On the relation between weather-related disaster impacts, vulnerability and climate change

We estimated trends and uncertainties for normalized economic losses L_j,t′ as described in Section 2.3. Data and IRW trends are given in Fig. 3 (upper panel) for all four regions j: Global, OECD, BRIICS and RoW. Due to outliers in the data, we log-transformed all data prior to estimating IRW trends. The trends shown were back-transformed by taking exponentials as explained in Section 2.3. All four trends show a stabilized, constant pattern. As an example, we give the trend ratios (μ₂₀₁₀/μ₁₉₈₀): 1.5 (0.7–3.0) for the global trend, with a 95 % confidence limit between brackets, 2.0 (0.8–5.1) for OECD countries, 1.6 (0.6–4.4) for BRIICS countries and 0.66 (0.31–1.4) for RoW countries. None of these trend ratios are therefore statistically different from 1.0; in other words, there was no significant change over the full sample period (1980–2010; α = 0.05)³.

Data and IRW trends for the normalized number of people affected A_j,t′ are shown in Fig. 3 (lower panel). As for the L_j,t′ data, we log-transformed prior to estimating IRW trends, compensating for skewness in the data. There appears to be no significant trend for two of the four regions (α = 0.05)⁴: global and RoW countries (black and blue lines). Examples are the ratios (μ₂₀₁₀/μ₁₉₈₀), which are 2.6 (0.9–7.3) for the global trend and 0.80 (0.30–2.11) for the RoW countries.

Trend ratios for OECD and BRIICS also stabilize, but not from 1980 onwards. The situation in BRIICS countries is shown in Fig. 2. As can be seen, the trend stabilizes from 1985 onwards. Similarly, the trend for OECD countries stabilizes from 1982 onwards. The ratios (μ₂₀₁₀/μ₁₉₈₀) are 13.8 (1.7–112) for OECD countries and 5.2 (1.5–18.7) for BRIICS countries. We note that the OECD ratio of 13.8 is very high; however, not in terms of absolute numbers. The trend value μ₂₀₁₀ for BRIICS countries is 0.040, and 0.002 for OECD countries (thus a factor of 20 lower).

Data and trends for the normalized number of people killed K_j,t′ are shown in Fig. 4 (upper panel). The trends were not estimated using IRW trends, as with L_j,t′ and A_j,t′, as none of the IRW trends estimated for these four regional datasets yielded acceptable white noise innovation data (this whiteness of noise process is a prerequisite for the time series models to hold). This failure is explained by the presence of outliers in these datasets, as can be seen in the lower panel of Fig. 4. We also applied a Gumble and a GEV distribution to the data (constant means). Again, we found no satisfactory results (method shown in Visser and Petersen 2012, their figure 4), and for none of the cases could a satisfactory Kolmogorov-Smirnov test statistic be found (α = 0.05).

Statistical trend techniques are therefore not appropriate for the data at hand. We therefore chose to give a trend presentation by fitting Lowess trends–a non-statistical robust trend technique. The trend estimates in Fig. 4 show that trends are more or less stable over time and not influenced by outliers. The only exceptions are global data over the 1980–1988 period. Our cautious, more qualitative, conclusion is that the trends in the number of people killed show constant, stabilized patterns over time.

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Our overall conclusion from Figs. 3 and 4 is that normalized trends generally show stabilized, constant patterns, although some differences exist across the disaster impact indicators L_j,t′, A_j,t′ and K_j,t′. With regard to economic losses, these results are consistent with those reported by Bouwer (2011a) for studies at the national level. Neumayer and Barthel (2011), reporting at the continental and global scales, also found stabilized losses (their figures 4 and 5, upper panels).

The upper panel of Table 1 shows a number of important differences across regions and weather categories. Firstly, climatological events have only a limited influence on economic losses L_t′: at the most they account for 17 %. Secondly, losses in OECD countries are dominated by meteorological events (64 %), which is not the case in BRIICS countries, where hydrological events dominate (66 %). Thirdly, meteorological and hydrological events are of equal weight (43 and 45 % respectively) for the RoW countries. Finally, meteorological events dominate at the global scale (50 %). We note that the uncertainties in these percentages do not influence the inferences made previously (16 and 84 percentiles given in Table 1).

The middle panel of Table 1 shows a pattern quite different from that shown in the upper panel. For all four regions, the contribution from floods is most pronounced. Percentages range from 43 % (OECD countries) to 65 % (BRIICS countries). Meteorological events have some effect on disasters in OECD and RoW countries: 36 and 30 % respectively. However, their influence in BRIICS countries and globally is small: 8 and 12 % respectively. The influence of climatological events is rather limited, ranging from 14 % (RoW countries) to 27 % (BRIICS countries).

The lower panel of Table 1 shows that the percentages differ from those in the upper and middle panels. With regard to the number of people killed, climatological events dominate in three of the four regions: 84, 64 and 60 % for OECD, RoW and global data respectively. The exception is the BRIICS countries. Here, the contribution is 35 %, which is lower than the influence of hydrological events (48 %), although uncertainties are wide in these two estimates.

Historic trend estimates for weather and climate variables and phenomena are presented in IPCC-SREX (2012, see their table 3-1). The categories ‘winds’, ‘tropical cyclones’ and ‘extratropical cyclones’ coincide with the ‘meteorological events’ category in the CRED database. In the same way, the ‘floods’ category coincides with the CRED ‘hydrological events’ category. The IPCC trend estimates hold for large spatial scales (trends for smaller regions or individual countries could be quite different).

