NSF2026: EAGER: An Ecologically Inspired Human-Machine Intelligence Approach to Recognizing Similitude in Multi-Scale Watershed Research

在地球科学部（GEO/GEO）和综合活动办公室的NSF 2026基金项目的支持下，佛蒙特州立农业学院的Hamshaw，Lee，Pespeni，Rizzo和安德伍德教授获得了EAGER赠款。该项目涉及NSF 2026 Idea Machine主题的跨尺度通用相似性，专注于地球科学的广泛领域。淡水资源面临着来自极端事件和土地使用变化的越来越大的压力，这导致水量和水质在多个尺度上（从小溪到大河，从风暴事件到十年周期）的变化趋势。为了更好地了解对水质的影响，这个跨学科的研究项目将调查短期和长期水质和流量数据的趋势，以及与流域属性（例如土地利用，地形）的可能相似性和关联。该项目依赖于美国大陆研究站存档的水质数据，这些数据可通过流量和水质数据库获得。为了分析这些大量的水质数据，将使用人工智能和传统科学方法相结合的方法。该项目的社会重要性包括应用机器学习和人工智能的进步，帮助研究人员和环境管理人员分析和利用在不同地点收集的有限监测资源，并制定监测环境变化的最佳实践。该项目将资助一个讲习班，将工程和水文科学的研究生与生物学和微生物学的研究生聚集在一起，分享数据、分析方法，并追求跨学科领域的跨学科方法。确定美国大陆流域特征和水质条件的相似性可以帮助确定环境变化的原因，并使研究从个别研究到更大的区域。由于这些长期监测数据的庞大规模和多样性对传统的科学和统计方法提出了挑战，我们将在集成的人机学习框架中与水文和生态领域的专家一起使用人工智能方法沿着。在这样做的过程中，我们的目标是确定与跨尺度相似性相关的常见环境变量和参数，并调查短期和长期水质和流量数据的趋势。从生态和生物学科的组织框架将被应用到创建一个新的方法来组织和总结流域信号的相似性，流域被分组为行会的基础上，他们相似的功能特性。 通过识别这些大型数据集中的相似模式，并提取与这些模式相关的流域属性，在单个流域研究中进行的生态系统过程研究的结果可以更容易地转化为更大的区域。该奖项反映了NSF的法定使命，并被认为值得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来支持。

With support from the Division of Earth Sciences (GEO/EAR) and the NSF 2026 Fund Program in the Office of Integrated Activities, Professors Hamshaw, Lee, Pespeni, Rizzo and Underwood at the University of Vermont & State Agricultural College are awarded this EAGER grant. This project addresses the NSF 2026 Idea Machine topic of Universal Similitude Across Scales, focusing on the broad area of Earth Sciences. Freshwater resources face growing pressures from extreme events and land use changes, which result in varying trends in water quantity and quality at multiple scales (from small creeks to large rivers and from storm events to decadal cycles). To build a greater understanding of the impacts to water quality, this interdisciplinary research project will investigate trends in short-term and long-term water quality and streamflow data and possible similarities and associations to watershed attributes (e.g. land use, topography). The project relies on water quality data archived at research stations across the continental U.S. available through databases of streamflow and water quality. To analyze these large quantities of water quality data, a combination of artificial intelligence and traditional scientific methods will be used. The societal importance of this project includes applying advances in machine learning and artificial intelligence to help researchers and environmental managers analyze and leverage limited monitoring resources collected at different sites and to develop best practices for monitoring environmental change. The project will fund a workshop that brings together graduate students from engineering and hydrological sciences with those from biology and microbiology to share data, analytical methods, and pursue interdisciplinary approaches that translate across disciplinary fields.Identifying similarities in watershed characteristics and water quality conditions across the continental U.S. can help identify the causes of environmental change and enables the translation of research from individual studies to larger regions. Because the sheer size and diversity of these long-term monitoring data present challenges for traditional scientific and statistical methods, we will employ artificial intelligence methods along with domain experts in hydrology and ecology in an integrated human-machine learning framework. In so doing, we aim to identify the common environmental variables and parameters that are linked to similarity across scales and investigate trends in short-term and long-term water quality and streamflow data. Organizational frameworks from ecological and biological disciplines will be applied to create a new approach to organizing and summarizing similarities in watershed signals, where watersheds are grouped into guilds based on their similar functional traits. By identifying patterns of similitude in these large data sets and extracting watershed attributes with linkages to these patterns, findings of research on ecosystem processes conducted in individual watershed studies can be more readily translated to larger regions.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.