This dataset contains the mean and variance of Deuterium delta value from precipitation water sampled at weather stations between 1961 and 2013 in Germany. These data have been kindly provided by Christine Stumpp and processed by the International Atomic Energy Agency IAEA in Vienna (GNIP Project: Global Network of Isotopes in Precipitation). These data are free to reuse provided the relevent citations (see references). These data represent a small sample of the much larger dataset compiled by the GNIP. We no longer provide larger GNIP dataset in the package as those are not free to reuse. You can still download the complete GNIP dataset for free, but you will have to proceed to a registration process with GNIP and use their downloading interface WISER (http://www-naweb.iaea.org/napc/ih/IHS_resources_isohis.html).
The dataframe includes 8591 observations on the following variables:
| [, 1] | lat | (numeric) | Latitude coordinate [decimal degrees] |
| [, 2] | long | (numeric) | Longitude coordinate [decimal degrees] |
| [, 3] | elev | (numeric) | Elevation asl [m] |
| [, 4] | isoscape.value | (numeric) | Deuterium stable hydrogen delta value [per thousand] |
| [, 5] | year | (numeric) | Year of sampling |
| [, 6] | month | (numeric) | Month of sampling |
| [, 7] | stationID | (Factor) | The unique identifier of the weather station |
The dataset contains non-aggregated data for 27 weather stations across Germany.
This dataset is the raw data source and should not be directly used for fitting isoscapes.
Please use prepdata to filter the dataset by time and
location.
If you want to use your own dataset, you must format your data as those
produced by the function prepdata.
GNIP Project IAEA Global Network of Isotopes in Precipitation: http://www.iaea.org
Stumpp, C., Klaus, J., & Stichler, W. (2014). Analysis of long-term stable isotopic composition in German precipitation. Journal of hydrology, 517, 351-361.
Klaus, J., Chun, K. P., & Stumpp, C. (2015). Temporal trends in d18O composition of precipitation in Germany: insights from time series modelling and trend analysis. Hydrological Processes, 29(12), 2668-2680.
prepdata to prepare the dataset for the analyses and
to filter by time and location.
# NOT RUN {
head(GNIPDataDE)
# }
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