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| Titel |
Using expert knowledge to increase realism in environmental system models can dramatically reduce the need for calibration |
| VerfasserIn |
S. Gharari, M. Hrachowitz, F. Fenicia, H. Gao, 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.4839-4859 |
| Datensatznummer |
250120544
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| Publikation (Nr.) |
 copernicus.org/hess-18-4839-2014.pdf |
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| Zusammenfassung |
| Conceptual environmental system models, such as
rainfall runoff models, generally rely on calibration for parameter
identification. Increasing complexity of this type of models for better
representation of hydrological process heterogeneity, typically makes
parameter identification more difficult. Although various, potentially
valuable, approaches for better parameter estimation have been developed,
strategies to impose general conceptual understanding of how a catchment
works into the process of parameter estimation has not been fully explored.
In this study we assess the effects of imposing semi-quantitative, relational
inequality constraints, based on expert-knowledge, for model development and
parameter specification, efficiently exploiting the complexity of a
semi-distributed model formulation. Making use of a topography driven
rainfall-runoff modeling (FLEX-TOPO) approach, a catchment was delineated
into three functional units, i.e., wetland, hillslope and plateau. Ranging
from simple to complex, three model setups, FLEXA,
FLEXB and FLEXC were developed based on these
functional units, where FLEXA is a lumped representation of the
study catchment, and the semi-distributed formulations FLEXB and
FLEXC progressively introduce more complexity. In spite of
increased complexity, FLEXB and FLEXC allow modelers
to compare parameters, as well as states and fluxes, of their different
functional units to each other, allowing the formulation of constraints that
limit the feasible parameter space. We show that by allowing for more
landscape-related process heterogeneity in a model, e.g., FLEXC,
the performance increases even without traditional calibration. The
additional introduction of relational constraints further improved the
performance of these models. |
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