Global isoscapes for δ<sup>18</sup>O and δ<sup>2</sup>H in precipitation: improved prediction using regionalized climatic regression models
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 (δ<sup>2</sup>H, δ<...
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doaj-bff9a8273b804f2fb75d592d643af4a52020-11-24T22:30:21ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382013-11-0117114713472810.5194/hess-17-4713-2013Global isoscapes for δ<sup>18</sup>O and δ<sup>2</sup>H in precipitation: improved prediction using regionalized climatic regression modelsS. Terzer0L. I. Wassenaar1L. J. Araguás-Araguás2P. K. Aggarwal3International Atomic Energy Agency, Isotope Hydrology Section, Vienna International Centre, Vienna, 1400, AustriaInternational Atomic Energy Agency, Isotope Hydrology Section, Vienna International Centre, Vienna, 1400, AustriaInternational Atomic Energy Agency, Isotope Hydrology Section, Vienna International Centre, Vienna, 1400, AustriaInternational Atomic Energy Agency, Isotope Hydrology Section, Vienna International Centre, Vienna, 1400, AustriaA 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 (δ<sup>2</sup>H, δ<sup>18</sup>O) 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 δ<sup>18</sup>O/δ<sup>2</sup>H 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 (<a href="www.iaea.org/water"target="_blank">www.iaea.org/water</a>) and are for use in hydrology, climatology, food authenticity, ecology, and forensics.http://www.hydrol-earth-syst-sci.net/17/4713/2013/hess-17-4713-2013.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
S. Terzer L. I. Wassenaar L. J. Araguás-Araguás P. K. Aggarwal |
spellingShingle |
S. Terzer L. I. Wassenaar L. J. Araguás-Araguás P. K. Aggarwal Global isoscapes for δ<sup>18</sup>O and δ<sup>2</sup>H in precipitation: improved prediction using regionalized climatic regression models Hydrology and Earth System Sciences |
author_facet |
S. Terzer L. I. Wassenaar L. J. Araguás-Araguás P. K. Aggarwal |
author_sort |
S. Terzer |
title |
Global isoscapes for δ<sup>18</sup>O and δ<sup>2</sup>H in precipitation: improved prediction using regionalized climatic regression models |
title_short |
Global isoscapes for δ<sup>18</sup>O and δ<sup>2</sup>H in precipitation: improved prediction using regionalized climatic regression models |
title_full |
Global isoscapes for δ<sup>18</sup>O and δ<sup>2</sup>H in precipitation: improved prediction using regionalized climatic regression models |
title_fullStr |
Global isoscapes for δ<sup>18</sup>O and δ<sup>2</sup>H in precipitation: improved prediction using regionalized climatic regression models |
title_full_unstemmed |
Global isoscapes for δ<sup>18</sup>O and δ<sup>2</sup>H in precipitation: improved prediction using regionalized climatic regression models |
title_sort |
global isoscapes for δ<sup>18</sup>o and δ<sup>2</sup>h in precipitation: improved prediction using regionalized climatic regression models |
publisher |
Copernicus Publications |
series |
Hydrology and Earth System Sciences |
issn |
1027-5606 1607-7938 |
publishDate |
2013-11-01 |
description |
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 (δ<sup>2</sup>H,
δ<sup>18</sup>O) 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
δ<sup>18</sup>O/δ<sup>2</sup>H 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 (<a href="www.iaea.org/water"target="_blank">www.iaea.org/water</a>) and are for use in hydrology,
climatology, food authenticity, ecology, and forensics. |
url |
http://www.hydrol-earth-syst-sci.net/17/4713/2013/hess-17-4713-2013.pdf |
work_keys_str_mv |
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