Technical Assistance Consultant’s Report (Ca Mau Atlas)

Project Number: 43295 December 2011

Socialist Republic of Viet Nam: Impact and Adaptation Study in the Delta (Cofinanced by the Climate Change Fund and the Government of Australia)

Prepared by Peter Mackay and Michael Russell

Sinclair Knight Merz (SKM) Melbourne, Australia

For Institute of Meteorology, Hydrology and Environment (IMHEN) and the Ca Mau Peoples Committee

This consultant’s report does not necessarily reflect the views of ADB or the Government concerned, and ADB and the Government cannot be held liable for its contents. (For project preparatory technical assistance: All the views expressed herein may not be incorporated into the proposed project’s design.

Climate Change Impact and Adaptation Study in the

Ca Mau Atlas December 2011

Ca Mau Peoples Committee Institute of Meteorology, Hydrology and Environment

Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

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CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

Contents

INTRODUCTION...... 4

CLIMATE CHANGE MODELLING AND EMISSIONS SCENARIOS...... 5

CLIMATE CHANGE VARIABLES ...... 6

CLIMATE CHANGE HAZARDS ...... 8

FLOODING AND INUNDATION ...... 10 SALINE INTRUSION ...... 11 STORM SURGE...... 12 TYPHOONS ...... 13

VULNERABILITY ASSESSMENT ...... 14 Climate Change Impact and Adaptation IDENTIFYING AND ANALYSING FUTURE RISK ...... 16 CA MAU PROVINCE...... 18 Study in The Mekong Delta – Part A CA MAU PROVINCE POPULATION VULNERABILITY ...... 20

CA MAU PROVINCE POVERTY VULNERABILITY ...... 21

CA MAU PROVINCE AGRICULTURE AND LIVELIHOODS VULNERABILITY ...... 22

CA MAU ATLAS CA MAU PROVINCE ENERGY AND INDUSTRY VULNERABILITY ...... 23 Decem ber 2011 CA MAU PROVINCE URBAN SETTLEMENTS AND TRANSPORTATION VULNERABILITY ...... 24 CA MAU PROVINCE REGIONAL SYNTHESIS VULNERABILITY ...... 25

CA MAU PROVINCE – SUMMARIES ...... 27

CA MAU CITY...... 28 CAI NUOC ...... 30 DAM DOI ...... 32 Although all effort is made to ensure that the information, opinions and analysis contained in this NAM CAN ...... 34 document are based on sources believed to be reliable - no representation, expressed or implied, is NGOC HIEN ...... 36 made guaranteeing accuracy, completeness or correctness. The boundaries, colours, denominations, PHU TAN ...... 38 and other information shown on any map in this work do not imply any judgment on the part of the THOI BINH...... 40 TRAN VAN THOI ...... 42 Authors concerning the legal status of any territory or the endorsement or acceptance of such U MINH ...... 44 boundaries. No legal liability or responsibility for the consequences of any third party’s use of the information, opinions and analysis contained in this report will be assumed. ABOUT THIS ATLAS...... 46

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Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

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CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

Introduction This study on the two Mekong Delta provinces of Kien Giang and Ca Mau has been undertaken in order to provide provincial and district policy makers with an understanding of the key areas of vulnerability and hotspots with regards to climate change in the period up to 2050. The study focuses on vulnerability in the socio-economic, agriculture, livelihoods, urban settlements, transport, energy and industry sectors. The baseline period used for this study is 1980-1999 (and the September 2000 flood event), with the periods 2030 and 2050 also modelled under selected greenhouse gas emissions scenarios (B2 and A2). Modelling that has been conducted includes regional climate modelling (i.e. downscaling), hydrological (B2 and A2) and coastal modelling (B2). The primary purpose of this vulnerability assessment study is to identify and evaluate the ‘net biophysical and social vulnerability’ of Ca Mau and Kien Giang provinces. In this context and for the purposes of this Report, ‘vulnerability’ is considered to be a function of: • Exposure to climatic conditions and sensitivity to the impacts of climate change; • The frequency, magnitude and extent of climate-related risks to the community, assessed in terms of the probability of occurrence (likelihood) and magnitude of hazards (consequence); and • The ability or adaptive capacity to respond to climate-related risks (including adaptive measures, coping strategies or actions taken in reaction to the impacts or to mitigate the risks). The Approach This study has adopted a standard ‘comparative vulnerability and risk assessment (CVRA) methodology and framework’ for estimating aggregate vulnerability for five of dimensions, these being: population; poverty; agriculture and livelihoods; industry and energy; urban settlements and transportation. This approach is based on the generally accepted IPCC approach to vulnerability assessment for natural system, in combination with a risk-based approach for assessing the impacts of natural hazards such as flooding, inundation and on human systems. The integration of the risk-based and vulnerability-based approaches was seen as both a necessary and practical means of for addressing the numerous threats that human and natural systems of the Mekong will face in the future as a result of climate variability and change, and also from non-climate hazards. Placing social vulnerability within the context of risk, and viewing biophysical vulnerability and risk as broadly equivalent, provides us with a relatively simple but pragmatic framework for assessing both the comparative geospatial and sectoral vulnerability on the Mekong Delta. This approach recognises the need to not only identify ‘who’ are the most socially vulnerable – but ‘what’ infrastructure and services are physically more exposed and vulnerable, and reflects the variation and complexity of both human and natural systems, and incorporates social dimensions such as population and poverty., as well as bio-physical attributes. Climate Change in the Mekong Delta Climate change is a major environmental challenge for Vietnam. The main indicator of climate change is global warming due to greenhouse gas emissions from human activities. Climate change also leads to a strong fluctuation in rainfall and an increase in weather and climate extremes such as floods and droughts. Rising sea levels will also directly affect coastal areas, potentially inundating land or increasing salinity, with the gradual loss of mangrove forests, and increasing the coastal infrastructure costs, such as renovation of port facilities and coastal urban areas. The Mekong Delta is almost entirely below 5 m above sea level, making it one of the 3 most vulnerable deltas in the world to sea level rise. A study by ICEM indicated that about 38% of the delta will be submerged under water if the sea water rises 1 m (Carew-Reid 2007). With global climate change, impact and frequency of extreme weather events are expected to intensify. Increased extent and duration of flooding, changes in wet season and dry season precipitation, inundation from sea level rise and changes to salinity intrusion could be significant threats to the region’s agricultural and fisheries productivity, as well as remaining natural coastal ecosystems. The climate in the delta is tropical monsoon and is influenced by both the southwest and monsoons. In general the dry season runs from December to April while the wet season is from May to November. The average annual temperature in the delta is close to 28°C. The mean monthly evaporation is around 150 mm. Monthly precipitation ranges between 0 mm in the dry season and around 250 mm in the wet season. There is a considerable spatial variation in annual rainfall across the delta. The average annual rainfall ranges from less than 1,500 mm in the central region and to over 2,350 mm in the south. Floods are a common feature in the Delta, and one which local people have learned to cope with. Recently the adopted a ‘Living with Floods’ Strategy for the Mekong , meaning more attention to flood protection and the conservation of natural systems and ecosystem services.

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Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

Climate Change Modelling and Emissions Scenarios Greenhouse Gas Emissions Scenarios Previous Modelling In 2000 the IPCC published a series of projected greenhouse gas emissions scenarios that could be used to assess Based on the review of relevant literature relating to climate change impacts and adaptation and the preliminary potential climate change impacts. The Special Report on Emission Scenarios, known as the ‘SRES scenarios’, grouped analysis of secondary data for the Mekong Delta region undertaken it was evident that there are significant scenarios into four families of greenhouse gas emissions (A1, A2, B1, and B2) that explore alternative development knowledge gaps and limitations surrounding the quantification of climate change impacts in Vietnam and the pathways, covering a wide range of demographic, economic, and technological driving forces: Mekong Delta region (MONRE, 2010). The main reason is that projecting the future impacts of climate change (for

Vietnam or anywhere else in the world) is an evolving science, and providing locally specific (i.e. less than the ~250  A1 – the story line assumes a world of very rapid economic growth, a global population that peaks mid-century km2 GCM grid resolution) interpretations of those projections is even more complex. The official climate model and the rapid introduction of new and more efficient technologies. A1 is divided into three groups that describe prepared by the Vietnamese government uses MAGICC/SCENGEN 5.3, and identifies climate change and sea level alternative directions of technological change: fossil intensive (A1Fi), non-fossil energy resources (A1T), and a scenarios for Vietnam in the 21st century. balance across all sources (A1B).

