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| Titel |
Improving runoff estimates from regional climate models: a performance analysis in Spain |
| VerfasserIn |
D. González-Zeas, L. Garrote, A. Iglesias, A. Sordo-Ward |
| 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. 6 ; Nr. 16, no. 6 (2012-06-25), S.1709-1723 |
| Datensatznummer |
250013330
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| Publikation (Nr.) |
 copernicus.org/hess-16-1709-2012.pdf |
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| Zusammenfassung |
| An important step to assess water availability is to have monthly time
series representative of the current situation. In this context, a simple
methodology is presented for application in large-scale studies in regions
where a properly calibrated hydrologic model is not available, using the
output variables simulated by regional climate models (RCMs) of the European
project PRUDENCE under current climate conditions (period 1961–1990). The
methodology compares different interpolation methods and alternatives to
generate annual times series that minimise the bias with respect to observed
values. The objective is to identify the best alternative to obtain
bias-corrected, monthly runoff time series from the output of RCM
simulations. This study uses information from 338 basins in Spain that cover
the entire mainland territory and whose observed values of natural runoff
have been estimated by the distributed hydrological model SIMPA. Four
interpolation methods for downscaling runoff to the basin scale from 10 RCMs
are compared with emphasis on the ability of each method to reproduce the
observed behaviour of this variable. The alternatives consider the use of the
direct runoff of the RCMs and the mean annual runoff calculated using five
functional forms of the aridity index, defined as the ratio between
potential evapotranspiration and precipitation. In addition, the comparison
with respect to the global runoff reference of the UNH/GRDC dataset is
evaluated, as a contrast of the "best estimator" of current runoff on a
large scale. Results show that the bias is minimised using the direct
original interpolation method and the best alternative for bias correction
of the monthly direct runoff time series of RCMs is the UNH/GRDC dataset,
although the formula proposed by Schreiber (1904) also gives good results. |
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