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2024 (English)In: Climate Risk Management, ISSN 2212-0963, Vol. 46, article id 100673Article in journal (Refereed) Published
Abstract [en]
In October 2021, the Swedish Meteorological and Hydrological Institute (SMHI) launched a novel national system for impact-based weather warnings, moving from the traditional format for meteorological, hydrological, and oceanographic warnings towards an assessment process that includes collaboration and consultation with regional stakeholders. For certain types of warnings, joint assessments of the potential impacts of weather events for a specific geographic area and time frame are made in collaboration with local and regional actors. As part of this new system, local and regional administrative efforts are made to create assessment-support documentation which are collated by practitioners at the municipal or organizational level, drawing on local knowledge, and subsequently compiled by the County Administrative Board. This process aims to support the collaborative decision-making processes ahead of the publication and in the evaluation of issued weather warnings. This paper explores the potential of integrating long- and short-term perspectives in societal response to climate change impacts with focus on extreme weather events. We present a case of AI-based technology to support processes linked to the national system for impact-based weather warnings and its integration with local and regional climate adaptation processes. We explore opportunities to integrate an AI-based pipeline, employing AI-based image and text analysis of crowdsourced data, in the processes of the warning system, and analyse barriers and enablers identified by local, regional, and national stakeholders. We further discuss to what extent data and knowledge of historical extreme weather events can be integrated with local and regional climate adaptation efforts, and whether these efforts could bridge the divide between long-term adaptation strategies and short-term response measures related to extreme weather events. Thus, this study unfolds the existing and perceived barriers to this integration and discusses possible synergies and ways forward in risk management and climate adaptation practice.
Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Extreme Weather Events, Impact-based weather warnings, Machine Learning, Flooding, Climate Resilience, Boundary Object
National Category
Climate Science
Identifiers
urn:nbn:se:liu:diva-210100 (URN)10.1016/j.crm.2024.100673 (DOI)001465671000001 ()2-s2.0-85210114904 (Scopus ID)
Projects
AI4ClimateAdaptation
Funder
Vinnova, 2020-03388
Note
This research was funded by Sweden's Innovation Agency, VINNOVA, grant number 2020-03388, 'AI for Climate Adaptation'.
2024-11-282024-11-282025-06-02