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| Titel |
Spatial analysis of precipitation in a high-mountain region: exploring methods with multi-scale topographic predictors and circulation types |
| VerfasserIn |
D. Masson, C. Frei |
| 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 ; 18, no. 11 ; Nr. 18, no. 11 (2014-11-17), S.4543-4563 |
| Datensatznummer |
250120527
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| Publikation (Nr.) |
 copernicus.org/hess-18-4543-2014.pdf |
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| Zusammenfassung |
Statistical models of the relationship between precipitation and topography
are key elements for the spatial interpolation of rain-gauge measurements in
high-mountain regions. This study investigates several extensions of the
classical precipitation–height model in a direct comparison and within two
popular interpolation frameworks, namely linear regression and kriging with
external drift. The models studied include predictors of topographic height
and slope at several spatial scales, a stratification by types
of a circulation classification, and a predictor for wind-aligned
topographic gradients. The benefit of the modeling components is
investigated for the interpolation of seasonal mean and daily precipitation
using leave-one-out cross-validation. The study domain is a north–south
cross section of the European Alps (154 km × 187 km) that is inclined towards
dense rain-gauge measurements (approx. 440 stations, 1971–2008).
The significance of the topographic predictors was found to strongly depend
on the interpolation framework. In linear regression, predictors of slope
and at multiple scales reduce interpolation errors substantially. But with
as many as nine predictors, the resulting interpolation still poorly
replicates the across-ridge variation of climatological mean precipitation.
Kriging with external drift (KED) leads to much smaller interpolation errors
than linear regression, but this is achieved with a single predictor (local
topographic height), whereas the incorporation of more extended
predictor sets brings only marginal further improvement. Furthermore, the
stratification by circulation types and the wind-aligned gradient predictor
do not improve over the single predictor KED model. As for daily
precipitation, interpolation accuracy improves considerably with KED and
the use of a single predictor field (the distribution of seasonal mean
precipitation) as compared to ordinary kriging (i.e., without any predictor).
Nonetheless, information from circulation types did not improve
interpolation accuracy.
Our results confirm that the consideration of topography effects is
important for spatial interpolation of precipitation in high-mountain
regions. But a single predictor may be sufficient and taking appropriate
account of the spatial autocorrelation (by kriging) can be more effective
than the development of elaborate predictor sets within a regression model.
Our results also question a popular practice of using linear regression for
predictor selection in spatial interpolation; however they support the common
practice of using a climatological mean field as a background in the
interpolation of daily precipitation. |
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