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Watershed Planning within a Quantitative Scenario Analysis Framework
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Flood probability quantification for road infrastructure: Data-driven spatial-statistical approach and case study

Zahra Kalantari1, Marco Cavalli2, Carolina Cantone3

  • 1Stockholm University, Department of Physical Geography and Bolin Centre for Climate Research, SE-106 91 Stockholm, Sweden.

The Science of the Total Environment
|January 8, 2017
PubMed
Summary
This summary is machine-generated.

Extreme weather events increasingly threaten roads. This study maps flood risk at road-stream crossings using watershed data and sediment connectivity, improving infrastructure resilience.

Keywords:
Climate change adaptationDecision makingGISMultivariate statistical modelSediment connectivity

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Area of Science:

  • Environmental Science
  • Hydrology
  • Civil Engineering

Background:

  • Climate change is increasing extreme hydrological events, stressing transportation infrastructure.
  • Roads are vulnerable to flooding and sediment accumulation at stream intersections.
  • Existing models often lack integrated analysis of watershed and sediment factors.

Purpose of the Study:

  • To develop a data-driven spatial-statistical approach for mapping road flood probability.
  • To quantify flood risk at critical road-stream intersections.
  • To improve the accuracy of road flood prediction models.

Main Methods:

  • Integrated watershed characteristics (soil type, land use, precipitation-runoff variability, road drainage) with sediment connectivity measures.
  • Developed a spatial-statistical model for flood probability assessment.
  • Applied and validated the approach in two case study areas in southwest Sweden.

Main Results:

  • The novel approach accurately quantifies and maps road flood probability at critical locations.
  • Integration of sediment connectivity significantly improved model accuracy.
  • Case studies demonstrated the model's effectiveness in areas with documented flood events.

Conclusions:

  • The developed approach enhances the prediction of road flooding under extreme hydrological conditions.
  • This methodology provides a valuable tool for infrastructure management and climate adaptation.
  • Improved understanding of flood risk at road-stream intersections is crucial for resilient transportation networks.