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| Titel |
Value of river discharge data for global-scale hydrological modeling |
| VerfasserIn |
M. Hunger, P. Döll |
| 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 ; 12, no. 3 ; Nr. 12, no. 3 (2008-05-29), S.841-861 |
| Datensatznummer |
250010661
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| Publikation (Nr.) |
 copernicus.org/hess-12-841-2008.pdf |
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| Zusammenfassung |
| This paper investigates the value of observed river discharge data for
global-scale hydrological modeling of a number of flow characteristics that
are e.g. required for assessing water resources, flood risk and habitat
alteration of aquatic ecosystems. An improved version of the WaterGAP Global
Hydrology Model (WGHM) was tuned against measured discharge using either the
724-station dataset (V1) against which former model versions were tuned or an
extended dataset (V2) of 1235 stations. WGHM is tuned by adjusting one model
parameter (γ) that affects runoff generation from land areas in order
to fit simulated and observed long-term average discharge at tuning stations.
In basins where γ does not suffice to tune the model, two correction
factors are applied successively: the areal correction factor corrects local
runoff in a basin and the station correction factor adjusts discharge
directly the gauge. Using station correction is unfavorable, as it makes
discharge discontinuous at the gauge and inconsistent with runoff in the
upstream basin. The study results are as follows. (1) Comparing V2 to V1, the
global land area covered by tuning basins increases by 5% and the area where
the model can be tuned by only adjusting γ increases by 8%.
However, the area where a station correction factor (and not only an areal
correction factor) has to be applied more than doubles. (2) The value of
additional discharge information for representing the spatial distribution of
long-term average discharge (and thus renewable water resources) with WGHM is
high, particularly for river basins outside of the V1 tuning area and in
regions where the refined dataset provides a significant subdivision of
formerly extended tuning basins (average V2 basin size less than half the V1
basin size). If the additional discharge information were not used for
tuning, simulated long-term average discharge would differ from the observed
one by a factor of, on average, 1.8 in the formerly untuned basins and 1.3 in
the subdivided basins. The benefits tend to be higher in semi-arid and
snow-dominated regions where the model is less reliable than in humid areas
and refined tuning compensates for uncertainties with regard to climate input
data and for specific processes of the water cycle that cannot be represented
yet by WGHM. Regarding other flow characteristics like low flow, inter-annual
variability and seasonality, the deviation between simulated and observed
values also decreases significantly, which, however, is mainly due to the
better representation of average discharge but not of variability. (3) The
choice of the optimal sub-basin size for tuning depends on the modeling
purpose. While basins over 60 000 km2 are performing best,
improvements in V2 model performance are strongest in small basins between
9000 and 20 000 km2, which is primarily related to a low level of V1
performance. Increasing the density of tuning stations provides a better
spatial representation of discharge, but it also decreases model consistency,
as almost half of the basins below 20 000 km2 require station
correction. |
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