Clustering of concurrent flood risks via Hazard Scenarios

Pappadà, Roberta; Durante, Fabrizio; Salvadori, Gianfausto; Michele, Carlo De

doi:10.1016/j.spasta.2017.12.002

Cited by 22 publications

(19 citation statements)

References 36 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They were regionalized using a simple linear model which led to an acceptable performance. However, more sophisticated regionalization techniques such as the use of nonlinear regression techniques (Brunner et al, 2018a), the use of the region of influence approach (Hosking and Wallis, 1997), or the copula-based clustering algorithm proposed by Pappadà et al (2018) could be applied if the marginal distributions were of particular interest and if a larger dataset was available. Alternatively, new regionalization techniques based on catchment similarity in terms of empirical copulas (Grimaldi et al, 2016) could be developed.…”

Section: Discussionmentioning

confidence: 99%

“…The Gumbel copula belongs both to the class of Archimedean and extreme value copulas. It allows for upper tail dependence (Poulin et al, 2007).…”

Section: Copula Modelsmentioning

confidence: 99%

“…This allowed for the evaluation of whether the dependence in relation to the distance between stations was captured by the models. Tail dependence describes the dependence in the upper or lower tail of a distribution and the upper tail dependence coefficient describes the probability that one margin exceeds a high threshold given that the other margin also exceeds a high threshold (Poulin et al, 2007). We used the tail dependence estimator of Schmidt and Stadtmüller (2006) with a parameter κ = 5 to produce upper tail dependence plots for the simulated samples and the observations.…”

Section: Validationmentioning

confidence: 99%

“…We used the tail dependence estimator of Schmidt and Stadtmüller (2006) with a parameter κ = 5 to produce upper tail dependence plots for the simulated samples and the observations. Since tail dependence estimators react quite sensitively to small sample sizes (Serinaldi, 2015), we also computed Kendall's tau, which is a non-parametric measure of dependence and does not only focus on the tails of the distributions (Poulin et al, 2007).…”

Section: Validationmentioning

confidence: 99%

See 3 more Smart Citations

Modeling the spatial dependence of floods using the Fisher copula

Brunner

Furrer

Favre

2019

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Floods often affect not only a single location, but also a whole region. Flood frequency analysis should therefore be undertaken at a regional scale which requires the considerations of the dependence of events at different locations. This dependence is often neglected even though its consideration is essential to derive reliable flood estimates. A model used in regional multivariate frequency analysis should ideally consider the dependence of events at multiple sites which might show dependence in the lower and/or upper tail of the distribution. We here seek to propose a simple model that on the one hand considers this dependence with respect to the network structure of the region and on the other hand allows for the simulation of stochastic event sets at both gauged and ungauged locations. The new Fisher copula model is used for representing the spatial dependence of flood events in the nested Thur catchment in Switzerland. Flood event samples generated for the gauged stations using the Fisher copula are compared to samples generated by other dependence models allowing for modeling of multivariate data including elliptical copulas, R-vine copulas, and max-stable models. The comparison of the dependence structures of the generated samples shows that the Fisher copula is a suitable model for capturing the spatial dependence in the data. We therefore use the copula in a way such that it can be used in an interpolation context to simulate event sets comprising gauged and ungauged locations. The spatial event sets generated using the Fisher copula well capture the general dependence structure in the data and the upper tail dependence, which is of particular interest when looking at extreme flood events and when extrapolating to higher return periods. The Fisher copula was for a medium-sized catchment found to be a suitable model for the stochastic simulation of flood event sets at multiple gauged and ungauged locations.

show abstract

Section: Discussionmentioning

confidence: 99%

“…The Gumbel copula belongs both to the class of Archimedean and extreme value copulas. It allows for upper tail dependence (Poulin et al, 2007).…”

Section: Copula Modelsmentioning

confidence: 99%

Section: Validationmentioning

confidence: 99%

Section: Validationmentioning

confidence: 99%

See 2 more Smart Citations

Modeling the spatial dependence of floods using the Fisher copula

Brunner

Furrer

Favre

2019

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Storms and cyclones were also clustered, which can be a triggering factor for extreme floods [31,32]. Flood clustering is reported in some new research [28,[33][34][35][36][37], but they mainly worked on extreme floods and some rare research is assessed the simultaneous floods [38], which is a flood risks clustering based on hazard scenarios.…”

Section: Introductionmentioning

confidence: 98%

Clustering Simultaneous Occurrences of the Extreme Floods in the Neckar Catchment

Modiri¹,

Bàrdossy²

2021

Water

View full text Add to dashboard Cite

Flood protection is crucial for making socioeconomic policies due to the high losses of extreme floods. So far, the synchronous occurrences of flood events have not been deeply investigated. In this paper, multivariate analysis was implemented to reveal the interconnection between these floods in spatiotemporal resolution. The discharge measurements of 46 gauges with a continuous daily time series for 55 years were taken over the Neckar catchment. Initially, the simultaneous floods were identified. The Kendall correlation between the pair sets of peaks was determined to scrutinize the similarities between the simultaneous events. Agglomerative hierarchical clustering tree (AHCT) and multidimensional scaling (MDS) were employed, and obtained clusters were compared and evaluated with the Silhouette verification method. AHCT shows that the Average and Ward algorithms are appropriate to detect reasonable clusters. The Neckar catchment has been divided into three major clusters: the first cluster mainly covers the western part and is bounded by the Black Forest and Swabian Alps. The second cluster is mostly located in the eastern part of the upper Neckar. The third cluster contains the remaining lowland areas of the Neckar basin. The results illustrate that the clusters act relatively as a function of topography, geology, and anthropogenic alterations of the catchment.

show abstract

A spatially‐weighted AMH copula‐based dissimilarity measure for clustering variables: An application to urban thermal efficiency

Di Lascio,

Menapace,

Pappadà

2023

Environmetrics

View full text Add to dashboard Cite

Investigating thermal energy demand is crucial for developing sustainable cities and the efficient use of renewable sources. Despite the advances made in this field, the analysis of energy data provided by smart grids is currently a demanding challenge due to their complex multivariate structure and high dimensionality. In this article, we propose a novel copula‐based dissimilarity measure suitable for analyzing district heating demand and introduce a procedure to apply it to high‐temporal resolution panel data. Inspired by the characteristics of the considered data, we explore the usefulness of the Ali‐Mikhail‐Haq copula in defining a new dissimilarity measure to cluster variables in the hierarchical framework. We show that our proposal is particularly sensitive to small dissimilarities based on tiny differences in the strength of the dependence between the involved random variables. Therefore, the measure we introduce is able to distinguish between objects with low dissimilarity better than standard rank‐based dissimilarity measures. Moreover, our proposal considers a weighted version of the copula‐based dissimilarity that embeds the spatial location of the involved objects. We investigate the proposed measure through Monte Carlo studies and compare it with an analogous dissimilarity measure based on Kendall's correlation. Finally, the application to real data concerning the Italian city Bozen‐Bolzano makes it possible to find clusters of buildings homogeneous with respect to their main characteristics, such as energy efficiency and heating surface. In turn, our findings may support the design, expansion, and management of district heating systems.

show abstract

Clustering of concurrent flood risks via Hazard Scenarios

Cited by 22 publications

References 36 publications

Modeling the spatial dependence of floods using the Fisher copula

Modeling the spatial dependence of floods using the Fisher copula

Clustering Simultaneous Occurrences of the Extreme Floods in the Neckar Catchment

A spatially‐weighted AMH copula‐based dissimilarity measure for clustering variables: An application to urban thermal efficiency

Contact Info

Product

Resources

About