ADPC Final ACCCRN Report Oct 2009 0

ASIAN CITIES CLIMATE CHANGE

RESILIENCE NETWORK (ACCCRN)

THAILAND - PHASE I

October 2009

adpc

ii

Foreword

 

Secondary and tertiary cities in Asia are often planned for development to relocate evenly both economic growth and populace. This economic goal makes smaller cities the site of increasing urbanization and land use change as they grow into the surrounding peri-urban space. However, these cities usually do not strictly enforce building regulations and monitor land use, nor do they have enough qualified technical staff to engage in planning, development control and construction regulation. This challenge is expected to be harder in the future.

Coastal erosion and the projected increase in sea levels from climate change scenarios underline the need for city governments to re-plan their infrastructure to be ready for the future. There is a lot of indication of a global climate change; events around the world that can be evidence to it include the heat wave in Western Europe, the recent flooding in England, and the monsoon floods in South Asia. Urban planning of coastal cities should include a long-term climate scenario, and appropriately design the infrastructure.

Unfortunately, city governments in general seem to fail to design action plans to address the problems associated with current vulnerability. City governments have limited institutional capacity to assess risk trends. This is related to the lack of technical information and probable scenarios of hazard exposure, socio-economic and physical vulnerabilities, risk assessment tools, early warning mechanism, and historical information on destructive events. It is also related to the ability to use technical information and probable scenarios within the urban planning and decision-making processes.

The RF Climate Change Resilience Initiative is an important initiative that can be utilized by city governments to devise strategies to cope with the climate changes already happening, and other hydro-meteorological deviations that will accelerate in the next decades. Within this grant initiative, the Asian Disaster Preparedness Center has tried to emphasize the interface between climate and urban disaster risk:

• Urban areas will increasingly play a major role in any climate change-related strategy, because cities have the potential to be either major polluters or major engines for green technological innovations and adoption thereof.

• The urban poor are increasing in proportion of the urban population, but are the most disadvantaged sector vis-à-vis climate-induced disasters. They have the fewest resources to prepare and plan for the impacts, the lowest capacity to respond in their own survival, the exposed to climate extremes, and the most reliant on the climate for livelihood.

• Urban governments need to step up their capacities and plan for longer-term futures with greater uncertainties that are attendant with climate change.

This document reports a six-month study for the purpose of creating “snapshots” of the climate scenarios prevailing in the five short listed urban areas, namely Muang Chiang Rai,

iii Muang Hat Yai, Muang Udon Thani, Muang Phuket, Muang Samut Sakorn. These snapshots are our contribution to a selection of two cities that can serve as urban climate resilience demonstration sites for the rest of Thailand. We tried to ascertain the real status of the readiness of the five cities by answering the following questions:

• How are disasters and climate hazards influencing the life of people in the selected cities?

• What impacts have climate-related hazards brought to these cities in the recent past?

• What sectors are the most affected by disasters and climate hazards?

• What data gaps exists in doing detailed studies on the changes in climate profiles?

• What is the technical capacity of cities to implement relevant projects and programs?

TEI report is the companion report that provides details on the social, governance and institutional issues surrounding urban climate resilience.

We wish to thank RF for providing Thailand an opportunity to open the discourse on urban climate resilience. We extend our heartfelt congratulations to the target cities for taking the steps to cooperate in this important project. ADPC wishes to acknowledge the government agencies that provided data for our study:

• Department of Disaster Prevention and Mitigation (DDPM), • Thailand Meteorological Department,

• Geo-Informatics and Space Technology Development Agency (GISTDA) • Department of Mineral Resources (DMR)

• Pollution Control Department (PCD) • National Housing Authority (NHA)

• Office of National Environment Policy and Planning (ONEP)

• Municipality of Chiang Rai, Udon Thani, Hat Yai, Phuket, Samut Sakhon

This study will be the beginning of more work from the Asian Disaster Preparedness Center to build the technical capacities of urban governments and their stakeholders for understanding climate risks, as well as for our advocacy for urban climate resilience.

Dr. Bhichit Rattakul Executive Director

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Contents

 

Foreword ... ii 

Dr. Bhichit Rattakul ... iii 

Table of Contents ... iv 

List of Figures ... vii 

List of Tables ... xi 

List of Abbreviation ... xiii 

1  Background at country-level ... 1 

1.1  Trend of Urbanization trend ... 1 

1.2  Climate change overview ... 2 

1.3  Management framework on climate change impacts ... 3 

2  Objectives of the study under ACCCRN phase I ... 5 

2.1  Overall objectives of the project ... 5 

2.2  Main objectives of the study by ADPC under phase I ... 8 

2.3  Key expected outcomes ... 9 

3  Methodology ... 11 

3.1  Data needs for the study ... 11 

3.2  Data collection ... 11 

3.3  Steps used for delivery of final outputs ... 12 

3.4  Detail explanation of study approach ... 13 

3.5  Identification of source agencies ... 14 

3.6  Establishment of survey teams and conducting survey ... 15 

3.7  Analysis of data ... 15 

3.8  Scoring and methodology for prioritization ... 16 

4  Brief introduction to 5 selected cities ... 17 

4.1  Chiang Rai ... 17 

4.1.1  Location ... 17 

4.1.2  Resources ... 18 

4.1.3  Climate ... 18 

4.1.4  Climate hazard and extreme events ... 22 

4.2  Udon Thani ... 22 

4.2.1  Location ... 23 

4.2.2  Resources ... 23 

4.2.3  Climate ... 24 

4.2.4  Climate hazard and extreme events ... 27 

4.3  Hat Yai ... 28 

4.3.1  Location ... 28 

4.3.2  Resources ... 29 

4.3.3  Climate ... 29 

4.3.4  Climate hazard and extreme events ... 31 

4.4  Phuket ... 31 

4.4.1  Location ... 31 

4.4.2  Resources ... 32 

4.4.3  Climate ... 33 

4.4.4  Climate hazard and extreme events ... 36 

4.5  Samut Sakhon ... 36 

4.5.1  Location ... 37 

v

4.5.3  Climate ... 38 

4.5.4  Climate hazard and extreme events ... 38 

4.6  Comparative assessment of Air and Water Quality in Five cities ... 39 

5  Climate scenario and observed trends in 5 cities ... 42 

5.1  Change of precipitation pattern ... 43 

5.1.1  Chiang Rai ... 43 

5.1.2  Udon Thani ... 48 

5.1.3  Hat Yai ... 51 

5.1.4  Phuket ... 56 

5.1.5  Samut Sakhon ... 60 

5.2  Change in temperature pattern ... 62 

5.2.1  Chiang Rai ... 62 

5.2.2  Udon Thani ... 64 

5.2.3  Hat Yai ... 67 

5.2.4  Phuket ... 68 

5.3  Sea level rise and storm surges ... 71 

5.3.1  Future sea level rise ... 72 

5.3.2  Effects of sea level rise ... 73 

6  Disaster impacts and extreme natural hazard events ... 75 

6.1  Natural disaster events and impacts within the target provinces. ... 77 

6.1.1  Muang Udon Thani ... 77 

6.1.2  Muang Chiang Rai ... 81 

6.1.3  Muang Hat Yai ... 84 

6.1.4  Muang Phuket ... 86 

6.1.5  Muang Samut Sakhon ... 89 

6.2  Natural Disaster Impacts in Muang district areas recorded by DDPM ... 91 

6.3  Actions taken to reduce impacts of climate hazard extreme events ... 92 

7  Air and water quality observations ... 103 

7.1  Air Quality Variations ... 103 

7.2  Water quality indicators / Water Pollution ... 107 

8  Assessment of sector based vulnerability ... 109 

8.1  Vulnerability of housing and human settlements. ... 110 

8.2  Health and Sanitation ... 117 

8.3  Education sector ... 120 

8.4  Infrastructure ... 123 

8.5  Utility services ... 130 

8.6  Industrial/commercial sector. ... 132 

8.7  Urban Planning ... 133 

9  Data availability for further study ... 140 

9.1  Availability of spatial data ... 140 

9.1.1  Chiang Rai ... 140 

9.1.2  Udon Thani ... 140 

9.1.3  Phuket ... 141 

9.1.4  Hat Yai ... 141 

9.1.5  Samut Sakhon ... 141 

9.1.6  Comparative assessment of spatial data ... 142 

9.2  Availability of meteorological and atmospheric data ... 143 

9.2.1  Chiang Rai ... 143 

9.2.2  Udon Thani ... 143 

vi

9.2.4  Hat Yai ... 144 

9.2.5  Samut Sakhon ... 144 

9.2.6  Comparative assessment on meteorological condition data availability ... 145 

9.3  Availability of disaster related data, damage and loss data ... 145 

9.3.1  Chiang Rai ... 145 

9.3.2  Udon Thani ... 146 

9.3.3  Phuket ... 146 

9.3.4  Hat Yai ... 147 

9.3.5  Samut Sakhon ... 147 

9.3.6  Comparative assessment on disaster related data availability ... 148 

10  Recommendations ... 149 

10.1  Comparative assessment of Impact of Climate hazard r events ... 149 

10.2  Comparative Assessment in relation to Climate Change scenario of target cities 150  10.3  Comparative assessment of air and water quality ... 150 

10.4  Comparative assessment of the Vulnerability of various sectors to climate change 152  10.5  Overall assessment ... 153 

References ... 155 

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Figures

 

