In cases where quantitative results are needed, optimality criteria will likely involve a mathematical summary of information or prediction uncertainty. The computations required to carry out such optimization searches are typically quite demanding, making the discussion in Chapter 4 on emulation and reduced-order models relevant here. Also, even when qualitative information is desired, it is often obtained through a quantitative analysis. This was the case in the case study presented in Section 6.5, in which quantitative information about ground surface subsistence was used to produce qualitative information regarding the prediction uncertainty for the QOI.

Finding: High-consequence decisions have been and continue to be informed by UQ assessments.


Ben-Tal, A., and A. Nemirovski. 2002. Robust Optimization—Methodology and Applications. Mathematical Programming, Series B 92, pp. 453-480.

Doherty, J. 2009. Addendum to the PEST Manual. Corinda, Australia: Watermark Numerical Computing.

Ermoliev, Y. 1988. Nonlinear Multiobjective Optimization. New York: Springer.

Fenelon, J.M. 2005. Analysis of Ground-Water Levels and Associated Trends in Yucca Flat, Nevada Test Site, Nye County, Nevada, 1951-2003. U.S. Geological Survey Scientific Investigations Report 2005-5175. Washington, D.C.: U.S. Department of the Interior.

Goodwin, B.T., and R.J. Juzaitis. 2006. National Certification Methodology for the Nuclear Weapon Stockpile. UCRL-TR-223486. Available at Accessed March 19, 2011.

Heyman, D.P., and M.J. Sobel. 2003. Stochastic Models in Operations Research, Vol. II: Stochastic Optimization. Mineola, N.Y.: Dover Publications.

Hunt, R.J., J. Doherty, and M.J. Tonkin. 2007. Are Models Too Simple? Arguments for Increased Parameterization. Ground Water 45(3):254-262.

Keating, E.H., J. Doherty, J.A.Vrugt, and Q. Kang. 2010. Optimization and Uncertainty Assessment of Strongly Nonlinear Groundwater Models with High Parameter Dimensionality. Water Resources Research 46(10):W10517.

Miettinen, K. 1999. Nonlinear Multiobjective Optimization. New York: Springer.

NRC (National Research Council). 2007. Models in Environmental Regulatory Decision Making. Washington, D.C.: National Academies Press.

NRC. 2009. Evaluation of Quantification of Margins and Uncertainties Methodology for Assessing and Certifying the Reliability of the Nuclear Stockpile. Washington, D.C.: The National Academies Press.

Oldenburg, C.M., B.M. Freifeld, K. Pruess, L. Pan, S. Finsterle, and G.J. Moridis. 2011. Numerical Simulations of the Macondo Well Blowout Reveal Strong Control of Oil Flow by Reservoir Permeability and Exsolution of Gas. Proceedings of the National Academy of Sciences: July.

Taguchi, G., L.W. Tung, and D. Clausing. 1987. System of Experimental Design: Engineering Methods to Optimize Quality and Minimize Costs. New York: Unipub.

Tonkin, M., and J. Doherty. 2009. Calibration-Constrained Monte Carlo Analysis of Highly Parameterized Models Using Subspace Techniques. Water Resources Research 45(12):w00b10.

U.S. Congress. 1994. Section 3138, National Defense Authorization Act for the Year 1994. Public Law 103-160; 42 U.S.C. 2121 Note.

Zyvoloski, G.A., B.A. Robinson, Z.V. Dash, and L.L. Trease. 1997. User’s Manual for the FEHM Application—A Finite-Element Heat- and Mass-Transfer Code. Los Alamos, N.Mex.: Los Alamos National Laboratory.

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