Treffer: Introducing a severe impacts approach to guide adaptation to pluvial floods in residential and public buildings.

Title:
Introducing a severe impacts approach to guide adaptation to pluvial floods in residential and public buildings.
Authors:
Hjerpe, Mattias1 (AUTHOR) Mattias.hjerpe@liu.se, Glaas, Erik1 (AUTHOR), Storbjörk, Sofie1 (AUTHOR)
Source:
Building Research & Information. Oct2025, Vol. 53 Issue 7, p817-832. 16p.
Geographic Terms:
Database:
Business Source Premier

Weitere Informationen

Damages to buildings from pluvial flooding pose key risks to cities, impacting both economy and health, and drawing increased policy attention. While Swedish buildings are known to be at risk, most research focuses on homeowners and flood exposure. Despite municipal property companies owning a quarter of all apartments in Sweden, less is known about their buildings' risk and adaptation actions. This study addresses this gap by investigating: What do property company staff view as severe pluvial flood impacts? Which building characteristics contribute to pluvial flood risk, particularly severe impacts? What adaptation actions can property companies implement to reduce severe flood impact risk? The study involves collaboration with four Swedish municipal property companies, mapping 2,358 buildings, inspecting 604 buildings, and conducting nine expert workshops. The study suggests a severe impacts approach to guide adaptation to pluvial floods, reducing impacts on: Human lives or health, Critical operations, Evacuation, Critical technical installations, and High-value loss and damage. The study identifies a set of scalable property-level adaptation actions to reduce severe impacts and a typology of adaptation actions, emphasizing property owner responsibility and ability to act. Implementing the severe impacts approach, the typology and scalable actions will increase property companies' capacity to adapt. [ABSTRACT FROM AUTHOR]

Copyright of Building Research & Information is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)