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A nonparametric statistical framework using a kernel density estimator to approximate flood marginal distributions - a case study for the Kelantan River Basin in Malaysia

Floods are becoming the most challenging hydrologic issue in the Kelantan River basin in Malaysia. All three flood characteristics, i.e. peak flow, flood volume and flood duration, are important when formulating actions and measures to manage flood risk. Therefore, estimating the multivariate design...

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Main Authors:	Latif, Shahid, Mustafa, Firuza Begham
Format:	Article
Published:	IWA Publishing 2020
Subjects:	H Social Sciences (General) HN Social history and conditions. Social problems. Social reform
Online Access:	http://eprints.um.edu.my/36658/
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id	my.um.eprints.36658
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spelling	my.um.eprints.366582023-12-31T03:04:19Z http://eprints.um.edu.my/36658/ A nonparametric statistical framework using a kernel density estimator to approximate flood marginal distributions - a case study for the Kelantan River Basin in Malaysia Latif, Shahid Mustafa, Firuza Begham H Social Sciences (General) HN Social history and conditions. Social problems. Social reform Floods are becoming the most challenging hydrologic issue in the Kelantan River basin in Malaysia. All three flood characteristics, i.e. peak flow, flood volume and flood duration, are important when formulating actions and measures to manage flood risk. Therefore, estimating the multivariate designs and their associated return periods is an essential element of making informed risk-based decisions in this river basin. In this paper, the efficacy of a kernel density estimator is tested by assessing the adequacy of kernel functions for capturing flood marginal density of 50 years (from 1961 to 2016) of daily streamflow data collected at Gulliemard Bridge gauge station in the Kelantan River basin. Tests for stationarity or the existence of serial correlation within the flood series is often a pre-requisite before introducing the random samples into a univariate or a multivariate framework. It was found that homogeneity existed within the flood vector series. It was concluded therefore that time series of the flood vectors do not exhibit any significant trend. Based on analytically based fitness measures, it was concluded that it is likely that Triweight kernel function is the best-fitted distribution for defining the marginal distribution of peak flows, flood volumes and flood durations in the Kelantan River basin. IWA Publishing 2020-06 Article PeerReviewed Latif, Shahid and Mustafa, Firuza Begham (2020) A nonparametric statistical framework using a kernel density estimator to approximate flood marginal distributions - a case study for the Kelantan River Basin in Malaysia. Water Supply, 20 (4). pp. 1509-1533. ISSN 1606-9749, DOI https://doi.org/10.2166/ws.2020.081 <https://doi.org/10.2166/ws.2020.081>. 10.2166/ws.2020.081
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	H Social Sciences (General) HN Social history and conditions. Social problems. Social reform
spellingShingle	H Social Sciences (General) HN Social history and conditions. Social problems. Social reform Latif, Shahid Mustafa, Firuza Begham A nonparametric statistical framework using a kernel density estimator to approximate flood marginal distributions - a case study for the Kelantan River Basin in Malaysia
description	Floods are becoming the most challenging hydrologic issue in the Kelantan River basin in Malaysia. All three flood characteristics, i.e. peak flow, flood volume and flood duration, are important when formulating actions and measures to manage flood risk. Therefore, estimating the multivariate designs and their associated return periods is an essential element of making informed risk-based decisions in this river basin. In this paper, the efficacy of a kernel density estimator is tested by assessing the adequacy of kernel functions for capturing flood marginal density of 50 years (from 1961 to 2016) of daily streamflow data collected at Gulliemard Bridge gauge station in the Kelantan River basin. Tests for stationarity or the existence of serial correlation within the flood series is often a pre-requisite before introducing the random samples into a univariate or a multivariate framework. It was found that homogeneity existed within the flood vector series. It was concluded therefore that time series of the flood vectors do not exhibit any significant trend. Based on analytically based fitness measures, it was concluded that it is likely that Triweight kernel function is the best-fitted distribution for defining the marginal distribution of peak flows, flood volumes and flood durations in the Kelantan River basin.
format	Article
author	Latif, Shahid Mustafa, Firuza Begham
author_facet	Latif, Shahid Mustafa, Firuza Begham
author_sort	Latif, Shahid
title	A nonparametric statistical framework using a kernel density estimator to approximate flood marginal distributions - a case study for the Kelantan River Basin in Malaysia
title_short	A nonparametric statistical framework using a kernel density estimator to approximate flood marginal distributions - a case study for the Kelantan River Basin in Malaysia
title_full	A nonparametric statistical framework using a kernel density estimator to approximate flood marginal distributions - a case study for the Kelantan River Basin in Malaysia
title_fullStr	A nonparametric statistical framework using a kernel density estimator to approximate flood marginal distributions - a case study for the Kelantan River Basin in Malaysia
title_full_unstemmed	A nonparametric statistical framework using a kernel density estimator to approximate flood marginal distributions - a case study for the Kelantan River Basin in Malaysia
title_sort	nonparametric statistical framework using a kernel density estimator to approximate flood marginal distributions - a case study for the kelantan river basin in malaysia
publisher	IWA Publishing
publishDate	2020
url	http://eprints.um.edu.my/36658/
_version_	1787133827019177984
score	13.211869

A nonparametric statistical framework using a kernel density estimator to approximate flood marginal distributions - a case study for the Kelantan River Basin in Malaysia

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