Chapter 3 – Deconstructing disaster: patterns and trends in risk
and poverty at the sub-national and local level
3.1 - Introduction
Most disaster mortality and direct economic loss is concentrated in internationally reported large-scale
disasters, which configure the global patterns and trends in disaster risk reviewed in Chapter 2.1 While
such disasters capture international political and media attention, they are only the visible tip of a much
larger risk iceberg.
Largely invisible to global observers, this chapter opens a window on rapidly evolving and emerging
trends in disaster risk at the sub-national and local levels and on their relationship with poverty. For
example, we will explore evidence from Asia, Latin America2 and Africa that suggests a rapid increase in
disaster risk associated with floods and heavy rains since 1990. These flood disasters are not associated
with massive mortality and economic loss. Rather such emerging risks are highly dynamic, mirroring
patterns of urban growth and economic, territorial and environmental change. We will also explore the
evidence that different kinds of disaster have a disproportionate impact on the poor. In particular, we will
highlight the hidden and sometimes long-term impact disasters have on poor households, and on
particularly vulnerable groups such as women and children.
The analysis builds on important recent advances in the compilation of national disaster data in both Asia
as well as Latin America, which for the first time enables the exploration of disaster risk patterns and
trends at a high level of resolution. As such, in this chapter global disaster risk is deconstructed and
viewed from a national level of observation and a local, municipal level of resolution. Poverty data at a
local or household resolution also exists in many of the same countries and has been compared with
disaster data in a series of case studies in nine countries3 Together with a systematization of the findings
of previous studies on disaster and poverty in Africa, this has enabled the identification of the different
mechanisms through which disaster risk and poverty interact
The country sample analyzed is characterized by a wide range of hazard types, development contexts and
geographic conditions. Nevertheless, high resolution disaster data is still not widely available in Africa,
Europe as well as in many small-island developing states and developed countries. As such, while the
findings point to many broad patterns and trends across different contexts they may not be globally valid.
3.2 - A question of resolution
Disaster risk reveals increasing complexity when observed at higher resolutions. Explored at the subnational and local levels, patterns and trends come into focus, which are essentially invisible when
observed from a global perspective.
International observers view large-scale disasters as a point or area on a map, which represents the
statistics of mortality and direct economic loss associated with the disaster. A closer examination,
however, reveals that losses are not evenly spread over the area affected: most of the losses tend to be
concentrated intensively in specific locations. At the same time, diffuse patterns of damage and
destruction spread extensively over wide areas become visible alongside these concentrations of intensive
1
Globally the vast majority of mortality and economic loss is concentrated in a few mega-disasters. An analysis of the EMDAT database, carried out for the ISDR, Disaster Risk Reduction: 2007 Global Review indicated that between 1975 and 2005,
82% of the disaster mortality registered in EM-DAT was associated with only 20 mega-events with over 10,000 deaths each.
Economic loss is less concentrated but 38.5% of total economic losses were found to be concentrated in just 21 mega-events
that caused more than US$10 billion of loss.
2
The Andean region (Bolivia, Colombia, Ecuador, Peru and Venezuela), Argentina, Mesoamerica (Costa Rica, and Mexico),
South Asia (the Indian States of Orissa and Tamil Nadu, Nepal and Sri Lanka) and Iran.
3
Case studies on Bolivia, Ecuador, El Salvador, India, Iran, Mexico, Peru, Nepal and Sri Lanka were commissioned specially
for this report by UNDP. Additional case-study material on Indonesia was contributed by the World Bank and on Fiji by
SOPAC.
1
risk. For example, in the case of the Armenia earthquake, Colombia, 25 January 1999, eight
municipalities intensively concentrated 98% of the deaths and 95% of the destroyed houses. However, the
impacts of the earthquake were extended over a much wider area (Map 3.1).
Map 0.1: Armenia, Columbia earthquake extensive and intensive impacts (1999)
At a high resolution, other risk patterns also come into focus, associated with thousands of frequently
occurring small-scale hazard events, such as floods, heavy rains, landslides, fires, and storms, which are
invisible to the global observer and go largely unrecorded.
In this chapter, the term intensive risk is used to describe those areas nesting within global disaster
hotspots that concentrate most of the mortality and destruction. Intensive risk is generally associated with
major hazards, such as large earthquakes, tsunamis, cyclones and volcanic eruptions and major
concentrations of exposed and vulnerable population and assets. The term extensive risk is used to
describe the diffuse, spatially extended risk patterns that only become visible at a higher resolution.
Extensive risk is associated with specific local concentrations of exposed, vulnerable population and
assets spread over wide areas and highly localized small-scale as well as major hazard events.
Box 0.1: Intensive, extensive and everyday risk in Cali, Colombia 4
If the resolution is further increased, extensive risk can also be deconstructed. Risk patterns at the neighbourhood or household
level come into focus, associated with everyday hazards such as house fires, domestic and occupational accidents. The number
of disasters reported in four different disaster databases, illustrate in the case of Cali, Colombia, how different patterns of
intensive, extensive and everyday risks become visible when risk is viewed at different resolutions.
From a global perspective, the EM-DAT database reports 8 intensive disasters that affected Cali between 1906 and 2007.
Increasing the resolution, a national DesInventar5 database reported 720 mainly extensive disasters between 1970 and 2007 and
a city-wide database, 1,280 events. At an even more detailed resolution, between 1987 and 2007, the Cali Fire Corps reported
80,121 emergencies, reflecting everyday risk.
Intensive and extensive disaster risk, therefore, are not mutually exclusive categories. Both have similar
causes (hazards, physical exposure and vulnerability) and outcomes (mortality, physical destruction and
damage, economic loss and livelihood interruption). The term intensive or extensive refers to the degree
of concentration of the risk in space and time, at a given resolution. When risk is highly concentrated in
space and time then it is more intensive in character. When it is spread out it is more extensive in
character.
4
Jimenez, Nayibe, 2007, Urbanizacion, marginalizacion y prefiguracion de desastres en ciudades medianas de paises en
desarrollo, Corporacion OSSO, Colombia.
5
See DesInventar at: http://gar-isdr.desinventar.net/DesInventar/main.jsp or http://online.desinventar.org/
2
Whether a risk is described as intensive or extensive depends on the level of observation and resolution.
As described in Box 0.1, the Armenia earthquake disaster appears intensive when observed globally but
unfolds into a complex pattern of intensive and extensive impacts when viewed at a higher resolution.
Some disasters, such as the landslide in Villa Tina, Colombia in 1987, which killed more than 500 people
in a single neighbourhood are intensive, whether observed globally or locally. Except in the case of largescale disaster, however, it is not possible to fold extensive into intensive risk by decreasing the resolution.
Extensive risk manifestations, associated with localized small-scale hazards become invisible if examined
from a global perspective.
3.3 – Identifying extensive risk
Extensive risk patterns in the sample were identified from a dataset specially updated, revised and
compiled for this report from national DesInventar disaster inventories.6 The dataset consists of 126,620
disaster reports associated with both geological7 and hydrometeorological8 hazards occurring between
1970 and 2007 in the 12 countries:9 a statistically robust sample that allows a high degree of confidence
in the trends and patterns identified.
Disaster losses, reported in a given local area, by definition manifest the specific configurations of risk
present in the area at the time. In DesInventar, a disaster report documents the losses that occurred at a
given time in a given local administrative area, such as a municipality, district or block, together with
information regarding the associated hazard type and cause. The impact of a large-scale earthquake or
cyclone, therefore, is documented as multiple local disaster reports. The impact of a small-scale hazard
affecting only one municipality appears as a single report. Relatively robust and comparable disaster loss
data exists for attributes such as mortality, housing destruction and damage. Loss data on crop and
livestock loss is far less robust. As a result, risks associated with droughts and rural agricultural
livelihoods are not addressed in the first part of this chapter. This imbalance is to some extent redressed
in the second part of the chapter, through the use of household poverty data from rural households.
In order to identify extensive risk, a loss threshold of 50 deaths or 500 houses destroyed was established
on the basis of a statistical analysis of the dataset (Technical Note 3.1). A disaster in a given local
administrative area above the threshold is considered to represent intensive risk. A disaster with 49 deaths
and 449 destroyed houses or less is considered to represent extensive risk. While any quantitative
definition of extensive risk is arbitrary, the threshold of 50 deaths and 500 destroyed houses was
considered both reasonable as well as statistically valid for the data sample at a local resolution.
Applying the threshold to the sample, manifestations of intensive and extensive risk associated with both
hydro-meteorological and geological hazards were identified. See Technical Note 3.1 for the distribution
o
3.4 – The underlying risk patterns
Extensive risk in time and space
Of the 126,620 disasters in the sample, only 0.7% represent manifestations of intensive risk. Intensive
risk disasters occur infrequently. Across the sample there is an annual average of only 27 disasters,
6
The dataset excludes hazard events without reported damage and any disaster reports without adequate source documentation,
see DesInventar at: http://gar-isdr.desinventar.net/DesInventar/main.jsp or http://online.desinventar.org/
7
Earthquakes, tsunamis and volcanic eruptions are considered as geological hazards in the analysis that follows. Landslides
may be either geological or hydrometeorological and are often both. For the purposes of this report they have been classified as
hydrometeorological, although recognising that many are related to earthquake occurrence.
8
Floods, flash floods, urban floods, rains, fires, forest fires, mudslides, avalanches, landslides, cyclones, storms, gales, strong
winds, hailstorms, tornados, electric storms, hailstorms, lightning, thunderstorms, storms, droughts, heat waves, cold waves,
frost, snowstorms.
9
The Peru database covers the time period 1997-2006; the Mexico database covers the time period 1980-2007 and the Tamil
Nadu database 1976-2007.
3
approximately 1 disaster every two weeks. Intensive risk is also spatially concentrated. As Map 0.2
shows, only a handful of cantons manifested intensive risk in Ecuador over the reporting period.
Map 0.2: Ecuador intensive disaster risk events (1970-2007)
In contrast, 99.3% of the disasters represented manifestations of extensive risk. Extensive risk is
continuous in time with an annual average of 3,395 disasters, equivalent to 9 disasters per day across the
sample. Extensive risk is also widely spread in territorial terms. Map 0.3 shows, all except a handful of
cantons in Ecuador manifested extensive risk in the reporting period.
Map 0.3: Ecuador extensive disaster risk events (1970-2007)
The spatial distribution of extensive risk is further highlighted by
Table 0.1 which shows that across the 12 countries, 82% of local administrative areas were
affected at least once during the reporting period and 48% have been affected 6 or more times.
Table 0.1: Spatial distribution of extensive risk (1970-2007)
Number of reports
0
Number of local
administrative areas
982
4
%
Cum % inv
Cum %
17.90%
100.00%
17.90%
1
2 to 5
6 to 10
11 to 20
21 to 50
51 to 100
more than 100
Total
639
1218
717
729
647
291
262
5485
11.65%
22.21%
13.07%
13.29%
11.80%
5.31%
4.78%
100.00%
82.10%
70.45%
48.24%
35.17%
21.88%
10.08%
4.78%
29.55%
51.76%
64.83%
78.12%
89.92%
95.22%
100.00%
Contemplating these maps and figures provides insight into a vision of disaster risk very different from
that which most international observers are familiar. Rather than occasional catastrophes occurring along
earthquake fault lines or on tsunami exposed shorelines, extensive disaster risk appears as a continuous
ongoing manifestation of small-scale impacts affecting to a greater or lesser extent all of a country’s
territory.
The hazard profile of extensive risk
Most intensive risk disasters in the sample were associated with the major hazard types reviewed in
Chapter 2: earthquakes, volcanic eruptions, tsunamis, cyclones, floods and in several cases with
landslides. Given the data limitations described in Section 3.2 intensive risk disasters could not be
associated with drought. Across the sample there were four times more intensive risk disasters associated
with hydro-meteorological than geological hazard. This reflects the hazard profile of the countries in the
sample and the events that actually occurred in the reporting period, given that hazard events, such as
large-scale earthquakes, eruptions or tsunamis, occur with long return periods in specific locations.
In contrast, more than 92% of extensive risk disasters were associated with hydrometeorological hazard
events. These include periodic cyclones and major floods but also large numbers of small-scale floods,
landslides, storms, mudslides and other localized hydrometeorological events. As Figure 0.1 shows,
floods, flash floods and heavy rains represent 40.9% of the extensive hydrometeorological risk in the
sample. 24.6% of that risk is associated with fires and forest fires, 14% with landslides, mud-slides and
avalanches, 12.3% with storm events, 4.6% with drought and heat waves and 3.5% with cold waves, frost
and snowstorms.