 B1 – describes a convergent world, with the same global population as A1, but with more rapid changes in economic structures toward a service and information economy. Climate Change Modelling (Regional Downscaling)

 B2 – describes a world with intermediate population and economic growth, emphasising local solutions to IMHEN recently completed statistical downscaling for the whole Mekong Delta for the primary climate variables, economic, social, and environmental sustainability. and we have utilised the statistically downscaled data for temperature and rainfall, together with the regionally downscaled scenarios for sea level rise (IMHEN 2010) and the latest hydrological river flow scenarios developed for  A2 – describes a very heterogeneous world with high population growth, slow economic development and slow the Mekong mainstream above Kratie by the Mekong River Commission (MRC), in order to assess the impacts of technological change. climate change in the Mekong Delta and Ca Mau and Kien Giang. The scenarios developed by the MRC were based The emission projections are widely used in the assessments of future climate change, and their underlying on PRECIS, and have been used in a number of reports prepared by IMHEN relating to climate change impacts in assumptions with respect to socioeconomic, demographic and technological change serve as inputs to many climate the Mekong River upstream of Vietnam. As of mid-March 2011, the official Digital Elevation Model for the Mekong change vulnerability and impact assessments. Greenhouse gas emissions trajectories under various scenarios are Delta was released and a copy was made available for use by the project. The specific modelling applications that depicted in Figure 1. were selected for use in this study are outlined in Table 1 (below) SDSM and SIMCLIM software were also used for Global emissions are currently tracking close to (or possibly higher than) the worst case emissions scenario (i.e. the reference and comparison. A1Fi or A2) and it is unlikely that emissions will contained to the low or medium emissions target by 2030. The climate change and sea level rise scenarios developed and published for Vietnam in 2009 were based on the low Table 1. Methods and Outputs of the Regional Climate modeling Used. (B1), medium (B2) and high (A2, A1Fi) scenarios. The average B2 scenario was recommended for all ministries, sectors Variable Modeling method Emission scenarios and localities to initially assess the impact of climate change and sea level rise and to build action plans to respond to climate change. Using results of previous studies as a basis, the 2011 updated climate change and sea level rise Monthly, seasonal, annual average Statistical downscaling using SimCLIM A2, B2, B1 modeling selected the following greenhouse gas emissions scenarios: B1 (low scenario), B2, A1B (middle scenario), A2 temperature and precipitation which incorporates outputs from 21 GCMs and A1Fi (high scenario). used in IPCC AR4 For the purposes of this study, climate change modelling (including regional downscaling) has been completed by Annual average sea level rise Dynamical downscaling using PRECIS A1Fi, B2, B1 IMHEN using B2 and A2 scenarios, which have been used as inputs for the hydrological modelling. Coastal modelling has also been completed using the B2 scenario only. This is due to the very minor differences between the two Maximum and minimum monthly, Dynamical downscaling using PRECIS B2 scenarios up to 2050. seasonal, annual temperature Figure 1 Scenarios for greenhouse gas Number of days >35°C Output from Japan’s MRI AGCM A1B emissions and projected change Seasonal and annual average Dynamical downscaling using PRECIS B2 in surface relative humidity and wind speed temperature (Source: IPCC, 2007) Information for all downscaling except Japan’s MRI AGCM was produced for a baseline period (1980-1999,

consistent with IPCC AR4) and future 20 year time slices centred on 2030, 2050, 2070 and 2090 (i.e. 2020- 2039, 2040-2059, 2060-2079, 2080-2099 respectively). For Japan’s MRI AGCM the baseline (i.e. current) period is 1979- 2003 and future scenarios are available for ‘near future’ (2015-2039) and ‘distant future’ (2075-2099). The spatial resolution of the climate change scenarios was to 20 km (for outputs of AGCM / MRI), 25 km (for outputs of PRECIS) and about 30 - 50 km for outputs of statistical downscaling.

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CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

Climate Change Variables Rainfall Temperature The change in monthly average rainfall and in seasonal average rainfall for both Ca Mau and Kien Giang provinces is presented in Table 4 below. As compared to the 1980-1999 baseline, under the medium B2 scenario by 2050 the average increase to temperature would increase by approximately 1.4°C in Ca Mau and 0.9°C in Kien Giang. Table 4: Change in Rainfall (%), Scenario B2 and A2 Ca Mau Kien Giang Table 2: Average Temperature Increase from 2010 baseline The expected average B2 A2 B2 A2 Ca Mau Province Kien Giang Province increase varies according to 2030 2050 2030 2050 2030 2050 2030 2050 season. The predicted B2 Scenario A2 Scenario B2 Scenario A2 Scenario increase in seasonal average January -3.7 -6.7 -3.7 -6.3 -5.8 -10.5 -5.9 -10.1 Season 2030 2050 2030 2050 2030 2050 2030 2050 temperature both provinces February -2.2 -4 -2.3 -3.8 -2.1 -3.8 -2.1 -3.6 for the two time periods 2030 Winter (Dec-Feb) 0.6 1.1 0.6 1.1 0.4 0.8 0.4 0.8 March -4.1 -7.4 -4.1 -7.1 -10.8 -19.5 -10.9 -18.7 and 2050 are presented in Spring (Mar-May) 0.7 1.2 0.7 1.1 0.4 0.7 0.4 0.7 Table 2. The increase in April -2.6 -4.7 -2.6 -4.5 -4 -7.2 -4 -6.9 temperature is expected to Summer (Jun-Aug) 0.8 1.5 0.9 1.5 0.5 0.9 0.5 0.9 May -0.2 -0.3 -0.1 -0.3 -0.3 -0.6 -0.4 -0.6 be in the range of 0.7°C to Autumn (Sep-Nov) 0.9 1.6 0.9 1.5 0.6 1 0.6 1 1.4°C for Ca Mau, and June 1.2 2.1 1.2 2 1.5 2.7 1.5 2.6 between 0.5°C to 0.9°C for Average 0.7 1.4 0.8 1.3 0.5 0.9 0.5 0.8 July 1.6 3 1.7 2.8 1.8 3.3 1.8 3.1 Kien Giang. August 0.6 1.1 0.6 1 0.7 1.2 0.7 1.2

September 0.6 1.2 0.7 1.1 0.9 1.6 0.9 1.5 By the end of the century Table 3: Average maximum and minimum temperature increase (°C) (2100) the maximum Ca Mau Province Kien Giang Province October 6.5 11.9 6.7 11.4 7.4 13.5 7.6 12.9 predicted average increase Scenario B2 Minimum Maximum Minimum Maximum November 2.2 4 2.3 3.9 1.9 3.4 1.9 3.2 would be approximately 3 to 3.5°C for the high 2030 2050 2030 2050 2030 2050 2030 2050 December -5.1 -9.3 -5.2 -8.9 -3.6 -6.5 -3.6 -6.3 emissions scenario (A2). Winter (Dec-Feb) 1 1.3 0.7 0.2 1 1.5 2.4 1.6 Winter (Dec-Feb) -4.3 -7.8 -4.3 -7.4 -3.6 -6.6 -3.7 -6.3 The average maximum and Spring (Mar-May) 0.9 1.7 0.2 0.8 0.3 0.7 -0.2 0.8 Spring (Mar-May) -1.2 -2.3 -1.3 -2.2 -1.8 -3.2 -1.8 -3.1 minimum temperature is Summer (Jun-Aug) 1.1 2 1.1 1.9 1.3 2.3 1.3 2.2 also anticipated to increase Summer (Jun-Aug) 1.4 2 0.8 1.7 0.6 1.5 2.2 3 as compared to the Autumn (Sep-Nov) 1.5 1.7 1.5 1.4 1.4 2.1 1.3 0.9 Autumn (Sep-Nov) 3.3 6.1 3.4 5.8 3.6 6.6 3.7 6.2 baseline. Average 1.3 2.4 1.3 2.3 1.5 2.8 1.5 2.6

1. According to the low • By the end of 21st century, the annual temperature would increase by A summary of rainfall data is as follows: emissions scenario about 1.5 to 2.0°C in Ca Mau and Kien Giang. The increase of Ca Mau is 1. According to the low • By the end of 21st century, rainfall is expected to increase by (B1): higher than in Kien Giang. emissions scenario about 3 to 4% in both Kien Giang and Ca Mau compared to the 2. In the medium • By the end of 21st century, the annual temperature would increase in both (B1): baseline. emissions scenario Ca Mau and Kien Giang, with the increase of approximately 1.5 to 2.5°C 2. In the medium • Rainfall tends to increase in rainy months (by up to 25% by the (B2): relative to the baseline period. Again, the increase is greater for Ca Mau emissions scenario end of the century) and decrease in dry months (can be from 30 (B2): than for Kien Giang. to 35%). By the end of the 21st century, rainfall would increase • The maximum temperature increases by less than the minimum in both Kien Giang and Ca Mau with an increase of 5-10% temperature. By the end of 21st century, the maximum temperature can compared with the baseline period. be higher than current record about from 2 to 2.5°C compared with an • By the end of the 21st century, the heaviest daily rainfall increase of 3.5 to 4.0°C for the minimum temperature. decreases in both Ca Mau and Kien Giang at a rate of about 20 to 30%. However, rainy days with rainfall anomalies of half or • By the end of 21st century, the number of hot days (maximum twice the current record will continue to exist. temperature more than 35°C) would increase by about 15 to 20 days 3. For the high emissions • The annual rainfall would increase in the 21st century in both Ca relative to the baseline period in both Ca Mau and Kien Giang. scenario (A2): Mau and Kien Giang; however, it is higher in Ca Mau. 3. For the high emissions • By the end of 21st century, the increase is about 2.5 to 3.5°C in both Kien scenario (A2): Giang and Ca Mau; however, it is higher in Ca Mau.

