Determination of Suitable Probability Distribution of Rainfall in Pakistan Considering Multiplicity
1
School of Mathematics and Statistics, Gansu Key Laboratory of Applied Mathematics and Complex Systems, Lanzhou University, Lanzhou 730000, China
2
School of Environmental Science and Engineering, North China Electric Power University, Beijing, China
3
School of Public Administration, Department of Land Resources Management, Zhejiang Gongshang University, Zhejiang Hangzhou 310018, China
4
Department of Environmental Science, Abdul Wali Khan University, Mardan, Pakistan
5
School of geography and environment, Shandong normal university, Jinan, China
6
Department of Environmental Sciences, COMSATS University Islamabad, Abbottabad, Pakistan
Submission date: 2023-12-19
Acceptance date: 2023-12-28
Publication date: 2023-12-30
Trends in Ecological and Indoor Environmental Engineering, 2023;1(1):24-34
KEYWORDS
ABSTRACT
Extreme rainfall events are occasional, and understanding their intensity and frequency is important for long-term planning and for public safety. The current study aims to investigate the stability of extreme precipitation events in different regions of Pakistan, their independence and the uniformity of their occurrence. A rainfall frequency analysis (RFA) was conducted based on information from 8 weather stations namely, the distribution and moments L of the annual maximum precipitation in Pakistan were investigated; Using goodness-of-fit criteria, the study determined the possible distribution of precipitation. Relatively Absolute Error (RAE) results are based on the most appropriate GUM distribution, revealed that the Sialkot, Multan, Faisalabad and DI Khan stations produce very low errors (0.266, 0.847, 0.075, 0.856, 1.671, 2.522, 3.659, 4.524). It was found that the most suitable distribution is LN distribution for Peshawar, Sibbi and Karachi Stations. The GEV distribution also performs well with small errors for various return periods (0.266, 0.847, 0.075, 0.856, 1.671, 2.522, 3.659, 4.524), corresponding to the return periods 2, 5, 25, 50, 100, 200, 500 and 1000 years. In contrast, P3 has the advantage of a return period of 10 years for all stations, as they all produce similar results for this particular return period. The current research provides valuable insights into estimating extreme rainfall at stations where rainfall data are available.
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