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Spatial Modeling of Urban Pluvial Flood Risk on Sewer Networks: A Bayesian Approach for Climate Adaptation in Swedish Municipalities

Pirzamanbin, Behnaz LU orcid and Mobini, Shifteh LU orcid (2026) The Swedish Climate Symposium 2026
Abstract
Climate change is intensifying short-duration rainfall extremes, increasing urban pluvial flood risk across Swedish municipalities. This study develops a novel spatial statistical framework for basement flood risk assessment using Log-Gaussian Cox Process (LGCP) models on metric graphs, applied to 17 years of flood records from Trelleborg, southern Sweden.
Unlike conventional approaches that treat flood locations independently, our method captures spatial correlation along the sewer network topology. Results reveal that flood risk is correlated within approximately 400 meters of pipe network, identifying neighborhood-scale vulnerability clusters.
Model comparison demonstrates that network-based spatial models significantly... (More)
Climate change is intensifying short-duration rainfall extremes, increasing urban pluvial flood risk across Swedish municipalities. This study develops a novel spatial statistical framework for basement flood risk assessment using Log-Gaussian Cox Process (LGCP) models on metric graphs, applied to 17 years of flood records from Trelleborg, southern Sweden.
Unlike conventional approaches that treat flood locations independently, our method captures spatial correlation along the sewer network topology. Results reveal that flood risk is correlated within approximately 400 meters of pipe network, identifying neighborhood-scale vulnerability clusters.
Model comparison demonstrates that network-based spatial models significantly outperform traditional 2D Euclidean approaches, confirming that sewer topology—not geographic proximity—governs flood propagation. Notably, significant flooding occurs during moderate rainfall events (20-25 mm/h over 60 minutes), well below extreme storm thresholds, highlighting the importance of routine capacity assessment. Combined sewer systems show elevated risk compared to separated systems.
These findings provide municipalities with data-driven tools for identifying high-risk network segments, prioritizing infrastructure investments, and developing early-warning systems. The methodology is transferable to other urban contexts, supporting evidence-based climate adaptation aligned with Sweden's 2024 national adaptation strategy.

Keywords: Urban Flooding, Spatial Statistics, Sewer Networks, Metric Graph, Bayesian Modeling, Climate Adaptation (Less)
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author
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organization
publishing date
type
Contribution to conference
publication status
unpublished
subject
conference name
The Swedish Climate Symposium 2026
conference location
Lund, Sweden
conference dates
2026-05-20 - 2026-05-22
language
English
LU publication?
yes
id
5f1bcdf0-0e72-430f-8249-23fb044677d5
date added to LUP
2026-06-04 10:58:25
date last changed
2026-06-08 16:22:31
@misc{5f1bcdf0-0e72-430f-8249-23fb044677d5,
  abstract     = {{Climate change is intensifying short-duration rainfall extremes, increasing urban pluvial flood risk across Swedish municipalities. This study develops a novel spatial statistical framework for basement flood risk assessment using Log-Gaussian Cox Process (LGCP) models on metric graphs, applied to 17 years of flood records from Trelleborg, southern Sweden.<br/>Unlike conventional approaches that treat flood locations independently, our method captures spatial correlation along the sewer network topology. Results reveal that flood risk is correlated within approximately 400 meters of pipe network, identifying neighborhood-scale vulnerability clusters.<br/>Model comparison demonstrates that network-based spatial models significantly outperform traditional 2D Euclidean approaches, confirming that sewer topology—not geographic proximity—governs flood propagation. Notably, significant flooding occurs during moderate rainfall events (20-25 mm/h over 60 minutes), well below extreme storm thresholds, highlighting the importance of routine capacity assessment. Combined sewer systems show elevated risk compared to separated systems.<br/>These findings provide municipalities with data-driven tools for identifying high-risk network segments, prioritizing infrastructure investments, and developing early-warning systems. The methodology is transferable to other urban contexts, supporting evidence-based climate adaptation aligned with Sweden's 2024 national adaptation strategy.<br/><br/>Keywords: Urban Flooding, Spatial Statistics, Sewer Networks, Metric Graph, Bayesian Modeling, Climate Adaptation}},
  author       = {{Pirzamanbin, Behnaz and Mobini, Shifteh}},
  language     = {{eng}},
  month        = {{05}},
  title        = {{Spatial Modeling of Urban Pluvial Flood Risk on Sewer Networks: A Bayesian Approach for Climate Adaptation in Swedish Municipalities}},
  year         = {{2026}},
}