2014
DOI: 10.1007/s00704-014-1180-5
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Automatic threshold and run parameter selection: a climatology for extreme hourly precipitation in Switzerland

Abstract: Extreme value analyses of a large number of relatively short time series are in increasing demand in environmental sciences and design. Here, we present an automated procedure for the peaks-over-threshold (POT) approach to extreme value theory and use it to provide a climatology of extreme hourly precipitation in Switzerland. The POT approach fits the generalized Pareto distribution (GPD) to independent exceedances above some high threshold. To guarantee independence, the time series is pruned: exceedances sep… Show more

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Cited by 50 publications
(33 citation statements)
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“…However, since such an analysis is not our goal, we follow a practical approach and apply the runs declustering for three different thresholds, the 95th, 98th, and 99th percentiles of the daily precipitation accumulation of all days. These thresholds are consistent with the threshold selection plots recommended by Coles (2001) and have been applied before to Swiss precipitation time series (Fukutome et al 2015). We found a maximum declustering run length r of about two days for all thresholds and all seasons, which is in good agreement with previous analyses: Fukutome et al (2015) found r to lie between 20 and 40 h (winter) and 10 and 20 h (summer) in the southern Alps.…”
Section: B Statistical Proceduressupporting
confidence: 90%
“…However, since such an analysis is not our goal, we follow a practical approach and apply the runs declustering for three different thresholds, the 95th, 98th, and 99th percentiles of the daily precipitation accumulation of all days. These thresholds are consistent with the threshold selection plots recommended by Coles (2001) and have been applied before to Swiss precipitation time series (Fukutome et al 2015). We found a maximum declustering run length r of about two days for all thresholds and all seasons, which is in good agreement with previous analyses: Fukutome et al (2015) found r to lie between 20 and 40 h (winter) and 10 and 20 h (summer) in the southern Alps.…”
Section: B Statistical Proceduressupporting
confidence: 90%
“…The spatial distribution of the hourly and daily return levels shown in Fig. 5 and 6 mirrors the findings of previous alpine climatologies (e.g., Frei and Schär, 1998;Isotta et al, 2014;Fukutome et al, 2015). Figure 6, moreover, shows that the regional statistic considered for the extreme value analysis has a huge impact on the thresholds which should be used by NowPAL to issue alerts, and therefore it should be carefully chosen when designing a regional alert system, depending on the nowcasting application and specific customer needs.…”
Section: Return Level Mapssupporting
confidence: 86%
“…In order to guarantee the temporal independence of maxima of blocks as short as months, a minimum lag time of 48 h among maxima occurring in 2 successive months but close in time was imposed for accumulations ranging from 1 h to 1 day. In fact, Fukutome et al (2015) and Barton et al (2016) found that this is the maximum declustering run length in Switzerland for hourly and daily precipitation, respectively. For 2-day rainfall accumulations, such lag time was extended to 72 h. In case of maxima of 2 separate months occurring within this lag time, the largest was assigned to the corresponding month, while the smallest was substituted with the second maximum of the other month.…”
Section: Extreme Rainfall Analysismentioning
confidence: 93%
“…In recent years, efforts have been made to overcome the problem of visual threshold selection, e.g., by robust threshold selection (Dupuis, 1999), likelihood-based visual diagnostics (Wadsworth and Tawn, 2012;Wadsworth, 2016), Bayesian approaches (Tancredi et al, 2006;Lee et al, 2014), approaches based on goodnessof-fit tests (Roth et al, 2016) and extreme value mixture models (MacDonald et al, 2011). In addition, attempts were made to develop more automated approaches for extreme value threshold estimation, including the automated threshold selection approach (ATSM) by Thompson et al (2009), the multiple threshold method (MTM) by Deidda (2010) and the automatic threshold and run parameter selection by Fukutome et al (2015). While these approaches are appealing from a theoretical perspective, their practical value is of- Figure 7.…”
Section: Discussionmentioning
confidence: 99%