Chapter 1: Hazards, Vulnerability and Risks � Trends and Analysis by kUR2soX5

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									Chapter I.
 Emerging trends of nNatural hazards and disaster risk in Asia-Pacific –
 Trends and Prospects

 This chapter reviews the trends on the frequency and impact of disasters caused by natural
 hazards and assesses the vulnerability and exposure of the countries in the Asia-Pacific region to
 disasters, highlighting the links across disaster risk reduction (DRR), climate change adaptation
 and social and economic development. To assess disaster trends, it uses data from the
 International Disaster Database (EM-DAT) covering a period of 30 years (1980-2009). Issues
 related to generally improved disaster reporting mechanisms as well as neglected and unreported
 disasters were also examined to identify the need for review and improvement of the existing
 data sources and information sharing platforms at regional level. Low-intensity but high-
 frequency hazards have a significant overall impact on human life, livelihoods and economic
 assets in many developing countries of the region, but usually they are not adequately prioritized
 in national DRR programs as compared with the more visible or widely reported major disasters
 that attract international attention.

 The chapter is intended to serve as a rational basis for adopting a more comprehensive approach
 to various socio-economic and technical aspects of disaster risk reduction in the face of (i)
 gradually mounting evidence of climate change impacts on natural hazards, (ii) increasing
 human population, economic assets and infrastructure in disaster prone areas, and (iii) uneven
 economic development within countries (rural vs. urban) and across the region (low, medium and
 high income countries). Some mega-disasters overwhelm smaller countries both in terms of
 physical damage and economic losses, and therefore, such countries can benefit significantly
 from a regional platform for DRR through better sharing of data, information, and knowledge.


 A region challenged by disasters

 The Asia-Pacific region is very prone to disasters caused by natural hazards.1 Between 1980 and
 2009, this region, home of 61% of the world’s population and generating 29% of world's GDP,
 experienced 45% of world's disasters, 42% of world’s economic losses, 58% of total number of
 deaths and 86% of the total affected population.

 The number of disasters reported globally has increased in the last decades – from 1690 disasters
 in the period of 1980-1989 to 3886 disasters in the last 10 years. Figure I-1 shows such increase
 by regions. Asia-Pacific has been the region that suffered the large number of disasters over these
 years. Both Asia-Pacific and Africa have experienced a sharp increase in the number of disasters
 in the last decade.



 1
  Natural hazards considered are: droughts, floods, storms, mass movement (e.g. landslides and avalanches),
 earthquakes (including tsunami generated by earthquake), extreme temperature, volcano and wildfire.

                                                                                                              1
Figure I-1 Increasing number of disasters reported in the last decades

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                                Africa    Asia-Pacific   Caribbean      Europe   Latin America    North America

Source: ESCAP based on data from EM-DAT: The OFDA/CRED International Disaster Database – www.emdat.be – Université Catholique de
Louvain – Brussels – Belgium


Figure I.2 presents the number of disasters by the various types of natural hazards in Asia-
Pacific. Floods, storms and earthquakes are the main causes of disasters in the region and the
number of these disasters has increased in the last decade. Such increase could be related to
many factors including increasing population exposed to hazards and improvements in reporting
and collection of disaster data in the International Disaster Database.

Figure I-2 Number of disasters in Asia-Pacific by type of natural hazard




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                                                                Drought                                   Earthquake (seismic activity)             Extreme temperature
                                                                Flood                                     Mass Movement Dry                         Mass Movement Wet
                                                                Storm                                     Volcano                                   Wildfire




Source: ESCAP based on data from EM-DAT: The OFDA/CRED International Disaster Database – www.emdat.be – Université Catholique de
Louvain – Brussels – Belgium



The number of people affected by disasters has soared over the past 40 years, rising from an
average 115 million annually between 1970 and 1989 to 196 million in the 1990s and 3.0 billion
between 2000 and 2008.2 Many of these people resided in Asia and the Pacific. In this region
alone, average numbers affected rose from just under 100 million per annum between 1970 and
1989 to 178 million in the 1990s and 2.5 billion between 2000-2008. In contrast, annual average
mortality figures fell marginally between 1970-89 and 1990-99, declining globally from 162,000
to 59,000 deaths, in part thanks to improvements in early warning systems. In Asia and the
Pacific, annual average figures fell from 56,000 to 41,000 between the same two periods.
However, there was a sharp increase in deaths between 2000 and 2008, totaling 1.4 million
globally and 1.1 million in Asia and the Pacific alone. These devastating figures reflected a
succession of severe events, including the 2004 Indian Ocean tsunami (226,408 deaths), the 2005
India/Pakistan Kashmir earthquake (73,338 deaths), the 2008 Cyclone Nargis in Myanmar
(133,655 deaths) and the 2008 Sichuan earthquake (87,476 deaths) (UNISDR 2009). The region
accounted for a staggering 83 per cent of total global deaths as a consequence of natural hazards
between 2000 and 2008, far higher than its 55 per cent share in the world’s population.3

2
  EM-DAT CRED data (www.emdat.be) as reported in the 1996, 2000 and 2009 IFRC World Disasters Reports.
3
 The Asia-Pacific region’s share in total global disaster-related deaths is expected to be considerably lower in 2010,
following the January 2010 Haiti earthquake in which an estimated 230,00 people died.

                                                                                                                                                                                                               3
During the second half of the twentieth century, reported “economic” losses from major4 disaster
events also rose significantly. Real losses more than doubled each decade from the 1950s to
1980s but in the 1990s rose almost three-fold over the previous ten years, with relatively high
losses throughout much of the decade (Munich RE 2002). Losses peaked in 1995, the year of the
Kobe earthquake, at US$178 billion, equivalent to 0.7 per cent of annual global gross domestic
product. During the early 2000s, losses fell back to levels similar to those experienced during
much of the1980s but rose sharply again in 2004, to the second highest level ever. A new record
was set the following year due to a series of severe windstorms, including Hurricane Katrina, the
most expensive natural catastrophe on record. Further extreme losses in 2008 pushed 2004 levels
into fourth place (Munich RE 2009a). Total losses from major disaster events during the first
decade of the twenty-first century were lower than those experienced in the 1990s but, as it
included three of the most costly years on record, fears of a continuing trend of rising losses have
by no means been allayed. The growth of overall disaster losses reflects a range of factors, but
most fundamentally the increases in the level of capital assets exposed to hazard events both in
the developed and developing worlds. Also, while difficult to delineate its impact, enhanced
capabilities for reporting disaster losses have also played an important role in explaining rising
losses.




Figure I-3 Increase of damage and loss mirrors the increase in GDP




4
  Munich RE defines ‘major’ disaster events as those events where there “are thousands of fatalities, when hundreds
of thousands of people are left homeless, and/or when the overall losses –considering the economic circumstances of
the country concerned – and/or insured losses reach exceptional proportions” (Munich RE 2009, 38).

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                                                                 estimated damage and loss   GDP    Linear (estimated damage and loss)

Source: ESCAP based on data from EM-DAT: The OFDA/CRED International Disaster Database – www.emdat.be – Université Catholique de
Louvain – Brussels – Belgium and GDP data from World Development Indicators.




The developed world has accounted for a significant share of economic losses. Between 2000
and 2008, high human development countries accounted for 60 percent of total reported direct
damage (in real terms), medium human development countries for 30 percent and low income
countries for just 1 percent of total reported damage.5 In comparison, low and medium human
development countries accounted for nearly 94% of all fatalities caused by hazard events
excluding epidemics between 2000 and 2009.6 In fact, data compiled over the past 30 years show
that the risk of disaster-related death is four times higher in poor countries than in high income
countries (IDB and ECLAC, 2007). In a developing country context, the greater toll on human
life is found to be primarily related to poverty and the decisions and actions of the poor and near-
poor. They include rural-urban migration and related demographic pressures, leading to the
growth of informal settlements on riverbanks and unstable slopes; relatively weak land use
planning; poor construction methods; steep land farming practices; the encroachment of river
plain and forest areas; environmental degradation; and pollution of urban waterways.


All sub-regions of the Asia-Pacific get their share of the disasters caused by natural hazards and
related impacts (Table I.1). South and South-West Asia had most disasters (1,283) over a 30 year

5
    EM-DAT CRED data (www.emdat.be) as reported in the 2009 IFRC World Disasters Report.
6
    EM-DAT CRED data (www.emdat.be) accessed on 27 April 2009

                                                                                                                                                                                5
period (1980-2009), followed by South-East Asia (1,069)), and, consequently, these two sub-
regions experienced greater fatalities. However, East and North-East sub-region suffers more
economically and in terms of number of affected people. It should be noted that to some extent
the 2004 Indian Ocean Tsunami does spike the disaster statistics of South-East Asia (see Box 1).
Considering the smaller size of the Pacific sub-region, the relative loss of human life and
economic damage are significantly high. Appendix 1 gives country-wise disaster statistics for
the Asia-Pacific region.

Table I-1 Disasters and Impacts in Asia-Pacific Sub-Regions over the Period 1980-2009.

         Asia-Pacific          Events       Killed       Affected       Estimated Damage
          Sub-region          (Number) (Thousand)       (Thousand)        (,000,000US$)
 East and North-East Asia           908      162,804       2,567,214               578,602
 South-East Asia                  1,069      394,687         272,777                48,220
 South and South-West Asia        1,283      566,423       1,914,696               141,506
 North and Central Asia             297       34,644          17,231                15,636
 Pacific                            406        5,425          19,126                39,078
 Asia-Pacific (Total)             3,963    1,163,983       4,791,044               823,042
Source: ESCAP based on data from EM-DAT: The OFDA/CRED International Disaster
Database – www.emdat.be – Université Catholique de Louvain – Brussels – Belgium.

Tables I.2 and I.3 below provide various disaster-related statistics over the last three decades for
the top 10 worst affected countries of the Asia-Pacific region. The highest number of people
exposed to flooding are all in Asia as absolute physical exposure to floods is highest in
Bangladesh, China, part of Russian Federation and India whereas relative exposure is
particularly high in Cambodia, Bangladesh and Vietnam. Asian countries also have the highest
absolute exposure to storms and storm surges while the Pacific Island States such as Fiji and
Vanuatu with their smaller populations have a high relative exposure to these events. The
presence of high concentrations of population in seismically active areas leads to very high
absolute exposure to earthquakes, particularly in China, India, Indonesia, Kyrgyzstan, and
Tajikistan. In contrast, relative exposure is higher in small countries such as Bhutan and several
of the Pacific Island States, including Solomon Islands, Tuvalu and Fiji, that are geographically
located in seismically active areas. These high exposure levels are reflected in impacts on
human population and economic resources as shown in Table I.2 and I.3.

