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| Titel |
Reducing structural uncertainty in conceptual hydrological modelling in the semi-arid Andes |
| VerfasserIn |
P. Hublart, D. Ruelland, A. Dezetter, H. Jourde |
| 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 ; 19, no. 5 ; Nr. 19, no. 5 (2015-05-13), S.2295-2314 |
| Datensatznummer |
250120712
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| Publikation (Nr.) |
 copernicus.org/hess-19-2295-2015.pdf |
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| Zusammenfassung |
| The use of lumped, conceptual models in hydrological impact studies requires
placing more emphasis on the uncertainty arising from deficiencies and/or
ambiguities in the model structure. This study provides an opportunity to
combine a multiple-hypothesis framework with a multi-criteria assessment
scheme to reduce structural uncertainty in the conceptual modelling of a
mesoscale Andean catchment (1515 km2) over a 30-year period
(1982–2011). The modelling process was decomposed into six model-building
decisions related to the following aspects of the system behaviour: snow
accumulation and melt, runoff generation, redistribution and delay of water
fluxes, and natural storage effects. Each of these decisions was provided
with a set of alternative modelling options, resulting in a total of 72
competing model structures. These structures were calibrated using the
concept of Pareto optimality with three criteria pertaining to streamflow
simulations and one to the seasonal dynamics of snow processes. The results
were analyzed in the four-dimensional (4-D) space of performance measures using a
fuzzy c-means clustering technique and a differential split sample test,
leading to identify 14 equally acceptable model hypotheses. A filtering
approach was then applied to these best-performing structures in order to
minimize the overall uncertainty envelope while maximizing the number of
enclosed observations. This led to retain eight model hypotheses as a
representation of the minimum structural uncertainty that could be obtained
with this modelling framework. Future work to better consider model
predictive uncertainty should include a proper assessment of parameter
equifinality and data errors, as well as the testing of new or refined
hypotheses to allow for the use of additional auxiliary observations. |
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