Evaluation of the SpatioTemporal Asset Catalog for management and discovery of FAIR flood hazard models

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Environmental Modelling & Software Pub Date : 2024-09-26 DOI:10.1016/j.envsoft.2024.106230
Seth Lawler , Thomas Williams , William Lehman , Christina Lindemer , David Rosa , Celso Ferreira , Chen Zhang
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Abstract

Approaches for performing flood hazards modeling and risk assessment at federal, state, and local agencies are undergoing emergent challenge for consistent metadata and cataloging systems to ensure the sharing of flood risk data in a Findable, Accessible, Interoperable, and Reusable (FAIR) manner. This paper explores the suitability of a suite of software and specifications developed by the Earth observation community for environmental modeling, which adhere to the FAIR principles not only for managing published or authoritative data but throughout the model development and flood hazard analysis phases. Specifically, we evaluate the SpatioTemporal Asset Catalog (STAC) in a pilot study undertaken as part of the Future of Flood Risk Data (FFRD) initiative of FEMA. The experimental results indicate the STAC ecosystem offers a flexible cloud native approach for linking data, managing metadata, and cataloging collections of models. Further, the STAC framework shows favorable results in a probabilistic and other use cases.
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评估用于管理和发现 FAIR 洪水灾害模型的时空资产目录
联邦、州和地方机构进行洪水灾害建模和风险评估的方法正面临着新的挑战,即需要一致的元数据和编目系统,以确保以可查找、可访问、可互操作和可重用(FAIR)的方式共享洪水风险数据。本文探讨了地球观测界为环境建模开发的一套软件和规范的适用性,这些软件和规范不仅在管理已发布或权威数据方面遵循 FAIR 原则,而且在整个模型开发和洪水灾害分析阶段都遵循该原则。具体而言,我们在一项试点研究中对时空资产目录(STAC)进行了评估,该研究是联邦紧急事务管理局未来洪水风险数据(FFRD)计划的一部分。实验结果表明,STAC 生态系统为连接数据、管理元数据和编目模型集合提供了灵活的云本地方法。此外,STAC 框架在概率和其他用例中也显示出良好的效果。
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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