Figure 0.1: Hydrometeorological extensive risk disaster reports by hazard type across the sample
While the underlying risk patterns in sample, therefore are principally linked to hydrometeorological
hazard, particularly to floods and heavy rains, each country has its own extensive risk profile. In Orissa,
India, for example, fires account for almost 59% of extensive risk disasters, given rural villages of tightly
packed thatch houses that are extremely vulnerable to fire. The relationship between fires and weather in
5
Orissa is shown in Figure 0.2: seasonally the number of fire disasters peaks in the dry season between
February and May and drops in the monsoon between June and November. In Iran, 42.9% of extensive
risk disasters are associated with earthquakes and as Map 0.4 shows are widely distributed throughout the
countries territory.
Figure 0.2: Average seasonal occurrences of disaster
reports associated with fire, Orissa, India (1970 – 2007)
Map 0.4: Number of extensive geological disaster risk
reports from the Islamic Republic of Iran (1970 – 2007)
Loss and damage patterns
Intensive risk concentrates a very large proportion of the mortality and direct economic loss10 that occurs
in the sample. While, mortality statistics need to be approached with caution, as Box 0.2 indicates, the
0.7% of the disaster reports that manifest intensive risk account for 83.7% of the total deaths, as well as
74.6% of the houses destroyed. This kind of loss pattern is coherent with the effects of highly destructive
hazards such as earthquakes, tsunamis, cyclones and volcanic eruptions. Mortality is particularly
associated with geological hazard, which, while associated with only 19% of the intensive disaster
reports, accounts for approximately 78% of the mortality.
The distribution of these loss and damage patterns in time is shown in Figure 0.3. Mortality in intensive
risk disasters is concentrated in a small number of major peaks, occurring every five years or so during
the reporting period. In contrast, extensive risk mortality appears as a continuous underlying flow.
Figure 0.3: Distribution of mortality associated with intensive and extensive risk across the data set (1980-2006)
Box 0.2: When the dead go missing
10
Destruction in the housing sector is taken here to be a proxy for direct economic loss.
6
In the year 2000, the World Bank, describing the impact of natural catastrophes in 1999, stated that “the landslides in
Venezuela alone caused 50,000 fatalities”.11 The EM-DAT database records 30,000 deaths due to the same set of floods,
mudslides and landslides, which occurred in December 1999 and affected Vargas, Miranda, the Federal District of Caracas and
to a lesser extent other states.
Two recent papers published by anthropologist Rogelio Altez 12 of the Universidad Central de Venezuela paint a very different
picture. After a lengthy forensic investigation in Vargas state, Altez documented a total of only 521 corpses found after the
disaster, of which 290 were unidentified, and only 331 people reported missing. Given that number of missing was similar to
the number of unidentified corpses, Altez concluded that “the total number of deaths does not exceed 700”.
After flying over the affected area, the then Secretary General of the IFRC declared to the BBC that Venezuela's disaster was
"certainly at least two or three times worse than Mitch as far as the death toll is concerned" and that “as many as 50,000 people
may have been killed”.13 According to Altez, statements of this kind, disseminated in the media, later became widely accepted
international statistics.
The key message from Altez’s study is that there are still major deficiencies in the way corpses are dealt with after many largescale disasters. Documented cases exist of mass cremations and burials without an adequate process of identification or even
quantification of the victims, often due to unjustified fear of epidemics. The enormous discrepancy in mortality in Venezuela,
between the international statistics and the study carried out by Altez, may be unique. But it does highlight both serious
deficiencies in the post-disaster treatment of corpses and the need for a critical approach when dealing with disaster mortality
data.
In general, the distribution of intensive risk mortality and housing destruction across the sample is
coherent with the global patterns of risk outlined in Chapter 2. Higher risk countries, such as Iran have a
higher percentage of intensive mortality and housing destruction. In lower risk countries, such as Costa
Rica or Argentina, mortality and housing destruction is more extensive.
While mortality and housing destruction is intensively concentrated, extensive risk disasters account for
51.3% of damaged housing across the sample. In most of the Latin American countries, over 75% of
housing damage is extensive. In Asia the percentage is less given that no less than 58.5 % of the housing
damage reported in Asia occurred in Orissa where 84.8% of the housing damage was intensive.
This loss pattern is coherent with the effects of floods, rains and storms, which are more likely to damage
housing rather than cause mortality or destruction. Research from other countries suggests that mortality
and injury only increase significantly in very severe floods, with a large number of affected buildings14.
Effectively, flood mortality in the sample is mainly concentrated in intensive events. Housing damage
across the sample is particularly associated with floods, flash floods and heavy rains. Floods accounted
for 34.7% of hydrometeorological extensive risk disasters but over 60% of corresponding housing
damage. Heavy rains were associated with 6% of the disasters but 26.7% of the housing damage.
Spatially, extensive housing damage is widely spread while intensive housing destruction is heavily
concentrated as is illustrated in the case of Tamil Nadu by Map 0.5 and Map 0.6.
11
World Bank, 2000, Managing Disaster Risk in Emerging Economies, Volume 1.
Altez, Rogelio, 2007, Muertes Bajo Sospecha: Investigación sobre el numero de fallecidos en el desastre del estado Vargas,
Venezuela en 1999, Cuadernos de Medicina Forense, 13 (50); and Altez, Rogelio, Revet, Sandrine, 2005, Contar los muertos
para contar la muerte: discusión en torno al numero de fallecidos en la tragedia de 1999 en el estado Vargas – Venezuela,
Revista Geografica Venezolana, Numero especial 2005, 21 – 43.
13
http://news.bbc.co.uk/2/hi/americas/581579.stm
14
In Japan, the threshold above which flood mortality increases has been calculated at 1,000 inundated buildings by Zhai,
Fukuzono and Ikeda (2006) reference…. Clearly this threshold will be different in other countries but suggest that in general
extensive flood disasters are unlikely to cause major mortality.
12
7
Map 0.5: Extensive housing damage in Tamil Nadu, India
(1976-2007)
Map 0.6: Intensive housing destruction in Tamil Nadu, India
(1976-2007)
The cost of extensive risk
Characterized by damage rather by destruction and by continuous small-scale manifestations rather than
occasional catastrophic disasters, the economic losses associated with extensive risk are largely
unaccounted for. Mechanisms which the international community uses to register and account for disaster
loss include UN situation reports disseminated by OCHA, reports in an international database such as
EM-DAT of which approximately one third include an estimate of economic loss; international appeals
for assistance launched by the UN or by IFRC and post-disaster damage and loss assessments by the
World Bank, other IFIs or the UN.15 Almost all intensive risk disasters are documented in EM-DAT, a
few are described in situation reports and a small number lead to an international appeal and/or a damage
and needs assessment. Figure 0.4 compares patterns of mortality and housing destruction and damage in
both intensive and extensive risk disasters in Sri Lanka with the periodicity of disasters reported in EMDAT, OCHA situation reports, UN appeals and international damage and needs assessments and
indicates. While most years with intensive disaster reports have a corresponding EM-DAT report, the
production of OCHA situation reports is more sporadic. There were only three international appeals for
assistance and only one international damage and needs assessment, following the 2004 tsunami. It has to
be assumed that the losses associated with extensive risk between these international reports went
unaccounted for, as well as losses in entire years such as 1997. [MESSY GRAPH: REDRAW]
Figure 0.4: Deaths, houses destroyed and houses damaged in Sri Lanka and
international awareness (1992-2005)
15
In general post-disaster damage and loss assessments use a methodology developed by the Economic Commission for Latin
America and the Caribbean (ECLAC). Economic Commission for Latin America and the Caribbean, 2003, Handbook for
Estimating the Socio-Economic and Environmental Effects of Disasters, Santiago, Chile:
http://www.preventionweb.net/files/1099_eclachandbook.pdf
8
Despite its invisibility, it is likely that extensive risk represents a major economic cost for the countries in
the sample and a significant percentage of global disaster loss that goes unaccounted for. In Mexico, for
example, 316,928 houses were destroyed in intensive risk disasters between 1980 and 2006 and 471,708
houses were damaged. In contrast, 1,468,509 houses were damaged in extensive risk disasters and 29,510
houses destroyed. The cost of a destroyed house in Mexico has been estimated as US$16,80016 and the
cost of a damaged house as 20% of that value.
Based on these estimates, while the cost of destroyed and damaged housing in intensive risk disasters
between 1980 and 2007 was US$5,477 million, the cost in extensive risk disasters represented another
US$4,831 million. In Mexico, in the housing sector, therefore, extensive risk represented almost 47% of
economic losses over the period. Applying the same methodology across the whole sample, extensive risk
would represent 34% of the total cost of disaster loss in the housing sector.
Additionally, in Mexico 55% of the costs in the housing sector in intensive risk disasters are concentrated
in only two years, 2005 and 1988. In contrast, while 2005 also represented a peak, extensive risk housing
costs are more evenly distributed over the whole 27 year period.
Table 0.2 illustrates, across the sample, that losses in other sectors such as education,
health and transport as well as people affected tend to be extensively distributed. 58% of schools, 65% of
hospitals, 89% of roads damaged and destroyed as well as 89% of the people affected occurred in
extensive risk disasters. Disaster loss data in other sectors is less robust than in housing, meaning that the
absolute figures are not meaningful. The proportional distribution between extensive and intensive risk,
however, is valid given the size of the sample.
Table 0.2: Loss attributes by risk category across the sample (1970–2007)
Risk category
Loss attribute
Schools
Hospitals
Kilometres of roads
People Affected
Total
Extensive risk
%
Intensive risk
%
32,157
1,037
64,917
182,989,857
18,488
677
57,695
144,627,235
57
65
89
79
13,669
360
7,221
38,362,622
43
35
11
21
This extensive distribution of a significant proportion of disaster costs has very significant implications.
To the extent that international assistance following disasters is sensitive only to intensive risk disasters
with major mortality and direct economic loss, the losses and costs of extensive risk are largely ignored as
well as other less visible but more pervasive long-run impacts on poverty. If, as will be explored later,
most extensive risk is associated with urban informal settlements and vulnerable rural livelihoods,
international disaster assistance is failing to address a significant proportion of the impact of disasters on
the poor.
3.5 – Underlying risk trends
Intensive risk is relatively static in geographical terms. Located on seismic fault lines, around active
volcanoes, on cyclone paths or on tsunami exposed coastlines, concentrations of intensive risk may
change over time, according to the growth or decline of vulnerable population and economic assets
exposed. But without a fundamental shift in global hazard patterns they do not move. While intensive risk
patterns manifest in different hotspots in different periods, reflecting the return period of different
hazards, they are strongly controlled by the global geography of hazard.
In contrast, extensive risk is far more dynamic and geographically mobile, given that it is associated with
myriad frequently occurring, spatially dispersed and small-scale hazards affecting highly localized
concentrations of population and economic assets. Many small-scale hazards, such as localized flooding
or fire, are socio-natural in character, given that the hazard itself is shaped by factors such as urbanization
16
Costs normalized using as a baseline the Indice de Precios de la Construccion, 2003 on the basis of an average sized social
house of 42 m2 and an average construction cost per m2 of US$400.
9
and change in the use of ecosystem services. An observation of how patterns of extensive risk manifest
and transform over time, therefore, provides a unique window to observe underlying trends in disaster
risk accumulation and to gain insight into how risk is shaped by development.
Extensive hydrometeorological risk
Only 3% of the extensive risk disasters in the sample correspond to geological hazard, mainly the
extensive impacts of large-scale earthquakes and tsunami. As such, we will concentrate our analysis on
trends in extensive risk associated with hydrometeorological hazard.
Figure 0.5 illustrates that the average annual occurrence of extensive hydrometeorological disasters has
quintupled over the last 37 years across the sample. While the associated mortality has doubled, this
implies that the average number of deaths per disaster is actually going down: possibly reflecting
improved development conditions and local capacities in disaster preparedness and emergency
management. As will be explored in chapter 5, most of the countries in the sample report substantial
achievements in the development of institutional systems, policy and legislation, plans and mechanisms
for disaster preparedness and response. In contrast, Figure 0.6 illustrates that the associated housing
damage has increased at least ten fold. If the average number of damaged houses per disaster has
approximately doubled then this can only be explained by an increase in the number of exposed and
vulnerable houses or by an increase in the intensity of hydrometeorological hazard. The rapid growth of
exposed and vulnerable settlements and the magnification of hazard levels through human intervention
would thus seem to characterise urban development and territorial occupation in the countries of the
sample. As will be explored in chapter 5, few of the countries in the sample report progress in
mainstreaming disaster risk reduction considerations into economic and social development, land-use
planning and building or into environmental management.