The IPCC table shows that little evidence is found for historic trends in meteorological and hydrological events. Furthermore, Table 1 shows that these two events are the main drivers for (1) economic losses (all regions), (2) the number of people affected (all regions) and (3) the number of people killed (BRIICS countries only). Thus, trends in normalized data and climate drivers are consistent across these impact indicators and regions.

The IPCC categories ‘temperature’ and ‘droughts’ coincide with ‘climatological events’. With regard to extreme temperatures, IPCC-SREX concludes that the number of hot extremes has increased since the 1950s, leading to an increase in fatalities; however, the number of cold extremes has decreased, leading to a decrease in fatalities. Furthermore, historic trends in the severity of droughts are unclear, although some regions have experienced more intense and longer droughts, in particular southern Europe and West Africa. Additionally, a more recent study by Sheffield et al. (2012) reports little change in global drought over the past 60 years.

It is difficult to say if, and how, these three different patterns ‘cancel’ each other out over large spatial scales. A positive trend in climatological events can not therefore be ruled out, which applies to those regions where climatological events play a dominant role (cf. lower panel of Table 1, the number of people killed in OECD countries, RoW countries and globally). The difficulty with estimating trends in K_j,t′ adds to that conclusion (cf. Figure 4).

We note that the IPCC-SREX conclusions on weather and climate extremes largely coincide with those drawn in IPCC (2007), except for tropical cyclones and droughts (Nicholls and Seneviratne 2013). For these events, the IPCC-SREX conclusions are more cautious; the same holds for conclusions drawn in IPCC (2013–see their sections 2.6.2.3, 2.6.3 and 2.6.4).

As a direct consequence of the findings above, the third situation—both C_t and V_t show stabilized patterns over the sample period—should hold. In other words, this analysis shows that historic vulnerability is expected to be stable (constant) over the period of analysis. This conclusion is consistent with the reasoning of Bouwer (2011a); however, Nicholls points to a number of factors which should contribute to reductions in vulnerability, notably improvements in early warning systems (extreme weather forecasts) and improvements in building techniques. How can the constant vulnerability patterns therefore be explained?

This question is difficult to answer since quantitative information on vulnerability and vulnerability changes is sparse. Aon (2012) and BEH (2012) presented global vulnerability maps (the WorldRiskIndex), and Cutter et al. (2003) derived a social vulnerability index for the United States, but this did not include time-dependency. IPCC-SREX (2012, box 2-3) discussed vulnerability indices for the Americas, but these estimates are also static, and Preston et al. (2011) and Birkmann (2013) gave extensive reviews of vulnerability mapping.

Two recent developments in this field are however particularly worth mentioning. Preston (2013) presented an index for potential socio-economic exposure (PSE) that encompasses aspects of vulnerability. The PSE index is a function of both population and GDP changes and was derived for individual US counties. Also, Birkmann et al. (2014) presented ‘socioeconomic pathways’ (SSPs), which were derived for future developments in vulnerability but do not provide historic hindcasts.

More qualitatively, we give four explanations for the seemingly contradictory results found here, that of stabilized vulnerability patterns versus ongoing adaptation measures in many countries and regions (Adger et al. 2007, see their table 17.1; Nicholls 2011; Preston 2013, see their section 4; IPCC 2014). Firstly, global disaster management initiatives have only recently been put in place. The Hyogo Framework for Action (HFA) was adopted by 168 Member States of the United Nations in 2005 to take action to reduce vulnerabilities and risks to disasters (UNISDR, 2011). Although these highly important efforts will certainly pay off in the near future, it is unclear whether they are reflected in the sample period chosen for this study. Similar conclusions are drawn in IPCC (2014). The section on adaptation experience (SPM, A.2) sums up a number of adaptation examples across regions, but also states that ‘adaptation is becoming embedded in some planning processes, with more limited implementation of responses (high confidence)’.

Secondly, it is unclear to what extent adaptation measures work in practice. Heffernan (2012) argues that many countries, and even the richest, are ill-prepared for weather extremes. As an example, he names Hurricane Sandy, which wreaked a loss of 50 billion USD along the northeast coast of the US in 2012. As for early warning systems, Heffernan states that not all systems are functioning well. For example, in 2000, Mozambique was hit by a flood worse than any in its history, and the event was not at all anticipated. Warnings of above-average rainfall came too late and failed to convey the magnitude of the coming flood.

Thirdly, a positive trend in vulnerability may be offset by the increasing number of people moving from rural to urban environments, often situated in at-risk areas (UN 2012). Since many large cities lie along coastlines, these movements will make people more vulnerable to land-falling hurricanes (Pielke et al. 2008), coastal flooding and heatwaves (due the urban heat island effect). With regard to economic losses, Hallegatte (2011) argues that these migration movements may have caused disaster losses to grow faster than wealth.

Fourthly, it is unclear how political tensions and violent conflicts have evolved over large regional scales since 1980. On the one hand, Theisen et al. (2013) show that the number of armed conflicts and the number of battle deaths have decreased slightly at the global scale since 1980. On the other hand, these methods are rather crude as far as covering all aspects of political tensions are concerned (Leaning and Guha-Sapir et al. 2013).

We conclude that quantitative information on time-varying vulnerability patterns is lacking. More qualitatively, we judge that a stable vulnerability V_t, as derived in this study, is not in contrast with estimates in the literature.