Magnitude and Frequency of Rural Floods in the Southeastern United States, through 2006: Volume 2, North Carolina

Feaster, Toby D.; Gotvald, Anthony J.; Weaver, J. Curtis

doi:10.3133/sir20095158

Cited by 19 publications

(13 citation statements)

References 24 publications

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“…The effects of these various types of uncertainties will influence the flood damage estimations, annualised risk and the selection of alternative design events. While this study does not include the effects of all of these uncertainties, a simplistic uncertainty estimation is performed by selecting the 5th and 95th percentile confidence intervals from the regional regression equations (Weaver et al ., ). Corresponding to these percentile estimates, two different continuous flow distribution curves are generated and the hydraulic modelling process was repeated, and consequently the flood damage frequency curves were estimated as shown in Figure .…”

Section: Resultsmentioning

confidence: 97%

“…Based on the regional regression equations developed for the study area (Weaver et al ., ), the continuous flow distribution curve for the Swannanoa River reach is generated as explained above in the Input Flow Estimation section (Figure ). From this flow distribution curve, 54 flow samples are uniformly selected with different exceedance probabilities through application of the Monte Carlo sampling framework.…”

Section: Resultsmentioning

confidence: 99%

“…In this study, the input flows are estimated using regional regression equations developed using a flood frequency analysis technique for ungaged basins (Weaver et al ., ). A continuous flow distribution is used in this study and compared with discrete return periods (2 years, 5 years, 10 years, 25 years, 50 years, 100 years, 250 years, and 500 years) in evaluating annualised flood risk.…”

Section: Methodsmentioning

confidence: 97%

“…The continuous flow distribution is synthesised for an ungaged basin by assuming that the regression flow estimates represent the peak flows experienced in the basin. Exceedance probabilities are identified ranging from 99.99% to 0.1%, and their corresponding flows are estimated using the United States Geological Survey (USGS) regression equations (Weaver et al ., ). These calculated peak flows and their probabilities, which are estimated using log‐Pearson Type III distribution, are plotted on a probability paper, and straight line is fit to the data.…”

Section: Methodsmentioning

confidence: 97%

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Annualised risk analysis approach to recommend appropriate level of flood control: application to Swannanoa river watershed

Kalyanapu

Judi

McPherson

et al. 2014

J Flood Risk Management

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This study presents an analysis approach using an existing Monte Carlo (MC) flood risk framework to compare annualised risk reductions from flood control alternatives targeting various recurrence interval events. The annualised risk approach is demonstrated by analysing the relative flood risk mitigation benefits of flood proofing in Swannanoa watershed, North Carolina. Using the MC framework, 54 design flows are sampled from the flow distribution and used to drive a graphics card based two‐dimensional flood model. The computed flood depths are used to create flood damage frequency and annualised risk curves. It was hypothesised that flood proofing for a higher probability event would result in a greater relative level of damage reduction for lower cost compared with the traditional use of a 1% annual exceedance flood event. The MC framework was used to test the hypothesis by producing a distribution of annualised risk over various return periods. Confirming the hypothesis, the 12% flood event was found to have the highest annualised risk for the case study in this paper. Simulations were executed to determine the relative costs and benefits of the 12% flood proofing (alternate case) and 1% flood proofing (base case) alternatives. The results showed that the base case reduced the expected annual damage (EAD) by 98.9% compared with the status quo. The alternate case reduced the EAD by 80.4% compared with the status quo. Comparing estimated annual implementation cost with flood damage reductions, annually, for every dollar spent on flood proofing, the 12% design resulted in twice the reduction in flood damages compared with the 1% design. This preliminary study thus confirms the potential of an alternative analysis approach that may be applied for identifying desired flood control level based on flood risk mitigation potential.

