Enhancing Capacities for Climate Risk Management in Myanmar’s Dry Zone through Climate Information and Services

April 2017

RISK ASSESSMENT REPORT

Contents Contents ...... i List of Tables ...... ii Table of Figures ...... iii Acronyms ...... iv I. INTRODUCTION ...... 1 METHODOLOGY ...... 1 RISK ASSESSMENT PROCESS ...... 3 THE PILOT TOWNSHIPS AND VILLAGE TRACTS ...... 4 BASELINE INFORMATION ...... 8 II. HAZARD ASSESSMENT ...... 19 TEMPERATURE IN THE PILOT TOWNSHIPS ...... 19 RAINFALL IN THE PILOT TOWNSHIPS ...... 21 DROUGHT HAZARD ASSESSMENT ...... 23 FLOOD HAZARD ASSESSMENT ...... 27 CYCLONE HAZARD ASSESSMENT ...... 30 EARTHQUAKE HAZARD ASSESSMENT ...... 32 THUNDERSTORMS AND OTHER HAZARDS ...... 34 III. EXPOSURE, VULNERABILITY AND CAPACITY ASSESSMENT ...... 35 EXPOSURE ...... 35 SOCIAL VULNERABILITY ASSESSMENT ...... 36 VULNERABILITY AND CAPACITY ASSESSMENT – PILOT VILLAGE TRACTS ...... 38 DROUGHT VULNERABILITY ASSESSMENT ...... 41 IV. RISK ASSESSMENT ...... 44 DROUGHT RISK ASSESSMENT ...... 44 FLOOD RISK ASSESSMENT ...... 44 CYCLONE RISK ASSESSMENT ...... 46 EARTHQUAKE RISK ASSESSMENT ...... 48 V. RECOMMENDATIONS AND WAY FORWARD ...... 51 REFERENCES ...... 52

i

List of Tables Table I-1. Pilot village tracts in the five townships ...... 6 Table I-2. Land cover in the pilot townships ...... 10 Table I-3. Population profile in the five townships ...... 13 Table II-1. Maximum temperature in the townships, 1981-2015 ...... 19 Table II-2. Village tracts reporting extreme heat as a hazard ...... 20 Table II-3. Minimum temperature in the townships, 1981-2015 ...... 20 Table II-4. Village tracts reporting extreme cold as a hazard ...... 21 Table II-5. Rainfall profile in the townships, 1981-2015 ...... 22 Table II-6. Wet season rainfall in the townships ...... 22 Table II-7. Village tracts reporting extreme rain as a hazard ...... 23 Table II-8. Dry season rainfall in the townships ...... 23 Table II-9. SPI values and drought categories ...... 24 Table II-10. Drought hazard index values ...... 26 Table II-11. Village tracts reporting drought as a hazard ...... 27 Table II-12. Village tracts reporting flood as a hazard ...... 30 Table II-13. Classification of tropical cyclone warnings in Myanmar ...... 32 Table II-14. Village tracts reporting earthquake as a hazard ...... 32 Table II-15. Village tracts reporting thunderstorm as a hazard ...... 34 Table III-1. Buildings and population exposed to 100-year flood ...... 36 Table III-2. Buildings and population exposed to earthquake ...... 36 Table III-3. Duration of common activities, problems and adaptation responses ...... 38 Table III-4. Capacity level of village tracts ...... 39 Table III-5. Irrigation and livelihood profile in the townships ...... 41 Table IV-1. Buildings affected by flooding ...... 46 Table IV-2. Cyclone damage potential ...... 46 Table IV-3. Building count per PGA ...... 48 Table IV-4. Population count per PGA ...... 50

ii Table of Figures Figure I-1. Risk assessment methodology ...... 2 Figure I-2. Hazard, vulnerability and risk assessment process ...... 4 Figure I-3. Pilot townships in Myanmar’s Dry Zone ...... 5 Figure I-4. Pilot village tracts ...... 7 Figure I-5. Topography ...... 8 Figure I-6. Land cover/land use map ...... 9 Figure I-7. Population count and population density maps ...... 10 Figure I-8. Building count and building density maps ...... 11 Figure I-9. Settlement area ...... 12 Figure I-10. Housing types in the pilot townships ...... 14 Figure I-11. Roofing type ...... 14 Figure I-12. Wall type ...... 15 Figure I-13. Floor type ...... 15 Figure I-14. Critical facilities map ...... 16 Figure I-15. Source of lighting ...... 17 Figure I-16. Source of drinking water ...... 17 Figure I-17. Source of non-drinking water ...... 18 Figure II-1. Spatial extent of 3, 6 and 12-month moderate (MD), severe (SD) and extreme (ED) drought ...... 25 Figure II-2. Drought hazard index (3, 6 and 12 months) ...... 26 Figure II-3. Flood hazard map (5-year flood event) ...... 28 Figure II-4. Flood hazard map (100-year flood event) ...... 29 Figure II-5. Cyclone hazard map (track and wind speed) ...... 31 Figure II-6. Earthquake hazard map ...... 33 Figure III-1. Flood and earthquake hazard index ...... 35 Figure III-2. Social vulnerability map of village tracts ...... 37 Figure III-3. Drought vulnerability map of village tracts ...... 42 Figure IV-1. Drought risk map ...... 44 Figure IV-2. Flood risk map ...... 45 Figure IV-3. Earthquake risk map ...... 49

iii Acronyms

CCA Climate Change Adaptation DEM Digital Elevation Model DMH Department of Meteorology and Hydrology DoA Department of Agriculture DoI Department of Irrigation DoL Department of Livestock DRM Disaster Risk Management DRR Disaster Risk Reduction DZGD Dry Zone Greening Department FGD Focus Group Discussion GAD General Administration Department GIS Geographic Information System IPCC Intergovernmental Panel on Climate Change RRD Relief and Resettlement Department

iv

I. INTRODUCTION

Myanmar’s Dry Zone is considered as the most water-stressed and food insecure region in the country. Located in the central plains, the Dry Zone covers about 13% of the country’s land area but is home to nearly one-third of its population of over 50 million. It is mostly flat, with the Irrawaddy river flowing through it. Seasonal water scarcity is very common, with about a quarter of all householders reporting insufficient water during the dry summer season

The Dry Zone receives considerably low average annual rainfall ranging from 500 to 1000mm compared to the national average precipitation, which ranges between 2,000 and 5,000mm. Rainfall patterns also differ widely among districts and across time. In recent years, monsoon duration has reduced and rainfall events have become shorter though more intense. Additionally, the onset of the wet season has become more variable than its culmination, presenting a major challenge for farmers relying mainly on rainfall for water.

To help reduce the vulnerability of farmers to increasing drought and rainfall variability, as well as enhance their capacity to plan for and respond to future climate change impacts, the project, “Addressing Climate Change Risks on Water Resources and Food Security in the Dry zone of Myanmar” was launched by the United Nations Development Programme (UNDP) with funding from Adaptation Fund. The project is implemented in the townships of Shwebo, Monywa, Myingyan, Nyaung U and Chauk. Under this, a sub-project “Enhancing Capacities for Climate Risk Management in Myanmar’s Dry Zone through Climate Information and Services” was designed and implemented with technical assistance from the Regional Integrated Multi-Hazard Early Warning System (RIMES). The latter is comprised of various activities including, among others, hazard, vulnerability and risk assessments in the five pilot townships.

The assessment component of the project aim to: i) map the various hazards affecting the pilot townships, ii) assess the exposure and vulnerability of populations and critical infrastructures, iii) assess existing resource and current capacity to respond to risks, iv) analyse the risks, and v) provide potential risk reduction and management recommendations.

1 METHODOLOGY

The entire risk assessment methodology is comprised of four phases as follows (see Figure I- 1):

Phase I. Baseline Data Gathering. This phase forms the basis for assessing and mapping hazards and vulnerabilities for the five pilot townships. Geographic Information System (GIS) information, satellite data and other information were gathered from various sources and utilized in the development of the base maps that show the administrative boundaries, land cover/land use, and/or topography. This phase also helped establish the hazard and vulnerability profile of particular locations based on data gathered from disaster databases.

Phase II. Hazard, Vulnerability and Capacity Assessment. The second phase aims to gather and organize observational data related to the hazards affecting the pilot townships, their

1 vulnerability as well as capacity. Various data gathering methods and tools were used for this phase including i) a comprehensive review of literature, census data, reports and other sources of information, ii) field surveys and site observations, iii) interviews with key representatives at the township level, and iv) participatory risk assessment activities at the village tract level.

Establishing Baseline Information Phase I. Baseline Data Administrative Boundary, Disaster Statistics, Gathering Land Cover/Land Use, Topography/DEM

Review of Literature, Census Data, Reports

Field Survey/Site Observation

Phase II. Hazard, Vulnerability and Capacity Township-Level Assessments Assessments DMH, DoA, DoI, LBVD, RRD, GAD, DZGD

Participatory Risk Assessment Training

Village-Tract Level Assessments FGD and Risk Mapping of Pilot Village Tracts

Participatory Risk Assessment Workshop

Risk Assessments Phase III. Risk Analysis GIS Overlay of Hazard and Vulnerability Maps to Develop Risk Assessments

Phase IV. Validation Validation of Assessment Outputs DMH, DoA, DoI, LBVD, RRD, GAD, DZGD

Figure I-1. Risk assessment methodology

In particular, there were two major assessment activities conducted with stakeholders at township and village tract levels:

1. Township Level Assessments. This process involves interviewing key people from the Department of Meteorology and Hydrology (DMH) Mandalay Regional Office and representatives from the General Administration Department (GAD), Department of Agriculture (DoA), Department of Irrigation (DoI), Livestock Breeding and Veterinary Department (LBVD), Dry Zone Greening Department (DZGD), and Relief and Resettlements Department (RRD) in each of the pilot townships. The township-level

2 interviews and data gathering helped provide meso-level perspective on the hazards and risks affecting the townships.

2. Village Tract Level Assessment. This assessment is comprised of four activities namely: i) risk ranking, ii) risk mapping, iii) seasonal calendar, and iv) survey. The activities were conducted with key people at the village tract level including the village tract administrator, village chairmen, leaders and/or representatives of farmers’ groups, women’s groups, fishermen’s groups or senior citizens’ groups, among others1.

Using outputs from the various data gathering methods above, Phase II helped: i) define and map the priority hazards affecting the pilot townships using hazard-specific data (e.g., rainfall, hydrological and hydraulic information as well as seismic data), ii) outline the different elements-at-risk with the digitized buildings and critical facilities map, and iii) assess the vulnerability and capacity levels of the villagers using census data from MIMU (http://www.themimu.info/census-data) as well as outputs from the village tract level activities. Although most of the hazard-related information came from the Department of Meteorology and Hydrology (DMH), these were validated with on-the-ground information from township-level officers and village tract level representatives.

