Moving toward probabilistic dam breach modeling and flood inundation mapping

Intensifying hydrologic and environmental extremes are making aging dams around the world increasingly vulnerable as many of them have surpassed their design service life and are classified as high-hazard potential dams. Traditionally used deterministic dam breach analysis methods can underestimate flood risk since they do not necessarily capture an entire spectrum of possible outcomes and their associated uncertainties. This study presents an alternative probabilistic approach for dam breach analyses and flood risk assessments accommodating various modeling approaches and uncertainty quantification techniques. We integrate stochastic simulation methods with a computationally efficient flood modeling tool to enable large-ensemble analysis. We explicitly consider uncertainties in three key components: (1) breach parameters - breach geometry and timing derived from empirical prediction methods, (2) forcings - rainfall characteristics, including depth, temporal patterns, and antecedent soil moisture, and (3) downstream hydraulic conditions and hydrodynamic responses. Advanced sampling strategies, such as copula methods, are adopted to propagate uncertainties from different sources through the modeling chain while maintaining computational efficiency.
The framework is demonstrated through the application to a recent Sanford Dam failure case, where we compare different breach prediction methods and evaluate their impacts on downstream flood characteristics. The deterministic breach model is first validated against observed failure characteristics and downstream flood impacts before extending to probabilistic analysis. Multivariate analysis reveals that breach formation timing characteristics exert stronger influence on peak outflow and flood wave arrival time than geometric breach parameters. Generated probabilistic flood inundation maps showing overall probability, flood depth, and timing provide critical information for emergency response and resilience planning. Initial results that probabilistic approach provides refined confidence bounds for flood risk estimates, informing decision-making with quantified uncertainty. Incorporating future projections highlights that extreme rainfall intensification could increase dam overtopping probabilities, with the magnitude depending on projection scenarios. Our study infuses evidence-based comparison of modeling approaches and supports a more realistic dam-break flood risk assessment for aging dams under non-stationary environmental conditions, informing emergency planning, and infrastructure management strategies.