EAGER: A Comprehensive Approach for Generating, Sharing, Searching, and Using High-Resolution Terrain Parameters

EAGER：生成、共享、搜索和使用高分辨率地形参数的综合方法

• 批准号: 2334945
• 负责人: Michela Taufer
• 金额: $ 22.5万
• 依托单位: University of Tennessee Knoxville
• 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: 2023-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2334945&HistoricalAwards=false
• 关键词: EAGER; Comprehensive; Approach; Generating; Sharing

Terrain parameters quantitatively describe a landscape's surface properties (for example, slope or topographic wetness). Terrain parameters hold significant potential for advancing climate-related science and engineering efforts. As terrain parameters can be generated at different spatial resolutions, they are valuable resources for scientists working on soil moisture prediction, fire propagation, estimation of soil carbon content, soil respiration, and hydrology. These applications are critical for understanding land-atmosphere interactions and mitigating the impacts of climate change across ecosystems and landscapes. However, the process of deriving terrain parameters from digital elevation models is accurate, but expensive in terms of computational resources and time. For more efficient generation of terrain parameters, this project implements a flexible workflow to generate terrain parameter datasets at different resolutions for different regions of interest. All the products of this project (data, metadata, and software) are stored in an open-access commons to ensure they are Findable, Accessible, Interoperable, and Reusable (FAIR). The team of researchers promotes increased participation of underrepresented students, particularly women, through mentoring students in Systers (the organization for women in Electrical Engineering and Computer Science at the University of Tennessee Knoxville) and the collaboration with the Women in Data Science (WiDS) at Stanford.Terrain parameters are derived from Digital Elevation Models. High-resolution terrain parameters enable accurate spatial analyses and decision-making in climate-related science and engineering domains, but generating high-resolution data is computationally expensive, hindering the usability of terrain parameters for multiple applications. The project addresses this challenge to make terrain parameters available for climate study in three ways. First, the project implements a workflow to generate 15 terrain parameters at any resolution (from 30 km to 3 m) while preserving performance and accuracy. Performance is evaluated by measuring wall times and memory usage cloud platforms. The accuracy is validated by comparing the data with the derived terrain parameters. Second, the project uses the workflow and exploits data parallelism to generate large high-resolution datasets (i.e., down to 3 m) for North America (i.e., Canada, the United States, and Mexico). The project deliverable comprises rich metadata annotating the parameter values and Jupyter Notebooks for data search and access, reproducible data generation, accuracy validation, and performance measurement. Third, by bringing together an interdisciplinary research team of leading scientists with experience in data science and soil moisture dynamics, the project facilitates collaboration among federal agencies (including NSF, NASA, and USDA, among others) and institutions to pursue interdisciplinary research, share insights, and deliver innovative solutions for climate-related issues.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.

地形参数定量描述景观的表面特性（例如坡度或地形湿度）。地形参数对于推进气候相关科学和工程工作具有巨大潜力。由于地形参数可以在不同的空间分辨率下生成，因此对于从事土壤湿度预测、火灾蔓延、土壤碳含量估计、土壤呼吸和水文学工作的科学家来说，它们是宝贵的资源。这些应用对于了解陆地与大气的相互作用以及减轻气候变化对生态系统和景观的影响至关重要。然而，从数字高程模型导出地形参数的过程是准确的，但在计算资源和时间方面是昂贵的。为了更高效地生成地形参数，该项目实现了灵活的工作流程，为不同感兴趣区域生成不同分辨率的地形参数数据集。该项目的所有产品（数据、元数据和软件）都存储在开放访问的公共资源中，以确保它们可查找、可访问、可互操作和可重用（公平）。研究人员团队通过指导 Systers（田纳西大学诺克斯维尔大学电气工程和计算机科学领域的女性组织）的学生以及与数据科学女性 (WiDS) 的合作，促进代表性不足的学生（尤其是女性）的参与。斯坦福地形参数源自数字高程模型。高分辨率地形参数可以在气候相关的科学和工程领域实现精确的空间分析和决策，但生成高分辨率数据的计算成本很高，阻碍了地形参数在多种应用中的可用性。该项目通过三种方式解决这一挑战，使地形参数可用于气候研究。首先，该项目实施了一个工作流程，可以在任何分辨率（从 30 km 到 3 m）下生成 15 个地形参数，同时保持性能和准确性。通过测量云平台的挂起时间和内存使用情况来评估性能。通过将数据与导出的地形参数进行比较来验证准确性。其次，该项目使用工作流程并利用数据并行性为北美（即加拿大、美国和墨西哥）生成大型高分辨率数据集（即低至 3 m）。该项目可交付成果包括注释参数值的丰富元数据以及用于数据搜索和访问、可重复数据生成、准确性验证和性能测量的 Jupyter Notebook。第三，通过汇集具有数据科学和土壤湿度动力学经验的领先科学家的跨学科研究团队，该项目促进了联邦机构（包括 NSF、NASA 和 USDA 等）和机构之间的合作，以开展跨学科研究、分享见解，并为气候相关问题提供创新解决方案。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。