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| Titel |
Global evaluation of runoff from ten state-of-the-art hydrological models |
| VerfasserIn |
Hylke Beck, Ad de Roo, Albert van Dijk, Jaap Schellekens, Emanuel Dutra, Gabriel Fink, Rene Orth |
| Konferenz |
EGU General Assembly 2016
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 18 (2016) |
| Datensatznummer |
250127879
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| Publikation (Nr.) |
 EGU/EGU2016-7802.pdf |
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| Zusammenfassung |
| Observed streamflow data from 966 medium sized catchments (1000 to 5000 km2) around
the globe were used to comprehensively evaluate the daily runoff estimates (1979–2012) of
six global hydrological models (GHMs) and four land surface models (LSMs) produced as
part of Tier-1 of the eartH2Observe project. The models were all driven by the WATCH
Forcing Data ERA-Interim (WFDEI) meteorological dataset, but used different datasets for
non-meteorologic inputs and were run at various spatial and temporal resolutions, although
all data were re-sampled to a common 0.5∘ spatial and daily temporal resolution. For the
evaluation, we used a broad range of performance metrics related to important aspects
of the hydrograph. We found pronounced inter-model performance differences,
underscoring the importance of hydrological model uncertainty in addition to climate input
uncertainty, for example in studies assessing the hydrological impacts of climate
change. The (uncalibrated) GHMs were found to perform better than the LSMs
in snow-dominated regions, and the ensemble mean was found to perform only
slightly worse than the best (calibrated) model. The models generally showed an
early bias in the spring snowmelt peak. We further found that, despite adjustments
using gauge observations, the WFDEI precipitation data still contain substantial
biases which propagate in the simulated runoff. Overall, more effort should be
devoted to calibrating and regionalizing the parameters of macro-scale models. |
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