Post-Calibration Uncertainty Analysis for Travel Times at a Naval Weapons Industrial Reserve Plant

Abstract

The Naval Weapons Industrial Reserve Plant (NWIRP) in McGregor, Texas began manufacturing explosives in 1980 and several hazardous chemicals were discovered in lakes and streams surrounding the site in 1998. Contaminants traveled to local lakes and streams much faster than initially predicted. This research estimated contaminant travel times and identified locations where monitoring wells should be installed to yield the greatest reductions in uncertainties in travel-time predictions. To this end, groundwater and particle-tracking models for NWIRP site were built to predict hydraulic heads and contaminant travel times. Next, parameter (hydraulic conductivities) uncertainties, parameter identifiabilities, observation (hydraulic heads) worth, and predictive (contaminant travel times) uncertainties were quantified. Parameter uncertainties were reduced by up to 92%; a total of 19 of 158 parameters were at least moderately identifiable; travel-time uncertainties were reduced up to 92%. Additionally, travel-time predictions and post-calibration parameter distributions were generated using the null-space Monte Carlo (NSMC) technique. NSMC predicted that conservative tracers exited the flow system within a year, which matches with field data. Finally, an observations-worth analysis found that additional 11 more measurements would reduce travel-time uncertainties by factors from 1.04 to 4.3 over existing data if monitoring wells were installed at the suggested locations.

Towards Improved Remedial Outcomes in Categorical Aquifers with an Iterative Ensemble Smoother

“…Because the inverse problem does not have a unique solution (Tarantola 2005), predictions made with a calibrated numerical model do not always bear fruit (Moore and Doherty 2005; Ahmmed et al 2020). Since it is nearly impossible to characterize heterogeneous subsurface environments, Rajaram 2016, suggests that predicting solute behavior in such settings for deterministic predictions at relevant scales necessitates a stochastic approach, which is in line with a typical decision‐making agency's expectation that predictions should be accompanied by uncertainty measures that allow risk assessment.…”