气候变化领域集成服务门户(CCPortal): Precipitation isotope time series predictions from machine learning applied in Europe

CCPortal

DOI	10.1073/pnas.2024107118
	Precipitation isotope time series predictions from machine learning applied in Europe
	Nelson D.B.; Basler D.; Kahmen A.
发表日期	2021
ISSN	0027-8424
卷号	118 期号:26
英文摘要	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 long-term 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/. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Hydrogen isotopes; Machine learning; Oxygen isotopes; Precipitation
语种	英语
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/238500
作者单位	Department of Environmental Sciences–Botany, University of Basel, Basel, CH-4056, Switzerland
推荐引用方式 GB/T 7714	Nelson D.B.,Basler D.,Kahmen A.. Precipitation isotope time series predictions from machine learning applied in Europe[J],2021,118(26).
APA	Nelson D.B.,Basler D.,&Kahmen A..(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).
MLA	Nelson D.B.,et al."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(2021).