We emphasize the need for integrated approaches, coordinated processes, open science, and networked efforts (ICON) for multihazard multisector risk management. Here, we review these challenges, highlight current research and operational endeavors, and underscore diverse research opportunities. Multihazard multisector risk management poses several nontrivial challenges, including: more » (a) integrated risk assessment, (b) Earth system data-model fusion, (c) uncertainty quantification and communication, and (d) crossing traditional disciplinary boundaries. It is important to quantify and effectively communicate risks to inform the design and implementation of risk mitigation and adaptation strategies. Natural hazards’ risks are expected to increase in the future due to environmental, demographic, and socioeconomic changes. Hazard interactions and their cascading phenomena in space and time can further intensify the impacts. Natural hazards pose risks to society, infrastructure, and the environment. (2021), in natural hazards and a discussion on the opportunities and challenges of adopting them. This article is about the state of ICON principles Goldman et al. This is a major endeavor that could greatly increase the pace and potential of interdisciplinary scientific discovery. Our challenge is then to coordinate the development of standards, curation practices, and tools that enable integrating and reusing multiple data types, software, multi-scale models, and machine learning approaches across disciplines in a way that is as open and/or FAIR as ethically possible. Networks of diverse people with expertise across Earth, space, and data science disciplines are essential for efficient and ethical exchanges of findable, accessible, interoperable, and reusable (FAIR) research products and practices. Our role in ICON science more » therefore involves collaborative work to assess, design, implement, and promote practices and tools that enable effective data management, discovery, integration, and reuse for interdisciplinary work in Earth and space science disciplines. ESSI addresses data management practices, computation and analysis, and hardware and software infrastructure. Each commentary focuses on a different topic: (Section 2) Global collaboration, cyberinfrastructure, and data sharing (Section 3) Machine learning for multiscale modeling (Section 4) Aerial and satellite remote sensing for advancing Earth system model development by integrating field and ancillary data. This article is composed of three independent commentaries about the state of Integrated, Coordinated, Open, Networked (ICON) principles (Goldman, et al., 2021b, ) in Earth and Space Science Informatics (ESSI) and includes discussion on the opportunities and challenges of adopting them. (LANL), Los Alamos, NM (United States) Sponsoring Org.: USDOE National Nuclear Security Administration (NNSA) OSTI Identifier: 1867856 Alternate Identifier(s): OSTI ID: 1974985 Report Number(s): PNNL-SA-172282 LA-UR-23-23197 Journal ID: ISSN 2333-5084 Grant/Contract Number: AC05-76RL01830 89233218CNA000001 Resource Type: Accepted Manuscript Journal Name: Earth and Space Science Additional Journal Information: Journal Volume: 9 Journal Issue: 4 Journal ID: ISSN 2333-5084 Publisher: American Geophysical Union (AGU) Country of Publication: United States Language: English Subject: 58 GEOSCIENCES Hydrology machine leaning Community Science diversity, stakeholder (ICON) principles to address Computer Science Earth Sciences Mathematics (PNNL), Richland, WA (United States) Los Alamos National Lab. Publication Date: Research Org.: Pacific Northwest National Lab. Oregon State Univ., Corvallis, OR (United States).College of Earth Ocean and Atmospheric Sciences of Wisconsin, Madison, WI (United States) of Utah, Salt Lake City, UT (United States) National Ecological Observatory Network, Boulder, CO (United States).of Tulsa, Tulsa, OK (United States) Oklahoma State Univ., Tulsa, OK (United States) of America, Washington, DC (United States) Department of Mines, Oklahoma City, OK (United States).
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