Phase III. Risk Analysis. This phase involves the creation of risk maps as well as the assessment of risks and their potential impacts. The hazard and vulnerability data gathered is organized, synthesized and analyzed in order to i) identify risks for priority action, and ii) determine potential risk management and resource allocation recommendations for decision-makers.

Phase IV. Validation. This phase involves a Validation Workshop where outcomes of the hazard, vulnerability and risk assessments are presented to key stakeholders for discussion and feedback. The purpose of this is to validate preliminary conclusions, deepen the understanding of findings, and discuss recommendations for further actions that may be adopted by concerned agencies as a way forward.

2 RISK ASSESSMENT PROCESS

The assessment of hazard, vulnerability and risk is a prerequisite to disaster risk reduction and resource management. These assessments may be made using only qualitative or

1 At least two 2-day trainings were conducted in relation to the village tract level assessments. The first one, entitled Participatory Risk Assessment Training, was conducted prior to the village tract level activities to i) introduce the concepts of hazard, vulnerability, risk, and risk management, and ii) demonstrate the process for conducting and documenting the activities at the pilot village tracts. The second, called Participatory Risk Assessment Workshop, was held after the village tract level activities to i) gather outputs and feedback from field experience, and ii) discuss areas or ways to improve the assessment process, methods, and/or tools. The trainings and village tract level assessments involved 10 participants from each of the 5 pilot townships for a total of 50. Participants invited to the training and the village tract assessments were all connected with a government agency in the township they were representing. The majority were agriculture extension workers with background on farm risk management and/or community organizing work.

3 quantitative methods or through a mix of both in a semi-quantitative type of assessment. The choice of method depends on the hazard type, the time and resources, data availability, and objective of the assessment. The more the resources and data available, the more detailed the assessments.

Figure I-2 shows the overall process for generating the hazard, vulnerability and risk assessments based on data gathered from the methodology outlined in the previous section.

!!!!!!!!Hazard!Assessment!!!!!!!!!!!!!!!!!!!!!!!Vulnerability!Assessment!!!!!!!!!!!!Risk!Assessment!

Administra*ve-Boundary- Disaster-Sta*s*cs- Baseline-Data- - ! Land-Cover/Land-Use- - Popula*on- ! Topography/DEM- -MAPPING- - -DROUGHT-- Buildings-&- DroughtORelated-Data- - - Infrastructure- Risk-Assessments- ! Rainfall-Data- - - ! Drought-Records- - Agricultural- - - Produc*on- - - FLOOD- - FloodORelated-Data- Capacity- - - ! Hydrological-Data- - ! Hydraulic-Data- Vulnerability- - - - - EARTHQUAKE-- - EarthquakeORelated-Data- - - Recommenda*ons- ! Seismic-Records- for-DRR- ! Earthquake-Events------

Figure I-2. Hazard, vulnerability and risk assessment process

Data from Phase I provided baseline information particularly useful in generating hazard maps and assessments. As well, information on the administrative boundaries and on previous disasters was helpful in generating the vulnerability maps and assessments. All of these help shape the risk maps and assessments, which became the basis for developing recommendations for reducing risks and managing resources.

3 THE PILOT TOWNSHIPS AND VILLAGE TRACTS

The project is implemented in the townships of Shwebo and Monywa in Sagaing Region, Myingyan and Nyaung U in Mandalay Region, and Chauk in Magway Region. Within the five townships, about 146 village tracts were piloted for the study, particularly in the aspect of vulnerability and capacity assessment. Figure I-3 shows the locations of the five pilot townships.

4 Shwebo. Another city in Sagaing Region, Shwebo is about 110km northwest of Mandalay between the Irrawaddy and Mu rivers. The city’s climate is tropical. Like Monywa, Schwebo is also a trade center for agricultural products like beans, rice and sesame from the surrounding plains between the Irrawaddy and Mu rivers.

Monywa. Monywa is a city in Sagaing Region, located on the eastern bank of the Chindwin River and about 136km northwest of Mandalay. It has a tropical savanna climate, with very warm temperatures throughout the year. The city is a major center for trade and commerce of agricultural products like beans, orange, pulses, and palm sugar from the surrounding Chindwin valley.

Myingyan. Myingyan is both a city and district in Mandalay Region, located in the eastern bank of the Irrawaddy River south of Mandalay. The city’s climate is dry, with little rainfall. Crops grown in the area include millet, sesame, cotton, maize, rice and various peas and beans.

Nyaung U. Also located in Mandalay Region, the administrative town of Nyaung U is located on the eastern bank of the Irrawaddy River. The town has tropical climate.

Chauk. Chauk is a town and river port in Magway Region, on the eastern side of the Irrawaddy River. The town has tropical climate with little rainfall throughout the year. Chauk’s local economy is driven by its oil field refinery, which processes crude oil. Figure I-3. Pilot townships in Myanmar’s Dry Zone

The figure highlights (in yellow) the pilot townships with Shwebo on the upper right, Monywa on the upper middle, Myingyan on the center, then Nyaung U and Chauk on the lower left. Below is a list of the pilot village tracts in the five townships.

5 Table I-1. Pilot village tracts in the five townships Shwebo Monywa Myingyan Nyaung U Chauk 1 Boe Daw Taw Dan Pin Te Ba Lon Byu Gyi Chaung Tet 2 Bone Bweit Hpan Khar Kyin Chay Say Chaung Shey Gway Cho 3 Chi Par In Taing Gway Pin Yoe Chaung Wa Gway Pin 4 Gway Pin Kone Kan Pyar Hpet Pin Aing Da Hat See Gway Pin Gyi 5 Kha Tet Kan Hpoke Kone Kan Chaw Dan Htein Kan (North) 6 Kha Tet Kan Hta Naung Wun Kan Ni Htan Pauk Kone Htein San (South) 7 Inn (Sin) Kwet Kyaung Kone Kan Sint Htee Pu Kyauk Ye 8 Ka Hpyu Kyee Oke Koke Ke Kamma Kyaung Yar Taw 9 Kan Gyi Taw Kyoe Kyar Kan Ku Ywar Kan Ni Gyi Ma Gyi Kone 10 Kawt Ma Yoe Taw Kun Saik Kan Ni Pauk Myay Pa Don 11 Ku Lar Ma Myin Mee Laung Kyee Pin Kan Kan Tein Myay Sun 12 Kyoe Kyar Nyaung Pin Lel Thit Kan Thar Yar Nyaung Chaung 13 Ma Au Pa Lin Kone Mee Pauk Ku Taw Nyaung Zin 14 Ma Khauk Se Gyi Taw Na Bu Aing Kya Oh Nyee Su 15 Ma Lar Taung Kyar Nat Htar Kyauk Kan Poke Pa Kan 16 Min Kone Taung Mar Taw Nyaung Wun Kyauk Pyin Kan Sa Lay 17 Min Kyaung Te Gyi Kone Pay Taw Kyun Khin Gyi Sar Taung 18 Na Maw Thar Si Pin Lel Mee Laung Pyar Taung Ba Lu 19 Ohn Pauk Yae Kan Su Pyar Myay Ne Gyi Tha Lone Thway 20 Pa Laing Yaung Taw Tone Pyawt Myay Ni Than Bo 21 Pauk Tone Ywar Ton Sar Khar Myay Thin Twin Thit To Kan 22 Seik Khun Zee Kyun Thin Pyun Nga Min May Twin Lat 23 Shar Taw Tu Ywin Bo Nyaung Ni Kyin Wa Thae San 24 Shwe Gun Yon Htoe Pan Kone Pin Ya Thit 25 Ta Ga Nan Ywar Si Pya Taing Ywar Ma 26 Ta Kan Thar Ywar Thar Yar Pyawt Kan 27 Ta Khun Taing Zee Pin Kan Pyin Chaung 28 Ta Ohn Sa Par Thin 29 Tei Pin Set Set Yo 30 Tha Pyay Thit Cho Shwe Ka Hpyu 31 Thit Cho Pin Sin Thar Mway 32 Su Ti 33 Taw Pyar 34 Taw Ywar 35 Tet Ma (Nyaung

Pin) 36 Tha Pyay Aing 37 War Khin Gyi 38 Yan San 39 Ywar Pale 40 Ywar Thit (South) 41 Zee Sa Hmyin

The table shows that Shwebo has 31 pilot village tracts, Monywa 22, Myingyan 27, Nyaung

6 U 41 and Chauk 25 for a total of 146 pilot village tracts. Figure I-4 shows the location of these village tracts in the townships.

Figure I-4. Pilot village tracts

7 The figure shows the location of pilot village tracts in the five townships. Selected on the basis of their vulnerability to climate-related stresses, the majority of the pilot village tracts are located in the middle to western side of the townships, mostly away from the river and/or from irrigation facilities.

4 BASELINE INFORMATION

Topography. Topography was generated using the global digital elevation model (DEM) Shuttle Radar Topography Mission (SRTM) with 30-meter resolution. Figure I-5 shows the topography of the pilot townships and adjacent areas. Shwebo, Monywa and Myingyan generally have low elevation, flat terrain particularly in the riverine areas. Nyaung U and Chauk also have low elevation in or near Ayerwaddy river but higher elevation from the middle towards their eastern sections.

Land Cover/Land Use. The Moderate Resolution Imaging Spectro- radiometer (MODIS) Type 4 Land Cover/Land Use was used along with definitions from the Food and Agriculture Organization (FAO) to generate six land cover/land use classifications: i) bare land, ii) cropland – broadleaf such as cotton, soybeans and sugar beets, iii) crop land – grass which encompasses rice, wheat, maize, iv) vegetation including evergreen needle leaf, evergreen broadleaf, deciduous needle leaf and deciduous broadleaf, v) urban, and vi) water (excluding the Ayerwaddy river). Land cover in the pilot townships is generally classified as cropland – grass and broadleaf.

Figure I-5. Topography

Figure I-6 shows the land cover/land use map of the townships and adjacent areas while

8 Table I-2 outlines the land cover profile under each category.

Figure I-6. Land cover/land use map

9 Table I-2. Land cover in the pilot townships Shwebo Monywa Myingyan Nyaung U Chauk Water 0.28% 0.03% 0.83% 0.73% 1.08% Bareland 0.35% 0.37% 0.90% 0.61% 1.12% Urban 0.63% 3.63% 2.57% 1.05% 0.70% Cropland – Grass 77.99% 70.22% 58.82% 76.69% 69.67% Cropland – Broadleaf 18.28% 25.70% 36.04% 20.59% 27.36% Vegetation 2.46% 0.03% 0.83% 0.33% 0.07%

The figure and the table show that the pilot townships and adjacent areas are generally made up of grass and broadleaf cropland ranging between 94 and 97%. This means only 3 to 6% of the townships are classified as water, bare land, urban and vegetation areas.

