CAREER: Leveraging Hydro-climatic Processes to Advance Season-ahead Cyanobacteria Prediction and Beach Management

职业：利用水文气候过程推进季前蓝藻预测和海滩管理

• 批准号: 1845783
• 负责人: Paul Block
• 金额: $ 50.99万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845783&HistoricalAwards=false
• 关键词: CAREER; Leveraging; Hydro; climatic; Processes

Eutrophication is the excessive accumulation of nutrients in bodies of water leading to overly dense blooms of plants and algae. Intensification of agricultural and urban practices in recent decades has led to the eutrophication of many inland lakes and streams. Cyanobacteria (also known as blue-green algae) often thrive in such nutrient-rich waters. Excessive growth of cyanobacteria can cause ecological, aesthetic, and health hazards, and may cause disruptions to drinking water systems, foul beaches, and produce toxins dangerous to human and aquatic life. Lake managers would like to predict the intensity of cyanobacteria abundance and overall ecosystem health prior to the summer season, but current methods for doing so are limited, often leading to only reactive lake management and recreational and beach safety. This research and education project will explore methods to better predict cyanobacteria abundance from hydroclimatic variables (such as temperature, precipitation and streamflow) to enable better lake management. The objective of this research is to answer four questions: Q1. How well can season-ahead prediction models conditioned on hydroclimatic variables (explicitly or in conjunction with biological factors) anticipate cyanobacteria abundance? Q2. Can physically-based models coupled with prediction models further enhance predictability and lead toward an improved understanding of local environmental and biological mechanisms? Q3. Can prediction models be extended to describe expected bloom formation and seasonal beach closings to inform beach planning and management? Q4. Is the prediction framework transferable and how well does it perform for various eutrophic lakes, particularly ones that are less well monitored? Research themes will be integrated into educational objectives designed to promote learning and development across several age groupings to: 1) foster citizen science participation, training, and analytical skills; 2) excite and prepare students and teachers from middle school through graduate school; and 3) enhance local agency decision-making capacity. These goals will be accomplished by introducing new water quality tests for citizen science monitors, developing new games and simulations targeting middle and high school students for delivery at science fairs and after school programs, hosting research retreats, and providing models, tools, and trainings to local agencies.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.

富营养化是指水体中营养物质的过度积累，导致植物和藻类过度密集繁殖。近几十年来，农业和城市实践的加剧导致许多内陆湖泊和溪流的富营养化。蓝藻（也被称为蓝绿藻）通常在这种营养丰富的水域中茁壮成长。蓝藻的过度生长会造成生态、美学和健康危害，并可能导致饮用水系统中断，污染海滩，并产生对人类和水生生物有害的毒素。湖泊管理者希望在夏季之前预测蓝藻丰度的强度和整体生态系统的健康状况，但目前这样做的方法有限，往往导致只有被动的湖泊管理和娱乐和海滩安全。这个研究和教育项目将探索从水文气候变量（如温度、降水和流量）中更好地预测蓝藻丰度的方法，以实现更好的湖泊管理。本研究的目的是回答四个问题：基于水文气候变量（明确地或结合生物因素）的季前预测模型对蓝藻丰度的预测有多好？Q2。以物理为基础的模型与预测模型相结合，能否进一步提高可预测性，并导致对当地环境和生物机制的更好理解？第三季。预测模型是否可以扩展到描述预期的水华形成和季节性海滩关闭，以便为海滩规划和管理提供信息？第四季度。预测框架是否可转移？它对各种富营养化湖泊，特别是那些监测不太好的湖泊的效果如何？研究主题将融入旨在促进不同年龄组学习和发展的教育目标，以：1)促进公民科学参与、培训和分析技能；2)激发和培养从中学到研究生的学生和教师；3)提高地方机构决策能力。这些目标将通过为公民科学监测器引入新的水质测试，开发针对中学生和高中生的新游戏和模拟，在科学博览会和课后项目中提供，举办研究静修活动，并向当地机构提供模型、工具和培训来实现。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

Large scale seasonal forecasting of peak season algae metrics in the Midwest and Northeast U.S.

• DOI: 10.1016/j.watres.2022.119402
• 发表时间: 2022-11
• 期刊: Water research
• 影响因子: 12.8
• 作者: M. Beal;G. Wilkinson;P. Block

Variability of summer cyanobacteria abundance: can season-ahead forecasts improve beach management?

• DOI: 10.1080/10402381.2022.2084799
• 发表时间: 2022-06
• 期刊: Lake and Reservoir Management
• 影响因子: 1.5
• 作者: M. Beal;B. O'Reilly;Caitlin Soley;Kaitlynn R. Hietpas;P. Block

Development of a sub-seasonal cyanobacteria prediction model by leveraging local and global scale predictors

• DOI: 10.1016/j.hal.2021.102100
• 发表时间: 2021-09-09
• 期刊: HARMFUL ALGAE
• 影响因子: 6.6
• 作者: Beal, Maxwell R. W.;O'Reilly, Bryan;Block, Paul
• 通讯作者: Block, Paul