Quantifying the effects of uncertainty on optimal groundwater bioremediation policies

Abstract

This paper describes a method for quantifying the economic and environmental effects of uncertainty in biological parameter values on optimal in situ bioremediation design. The range of uncertainty in model results associated with a range of input parameter values is quantified for both individual parameter errors and errors in combinations of parameters. Three measures of sensitivity are presented that quantify different aspects of the effects of model error on an implemented optimal policy. Numerical results are presented for an example site contaminated with phenol, with parameter ranges derived from values reported in the literature. For the example site, Ks (the substrate half‐velocity coefficient in the Monod kinetic equation for biodegradation) was found to be the most sensitive biological parameter and this sensitivity was asymmetric; i.e., reductions in the value of Ks have a much greater effect than increases in the value of Ks. The methodology applied in this paper could also be applied to other water resource management problems, allowing the user to quantify the effects of wide ranges of possible parameter values on model results. The method is particularly useful for computationally intensive optimization models, as it requires a manageable number of model runs, and for the many situations where insufficient data are available to permit accurate estimation of probability distributions.

Organic contaminant transport and fate in the subsurface: Evolution of knowledge and understanding

“…Active remediation schemes can be very expensive to install and operate, so some studies have addressed optimization and uncertainty of injection and extraction well placement, pumping rates, and injected concentrations Huang et al, 2006;Shoemaker, 1996, 1998;Smalley et al, 2000]. Minsker and Shoemaker [1998] concluded that the Monod half-saturation constant (K s , equation (7)) was the largest source of model uncertainty. Smalley et al [2000] applied a noisy genetic algorithm to account for parameter uncertainty and incorporated risk-based cleanup criteria.…”

An integrated fuzzy-stochastic modeling approach for risk assessment of groundwater contamination

“…However, most of the previous works have only dealt with parameter uncertainties in modeling of contaminant transport, while the uncertainties in environmental quality guidelines and health risk evaluation criteria have received less attention (Minsker and Shoemaker, 1998;Chen et al, 2003). In addition, few previous works were reported to effectively link different types of uncertainties in a risk assessment framework.…”

Inverse modeling of surface-water discharge to achieve restoration salinity performance measures in Florida Bay, Florida

“…Inverse modeling can be used for decision analysis in ground-water systems, using parameter-estimation techniques (Tiedeman and Gorelick, 1993). This method also can be used for ground-water-management tasks, such as optimizing ground-water pumpage, and so has similarities in usage to optimization models (Minsker and Shoemaker, 1998).…”