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| Titel |
The ability of a GCM-forced hydrological model to reproduce global discharge variability |
| VerfasserIn |
F. C. Sperna Weiland, L. P. H. Beek, J. C. J. Kwadijk, M. F. P. Bierkens |
| 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. 8 ; Nr. 14, no. 8 (2010-08-19), S.1595-1621 |
| Datensatznummer |
250012403
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| Publikation (Nr.) |
 copernicus.org/hess-14-1595-2010.pdf |
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| Zusammenfassung |
Data from General Circulation Models (GCMs) are often used to investigate
hydrological impacts of climate change. However GCM data are known to have
large biases, especially for precipitation. In this study the usefulness of
GCM data for hydrological studies, with focus on discharge variability and
extremes, was tested by using bias-corrected daily climate data of the 20CM3
control experiment from a selection of twelve GCMs as input to the global
hydrological model PCR-GLOBWB. Results of these runs were compared with
discharge observations of the GRDC and discharges calculated from model runs
based on two meteorological datasets constructed from the observation-based
CRU TS2.1 and ERA-40 reanalysis. In the first dataset the CRU TS 2.1 monthly
timeseries were downscaled to daily timeseries using the ERA-40 dataset
(ERA6190). This dataset served as a best guess of the past climate and was used to
analyze the performance of PCR-GLOBWB. The second dataset was created from
the ERA-40 timeseries bias-corrected with the CRU TS 2.1 dataset using the
same bias-correction method as applied to the GCM datasets (ERACLM). Through this
dataset the influence of the bias-correction method was quantified. The
bias-correction was limited to monthly mean values of precipitation,
potential evaporation and temperature, as our focus was on the reproduction
of inter- and intra-annual variability.
After bias-correction the spread in discharge results of the GCM based runs
decreased and results were similar to results of the ERA-40 based runs,
especially for rivers with a strong seasonal pattern. Overall the
bias-correction method resulted in a slight reduction of global runoff and
the method performed less well in arid and mountainous regions. However, deviations between GCM results and GRDC statistics did decrease for
Q, Q90 and IAV. After bias-correction consistency amongst models
was high for mean discharge and timing (Qpeak), but relatively low for
inter-annual variability (IAV). This suggests that GCMs can be of use in
global hydrological impact studies in which persistence is of less relevance
(e.g. in case of flood rather than drought studies). Furthermore, the
bias-correction influences mean discharges more than extremes, which has the
positive consequence that changes in daily rainfall distribution and
subsequent changes in discharge extremes will also be preserved when the
bias-correction method is applied to future GCM datasets. However, it also
shows that agreement between GCMs remains relatively small for discharge
extremes.
Because of the large deviations between observed and simulated discharge, in
which both errors in climate forcing, model structure and to a lesser extent
observations are accumulated, it is advisable not to work with absolute
discharge values for the derivation of future discharge projections, but
rather calculate relative changes by dividing the absolute change by the
absolute discharge calculated for the control experiment. |
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