Title: The best fitted probabilistic modelling for seasonal extreme rainfall of Gilgit-Baltistan, Pakistan

Authors: Muhammad Ali; Bulbul Jan; Faisal Ahmed Khan Afridi; Muhammad Yonus

Addresses: Department of Artificial Intelligence and Mathematical Sciences, Sindh Madressatul Islam University, Aewan-e-Tijarat Road, Karachi, 74000, Pakistan ' Department of Basic Sciences and Mathematics, Dawood University of Engineering and Technology, Karachi, Pakistan ' Institute of Space Technology, University of Karachi, Pakistan ' Department of Computer Science, Indus University, Karachi, Pakistan

Abstract: Extreme rainfall plays a key role in hydrological designs, resources of water management, flood hazard and land sliding in mountainous regions. In hydrological analysis, it has been a topic of interest to establish a suitable probabilistic approach for extreme rainfall. Monthly extreme rainfall of five meteorological stations of Gilgit-Baltistan (GB) from 1971-2010 have been used. The percentile deviation and probabilistic models have been applied to calculate variation and return levels of seasonal extreme rainfall. The outcome of this study shows that the preferred stations of GB (Gilgit, Skardu, Bunji, Gupis and Astore) will receive rainfall greater than 107.76 mm, 140.64 mm, 69.23 mm, 85.34 mm and 153.44 mm, respectively for the incoming 50-year return period, which are more than the extreme rainfall of 2010. The analysis also predicts that on average, Gilgit-Baltistan will receive 111.28 mm return level against the 50year return period in the summer season. These results are useful for agriculture, environmental researchers, planners and decision makers to get precautionary measures in Gilgit-Baltistan.

Keywords: seasonal extreme rainfall; probabilistic modelling; return levels; percentile deviation; return period; Pakistan.

DOI: 10.1504/IJGW.2021.114341

International Journal of Global Warming, 2021 Vol.23 No.4, pp.355 - 369

Received: 23 Apr 2020
Accepted: 15 Aug 2020

Published online: 12 Apr 2021 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article