Figure 2.1. Map of study area ... 7  Figure 2.2. Map of UN-Habitat on the Asian LECZ cities ... 10  Figure 4.1. Map of Chiang Rai district (Source: GISTDA) ... 17  Figure 4.2. Normal monthly mean temperature over Chiang Rai from 1980 to 2008. (a)

maximum temperature(Tmax) and highest Tmax (Ext. Tmax), (b) same as a but for minimum temperature and (c) mean temperature (Source: Thailand

Meteorological Dept.) ... 20 

Figure 4.3. Normal monthly mean rain parameters over Chiang Rai from 1980 to 2008. (a) total rain (b) number of rainy days and (c) maximum rain in 1 day

(Source: Thailand Meteorological Dept.) ... 21 

Figure 4.4. Map of Udon Thani district (Source: GISTDA) ... 23  Figure 4.5. Normal monthly mean temperature over Udon Thani from 1980 to 2008. (a)

maximum temperature(Tmax) and highest Tmax (Ext. Tmax), (b) same as (a) but for minimum temperature and (c) Mean temperature (Source:

Thailand Meteorological Dept.) ... 26 

Figure 4.6. Normal monthly mean rain parameters over Udon Thani from 1980 to 2008. (a) total rain (b) number of rainy days and (c) maximum rain in 1 day

(Source: Thailand Meteorological Dept.) ... 27 

Figure 4.7. Map of Hat Yai district (Source: GISTDA) ... 29  Figure 4.8. Normal monthly mean temperature over Hat Yai from 1980 to 2008 (Source:

Thailand Meteorological Dept.) ... 30 

Figure 4.9. Monthly normal rainfall over Hat Yai (mm) from 1980 to 2008 (Source:

Thailand Meteorological Dept.) ... 30 

Figure 4.10. Map of Phuket Province (Source: GISTDA) ... 32  Figure 4.11. Monthly mean normal of temperature; Tmax, Tmin, mean Temperature, Ext.

Tmax and Ext. Tmin obtained from the data 1980 to 2008 over Phuket

(Source: Thailand Meteorological Dept.) ... 34 

Figure 4.12. Normal monthly mean rain parameters over Phuket from 1980 to 2008. (a) total rain (b) number of rainy days and (c) maximum rain in 1 day (Source:

Thailand Meteorological Dept.) ... 35 

Figure 4.13. Map of Samut Sakhon Provice (Source: GISTDA) ... 37  Figure 4.14. Particulate matters .PM10 concentration in urban areas from 1997 to 2008

(Source: Thailand Pollution Control Dept.) ... 39 

Figure 4.15. O3 Low-Ozone concentration in urban areas from 1997 to 2009 (Source:

Thailand Pollution Control Dept.) ... 40 

Figure 4.16. SO2- Sulfur Dioxide concentration in urban areas from 1997 to 2009

(Source: Thailand Pollution Control Dept.) ... 40 

Figure 5.1. Annual mean temperature & yearly total rainfall over Chiang Rai (Source:

Thailand Meteorological Dept. and Analysis) ... 43 

Figure 5.2. Frequency of rainy days & different rainy days over Chiang Rai in Apr-May

(Source: Thailand Meteorological Dept. and Analysis) ... 45 

Figure 5.3. Frequency of rainy days & different rainy days over Chiang Rai in Oct-Nov

(Source: Thailand Meteorological Dept. and Analysis) ... 46 

Figure 5.4. Frequency of rainy days & different rainy days over Chiang Rai in JJAS

(Source: Thailand Meteorological Dept. and Analysis) ... 47 

Figure 5.5. Annual mean temperature & yearly total rainfall over Udon Thani (Source:

viii Figure 5.6. Frequency of rainy days & higher than heavy rainfall days over Udon Thani

during April-May(Source: Thailand Meteorological Dept. and Analysis) ... 49  Figure 5.7. Frequency of rainy days & higher than heavy rainfall days but for Oct-Nov

over Udon Thani (Source: Thailand Meteorological Dept. and Analysis) ... 50  Figure 5.8. Frequency of rainy days & higher than heavy rainfall days for

June-September over Udon Thani (Source: Thailand Meteorological Dept. and

Analysis) ... 51 

Figure 5.9. Annual mean temperature & yearly total rainfall over Hat Yai (Source:

Thailand Meteorological Dept. and Analysis) ... 52 

Figure 5.10. Monthly rainfall over Hat Yai during the peak monsoon months of October- December during the period from 1980-2008 (Source: Thailand

Meteorological Dept. and Analysis) ... 53 

Figure 5.11. Frequency of rainy days & heavy rainy days over Hat Yai in Apr-May

(Source: Thailand Meteorological Dept. and Analysis) ... 54 

Figure 5.12. Frequency of rainy days & heavy rainy days over Hat Yai in Oct-Nov

(Source: Thailand Meteorological Dept. and Analysis) ... 55 

Figure 5.13. Frequency of rainy days & heavy rainy days over Hat Yai in June-September (Source: Thailand Meteorological Dept. and Analysis) ... 56  Figure 5.14. Annual mean temperature & yearly total rainfall over Phuket (Source:

Thailand Meteorological Dept. and Analysis) ... 57 

Figure 5.15. Frequency of rainy days & higher than heavy rainfall days over Phuket during April-May (Source: Thailand Meteorological Dept. and Analysis) ... 58  Figure 5.16. Frequency of rainy days & higher than heavy rainfall days for

October-November (Phuket) (Source: Thailand Meteorological Dept. and Analysis) ... 59  Figure 5.17. Frequency of rainy days & higher than heavy rainfall days for

June-September (Phuket) (Source: Thailand Meteorological Dept. and Analysis) ... 60  Figure 5.18. Frequency of annual rainy days and other rain days over Samut Sakhon

(Source: Thailand Meteorological Dept. and Analysis) ... 61 

Figure 5.19. Monthly mean temperature variation during hot weather season (March to May) from 1980 to 2008 over Chiang Rai. (a) Maximum temperature, (b) Minimum Temperature and (c) Mean Temperature. The linear trend line for April is plotted (Source: Thailand Meteorological Dept. and Analysis) ... 62  Figure 5.20. Monthly mean temperature variation during cold weather season (December

to February) from 1980 to 2008 over Chiang Rai. (a) Maximum temperature, (b) Minimum Temperature and (c) Mean Temperature. The linear trend line for December is plotted (Source: Thailand Meteorological Dept. and

Analysis) ... 63 

Figure 5.21. Monthly mean extreme maximum temperature (highest Tmax) variation during April and monthly mean extreme minimum temperature (lowest Tmin) during December over Chiang Rai (Source: Thailand Meteorological

Dept. and Analysis) ... 64 

Figure 5.22. Monthly mean temperature variation during hot weather season (March to May) from 1980 to 2008 over Udon Thani. (a) Maximum temperature, (b) Minimum Temperature and (c) Mean Temperature. The linear trend line for April is plotted (Source: Thailand Meteorological Dept. and Analysis) ... 65  Figure 5.23. Monthly mean temperature variation during cold weather season (December

to February) from 1980 to 2008 over Udon Thani. (a) Maximum temperature, (b) Minimum Temperature and (c) Mean Temperature. The linear trend line for December is plotted (Source: Thailand Meteorological

ix Figure 5.24. Monthly temperature variations (Tmax, Tmin) from 1980 to 2008 during

winter season (Apr) over Hat Yai (Source: Thailand Meteorological Dept.

and Analysis) ... 67 

Figure 5.25. Monthly temperature variations (Tmax, Tmin) from 1980 to 2008 during winter season (Jan) over Hat Yai (Source: Thailand Meteorological Dept.

and Analysis) ... 68 

Figure 5.26. Number of days with Tmax more that 350C during hot weather season from March to May over Hat Yai. The linear trend for April is plotted in the figure (Source: Thailand Meteorological Dept. and Analysis) ... 68  Figure 5.27. Monthly mean temperature variation during hot weather season (March to

May) from 1980 to 2008 over Phuket. (a) Maximum temperature, (b) Minimum Temperature and (c) Mean Temperature. The linear trend line for April is plotted (Source: Thailand Meteorological Dept. and Analysis) ... 69  Figure 5.28. Monthly mean temperature variation during cold weather season (December

to February) from 1980 to 2008 over Phuket. (a) Maximum temperature, (b) Minimum Temperature and (c) Mean Temperature. The linear trend line for January is plotted (Source: Thailand Meteorological Dept. and Analysis) ... 70  Figure 6.1. Map of LECZ of Thailand (Source: CIESIN, Columbia University) ... 76  Figure 6.2. Damages and destruction in Muang Udon Thani due to disaster (Source:

DDPM provincial office, Municipality) ... 80 

Figure 6.3. Damages and destruction in Chiang Rai province (Source: DDPM provincial

office, Municipality) ... 83 

Figure 6.4. Damages and destruction in Hat Yai (Source: Municipality) ... 85  Figure 6.5. Landslide events map in Phuket province (Source:GERD Kasetsart

University, ADPC RECLAIM II ) ... 87 

Figure 6.6. Damages and destruction in Phuket (Source: RECLAIM II, Municipality) ... 88  Figure 6.7. Damages and destruction in Samut Sakhon (Source: Municipality) ... 90  Figure 6.8. Projects relevant to climate change and disaster risk reduction (Source:

Provincial office, Municipality, field survey) ... 95 

Figure 6.9. Flood protection scheme in Muang districts (Source: Municipality) ... 96  Figure 6.10. Landslide early warning measures developed by ADPC and Kaasetsart

universities in Phuket (Source: ADPC RECLAIM II) ... 97  Figure 6.11. Tsunami warning system in Phuket (Source: field survey and National

