Spatial dependence in extreme river flows and precipitation for Great Britain

Keef, Caroline; Svensson, Cecilia; Tawn, Jonathan A.

doi:10.1016/j.jhydrol.2009.09.026

Cited by 100 publications

(142 citation statements)

References 7 publications

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“…The HT model relies on the assumption that fluvial flooding has the same spatial characteristics in winter months and in summer months [22]. Thus, the knowledge about the runoff seasonality may help to analyse if this assumption is fulfilled.…”

Section: Seasonality Of Runoffmentioning

confidence: 99%

“…The first measure of spatial dependence can be interpreted as an exploratory summary measure of bivariate dependence and is defined as [22] …”

Section: Spatial Dependence Measuresmentioning

confidence: 99%

“…Section 3.3 and [22]). The spatial dependence measure N j (p) is an appropriate tool to investigate whether the spatial characteristics of different series (all-year (AY), May to October (M2O), and November to April (N2A) series) differs significantly.…”

Section: Ht Model and Seasonal Characteristics Of Runoffmentioning

confidence: 99%

“…Besides the aspects mentioned above, the idea of the spatial dependence measure P i,j (p) can be applied to investigate the temporal dependence in individual series. Therefore, Equation (3) has to be adapted to account for daily maximum data [22].…”

Section: Event Definition and Seasonality Of Runoffmentioning

confidence: 99%

“…The null hypothesis that the spatial dependence of winter and summer half-year do not differ was rejected for a significance level of 1%. However, the HT model relies on the assumption that flooding in the winter half-year has the same spatial characteristics as flooding in summer half-year [22]. Thus, half-year series were used for further analyses.…”

Section: Event Definition and Seasonality Of Runoffmentioning

confidence: 99%

See 4 more Smart Citations

Generation of Spatially Heterogeneous Flood Events in an Alpine Region—Adaptation and Application of a Multivariate Modelling Procedure

Schneeberger¹,

Steinberger²

2018

Hydrology

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Flooding often has a negative impact on society. In particular, widespread flood events can cause a lot of damage. These events are often spatially and temporally heterogeneous and should be duly considered for an appropriate analysis of flooding. Therefore, a conditional multivariate approach is adapted and applied in order to (i) contribute to a better understanding of the spatial characteristics of fluvial floods and (ii) to deliver sets of synthetically generated flood events. The present paper focuses on a simulation procedure consisting of careful data preparation and selection and the application of a conditional multivariate approach. The conditional approach is adapted to account for the seasonality of runoff data. Model checks attuned to the model are presented to ensure the consistence of simulated and observed data. The Austrian Province Vorarlberg was chosen as the study area. A thorough data analysis of runoff time series showed that the hydrological behaviour is characterized by a strong seasonality that was considered within the applied modelling procedure. The analysis of the spatial dependence of high river flows identified regions where floods likely occur simultaneously and regions with low spatial dependence. The main result of the modelling procedure, a large set of widespread flood events, was successfully generated.

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Section: Seasonality Of Runoffmentioning

confidence: 99%

“…The first measure of spatial dependence can be interpreted as an exploratory summary measure of bivariate dependence and is defined as [22] …”

Section: Spatial Dependence Measuresmentioning

confidence: 99%

Section: Ht Model and Seasonal Characteristics Of Runoffmentioning

confidence: 99%

Section: Event Definition and Seasonality Of Runoffmentioning

confidence: 99%

Section: Event Definition and Seasonality Of Runoffmentioning

confidence: 99%

See 3 more Smart Citations

Generation of Spatially Heterogeneous Flood Events in an Alpine Region—Adaptation and Application of a Multivariate Modelling Procedure

Schneeberger¹,

Steinberger²

2018

Hydrology

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Modeling dependence between extreme rainfall and storm surge to estimate coastal flooding risk

Zheng

Westra

Leonard

et al. 2014

Water Resources Research

167

142

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Accounting for dependence between extreme rainfall and storm surge can be critical for correctly estimating coastal flood risk. Several statistical methods are available for modeling such extremal dependence, but the comparative performance of these methods for quantifying the exceedance probability of rare coastal floods is unknown. This paper compares three classes of statistical methods-thresholdexcess, point process, and conditional-in terms of their ability to quantify flood risk. The threshold-excess method offers approximately unbiased estimates for dependence parameters, but its application for quantifying flood risk is limited because it is unable to handle situations where only one of the two variables is extreme. In contrast, the point process method (with the logistic and negative logistic models) and the conditional method describe the full distribution of extremes, but they overestimate and underestimate the dependence strength, respectively. We conclude that the point process method is the most suitable approach for modeling dependence between extreme rainfall and storm surge when the dependence is relatively strong, while none of the three methods produces satisfactory results for bivariate extremes with very weak dependence. It is therefore important to take the bias of each method into account when applying them to flood estimation problems. A case study is used to demonstrate the three statistical methods and illustrate the implication of dependence to flood risk.

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Combining regional estimation and historical floods: A multivariate semiparametric peaks‐over‐threshold model with censored data

Sabourin

Renard²

2015

Water Resources Research

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The estimation of extreme flood quantiles is challenging due to the relative scarcity of extreme data compared to typical target return periods. Several approaches have been developed over the years to face this challenge, including regional estimation and the use of historical flood data. This paper investigates the combination of both approaches using a multivariate peaks-over-threshold model that allows estimating altogether the intersite dependence structure and the marginal distributions at each site. The joint distribution of extremes at several sites is constructed using a semiparametric Dirichlet Mixture model. The existence of partially missing and censored observations (historical data) is accounted for within a data augmentation scheme. This model is applied to a case study involving four catchments in Southern France, for which historical data are available since 1604. The comparison of marginal estimates from four versions of the model (with or without regionalizing the shape parameter; using or ignoring historical floods) highlights significant differences in terms of return level estimates. Moreover, the availability of historical data on several nearby catchments allows investigating the asymptotic dependence properties of extreme floods. Catchments display a significant amount of asymptotic dependence, calling for adapted multivariate statistical models.

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Spatial dependence in extreme river flows and precipitation for Great Britain

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Cited by 100 publications

References 7 publications

Generation of Spatially Heterogeneous Flood Events in an Alpine Region—Adaptation and Application of a Multivariate Modelling Procedure

Generation of Spatially Heterogeneous Flood Events in an Alpine Region—Adaptation and Application of a Multivariate Modelling Procedure

Modeling dependence between extreme rainfall and storm surge to estimate coastal flooding risk

Combining regional estimation and historical floods: A multivariate semiparametric peaks‐over‐threshold model with censored data

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