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Flood Frequency Analysis and Trend Detection in the Brisbane River Basin, Australia
by
Rahman, Ataur
, Alim, Mohammad A.
, Mim, Sadia T.
, Hossain, S M Anwar
in
Analysis
/ Comparative analysis
/ Distribution
/ Distribution (Probability theory)
/ Emergency preparedness
/ Environmental aspects
/ Floods
/ Forecasts and trends
/ Infrastructure
/ Nonparametric statistics
/ Observations
/ Parameter estimation
/ Probability distribution
/ Software
/ Storm damage
/ Trends
/ Watersheds
2025
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Flood Frequency Analysis and Trend Detection in the Brisbane River Basin, Australia
by
Rahman, Ataur
, Alim, Mohammad A.
, Mim, Sadia T.
, Hossain, S M Anwar
in
Analysis
/ Comparative analysis
/ Distribution
/ Distribution (Probability theory)
/ Emergency preparedness
/ Environmental aspects
/ Floods
/ Forecasts and trends
/ Infrastructure
/ Nonparametric statistics
/ Observations
/ Parameter estimation
/ Probability distribution
/ Software
/ Storm damage
/ Trends
/ Watersheds
2025
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Do you wish to request the book?
Flood Frequency Analysis and Trend Detection in the Brisbane River Basin, Australia
by
Rahman, Ataur
, Alim, Mohammad A.
, Mim, Sadia T.
, Hossain, S M Anwar
in
Analysis
/ Comparative analysis
/ Distribution
/ Distribution (Probability theory)
/ Emergency preparedness
/ Environmental aspects
/ Floods
/ Forecasts and trends
/ Infrastructure
/ Nonparametric statistics
/ Observations
/ Parameter estimation
/ Probability distribution
/ Software
/ Storm damage
/ Trends
/ Watersheds
2025
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Flood Frequency Analysis and Trend Detection in the Brisbane River Basin, Australia
Journal Article
Flood Frequency Analysis and Trend Detection in the Brisbane River Basin, Australia
2025
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Overview
This study presents a comprehensive flood frequency analysis for Australia’s Brisbane River basin using annual maximum flood (AMF) data from 26 stream gauging stations. This evaluates five different probability distributions in fitting the AMF data of the selected stations, which are the Lognormal, Log Pearson Type III (LP3), Gumbel, Generalized Extreme Value (GEV), and Generalized Pareto (GP) distributions (the recommended distributions in FLIKE software (School of Civil Engineering, University of Newcastle Australia, Australia, Release_x86_5.0.306.0). Three different goodness-of-fit tests (Chi-Squared, Anderson–Darling, and Kolmogorov–Smirnov) are adopted. This study also examines trends in the observed AMF data using several trend tests. It is found that the LP3 is the best-fit probability distribution at majority of the selected stations, followed by the GP distribution. Although the AMF data at most of the stations show an increasing linear trend, these trends are generally statistically non-significant.
Publisher
MDPI AG
Subject
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