Collaborative Research: CIBR: Cyberinfrastructure Enabling End-to-End Workflows for Aquatic Ecosystem Forecasting

合作研究：CIBR：网络基础设施支持水生生态系统预测的端到端工作流程

• 批准号: 1933102
• 负责人: Renato Figueiredo
• 金额: $ 65.17万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1933102&HistoricalAwards=false
• 关键词: Collaborative; Research; CIBR; Cyberinfrastructure; Enabling

Aquatic ecosystems in the United States and around the globe are experiencing increasing variability due to human activities. Provisioning drinking water in the face of rapid change in environmental conditions motivates the need to develop forecasts of future water quality. Near-term water quality forecasts can guide management actions over day to week time scales to mitigate potential disruptions in drinking water and other essential freshwater ecosystem services. To maximize the utility of water quality forecasts for managers and decision-makers, the forecasts must be accessible in near-real time, reliable, and continuously updated with environmental sensor data. However, developing iterative, near-term ecological forecasts requires complex cyber-infrastructure that is widely distributed, from sensors and computers collecting information at freshwater lakes and reservoirs to cloud computing services where forecast models are executed. Consequently, significant software challenges still remain for environmental scientists to easily and effectively deploy forecasting workflows. This project will address this need by designing, implementing, and deploying open-source software — FLARE: Forecasting Lake And Reservoir Ecosystems — that will enable the creation of flexible, scalable, robust, and near-real time iterative ecological forecasts. This software will be tested and widely disseminated to water utilities, drinking water managers, and many other decision-makers. FLARE will greatly advance the capability of the ecological research community to perform near-real time aquatic forecasts.The FLARE forecasting system is novel in its architecture, as it integrates a software-defined virtual distributed infrastructure spanning resources from sensor gateway devices at the edge of the network to cloud computing and storage. FLARE will support the flexible deployment of software in close proximity to water quality sensors in lakes and reservoirs, and in cloud resources for end-to-end data acquisition and processing. FLARE interconnects its distributed resources through a virtual private network to ensure data integrity and privacy in communications, and supports a flexible model applicable across a variety of lakes and reservoirs. Reusing best-of-breed technologies, FLARE builds upon and integrates several contemporary, widely-used open-source software frameworks in a manner that lowers the barrier to the deployment and management of ecological forecasting workflows by ecologists. Importantly, this project’s development of scalable and open-source cyberinfrastructure tools and end-to-end workflows for creating iterative aquatic forecasts will provide a critical resource for advancing the ecological forecasting research community, as well as provide a template for forecasting in other ecosystems. This project will build on and expand an existing program for cross-disciplinary teaching tools and research exchanges of undergraduate and graduate students to provide training at the intersection of computer science, freshwater science, and ecosystem modeling. Ultimately, this project will develop scalable, robust, secure workflows that will advance the capacity, practice, and training opportunities for ecological forecasting worldwide. Results from this project can be found at http://flare-forecast.orgThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

