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Disaster-Resilience Analytics & Solutions KSB

The Disaster-Resilience Analytics and Solutions (D-RAS) KSB is a SWAT Team of highly skilled, technical experts whose mission is to troubleshoot disaster risk and resilience challenges encountered by clients internal and external to the World Bank. The D-RAS Team also advises and collaborates with teams to develop tailored and innovative solutions to their specific disaster risk and resilience challenges.

About

The Disaster-Resilience Analytics and Solutions (D-RAS) KSB is a SWAT Team of highly skilled, technical experts who troubleshoot disaster risk and resilience challenges encountered by clients internal and external to the World Bank. The D-RAS Team advises and collaborates with teams to develop tailored, innovative solutions to the problems they face. These solutions include developing country disaster risk profiles, robust investment plans, economic loss models / cost benefit-analyses and much more.


Disaster Risk Analytics

The World Bank’s Disaster Risk Management (DRM) portfolio of hazard and risk identification and analysis projects incorporates a wide range of natural hazards, including earthquakes, hurricanes, floods and landslides. Under the CAPRA (Probabilistic Risk Assessment Program), the World Bank is developing a series of disaster risk profiles for countries in the Latin American and Caribbean region, with an initial focus on Central America. These Country Disaster Risk Profiles (CDRPs) provide estimates of potential economic losses and future risk to property and other asset classes from natural disasters.

Resilience Analytics

While traditional quantitative decision support analyses begin by asking: “What will the future bring?” Decision Making Under Uncertainty (DMU) methods ask, “What are the strengths and limitations of our strategies, and what can we do to improve them?” To answer this, rather than using models and data to evaluate plans under a single or handful of scenarios, DMU methods run models over hundreds of different scenarios – built around changing climate conditions, but also the changing socio-economic conditions that may affect the project’s performance. Statistical analyses of these model runs identify the key conditions under which each strategy satisfies or fails to satisfy decision makers’ objectives and visualizations help decision makers understand how robust different strategies are by benchmarking those key conditions against the range of plausible outcomes.

Recent successful applications of disaster risk and resilience analytics include:

Disaster Risk Analytics
  • Quantification of building stock value of cities (Dar es Salaam)
  • Creation of country disaster risk profiles (Central America)
  • Rapid post-disaster loss assessments (April 2015 Nepal earthquake)
  • Development of economic loss models / cost-benefit analyses (Can-Tho)

Resilience Analytics

 

  • Development of robust investment plans (Peru)
  • Evaluation of tradeoffs among multiple flood mitigation interventions (Colombo)
  • Assessment of infrastructure vulnerabilities (Road network in Peru)
  • Design of resilient hydropower infrastructure (Nepal)e.