Disaster Warning Center) ... 98 

Figure 6.12. Community level training and capacity building program organized by municipality and DDPM in Hat Yai and Phuket (Source: Municipality) ... 99  Figure 6.13. Community level training and capacity building program organized by

municipality and DDPM in Udon Thani and Chiang Rai (Source:

Municipality) ... 100 

Figure 6.14. Community and municipality capacity building program organized by municipality, DDPM, and ADPC in Phuket (Source: Municipality and

ADPC RECLAIM II) ... 101 

Figure 6.15. Various publication relevant to disaster capacity building development ... 102  Figure 7.1. Pm10 and O3 concentration (Source: Thailand Pollution Control Dept.) ... 105  Figure 7.2. CO, SO2 and NO2 concentration (Source: Thailand Pollution Control Dept.) ... 106  Figure 8.1. Inundation map of Chiang Rai (Source: DDPM Chiang Rai province,

Municipality, Field survey) ... 111 

Figure 8.2. Inundation map of Hat Yai (Source: ADPC TUDMP, Municipality, Field

survey) ... 112 

x

Figure 8.4. Pictures of occupancy types in Chiang Rai (Source: Field survey) ... 113 

Figure 8.5. Pictures of occupancy types in Udon Thani (Source: Field survey) ... 113 

Figure 8.6. Pictures of occupancy types in Hat Yai (Source: Field survey) ... 114 

Figure 8.7. Pictures of occupancy types in Phuket (Source: Field survey) ... 114 

Figure 8.8. Pictures of occupancy types in Samut Sakhon (Source: Field survey) ... 115 

Figure 8.9. Vulnerable buildings in Udon Thani (Source: Field survey) ... 115 

Figure 8.10. Vulnerable buildings in Chiang Rai (Source: Field survey) ... 115 

Figure 8.11. Vulnerable buildings in Hat Yai (Source: Field survey) ... 116 

Figure 8.12. Vulnerable buildings in Phuket (Source: Field survey) ... 116 

Figure 8.13. Ratio of malaria cases per 100,000 of population (Source: Department of disease control, Ministry of Public Health) ... 118 

Figure 8.14. Ratio of leptospirosis cases per 100,000 of population (Source: Department of disease control, Ministry of Public Health) ... 118 

Figure 8.15. Ratio of dengue hemorrhagic fever (DHF) cases per 100,000 of population (Source: Department of disease control, Ministry of Public Health) ... 119 

Figure 8.16. Hospitals and health public awareness program (Source: Field survey) ... 120 

Figure 8.17. School building in Muang district (Source: Field survey) ... 121 

Figure 8.18. Flooded school in Hat Yai (Source: Municipality) ... 121 

Figure 8.19. Disaster capacity building program in school (Source: DDPM Udon Thani municipality) ... 122 

Figure 8.20. Inundation area due to flood (Source: ADPC TUDMP, ADPC RECLAIM II, Municipality) ... 123 

Figure 8.21. Damaged infrastructure due to disaster in Muang district (Source: DDPM, Municipality) ... 124 

Figure 8.22. Flood protection systems in Udon Thani (Source: Municipality) ... 125 

Figure 8.23. Flood protection systems in Chiang Rai (Source: Field survey) ... 126 

Figure 8.24. Flood protection systems in Hat Yai (Source: Field survey) ... 127 

Figure 8.25. Coastal erosion protection system in Samut Sakhon (Source: Municipality, Field survey) ... 127 

Figure 8.26. Example of flood protection scheme in Udon Thani (Source: Municipality) ... 128 

Figure 8.27. Example of flood protection scheme in Hat Yai (Source: Municipality) ... 129 

Figure 8.28. Utilities in Muang district (Source: Municipality, Field survey) ... 131 

Figure 8.29. Commercial areas in Muang district (Source: Field survey) ... 132 

Figure 8.30. Vulnerable commercial and tourism facilities (Source: ADPC RECLAIM II, Municipality) ... 133 

Figure 8.31. Land use plan of Udon Thani (Source: Municipality) ... 134 

Figure 8.32. Land use plan (Top) and flood inudation map (Bottom) of Chiang Rai (Source: Municipality) ... 135 

Figure 8.33. Land use plan (Top) and flood inundation map of Hat Yai (Source:ADPC TUDMP, Municipality) ... 136 

Figure 8.34. Land use plan (Top) and landslide hazard map (Bottom) of Phuket (Source: ADPC RECLAIM II, Municipality) ... 137 

Figure 8.35. Land use plan of Samut Sakhon (Source: Municipality) ... 138 

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Tables

 

Table 1.1. Population Projection for the whole Kingdom in Thousand (Source NESDB) ... 2 

Table 4.1. Income of Phuket (Department of Provincial Administration) ... 33 

Table 5.1. Annual mean temperature and rainfall comparison during two periods over Chiang Rai; 1980-1993 and 1994-2008 (Source: Thailand Meteorological Dept. and Analysis) ... 44 

Table 5.2. Annual mean temperature and rainfall comparison during two periods over Udon Thani; 1980-1993 and 1994-2008 (Source: Thailand Meteorological Dept. and Analysis) ... 48 

Table 5.3. Annual mean temperature and rainfall comparison during two periods over Hat Yai; 1980-1993 and 1994-2008 (Source: Thailand Meteorological Dept. and Analysis) ... 52 

Table 5.4. Annual mean temperature and rainfall comparison during two periods over Phuket; 1980-1993 and 1994-2008 (Source: Thailand Meteorological Dept. and Analysis) ... 57 

Table 5.5. SRES scenario (Source: United Nations – IPCC) ... 72 

Table 6.1. Worst cases of flood events in provinces (Source: DDPM main office) ... 77 

Table 6.2. Drought data from 2004 to 2009 (Source DDPM main office) ... 78 

Table 6.3. Tropical storm data (Source: Thailand Meteorological Dept) ... 79 

Table 6.4. Summary of flood disaster in Muang district areas (Source: DDPM provincial office) ... 91 

Table 6.5. Summary of strong wind disaster in Muang district areas (Source: DDPM provincial office) ... 91 

Table 6.6. Summary of fire disaster in Muang district areas (Source: DDPM provincial office) ... 91 

Table 6.7. Summary of landslide disaster in Muang district areas (Source: DDPM provincial office) ... 92 

Table 6.8. Summary of drought disaster in Muang district areas (Source: DDPM provincial office) ... 92 

Table 6.9. Existing and previous major projects relevant to climate change and disaster risk reduction in Udon Thani (Source: Udon Thani Municipality) ... 93 

Table 6.10. Existing and previous major projects relevant to climate change and disaster risk reduction in Chiang Rai (Source: Chiang Rai Municipality) ... 93 

Table 6.11. Existing and previous major projects relevant to climate change and disaster risk reduction in Hat Yai (Source: Hat Yai Municipality) ... 94 

Table 6.12. Existing and previous major projects relevant to climate change and disaster risk reduction in Phuket (Source: Phuket Municipality) ... 94 

Table 7.1. Availability of pollution (Source: Thailand Pollution Control Dept.) ... 104 

Table 7.2. Summarize of the pollution types (Source: Thailand Pollution Control Dept.) .. 108 

Table 8.1. Building occupancy type (Source: Municipality) ... 111 

Table 8.2. Building in flood prone area (Source: ADPC TUDPM, DDPM provincial office, Municipality, Calculation by ADPC) ... 111 

Table 8.3. Ratio of disease per 100,000 population of Hat Yai municipality (Source: Municipality) ... 119 

Table 8.4. Ratio of disease per 100,000 population of Udon Thani municipality (Source: Municipality) ... 119 

Table 8.5. Ratio of disease per 100,000 population of Phuket municipality (Source: Municipality) ... 120 

xii Table 8.7. Location of water supply source and sanitary landfill in Muang district

(Source: Municipality) ... 130  Table 9.1. Spatial data source ... 142  Table 9.2. Comparative assessment of spatial data availability ... 142  Table 9.3. Comparative assessment of meteorological and atmospheric data availability .... 145  Table 9.4. Comparative assessment on disaster related data availability among five cities .. 148  Table 10.1. Comparative assessment of five provinces evaluating their relative impact

due to disaster events ... 150  Table 10.2. Comparative assessment of five provinces evaluating on climate change

scenario and water-air quality ... 151  Table 10.3. Comparative assessment on five cities evaluating on vulnerability of various

sectors to climate change (Rate as "Highest vulnerable = 5 to Lowest vulnerable = 1") ... 152  Table 10.4. Summary of comparative assessment of data availability ... 153 

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Abbreviation

 

ACCCRN Asian Cities Climate Change Resilience Network ADPC Asian Disaster Preparedness Center

BMA Bangkok Metropoltan Administration BOD Bio chemical Oxygen Demand

CO Carbon Monoxides

CO2 Carbon Dioxides

DDPM Department of Disaster Preparedness and Mitigation DHF Dengue Hemorrhagic Fever

DMCR Dept of Marine and Coastal Resources DMR Dept of Mineral Reources

DO Dissolved Oxygen

FCB Fecal Coliform Bacteria GIS Geographical Information System

GISTDA Geo- Informatics and Space Technology Development Agency IPCC Intergovernmental Panel on Climate Change

LECZ Low Elevation Coastal Zone

MoU Memorandum of Understanding NESDB National Economic and Social Development Board NHA National Housing Authority

NOX Nitrogen Oxides

O3 Ozone

ONEP Office of National Environmnet Policy and Planning PCD Pollution Control Dept

PM Particulate Matter

PMBC Phuket Marine Biological Centre

ppm Part per Million

ppb Part per Billion RECLAIM

Regional Capacity Enhancement for Landslides Impact Mitigation

RF Rockefeller Foundation

SOX Sulfur Oxides

TCB Total Coliform Bacteria TEI Thailand Environment Institute

TMD Thailand Meteorological Department

1

1

Background

 

at

 

country

­

level

 

1.1

Trend of Urbanization trend 

Bangkok Metropolis is still considered the prime city of the country. The size of the city’s population is 36 times of the second largest city, Nakhonrajchasima Municipality. There are 10 large sized cities (100,000 – 300,000 persons), 27 medium sized cities (50,000 – 100,000 persons), 91 urban communities (25,000 – 50,000 persons), 962 small urban communities (10,000 – 25,000 persons), and 6,687 rural communities in the country. Source (NESDB,

2006). The majority of urban population agglomerated in the central region of the country,

especially in the Bangkok Metropolitan region, the Eastern Seaboard and the Western Seaboard. The urban expansion has occurred in the industrial region and peri-urban areas such as Ayuthaya, Chonburi and Rayong.