Figure 0.5: Number of extensive hydrometeorological
disaster reports and number of associated deaths for data
set (1980-2006)
Figure 0.6: Number of extensive hydrometeorological
disaster reports and number of associated deaths for data
set (1980-2006)
Error! Reference source not found. provides further insight into these trends by comparing the annual
average increase in mortality and housing damage with average annual population growth in each
country. If population increase is accepted as a proxy for increased physical exposure,17 then increases or
decreases in mortality and housing destruction and damage indicate changes in vulnerability. In most of
Latin America, with the exception of Mexico and Ecuador, mortality is falling relative to population size,
whereas in Asia it is increasing. This may indicate a greater achievement in disaster preparedness and
response in Latin America or simply higher development levels. As was highlighted in chapter 2, risks
associated with floods and cyclones heavily correlated with GDP per capita. While the Latin American
countries in the sample are upper or lower middle income countries, all the Asian countries, with the
exception of Iran are low-income.
17
Unlike the calculation of physical exposure presented in Chapter 2, it is impossible to compute the areas prone to small-scale
hydrometeorological hazards, such as localised floods and fires, with a level of resolution to inform the analysis of extensive
risk presented here.
10
In contrast, with the exception of Argentina and Orissa, housing destruction is increasing faster than
population, indicating increased vulnerability in the housing sector. With the exception of Bolivia,
housing damage is increasing faster still. Given the strong association of housing damage with flooding
this confirms the trend of a combination of more intense flooding and increased vulnerability of human
settlements.
annual % change in
mortality 1970-2007
from EXT
HYDROMET events
annual % change in
houses destroyed
1970-2007 from EXT
HYDROMET events
annual % change in
houses damaged
1970-2007 from EXT
HYDROMET events
annual %
increase in
population
1970-2007*
Argentina
0.90
-1.88
2.92
1.33
Bolivia
1.48
6.52
0.03
2.17
Colombia
-0.95
3.22
9.48
1.89
costa rica
0.93
3.71
8.18
2.35
Ecuador
3.93
9.41
26.12
2.09
Iran
13.11
/
/
2.37
mexico (1980-2007)
6.48
3.48
17.94
1.88
nepal (1971-2007)
4.47
2.80
8.28
2.25
Orissa(1)
5.75
-3.55
7.80
1.66
peru (1970-2006)
1.31
2.80
3.03
1.96
sri lanka (1974-2007)
1.70
11.99
5.68
1.15
tamil nadu(1) (1976-2007)
11.67
4.66
12.23
1.25
Venezuela
0.51
2.56
5.96
2.49
country name
*UNEP GEO Data: 1970-2010 5 year average (geodata.grid.unep.ch)
(1) annual population increase over 1981-2001 period (Census if India, 2001)
Error! Reference source not found., naturally, highlights important national variations. For example
Figure 0.7 and Figure 0.8 illustrate that housing destruction in extensive risk disasters in Orissa, India
experienced a drastic reduction over the reporting period. Fire is the cause of 92.5% of the houses
destroyed in extensive risk disasters in Orissa, India. Over the reporting period thatch houses have been
rebuilt with non-flammable materials, leading to a dramatic reduction in housing destruction.
Figure 0.8: Housing destruction in Orissa, India (1970 –
2007)
Figure 0.7: Causes for housing destruction in Orissa, India
(1970 – 2007)
An analysis of spatial trends indicates a centrifugal territorial expansion of extensive hydrometeorological
risk. Figure 0.9 illustrates that the number of local administrative areas with manifestations of this kind of
risk has increased consistently from decade to decade. It is interesting to note that the number of local
areas affected by more than 50 disasters over the reporting period, or approximately 2 disasters per year
has remained more or less constant. In other words there is a core of local areas, which are always
affected. But the number of local areas affected by 1 to 9 disasters has doubled and those affected by 10
11
– 49 disasters has almost quintupled between 1980 and 2006, indicating that many new local areas,
previously not affected by flood disasters now experience them while the number of disasters per
municipality has also grown.
Figure 0.9: Number of municipalities affected by extensive hydrometeorological risk (1980-2006)
For example, in Colombia, 373 municipalities reported housing damage in extensive risk disasters in the
1970s. These figures rose to 661 in the 1990s and 692 in the first 7 years of the present decade. The
number of municipalities reporting deaths and housing destruction rose from 147 and 388 respectively in
the 1970s and 1990s to 507 in the first 7 years of the present decade. Simiarly, as Map 0.7 highlights
extensive hydro-meteorological risk in Mexico was concentrated in only a small number of states in the
1980s. Over the last ten years almost all Mexican states now manifest this kind of risk.
Map 0.7 Number of extensive risk events (1980-1989, 1990/1999, 2000/2007)
Importantly, extensive risk patterns underpin the configuration of new patterns of intensive risk. When a
large-scale hazard event, such as a major cyclone, flood or earthquake affects a region, it simultaneously
activates a large number of extensive risks. For example, as can be seen in Figure 3.12 and 3.13 the
12
impacts of the super cyclone in Orissa, were concentrated in the same areas that manifest high levels of
extensive risk. In other words the expansion of extensive risk constructs intensive risk over time.
Map 0.8: Orissa, India extensive disaster risk reports (1970
– 2007)
Map 0.9: Orissa, India intensive disaster risk reports (1970
– 2007)
Floods and heavy rains
Within this overall trend of emerging hydrometeorological risk, as Figure 0.10 illustrates, the number of
disasters associated with floods and heavy rains, in the sample, is increasing at a far faster rate than other
categories of hydrometeorological hazards, particularly since 1990.
Figure 0.10: Number of disasters associated with floods and heavy rains (1980
– 2006)
In Mexico, for example, the annual average number of extensive risk disasters associated with floods,
rains and flash floods increased 8 fold increase since 1980 (Figure 0.11), accounting for 31% of all
extensive hydrometeorological risk disasters in the 1980s but over 40% in the last decade. Similarly, in
Colombia (Figure 0.12), floods, flash floods and heavy rains accounted for 43% of extensive
hydrometeorological risk disasters in the 1970s but 53% in the last decade.
13
Figure 0.11: Extensive risk disasters in Mexico associated with
floods, rains and flash floods (1980–2006)
Figure 0.12: Floods and rains in Colombia as a proportion of all extensive risk disasters (1970-1979) and (1998 – 2007)
Exceptions to this trend include Peru, where landslides and mudslides constitute the principal
hydrometeorological hazard, given that the majority of the urban population is concentrated on a dry
coastal desert strip in areas largely not prone to flooding. The city of Lima concentrates approximately
one third of the countries population but has recorded an average of only 10 flood events per year,
affecting regularly only two or three districts.
As was emphasized in section 3.4 housing damage is a quintessential characteristic of disasters associated
with flooding and heavy rains. A rapidly increasing number of flood and rain disasters affecting an
increasing number of localities is therefore coherent with the rapid increase in housing damage noted
above. A key emerging trend in extensive risk in Asia and Latin America, documented in this report,
therefore, is a rapid and concurrent in increase in flood hazard, affecting exposed and vulnerable
settlements in an ever expanding number of areas.
3.6 Interpreting the trend: urbanization, territorial occupation and extensive
risk
A number of factors could explain this trend of rapidly increasing and expanding extensive flood risk.
Improved disaster reporting and an urban bias in the data ?
There have been major improvements in the reporting of disaster events in recent years, due to improved
communications, the development of decentralised institutional systems in many countries and the
14
introduction of the internet. In some disaster databases, such as Colombia, systematic government
reporting contributed additional reports since the 1990s.
We believe, however, that while improved reporting certainly will have lead to an increased number of
reports, it is not sufficient to explain the trend, for a number of reasons. Improved reporting could be
expected to lead to more reports across all hydrometeorological hazards and not just floods and rains. The
contribution of new data sources would increase the average number of annual reports but not
exponentially. At the same time, extensive flood risk is increasing not only in remote rural areas, where
disasters may have gone unreported, but also in major metropolitan areas, such as Buenos Aires or
Mexico, where reporting deficits are unlikely. In other words, while disaster reporting has improved since
the 1970s, it, alone, does not convincingly explain the trend.
At least in some countries, such as Peru and Colombia, many more disasters are reported from the major
cities and from provincial capitals than from isolated rural areas18. This at least partly reflects a bias in
the reporting, given that most databases are compiled from national sources. As Box 3.1 indicates local
databases have generally registered many times more disasters than national databases, however, most of
the additional reports are of very small disasters that are not visibly nationally. All the disasters reported
from important areas certainly exist, while it is unlikely that nationally significant disasters from remote
rural areas have gone unreported. While the bias certainly exists, again it is not sufficient to explain the
trend.
Increased precipitation ?
Another possible explanation could be increases in precipitation levels or their concentration in more
intense precipitation events. In some countries such as Colombia, Costa Rica, Ecuador and Venezuela the
increase in flood and rain related disasters since the mid 1990s has coincided with a period of increased
precipitation, as Figure 0.13 and Figure 0.14 highlights illustrates in the case of Costa Rica.19
Figure 0.13: Flood and rain disasters in Costa Rica (19902007)
Figure 0.14: Precipitation Costa Rica 10 years averages
(1957-2007)
Again, however, while increases in precipitation certainly would increase flood hazard the data available
does not validate the hypothesis. In Mexico and Nepal, for example, extensive flood risk is increasing,
while average precipitation is decreasing. In the case of Nepal this may reflect increased glacier melt. In
Peru, in contrast, average precipitation is increasing while extensive flood risk is decreasing. There is no
consistent relationships between both variables, therefore, across the sample.
Even in the countries where precipitation is increasing in the 1990s, this may be a short term trend within
a longer term cycle. At the same time, a national average increase in precipitation may hide drastic local
18
Reference GRADE paper and Fernando Ramirez from Part II background papers
Global Precipitation Analysis Products of the Global Precipitation Climatology Centre (GPCC), (2008). Full Data
Reanalysis Product Version 4.
19
15
variations even in small countries like Costa Rica, implying that meaningful correlations between
increased flooding and precipitation could only be explored with local level precipitation data, which had
not been possible in the scope of this report. Similarly, average annual increases in precipitation do not
reflect changes in seasonal distribution and intensity, which can have a dramatic influence on flooding.
Finally, even if an increase in precipitation could be proved and a causal relationship with more intense
flood disasters established, while this would explain the number of disasters and their impact it would not
account for the drastic territorial expansion of risk highlighted in section 3.3.
Urbanization, environment and territorial occupation ?
Aggregated national statistics on urbanization and environment also provide little insight into the trend of
increasing and expanding extensive flood risk. National level indicators on deforestation indicate that
most countries have continued to lose forest cover at a rapid rate in the last decade and there is a
relationship between declining regulatory services from ecosystems and increased flooding. However, as
in the case of precipitation, it is impossible to infer from national averages whether this has contributed to
increased extensive flood risk20 in specific watersheds. For example, in Costa Rica environmental
protection policies and the application of a system of payment for ecosystem services have led to an
increase in national forest cover at the same time as flood risk is increasing. An analysis of data in eacah
watershed would be required to establish a relationship.
Similarly as Table 0.321 indicates, all countries have been experiencing progressively slower urban
growth rates over recent decades. As with precipitation, national averages of urban growth, therefore do
not explain the trend.
Table 0.3: Urban growth rates in %
Accumulated Percentage increase in Urban Population
Argentina
Bolivia
Colombia
Costa Rica
Ecuador
India
Orissa
Tamil Nadu
Iran
Mexico
Nepal
Peru
Sri Lanka
Venezuela
Average
78-87
22
54
38
49
56
41
36
18
69
35
88
38
7
44
40
88-97
19
44
29
55
37
34
32
34
39
26
91
25
2
33
30
98-07
14
32
21
34
25
27
22
16
75
15
-2
28
21
It is impossible, therefore, to easily explain the trend through an examination of national data. In contrast,
case studies from different countries, point to a complex combination of interlocked factors related to
changes in territorial occupation, declines in the regulatory services of ecosystems, models of urban
growth and both rural and urban poverty as being factors that explain the trend, as well as its specific
national and regional manifestations.
Latin America
In Latin America, more than 80% of the total population is now “urban” although definitions of what
constitutes urban and rural vary from country to country. Extensive risk in Latin America, therefore, is
essentially an urban phenomenon, with loss patterns in the region broadly reflecting the proportion of
urban and rural population as shown in Table 0.4.
20
21
For national level data on deforestation rates see: http://faostat.fao.org/site/377/DesktopDefault.aspx?PageID=377#ancor
Department of Economic and Social Affairs Population Division, 2008, World Urbanisation Prospects: The 2007 Revision.
16
Table 0.4: Urban and rural distributionof extensive risk in Peru and Colombia [CHANGE TABLE HEADINGS]
Table 0.5: I am asking Cristina since the 15 October in 5 spererate emails to send me the talbe, but she refuses to send
an excel version. Only what I have here, and another one with Mexico in Power Point!
