12 March 2009
A very interesting report was released last week by the Economy and Environment Program for Southeast Asia (EEPSEA) in which an attempt has been made to map the pattern of vulnerability across the region to climate change. The aim of the project, for which the report is available online, was to “identify which regions in Southeast Asia are the most vulnerable to climate change.” This is a fairly bold thing to try to do, so lets take a look at what they have achieved and how they have done so.
The researchers have taken as a starting point maps of five hazards – cyclones, drought, floods, landslides and coastal inundation (sea level change). In each case they have used an external dataset to indicate the hazard associated with each of these events – so, for example, for landslides they have used the NGI landslide hazard maps produced in the World Bank Natural Disaster Hotspots project (Fig. 1), whereas for sea level change they have modelled the inundation associated with a 5 m increase in sea level.
For each hazard a scale of 1-10 has been used, with 10 being highest hazard and 1 the lowest. For each cell on the map, the average score across the five hazards was then taken as an indication of the overall hazard. I will return to this below. The outcome of this analysis is termed the “Multiple Climate Hazard Index”. The resultant map is shown in Fig. 2 below.
This map has then been combined with data for population density (incorporating areas that are ecologically protected) and “adaptive capability” (defined as “the degree to which adjustments in practices, processes, or structures can moderate or offset potential damage or take advantage of opportunities (from climate change).” The latter has used expert judgement to create an index based upon data on education, poverty, income inequality and suchlike. These three indicators have then been averaged to determine the level of vulnerability to climate change (Fig. 3).
Hmmmm! First, let me say that this is a brave thing to do – such exercises are really challenging given the complexity of the dataset. Such exercises are important and useful given the need to prioritise. I am hesitant to be too critical. I would however like to point out four things that are worth thinking about.
1. Perhaps most importantly, I don’t see how this is a map of climate change vulnerability. An argument can be made that this is a map of vulnerability to meteorologically driven hazards, but most of the parameters do not appear to consider a changing climate. The data for floods, droughts and cyclones used historic data of occurrence. This does not consider change. The only parameter that considered climate change was the sea level inundation, but this used a terribly simplistic model (a binary switch at 5 m sea level rise).
2. The decision to average across the five hazards is strange. The problem can be illustrated with an extreme example. Imagine you live on a flat, tropical plain 20 cm above sea level. Most of the hazards are likely to be low – no landslides, no river floods, no droughts, no tropical cyclones. However, a comparatively small rise in sea level wipes you out. In the system used here, your hazard comes out low whereas actually it is very high. It might be more rationale to take the highest value of hazard, or a more subtle measure.
3. The decision to weight the parameters equally is also interesting and surprising. Given the vastly different impact of the hazards, it might be worth weighting the hazards appropriately.
4. The decision to average the hazard, the population and adaptive capability is also odd. I would have thought that these parameters should be combined so that they interact (r.g. through multiplication and/or division). Clearly, the case where the level of hazard is high, the population is high and the adaptive capacity is low is exactly where really serious disasters occur.
I suspect that this map needs another iteration or two, perhaps backed up with a sensitivity analysis, but as a first step the authors deserve praise.