When looking at the general global urban development in the future, the United Nations stated that within next 20 years there will be more urban than rural population. Six out of ten world populations will reside in urban areas. The increase in number of population will be mainly in the large cities as people will migrate from rural to urban area. There will be more megacities and these megacities will especially locate in Asia.

It is expected that in 2010, 50% of the country’s population will live in cities. The Bangkok Metropolis will be one of the megacities in the year 2030 with more than 10 million populations. The five cities in the selection process will also be more populated as they are the major growth centers in different regions of the country.

There will be 14 million population increases in urban area in 20 years (2030). In the year 2006, there were 21.5 million people out of the country’s 62.8 million people residing in urban areas. If the projected population in 2030 is 70.6 million, then, there will be 35 million urban populations. Of all these urban population, 14 million people will reside in the Bangkok Metropolitan Region. As analyzed by the Department of City Planning, BMA, about 10 percent of the country’s population will be in the Bangkok Metropolis whereas the rest (10%) will be in the region (BMA, Department of City Planning, 2006).

When looking at the population pyramid of the country’s population in 2030 projected by NESDB as seen in Table 1.1 , there will be changes in the age and sex structure. The trend for increase of female than male, making the sex ratio change to 51.15% of female and 48.85% of male is predicted. The age structure shows that the youth population will decrease meanwhile the middle aged (15-59) and the aging population (60+) will increase. The NESDB reveals that the country is moving forward to an aging population society.

2

Table 1.1. Population Projection for the whole Kingdom in Thousand (Source NESDB)

Age Sex Year

2000 2010 2020 2030 0-14 Male

Female 7,866 7,477 7,698 7,381 6,924 6,605 7,404 6,995 15-59 Male Female 20,143 20,882 22,462 23,025 2,3330 23,574 2,2422 2,2193 60+ Male

Female 2,656 3,211 3,525 4,468 5,334 6,918 7,656 10,064

Total 62,236 68,559 72,685 76,734

Moreover, NESDB proposed that the country will move towards a creative and green economy for the next 5 years and onwards. This state of economy is based in more urban than in rural areas. As a result, they will draw the population of 15-59 years old to the cities. The aging population will reside both in urban and rural areas.

1.2

 Climate change overview 

The utilization of fossil fuel in the country is among many causes resulting in climate change phenomena in the country. From research conducted for MANRES project on the economics of watershed management with a case study of Mae Teng (TDRI, 1995), it is found that urban areas, especially those locate along the waterways (canals and rivers) and coastal area are subjected to impacts from climate change. The effects are the result from release of green house gases to the global atmosphere. The phenomena include change in temperature (average temperatures high and low temperature, number of hot and cool days in a year) and changes in precipitation pattern (amount of rainfall, number of rainy days in a year and intensity).

The study on the impact of climate change on natural resources and environment in Thailand conducted by Mahidol University in 2008 divided the time period into 3 periods; 2010- 2039, 2050-2059, and 2080-2089. The assessment is based on SEA START (2008) study.

The findings for the climate change scenario for the country area:

1) The average highest temperature during the first period (2010 –2039) will not change much and it will be 34-36 °C. But, the area coverage will expand to cover the whole country region, lower northern region and part of the southern region. By the end of the century, the average high temperature will increase about 4°C (38-40°C) covering the

3 whole country. The country will experience longer summer periods. By 2039, all the central region and part of the southern region will have summer periods longer than 8 months in a year. By the end of the century, the summer for the whole country will last longer than 8 months/year (Mahidol University (2008) pp 24-25).

2) Secondly, the average lowest temperature for every region, except the northern region, will be 24°C from 2010-2039 which will be higher than the past minimum temperatures. The average lowest temperature in the north will maintain at 18-22°C. Further, the extent of area coverage for lowest temperature will be decreased. By the end of the century (2089), more than 90 percent of the area of the country will have the lowest temperature as high as 24 °C. The period of cold season will be decreased. In the area of the northern and the northeastern regions, the duration of cold period with 16°C will be reduced from 2 - 2.5 months during the beginning of the century to 1 month (Source

Report, Mahidol University (2008), pp 24-25).

3) Thirdly, there will be increase in annual rainfall for the whole country. However, the total annual rainy days will remain more or less the same as before (Mahidol University

(2008), pp 25).

As a result, Institute of Public Studies of Chiang Mai University has assessed that there are 3 areas of risks and vulnerability for the country:

• Physical, infrastructure, settlement and tourism; • Agriculture and food security; and

• Health and public health services

The assessment is in the process to propose national plan to encounter climate change.

1.3

Management framework on climate change impacts 

Following the Kyoto Protocol 2002, Thailand is classified in the group of the countries under Non-Annex I Countries. Thailand does not have obligation to reduce greenhouse gases. Nevertheless, the government considered that the country is in the process of continuous socio-economic development, there may be increase in greenhouse gas emission in the future. The country should participate in global forum on alleviating the impact of climate change. As a result, the Ministry of Natural Resource and Environment started establishing work plan to follow the international agreement on climate change in 2005. This project incorporated all related government and non-government agencies to work together. The Office of Environment Policy and Planning (ONEP) have integrated many ideas, policies, and plans from related agencies. Then the first national strategy on climate change was formulated in 2006 under the participatory process of involving all concerned parties. By the beginning of 2008 the cabinet approved the national strategies (2008-2012). The related government agencies had to use as the framework for their own organizations policies, plans, and actions. The objective of the national strategy on climate change management 2008-2012 is as follow;

4 1. To prepare the country to understand the issue, to mitigate, and to adapt to the

impacts from climate change.

2. To join the global panel in reducing the emission of greenhouse gases on the sustainable development basis following the international agreement at the Kyoto Protocol.

3. To promote the integration from concerned parties in planning and implementation in order to solve the country’s climate change problems systematically.

There are 6 strategies to fulfill the above objectives. These strategies for 2008-2012 action plans are:

1) To build capacity in adaptation to mitigate and reduce the vulnerability results from climate change.

2) To support the reduction of greenhouse gas emission and increase of area absorbing gas on the sustainable development basis.

3) To support research and development in order to promote clear understanding on climate change.

4) To build awareness and participation in problem solving related to climate change. 5) To increase potentiality of related personnel and agencies in implementing climate

change related projects and

6) To develop international corporation tasks with foreign counties

The proposed strategies were proposed by ONEP, the Ministry of Natural Resources and Environment. The implementations of these strategies are suggested to be performed by almost all government agencies. The local administrative organization is also play important roles in the strategies suggested.

As seen from above the objectives of the National Strategy proposed are in line with the objectives of the Program under Asian Cities Climate Change Resilience Networks implemented jointly by Thai Environmental Institute (TEI) and Asian Disaster Preparedness Center (ADPC) in selected cities in Thailand and supported by Rockefeller Foundation.

5

2

Objectives

 

of

 

the

 

study

 

under

 

ACCCRN

 

phase

 

I

 

The environmental issue of climate change poses great challenges facing decision makers at many levels. Shifting weather patterns threatens food production, infrastructure durability and livelihood sustainability through increased unreliability of precipitation and increased unpredictability of temperature; Storm surges demolish fragile economic activities; rising sea levels contaminate water bodies and increase the risk of catastrophic flooding; and a warming atmosphere expands the risk of pests and diseases once limited to the tropics to widespread pole-ward.

Climate change still relies on potential climate patterns. Uncertainties remain in climate change science, especially regarding the operation and interaction of Earth systems over various timeframes and how subsystems react to feedbacks. But, unpredictability of climate change is part of what it makes it so dangerous (Smith 2008). Subsequently, building climate change scenarios is crucial to encompass the whole range of climatic possible evolutions. Each scenario will be attributed a percentage reflecting its reliability and its propensity to occur. The linkage between the climate change effects and its impacts, logically demonstrated through the scenarios, will enable the decision makers to get a clear picture of the potential changes and will guide them to take the necessary steps to adapt to climate change impacts. The project is working in urban context, where most of the local assets in terms of economic, social and cultural values are concentrated. The vulnerability assessment ADPC will further conduct on infrastructure and built up environment will give a comprehensive understanding of what is at risk at the micro-scale level in the two selected cities. TEI will further identify the most accurate stakeholders to carry on climate change adaptation initiatives at the city level. Decision makers can consequently identify and advisedly take decision on the most accurate mechanisms towards climate change at local adaptation.