美国和全球各地的水生生态系统正在经历人类活动引起的可变性。面对环境条件快速变化的饮用水促进了发展未来水质森林的需求。近期的水质预测可以指导管理行动，每天都有时间尺度，以减轻饮用水和其他必要的淡水生态系统服务的潜在中断。为了最大程度地利用经理和决策者的水质森林效用，必须在近乎真实的时间内访问森林，可靠，并通过环境传感器数据不断更新。但是，开发迭代的近期生态森林需要广泛分布的复杂网络基础结构，从收集淡水湖泊和储层信息的传感器和计算机到执行预测模型的云计算服务。因此，对于环境科学家来说，仍然存在重大的软件挑战，可以轻松有效地部署预测工作流程。该项目将通过设计，实施和部署开源软件（Flare：预测湖泊和水库生态系统）来满足这一需求，从而可以创建灵活，可扩展，稳定且近乎实现的时间迭代的生态预测。该软件将经过测试，并广泛传播到水电图，饮用水管理人员和许多其他决策者。耀斑将大大提高生态研究界进行近乎现实的水生森林的能力。耀斑预测系统在其体系结构中是新颖的，因为它集成了一个软件定义的虚拟分布式基础架构，从网络边缘的传感器网关设备到云计算和存储。 Flare将支持软件的灵活部署，即与湖泊和水库中的水质传感器以及云资源中的水质传感器紧密相关，以端到端的数据获取和处理。 Flare通过虚拟专用网络互连其分布式资源，以确保通信中的数据完整性和隐私，并支持适用于各种湖泊和储层的灵活模型。 Reusing best-of-breed technologies, FLARE builds upon and integrates several contemporary, wide-used open-source software frameworks in a manner that lowers the barrier Importantly, this project’s development of scalable and open-source cyberinfrastructure tools and end-to-end workflows for creating iterative aquatic forests will provide a critical resource for advancing the ecological forecasting research community, as well as provide a template for forecasting in other生态系统。该项目将在本科生和研究生的跨学科教学工具和研究交流的现有计划基础上进行，并在计算机科学，淡水科学和生态系统建模的交汇处提供培训。最终，该项目将开发可扩展，健壮，安全的工作流程，以促进全球生态预测的能力，实践和培训机会。该项目的结果可以在http：//flare-forecast.orgt.orgthis Award上找到反映NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，被视为通过评估而被视为珍贵的支持。

项目成果

Predicting the effects of climate change on freshwater cyanobacterial blooms requires consideration of the complete cyanobacterial life cycle

• DOI: 10.1093/plankt/fbaa059
• 发表时间: 2021-01-01
• 期刊: JOURNAL OF PLANKTON RESEARCH
• 影响因子: 2.1
• 作者: Cottingham, Kathryn L.;Weathers, Kathleen C.;Carey, Cayelan C.
• 通讯作者: Carey, Cayelan C.

Advancing lake and reservoir water quality management with near-term, iterative ecological forecasting

• DOI: 10.1080/20442041.2020.1816421
• 发表时间: 2021-01-14
• 期刊: INLAND WATERS
• 影响因子: 3.1
• 作者: Carey, Cayelan C.;Woelmer, Whitney M.;Thomas, R. Quinn
• 通讯作者: Thomas, R. Quinn

Ecosystem-Scale Oxygen Manipulations Alter Terminal Electron Acceptor Pathways in a Eutrophic Reservoir

生态系统规模的氧气操纵改变富营养化水库中的末端电子受体途径

• DOI: 10.1007/s10021-020-00582-9
• 发表时间: 2021
• 期刊: Ecosystems
• 影响因子: 3.7
• 作者: McClure, Ryan P.;Schreiber, Madeline E.;Lofton, Mary E.;Chen, Shengyang;Krueger, Kathryn M.;Carey, Cayelan C.
• 通讯作者: Carey, Cayelan C.

Near‐term forecasts of NEON lakes reveal gradients of environmental predictability across the US

NEON 湖泊的近期预测揭示了美国各地环境可预测性的梯度

• DOI: 10.1002/fee.2623
• 发表时间: 2023
• 期刊: Frontiers in Ecology and the Environment
• 影响因子: 10.3
• 作者: Thomas, R Quinn;McClure, Ryan P;Moore, Tadhg N;Woelmer, Whitney M;Boettiger, Carl;Figueiredo, Renato J;Hensley, Robert T;Carey, Cayelan C
• 通讯作者: Carey, Cayelan C

Embedding communication concepts in forecasting training increases students' understanding of ecological uncertainty

在预测培训中嵌入沟通概念可以增加学生对生态不确定性的理解

• DOI: 10.1002/ecs2.4628
• 发表时间: 2023
• 期刊: Ecosphere
• 影响因子: 2.7
• 作者: Woelmer, Whitney M.;Moore, Tadhg N.;Lofton, Mary E.;Thomas, R. Quinn;Carey, Cayelan C.
• 通讯作者: Carey, Cayelan C.