Rangos de
población
< 100
100-500
500-1000
1000-5000
> 5000
Totales
Población
113 606 846
50 735 724
20 658 731
37 989 536
46 414 503
269 405 340
%
42
19
8
14
17
100
Registros
42149
9516
5978
4260
6330
68 233
%
Muertos
1397
62
14
9
6
9
100
4142
1302
3137
12 490
22 468
%
6
18
6
14
56
100
Viviendas
destruidas
39 151
29 772
5390
14 330
61 282
149 925
%
26
20
4
10
41
100
Viviendas
afectadas
1 510 855
368 756
130 724
73 245
224 492
2 308 072
%
65
16
6
3
10
100
As Table 0.5 compares the number of extensive risk disasters and their attributes in eight countries in
Latin America with the population size of the affected administrative areas. Areas with a population
inferior to 500,000 contain 61% of the total population. These administrative areas include most of the
rural population as well as many small and medium urban centres. They account for 76% of the extensive
risk disasters and 81% of the housing damage but only 24% of the deaths and 46% of the housing
destruction.
At the other extreme, administrative areas with populations that exceed 1 million, including all the major
cities in the region, account for 31% of the population,15% of the extensive risk disasters and 13% of the
housing damage but 70% of the deaths and 51% of the housing destruction.
This distribution of extensive disaster risk suggests two different but complementary processes of
urbanization and territorial occupation operating simultaneously:

The large number of extensive risk disasters and housing damage occurring in rural areas and
small urban centres is coherent with the geographical expansion of extensive risk described above.
As can be clearly visualized in the case studies of Ecuador and Peru presented below, extensive
risk follows processes of territorial occupation, characterised by the opening or improvement of
roads, the extension of the agricultural frontier and the growth of small and medium urban centres.
Increasing extensive risk is usually due to a concatenation of factors including a decline in the
regulatory services provided by ecosystems, inadequate water management, land-use changes,
rural-urban migration, unplanned urban growth which increases run-off, the expansion of informal
settlements in low-lying areas and an under-investment in urban drainage.

The reduced number of disasters and housing damage but the relatively high proportion of
mortality and housing destruction, occurring in large and mega-cities, indicates a process of
intensification of risk over time. As the case studies of Buenos Aires, Mexico and Costa Rica
show extensive flood risk is closely linked to the increased run-off caused by new urban
17
development, a chronic underinvestment in city-wide pluvial drainage, the location of informal
settlements and social housing projects in low-lying flood prone areas and inadequate water
management in the surrounding watersheds. As cities develop and consolidate, however, many
localized extensive risks are gradually reduced, as drains are installed, houses are improved and
flood protection works are carried out. However, risk becomes more intensive over time as the
underlying city-wide causes of risk are not addressed. When disasters do happen, therefore, they
tend to be more destructive with high rates of mortality and housing destruction. The cases of
Tabasco, Mexico and the Federal District of Mexico itself illustrate this process in the contexts of
a mega-city and of a large city in a complex hydrographic basin
Box 3.x Flooding in Tabasco, Mexico, 2007: In 2007, 62% of the area of the state of Tabasco in Mexico and 40% of the
city of Villahermosa were affected by the most severe floods in the history of the state. At least 1.2 million people were
affected, 168,000 houses damaged, 570,000 hectares of crops lost as well as major damages to infrastructure and the economy.
The exceptionally high rainfall that contributed to the flooding meant that climate change and variability were invoked as the
principal culprit of the disaster. In reality, the causes were a combination of inadequate water management in a very complex
watershed that covers 89,000 km2 and a process of urbanisation of low-lying areas without a corresponding investment in
drainage. The canalisation of rivers in the upper basin, through a series of isolated and uncoordinated civil engineering works,
has tended to increase flooding in urban areas built on the floodplains of the Grijalva and Carrizal rivers further downstream.
Four of the six most devastating floods that have occurred in Villahermosa in the last 95 years have occurred since 1980.
[IF WE HAVE SPACE INLCUDE TWO MAPS OF POP. DENSITY AND FLOODED AREAS FROM UNOSAT
http://unosat.web.cern.ch/unosat/freeproducts/mexico/Tabasco_Inundations_Nov2007/UNOSAT_pop_dens_Tabasco_Nov200
7_A3_Spanish_highres.jpeg
http://unosat.web.cern.ch/unosat/freeproducts/mexico/Tabasco_Inundations_Nov2007/UNOSAT_Tabasco_Nov2007_A3_Spa
nish_highres.jpeg
Floods in Mexico: The area now occupied by Mexico’s mega-capital was originally a series of lakes in a large basin
surrounded by high mountains. Tenochtitlan, capital of the Aztecs, was founded on an island in these lakes and after the
Spanish conquest became the capital of Nueva Espana and later Mexico city. Given the geography and hydrology of the basin
floods occurred both in the pre-hispanic period and with increasing intensity in the colonial period. One flood between 1629
and 1635 may have caused 30,000 deaths. The problem was addressed by the government of Porfirio Diaz which drained the
basin through the construction of a 50km long canal and a tunnel through the mountains of Tequixquiac allowing the waters of
the basin to drain into the Gulf of Mexico. While this monumental example of civil engineering initially greatly reduced
flooding, the exponential growth of the city and it’s subsidence due to the drainage and urbanisation of wetlands have
progressively increased hazard levels and increased the frequency of urban flooding. Further engineering works, such as the
construction of a deep drainage system between 1967 and 1975, have partially addressed the problem. However, the
subsidence has reduced the gradient of the canal of Porfirio Diaz and even threatens to invert it, which would mean even more
destructive floods in the future.
As will be explored in more depth in the next chapter, cities with poor urban governance and which have
only been able to absorb growth through the expansion of informal settlements often tend to have the
highest levels of risk. In Mexico, for example, almost 20% of the disasters occurred between 1980 and
2007 were concentrated in only 22 municipalities. 7 of these municipalities have more than 1 million
inhabitants, 8 with between 500,000 and 1 million inhabitants and 7 with between 200,000 and 500,000
inhabitants. Most of the most affected municipalities, had high percentages of their population with high
or very high levels of marginality, for example: Acapulco (54.4%), Coatzocoalcos (54.1%) (Tijuana
(31.3%), Juarez (45%), Veracruz (31%) or Tapachula (54.1%)22. In the metropolitan areas of San
Salvador, both disaster incidence and housing damage and destruction were found to be closely
correlated with urban growth. The municipalities with recurrent extensive disasters were those with the
most rapid urban growth23, in some cases up to 16% per year.
Argentina
In Argentina, floods and rains account for approximately 45% of extensive hydrometeorological disasters. At the national
level, unlike in other countries in the sample, the annual average number of events is actually declining, along with mortality,
housing destruction and housing damage. However, no less than 32% of the disasters occur in the Province of Buenos Aires or
in the Federal Capital. In contrast to the country as a whole, extensive flood and rain risk in the urban area has increased
between 1970 and 2007 as illustrated in Figure 0.15.
22
23
Mansilla, Mexico document #2
Mansilla, El Salvador document.
18
Figure 0.15: Extensive flood and heavy rain disasters in the province of
Buenos Aires and the Federal Capital (1970-2007)
This increase in extensive flood risk is closely
related to the increased run-off caused by the
paving of the land area and the reduction in
woodland and parks, as well as by underinvestment in drainage. Whereas in the 1970’s
the average annual investment in urban
infrastructure in Buenos Aires was US$350
million per year, by 1989 this figure had fallen
to US$69.9 million. The city’s drainage system
was completed in 1953 for a calculated
population of 800,000. The city proper of
Buenos Aires had a population of 2,776,138 in
2001, while the Greater Buenos Aires area had
a population of 11.4 million people. The
greatest impact of the floods is in
neighbourhoods such as La Boca and Barracas,
which were traditionally inhabited by lowincome groups.24
Costa Rica
In Costa Rica, floods, heavy rains and flash floods accounted for 63% of the extensive risk disasters reported since 1996 and
83% of the housing damage. Since 1996, the number of disasters has increased 10 fold, while the number of houses damaged
has increased more than 3 fold. In contrast, the number of deaths and destroyed houses have actually decreased, reflecting
relatively efficient emergency management and an overall improvement in housing conditions, except in the case of informal
precario settlements.
According to the Municipality of San Jose, more than 80% of the floods occurring in the countries capital are caused by either
inadequate drainage to cope with the increased run-off caused by urban growth or by the accumulation of garbage and waste in
drainage channels. Most housing damage is concentrated in precarios occupying marginal land adjacent to the streams and
torrents that drain the city. Some cities such as Turrialba have historically prohibited construction within 15 metres of a river
bank. This kind of urban zoning paradoxically guides formal urban development to safe areas, while leaving hazard prone
areas for illegal or informal occupation by poor households.
According to the World Bank25, Costa Rica has one of the lowest poverty headcounts in Latin America. Using a US$2 per day
poverty line, Costa Rica has a headcount of only 9% compared to a regional average of 25%. Key socio-economic indicators
are considerably better than the average in upper-middle income countries. However, since the mid 1990’s, while the country
experienced relatively consistent economic growth, income inequality rose, per capita household income of the poor fell and
the decline in poverty experienced in previous decades stagnated. In the same period, the number of extremely poor people
living in precarios has almost doubled to 8% of the population. This period coincides with the increase in flood events and
damage to housing.
The rapid increase in the number of extensive flood risk in Costa Rica is linked therefore to urban growth, inadequate water
management, a lack of investment in drainage and an increase in precario housing. The latter is a symptom of the lack of
progress made in addressing poverty and inequality over the last decade.
Cali, Colombia
Box. 3.x Floods and informal settlements in Cali, Colombia. The city experienced 179 extensive flood disasters between
1970 and 2007, representing 53% of the extensive hydrometeorological disasters and accounting for 95.6% of the housing
damage. Between 1950 and 2000, risk patterns arose in different areas of the city as informal settlements were created through
24
Herzer, Hilda and Clichevsky, Nora, 2000, Floods in Buenos Aires, Learning from the Past, in World Bank, 2000, Managing
Disaster Risk in Emerging Economies, Washington DC.
25
World Bank, 2007, Costa Rica Poverty Assessment: Recapturing Momentum for Poverty Reduction. http://wwwwds.worldbank.org/external/default/WDSContentServer/WDSP/IB/2007/05/23/000020953_20070523093115/Rendered/PDF/3
59100CR.pdf
19
land invasions by poor families and with inadequate drainage.
In the 1950s and 1960s flood disasters were concentrated in informal settlements along the flood plains of the Cali and Cauca
rivers, with inadequate or non-existent storm drains. In the 1970s, the focus of flood disasters shifted to the right bank of the
river Cali and the flood plain of the river Canaveralejo. Again, the incapacity of the drainage system to deal with the increased
run-off posed by urban growth was responsible for the flooding. In the 1980s and 1990s, the focus of flood disasters shifted to
Aguablanca, an area which had been subject to major investments in hydraulic infrastructure to habilitate 640 hectares of land
for intensive agriculture. In the 1980s this area was occupied by informal settlements and by social interest housing promoted
by the city government. 29 neighbourhoods and 24,000 houses sprung up on an area intended for agriculture and where the
construction of drainage infrastructure was costly and difficult. The draining of lakes, intended to regulate flood waters, the
leveling of dykes for urbanization and the obstruction of natural drainage channels with garbage were additional causes of
flooding in newly urbanized areas in the east of the city since the 1990’s.
Ecuador
In Ecuador, 96.1% of disaster reports are extensive, accounting for 67.9% of the mortality, 75.6% of the destroyed housing and
90.3% of the damaged housing. Even the impacts of major climate events such as the 1997-1998 ENSO were extensive in
Ecuador, given that they were spread over large areas of the country.
The spatial accumulation of extensive hydrometeorological risk is closely associated with the evolution of territorial
occupation and urbanization in the country. Map 0.10 shows the spatial evolution of extensive hydrometeorological disasters
since 1970. In the 1970’s, disasters were concentrated in the metropolitan areas of Quito and Guayaquil. In successive decades,
extensive hydrometeorological risk spread first along the country’s Andean backbone, then into the tropical lowlands of the
Pacific Coast and finally into the Amazon basin, closely reflecting the dynamics of territorial occupation in the country.
Map 0.10: Spatial evolution of extensive hydrometeorological disasters in Ecuador from 1970-2007
20
Peru
Extensive hydrometeorological risk in Peru is characterized by landslides and mudslides. Traditionally these hazards occurred
in the coastal valleys on the western slopes of the Andes. Over the last two decades there has been a drastic redistribution of
hazard patterns as the opening of roads down the forested eastern slopes of the Andes and into the Amazon basin led to the
clearing of forest for agricultural purposes, including coca cultivation, and rapid urban growth. Given the topography and land
cover of the area, this process of territorial occupation was accompanied by a rapid growth in the number of landslides and
mudslides.
