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: | , |
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| Format: | Article |
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IWA Publishing
2020
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| Online Access: | http://eprints.um.edu.my/36658/ |
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| Summary: | 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. |
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