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| Titel |
Informal uncertainty analysis (GLUE) of continuous flow simulation in a hybrid sewer system with infiltration inflow – consistency of containment ratios in calibration and validation? |
| VerfasserIn |
A. Breinholt, M. Grum, H. Madsen, F. Örn Thordarson, P. S. Mikkelsen |
| 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 (2013-10-24), S.4159-4176 |
| Datensatznummer |
250085971
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| Publikation (Nr.) |
 copernicus.org/hess-17-4159-2013.pdf |
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| Zusammenfassung |
| Monitoring of flows in sewer systems is increasingly applied to calibrate
urban drainage models used for long-term simulation. However, most often
models are calibrated without considering the uncertainties. The
generalized likelihood uncertainty estimation
(GLUE) methodology is here applied to assess parameter and flow simulation
uncertainty using a simplified lumped sewer model that accounts for three
separate flow contributions: wastewater, fast runoff from paved areas, and
slow infiltrating water from permeable areas. Recently GLUE methodology
has been critisised for generating prediction limits without statistical
coherence and consistency and for the subjectivity in the choice of a
threshold value to distinguish "behavioural" from "non-behavioural" parameter
sets. In this paper we examine how well the GLUE methodology performs when
the behavioural parameter sets deduced from a calibration period are applied
to generate prediction bounds in validation periods. By retaining an
increasing number of parameter sets we aim at obtaining consistency between
the GLUE generated 90% prediction limits and the actual containment ratio
(CR) in calibration. Due to the large uncertainties related to
spatio-temporal rain variability during heavy convective rain events, flow
measurement errors, possible model deficiencies as well as epistemic
uncertainties, it was not possible to obtain an overall CR of more than 80%.
However, the GLUE generated prediction limits still proved rather consistent,
since the overall CRs obtained in calibration corresponded well with the
overall CRs obtained in validation periods for all proportions of retained
parameter sets evaluated. When focusing on wet and dry weather periods
separately, some inconsistencies were however found between calibration and
validation and we address here some of the reasons why we should not expect
the coverage of the prediction limits to be identical in calibration and
validation periods in real-world applications. The large uncertainties result
in wide posterior parameter limits, that cannot be used for interpretation of, for example, the relative size of paved area vs. the size of infiltrating area. We
should therefore try to learn from the significant discrepancies between
model and observations from this study, possibly by using some form of
non-stationary error correction procedure, but it seems crucial to obtain
more representative rain inputs and more accurate flow observations to reduce
parameter and model simulation uncertainty. |
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