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On the relation between
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weather-related disaster impacts, vulnerability and climate change
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Authors: Hans Visser1*, Arthur C. Petersen1,2,3 and Willem Ligtvoet1
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SUPPLEMENTARY MATERIAL
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Appendix A The EM-DAT database
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As described in Section 2.1, all the analyses in this article are based on the EM-DAT
emergency database. This database is open source and has been referenced in many
publications (e.g. Birkmann 2013). Comparable databases are NatCat (Munich Re) and Sigma
(Swiss Re), which are run on a commercial basis. Regional databases are also in use, for
example the SHELDUS database for the United States (Gall et al. 2009; Preston 2013). EMDAT is a global database maintained by the World Health Organization (WHO) and the
Centre for Research on the Epidemiology of Disasters (CRED) at the University of Louvain,
Belgium (Guha-Sapir et al. 2012). The database contains disaster events from 1900 onwards,
presented on a country basis. Applications can be found in Guha-Sapir and Santos (2012) and
references therein.
The EM-DAT database provides three disaster impact indicators for each disaster event:
(i) economic losses, (ii) number of people affected, and (iii) number of people killed. These
are defined as follows (Guha-Sapir et al. 2012): economic losses are direct damage costs and a
direct consequence of weather or climate events. They refer to the cost of all physical impacts,
including the lives and health of directly-affected persons, on all types of tangible assets,
including private dwellings, agriculture, commercial and industrial stocks and facilities,
infrastructure (roads, bridges, ports, water supplies, telecommunications) and natural
resources. The number of people affected is the sum of people injured, people needing
immediate assistance for shelter and people requiring immediate assistance during a period of
emergency (this may include displaced or evacuated people). The number of people killed is
the sum of people confirmed dead and/or missing and/or presumed dead.
Since the quality of the analyses is as good as the quality of the underlying data, we
briefly address a number of uncertainties associated with the use of EM-DAT (and related
databases). We address three issues: (i) the role of ‘reporting bias’, (ii) data comparison across
databases, and (iii) definitional issues. More information is given in Guha-Sapir and Below
(2002) and Visser et al. (2012 – Appendix A).
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Reporting bias
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Reporting bias is an important source of uncertainty in disaster databases. Reporting bias is
the phenomenon that the number of disasters coded in a database increases over time. The
reason is not only an increasing population, increasing wealth or climate change, but also the
fact that sources of disaster reporting become sparse further back in time. For example, we
checked the disasters reported for a small country – the Netherlands – in EM-DAT and
compared these data with a detailed overview of disasters in Buisman (2011). No disasters
were reported in EM-DAT before 1950, while all disasters after 1950 were correct. For these
reasons CRED advises using its database from 1980 onwards, even though the disaster
database starts in the year 1901. This advice has been followed in this study.
A second measure was also taken to remove reporting bias as much as possible. We only
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selected major disasters, that is disasters in the severity classes 4, 5 and 6, following the
severity definitions of Munich Re. These are disasters with an economic loss of over 250
million USD2010, and/or 100 or more fatalities. The reason for this precaution is that reporting
bias will manifest itself mainly in disasters with smaller impacts.
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Data comparison across databases
One way of checking the reliability of EM-DAT is to compare this database with other
databases. This has been done by Guha-Sapir and Below (2002). They compared EM-DAT
with two commercial databases: NatCat, maintained by Munich Re, and Sigma, maintained by
Swiss Re. Database comparison is not easy, as each institute uses its own definitions, disaster
thresholds and geographical units. The same conclusion was drawn by Gall et al. (2009) in a
comparison of four economic loss databases (EM-DAT, NATHAN, SHELDUS and Storm
Events). Despite these differences, we found a good correspondence between global economic
loss data in EM-DAT and the Munich Re NatCat database (R=0.94 over the period 1980 to
2009). We also compared global numbers of people affected over the period 1990 to 2010 and
found a correlation of 0.84 (this relative low value is due to one outlier in 1993).
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Definitional issues
There are three issues worth mentioning with relation to the CRED database. Firstly, the loss
data in EM-DAT are direct losses. Direct losses reflect damages sustained by public
infrastructure, buildings, machinery or crops. In the case of complete destruction, direct losses
are often equivalent to the replacement costs. However, indirect losses may outweigh direct
losses. As a result, the losses presented in this study may be a fraction of the total losses due
to a specific disaster (Gall et al. 2009).
Secondly, the definition of disaster type (climatological, hydrological or meteorological)
is not always clear. For example, Hurricane Katrina is categorized in EM-DAT as a
meteorological disaster. However, much of the disaster burden was due to flooding, which is a
hydrological disaster. CRED does not apply any ‘fuzzy attribution’ if a disaster belongs to
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two disaster types.
