Finding Probability Distributions for Annual Daily Maximum Rainfall in Pakistan Using Linear Moments and Variants

Ahmad, Ishfaq; Abbass, Aamar; Saghir, Aamir; Fawad, Muhammad

doi:10.15244/pjoes/61715

Cited by 12 publications

(18 citation statements)

References 34 publications

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“…The guidelines were first published in 1958and updated thereafter in 1977and 1978(Pilgrim, 1984, and have been continually updated. Compared to the previous revision, the most recent update makes use of nearly 30 years of additional daily rainfall data from 2 300 additional rainfall stations and was published in 2016 (Ball et al, 2016). Design rainfalls are categorised into five broad classes, based on the frequency of occurrence, as summarised in Table 1.…”

Section: Design Rainfalls In Australiamentioning

confidence: 99%

“…This is considered to be a good compromise between providing a better fit to the higher probabilities of the distribution and giving too much influence to the high outliers . Many countries are exploring the use of LH-moments for estimating extreme rainfall and flood events (Ahmad et al, 2016;Deka et al, 2011;Hossein and Gheidari, 2013).…”

Section: Design Rainfalls In Australiamentioning

confidence: 99%

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Review: Methods for the estimation of extreme rainfall events

Johnson

Smithers

2019

WSA

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The estimation of design rainfalls is necessary to estimate the exceedance probabilities of extreme floods required to design hydraulic structures and to quantify the risk of failure of these structures. New approaches to estimating extreme rainfall events are being developed internationally. This paper reviews methods for estimating design rainfalls, particularly extreme events, in South Africa and internationally, and highlights the need to update methods used for estimating extreme rainfall events in South Africa as a platform for future research.

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Section: Design Rainfalls In Australiamentioning

confidence: 99%

Section: Design Rainfalls In Australiamentioning

confidence: 99%

Review: Methods for the estimation of extreme rainfall events

Johnson

Smithers

2019

WSA

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“…According to the United Nations Framework on Climate Change (UNFCC) the human lives, animal lives, and water resources, agricultural lands, food security, and coastal zones will be heavily affected Value (GEV), Gumbel, Generalized Pareto (GP), Generalized Logistic (GLO) and the lognormal distribution. Because of their suitability to modelling of extreme value, these probability distributions have numerous applications for environmental data (e.g., An and Pandey, 2005;Furrer and Katz, 2008;Chikobvu and Sigauke, 2013;Jonathan and Ewans, 2013;Diriba et al, 2015;Ahmad et al, 2016). Several studies based on frequentist approaches have been conducted for finding the best fit distribution and modelling the AMDR data in Pakistan by Rasul et al (2004), Haroon and Rasul (2009), Rasul et al (2009) and Ahmad et al (2014Ahmad et al ( , 2016.…”

Section: Introductionmentioning

confidence: 99%

“…Because of their suitability to modelling of extreme value, these probability distributions have numerous applications for environmental data (e.g., An and Pandey, 2005;Furrer and Katz, 2008;Chikobvu and Sigauke, 2013;Jonathan and Ewans, 2013;Diriba et al, 2015;Ahmad et al, 2016). Several studies based on frequentist approaches have been conducted for finding the best fit distribution and modelling the AMDR data in Pakistan by Rasul et al (2004), Haroon and Rasul (2009), Rasul et al (2009) and Ahmad et al (2014Ahmad et al ( , 2016. Ahmad et al (2016) suggested that the (GEV) and (GP) are the most suitable distribution for the AMDR series in the different regions of Pakistan.…”

Section: Introductionmentioning

confidence: 99%

Modelling of Extreme Rainfall in Punjab: Pakistan Using Bayesian and Frequentist Approach

Ahmad¹

2019

Appl. Ecol. Env. Res.

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In this study we compared the efficiency of frequentist approach with Bayesian approach by carrying out extreme value analysis of Annual Maximum Daily Rainfall (AMDR). For frequentist frequency analysis of AMDR, we used the data of one station i.e. Lahore in Punjab province, Pakistan while for Bayesian analysis we used the data of three other neighboring stations as prior information. During frequentist approach, Generalized Extreme Value (GEV) was found to be a best-fit distribution on the data. In frequentist method, the parameters of GEV distribution were estimated using Maximum Likelihood Estimation (MLE), while in the Bayesian framework the Markov Chain Monte Carlo (MCMC) simulation technique along with Metropolis-Hasting algorithm and Gibbs sampler were implemented. Findings of this study indicate that despite the asymptotic requirements of the MLE, its performance can be enhanced by adopting the Bayesian MCMC paradigm. In order to acquire the posterior densities of GEV parameters, non-informative and informative priors based on the historical data of surrounding weather stations were employed. The result of Bayesian MCMC might be affected by the choice of priors. In addition, the performance of the parameters estimation methods was verified by employing several robustness measures. Robustness measures results proved that the Bayesian MCMC method performed better than MLE in estimating GEV parameters and future return levels. Therefore, the findings of these analyses could be helpful in adopting proper flood protection measures and designing infrastructures of culverts, buildings, bridges and dams in the region.

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“…Hussain -Abbas: Rainfall frequency analysis using Frechet and Log-logistic distributions of sites of Azad Jammu and Kashmir, Hungary rainfall of 30 met sites of Pakistan. The study area (includes sites from Sindh, Punjab, Balochistan and Northern Areas) was divided into four homogeneous regions with different three-parameters probability distributions identified as best-fitted probability distributions Ahmad et al (2016). performed at site frequency analysis using annual daily maximum rainfall series.…”

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confidence: 99%

Rainfall Frequency Analysis Using Frechet and Log-Logistic Distributions of Sites of Azad Jammu and Kashmir, Pakistan

Hussain¹

2019

Appl. Ecol. Env. Res.

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The paper illustrates the application of at-site frequency analysis using relatively nontraditional probability distributions adopting four methods of parameter estimation using annual maximum rainfall series (AMRS) (from 1980 to 2015) of three sites; Muzaffarabad, Garhi Dupatta and Kotli of Azad Jammu and Kashmir, Pakistan. Critical assumptions related to AMRS for frequency analysis have been validated through run test (to check the randomness of AMRS at each site) and Mann Whitney test (to check the distribution of AMRS at given sites as identical or not). Two probability distributions; Frechet and Log-logistic have been used as candidate distributions to model AMRS at given sites. The parameters of these distributions have been estimated using four methods; Probability Weighted Moments Estimation (PWME), Maximum Likelihood Estimation (MLE), Bayesian Estimation (BE) and Moments Estimation (ME) methods. The Kolmogorov Smirnov test has been used as goodnessof-fit criteria. The results show suitability of the selected probability distributions for the observed AMRS series at given sites with BE and MLE methods as adequate for estimation. Moreover, the results indicate that ME method is not a preferred choice for the estimation of parameters and quantiles for various return periods.

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Finding Probability Distributions for Annual Daily Maximum Rainfall in Pakistan Using Linear Moments and Variants

Cited by 12 publications

References 34 publications

Review: Methods for the estimation of extreme rainfall events

Review: Methods for the estimation of extreme rainfall events

Modelling of Extreme Rainfall in Punjab: Pakistan Using Bayesian and Frequentist Approach

Rainfall Frequency Analysis Using Frechet and Log-Logistic Distributions of Sites of Azad Jammu and Kashmir, Pakistan

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