As seen in Table 4, the biggest increase in rainfall is in the autumn months, while the biggest decrease is in the winter months, leading to a more marked change in the start of the dry season.

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Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

Climate Change Variables Other Climatic Variables Sea Level Rise  Average surface pressure increases over the country. Sea Level Rise (SLR) scenarios for the coast of Ca Mau to Kien Giang under the low, medium and high scenarios.  Relative humidity decreases in the dry months, increase in rainy months. However, the annual relative Table 5: Sea Level Rise humidity tends to decrease slightly over both 2 provinces.

Emission Scenario Periods In The Future 2030 2050 2070 2090 Impact Modeling

Low (B1) 15 28 45 63 Two impact models have been used in this study, these being:

Medium (B2) 15 30 49 70 1. The IMHEN hydrological model to simulate impacts associated with changes in hydrology, sea level rise, flooding and inundation, and saline intrusion. Under baseline (1980-1999 and for the 2000 flood event), 2030 High (A1F1) 16 32 57 88 (2020-2039) and 2050 (2040-2059) time horizons with flood inundation projections produced for both A2 and By the end of the 21st century, the sea level from Ca Mau to Kien Giang could rise up to 72 cm (low B2 emission scenarios and salinity intrusion projections projected for B2.; scenario), 82 cm (medium scenario) and 105 cm (high scenario) compared with 1980-1999. However for the The hydrological modeling was performed using the Integrated Quality and Quantity Model (IQQM) to simulate the purpose of this study, we are referencing the periods 2030 and 2050 with corresponding SLR of 15 cm and flow of water through the Mekong Delta river systems, making allowance for control structures such as dams and 30 cm under the high A2 scenario. irrigation abstractions. Flow coming from the upper Mekong was obtained for Kratie from the Mekong River Commission. Hydrodynamic modeling was also performed using the ISIS software which enabled representation of the Wind Speed complex interactions caused by tidal influences, flow reversals between wet and dry seasons, and overbank flow in the The change in seasonal mean wind speed for Ca Mau and Kien Giang provinces for the B2 scenario are flood season. Salinity intrusion modeling was also performed using ISIS. shown in Table 6 below. Average wind speed increases in winter, spring and autumn months, but decreases in the summer months. Annual average wind speed increases in most areas of Ca Mau and does not have a 1. The Institute of Coastal and Offshore Engineering HydroGIS model to simulate coastal inundation associated clear trend in Kien Giang. with sea level rise and Typhoons, storm surge simulation and coastal erosion and sedimentation. A baseline scenario (2000-2009) was modeled as was a future scenario for 2050 (2050-2059) for the B2 scenario. Table 6. Change in Seasonal Mean Wind Speed (m/s), Scenario B2 This coastal modeling utilizes the MIKE 21/3 Coupled Model Flow Model to simulate the combined processes of Ca Mau Kien Giang hydrodynamics, wind induced waves, mud transport, sand transport, erosion/deposition, storm surge, and typhoons in the near shoreline coastal zone of Kien Giang and western Ca Mau provinces under current and projected future 2030 2050 2030 2050 conditions. Winter (Dec-Feb) 0.6 0.6 0.3 0.2

Spring (Mar-May) 0.6 0.7 0.1 0.1 Comparison of B2 and A2 Scenarios Summer (Jun-Aug) -0.2 -0.2 -0.2 -0.2 Autumn (Sep-Nov) 0.2 0.1 0.2 0.2 Difference between predicted percentage change for the two modeled scenarios A2 and B2 is shown in Table 7. Average 0.4 0.4 -0.1 -0.1 Table 7. Difference between predicted percentage change for A2 and B2.

Variable 2030 2050

Known issues With the Modeling results Sea Level Rise 1.0% 14.0%

There are some aspects of climate change not covered by the modeling and some of the results that have emerged from the Inundation Area 2.7% 12.8% downscaling are confusing and will require further detailed investigation to clarify: Annual Average Temperature 0.0% 2.0%  impacts induced by climate change from other natural phenomena (e.g. El Nino/Southern Oscillation etc) are not assessed;  The A2 temperature results are very close to the B2 results and in some cases not as warm, even out to 2050, which is Average Maximum Temperature 0.0% 1.5% contrary to IPCC (2007) projections. Average Minimum Temperature 0.0% -3.2%  The change in temperature out to 2050 is sometimes not as large as the change to 2030 which is inconsistent with the known physics of climate change; Rainfall 0.0% 0.0%  The projected rainfall change under A2 is sometimes less than the change under B2 (this is for both the seasonal and monthly projections). Wind Speed 0.0% 1.2%

 There is a lack of significant differences in wind speed between the 2030 and 2050 scenarios.  the current hydro-meteorological observation network is insufficient and inadequately distributed across climate zones;

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CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

Climate Change Hazards The climate change modeling indicates that there are a range of climate change related hazards that are most The hazards that are used therefore for vulnerability analysis are as follows: relevant in the context of the Mekong Delta, namely:  Flooding and Inundation – includes sea level rise and increased rainfall or river flow  Saline Intrusion – combines sea level rise with increased flow of water through river and canal system  Storm Surge – combines overtopping of water due to a storm event with sea level rise and storm event or  Sea level rise (SLR) – associated with gradual inundation and saline intrusion typhoon  Extreme weather events, such as typhoons, storm surges, storms or floods (also associated with reduced salinity due to increased volume of fresh water) Typhoons are considered to have a very high potential impact, however the modeling of typhoons at the moment is  Increased average temperature , as well as increased temperature range (minimum and maximum) relatively poor, and as such whilst it is not possible to differentiate between districts or provinces with regards to  Reduced precipitation during dry season and more marked transition between seasons their vulnerability to typhoons, it is recommended that further modeling of the potential changes to typhoon  Additional precipitation during the wet season (especially October) frequency, intensity, duration and paths is conducted subsequent to this study so that the impact can be better  Changes in wind speed understood to guide appropriate adaptation measures. Aside from storm surge, the effects of typhoons on land are therefore not included in the vulnerability analysis, but it should be considered a significant hazard to the entire study

area with potentially significant impacts. The impacts of these hazards are outlined in Table 8 below from a cross-sectoral perspective, which enables an analysis of the overall severity of the hazard for the target provinces. In reality, the most extreme hazards from climate change would be a combination of the below events. For example, sea level rise of 30 cm combined with a typhoon bringing additional rain and storm surge would have a total combined impact that would be greater than the individual events taken in isolation.

Hazards such as increased temperature, changes to precipitation during wet and dry season or changes in wind speed are of relatively low impact during the study period to 2050, and therefore have not been included in the assessment of vulnerability for each province and district. Photo; M. Russell

Table 8. Cross-sectoral Impacts of Hazards Impact Climate Change Hazard Energy & Industry Urban planning & Transportation Livelihood and Agriculture Socio-Economic Pattern Severity

Sea Level Rise - 15 cm Increased amount of short-term urban drainage Some poor household may have (Inundation & Salinity) Power poles life time is slightly reduced Little impact Low issues at high tide/with heavy rain trouble dealing with impacts A2 2030 Predicted SLR Sea Level Rise - 30 cm Some power system impacts, medium and low Some low lying roads might be affected Trend towards reducing agricultural Some rice paddies and shrimp ponds inundated (Inundation & Salinity) Moderate voltage system Increased amount of short-term urban drainage activities and more services and Crops are damaged or yields reduced A2 2050 Predicted SLR issues at high tide/with heavy rain industry Floor heights in factories needs to be Low lying urban areas flooded during high tides Need for higher and therefore less stable dykes for progressively raised, minor cost if piggybacked and heavy rainfall for short periods: will affect the three major livelihood systems onto regular refurbishment cycles small but growing numbers of people. Erosion of coasts reducing land that is available for Some consolidation of factories to raised Erosion of coasts protecting urban areas and shrimp farming elevation sites and/or defendable industrial roads: will increase in some areas (but may be Loss of mangroves reducing non-timber forest zones accretion in other areas) product availability Significant relocation of all people Sea Level Rise - 50 cm Some low lying factories relocate to higher National/Provincial roads under Changes in ecology reducing harvest of shellfish and and social and industrial fabric. 20% (Inundation and Salinity) elevation/more defendable sites at end of their construction/planned still function due to raised crabs (both juveniles for farming and mature) and High less agricultural GDP and 10% economic lives heights native prawn resources reduction in services and industry Slight decrease in life of power poles from Newer bridges raised above SLR level Increase in salinity of groundwater leading to A2 2070 Predicted SLR increased inundation Low lying modal interchanges (jetties, wharves) reduced crop yields and loss of fruit trees Low and medium voltage power distribution flooded: will affect users until rebuilt Salinity changes resulting in costs of change to new system to be adjusted as part of normal Erosion and overtopping of banks of cropping systems replacement cycle to match new livelihoods and rivers/inland waterways: will mostly affect those Changes in canal salinity dynamics leading to power load patterns living alongside. changes in fish population dynamics (both Minor potential rust issue for older, less robust Some water control devices become less freshwater and juvenile saltwater species) 9

Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

Impact Climate Change Hazard Energy & Industry Urban planning & Transportation Livelihood and Agriculture Socio-Economic Pattern Severity

structures efficient: may affect water transfer and water Reduction in yield of rice crops and possible dry Salinity will limit extraction time from surface transport season crop failure if salt intrusion lasts too long water sources (Kien Giang) Increased costs in changing to new livelihood Salinity would increase corrosion of steel and system E.g. from rice to rice/shrimp or from rice concrete structures (e.g. power poles, shrimp to intensive shrimp transformers, wires, etc) Increased variability at the salt/fresh interface increasing the risk of crop failure Failure of crop if increased saline intrusion stops irrigation Reduction in yield / loss of rice crop due to flooding Loss of shrimp crop in intensive system due to Loss of power supply from wind damage flooding or loss of power Slow restoration of rural power supply, esp. if in Damage to dykes in both rice and shrimp systems wet season. Could be months to full power Damage to people and structures in urban areas Damage to fishing boats Relocation of all people and social supply restoration in rural areas. from high winds and rain/flooding Loss of fishing time and industrial fabric, 40% reduction in Typhoon Very High Economic impact of diesel standby generators Potential damage to flimsy/poorly designed Possible changes to ocean habitat reducing fish catch agriculture and 30% reduction in needing to be used for shrimp and wet fish structures such as typical bridges over canals services and industry processing and jetties: will disrupt travel in rural areas. Loss of non-timber forest products and other aquatic animals due to damage / flooding of forests and Increased price of ice from use of standby wetlands generators Loss of sandbanks for shellfish spawn capture Loss of offshore crab infrastructure and crop Could be loss of power to flooded areas from Low lying urban areas flooded during high tides Damage to dyke structures flooding of some 110/22kV substations if flood and heavy rainfall for long periods (especially in Reduction in yield due to early / late rice harvest in Significant relocation of all people well above normal wet season levels. Limited Kien Giang from Mekong flows): may affect large some areas and social and industrial fabric. 20% long term damage. and growing numbers of people. Complete loss of rice crop due to extended flooding Flood High less agricultural GDP and 10% Temporary loss of industrial production from period or flood damage National/Provincial roads under reduction in services and industry flooded factory sites. construction/planned still function due to raised Loss of shrimp crop due to fresh water intrusion

Minimal impact as power system already built heights and may actually act as a barrier to Damage to fruit trees for seasonal flooding. water dispersal. Loss of non rice crops Increase in loads for ice making and air conditioning. But overall impact low compared to industrial power demands. Any increase will be more regional leading to greater power cuts Loss of shrimp crop due to increased disease risk Heat sink effect in urban areas: can impact on Heat wave Low if heat wave comes during dry season when Reduction in yield of rice crop vulnerable persons during heat waves Vietnam hydro capacity is reduced. Reduced power system capacity Increased frequency/duration of power cuts (regional not provincial or district issue) Damage to dykes and pond infrastructure complete As for flooding, some impact on energy and Loss of shrimp crop industry assets’ corrosion as salt water Low lying coastal urban areas, notably centers Loss of shrimp crop due to aerators failing if power Relocation of all people and social inundation rather than fresh water inundation such as Ha Tien, Rach Gia, Song Doc and Nam fails and industrial fabric, 40% reduction in Storm surge High in flood. Coastal 110/22kV substations may Can: may affect large and growing numbers of Loss of other perennial crops agriculture and 30% reduction in have to come off line, some linger term impact people. services and industry from salt water inundation. Complete loss of established orchards Damage to fishing boats Loss of fishing time

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CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

Flooding and Inundation

Flooding is already a regular seasonal feature of the Mekong Delta and people are accustomed to dealing with it on Modelling shows that significant increased inundation is projected to occur in the central delta and subsequently an annual basis. spread outwards towards the coast. By 2050, flood events are projected to cover much of northern and western Kien Giang with depths to over 1 m in many areas. The southern districts of Kien Giang and much of Ca Mau

experience less severe flooding of depths of less than 1 m. Modelling indicates that a more extensive part of this The baseline map below shows the extent of the 2000 flood which was an extreme event (considered to be a 1 in area will experience inundation and to a greater depth. 100 year flood). This flood event has been combined with projected sea level rise, and Mekong Basin rainfall and

river flows from 2 climate scenarios (B2 and A2) to produce the flood maps for 2030 and 2050. The flood maps shown below are for the A2 scenario. As these are based on an extreme flood event, the inundation depicted If inundation levels are deep then dikes along canals and around paddy fields can be overtopped leading to doesn’t represent permanent inundation, but shows expected inundation during periods of extreme flood. The flooding of houses and crops. Flood waters can damage dikes and other farm infrastructure. frequency of the “1 in 100 year” flood may or may not vary, and would be dependent on rainfall across the whole

Mekong basin, covering several countries (beyond the area of the study).

Flooding and Inundation - 2000 Flooding and Inundation – 2030 Flooding and Inundation - 2050

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Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

Saline Intrusion The hydrological modelling indicates that in 2030 and 2050, coastal areas of Hon Dat in Kien Giang province that are currently affected by saline intrusion will have a reduction of salinity during the dry season. During the wet season The maximum extent of saline intrusion occurs in the dry season when there is reduced flow of fresh water in the canals through Kien Giang and Ca Mau are full of fresh water and form the purpose of draining rain water away. canals and rivers from rainfall and the Mekong River. At the height of the dry season, the month of May, saline They are full of either local rainfall or flow through the Mekong and major canal systems. The anticipated reduction intrusion will vary depending on the rainfall of that season. The major ramification of salinity in Ca Mau and Kien of salinity in the Hon Dat district of Kien Giang is largely due to anticipated increased water flow through the river Giang is related to agricultural production. and canal system. In the provinces of Ca Mau and Kien Giang, there is already a large area that is affected annually by saline intrusion in the dry season. In parts of Ca Mau, farmers adjust to changes in salinity by shifting crops seasonally Isohalines – are lines of equal salinity concentration. from shrimp production (from during the dry season, to rice production for the wet season). The impact of this is In the maps below the 4 ppt (parts per thousand) isohaline is used. that they are unable to produce a second rice crop in the dry season as farmers are able to elsewhere. The maps show the projected variability of salinity for three 20 year periods. In some years lower amounts of 4 ppt (parts per thousand) is the level of salinity that effects agricultural production of crops such as rice. water flow and rainfall will result in saline water extending further inland than normal. The projected extent of The map “Salinity Range in May (2000 – 2010)” shows the range of 4 ppt saline intrusion over a 10 year period during the month of May each year. this intrusion is shown by the maximum isohaline. In other years higher than average river flows and rainfall will result in less saline intrusion. The projected extent of intrusion in these years is shown by the minimum isohaline

4 ppt Salinity Range in May (1980 – 1999) Projected 4 ppt Salinity Range in May (2020 – 2039) Projected 4 ppt Salinity Range – May (2040 - 2059)

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CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

Storm Surge The delta experiences two different monsoons, the South West (SW) Monsoon (in the wet season) that blows onshore along the West coast of Large waves during a storm surge can cause destruction of exposed infrastructure both provinces and the North East (NE) Monsoon (in the dry season) that blows onshore on the East coast of Ca Mau. Strong monsoon winds along the coast. These waves will undermine mangroves and erode exposed earth can lead to higher water elevations downwind. This produces a storm surge which when combined with a high tide produces water levels that banks. Earth dykes that have been exposed by mangrove removal or erosion will are elevated by up to 0.8 – 0.9 m currently. Strong SW monsoons also create waves of over 3 m offshore that impact on the west coast of both be breached within a single wet season. Waves will penetrate through a thin line of provinces as waves of around 1 m. Waves 10 – 20 % larger are projected to impact the coast in 2050. Strong northeast to east monsoons in the mangroves and erode earth dykes. The conversion of mangroves into aquaculture dry season bring large waves over 3 m offshore and 2 m at the shore to the East coast of Ca Mau. Waves of 12 – 25 cm larger are projected to ponds has made considerably more infrastructure potentially exposed to storm impact the shore line in 2050. surge. Maps have been included only for baseline and 2050 as there is limited difference between the maps resulting from the modeling for 2030. The sea level rise projected by climate models will increase the height of water levels during storm surges to over 1 m. Water levels of this height combined with South West Monsoon Conditions – Kien Giang Province waves of 1 – 2 m will lead to overtopping of dykes that are built to the current recommended dyke standards. Waves will also be able to penetrate further into Significant Wave Height (Baseline) Difference in Significant Wave Height (2009 - 2050) mangrove forests and a thin band a mangroves of 20-30 m will not offer sufficient protection for dykes or pond bunds. When winds are from the northwest, Ngoc Hien district (the island at the tip of Ca Mau province) is downwind of a considerable fetch and is subject to higher water elevation in strong Northeast winds. Ngoc Hien is thus exposed to storm surge from strong monsoon winds from both SW and NW wind and is extremely vulnerable to storm surge. North East Monsoon Conditions – Ngoc Hien (Ca Mau)

