Synthesizing hydrologic process knowledge to determine global drivers of dominant processes

水文学的一个基本挑战是解释不同的水文过程发生在何时何地，以及它们如何受到气候和景观的控制。水文过程描述了水如何从降雨或降雪到河流流动的路径上移动并储存在景观中。大规模的过程知识对于广泛的应用是有价值的，例如设计现实和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了密集监测，以深入了解特定地点的水文过程，但这些知识是零散的，难以跨区域和跨大陆整合。该项目将通过创建和探索一个可搜索的水文过程知识数据库，将实地水文研究纳入全球范围。该项目将使水文学界能够应用现代大数据方法进行知识发现，并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开发教程和实践活动，以学生工具包的形式探索数据库。为了与水文学界互动并增强成果的影响力，项目团队将为数据库建立一个网络应用程序和在线地图发现界面，该项目将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广泛知识填充数据库，使用标准化工作流程来保持质量。该数据库将使用水文过程分类法，以便能够对过程信息进行标记、组织和分级搜索。研究人员将探索数据库，以综合水文功能的全球模式，分析如何通过物理流域特征解释主导过程，并揭示事件和季节时间尺度过程的转变。他们将应用该数据库来评估新一代大陆域水文模型，这些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识别出与实验发现的相同的主导过程。该数据库的长期愿景包括为未来深远的机器学习工作（如自动文献和过程预测的数据分析）发挥“基础事实”的作用。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。

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.