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A Blueprint for Full Collective Flood Risk Estimation: Demonstration for European River Flooding.

Francesco Serinaldi1,2, Chris G Kilsby1,2

  • 1School of Civil Engineering and Geosciences, Newcastle University, Newcastle Upon Tyne, UK.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|December 30, 2016
PubMed
Summary

This study models continuous stream flow to assess collective flood risk, overcoming limitations of rare extreme flood data. Dynamic copulas preserve spatio-temporal flood loss correlations for better risk assessment.

Keywords:
Collective flood riskEuropean riversdynamic copula space-time modeling

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Area of Science:

  • Hydrology and Environmental Science
  • Climate Change Adaptation
  • Risk Management

Background:

  • Floods are dynamic natural hazards with complex spatio-temporal impacts across regions.
  • Assessing collective flood risk is challenging due to the rarity of extreme flood events and limited historical data.
  • Existing methods often fail to capture the intricate spatio-temporal dependencies in flood occurrences and their associated losses.

Purpose of the Study:

  • To develop a novel approach for modeling continuous stream flow data to better assess collective flood risk.
  • To overcome the limitations of traditional frequency analysis for rare extreme flood events.
  • To effectively utilize spatio-temporal correlation structures within stream flow records for improved flood risk assessment.

Main Methods:

  • Employed a dynamic copula framework to model spatio-temporal properties of stream flow.
  • Coupled time series models for temporal dynamics with multivariate distributions for spatial dependence.
  • Applied the model to 490 stream flow sequences across 10 major European river basins.

Main Results:

  • Demonstrated that temporal dependence is crucial for accurately reproducing interannual persistence in flood losses.
  • Showcased the effectiveness of dynamic copulas in preserving spatial dependence of flood losses at weekly and annual scales.
  • Validated the model's ability to improve the magnitude and frequency estimation of basin-wide annual flood losses.

Conclusions:

  • Continuous stream flow modeling using dynamic copulas offers a more efficient use of data for flood risk assessment.
  • The proposed framework effectively captures both temporal and spatial dependencies in flood processes.
  • This approach enhances the reliability of collective flood risk assessments, particularly in data-scarce scenarios.