ARCHIVED DEC 2018

Investigators: Shlomo P. Neuman (PI), Post-Doctoral Researcher TBD, Phoolendra K. Mishra, and Liang Xue (University of Arizona).

Project Objectives: The overall goal of this project is to develop and demonstrate tools that would provide quantitative information to decision makers about (a) uncertainties associated with characterization, flow-transport analysis, and monitoring of subsurface remediation and waste storage sites and (b) the potential of additional characterization and monitoring data to help reduce these uncertainties and risks associated with particular decisions. The tools will be based on a comprehensive and rigorous theoretical and algorithmic framework that considers jointly significant sources of uncertainty, whether aleatory (controlled by chance) or epistemic (due to incomplete knowledge of site conditions and processes, hence reducible in principle through conditioning on site data). Specific sources of uncertainty we propose to consider stem from data errors (due to sparse and erroneous measurements conducted on disparate spatial and/or temporal scales), model errors (due to incorrect conceptualization and/or mathematical representation of site conditions and processes on scales that may not be compatible with those of available or future data), parameter estimation errors (due to errors in estimating model parameters directly from available data or indirectly through model calibration against such data, and lack of scale consistency between parameters and measured data), scenario uncertainties (due to lack of knowledge about conditions under which the system has operated, operates and/or will operate in the future), and predictive uncertainties (due to the cumulative effect of all the above).

We propose to achieve our goal by pursuing the following specific objectives:

1. Develop and demonstrate multimodel approaches to uncertainty quantification and reduction with focus on Maximum Likelihood Bayesian Model Averaging (MLBMA).
2. Develop and demonstrate methods to assess and optimize the potential worth of new site characterization and monitoring data using MLBMA within a formal cost-risk-benefit framework.
3. Develop and demonstrate ways to render the space-time scales of data, models, parameters, model predictions and their statistics compatible with each other and with decision needs.
4. Develop and demonstrate methods to infer field-scale vadose zone flow and transport properties from well tests spanning the vadose and underlying saturated zones.
5. Explore the compatibility of such field-scale vadose zone properties with those of laboratory-scale measurements.

Relevance and Impact to DOE: The need to address uncertainties in a comprehensive manner is formally recognized in a joint Memorandum of Understanding (MOU) signed a few years ago by the U.S. Department of Energy, U.S. Nuclear Regulatory Commission, U.S. Army Corps of Engineers, Agricultural Research Service of the U.S. Department of Agriculture, U.S. Environmental Protection Agency, and the U.S. Geological Survey. The MOU recognizes the inherent uncertainty associated with conceptual-mathematical models and their parameter input estimates. It further recognizes that in using models to assess risk to public health and/or the environment, comprehensive uncertainty assessments coupled to robust parameter estimation methods would greatly enhance the insights and predictions derived from such models. There also is growing recognition that subsurface flow and transport variables are ubiquitously heterogeneous and dependent on scales of measurement (data support), observation (extent of phenomena such as a dispersing contaminant plume), sampling window (domain of investigation), spatial correlation (structural coherence), and spatial resolution (descriptive detail). Accounting for these scale phenomena within the framework of a comprehensive uncertainty assessment methodology, such as proposed here, would greatly enhance confidence in DOE decisions concerning subsurface remediation and waste storage sites.