Regional parent flood frequency distributions in Europe – Part 1:  Is the GEV model suitable as a pan-European parent?

Salinas, J. L.; Castellarin, Attilio; Viglione, Alberto; Kohnová, Silvia; Kjeldsen, Thomas

doi:10.5194/hess-18-4381-2014

Cited by 77 publications

(68 citation statements)

References 34 publications

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“…Salinas et al [15] proposed the use of WMA series (moving average series weighted with record length) to reduce noise and effectively examine the most appropriate PD that reflects the statistical characteristics of the observed data. The WMA is obtained by calculating the weighted average of 10 adjacent L-Cs samples and plotting them against the weighted average of the 10 corresponding L-Ck samples on an L-moment ratio diagram.…”

Section: Graphical Inspection Using Moving Average Seriesmentioning

confidence: 99%

“…The WMA is obtained by calculating the weighted average of 10 adjacent L-Cs samples and plotting them against the weighted average of the 10 corresponding L-Ck samples on an L-moment ratio diagram. Therefore, to decrease the effect of sampling variability in small samples, each L-moment ratio is proportionally weighted to the sample record length [15]. Fig.…”

Section: Graphical Inspection Using Moving Average Seriesmentioning

confidence: 99%

“…Moreover, in Slovenia the Agency of the Environment recommends the use of five distribution models (i.e., N, LN, P3, LP3, and GUM), Slovakia often uses the GAM, LN3, LP3, and GEV distributions, and the UK generally prefers the generalized logistic distribution (GLO) [15].…”

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confidence: 99%

“…Salinas et al [15] attempted to find a parent PD on a European scale by using a database for Europe that contained the yearly peak flows of 4,105 sites in 15 European countries. They also investigated how an appropriate PD could be selected using L-moment ratio diagrams.…”

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confidence: 99%

“…Identifying the appropriate PD of floods is probably the most important step in flood frequency analysis. Accordingly, many studies all over the world have investigated how to determine an appropriate PD of floods [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. The selection of techniques for assessing flood frequency in specific conditions depends on several dynamics, including national tradition, knowledge of the modeler, the aim of a study, governmental necessities, and data accessibility [17].…”

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Parent Flood Frequency Distribution of Turkish Rivers

Aşıkoğlu¹

2018

Pol. J. Environ. Stud.

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Determining the probability distribution (PD) of such random events as floods by estimating their behavior has always been a significant problem. Identifying the appropriate PD of floods is probably the most important step in flood frequency analysis. Accordingly, many studies all over the world have investigated how to determine an appropriate PD of floods [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. The selection of techniques for assessing flood frequency in specific conditions depends on several dynamics, including national tradition, knowledge of the modeler, the aim of a study, governmental necessities, and data accessibility [17].Cunnane [18] investigated the flood frequency determination techniques adopted by 55 organizations from 28 countries. Gumbel, two-parameter lognormal (LN2), P3, and log-Pearson type 3 (LP3) distributions appear to be the most common of the six extant distributions, with the other two being extreme value type 2 (EV2) and GEV distributions.Pol. J. Environ. Stud. Vol. 27, No. 2 (2018) AbstractThis study investigates the existence of a single parent flood frequency distribution on a Turkish scale. In the design of hydraulic structures, estimating the project flood of a given period or probability is usually the first step. Determining an acceptable design criterion depends substantially on the probability distribution of floods. In this study, annual peak series from 268 Turkish rivers was collected and a database of L-Moment ratios was constructed. The best-fit probability distribution (PD) model was investigated among seven distribution models (generalized logistic, GLO, generalized extreme value, GEV, generalized Pareto, GPA, three-parameter lognormal, LN3, Pearson type 3, P3, Gumbel, GUM, and normal distribution, N) by using a framework of L-moment ratio diagrams. The L-Moment ratio framework used in this study consisted of two sequential procedures. The first graphical procedure evaluated the L-Moment diagrams visually while the second procedure is based on the hypothetical testing procedure of L-Moment diagrams. The results of a graphical inspection of the dataset show GEV distribution as a potential parent PD of the floods in Turkey. A more detailed hypothetical testing procedure comprises Monte Carlo simulations produced from a GEV model. In a hypothetical testing procedure the variability of L-skewness and L-kurtosis values of sample data are situated within the theoretical limits of GEV distribution. Consequently, the GEV distribution is accepted as a single parent PD for annual maximum flow series of Turkey.

