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| Titel |
Imposing constraints on parameter values of a conceptual hydrological model using baseflow response |
| VerfasserIn |
S. M. Dunn |
| 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 ; 3, no. 2 ; Nr. 3, no. 2, S.271-284 |
| Datensatznummer |
250000915
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| Publikation (Nr.) |
 copernicus.org/hess-3-271-1999.pdf |
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| Zusammenfassung |
| Calibration of conceptual hydrological models is frequently limited by a lack
of data about the area that is being studied. The result is that a broad range
of parameter values can be identified that will give an equally good calibration
to the available observations, usually of stream flow. The use of total stream
flow can bias analyses towards interpretation of rapid runoff, whereas water
quality issues are more frequently associated with low flow condition. This
paper demonstrates how model distinctions between surface an sub-surface runoff
can be used to define a likelihood measure based on the sub-surface (or baseflow)
response. This helps to provide more information about the model behaviour,
constrain the acceptable parameter sets and reduce uncertainty in streamflow
prediction. A conceptual model, DIY, is applied to two contrasting catchments in
Scotland, the Ythan and the Carron Valley. Parameter ranges and envelopes of
prediction are identified using criteria based on total flow efficiency,
baseflow efficiency and combined efficiencies. The individual parameter ranges
derived using the combined efficiency measures still cover relatively wide
bands, but are better constrained for the Carron than the Ythan. This reflects
the fact that hydrological behaviour in the Carron is dominated by a much
flashier surface response than in the Ythan. Hence, the total flow efficiency is
more strongly controlled by surface runoff in the Carron and there is a greater
contrast with the baseflow efficiency. Comparisons of the predictions using
different efficiency measures for the Ythan also suggest that there is a danger
of confusing parameter uncertainties with data and model error, if inadequate
likelihood measures are defined. |
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