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| Titel |
Generating spatial precipitation ensembles: impact of temporal correlation structure |
| VerfasserIn |
O. Rakovec, P. Hazenberg, P. J. J. F. Torfs, A. H. Weerts, R. Uijlenhoet |
| 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 ; 16, no. 9 ; Nr. 16, no. 9 (2012-09-24), S.3419-3434 |
| Datensatznummer |
250013480
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| Publikation (Nr.) |
 copernicus.org/hess-16-3419-2012.pdf |
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| Zusammenfassung |
| Sound spatially distributed rainfall fields including a proper spatial and
temporal error structure are of key interest for hydrologists to force
hydrological models and to identify uncertainties in the simulated and
forecasted catchment response. The current paper presents a temporally coherent
error identification method based on time-dependent multivariate spatial
conditional simulations, which are conditioned on preceding
simulations.
A sensitivity analysis and real-world experiment are carried out within the
hilly region of the Belgian Ardennes. Precipitation fields are simulated for
pixels of 10 km × 10 km resolution. Uncertainty analyses in the
simulated fields focus on (1) the number of previous simulation hours on which
the new simulation is conditioned, (2) the advection speed of the rainfall event,
(3) the size of the catchment considered, and (4) the rain gauge density within
the catchment. The results for a sensitivity analysis show for typical advection
speeds >20 km h−1, no uncertainty is added in terms of across ensemble
spread when conditioned on more than one or two previous hourly simulations. However, for
the real-world experiment, additional uncertainty can still be added when conditioning
on a larger number of previous simulations. This is because for actual precipitation
fields, the dynamics exhibit a larger spatial and temporal variability. Moreover, by
thinning the observation network with 50%, the added uncertainty increases only
slightly and the cross-validation shows that the simulations at the unobserved locations
are unbiased. Finally, the first-order autocorrelation coefficients show clear temporal
coherence in the time series of the areal precipitation using the time-dependent
multivariate conditional simulations, which was not the case using the time-independent
univariate conditional simulations. The presented work can be easily implemented within
a hydrological calibration and data assimilation framework and can be used as an
improvement over currently used simplistic approaches to perturb the interpolated
point or spatially distributed precipitation estimates. |
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