Table I-2 Disaster Statistics over the Period 1980-2009

Rank               Country                 Events              Country                  Killed
                                                                                      (Thousand)
  1        China                                  574   Bangladesh                           191,650
  2        India                                  416   Indonesia                            191,164
  3        Philippines                            349   China                                148,419
  4        Indonesia                              312   India                                141,888
  5        Bangladesh                             229   Myanmar                              139,095
  6        Russian Federation                     176   Pakistan                              84,841

                                                                                                       6
  7           Japan                                           155        Iran (Islamic Rep. of)                        77,987
  8           Australia                                       154        Sri Lanka                                     36,871
  9           Viet Nam                                        152        Philippines                                   32,578
 10           Iran (Islamic Rep. of)                          140        Russian Federation                            31,795
Rank                   Country                        Affected                   Country                        Damage*
                                                      (Million)                                               (US$ Million)
   1          China                                     2,549.85         China                                     321,544.61
   2          India                                     1,501.21         Japan                                     188,183.82
   3          Bangladesh                                  316.34         India                                      51,644.78
   4          Philippines                                 109.42         DPR Korea                                  46,331.29
   5          Viet Nam                                      67.73        Turkey                                     35,144.61
   6          Thailand                                      53.76        Australia                                  34,690.13
   7          Iran (Islamic Rep. of)                        42.05        Iran (Islamic Rep. of)                     24,977.98
   8          Pakistan                                      29.96        Indonesia                                  22,581.81
   9          Indonesia                                     17.54        Republic of Korea                          19,818.30
  10          Cambodia                                      16.40        Bangladesh                                 16,273.08
Source: ESCAP based on data from EM-DAT: The OFDA/CRED International Disaster Database – www.emdat.be – Université Catholique de
Louvain – Brussels – Belgium, and data on implicit price deflators in US$ from UNSD National Accounts Main Aggregates Database.
Note: * Damage and loss reported in US$ Million 2005 constant prices.



While it is obvious that geographically larger countries experience more disasters and damage in
absolute terms, the impact could be greater and/or more long-term on smaller countries. For
example, in 2008, Islands of Samoa, American Samoa and Tonga were among the top 10
countries in the world in terms of number of deaths per 100,000 inhabitants, indicating
vulnerability of small island states.
While low-intensity earthquakes are continuously recorded by multiple seismic activity
measurement stations spread around the world, they are not felt by general population as they
cause little or no damage. Major earthquakes are low-frequency short-duration events, but cause
more intense damage both in terms of loss of human life and economic damage (Table I.3).
However, most of the disasters listed in Table I.3 are hydro-meteorological type such as flood,
drought, storm, extreme temperature, and wild fires. These events may be visibly less intense
when compared to the earthquakes, but cause more long-term losses due to their frequent
occurrences and sustained impact on land-based and coastal food production activities, the major
source of livelihood in most of the Asia-Pacific countries. Also, these events are directly or
indirectly liked to climatic processes and these linkages cannot be simply ignored due to lack of
availability of very long-term data.




Table I-3 Ranking of Top 10 Disaster Types and Their Impact in Asia-Pacific over 1980-2009

Rank                   Disaster                Number of                       Disaster                          Killed
                        Type                    Events                          Type                           (Thousand)

                                                                                                                                   7
  1           Flood                                  1,317        Earthquake                                         570.80
  2           Cyclone                                1,127        Cyclone                                            384.20
  3           Earthquake                                444       Flood                                              128.95
  4           Mass Movement Wet                         264       Volcano                                             17.51
  5           Extreme Temperature                       119       Extreme Temperature                                 17.51
  6           Drought                                   108       Mass Movement (Wet)                                 14.28
  7           Wild Fire                                  96       Drought                                              5.33
  8           Volcano                                    71       Mass Movement (Dry)                                  1.53
  9           Mass Movement Dry                          20       Wild Fire                                            1.06
 10           Insect Infestation                          8       Insect Infestation                                   0.00
Rank                 Disaster                   Affected                    Disaster                       Damage* (Million
                       Type                     (Million)                     Type                              US$)
     1        Flood                               2,676.16        Flood                                             301,590
     2        Drought                             1,296.27        Earthquake                                        264,530
     3        Cyclone                               664.03        Cyclone                                           165,770
     4        Earthquake                            109.71        Drought                                            53,330
     5        Extreme Temperature                    85.90        Extreme Temperature                                18,080
     6        Wild Fire                                3.31       Wild Fire                                          16,210
     7        Volcano                                  2.36       Mass Movement (Wet)                                 2,130
     8        Mass Movement Wet                        1.36       Volcano                                               710
     9        Mass Movement Dry                        0.02       Insect Infestation                                    190
    10        Insect Infestation                       0.00       Mass Movement (Dry)                                    10
Source: ESCAP based on data from EM-DAT: The OFDA/CRED International Disaster Database – www.emdat.be – Université Catholique de
Louvain – Brussels – Belgium, and data on implicit price deflators in US$ from UNSD National Accounts Main Aggregates Database.
Note: * Damage and loss reported in US$ Million 2005 constant prices.



Neglected and Unreported Disasters
The Asia-Pacific region experiences a significant number of smaller disasters that go unreported
by EM-DAT7 and similar international database systems. Most of these are low-intensity, high-
frequency disasters that often do not make headlines in international press or publications, but
actually inflict serious damage on a constant basis to highly vulnerable populations. Ironically,
many local communities take these as an integral part of their existence and learn to live with
them with varying degree of success. On the positive side, these long-term experiences have
helped them develop indigenous knowledge and practices which need to be better documented
and shared among DRR communities.

Many of the developing countries simply do not have modern technical and adequately qualified
human resources for community level disaster monitoring programs, particularly in the rural
areas where majority of the region’s population lives, and hence it never became a function of
local government to identify potential local hazards, map them and develop rescue, recovery and
re-construction plans accordingly. The other part of the problem lies with internationally
accepted disaster definition and reporting methodologies which are based on absolute number of

7
 For a disaster to be entered into the database at least one of the following criteria must be fulfilled: Ten (10) or
more people reported killed; hundred (100) or more people reported affected; declaration of a state of emergency;
call for international assistance.

                                                                                                                                   8
deaths and economic damages due to single disaster event, ignoring the frequency of such
events and their sustained impact on local communities. The aggregate impacts of such disasters
are difficult to quantify based on the available data. To highlight this issue of neglected and
unreported disasters, this report compares two disaster databases - EM-DAT and
DesinventerDesinventar8, to compare the number of disaster events from Indonesia (1998-2009)
and Sri Lanka (1980-2008).

In case of Indonesia, DesinventerDesinventar data are available from 1998 only. Four types of
disasters were considered for the analysis: floods, landslides, cyclones and droughts.
Earthquakes and tsunamis were not considered, those being generally catastrophic events and
well captured by both DesinventerDesinventar and EM-DAT.

Table I-4 Comparison of DesinventerDesinventar and EM-DAT data in Indonesia (1998-
2009)

                               No. of Events                                   People Killed                                 People Affected
    Disasters
                    EM-       DesinventerDesinventar               EM-         DesinventerDesinventar              EM-DAT DesinventerD
    Flood           DAT
                     63                        2,296               DAT
                                                                   2,826                        1,233              3,525,309               1
    Lanslide         29                          735               1,115                        1,273                332,330
    Cyclone           2                          680                   4                          109                  3,715
    Drought           1                        1,149                   0                            0                 15,000
    Total            95                        4,860               3,945                        2,615              3,876,354               1
Source: ESCAP based on data from EM-DAT: The OFDA/CRED International Disaster Database – www.emdat.be – Université Catholique de
Louvain – Brussels – Belgium



In case of Sri Lanka, the total numbers of recorded disaster events in DesinventerDesinventar
database were 3,076 from 1980 to 2008, whereas only 49 events were recorded in EM-DAT
database for the same period (Table 1.2.2). The reported number of flood events during the
period was 2,210 and 39 in Desinventar and EM-DAT respectively. However, the reported the
number of casualties due to flood in EM-DAT is nearly 3 times higher than that of
DesinventerDesinventar. Further investigations show that a large number of deaths (325
casualities) were reported in EM-DAT in 1989. A significant difference were also found in
people affected by the disasters in these two datasets, but it is not as severe as in the case of
Indonesia.

Table I-5 Comparison of DesinventerDesinventar and EM-DAT data in Sri Lanka (1980-
2008)
    Disasters                       No. of Events                                         People Killed                                Peopl

8
  DesInventar is a data collection and analysis methodology which uses a set of open-sourced software programmes
to help record the impacts of highly localized small scale events, assess disaster trends as well their impact on
communities. It has been used by local officials to build disaster inventories following local conditions and
requirements. DesInventar was created in 1992 by La Red (The Network for Social Studies on Disaster Prevention
in Latin America).


                                                                                                                                   9
                      EM-DAT           DesinventerDesinventar                    EM-DAT              DesinventerDesinventar                  EM-DAT       De
  Flood                   39               2,210                                      933                 317                                9,283,426     8
  Lanslide                  1                293                                       65                 490                                      130
  Cyclone                   3                 41                                       14                    9                                 433,000
  Drought                   6                532                                        0                    0                               6,006,000     7
  Total                    49              3,076                                    1,012                 816                               15,722,556    17

Both in case of Indonesia and Sri Lanka, the number of events reported in Desinventar is very
large in comparison to EM-DAT. On the contary, the reported number of people killed in
Desinventar is lower than the EM-DAT. Desinventar has reported nearly 50 times more numbers
of disasters in case of Indonesia and 60 times more in case of Sri Lanka. This shows that with
the present criteria in EM-DAT for data capturing does misses quite a big numbers of disaster
events. Secondly, reporting of higher number of casualties in EM-DAT (although the reported
number of disaters events are very low) highlights that data varification is an issue to which
attention should be paid.


Risk, exposure and vulnerability

According to the United Nations Global Assessment Report on DRR (GAR, 2009) the risk levels
for most of the hazards are increasing over time, even assuming constant frequency and intensity
of hazards. The increase in risk is by and large attributed by the growing exposure of people and
assets and this has been the main driver for increased risk in last 20 years. GAR report notes that
in fact there has been a reduction in vulnerability due to improved development conditions.
However, reduction in vulnerability alone is insufficient to offset the drastic increase in exposure
in recent years.

Flood, cyclone and earthquake are the top 3 disasters in the Asia-Pacific region in terms of
number of events, casualties and damage (See Table I.3). The absolute and relative physical
exposure of top 10 countries in the Asia-Pacific region is shown in Table I.7. Absolute exposure
is the expected average number of people exposed per year whereas the relative exposure
describes the expected average number of people exposed per year as a proportion of national
population.

