USACE Experience in Implementing Risk Analysis for Flood Damage Reduction Projects

Davis, Darryl W.; Faber, Beth A.; Stedinger, Jery R.

doi:10.1111/j.1936-704x.2008.00023.x

Cited by 22 publications

(12 citation statements)

References 5 publications

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“…USACE, the owner and self-regulator of 708 large dams and regulator of over 2500 levee systems in the United States, provides a broad spectrum of risk management, in which has pioneered the implementation of different risk analysis techniques (Davis, Faber, and Stedinger 2008), as well as hydropower, water supply, navigation, and environmental benefits within USA. A major challenge of infrastructure management in USACE has been the replacement of the experience and institutional knowledge of its engineers gained during the agency's major construction era but now passed for several decades.…”

Section: United States Army Corps Of Engineers (Usace)mentioning

confidence: 99%

Overcoming failure in infrastructure risk governance implementation: large dams journey

Escuder‐Bueno

Halpin

2016

Journal of Risk Research

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Section: United States Army Corps Of Engineers (Usace)mentioning

confidence: 99%

Overcoming failure in infrastructure risk governance implementation: large dams journey

Escuder‐Bueno

Halpin

2016

Journal of Risk Research

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“…In hydrosystems' design and management, studies for evaluating annual expected flood damage considering epistemic uncertainties associated with frequency distribution models and parameters, along with the inherent hydrological uncertainty, can be found elsewhere (Bodo and Unny, 1976;Wood, 1977;Mays, 1981a, 1981b;Bao et al, 1987;Greco et al, 1999;Tung et al, 2006). The US Army Corps of Engineers (1996) and Davis et al (2008) further consider epistemic uncertainties arising from stage-discharge and stage-damage relationships in risk analysis of FDR projects.…”

Section: Uncertainties In Risk-based Decision Makingmentioning

confidence: 99%

“…The US Army Corps of Engineers () and Davis et al . () further consider epistemic uncertainties arising from stage‐discharge and stage‐damage relationships in risk analysis of FDR projects.…”

Section: Introductionmentioning

confidence: 99%

Effects of statistical sampling errors on flood‐damage‐reduction project evaluation

Tung

2016

J Flood Risk Management

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Risk‐based decision making of flood‐damage‐reduction (FDR) projects evaluates different design alternatives that have uncertain inundation–reduction benefits and costs. Uncertainties in FDR projects arise from, but are not limited to, the natural randomness of hydrological events, knowledge deficiency in hydrologic models, and the parameters, among others. This study investigates how the flood damage estimation is affected by the epistemic uncertainty resulting from using finite flood data in defining the flood‐frequency relationship and its effects on risk‐based decision making. A Monte Carlo simulation is applied in the study to simulate the epistemic uncertainty associated with the sampling error of the flood magnitude. The model parameter uncertainty is explicitly considered in the estimation of statistical features of flood damage. A recently developed decision rule on the basis of expected opportunity loss (EOL) is applied to the risk‐based evaluation of the relative merits of several competing flood mitigation projects. EOL‐based decision rule has the advantages of considering a decision maker's risk‐aversion attitude and incorporating more complete statistical features of project outcomes, including their correlations. The influence of the model parameter uncertainty on the project evaluation results is examined through an example FDR project with five design alternatives in which flood magnitude follows a Gumbel distribution.

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“…USACE, 1994;Carsell et al, 2004;de Bruijn, 2005;Loucks et al, 2005). To determine the probability distribution of flood damage, the hydro-economic EAD model (USACE, 1994;Davis et al, 2008;Dingman, 2002;Loucks et al, 2005) links the flood frequency distribution through flood stages to flood damage. The model can best be explained graphically (Fig.…”

Section: Expected Annual Flood Damagementioning

confidence: 99%

Estimating the benefits of single value and probability forecasting for flood warning

Verkade¹,

Werner²

2011

Preprint

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Flood risk can be reduced by means of flood forecasting, warning and response systems (FFWRS). These systems include a forecasting sub-system which is imperfect, meaning that inherent uncertainties in hydrological forecasts may result in false alarms and missed floods, or surprises. This forecasting uncertainty decreases the potential reduction of flood risk, but is seldom accounted for in estimates of the benefits of FFWRSs. In the present paper, a method to estimate the benefits of (imperfect) FFWRSs in reducing flood risk is presented. These benefits include not only the reduction of flood losses due to a warning response, but also consider the costs of the warning response itself, as well as the costs associated with forecasting uncertainty. The method allows for estimation of the benefits of FFWRSs that use either deterministic or probabilistic forecasts. Through application to a case study, it is shown that FFWRSs using a probabilistic forecast have the potential to realise higher benefits at all lead-times. However, it is also shown that provision of warning at increasing lead-time does not necessarily lead to an increasing reduction of flood risk, but rather that an optimal lead-time at which warnings are provided can be established as a function of forecast uncertainty and the cost-loss ratio of the user receiving and responding to the warning

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USACE Experience in Implementing Risk Analysis for Flood Damage Reduction Projects

Cited by 22 publications

References 5 publications

Overcoming failure in infrastructure risk governance implementation: large dams journey

Overcoming failure in infrastructure risk governance implementation: large dams journey

Effects of statistical sampling errors on flood‐damage‐reduction project evaluation

Estimating the benefits of single value and probability forecasting for flood warning

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