The Climate Impacts: Global and Regional Adaptation Support Platform

A wave of unprecedented urbanization in human history is underway in a coastal band in non-OECD countries (UNDESA, 2006). Whether in Port-au-Prince in Haiti or Shanghai in China, emerging problems are compounding with unsolved long-standing ones to create a herculean management challenge for numerous coastal cities (Tanner et al, 2009). This challenge comprises different aspects of global change, such as climate change, urban sprawl, and urbanization on the one hand, and settlement distribution and natural hazards such as tropical cyclones, floods and landslides on the other. Combinations of these aspects generate vulnerability for urban livelihoods through a multitude of mechanisms, threatening both human well-being and ecosystems.

Based on similarities, we present a global categorization of these mechanisms into six typical, generalized manifestations and group the areas and coastal cities they are prevalent in. By doing so, we can identify coastal cities that share similar properties and challenges with respect to socio-ecological challenges under unprecedentedly rapid growth. A similar approach has been applied to drylands by Sietz et al (2011) to drylands.

Figure 1. Module for displaying of spatial distribution and vulnerability profiles. Left: Map of six groups of cities under rapid coastal urbanization. Right: Example of a city vulnerability profile (Mumbai) and the average profile of the group it belongs to.

Important messages of the study

The two poorest groups (pink and orange) are most vulnerable to natural hazards. At the same time they comprise some of the World's most rapidly growing populations with the poorest coping capacities. City examples include Dacca, Maputo, Port-au-Prince, Manila, and Fuzhou.

The wealthiest group, showing a high percentage of people living in low-lying areas (dark blue) presents damages due to flood exposure and sensitivity do not act as an an 'early warning' for the much larger damages to be expected under climate change: Here the flood sensitivity is high despite low exposure, the highest management effectiveness and income, showing that capacity to adapt does not mean adapting. City examples include including Mumbai, Belém, and Cape Town.

Similar natural hazard damages are observable under contrasting exposures and abilities to adapt. This implies that there is much to learn from disadvantaged cities that are efficiently translating their limited means into reducing vulnerability. This study can help identify these "poster cities" and similar cities their efficacy may be transferrable to.

Interpretation aid and possible limitations

Each group has a typical combination of indicator values that is displayed in a profile under the map. These profiles are averages of all cities contained in them. Their spatial distribution is shown on the map, and each city on this map can be selected and displayed a) in a separate, city specific profile either together with its group profile (Fig. 1), or alongside two other cities of choice to aid comparisons (Fig. 2).

Figure 2: Module for comparing up to three cities under rapid coastal urbanization with respect to their vulnerability profiles.

What are assumptions and other reasons that limit the use of the results?

This study relies on globally available subnational and national gridded datasets of a 0.5° lon x 0.5° lat spatial resolution. While it allows for a global overview and comparing between cities with respect to the indicators used, the data does not, and cannot, replace in-depth case studies. This point is particularly the case when looking into the indicators that are only available on a national basis (see Table 1).

How was the data prepared/analysis performed?

After using case studies to identify typical vulnerability generating mechanisms, we formalized and indicated their most relevant elehnents with 17 globally available indicators (see Table 1 for examples). They include, inter alia, exposures and sensitivities to climate related extreme events, sea-level rise, marginalization, urban population growth, urban sprawl, management capacity, and prevalence of wetland ecosystems. The indicator datasets were fed into an established cluster analysis, revealing a typology of six clearly distinguishable mechanisms with typical combinations of indicator values.

Table 1. Quantification of vulnerability creating mechanisms: Identified indicators and subnational proxy datasets to represent them. Indicators addressing the state of the environment are predominantly from 2000. Indicators addressing change are predominantly from the time interval from 1990 to 2000.