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; 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.

地形参数定量地描述了景观的表面特性（例如，坡度或地形湿度）。地形参数具有推进与气候相关的科学和工程工作的巨大潜力。由于可以在不同的空间分辨率下生成地形参数，因此对于从事土壤水分预测，火灾繁殖，土壤碳含量，土壤呼吸和水文学的估计的科学家来说，它们是宝贵的资源。这些应用对于理解土地 - 大气相互作用以及减轻生态系统和景观气候变化的影响至关重要。但是，从数字高程模型中得出地形参数的过程是准确的，但在计算资源和时间方面昂贵。为了更有效地生成地形参数，该项目实现了灵活的工作流程，以在不同分辨率的不同分辨率下生成地形参数数据集。该项目的所有产品（数据，元数据和软件）都存储在开放式公共场中，以确保它们可以找到，可访问，可互操作和可重复使用（公平）。研究人员团队通过指导Systers（田纳西·诺克斯维尔大学电气工程和计算机科学的女性组织）和与Stanford的妇女合作的指导学生（Terrain.Terrain参数来自数字高度标准模型），从而促进了代表性不足的学生，尤其是女性的参与。高分辨率的地形参数可以在与气候相关的科学和工程领域中进行准确的空间分析和决策，但是生成高分辨率数据在计算上是昂贵的，从而阻碍了多个应用程序的地形参数的可用性。该项目解决了这一挑战，以使地形参数以三种方式用于气候研究。首先，该项目实现工作流程，以任何分辨率（从30 km到3 m）生成15个地形参数，同时保持性能和准确性。通过测量墙壁时间和内存使用云平台来评估性能。通过将数据与派生的地形参数进行比较来验证精度。其次，该项目使用工作流程并利用数据并行性来为北美（即加拿大，美国和墨西哥）生成大型高分辨率数据集（即降至3 m）。该项目可交付的元数据包括注释参数值和jupyter笔记本，以进行数据搜索和访问，可再现的数据生成，准确性验证和性能测量。 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通过使用基金会的智力优点和更广泛影响的评论标准进行评估。