Estimating extreme flood events – assumptions, uncertainty and error
Hydrological extremes are amongst the most devastating forms of natural disasters both in terms of lives lost and socio-economic impacts. There is consequently an imperative to robustly estimate the frequency and magnitude of hydrological extremes. Traditionally, engineers have employed purely s...
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doaj-ea662d47ac364a0092b297de1f6da1122020-11-24T20:52:16ZengCopernicus PublicationsProceedings of the International Association of Hydrological Sciences2199-89812199-899X2015-06-01369313610.5194/piahs-369-31-2015Estimating extreme flood events – assumptions, uncertainty and errorS. W. Franks0C. J. White1M. Gensen2School of Engineering and ICT, University of Tasmania, Hobart, AustraliaSchool of Engineering and ICT, University of Tasmania, Hobart, AustraliaUniversity of Twente, Faculty of Engineering Technology, Enschede, the NetherlandsHydrological extremes are amongst the most devastating forms of natural disasters both in terms of lives lost and socio-economic impacts. There is consequently an imperative to robustly estimate the frequency and magnitude of hydrological extremes. Traditionally, engineers have employed purely statistical approaches to the estimation of flood risk. For example, for an observed hydrological timeseries, each annual maximum flood is extracted and a frequency distribution is fit to these data. The fitted distribution is then extrapolated to provide an estimate of the required design risk (i.e. the 1% Annual Exceedance Probability – AEP). Such traditional approaches are overly simplistic in that risk is implicitly assumed to be static, in other words, that climatological processes are assumed to be randomly distributed in time. In this study, flood risk estimates are evaluated with regards to traditional statistical approaches as well as Pacific Decadal Oscillation (PDO)/El Niño-Southern Oscillation (ENSO) conditional estimates for a flood-prone catchment in eastern Australia. A paleo-reconstruction of pre-instrumental PDO/ENSO occurrence is then employed to estimate uncertainty associated with the estimation of the 1% AEP flood. The results indicate a significant underestimation of the uncertainty associated with extreme flood events when employing the traditional engineering estimates.https://www.proc-iahs.net/369/31/2015/piahs-369-31-2015.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
S. W. Franks C. J. White M. Gensen |
spellingShingle |
S. W. Franks C. J. White M. Gensen Estimating extreme flood events – assumptions, uncertainty and error Proceedings of the International Association of Hydrological Sciences |
author_facet |
S. W. Franks C. J. White M. Gensen |
author_sort |
S. W. Franks |
title |
Estimating extreme flood events – assumptions, uncertainty and error |
title_short |
Estimating extreme flood events – assumptions, uncertainty and error |
title_full |
Estimating extreme flood events – assumptions, uncertainty and error |
title_fullStr |
Estimating extreme flood events – assumptions, uncertainty and error |
title_full_unstemmed |
Estimating extreme flood events – assumptions, uncertainty and error |
title_sort |
estimating extreme flood events – assumptions, uncertainty and error |
publisher |
Copernicus Publications |
series |
Proceedings of the International Association of Hydrological Sciences |
issn |
2199-8981 2199-899X |
publishDate |
2015-06-01 |
description |
Hydrological extremes are amongst the most devastating forms of natural
disasters both in terms of lives lost and socio-economic impacts. There is
consequently an imperative to robustly estimate the frequency and magnitude
of hydrological extremes. Traditionally, engineers have employed purely
statistical approaches to the estimation of flood risk. For example, for an
observed hydrological timeseries, each annual maximum flood is extracted and
a frequency distribution is fit to these data. The fitted distribution is
then extrapolated to provide an estimate of the required design risk (i.e.
the 1% Annual Exceedance Probability – AEP). Such traditional approaches
are overly simplistic in that risk is implicitly assumed to be static, in
other words, that climatological processes are assumed to be randomly
distributed in time. In this study, flood risk estimates are evaluated with
regards to traditional statistical approaches as well as Pacific Decadal
Oscillation (PDO)/El Niño-Southern Oscillation (ENSO) conditional
estimates for a flood-prone catchment in eastern Australia. A
paleo-reconstruction of pre-instrumental PDO/ENSO occurrence is then
employed to estimate uncertainty associated with the estimation of the 1%
AEP flood. The results indicate a significant underestimation of the
uncertainty associated with extreme flood events when employing the
traditional engineering estimates. |
url |
https://www.proc-iahs.net/369/31/2015/piahs-369-31-2015.pdf |
work_keys_str_mv |
AT swfranks estimatingextremefloodeventsassumptionsuncertaintyanderror AT cjwhite estimatingextremefloodeventsassumptionsuncertaintyanderror AT mgensen estimatingextremefloodeventsassumptionsuncertaintyanderror |
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