Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1073/pnas.2024107118 |
Precipitation isotope time series predictions from machine learning applied in Europe | |
Daniel B. Nelson; David Basler; Ansgar Kahmen | |
2021-06-29 | |
发表期刊 | Proceedings of the National Academy of Science |
出版年 | 2021 |
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/. |
领域 | 地球科学 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/333912 |
专题 | 地球科学 |
推荐引用方式 GB/T 7714 | Daniel B. Nelson,David Basler,Ansgar Kahmen. Precipitation isotope time series predictions from machine learning applied in Europe[J]. Proceedings of the National Academy of Science,2021. |
APA | Daniel B. Nelson,David Basler,&Ansgar Kahmen.(2021).Precipitation isotope time series predictions from machine learning applied in Europe.Proceedings of the National Academy of Science. |
MLA | Daniel B. Nelson,et al."Precipitation isotope time series predictions from machine learning applied in Europe".Proceedings of the National Academy of Science (2021). |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论