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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

基本信息

批准号：
1933016
负责人：
Cayelan Carey
金额：
$ 63.57万
依托单位：
Virginia Polytechnic Institute and State University
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=1933016&HistoricalAwards=false
关键词：
Collaborative Research CIBR Cyberinfrastructure Enabling

项目摘要

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.

由于人类活动，美国和全球的水生生态系统正在经历越来越多的变化。在环境条件迅速变化的情况下提供饮用水，促使需要制定对未来水质的预测。近期水质预报可以指导管理部门在每天到每周的时间尺度上采取行动，以减轻对饮用水和其他基本淡水生态系统服务的潜在干扰。为了最大限度地提高水质预报对管理人员和决策者的效用，预报必须近乎实时、可靠并持续更新环境传感器数据。然而，开发迭代的短期生态预测需要广泛分布的复杂网络基础设施，从在淡水湖和水库收集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，要轻松有效地部署预测工作流，仍然存在重大的软件挑战。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，该软件将能够创建灵活、可扩展、强大和接近实时的迭代生态预测。该软件将经过测试，并广泛传播给自来水公司、饮用水管理人员和许多其他决策者。FLARE将极大地提高生态研究社区执行近实时水生预报的能力。FLARE预报系统的体系结构新颖，因为它集成了一个软件定义的虚拟分布式基础设施，涵盖了从网络边缘的传感器网关设备到云计算和存储的资源。FLARE将支持在湖泊和水库的水质传感器附近以及云资源中灵活部署软件，以进行端到端的数据采集和处理。FLARE通过虚拟专用网络将其分布式资源互连，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水库的灵活模型。FLARE重复使用同类最好的技术，构建并集成了几个当前广泛使用的开源软件框架，从而降低了生态学家部署和管理生态预测工作流程的障碍。重要的是，该项目开发了可扩展和开源的网络基础设施工具和端到端工作流程，用于创建迭代的水生预报，将为推进生态预报研究界提供关键资源，并为其他生态系统的预报提供模板。该项目将建立和扩大现有的本科生和研究生的跨学科教学工具和研究交流计划，以提供计算机科学、淡水科学和生态系统建模交叉领域的培训。最终，该项目将开发可扩展的、强大的、安全的工作流程，以提高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.orgThis上找到，该奖项反映了国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（48）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Water chemistry time series for Beaverdam Reservoir, Carvins Cove Reservoir, Falling Creek Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2013-2022

美国弗吉尼亚州西南部 Beaverdam 水库、Carvins Cove 水库、Falling Creek 水库、Gatewood 水库和 Spring Hollow 水库的水化学时间序列 2013-2022

DOI：
10.6073/pasta/457120a9de886a1470c22a01d808ab2d
发表时间：
2023
期刊：
Environmental Data Initiative
影响因子：
0
作者：
Carey, Cayelan C.;Wander, Heather L.;Howard, Dexter W.;Breef-Pilz, Adrienne;Niederlehner, B. R.
通讯作者：
Niederlehner, B. R.

Manually-collected discharge data for multiple inflow tributaries entering Falling Creek Reservoir, Beaverdam Reservoir, and Carvins Cove Reservoir, Virginia, USA from 2019-2022

2019-2022年进入美国弗吉尼亚州Falling Creek水库、Beaverdam水库和Carvins Cove水库的多条流入支流手动收集的流量数据

DOI：
10.6073/pasta/dd75aba9ea4a87904091c49b77795588
发表时间：
2023
期刊：
Environmental Data Initiative
影响因子：
0
作者：
Carey, Cayelan C;Breef-Pilz, Adrienne;Howard, Dexter W;Woelmer, Whitney M;Geisler, Beckett;Haynie, George
通讯作者：
Haynie, George

Whole‐ecosystem oxygenation experiments reveal substantially greater hypolimnetic methane concentrations in reservoirs during anoxia

整个生态系统充氧实验揭示了缺氧期间水库中低浅层甲烷浓度显着升高

DOI：
10.1002/lol2.10173
发表时间：
2020
期刊：
Limnology and Oceanography Letters
影响因子：
7.8
作者：
Hounshell, Alexandria G.;McClure, Ryan P.;Lofton, Mary E.;Carey, Cayelan C.
通讯作者：
Carey, Cayelan C.

Macrosystems EDDIE Teaching Modules Increase Students’ Ability to Define, Interpret, and Apply Concepts in Macrosystems Ecology

宏观系统 EDDIE 教学模块提高学生定义、解释和应用宏观系统生态学概念的能力

DOI：
10.3390/educsci11080382
发表时间：
2021
期刊：
Education Sciences
影响因子：
3
作者：
Hounshell, Alexandria G.;Farrell, Kaitlin J.;Carey, Cayelan C.
通讯作者：
Carey, Cayelan C.

Secchi depth data and discrete depth profiles of water temperature, dissolved oxygen, conductivity, specific conductance, photosynthetic active radiation, redox potential, and pH for Beaverdam Reservoir, Carvins Cove Reservoir, Falling Creek Reservoir, Ga

Beaverdam 水库、Carvins Cove 水库、Falling Creek 水库、佐治亚州的 Secchi 深度数据和离散深度剖面，包括水温、溶解氧、电导率、比电导、光合活性辐射、氧化还原电位和 pH 值

DOI：
10.6073/pasta/eb17510d09e66ef79d7d54a18ca91d61
发表时间：
2023
期刊：
Environmental Data Initiative
影响因子：
0
作者：
Carey, Cayelan C.;Breef-Pilz, Adrienne;Wander, Heather L.;Geisler, Beckett;Haynie, George
通讯作者：
Haynie, George