Population. The analysis of population characteristics is integral to risk assessment. The very location and density of populations help determine their level of risk. For instance, the pilot townships’ urban areas, except for Shwebo, are located close to, or beside, the river making residents vulnerable to riverine flooding.

Figure I-7 shows the spatial distribution and density of populations while Figure I-8 shows the number and density of housing units in each of the village tract in the pilot townships.

Figure I-7. Population count and population density maps

10 The figure above (left) shows that most village tracts have between 1,000 – 5,000 residents, with a typical population density that is less than 200 residents per km2. For Shwebo, the urban area is located in the middle of the township. With approximately 64,095 residents, the population density of Shwebo’s urban area is at 5,258 residents per km2, about 17.5 times more than the township average population density of 299 per km2.

Monywa’s urban area is situated in the western side of the township, along the Chindwin River. The most urbanized township among the five, Monywa’s 202,959 urban residents live on 30 km2 resulting in a high population density of 6,667 residents per km2, which is 14 times more than the township average population density of 467 km2. Myingyan’s urban area is situated towards the southwestern part of the township near Ayerwaddy River. About 84,941 people live in the 12.7 km2 town for a high population density of 6,688 people per km2, 18 times more than the township’s average population density of 372 per km2.

Nyaung U and Chauk’s denser settlements are also located in the western parts of the townships, very close to Ayerwaddy River. Nyaung U has an estimated 30,477 residents in its 25 km2 urban area resulting in a relatively low urban population density of 1,178 per km2, which is 6 times more than the township’s average population density of 189 per km2. Chauk also has relatively less urban residents at 46,470 and population density of 1,526 km2, 6.6 times more than the average 231 per km2 population density in the township.

Figure I-8. Building count and building density maps

11

Figure I-9. Settlement area

Figure I-8 shows that the structures in the village tracts generally range between 501 – 2000 units, averaging less than 50 units per km2. This means that settlements are scattered across the village tracts. Figure I-9 shows the specific locations of these buildings and structures in the five townships. Although the relatively low population density helps decrease

12 vulnerability, the very location of settlements along the riverbanks makes them more exposed, and potentially vulnerable to flooding.

The following table outlines in more detail the population profile of the townships based on 2016 census data from the Myanmar Information Management Unit (MIMU)2.

Table I-3. Population profile in the five townships Shwebo Monywa Myingyan Nyaung U Chauk Urban 29.3% 55.8% 31.8% 24.5% 24.3% Total population 251,873 347,124 279,946 232,577 212,589 Male 46% 46% 45% 46% 47% Female 54% 54% 55% 54% 53% # of Households 50,247 75,962 62,340 44,662 44,618 Average household size 4.5 4.6 4.3 4.3 4.1 % Female-headed 25.5% 26.1% 38% 24.7% 30.7 Aged 15-64 162,231 259,250 186,377 134,724 121,345 Employed as % of aged 15-64 69.7% 69.7% 65.7% 65.7% 55.4% population** Child dependent (0-14) 34.6% 33.9% 36.6% 36.2% 40.6% Old dependent (64 and above) 10.6% 9.6% 11.5% 10.9 12% Disability prevalence 3.1% 3.6% 6.6% 5% 4.1% Literacy Rate 89.72% 88.89% 87.96 83.53 85.26% **Based on regional rates

Total population in the pilot townships range between a low of 212,589 in Chauk and a high of 347,124 in Monywa, which is the most urbanized township. All townships show relatively similar gender profiles with males ranging between 45–47% and females 53–55% of the total population. Average household size for all townships is also similar, with a low of 4.1 in Chauk and a high of 4.6 in Monywa. Nyaung U has the lowest number of female-headed households while Myingyan has the highest at 38%.

Between 121,345 – 259,250 (57 – 75%) of the townships’ total population is aged 15-64, the range considered in Myanmar as the working ages – 55.4% in Chauk, and highest at 69.7% in both Shwebo and Monywa. An estimated 41.8 to 48.1% are either children aged between 0–14 or adults aged 64 and above. The young and old are often considered a population dependent on the working group (i.e., those aged between 15 to 64), and are therefore seen to increase overall vulnerability of a particular locality.

The prevalence of disabilities related to seeing, hearing, walking and remembering ranges between Shwebo’s lower rate of 3.1% and Myingyan’s 6.6%. Meanwhile, literacy rates are higher in Shwebo at 89.72 compared to other townships.

Housing Types. The number and type of houses is relevant in determining the elements-at-

2 Where possible, data is compared with information received from the General Administration Department (GAD) of the pilot townships. There may be discrepancies in the absolute values such as population or number of households. In general, however, this is not considered to significantly affect the outcomes of the vulnerability and risk assessments.

13 risk and vulnerability components of the risk assessment especially for flood, cyclones and earthquakes. Figure I-10 shows the infrastructure and housing types in the five townships while Figures I-11, I-12 and I-13 show the materials commonly used for each component of the houses and buildings in the pilot townships.

45000

40000 Apartment/condominium 35000 Bungalow/brick house 30000 Semi-pacca house 25000 Wooden house 20000 Bamboo

15000 Hut 2–3 years

10000 Hut 1 year

5000 Other 0 Shwebo Monywa Myingyan Nyaung U Chauk

Figure I-10. Housing types in the pilot townships

A majority (73 – 88% or 32,570 – 55,124) of the houses in the five townships is made up of bamboo and other wooden materials. Only about 5 – 10% are bungalows or brick houses, which are comparatively stronger.

60,000

50,000 Dhani/Theke/In leaf 40,000 Bamboo

30,000 Wood Corrugated sheet

20,000 Tile/Brick/Concrete

Other 10,000

0 Shwebo Monywa Myingyan Nyaung U Chauk

Figure I-11. Roofing type

Between 60 to 72% (26,776 – 44,810) houses use corrugated sheet for roofing, 28% (12,270 – 14,096) use dhani/theke/inleaf, and in the case of Myingyan and Monywa, 10,824 and 18,251 use bamboo as roofs.

14 60000

50000 Dhani/Theke/In leaf

40000 Bamboo Earth 30000 Wood

20000 Corrugated sheet Tile/Brick/Concrete 10000 Other

0 Shwebo Monywa Myingyan Nyaung U Chauk

Figure I-12. Wall type

Most houses again use bamboo materials as walls. This ranges between 30,849 – 53,331 (69 – 84%) houses. Tile/brick/concrete and wood rank second and third respectively as materials for walls.

45,000

40,000

35,000

30,000 Bamboo

25,000 Earth

20,000 Wood

15,000 Tile/Brick/Concrete

10,000 Other

5,000

0 Shwebo Monywa Myingyan Nyaung U Chauk

Figure I-13. Floor type

The floor type differs in the pilot townships. For Shwebo, Monywa and Myingyan, majority of the houses are made of earth soil flooring with some houses on tile/brick/concrete floors. On the other hand, majority of the house floor types in Nyaung U and Chauk are made of bamboo and some earth soil or tile/brick/concrete.

Houses in the five townships are generally made of light materials, which may be more vulnerable to fire, floods and cyclones but less vulnerable to earthquakes.

Infrastructure and Facilities. Figure I-10 shows the existing road and railway networks, critical facilities such as churches/temples/pagodas, schools and hospitals in the pilot

15 townships and adjacent areas.

Figure I-14. Critical facilities map

16 The figure above shows the major road as well as railway network in the townships. A few other facilities such as community centers, markets and temples among others are also shown in the map to a certain degree. As for utilities, Figures I-15, I-16 and I-17 outline household access to light, drinking water as well as non-drinking water.

60,000

50,000 Electricity Kerosene 40,000 Candle

30,000 Baery Generator (private) 20,000 Water mill (private)

10,000 Solar system/energy Other 0 Shwebo Monywa Myingyan Nyaung U Chauk

Figure I-15. Source of lighting

While most residents already have access to electricity, it only serves between 32 – 45% of the townships’ households, except for Monywa where electricity service is available to 67% of the households. Additionally, electricity connection suffers from frequent power interruptions. Other households use batteries, generators and solar energy for lighting.

40,000 Tap water/ Piped 35,000 Tube well, borehole 30,000 Protected well/ Spring 25,000 Unprotected well/Spring 20,000 Pool/Pond/ Lake

15,000 River/stream/ canal

10,000 Waterfall/ Rain water

5,000 Boled water/ Water purifier

0 Tanker/ Truck Shwebo Monywa Myingyan Nyaung U Chauk

Figure I-16. Source of drinking water

For drinking and non-drinking water, the majority of residents use ground water via tube wells/boreholes. This is followed by tap/piped water, protected well/spring, ponds and rivers/streams. In recent years, however, there have been reports of tube wells will saline water, further limiting the already short supply of water in the townships. Perhaps this helps

17 explain why there are more household who use tube wells and boreholes as a source of non-drinking water (see Figure I-17).

45,000 Tap water/ Piped 40,000 35,000 Tube well, borehole 30,000 Protected well/ Spring 25,000 Unprotected well/Spring 20,000 Pool/Pond/ Lake 15,000 River/stream/ canal 10,000 Waterfall/ Rain water 5,000 Boled water/ Water purifier 0 Tanker/ Truck Shwebo Monywa Myingyan Nyaung U Chauk

Figure I-17. Source of non-drinking water

The figure above shows the sources of non-drinking water having a similar trend to the sources of drinking water in the five townships. Tube wells and boreholes are a major source of water followed by protected well/spring and tap water. But in Chauk, pools/ponds/lakes are considered as good sources of water next to tube wells, which serve lesser households when compared to other townships.

18

II. HAZARD ASSESSMENT

An important step in the risk assessment process, hazard assessment involves the analysis of the nature, behavior, frequency and/or probability of occurrence of hazards using meteorological, hydrological and geological data. The choice of method and the process for assessing hazards depends on availability of (historical) data, time and/or resources, and the purpose for which the assessment is done.

The following section presents the temperature– as well as rainfall–related hazards in the pilot township. This is followed by drought, flood, cyclone and earthquake hazard assessments in the pilot townships.

1 TEMPERATURE IN THE PILOT TOWNSHIPS

The Dry Zone has two seasons: wet and dry. The wet season, which is from May to October, coincides with the southwest monsoon. Meanwhile, the dry season is divided further into the “winter” months of November to February, and “summer” months of March to April.

