Precipitation isotope time series predictions from machine learning applied in Europe.

ABSTRACT: 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

SUBMITTER: Nelson DB 

PROVIDER: S-EPMC8256050 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature