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

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强导致许多内陆湖泊和溪流的富营养化。蓝细菌（也称为蓝绿藻）通常在这种营养丰富的沃茨中茁壮成长。 蓝细菌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危险的毒素。湖泊管理者希望在夏季之前预测蓝藻丰度和整体生态系统健康的强度，但目前这样做的方法是有限的，往往导致只有反应性湖泊管理和娱乐和海滩安全。该研究和教育项目将探索从水文气候变量（如温度，降水量和流量）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：Q1。以水文气候变量（明确或与生物因素结合）为条件的季节预测模型能预测蓝藻丰度吗？Q2.基于物理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？Q3.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，以告知海滩规划和管理？Q4.预测框架是否可转移，以及它对各种富营养化湖泊的表现如何，特别是那些监测得不太好的湖泊？研究主题将被纳入教育目标，旨在促进学习和发展，在几个年龄组：1）促进公民科学参与，培训和分析技能; 2）激发和准备从中学到研究生院的学生和教师;和3）提高地方机构的决策能力。这些目标将通过为公民科学监测员引入新的水质测试，开发针对初中和高中学生的新游戏和模拟，在科学博览会和课后项目中提供，举办研究务虚会，并提供模型，工具，该奖项反映了NSF的法定使命，并通过使用基金会的知识产权进行评估，被认为值得支持。优点和更广泛的影响审查标准。

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.