During the dry season, temperatures reach 40°C, so that rates of evaporation are potentially more than double those of rainfall. Because of this, the Dry Zone is sometimes considered as a semi-arid area (see IWMI 2015).

Heat Hazard. An analysis of temperature data in the pilot townships for the last 35 years (1981-2015) shows potential (extreme) heat hazard with slightly higher temperatures in Myingyan. Table II-1 summarizes the maximum temperature variability as well as trends in the five townships.

Table II-1. Maximum temperature in the townships, 1981-2015 Shwebo Monywa Myingyan3 Nyaung U Chauk4 Annual average maximum 33.27°C 33.70°C 34.43°C 33.98°C 34.02°C temperature Average maximum 42.3°C 43.08°C 44.12°C 43.63°C 43.79°C temperature Highest recorded 44.2°C 46°C 46.9°C 46.10°C 46°C temperature Frequency that 34.29% (≥37) 43.64% 47.91% 38.53% 37.45% temperature ≥35 Frequency that 4.81% 8.23% 11.32% 8.56% 12.33% temperature ≥40 Frequency that .2% 1.07% 1.93% 1.01% 1.57% temperature ≥43 Trend for maximum increasing increasing increasing increasing increasing temperature

3 Analysis for Myingyan does not include the years 1981 and 1982 due to serious gaps in observation data in those years. 4 Due to data quality concerns, the climate analysis for Chauk Township utilized historical observation data for rainfall and temperature from adjacent Minbu Township.

19 • Annual average maximum temperature. This represents the annual maximum daily temperature averaged for the last 35 years. The figures range between the low of 33.27°C for Shwebo and high of 34.43°C for Myingyan. • Average maximum temperature. This is based on the highest maximum temperature for each year averaged for the last 35 years for each township. Again, Shwebo registers the lower limit of 42.3°C and Myingyan, 44.12°C. • Highest recorded temperature. This shows the maximum temperature recorded during the last 35 years for each of the pilot township. Shwebo, once again registered the lower limit of 44.2°C while Myingyan the highest at 46.9°C. • Frequency that temperature is equal to or more than 35°C, 40°C and 43°C. Shwebo shows lower frequency at 34.25% and again Myingyan has the highest at 47.91% of total days during the last 35 years that the temperature was 35°C or higher (≥ 37°C for Monywa). The rates for ≥ 40°C went down to a low of 4.81% for Shwebo and 12.33% for Chauk. The figures further reduced to 0.2% for Shwebo and 1.93% for Myingyan for days equal to or exceeding 43°C.

Overall, there is an increasing trend for maximum temperature for all the pilot townships during the last 35 years. This trend supports and is complemented by local perceptions of extreme heat as a local hazard as shown in Table II-2.

Table II-2. Village tracts reporting extreme heat as a hazard Shwebo Monywa Myingyan Nyaung U Chauk # of pilot village tracts 31 22 27 41 25 Village tracts reporting 31 11 27 26 25 extreme heat Average ranking of 7 14 16.78 11.92 13 extreme heat Level of priority5 medium medium high medium medium

Similar to the trends highlighted in the table and the map, at least half of the pilot village tracts in each township consider extreme heat as a hazard. In particular, village tracts in Myingyan find extreme heat as a high priority hazard compared to all other townships. As well, Shwebo registered a low medium priority rating for the hazard.

Cold Hazard. The analysis of minimum temperature data and trends in the pilot townships for the last 35 years (1981-2015) appear to be inconclusive in relation to cold hazard. Table II-3 summarizes the minimum temperature variability as well as trends in the five townships.

Table II-3. Minimum temperature in the townships, 1981-2015 Shwebo Monywa Myingyan Nyaung U Chauk Annual average minimum 21.02°C 19.1°C 21.91°C 22.10°C 19.08°C temperature Average minimum 9.59°C 10.2°C 9.32°C 10.22°C 9.13°C temperature Lowest recorded 3°C 7°C (Jan 1992) 6.5°C (Jan 6.5°C (Jan 6.4°C (Feb

5 Hazard scores of 1–4 are of low priority, 5–14 are of medium priority and 15–25 of high priority.

20 Shwebo Monywa Myingyan Nyaung U Chauk temperature 1983) 2001) 1997) Days with temperature ≤ 47 1 1 2 4 7°C slightly slightly Trend decreasing increasing no trend decreasing decreasing

• Annual average minimum temperature. This represents the annual minimum daily temperature averaged for the last 35 years. The figures range between the low of 19.08°C for Chauk and high of 22.10°C for Nyaung U. • Average minimum temperature. This is based on the lowest minimum temperature for each year averaged for the last 35 years for each township. Again, Chauk registers the lower limit of 9.13°C and Nyaung U, 10.22°C. • Lowest recorded temperature. This shows the minimum temperature recorded during the last 35 years for each of the pilot township. Shwebo registered the lowest at 3°C while Monywa the highest at 7°C. • Days with temperature equal to or less than 7°C. Again, Shwebo registers significantly more days than the other townships.

The minimum temperature in Shwebo appears to go further down during the last 35 years. This is also observed very slightly in Monywa and Nyaung U. Chauk did not show any trend while in Myingyan, the temperature appears to go up. Similarly, local perceptions remain inconclusive and extreme cold is not considered a high priority risk as shown in Table II-4.

Table II-4. Village tracts reporting extreme cold as a hazard Shwebo Monywa Myingyan Nyaung U Chauk # of pilot village tracts 31 22 27 41 25 Village tracts reporting 31 5 24 11 1 extreme cold Average ranking of 3.19 5 8.79 6.9 12 extreme cold Level of priority low medium medium medium medium

A few of the pilot village tracts in Monywa, Nyaung U and Chauk, and a majority of Myingyan consider extreme cold as a slightly medium priority hazard. Although all pilot village tracts in Shwebo consider it a hazard, this was a low priority.

2 RAINFALL IN THE PILOT TOWNSHIPS

Sandwiched between the Rakhine mountain range on the west and the highlands on the east, the Dry Zone receives considerably low average annual rainfall ranging from 500 to 1000mm compared to the national average precipitation, which ranges between 2,000 and 5,000mm (IWMI, 2015). Rainfall patterns also differ widely among districts and across time. The central area receives about 600mm rainfall per year while the peripheries receive up to 1,000mm annually.

21 Rainfall season for the five pilot townships is from May to October. Rainfall quantity is relatively high during the months of May and/or June but is suppressed in July, then peaks in September except for Shwebo where rainfall peaks in August. Table II-5 shows the rainfall profile of the pilot townships.

Table II-5. Rainfall profile in the townships, 1981-2015 Shwebo Monywa Myingyan Nyaung U Chauk Average annual rainfall 815mm 696mm 647mm 628mm 776mm Normal rainfall 719-1083 mm 557-835 mm 550-787 mm 494-742 mm 619-929 mm 1399mm 1195mm 891mm 1023mm 1295mm Highest annual rainfall (1983) (2010) (1989) (2011) (1995) 459mm 290mm 389mm 248mm 438mm Lowest annual rainfall (1985) (1982) (2009) (1982) (1998) # of rainfall days 49 (41 + 8) 48 (41 + 7) 48 (44 + 4) 49 (43 + 6) 60 (55 + 5)

Shwebo with average annual rainfall of 815mm has the highest rainfall quantity among the pilot townships, while Nyaung U has the lowest. The number of rainfall days in the townships is relatively the same at 48 – 49 except for Chauk, which has 60 rainfall days in a year.

Wet Season. Rainfall data indicate a general increase in rainfall quantity and number of rainfall days during the wet season. Table II-6 shows the wet season rainfall profile of the pilot townships.

Table II-6. Wet season rainfall in the townships Shwebo Monywa Myingyan Nyaung U Chauk Average wet season rainfall 823mm 620mm 594mm 569mm 709mm # of years with above 18 of 35 years 19 of 35 years 18 of 33 years 15 of 35 years 20 of 35 years average wet season rainfall (51.43%) (54.29%) (54.55%) (42.86%) (57.14%) Average # of rainfall days 53 47 48 43 63 # of years with above 14 of 35 years 20 of 35 years 15 of 33 years 14 of 35 years 18 of 35 years average # of rainfall days (40%) (57%) (45.45%) (40%) (51.43%) Extreme rainfall events (≥ 228 days 147 days 130 days 138 days 144 days 38 mm for Dry Zone) Wet season rainfall increasing increasing increasing increasing increasing quantity Wet season rainfall days increasing increasing increasing increasing increasing

The table shows slightly higher number of years with above average wet season rainfall for all townships except Nyaung U. But the number of years with above average number of rainfall days is slightly less than half except in Monywa and Chauk. Table II-6 also shows the number of days with rainfall measuring at least 38mm. While the figures only represent about 1% (130 days) to 1.78% (228 days) of the total number of days in 35 years, the trends in annual rainfall quantity and number of rainfall days is increasing for all townships. Nevertheless, data remains inconclusive in terms of the hazards directly related to precipitation during the wet season (i.e., extreme rain and/or wetter wet seasons). Similarly, local perceptions of extreme rainfall differ across the pilot townships as shown in Table II-7.

22 Table II-7. Village tracts reporting extreme rain as a hazard Shwebo Monywa Myingyan Nyaung U Chauk # of pilot village tracts 31 22 27 41 25 Village tracts reporting - 10 - 17 - extreme rain Average ranking of - 12 - 7.65 - extreme rain Level of priority N/A medium N/A medium N/A

Less than half of the pilot village tracts in Monywa and Nyaung U perceive the event as a medium priority hazard. On the other hand, it appears that representatives from the pilot village tracts of Shwebo, Myingyan and Chauk welcome the rain as it comes. This perception is in line with the infrequent incidence of extreme rainfall events as shown in Table II-6. It also reflects the local conditions and circumstances in the pilot village tracts, where rainfall (even extreme rainfall) may be considered a resource as opposed to a hazard for some villagers.

Dry Season. The analysis of rainfall during the dry season provides results that can help the drought hazard assessment in the next section. Additionally, it provides insights to trends towards drier dry season. Table II-8 provides a snapshot of the dry season rainfall characteristics in the pilot townships.

Table II-8. Dry season rainfall in the townships Shwebo Monywa Myingyan Nyaung U Chauk Average dry season rainfall 81mm 75mm 52mm 64mm 71mm # of years with above 14 years 11 years 16 years 12 years 8 years average dry season rainfall # of years with below 21 years 17 years 17 years 23 years 19 years average dry season rainfall Average # of rainfall days 8 days 7 days 6 days 6 days 5 days # of years with above 13 years 18 years 11 years 16 years 14 years average # of rainfall days # of years with below 19 years 15 years 15 years 15 years 19 years average # of rainfall days # of years ≤1 rainfall day 1 0 3 years 3 years 4 years Trend in dry season rainfall decreasing decreasing decreasing decreasing decreasing quantity Trend in dry season rainfall decreasing decreasing decreasing decreasing decreasing days

The table shows lesser number of years with above average rainfall and above average rainfall days, and relatively higher number of years with below average dry season rainfall and below average rainfall days. In other words, there is lower dry season rainfall quantity and lesser rainfall days. This decreasing trend is true for all townships.

