Using climate change models to assess the probability of weather extremes events: a local scale study based on the generalized extreme value distribution

Fontolan, Mariana; Xavier, Ana Carolina Freitas; Pereira, Heloisa Ramos; Blain, Gabriel Constantino

doi:10.1590/1678-4499.2018144

Cited by 11 publications

(5 citation statements)

References 54 publications

(84 reference statements)

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“…However, according to Raynal (), since the choice of which type is the most appropriate for the data under investigation is not always obvious, the GEV becomes a more appropriate alternative for the investigation of statistical extremes values. In addition, the GEV has been used by several studies in the analysis of extreme precipitation events, including those that quantify the influence of climate changes on the spatial and temporal variability of this meteorological element (e.g., Manton et al ., ; Katz et al ., ; Alexander et al ., ; Feng et al ., ; Choi et al ., ; Re and Barros, ; Fontolan et al ., ).…”

Section: Introductionmentioning

confidence: 97%

Stationary and non‐stationary detection of extreme precipitation events and trends of average precipitation from 1980 to 2010 in the Paraná River basin, Brazil

Xavier

Rudke

Fujita

et al. 2019

Intl Journal of Climatology

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The main objective of this study was to investigate the trends on average and extreme events in time series of daily precipitation from 1980 to 2010 in the Paraná River basin, Brazil. The nonparametric Mann–Kendall test was applied to detect monotonic trend in the precipitation series. The occurrence of extreme values was analysed based on three generalized extreme values (GEV) models: Model 1 (stationary), Model 2 (non‐stationary for location parameter), and Model 3 (non‐stationary for location and scale parameters). The GEV parameters were estimated by the Generalized Maximum Likelihood method (GMLE) and for the non‐stationary models, the parameters were estimated as linear functions of time. To choose the most suitable model, the maximum likelihood ratio test (D) was used. From the results observed at the monthly scale, it was possible to infer that the months with the highest probability of an extreme weather event occurrence are February (climates Aw and Cfa), July (Cfa and Cfb), and October (Aw, Cfa, and Cfb). Approximately 90% of the 1,112 stations presented no trend regarding the GEV parameters. The non‐stationarity showed by other stations (Models 2 and 3) might be associated with several factors, such as the alteration of land use due to the north expansion of the agricultural border of the Paraná River basin.

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Section: Introductionmentioning

confidence: 97%

Stationary and non‐stationary detection of extreme precipitation events and trends of average precipitation from 1980 to 2010 in the Paraná River basin, Brazil

Xavier

Rudke

Fujita

et al. 2019

Intl Journal of Climatology

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“…First, our methodology based on adjustment coefficients can be seen as an extension of Brown et al (2014), which estimates non-stationary GEV distribution simultaneously with both observations and a single GCM-RCM pair and introduces constant bias terms for each GEV parameter. There are also some links to a debiasing method proposed for annual maxima from GCM-RCM projections (Fontolan et al, 2019). For the location parameter, we consider additive adjustment coefficients that can be seen as bias terms, while the adjustment coefficients of the scale parameter that are multiplicative (due to the log link function) can be viewed as bias-correction factors (Hosseinzadehtalaei et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

A non-stationary extreme-value approach for climate projection ensembles: application to snow loads in the French Alps

et al. 2022

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Abstract. Anticipating risks related to climate extremes often relies on the quantification of large return levels (values exceeded with small probability) from climate projection ensembles. Current approaches based on multi-model ensembles (MMEs) usually estimate return levels separately for each climate simulation of the MME. In contrast, using MME obtained with different combinations of general circulation model (GCM) and regional climate model (RCM), our approach estimates return levels together from the past observations and all GCM–RCM pairs, considering both historical and future periods. The proposed methodology seeks to provide estimates of projected return levels accounting for the variability of individual GCM–RCM trajectories, with a robust quantification of uncertainties. To this aim, we introduce a flexible non-stationary generalized extreme value (GEV) distribution that includes (i) piecewise linear functions to model the changes in the three GEV parameters and (ii) adjustment coefficients for the location and scale parameters to adjust the GEV distributions of the GCM–RCM pairs with respect to the GEV distribution of the past observations. Our application focuses on snow load at 1500 m elevation for the 23 massifs of the French Alps. Annual maxima are available for 20 adjusted GCM–RCM pairs from the EURO-CORDEX experiment under the scenario Representative Concentration Pathway (RCP) 8.5. Our results show with a model-as-truth experiment that at least two linear pieces should be considered for the piecewise linear functions. We also show, with a split-sample experiment, that eight massifs should consider adjustment coefficients. These two experiments help us select the GEV parameterizations for each massif. Finally, using these selected parameterizations, we find that the 50-year return level of snow load is projected to decrease in all massifs by −2.9 kN m−2 (−50 %) on average between 1986–2005 and 2080–2099 at 1500 m elevation and RCP8.5. This paper extends the recent idea to constrain climate projection ensembles using past observations to climate extremes.

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“…The use of this approach is based on the extremal types theorem, that postulates that the probability function towards which the sampling distribution of the X largest values of independent and identically distributed (iid) observations converges as X increases is the GEV distribution (e.g., Coles 2001, Wilks 2011. This theorem can also be applicable to distributions of extreme minima (Coles 2001, Wilks 2011, Fontolan et al 2019, which are concerned with the X smallest values of iid observations. Despite this theoretical basis, in practical applications, the iid assumption is rarely met, and the GEV may not be the most suitable distribution to represent a set of extreme-value data (Wilks 2011).…”

Section: Case Studymentioning

confidence: 99%

Is it possible to apply the regional frequency analysis to daily extreme air temperature data?

et al. 2022

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The improvement of probabilistic assessments of extreme air temperature events is a major goal for agrometeorological studies.The regional frequency analysis based on L-moments (RFA-Lmom) has been successfully used to improve the study of hydrometeorological variables such as extreme rainfall events. This study investigated the hypothesis that the RFA-Lmom can be applied to extreme maximum (Tmax) and minimum (Tmin) air temperature data. The RFA-Lmon was calculated considering its original algorithm (multiplicative approach)and a new procedure referred as to additive approach. The suitability of both approaches was evaluated through Monte Carlo experiments, which simulated homogeneous and heterogeneous groups of Tmin and Tmax series, and through a case study based on weather stations situated in the state of São Paulo, Brazil. The results found in this study indicated that the RFA-Lmom can be applied to Tmax and Tmin data in tropical/subtropical regions such as the state of São Paulo. In addition, the additive approach consistently outperformed the multiplicative approach. Both discordance and heterogeneous measures presented their best performances when calculated under this new approach.The original goodness-of-fit measure may also be replaced by its bivariate extension when the group is formed by more than 15 series.

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Using climate change models to assess the probability of weather extremes events: a local scale study based on the generalized extreme value distribution

Cited by 11 publications

References 54 publications

Stationary and non‐stationary detection of extreme precipitation events and trends of average precipitation from 1980 to 2010 in the Paraná River basin, Brazil

Stationary and non‐stationary detection of extreme precipitation events and trends of average precipitation from 1980 to 2010 in the Paraná River basin, Brazil

A non-stationary extreme-value approach for climate projection ensembles: application to snow loads in the French Alps

Is it possible to apply the regional frequency analysis to daily extreme air temperature data?

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