Methodologically Enhanced Virtual Labs for Early Warning of Significant or Catastrophic Change in Ecosystems: Changepoints for a Changing Planet

方法论增强的虚拟实验室，用于生态系统重大或灾难性变化的早期预警：变化中的地球的变化点

基本信息

批准号：
NE/T006102/1
负责人：
Gordon Blair
金额：
$ 25.92万
依托单位：
Lancaster University
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2019
资助国家：
英国
起止时间：
2019 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FT006102%2F1
关键词：
Methodologically Enhanced Virtual Labs Early

项目摘要

Virtual labs are emerging as a key component in the construction of future digital environments, particularly to abstract over the complexities of the underlying distributed networks of sensors and associated computational infrastructure. We define a virtual lab as a transdisciplinary collaboration space hosted in the cloud (public/private/hybrid) that allows stakeholders to access a range of data, analytical methods and assessment tools (e.g. visualisation tools and/or statistical tools), and to execute these analyses using the elastic capacity of a cloud. In the environmental science community, most existing virtual labs focus on the problem of integrating often complex and heterogeneous data. We seek to significantly advance the state-of-the-art by enhancing virtual labs with sophisticated methodological capability, embracing state-of-the-art data science techniques to assist in the societally-relevant interpretation of these data. This is a bold and broad vision and, to make this feasible in a year, we elect to work with a particular family of data science techniques, that is, changepoint detection methods, designed to identify fundamental changes and anomalous behaviour in data, typically within time-series, but also applicable across space and time and to complex, multivariate problems.This feasibility study will therefore bring together a cross-disciplinary team working on virtual labs, changepoint methods and evidence for impacts of global environmental change on ecosystem structure and function. Our approach will foster a deep, cross-disciplinary dialogue through workshops, enhanced by rapid prototyping of virtual labs to stimulate thinking about what is possible/desirable w.r.t. ecosystem early warning methods.The project will build on the rich, complex, multi-faceted data available from the Environmental Change Network (ECN), that offers detailed multivariate 25-year long data sets for a range of ecosystems in the UK. We seek to understand the role of data science, including, but not limited to changepoint detection, in the construction of environmental early warning alert systems capable of operating at a variety of scales, from catchments to global planetary level systems.

虚拟实验室正在成为未来数字环境的构建中的关键组成部分，尤其是在基础传感器和相关计算基础架构的基础分布网络的复杂性上抽象。我们将虚拟实验室定义为托管在云（公共/私人/混合动力）中的跨学科协作空间，该空间允许利益相关者访问一系列数据，分析方法和评估工具（例如可视化工具和/或统计工具），并使用云的弹性能力来执行这些分析。在环境科学界，大多数现有的虚拟实验室都集中在整合复杂且异构数据的问题上。我们试图通过以精致的方法学能力来增强虚拟实验室来显着推进最先进的方法，并采用最先进的数据科学技术，以帮助对这些数据的社会相关解释。这是一个大胆而广阔的视野，为了使这一一年可行，我们选择与特定的数据科学技术家庭合作，即变更点检测方法，旨在识别数据中的基本变化和异常行为，通常在时间序列中，通常在时间序列中，通常在时间序列中，但也适用于跨越时空的时间和跨越时间和复杂的实验，因此可以使多样化的多样化的良好的态度汇总到跨越的依据。全球环境变化对生态系统结构和功能的影响。我们的方法将通过研讨会来促进深度，跨学科的对话，并通过快速的虚拟实验室进行原型制作来刺激对可能/可取的W.R.T.的思考。生态系统预警方法。该项目将建立在环境变化网络（ECN）中可获得的丰富，复杂，多方面的数据上，该数据为英国的一系列生态系统提供了详细的多元25年数据集。我们试图了解数据科学的作用，包括但不限于更改点检测，在构建能够以各种规模运行的环境预警警报系统中，从集水区到全球行星级别系统。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

The evolving role of weather types on rainfall chemistry under large reductions in pollutant emissions.

DOI：
10.1016/j.envpol.2022.118905
发表时间：
2022-01
期刊：
Environmental pollution
影响因子：
8.9
作者：
Chak‐Hau Michael Tso;D. Monteith;T. Scott;H. Watson;Beverley Dodd;M. Pereira;P. Henrys;M. Hollaway;S. Rennie;Aaron Lowther;J. Watkins;Rebecca Killick;Gordon S. Blair
通讯作者：
Chak‐Hau Michael Tso;D. Monteith;T. Scott;H. Watson;Beverley Dodd;M. Pereira;P. Henrys;M. Hollaway;S. Rennie;Aaron Lowther;J. Watkins;Rebecca Killick;Gordon S. Blair

Integration of long-term collocated ecological datasets: examples from the UK Environmental Change Network (ECN)

长期并置生态数据集的整合：来自英国环境变化网络 (ECN) 的示例