Table I-6 Top 10 countries in the Asia-Pacific based on absolute and relative physical exposure
                         Flood                                          Cyclone                                          Earthquake
 Rank




             Absolute              Relative               Absolute                  Relative                 Absolute                 Relative
             (Million)               (%)                  (Million)                    (%)                   (Million)                   (%)
  1     Bangladesh1 (19.2)     Cambodia1 (12.2)            Japan1 (30.9)    North Marina Isl.2 (58.2)          Japan1(13.4)            Vanuatu1 (60.4)
  2           India2 (15.8)   Bangladesh2 (12.1)      Philippines2(20.7)                 Niue9 (25.4)    Philippines2(12.1)       Solomon Isl.2 (36.3)
  3            China3 (3.9)       Vietnam3 (3.9)            China3(11.1)               Japan10 (24.2)     Indonesia3 (11.0)              Tonga6 (21.1)
  4         Vietnam4 (3.4)          Bhutan4 (1.7)            India4(10.7)        Philippines11 (23.6)          China4 (8.1)     Papua New G..9 (17.5)
  5       Cambodia5 (1.7)             India5 (1.4)     Bangladesh6(7.5)                  Fiji12 (23.1)          India8 (3.3)       Philippines12 (13.8)
  6        Indonesia6 (1.1)       Thailand6 (1.3)    Rep. of Korea9(2.4)              Samoa15 (21.4)        Pakistan9 (2.8)       Timor Leste14 (11.3)
  7         Thailand7 (0.8)          Nepal7 (1.2)      Myanmar11 (1.2)       New Caledonia18 (20.7)              Iran15 (1.7)            Japan15 (10.5)
  8      Philippines8 (0.7)      Lao PDR8 (1.1)          Vietnam13 (0.8)           Vanuatu20 (18.3)      Bangladesh17 (1.3)             Bhutan17 (0.8)
  9         Pakistan9 (0.5)      Myanmar9 (0.9)       Hong Kong17 (0.4)               Tonga21 (18.1)     Papua N. G..9 (1.1)         Indonesia31 (0.4)
 10       Myanmar10 (0.4)      Philippines10 (0.9)       Pakistan19 (0.3)      Cook Islands32 (10.5)      Afghanistan (1.0)          Kyrgystan35 (0.4)


                                                                                                                                           10
Source: P. Peduzzi, UNEP/GRID-Europe
Note: Number in superscript against each country shows its global rank.



It is interesting to note that the top 10 countries in the world in terms of both absolute and
relative physical exposure to floods are from the Asia-Pacific region and this is primarily due to
the high concentration of the exposed population in the river flood plains and deltas. Bangladesh
and Cambodia respectively have the highest absolute and relative exposures to flood in the
world. In case of absolute exposure to cyclones, the top four countries in the world are from the
Asia-Pacific region whereas the North Marina Islands in the South-Pacific has the second highest
relative exposure in the world. Similarly, in case of earthquakes, top four countries in the world
in terms of physical exposure is also from the region and Vanuatu and Solomon Islands are the
top two countries in the world which have the highest relative exposures to earthquakes. Figure
1.1.4 shows the Asia-Pacific region having absolute physical exposure to floods, cyclones and
earthquakes.


Seven countries from the Asia-Pacific region find their place among top 10 countries in the
world in terms of absolute as well as relative GDP exposure to floods (Table I.8). In case of
absolute economic exposure to cyclone, 6 countries from the region are found among the top 10
countries. Small island countries from the South-Pacific have the highest relative GDP exposure
to cyclones. Japan has the highest absolute GDP exposure to earthquakes in the world followed
by China (7th) and the Philippines (9th). Vanuatu has the highest relative GDP exposure (96.5%)
to earthquakes in the world. Several other countries from the Pacific, South Asia and North &
Central Asia have also high relative exposure to earthquakes. Figure 1.1.5 shows the Asia-
Pacific region having absolute GDP exposure to floods, cyclones and earthquakes.
Table I-7 Top 10 countries in the Asia-Pacific based on absolute and relative GDP exposure
                           Flood                                          Cyclone                                      Earthquake
  Rank




               Absolute                 Relative           Absolute                     Relative             Absolute                   Relative
            (Billion US$)                 (%)            (Billion US$)                     (%)             (Billion US$)                  (%)
   1            China1 (12.5)     Bangladesh1 (14.5)       Japan1 (1,226.7)     North Marina Isl.2 (59.4)    Japan1 (340.7)           Vanuatu1 (96.5)
   2       Bangladesh3 (9.7)       Cambodia2 (14.0)   Rep. of Korea4 (35.6)             Vanuatu9 (27.1)        China7 (16.0)     Solomon Isl.2 (46.3)
   3               India4 (9.3)       Vietnam3 (4.4)           China5 (28.5)                     11
                                                                                            Niue (24.9)   Philippines9(11.4)            Tonga6 (22.7)
   4              Japan6 (4.5)     Philippines5 (2.5)    Philippines6 (24.3)                Fiji13 (24.1)  Indonesia11 (7.9)    Papua New G.8(22.1)
   5           Thailand8 (3.0)       Thailand6 (1.8)     Hong Kong7 (13.3)                       8
                                                                                             Fiji (16.0)      Turkey14 (5.7)    Timor Leste13 (14.9)
   6        Philippines9 (2.5)           India8 (1.3)           India9 (8.0)              Japan14 (23.9)         Iran17 (3.8)     Philippines14 (11.2)
   7          Vietnam10 (2.2)        Myanmar9 (1.1)      Bangladesh13 (3.9)           Philippines5(23.9)    Australia25 (2.7)            Japan23(6.6)
   8     Rep. of Korea18 (1.2)      Lao PDR11 (1.1) North Marina Isl.19 (1.5)    New Caledonia16 (22.4)         India25 (2.1)       Kyrgystan35 (4.0)
   9         Indonesia19 (1.0)          Nepal13 (0.9)       Australia23(0.8)             Samoa21 (19.2)     Pakistan31 (1.4)       Azerbaijan36 (4.0)
  10        Cambodia21 (0.9)                  18
                                    Sri Lanka (0.6) New Caledonia25 (0.7)                Tonga24 (17.4) New Zealand34 (1.0)          Indonesia41(3.5)

Source: P. Peduzzi, UNEP/GRID-Europe
Note: Number in superscript against each country shows its global rank.




                                                                                                                                          11
Figure I-4 Absolute physical exposure map for floods, cyclones and earthquakes




Figure I-5 Absolute GDP exposure map for floods, cyclones and earthquakes




Exposure is one of the components of the risk, the other is vulnerability. Countries with the same
exposure to a hazard would have very different levels of risk if they have different vulnerability.
And when the analysis accounts for that factor, it suggested that the link between exposure and
risk is not straightforward. Figure I-6 shows the relationship between risk and exposure to floods
that have relatively higher frequency and lower impact. The quantitative risk in the vertical axis,
presented as potential deaths per million inhabitants caused by the floods that occur within 5

                                                                                                 12
years recurrence interval, was estimated based on a model of probability of damage level caused
by floods using 20 years-data, from 1990 to 2009. The horizontal axis present the population
exposed per year per million inhabitants. Higher potential causalities in countries with low
population exposure indicate a higher level of vulnerability. Nepal, for instance, has about the
same level of exposure than Thailand but have a higher risk of deaths, indicating its higher
vulnerability. On the other hand, Bangladesh has a risk level; compared with countries that face a
much lower exposure to these low- recurrence-interval floods.


Figure I-6 Human vulnerability to floods of higher frequency and lower impact (5 years
recurrence interval)

                                                              10


                                                              9
                                                                                                                    NPL



                                                              8
   Modelled potential casualities (per million inhabitants)




                                                                                               AFG


                                                              7


                                                              6

                                                                                                                                                   KHM

                                                              5


                                                              4

                                                                                                                                   VNM
                                                              3
                                                                                                PAK
                                                                                                                                                   BGD
                                                                                       MNG                                BTN

                                                              2
                                                                                                                     THA
                                                                                                                      IND
                                                                                             CHN
                                                                                              KOR             PHL
                                                                                                      IDN
                                                              1                                        LKA
                                                                                  TUR
                                                                                MYS                           MMR
                                                                     AUS     TLS       RUS    TJK
                                                                                 JPN     AZE
                                                                                       KGZ ARM GEO     NZL
                                                              0            PNG
                                                                                     UZB
                                                                                        KAZ TKM
                                                               100             1,000                          10,000                           100,000   1,000,000
                                                                                       Population exposed per year (per million inhabitants)


Source: ESCAP based on data from EM-DAT: The OFDA/CRED International Disaster Database – www.emdat.be – Université Catholique de
Louvain – Brussels – Belgium and exposure data from P. Peduzzi, UNEP/GRID-Europe.


Vulnerability to disasters depends on the type and intensity of the hazard. Countries that are not
particularly vulnerable to higher frequency and lower impact hazards may become highly
vulnerable if higher impact hazards start to happen more frequently. As indicated in Figure 1-7,
which presents the relationship between risk and exposure to floods that have relatively lower
frequency and higher impact, Bhutan and Tajikistan are highly vulnerable to floods of 20 years
recurrence interval, but are not particularly vulnerable to floods of 5 years recurrence interval
(Figure 1-6). Similarly, Myanmar and Bangladesh on their vulnerability to storms as shown in
Figure 1-8 and 1-9.



                                                                                                                                                                     13
Figure I-7 Human vulnerability to floods of lower frequency and higher impact (20 years
recurrence interval)

                                                              1,000




                                                                                                                       BTN
   Modelled potential casualities (per million inhabitants)




                                                                                           TJK



                                                               100



                                                                                              AFG
                                                                                                                 NPL                              KHM




                                                                                     MNG

                                                                                                      LKA
                                                                10                             PAK                                VNM             BGD
                                                                                              KOR


                                                                                                             PHL THA

                                                                                              CHN
                                                                                                     IDN
                                                                            TLS                                    IND

                                                                            MYS     RUS
                                                                                               GEO           MMR
                                                                              TUR
                                                                                    AZE ARM
                                                                        PNG JPN      KGZ              NZL
                                                                 1
                                                                                    UZB TKM
                                                                  100       1,000                            10,000                          100,000    1,000,000
                                                                                               Population exposed per year (per million inhabitants)
 Less than 1 per million: KGZ, NZL, PNG, JPN, TKM, UZB

Source: ESCAP based on data from EM-DAT: The OFDA/CRED International Disaster Database – www.emdat.be – Université Catholique de
Louvain – Brussels – Belgium and physical exposure data from P. Peduzzi, UNEP/GRID-Europe.



Figure I-8 Human vulnerability to storm of higher frequency and lower impact (5 years
recurrence interval)




                                                                                                                                                                    14
                                                             14
                                                                                                                                                 WSM
                                                             13

                                                             12                                                                                      PHL
  Modelled potential casualities (per million inhabitants)




                                                             11

                                                             10
                                                                                                                                                     FJI

                                                             9

                                                             8

                                                             7

                                                             6

                                                             5

                                                             4

                                                             3                          VNM
                                                                                                                                               VUT

                                                                                                                                       COK
                                                             2
                                                                                                                                                           GUM
                                                                                                                        KOR
                                                             1                         IND                           BGD
                                                                               PAK   CHN                                                             JPN
                                                                                                                              HKG
                                                                  MHLLKA AUS                             MMR      MAC          PYF   ASM         NCL NIU
                                                                                                                                               TON
                                                             0
                                                             1,000                    10,000                                         100,000                     1,000,000
                                                                                     Population exposed per year (per million inhabitants)


Source: ESCAP based on data from EM-DAT: The OFDA/CRED International Disaster Database – www.emdat.be – Université Catholique de
Louvain – Brussels – Belgium and physical exposure data from P. Peduzzi, UNEP/GRID-Europe.




