A Generic Closed-Loop Contaminant Treatment System Package for MODFLOW 6.

We present a contaminant treatment system (CTS) package for MODFLOW 6 that facilitates the simulation of pump-and-treat systems for groundwater remediation. Using the "nonintrusive" MODFLOW 6 application programming interface (API) capability, the CTS package can balance flows between extraction and injection wells within the outer flow solution loop and applies blended concentration/mass treatment efficiency within the outer transport solution loop. The former can be important when the requested extraction rate cannot be satisfied by the current simulated groundwater system conditions, while the latter can be important for simulating incomplete/imperfect treatment schemes. Furthermore, the CTS package allows users to temporally vary all aspects of a simulated CTS system, including the configuration and location of injection and extraction wells, and the CTS efficiency. This flexibility combined with the API-based implementation provide a generic and general CTS package that can be applied across the wide range of MODFLOW 6 simulation options and that evolves in step with MODFLOW 6 code modifications and advancements without needing to update the CTS package itself.

Model averaging techniques for quantifying conceptual model uncertainty.

In recent years a growing understanding has emerged regarding the need to expand the modeling paradigm to include conceptual model uncertainty for groundwater models. Conceptual model uncertainty is typically addressed by formulating alternative model conceptualizations and assessing their relative likelihoods using statistical model averaging approaches. Several model averaging techniques and likelihood measures have been proposed in the recent literature for this purpose with two broad categories--Monte Carlo-based techniques such as Generalized Likelihood Uncertainty Estimation or GLUE (Beven and Binley 1992) and criterion-based techniques that use metrics such as the Bayesian and Kashyap Information Criteria (e.g., the Maximum Likelihood Bayesian Model Averaging or MLBMA approach proposed by Neuman 2003) and Akaike Information Criterion-based model averaging (AICMA) (Poeter and Anderson 2005). These different techniques can often lead to significantly different relative model weights and ranks because of differences in the underlying statistical assumptions about the nature of model uncertainty. This paper provides a comparative assessment of the four model averaging techniques (GLUE, MLBMA with KIC, MLBMA with BIC, and AIC-based model averaging) mentioned above for the purpose of quantifying the impacts of model uncertainty on groundwater model predictions. Pros and cons of each model averaging technique are examined from a practitioner's perspective using two groundwater modeling case studies. Recommendations are provided regarding the use of these techniques in groundwater modeling practice.

Global sensitivity analysis techniques for probabilistic ground water modeling.

Global sensitivity analysis techniques are better suited for analyzing input-output relationships over the full range of parameter variations and model outcomes, as opposed to local sensitivity analysis carried out around a reference point. This article describes three such techniques: (1) stepwise rank regression analysis for building input-output models to identify key contributors to output variance, (2) mutual information (entropy) analysis for determining the strength of nonmonotonic patterns of input-output association, and (3) classification tree analysis for determining what variables or combinations are responsible for driving model output into extreme categories. These techniques are best applied in conjunction with Monte Carlo simulation-based probabilistic analyses. Two examples are presented to demonstrate the applicability of these methods. The usefulness of global sensitivity techniques is examined vis-a-vis local sensitivity analysis methods, and recommendations are provided for their applications in ground water modeling practice.