3 DROUGHT HAZARD ASSESSMENT

The Dry Zone is considered as the most water-stressed region of the country. Seasonal

23 water scarcity is very common, with about a quarter of all householders reporting insufficient water during the dry summer season (WFP, 2011). This is because domestic water is limited to groundwater sources in the form of shallow wells or through rainwater collection ponds, which could easily dry up.

The IWMI (2015) reports at least three “dry-related” hazards affecting the Dry Zone region, including: • Dry Spells. Brief dry spells – about 6 to 14 days without rain – are very common during the early to middle part of the monsoon, i.e., months of June, July and August, although their intensities vary geographically (central part is more prone) and over time. • Early Retreat of the Monsoon. The dry spells in August are sometimes considered as an early retreat of the monsoon, which is another hazard affecting communities in the Dry Zone. The usual end of the monsoon is late October but in recent years, communities found that the rainy season sometimes ends in September, at least four weeks prior (Sellamuttue et al, 2013). In effect, the duration of the monsoon period in the Dry Zone varies from 115 to 175 days, with the shortest at the central part. The early end of the monsoon means there is less water available in the soil and temperatures are higher, which could also result to limited forage for cattle and/or potential pest infestations on crops such as groundnuts. • Drought. The Dry Zone is generally vulnerable to drought (defined as low rainfall for the entire season), the most recent (2015-2016) and allegedly the strongest during the last five centuries, was linked to the El Nino climate pattern (Thu, 2016).

In view of the above issues, an analysis of 3-, 6- and 12-month drought hazard was done using the WMO-recommended Standard Precipitation Index (SPI). Each SPI calculation is compared with the drought category range to obtain the probabilities for each drought category in the pilot townships. The results yielded the following frequencies of occurrence under each drought category.

Table II-9. SPI values and drought categories Frequency of Occurrence (%) Month SPI Range Drought Category Shwebo Monywa Myingyan Nyaung U Chauk Near Normal or Mild 0 to –0.99 77.25% 76.27% 73.05% 73.58% 79.08% Drought (NND) 3 –1 to –1.49 Moderate Drought (MD) 13.76% 16.38% 16.77% 16.06% 12.76% –1.5 to –1.99 Severe Drought (SD) 4.76% 6.78% 5.39% 8.81% 3.57% ≤ 2 Extreme Drought (ED) 4.23% 0.56% 4.79% 1.55% 4.59% Near Normal or Mild 0 to –0.99 73.02% 70.95% 64.67% 67.51% 65.41% Drought (NND) 6 –1 to –1.49 Moderate Drought (MD) 14.81% 18.44% 20.11% 17.26% 20.00% –1.5 to –1.99 Severe Drought (SD) 8.47% 7.82% 7.61% 10.15% 8.11% ≤ 2 Extreme Drought (ED) 3.70% 2.79% 7.61% 5.08% 6.49% Near Normal or Mild 0 to –0.99 69.35% 75.42% 64.80% 74.27% 64.80% Drought (NND) 12 –1 to –1.49 Moderate Drought (MD) 24.73% 12.29% 19.55% 9.22% 19.90% –1.5 to –1.99 Severe Drought (SD) 4.30% 5.03% 10.61% 10.19% 7.65% ≤ 2 Extreme Drought (ED) 1.61% 7.26% 5.03% 6.31% 7.65%

24

Figure II-1. Spatial extent of 3, 6 and 12-month moderate (MD), severe (SD) and extreme (ED) drought

25 Table II-9 shows that over the span of 35 years, near normal drought for 3, 6 and 12 months occurred between 64.67 – 79.08%, moderate drought occurred 9.22 – 24.73%, severe drought between 3.57 – 10.61% and extreme drought happened .56 – 7.65% of the time. Figure II-1 shows the spatial extent of moderate, severe and extreme drought occurrences for 3, 6 and 12–month time-frames. Moderate drought is highest in Myingyan for both the 3 and 6-month period, then in Shwebo for the 12-month period. For severe drought, Nyaung U has highest occurrence for both the 3 and 6-month time frame and Myingyan for the 12- month period. Extreme drought is again highest in Myingyan for both 3 and 6-month period, and then in Chauk for the 12-month time frame.

Based on severity and frequency, weights and ratings were assigned on drought occurrence probabilities to generate the following drought hazard indices (DHI).

Table II-10. Drought hazard index values Shwebo Monywa Myingyan Nyaung U Chauk 3-month 0.467 0.467 0.700 0.500 0.500 6-month 0.200 0.233 0.600 0.533 0.600 12-month 0.233 0.500 0.700 0.800 0.733

The table shows that Shwebo’s drought hazard decreased from its 3-month high of 0.467 to a low of 0.200 and 0.233 for 6 and 12 months respectively. For Monywa and Myingyan, the values decreased during the 6-month period only to go back up again in the 12-month period. Nyaung U and Chauk, on the other hand, increased towards the 6 and 12-month periods. Figure II-2 shows the drought hazard map of the townships based on the DHI values.

Figure II-2. Drought hazard index (3, 6 and 12 months)

Figure II-2 shows that 3-month drought hazard is highest in Myingyan, while 6 and 12- month drought hazards affect the townships of Myingyan, Nyaung U and Chauk. Similarly,

26 local perceptions consider drought as a high priority hazard in these three townships as shown in the following table.

Table II-11. Village tracts reporting drought as a hazard Shwebo Monywa Myingyan Nyaung U Chauk # of pilot village tracts 31 22 27 41 25 Village tracts reporting 31 19 25 35 25 drought Average ranking of drought 9.89 12.95 21.64 15.63 15.84 Level of priority medium medium high high high # of pilot village tracts reporting drought as part 31 11 27 41 23 of season Average duration (months) 3.19 2.55 2.82 3.32 3.43

A majority, if not all, of the pilot village tracts in the five townships consider drought as a hazard. Myingyan, Nyaung U and Chauk villagers consider drought as a high priority hazard while Shwebo and Monywa townships consider it medium priority. Majority of the villagers consider drought as part of the season. This is perceived to last between a low of 2.55 months in Monywa, and a high of 3.43 months in Chauk.

4 FLOOD HAZARD ASSESSMENT

Riverine flooding is a problem in the Dry Zone. For the Irrawaddy and Chindwin rivers, flooding happens when there is intense rain in Northern Myanmar for at least 3 days. When these headwaters coincide, flooding occurs in the lower Irrawaddy River and the delta. For some, annual riverine flooding is a natural phenomenon in the river basins that is considered to help clean the farmlands and replenish the ground with nutrients from upstream (Union of Myanmar, 2009). Others in the fishing industry welcome the overflow since they facilitate the fish spawning process. However, for crop farmers such is a big loss in terms of crop losses and opportunity costs since riverine floods inundate their farmlands for as long as 7 weeks during the wet season (IWMI, 2015).

For flood hazard assessment, HEC-RAS hydrologic modeling system was used to compute water surface elevation. These elevations were then mapped in GIS to form flood inundation maps with 5– and 100–year flood return periods6. Figure II-3 show the 5-year return period while Figure II-4 presents the 100-year flood inundation map affecting the five pilot townships. Due to challenges related to data availability, flood inundation levels/depths could only be mapped at certain points downstream of Shwebo and Monywa. This means the map could only generate inundation width and not flood hazard levels, hence the limited (blue) color in both townships although some flood depth information is available on the southwestern tip of Monywa. For the other townships, both flood scenarios show that Myingyan township is most flood-prone due to its low elevation compared to Nyaung U and Chauk.

6 There was a lack of parameter data upstream of Monywa and Shwebo so that only satellite images of historical flood events were used to generate flood inundation maps of these townships. These were combined with data extrapolated from the DEM and hazard map generated downstream.

27

Figure II-3. Flood hazard map (5-year flood event)

28

Figure II-4. Flood hazard map (100-year flood event)

29 Table II-12 summarizes the flood hazard-related outputs from the village tract assessments.

Table II-12. Village tracts reporting flood as a hazard Shwebo Monywa Myingyan Nyaung U Chauk # of pilot village tracts 31 22 27 41 25 Village tracts reporting 31 6 11 8 1 flood Average ranking of flood 4.23 9 13.36 8.38 9 Level of priority low medium medium medium medium # of pilot village tracts reporting flood as part of 20 5 13 1 2 season Average duration (months) 2.45 2.4 1.77 3 4

Although there were only about 7 out of 146 pilot village tracts situated beside the river, flooding was reported as a hazard by more than 1/3 of the pilot village tracts assessed. In particular, all pilot village tracts in Shwebo report flood as a hazard although this is considered a low priority hazard. Other townships report it as low medium priority. Similarly, a few pilot village tracts consider flood as part of the season occurring between a low of 1.77 months in Myingyan, and a high average 4 months in Chauk. Much of the reported flooding were either local stream or canal flooding with relatively lower flood water depth compared to riverine flooding hence the relatively low to lower medium priority.

5 CYCLONE HAZARD ASSESSMENT

Among all natural disasters in Myanmar, cyclones have historically caused the most destruction. The cyclone season falls between April to May and from October through December. Since 1947, about 36 cyclones have made landfall on the Myanmar coast. From year 2000, about one cyclone crosses Myanmar coast compared to a previous average of one every 3 years (Union of Myanmar, 2009). Some of the more recent devastating cyclones to hit the country – Cyclone Nargis in May 2008 and Cyclone Giri in October 2010 – left over 140,000 people dead and missing and affected a total of over 2.6 million people who lost their homes and livelihoods.

In general, cyclones are more common in the coastal regions of Rakhine State and Ayeyarwady Delta. The Dry Zone’s location in the central plains of Myanmar means that the area may be exposed to leftover winds and rain from cyclones that have weakened since landfall in coastal areas. Regardless, these winds and rain can still damage (rain fed) crops. This was the case when Cyclone Giri struck Myanmar in October 2010 as a Category 4 (Very Severe Cyclonic Storm). Although Giri degenerated to a tropical depression as it continued its path and crossed the Dry Zone area, it still brought wind speeds up to 55 knots (63 miles per hour), further dissipated to 45 (51.7 mph) and finally to 35 knots (40 mph).

