Preface: Advances in extreme value analysis and application to natural hazards

Hamdi, Yasser; Haigh, Ivan D.; Parey, Sylvie; Wahl, Thomas

doi:10.5194/nhess-21-1461-2021

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…Extreme value theory (EVT) provides a framework to analyze statistical distributions of a physical process in terms of their behavior at the extremes (Coles, 2001). EVT has been frequently used in the fields of hydrology and climatology (Hamdi et al., 2021). From the Fisher‐Tippett‐Gnedenko theorem (Fisher & Tippett, 1928; Gnedenko, 1943), the GEV distribution arises asymptotically as a model for block maxima M n with increasing block size n ; for sufficiently large n , this theorem suggests using the approximation

\mathrm{Pr}\left({M}_{n}\le x\right)\approx \text{GEV}(x;\,\xi )=\left\{\begin{array}{@{}c@{}}\mathrm{exp}\left(-{\left[1+\xi \left(\frac{x-\mu }{\sigma }\right)\right]}^{\sfrac{-1}{\xi }}\right),\xi \ne 0\\ \qquad \mathrm{exp}\left(-\mathrm{exp}\left(-\frac{x-\mu }{\sigma }\right)\right),\xi =0,\end{array}\right.\qquad \quad x\mathbb{\in }\mathbb{R},

for suitably chosen distributional parameters μ (location), σ (scale), and ξ (shape).…”

Section: Theory and Methodsmentioning

confidence: 99%

“…Extreme value theory (EVT) provides a framework to analyze statistical distributions of a physical process in terms of their behavior at the extremes (Coles, 2001). EVT has been frequently used in the fields of hydrology and climatology (Hamdi et al, 2021). From the Fisher-Tippett-Gnedenko theorem (Fisher & Tippett, 1928;Gnedenko, 1943), the GEV distribution arises asymptotically as a model for block maxima M n with increasing block size n; for sufficiently large n, this theorem suggests using the approximation…”

Section: Extreme Value Theory: Generalized Extreme Value Distributionmentioning

confidence: 99%

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Convection‐Permitting Climate Models Can Support Observations to Generate Rainfall Return Levels

Poschlod,

Koh

2024

Water Resources Research

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Information about the frequency and intensity of extreme precipitation is generally derived from fitting extreme value models using point‐observations, but the regionalization of these models is challenging. Here we propose using high‐resolution convection‐permitting climate model output as covariates for the estimation of observation‐based spatial rainfall return levels. We apply the Weather and Forecasting Research (WRF) model at a 1.5 km resolution driven by ERA5 reanalysis data over southern Germany, where 1,132 rain gauges provide observations of daily rainfall. For this complex topography, we build three different smooth spatial Generalized Extreme Value (GEV) models: (a) a reference model using latitude, longitude and elevation as covariates; (b) a model adding mean annual precipitation from the WRF; (c) a model adding extreme value statistical model estimates using WRF output. We show that the additional information provided by the WRF model can improve the representation of 10‐year and 100‐year return levels of daily rainfall by lowering the percentage bias, mean absolute error, and root‐mean‐square error. Furthermore, we conduct an extensive cross‐validation, where only 5%, 10%, 20%, 50%, 80%, 90%, and 95% of all rain gauges are considered when building spatial GEV models. Again, the additional information provided by the WRF model can improve results here. This cross‐validation study also highlights the robustness of our approach, showing great potential for use in data‐scarce regions.

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\mathrm{Pr}\left({M}_{n}\le x\right)\approx \text{GEV}(x;\,\xi )=\left\{\begin{array}{@{}c@{}}\mathrm{exp}\left(-{\left[1+\xi \left(\frac{x-\mu }{\sigma }\right)\right]}^{\sfrac{-1}{\xi }}\right),\xi \ne 0\\ \qquad \mathrm{exp}\left(-\mathrm{exp}\left(-\frac{x-\mu }{\sigma }\right)\right),\xi =0,\end{array}\right.\qquad \quad x\mathbb{\in }\mathbb{R},

for suitably chosen distributional parameters μ (location), σ (scale), and ξ (shape).…”

Section: Theory and Methodsmentioning

confidence: 99%

“…Extreme value theory (EVT) provides a framework to analyze statistical distributions of a physical process in terms of their behavior at the extremes (Coles, 2001). EVT has been frequently used in the fields of hydrology and climatology (Hamdi et al, 2021). From the Fisher-Tippett-Gnedenko theorem (Fisher & Tippett, 1928;Gnedenko, 1943), the GEV distribution arises asymptotically as a model for block maxima M n with increasing block size n; for sufficiently large n, this theorem suggests using the approximation…”

Section: Extreme Value Theory: Generalized Extreme Value Distributionmentioning

confidence: 99%

Convection‐Permitting Climate Models Can Support Observations to Generate Rainfall Return Levels

Poschlod,

Koh

2024

Water Resources Research

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“…The problem of extreme values in statistical theory is very common. The behavior of extreme values is studied even if it has a low likelihood of occurring but can have a major impact on the observed events [1]. Extensive literature is available on the applications of extreme values in different fields.…”

Section: Introductionmentioning

confidence: 99%

Modeling Extreme Values with Alpha Power Inverse Pareto Distribution

Ihtisham¹,

Manzoor²,

Khalil³

et al. 2023

Measurement Science Review

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The study focuses on the development of a new probability distribution with applications to extreme values. The distribution is proposed by incorporating an additional parameter into the inverse Pareto distribution using the α-Power Transformation. Various properties of the new distribution are derived. The paper also explores the estimation of the parameters by the Maximum Likelihood Estimation (MLE) technique. Simulations are performed to evaluate the performance of the MLEs. In addition, two real data sets with extreme values are used to evaluate the efficacy of the proposed model. It is concluded that the proposed model performs well in the case of extreme values compared to the existing distributions.

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“…[18]. Therefore, the issue of OTC in urban areas has become the subject of the numerous studies over the past two decades [1,2,11,[19][20][21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Outdoor Thermal Comfort in Serbia’s Urban Environments during Different Seasons

et al. 2021

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The urban microclimate is gradually changing due to climate change, extreme weather conditions, urbanization, and the heat island effect. In such an altered environment, outdoor thermal comfort can have a strong impact on public health and quality of life in urban areas. In this study, three main urban areas in Serbia were selected: Belgrade (Central Serbia), Novi Sad (Northern Serbia), and Niš (Southern Serbia). The focus was on the temporal assessment of OTC, using the UTCI over a period of 20 years (1999–2018) during different seasons. The main aim is the general estimation of the OTC of Belgrade, Novi Sad, and Niš, in order to gain better insight into the bioclimatic condition, current trends and anomalies that have occurred. The analysis was conducted based on an hourly (7 h, 14 h, and 21 h CET) and “day by day” meteorological data set. Findings show the presence of a growing trend in seasonal UTCI anomalies, especially during summer and spring. In addition, there is a notable increase in the number of days above the defined UTCI thresholds for each season. Average annual UTCIs values also show a positive, rising trend, ranging from 0.50 °C to 1.33 °C. The most significant deviations from the average UTCI values, both seasonal and annual, were recorded in 2000, 2007, 2012, 2015, 2017, and 2018.

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Preface: Advances in extreme value analysis and application to natural hazards

Cited by 8 publications

References 21 publications

Convection‐Permitting Climate Models Can Support Observations to Generate Rainfall Return Levels

Convection‐Permitting Climate Models Can Support Observations to Generate Rainfall Return Levels

Modeling Extreme Values with Alpha Power Inverse Pareto Distribution

Assessment of Outdoor Thermal Comfort in Serbia’s Urban Environments during Different Seasons

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