Chapter I. Emerging trends of natural hazards and disaster risk in Asia-Pacific 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 highfrequency 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 400 350 number of disasters 300 250 200 150 100 50 19 80 19 81 19 82 19 83 19 84 19 85 19 86 19 87 19 88 19 89 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 0 year 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 AsiaPacific. 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 2 110 100 90 Number of disasters 80 70 60 50 40 30 20 10 08 07 06 05 04 03 02 01 00 99 09 20 20 20 20 20 20 20 20 20 20 97 96 98 19 19 19 94 93 92 91 90 89 88 95 19 19 19 19 19 19 19 19 86 85 84 83 82 81 87 19 19 19 19 19 19 19 19 19 80 0 year 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. 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 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$178 billion, equivalent to 0.7 per cent of annual global gross domestic product. During the early 2000s, losses fell back to levels similar to those experienced during much of the1980s but rose sharply again in 2004, to the second highest level ever. A new record was set the following year due to a series of severe windstorms, including Hurricane Katrina, the most expensive natural catastrophe on record. Further extreme losses in 2008 pushed 2004 levels into fourth place (Munich RE 2009a). Total losses from major disaster events during the first decade of the twenty-first century were lower than those experienced in the 1990s but, as it included three of the most costly years on record, fears of a continuing trend of rising losses have by no means been allayed. The growth of overall disaster losses reflects a range of factors, but most fundamentally the increases in the level of capital assets exposed to hazard events both in the developed and developing worlds. Also, while difficult to delineate its impact, enhanced capabilities for reporting disaster losses have also played an important role in explaining rising losses. Figure I-3 Increase of damage and loss mirrors the increase in GDP Munich RE defines ‘major’ disaster events as those events where there “are thousands of fatalities, when hundreds of thousands of people are left homeless, and/or when the overall losses –considering the economic circumstances of the country concerned – and/or insured losses reach exceptional proportions” (Munich RE 2009, 38). 4 4 35 30 200 25 150 20 15 100 GDP (US$ Trillion, 2005 prices) Estimated damage and loss(US$ Million, 2005 prices) 250 10 50 5 0 19 80 19 81 19 82 19 83 19 84 19 85 19 86 19 87 19 88 19 89 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 0 year 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 nearpoor. 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 6 EM-DAT CRED data (www.emdat.be) as reported in the 2009 IFRC World Disasters Report. 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 subregions 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$) 908 East and North-East Asia 162,804 2,567,214 578,602 1,069 South-East Asia 394,687 272,777 48,220 1,283 South and South-West Asia 566,423 1,914,696 141,506 297 North and Central Asia 34,644 17,231 15,636 406 Pacific 5,425 19,126 39,078 3,963 Asia-Pacific (Total) 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 1 2 3 4 5 6 Country China India Philippines Indonesia Bangladesh Russian Federation Events 574 416 349 312 229 176 Country Bangladesh Indonesia China India Myanmar Pakistan Killed (Thousand) 191,650 191,164 148,419 141,888 139,095 84,841 6 7 8 9 10 Rank Japan Australia Viet Nam Iran (Islamic Rep. of) Country 1 2 3 4 5 6 7 8 9 10 China India Bangladesh Philippines Viet Nam Thailand Iran (Islamic Rep. of) Pakistan Indonesia Cambodia 155 154 152 140 Affected (Million) 2,549.85 1,501.21 316.34 109.42 67.73 53.76 42.05 29.96 17.54 16.40 Iran (Islamic Rep. of) Sri Lanka Philippines Russian Federation Country China Japan India DPR Korea Turkey Australia Iran (Islamic Rep. of) Indonesia Republic of Korea Bangladesh 77,987 36,871 32,578 31,795 Damage* (US$ Million) 321,544.61 188,183.82 51,644.78 46,331.29 35,144.61 34,690.13 24,977.98 22,581.81 19,818.30 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 Type Number of Events Disaster Type Killed (Thousand) 7 1 2 3 4 5 6 7 8 9 10 Rank 1 2 3 4 5 6 7 8 9 10 Flood Cyclone Earthquake Mass Movement Wet Extreme Temperature Drought Wild Fire Volcano Mass Movement Dry Insect Infestation Disaster Type Flood Drought Cyclone Earthquake Extreme Temperature Wild Fire Volcano Mass Movement Wet Mass Movement Dry Insect Infestation 1,317 1,127 444 264 119 108 96 71 20 8 Affected (Million) 2,676.16 1,296.27 664.03 109.71 85.90 3.31 2.36 1.36 0.02 0.00 Earthquake Cyclone Flood Volcano Extreme Temperature Mass Movement (Wet) Drought Mass Movement (Dry) Wild Fire Insect Infestation Disaster Type Flood Earthquake Cyclone Drought Extreme Temperature Wild Fire Mass Movement (Wet) Volcano Insect Infestation Mass Movement (Dry) 570.80 384.20 128.95 17.51 17.51 14.28 5.33 1.53 1.06 0.00 Damage* (Million US$) 301,590 264,530 165,770 53,330 18,080 16,210 2,130 710 190 10 Source: ESCAP based on data from EM-DAT: The OFDA/CRED International Disaster Database – www.emdat.be – Université Catholique de Louvain – Brussels – Belgium, and data on implicit price deflators in US$ 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, highfrequency 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 Desinventar8, to compare the number of disaster events from Indonesia (1998-2009) and Sri Lanka (19802008). In case of Indonesia, Desinventar 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 Desinventar and EM-DAT. Table I-4 Comparison of Desinventar and EM-DAT data in Indonesia (1998-2009) Disasters Flood Lanslide Cyclone Drought Total EMDAT 63 29 2 1 95 No. of Events Desinventar 2,296 735 680 1,149 4,860 EMDAT 2,826 1,115 4 0 3,945 People Killed Desinventar 1,233 1,273 109 0 2,615 People Affected EM-DAT Desinve 3,525,309 1 332,330 3,715 15,000 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 Desinventar 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 Desinventar. 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 Desinventar and EM-DAT data in Sri Lanka (1980-2008) Disasters Flood Lanslide No. of Events EM-DAT Desinventar 39 2,210 1 293 People Killed EM-DAT Desinventar 933 317 65 490 Peopl EM-DAT 9,283,426 130 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 8 Cyclone Drought Total 3 6 49 41 532 3,076 14 0 1,012 9 0 816 433,000 6,006,000 15,722,556 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. Rank Table I-6 Top 10 countries in the Asia-Pacific based on absolute and relative physical exposure 1 2 3 4 5 6 7 8 9 10 Flood Absolute (Million) Bangladesh1 (19.2) India2 (15.8) China3 (3.9) Vietnam4 (3.4) Cambodia5 (1.7) Indonesia6 (1.1) Thailand7 (0.8) Philippines8 (0.7) Pakistan9 (0.5) Myanmar10 (0.4) Relative (%) Cambodia1 (12.2) Bangladesh2 (12.1) Vietnam3 (3.9) Bhutan4 (1.7) India5 (1.4) Thailand6 (1.3) Nepal7 (1.2) Lao PDR8 (1.1) Myanmar9 (0.9) Philippines10 (0.9) Cyclone Absolute (Million) Japan1 (30.9) Philippines2(20.7) China3(11.1) India4(10.7) Bangladesh6(7.5) Rep. of Korea9(2.4) Myanmar11 (1.2) Vietnam13 (0.8) Hong Kong17 (0.4) Pakistan19 (0.3) Relative (%) North Marina Isl.2 (58.2) Niue9 (25.4) Japan10 (24.2) Philippines11 (23.6) Fiji12 (23.1) Samoa15 (21.4) New Caledonia18 (20.7) Vanuatu20 (18.3) Tonga21 (18.1) Cook Islands32 (10.5) Earthquake Absolute (Million) Japan1(13.4) Philippines2(12.1) Indonesia3 (11.0) China4 (8.1) India8 (3.3) Pakistan9 (2.8) Iran15 (1.7) Bangladesh17 (1.3) Papua N. G..9 (1.1) Afghanistan (1.0) Relative (%) Vanuatu1 (60.4) Solomon Isl.2 (36.3) Tonga6 (21.1) Papua New G..9 (17.5) Philippines12 (13.8) Timor Leste14 (11.3) Japan15 (10.5) Bhutan17 (0.8) Indonesia31 (0.4) Kyrgystan35 (0.4) Source: P. Peduzzi, UNEP/GRID-Europe Note: Number in superscript against each country shows its global rank. 10 7 17 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 AsiaPacific region having absolute GDP exposure to floods, cyclones and earthquakes. Rank Table I-7 Top 10 countries in the Asia-Pacific based on absolute and relative GDP exposure 1 2 3 4 5 6 7 8 9 10 Flood Absolute (Billion US$) China1 (12.5) Bangladesh3 (9.7) India4 (9.3) Japan6 (4.5) Thailand8 (3.0) Philippines9 (2.5) Vietnam10 (2.2) Rep. of Korea18 (1.2) Indonesia19 (1.0) Cambodia21 (0.9) Cyclone Relative Absolute (%) (Billion US$) Bangladesh1 (14.5) Japan1 (1,226.7) Cambodia2 (14.0) Rep. of Korea4 (35.6) Vietnam3 (4.4) China5 (28.5) Philippines5 (2.5) Philippines6 (24.3) Thailand6 (1.8) Hong Kong7 (13.3) India8 (1.3) India9 (8.0) Myanmar9 (1.1) Bangladesh13 (3.9) Lao PDR11 (1.1) North Marina Isl.19 (1.5) Nepal13 (0.9) Australia23(0.8) 18 Sri Lanka (0.6) New Caledonia25 (0.7) Earthquake Relative Absolute (%) (Billion US$) North Marina Isl.2 (59.4) Japan1 (340.7) Vanuatu9 (27.1) China7 (16.0) 11 Niue (24.9) Philippines9(11.4) Fiji13 (24.1) Indonesia11 (7.9) Fiji8 (16.0) Turkey14 (5.7) Japan14 (23.9) Iran17 (3.8) Philippines5(23.9) Australia25 (2.7) New Caledonia16 (22.4) India25 (2.1) Samoa21 (19.2) Pakistan31 (1.4) Tonga24 (17.4) New Zealand34 (1.0) Relative (%) Vanuatu1 (96.5) Solomon Isl.2 (46.3) Tonga6 (22.7) Papua New G.8(22.1) Timor Leste13 (14.9) Philippines14 (11.2) Japan23(6.6) Kyrgystan35 (4.0) Azerbaijan36 (4.0) Indonesia41(3.5) Source: P. Peduzzi, UNEP/GRID-Europe Note: Number in superscript against each country shows its global rank. Figure I-4 Absolute physical exposure map for floods, cyclones and earthquakes 11 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 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 12 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 Modelled potential casualities (per million inhabitants) 9 NPL 8 AFG 7 6 KHM 5 4 VNM 3 PAK MNG BTN BGD 2 CHN KOR 1 PHL LKA AUS 0 100 THA IND IDN TUR MYS TJK TLS RUS AZEARM GEO JPN KGZ PNG UZB KAZ TKM 1,000 MMR NZL 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. Figure I-7 Human vulnerability to floods of lower frequency and higher impact (20 years recurrence interval) 13 1,000 Modelled potential casualities (per million inhabitants) BTN TJK 100 AFG NPL KHM MNG LKA 10 VNM PAK KOR BGD PHL THA CHN IDN TLS IND RUS MYS TUR PNG JPN 1 100 1,000 MMR GEO AZE ARM KGZ NZL UZB TKM Less than 1 per million: KGZ, NZL, PNG, JPN, TKM, UZB 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-8 Human vulnerability to storm of higher frequency and lower impact (5 years recurrence interval) 14 WSM 13 Modelled potential casualities (per million inhabitants) 12 PHL 11 10 FJI 9 8 7 6 5 4 VNM 3 VUT COK 2 GUM KOR 1 PAK IND CHN AUS MHLLKA 0 1,000 BGD MMR 10,000 MAC HKG PYF JPN ASM 100,000 TON NCL NIU 1,000,000 Population exposed per year (per million inhabitants) 14 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) 10,000 Modelled potential casualities (per million inhabitants) MMR 1,000 COK BGD NIU VUT 100 WSM VNM PHL FJI PNG GUM 10 IND TON NCL KOR PAK HKG THA JPN IDN 1 1 TUV 10 100 Less than 1 per million: ASM, PYF, IDN, MAC, MHL, NZL, LKA, TUV AUS NZL MHL LKA 1,000 CHN MAC PYFASM 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. 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) 15 35.0 WSM Modelled potential economic damage and loss (% GDP) 30.0 25.0 20.0 15.0 10.0 5.0 VNM MHL IND CHN BGD MACKOR HKG * 0.0 0 * TON 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 Modelled potential economic damage and loss (% GDP) WSM 100 TON FJI 10 BGD VNM VUT PAK 1 0 5 10 15 20 25 30 Economic exposure per year (% GDP) * 16 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 Contribution to risk (casualities) Storms Floods 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Hazard type frequency of hazard population vulnerability other Source: ESCAP 17 And vulnerability to weather-related disasters has indeed decreased in all subregions in AsiaPacific 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 vulnerability index 4 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 18 8% 6% 4% change in risk 2% 0% ENEA NCA PacDeveloping SEA SSWA Vulnerability Population growth -2% -4% -6% -8% Source: ESCAP However, as shown in the simulation presented in Figure I-15, if the frequency of weatherrelated 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% chnage in risk 4.0% 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 19 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 hydrometeorological 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. 