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Related Experiment Video

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Watershed Planning within a Quantitative Scenario Analysis Framework
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Integrated Direct and Indirect Flood Risk Modeling: Development and Sensitivity Analysis.

E E Koks1, M Bočkarjova2, H de Moel1

  • 1Institute for Environmental Studies (IVM), VU University Amsterdam, Amsterdam, The Netherlands.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|December 18, 2014
PubMed
Summary

This study introduces an integrated flood risk model to dynamically assess economic losses and recovery. Findings reveal indirect losses dominate in low-probability events, necessitating detailed regional flood risk assessments.

Keywords:
Flood durationflood risk modelingindirect lossesinput-output model

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Area of Science:

  • Environmental Science
  • Economics
  • Risk Management

Background:

  • Floods cause significant economic damage, necessitating accurate risk assessment models.
  • Existing models often lack dynamic recovery modeling and consistent loss accounting.

Purpose of the Study:

  • To develop an integrated direct and indirect flood risk model for dynamic economic loss and recovery assessment.
  • To improve consistency in accounting for capital and labor losses using the Cobb-Douglas production function.
  • To analyze the impact of high- and low-probability flood events on economic recovery.

Main Methods:

  • Developed an integrated direct and indirect flood risk model.
  • Utilized the Cobb-Douglas production function to translate direct losses into production losses.
  • Employed a hybrid input-output model for economic recovery analysis.
  • Applied the model to the port of Rotterdam using six distinct flood event scenarios.
  • Conducted a global sensitivity analysis to explore parameter uncertainty.

Main Results:

  • Direct losses are approximately 50% larger than indirect losses for expected annual damage.
  • Indirect losses exceed direct losses for low-probability flood events.
  • High- and low-probability flood events differ qualitatively in damage scale and recovery duration.
  • Parameter influence varies significantly between high- and low-probability flood modeling.

Conclusions:

  • Integrated flood risk models are crucial for understanding dynamic economic impacts.
  • The distinction between high- and low-probability flood consequences requires tailored assessment approaches.
  • Detailed regional analysis is essential for accurate flood risk management and disaster preparedness.