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

地形参数定量地描述了一个景观的表面特性（例如，坡度或地形湿度）。地形参数在推进气候相关科学和工程工作方面具有巨大的潜力。由于地形参数可以在不同的空间分辨率下生成，因此它们对于从事土壤水分预测、火灾传播、土壤碳含量估算、土壤呼吸和水文研究的科学家来说是宝贵的资源。这些应用对于理解陆地-大气相互作用和减轻气候变化对生态系统和景观的影响至关重要。然而，从数字高程模型中提取地形参数的过程是准确的，但在计算资源和时间方面代价高昂。为了更有效地生成地形参数，该项目实现了一个灵活的工作流程，以不同的分辨率为不同的感兴趣的区域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）都存储在一个开放存取的公共空间中，以确保它们是可查找的、可访问的、可互操作的和可重用的（FAIR）。研究团队通过指导systems（田纳西大学诺克斯维尔分校电子工程和计算机科学女性组织）的学生，以及与斯坦福大学数据科学女性组织（WiDS）的合作，促进了代表性不足的学生，特别是女性的参与。地形参数来源于数字高程模型。高分辨率地形参数可以在气候相关的科学和工程领域进行准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了这一挑战，使地形参数可用于气候研究。首先，该项目实现了一个工作流，在保持性能和精度的同时，以任何分辨率（从30公里到3米）生成15个地形参数。性能是通过测量墙时间和云平台的内存使用来评估的。通过与导出的地形参数进行比较，验证了其精度。其次，该项目使用工作流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米）。项目交付成果包括丰富的元数据注释参数值和Jupyter notebook，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。第三，通过汇集具有数据科学和土壤水分动力学经验的顶尖科学家组成的跨学科研究团队，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。该奖项反映了美国国家科学基金会的法定使命，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。

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.