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| Titel |
Bayesian uncertainty assessment of flood predictions in ungauged urban basins for conceptual rainfall-runoff models |
| VerfasserIn |
A. E. Sikorska, A. Scheidegger, K. Banasik, J. Rieckermann |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 16, no. 4 ; Nr. 16, no. 4 (2012-04-12), S.1221-1236 |
| Datensatznummer |
250013259
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| Publikation (Nr.) |
 copernicus.org/hess-16-1221-2012.pdf |
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| Zusammenfassung |
| Urbanization and the resulting land-use change strongly affect the water
cycle and runoff-processes in watersheds. Unfortunately, small urban
watersheds, which are most affected by urban sprawl, are mostly ungauged.
This makes it intrinsically difficult to assess the consequences of
urbanization. Most of all, it is unclear how to reliably assess the
predictive uncertainty given the structural deficits of the applied models.
In this study, we therefore investigate the uncertainty of flood predictions
in ungauged urban basins from structurally uncertain rainfall-runoff models.
To this end, we suggest a procedure to explicitly account for input
uncertainty and model structure deficits using Bayesian statistics with a
continuous-time autoregressive error model. In addition, we propose a
concise procedure to derive prior parameter distributions from base data and
successfully apply the methodology to an urban catchment in Warsaw, Poland.
Based on our results, we are able to demonstrate that the autoregressive
error model greatly helps to meet the statistical assumptions and to compute
reliable prediction intervals. In our study, we found that predicted peak
flows were up to 7 times higher than observations. This was reduced to
5 times with Bayesian updating, using only few discharge measurements. In
addition, our analysis suggests that imprecise rainfall information and
model structure deficits contribute mostly to the total prediction
uncertainty. In the future, flood predictions in ungauged basins will become
more important due to ongoing urbanization as well as anthropogenic and
climatic changes. Thus, providing reliable measures of uncertainty is
crucial to support decision making. |
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