Climate Change Data Portal
| Machine learning for detecting compound climate drivers of extreme impacts | |
| 项目编号 | IZCOZ0_189908 |
| Zscheischler Jakob | |
| 项目主持机构 | University of Berne - BE |
| 开始日期 | 2020-05-01 |
| 结束日期 | 2022-10-31 |
| 英文摘要 | Large climate-related impacts on ecosystems or human societies are often related to multiple compounding weather and climate processes, which interact at different spatial and temporal scales. For instance, devastating floods are caused by specific spatiotemporal precipitation patterns in combination with antecedent soil moisture. Extremely low crop yields and vegetation activity are often related to multiple climate events occurring at different points in time, whose impacts compound each other. Identifying such compounding weather and climate conditions is very challenging due to the complexity of the involved systems as well as the very high dimensionality of multiscale climate and weather processes. The limited availability of observational data on impacts makes the development of new statistical approaches very difficult. Here, impact models such as vegetation models, crop models and hydrological models can serve as a tool to generate large amounts of realistic data. In this project, we will make use of a dynamic global vegetation model (LPX-Bern) to explore the ability of state-of-the-art machine learning techniques to identify climate and weather features that cause extreme reduction in vegetation productivity. LPX-Bern will be forced with very long simulations from a climate model to generate large amounts of impact data. We will then employ machine learning approaches such as Convolutional Neural Networks that allow an identification of the learned features to identify climate conditions that are associated with extremely low vegetation productivity through classification. We will assess the sensitivity of the classification to sample size and uncertainties in the impact variable, attributes that are important when transferring the approach to observational data. The resulting climate-feature impact relationships will be compared with output from other vegetation models.The highly interdisciplinary project will develop a novel approach to link extreme impacts with their multiple climatic drivers and will therefore provide new tools for compound event research. The comparison between climate-feature impact relationships across different models will help identify model uncertainties. The approach can also be used to assess climate-related risks in other sectors such as agriculture and flood protection. Ultimately, the project will pave the way to an improved assessment of climate risks. |
| 英文关键词 | compound events; deep learning; machine learning; vegetation modelling |
| 学科分类 | 10 - 材料科学 |
| 资助机构 | CH-SNSF |
| 项目经费 | 182492 |
| 项目类型 | COST (European Cooperation in Science and Technology) |
| 国家 | CH |
| 语种 | 英语 |
| 文献类型 | 项目 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/191177 |
| 推荐引用方式 GB/T 7714 | Zscheischler Jakob.Machine learning for detecting compound climate drivers of extreme impacts.2020. |
| 条目包含的文件 | 条目无相关文件。 | |||||
| 个性服务 |
| 推荐该条目 |
| 保存到收藏夹 |
| 导出为Endnote文件 |
| 谷歌学术 |
| 谷歌学术中相似的文章 |
| [Zscheischler Jakob]的文章 |
| 百度学术 |
| 百度学术中相似的文章 |
| [Zscheischler Jakob]的文章 |
| 必应学术 |
| 必应学术中相似的文章 |
| [Zscheischler Jakob]的文章 |
| 相关权益政策 |
| 暂无数据 |
| 收藏/分享 |
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。
