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| Titel |
Improving uncertainty estimation in urban hydrological modeling by statistically describing bias |
| VerfasserIn |
D. Giudice, M. Honti, A. Scheidegger, C. Albert, P. Reichert, 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 ; 17, no. 10 ; Nr. 17, no. 10 (2013-10-28), S.4209-4225 |
| Datensatznummer |
250085974
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| Publikation (Nr.) |
 copernicus.org/hess-17-4209-2013.pdf |
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| Zusammenfassung |
| Hydrodynamic models are useful tools for urban water management.
Unfortunately, it is still challenging to obtain accurate results and
plausible uncertainty estimates when using these models. In particular, with
the currently applied statistical techniques, flow predictions are usually
overconfident and biased. In this study, we present a flexible and relatively
efficient methodology (i) to obtain more reliable hydrological simulations in
terms of coverage of validation data by the uncertainty bands and (ii) to
separate prediction uncertainty into its components. Our approach
acknowledges that urban drainage predictions are biased. This is mostly due
to input errors and structural deficits of the model. We address this issue
by describing model bias in a Bayesian framework. The bias becomes an
autoregressive term additional to white measurement noise, the only error
type accounted for in traditional uncertainty analysis. To
allow for bigger discrepancies during wet weather, we make the variance of
bias dependent on the input (rainfall) or/and output (runoff) of the system.
Specifically, we present a structured approach to select, among five
variants, the optimal bias description for a given urban or natural case
study. We tested the methodology in a small monitored stormwater system
described with a parsimonious model. Our results clearly show that flow
simulations are much more reliable when bias is accounted for than when it is
neglected. Furthermore, our probabilistic predictions can discriminate
between three uncertainty contributions: parametric uncertainty, bias, and
measurement errors. In our case study, the best performing bias description
is the output-dependent bias using a log-sinh transformation of data and
model results. The limitations of the framework presented are some ambiguity
due to the subjective choice of priors for bias parameters and its inability
to address the causes of model discrepancies. Further research should focus
on quantifying and reducing the causes of bias by improving the model
structure and propagating input uncertainty. |
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