For cyclone hazard assessment, cyclone data between 1840 to 2016 was downloaded from International Best Track Archive for Climate Stewardship (IBTrACS, https://climatedataguide.ucar.edu/climate-data/ibtracs-tropical-cyclone-best-track-data) and mapped in GIS to develop the following figure.

30

Figure II-5. Cyclone hazard map (track and wind speed)

Figure II-5 shows the tracks of cyclones that crossed the Dry Zone area during the last 119 years. Thus far, an average of 1 cyclone every 8.5 years reaches the Dry Zone. The colors in the dots and the corresponding colors in the Dry Zone Region map signify the cyclone wind speed in knots (1 knot = 1.15 miles per hour). Higher wind speeds mean higher hazard categories based on Myanmar’s tropical cyclone warning system (WMO, 2016).

31 Table II-13. Classification of tropical cyclone warnings in Myanmar Knots Miles per hour Kilometers per hour Low pressure area < 17 < 20 < 31 Depression 17-27 20-31 31-50 Deep Depression 28-33 32-38 51-61 Cyclonic Storm 34-47 39-54 62-87 Severe Cyclonic Storm 48-63 55-72 88-117 Very Severe Cyclonic Storm 64-119 73-137 118-221 Super Cyclonic Storm 120 above 138 above 222 above

Unfortunately, wind speed data is generally unavailable for cyclones occurring prior to the 1990s. In view of this, the hazard map was developed by interpolating data mostly from 2010’s Giri and then from two other unnamed depressions in 1992 as well as a storm in 2003. The resulting hazard map provides a relatively conservative estimate of the potential cyclone category that reaches the Dry Zone area7. The westernmost portion of the Dry Zone may experience higher wind speed hazards up to 80 knots (92 mph, dark orange), which then weakens to 55 knots (63 mph, light orange), and further degrades between 35 (40 mph) and 45 knots (51.7 mph, light green).

6 EARTHQUAKE HAZARD ASSESSMENT

Myanmar sits on one of the two main earthquake belts in the world so that much of the country is also prone to earthquake. A major active fault called the Sagaing Fault passes through some populated cities within the Dry Zone.

To assess earthquake hazards in the pilot townships, deterministic method was used. This method is good for testing emergency preparedness and coping with disaster loss of various magnitudes. It involves determining the largest possible earthquake that could happen from the identified active fault nearest to each of the five townships. Figure II-6 shows the locations for peak ground acceleration in each of the townships while Table II-14 presents the outcomes of the village tract-level assessments in relation to earthquake hazards8.

Table II-14. Village tracts reporting earthquake as a hazard Shwebo Monywa Myingyan Nyaung U Chauk # of pilot village tracts 31 22 27 41 25 Village tracts reporting 31 22 20 29 22 earthquake Average ranking of 4.16 2.64 6.5 4.97 2.09 earthquake Level of priority low low medium medium low

The table shows that a majority of the pilot village tracts consider earthquake as a hazard. However, it is only seen as a low priority hazard with only Myingyan and Nyaung U treating it as a slightly medium priority hazard.

7 The term conservative is used in hazard assessment to denote value that maximizes safety. 8 Peak ground acceleration (PGA) is the maximum ground acceleration during earthquake shaking at a certain location. PGA is typically used as a hazard index for buildings up to 7 stories high.

32

Figure II-6. Earthquake hazard map

33 The figure shows the different sources of maximum ground shaking for the five townships. For Shwebo, the source is located on the eastern part of the township, Monywa and Chauk on their southwest, while Myingyan and Nyaung have the same PGA source located at Myingyan’s southwest and Nyaung U’s northeast. This means that the buildings and villagers living in or nearest to those sources are exposed to relatively higher earthquake hazards.

7 THUNDERSTORMS AND OTHER HAZARDS

The village tract-level assessments highlighted thunderstorm as a relatively important hazard in the pilot townships in addition to the other hazards outlined in the previous sections. The table below outlines the pilot village tracts’ perceptions of thunderstorm9.

Table II-15. Village tracts reporting thunderstorm as a hazard Shwebo Monywa Myingyan Nyaung U Chauk # of pilot village tracts 31 22 27 41 25 Village tracts reporting 31 21 27 38 24 thunderstorms Average ranking of 4.6 9.52 12.48 7.24 6.46 thunderstorms Level of priority medium medium medium medium medium

The table above shows that a majority of the pilot village tracts consider thunderstorm as a hazard with relatively medium priority across all townships.

Other hazards that affect some areas in the pilot townships result from a combination of natural and man-made activities. For instance, flashfloods are already happening in recent years as decreasing forest cover and soil erosion now put communities at greater risk of localized flash floods during heavy rainfall events (MIMU, 2016). Forest fire is also another hazard, which may be caused by natural events such as lightning and friction of tightly packed trees. However, the Union of Myanmar (2009) states that the majority of forest fire incidents are due to man-made activities including shifting (slash and burn) cultivation, deliberate burning of forests for hunting purposes, careless use of fire (smoking and/or cooking) in the forest, blazing the trunk intentionally to collect lacquer, and purposeful burning of fodder to grow new grass.

9 The National Oceanic and Atmospheric Administration (NOAA) defines thunderstorms as rain showers during which one hears thunder. A thunder event is composed of lightning and rainfall, and could intensify with damaging hail (one inch or greater), high or strong winds/windstorms (winds gusting from 50 knots/57.5 mph up to 150mph or 240km/h), tornadoes and flash flooding. The thunderstorm classification in the village tract-level assessments therefore combined all local hazards related to thunderstorms – lightning, strong winds, tornadoes and flashfloods. Unfortunately, local data currently available cannot warrant reliable assessment of these hazards.

34 III. EXPOSURE, VULNERABILITY AND CAPACITY ASSESSMENT

This section highlights the elements that make up another important component of the risk assessment process. As a concept, vulnerability has evolved to encompass concepts of exposure and capacity. Vulnerability assessment involves the identification and analysis of the reasons why various elements such as people, structures/buildings, facilities, livelihood, economic activities and environment are at risk.

1 EXPOSURE

Exposure refers to a situation where people, livelihoods, environmental services and resources, infrastructure, or economic, social, or cultural assets in place could be adversely affected by physical events and thereby subjected to potential harm, loss or damage. Here, the elements-at-risk that are assessed are the buildings and populations. Figure III-1 shows the village tracts exposed to both flood and earthquakes of different hazard levels.

Figure III-1. Flood and earthquake hazard index

The figure above shows that village tracts adjacent to Chindwin and Ayerwaddy rivers are affected by flood. Earthquake, on the other hand, affects all areas of the township although highest in village tracts nearest to the source of PGA. The same may be said of cyclones,

35 which affect everything in its track across the Dry Zone. Below is a summary of the buildings and populations exposed to different levels of flood and earthquake hazards.

Table III-1. Buildings and population exposed to 100-year flood Buildings Vey Low Low Medium High Very High Shwebo 45,132 1,546 400 316 Monywa 14,342 7,157 41,891 9,017 Myingyan 19,581 19,363 1,515 4,165 12,615 Nyaung-U 30,951 2,743 6,127 9,174 1,087 Chauk 24,344 2,209 14,171 1,757 Population Shwebo 240,121 8,346 2,136 1,270 Monywa 65,711 33,071 216,742 43,362 Myingyan 95,735 99,087 7,734 18,844 58,546 Nyaung-U 142,935 12,261 28,439 43,921 5,553 Chauk 119,531 9,964 74,702 8,383

The table shows about 22% of total structures and 21% of the population in Myingyan are exposed to very high level of flood hazard, this is followed by Monywa’s 12%.

Table III-2. Buildings and population exposed to earthquake Buildings Vey Low Low Medium High Very High Shwebo 11,532 19,175 16,687 Monywa 1,717 14,439 51,283 4,968 Myingyan 2,055 22,977 26,489 5,718 Nyaung-U 39,990 7,187 2,905 Chauk 15,548 11,914 6,436 8,583 Population Shwebo 64,097 102,376 85,400 Monywa 7,649 65,292 261,687 24,258 Myingyan 9,499 110,940 132,244 27,263 Nyaung-U 188,000 31,028 14,081 Chauk 79,450 57,221 34,509 41,400

Exposure to earthquake hazard is very high for 35% of structures and 34% of the population in Shwebo, followed by 20% of structures and 19% of the population in Chauk township. Because of the limited data on cyclone hazards, the analysis only renders the five townships to similar hazard categories so these were no longer mapped nor highlighted herewith.

2 SOCIAL VULNERABILITY ASSESSMENT

Social vulnerability refers to a person or group’s characteristics and situation, which influence their capacity to anticipate, cope with, resist, or recover from the impact of hazards (Wisner et al 2004). Here, social vulnerability analysis was done using female population, population under 18, and population density data at the village tract level. Figure III-2 shows the outcomes of the analysis of the said variables.

36

Figure III-2. Social vulnerability map of village tracts

37 The map shows the relative social vulnerability levels of the village tracts in the five townships10. It is clear that the vulnerability levels across and within the five townships is relatively homogenous, with only very few village tracts denoting low vulnerability levels, and then a few others denoting high and very high social vulnerability. Among those with very high social vulnerability are the urban areas of the townships. This is because of the very high population density in those areas compared to the village tracts.

3 VULNERABILITY AND CAPACITY ASSESSMENT – PILOT VILLAGE TRACTS

The village tract assessments provided more information on the annual activities, common problems as well as responses of villagers. The table below highlights the length of time villagers engage in activities affected by the seasons.