North East Monsoon Conditions Ca Mau Province Significant Wave Height (Baseline) Difference in Significant Wave Height (2009 - 2050)

(Baseline on top, 2050 below)

Tidal Current

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Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

Typhoons Significant Wave Heights during Typhoon Linda (1997) In 1997, Typhoon Linda moved across the southern tip of the Ca Mau peninsula and caused widespread damage across the two provinces. It resulted in flooding, damage to mangrove and plantation forests, damage to housing and power infrastructure and inundation and associated damage to agricultural production. It was reported that Typhoon Linda, had a purely financial cost of some US$593 million, mostly from destroyed/damaged housing. Fishing villages along the coast on the mainland and particularly on the islands experienced widespread destruction with loss of housing and boats. This destruction would also have been accompanied by widespread coastal erosion and damage due to strong winds and inland flooding. The greatest physical effects of Linda on the mainland would have been felt on the lightly populated East coast of Ca Mau when the typhoon approached and crossed the coast. This occurred at high tide and the strong onshore winds and associated low atmospheric pressure would have led to severe storm surge conditions. The accompanying wave field had a long fetch which meant that waves of over 3 meters were directed onto the shore. The sea level rise projected by climate models means that storm surge during a typhoon will be enhanced. As shown in the figures for Ca Mau and Kien Giang provinces to the right, water surface elevation can be up to 2 m high and combined with 4- 5 m waves will result in severe damage to coastal protection dykes, and fishing villages in estuaries and Ca Mau Province Kien Giang canal mouths along the entire coast. Ngoc Hien district (the island at the tip of Ca Mau province) will be almost completely inundated and Projected Water Surface Elevation and Currents for Typhoon – B2 Scenario, 2050 extremely strong currents are predicted to flow through the Grand River If a typhoon with the same characteristics of resulting in erosion along the southern border of Nam Can district. Typhoon Linda were to cross the Ca Mau peninsular at high tide, the projected water surface elevation and currents in 2050 under a B2 scenario are modeled as shown to the left.

Scale

70 km / hr

Wind Fields during Typhoon Linda (1997)

During Typhoon Linda very strong onshore winds of over 140 km /hr battered the east coast of Ca Mau. Inland Ca Mau and southern Kien Giang experienced very strong winds and wind speeds over the rest of Kien Giang were over 70 Km / hr. The direction of winds around the cyclone meant that winds were offshore on the west coast which limited the formation of storm surge and meant that large waves did not strike the shore. Phu Quoc and the islands off Kien Giang would have experienced very strong winds.

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CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

Vulnerability Assessment The primary purpose of the vulnerability assessment study is to identify and evaluate the ‘net biophysical and social vulnerability’ of Ca Mau and Kien Giang provinces. In this context and for the purposes of this Report, ‘vulnerability’ is considered to be a function of: Table 9. Vulnerability Indicators Exposure to climatic hazards Sensitivity to the impacts of climate change hazards;

Baseline Vulnerability Index Future Projection Indicators The ability or adaptive capacity to respond to climate-related risks (including adaptive measures, coping strategies or actions taken in reaction to the impacts Total Population Total Population or to mitigate the risks); and Population Density Population density Average Family Size No. People Affected by each Hazard The frequency, magnitude and extent of climate-related risks to the community, assessed in terms of the probability of occurrence (likelihood) and magnitude of hazards (consequence). Number of Households No. Households Affected by each Population at working age Hazard The Vulnerability assessment process started with team meetings designed to develop questionnaires that were used to survey officials in the two provinces. The questionnaire was designed to make sure the information required to provide data for measures and indices considered to be useful by the experts in Population Average Natural Population Growth Rate each sector was included. The field district survey was executed in March and April 2011. We used statistical development indicators which included measures of human resources capacity (i.e. literacy rates, health statistics etc), economic capacity Annual Average Income per Capita Total no. of Poor Households (i.e. GDP per capita and measures of income inequality), Livelihood measures (diversity of occupations, income streams, number of adults in employment Number of Poor Households Density of Poor Households etc.) and social capacity (population density, percentage of productive land, together with governance and institutional measures.

% of Poor Households No Poor Households Affected by each Number of Teachers Hazard Analysing Adaptive Capacity Number of Doctors % of Poor Households Affected by It is important to understand at this stage that the methodology for vulnerability assessment must not only represent and highlight the coupled natural– Poverty human system, but also the interaction between these components. In this light it is imperative to outline the existing adaptation capacity within a Agricultural Land per person each Hazard community as part of the evaluation of vulnerability required by our CVRA framework. Adaptive capacity can be defined as “the ability or capacity of a % Ethnic Households system to modify or change its characteristics or behaviour so as to cope better with existing or anticipated external stresses.” Adaptive capacity then can be Number of Rural Households Total Population characterised as a set of potential actions that contribute to reducing vulnerability, and can either influence either the existing or future exposure or Number of Livelihood Streams Agriculture land per Person sensitivity – or both. Streams Employing > 10,000 or No. Rural People Affected by each producing >250 Billion VND Hazard The approach used has been to measure adaptive capacity in the same manner as for exposure and sensitivity through the adoption of a number of Average Annual GDP per No Rural Households Affected by indicators that indicate the capacity of a community or system to build resilience (such as income levels, number of income streams, % people in full Household each Hazard employment). These measures are then incorporated into our district profiles. Livelihoods Rice Crop Land per Person Adaptive capacity can also reflect the abilities of provincial and district agencies or organization responsible for managing natural and human systems. Their Aquaculture Land per Person ability to adapt is determined by a range of issues, including their ability to collect and analyse information, communicate, plan, and implement adaptation Households reliant on Industry No. Households reliant on Industry strategies that ultimately reduce vulnerability to climate change impacts. As such Institutional capacity was not incorporated into the CVRA process. Average Annual GDP per No. Households connected to the The Vulnerability Ranking Process Household contributed by Industry National Grid The first phase of the vulnerability assessment is an evaluation of how specific systems, both natural and human, such as roadways, water resources, and

Households Connected to National No. Industrial Households Affected by industrial areas etc., were “exposed” to climate hazards and impacts. Grid each Hazard Length of High/Medium Voltage Households connected to the Baseline indicators describe the current situation and represent a measure of the current sensitivity and adaptive capacity. Maps of the current geographic Power Lines National Grid Affected by each extent of the exposure to the three climate change provide hazard indicators. The baseline indicators used are as outlined in Table 2Table 9. For each of the five sectors the districts were evaluated as a function of their comparative vulnerability across the key indices. To do this each district is first ranked by Number of Power Plants/High Hazard indicator, then, an average ‘comparative baseline exposure’ is calculated for each district. Voltage Substations No Km of High Voltage Power Lines

Energy & Industry & Energy % off-farm Income Affected by each Hazard In the second phase of the assessment, districts were rated according to their ‘respective sensitivity’ (low to very high) to future hazard projections Number of Factories generated from the hydrological modeling and coastal modeling work. Number of Different Industries Forward Projection Indicators describe things that we can project forward. Population growth can be used to project changes in sensitivity indicators. And the output of the climate models showing future exposure to climate change impacts can be used to project changes in hazard indicators. The Forward Urban Population Urban Population Projection indicators used are as outlined in Table 9. The area affected by each hazard can be used to estimate the number of people that are projected to

Urban Households Urban Area Per Person (Ha) be affected. Hazard maps for flooding, inundation, saline intrusion and storm surge for 2030 and 2050 were used to forecast vulnerability under future Urban Area Urban Households Affected by each conditions. % Urban Hazard These indicators were used to establish specific sectoral baseline characteristics for population, poverty, agriculture and livelihoods, industry and energy and Sewer/Septic Tank Population of settlements Affected by urban settlements and transportation. Forward projection indicators were used to develop future vulnerability profiles, assess the future impacts and for Water Supply each Hazard measuring risk associated with our different climate change scenarios for 2030 and 2050. Major Waterways No Km Roads Affected by each Assessment of Existing Control Measures Hazard Major Roads Vulnerability to a hazard will also depend on the extent to which populations and systems are exposed to the direct physical impacts of that hazard. Exposure District Roads will depend on a number of factors such as where populations live and how they protect their communities and livelihoods. Furthermore, the resilience of

Settlements & Transport & Settlements Transport Hubs agricultural systems is determined by the extent to which existing coping measures such as dykes and irrigation structures are in place. The resilience of

settlements and industry will depend on their location and the existence and status of dykes and other protection measures, while that of transport and energy infrastructure will depend on the depend on their location and building standards. These factors are specific to particular hazards and are not explicitly incorporated into the indices described above.