Error! Reference source not found. shows the redistribution of extensive risk associated with these hazards between 19701985 and 1986-2006. In the first period, risk was concentrated in the coastal valleys, in the Andes and in areas such as the
province of La Convention in Cuzco, an early area of territorial occupation in the Amazon. In the second period,
concentrations of risk appear in the central jungle, along the upper Huallaga Valley and in the northern Amazon, closely
mirroring the process of territorial occupation.
Asia
The interpretation of the trend in Asia poses a very different set of challenges. Firstly, the five databases
from India (Orissa and Tamil Nadu), Iran, Nepal and Sri Lanka are not a representative sample of Asia as
a region. They provide a north-south cross section of south Asia, and a glimpse of west Asia. East, southeast and central Asia are unrepresented. Secondly, the compilation of the disaster databases is far more
recent. The Latin America databases were first compiled in 1995 and subsequently have been subject to
numerous updates, revisions and analysis. The Asian databases in contrast were initiated between 2003
and 2006. 2003. Only Sri Lanka has published a summary of the disaster risk patterns and trends26,
although a similar publication is underway in Tamil Nadu. Given that the database themselves are so
26
Reference Historical disaster database in Sri Lanka publication.
21
recent it is not surprising that current analysis of disaster data in Asian countries focuses principally on
describing the main spatial patterns and temporal trends of the different risks and of hazards. Very little
research would seem to have taken place into the role that underlying risk factors, such as urbanisation
and rural livelihoods, play in explaining the risk trends and patterns manifest in the disaster databases.
Given these limitations, our interpretation of the trends in extensive risk in Asia is less comprehensive
and more incipient than in Latin America.
The rural-urban contrast
In contrast to Latin America, all of the Asian countries, except for Iran, are still markedly rural. Iran had
66.9% urban population in 2005. In contrast, Nepal had 15.8%, Sri Lanka 15.1% and India 28.7%.
At the same time, however, there is evidence that cities in Asia are experiencing the problems of
increasing damage due to urban floods, for the same reasons as in Latin America. For example, Colombo
experienced 240 flood disasters since 1974. Almost half the reported disasters and about 80% of the
reported housing damage has occurred since 2005. Chennai, similarly experienced major flooding in
1990, 1994 and 1996 and Kathmandu in 2000 and 2002. Flooding in these, as in other cities in south
Asia, would tend to suggest that in urban areas, many of the same factors that are driving risk in Latin
America, such as rapid urban growth, the expansion of informal settlements, inadequate water
management and an underinvestment in drainage. In many cities in the region, given higher levels of
poverty and a greater proportion of the urban population living in unserviced informal settlements and
slums than in Latin America, it is possible that the problem may be even more severe.
Elsewhere, however, flooding has very different causality and manifestations. In Iran, flooding would
appear to be mainly associated with torrential flash flooding as the snows melt in spring. In Nepal,
Orissa and Tamil Nadu, flood disasters are also associated with major concentrations of rural population
living on the floodplains and deltas of major rivers. Environmental degradation, in particular declines in
the regulatory services provided by forest ecosystems, may be contributing to increased flooding in some
watersheds. In Nepal, increased flooding in the south-eastern Terai region may be related to increased
glacier melt in the Himalaya as well as to environmental changes in upland watersheds. In both Indian
states as well as Sri Lanka and Nepal, housing damage in rural areas would seem to be closely associated
with the high density of rural settlement in flood prone areas and the vulnerability of rural housing.
Box 3.x Iran: The number of people killed and houses destroyed and damaged in extensive flood disasters in Iran is minimum
compared to the losses in major earthquakes. Extensive risk floods are responsible for less than 2% of the total deaths over the
reporting period and thus have tended not to attract the attention of policy-makers in the country. Nevertheless, there were a
total of 2,481 extensive flood disasters over the reporting period. While as Figure 3.x shows the number of disasters is
trending up as in other countries and regions, most of the reports are concentrated in a few peak years, for example 1982,
1987, 1993, 1995 and 1998, 2001 and 2007. Most flood events in Iran occur from March to May and are torrential floods
related to winter snow melt. Unlike other countries, the number of damaged houses is trending down which may indicate
either decreasing exposure due to migration to cities or else improvements in housing. As in other countries, the number of
areas affected by extensive flood disasters has increased over the reporting period. In general geographic distribution of
flooding, is controlled by the countries topography and associated with the mountain ranges and fertile valleys in the east and
south-east, north and south-west of the country.
STEPHANE: INSERT FIGURE OF TEMPORAL EVENT DISTRIBUTION WITH REGRESSION OF EXT. FLOOD/
FLASH FLOOD/HEAVY RAIN EVENTS + MPA OF SPATIAL DISTRIBUTION
Box 3.x Orissa: Orissa is one of the most least developed states in India with a per capita Net State Domestic Product about
two thirds of the national average, as low HDi plcing it 11 th out of 15 Indian states, and a rural poverty head count approaching
50%. The economy is primarily rural with 64% of the population working in the agricultural sector. A large part of the
mortality and housing damage reported in Orissa occurred in the 1999 super-cyclone and a large percentage of the housing
destruction, as mentioned is due to rural fires. Extensive riverine and coastal flood disaster reports are concentrated along the
eastern flood-plain, both in the northern districts of Bhadrak, Kendrapara and Jajapur, which have low-incomes, low
urbanisation and a high proportion of scheduled tribes as well as in more urbanised Districts to the south, in the floodplain and
delta of the Mahanadi river. Factors, which could be contributing to both increased extensive flood disaster risk as well as
drought in Orissa are the lack of access to and control over land and common property resources like water, forest and
22
common land, land degradation, the decline in regulatory ecosystem services provided by forests, and ineffective water
management.
The distribution of mortality and affected people, however, highlights the extreme vulnerability of southern Orissa. The
Districts of Kalahandi, Bolangir and Koraput are characterised by repeated droughts, floods, extreme poverty, food insecurity
and chronic income poverty and localised near famine conditions. While the distribution of extensive risk disasters is skewed
towards the central eastern coastal region, the distribution of mortality is skewed towards the extremely poor rural Districts
mentioned above. In other words, while flood incidence would seem to be associated with higher levels of urbanisation and
with relatively affluent agricultural areas on floodplains and deltas, mortality is far higher in the poorest most food insecure
inland areas. INSERT MAP OF DISTRIBUTION OF EXT. RISK REPORTS + EXT. RISK DEATHS AT THE BLOCK
LEVEL
Box 3.x Sri Lanka. In Sri Lanka the number of houses damaged in extensive flood disasters has approximately doubled
since the early 1990s. while the number of disasters reported has also increased. As Map 3.x shows, there are two major
concentrations of extensive flood risk in Sri Lanka. There is a major concentration of housing damage due to flooding in
south-western Sri Lanka, which is relatively highly urbanised, with associated issues of inadequate drainage. For example,
flooding reported in early November 2006 in Colombo destroyed 221 houses, damaged 1,674 houses and affected 80,128
people. Map 3.x
(http://unosat.web.cern.ch/unosat/freeproducts/srilanka/UNOSAT_Sri_Lanka_radarsat_Colombo_v1.0_highres.jpeg) shows
the distribution of flooded areas while Map 3.x shows
http://unosat.web.cern.ch/unosat/freeproducts/srilanka/UNOSAT_Sri_Lanka_popden_v1.0_highres.jpeg
the population density. Floods were concentrated in urban areas indicating typical problems of settlement of low-lying areas
and in adequate drainage. [ASK FRANCESCO IF THEY CAAN OVERLAY BOTH IMAGES FOR US]
In eastern Sri Lanka, there is another concentration of extensive flood risk primarily associated with monsoon rains in the lowlying flood plains in north-central and eastern Sri Lanka, for example around Polonnaruwa.
Tamil Nadu: Tamil Nadu is India’s most urbanized large state, with 44% of its population living in urban areas and a
diversified economy. On the Human Development Index it is third among India’s 15 largest states and has been successful in
reducing poverty. The rural poverty head count ration declined from 32 to 23 % between 1993 and 2004., while the urban
head count ratio fell even faster, from 40 to 22%. However, the state has high differential vulnerability, with Scheduled Castes
having twice the mean poverty head count in urban and 50% higher in rural areas. Extensive risk is spread throughout the
state, including droughts, floods, fires and epidemics. While most of the mortality was concentrated in the 2004 tsunami,
housing damage and destruction is much more distributed in extensive risk disasters. Extensive flood risk, as elsewhere in
south Asia would seem to present two different patterns. As Map 3.x shows [[INSERT MAP SHOWING DISTRIBUTION
OF EXT. RISK FLOOD /RAIN HOUSING DAMAGE ] there is a concentration of extensive risk housing damage around
urban centres in the north-east of the state. While poorly studied, flooding would seem to be associated with the high level of
urbanization, and associated problems of settlement of low-lying areas, increased run-off and inadequate drainage. In Chennai,
for example, 17.7 % of the urban population was living in slums in 2001 27 The other concentration of housing damage is along
the floodplain of the Kaveri river, in the watershed of the Ponnaiyar river and in the Kambam valley. Tamil Nadu has a long
history of chain tank irrigation , which has fallen into disuse. Much flood damage in rural areas is associated with chain-tank
failure and the silting of irrigation channels. In upland areas, increasing extensive flood risk may be associated with issues of
environmental degradation.
Box 3.x Nepal: In Nepal, extensive risk flood disaster and associated housing damage is concentrated in the densely
populated alluvial plains of the Terai in south-east Nepal See Map3.x. Floods in this case are not a consequence of
urbanisation or population density, but a cause. The richness of the soils is a consequence of the frequent replenishment of
nutrients through flooding and the reason why the region can support such a large dense population. Other extensive risks,
associated with landslides for example are spread across the mountainous centre, north and west of the country and are
responsible for more mortality than floods. [INSERT MAP OF EXT FLOOD HOUSING DAMAGE BY DISTRICT]
Africa
Systematic historical disaster data is not readily available in African countries. In South Africa, a detailed
database has been developed and maintained in Capetown28. A national database in now being compiled
in Mozambique. However, these are still exceptions in the region.
Given this lack of data, it is not possible to explore extensive risk patterns and trends in the same way as
in Latin America and Asia. Nevertheless, a series of case studies from African cities29 highlight that most
27
Maiti, Sutapa, Agrawal, Praween, 2005, Environmental Degradation in the context of growing urbanisation: a focus on the
metropolitan cities of India, Journal of Human Ecology, 17(4): 277-287
28
Reference MANDISA from DiMP
23
of the underlying factors of extensive flood risk in Latin American and Asian cities are also manifest in
Africa and that, if anything, these manifestations are more extreme.
Four types of flood have been identified in African cities: (1) localised flooding due to inadequate
drainage (2) flooding from streams whose catchment is entirely within the urban areas (3) flooding from
major rivers on whose banks the cities and towns are located and 94) coastal flooding from the sea or by a
combination of high tides and river flows. According to Action Aid, the first two kinds of flood are most
prevalent.
The underlying causes are very rapid urban growth is fuelled by migration from rural areas, which
themselves suffer from the combined impacts of flood, drought, conflict, HIV/AIDS and other hazards
on the vulnerable, stressed livelihoods of poor rural households. Due to poor urban governance most
cities absorb growth through the expansion of informal settlements, which often occupy low-lying flood
prone areas. 72% of Africa’s urban population lives in such settlements, meaning that floods
predominantly affect the urban poor. Investment in drainage infrastructure is often non-existent while
little or no maintenance of any existing drainage infrastructure is carried out. While climate change and
sea level rise is already magnifying flood hazard levels, the principal causes are related to the urbanisation
process itself.
[INSERT CITY EXAMPLES FROM ACTION AID PUBLICATION ON SPECIFIC CITIES +
CONSIDER MOVING AURAN ST. LOUIS BOX FROM CHAPTER 4]
3.7 Poverty and disaster risk: the empirical evidence
[NEED TO REFRENCE ALL THIS SECTION]
The evidence presented in this chapter so far tends to validate the hypothesis that both the impact and
incidence of disasters at the national and local level tends to be disproportionately concentrated amongst
the poor. Across Latin America, in parts of Asia and in African cities, urban informal settlements are
certainly experiencing rapidly increasing flood disasters, with significant patterns of housing damage.
There is also an expansion of extensive risk in small towns and rural areas, associated with processes of
territorial occupation and a decline in the regulatory services provided by ecosystems. While it is less
clear which social groups are affected by such disasters, it is unlikely that the housing damage reported
corresponds to high-income groups. Given that intensive risk is overlaid on patterns of extensive risk,
the impacts of major disasters will also disproportionately affect the same social groups affected by
extensive risk disasters.
