Governance of flood risk data: A comparative analysis of government and insurance geospatial data for identifying properties at risk of flood
Flood risk maps are essential sources of information for flood risk management (FRM) decisions. Commercial flood models used by the insurance industry are rarely studied in the academic literature which has led to difficulties in understanding their sources of uncertainty and opportunities for impro...
Gespeichert in:
Veröffentlicht in: | Computers, environment and urban systems environment and urban systems, 2021-07, Vol.88, p.101636, Article 101636 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Flood risk maps are essential sources of information for flood risk management (FRM) decisions. Commercial flood models used by the insurance industry are rarely studied in the academic literature which has led to difficulties in understanding their sources of uncertainty and opportunities for improvement. This paper compares regions and residential properties identified as exposed to floods by an insurance industry model and by government authorities responsible for FRM in three Canadian cities. Findings show that the insurance model is identifying substantially greater number of regions and properties as at-risk of flood, and little overlap exists between public and private flood maps. The paper discusses opportunities for data integration and increased data transparency for supporting flood resiliency efforts in Canada.
•This study leverages flood maps used by governments and insurance companies to detect residential flood exposure.•Findings show large differences in the number and location of residential properties in flood zones recognized by each sector.•The paper discusses opportunities for cross-sectoral collaboration in data management for improving flood risk management. |
---|---|
ISSN: | 0198-9715 1873-7587 |
DOI: | 10.1016/j.compenvurbsys.2021.101636 |