Figure I-9 Human vulnerability to storms of lower frequency and higher impact (20 years
recurrence interval)




                                                                                                                                                                             15
                                                              10,000




                                                                                                                                               MMR
   Modelled potential casualities (per million inhabitants)




                                                               1,000                                                                                         COK
                                                                                                                                                     BGD


                                                                                                                                                                       NIU




                                                                                                                                                                     VUT
                                                                100
                                                                                                                                                                 WSM
                                                                                                                                                                           PHL

                                                                                                                                      VNM
                                                                                                                                                                       FJI

                                                                           PNG
                                                                                                                                                                                 GUM



                                                                 10                                                                   IND

                                                                                                                                                                   TON
                                                                                                                                                     KOR
                                                                                                                                                                      NCL
                                                                                                                           PAK
                                                                                            THA                                                        HKG
                                                                                                                                                                       JPN


                                                                                                                    AUS             CHN
                                                                                 IDN               TUV       NZL MHL                               MAC PYFASM
                                                                  1                                                  LKA
                                                                       1               10    100                1,000                10,000                100,000               1,000,000
                                                                                                         Population exposed per year (per million inhabitants)
 Less than 1 per million: ASM, PYF, IDN, MAC, MHL, NZL, LKA, TUV



Source: ESCAP based on data from EM-DAT: The OFDA/CRED International Disaster Database – www.emdat.be – Université Catholique de
Louvain – Brussels – Belgium and physical exposure data from P. Peduzzi, UNEP/GRID-Europe.


Similar analysis can be done on the economic impact of disasters. Figures I-10 and I-11 show, for
storm hazards with the recurrence interval of 5 and 20 years respectively, the relationship
between the economic risk - measured as potential damage and loss as a percentage of the GDP –
and the economic exposure of countries. They show that some of the Pacific Developing
Countries are are highly vulnerable to storms. In fact, the economic impact of 20-years recurrent
storms could be unbearable to some countries. In the case of Samoa, such hazards may represent
damage and loss over 100% of its GDP. Such high vulnerability of a LDC on the process for
graduating suggests the need to consider the vulnerability to disasters as one of the criteria for
the graduating process.

Figure I-10 Economic vulnerability to storms of higher frequency and lower impact (5 years
recurrence interval)




                                                                                                                                                                                             16
                                                               35.0
                                                                                                                                                                                        WSM




                                                               30.0
         Modelled potential economic damage and loss (% GDP)




                                                               25.0




                                                               20.0




                                                               15.0




                                                               10.0




                                                                5.0


                                                                              VNM
                                                                             MHL IND CHN             MACKOR BGD
                                                                                                            HKG                                                              TON
                                                                               *
                                                                0.0
                                                                         0 *                           5                              10                    15                                20
                                                                                                                     Economic exposure per year (% GDP)
                                                               * KHM IDN AUS PNG NZL PAK LKA THA
Source: ESCAP based on data from EM-DAT: The OFDA/CRED International Disaster Database – www.emdat.be – Université Catholique de
Louvain – Brussels – Belgium and economic exposure data from P. Peduzzi, UNEP/GRID-Europe.




Figure I-11 Economic vulnerability to storms of lower frequency and higher impact (20 years
recurrence interval)
                                                               1,000




                                                                                                                                                 WSM
  Modelled potential economic damage and loss (% GDP)




                                                                100

                                                                                                                                           TON




                                                                                                                                                            FJI
                                                                    10




                                                                                               BGD
                                                                             VNM                                                                                       VUT



                                                                         PAK
                                                                    1
                                                                         0                 5                 10                  15                    20         25               30
                                                                                                                  Economic exposure per year (% GDP)
                                                                *




                                                                                                                                                                                                   17
Source: ESCAP based on data from EM-DAT: The OFDA/CRED International Disaster Database – www.emdat.be – Université Catholique de
Louvain – Brussels – Belgium and economic exposure data from P. Peduzzi, UNEP/GRID-Europe.


Figures I-6 to I-11 indicate that poorer countries are more vulnerable to disasters. That is
consistent with findings that show an inverse relationship between GDP per capita and the
impact of disasters. This report tested the 68 official MDG indicators to verify if they explain the
variation of risk among countries with the same physical exposure (i.e. same population size
facing the same frequency of hazards). The study suggests that vulnerability to disasters can be
explained by the following social and economic indicators:
    – GDP per capita (US$ 2005 constant prices)
    – Percentage of Population undernourished (MDG1)
    – Percentage of Seats held by women in national parliament (MDG3)
    – Infant mortality rate (0-1 year) per 1,000 live births (MDG4)
    – Tuberculosis prevalence rate per 100,000 population (MDG6)
    – Proportion of the population using improved sanitation facilities (MDG7)
    – Internet users per 100 population (MDG8).

A composite index based on these indicators was used, together with the frequency of hazard and
the population size of countries, in a multiple regression analysis and these variables explain
73% of the variation on total causalities by floods between 1980-2009 among 95 countries and
68% of the variation on total causalities by storms among 79 countries.

Figure I-12 shows the contributions of the frequency of hazards, the size of the population and
the vulnerability to the risk of deaths owing to floods and storms. It suggests that, to reduce risk,
vulnerability has to be reduced at a fast pace to compensate for the increase in population and
increase in the frequency of weather-related hazards.

Figure I-12 Effects of vulnerability, population growth and hazards on risk to weather-related
disasters




                                       Storms
  Contribution to risk (casualities)




                                       Floods




                                                0%   10%   20%   30%         40%             50%           60%         70%           80%   90%   100%
                                                                                      Hazard type

                                                                       frequency of hazard    population     vulnerability   other

Source: ESCAP

                                                                                                                                                        18
And vulnerability to weather-related disasters has indeed decreased in all subregions in Asia-
Pacific in the last 20 years. As indicated in Figure I-13, South-East Asia has made the faster
progress while the Pacific Developing Countries may lag behind if they continue business as
usual. Figure I-13 shows the estimated vulnerability of five ESCAP subregions to floods and
storms. The estimates for 1990 to 2009 are based on the available GDP and MDG data. The
estimates for 2015 are based on the assumption that countries would mirror their past
performance while they progress towards the MDGs.

Figure I-13 Vulnerability to weather-related disasters

                        7



                        6



                        5



                        4
  vulnerability index




                                                                                         ENEA
                        3
                                                                                         NCA
                                                                                         PacDeveloping
                                                                                         SEA
                        2
                                                                                         SSWA



                        1



                        0
                             1990   2000             2009                2015


                        -1



                        -2
                                            year


Source: ESCAP


Such fast reduction in vulnerability has helped to counterbalance the increase in risk owing to
increase in the exposed population. Figure I-14 shows the contribution to percentage change in
risk owing to the decreasing vulnerability and the increasing population in the ESCAP
subregions. It indicates that the reduction in vulnerability has occurred evenly in all subregions
and that it has been higher than the effect of the increase in population.

Figure I-14 Contribution to percentage change in risk by reduction in vulnerability and increase
in population, 2000-2009




                                                                                                    19
                    8%




                    6%




                    4%




                    2%
   change in risk




                                                                          Vulnerability
                    0%
                                                                          Population growth
                            ENEA    NCA    PacDeveloping     SEA   SSWA


                    -2%




                    -4%




                    -6%




                    -8%


Source: ESCAP


However, as shown in the simulation presented in Figure I-15, if the frequency of weather-
related hazards increases because of climate change by, for instance, 2%, South-East Asia, West
and South Asia and The Pacific Developing Countries would start to experience increased risks.

Figure I-15 Simulation of change in risk owing to increase in frequency of weather-related
hazard by 2%
                    10.0%



                    8.0%



                    6.0%



                    4.0%
  chnage in risk




                    2.0%
                                                                          exposure
                                                                          vulnerability
                    0.0%
                             ENEA   NCA     PacDeveloping    SEA   SSWA


                    -2.0%



                    -4.0%



                    -6.0%



                    -8.0%
                                          ESCAP subregions




Source: ESCAP


In summary, whatever way we look at recent disaster statistics, it is quite clear that larger

                                                                                                20
conceptual framework of economic development policies in the Asia-Pacific countries need due
consideration of future disaster risks and integration of climate change scenarios in DRR
components, particularly when mega infrastructure developments in both rural and urban areas
are planned.



Projected Climate Change Impacts on Disaster Risks
Climate change and its potential impact on various aspects of human life, environment and
economic development have become the focus of intense debate in a variety of forums around
the world and the Asia-Pacific region is not an exception. It appears each stakeholder has its own
perspective on this issue depending upon their own “stake.” In DRR communities, the most
frequent question raised is whether there is scientifically proven evidence to link the upward
trend in the observed number of disasters caused by natural hazards with gradually emerging
evidence on global climate change. Obviously, based on the limited available data of last three
decades presented in Section 1.1, it is statistically difficult to quantify and isolate the exact
impact of climate change on frequency of occurrence and magnitude of disaster events,
considering the time dimension and randomness involved in both the climate and disaster
processes. However, improved reporting mechanisms of present day cannot entirely explain the
significant increases in disaster events over the last three decades, particularly hydro-
meteorological type, when the emerging evidences of linkages between physical changes in the
atmospheric, terrestrial and oceanic conditions, and their dynamic relationships with weather
processes that lead to disaster events are taken into account (see also Table 1.1.4 and Figure 1.1.3
in Section 1.1). This section analyzes the Asia-Pacific related climate change findings of various
international, regional and national agencies and discusses the implications for future DRR
approaches in the region, particularly for hydro-meteorological disasters such as floods, drought,
extreme temperature, typhoons, hurricanes, and wildfires.

Climate Change Projections for the Asia-Pacific Region

The future projections of global climate patterns are largely based on scientifically sophisticated
mathematical models of the global climate system that incorporate the long-term historical
observations of important weather factors and the physical processes of the atmosphere and the
oceans, including the expected growth in greenhouse gases from socio-economic scenarios for
the coming decades. The IPCC has examined the published results from many different models
and generally summarizes that globally by 2100:

   The global average surface warming (surface air temperature change), will increase by 1.1 -
    6.4 °C.
   The sea level will rise between 18 and 59 cm.
   The oceans will become more acidic.
   It is very likely that hot extremes, heat waves and heavy precipitation events will continue to
    become more frequent.
   It is very likely that there will be more precipitation at higher latitudes and it is likely that
    there will be less precipitation in most subtropical land areas.


                                                                                                   21
   It is likely that tropical cyclones (typhoons and hurricanes) will become more intense, with
    larger peak wind speeds and more heavy precipitation associated with ongoing increases of
    tropical sea surface temperatures.