While a clear relationship between disaster risk and poverty can be inferred from the evidence presented
above it is far less easy to demonstrate empirically for a number of reasons:





Empirical studies tend to be opportunistic rather than systematic, given the limited availability of
both disaster and poverty data at a suitable scale.
Spatially aggregated data may smooth important local differences in disaster risk and poverty
making it difficult or impossible to identify relationships
Disaster incidence and loss may be highly correlated with hazard intensity as well as with
poverty. Information on the distribution and intensity of localized hazards is often not available,
making it difficult to control for what may be a key variable.
As mentioned in 3.6 national disaster databases are biased towards major cities and towards local
political capitals and may underestimate disaster risk in poor rural areas.
When disaster risk is compared correlated nationally with data on the poverty headcount, poverty
gap or unsatisfied basic needs, it may produce counter-intuitive results in countries with
predominantly urban populations, given relative differences between urban and rural poverty.
The case studies referred to in this chapter are drawn from publications by Action Aid (unjust waters – reference) and by
AURAN.
29
24
Despite these challenges, the case studies from Latin America and Asia commissioned for this report,
together with the results from other research in Africa do provide empirical insight into the relationship
between disaster risk and poverty. Many of these empirical studies address the issue of drought and it’s
impact on rural livelihoods, thus balancing the analysis presented in the first part of this chapter, which
focused particularly on the impact of floods on the housing sector.
3.7.1 The poor are more at risk
A number of the case studies provide empirical evidence that the incidence and impact of disasters is
greater in areas inhabited by poor households, although the studies do not show whether this was due to
greater exposure to hazard, due to physical vulnerability or due to the magnification of hazard levels.
Additionally, the studies do not take into account other less quantifiable variables such as social capital,
which often plays a significant role in reducing risk30.
In Mexico, the number of destroyed and damaged houses between 1980 and 2007 was compared with the
total housing stock in each municipality and with an Index of Municipal Marginality developed by the
national population council. It was found that in absolute terms most houses were affected in urban
municipalities with a relatively low level of marginality. 69% of the houses lost occurred in
municipalities with very low levels of marginality and a further 12.7% in municipalities with a low level
of marginality, in general in large cities. However, when compared with the total housing stock, disaster
losses affected an average of only 8% of the housing stock across these municipalities.
In contrast, while in absolute terms, only 18% of the destroyed and damaged houses occurred in
municipalities with high or very levels of marginality, these destroyed a far higher percentage of the
housing stock. In a third of these municipalities less than 10% of the housing stock was affected over the
27 year period. In contrast, in another third, between 10 and 25% of the housing stock was lost, while in
another third more than 25%. Over 20% of the municipalities with high and very high levels of
marginality had more than 50% of their housing stock affected. Most of these municipalities contained a
mixture of rural areas and small towns. This indicates that while disaster risk in absolute terms may be
principally urban, it’s impact may be far greater in marginal rural areas and small towns.
In Peru it was found that rural households that reported a disaster impact in 2002 had on average less
access to public services, were less well integrated into the market and had a higher proportion of
agricultural income. It was also shown that households that experienced disaster were between 2.3 and
4.8 times more likely to be always poor than never poor. Those rural families with more livestock
holdings had slightly less probability of being always poor, which indicates that livestock is important as
a buffer against lost income.
In Bolivia, it was found that at the municipal level, factors such as improved education, immigration,
declining child mortality (associated with improving living conditions) and a decrease in unsatisfied basic
needs all contributed to a reduction in monetary poverty. However, in municipalities exposed to natural
hazards, the effectiveness of these variables in reducing poverty declines by about 50%. In Bolivia,
poverty was least 3% higher in municipalities exposed to hazard. Similarly, in Ecuador it was found that
drought exposure led to an increase in poverty incidence by 2% approximately.
In Nepal areas affected by floods tended to have lower poverty rates and higher per capita expenditures
while those affected by epidemics and landslides tended to have higher poverty rates and lower per capita
expenditure rates. As described in 3.6, flooding incidence and impacts are concentrated in the highlyproductive lowland agricultural plains of the Terai in south-eastern Nepal. As flooding contributes to the
fertility of the soil of the region, it contributes to the wealth of the area. In contrast, epidemics are heavily
concentrated in districts in western Nepal and in mountainous western Nepal, which concentrate the
30
Yamamura, Eiji ……… see list of papers consulted for second part.
25
countries rural poverty. Poverty rates are significantly higher in the mid-western (44.8%) and far western
(41%) of the country than in the central (27.1%) and eastern regions (29.3%), where the Terai is located.
In urban areas, the municipalities with most disaster affected people had a significantly higher poverty
rate and poverty gap.
In Orissa, India the absence of disaggregated data on household expenditure, assets and income meanst
that it was not possible empirically to explore correlations between disaster incidence and loss and
poverty. However, as in Nepal the areas most subject to floods, along the coast and floodplains and deltas
of major rivers are relatively wealthy compared to extremely poor southern Orissa. It is unlikely,
therefore, that there would be a clear relationship between poverty and flooding. The poorest areas of
Orissa in the south are drought prone, but as already stressed, the disaster database does not provide
adequate data on drought impacts on livelihoods. A statistically significant relationship, however, was
found between households living in houses with earth walls and thatch roofs and those most affected by
cyclone, flood, fire and lightening.
In the case of Sri Lanka, a very strong correlation was found between population living below the poverty
line and the number of houses damaged due to floods and a less strong but significant correlation between
population living below the poverty line and houses damaged due to landslides. As in the case of
Mexico, mentioned above this does not necessarily imply that there is a high incidence of floods in the
areas where the poor live but it does confirm that unsafe, vulnerable housing is a poverty factor that
conditions the likelihood of suffering greater loss in a disaster.
In Tamil Nadu, as in Orissa, the absence of disaggregated data on household expenditure, assets and
income made it difficult to explore disaster risk poverty interactions. However, the use of poverty proxies
demonstrated a strong relationship between the vulnerability of housing and disaster loss. As in Sri
Lanka, this confirms that unsafe, vulnerable housing is a poverty factor that conditions the likelihood of
suffering greater loss in a disaster. For example, mortality in extensive flood disasters was highly
correlated with vulnerable housing. Similarly, cyclone housing damage was inversely related to the
literacy rate. If literacy is taken to be a proxy for poverty again this indicates that the poor were more
likely to suffer housing damage. Mortality amongst scheduled castes was also higher in Blocks with a
high proportion of vulnerable housing.
3.6.2 Disaster losses increase poverty
The empirical evidence also confirms that disaster impacts, associated with both intensive and extensive
risks, have a direct and negative impact on the welfare at both the household as well as local and regional
levels. Impacts may include an immediate increase in monetary poverty, both in terms of its depth and
breadth, as well as a deterioration in other welfare indicators. If as examined in 3.6.1, the poor are more
likely to experience disaster incidence and loss, they are therefore at the same time less able to absorb the
impact of the loss and recover. In hazard prone areas, therefore, the recurrent losses due to disasters is a
central component of the experience of poverty.
In Mexico, municipalities that experienced disasters between 2000 and 2005 experienced a 0.8% drop in
the municipal-level Human Development Index (HDI), a 3.6% increase in food poverty a 3% increase in
capacities poverty and a 1.5% increase in assets poverty. Drought increased these different poverty
indicators more than floods. Droughts led to a decrease of 1.2% in the municipal HDI, increased food
poverty by 4.1%, capacities poverty in 3.7% and assets poverty in 2.5%.
In Iran, only a minor percentage of the population lives below the World Bank’s US $1 per day poverty
line. This percentage fell from 3.5% in 1991 to 0.3% in 2004. Other human development indicators also
steadily improved over the period, including life expectancy, education and health. In Iran, unlike any of
the other countries surveyed in this chapter, intensive earthquake disasters, affecting entire provinces
account for the vast majority of mortality (95%) and houses destroyed (73%), while 69% of the
population is urban. Given this pattern of major earthquakes affected entire provinces and their urban
26
centres, it was therefore possible to identify correlations between high disaster risk and decreased
household expenditure a high level of spatial aggregation, at the provincial level. Those provinces
(Ardebil, Chaharmahal-o-Bakhtiari, Lorestan, Mazandaran, Khorasan, Hormozgan and Yazd )which had
experiences the largest number of deaths and houses destroyed per 10,000 inhabitants had also
experienced the greatest negative effect on the expenditures of urban households. However, in some
provinces with major housing destruction, such as Mazandaran, Lorestan and Khorasan, household
expenditure actually increased, probably due to the positive impact of financial assistance from
government or charities.
In El Salvador, the 2001 earthquakes killed more than 1,200 people, affected approximately 300,000
houses (or 32% of the stock) and caused US $1.6 billion in direct and indirect damage (12% of GDP in
2000). Between 2000 and 2002, average household income per capita was actually increasing in El
Salvador (from 5449 to 6957 colones) while extreme poverty rates had fallen from 33.8% to 26.6%. In
poor rural households, affected by the earthquakes, average household income per capita was reduced by
approximately one third. Those most affected by the earthquakes suffered higher loss of housing, land,
livestock, farm machinery and other physical capital, which reduces the future earning capacity.
In the city of Trinidad, following a major flood in 2006, the level of poverty increased more than five
times the national increment while the poverty gap and the severity of poverty also increased. The impact
of education on reducing poverty also fell from 8.7% in 2006 to 6.6% in 2007
3.6.3 Disaster loss disproportionately affects the most poor and vulnerable
The ability of households to access, use and transform both tangible and intangible assets, such as
reserves and savings, an extended family, social safety nets provided by governments, land and property
and skills and knowledge influences their income-generating potential and welfare in normal times. In
disaster situations, asset holdings offer a crucial means to buffer impacts and to recover and therefore,
condition their level of resilience or vulnerability to hazards. Households susceptible to experience
hazard impacts and disaster losses, typically suffer a reduction in return to assets, leading to a collapse in
income and a loss in assets themselves. The most vulnerable, who are less able to access, use and
transform assets in normal times are the most affected by disaster. Disasters therefore not only increase
poverty, they increase inequality.
In Peru, it was found that the occurrence of disasters between 2002 and 2006 had a drastic effect on
households monthly per capita consumption after 2006. This impact was significantly greater for poor
families, whose consumption was reduced by 2.6 % than for wealthier families, whose consumption was
reduced by only 1.2%.
In Trinidad, Bolivia, the impact of floods in 2006 had a negative impact on inequality. The Gini
coefficient rose from 0.37 to 0.43 in the city between 2006 and 2007.
The disproportionate affect of disasters in the poorest households is further substantiated by evidence
from Africa. In Burkino Faso, the 1984-1985 drought affected the poorest third of the population 10%
more than the wealthier third. In Madagascar, cyclone impacts on the volume of agricultural production
of the poorest quintile were 11% compared to only 6% on the riches quintile. In Ethiopia, low rainfall
mainly affected households with low initial asset holdings.
Other studies have shown that in countries where the socio-economic status of women is low disasters
have a significant effect on the gender gap in life-expectancy31 given that disasters exacerbate previously
existing patterns of discrimination that make women more vulnerable. In countries, where the socioeconomic status of women improves the effect vanishes. For example, following the 2006 floods in
Trinidad, Bolivia, women’s income was negatively affected more than that of men.
31
Neumayer and Piper 2007, see reference papers.
27
3.6.4 Disaster has long term effects on the poor
Disaster losses are often measured only in terms of mortality or direct economic impact. There is
evidence, however, that disasters can have longer term impacts in areas such as health, education and
nutrition that can seriously affect the possibilities of recovery and further development. While strategic
economic sectors and wealthy households can recover quite quickly after a disaster, the setbacks to poor
households may last for years and in some cases decades.
If households have few assets to buffer the impact of a disaster, the transitory impacts of a hazard can turn
into longer-term impacts, particularly on highly vulnerable groups such as children. Nutrition shortfalls
in children can affect their human development later in life, while households may adopt short-term
strategies with high costs in the future, such as taking children out of school or depleting their few
remaining assets. In other words, disaster loss not only has an immediate impact on welfare but can erode
medium and long term welfare through an erosion of human and physical capital
Within poor households, children and women may be particularly affected. Temporary poor health and
malnutrition during drought can produce stunting, lower school achievement and attainment as well as
lower health and lower wages and productivity as an adult. In the case of children, there are quite
dramatic negative impacts if nutritional deficiencies occur from the womb to about 2 years old. In
Zimbabwe, for example, 16 years after the 1982-84 drought in Zimbabwe affected children had 7% loss
of adult earnings.
Drought shocks in Ethiopia, for example, had a major detrimental effect on child health. Children aged 624 months at the time of the 1994-1995 drought experienced about a 0.9 cm growth loss in height over a
six month period in those households where half the crop was damaged. In Bangladesh, the growth of
children in landless and credit constrained households was severely affected by the 1988 floods32.