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Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

Incorporating control measures in the vulnerability assessment While the specific functional form of vulnerability will vary by context and location, the general relationship between In order to incorporate measures of specific control factors in the vulnerability assessment, an the sectoral dimensions and indicators allows us to characterize the vulnerability profile for each district. The goal is appraisal of the existence and quality of hard measures to control the impacts of specific hazards not to simply define quantifiable measures, but rather to represent inter-relationships between natural and human was developed. Expert opinion was used to evaluate the adequacy of the control measures for systems in a standard form that can be used as a tool to compare and contrast vulnerability in both a temporal and each of the five sectors as outlined in Table 10 below. Information on control measures was geospatial context. The profile can be used to predict the changes in scale and extent of sectoral vulnerabilities over obtained through discussions and interviews with authorities at provincial and district levels, as time and provide an insight into which sectors and locations to intervene to build climate change resilience. well as from literature reviews for each sector. The assessments assumed that no further adaptation response occurs to mitigate against climate change impacts. The expert opinion of the existence and quality of control measures in each time period was incorporated into the vulnerability assessment as a weighting factor in the vulnerability ranking. Table 10. Components of Hard Control Measures Assessed by Expert Opinion.

All hazards Inundation Salinity Storm surge Component Agriculture Crop & handling and Dyke system Sluice gates Coastal Dyke, Livelihoods processing, Warning system Thick mangrove Rice varieties, belt Cultivation methods Warning system Settlements, Suitable elevation of Urban drainage, Water and Coastal Population andinfrastructure Adherence to sanitation protection Poverty suitable building infrastructure infrastructure codes Warning system Transport andSuitable elevation of Adherence to Suitable Building Coastal dyke and Energy infrastructure suitable building Materials protection Photo M. Russell Infrastructure Dykes codes systems The figure below illustrates how the vulnerability assessment is used to develop ‘vulnerability profiles’ for each district. Conceptualising the dimensions of vulnerability using the radial graphs allowed us to examine Interpreting Vulnerability Profiles and describe how the different aspects of vulnerability are related to each other, and for combining the findings from district and sectoral surveys with the outputs from the hydrological and coastal impact The vulnerability profile illustrates the change in the vulnerability of a district over time in each of the five sectors. The shape models. The shape and area of the vulnerability profile expressed in this form is proportional to sensitivity of the profile for each time period can be interpreted on a sector by sector basis. Equal vulnerability for each sector will and exposure less the adaptive capacity. This approach links environmental and socio‐economic produce a balanced star shape, and deviations from the star indicate sectors that are more or less vulnerable. The hypothetical dimensions with the capacity for local communities and institutions to adapt to climate change. example above can be interpreted as:

Current situation - while the control measures currently in place keep the vulnerability of the district low across all sectors, the Hypothetical District Vulnerability Profile vulnerability of the agriculture and livelihood sector is higher and the vulnerability of the energy and industry sector is lower. Population An analysis of the values of the various indicators that make up each sector will reveal the factors that contributing to the 20 different vulnerabilities. Low energy and industry vulnerability would likely reflect a low amount of development in the 15 province. And high vulnerability to agriculture could be due to a high reliance on rice based systems that are exposed to 10 inundation or salinity. Settlements & Transport Poverty 5 Current As the population grows and the exposure to hazards increases districts become increasingly vulnerable and the - 2030 A2 ability of the existing control measures to cope with the projected impacts is reduced expanding the star in size. 2050 A2 2030 - the district becomes increasingly vulnerable. While the profile maintains a similar shape, the vulnerability to poverty increases more that for the other sectors. This may be due to a high initial proportion of poor households that is projected to increase and become exposed to climate change hazards. Energy & Industry Agriculture & Livelihoods 2050 – the population growth, lack of adequate control measures and increased exposure combine to increase vulnerability in all sectors. Agriculture and livelihoods show a more pronounced increase in vulnerable. The particular causes can be determined by an analysis of the indicators. An example of causes may be that vulnerability due to a large rural population and limited alternative income sources is exacerbated by a dwindling amount of available rural land per head of population.

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CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

For each province the calculated vulnerability profiles were then mapped to show the spatial distribution of vulnerability.

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Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

Identifying and Analysing Future Risk 3) The negative impacts to human-made physical infrastructure and the intended service it provides to the community, industry, government and the natural environment (including buildings, roads, ports, water and Various risk assessment methods and tools have been developed around the world, encompassing a broad electricity infrastructure etc.); and range of application from cross cutting methods to specific sectoral methods from a local to global scale. Most 4) The biophysical vulnerability that may be related to the disturbance of coastal and riverine environments and methodologies are designed to evaluate risk according to morphological or economic terms, whereas social and systems. ecological assessments have focused on vulnerability and sensitivity. This study used the results from the exposure modelling together with the key observations and findings from the sectoral People are considered at ‘risk’ when they are unable to cope with a hazard. A disaster occurs when a significant consultations and surveys to determine the relative levels of risk for a particular threat source - expressed as a function of number of vulnerable people experience a hazard and suffer from severe damage and/or disruption of their ‘likelihood’ and ‘consequence’ to highlight the major risks at the district and provincial levels. livelihood system in such a way that recovery is unlikely without external assistance. The risk assessment process involved rating the future risk, using the qualitative measures of ‘likelihood ’ and ‘consequence’ Risk is defined in terms of the probability of a particular climatic outcome multiplied by the consequences of of potential climate change impacts on each of the target sectors as highlighted in Table 12 and the descriptors in Table 11 that outcome. For the purposes of this Study we focused on four main considerations for assessing risks relating and Table 13. to sea level rise, flooding, inundation, salinity and the consequences of storm surges: Table 12. Risk Rating Matrix 1) The negative impacts on the sustainability of the local economy, and especially to household Consequences livelihoods; Insignificant Minor Moderate Major Catastrophic Likelihood 1 2 3 4 5 2) The negative impacts social vulnerability e.g. the incidence of extreme events with respect to mortality Almost certain (5) M (5) M (10) H (15) E (20) E (25) or social disruption; Likely (4) L (4) M (8) H (12) H (16) E (20) Table 11. Qualitative Measures of Likelihood Possible (3) L (3) M (6) M (9) H (12) H (15) Level Descriptor Recurrent risks Single events Rare (2) L (2) L (4) M (6) M (8) M (10) 5 Almost Certain Could occur several times per year More likely than not - Probability greater than 50% Unlikely (1) L (1) L (2) L (3) L (4) M (5) 4 Likely May arise about once per year As likely as not - 50/50 chance E = >20 Extreme risks; require urgent attention to implement adaptation options immediately. 3 possible May arise once in ten years Less likely than not but still appreciable - Probability H = 12 – 20 High risks; requiring attention to developing adaptation options in the near term. less than 50% but still quite high M = 5 – 12 Medium risks; it is expected that existing controls will be sufficient in the short term but will require attention in the 2 Unlikely May arise once in ten years to 25 Unlikely but not negligible - Probability low but medium term and should be maintained under review. years noticeably greater than zero L = <5 Low risks; Control measures should be maintained under review but it is expected that existing controls will be 1 Rare Unlikely during the next 25 years Negligible - Probability very small, close to zero sufficient and no further action will be required to treat them unless they become more severe.

Table 13. Quantitative Measures of Consequence.

Level Infrastructure Services Community Local Economy Natural Environment No infrastructure damage. No adverse human health effects or complaint. Minor negative impacts on key economic No environmental damage elements (i.e. rice production, aquaculture, tourism, fisheries) Insignificant 1 Localized infrastructure service disruption. No permanent Short-term disruption to employees, customers and all Temporary disruption to one key economic Minor instances of environmental damage damage. community. element (i.e. agricultural production, tourism, that could be reversed i.e. negative impact on

Some minor restoration work required. Early renewal of Slight adverse human health effects or general amenity issues. fisheries) a specific species infrastructure by 5-10%. Isolated but noticeable examples of decline in social cohesion 2 Minor

Widespread infrastructure damage and loss of service. Damage Frequent disruptions to employees, customers or neighbors. Temporary disruption to one or more key Isolated but significant instances of recoverable by maintenance and minor repair. General appreciable decline in social cohesion economic elements (i.e. agricultural production, environmental damage that might be Partial loss of local infrastructure. Early renewal of tourism, fisheries) reversed with intense efforts Infrastructure by 10-20% i.e. reduced fish stock 3 Moderate Extensive infrastructure damage requiring extensive repair. Permanent physical injuries and fatalities may occur from an A key element of the economy is disrupted for Sever loss of environmental enmity and a

Permanent loss of regional infrastructure services, e.g. a bridge individual event. an extended period of time (i.e. phosphate danger of continuing environmental damage washed away by a flood event. Negative reports in national media. mines, tourism or fisheries) Early renewal of Infrastructure by 20-50%. Retreat of usable Severe and widespread decline in services and quality of life 4 Major land i.e. agricultural and residential land within the community

Permanent damage and/or loss of infrastructure service across Severe adverse human health effects – leading to multiple More than one key element of the economy is Major widespread loss of environmental state. events of total disability or fatalities. disrupted for an extended period of time (i.e. amenity and progressive irrecoverable Retreat of infrastructure support and translocation of Emergency response. phosphate mines, tourism or fisheries) environmental damage residential and commercial development. Region would be seen as unable to support its community i.e. death of coral reef 5 Catastrophic

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CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

Risk Values As the spatial extent of the three major impacts under consideration can be mapped, a range of consequence ratings could be determined depending on the exposure to the impact. Similarly the likelihood of each hazard could also be determined. The risk values used in the study are presented in Table 14. The consequences and likelihoods were considered using the current (2010) level of adaptation response to climate change and do not include any uptake of potential adaptation responses by 2030 and 2050.

