Annual maximum and peaks-over-threshold analyses of daily rainfall accumulations for Austria

Villarini, Gabriele; Smith, James A.; Ntelekos, Alexandros A.; Schwarz, Ulrich

doi:10.1029/2010jd015038

Cited by 55 publications

(53 citation statements)

References 116 publications

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“…construction of dams, river engineering). These results are also in agreement with results found by Villarini et al (2011c) regarding extreme rainfall over Austria. When investigating the presence of trends in the frequency of POT floods, seven stations showed a statistically significant decreasing trend.…”

Section: Discussionsupporting

confidence: 83%

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Analyses of extreme flooding in Austria over the period 1951–2006

Villarini

Smith

Serinaldi

et al. 2011

Intl Journal of Climatology

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ABSTRACT:Analyses of extreme flooding in Austria is performed using daily discharge time series from 27 stations over the period . The main research questions revolve around: (1) temporal non-stationarities in the flood record, (2) upper tail and scaling properties of the flood peak records, and (3) relation between magnitude and frequency of flooding and the North Atlantic Oscillation (NAO). Two datasets are derived from the daily discharge time series: annual maximum daily discharge and peaks-over-threshold (POT) data. The validity of the stationarity assumption in the annual maximum discharge record is assessed by investigating the presence of abrupt and slowly varying changes using nonparametric tests. The time series are tested for abrupt changes both in the mean and variance of the flood peak distributions by means of the Pettitt test. The presence of monotonic trends is investigated by means of the Mann-Kendall and Spearman tests. Violations of the stationarity assumption are associated with abrupt rather than gradual changes. These step changes generally involve river regulation through construction of dams or other major engineering works. It is not possible to make conclusive statements about the presence of an anthropogenic climate change signal in the flood peak record. Similar conclusions are obtained when focussing on the frequency of POT floods. The Generalised Extreme Value distribution is used to study the upper tail and scaling properties of annual maximum daily discharge records. The location and scale parameters exhibit power-law behaviour as a function of drainage area. The shape parameters indicate that the flood peak distributions for Austria have a heavy tail. Non-stationary modelling of the annual maximum daily discharge and POT time series is used to explore the relation between flood magnitude and frequency and NAO. The results indicate that NAO is a significant covariate in explaining the magnitude and frequency of occurrence of flooding over a large part of Austria.

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Section: Discussionsupporting

confidence: 83%

“…The lack of a climate change signal in heavy rainfall over Austria was also found by Villarini et al (2011c). Moreover, the results agree with the findings by Barredo (2009), who analysed normalised European flood losses without finding a climate change signal (he related the increase in the original flood loss record to societal factors).…”

Section: Stationaritysupporting

confidence: 81%

Analyses of extreme flooding in Austria over the period 1951–2006

Villarini

Smith

Serinaldi

et al. 2011

Intl Journal of Climatology

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“…Two basic approaches have been proposed for deriving extreme value series (Coles, 2001), which are both widely applied in studying extreme meteorological events (e.g., Smith, 1989;Davison and Smith, 1990;Parey et al, 2010;Villarini, 2011;Papalexiou and Koutsoyiannis, 2013). On the one hand, the maximum value per year can be used in the block maxima approach, resulting in an annual maxima series (AMS).…”

Section: Introductionmentioning

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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“…The AMF series can be simply constructed by extracting the maximum daily discharge in each year, while the POT series consists of peak flows above a threshold. The POT series allow for more peaks to be selected and included in extreme flow analysis, as shown by, for instance, Parent and Bernier (2003) and Villarini et al (2011). Application of the AMF method, naturally leads to peak flows that are independent.…”

Section: Peak Flow Extractionmentioning

confidence: 99%

Improved Simulation of Peak Flows under Climate Change: Postprocessing or Composite Objective Calibration?

Zhang

Booij

2015

Journal of Hydrometeorology

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Climate change is expected to have large impacts on peak flows. However, there may be bias in the simulation of peak flows by hydrological models. This study aims to improve the simulation of peak flows under climate change in Lanjiang catchment, east China, by comparing two approaches: postprocessing of peak flows and composite objective calibration. Two hydrological models [Soil and Water Assessment Tool (SWAT) and modèle du Génie Rural à 4 paramètres Journalier (GR4J)] are employed to simulate the daily flows, and the peaks-over-threshold method is used to extract peak flows from the simulated daily flows. Three postprocessing methods, namely, the quantile mapping method and two generalized linear models, are set up to correct the biases in the simulated raw peak flows. A composite objective calibration of the GR4J model by taking the peak flows into account in the calibration process is also carried out. The regional climate model Providing Regional Climates for Impacts Studies (PRECIS) with boundary forcing from two GCMs (HadCM3 and ECHAM5) under greenhouse gas emission scenario A1B is applied to produce the climate data for the baseline period and the future period 2011-40. The results show that the postprocessing methods, particularly quantile mapping method, can correct the biases in the raw peak flows effectively. The composite objective calibration also resulted in a good simulation performance of peak flows. The final estimated peak flows in the future period show an obvious increase compared with those in the baseline period, indicating there will probably be more frequent floods in Lanjiang catchment in the future.

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Annual maximum and peaks-over-threshold analyses of daily rainfall accumulations for Austria

Cited by 55 publications

References 116 publications

Analyses of extreme flooding in Austria over the period 1951–2006

Analyses of extreme flooding in Austria over the period 1951–2006

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

Improved Simulation of Peak Flows under Climate Change: Postprocessing or Composite Objective Calibration?

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