During the 1994-95 drought in Zimbabwe, women’s body mass fell by about 3% while no impact was
found on men’s health. Impacts on adult health were also observed in Ethiopia, where during the 19941995 drought the Body Mass Index in communities with poor rainfall and low landholdings dropped by
0.9%. Impacts on adult health can impair productivity and increase the risk of premature mortality
In Nepal, people living in areas which had been affected by floods in the past, were more likely to suffer
from wasting and low weights. Similarly the population in areas affected by landslides, were associated
with higher percentages of stunting.
In El Salvador, following the 2001 earthquakes, the probability of school enrollment for children in the
most affected households decreased by 5.3%. Similarly, there was a worsening in school retention and
progression in some areas of Nicaragua affected by Hurricane Mitch33. In Cote d’Ivoire, enrollment
declined by about 20% in drought prone areas.
3.6.5 Coping with risk and loss can further erode development
In rural areas, options to recover from major asset depletion are far more constrained than in urban areas,
which can lead to permanently decreased welfare. Informal coping mechanisms such as selling livestock
are only partially effective as a buffer. During the 1999 drought in Ethiopia, livestock herds declined by
almost 40% and it was estimated that 25% of livestock reductions were distress sales where the seller
received less than 50% of the normal price of the animal sold. Similarly, in Burkino Faso, livestock sales
only covered 20 -30 % of crop income short-falls due to rainfall deficiencies. Only a quarter of the
income variability was smoothed via stored grains. The limited success of such strategies is evident from
the experience of the 2004 drought in Ethiopia, where despite limited mortality, consumption levels of
32
33
Foster, 2005 (from Baez background paper)
Ureta, 2005 (from Baez background paper0
28
those affected by the drought fell by 16%. Similarly in Burkino Faso, the 1984 -85 drought apparently
produced no mortality but lead to increases in poverty of 2 – 19% in the Sahelian region and 12 – 15% in
the Sudanian region.
In El Salvador, households had to sell productive assets such as animals or land, use savings or borrow
and stop or cancel planned investments in physical capital as a result of the earthquake. Alternative
sources of income and consumption played only a limited role in absorbing losses associated with the
disaster. 26.8% of the households reported less food consumption because of the earthquake.
Given that the vast majority of households in rural areas in Africa, Asia and Latin America do not have
access to insurance and credit, it is very difficult and long to recover assets lost in a disaster. This means
that many years after a drought or famine many rural households are still facing difficulties in recovery.
10 years after the devastating famines in Ethiopia in the 1980s cattle holdings in asset-poor households
were still two-thirds of what they were before. In some cases, recovery may be so long that welfare is
permanently affected creating what are known as poverty traps. Given the risk of poverty traps, evidence
from Burkino Faso, for example, shows that some households prefer to drastically reduce consumption in
order to protect livestock assets. Consumption growth loss would seem to be common 10 years or more
after drought disasters. Recovery would also seem to be wealth differentiated meaning that the poorest
recover slower than the wealthy.
In the case of successive disasters, or where extensive risk disasters have eroded assets, the use of stores
of assets to smooth consumption and buffer against loss is less likely to be successful. The impact of
disaster loss on poverty is likely to be particularly drastic in contexts that experience frequent successive
droughts and floods. The poorest households do not have the ability to recover assets from one disaster
before being affected by the next, which leads into accelerated decline in welfare.
Livelihoods in hazard prone areas are affected not only by asset loss itself but by as yet unrealized risks.
For example, farmers are less likely to invest in fertilizer in drought prone areas, given that it a harvest
fails due to drought the investment is lost. However, without fertilizer productivity is very low
constraining the capacity of farmers to accumulate sufficient assets to absorb loss when a drought occurs.
For example, poor households in drought prone areas of Tanzania tend to grow sweet potatoes more than
any other crop because it is drought resistant, despite the fact that it’s returns per hectare are about 25%
less than sorghum, maize or cotton. A simulation exercise in Zimbabwe, for example, found that exposure
to hazards would reduce the capital stock across households by 46% after 50 years, due to such ex-ante
strategies to minimize risk.. Similalry, in urban areas, households may prefer a central but hazard prone
location if it improves access to work and income, even though asset loss due to flooding, for example
impairs future welfare.
3.7 Conclusions
For most international observers, it is no exaggeration to say that the disaster problematic per se is largely
associated with manifestations of intensive risk. The national and international political impact of
intensive loss events, the continued predominance of disaster response and international humanitarian
assistance as approaches to deal with disaster risk, the focus of scientific research on major geological and
climatic processes, including climate change and the compilation of disaster loss statistics centred on
mortality and direct economic loss have all contributed to this perception.
As stressed in Chapter 2 of this report, major concentrations of intensive risk, associated with both
mortality and economic asset loss risk do exist and represent an enormous challenge to global stability
and sustainability. However, in the shadow of the intensive risk disasters that capture media and political
attention, patterns of extensive risk are emerging, manifested through thousands of smaller disasters each
year, expanding geographically over wide areas and associated mainly with hydrometeorological hazards,
such as floods, landslides and fires. Extensive risk has been largely ignored by the humanitarian
29
community, the media and the private sector. In a recent IFRC report, extensive risk was described as
uncounted risk34 within an overall typology of neglect.
Since about 1990, the number of extensive risk disasters has increased rapidly and has expanded
territorially: in particular disasters associated with floods and heavy rains. This expansive trend is driven
by different factors in each country, including: the decline in the regulatory services provided by
ecosystems, land use changes in rural areas, the increase in the urban population living in informal
settlements, increases in run-off due to urban growth and in appropriate water management and a chronic
underinvestment in drainage in urban areas. While extensive risk manifests more in terms of recurrent
damage than in terms of massive death and destruction, it is now both perennial and pervasive in many
countries and importantly prefigures new patterns of intensive risk. Put simply the extensive risks of
today are the intensive risks of tomorrow. While it is the way countries are managing their economic,
social and urban development and their environments that is driving the expansion of extensive risk,
climate related variables such as more intense precipitation also increase risk. Given that most extensive
risk is related to hydrometeorological hazard, it is extremely sensitive to an intensification of the watercycle, as well as to other climate change outcomes such as glacier melt. Extensive risk, therefore, also
represents a major challenge for climate change adaptation.
There is evidence that poor urban and rural households are disproportionately affected by manifestations
of extensive risk. The erosion of assets and loss of income caused by housing damage and crop loss, as
well as by the destruction of local infrastructure generally goes uncounted and represents the invisible
side of disaster costs. According to our calculations its total cost may be very significant, however.
When extensive risk is overlaid with intensive risk disasters causing covariate impacts in a large number
of households, those effects are multiplied and can lead to increased poverty in the short-run and a series
of very damaging effects on health, welfare and productivity in the long-run. These disaster impacts
particularly affect the poorest households and their most vulnerable members such as young children and
women. The impact on children is particularly critical given that it compromises the development
potential of future generations.
The ex-post mechanisms designed by the international community to respond to disasters, such as
humanitarian assistance and reconstruction loans, are designed to respond to events with major mortality
or direct economic loss. Recurrent asset loss in poor households due to extensive risk as not addressed by
international appeals for assistance, damage and needs assessments and similar mechanisms. Neither is
much attention paid to the longer term effects of disasters on the most poor and vulnerable. This implies
that poor rural and urban households are not disproportionately affected by disaster, they also receive the
least assistance. The underlying risk factors that are configuring extensive risk and future intensive risk
are explored in the next chapter. Given the relationship between disaster risk and poverty highlighted in
this chapter, addressing those factors is a fundamental challenge, for both disaster risk and poverty
reduction.
34
International Federation of Red Cross and Red Crescent Societies (IFRC), 2006, World Disaster Report 2006 Neglected
Crisis.
30
Technical Note 3.1 – Establishment of the threshold between intensive and
extensive risk
Previous studies undertaken in 2002 by the ISDR Working Group 3 on Risk, Vulnerability and Disaster
Impacts35 and in 2005 at the National University of Colombia36 applied different methodologies to
identify the number and impact of small disasters in DesInventar. These methods were reviewed in order
to see if they provided a viable approach for the identification of extensive risk.
The Working Group 3 study compared the DesInventar with EM-DAT in four countries: Chile,
Colombia, Jamaica and Panama. A large number of reports in DesInventar with losses below the EMDAT threshold of 10 deaths or 100 people affected were identified, most of which would reflect extensive
risk. However, large disasters above the EM-DAT threshold would generally have intensive as well as
extensive impacts in different local areas. At the same time, the EM-DAT threshold was not established
as a measure of intensive risk. Identifying DesInventar events which were not reported in EM-DAT was
therefore not a valid approach to identify extensive risk.
The second study, which examined the impact of small disasters in Colombia, used a statistical procedure
to calculate outlier events which exceeded 3.5 standard distributions from the mean distribution of
DesInventar reports in Colombia. While this approach has a transparent statistical procedure, the
definition of the number of standard deviations from the mean is by definition arbitrary and relative. If the
number of standard deviations is increased or decreased then the threshold between intensive and
extensive risk would change and the number of outliers would increase or decrease.
After ruling out the above methods, the approach chosen was to compare the distribution of DesInventar
reports across the dataset of 126,620 disaster reports in the 13 countries and states between 1970-2007
with the numbers of deaths and of houses destroyed. As Figure 0.16 and Figure 0.17 show a statistical
analysis was carried out to identify the point where the maximum percentage of losses is concentrated in
the minimum number of reports for both the variables, in this case where the fatality slope fell below a
gradient of 0.1%. A similar exercise was carried out for destroyed houses. 50 deaths and 500 destroyed
houses were considered to represent a reasonable threshold that was both easy to understand and met the
above criteria.
Figure 0.16: Maximum percentage of losses
GAR Asian & LAC DesInventar Fatality profile for All & Fatal Events
(percentile)
F atality s hare (perc entile)
100.00%
95.00%
90.00%
85.00%
80.00%
75.00%
70.00%
65.00%
60.00%
-
20
40
60
80
100
Number of Deaths
All As ia events
All As ian F atal events
All L AC events
All L AC F atal events
35
OSSO, LA RED, 2002, Comparative Analysis of Disaster Databases, report produced for ISDR Working Group 3..