2.1

Overall objectives of the project  

The primary objective of the ACCCRN Program is to build climate change resilient urban communities with a focus on poor and vulnerable groups by creating robust models and methodologies for assessing and addressing risk through active engagement and analysis of various cities. India, Indonesia, Vietnam and Thailand are the selected countries by ACCCRN initiatives to foster urban resilience against changing climate patterns.

Under Phase I, ACCCRN has a broad review for selection of number of cities to • Understand the vulnerability to climate change in those cities

• Investigate the readiness of those cities to engage with the project on resilience • Understand the physical. Social, political and economic context of the selected cities In line with the broader objectives of ACCRN, through the Phase I of the Thailand Project, a screening survey of some of the selected secondary cities in Thailand is carried out to help identifying most promising two cities to be the Pilot cities of the Phase II of the Project.

6 The main objective of the project phase I is to:

1. Develop the potential climate change scenarios and short, medium and long term impacts to urban built up

2. Assess previous or/and existing adaptation measures on climate change which have been implemented at city level

3. Present data gaps observed in five cities for developing the profile of physical vulnerability in relation to climate change adaptation

4. Identify two cities which can be studied in detail to identify options for integrating climate change adaptations in the governance process considering pre identified indicators for modifications to built environment

Initially, the Rockefeller Foundation has identified five (5) cities in Thailand which have different background on the geographical location, types of main income generation activities, population structure and other key parameters. The ADPC executed project activities in association with Thai Environmental Institute (TEI) in the following urban areas in Thailand. They are

1. Udon Thani Province - Muang District 2. Chiang Rai Province - Muang District 3. Phuket Province - Muang District 4. Songkhla Province - Hat Yai District

7

Figure 2.1. Map of study area

The Rockefeller Foundation under its ACCCRN initiative has requested ADPC and TEI to study jointly and report in the suitability of above cities to be identified as potential Pilot

8 Demonstration Cities under the Phase II. The study area of the selected cities is within Muang District boundary but in some cases (such as Samut Sakhon, Phuket) the study area are extended beyond the Muang District limits in order to capture potential climate change impacts and other variations of physical vulnerability considering the rapid urban expansion. The decision on Pilot Demonstration Cities under the Phase II, will be largely depending on the availability of data for further studies for delineating the degree of vulnerability to climate change impacts and associate risk which is the focus area of the study carried out by ADPC. It also based on other criteria such as availability of institutional support for undertaking reforms for building Climate Change resilient urban communities, which are the focus of the study carried out by TEI.

Although two cities are selected for further activities, studies carried out in other cities will still have a benefit as the study findings will be useful to respective cities as well as to ACCCRN for future interventions. Adaptation of a long term climate risk reduction strategy and promoting a risk based development agenda for sustainable development is the ultimate aim. The study will benefit the potential decision making, planning and implementation of short, medium and long term strategies for building climate resilient cities and urban communities.

2.2

Main objectives of the study by ADPC under phase I 

The purpose of the present initiative by ADPC is to conduct an evaluation which will establish the preliminary findings and its status on data availability for assessment. Finally, it helps identification of most promising two Pilot cities in the context of data availability and assessment of climate change impacts and risk factors.

Through the project phase I, ADPC, based on the technical parameters, addresses the issues on physical risk factors such as vulnerability of built environment, infrastructure, lifeline facilities, natural and environmental resources etc. where as TEI evaluates the human, social, economic aspects of vulnerability. ADPC and TEI work very closely having a partnership arrangement to support each other ensuring close association with local, provincial authorities, cities and other relevant agencies to obtain necessary inputs for the study.

The findings of this city assessment would create the basis for the design of the project phase II of ACCCRN program in Thailand. Through this city level engagement in subsequent phases, awareness creation and capacity development will be expected to undertake based on the detailed comparative assessment among the selected cities and findings.

The study aims at:

1)Understanding risk profile of five cities/provinces on climate change and trends observed

2)Assessing the context of vulnerability to climate change impacts of five cities/ provinces in terms of physical, environment point of view

3)Identifying the gaps in terms of availability of Technical data and data sources for building the climate change scenarios and impacts to urban built up area

9 Under the Phase I, ADPC expects that the recommendations (Phase I) will facilitate Rockefeller Foundation to decide on selection of priority cities for the 2nd Phase. A parallel study on institutional, political, economical aspects and city readiness to undertake climate adaptation measures is carried out by TEI.

2.3

Key expected outcomes 

In most cases, climate change risk reduction is not viewed as a priority action in sustaining the urban development gains since currently no or limited attempts are being made for such analysis to be carried out for vulnerable secondary cities in Thailand. Further, there is a scarcity of monitoring tools available for analyzing the impacts. It is globally accepted that the human induced environmental degradation affects temperatures, precipitation patterns, sea levels and storm frequencies etc. in cities. These urban areas experience frequent occurrences of hydro-meteorological disaster events with direct and indirect losses of social and economic nature.

Preliminary studies in the Phase I will be useful for city governing bodies and provincial authorities to initiate productive actions leading to long term solutions, local level adaptation options and identifying gaps. Using the results of these studies, city authorities can have opportunities for climate change adaptation strategies included in development planning, in the long run. For example, cities of Phuket, Hat Yai and Chiang Rai being predominantly cities of tourist attraction, city authorities will be able to mitigate climate change impact on the respective sectors in the long term. The outcome will be useful for the city authorities to initiate proactive actions to undertake appropriate long term solutions through integration of adaptation measures in urban development.

In the light of above mentioned potential advantages, the key outcome of the proposed study can be identified as the following:

• Assessment of the status of impacts of climate change in five selected cities/provinces • Assessment of the extent of vulnerability for disasters and the trend of change of

disasters

• Evaluation of data availability for study on previous events and trends as well as for study of potential exposures to future climate change scenarios

• Identification of source agencies and data providers, availability of essential data and information gaps, outlining the areas for further qualitative and quantitative assessment

• Identification of the sources of most useful information for developing strategy for facing future challenges on climate change impacts

10

Figure 2.2. Map of UN-Habitat on the Asian LECZ cities

Reducing physical vulnerability is thereby one of the priorities towards climate change adaptation. Hazard prone areas are firstly identified and well demarcated; the physical features within each defined areas are then listed and their weaknesses assessed. This study gives a clear idea of the current built up resilience statement and enables to formulate specific recommendations on adaptation measures to strengthen local resilience towards climate change effects.

11

3

Methodology

 

3.1

Data needs for the study 

Thailand country project phase I of ACCCRN program, considered five cities, and main objective was the selection of two cities for the study under next phase. In order to meet study objectives, ensuring the availability of data under each study component was a crucial factor. Outcome of phase I study expected to help evaluation of the potential and the feasibility of further analysis under phase 2 for the two cities selected as the core beneficiaries of ACCCRN program.

To proceed towards in-depth studies, it was felt that a certain amount of base data is required covering a considerable period. The consistency in terms of data coverage for all cities and short time period available for the study were seen as challenges. Furthermore, even if data is available, reliability of data also needs to be evaluated. ACCCRN initiative focuses on urban areas; thereby district as well as city level information are needed to conduct a sector based urban climate change vulnerability assessment. The resolution of data for instance is of high importance for study on spatial distribution of relevant sectors at risk from climate hazards. With regards to climate parameters, the time period covered by the data set is important as it primarily will be used for trend analysis and also to draw some projections. Higher the coverage the team felt it will help to produce better future projections and also to come up with a better scenario assessment.

Therefore, the team has developed initial plans to collect data concerning three categories; firstly infrastructure data including roads, buildings, drainage and sewerage network, power and communication; then meteorological data such as temperature data, precipitation data, wind data and air and water quality data and finally disaster related past data in terms of number of events, damages, and loss per disaster type etc. Each data category has been assessed and rated according to two other criteria also: namely data availability and data reliability. Rating has been assigned in a scale of 1 to 5 with 1 as the lowest and 5 as the highest. Zero 0 implies that no data exists.

3.2

Data collection 

Mode of data collection by ADPC team was through a combination of desk and field surveys by a team of specialists. The survey team made an attempt to collect data directly from source agencies as much as possible but in certain cases relied on secondary data sources also. Some of the information has been obtained through meetings and interviews with appropriate personnel from the relevant technical agencies located in Bangkok, in the target provinces, cities as appropriate. It was felt important to share the objectives and the method adopted by the study team with source agencies concerned in order to provide a clear understanding on the study objectives, expected outputs etc. Study team was careful not to raise expectations unnecessarily on the final outcome and subsequent process.

12 ADPC team representatives participated in initial meetings between city level officials and TEI to reinforce team efforts as well as to have better coordination. Subsequently ADPC study team made several field visits to cities as needed to gather information. In addition, information was obtained thro’ web search and also study of references, project reports available elsewhere. One such source was ADPC and AIT libraries. ADPC has already conducted few projects in target cities and such study reports produced by ADPC were found to be useful.

ADPC team had meetings for checking the availability and field verification of data. Meetings were held with individuals and groups from target cities, government agencies, professional bodies, universities, relevant NGOs and CBOs.

