Site characterization is a process of reducing uncertainty, with the eventual aim of developing an accurate conceptual site model (CSM) that is appropriate for the remedial objectives of the site. The economic cost of a CSM is highly variable and dependent on many factors, and little in the way of formal guidance exists on balancing the production of data from intensive and costly site characterization activities, the associated cost of the activities, and the ultimate benefit to achieving remedial goals more cost effectively. A similar lack of guidance exists for understanding which elements of site characterization translate to the highest value data for remediation planning, as well as in understanding which performance metrics are the most critical in assessing operational performance and in predicting long-term effectiveness of the applied remedy.

The DIVER (Data Information Value to Evaluate Remediation) project will develop technical guidance on the value of data in both the site characterization and remediation contexts based on detailed field data, empirical evidence gathered from some of the most respected and successful practitioners in the field, highly detailed virtual site investigations, and stochastic approaches to quantifying the value of additional information. The primary research objective is to develop a framework for optimizing the site characterization process, such that the total cost of investigation, the cost of achieving remedial goals, and the likelihood of failure of remedial approaches are minimized.

Technical Approach

This project will develop detailed, large-scale data sets through simulation, which will act as the “sites” under investigation and remediation. These “virtual” sites will provide data to two different assessment approaches, the “Evidence Based (EB)” approach and the “Decision Theoretic (DT)” approach. In the EB approach, these “sites” will be investigated (virtually) by several decision maker (DM) teams, comprised of some of the most experienced and senior practitioners in the industry. The DM teams will develop site investigation plans, which will be “answered” by interrogating the large-scale data sets and producing site investigation data. The DM teams will develop CSMs for each site and remediation plans to achieve pre-defined goals and end-points. The CSMs and remediation performance will be assessed against metrics, and the worth of each investigation approach used will be evaluated in terms of the benefit to the degree of effort. In the DT approach, the same data used by the EB approach teams will be used in a stochastic framework, where the most likely remedial design will be arrived at through the minimization of cost functions given probabilistic distributions of the input data, and the relative value of additional information concerning the distributions will be assessed against the results of the EB approach. The final stage of the project will test the validity of the developed guidance on an existing Department of Defense (DoD) site where remediation performance has been found to be suboptimal. This will be accomplished through use of the guidance on additional site investigation approaches to refine the understanding of the CSM and identify the elements that are resulting in lowered performance of the remediation system.


This project will have the immediate benefit of reducing future remediation failures because of improper or inefficient characterization across DoD, as well as identifying the key parameters which, through reduction of uncertainty, lead to cost-effective and efficient remediation. The project will develop guidance for early assessment of the likelihood of failing to meet certain remediation goals at DoD sites, as well as produce a framework and guidance based on scientific principles that determine the value of data and the value of current site investigation practices, which in turn can be used by DoD to assess proposals and work plans submitted for its sites. (Anticipated Project Completion - 2018)


Mumford, K., S. Bryck, B.H. Kueper, S. Mancini, M. Kavanaugh, and D. Reynolds. 2022. Virtual Site Investigation to Evaluate Conceptual Site Model Development at DNAPL-Impacted Sites. National Groundwater Association, 4(3):44-58. doi.org/10.1111/gwmr.12537.