Supplementary material
The supplementary material (SM) with supplementary figures (SF) and tables (ST) is
provided in 4 sections: SM1-4.
SM1: Trends in losses for two statistical models
Figure SF1 shows trends in losses for two statistical models as reported in Visser and Petersen
(2012). The first model is a simple exponential fit, while the second one uses and integrated
random walk. Visser and Petersen suggest that both statistical models are equally valid.
Figure SF1: Loss trends in different statistical models
Sources: Visser and Petersen, 2012
Note: the source of the loss data is both times Munich Re, 2011
SM2: Loss detection and projection studies
Table ST1 provides a comprehensive overview of loss trend detection studies. The table and
our analysis show that the different approaches that are used for adjusting loss lead to
different outcomes. On the other hand, the strength of the different studies is that different
approaches and use of indices and different datasets lead to very similar outcomes. In general,
many studies have studied loss databases with a limited length (periods shorter than 40-50
years), which reduces the possibility to determine impacts of anthropogenic climate change.
Table ST1 provides a comprehensive overview of loss trend detection studies.
Table ST1: Disaster loss trend detection studies
Hazard
Bushfire
Flood
Flood
Flood
Flood
Flood
Flood
Hail
Hail
Windstorm
Windstorm
Thunderstorm
Tornado
Tornado
Tornado
Tropical storm
Tropical storm
Tropical storm
Tropical storm
Region
Australia
USA
China
Europe
Spain
Korea
Bangladesh
USA
Germany
USA
Europe
USA
USA
USA
USA
Latin America
India
USA
USA
Period
1900-2009
1926-2000
1950-2001
1970-2006
1971-2008
1971-2005
1970-2007
1951-2006
1974-2003
1952-2006
1970-2008
1949-1998
1890-1999
1900-2000
1950-2011
1944-1999
1977-1998
1900-2005
1950-2005
Normalization
None
Wealth, population
GDP
Wealth, population
Insurance premiums, dwelling values
Population
GDP, population, vulnerability
Property, insurance market values
Insured value, number of contracts
Property, insurance market values
Wealth, population
Insurance coverage, population
Wealth
None
Wealth, population
Wealth, population
Income, population
Wealth, population
GDP, population
Tropical storm
Tropical storm
Storm
Thunderstorm,
hail
Weather
Weather
Weather
Weather
Weather
Weather
Weather
China
USA
USA
USA
1983-2006
1900-2008
1952-2006
1970-2009
GDP
GDP
Property, insurance market values
GDP
Global
Global
Germany
USA
Australia
USA
World
1980-2009
1990-2008
1973-2008
1980-2008
1967-2006
1951-1997
1950-2005
Wealth
Wealth, insurance conditions
Wealth, insurance conditions
Wealth, insurance conditions
Dwellings, dwelling value
Wealth, population
GDP, population
Source: Bouwer, 2011a, updated with recent papers, indicated in bold
Normalized loss
No trend
No trend
Increase since 1987
No trend
No trend
Increase
Decrease
Increase since 1992
Increase
Increase since 1952
No trend
Increase
No trend
No trend
No trend
No trend
No trend
No trend
Increase since 1970, no trend
since 1950
No trend
Increase since 1900
Increase
Increase
Reference
Crompton et al. 2010
Downton et al. 2005
Fengqing et al. 2005
Barredo 2009
Barredo et al. 2012
Chang et al. 2009
Tanner et al. 2007
Changnon, 2009a
Kunz et al. 2009
Changnon 2009b
Barredo 2010
Changnon 2001
Brooks and Doswell 2001
Boruff et al. 2003
Simmons et al. 2013
Pielke et al. 2003
Raghavan and Rajesh 2003
Pielke et al. 2008
Schmidt et al. 2009
No trend
No trend
Increase
Increase
No trend
No trend
Increase since 1970, no trend
since 1950
Neumayer and Barthel 2011
Barthel and Neumayer 2012
Barthel and Neumayer 2012
Barthel and Neumayer 2012
Crompton and McAneney 2008
Choi and Fisher 2003
Miller et al. 2008
Zhang et al. 2009
Nordhaus 2010
Changnon 2009
Sander et al. 2013
SM3: Trends in drivers of risk
UNISDR for work on a global model for their bi-annual Global Disaster Assessment reports models
exposure, vulnerability and risk for a number of hazards with a regional and decadal resolution. Figure SF2
shows trends in flood exposure, vulnerability and risk in relation for direct economic losses from 1980 to
2010 for South and South West Asia, and SF3 for the OECD region for 1990 to 2010 as reported in
UNISDR (2011) and UNESCAP/UNISDR (2012).
Figure SF2: Trends in flood disaster risk, exposure and vulnerability between 1980-2010 for the SSW
region
Note: In addition to Bangladesh the South and South-West Asian region comprises Afghanistan, Bhutan, India, the
Islamic Republic of Iran, Maldives, Nepal, Pakistan, Sri Lanka and Turkey.
Source: UNESCAP&UNISDR, 2012
Figure SF3: Trends in flood disaster risk, exposure and vulnerability between 1990-2010 for OECD
countries
Source: UNISDR, 2011
SM4: Modelling economic flood risk in Bangladesh
Table ST2 reports data detail on large riverine flood disasters in Bangladesh in terms of hazard (return
period, flooded area), exposure (population, assets), and risk/impacts (fatalities, losses). As vulnerability
cannot be observed directly, it is derived as normalized losses per GDP/flooded area (and fatalities per
population/flood area), which can be considered a normalization procedure.
Table ST2: Selected impacts for the worst floods in Bangladesh over the last four decades