3.3

Steps used for delivery of final outputs 

The delivery of final outputs was carried out in several steps, as given below: i. Preliminary investigation

• Identification of partners, stakeholders and source agencies

• Establishment of survey teams and appraisal of methodology adopted ii. Conduct literature survey for collecting information on previous studies on

• Assessment of vulnerability to disaster events

• Assessment of vulnerability to Climate change targeting cities, provinces of Thailand

iii. Participation in meetings organized to introduce the project objectives to target cities/districts

• City workshop in Bangkok organized by TEI • Workshops organized in respective cities

iv. Detailed analysis for development of climate risk profile

• Scenario buildings on potential Climate Change impacts and trends • Qualitative and quantitative analysis of data availability

• Setting up a criteria for study using different indicators • Analysis under 3 main indicators

• Prioritization of Cities based on the scoring as per the selected criteria.

v. Collection of disaster related data for target cities and sector based vulnerability assessment

• Visit source agencies in Bangkok

• Visit source agencies in the provincial officers • Develop building foot print maps

13 • Sector based vulnerability assessment in the field

• Capacity assessment for undertaking relevant projects by cities vi. Reporting and recommendation

• Reporting the preliminary assessments and recommendations • Data presentation to the Advisory committee

• City level meeting to present the final assessment results • Submission of reports to ACCCRN/RF

3.4

Detail explanation of study approach 

The objective of data collection was to get a clear understanding of the availability and accuracy of technical data required to conduct appropriate assessments under three categories for 5 target cities;

i. Climate scenario assessment ii. Assessment of disaster impacts

iii. Sector based vulnerability assessment and assessment of capacity of cities to undertake relevant projects

The available technical data has been evaluated methodically for each of the five cities. The following data categories were used in data collection.

1) Geographical data; The locality of the city/ province in Thailand, physical features such as rivers, lagoons, water bodies, boundaries on Province, municipal area, Amp hoe, and Tambon

2) Building footprint maps produced by National Housing Authority 3) Infrastructure and utility data; - roads, water supply, transportation,

4) Land use planning; urban built up area, housing and buildings, productive sectors such as agriculture, tourism, industry

5) Meteorological data; Temperature, precipitation, wind parameters 6) Pollution data; air quality, surface water /ground water data

7) Disaster related information; losses, affected numbers, agricultural areas records 8) Health (event data): Seasonal disorders such as Malaria, Dengue, Chikungunya,

Lepropitis

Considering the urban Land use planning data, sub-indicators identified are: land uses over the past 20 years and existing land use projections; physical coverage of the different productive sectors; availability of land for new developments etc. In terms of disaster related information following data categories have been used; records on past disasters, past losses and damages. It was planned to collect such information over the past 25 years but later it

14 was found that the data is available only with DDPM (they started systematic collection and recording of data after establishment of the institution) and they can provide authentic data only for the years they have in their database. ADPC study team could acquire adequate information only for the past 5 years.

The first above mentioned five categories have been presented as GIS maps and overlaid with hazard maps when available. The percentage of different building typologies has been analyzed to see what category of buildings is located in hazard prone areas for respective types of hazards. For Meteorological data, six sub-indicators have been identified and evaluated according to see the trends to project the future scenarios. They are: variations over annual mean temperature, variations over seasonal mean temperature (During hot weather season and cold weather season), and variations over extreme temperature (T max and T min), variations over frequency (Number of extreme cold/ warm days)

Similarly, Precipitation data also categorized in to few sub parameters: variations in seasonal and annual mean precipitation, frequency on deficiency rain fall or drought, variation on the number of rainy days and variations over intense precipitation days (heavy, moderate, light).

3.5

Identification of source agencies 

The following technical source agencies and organisations were identified as potential data sources to obtain relevant data.

• Dept of Public Works and Town and Country Planning • Dept of Natural Resources and Environment

• Geo- Informatics and Space Technology Development Agency (GISTDA) • Thai Meteorological Department

• National Housing Authority

• Dept of Disaster Preparedness and Mitigation (DDPM) • Pollution Control Dept

• Royal Thai Navy • Irrigation Dept

• Dept of Mineral Resources; Mekong River Commission • Ministry of Health

ADPC had good working relationship with some agencies but with some other agencies ADPC team had to establish new partnerships or sign MOU to collect data. The procedural problems created some delays but all agencies were corporative and supportive.

15

3.6

Establishment of survey teams and conducting survey 

Survey team members were selected from in-house staff of ADPC and thro’ short term hire and survey teams were provided with a comprehensive awareness and training on conducting the desk survey and subsequent field verifications. Each study team was headed by a specialist and other members included were research assistants/coordinators. Appraisal on the indicators, data quality, quantity and manners to conduct a survey was arranged to foster a common framework among the survey team members.

A Meteorology and air and water quality related desk studies were carried out initially by Research assistants to avail information on climate related parameters and to identify best sources. Subsequently, ADPC has employed subject specialists to verify the relevant information needed for the study (as there were several data sets obtained from different data collection centres) and to conduct the scenario assessment.

3.7

Analysis of data 

Gathered information and data were thoroughly examined by ADPC team with a broad focus on whether the city/province is prone to single hazard or multiple hazards; whether the Climate Change impacts at present are prominent or minor. More than one hazard will give more visibility and possibility for easy replication.

i. Vulnerability to disasters

During the process of analysing, ADPC has observed that floods. Landslides, droughts and cyclones are the recurring major natural disasters in the selected provinces. Information recorded are mainly from 2000 – 2008 with details of the number of events, number of people affected, number of households affected, area of agriculture affected and the estimated losses. Although the information is available for damaged infrastructure, it was not possible to arrive at a qualitative or quantities assessment as the units measured were so broad. For example, the extent of roads damaged is indicated as a number which is difficult in evaluation as the category and the length of the road was not specified. In some cases data for flash floods and landslides were given as a combined loss. In the same way fire incidents have been recorded without identifying the same as a man-made event or natural event. How ever, ADPC team considers that the available data assessment is adequate for the phase 1.

ii.Climate change scenario study

ADPC team was able to collect data for the last 29 years (1980 – 2008) with details of precipitation and temperature as main parameters. For a comprehensive study, data were accessible and obtainable even to a level of details up to daily as well as monthly mean values. Accordingly, analysis for temperature variations for annual mean temperature, annual maximum and minimum temperatures, and monthly mean temperatures for cold weather season and hot weather seasons could be undertaken.

16 Similarly, precipitation data were also recorded and scrutinized for variations over average annual rain fall, heavy and very heavy rain fall intensities during monsoon seasons.

3.8

Scoring and methodology for prioritization 

Natural hazards occurrence and their past impacts within target urban areas were evaluated against several sectors taking in to consideration the natural hazard exposure levels. A matrix was created to evaluate and assign a score as per the degree of impact from respective hazard types. This approach has helped to identify and prioritize the cities in terms of impact from extreme events. The appraisal was on qualitative terms based on a comparative subjective assessment. .For easy reference, a score of five (5) was assigned for a worst case and one (1) was for the least case. When data is not available, the score of zero was allocated. Similar approach was used for presentation of the results of Climate scenario study as well as sector based vulnerability assessment and study of data availability and to prioritize the cities for detail study under Phase 2.

17

4

Brief

 

introduction

 

to

 

5

 

selected

 

cities

 

4.1

Chiang Rai 

Population : 141,291

Economy : Agriculture, Trade

Role in the province : Administrative, Commercial and Educational Center Major Climate hazards and impacts : Floods, Landslide, Tropical storm; River erosion; :

Water pollution

4.1.1 Location 

Chiang Rai province is Thailand’s northernmost province. Clipped to the north by the Mae Kok River, Chiang Rai city is 60 kilometers far from Thailand’s northern border where the Mekong River skirts the boundaries of Thailand, Lao PDR and Myanmar. Muang Chiang Rai, the district capital of the province of the same name, approximately 785 km north of the nation’s capital, Bangkok and located at 19055’N, Longitude-99050’E and with an elevation of 395m is included in the Lower Mekong basin with its boundary stretching to Lampang in the south, Phayao in the east and Chiang Mai in the west. The watershed area of the Mae Kok River is 7,895 square kilometers with an annual run-off of 5,119 million cubic meters. The general landscape consists of a large fertile plain, set within a midst of crisp and scenic mountain ranges. These form into a pan shape with elevations at approximately 580 m above sea level.

18

4.1.2 Resources 

Formerly part of the Golden Triangle, the substitution program to replace opium production with tea, coffee and rice cropping has led to the increase of border trade and commerce. Thus the economy of the city relies mostly on agricultural resources, small business owners, especially with the extension of tourism. Although over 30% of the land area is officially classed as farmland, only about 19% is really considered suitable for cultivation – and most of the cultivatable area is located along the Phaholyothin highway, which runs through the districts of Phan, Muang and Mae Chan and ends in Mae Sai. Moreover through the development of tourism and its associated increasing transport facilities, the city is gradually becoming a privileged gateway to reach China, Myanmar, Lao PDR, Vietnam and Cambodia. The city currently plays the role of a major transport hub in the whole region, using also the river as a transportation channel. These dynamics contribute to the urban extension of Muang Chiang Rai over the past years.

4.1.3 Climate 

Because of its higher elevations, the climate in Chiang Rai province is generally somewhat cooler when compared to the rest of the country. However, there are still three distinct seasons; the hot season, the rainy season and the cool season. Throughout the winter months, nighttime temperatures can drop considerably from the average daytime figures. At other times during the year, day and night temperatures do not vary significantly.

The Cool Season lasts from late October until the end of February with temperatures ranging from 13°C to 28°C. The coldest months are December and January. The Hot Season begins at the end of March and lasts until the end of May, with temperatures ranging between 17° C and 36° C. The hottest month is April.

In Chiang Rai, the monsoon or rainy season starts around May and ends in October - earlier than in Central Thailand. The average rainfall in Chiang Rai is considerably higher than its neighbor, Chiang Mai province. So much so that during the months of August and September, (when rainfall is heaviest), many of the streets throughout the province will flood. At most other times the rain will normally fall sporadically

The hot season lasts from March to May with a daytime average temperature of 26°C. The rainy season actually presents two peaks: one in May-June (200mm average in one day) and one in August-September (up to 380mm average in one day). The average precipitation amount in a year is around 150 millimeters. The refreshing season starts in October and continues until the month of January. The average daytime temperature drops to 21°C during this period.