Table III-3. Duration of common activities, problems and adaptation responses Shwebo Monywa Myingyan Nyaung U Chauk Average # of pilot village tracts 31 22 27 41 25 146 Economic activities • Planting 7.9 (31) 5.91 (22) 5.15 (27) 7.44 (41) 5.96 (24) 6.47 (145) • Grazing 5.2 (5) 2.25 (4) 3.06 (16) 6.68 (37) 8.33 (12) 5.10 (74) • Wage Labor 5.88 (17) 5 (17) 9.3 (10) 3.58 (40) 6.11 (19) 5.97 (103) • Producing/selling local products (e.g., charcoal, 9.55 (11) 6 (16) 4.28 (18) 3.56 (27) 8.75 (12) 6.43 (84) timber, handicrafts) Food and nutrition • Eating less than 3 meals/day N/A N/A 3 (3) 2 (1) N/A 2.5 (4) • -Lack of food nourishment N/A N/A 3 (1) N/A N/A 3 (1) Common problems • Illness in people 3.26 (31) 2.64 (22) 2.08 (25) 2.95 (41) 3.08 (25) 2.8 (144) • Illness in livestock 2.77 (31) 2.73 (22) 2.58 (26) 4.05 (41) 2.76 (25) 2.98 (145) • Pest outbreaks 2.87 (31) 2.16 (19) 2.84 (25) 2.64 (39) 5.75 (8) 3.25 (122) Adaptation response • Temporary evacuation 2 (1) 2.13 (8) 1.33 (6) 2.75 (4) 9 (3) 3.44 (22) • Migration N/A 2 (2) 1.5 (4) 5.2 (5) N/A 2.9 (11) • Storage of Food Supplies 4.91 (11) 2.33 (15) 2 (9) 3.26 (35) 2.46 (11) 2.99 (81) • Debt 8.23 (30) 1.9 (20) 4.67 (27) 4.15 (40) 4.28 (25) 4.65 (142) • Asset sharing 4 (3) 2.33 (3) 1.4 (5) 2.4 (5) 2.67 (3) 2.56 (19) • Selling valuable items (e.g., 3 (4) 1.88 (17) 1.6 (25) 1.34 (35) 3.37 (19) 2.24 (100) jewelry)

10 The map was generated following the process originally introduced by Cutter et al (2000), which is a comparative metric that shows an area’s relative social vulnerability. Because of the limited data available at the village tract level, only three indicators were included in the analysis – number of females, number of people under the age of 18, and population density in each of the village tracts. These indicators are considered to relatively affect social vulnerability. Values for these indicators were computed using mean, standard deviation (SD) and z-score. The map is based on the average z- score of the three indicators, where low and very low vulnerability is equal to 1 and 2 SD below the mean, while high and very high vulnerability is 1 and 2 SD above the mean.

38 Economic Activities. Planting is tied to the rainy season. Villagers plant between 5 to 8 months a year depending on he availability of water for their crops. Grazing is not tied to the season perhaps because villagers were mostly small-scale livestock farmers. Wage labor and producing/selling other products are tied to farm labor as these are considered alternative sources of income during the dry season.

Food and Nutrition. Only 5 out of 146 village tracts indicate lack of food and/or nourishment. Unfortunately, such incidence is believed to last for 2.5–3 months.

Common Problems. Most illnesses in people occur between the months of May and June and September through December. For livestock, foot and mouth disease is normally associated with the colder months of September and November, while pest outbreaks are typical during the months of July and August. These problems are believed to occur for a an average of 3 months.

Adaptation Response. The most common adaptation response considered by villagers is getting into debt, which happens during the months of May, June and December. This is typically tied with farming specially the planting season, and when there are problems besetting the household. Debts are normally paid during harvest time, but most farmers typically get into the borrowing cycle again the following planting season. Another popular response is selling valuable items, which normally happens during the months of December and January. A number of village tracts also indicate storage of food as an important response, which typically lasts for 3 months.

Table III-2 summarizes the outcomes of the village tract survey, which assesses the societal, economic as well as management and institutional capacity of villagers.

Table III-4. Capacity level of village tracts Shwebo Monywa Myingyan Nyaung U Chauk Average # of pilot village tracts 31 22 27 41 25 146 Societal capacity • Priority of villagers to protect 2.58 3 1.78 2.78 2.08 2.44 themselves against hazard • Measures that promote or enforce nature conservation 2.42 2.09 3.04 2.35 1.68 2.32 (e.g. reforestation) • Public awareness programs 2.42 3.27 2.96 3.15 3.76 3.11 on disaster and climate risks • Integration of disaster and climate risk information in 1.48 2.09 1.74 2.28 1.72 1.86 schools • Emergency response drills 1.9 2.14 2.81 1.5 1.96 2.06 • Emergency committee 1.61 2.82 2 2.5 2.64 2.31 • Public representatives in 1.74 3.5 2.3 3.05 2.96 2.71 emergency committee • Village tract risk management/ emergency 1.61 2.09 1.74 2.36 1.96 1.95 groups

39 Shwebo Monywa Myingyan Nyaung U Chauk Average Average 1.97 2.63 2.30 2.50 2.35 2.37 Indicator11 low medium low low low low Economic capacity • Local emergency funds (e.g., 1.06 1.27 1.3 1.53 1.08 1.25 Township GAD) • Release period of national 1.39 1.59 1.15 1.05 1.76 1.39 emergency funds • Access to international 1.32 1.05 1.19 1.05 1.08 1.14 emergency funds • Availability of insurance 1.16 1.55 1.26 1.08 1.12 1.23 • Availability of loans/credit for 1.84 1.68 1.63 2.26 1.72 1.83 reconstruction/recovery Average 1.35 1.43 1.31 1.39 1.35 1.37 Indicator very low very low very low very low very low very low Management and institutional capacity • Availability and circulation of 1 1.05 1 1.78 1.48 1.26 risk map • Availability and circulation of 1.13 1.41 1 1.73 1.68 1.39 emergency plans • Effectiveness of early 3.06 3.73 3.78 3.98 3.68 3.65 warning system • Frequency of training of local 1.39 1.09 2.22 2.4 1.28 1.68 institutions • Communication with 2.58 2.86 3.44 3.28 2.88 3.01 township level risk institution Average 1.83 2.03 2.29 2.63 2.20 2.20 Indicator low low low moderate low low Sources of early warning information • TV 30 21 24 31 21 127 • Radio 29 22 27 38 24 140 • Newspaper 8 3 3 8 2 24 • Social media (e.g., FB) 28 15 16 29 15 103 • Online (e.g., websites) 0 0 4 15 0 19 • Village Warnings (e.g., through Loud Speaker 30 20 25 35 13 123 System) • Text message 31 14 12 4 3 64 • Personal (e.g., family, 31 16 24 34 16 121 neighbors)

Societal Capacity. This category assesses the social capacity of village tracts. It includes questions related to attitudes, actions, awareness, information and social groups and committees responsible in cases of emergencies. Survey responses generated reveal low societal capacity for all townships except Monywa, which has moderate levels.

11 Categories for the indicators are as follows: 1–1.75 very low, 1.76–2.50 low, 2.51–3.25 moderate and 3-25–4 high.

40 Economic Capacity. This category focuses on funding (emergency fund, insurance, loans and credits) for emergencies, recuperation, reconstruction and rehabilitation whether at the local, township, national or international levels. The answers indicate very low levels of economic capacity for all townships.

Management and Institutional Capacity. This category of questions focuses on disaster risk reduction, to emergency preparedness to early warning. In general, there are low levels of management and institutional capacity for all townships except Nyaung U, which has moderate level. Two indicators that are worth noting in this category are the loud speaker early warning system and communication with township level risk institution, where most village tract respondents rated highly.

Sources of Early Warning Information. Respondents considered the radio as their number one source of early warning information. This is followed by village warnings through the load speaker system, personal messages, TV, social media and text messages.

4 DROUGHT VULNERABILITY ASSESSMENT

Because of the unique nature of drought as a hazard, a separate drought vulnerability assessment was conducted. But due to data limitations, the analysis of drought vulnerability included only four indicators – population density as well as female population at the village tract level, then irrigation and farming occupation at the township level. Categories and ratings were assigned for each indicator to create the drought vulnerability index (DVI), which was used as input to the vulnerability map12. Table III-5 outlines the irrigation and livelihood profile of the townships while Figure III-3 shows the drought vulnerability map.

Table III-5. Irrigation and livelihood profile in the townships Irrigation Shwebo Monywa Myingyan Nyaung U Chauk Irrigated Land (Ika) 149,399 103,618 164,830 287,374 135,703 Total Land Area (Ika) 185,320 170,240 240,881 366,563 245,013 Non-irrigated (% of Total) 19.38% 39.13% 31.57% 21.60% 44.61% Livelihood Government 3,513 10,562 4,138 4,345 7,575 Service 159 10,154 37,975 25,243 3,298 Farmer 71,157 21,986 48,900 31,020 57,804 Livestock 32,758 627 15,995 2,101 5,021 Businesswoman 20,839 26,801 6,897 8,112 13,140 Industrial Worker 6,278 7,806 450 1,198 1,370 All-Around 121,573 98,338 45,577 43,112 40,228 Other 4,914 20,609 17,966 30,277 8,257 Fishermen 684 903 429 Total 261,875 196,883 177,898 146,311 137,122

12 For each of the indicators, four classes were derived using the natural break method in GIS. These classes were used to categorize the indicator values. Once categorized, ratings were assigned to these indicators. They were then averaged and normalized as DVI values.

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Figure III-3. Drought vulnerability map of village tracts

42 The map shows that Chauk and Myingyan are slightly more vulnerable to drought compared to the other townships. This is because both townships have relatively high percentage of non-irrigated land (44.61 and 31.57% of total land area), and more number of people (42 and 27% of the working population) whose income depend on agriculture.

The village tracts in Chauk that are more vulnerable to drought are mostly on the northern half of the township while for Myingyan, these were concentrated on the southern half, near the urban area.

43 IV. RISK ASSESSMENT

This section focuses on integrating the outputs from the hazard and vulnerability assessment to create risk maps, assess risks and potential impacts, and develop risk reduction as well as resource management recommendations. It must be noted that the resulting risk maps are based on indices, which define relative levels of risk among different village tracts and townships.

1 DROUGHT RISK ASSESSMENT

The drought hazard as well as drought vulnerability indices were multiplied to define the drought risk index of the village tracts. These indices were then used to develop the following drought risk maps for 3, 6 and 12-month time periods.

Figure IV-1. Drought risk map

The map shows a relatively high drought risk for Myingyan for all time periods. Chauk follows with high drought risk for 3 and 6-month time frames. Shwebo appears to have lower drought risk levels compared to the other pilot townships.

2 FLOOD RISK ASSESSMENT

Flood risk index was generated using the flood hazard map as well as the population and building/structure density in the townships. The resulting flood risk map is Figure IV-2 while Table IV-1 highlights the number of buildings/structures affected by different flood levels.

High risk is concentrated in village tracts adjacent to the river. Higher risk can be seen in the southwestern portion of Monywa, and then on the western side of Myingyan, which has very low elevation. Nyaung U and Chauk have low to moderate flood risk levels except their urban areas, which have high flood risk levels.

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Figure IV-2. Flood risk map

45 Table IV-1. Buildings affected by flooding Shwebo Monywa Myingyan Nyaung U Chauk Flood level13 842 13,649 10,993 4,432 4,066 1 – 6 meters 9,531 4,000 2,773 7 – 12 meters 1,418 409 1,047 842 13,649 13 – 18 meters 44 23 220 19 – 25 meters 26

The table shows the building count, an estimated 34,000 buildings/ structures, affected by flood of various levels. Flood risk levels were difficult to establish in Shwebo and Monywa due to the lack of flood parameter data upstream of both townships.