Table 14. Values of Likelihood and Consequence Used for Levels of Exposure to Each Climate Change Impact.

Hazard Exposure Consequence Likelihood Risk < 25% of Area Insignificant 1 possible 3 3 Low Inundation < 75% of Area Minor 2 Likely 4 8 Moderate >75% of Area and deep Moderate 3 possible 3 9 Moderate < 50% of Area Insignificant 1 certain 5 5 Moderate Salinity >50% of Area Minor 2 certain 5 10 Moderate

Localised Minor 2 Rare 2 4 Low Storm Widespread Moderate 3 Rare 2 6 Moderate Surge Extensive Major 4 Rare 2 8 Moderate

Permanent Catastrophic 5 Rare 2 10 Moderate

Hotspots Risk hotspots are defined as those areas that inherently have the highest scale of hazard- and place- specific risk for the combined effects of climate change, and are considered to be the highest-risk areas and include urban settlements, specifically the transport, energy and industrial infrastructure, and rural areas that are highly exposed to the impacts of climate change (such as areas affected by sea level rise and inundation). The following areas have been identified as hotspots in the different sectors.

Table 15. Hot Spots Identified in the Vulnerability and Risk Analysis.

Agriculture & Industry & Urban Settlements & All Photos M. Population Poverty Livelihoods Energy Transport Kowel, except top Ca Mau Ngoc Hien U Minh Dam Doi Cai Nuoc right Wikipedia Tran Van Thoi Dam Doi Dam Doi Tran Van Thoi Tran Van Thoi Tran Van Thoi Ca Mau Ca Mau

It must be stressed that the estimates developed for future vulnerability under different climate change scenarios are at a relatively coarse-scale, and whilst vulnerability profiles were developed for both A2 and B2 Emission Scenarios for three time slices (current 2010, 3030 and 2050), only the A2 maps are presented in this Report for illustrative purposes. These scenarios are considered mid-range (B2) and high (A2), however as global emissions are currently tracking above the highest A1Fi scenario it should be considered that even the A2 hazard maps are potentially conservative with regards to actual climate impacts. In reality, there was little appreciable difference between vulnerability assessed under the A2 and B2 scenarios over the 2030 and 2050 time periods – and these differences were not sufficient to affect the vulnerability mapping to any appreciable degree.

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Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

Ca Mau Province Population and Demographics Topography

Ca Mau’s total population is over 1.2 million (2010). The capital city is Ca Mau City Ca Mau Province is the and there are eight districts and 101 communes and towns. The average population Southern-most tip of density is 226 person/km2 which is lower than the national density (260 persons/ the Mekong Delta km2) and that of other provinces in the Mekong delta (425 person/km2). Population growth is 1.3 percent per year and females account for 49.6 percent of total floodplain. The area is provincial population. low lying, with some 2 elevation along the Ca May City has the highest population density with 863 person/km followed by Cai Nuoc district and Tran Van Thoi district with population densities of respectively 331 South-East coast. The 2 2 person/km and 260 person/km . Ngoc Hien district has the lowest population district located at the 2 density with only 107 person/km . Urban population is 20% versus 80% living in rural very tip of Ca Mau, areas. Its immigration rate is 0.4% and emigration rate is 0.7% resulting in a net Ngoc Hien, is actually migration rate of -0.3%. Ca Mau can be classified as a rural province with a high percentage of the population living in rural areas. an island separated from the neighboring Commencing in 2000, the Government implemented a wide array of interventions targeting the opening of the provincial economy. In particular, a significant area of district, Nam Can, by a paddies has been transformed into high yielding shrimp cultivation. This has further river. This area is improved Ca Mau’s competitive advantages in shrimp cultivation with significant habited, but is spin-offs and creation of induced jobs in upstream and downstream components of technically classified as the value chain (transport, processing of shrimps, agro-processing). a marine area.

Current Population Density

Irrigation System

Ca Mau has an extensive canal system crisscrossing the entire province. The canal system plays multifunctional roles of drainage, water storage and transport. Administrative Center Ca Mau City Major canal Land Area (ha) 533,318 construction was started early in the Population 1,218,500 20th century by the French. This system Population Density (person/ha) 2.28 has sluice gates that are coordinated in No. of Households 285,000 irrigation areas. Average Family Size 4.27 Average Annual Per Capita Income 18,757,400 VND GDP contribution from Industry (HH) 1,918,806 VND Unemployment Rate 6.0% Education (Teachers/1000p.) 8.8 Health (Doctors/1000p.) 0.47

Ethnicity (% Kinh/non-Kinh) 96.6/3.4

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CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

Ca Mau Province Energy and Industry Transport Network Livelihoods and Agriculture The largest single existing energy sector project in Ca Mau province is the (PVN) Ca Mau gas-power-fertilizer complex. This project comprises a 325 km long gas pipeline from the Malaysian-Vietnamese shared offshore PM3-CAA field (the pipeline was completed in 2007 at a cost of $300M,) and at Khanh An , and 9 km Northwest from Ca Mau City there is the gas- power-fertilizer complex itself. The Ca Mau gas-power-fertilizer complex is located at the confluence of the Ong Doc, Cai Tau and Trem rivers, is affected by the tides of the South-western sea with a maximum tidal amplitude of 60 cm, and the area around the site is usually flooded in the rainy season for 2 - 3 months with general flooding depth of 30 - 50 cm (CM2 DEIA). Total capital investment is $1,760 – $2,060 million The Ca Mau 1 and 2 power plants comprise two separate 750MW CCGT plants designed to run on natural gas and diesel oil. The Ca Mau 1 power plant was approved in October 2001. The feasibility study (FS) was completed in 2005, construction started in 2006 for Ca Mau 1 and in 2007 for Ca Mau 2. The two plants were completed in 2007 and 2008 with 720MW net export power output per plant over a 25 year design life with a total investment capital for the two power stations of US$860 million. Electricity Network

Amongst the 9 Districts in the province, Ca Mau City has a high level of

centrality in terms of its provision of high level services and is a magnet for Livelihoods and Agriculture investment and residency compared to other centers. In the 5 official standard classes of urban centers Ca Mau City is the only one not considered Ca Mau has an area of 533,318 ha, with about 300,000 ha used for aquaculture. The as Class 5. predominant crop is still rice, mainly double cropping in salt free zones. Total land area Ca Mau City is the administrative and commercial centre: it is also the main under rice is 130,000 ha, divided into 70,000 ha with double cropping and 60,000 ha with location for processing of the outputs from the region’s primary sector. It is single cropping only. Over the last decade the area under cereals cultivation in Ca Mau located inland but is well served regionally by both water and road (Highway has decreased by 43 percent. The province can be divided into 3 eco-zones: 1 linking east to Can Tho) access. Nam Can town, the southern port, has been chosen as one of 15 identified (i) The southern 5 districts of Cai Nuoc, Dam Doi, Nam Can, Ngoc Hien and Phu Tan, coastal economic zones in the country. It is currently a key transit centre for experiencing minimal flooding but high salinity for long periods. This area is aquaculture products in the southern part of the Province. almost entirely under aquaculture. As the main port on the west coast, Song Doc town has Marine Economic (ii) The two northern coastal districts of Tran Van Thoi and U Minh with a mixture of Town status but is not the District’s administrative centre (Tran Van Thoi forests, rice and, increasingly, aquaculture. This area contains the U Minh Ha Town). It serves as the base for small (<2,000 tons) fishing boats. national Park and most of the production forests of the province. A series of sluice gates along rivers and canals along the coastline is used to minimize saline Water is key to the transport of heavy/bulky products to processing centers intrusion to enable rice cultivation. because it: (iii) The two northeastern districts of Ca Mau and Thoi Binh which experience  can take bigger loads moderate flooding and saline intrusion. This area contains much of the extensive  is cheaper (up to 60% according to discussions with the DoT), fruit production of the province and still has mostly rice crops around and north of  can reach remote areas whereas the road system has limited Ca Mau city. Aquaculture is also becoming increasingly important in this eco-zone.