The monthly mean data of temperature and rainfall over Chiang Rai during the period 1980-2008 is used in the present analysis. The monthly mean data of maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (mean T), total rain, number of rainy days and maximum rain in 1 day is analyzed for 29 years from 1980 to 2009 to prepare the monthly normal. In addition, the highest Tmax and lowest Tmin in a given month are also considered to calculate the normal of these two parameters during the 29 years period

19 mentioned here. The normal of temperature parameters are shown in Figure 4.2 and normal rainfall parameters during this period is discussed in Figure 4.3.

It is seen from Figure 4.2 that the peak summer is in April (about 35°C) with March to May as the hot weather season. The extreme Tmax normal is about 4 to 5 degree higher than that of normal Tmax during whole season (Figure 4.2a). Similarly extreme cold temperature reported during Dec to Feb (Figure 4.2b). There is not much variation in the mean temperature from April to October, 2009 (Figure 4.2c)

The peak rainfall months and the observation of highest rainfall in a day (of the order to 6 cm/day) are reported mainly during July to September (Figure 4.3a&c). The number of rainy days is also highest in August followed by July, June, May, September and October (Figure 4.3b).

20

Figure 4.2. Normal monthly mean temperature over Chiang Rai from 1980 to 2008. (a) maximum

temperature(Tmax) and highest Tmax (Ext. Tmax), (b) same as a but for minimum temperature and (c) mean temperature (Source: Thailand Meteorological Dept.)

21

Figure 4.3. Normal monthly mean rain parameters over Chiang Rai from 1980 to 2008. (a) total rain (b)

22

4.1.4 Climate hazard and extreme events 

During the monsoon period, heavy rainfall trigger every year flooding of various intensity. The carriage way of the main river and associated drainage network is not sufficient to cope with the sudden amount of water. The water level thereby rises rapidly, leading to flooding and further erosion. Recently, in July 2000 massive floods, destroying the surrounding fields, affecting livelihood and leading to business interruption, overwhelmed the city. In 2006, flash floods occurred and affected the built-up environment as well as the utility and facility networks within the city. Transportation system was blocked and tourist activities ceased for various days. Loss of crop and economic breakdown led to migration wave to Bangkok. Water run-off is constantly increasing due to inadequate land-use planning coupled with increasing climate variability. Landslide and mudslide are substantial issues Chiang Rai has to face over the past decade. The topography of the zone associated with heavy rainfalls and unplanned human settlements, leads to increase the landslide risk and therefore the correlated damages on livelihoods, infrastructures and property. In fact, each year landslide occurs in the zone. For instance, in 2005, seven events have been reported in Chiang Rai province, affecting a total area of 800 square meters; and in 2007, four landslide events occurred, affecting 300 square meters.

Tropical storms are highly frequent in Chiang Rai. Since the beginning of the eighties, the cyclone frequency tends to be accelerated. Strong winds, comprised between 63 km/h and 118 km/h and heavy rainfalls are associated to cyclone period. In recent times several ice rains and gale has been common phenomena.

Drought is also of main concern in the province. In April 2008, a period of extreme drought hit the province. The Mekong River was very shallow, impeding the boat tour to work. According to TNA, 314,000 families in 18 districts have been hardly affected by water shortages. The lack of irrigation facilities for agriculture has led to the lost of the crops. In fact, more than 68,000 rai (27,700 acres) of agricultural lands have been damaged. The provincial authorities have declared the 18 districts of Chiang Rai as a drought ravaged area.

4.2

Udon Thani 

Population : 174,531

Economy : Agriculture, Trade, Industry

Role in the province : Administrative, Commercial, Industrial and Educational Center

Major Climate change issues : Flooding; Soil acidity and salinity; Tropical storms erosion, water shortage

23

4.2.1 Location 

In the northern part of the Isan region, Udon Thani is the capital (Amphoe Muang) of Udon Thani province (latitude 17°24'27''N and longitude 102°47'35''E) with its boundary stretching to Nong Khai in the north, Khon Kaen in the south, Sakon Nakhon in the east and Nong Bua Lamphu in the west.

Located 60 kilometers away, from the Lao PDR border and the Nong Khai checkpoint, Muang Udon Thani forms a plateau, 187 meters above sea level. Muang Udon Thani is part of the Lower Mekong Basin. Three affluents of the Songkhram River, which is the third largest tributary of the Mekong, run through the city. The riverbanks are currently and increasingly endangered by human population growth and it’s correlated increasingly with urbanization trends and associated activities. The erosion process is thus accelerated, with the risk of riverbank slope destabilization and river widening. Increasing human settlements nearby the canals amplify the potentiality of these areas to be badly affected in case of heavy rainfalls coupled with insufficient water retaining capacity. In fact, the river flows within concrete channels, which reduce the drainage.

Figure 4.4. Map of Udon Thani district (Source: GISTDA)

4.2.2 Resources 

The city plays a major role as a regional and international transport hub as the gateway to Laos, North Vietnam and Southern China. Muang Udon Thani is thereby easily accessible from Bangkok by train or by plane or even by route. Furthermore the main agricultural productions of the region, such as sugarcane, rice straw, cassava, corns, converge to Muang

193

Stat

ion

Name Stat

ion Co d e yea r mont h Mean Ma x Temp Mean Min Temp To ta l Rai n Ra in iy D Max Ra in 24 h rs A verage Rai n ex trmax extrmi n meant emp

Songkhla* 568501 2001 10 31 24.7 277.7 24 74.8 83 33.9 23.5 27.8 Songkhla* 568501 2001 11 29.5 24.8 459.5 23 103.3 83 32.3 23.2 27.2 Songkhla* 568501 2001 12 29.7 24.7 505.2 18 120.6 82 31.5 22.9 27.2

Songkhla* 568501 2002 1 29.6 25.5 4.9 4 2.8 76 30.4 24 27.5

Songkhla* 568501 2002 2 30.3 24.9 153.5 28 6.9 76 31.6 22.8 27.6

Songkhla* 568501 2002 3 31.6 25.2 44.5 4 41.4 77 33 23 28.3

Songkhla* 568501 2002 4 32.6 26 32.7 9 12.4 77 35.5 24.5 29.3

Songkhla* 568501 2002 5 33.1 26 112.2 11 35 76 35.2 24.5 29.5

Songkhla* 568501 2002 6 34 25.7 73.1 14 16.6 74 35.9 24.2 29.9

Songkhla* 568501 2002 7 34.4 25.7 47 9 26.1 71 36.6 23.2 30

Songkhla* 568501 2002 8 34.1 25.4 55.2 10 26.1 73 36.5 24.1 29.7 Songkhla* 568501 2002 9 32.7 24.9 80.1 12 47.5 76 35.5 23.3 28.8 Songkhla* 568501 2002 10 32 24.8 184.5 18 52.5 80 34.4 23.5 28.3 Songkhla* 568501 2002 11 30.1 24.8 503.4 25 85.3 85 31.8 23.3 27.4

Songkhla* 568501 2002 12 30.7 25.8 260 22 86.4 80 32.3 24 28.2

Songkhla* 568501 2003 1 30.2 25.7 86.2 7 59.4 74 30.9 23.4 27.9 Songkhla* 568501 2003 2 30.4 25.5 63.6 4 28.9 75 31.5 23.9 27.9 Songkhla* 568501 2003 3 31.8 25.5 123.4 13 42.8 77 33.3 23.9 28.6

Songkhla* 568501 2003 4 32.6 25.9 18.7 4 8.4 75 34.2 24.5 29.3

Songkhla* 568501 2003 5 33.4 25.9 78.7 12 41.8 77 35.7 24.5 29.6

Songkhla* 568501 2003 6 33.5 25.5 79.2 9 25.6 76 36.5 24 29.5

Songkhla* 568501 2003 7 32.9 25 54.4 13 16.5 77 34.8 23.2 29

Songkhla* 568501 2003 8 33 25.3 69.2 17 17.6 75 34.8 23.5 29.1

Songkhla* 568501 2003 9 33.4 24.9 156.5 13 43.3 77 35.6 23.4 29.1 Songkhla* 568501 2003 10 31 24.4 579.7 20 150.8 82 32.9 23.2 27.7 Songkhla* 568501 2003 11 29.7 24.8 488.6 19 81.4 83 31.9 23.5 27.2 Songkhla* 568501 2003 12 29.3 24.5 449.5 22 130.7 80 31.5 22.5 26.8

Songkhla* 568501 2004 1 30.1 25.6 15.1 7 4.5 77 31.7 23.9 27.8

Songkhla* 568501 2004 2 30.5 25.1 20.5 3 14.1 75 31.3 23.1 27.8 Songkhla* 568501 2004 3 31.8 25.8 14.6 2 13.9 75 34.3 23.5 28.8

Songkhla* 568501 2004 4 33.5 26.1 28 7 13.1 74 36.8 24.2 29.8

Songkhla* 568501 2004 5 33.7 25.7 105.8 9 38.6 75 36.8 23.5 29.7 Songkhla* 568501 2004 6 33.6 25.4 58.2 13 10.9 74 35.8 23.6 29.5

