IAHR, founded in 1935, is a worldwide independent member-based organisation of engineers and water specialists working in fields related to the hydro-environmental sciences and their practical application. Activities range from river and maritime hydraulics to water resources development and eco-hydraulics, through to ice engineering, hydroinformatics, and hydraulic machinery.
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| Estimation of design floods with uncertain hydrological data: testing model averaging |
| Author : |
KENECHUKWU OKOLI(1), LUIGIA BRANDIMARTE(2) , FRANCESCO LIAO(3) , ANNA BOTTO(4) & GIULIANO DI BALDASSARE(5) |
ABSTRACT
This study investigated the performance of ˇ°model averagingˇ± in the estimation of the design floods in view of uncertainty in
model choice (i.e., probability distribution), as well as inaccuracy of hydrological data (i.e., times series of annual maximum
flows). Two model averaging techniques were tested: i) simple model averaging, whereby a number of probability distribution
models are used to infer the data and the design flood is computed as a simple average, and (ii) weighted model averaging,
whereby the estimates provided by the diverse probability models are combined by giving higher weights to the distribution
models that fit better the hydrological data. Model averaging outcomes were also compared to the results of model selection,
whereby a single best probability model was selected by means of the Aikaike Information Criterion (AIC). In particular,
numerical experiments were carried out by generating synthetic time series of annual maximum flows using the Wakeby
probability model as parent distribution. For this study, comparisons were made in terms of relative errors and referring to
the 1-in-100 year flood, i.e the quantile corresponding to a return period of 100 years. Weighted model averaging and simple
model averaging showed a similar level of performance in improving the estimate of design floods. Interestingly, for shorter
sample size drawn from a highly skewed population (not rare in hydrology), the experiments showed that model averaging
(that might lead to over-fitting) improves design flood estimates even for short sample size in comparison to blindly selecting
the parsimonious Gumbel distribution (also known as EV1).
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| File Size : |
493,516 bytes |
| File Type : |
Adobe Acrobat Document |
| Chapter : |
IAHR World Congress Proceedings
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| Category : |
36th Congress - The Hague (2015) ALL CONTENT
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| Article : |
Flood risk management and adaptation
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| Date Published : |
18/08/2015 |
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