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| Titel |
Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China |
| VerfasserIn |
G. H. Fang, J. Yang, Y. N. Chen, C. Zammit |
| 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 ; 19, no. 6 ; Nr. 19, no. 6 (2015-06-02), S.2547-2559 |
| Datensatznummer |
250120726
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| Publikation (Nr.) |
 copernicus.org/hess-19-2547-2015.pdf |
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| Zusammenfassung |
| Water resources are essential to the ecosystem and social economy in the
desert and oasis of the arid Tarim River basin, northwestern China, and
expected to be vulnerable to climate change. It has been demonstrated that
regional climate models (RCMs) provide more reliable results for a regional
impact study of climate change (e.g., on water resources) than general
circulation models (GCMs). However, due to their considerable bias it is
still necessary to apply bias correction before they are used for water
resources research. In this paper, after a sensitivity analysis on input
meteorological variables based on the Sobol' method, we compared five
precipitation correction methods and three temperature correction methods in
downscaling RCM simulations applied over the Kaidu River basin, one of the
headwaters of the Tarim River basin. Precipitation correction methods
applied include linear scaling (LS), local intensity scaling (LOCI), power
transformation (PT), distribution mapping (DM) and quantile mapping (QM),
while temperature correction methods are LS, variance scaling (VARI) and DM.
The corrected precipitation and temperature were compared to the observed
meteorological data, prior to being used as meteorological inputs of a
distributed hydrologic model to study their impacts on streamflow. The
results show (1) streamflows are sensitive to precipitation, temperature and solar radiation but not to relative humidity and wind speed; (2) raw RCM
simulations are heavily biased from observed meteorological data, and its
use for streamflow simulations results in large biases from observed
streamflow, and all bias correction methods effectively improved these
simulations; (3) for precipitation, PT and QM methods performed equally best
in correcting the frequency-based indices (e.g., standard deviation,
percentile values) while the LOCI method performed best in terms of the
time-series-based indices (e.g., Nash–Sutcliffe coefficient, R2); (4)
for temperature, all correction methods performed equally well in correcting
raw temperature; and (5) for simulated streamflow, precipitation correction
methods have more significant influence than temperature correction methods
and the performances of streamflow simulations are consistent with those of
corrected precipitation; i.e., the PT and QM methods performed equally best in
correcting flow duration curve and peak flow while the LOCI method performed
best in terms of the time-series-based indices. The case study is for an
arid area in China based on a specific RCM and hydrologic model, but the
methodology and some results can be applied to other areas and models. |
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