Widespread changes in extreme temperatures have been observed in many regions of the world
over the last 50 years; most notably the higher frequency of high temperature days and heat
(IPCC AR4). The IPCC predicts that the warming is likely to be well above the global mean in
central Asia, the Tibetan Plateau and northern Asia, above the global mean in East and South
Asia, and similar to the global mean in Southeast Asia. It is very likely that summer heat
waves/hot spells in East Asia will be of longer duration, more intense, and more frequent. It is
very likely that there will be fewer very cold days in East Asia and South Asia. Boreal winter
precipitation is very likely to increase in northern Asia and the Tibetan Plateau, and likely to
increase in eastern Asia and the southern parts of Southeast Asia. Summer precipitation is likely
to increase in northern Asia, East and South Asia and most of Southeast Asia, but it is expected to
decrease in central Asia. An increase in the frequency of intense precipitation events in parts of
South Asia, and in East Asia, is very likely.

There is good evidence for an increase of the more damaging intense tropical cyclone activity in
the North Atlantic since 1970s, which is correlated with increases in tropical sea surface
temperatures. However, according to the IPCC, to date there is no clear trend evident in the
global annual number of tropical cyclones.

Extreme rainfall and winds associated with tropical cyclones are likely to increase in East,
Southeast and South Asia. Monsoonal flows and the tropical large scale circulation are likely to
be weakened.
However the report notes that due to lack of observational data there has been little assessment of
the projected changes in regional climatic means and extremes. Also, there are substantial inter-
model variances in representing monsoon processes, and a lack of clarity over changes in ENSO
further contributes to uncertainty about future regional monsoon and tropical cyclone behavior.
Consequently, quantitative estimates of projected precipitation change are difficult to obtain. It is
likely that some local climate changes will vary significantly from regional trends due to the
region’s very complex topography and marine influences.

Many long-term precipitation trends (1900-2005) examined by IPCC AR-4 indicate significant
increases in Northern and Central Asia, and more dry conditions in parts of Southern Asia. More
intense and longer droughts have been found over wider areas since the 1970s, particularly in the
tropics and subtropics. Higher temperatures and decreased precipitation have increased the
prevalence of drier conditions as well as contributing to changes in the distribution of droughts.
Changes in sea surface temperatures, wind patterns, and decreased snow pack and snow cover
also have been linked to drought occurrences.

According to UNEP’s Climate Change Science Compendium (2007) global average sea level is
rising as a consequence of three factors—thermal expansion of warming ocean water, addition of
melted water from the ice sheets of Greenland and Antarctica and glaciers and ice caps, and from
increased surface runoff. The average rate of global mean sea-level rise over the 20th century
was about 1.7 mm/year. During1993-2003 global mean sea level rose about 3.1 mm/year, and

                                                                                                  22
since 2003 the rate of rise has been about 2.5 mm/year. Prior to 1990, ocean thermal expansion
accounted for more than 50% of global sea-level rise. Since then, the contribution from thermal
expansion has declined to about 15% but this decrease has been countered by increases in
glacier, ice cap, and ice sheet contributions. While glaciers and ice caps exclusive of the ice
sheets dominate present-day contributions to sea-level rise, they collectively constitute a far
smaller total sea-level rise owing to their much smaller global volume. If current trends continue,
the glacier and ice cap reservoir will be exhausted by 2200 (UNEP, 2007).

While IPCC and UNEP present the global climate change scenarios and their implications,
though scattered, there is an emerging body of evidence from the Asia-Pacific region also that
highlights the possible linkages between changing weather processes and their effect on natural
hazards of the region. The remaining part of this section presents a collection of case studies
from the region on possible climate change impacts on GLOF, drought, sea-level rise, extreme
precipitation events, and forest fires.

Glacial Lake Outburst Flood (GLOF)

It is estimated that over the last one hundred years, the air temperature has increased by 0.3 to
0.6 ºC and by 2100 the temperature of the Indian sub-continent may increase further by 3.5 to
5.5ºC (IPCC-AR4). High-altitude glacial environments, being specially sensitive to the
temperature changes, serve as prominent indicators of global climate change. Several studies by
ICIMOD (2007) and other agencies such as SAARC (2008) show that Himalayan glaciers have
been melting at unprecedented rates in recent decades (Table and Figure below). One
phenomenon associated with glacial retreat is the formation of glacial lakes. As the size of these
lakes increases, so too does the risk of breaching of the unstable moraine dam, with a sudden
release of the stored water giving rise to a ‘glacial lake outburst flood’ or GLOF. Most of the
glacial lakes in the Himalaya have appeared within the last five decades, and the region has faced
devastating consequences as a result of such floods according to ICIMOD.
A comprehensive study aimed to investigate the impact of climate change on glaciers and glacial
lakes in the Himalayas based on empirical evidence and time-series data and information was
conducted by ICIMOD and UNEP (2007). The Dudh Koshi sub-basin of Nepal and the Pho Chu
sub-basin of Bhutan are two known hotspots of glacial activity and have both witnessed
devastating GLOFs in the past, thus these two areas were chosen as the focus of the case studies.
The case studies revealed some interesting insights on retreating glaciers and the growth of
glacial lakes, and the main observations and specific findings were as follows:

• It is apparent that the glacier retreat rate has accelerated in recent times as compared to the
1970’s. The valley glaciers and small glaciers are retreating fast. The Imja glacier retreated at an
average rate of 42m per year in the period from 1962 to 2000. The retreat rate increased to 74m
per year during 2001 and 2006, when it became one of the fastest-retreating glaciers in the
Himalayas.

• Some of the smaller glaciers in Bhutan have completely disappeared as confirmed by the
satellite images of 2000–2001. In the Bhutan Himalaya the average retreat rate of glaciers was
around 30m per year between 1963 and 1993. Some of the glaciers in the Lunana region of the
Pho Chu sub-basin were retreating as fast as 57m per year in 2001, with an increase in retreat

                                                                                                  23
rate as high as 800% since 1970.

• During a glacier retreat, there is a high probability of formation of new lakes, as well as
merging and expansion of existing ones, at the toe of a valley glacier. In the Dudh Koshi sub-
basin of Nepal, the total number of lakes has decreased by 37%, but their total area has increased
by 21%. Similarly in the Pho Chu sub-basin of Bhutan, the total number of lakes has decreased
by 19% but the total area has increased by 8%.

• The Luggye Tso in the Pho Chu sub-basin of Bhutan, from which a GLOF originated in 1994,
is once again in the process of enlargement. The Thorthormi glacier in Bhutan had no
supraglacial ponds during the 1950s, but now there is a cluster of newly formed supra-glacial
lakes which are merging. If this trend continues, they will further merge to form a large lake
posing a serious GLOF threat in the near future.

• The hazard assessment of the Imja Tsho indicated that the lower terraces at several villages
have a possibility of overtopping by a GLOF.

• Monitoring of Lake Imja Tsho using ESA RADAR satellite imagery provided a useful means
for detecting growth (change) of the lake over a short time (as quickly as monthly). Such a
technique may prove useful for issuing early warnings in a cost effective manner.

These findings, howsoever localized, do warrant a concerted attempt to improve our scientific
understanding of the impact of global warming on melting of glaciers. By investigating much
larger areas, it will be possible to assess the effects that the change in global climatic patterns is
having in the Himalayas. Concerned agencies such as ICIMOD and SAARC also caution that
action is needed now by the international community to safeguard these precious regional
resources. GLOF mitigation measures and commissioning of early warning systems are daunting
and challenging tasks, and also quite expensive. Satellite-based techniques using RADAR
imageries may prove a useful tool for monitoring a glacial lake independent of local weather
conditions. Considering the investments required for such early warning systems by smaller
countries, a regional approach may prove more useful.

Drought

According to United Nations estimates, one third of the world’s population lives in areas with
water shortages and 1.1 billion people lack access to safe drinking water. Globally droughts are
the second most geographically extensive hazard after floods, i.e., covering 7.5 per cent and 11
per cent of the global land area each (Liu, 2007). The land area, population and GDP loss
affected by drought amount to 970 million km2, 57.3 billion and US$108.6 billion, respectively
(Liu, 2007). Unlike earthquakes, floods, and storms, droughts do not cause immediate physical
damage, but their impact is long lasting and widespread as food and water security of a large
proportion of population is affected.

In Asia, drought is the second highest disaster after flood in terms of affected population and it
occupies fourth position in terms of damage. Drought is considered China’s greatest disaster. For
example, in 2006, a severe drought in Southern China left 520,000 people short of drinking water

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and damaged 102,000 hectares of crops, amounting to economic losses of over US$50 million
(Liu, 2007). China’s South-Western city of Chongqing, located along the upper reaches of the
Yangtze River, suffered from its worst drought in half a century. The 2006 drought caused
Chongqing financial losses of nearly US$1.04 billion. Nearly 8 million local residents had
difficulty accessing drinkable water, and some 2.07 million hectares of farmland were affected.
Droughts in areas across China that summer left 18 million people short of drinking water.
Chongqing drought raised climate change worries, some experts believe the unusual drought in
Chongqing and Sichuan in the summer of 2006 was an evidence of increase in abnormal climatic
occurrences related to global warming (Liu, 2007).

Australia is the driest inhabited continent even though some areas have annual rainfall of over
1200 mm. Large areas of Eastern Australia suffered generally drier than normal conditions from
mid-1979 through to the end of 1981. For the 10-month period from April 1982 to February
1983, almost all of Eastern Australia was severely affected and large parts of South-Eastern
Australia suffered their lowest rainfall on record. The worst losses occurred during this latter
period, accounting for an amount in excess of A$3 billion of the total estimated loss (Liu, 2007).
Research indicates that severe drought affects some part of Australia about once every 18 years;
intervals between severe droughts have varied from 4 to 38 years (Liu, 2007). Severe drought
occurred in 1982, 1994 and 2002. Severe long-term drought, stemming from at least three years
of rainfall deficits, continued during 2005. The most serious drought occurred in 2006 and was
estimated to be the worst in 1,000 years (Liu, 2007).

Severe drought has hit much of Central and South-West Asia since 1999. The persistent multi-
year drought in Central and South-West Asia has affected close to 60 million people. Agriculture,
animal husbandry, water resources, and public health have been particularly stressed throughout
the region.

Preliminary analysis suggests that the drought is related to large-scale variations in the climate
across the Indian and Pacific Oceans due to global warming and related weather disturbances
such as ENSO (Liu, 2007). Using satellite remote sensing techniques and surface observation
stations, modern drought monitoring and prediction are particularly useful for drought planning
and mitigation . China has made substantial progress in dynamic monitoring of soil moisture and
drought using these techniques. To further address this, the Drought-Flood Monitoring System
and Operational System for Climate Impact Assessment and for Short-term Climate Prediction
have been developed at the National Climate Program of China (Li, unknown year).

Due to low-intensity and long-duration impact, drought related disasters often receive low
priority in DRR. This perspective needs a critical review as droughts become more frequent and
widespread.

