Assessment and management of risk in subsurface hydrology: A review and perspective

• Published in: Advances in water resources Vol. 51; pp. 247 - 260
• Main Author: Tartakovsky, Daniel M.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.01.2013; Elsevier
• Subjects: Algorithms; Assessments; Computation; Decision making; Decision under uncertainty; Earth sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; Exact sciences and technology; exposure assessment; exposure pathways; Hydrogeology; Hydrology; Hydrology. Hydrogeology; Pollution, environment geology; Probabilistic risk analysis; probabilistic risk assessment; probability analysis; Probability density functions; Risk; risk management; Stochastic optimization; Uncertainty; Uncertainty quantification; water resources; Uncertainty quantification; Probabilistic risk analysis; Decision under uncertainty; Stochastic optimization; algorithms; risk assessment; models; Decision making; decomposition; hydrogeology; subsurface; faults; management; optimization; uncertainties; theory; probability density function
• ISSN: 0309-1708; 1872-9657
• DOI: 10.1016/j.advwatres.2012.04.007

► Review of probabilistic risk assessment and management in hydrogeology. ► Comprehensive treatment of parametric, geologic and model uncertainties. ► Review of decision-theoretic and stochastic optimization approaches to risk management. Uncertainty plagues every effort to model subsurface processes and every decision made on the basis of such models. Given this pervasive uncertainty, virtually all practical problems in hydrogeology can be formulated in terms of (ecologic, monetary, health, regulatory, etc.) risk. This review deals with hydrogeologic applications of recent advances in uncertainty quantification, probabilistic risk assessment (PRA), and decision-making under uncertainty. The subjects discussed include probabilistic analyses of exposure pathways, PRAs based on fault tree analyses and other systems-based approaches, PDF (probability density functions) methods for propagating parametric uncertainty through a modeling process, computational tools (e.g., random domain decompositions and transition probability based approaches) for quantification of geologic uncertainty, Bayesian algorithms for quantification of model (structural) uncertainty, and computational methods for decision-making under uncertainty (stochastic optimization and decision theory). The review is concluded with a brief discussion of ways to communicate results of uncertainty quantification and risk assessment.