Modelling maximum daily yearly rainfall in northern Algeria using generalized extreme value distributions from 1936 to 2009

Boudrissa, Naima; Cheraitia, Hassen; Halimi, Lotfi

doi:10.1002/met.1610

Cited by 24 publications

(12 citation statements)

References 31 publications

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“…Based on a sufficiently long observation series of the maximum annual daily precipitation, it is possible to predict P maxp% by using probability distributions of random variables. In hydrological studies, P maxp% is commonly determined by using the following probability distribution functions: normal distribution, log-normal distribution, Pearson's type III, exponential function, Gumbel's distribution, generalized extreme value distribution (GEV), and Weibull's and Pareto's distributions [7][8][9][10][11][12][13]. Wdowikowski et al [14] recommend the generalized exponential distribution for such a purpose.…”

Section: Introductionmentioning

confidence: 99%

Estimating Maximum Daily Precipitation in the Upper Vistula Basin, Poland

et al. 2019

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The aim of this study was to determine the best probability distributions for calculating the maximum annual daily precipitation with the specific probability of exceedance (Pmaxp%). The novelty of this study lies in using the peak-weighted root mean square error (PWRMSE), the root mean square error (RMSE), and the coefficient of determination (R2) for assessing the fit of empirical and theoretical distributions. The input data included maximum daily precipitation records collected in the years 1971–2014 at 51 rainfall stations from the Upper Vistula Basin, Southern Poland. The value of Pmaxp% was determined based on the following probability distributions of random variables: Pearson’s type III (PIII), Weibull’s (W), log-normal, generalized extreme value (GEV), and Gumbel’s (G). Our outcomes showed a lack of significant trends in the observation series of the investigated random variables for a majority of the rainfall stations in the Upper Vistula Basin. We found that the peak-weighted root mean square error (PWRMSE) method, a commonly used metric for quality assessment of rainfall-runoff models, is useful for identifying the statistical distributions of the best fit. In fact, our findings demonstrated the consistency of this approach with the RMSE goodness-of-fit metrics. We also identified the GEV distribution as recommended for calculating the maximum daily precipitation with the specific probability of exceedance in the catchments of the Upper Vistula Basin.

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Section: Introductionmentioning

confidence: 99%

Estimating Maximum Daily Precipitation in the Upper Vistula Basin, Poland

et al. 2019

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“…Seebocus et al 11 showed that the frequency distribution of rainfall data for Mauritius (see Figure 3) can be represented using the GEV distribution. In this study, the block maxima method is used with the GEV distribution, where the cumulative distribution function is formulated as 21 :…”

Section: Extreme Value Analysismentioning

confidence: 99%

Analysis of extreme rainfall and drought events using statistical and fractal methods: A case study of Mauritius

2021

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Due to climate change, extreme rainfall and drought events are becoming more and more frequent in several regions of the globe. We investigated the suitability of employing statistical and fractal (or scaling) methods to characterise extreme precipitation and drought events. The case of the island of Mauritius was considered, for which monthly mean rainfall data for the period January 1950 to December 2016 were analysed. The generalised extreme value distribution was used to extract the 10- and 20-year return levels and the Standardised Precipitation Index (SPI) was used to identify anomalous wet and dry events. A log-term correlation analysis was also performed to characterise the relationship between maximum rainfall and its duration. The results indicate that the 10-year return level is approximately between 500 mm and 850 mm and the 20-year return level is between 600 mm and 1000 mm. Results also show that the extreme maximum rainfall events occur mostly during austral summer (November to April) and could be related to the effects of tropical cyclones and La Niña events, while anomalous dry events were found to be significantly persistent with very long periods of drought. Moreover, there was a strong correlation between maximum rainfall and its duration. The methodology used in this work could be very useful in similar studies for other Small Island Developing States.

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“…Several other published papers have analysed extreme events in other areas using the GEVD, showing the importance of modelling extreme events and providing information such as return levels and periods as measures of extreme risk. [13] modelled annual maximum of daily rainfall in northern Algeria using the GEVD from 1936 to 2009 to estimate return levels at 2, 10, 20, 50 and 100 years return periods. Their results show an average of 100 years return period needed to record similar levels of 181.9mm of rainfall in Algiers, 173mm in Miliana and 109.54mm in Oran.…”

Section: Review Of Literaturementioning

confidence: 99%

Analysing Extreme Risk in the South African Financial Index (J580) using the Generalised Extreme Value Distribution

Chikobvu

Jakata²

2020

Stat., optim. inf. comput.

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The aim of this study is to model the probabilistic behaviour of unusually large financial losses (extreme-risk)and gains of the South African Financial Index (J580). Risk is defined as uncertainty in return in this paper. This study makes use of Extreme Value Theory (EVT) for the period years: 1995-2018 to build models that are used to estimate extreme losses and gains. The quarterly block maxima/minima of monthly returns are tted to the Generalised Extreme Value Distribution (GEVD). Return levels (maximum loss/gain) based on the parameters from the GEVD are estimated. A comparative analysis with the Generalised Pareto Distribution (GPD) is carried out. The study reveals that EVT provides an efficient method of forecasting potentially high risks in advance. The conclusion is that analysing extreme risk in the South African Financial Index helps investors understand its riskness better and manage to reduce the risk exposure in this portfolio.

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Modelling maximum daily yearly rainfall in northern Algeria using generalized extreme value distributions from 1936 to 2009

Cited by 24 publications

References 31 publications

Estimating Maximum Daily Precipitation in the Upper Vistula Basin, Poland

Estimating Maximum Daily Precipitation in the Upper Vistula Basin, Poland

Analysis of extreme rainfall and drought events using statistical and fractal methods: A case study of Mauritius

Analysing Extreme Risk in the South African Financial Index (J580) using the Generalised Extreme Value Distribution

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