3 CYCLONE RISK ASSESSMENT

Cyclones bring strong winds (as high as 120mph), heavy rains (more than 5 inches within 24 hours) and storm surges (higher than 10 feet and consequently, flooding), which cause the most damage. But since the Dry Zone is situated in the central plains of Myanmar, it is only at risk to strong winds and rain.

Global data on cyclone tracks and wind speed indicate similar category and potential impact across the five pilot townships, so that vulnerability and risk is no longer mapped herewith. Below is the cyclone damage classification that Myanmar uses, bearing in mind that the Dry Zone area, and consequently the townships could experience severe cyclonic storms with wind speeds up to 55 knots (63 mph).

Table IV-2. Cyclone damage potential Deep Severe Cyclonic Very Severe Super Cyclonic Depression Cyclonic Storm Storm Cyclonic Storm Storm Knots 28-33 34-47 48-63 64-119 120 above Kmph 52-61 62-87 88-117 118-221 222 above Structures Minor Damage to Major damage Total Extensive damage damage to thatched huts. to thatched destruction of to non-concrete loose / houses/ huts. thatched residential and unsecured Rooftops may houses/ industrial structures blow off. extensive buildings. Unattached damage to Structural damage metal sheets kutcha houses. to concrete may fly. Some damage structures. Air full to pucca of large projectiles houses. Potential threat from flying objects. Communicatio Minor damage Minor damage Bending/ Uprooting of n & Power to power and to power and uprooting of communication communication communication power and and power poles.

13 Building/structure count is based on RIMES’ digitized buildings and structures in the five pilot townships.

46 Deep Severe Cyclonic Very Severe Super Cyclonic Depression Cyclonic Storm Storm Cyclonic Storm Storm lines due to lines. communication Total disruption of breaking of poles communication branches. and power supply. Road/Rail Some Major damage Major damage Major damage Extensive damage breaches in to Kutcha and to Kutcha and to Kutcha and to Kutcha roads Kutcha road minor damage some damage Pucca roads. and some damage due to to Pucca roads. to Pucca roads. Flooding of to poorly repaired flooding. Flooding of escape routes. pucca roads. Large escape routes. Disruption of scale submerging rail/road link at of coastal roads several places due to flooding and sea-water inundation. Total disruption of railway and road traffic due to major damages to bridges, signals and railway tracks. Washing away of rail/road links at several places Agriculture Minor Some damage Breaking of tree Widespread Total destruction damage to to paddy crops, branches, damage to of standing crops/ Banana trees banana, papaya uprooting of standing crops, orchards. and near trees and large avenue plantations, Uprooting of large coastal orchards. trees. Moderate orchards, falling trees and blowing agriculture damage to of green away of palm and due to salt banana and coconuts and coconut crowns, spray. papaya trees. tearing of palm stripping of tree Damage to Large dead fronds. Blowing barks. ripe paddy limbs blown down or crops. from trees. uprooting of bushy trees like mango. Marine Very rough High to very Phenomenal Phenomenal Phenomenal seas seas. Sea high sea waves seas with wave seas with wave with wave heights waves about about 6-9 m height 9-14 m. height more of more than 14m. 4-6 m high. high. Movement in than 14 m. All shipping motorboats Visibility activities unsafe. unsafe. severely affected. Movement in motorboats and small ships unsafe/not advisable Coastal Zone Minor Seawater Major damage Storm surge up Extensive damage damage to inundation in to coastal crops. to 2-5m. to port

47 Deep Severe Cyclonic Very Severe Super Cyclonic Depression Cyclonic Storm Storm Cyclonic Storm Storm Kutcha low-lying areas Storm surge up Inundation up installations. embankment after erosion of to 1.5 m (area to 10-15km in Storm surge more s. Kutcha specific) causing specific areas. than 5m, embankments. damage to Small boats, inundation up to embankments/ country crafts, 40 km in specific saltpans. large boats and areas and Inundation up ships may get extensive beach to 5 km in torn from their erosion. All ships specific areas. moorings torn from their moorings. Flooding of escape routes. Overall Minor Minor to Moderate Large - Catastrophic Damage moderate Extensive Action Fishermen Fishermen Fishermen Fishermen not Fishermen not to Suggested advised not advised not to advised not to to venture into venture into sea. to venture venture into venture into sea. Evacuation Large-scale into sea. sea. sea. from coastal evacuations Coastal hutment areas essential. needed. dwellers advised Diversion/ Total stoppage of to move to safer suspension of rail and road places. Other rail and road traffic needed in people in the traffic may be vulnerable areas. affected areas required. to remain indoors Source: WMO, 2016

4 EARTHQUAKE RISK ASSESSMENT

The earthquake risk index was generated by multiplying the earthquake hazard and vulnerability indices. Tables IV-3 and IV-4 show the building and population counts under different PGA values while Figure IV-3 shows the earthquake risk map.

Table IV-3. Building count per PGA PGA Shwebo Monywa Myingyan Nyaung U Chauk 00 – 0.1 0 0 0 0 0 0.1 – 0.2 0 0 0 14,642 0 0.2 – 0.3 0 0 6,861 27,321 0 0.3 – 0.4 0 4,972 39,931 5,214 19,122 0.4 – 0.5 5,762 54,786 7,530 2,905 11,757 0.5 – 0.6 33,755 11,693 1,157 0 3,996 0.6 – 0.7 7,877 956 0 0 4,248 0.7 – 0.8 0 0 0 0 2,057 0.8 – 0.9 0 0 0 0 1,301 0.9 – 1.0 0 0 0 0 47,394 72,407 55,479 50,082 42,481

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Figure IV-3. Earthquake risk map

49 Table IV-4. Population count per PGA Shwebo Monywa Myingyan Nyaung U Chauk 00 – 0.1 0 0 0 0 0.1 – 0.2 0 0 0 67,203 0.2 – 0.3 0 0 32,670 129,409 0.3 – 0.4 0 21,810 197,783 22,416 98,218 0.4 – 0.5 28,711 275,834 35,944 14,081 56,408 0.5 – 0.6 183,206 56,957 4,919 0 21,810 0.6 – 0.7 39,956 4,285 0 0 19,757 0.7 – 0.8 0 0 0 0 9,677 0.8 – 0.9 0 0 0 0 6,710 0.9 – 1.0 0 0 0 0 251,873 358,886 271,316 233,109 212,580

Tables IV-3 and IV-4 show that all buildings/structures and populations are affected when an earthquake from a particular source happens. Chauk is affected by higher PGA values while Nyaung U by lower PGA. But because of other indicators like building and population density, the townships of Shwebo, Monywa and Myingyan are at relatively higher risk to earthquakes.

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V. RECOMMENDATIONS AND WAY FORWARD

This risk assessment report analyzed and assessed drought, flood, cyclone and earthquake hazard, vulnerability and risks in the townships of Shwebo, Monywa, Myingyan, Nyaung U and Chauk. In a way, the assessments helped provide a hazard, vulnerability and risk profile and baseline of the townships, and to some extent, the village tracts therein. In view of this, the following recommendations are developed.

Risk-Sensitive Development. Decision-makers must be aware of the potential hazards and risks that affect their location and must integrate those in their on-going as well as upcoming programs and projects keeping in mind areas that are at higher risk to different hazards. Additionally, townships could begin implementing/enforcing housing types that are structurally better and safer from floods, cyclones and/or earthquake hazards and risks.

Township and Village Tract Disaster Management. The maps are envisaged to help in disaster risk reduction. Township RRD, GAD and village tract administrators are encouraged to make use of the maps together with early warning information in creating disaster risk reduction programs, emergency management/contingency plans at the township as well as village tract levels.

Township and Village Tract Resource Management. Risk maps and assessments are also useful in resource allocation and management. Agencies like DZGD, DoI, DoA and LBVD may integrate risk assessment information in planning for their own medium- to long-term institutional programs. Information on, for instance, the spatial extent of drought provides guidance useful in making decisions related to agriculture, water resources, livestock and environmental management.

Risk Awareness and Education Programs. Maps are useful in advancing awareness of the hazards and risks affecting a particular location. These may be used to help educate people of the impacts and implications of different risks on their daily lives.

Data Management and Access. It must be noted that more detailed data is essential to generate more detailed and accurate assessments. The lack of which also means that the assessments are limited. One of the major challenges in the risk assessment exercise was the absence of, or lack of access to, data at the village tract level. For instance, population data at the village tract level was only limited to population segregated by gender and age, housing units and households. There was also limited data on some hazards like flood (upstream of Shwebo and Monywa), and thunderstorms in the Dry Zone region. In view of this, it is recommended that Myanmar agencies set-up an effective data collection and management system and make it accessible across other agencies whose work and program is for the public good of Myanmar citizens.

Adoption and Updating of Risk Maps and Assessments. Agencies need to discuss their mandates vis-à-vis continued updating or scaling up of hazard, vulnerability and risk assessments in other townships and village tracts outside the pilots. Information agencies like DMH play an important role as hazard information provider, while other agencies like GAD, RRD, DoA, LBVD, DZGD and DoI take response and planning roles.

51 REFERENCES

Cutter, S., J. Mitchell, and M Scott (2000). Revealing the vulnerability of People and Places: A Case Study of Georgetown County, South Carolina. Annals of the Association of American Geographers 90(4): 713-737 IWMI (2013). IWMI Recommendations for Priority Action in the Dry Zone of Myanmar. http://www.themimu.info/sites/themimu.info/files/documents/Ref%20Doc_DryZone_Recom endationsForPriorityActions_IWMI.pdf Accessed 12 April 2016 IWMI (2015). Improving water management in Myanmar’s dry zone for food security, livelihoods and health. Colombo, Sri Lanka: International Water Management Institute (IWMI). 52p. http://www.lift-fund.org/sites/lift-fund.org/files/uploads/Publications/IWMI.pdf Accessed 12 April 2016 MIMU (2016). Dry Zone. Myanmar Information Management Unit. http://www.themimu.info/special-interest-region/dry-zone Accessed 12 April 2016 Thu, M.K. (2016). Drought in the Dry Zone. Frontier Myanmar. http://frontiermyanmar.net/en/drought-the-dry-zone Accessed 12 April 2016 Union of Myanmar (2009). Hazard Profile of Myanmar. http://www.preventionweb.net/files/14567_14567HazardReport25.8.091.pdf Accessed 12 April 2016 WFP (2011). Food Security Assessment in the Dry Zone Myanmar. World Food Program, Food and Agricultural Research Organization. Wisner, B., P. Blaikie, T. Cannon and I. Davis (2004). At Risk: Natural Hazards, People’s Vulnerability and Disasters. London: Routledge WMO (2016). Tropical Cyclone Operation Plan for the Bay of Bengal and the Arabian Sea. Tropical Cyclone Programme Report No. TCP 21 Ed 2016

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