Use of historical information in extreme-surge frequency estimation: the case of marine flooding on the La Rochelle site in France

Hamdi, Yasser; Bardet, Lise; Duluc, Claire-Marie; Rebour, Vincent

doi:10.5194/nhess-15-1515-2015

Cited by 23 publications

(27 citation statements)

References 36 publications

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“…These three distributions come from the same family, the generalized extreme value (GEV) distribution. The GEV distribution has been applied in a large number of studies to estimate and analyze extremes [11,[35][36][37]. The frequency model using this family of distributions is often referred to as the block maxima approach (e.g., [34]).…”

Section: Extreme Values In a Non-stationary Environment-theorymentioning

confidence: 99%

Temperature Extremes: Estimation of Non-Stationary Return Levels and Associated Uncertainties

Hamdi¹,

Duluc²,

Rebour³

2018

Atmosphere

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Estimating temperature extremes (TEs) and associated uncertainties under the non-stationary (NS) assumption is a key research question in several domains, including the nuclear safety field. Methods for estimating TEs and associated confidence intervals (CIs) have often been used in the literature but in a stationary context, separately and without detailed comparison. The extreme value theory is often used to assess risks in a context of climate change. It provides an accurate indication of distributions describing the frequency of occurrence of TEs. However, in an NS context, the notion of the return period is not easily interpretable. For instance, to predict a high return level (RL) in a future year, time-varying distributions must be used and compared. This study examines the performance of a new concept to predict RLs in an NS context and compares three methods for constructing the associated CIs (delta, profile likelihood, and parametric bootstrap). The present work takes up the concept of integrated return periods that define the T-year RL as the level for which the expected number of events in a T-year period is one and proposes a new method based on conditional predictions that is useful for predicting high RLs of extreme events in the near future (the 100-year RL in the year 2030, for instance). The daily maximum temperature (DMT) observed at the Orange Station in France was used as a case study. Several trend models were compared and a new likelihood-based method to detect breaks in TEs is proposed. The analyses were conducted assuming the time-varying Generalized Extreme Value (GEV) distribution. The concepts have been implemented in a software package (Non-Stationary Generalized Extreme Value (NSGEV)). The application demonstrates that the RL estimates for NS situations can be quite different from those corresponding to stationary conditions. Overall, the results suggest that the NS analysis can be helpful in making a more appropriate assessment of the risk for periodic safety reviews during the life of a nuclear power plant (NPP).

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Section: Extreme Values In a Non-stationary Environment-theorymentioning

confidence: 99%

Temperature Extremes: Estimation of Non-Stationary Return Levels and Associated Uncertainties

Hamdi¹,

Duluc²,

Rebour³

2018

Atmosphere

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“…Previous works on local sea levels (Val Gelder, 1996;Bulteau et al, 2015) and on local skew surges (Hamdi et al, 2015) pointed out that the incorporation of historical data leads to a positive effect on extreme frequency analysis.…”

Section: R Frau Et Al: Historical Information For Regional Analysismentioning

confidence: 99%

“…This is quite a sweeping hypothesis, which is difficult to verify and validate. Moreover, the duration associated to the perception threshold is usually defined as the duration between the oldest historical data and the start of the systematic series (Payrastre et al, 2011;Hamdi et al, 2015).…”

Section: R Frau Et Al: Historical Information For Regional Analysismentioning

confidence: 99%

The use of historical information for regional frequency analysis of extreme skew surge

Frau

Andreewsky

Bernardara

2018

Nat. Hazards Earth Syst. Sci.

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Abstract. The design of effective coastal protections requires an adequate estimation of the annual occurrence probability of rare events associated with a return period up to 10 3 years. Regional frequency analysis (RFA) has been proven to be an applicable way to estimate extreme events by sorting regional data into large and spatially distributed datasets. Nowadays, historical data are available to provide new insight on past event estimation. The utilisation of historical information would increase the precision and the reliability of regional extreme's quantile estimation. However, historical data are from significant extreme events that are not recorded by tide gauge. They usually look like isolated data and they are different from continuous data from systematic measurements of tide gauges. This makes the definition of the duration of our observations period complicated. However, the duration of the observation period is crucial for the frequency estimation of extreme occurrences. For this reason, we introduced here the concept of "credible duration". The proposed RFA method (hereinafter referenced as FAB, from the name of the authors) allows the use of historical data together with systematic data, which is a result of the use of the credible duration concept.

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“…The maximum likelihood method was selected for its statistical features, especially for large series and for the ease with which any additional information (i.e., the HI) is incorporated in it. On the other hand, the plotting positions exceedance formula based on both systematic observations and HI (Hirsch, 1987;Hirsch and Stedinger, 1987;Guo, 1990) is proposed to calculate the observed probabilities and has been incorporated into the POTH FM considered herein. For systematic data, there are several formulas that can be used to calculate the observed probabilities.…”

Section: Settings Of the Frequency Model With Hi (Poth)mentioning

confidence: 99%

Analysis of the risk associated with coastal flooding hazards: a new historical extreme storm surges dataset for Dunkirk, France

Hamdi¹,

Garnier²,

Giloy³

et al. 2018

Nat. Hazards Earth Syst. Sci.

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This paper aims to demonstrate the technical feasibility of a historical study devoted to French nuclear power plants (NPPs) which can be prone to extreme coastal flooding events. It has been shown in the literature that the use of historical information (HI) can significantly improve the probabilistic and statistical modeling of extreme events. There is a significant lack of historical data on coastal flooding (storms and storm surges) compared to river flooding events. To address this data scarcity and to improve the estimation of the risk associated with coastal flooding hazards, a dataset of historical storms and storm surges that hit the Nord-Pasde-Calais region during the past five centuries was created from archival sources, examined and used in a frequency analysis (FA) in order to assess its impact on frequency estimations. This work on the Dunkirk site (representative of the Gravelines NPP) is a continuation of previous work performed on the La Rochelle site in France. Indeed, the frequency model (FM) used in the present paper had some success in the field of coastal hazards and it has been applied in previous studies to surge datasets to prevent coastal flooding in the La Rochelle region in France.In a first step, only information collected from the literature (published reports, journal papers and PhD theses) is considered. Although this first historical dataset has extended the gauged record back in time to 1897, serious questions related to the exhaustiveness of the information and about the validity of the developed FM have remained unanswered. Additional qualitative and quantitative HI was extracted in a second step from many older archival sources. This work has led to the construction of storm and coastal flooding sheets summarizing key data on each identified event. The quality control and the cross-validation of the collected in-formation, which have been carried out systematically, indicate that it is valid and complete in regard to extreme storms and storm surges. Most of the HI collected is in good agreement with other archival sources and documentary climate reconstructions. The probabilistic and statistical analysis of a dataset containing an exceptional observation considered as an outlier (i.e., the 1953 storm surge) is significantly improved when the additional HI collected in both literature and archives is used. As the historical data tend to be extreme, the right tail of the distribution has been reinforced and the 1953 "exceptional" event does not appear as an outlier any more. This new dataset provides a valuable source of information on storm surges for future characterization of coastal hazards.

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Use of historical information in extreme-surge frequency estimation: the case of marine flooding on the La Rochelle site in France

Cited by 23 publications

References 36 publications

Temperature Extremes: Estimation of Non-Stationary Return Levels and Associated Uncertainties

Temperature Extremes: Estimation of Non-Stationary Return Levels and Associated Uncertainties

The use of historical information for regional frequency analysis of extreme skew surge

Analysis of the risk associated with coastal flooding hazards: a new historical extreme storm surges dataset for Dunkirk, France

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