Searching for the Most Suited Distribution and Estimation Method for At-Site Flood Frequency Analysis: A Case of the Chenab River
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College of Statistical Sciences, University of the Punjab, Lahore, Pakistan
Department of Statistics, Government College University Faisalabad, Pakistan
Department of Mathematical Statistics and Actuarial Science, University of the Free State, Bloemfontein, South Africa
Institute of Environmental Medicine, Division of Biostatistics, Karolinska Institute, Stockholm, Sweden
Submission date: 2023-10-08
Final revision date: 2023-12-17
Acceptance date: 2024-03-11
Online publication date: 2024-04-03
Corresponding author
Sajjad Haider Bhatti   

College of Statistical Sciences, University of the Punjab, Pakistan
The article deals with at-site flood frequency analysis for different gauging stations of the Chenab River in Pakistan. The study aimed at recommending the most suitable probability distribution and efficient method of parameter estimation for each gauging site. Generalized extreme value, generalized logistic, Gumbel, generalized Pareto, and reverse Gumbel probability models are fitted to the annual peak flow/discharge. For each gauging site, the parameters of these distributions are estimated through L-moments, maximum likelihood, least squares, weighted least squares, and relative least squares methods. For each site, the probability models with a particular estimation method are ranked on the basis of goodness-of-tests and accuracy measures, and then the most suitable pair of model and estimation method is identified through a total rank. The results indicate that the generalized Pareto distribution is the best fit for Marala, Khanki, Qadirabad, and Punjnad, while the generalized extreme value distribution is the most suited for the Trimmu gauging site. As far as the estimation method is concerned, least squares and weighted least squares methods are more accurate for most of the gauging sites. Finally, for each gauging site, the best-suited probability model is used to estimate the annual peak flow and to construct associated confidence intervals for different return years.
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