⇒ I will be presenting a talk (H42D-06) at AGU Fall Meeting on December 16, 2010

Title:
     Context-Specific Measures of Uncertainty in Groundwater Remediation.
Abstract:
    The remediation of groundwater sites contaminated with nonaqueous-phase liquids (NAPLs) has been recognized as a difficult and expensive task. One of the challenges is that the success of remediation is usually contingent upon an appropriate level of characterization of the physical, chemical, and biological properties of the site. For example, thermal treatment cannot be economically applied when the location of the NAPL source is unknown. Both characterization and remediation are expensive and need to be optimized. Here, we focus on the representation of uncertainty in the context of a specific remediation project. Traditional measures of uncertainty, such as mean square errors and correlation coefficients, do not necessarily depict the severity of uncertainty. For example, a small error in a parameter to which performance is sensitive may affect the prospect for remediation success much more than a large error in an insensitive parameter. We quantify uncertainty as the expected increase in the cost of achieving clean-up objectives that is associated with uncertainty, i.e., the expected cost with the present state of uncertainty minus the expected cost if uncertainty were fully removed. This measure of uncertainty is context-specific, i.e., it is dependent on site conditions and remediation strategies as well as specific remediation objectives and unit costs. We consider clean-up objectives, cost formulations, and sensitivity of costs to uncertainty in parameters, measurements, and the model itself and seek to minimize expected cost under conditions of incomplete information. We follow a similar approach in quantifying the uncertainty attributable to individual parameters by evaluating how the expected cost would be reduced by eliminating the uncertainty of each individual parameter. We present results from a synthetic case study and from the investigation at an actual field site, at Dover Air Force Base, of source removal and plume treatment. The results quantify the cost associated with uncertainty, through required over-design, thus setting an upper limit on how much one should spend on characterization, and attest that uncertainty in many parameters does not entail significant cost.


⇒ Large Scale Inverse Modeling with Half A Million Unknowns



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