Songkhla* 568501 2004 7 32.9 25 82.5 9 46.5 76 35.7 23.2 28.9

Songkhla* 568501 2004 8 33.8 25.3 82.5 12 30.5 74 37.3 23.5 29.5 Songkhla* 568501 2004 9 31.9 24.6 326.1 17 68.7 79 34.2 22.9 28.2

Songkhla* 568501 2004 10 35 24.3 197.3 20 63 80 38.5 22.3 29.6

Songkhla* 568501 2004 11 30.5 25.2 417.4 17 146 81 31.4 23.3 27.8 Songkhla* 568501 2004 12 29.8 24.8 207.1 12 48.6 78 31.3 23.3 27.3

Songkhla* 568501 2005 1 29.5 24.4 13.3 5 5.6 76 30.3 22.3 26.9

Songkhla* 568501 2005 2 30.8 25.2 26.9 1 26.9 76 32.5 22.8 28

194

Stat

ion

Name Stat

ion Co d e yea r mont h Mean Ma x Temp Mean Min Temp To ta l Rai n Ra in iy D Max Ra in 24 h rs A verage Rai n ex trmax extrmi n meant emp

Songkhla* 568501 2005 4 32.9 26.3 3.8 4 3 74 35.5 24.2 29.6

Songkhla* 568501 2005 5 33.7 26 144 15 31.6 77 35.3 24.4 29.8

Songkhla* 568501 2005 6 33.8 25.7 90.6 16 25.6 75 36.2 24.4 29.7 Songkhla* 568501 2005 7 33.6 25.2 93.5 12 30.8 73 35.7 23.8 29.4 Songkhla* 568501 2005 8 34 25.1 159.3 11 38.2 73 36.8 23.1 29.5 Songkhla* 568501 2005 9 32.1 25.2 182.2 15 83.3 80 35.2 23.3 28.6 Songkhla* 568501 2005 10 31.1 24.6 350.2 20 74.7 83 33.6 23.6 27.8 Songkhla* 568501 2005 11 30.1 24.4 872.2 19 521.8 85 32.7 22.8 27.2 Songkhla* 568501 2005 12 28 23.9 1215.

1

24 170.6 88 30 22.3 25.9

Songkhla* 568501 2006 1 29.6 24.1 69.5 12 40.5 78 32.4 22.4 26.8 Songkhla* 568501 2006 2 30.2 25.5 119.3 10 33.7 78 30.8 24 27.8

Songkhla* 568501 2006 3 31 25.2 106.7 9 38.2 78 32.2 22.9 28.1

Songkhla* 568501 2006 4 32.2 25.2 126.6 12 48.7 78 33.8 23.3 28.7 Songkhla* 568501 2006 5 32.4 24.5 153.1 19 35.4 80 34.3 23.4 27.9 Songkhla* 568501 2006 6 33.1 24.4 197.7 13 73.3 78 34.7 14.7 28.7

Songkhla* 568501 2006 7 33.3 25.3 41.7 11 12.4 75 35 24 29.3

Songkhla* 568501 2006 8 33.9 25.3 36.5 7 15.3 71 36.1 23 29.6

Songkhla* 568501 2006 9 32.5 24.2 91.4 19 15 78 34.3 21.4 28.3

Songkhla* 568501 2006 10 31.8 23.4 171 21 40 79 35 22 27.6

Songkhla* 568501 2006 11 30.6 24.4 303.8 23 37 81 31.7 23.1 27.5 Songkhla* 568501 2006 12 30.4 24.8 190 22 48.3 76 32.1 23.6 27.7

Songkhla* 568501 2007 1 29.7 24.8 316 16 182 79 31.8 23 26.9

Songkhla* 568501 2007 2 30.3 24.7 7.3 3 3.6 74 31.6 22.4 27.1

Songkhla* 568501 2007 3 31.8 25.1 70.6 7 49.6 76 33.4 22.1 28

Songkhla* 568501 2007 4 32.3 25.7 89.6 9 29 76 33.8 23.6 28.6

Songkhla* 568501 2007 5 32.7 25.3 203 19 36.9 80 35.3 23.3 28.1 Songkhla* 568501 2007 6 32.6 25.5 113.6 14 34.7 80 34.7 23.8 28.1 Songkhla* 568501 2007 7 33.1 25.2 146.6 14 78.8 76 36.4 23.3 28.1

Songkhla* 568501 2007 8 33.5 25.1 38 11 18.5 73 35.5 23.9 28.2

Songkhla* 568501 2007 9 33.5 25.1 125.8 14 25.2 75 35.6 24.1 28.1 Songkhla* 568501 2007 10 31.1 24.3 363.8 20 85.3 81 35.1 22.6 26.9 Songkhla* 568501 2007 11 29.6 24.3 217.1 21 57.8 82 31.1 23.7 26.6 Songkhla* 568501 2007 12 29.9 24.9 169.8 17 39.5 78 30.7 23.3 27.1

Songkhla* 568501 2008 1 29.8 24.8 103 10 36.2 78 31.4 23 27

Songkhla* 568501 2008 2 30.4 24.4 55.1 7 36 75 33.3 22.9 27

Songkhla* 568501 2008 3 30.8 25.4 36.7 7 21.3 76 32.8 23.2 27.5 Songkhla* 568501 2008 4 32.3 25.3 96.6 9 32.6 78 36.4 23.8 28.4 Songkhla* 568501 2008 5 33.7 25.1 137.3 19 37.3 77 36.2 23.9 28.2 Songkhla* 568501 2008 6 32.7 25 258.9 16 59.1 79 35.2 23.5 27.7 Songkhla* 568501 2008 7 32.7 24.6 189.7 15 86.2 77 34.5 23.2 27.7

Songkhla* 568501 2008 8 33 24.6 212.4 19 43.9 77 34.5 22 27.8

195

Stat

ion

Name Stat

ion

Co

d

e

yea

r

mont

h

Mean

Ma

x

Temp

Mean

Min

Temp To

ta

l

Rai

n

Ra

in

iy

D

Max

Ra

in

24

h

rs

A

verage

Rai

n

ex

trmax extrmi

n

meant

emp

Songkhla* 568501 2008 10 31.9 24.8 235.5 15 48.4 78 34.8 23.1 27.7 Songkhla* 568501 2008 11 29.2 24.3 1044 23 111.6 85 34 23.4 26.3 Songkhla* 568501 2008 12 28.7 24.4 396.9 21 93.5 82 30.5 22.6 26.2

196

Disease : Malaria

Chieng Rai

Year 2003 2004 2005 2006 2007 2008 Number of Cases 76 185 111 85 64 77 Ratio of cases per 100000

population 6.11 15.23 9.1 6.94 5.22 6.28

Udon Thani

Year 2003 2004 2005 2006 2007 2008

Number of Cases 3 3 6 11 8 2

Ratio of cases per 100000

population 0.19 0.2 0.39 0.72 0.52 0.13

Samut Sakhon

Year 2003 2004 2005 2006 2007 2008 Number of Cases 16 170 120 108 91 26 Ratio of cases per 100000

population 3.59 38.16 26.82 23.62 19.52 5.48

Phuket

Year 2003 2004 2005 2006 2007 2008 Number of Cases 35 44 102 66 88 101 Ratio of cases per 100000

population 12.75 15.59 35.29 22.26 28.56 31.44

Songkhla

Year 2003 2004 2005 2006 2007 2008 Number of Cases 151 54 194 1350 4140 805 Ratio of cases per 100000

population 11.77 4.19 15.02 103.06 313.35 60.51

197

Disease : Leotospirosis

Chieng Rai

Year 2003 2004 2005 2006 2007 2008

Number of Cases 137 50 41 91 135 95

Ratio of cases per 100000

population 11.01 4.12 3.36 7.43 11.02 7.75

Udon Thani

Year 2003 2004 2005 2006 2007 2008

Number of Cases 102 79 135 84 1014 168

Ratio of cases per 100000

population 6.63 5.16 8.87 5.51 6.61 10.96

Samut Sakhon

Year 2003 2004 2005 2006 2007 2008

Number of Cases 0 0 0 1 0 0

Ratio of cases per 100000

population 0 0 0 0.22 0 0

Phuket

Year 2003 2004 2005 2006 2007 2008

Number of Cases 4 5 12 15 20 16

Ratio of cases per 100000

population 1.46 1.77 4.15 5.06 6.49 4.89

Songkhla

Year 2003 2004 2005 2006 2007 2008

Number of Cases 11 14 55 72 58 58

Ratio of cases per 100000

population 0.86 1.09 4.26 5.5 4.39 4.36

198

Disease : Dengue Hemorrhagic

fever (DHF)

Chieng Rai

Year 2003 2004 2005 2006 2007 2008 Number of Cases 265 79 66 40 320 517 Ratio of cases per 100000

population 21.29 6.5 5.41 3.26 26.11 42.16

Udon Thani

Year 2003 2004 2005 2006 2007 2008 Number of Cases 455 254 335 450 513 220 Ratio of cases per 100000

population 29.57 16.6 22.02 29.49 33.55 14.35

Samut Sakhon

Year 2003 2004 2005 2006 2007 2008 Number of Cases 443 281 537 295 830 814 Ratio of cases per 100000

population 99.43 63.08 120.04 64.51 178.03 171.72

Phuket

Year 2003 2004 2005 2006 2007 2008 Number of Cases 142 85 127 94 423 609 Ratio of cases per 100000

population 51.74 30.12 43.93 31.7 137.29 189.57

Songkhla

Year 2003 2004 2005 2006 2007 2008 Number of Cases 1347 512 846 590 1672 1613 Ratio of cases per 100000

population 105.01 39.75 65.48 45.04 126.55 121.25