Research Article Regional Frequency Analysis of Extremes Precipitation Using L-Moments and Partial L-Moments Said Arab Khan, 1,2 Ijaz Hussain, 1 Tajammal Hussain, 3 Muhammad Faisal, 4,5 Yousaf Shad Muhammad, 1 and Alaa Mohamd Shoukry 6,7 1 Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan 2 Government Degree College, Lahore, Swabi, Khyber Pakhtunkhwa, Pakistan 3 Department of Statistics, COMSATS Institute of Information Technology, Lahore, Pakistan 4 Faculty of Health Studies, University of Bradford, Bradford, UK 5 Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK 6 Arriyadh Community College, King Saud University, Riyadh, Saudi Arabia 7 Workers University, Cairo, Egypt Correspondence should be addressed to Ijaz Hussain; ijaz@qau.edu.pk Received 21 December 2016; Revised 26 January 2017; Accepted 7 February 2017; Published 7 March 2017 Academic Editor: Mouleong Tan Copyright © 2017 Said Arab Khan et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Extremes precipitation may cause a series of social, environmental, and ecological problems. Estimation of frequency of extreme precipitations and its magnitude is vital for making decisions about hydraulic structures such as dams, spillways, and dikes. In this study, we focus on regional frequency analysis of extreme precipitation based on monthly precipitation records (1999–2012) at 17 stations of Northern areas and Khyber Pakhtunkhwa, Pakistan. We develop regional frequency methods based on L-moment and partial L-moments (L- and PL-moments). Te L- and PL-moments are derived for generalized extreme value (GEV), generalized logistic (GLO), generalized normal (GNO), and generalized Pareto (GPA) distributions. Te -statistics and L- and PL-moments ratio diagrams of GNO, GEV, and GPA distributions were identifed to represent the statistical properties of extreme precipitation in Northern areas and Khyber Pakhtunkhwa, Pakistan. We also perform a Monte Carlo simulation study to examine the sampling properties of L- and PL-moments. Te results show that PL-moments perform better than L-moments for estimating large return period events. 1. Introduction Hydraulic and hydrologic designs are key steps in planning of any water project. Any problem pitched at designing stage will result in the failure of design irrespective of the fact how correctly the other steps are taken. Hydrologists deal with water-related issues, problems of quantity, quality, and availability, in the society that known as hydrologic events. Stochastic methods are ofen used to understand sources of uncertainties in physical processes that give rise to observed hydrologic events, as precipitation and stream fow estimates depend on the past or future events. Several statistical methods ofered to minimize and summarize the uncertainties of observed data and frequency analysis is one of them. It determines that how ofen a particular event will occur by estimating the quantile   for return period of , where  is the magnitude of the event that occurs at a given time and location. Dalrymple [1] proposed regional frequency analysis (RFA) method for pooling various data samples. It is index- food procedure in hydrology. Hosking et al. [2] studied the properties of probability-weighted moments (PWMs) method based on RFA method. Tis method is frst used by Greis and Wood [3] and Wallis [4]. Cunnane [5] reviewed twelve methods of RFA and related regional PWMs algo- rithm. Initially, PWMs are considered as an alternative parame- ter estimation method; however, it was difcult to interpret directly as measures of the shape and scale parameters of distribution. RFA can forecast the food fow using empirical Hindawi Advances in Meteorology Volume 2017, Article ID 6954902, 20 pages https://doi.org/10.1155/2017/6954902