http://www.unisdr.org/eng/task%20force/tf-working-groups3-eng.htm
36
Mabel C. Marulanda, Omar D. Cardona, and Alex H. Barbat, 2007, Revealing the Impact of Small Disasters to the
Economic and Social Development. The need of a proposal to cover the losses of low-income people and a framework to
measure and reduce the vulnerabilityby: http://www.ehs.unu.edu/file.php?id=295
31
Figure 0.17: Asia and LAC DesInventarfatality profile (by percentile of all reported & fatal events)
Asia
Fatality
class
intervals
All Asia
events
All Asian fatal
events
1
2
3
5
7
10
15
25
50
75
100
250
500
1,000
2,500
5,000
7,500
10,000
15,000
25,000
0.00%
83.00%
91.50%
94.70%
96.90%
97.80%
98.40%
98.90%
99.30%
99.60%
99.70%
99.70%
99.80%
99.90%
99.90%
99.90%
99.90%
99.90%
99.90%
99.90%
99.90%
0.00%
50.30%
68.70%
81.90%
87.30%
90.70%
93.70%
95.90%
97.60%
98.30%
98.60%
99.30%
99.50%
99.70%
99.80%
99.80%
99.90%
99.90%
99.90%
99.90%
50,000
100%
100%
n' value
45,217
7,652
All
fatality
slope
LAC
Cumulative
deaths
%
Cumulative
death
0.000
0.503
0.184
0.066
0.027
0.011
0.006
0.002
0.001
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
3,851
6,663
10,084
12,294
14,346
16,959
20,139
24,495
27,637
29,365
37,525
43,633
53,646
63,486
75,220
112,265
112,265
112,265
112,265
203,536
1.89%
3.27%
4.95%
6.04%
7.05%
8.33%
9.89%
12.03%
13.58%
14.43%
18.44%
21.44%
26.36%
31.19%
36.96%
55.16%
55.16%
55.16%
55.16%
100.00%
0.00%
89.60%
94.20%
96.30%
98.00%
98.70%
99.10%
99.40%
99.70%
99.80%
99.90%
99.90%
99.90%
99.90%
99.90%
99.90%
99.90%
99.90%
99.90%
99.90%
100.00%
0.00%
44.20%
64.70%
81.50%
87.70%
91.60%
95.00%
97.20%
98.60%
99.20%
99.40%
99.80%
99.80%
99.90%
99.90%
99.90%
99.90%
99.90%
99.90%
100.00%
0.442
0.205
0.084
0.031
0.013
0.007
0.002
0.001
0.000
0.000
0.000
0.000
0.000
86,403
79.02%
109,345
100.00%
0.000
203,536
100.00%
100.00%
100.00%
-
109,345
100.00%
81,399
8,431
All LAC
events
All LAC
fatal
events
All
fatality
slope
Cumulative
deaths
%
Cumulati
ve death
3,728
3.41%
7,182
6.57%
11,945
10.92%
14,796
13.53%
17,290
15.81%
20,588
18.83%
23,945
21.90%
28,114
25.71%
30,920
28.28%
32,175
29.43%
36,317
33.21%
37,407
34.21%
40,903
37.41%
45,403
41.52%
48,403
44.27%
48,403
44.27%
48,403
44.27%
68,403
62.56%
No events
Fatal events with asymptotic fatality < 0.1% slope
Figure 0.18: Intensive and extensive risk in Asia and Latin America (1970 – 2007)
Country
Risk
Type
Hazard type
Disaster
reports
%
Deaths
%
Argentina
Extensive
Hydrometeorological
Extensive
Intensive
14,525
98.7
2,554
84.8
Geological
165
1.1
68
2.3
1,041
2.0
3,031
2.2
Hydrometeorological
25
0.2
360
12.0
24,775
46.8
28,600
20.5
Intensive
Geological
4
0.0
30
1.0
8,800
16.6
6,643
4.8
14,719
100.0
3,012
100.0
52,959
100.0
139,369
100.0
Extensive
Hydrometeorological
1,363
98.5
435
55.1
2,369
56.7
2,066
100.0
Extensive
Geological
16
1.2
0
0.0
8
0.2
0
0.0
Intensive
Hydrometeorological
4
0.3
249
31.6
0
0.0
0
0.0
Intensive
Geological
1
0.1
105
13.3
1,800
43.1
0
0.0
1,384
100.0
789
100.0
4,177
100.0
2,066
100.0
SUB
TOTAL
Bolivia
SUB
TOTAL
Colombia
18,343
%
34.6
Houses
Damaged
101,095
%
72.5
Extensive
Hydrometeorological
21,316
97.2
6,645
18.5
86,655
50.5
397,968
83.8
Extensive
Geological
548
2.5
221
0.6
6,489
3.8
35,386
7.4
Intensive
Hydrometeorological
58
0.3
2,387
6.7
27,224
15.9
2,991
0.6
Intensive
Geological
14
0.1
26,603
74.2
51,282
29.9
38,754
8.2
21,936
100.0
35,856
100.0
171,650
100.0
475,099
100.0
Extensive
Hydrometeorological
8,957
97.7
414
86.1
2,981
39.0
36,497
80.9
Extensive
Geological
207
2.3
21
4.4
835
10.9
2,538
5.6
Intensive
Hydrometeorological
0
0.0
0
0.0
0
0.0
0
0.0
Intensive
Geological
3
0.0
46
9.6
3,830
50.1
6,068
13.5
SUB
TOTAL
Costa Rica
Houses
Destroyed
32
SUB
TOTAL
Ecuador
100.0
7,646
100.0
45,103
100.0
3,237
96.1
1,944
67.9
8,223
75.6
36,088
90.3
124
3.7
43
1.5
406
3.7
3,687
9.2
Hydrometeorological
Geological
Intensive
Hydrometeorological
4
0.1
550
19.2
663
6.1
178
0.4
Intensive
Geological
2
0.1
327
11.4
1,590
14.6
0
0.0
3,367
100.0
2,864
100.0
10,882
100.0
39,953
100.0
Extensive
Hydrometeorological
2,065
55.3
3,389
2.5
3,379
2.4
119,705
37.1
Extensive
Geological
1,601
42.9
375
0.3
3,729
2.7
46,569
14.4
Intensive
Hydrometeorological
29
0.8
2,099
1.5
29,420
21.3
2,800
0.9
Intensive
Geological
36
1.0
131,430
95.7
101,485
73.5
153,606
47.6
3,731
100.0
137,293
100.0
138,013
100.0
322,680
100.0
71.5
Extensive
Hydrometeorological
11,978
96.8
7,692
32.5
28,198
8.1
1,387,289
Extensive
Geological
317
2.6
242
1.0
1,312
0.4
81,220
4.2
Intensive
Hydrometeorological
65
0.5
3,549
15.0
236,801
68.4
370,011
19.1
Intensive
Geological
14
0.1
12,171
51.5
80,127
23.1
101,697
5.2
12,374
100.0
23,654
100.0
346,438
100.0
1,940,217
100.0
40.8
SUB
TOTAL
Nepal
481
Extensive
SUB
TOTAL
Mexico
100.0
Extensive
SUB
TOTAL
Iran
9,167
Extensive
Hydrometeorological
11,295
98.8
8,513
80.6
113,580
58.1
59,936
Extensive
Geological
72
0.6
116
1.1
2,001
1.0
5,300
3.6
Intensive
Hydrometeorological
49
0.4
1,180
11.2
48,062
24.6
31,811
21.6
Intensive
Geological
19
0.2
757
7.2
31,709
16.2
50,023
34.0
11,435
100.0
10,566
100.0
195,352
100.0
147,070
100.0
Orissa,
Extensive
Hydrometeorological
7,298
94.8
6,572
22.0
281,154
23.4
398,913
15.2
India
Extensive
Geological
6
0.1
0
0.0
3
0.0
11
0.0
Intensive
Hydrometeorological
395
5.1
23,296
78.0
917,797
76.5
2,227,441
84.8
Intensive
Geological
0
0.0
0
0.0
0
0.0
0
0.0
7,699
100.0
29,868
100.0
1,198,954
100.0
2,626,365
100.0
SUB
TOTAL
SUB
TOTAL
Peru
Extensive
Hydrometeorological
13,134
92.1
5,191
13.0
39,860
9.2
244,275
67.1
Extensive
Geological
1,062
7.4
876
2.2
19,847
4.6
43,865
12.0
Intensive
Hydrometeorological
36
0.3
20,409
51.0
61,402
14.1
51,312
14.1
Intensive
Geological
29
0.2
13,550
33.9
312,894
72.1
24,732
6.8
14,261
100.0
40,026
100.0
434,003
100.0
364,184
100.0
Extensive
Hydrometeorological
9,745
98.8
1,801
8.7
30,416
13.0
242,128
55.3
Extensive
Geological
35
0.4
221
1.1
1,836
0.8
3,000
0.7
Intensive
Hydrometeorological
40
0.4
2,383
11.5
147,072
62.6
144,923
33.1
Intensive
Geological
42
0.4
16,360
78.8
55,472
23.6
47,515
10.9
9,862
100.0
20,765
100.0
234,796
100.0
437,566
100.0
12,325
98.7
1,847
36.6
109,869
48.5
550,346
60.9
SUB
TOTAL
Sri Lanka
SUB
TOTAL
Tamil
Nadu,
Extensive
Hydrometeorological
India
Extensive
Geological
62
0.5
213
4.2
3,171
1.4
1,703
0.2
Intensive
Hydrometeorological
81
0.6
839
16.6
90,511
40.0
326,994
36.2
Intensive
Geological
23
0.2
2,145
42.5
22,991
10.1
24,056
2.7
12,491
100.0
5,044
100.0
226,542
100.0
903,099
100.0
61.8
SUB
TOTAL
Venezuela
Extensive
Hydrometeorological
4,135
98.6
1,395
52.4
13,975
28.6
78,290
Extensive
Geological
44
1.0
10
0.4
6
0.0
235
0.2
Intensive
Hydrometeorological
15
0.4
1,258
47.2
34,955
71.4
48,115
38.0
Intensive
Geological
0
0.0
0
0.0
0
0.0
0
0.0
4,194
100.0
2,663
100.0
48,936
100.0
126,640
100.0
121,373
95.9
48,392
15.5
739,002
24.1
3,654,596
48.3
4,259
3.4
2,406
0.8
40,684
1.3
226,545
3.0
SUB
TOTAL
All
TOTAL
Extensive
Hydrometeorological
Extensive
Geological
Intensive
Hydrometeorological
801
0.6
58,559
18.7
1,618,682
52.7
3,235,176
42.7
Intensive
Geological
187
0.1
203,524
65.0
671,980
21.9
453,094
6.0
126,620
100.0
312,881
100.0
3,070,348
100.0
7,569,411
100.0
33
List of Figures
Figure 3.1: Number of extensive risk disaster reports seperated in several hydrometeorological categories
(1970-2007) ...........................................................................................................................................5
Figure 3.2: Average seasonal occurrences of disaster reports associated with fire, Orissa, India (1970 –
2007) ......................................................................................................................................................6
Figure 3.3: Distribution of mortality associated with intensive and extensive risk across the data set
(1980-2006) ...........................................................................................................................................6
Figure 3.4: Deaths, houses destroyed and houses damaged in Sri Lanka and international awareness
(1992-2005) ...........................................................................................................................................8
Figure 3.5: Number of extensive hydrometeorological disaster reports and number of associated deaths
for data set (1980-2006).......................................................................................................................10
Figure 3.6: Number of extensive hydrometeorological disaster reports and number of associated deaths
for data set (1980-2006).......................................................................................................................10
Figure 3.7: Causes for housing destruction in Orissa, India (1970 – 2007) ................................................11
Figure 3.8: Housing destruction in Orissa, India (1970 – 2007) .................................................................11
Figure 3.9: Number of municipalities affected by extensive hydrometeorological risk (1980-2006) ........12
Figure 3.10: Number of disasters associated with floods and heavy rains (1980 – 2006) ...................13
Figure 3.11: Extensive risk disasters in Mexico associated with floods, rains and flash floods (1980–2006)
.............................................................................................................................................................14
Figure 3.12: Floods and rains in Colombia as a proportion of all extensive risk disasters (1970-1979) and
(1998 – 2007) .......................................................................................................................................14
Figure 3.13: Flood and rain disasters in Costa Rica (1990-2007) ...............................................................15
Figure 3.14: Precipitation Costa Rica 10 years averages (1957-2007) .......................................................15
Figure 3.15: Extensive flood and heavy rain disasters in the province of Buenos Aires and the Federal
Capital (1970-2007) .............................................................................................................................19
Figure 3.16: Extensive flood disaster and housing damage is concentrated in urban areas (1996 – 2007)
............................................................................................................. Error! Bookmark not defined.
Figure 3.17: Maximum percentage of losses ...............................................................................................31
Figure 3.18: Asia and LAC DesInventarfatality profile (by percentile of all reported & fatal events) .......32
Figure 3.19: Intensive and extensive risk in Asia and Latin America (1970 – 2007) .................................32
List of Maps
Map 3.1: Armenia, Columbia earthquake extensive and intensive impacts (1999) ......................................2
Map 3.2: Ecuador intensive disaster risk events (1970-2007) .......................................................................4
Map 3.3: Ecuador extensive disaster risk events (1970-2007) ......................................................................4
Map 3.4: Number of extensive geological disaster risk reports from the Islamic Republic of Iran (1970 –
2007) ......................................................................................................................................................6
Map 3.5: Extensive housing damage in Tamil Nadu, India (1976-2007) .....................................................8
Map 3.6: Intensive housing destruction in Tamil Nadu, India (1976-2007) .................................................8
Map 3.7 Number of extensive risk events (1980-1989, 1990/1999, 2000/2007) ........................................12
Map 3.8: Spatial evolution of extensive hydrometeorological disasters in Ecuador from 1970-2007........20
Map 3.9: Redistribution of extensive risk associated with these hazards between 1970-1985 and 19862006. .................................................................................................... Error! Bookmark not defined.
Map 3.10: Houses damaged in extensive disasters in North-Central Venezuela (1980 – 2007)......... Error!
Bookmark not defined.
Map 3.11: Distribution of flood events (1950-1959; 1960-1969) ............ Error! Bookmark not defined.
Map 3.12: Orissa, India extensive disaster risk reports (1970 – 2007) .......................................................13
Map 3.13: Orissa, India intensive disaster risk reports (1970 – 2007) ........................................................13
List of Tables
Table 3.1: Spatial distribution of extensive risk (1970-2007) .......................................................................4
34
Table 3.2: Loss attributes by risk category across the sample (1970–2007) .................................................9
Table 3.3: Annual increase in mortality and housing damage compared with annual population growth
(1980-2006) ......................................................................................... Error! Bookmark not defined.
Table 3.4: Urban growth rates in % .............................................................................................................16
Table 3.5: .....................................................................................................................................................17
Table 3.6: I am asking Cristina since the 15 October in 5 spererate emails to send me the talbe, but she
refuses to send an excel version. Only what I have here, and another one with Mexico in Power
Point!....................................................................................................................................................17
ALSO INCORPORATE BOX INTO SECTION 4.2 9 (or eventually into chapter 3 from Action Aid (Climate change, urban
flooding and the rights of the urban poor in Africa: key findings from six African cities)
35