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<p>This study provides a national-scale assessment of flood exposure in Sweden by integrating high-resolution population and building data with inundation extents for the 100-year and highest possible flood scenarios. Exposure increases sharply under the extreme event, with building exposure tripling and population exposure doubling. Spatial patterns differ across scales, with building and population exposure responding differently to changes in hazard magnitude and forming distinct regional hotspots. By linking exposure metrics to water-related insurance claims, we show that population exposure strongly predicts both claim counts and compensation at the county scale, whereas building exposure is a weaker indicator of observed impacts. At the municipal scale, relationships weaken due to local variability and aggregation effects. The findings highlight the importance of population-based indicators and demonstrate the value of integrating empirical loss data into national flood risk assessments.</p>

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Population exposure predicts flood losses in Sweden

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月6日

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  • Konstantinos Karagiorgos,
  • Lars Nyberg,
  • Tonje Grahn

摘要

This study provides a national-scale assessment of flood exposure in Sweden by integrating high-resolution population and building data with inundation extents for the 100-year and highest possible flood scenarios. Exposure increases sharply under the extreme event, with building exposure tripling and population exposure doubling. Spatial patterns differ across scales, with building and population exposure responding differently to changes in hazard magnitude and forming distinct regional hotspots. By linking exposure metrics to water-related insurance claims, we show that population exposure strongly predicts both claim counts and compensation at the county scale, whereas building exposure is a weaker indicator of observed impacts. At the municipal scale, relationships weaken due to local variability and aggregation effects. The findings highlight the importance of population-based indicators and demonstrate the value of integrating empirical loss data into national flood risk assessments.