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Section: Resultsmentioning

confidence: 97%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 97%

Section: Methodsmentioning

confidence: 97%

See 2 more Smart Citations

Annualised risk analysis approach to recommend appropriate level of flood control: application to Swannanoa river watershed

Kalyanapu

Judi

McPherson

et al. 2014

J Flood Risk Management

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show abstract

“…The general hydrograph shape is scaled proportionally to a new hydrograph using the randomly sampled 1% peak discharge value drawn from a uniform distribution having a central value of 270 m 3 /s. The 270 m 3 /s value is the 1% annual peak discharge determined using the regression equations from the USGS Scientific Investigations Report (Weaver et al ., ). The range of the uniform distribution is set to be within the standard error of prediction of the regression estimates found in the USGS study (peak discharge range is from 180.7 to 404.6 m 3 /s), as shown in Figure .…”

Section: Case Studymentioning

confidence: 97%

Monte Carlo‐based flood modelling framework for estimating probability weighted flood risk

Kalyanapu

Judi

McPherson

et al. 2011

J Flood Risk Management

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This study presents a new Monte Carlo-based flood inundation modelling framework for estimating probability weighted flood risk using a computationally efficient graphics processing unit (GPU) two dimensional (2D) hydraulic model. The 2D flood model is programmed in the GPU framework providing a unique ability to run numerous simulations in a short period of time, permitting the integration of 2D hydraulic modelling into Monte Carlo analysis. The framework operates by performing many 2D flood simulations of randomly sampled input parameters to develop a spatially varied flood hazard map. The probabilistic framework is demonstrated using a 1% annual probability flood event and simulating 1000 different flood simulations by randomly selected peak flows of the Swannanoa River in Buncombe County, USA. The results, in general, display benefits of probabilistic flood risk approach compared with a single simulation approach. The latter approach underestimated 28% of flood risk relative to the former. As the number of simulations increased from 1 to 1000, areas identified as low danger and judgment zone increased by 87.4% and 36.8% respectively, whereas the high danger zone increased by 9.3%. In conclusion, the new Monte Carlo flood risk modelling framework has the ability to provide improved accuracy of flood risk and greater insight into the spatial distribution of flood risk. Predefined flow directionThe 1D models require prior knowledge of flow directions (e.g. in HEC-RAS, flow direction is represented by the main channel and banks). This is not appropriate in an urban J Flood Risk Management 5 (2012) 37-48

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Robust flood frequency analysis: Performance of EMA with multiple Grubbs‐Beck outlier tests

Lamontagne

Stedinger

et al. 2016

Water Resources Research

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Flood frequency analysis generally involves the use of simple parametric probability distributions to smooth and extrapolate the information provided by short flood records to estimate extreme flood flow quantiles. Parametric probability distributions can have difficulty simultaneously fitting both the largest and smallest floods. A danger is that the smallest observations in a record can distort the exceedance probabilities assigned to the large floods of interest. The identification and treatment of such Potentially Influential Low Floods (PILFs) frees a fitting algorithm to describe the distribution of the larger observations. This can allow parametric flood frequency analysis to be both efficient, and also robust to deviations from the proposed probability model's lower tail. Historically, PILF identification involved subjective judgement. We propose a new multiple Grubbs‐Beck outlier test (MGBT) for objective PILF identification. MGBT PILF identification rates (akin to Type I errors) are reported for the lognormal (LN) distribution and the log‐Pearson Type III (LP3) distribution with a variety of skew coefficients. MGBT PILF identification generally matched subjective identification from a recent California flood frequency study. Monte Carlo results show that censoring of PILFs identified by the MGBT algorithm improves the extreme quantile estimator efficiency of the expected moments algorithm (EMA) for negatively skewed LP3 distributions and has little effect for zero or positive skews; simultaneously it protects against deviations from the LP3 in the lower tail, as illustrated by distorted LN examples. Thus, MGBT generally makes flood frequency analysis based on the LP3 distribution with EMA both more accurate and more robust.

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Magnitude and Frequency of Rural Floods in the Southeastern United States, through 2006: Volume 2, North Carolina

Cited by 19 publications

References 24 publications

Annualised risk analysis approach to recommend appropriate level of flood control: application to Swannanoa river watershed

Annualised risk analysis approach to recommend appropriate level of flood control: application to Swannanoa river watershed

Monte Carlo‐based flood modelling framework for estimating probability weighted flood risk

Robust flood frequency analysis: Performance of EMA with multiple Grubbs‐Beck outlier tests

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