Improvement of overtopping risk evaluations using probabilistic concepts for existing dams

Abstract

Hydrologic risk analysis for dam safety relies on a series of probabilistic analyses of rainfall-runoff and flow routing models, and their associated inputs. This is a complex problem in that the probability distributions of multiple independent and derived random variables need to be estimated in order to evaluate the probability of dam overtopping. Typically, parametric density estimation methods have been applied in this setting, and the exhaustive Monte Carlo simulation (MCS) of models is used to derive some of the distributions. Often, the distributions used to model some of the random variables are inappropriate relative to the expected behaviour of these variables, and as a result, simulations of the system can lead to unrealistic values of extreme rainfall or water surface levels and hence of the probability of dam overtopping. In this paper, three major innovations are introduced to address this situation. The first is the use of nonparametric probability density estimation methods for selected variables, the second is the use of Latin Hypercube sampling to improve the efficiency of MCS driven by the multiple random variables, and the third is the use of Bootstrap resampling to determine initial water surface level. An application to the Soyang Dam in South Korea illustrates how the traditional parametric approach can lead to potentially unrealistic estimates of dam safety, while the proposed approach provides rather reasonable estimates and an assessment of their sensitivity to key parameters.

Modeling risk analysis for forecasting peak discharge during flooding prevention and warning operation

“…Apel et al (2004Apel et al ( , 2006) developed a stochastic flood risk model to reduce the complex nature and high computation time for the risk and uncertainty analysis by simplifying the process chain, including the hydrological load, flood routing, outflow through levee breach, and damage estimation using the Monte Carlo framework. Kwon and Moon (2006) modified the traditional process of risk analysis for dam safety due to appropriate statistical properties of random variables by incorporating a nonparametric probability density for select variables, Latin Hypercube sampling (LHS) for improving the efficiency of the Monte Carlo simulation, and a Bootstrap method for determining the initial value of the water level in the dam. Paik (2008) developed a stochastic reservoir routing model based on the uncertainty method, i.e.…”

Evaluation of dam overtopping probability induced by flood and wind

“…In earlier studies (Askew et al 1971;Cheng et al 1982;Afshar and Marino 1990;Meon 1992;Pohl 1999;Kwon and Moon 2006;Kuo et al 2007Kuo et al , 2008, dam overtopping probability was assessed without considering the possible errors arising from adopting a particular distribution model for flood data or the uncertainty of estimated flood quantiles. Therefore, this study takes into account the two types of errors and proposes a sampling scheme that combines the importance sampling (IS) and the Latin hypercube sampling (LHS) for generating random floods and wind speeds in dam safety evaluation.…”

Risk analysis for flood control operation of seasonal flood-limited water level incorporating inflow forecasting error

“…Apel et al (2004) parameterized the main model components based on the results of complex deterministic models and used them for risk and uncertainty analysis in a Monte Carlo framework. Kwon and Moon (2006) developed a hydrological dam risk model for overtopping risk evaluations, including nonparametric Monte Carlo simulation (MCS), Latin hypercube sampling and bootstrap sampling. Kuo et al (2007) applied five uncertainty analysis methods to evaluate the overtopping risk of a dam given the conditions of four initial water levels, five return periods and some malfunctioning spillway gates, by considering seven uncertainty factors.…”