Observed
variable
Model variable
Return
period
(years)
Probability
Flooded
area (000
sq km)
Proxy
hazard
1998
1988
2007
1987
2004
1974
1984
1%
2%
7%
8%
9%
11%
53%
100
90
62
57
58
53
28
Population
(million)
GDP current
(million $)
Fatalities
Proxy Proxy exposure Risk of loss of
exposure of
of assets
life
population
127
44092
1,050
105
26034
2,440
146
68415
405
102
23969
2,280
139
56561
761
74
12459
28,700
95
19258
1,200
Losses (million Fatalities/population
current $)
(per million)
Fatalities/population
Losses/GDP/flooded
(per million)/flooded
area
area
Risk of asset Relative risk of loss Relative risk of
Proxy vulnerability
Proxy vulnerability
losses
of life loss of assets (stage damage curve) assets damage curve)
2,128
1,424
1100
1,167
1,860
936
378
8%
23%
3%
22%
5%
388%
13%
Losses/GDP
4.8%
5.5%
1.6%
4.9%
3.3%
7.5%
2.0%
0.12
0.38
0.07
0.57
0.14
10.69
0.66
0.07
0.09
0.04
0.12
0.08
0.21
0.10
The empirical data form the basis for numerically modelling risk as a function of hazard, exposure and
vulnerability as well as climatic and socio-economic, which is displayed graphically in figure SF4.
Figure SF4: Methodological approach for assessing risk driven by societal and climate changes for the case
of riverine flooding in Bangladesh
Table ST3 tabulates key assessment modules including a discussion of functional relationship or drivers
and the sources.
Table ST3: Modules, functional relationships and input data
Module and output
Climate (warming)
Functional relationship or
drivers
SRES scenarios A2 and B1
Source
Nakicenovic and Swart, 2000
3
Precipitation
Function of mean temperature
change
PRECIS RCM for A2 and B1
(Tanner et al. 2007)
Maximum discharge
Function of precipitation
Economic vulnerability
Observed losses and vulnerability
Flooded area
Function of max discharge
Exposure
GDP, Population, assets
Based on
Conway et al., 2007 in Tanner et al., 2007
Bangladesh loss statistics
(Based on CRED 2013)
Statistical model
(based on Mirza 2002)
World Bank, 2013; SRES scenarios given by
Nakicenovic and Swart, 2000
Risk: economic losses
Function of flooded area,
economic vulnerability and
exposure
The data to model the relationship between temperature change (over mid 20th century levels) and change
in precipitation (%) is based on model results from 10 global circulation models). A simple relationship
(polynomial of degree 2) between years and mean regional temperature change has been assumed which is
based on the table above and the monsoon months. Modelled area flooded under projections of climate
change (B1) scenario is shown in figure SF5.
Figure SF5: Projected change in frequency of severe instances with areas flooded
Source: Hassan and Conway, chapter 5 in Tanner et al., 2007.
The spatial resolution is country-level and for such analysis spatially-explicit hydrological modelling is not
useful for informing hazard analysis. Instead, a statistical approach was applied for the monsoon months
from June to August based on observed river flows in the three largest river basins in Bangladesh: The
Ganges, the Brahmaputra and the Meghna. While rough, this appeared to be a valid approach, which may
not be applicable for other countries with less homogeneous terrain. The relationship between precipitation
change and (average) mean peak discharge is modeled based on Mirza (2002), who uses three global
circulation models for each of the river basins. Discharge is modelled as the sum of maximum discharges
(DMax_G, DMax_B, DMax_M in the 3 river basins (Meghna, Brahmaputra and Ganges), which are
DMax _G = 603.48DP + 52623
(1)
DMax _ B = 535.59DP + 65271
(1’)
DMax _ M = 227.73DP +14084
(1’’)
Dt = DMax _G + DMax _ B + DMax _ M
(1’’’)
A Gumbel model for event severity is used and the relationship between flooded area and the discharge
levels is estimated with a nonlinear regression model based on past maximum discharge levels from 19502010. The coefficient of variation for the flooded area was kept constant over time, i.e. it was assumed that
an increase in average flooded area due to climate change also would increase the standard deviation of
flooded area.
F ( x)  exp(  exp(  x))
(2)
A location and scale change re-parameterization (Fisher-Tippet) yields the following distribution:
4
x

F , ( x)  exp(  exp( 
   / 6 ))
 

(2’)
1


with   lim n k  log n   0.5772
k


The Fisher Tippet distribution for maximum discharge and the non-linear function for flooded area are
estimated based on past maximum discharge levels (1950-2010) and flooded areas. Overall, the relationship
between flooded area and discharge level Dt is estimated as
æ Dt ö
F(t) =1.2621ç
÷
è 10000 ø
3.778
(2’’)
Economic vulnerability is a function of flooded area Ft, and a time-dependent process VIt. Applying OLS
regression for losses over time, we estimated the vulnerability index VIt in order to adjust vulnerability
over the observed time period as well as future years.
V(Ft ,VI t ) = v0 * Ft *VI t
VI(t) = 5E + 25*e(-0.0308*t)
(3)
Losses (risk) are finally calculated via multiplication of vulnerability with exposure in the respective year,
and indicated in constant 2005 USD or as a percentage of GDP.
L(t) = Vt * Et
(4)
5
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