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| Titel |
Development and comparative evaluation of a stochastic analog method to downscale daily GCM precipitation |
| VerfasserIn |
S. Hwang, W. D. Graham |
| 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 ; 17, no. 11 ; Nr. 17, no. 11 (2013-11-13), S.4481-4502 |
| Datensatznummer |
250085993
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| Publikation (Nr.) |
 copernicus.org/hess-17-4481-2013.pdf |
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| Zusammenfassung |
There are a number of statistical techniques that downscale coarse climate
information from general circulation models (GCMs). However, many of them do
not reproduce the small-scale spatial variability of precipitation
exhibited by the observed meteorological data, which is an important factor
for predicting hydrologic response to climatic forcing. In this study a new
downscaling technique (Bias-Correction and Stochastic Analog method; BCSA)
was developed to produce stochastic realizations of bias-corrected daily GCM
precipitation fields that preserve both the spatial autocorrelation structure
of observed daily precipitation sequences and the observed temporal frequency
distribution of daily rainfall over space.
We used the BCSA method to downscale 4 different daily GCM precipitation
predictions from 1961 to 1999 over the state of Florida, and compared the
skill of the method to results obtained with the commonly used
bias-correction and spatial disaggregation (BCSD) approach, a modified
version of BCSD which reverses the order of spatial disaggregation and
bias-correction (SDBC), and the bias-correction and constructed analog
(BCCA) method. Spatial and temporal statistics, transition probabilities,
wet/dry spell lengths, spatial correlation indices, and variograms for wet
(June through September) and dry (October through May) seasons were
calculated for each method.
Results showed that (1) BCCA underestimated mean daily precipitation for both
wet and dry seasons while the BCSD, SDBC and BCSA methods accurately
reproduced these characteristics, (2) the BCSD and BCCA methods
underestimated temporal variability of daily precipitation and thus did not
reproduce daily precipitation standard deviations, transition probabilities
or wet/dry spell lengths as well as the SDBC and BCSA methods, and (3) the
BCSD, BCCA and SDBC methods underestimated spatial variability in daily
precipitation resulting in underprediction of spatial variance and
overprediction of spatial correlation, whereas the new stochastic technique
(BCSA) replicated observed spatial statistics for both the wet and dry
seasons. This study underscores the need to carefully select a downscaling
method that reproduces all precipitation characteristics important for the
hydrologic system under consideration if local hydrologic impacts of climate
variability and change are going to be reasonably predicted. For low-relief,
rainfall-dominated watersheds, where reproducing small-scale spatiotemporal
precipitation variability is important, the BCSA method is recommended for
use over the BCSD, BCCA, or SDBC methods. |
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