking approach allows the use of parameter estimation and optimization methods requiring numerous model calls. We present a Python module facilitating the pre- and post-processing operations of a modeling workflow based on the widely used USGS MODFLOW6 and MODPATH7 programs. The module handles particle seeding around the sink and estimation of the mixing ratio of water withdrawn from the sink. This ratio is computed with a mixing law from the particle endpoints, accounting for particle velocities and mixing in the source model cells. We investigate the best practice to obtain robust derivatives with this approach, which is a benefit for the screening methods based on linear analysis. We illustrate the interest of the approach with a real world case study, considering a drinking water well field vulnerable to a contaminated stream. The configuration is typical of many other drinking water production sites. The modeling workflow is fully script-based to make the approach easily reproducible in similar cases.

<p>The assimilation and prediction of concentration data is often impeded by the computation time of groundwater transport models based on the resolution of the advective-dispersive equation. This is unfortunate because such data is often rich in information and the prediction of concentration values is of great interest for decision making.  Particle tracking may be used as an efficient alternative under a series of simplifying assumptions, which are often reasonable at groundwater sinks. A rapid transport model allows the use of assimilation and optimization methods requiring many model calls.  We developed a Python package to facilitate the use of the USGS MODFLOW6 and MODPATH7 models to simulate the transfer of tracer or contaminant concentrations to a groundwater sink (typically a pumping well or a drain). The approach requires the identification of one or a series of sources of tracer/contaminant such as a contaminated stream or area in the model domain. The package handles particle seeding around the sink and estimation of the concentration of water withdrawn from the sinks. Both “strong” and “weak” sources can be considered. Concentrations are computed with a mixing law from the particle endpoints and velocities. We investigated the best practice to obtain robust derivatives with this approach, which is essential for all methods based on the linearized version of the model. We provide a step-by-step workflow from model construction to parameter estimation, linear uncertainty analysis, and chance-constraints optimization with the PEST suite. The interest and practical details of the approach are illustrated on a well field vulnerable to a stream, and a parametric analysis is provided in order to evaluate the impact of key numerical parameters on the presented results.</p>