LIMITATIONS AND UTILITY OF CONTAMINANT TRANSPORT MODELING IN COMPLEX GEOLOGIC SETTINGS – AN EXAMPLE FROM FRENCHMAN FLAT, NEVADA

One of the major values of ground-water modeling is that it can be used to test hypotheses and simulate consequences of management decisions. However, models for some environmental problems involve complex processes and settings that are nearly impossible to fully quantify or measure, and the problem becomes more difficult when uncertainty is considered. In this case it is necessary that the limitations of understanding developed and clearly communicated to decision makers using sound principles.

An illustration is drawn from Frenchman Flat, Nevada. Underground nuclear testing via deep vertical shafts was conducted at the Nevada Test Site (NTS) from 1951 until 1992. The Frenchman Flat area of the NTS was used for 7 years. The Underground Testing Area (UGTA) Project is currently conducting correction action investigations to ensure the protection of the public and the environment

Traditionally, the focus of uncertainty analysis in groundwater modeling has been on model parameters. However, significant uncertainties may also arise due to incomplete understanding of the subsurface geologic framework and other conceptual uncertainties. In Frenchman Flat this issue was directly addressed by developing multiple possible models of the geologic framework that are then tested for their ability to match observed hydrologic conditions. Another component considered the flow model parameter uncertainty for a given framework using calibration-constrained Monte Carlo analysis. Still another component was an alternative hydrologic conceptual model, also calibrated to the available data. This suite of flow models was combined with transport parameter uncertainty to provide contrasting views of uncertainty for use in regulatory decision making.

Uncertainty analysis, whether parameter or conceptual, that focuses on changes to model outcomes that are not informed by measured data may be more useful to decision makers than a general “uncertainty analysis”. Consideration of more complex forms of uncertainty – conceptual model uncertainty – has created a new problem in that it is not always obvious what uncertainty permissible with the data actually has an impact on the decision. These types of approaches begin to address the impact of uncertainty on decision making, which is a very real value of modeling.