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| Titel |
Ensemble modelling of nitrogen fluxes: data fusion for a Swedish meso-scale catchment |
| VerfasserIn |
J.-F. Exbrayat, N. R. Viney, J. Seibert, S. Wrede, H.-G. Frede, L. Breuer |
| 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 ; 14, no. 12 ; Nr. 14, no. 12 (2010-12-01), S.2383-2397 |
| Datensatznummer |
250012519
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| Publikation (Nr.) |
 copernicus.org/hess-14-2383-2010.pdf |
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| Zusammenfassung |
Model predictions of biogeochemical fluxes at the landscape scale are highly
uncertain, both with respect to stochastic (parameter) and structural
uncertainty. In this study 5 different models (LASCAM, LASCAM-S, a
self-developed tool, SWAT and HBV-N-D) designed to simulate hydrological
fluxes as well as mobilisation and transport of one or several nitrogen
species were applied to the mesoscale River Fyris catchment in mid-eastern
Sweden.
Hydrological calibration against 5 years of recorded daily discharge at two
stations gave highly variable results with Nash-Sutcliffe Efficiency (NSE)
ranging between 0.48 and 0.83. Using the calibrated hydrological parameter
sets, the parameter uncertainty linked to the nitrogen parameters was
explored in order to cover the range of possible predictions of exported
loads for 3 nitrogen species: nitrate (NO3), ammonium (NH4) and
total nitrogen (Tot-N). For each model and each nitrogen species,
predictions were ranked in two different ways according to the performance
indicated by two different goodness-of-fit measures: the coefficient of
determination R2 and the root mean square error RMSE. A total of 2160
deterministic Single Model Ensembles (SME) was generated using an increasing
number of members (from the 2 best to the 10 best single predictions).
Finally the best SME for each model, nitrogen species and discharge station
were selected and merged into 330 different Multi-Model Ensembles (MME). The
evolution of changes in R2 and RMSE was used as a performance
descriptor of the ensemble procedure.
In each studied case, numerous ensemble merging schemes were identified
which outperformed any of their members. Improvement rates were generally
higher when worse members were introduced. The highest improvements were
achieved for the nitrogen SMEs compiled with multiple linear regression
models with R2 selected members, which resulted in the RMSE decreasing
by up to 90%. |
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