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Section: Graphical Inspection Using Moving Average Seriesmentioning

confidence: 99%

Section: Graphical Inspection Using Moving Average Seriesmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Parent Flood Frequency Distribution of Turkish Rivers

Aşıkoğlu¹

2018

Pol. J. Environ. Stud.

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Attribution of regional flood changes based on scaling fingerprints

Viglione¹,

Merz

Dũng

et al. 2016

Water Resources Research

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Changes in the river flood regime may be due to atmospheric processes (e.g., increasing precipitation), catchment processes (e.g., soil compaction associated with land use change), and river system processes (e.g., loss of retention volume in the floodplains). This paper proposes a new framework for attributing flood changes to these drivers based on a regional analysis. We exploit the scaling characteristics (i.e., fingerprints) with catchment area of the effects of the drivers on flood changes. The estimation of their relative contributions is framed in Bayesian terms. Analysis of a synthetic, controlled case suggests that the accuracy of the regional attribution increases with increasing number of sites and record lengths, decreases with increasing regional heterogeneity, increases with increasing difference of the scaling fingerprints, and decreases with an increase of their prior uncertainty. The applicability of the framework is illustrated for a case study set in Austria, where positive flood trends have been observed at many sites in the past decades. The individual scaling fingerprints related to the atmospheric, catchment, and river system processes are estimated from rainfall data and simple hydrological modeling. Although the distributions of the contributions are rather wide, the attribution identifies precipitation change as the main driver of flood change in the study region. Overall, it is suggested that the extension from local attribution to a regional framework, including multiple drivers and explicit estimation of uncertainty, could constitute a similar shift in flood change attribution as the extension from local to regional flood frequency analysis.

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A fuzzy Bayesian approach to flood frequency estimation with imprecise historical information

Salinas

Kiss²,

Viglione³

et al. 2016

Water Resources Research

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This paper presents a novel framework that links imprecision (through a fuzzy approach) and stochastic uncertainty (through a Bayesian approach) in estimating flood probabilities from historical flood information and systematic flood discharge data. The method exploits the linguistic characteristics of historical source material to construct membership functions, which may be wider or narrower, depending on the vagueness of the statements. The membership functions are either included in the prior distribution or the likelihood function to obtain a fuzzy version of the flood frequency curve. The viability of the approach is demonstrated by three case studies that differ in terms of their hydromorphological conditions (from an Alpine river with bedrock profile to a flat lowland river with extensive flood plains) and historical source material (including narratives, town and county meeting protocols, flood marks and damage accounts). The case studies are presented in order of increasing fuzziness (the Rhine at Basel, Switzerland; the Werra at Meiningen, Germany; and the Tisza at Szeged, Hungary). Incorporating imprecise historical information is found to reduce the range between the 5% and 95% Bayesian credibility bounds of the 100 year floods by 45% and 61% for the Rhine and Werra case studies, respectively. The strengths and limitations of the framework are discussed relative to alternative (non‐fuzzy) methods. The fuzzy Bayesian inference framework provides a flexible methodology that fits the imprecise nature of linguistic information on historical floods as available in historical written documentation.

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Regional parent flood frequency distributions in Europe – Part 1: Is the GEV model suitable as a pan-European parent?

Cited by 77 publications

References 34 publications

Parent Flood Frequency Distribution of Turkish Rivers

Parent Flood Frequency Distribution of Turkish Rivers

Attribution of regional flood changes based on scaling fingerprints

A fuzzy Bayesian approach to flood frequency estimation with imprecise historical information

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