A dispiriting part of the aftermath of a disaster is hearing about the staggering number of deaths and seemingly insurmountable economic losses. Many of the disaster risk reduction programs that implement disaster prevention and preparedness capabilities are helping to create more resilient communities. These worthwhile programs require ongoing financing, and their success must be measured and evaluated to continue to justify the allocation of limited funds.
There are two global UN frameworks being renewed this year:
Both frameworks will run for 15 years. This is the first time explicit numerical targets have been set around disaster risk, and consequently, there is now a more pressing need to measure the progress of disaster risk reduction programs to ensure the goals are being achieved.
The most obvious way to measure the progress of a country’s disaster risk reduction would be to observe the number of deaths and economic losses from disasters.
However, as we have learned in the insurance industry in the early 1990s, this approach presents big problems around data sampling. A few years or even decades of catastrophe experience do not give a clear indication of the level of risk in a country or region because catastrophes have a huge and volatile range of outcomes. An evaluation that is purely based on observed deaths or losses can give a misleading impression of success or failure if countries or regions are either lucky in avoiding (or unlucky in experiencing) severe disaster events during the period measured.
A good example is the 2010 Haiti earthquake, which claimed more than 200,000 lives and cost more than $13 billion. Yet for more than 100 years prior to this devastating event, earthquakes in Haiti had claimed fewer than 10 lives.
Haiti shows that it is simply not possible to determine the true level of risk from 15 years of observations for a single country. Even looking at worldwide data, certain events dominate the disaster mortality data, and progress cannot be measured.
Global disaster-related mortality rate (per million global population), 1980–2013 (From Setting, measuring and monitoring targets for disaster risk reduction: recommendations for post-2015 international policy frameworks. Source: adapted from www.emdat.be)
A more reliable way to measure the progress of disaster risk reduction programs is to use a probabilistic methods, which rely on a far more extensive range of possibilities, simulating tens of thousands of catastrophic events. These can then be combined with data on exposures and vulnerabilities to output metrics of specific interest for disaster risk reduction, such as houses or lives lost. Such metrics can be used to:
- Measure disaster risk in a village, city, or country and how it changes over time
- Analyze the cost-benefit of mitigation measures:
- For a region: For example, the average annual savings in lives due to a flood defense or earthquake early warning system
- For a location: For example, choosing which building has the biggest reduction in risk if retrofitted
- Quantify the impact of climate change and how these risks are expected to vary over time
In the long term, probabilistic catastrophe modeling will be an important way to ensure improved measurement and, therefore, management of disaster risk, particularly in countries and regions at greatest risk.
The immediate focus should be on educating government bodies and NGOs on the valuable use of probabilistic methods. For the 15 year frameworks which are being renewed this year, serious consideration should be given on how to implement a useful and practical probabilistic method of measuring progress in disaster risk reduction, for example by using hazard maps. See here for further recommendations: http://www.preventionweb.net/english/professional/publications/v.php?id=39649
2015 is an important year for measuring disaster risk: let’s get involved.