Impacts of Sea Level Rise

The most direct impact of future sea level rise will be first felt by all coastal cities and
communities around the world. In the Asia-Pacific region, most vulnerable are Maldives,
Bangladesh and the Pacific sub-region, which has 22 small-island developing
countries/territories and many of these are low lying atolls with limited land space, and human

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and financial resources. Fishing, tourism and agriculture dominate the economies of the Pacific
Islands and these sectors will be affected by the sea level rise. The Pacific Islands already face
natural hazards such as cyclones, storm surges, droughts and flooding. WWF (2010) predicts sea
levels may rise to as much as 0.88 m in the 21st Century and will greatly threaten all key
development sectors in the Pacific. For example, In Fiji, half of the population lives within 60
km of the shore with 90% of villages located on the coast. Sea level rise may threaten village
livelihoods, and traditional settlement patterns, as people may have to move away from their
customary land, to higher ground. On Upolu Island, Samoa 70% of churches and 60% of
schools are located on coastal lowland. Many of the island people rely on fisheries as a source of
food and income from coral reef and mangrove habitats that are threatened by warming ocean
temperatures and sea level rise. Specifically, the following impacts are expected (WWF, 2010):

      There will be less land for use due to sea level rise, caused by climate change flooding
       coastal plains. Low lying atolls are especially at risk.
      There will be less freshwater available for use. Climate change increases the incidence of
       extreme events such as floods, droughts and cyclone which threaten freshwater supply.
      Agriculture will be affected. Coastal plains, where most of agriculture is based, can be
       salinised due to sea-level rise and become less productive. Increased disasters will
       damage crops and warmer, wetter climate will favor the breeding of pests.
      Reefs and marine resources will be affected. Increased ocean temperatures degrade coral
       reefs through coral bleaching. Some migratory species, such as Tuna, will move to areas
       where ocean conditions are more suited to their survival.
      Disease prevalence will increase as warmer, wetter conditions favor the breeding of
       disease carrying insects such as mosquitoes and aquatic pathogens.
      Tourism will be affected by the increase in disasters, biodiversity loss and increased
       prevalence of disease.
      A less productive resource-base, increases in the severity of disasters and poor human
       health will affect the economic development.

Not only the coastal communities, millions of people in generally low-lying nations such as
Bangladesh, along deltas and river systems like the Mekong (see Box below) will have to
respond to rising sea levels during the 21st century and beyond. UNU and UNHCR (2009)
mapped the effects of 1-m and 2-m sea level rise on human migration and displacement, and
concluded followings among others:

• Disasters continue to be a major driver of shorter-term displacement and migration. As climate
  change increases the frequency and intensity of natural hazards such as cyclones, floods, and
  droughts, the number of temporarily displaced people will rise. This will be especially true in
  countries that fail to invest now in disaster risk reduction and where the official response to
  disasters is limited.

• Sea level rise will worsen saline intrusions, inundation, storm surges, erosion, and other coastal
  hazards. The threat is particularly grave for island communities. There is strong evidence that
  the impacts will devastate subsistence and commercial agriculture on many small islands.

• In the densely populated Ganges, Mekong, and Nile River deltas, a sea level rise of 1 meter

                                                                                                  26
 could affect 23.5 million people and reduce the land currently under intensive agriculture by at
 least 1.5 million hectares. A sea level rise of 2 meters would impact an additional 10.8 million
 people and render at least 969 thousand more hectares of agricultural land unproductive.

• Many people will not be able to move far enough to adequately avoid the negative impacts of
  climate change as migration requires financial, social, and political resources that the most
  vulnerable populations frequently do not have. Case studies also indicate that poorer
  environmental migrants can find their destinations as precarious as the places they left behind.

Trend of Tropical Cyclones in the region

Typhoon Committee (ESCAP and WMO, 2009) has observed significant inter-decadal and inter-
annual fluctuations in the frequency of tropical cyclone (TC) formation and occurrence over the
Western North Pacific (WNP) in the last 50 years. However, based on available publications, the
Committee sees no clear long term trend in the TC frequency over WNP. An additional analysis
utilizing 5 different best track datasets with data up to 2008 and allowing adjustments for the
difference in averaging period between datasets shows that there is either a decreasing trend or
no trend in the annual number of TCs (tropical storm or above) and typhoons in WNP (ESCAP
and WMO, 2009). Further, the findings suggest that:

The number of land falling TC varies from one region to the other. There is no significant linear
trend in the frequency of land falling TCs in Japan and the Philippines. The trends of land falling
TCs in China and Thailand are decreasing. The trend of TC influencing Republic of Korea is
increasing in recent years, but it is not conclusive yet.

In China, there is a decreasing trend in the maximum intensity of land falling TCs in recent years
but the mean intensity of land falling TCs has no trend. The extreme wind induced by tropical
cyclone affecting China has a decreasing trend and the total amount and intensity of TC
precipitation has no significant trend.

With regard to the future ESCAP and WMO (2009) conclude that a majority of the climate
models project a reduction in the number of TCs in the WNP in different greenhouse gas
scenarios. While there are fewer studies on the change of TC intensity, some of the model
projections suggest an increase in the number of intense TCs in the WNP in a warmer climate.
Although climate models could provide us with projections for the future changes in TC activity,
there exists a variety of uncertainties and limitations in the climate modeling and associated
downscaling methods which may affect the skill and reliability of the projections, in particular at
regional scale.

More frequent extremely heavy rainfall in short time period

As per the IPCC AR4, the frequency of heavy precipitation events has increased over most land
areas, which is consistent with global warming and the observed increases of atmospheric water
vapor. However, this observation is based on existing methods of rainfall monitoring. The Japan
Meteorological Agency (JMA) has come up with an improved procedure to enhance our
understanding of such new weather events. JMA observes precipitation at one-hour intervals at

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about 1,300 regional meteorological observing stations (collectively known as the Automated
Meteorological Data Acquisition System, or AMeDAS) all over Japan. Observation was started
in the latter part of the 1970s at many points. Although the period covered by AMeDAS data is
shorter than that of Local Meteorological Observatories or Weather Stations (which have records
going back about 100 years), AMeDAS has about nine times as many stations. It is therefore
relatively easier to catch localized heavy precipitation using AMeDAS data.

Long-term changes in the frequency of heavy rainfall over the most recent 30-year period
covered by AMeDAS can be ascertained by tallying up the frequency of days with over 200 mm
and over 400 mm of heavy rain, and the frequency of hours with over 50 mm and over 80 mm of
strong rain observed by AMeDAS every year. The number of AmeDAS points has been about
1,300 since 1979, though the total in 1976 was about 1,100. JMA therefore normalizes the data
into rain frequencies per 1,000 points to eliminate the influence of differences in the number of
points from year to year.

The change in the frequency of strong hourly rain and the change in the frequency of heavy daily
rain, based on 11-year average values, show a gradual increase in all cases. Additionally,
statistical significance is found in the increasing tendency for frequencies of over 50 mm and
over 80 mm of strong hourly rain, and of over 400 mm of heavy daily rain, but not for the
frequency of over 200 mm of heavy daily rain. However, since the observation period of
AMeDAS is short and the frequencies of heavy and strong rain change considerably every year,
further data accumulation is necessary to accurately capture the long-term trend.

Using daily rainfall data from 1,803 weather stations across India, Goswami et. al. (2006) are
able to show (i) significant rising trends in the frequency and the magnitude of extreme rain
events and (ii) a significant decreasing trend in the frequency of moderate events over central
India during the monsoon seasons from 1951 to 2000. However, the seasonal mean rainfall does
not show a significant trend, because the contribution from increasing heavy events is offset by
decreasing moderate events. The authors conclude that substantial increase in hazards related to
heavy rain is expected over central India in the future.

Floods are a major cause of death and destruction in many countries of the region. So it would be
beneficial for all if similar rainfall monitoring and observation sharing programs are established
in the region for better DRR.

Forest Fires

A recent international symposium in Korea on Regional/National Impact of Climate Change on
Fire Regimes Ecosystems (GFMC, 2009) observed that throughout the Asian region forest fire
regimes are undergoing changes which are primarily induced by humans and aggravated by
climate extremes. In equatorial Asia the use of fire in converting native primary or secondary
vegetation is highest in the region. Main current burning activities are related to traditional
practices of conversion of peatlands to plantations, notably biofuel plantations. Wildfires
spreading from land-use fires are favored by dry spells or extended droughts during El Nino-
Southern Oscillation (ENSO) events. Increasing severity and frequency of ENSO events are a
consequence of global climate change (GFMC, 2009). In the seasonal forests of mainland South

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and Southeast Asia regular seasonal smoke pollution caused by forest fires are aggravated by
industrial pollution and other burning activities such as trash burning. The so called “Asian
Brown Cloud” or the seasonal smoke pollution in Northern Thailand are a consequence of
multiple sources of fire. In the mountain regions of the Himalayas regional warming linked to
climate change is predicted to alter the snow and ice regimes in high-altitude ecosystems.
Rapidly melting glaciers will not only impact the drinking water supply of around one billion
people but also may affect regional vegetation dryness and fire regimes. In Central Asia a trend
of regional desiccation as a consequence of climate change is observed. Unsustainable forestry
practices, often illegal, are influencing fire hazard and increase wildfire risk and severity. Besides
regional drying wildfires are becoming a major force of steppization of Central Asia. In the
current regions of continuous or discontinuous permafrost of Northern Asia regional warming
will affect permafrost, forest cover and fire regimes. In Northeast Asia, notably in the Far East of
Russia, mixed forest ecosystems are becoming increasingly vulnerable to fire as a consequence
of regional climate change, careless fire use and reduced institutional capacities to manage fires
(GFMC, 2009).



Climate Change Impacts and Future Disaster Risks
As is evident from the above projections, there is significant uncertainty and diversity associated
with exact future status of individual weather parameters, making it extremely difficult to
quantify the physical impact of any particular climate change process. However, based on serious
impacts of events that have occurred in past decades and climate change trends projected by
IPCC, some qualitative extrapolations have been drawn by international agencies such as IPCC
and the World Bank with regard to the future disaster risks (both physical and economic) and
development in the Asia-Pacific region.

Climate change in the absence of any counter measures is expected to influence future disaster
risks in three different ways, firstly through the likely increase in weather and climatic hazards
such as global warming, sea-level rise, and erratic precipitation patterns, secondly through
increases in the vulnerability of communities to natural hazards due to ecosystem degradation,
reductions in water resources and food availability, and changes in livelihoods, and thirdly by
pushing more populations to a higher level of exposure to hazards. Environmental degradation
and rapid unplanned urban growth in many parts of developing Asia-Pacific countries, coupled
with climate change, will further reduce the capacity of many local communities to cope with
even the existing levels of disastrous natural hazards.

The two boxes below demonstrate two different impact perspectives – one on key development
sectors (IPCC, 2007) and the other on Asia-Pacific sub-regions (IPCC, 2007 and World Bank,
2010). However, both perspectives highlight underlying relationships of climate change, disaster
risks and economic development.