20 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 intermodel 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 21 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 22 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 subbasin 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 23 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 multiyear 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 24 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 25 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 interannual 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 26 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 NinoSouthern 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 27 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. 28 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 AsiaPacific. (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. 29 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). 30 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 (OctoberNovember) 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 31 (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 32 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 33 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 climatechange 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 catastrophe 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) 34 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. 35 Magnitude (Mw) 9.2 8.5 8.0 7.5 7.0 Return Periods (year) 520 250 120 55 25 Tsunami height (m) at Banda Aceh 9.5 5.2 2.7 1.11 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 (300400)、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) 36 References 1. ADB, 2007. Rehabilitation and Reconstruction Program for Disaster-affected Areas of Solomon Islands, draft. 2. EM-DAT (Raw Data Source www.emdat.be as accessed in May 2010) 3. Prevention Web (www.preventionweb.net) 4. UNEP, 2009. Climate Change Science Compendium 2009. 5. World Bank, 2010. World Development Report 2010: Development and Climate Change. 6. IAWE, 2009. 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How does climate change impact our lives in the Pacific? www.wwfpacific.org.fj/what_we_do/climatechange/impacts/ 38 Appendix 1 Country-wise Disaster events and impacts in UNESCAP sub-regions (19802009) East & North East Asia China DPR Korea Hong Kong, China Japan Macau, China Mongolia Republic of Korea Sub-Total North & Central Asia Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Russian Federation Tajikistan Turkmenistan Uzbekistan Sub-Total Pacific (Oceania) American Samoa Australia Cook Islands Fiji French Polynesia Guam Kiribati Marshall Islands Micronesia (Federated States of) Nauru New Caledonia New Zealand Niue Northern Mariana Islands Palau Papua New Guinea Events 574 24 57 155 23 5 70 908 Events 5 11 14 14 20 176 49 2 6 297 Events 6 154 9 35 5 8 2 3 Killed 148,419 1,879 511 8,492 263 0 3,240 162,804 Killed 5 60 24 184 422 31,795 2,069 11 74 34,644 Killed 40 955 32 219 30 6 0 6 Affected ('000) 2,549,850 10,736 16 2,785 2,485 1 1,341 2,567,214 Affected 319 2,316 726 719 177 5,686 6,636 0 652 17,231 Affected 23 15,798 7 1,092 6 12 84 1 Damage (US$, Million)* 321,545 46,331 568 188,184 2,156 0 19,818 578,602 Damage (US$, Million)* 203 286 847 142 227 12,004 1,709 180 38 15,636 Damage (US$, Million)* 0 34,690 61 593 72 0 0 0 8 72 40 10 0 8 23 2 0 0 3,456 0 2 35 1 0 0 1,156 0 51 1,562 0 0 0 169 7 43 3 1 55 39 Samoa Solomon Islands Tonga Tuvalu Vanuatu Sub-Total South & South-West Asia Afghanistan Bangladesh Bhutan India Iran (Islamic Republic of) Maldives Nepal Pakistan Sri Lanka Turkey Sub-Total South-East Asia Brunei Darussalam Cambodia Indonesia Lao PDR Malaysia Myanmar Philippines Singapore Thailand Timor-Leste Viet Nam Sub-Total GRAND TOTAL 9 179 14 168 9 17 4 0 31 212 406 5,425 Events Killed 125 0 229 191,650 9 303 416 141,888 140 77,987 4 102 74 10,881 131 84,841 60 36,871 95 21,900 1,283 566,423 Events Killed 1 0 30 1,959 312 191,164 30 945 58 1,239 25 139,095 349 32,578 3 36 101 11,730 8 27 152 15,914 1,069 394,687 3,963 1,163,983 * All damage figures are converted to 2005 level using discounting 262 219 123 0 268 19,126 Affected 0 316,348 66 1,501,211 42,050 14 4,507 29,966 13,963 6,571 1,914,696 Affected 0 16,404 17,545 3,998 579 3,315 109,423 2 53,762 14 67,735 272,777 4,791,044 1,298 36 125 0 411 39,078 Damage (US$, Million)* 497 16,273 5 51,645 24,978 529 1,621 8,871 1,942 35,145 141,506 Damage (US$, Million)* 4 518 22,582 337 1,723 2,726 7,168 0 5,983 0 7,180 48,220 823,041 Source: EM-DAT 40