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Disaster risk assessment


Understanding, Managing, and Reducing Disaster RisksUnderstanding, Managing, and Reducing Disaster Risks

“We need to recognize that the decision to allow pre-code buildings to stay unretrofitted against local hazards means that the portion of our population that live and work in those buildings face higher than average risks than the populations that are in the newer buildings. [We] need to work to change the dynamic that the areas of town that are most affordable are also the areas facing greater hydrological, geological and ecological risks.
” Citizen from King County, Washington"


When performed at the national level, risk assessments range from qualitative national risk profiles for advocacy purposes to the quantitative assessment of risk to inform countries financial strategies for addressing the accumulating risks. Different types of risk assessment are applied at different scales. The table presents a selection of other types of risk assessment identified by the World Bank Global Facility for Disaster Reduction and Recovery.
ProductPurposeScaleData requirementsCost
Community-based disaster risk assessmentTo engage communities, communicate risk, and promote local actionCommunity levelLow: typically based on historical disaster events<$100,000
Asset-level risk assessments, including cost-benefit and engineering analysisTo inform design of building-level/asset-level risk reduction activites and promote avoidance of new riskBuilding/infrastructure levelModerate-high: requires high-resolution local data for large spatial areas with clear articulation100,000 to $500,000;
Catastrophic risk assessment for financial planningFor financial and fiscal assessment of disasters and to catalyze catastrophe risk insurance market growthNational to multi-countryHigh: Requires high-resolution, high-quality data of uncertainty>$500,000
Source: World Bank and GFDRR 2013, adapted from GFDRR (2014a)


Risk can be assessed both deterministically (single or few scenarios) and probabilistically (the likelihood of all possible events). Probabilistic models “complete” historical records by reproducing the physics of the phenomena and recreating the intensity of a large number of synthetic (computer-generated) events (UNISDR, 2015a). As such, they provide a more comprehensive picture of the full spectrum of future risks than is possible with historical data (UNISDR, 2015a). While the scientific data and knowledge used for modelling is still incomplete, provided that their inherent uncertainty is recognised, these models can provide guidance on the likely 'order of magnitude' of risks (UNISDR, 2015a).

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