Peaks-over-Threshold Study of Trends in Extreme Rainfall over the Iberian Peninsula

Acero, Francisco Javier; García, José Álvarez; Gallego, M. C.

doi:10.1175/2010jcli3627.1

Cited by 95 publications

(67 citation statements)

References 39 publications

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“…They pointed to an important consequence: autumn, instead of winter, would be the season with most extreme rainfall in the western IP due to the sharp increase in the RLs in the future. This is coherent with previous results [9,10] indicating a significant positive trend for extreme rainfall over the western half of the IP and with the findings of a regional study [12] of an area in the southwestern IP, which showed the same behavior. An increase in the frequency of extreme precipitation in autumn for the same area of the IP has been described as being due to increased northwesterly flows [25].…”

Section: Discussionsupporting

confidence: 93%

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Return Level Estimation of Extreme Rainfall over the Iberian Peninsula: Comparison of Methods

Acero

Parey

García

et al. 2018

Water

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Different ways to estimate future return levels (RLs) for extreme rainfall, based on extreme value theory (EVT), are described and applied to the Iberian Peninsula (IP). The study was done for an ensemble of high quality rainfall time series observed in the IP during the period 1961-2010. Two approaches, peaks-over-threshold (POT) and block maxima (BM) with the generalized extreme value (GEV) distribution, were compared in order to identify which is the more appropriate for the estimation of RLs. For the first approach, which identifies trends in the parameters of the asymptotic distributions of extremes, both all-days and rainy-days-only datasets were considered because a major fraction of values of daily rainfall over the IP is zero. For the second approach, rainy-days-only data were considered showing how the mean, variance and number of rainy days evolve. The 20-year RLs expected for 2020 were estimated using these methods for three seasons: autumn, spring and winter. The GEV is less reliable than the POT because fixed blocks lead to the selection of non-extreme values. Future RLs obtained with the POT are greater than those estimated with the GEV, mainly because some gauges show significant positive trends for the number of rainy days. Autumn, rather than winter, is currently the season with the heaviest rainfall for some regions.

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Section: Discussionsupporting

confidence: 93%

“…Some studies of extreme rainfall trends for the IP have used the statistical extreme value theory (EVT) and some the block maxima (BM) [9] or the peaks-over-threshold (POT) approaches [10,11]. These methods each have advantages and disadvantages, and it is important to compare the results they give.…”

Section: Introductionmentioning

confidence: 99%

Return Level Estimation of Extreme Rainfall over the Iberian Peninsula: Comparison of Methods

Acero

Parey

García

et al. 2018

Water

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“…A sensitivity analysis was performed by estimating four different GPD models. Acero et al (2011) use the POT-GDP approach where thresholds are made time-varying, allowing them to change linearly over time. An automatic declustering approach was used to select independent extreme events exceeding the threshold.…”

Section: Assuming a Non-stationary Climatementioning

confidence: 99%

Inferences on weather extremes and weather-related disasters: a review of statistical methods

Visser

Petersen

2012

Clim. Past

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Abstract. The study of weather extremes and their impacts, such as weather-related disasters, plays an important role in research of climate change. Due to the great societal consequences of extremes -historically, now and in the future -the peer-reviewed literature on this theme has been growing enormously since the 1980s. Data sources have a wide origin, from century-long climate reconstructions from tree rings to relatively short (30 to 60 yr) databases with disaster statistics and human impacts.When scanning peer-reviewed literature on weather extremes and its impacts, it is noticeable that many different methods are used to make inferences. However, discussions on these methods are rare. Such discussions are important since a particular methodological choice might substantially influence the inferences made. A calculation of a return period of once in 500 yr, based on a normal distribution will deviate from that based on a Gumbel distribution. And the particular choice between a linear or a flexible trend model might influence inferences as well.In this article, a concise overview of statistical methods applied in the field of weather extremes and weather-related disasters is given. Methods have been evaluated as to stationarity assumptions, the choice for specific probability density functions (PDFs) and the availability of uncertainty information. As for stationarity assumptions, the outcome was that good testing is essential. Inferences on extremes may be wrong if data are assumed stationary while they are not. The same holds for the block-stationarity assumption. As for PDF choices it was found that often more than one PDF shape fits to the same data. From a simulation study the conclusion can be drawn that both the generalized extreme value (GEV) distribution and the log-normal PDF fit very well to a variety of indicators. The application of the normal and Gumbel distributions is more limited. As for uncertainty, it is advisable to test conclusions on extremes for assumptions underlying the modelling approach. Finally, it can be concluded that the coupling of individual extremes or disasters to climate change should be avoided.

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“…The POT approach ensures that other extreme rainfall events apart from the annual maxima are included [3,4], but suffers from the subjective selection of a suitable threshold [5,6] and potential correlation between extreme events [7]. In contrast, the AM approach is simple, hence remaining popular in extreme rainfall studies [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Linear Moments and Uncertainty Analysis to Extreme Rainfall Events in Sabah

2017

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Abstract. Linear moments (LM) has been applied in extreme rainfall study for several countries, including China, United States of America, and Peninsular Malaysia. In this study, the LM procedures were applied to extreme rainfall data corresponding to locations provided in Malaysia Urban Stromwater Manual (MSMA) to derive new design rainfalls. Different record lengths were considered to assess the changes in design rainfall, and Monte Carlo simulations were carried out to compute confidence interval of the derived design rainfalls. Based on the Goodnessof-Fit (GoF) test results, the Generalized Extreme Value (GEV) probability distribution was chosen to derive the design rainfalls. The updated design rainfalls at all four locations showed significant reduction at design rainfalls of 50-year ARI and above. The difference of the design rainfalls from shorter record lengths with respect to the full record length and the confidence intervals do not necessarily reduce with a longer record. In hypothetical cases where 100-yr ARI rainfall was added, the increase in design rainfalls did not exceed the upper bound of the confidence intervals. The derived confidence intervals hence allow for better risk assessment, and should be considered in the design of critical structures, i.e. dams.

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Peaks-over-Threshold Study of Trends in Extreme Rainfall over the Iberian Peninsula

Cited by 95 publications

References 39 publications

Return Level Estimation of Extreme Rainfall over the Iberian Peninsula: Comparison of Methods

Return Level Estimation of Extreme Rainfall over the Iberian Peninsula: Comparison of Methods

Inferences on weather extremes and weather-related disasters: a review of statistical methods

Application of Linear Moments and Uncertainty Analysis to Extreme Rainfall Events in Sabah

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