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Conclusions and Recommendations

1.4.1 Conclusions

   (1) Last 30 years data demonstrate a continuous rise in occurrence of disasters caused by
       natural hazards in the Asia-Pacific region, economic losses/damages, number of people
       affected and in some cases number of people killed. The trends are more or less similar in
       all the sub-regions of the Asia-Pacific. Probable factors behind these upward trends
       include improved disaster reporting mechanisms, increase in human population, rapid
       economic development, and impacts climate change on hydro-meteorological processes.
   (2) The underlying factor for the increasing trend of casualties and damage is the high levels
       of absolute and relative exposures to natural hazards in all sub-regions of the Asia-
       Pacific.
   (3) Among the different types of disasters, hydro-meteorological disasters are the most
       frequent, causing a greater loss of human life, livelihoods and economic damages in
       Asia-Pacific as compared to the past. Last 30 years data confirm a much faster increase
       in the number of climate sensitive disasters – such as flood, drought, cyclone, extreme
       temperature and wet landslides – compared to the number of earthquakes which is the
       main geological disaster in the region. It is worth noting that these trends take into
       account both the improved reporting mechanisms and increased exposure due to
       population growth and urbanization, as the influence of these factors are same for both
       categories (hydro-meteorological and geological) of disasters.
   (4) Some countries in the Asia-Pacific region suffer significantly from neglected disasters
       such as tornadoes/cyclones in Bangladesh and the Pacific Island countries. A
       significantly large number of small but frequent disasters from Indonesia and Sri Lanka
       go unreported in official database such as EM-DAT, highlighting the need for review of
       existing diversity in disaster reporting mechanisms at national and international levels.
   (5) Climate change has been shown by a number of case studies from Asia-Pacific region to
       have an impact on GLOF events, drought, sea-level rise, ENSO related disturbances, and
       extreme weather phenomenon.
   (6) There is an urgent need for greater regional cooperation for disaster risk reduction
       through advocacy, knowledge-exchange, and capacity development. Mega-disasters
       usually strike several countries at the same time, thus regional approach to monitoring,
       data-sharing, and preparedness will be useful and mutually beneficial.

1.4.2 Recommendations

       Promote to develop an official disaster database in the region.
       Recommend to integrate a regional or national level climate change scenarios for
        better disaster risk reduction.
       Recommend to develop a regional network for climate change adaptation and disaster
        risk reduction.

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Boxes
Box 1: Recent Mega-disasters of Asia-Pacific Region
Cyclone Nargis (Myanmar) - May 2 and 3, 2008
  Category: 4 (Saffir-Simpson scale)
  People killed: 84,530 deaths and 53,836 missing
  People affected: 2.4 million
  Economic Damage: 4 billion US$

Sichuan Earthquake (China) - 12 May 2008
   Magnitude: 7.9 (Richter scale)
   People killed: 68,858 deaths and 8,618 missing
   People affected: 45.6 million
   Economic Damage: 85 billion US$

Asian Tsunami - 26 December, 2004
   Magnitude: 9.3 (Richter scale)
   People killed: 184,167 And 45,752 missing
   People affected: 5.0 million
   Economic Damage: 10 billion US$

Box 2 Disasters in Pacific Island Countries

Pacific Island Countries (PICs) are vulnerable to a range of natural hazards, such as cyclones,
volcanic eruptions, earthquakes, floods, tsunamis, landslides and droughts. The small, highly
dispersed land areas and populations, and changing nature of life in the Pacific, intensify this
vulnerability. Official statistics suggest that natural hazards have a considerable economic impact
on development in the Pacific. (SOPAC, 2008). The real total impact of disasters caused by
natural hazards, including long-term impacts on the living conditions, livelihoods, economic
performance and environmental assets of Pacific Island Countries, is likely to be much larger
(SOPAC, 2008). In addition, due to the small populations, economies and land areas of many
Pacific Island Countries, unreported disaster-related damages that are small relative to the
damages elsewhere in the world can have a large impact relative to the country’s total GDP and
population.

Many small islands are affected by random cyclonic events, which are a major problem for
communities, often causing significant storm damage and flooding. Storm surges have often
inundated land, caused loss of life and severely damaged infrastructure in some small islands, for
example, atolls in Tuvalu, the Marshall Islands, Federated States of Micronesia and the northern
Cook Islands. During
these events, freshwater lenses may receive considerable inputs from land inundation by
seawater and subsequent infiltration, and many months may pass before they return to a potable
condition.
The frequency of tropical cyclones has been related to the ENSO cycle (SOPAC, 2002).



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The unfortunate reality is that disasters caused by natural hazards can have a debilitating impact
upon Pacific island economies. According to the World Bank (2006), disasters in the Pacific have
reportedly directly affected more than 3.4 million people and led to more than 1,700 reported
deaths in the region (excluding PNG) since 1950. In the 1990s alone, reported disasters cost the
Pacific Islands region US$2.8 billion in 2004 terms (World Bank, 2006). However, it is only at
the national level that the true impact of disasters on the economy are visible. This is because,
compared to developed countries with larger reserves to draw on in times of disaster, the small
size of most Pacific island states means that disaster can have a disproportionately high impact
on their economy. Accordingly:

      During major disaster events, Samoa reported average economic disaster costs of 46
       percent of annual GDP. (World Bank, 2006);
      The 2007 earthquake and accompanying tsunami that hit the Solomon Islands cost the
       country around 90% of the 2006 Government Budget (ADB, 2007);
      Cyclone Heta which hit Niue in 2004 effectively completely wiped out the national GDP,
       with immediate losses in 2004 amounting to over five times that of the GDP (SOPAC,
       2008).

These are only the direct estimates of the costs of disasters and are based in immediate losses
such as the destruction of infrastructure and crops. However, disasters also indirectly impact
economic growth further by removing access to infrastructure such as inability to get produce or
producers to markets and lowering economic capacity such as loss of educational opportunities.

As there has been relatively little research on broader disaster impacts in the Pacific, the true
costs continue to be underestimated, creating problems in alerting policy makers and
international donors to the serious economic consequences of natural hazards and the imperative
for integrating comprehensive DRR into national development planning (SOPAC, 2008). Despite
the serious negative impacts of disasters caused by natural hazards in the Pacific, there is no
systematic collection of comprehensive data on these effects. The understanding and
documentation of these effects are vital to the development of long-term policies for
reconstruction, mitigation and preparedness. The lack of data also limits the scope for conducting
cost-benefit analyses of DRR measures (SOPAC, 2008).

Box 3 Tornadoes in Bangladesh

Bangladesh lies between the Himalayas to the north and Bay of Bengal to the south, and
geographically characterized by an intricate river system, complex coastal configuration, and
shallow bathymetry. This unique geography provides cold heavy air from the north and warm
moist air from the south, leading to favorable conditions for severe thunderstorms which spawn
tornadoes or other strong winds during pre-monsoon (March-May) and post-monsoon (October-
November) seasons. Tornadoes are identified as one of the unpredictable localized hazards in
Bangladesh which result in significant deaths and disabilities, loss of income, and destruction of
resources. In recent decades, they have drawn little attention, as the emphasis on disaster
management has been dominated by floods and cyclones. The frequency of tornadoes in
Bangladesh is similar to that of the central United States, and is among the highest in the world.
More than 10,000 deaths have been attributed to tornadoes during the period 1961 to 1996

                                                                                                32
(IAWE, 2009). A late 2009 forum of national and international experts in Bangladesh concluded
that climate change is expected to increase the frequency and intensity of severe events such as
tornadoes. Based on the number of casualties and overall impact on national economy, tornadoes
and thunderstorms are now considered as one of the major hazards and exceeded only by
cyclones and floods in Bangladesh. Bangladesh is making efforts to reduce the impact of such
neglected localized disasters within the larger context on national disaster risk reduction and
management (IAWE, 2009).



Box 4: Indirect Consequences of Sea-level Rise
Impacts of Climate Change on Pesticide Fate in the Mekong Delta, Vietnam

It is predicted that the Mekong Delta (MKD) will be seriously impacted by climate change
through sea level rise; warmer, longer and more arid dry seasons; increased flooding during the
rainy season and elevated CO2 concentrations. Some of the predicted changes are considered to
be first order climate drivers for pesticide fate in the environment e.g. temperature, rainfall
pattern and intensity. The predicted changes in climate will likely also influence pesticide fate
indirectly via changes in pesticide use mainly driven through altered development, reproduction
and/or dispersal of invertebrate pests; changes in resistance and cultivation conditions of
common crop varieties; and changing land use patterns. For example, since the beginning of the
renovation (“Doi moi”) period in 1986, the Mekong Delta in Vietnam experienced an extensive
transformation process in agricultural sector characterized by an enhanced use of agrochemicals.
IPM (Integrated Pest Management) and 3R3G (3 Reductions, 3 Gains) practices helped to curtail
pesticide use. Recent – assumed climate change influenced - severe outbreaks of insect pests and
diseases are undermining these positive developments. The share of climate change in these
outbreaks is not well understood. Meanwhile, climate change will increasingly influence land
use change which will probably remain the main driver for future changes in pesticide use
patterns.

Source: Zita Sebesvari, Huong TT Le and Fabrice G. Renaud




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Box 5: Climate Change Impacts on Key Development Sectors
While climate change will affect all countries, poor communities are more likely to suffer as they
tend to live in high-risk areas such as unstable slopes and flood plains, and often cannot afford
well-built houses. Moreover, many of them depend on climate-sensitive sectors, such as
agriculture, and have little or no means to cope with climate change with poor access to public
services. Climate change is expected to reduce already low incomes and increase illness and
death rates in many developing countries, making disaster risk reduction more challenging.

At the current pace of urbanization, environmental degradation and climate change the
vulnerability of major Asian cities in floodplains and coastal areas is growing rapidly and
effective urban risk reduction requires particular attention. However, rural vulnerability and
poverty feed into the exponential growth of cities in Asia and therefore risk (and poverty)
reduction in the country side is equally important.

The IPCC Fourth Assessment Report of the Working Group II “Impacts, Adaptation and
Vulnerability” describes the likely effects of climate change, including from increases in extreme
events. The effects on key sectors, if not tackled in time, may be summarized as follows:

Water: Drought-affected areas will likely become more widely distributed. Heavier precipitation
events are very likely to increase in frequency leading to higher flood risks. By mid-century,
water availability will likely decrease in mid-latitudes, in the dry tropics and in other regions
supplied by melt water from mountain ranges. More than one sixth of the world’s population is
currently dependent on melt water from mountain ranges.

Food: While some mid-latitude and high-latitude areas will initially benefit from higher
agricultural
production, for many others at lower latitudes, especially in seasonally dry and tropical regions,
the increases in temperature and the frequency of droughts and floods are likely to affect crop
production negatively, which could increase the number of people at risk from hunger and
increased levels of displacement and migration.

Industry, settlement and society: The most vulnerable industries, settlements and societies are
generally those located in coastal areas and river flood plains, and those whose economies are
closely linked with climate sensitive resources. This applies particularly to locations already
prone to extreme weather events, and especially areas undergoing rapid urbanization. Where
extreme weather events become more intense or more frequent, the economic and social costs of
those events will increase.

