Introduction
The new Regionalized Cluster-based Water Isotope Prediction (RCWIP) Model is a precipitation δ2H and δ18O isoscape (gridded isotopic 'landscape') based on climatically similar subsets of the GNIP and related data, developed by the IAEA. Existing precipitation isoscape prediction models usually suffer from a couple of major shortcomings: they are either fit to a geographically limited domain, or the global model parameterization blurs regionally important factors.
To help bridge this gap between global and regional modelling approaches, our multiple-regression/interpolation model groups GNIP stations according to their common climatic characteristics and uses flexible regressor combinations for each climatic cluster as well as a fuzzy transitions between these subsets. The result of the RCWIP approach is lower overall global predictive model uncertainty, adopting successful regional model parameterizations, but not limiting its geographical domain. The resulting data products and maps will provide value inputs not only for hydrological sciences, but also for climate and ecological research, food and commodity authentication and forensics.
Updates
The GNIP is a dynamic data network with new data and sampling locations added periodically. Hence, it is our goal to update the RCWIP model minimally on an annual basis. The current version of the RCWIP framework is 1.00; the last update took place on 2013-05-02.
Data products and maps
RCWIP results are available in two different formats (comma separated value tables and gridded GeoTIFF). In addition, readily prepared maps of monthly, annual and growing season δ18O and δ2H in PNG format can be accessed. The grid naming conventions are as follows:
RCWIP_grid|grid_err_5|6_month_version_build date.tif
'grid' indicates the final δ values; 'grid_err' indicates the model uncertainty. '5' stands for δ18O and '6' for δ2H. Month numbers 1 to 12 stand for January to December; 13 is the annual amount-weighted mean and 14 the amount-weighted growing season mean. The version (i.e. '100' means version '1.00') indicates the version of the RCWIP framework, while the build date (in YYYYMMDD format) indicates the most recent update with GNIP data.
| Tables CSV | ||
|---|---|---|
| Monthly & Annual δ18O (41 MB), Zip File | Growing Season δ18O (6 MB), Zip File | Growing Season δ2H (6 MB), Zip File |
| Grids (Geo tiff) | ||
|---|---|---|
| Monthly δ18O and δ2H, January-March (8 MB), Zip File | Monthly δ18O and δ2H, April-June (8 MB), Zip File | |
| Monthly δ18O and δ2H, July-September (8 MB), Zip File | Monthly δ18O and δ2H, October-December (8 MB), Zip File | |
| Annual and Growing Season δ18O and δ2H (6 MB), Zip File | ||
| Maps | ||
|---|---|---|
| Africa (12 MB), Zip File | Asia (20 MB), Zip File | Europe (24 MB), Zip File |
| North America (16 MB), Zip File | South America (11 MB), Zip File | Oceania (8 MB), Zip File |
| Global (11 MB), Zip File | ||
Citation
When using any of the RCWIP data products, please cite both the scholarly paper and the underlying GNIP dataset. Please do not hesitate to modify the sample citations below according to the submission guidelines you are asked to follow:
S. Terzer, L. I. Wassenaar, L. J. Araguás-Araguás, and P. K. Aggarwal: Global isoscapes for δ18O and δ2H in precipitation: improved prediction using regionalized climatic regression models. Hydrol. Earth Syst. Sci. Discuss., 10, 7351-7393, 2013. doi:10.5194/hessd-10-7351-2013
International Atomic Energy Agency: RCWIP (Regionalized Cluster-Based Water Isotope Prediction) Model – gridded precipitation δ18O|δ2H| δ18O and δ2H isoscape data. International Atomic Energy Agency, Vienna, Austria, <year>, available at http://www.iaea.org/water (downloaded at YYYY-MM-DD).
and IAEA/WMO (current Year). Global Network of Isotopes in Precipitation. The GNIP Database. Accessible at: http://www.iaea.org/water
Disclaimer
The gridded and tabulated data files below are the result of prediction algorithms. Though great efforts have been undertaken to improve the coverage of the GNIP network around the world (where most of the data comes from), there remain substantial spatial and temporal gaps, whose interpolation is associated with a prediction uncertainty.
The use of the RCWIP model data products is at the discretion of each user; however, model-derived prediction should always be verified with appropriate field data. RCWIP data should not be used as the sole basis for decision-making.
RCWIP data products have been produced diligently and strive to be as accurate as possible. Nevertheless, the IAEA as well as the authors of the corresponding publication(s) reserve the right to waive any liability arising from the use of these data products.