On Selection of Probability Distributions for Representing Annual Extreme Rainfall Series

Nguyen, Van‐Thanh‐Van; Tao, Diana; Bourque, Alain

doi:10.1061/40644(2002)250

Cited by 19 publications

(3 citation statements)

References 7 publications

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“…"The set of points (xi:n, yi:n) is plotted on normal graph with a 1:1 straight line extending from the origin. Theoretically, all points should fall on the 1:1 line if the assumed CDF is the true distribution" (Bourque, Nguyen, & Tao, 2002).…”

Section: Graphical Testmentioning

confidence: 99%

Instantaneous Flood Events Data Analyses in Northern Pakistan

Hussain

Shahab

2019

ESJ

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In large watersheds of Pakistan, a proper flood intensity assessment is a most important concern for many water management applications. The present study investigates the possible best-fit probability distributions technique of uttermost flood events using more than 30 years data (except Mastuj Bridge station) from 15 stream flow stations in northern Pakistan, operated and maintained by Surface Water Hydrology Project (SWHP), WAPDA. Different statistical distribution model, were used to estimate design floods for risk planning and the design of important civil infrastructure. Method of moments was used for estimation of parameters of selective distributions. Four goodness-of-fit statistics test K, A2, χ2 and Root mean square error (RMSE) were applied to evaluate the most appropriate probability distribution. P3, LP3, GEV, and LN were determined best fit distribution model. Among the eight-probability distribution's P3 fitted 33.3% of the stations, LP3 also fitted 33.3% of the stations, GEV fitted 26.7% of the stations and LN fitted 6.7% of the stations. The more practical result for peak flood were calculated for recurrence intervals of 5-year, 25-year, 50-year, 100-year, 500-year and 1000-years respectively. In future before planning and designing any project such as Hydropower, irrigation systems, Dike, flood disaster assessment and management at Indus and adjacent rivers, P3, LP3, GEV and LN distributions results should be considered more reliable on these locations.

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Section: Graphical Testmentioning

confidence: 99%

Instantaneous Flood Events Data Analyses in Northern Pakistan

Hussain

Shahab

2019

ESJ

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“…Koutsoyiannis and Baloutsos 2000;Koutsoyiannis 2004a, b;Bacro and Chaouche 2006). For this reason, and as recommended by Nguyen et al (2002), this study only considers the GEV distribution.…”

Section: Introductionmentioning

confidence: 99%

Bayesian estimation of intensity–duration–frequency curves and of the return period associated to a given rainfall event

Huard

Mailhot

Duchesne

2009

Stoch Environ Res Risk Assess

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Intensity-duration-frequency (IDF) curves are used extensively in engineering to assess the return periods of rainfall events and often steer decisions in urban water structures such as sewers, pipes and retention basins. In the province of Québec, precipitation time series are often short, leading to a considerable uncertainty on the parameters of the probabilistic distributions describing rainfall intensity. In this paper, we apply Bayesian analysis to the estimation of IDF curves. The results show the extent of uncertainties in IDF curves and the ensuing risk of their misinterpretation. This uncertainty is even more problematic when IDF curves are used to estimate the return period of a given event. Indeed, standard methods provide overly large return period estimates, leading to a false sense of security. Comparison of the Bayesian and classical approaches is made using different prior assumptions for the return period and different estimation methods. A new prior distribution is also proposed based on subjective appraisal by witnesses of the extreme character of the event.

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“…It has been shown by Fisher (1925), that the statistical distribution of rainfall is more important than the total amount of rainfall for the yield of crops. Therefore, detailed statistical studies of rainfall data for a variety of countries have been carried out for more than 70 years along with fits to multiple probability distribution (Ghosh et al, 2016;Sharma and Singh, 2010;Nguyen et al, 2002). We recap some of these studies for stations, both in India, as well as those outside India.…”

Section: Introductionmentioning

confidence: 99%

Multimodel response assessment for monthly rainfall distribution in some selected Indian cities using best-fit probability as a tool

2018

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We carry out a study of the statistical distribution of rainfall precipitation data for 20 cites in India. We have determined the best-fit probability distribution for these cities from the monthly precipitation data spanning 100 years of observations from 1901 to 2002. To fit the observed data, we considered 10 different distributions. The efficacy of the fits for these distributions was evaluated using four empirical non-parametric goodness-of-fit tests namely Kolmogorov-Smirnov, Anderson-Darling, Chi-Square, Akaike information criterion, and Bayesian Information criterion. Finally, the best-fit distribution using each of these tests were reported, by combining the results from the model comparison tests. We then find that for most of the cities, Generalized Extreme-Value Distribution or Inverse Gaussian Distribution most adequately fits the observed data.

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On Selection of Probability Distributions for Representing Annual Extreme Rainfall Series

Cited by 19 publications

References 7 publications

Instantaneous Flood Events Data Analyses in Northern Pakistan

Instantaneous Flood Events Data Analyses in Northern Pakistan

Bayesian estimation of intensity–duration–frequency curves and of the return period associated to a given rainfall event

Multimodel response assessment for monthly rainfall distribution in some selected Indian cities using best-fit probability as a tool

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