Synthesizing hydrologic process knowledge to determine global drivers of dominant processes

综合水文过程知识以确定主导过程的全球驱动因素

• 批准号: 2322510
• 负责人: Hilary McMillan
• 金额: $ 37.6万
• 依托单位: San Diego State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-11-15 至 2026-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322510&HistoricalAwards=false
• 关键词: Synthesizing; hydrologic; process; knowledge; determine

A fundamental challenge in hydrology is to explain where and when different hydrologic processes occur, and how they are controlled by climate and landscape. Hydrologic processes describe how water moves through and is stored within the landscape on its path from rainfall or snowfall to river flow. Large-scale process knowledge is valuable for a wide range of applications such as designing realistic and accurate flow forecasting models, and choosing effective interventions to improve water quality. Many scientific organizations maintain intensively monitored watersheds that provide deep understanding of hydrologic processes at specific locations, but the knowledge is fragmented and difficult to integrate across regions and continents. This project will unify field hydrology studies into a global picture, by creating and exploring a searchable database of hydrologic process knowledge. The project will enable the hydrology community to apply modern, big data approaches to knowledge discovery, and to draw out emergent patterns that relate landscape organization and hydrologic function. The project team will work with undergraduate students to develop tutorials and hands-on activities to explore the database in the form of a Student Toolkit. To engage with the hydrology community and enhance the impact of the results, the project team will build a web app and online map discovery interface for the database.The project will create a relational database to represent process knowledge, and populate the database with extensive knowledge from hundreds of experimental watersheds, using standardized workflows to maintain quality. The database will use a taxonomy of hydrologic processes to enable labelling, organizing and hierarchical searching for process information. The investigators will explore the database to synthesize global patterns of hydrologic function, to analyze how dominant processes can be explained by physical watershed features, and to reveal transitions in processes at events and seasonal timescales. They will apply the database to evaluate new generation continental-domain hydrologic models, which currently lack the process data needed to select model structure. They will test whether flow predictions improve when models accurately represent processes, and whether model sensitivity analysis identifies the same dominant processes as those found experimentally. The long-term vision for the database includes a role as “ground truth” for future far-reaching machine learning efforts such as automatic literature and data analysis for process prediction.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.

水文学的一个基本挑战是解释不同的水文过程发生的地点和时间，以及它们如何受到气候和景观的控制。水文过程描述了水在从降雨或降雪到河流的路径上如何穿过并储存在景观中。大规模过程知识对于广泛的应用非常有价值，例如设计真实且准确的流量预测模型，以及选择有效的干预措施来改善水质。许多科学组织对流域进行集中监测，以深入了解特定地点的水文过程。但知识碎片化、难度大该项目将通过创建和探索水文过程知识的可搜索数据库，将实地水文学研究统一到全球范围内。该项目将使水文学界能够应用现代大数据方法来发现知识。绘制出与景观组织和水文功能相关的新兴模式。项目团队将与本科生合作，以学生工具包的形式开发教程和实践活动，以探索数据库，并加强与水文学界的合作。项目结果的影响团队将为数据库构建一个网络应用程序和在线地图发现界面，创建一个关系数据库来表示过程知识，并使用来自数百个实验流域的丰富知识填充数据库，并使用标准化工作流程来保持数据库的质量。研究人员将利用水文过程的分类来对过程信息进行标记、组织和分层搜索，以综合水文功能的全局模式，分析如何通过物理流域特征来解释主要过程，并揭示水文过程的转变。活动和季节性流程他们将应用该数据库来评估新一代大陆域水文模型，该模型目前缺乏选择模型结构所需的过程数据，他们将测试当模型准确地表示过程时流量预测是否会得到改善，以及模型敏感性分析是否能识别出相同的情况。该数据库的长期愿景包括反映未来影响深远的机器学习工作的“基本事实”，例如用于过程预测的自动文献和数据分析。该奖项是 NSF 的法定使命，并具有通过评估认为值得支持利用基金会的智力优势和更广泛的影响审查标准。