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Thirdly, the geographical attribution of a disaster may become complicated if countries
fall apart. A recent example is the split-up of Sudan into Sudan and South Sudan. Other
examples are the split-up of the Soviet Union, Yugoslavia and Czechoslovakia. As long as
analyses are aggregated over large regions, as in this study, no distortion of data will arise.
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Appendix B Trend estimation and the Kalman filter
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The trend model almost exclusively applied in the field of disaster management is the OLS
straight line. This model has the advantage of being simple and generating uncertainty
information for any trend difference [μt - μs] (indices ‘t’ and ‘s’ are arbitrary time points
within the sample period). More formally, the OLS linear trend model reads as:
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𝑦𝑡 = 𝜇𝑡 + 𝜀𝑡
and
𝜇𝑡 = 𝑎 + 𝑏 𝑡 ,
(1)
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where parameter a is the intercept, b the slope and εt a white noise process. Now, the variance
of any trend difference [μt - μs] follows from var(μt - μs) = (t - s)2 var(b).
Throughout this study a sub-model from the class of Structural Time series Models
(STMs) has been applied: the Integrated Random Walk (IRW) model. This model is attractive
since it is flexible while generating uncertainty bands in the same way as model (1) (Visser
and Molenaar 1995; Visser 2004; Visser and Petersen 2009). The IRW trend model has the
following form:
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𝑦𝑡 = 𝜇𝑡 + 𝜀𝑡
and
𝜇𝑡 = 2𝜇𝑡−1 − 𝜇𝑡−2 + 𝜂𝑡 ,
(2)
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where yt denotes a measurement at time t, and 𝜂𝑡 and 𝜀𝑡 are independent, normally
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distributed, white noise processes with zero mean and variances 𝜎𝜂2 and 𝜎𝜀2 respectively. To
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estimate trends from this model using the Kalman filter, model (2) needs to be rewritten in the
state-space form:
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2 −1 𝜇𝑡
(𝜇𝜆𝑡+1 ) = [
] ( ) + (0𝜂𝑡 )
𝑡+1
1 0 𝜆𝑡
and
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𝑦𝑡 = (1 0) (𝜇𝜆𝑡 ) + 𝜀𝑡 ,
𝑡
(3)
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where the term λt equals μt-1, 𝜂𝑡 ~𝑁(0, 𝜎𝜂2 ) and 𝜀𝑡 ~𝑁(0, 𝜎𝜀2 ).
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Under these assumptions of normality, the Kalman filter provides optimal estimates
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μ
̂𝑡 for the trend 𝜇𝑡 : the filter yields the minimum mean square estimator (MMSE) for the
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vector (μt , λt )′, based on observations up to and including time t. If the noise processes are
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not normally distributed, the filter generates the minimum mean square linear estimator
(MMSLE). This still yields optimal estimates but the filter is less powerful. For more
information on the Kalman filter please refer to Harvey (1984; 1989), Durbin and Koopman
(2001) and Chandler and Scott (2011 – Section 5.5). A historical overview, with applications
in aerospace, is given by Grewal and Andrews (2010).
The IRW trend model yields both linear and flexible trends, depending on the noise
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variance σ2η . If this variance is set to zero, the IRW trend equals the OLS linear trend (model
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(1)). On the other hand, when σ2η is set to a large number, the trend will be extremely flexible.
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Since the value of this noise variance σ2η steers the flexibility of the trend, σ2η is also known as
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the ‘smoothing parameter’. The optimal value for σ2η can be obtained using maximum
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likelihood estimation (Harvey 1984; 1989). This implies a minimization of the sum of squared
one-step-ahead prediction errors. In this way the flexibility is ‘adapted’ to the data.
All the results shown in Figures 2 and 3 are gained by log-transforming the data first.
Thus, the data yt are transformed by zt = ln(yt) = µt + εt. Then, trends are estimated on zt and
back-transformed afterwards. If we denote the trend in yt by µt’, it easily follows that the trend
ratio [µt’/µs’] equals exp(µt -µs), where the trend difference [µt -µs], and uncertainties therein,
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follow from model (2). An example of such a trend ratio is given in Figure 2, lower panel.
For model (2) to hold, residuals should be white noise. Normality is an attractive property
but not a necessary condition. In multiple regression models such checks are performed on
model residuals. Here arises a difference with model checks for Kalman filtering. Here, the
one-step-ahead-predictions, or innovations in short, are used to check for whiteness and
normality. For details please see Harvey (1989 – Section 5.4). These conditions were fulfilled
for all the models estimated. The estimation of autocorrelation functions (ACFs) showed no
serial correlation in the innovation series at hand.
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