Semi-parametric Estimation for Selecting Optimal Threshold of Extreme Rainfall Events

Wendy Ling Shinyie, Noriszura Ismail, Abdul Aziz Jemain

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

The two primary approaches of extreme events analysis are annual maximum series (AMS), which fits Generalized Extreme Value (GEV) distribution to the yearly peaks of events in the observation period, and partial duration series (PDS), which fits Generalized Pareto (GP) distribution to the peaks of events that exceed a given threshold. The PDS is able to reduce sampling uncertainty and is more useful in dealing with extreme values and asymmetries in the tails, but the optimal threshold is required. The objective of this study is to compare and determine the best method for selecting the optimal threshold of PDS using the hourly, 12-h and 24-h aggregated data of rainfall time series in Peninsular Malaysia. The choice of the threshold, or the number of largest order statistics, can be estimated by the parameters of extreme events. In this study, thirteen semi-parametric estimators are considered and applied to estimate the shape parameter or extreme value index (EVI). A semi-parametric bootstrap is then used to estimate the mean square error (MSE) of the estimator at each threshold and the optimal threshold is selected based on the smallest MSE. Based on the smallest MSE, the majority of stations and data durations favor the Adapted Hill estimator, followed by the QQ, Hill and Moment Ratio 1 estimators. Therefore, this study proves that the application of different estimators on real data may result in different optimal values of threshold and the choice of the best method is very much data-dependent.

Original languageEnglish
Pages (from-to)2325-2352
Number of pages28
JournalWater Resources Management
Volume27
Issue number7
DOIs
Publication statusPublished - 2013

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Mean square error
Rain
partial duration series
rainfall
extreme event
Time series
Statistics
Sampling
asymmetry
time series
sampling
Uncertainty

Keywords

  • Extreme rainfall events
  • Semi-parametric bootstrap
  • Semi-parametric estimators
  • Threshold selection

ASJC Scopus subject areas

  • Water Science and Technology
  • Civil and Structural Engineering

Cite this

Semi-parametric Estimation for Selecting Optimal Threshold of Extreme Rainfall Events. / Shinyie, Wendy Ling; Ismail, Noriszura; Jemain, Abdul Aziz.

In: Water Resources Management, Vol. 27, No. 7, 2013, p. 2325-2352.

Research output: Contribution to journalArticle

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