Health: The projected changes in climate are likely to alter the health status of millions of people,
including through increased deaths, disease and injury due to heat waves, floods, storms, fires
and droughts. Increased malnutrition, diarrheal disease and malaria in some areas will increase
vulnerability to extreme. Public health and development goals will be threatened by longer term
damage to health systems from disasters.
                                                                             Source: IPCC (2007)

Box 6: Climate Change Impacts and the Asia-Pacific Region

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According to the IPCC, Asia’s sustainable development will be challenged as climate change compounds
the pressures that rapid urbanization, industrialization, and economic development have placed on natural
resources. One of the main issues will be the availability of adequate fresh water, which by the 2050s will
be a concern for possibly more than one billion people. The continued melting of glaciers in the
Himalayan region is projected to increase flooding and rock avalanches and to adversely affect water
resources in future in the region. Asia’s coastal areas, and especially its heavily populated delta regions,
will become even more prone to increased flooding because of both rising sea levels and river flooding.
Millions of people are likely to be affected by floods, storm surges and other coastal hazards every year
due to rising sea levels by the 2080s, particularly in the large deltas of Asia such as Greater Mekong Delta
and the small island states of the Pacific. By mid-century, reduced water resources are expected in many
small islands of the Pacific. Small island states in the Pacific, coastal systems and other low-lying areas
are especially vulnerable to the effects rising sea levels and extreme weather events. The World Bank’s
2010 World Development Report relates these projected climate change impacts on various sub-regions of
the Asia-Pacific.

For South Asia, owing to already degraded natural resource base resulting from geography coupled with
high levels of poverty and population density, water resources will be a major cause of concern. The
monsoon rains which provide 70 percent of annual precipitation in a four-month period and the rapidly
melting of Himalayan glaciers will have direct impact of climate change driven weather processes. Rising
sea levels are a dire concern in the sub-region region, which has long and densely populated coastlines,
agricultural plains threatened by saltwater intrusion, and many low-lying islands. In more severe climate-
change scenarios, rising seas would submerge much of the Maldives and inundate 18 percent of
Bangladesh’s landmass.

In East Asia and the Pacific one major driver of vulnerability is the large number of people living along
the coast and on low-lying islands—over 130 million people in China, and roughly 40 million, or more
than half the entire population, in Vietnam. A second driver is the continued reliance, particularly among
the poorer countries, on agriculture for income and employment. As pressures on land, water, and forest
resources increase—as a result of population growth, urbanization, and environmental degradation caused
by rapid industrialization—greater variability and extremes will complicate their management. In the
Mekong River basin, the rainy season will see more intense precipitation, while the dry season lengthens
by two months. A third driver is that the region’s economies are highly dependent on marine resources—
the value of well-managed coral reefs is $13 billion in Southeast Asia alone—which are already stressed
by industrial pollution, coastal development, overfishing, and runoff of agricultural pesticides and
nutrients.

Vulnerability to climate change in Central Asia is driven by environmental mismanagement during greater
part of the 20th century and the current poor state of much of the sub-region’s infrastructure. For example:
rising temperatures and reduced precipitation in Central Asia will exacerbate the environmental catastro-
phe of the disappearing Southern Aral Sea, particularly caused by the diversion of water to grow cotton in
a desert climate, while sand and salt from the dried-up seabed are blowing onto Central Asia’s glaciers,
accelerating the melting caused by higher temperature. Poorly constructed, badly maintained and aging
infrastructure and housing are ill suited to withstand storms, heat waves, or floods.

                                                         Source: IPCC (2007) and World Bank (2010)




                                                                                                         35
Box 7
           Low-frequency High-risk Tsunami in the Indian Ocean via Sedimentation Survey

 from “Estimating the Recurrence Interval and Behavior of Tsunamis in the Indian Ocean via a Survey of Tsunami-related Sedimentation”, 18-19
                                                                                                                March 2009, Tsukuba, Japan


        Recent studies indicate gigantic earthquakes repeat at several hundred years interval in
the subduction zones in the world, including the source region of the 2004 Sumatra-Andaman
earthquake. Forecasting location, time and size of earthquakes require information on recurrence
history of past earthquakes, from which probably of future earthquakes can be calculated. For
earthquakes with long recurrence intervals, geological data such as tsunami deposits are essential
to estimate the earthquake history. (p11,.K.Satake)
        Studies are crucial to understanding the hazard posed by Low Probability High
Consequences (LPHC) type disaster like tsunamis, because sediments left in the wake of
tsunamis are often the only discernable record that a coastline has been struck. (p14,. Andrew Moore)




                                                                                            Pangandaran
  <Indonesia>
        In Meulaboh, western coast of Nanggröe Aceh Darussalam Province, sand sheets
represent earlier tsunamis soon after AD 1290-1400 and after AD 780-990. An additional sand
sheet of limited extent might correlate with a documented smaller tsunami of AD 1907. (p25.
Monecke, et al., 2008. Nature, 455, 1232-1234. )
       In Simeulue Island, Nanggröe Aceh Darussalam Province, a fresh, uneroded coral
boulder from a paleo-tsunami layer yields an age consistent with a historically recorded
earthquake (M~8.5) in 1861. Another paleo-tsunami layer may have been deposited by a tsunami
associated with an earlier uplift event occurred around 1799, documented by an uplifted coral
microatoll at the site. (p22, 23, Fujino, et. al)(Meltzner et al., AGU Fall Meeting 2007)
       In Pangandaran, West Java, the deposit is correlated to the tsunami in 1921 (p20, Eko Yulianto,
et.al)

     <Probabilistic Tsunami Hazard Analysis>
     The 2004 earthquake is approximately predicted to be of 520 years return period earthquake
from probabilistic tsunami hazard analysis (PTHA) in Banda Ache.


                                                                                                                                        36
         Magnitude (Mw)                Return Periods (year)             Tsunami height (m) at Banda Aceh
               9.2                             520                                      9.5
               8.5                             250                                      5.2
               8.0                             120                                      2.7
               7.5                              55                                     1.11
               7.0                              25                                     0.48
                                                                                                                     (p48, Latief et al.)




   <Sri Lanka>
     At Karagan lagoon, Hambantota in southeast coast of Sri Lanka, a sand layer before 2130
year B.P. might be formed by the tsunami and correlate with the historical tsunami in Sri Lanka,
which was occurred during 2100-2300 year B.P. (p36, 37, Vijitha et al.)
     In the same lagoon, the possible tsunami sand layers, which might suggests the past
tsunami recurrence, were formed about 600 to 1000 years interval. (p40, Goto et al.)

     <Thailand>
     In Phra Thong Island, Thailand, the ages of four paleotsunami sand layers which are likely
to have been deposited by the predecessors of the 2004 tsunamis, are estimated 350±50 (300-
400)、990±130 (860-1120)、1410±190 (1220-1600) and 2100±260 (1840-2360) years ago
(Prendergast et al., submitted). (p42, Jankaew et. al.)
           (ref: http://www.nature.com/nature/journal/v455/n7217/full/nature07373.html, Dep. of Geology, Thailand, Dr. Kruawan Jankaew,
                                         http://www.aist.go.jp/aist_e/latest_research/2009/20090113/20090113.html, AIST Dr. Yuki Sawai)




                                                                                                                                    37
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                                                                                         38
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                                  Appendix 1

 Country-wise Disaster events and impacts in UNESCAP sub-regions (1980-
                                   2009)
East & North East Asia         Events    Killed   Affected ('000)   Damage (US$, Million)*
China                            574    148,419        2,549,850                  321,545
DPR Korea                         24      1,879           10,736                   46,331
Hong Kong, China                  57        511                16                      568
Japan                            155      8,492             2,785                 188,184
Macau, China                      23        263             2,485                    2,156
Mongolia                            5         0                 1                        0
Republic of Korea                 70      3,240             1,341                  19,818
                   Sub-Total     908    162,804        2,567,214                  578,602
North & Central Asia           Events    Killed         Affected    Damage (US$, Million)*
Armenia                             5         5               319                      203
Azerbaijan                         11        60             2,316                      286
Georgia                           14         24               726                      847
Kazakhstan                        14        184               719                      142
Kyrgyzstan                        20        422               177                      227
Russian Federation               176     31,795             5,686                  12,004
Tajikistan                        49      2,069             6,636                    1,709
Turkmenistan                        2        11                 0                      180
Uzbekistan                          6        74               652                       38
                   Sub-Total     297     34,644           17,231                   15,636
Pacific (Oceania)              Events    Killed         Affected    Damage (US$, Million)*
American Samoa                      6        40                23                        0
Australia                        154        955           15,798                   34,690
Cook Islands                        9        32                 7                       61
Fiji                              35        219             1,092                      593
French Polynesia                    5        30                 6                       72
Guam                                8         6                12                        0
Kiribati                            2         0                84                        0
Marshall Islands                    3         6                 1                        0
Micronesia (Federated States
                                   8        72                40                       10
of)
Nauru                                         0                0                         0
New Caledonia                      7          8                2                        51
New Zealand                       43         23               35                     1,562
Niue                               3          2                1                         0
Northern Mariana Islands           1          0                0                         0
Palau                                         0                0                         0
Papua New Guinea                  55      3,456            1,156                       169

                                                                                      40
 Samoa                                     9       179                     262                    1,298
 Solomon Islands                          14       168                     219                       36
 Tonga                                     9        17                     123                      125
 Tuvalu                                    4         0                       0                        0
 Vanuatu                                  31       212                     268                      411
                    Sub-Total            406     5,425                  19,126                  39,078
 South & South-West Asia              Events    Killed                Affected   Damage (US$, Million)*
 Afghanistan                             125         0                       0                      497
 Bangladesh                              229   191,650                 316,348                  16,273
 Bhutan                                    9       303                      66                        5
 India                                   416   141,888               1,501,211                  51,645
 Iran (Islamic Republic of)              140    77,987                  42,050                  24,978
 Maldives                                  4       102                      14                      529
 Nepal                                    74    10,881                   4,507                    1,621
 Pakistan                                131    84,841                  29,966                    8,871
 Sri Lanka                                60    36,871                  13,963                    1,942
 Turkey                                   95    21,900                   6,571                  35,145
                    Sub-Total          1,283   566,423               1,914,696                 141,506
 South-East Asia                      Events    Killed                Affected   Damage (US$, Million)*
 Brunei Darussalam                         1         0                       0                        4
 Cambodia                                 30     1,959                  16,404                      518
 Indonesia                               312   191,164                  17,545                  22,582
 Lao PDR                                  30       945                   3,998                      337
 Malaysia                                 58     1,239                     579                    1,723
 Myanmar                                  25   139,095                   3,315                    2,726
 Philippines                             349    32,578                 109,423                    7,168
 Singapore                                 3        36                       2                        0
 Thailand                                101    11,730                  53,762                    5,983
 Timor-Leste                               8        27                      14                        0
 Viet Nam                                152    15,914                  67,735                    7,180
                    Sub-Total          1,069   394,687                 272,777                  48,220
             GRAND TOTAL               3,963 1,163,983               4,791,044                 823,041
* All damage figures are converted to 2005 level using discounting                Source: EM-DAT




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