Collaborative Research: URoL:ASC: Applying rules of life to forecast emergent behavior of phytoplankton and advance water quality management

全球饮用水安全受到湖泊和水库中浮游植物大量繁殖的威胁，这些浮游植物通过有害毒素、浮渣以及味道和气味的变化对水质构成重大威胁。为了在全球变化的情况下改善饮用水管理，该项目建议开发第一个自动化的实时湖泊浮游植物预测系统，该系统量化水质预测的不确定性。如果管理人员能够预测浮游植物大量繁殖，他们就可以先发制人，采取行动减轻水质损害，例如调整水处理方法，从而降低成本，改善饮用水安全。该项目团队计划将先进的湖泊生态系统和统计建模与新的计算能力相结合，每天为美国几个湖泊的水管理人员提供1至35天的浮游植物水华预测。研究人员打算与水资源管理人员在预测系统上合作，以获得有关如何最有效地传达预测以改善水资源决策的宝贵知识。 项目小组还计划为参加水管理/废水证书课程的高中生和社区大学生开发关于预报和淡水生态系统的教学单元，从而改善阿巴拉契亚中部的水质和水工作人员培训。 这些教学模块将作为现有教育计划的一部分提供给美国各地的学院和大学，该计划迄今已惠及10万多名学生。湖泊中的浮游植物水华是一种突发行为，可能会通过降低水质产生生态系统规模和社会重要后果，但预测起来具有挑战性。一个基本的生命规则支配着这种行为：生态系统规模的出现是对个体生物体起作用的环境动力学的函数（例如，温度和光照对浮游植物生长速率的影响），由种群和群落过程介导（例如，促进浮游植物生物量增加的多物种相互作用）。该项目将应用生命规则方法，通过将浮游植物的突发行为实施到预测模型中，利用云和边缘计算工具生成实时湖泊水质预测，来解决一个重大的社会问题。这项研究是由一个跨学科的团队独特的，该团队拥有生物科学，社会和决策科学，物理科学，计算机和数据科学，统计学以及与管理人员，教育工作者和社区成员的长期合作伙伴关系的专业知识。这种融合的、受使用启发的研究方法的进展将包括：1）提高对生命规则如何用于预测紧急生态系统规模现象的理解; 2）将数据从环境传感器传输到云的新网络基础设施; 3）生成新颖的、可计算的统计方法，用于基于个人的模型的实时预测; 4）更好地理解水管理和生态系统动态如何相互作用以控制浮游植物; 5）创建新的工具，有效地传达预测的不确定性;以及6）通过为研究人员，管理人员和学生提供创新培训，扩大阿巴拉契亚中部的STEM参与，进行能力建设。通过新颖的跨学科整合，该项目旨在开发一个预测系统，该系统将成为全球社区饮用水系统的模型。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Drinking water safety is threatened globally by increasing phytoplankton blooms in lakes and reservoirs, which pose major threats to water quality via harmful toxins, scums, and changes in taste and odor. To improve drinking water management in the face of global change, this project proposes to develop the first automated, real-time lake phytoplankton forecasting system that quantifies uncertainty in water quality predictions. If managers had forecasts of phytoplankton blooms, they could preemptively act to mitigate water quality impairment, such as by adapting water treatment, thereby decreasing costs and improving drinking water safety. The project team plans to integrate cutting-edge lake ecosystem and statistical modeling with new computing capacity to deliver 1 to 35 day-ahead forecasts of phytoplankton blooms to water managers daily for several U.S. lakes. Researchers intend to work with water managers on the forecasting system to generate valuable knowledge about how best to effectively communicate forecasts for improved water resource decision-making. The project team also plans to develop teaching modules on forecasting and freshwater ecosystems for high school students and community college students in water management/wastewater certificate programs, thereby improving both water quality and water worker training in central Appalachia. The teaching modules will be made available to colleges and universities across the U.S. as part of an existing educational program that has reached over 100,000 students to date.Phytoplankton blooms in lakes are a type of emergent behavior that can have ecosystem-scale, societally important consequences by degrading water quality, yet are challenging to predict. A fundamental Rule of Life governs this behavior: ecosystem-scale emergence is a function of environmental dynamics operating on individual organisms (e.g., temperature and light effects on phytoplankton growth rates), mediated by population and community processes (e.g., multi-species interactions that promote increased phytoplankton biomass). This project will apply a Rules of Life approach to solve a major societal problem by implementing emergent phytoplankton behavior into predictive models to generate real-time lake water quality forecasts with cloud and edge computing tools. This research is uniquely enabled by a transdisciplinary team with expertise that spans the biological sciences, social and decision sciences, physical sciences, computer and data sciences, and statistics, as well as long-term partnerships with managers, educators, and community members. Advances from this convergent, use-inspired research approach will include: 1) improved understanding of how a Rule of Life can be used to predict emergent, ecosystem-scale phenomena; 2) new cyberinfrastructure for transferring data from environmental sensors to the cloud; 3) generation of novel, computationally-tractable statistical methods for real-time forecasting with individual-based models; 4) greater understanding of how water management and ecosystem dynamics interact to control phytoplankton; 5) creation of new tools that effectively communicate forecast uncertainty; and 6) capacity-building by providing innovative training for researchers, managers, and students that broadens STEM participation across central Appalachia. Through novel, cross-disciplinary integration, this project aims to develop a forecasting system that will become a model for drinking water systems in communities globally.This 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.