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| Titel |
Global isoscapes for δ18O and δ2H in precipitation: improved prediction using regionalized climatic regression models |
| VerfasserIn |
S. Terzer, L. I. Wassenaar, L. J. Araguas-Araguas, P. K. Aggarwal |
| 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 (2013-11-29), S.4713-4728 |
| Datensatznummer |
250086008
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| Publikation (Nr.) |
 copernicus.org/hess-17-4713-2013.pdf |
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| Zusammenfassung |
| A regionalized cluster-based water isotope prediction (RCWIP) approach, based on
the Global Network of Isotopes in Precipitation (GNIP), was demonstrated
for the purposes of predicting point- and large-scale spatio-temporal
patterns of the stable isotope composition (δ2H,
δ18O) of precipitation around the world. Unlike earlier global
domain and fixed regressor models, RCWIP predefined 36 climatic
cluster domains and tested all model combinations from an
array of climatic and spatial regressor variables to obtain the best
predictive approach to each cluster domain, as indicated by root-mean-squared error (RMSE) and
variogram analysis. Fuzzy membership fractions were thereafter used as the
weights to seamlessly amalgamate results of the optimized climatic zone
prediction models into a single predictive mapping product, such as global
or regional amount-weighted mean annual, mean monthly, or growing-season
δ18O/δ2H in precipitation. Comparative tests
revealed the RCWIP approach outperformed classical global-fixed
regression–interpolation-based models more than 67% of the time, and
clearly improved upon predictive accuracy and precision. All RCWIP
isotope mapping products are available as gridded GeoTIFF files from the
IAEA website (www.iaea.org/water) and are for use in hydrology,
climatology, food authenticity, ecology, and forensics. |
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