Barriers and Possibilities for Interdisciplinary Disaster Science Research: Critical Appraisal of the Literature
Publication: Natural Hazards Review
Volume 23, Issue 1
Abstract
The strength and speed of modeling software has increased drastically in recent years. As it does so, researchers across a variety of fields work to determine how most effectively to utilize this strength and speed. Many of them have turned to interdisciplinarity as a means of creating more representative models. This is the case for disaster research as this area of interest involves many intricately interdependent systems. In working on interdisciplinary projects, past research has noted several barriers to complete integration, including differences in language and methodology, institutional structures not conducive to interdisciplinary collaboration, and nuanced tension between disciplines. Many solutions to these issues have been presented: facilitated conversation, increased institutional support, and several others. However, one area of difficulty for which comprehensive solutions have not yet been realized is data integration. This is indeed a challenge that lays at the heart of meaningful interdisciplinarity. The data are frequently telling an intricately interwoven story, and the more these data can be analyzed in a cohesive manner the more likely it is that researchers will be able to harness their predictive power to reduce disasters. In order to understand what efforts have been made at data integration, 29 papers are systematically reviewed in order to extract the nature of previous attempts, reasons for integration, challenges, shortcomings, and recommendations for future work. The papers analyzed most commonly referenced the different syntax and data types as a challenge of integration. Regarding shortcomings of integration efforts, the most common concern was that of model parameterization bias and substantial uncertainties. As a recommendation for future work, the papers most commonly suggested more standardization of data and methods across collaborating disciplines and from one project to the next in order to avoid these shortcoming and challenges.
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Data Availability Statement
All data, models, or code generated or used during the study are available from the corresponding author by request.
Acknowledgments
The Center for Risk-Based Community Resilience Planning is a NIST-funded Center of Excellence; the Center is funded through a cooperative agreement between the US National Institute of Standards and Technology and Colorado State University (NIST Financial Assistance Award Number: 70NANB20H008). The views expressed are those of the authors and may not represent the official position of the National Institute of Standards and Technology or the US Department of Commerce.
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Natural Hazards Review
Volume 23 • Issue 1 • February 2022
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This work is made available under the terms of the Creative Commons Attribution 4.0 International license, https://creativecommons.org/licenses/by/4.0/.
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Received: Mar 20, 2021
Accepted: Aug 11, 2021
Published online: Sep 23, 2021
Published in print: Feb 1, 2022
Discussion open until: Feb 23, 2022
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