Allen, S. T., Jasechko, S., Berghuijs, W. R., Welker, J. M., Goldsmith, G. R., and Kirchner, J. W.: Global sinusoidal seasonality in precipitation isotopes, Hydrol. Earth Syst. Sci., 23, 3423–3436, https://doi.org/10.5194/hess-23-3423-2019, 2019.
Global sinusoidal seasonality in precipitation isotopes
|Author:||Allen, Scott T.1; Jasechko, Scott2; Berghuijs, Wouter R.1;|
1Department of Environmental Systems Science, ETH Zurich, Zurich, 8092, Switzerland
2Bren School of Environmental Science and Management, University of California at Santa Barbara, Santa Barbara, CA, 93117, USA
3Ecology and Genetics Research Unit, University of Oulu, 90014 Oulu, Finland
4Biological Sciences Department, University of Alaska, Anchorage, Alaska
5Schmid College of Science and Technology, Chapman University, Orange CA, 92866, USA
6Swiss Federal Research Institute WSL, Birmensdorf, 8903, Switzerland
7Department of Earth and Planetary Science, University of California, Berkeley, CA, 94709, USA
|Online Access:||PDF Full Text (PDF, 6.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019092329302
|Publish Date:|| 2019-09-23
Quantifying seasonal variations in precipitation δ2H and δ18O is important for many stable isotope applications, including inferring plant water sources and streamflow ages. Our objective is to develop a data product that concisely quantifies the seasonality of stable isotope ratios in precipitation. We fit sine curves defined by amplitude, phase, and offset parameters to quantify annual precipitation isotope cycles at 653 meteorological stations on all seven continents. At most of these stations, including in tropical and subtropical regions, sine curves can represent the seasonal cycles in precipitation isotopes. Additionally, the amplitude, phase, and offset parameters of these sine curves correlate with site climatic and geographic characteristics. Multiple linear regression models based on these site characteristics capture most of the global variation in precipitation isotope amplitudes and offsets; while phase values were not well predicted by regression models globally, they were captured by zonal (0–30∘ and 30–90∘) regressions, which were then used to produce global maps. These global maps of sinusoidal seasonality in precipitation isotopes based on regression models were adjusted for the residual spatial variations that were not captured by the regression models. The resulting mean prediction errors were 0.49 ‰ for δ18O amplitude, 0.73 ‰ for δ18O offset (and 4.0 ‰ and 7.4 ‰ for δ2H amplitude and offset), 8 d for phase values at latitudes outside of 30∘, and 20 d for phase values at latitudes inside of 30∘. We make the gridded global maps of precipitation δ2H and δ18O seasonality publicly available. We also make tabulated site data and fitted sine curve parameters available to support the development of regionally calibrated models, which will often be more accurate than our global model for regionally specific studies.
Hydrology and earth system sciences
|Pages:||3423 - 3436|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
1172 Environmental sciences
119 Other natural sciences
This project was funded by a grant from the Swiss Federal Office of the Environment to Gregory R. Goldsmith and James W. Kirchner.
© Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License.