Precipitation isotope time series predictions from machine learning applied in Europe

Nelson, Daniel B. and Basler, David and Kahmen, Ansgar. (2021) Precipitation isotope time series predictions from machine learning applied in Europe. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 118 (26). ARTN e2024107118.

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Official URL: https://edoc.unibas.ch/84978/

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Hydrogen and oxygen isotope values of precipitation are critically important quantities for applications in Earth, environmental, and biological sciences. However, direct measurements are not available at every location and time, and existing precipitation isotope models are often not sufficiently accurate for examining features such as long-term trends or interannual variability. This can limit applications that seek to use these values to identify the source history of water or to understand the hydrological or meteorological processes that determine these values. We developed a framework using machine learning to calculate isotope time series at monthly resolution using available climate and location data in order to improve precipitation isotope model predictions. Predictions from this model are currently available for any location in Europe for the past 70 y (1950-2019), which is the period for which all climate data used as predictor variables are available. This approach facilitates simple, user-friendly predictions of precipitation isotope time series that can be generated on demand and are accurate enough to be used for exploration of interannual and longterm variability in both hydrogen and oxygen isotopic systems. These predictions provide important isotope input variables for ecological and hydrological applications, as well as powerful targets for paleoclimate proxy calibration, and they can serve as resources for probing historic patterns in the isotopic composition of precipitation with a high level of meteorological accuracy. Predictions from our modeling framework, Piso.AI, are available at https://isotope. bot.unibas.ch/PisoAI/.
Faculties and Departments:05 Faculty of Science > Departement Umweltwissenschaften > Integrative Biologie > Physiological Plant Ecology (Kahmen)
UniBasel Contributors:Kahmen, Ansgar and Nelson, Daniel
Item Type:Article, refereed
Article Subtype:Research Article
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:27 Jan 2022 12:59
Deposited On:27 Jan 2022 12:59

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