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| Titel |
A constraint-based search algorithm for parameter identification of environmental models |
| VerfasserIn |
S. Gharari, M. Shafiei, M. Hrachowitz, R. Kumar, F. Fenicia, H. V. Gupta, H. H. G. Savenije |
| 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 ; 18, no. 12 ; Nr. 18, no. 12 (2014-12-05), S.4861-4870 |
| Datensatznummer |
250120545
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| Publikation (Nr.) |
 copernicus.org/hess-18-4861-2014.pdf |
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| Zusammenfassung |
| Many environmental systems models, such as conceptual rainfall-runoff models,
rely on model calibration for parameter identification. For this, an observed
output time series (such as runoff) is needed, but frequently not available
(e.g., when making predictions in ungauged basins). In this study, we provide
an alternative approach for parameter identification using constraints based
on two types of restrictions derived from prior (or expert) knowledge. The
first, called parameter constraints, restricts the solution space
based on realistic relationships that must hold between the different model
parameters while the second, called process constraints requires
that additional realism relationships between the fluxes and state variables
must be satisfied. Specifically, we propose a search algorithm for finding
parameter sets that simultaneously satisfy such constraints, based on
stepwise sampling of the parameter space. Such parameter sets have the
desirable property of being consistent with the modeler's intuition of how
the catchment functions, and can (if necessary) serve as prior information
for further investigations by reducing the prior uncertainties associated
with both calibration and prediction. |
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