Uncertainty in Natural Hazards, Modeling and Decision Support: An Introduction to This Volume

Modeling has been used to characterize and map natural hazards and hazard susceptibility for decades. Uncertainties are pervasive in natural hazards analysis, including a limited ability to predict where and when extreme events will occur, with what consequences, and driven by what contributing factors. Modeling efforts are challenged by the intrinsic variability of natural and human systems, missing or erroneous data, parametric uncertainty, model‐based or structural uncertainty, and knowledge gaps, among other factors. Further, scientists and engineers must translate these uncertainties to inform policy decision making, which entails its own set of uncertainties regarding valuation, understanding limitations, societal preferences, and cost‐benefit analysis. Thus, it is crucial to develop robust and meaningful approaches to characterize and communicate uncertainties. Only recently have researchers begun to systematically characterize and quantify uncertainty in the modeling of natural hazards. Many factors drive the emergence of these capabilities, such as technological advances through increased computational power and conceptual development of the nature and complexity of uncertainty. These advances, along with increased sophistication in uncertainty analysis and modeling, are currently enabling the use of probabilistic simulation modeling, new methods that use observational data to constrain the modeling approaches used, and other quantitative techniques in the subdisciplines of natural hazards. In turn, these advances are allowing assessments of uncertainty that may not have been possible in the past. Given the expanding vulnerability of human populations and natural systems, management professionals are ever more frequently called upon to apply natural hazard modeling in decision support. When scientists enter into predictive services, they share professional, moral, legal, and ethical responsibilities to account for the uncertainties inherent in predictions. Where hazard predictions are flawed, limited resources may be unjustifiably be spent in the wrong locations, property may be lost, already stressed ecosystems may be critically damaged, and potentially avoidable loss of human life may occur. These essential concerns for reliable decision support compel thorough characterization of the uncertainties inherent in predictive models.