Defining the hundred year flood: A Bayesian approach for using historic data to reduce uncertainty in flood frequency estimates

Parkes, Brandon; Demeritt, David

doi:10.1016/j.jhydrol.2016.07.025

Cited by 63 publications

(54 citation statements)

References 95 publications

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“…Further, the 2015 event was indeed very large and well above any of the systematic and historical events. Similar findings in terms of how excessively high the Winter 2015 events in Northern Britain were compared with a long historical record, are also discussed in Parkes and Demeritt (2016), which presents the history of flooding of the city of Carlisle from the river Eden. As a reference for how influential the most recent events might be for the estimation of flood risk in the area it is here noted that the return period for the value of 1700 m 3 =s when including the peak flow value of December 2015 in the sample would be approximately 60 years (annual exceedance probability equal to 0.0165) when using systematic records only and 240 years (annual exceedance probability equal to 0.0042) when historical data are used in the estimation.…”

Section: Simulation Studysupporting

confidence: 52%

German tanks and historical records: the estimation of the time coverage of ungauged extreme events

Prosdocimi

2017

Stoch Environ Res Risk Assess

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The use of historical data can significantly reduce the uncertainty around estimates of the magnitude of rare events obtained with extreme value statistical models. For historical data to be included in the statistical analysis a number of their properties, e.g. their number and magnitude, need to be known with a reasonable level of confidence. Another key aspect of the historical data which needs to be known is the coverage period of the historical information, i.e. the period of time over which it is assumed that all large events above a certain threshold are known. It might be the case though, that it is not possible to easily retrieve with sufficient confidence information on the coverage period, which therefore needs to be estimated. In this paper methods to perform such estimation are introduced and evaluated. The statistical definition of the problem corresponds to estimating the size of a population for which only few data points are available. This problem is generally refereed to as the German tanks problem, which arose during the second world war, when statistical estimates of the number of tanks available to the German army were obtained. Different estimators can be derived using different statistical estimation approaches, with the maximum spacing estimator being the minimum-variance unbiased estimator. The properties of three estimators are investigated by means of a simulation study, both for the simple estimation of the historical coverage and for the estimation of the extreme value statistical model. The maximum spacing estimator is confirmed to be a good approach to the estimation of the historical period coverage for practical use and its application for a case study in Britain is presented.

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Section: Simulation Studysupporting

confidence: 52%

German tanks and historical records: the estimation of the time coverage of ungauged extreme events

Prosdocimi

2017

Stoch Environ Res Risk Assess

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“…The second reason for the limited application is the lack of a consistent methodology for the integration of noncontinuous data with continuous gauging data. The development of the Peak‐Over Threshold (POT) method and other Bayesian type models [e.g., Parkes and Demeritt , ] in recent years is beginning to address this limitation. The main aim of this paper is to improve the estimation of design flood (1% AEP) for at‐site FFA by incorporating historical and paleoflood records in SEQ.…”

Section: Introductionmentioning

confidence: 99%

Reducing uncertainty with flood frequency analysis: The contribution of paleoflood and historical flood information

Lam

Thompson

Croke

et al. 2017

Water Resources Research

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Using a combination of stream gauge, historical, and paleoflood records to extend extreme flood records has proven to be useful in improving flood frequency analysis (FFA). The approach has typically been applied in localities with long historical records and/or suitable river settings for paleoflood reconstruction from slack‐water deposits (SWDs). However, many regions around the world have neither extensive historical information nor bedrock gorges suitable for SWDs preservation and paleoflood reconstruction. This study from subtropical Australia demonstrates that confined, semialluvial channels such as macrochannels provide relatively stable boundaries over the 1000–2000 year time period and the preserved SWDs enabled paleoflood reconstruction and their incorporation into FFA. FFA for three sites in subtropical Australia with the integration of historical and paleoflood data using Bayesian Inference methods showed a significant reduction in uncertainty associated with the estimated discharge of a flood quantile. Uncertainty associated with estimated discharge for the 1% Annual Exceedance Probability (AEP) flood is reduced by more than 50%. In addition, sensitivity analysis of possible within‐channel boundary changes shows that FFA is not significantly affected by any associated changes in channel capacity. Therefore, a greater range of channel types may be used for reliable paleoflood reconstruction by evaluating the stability of inset alluvial units, thereby increasing the quantity of temporal data available for FFA. The reduction in uncertainty, particularly in the prediction of the ≤1% AEP design flood, will improve flood risk planning and management in regions with limited temporal flood data.

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“…alternative methods should be implemented to ensure that in such cases, estimates can still be obtained which are comparable to the MLE method. For example, Parkes and Demeritt (2016) and Reis and Stedinger (2005) used a Bayesian approach with a Gibbs Markov Chain Monte Carlo sampler to incorporate historical events.…”

Section: Maximum Likelihoodmentioning

confidence: 99%

Technical Note: Approximate Bayesian Computation to improve long-return flood estimates using historical data

Griffin

Shaw

Stewart

2018

Preprint

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Abstract. For the Generalised Logistic distribution as used in UK flood frequency analysis, one standard approach for parameter estimation is through maximum likelihood methods. However, there can be problems with convergence to final estimates in cases where the true parameter values are extreme. This paper applies Approximate Bayesian Computation (ABC), a likelihood-free approach popularised in statistical genetics, which generates candidate parameters and compares data simulated from those candidates to the observed data. Candidates whose data have summary statistics (Partial Probability Weighted Moments, PPWM) sufficiently close to those of the observed data are accepted as draws from the posterior distribution. The ABC-PPWM approach is applied to new historical data points to estimate the flood frequency distribution for the River Severn at the Welsh Bridge in Shrewsbury, UK to improve the estimates of magnitudes of flood events with return period longer than the length of systematic records. Level data are derived from historical sources, and discharge estimates are obtained using data from upstream discharge gauging stations. When used in the ABC-PPWM approach, the results are at least as effective as the maximum likelihood methods, showing similar point estimates, and similar levels of variance. The estimates for the shape parameter for the GLO show some discrepancies, but this is known to be the most challenging to estimate given the availability of only censored historical data. Unlike maximum likelihood methods, for which the estimate may not be obtainable, the ABC-PPWM approach is always successful.

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Defining the hundred year flood: A Bayesian approach for using historic data to reduce uncertainty in flood frequency estimates

Cited by 63 publications

References 95 publications

German tanks and historical records: the estimation of the time coverage of ungauged extreme events

German tanks and historical records: the estimation of the time coverage of ungauged extreme events

Reducing uncertainty with flood frequency analysis: The contribution of paleoflood and historical flood information

Technical Note: Approximate Bayesian Computation to improve long-return flood estimates using historical data

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