<b>extRemes</b>2.0: An Extreme Value Analysis Package in<i>R</i>

Gilleland, Eric; Katz, Richard W.

doi:10.18637/jss.v072.i08

Cited by 450 publications

(328 citation statements)

References 29 publications

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“…Following the convention of the extRemes package (Gilleland and Katz, 2016), the PDS/GP-based T -year event definition is applied in this paper, and we transformed AMS/GEV return periods accordingly. Note, however, that the transformation difference is mostly relevant for small return periods, as differences between T GEV and T GP become negligible for return periods of more than 5 years (Langbein, 1949;Rosbjerg, 1977;WMO, 2009).…”

Section: Assessment Methodsmentioning

confidence: 99%

Extreme weather exposure identification for road networks – a comparative assessment of statistical methods

Schlögl

Laaha

2017

Nat. Hazards Earth Syst. Sci.

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Abstract. The assessment of road infrastructure exposure to extreme weather events is of major importance for scientists and practitioners alike. In this study, we compare the different extreme value approaches and fitting methods with respect to their value for assessing the exposure of transport networks to extreme precipitation and temperature impacts. Based on an Austrian data set from 25 meteorological stations representing diverse meteorological conditions, we assess the added value of partial duration series (PDS) over the standardly used annual maxima series (AMS) in order to give recommendations for performing extreme value statistics of meteorological hazards. Results show the merits of the robust L-moment estimation, which yielded better results than maximum likelihood estimation in 62 % of all cases. At the same time, results question the general assumption of the threshold excess approach (employing PDS) being superior to the block maxima approach (employing AMS) due to information gain. For low return periods (non-extreme events) the PDS approach tends to overestimate return levels as compared to the AMS approach, whereas an opposite behavior was found for high return levels (extreme events). In extreme cases, an inappropriate threshold was shown to lead to considerable biases that may outperform the possible gain of information from including additional extreme events by far. This effect was visible from neither the square-root criterion nor standardly used graphical diagnosis (mean residual life plot) but rather from a direct comparison of AMS and PDS in combined quantile plots. We therefore recommend performing AMS and PDS approaches simultaneously in order to select the best-suited approach. This will make the analyses more robust, not only in cases where threshold selection and dependency introduces biases to the PDS approach but also in cases where the AMS contains non-extreme events that may introduce similar biases. For assessing the performance of extreme events we recommend the use of conditional performance measures that focus on rare events only in addition to standardly used unconditional indicators. The findings of the study directly address road and traffic management but can be transferred to a range of other environmental variables including meteorological and hydrological quantities.

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Section: Assessment Methodsmentioning

confidence: 99%

Extreme weather exposure identification for road networks – a comparative assessment of statistical methods

Schlögl

Laaha

2017

Nat. Hazards Earth Syst. Sci.

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“…The GEV distribution is characterized by three parameters, the location, the scale, and the shape of the distribution, which describes the centre of the distribution, the deviation around the mean, and the shape or the tail of the distribution (Katz et al, 2002;Katz and Brown, 1992). The cumulative distribution function of the stationary (timeinvariant) GEV model is given by Coles et al (2001) and Gilleland and Katz (2016):…”

Section: Extreme Value Analysis Of Sub-daily and Daily Precipitation mentioning

confidence: 99%

Does nonstationarity in rainfall require nonstationary intensity–duration–frequency curves?

Ganguli

Coulibaly

2017

Hydrol. Earth Syst. Sci.

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Abstract. In Canada, risk of flooding due to heavy rainfall has risen in recent decades; the most notable recent examples include the July 2013 storm in the Greater Toronto region and the May 2017 flood of the Toronto Islands. We investigate nonstationarity and trends in the short-duration precipitation extremes in selected urbanized locations in Southern Ontario, Canada, and evaluate the potential of nonstationary intensity-duration-frequency (IDF) curves, which form an input to civil infrastructural design. Despite apparent signals of nonstationarity in precipitation extremes in all locations, the stationary vs. nonstationary models do not exhibit any significant differences in the design storm intensity, especially for short recurrence intervals (up to 10 years). The signatures of nonstationarity in rainfall extremes do not necessarily imply the use of nonstationary IDFs for design considerations. When comparing the proposed IDFs with current design standards, for return periods (10 years or less) typical for urban drainage design, current design standards require an update of up to 7 %, whereas for longer recurrence intervals (50-100 years), ideal for critical civil infrastructural design, updates ranging between ∼ 2 and 44 % are suggested. We further emphasize that the above findings need re-evaluation in the light of climate change projections since the intensity and frequency of extreme precipitation are expected to intensify due to global warming.

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“…Estimation of parameters was done with the help of the R software packages available on https://www.r-project. org/: extRemes (Gilleland and Katz 2016), FAdist (Aucoin 2015), PearsonDS (Becker and Klößner 2013).…”

Section: Maximum Likelihood Methodsmentioning

confidence: 99%

A comparison of three approaches to non-stationary flood frequency analysis

2017

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Non-stationary flood frequency analysis (FFA) is applied to statistical analysis of seasonal flow maxima from Polish and Norwegian catchments. Three non-stationary estimation methods, namely, maximum likelihood (ML), two stage (WLS/TS) and GAMLSS (generalized additive model for location, scale and shape parameters), are compared in the context of capturing the effect of non-stationarity on the estimation of time-dependent moments and design quantiles. The use of a multimodel approach is recommended, to reduce the errors due to the model misspecification in the magnitude of quantiles. The results of calculations based on observed seasonal daily flow maxima and computer simulation experiments showed that GAMLSS gave the best results with respect to the relative bias and root mean square error in the estimates of trend in the standard deviation and the constant shape parameter, while WLS/TS provided better accuracy in the estimates of trend in the mean value. Within three compared methods the WLS/TS method is recommended to deal with nonstationarity in short time series. Some practical aspects of the GAMLSS package application are also presented. The detailed discussion of general issues related to consequences of climate change in the FFA is presented in the second part of the article entitled ''Around and about an application of the GAMLSS package in non-stationary flood frequency analysis''.Keywords Non-stationary flood frequency analysis Á Maximum likelihood Á Two-stage methodology Á GAMLSS Á Multimodel approach Á Monte Carlo simulations

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extRemes2.0: An Extreme Value Analysis Package inR

Cited by 450 publications

References 29 publications

Extreme weather exposure identification for road networks – a comparative assessment of statistical methods

Extreme weather exposure identification for road networks – a comparative assessment of statistical methods

Does nonstationarity in rainfall require nonstationary intensity–duration–frequency curves?

A comparison of three approaches to non-stationary flood frequency analysis

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