 is convenient: it allows goods to be delivered door to door with minimal fuss

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Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

Ca Mau Province Population Vulnerability Provincial Population Vulnerability- A2 Scenario (Baseline, 2030, 2050 Population Vulnerability: refers to the vulnerability of people and populations in the study area to the effects of climate change, and recognises that there are distinct regional differences in the demographic composition and trends (such as the migration of people towards coastal urban areas which yields a greater than average growth of the population in Population Vulnerability some districts). Population growth is a major driver for change in the delta, especially in terms 30 of increasing the number of people and households exposed to climate change hazards, but also increase of demands on the available natural resources and its implications on sustainable 25 livelihoods. The relationship between population change and the associated demographic trends will affect the ability of local communities and households to build resilience to climate 20 change. Over the long term, population growth in the study area is likely to contribute to and 15 exacerbate not only the vulnerability to climate change, but exacerbate the difficulties in adapting to the potentially detrimental changes in climate. In this context a district is 10 considered to be vulnerable if it exhibits characteristics such as high population numbers, rates of growth or large family size. Using the comparative indicators and measures provided below 5 it is possible to estimate or rate the relative population vulnerability at the district levels. - The population vulnerability map was derived primarily from the population and demographic Ca Mau Cai Nuoc Dam Doi Nam Can Ngoc Hien Phu Tan Thoi BinhTran Van Thoi U Minh and data collected during the district survey. For each province we ranked the districts out of 40 according to these indicators and found that: The current population vulnerability for all the districts in Ca Mau was low; By 2030 two out of nine in Ca Mau, were assessed as being medium; By 2050, Cau Mau City and Tran Van Thoi in Ca Mau were assessed as being highly vulnerable, and a further three districts exhibiting medium vulnerability; Map of the Provincial Population Vulnerability- A2 Scenario (Baseline, 2030, 2050)

Hotspots The most vulnerable districts in Ca Mau with regard to population are Ca Mau city and Tran Van Thoi. Ca Mau: has high existing population density, combined with a high in migration rate that result in high vulnerability for this sector. However, the low number of rural households keeps the present vulnerability low, but exposure and sensitivity to inundation will increase into the future, and is likely to be significant by 2050. The high population, the large area of the urban area and the concentration of transport hubs increases vulnerability in this sector. In all sectors except poverty, vulnerability increases in the future due to population growth and inward migration which emphasizes the current susceptibility to impacts. Tran Van Thoi: With high population and inward migration, a coastal town subject to storm surge and with 46% of the areas currently subject to inundation the initial high exposure increases to 80% in the future, as inundation and storm surge affect larger areas. Combine this with a lower average income, more poor households and less access to health than the other urbanised districts results in a high exposure and sensitivity.

Risks Table 16. Risk from Climate Change for the Population Sector.

Sectoral Component Climate Change Impact Urban Settlements Rural Households Migration Patterns Temperature Negligible Risk Negligible Risk Negligible Risk

Sea Level Rise Definite Risk Definite Risk Definite Risk

Flooding & Inundation Definite Risk Definite Risk Definite Risk Salinity Negligible Risk Minor Risk Minor Risk The primary risks to population in the study area relate to the combined effects of SLR, flooding and inundation and the impacts associated with extreme events. The risks associated with salinity and Storm Surge Minor Risk Minor Risk Definite Risk temperature are relatively minor in comparison. Typhoons Definite Risk Definite Risk Definite Risk

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CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

30 Ca Mau Province Poverty Vulnerability Poverty Vulnerability Poverty Vulnerability: refers to the vulnerability of poor and near poor households and people in the study area to the effects of climate 25 change, and recognises that the incidence of poverty varies across the region, due to a range of ‘special difficulties’ such as ethnicity, lack of 20 access to agricultural land, education and health services, fresh drinking water, power and markets. Poverty diminishes the resilience and adaptive capacity of people and households, especially where people lack savings and capital for investment to adopt better production 15 technology and also lack awareness and knowledge of adaption options available. Like population, poverty encompasses dimensions relevant to climate change vulnerability, such as the vulnerability to impacts and future shocks – and the ability to build resilience and adapt to 10 climate change. In this study we recognize that poverty is multi-dimensional and includes health, wealth, education and access to natural resources in addition to income. Combining information on these indicators with different poverty measures at a the commune level allows 5 us not only to understand the spatial patterns of poverty but allows us to analyse the vulnerability of the poor and near poor communities - and households to climate change impacts and hazards into the future. Ca Mau Cai NuocDam Doi Nam Ngoc Phu Tan Thoi Tran Van U Minh Map of the Provincial Poverty Vulnerability- A2 Scenario (Baseline, 2030, 2050) Can Hien Binh Thoi

In Vietnam, poverty is officially measured by a standard government measure; according to Decision

170/2005/QĐ-TTg, poor households in rural areas have a monthly income per person of below 200,000 VND and below 260,000 VND for urban areas.

Risks Table 17. Risk from Climate Change in the Poverty Sector. Poverty Dimensions Climate Change Impact Household Incomes Health & Education Ethnic Minorities Temperature Negligible Risk Minor Risk Negligible Risk Sea Level Rise Definite Risk Definite Risk Minor Risk Flooding & Inundation Definite Risk Definite Risk Minor Risk In this study we recognize that poverty is multi-dimensional and includes health, wealth, education and access to natural resources in Salinity Minor Risk Definite Risk Negligible Risk addition to income. We have used a combination of the standard indicators for Vietnam (such as: household income levels; ethnicity; Storm Surge Definite Risk Minor Risk Negligible Risk education, literacy and access to schools; and access to health services) together with access to land resources. Using the indicators together with future projections of population growth we were able to estimate or rate the relative poverty Typhoons Definite Risk Definite Risk Definite Risk vulnerability at the district levels, and comparative vulnerabilities of each district are represented geographically above. We assume The primary risks to poverty in the study area relate to the combined effects of SLR, flooding that estimates of relative poverty levels will stay the same (i.e. without poverty reduction interventions). and inundation and the impacts associated with extreme events. The risks associated with For each province we ranked the districts out of 40 according to these indicators and found that: salinity and temperature again are minor in comparison. The current poverty vulnerability for all the districts was low; Hotspots By 2030 two out nine districts. Doi and Ngoc Hien assessed as being medium; and The most vulnerable districts in Ca Mau from with regard to poverty are Dam Doi and Ngoc Hien. By 2050, Dam Doi and Ngoc Hien were assessed as being highly vulnerable. Dam Doi: A high number of poor household is only slightly ameliorated by a moderate access to Whilst all the indicators of poverty are important, the primary driver of poverty vulnerability proved to be access to land resources. As health and education. Low population growth reduces the effect of increases in inundation by access to productive land is important for reducing rural poverty, the impacts of climate change on the productivity of land will further 2050. constrain efforts to combat rural poverty. In almost all districts limited space is either a problem now, or will be in the near future. In the Mekong delta pressure on space will increase dramatically in future, and this in turn will place unparalleled pressure on household Ngoc Hien: A very low income and high number of poor household and limited access to health livelihood systems and the regional economy in general. and education lead to increasing vulnerability as exposure to inundation and storm surge increases. The very high population growth increases the effect.

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Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

Ca Mau Province Agriculture and Livelihoods Map of the Provincial Livelihoods and Agriculture Vulnerability- A2 Scenario (Baseline, 2030, 205 Vulnerability Agriculture and Livelihoods Vulnerability: refers to the vulnerability of agricultural farming, infrastructure and livelihood systems in the study area to the effects of climate change, and recognizes that In Vietnam the single farmer household is recognized as the basic economic unit upon which the agricultural sector is built upon at the commune, district and provincial levels, and is central to understanding the current and future effects of climate change. In this context a household agricultural and livelihood system is considered to be vulnerable if there is a high probability of loss or damage from climate change from which there is a high probability of it not recovering quickly or fully because the effects are either irreversible or the opportunities of recouping the losses are negligible. Household vulnerability is determined by access to resources (land and water) and the level and diversity of income sources (occupations) as well as availability of productive assets and infrastructure.

40 Agriculture & Livelihoods Vulnerability

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30

25

20

15 Rural households in Ca Mau to tend rely heavily on climate-sensitive resources such as agricultural land and climate-sensitive activities such as rice farming 10 and aquaculture. Climate change impacts such as flooding and inundation, salinity and storm surge reduce the availability of these local natural resources, limiting the options for rural households that depend on natural resources for consumption or income generation. 5 The most vulnerable districts are those with a large number of households that are highly dependent on water-reliant - farming systems (such as the rice-based system), and are most exposed to river based flooding and inundation such as U Mihn. The coastal districts, whilst

Ca Mau Cai NuocDam DoiNam CanNgoc HienPhu TanThoi BinhTran Van ThoiU Minh being adversely affected by salinity and storm surge were assessed as less vulnerable, primarily due to the higher level of control and or protection afforded by the sea dyke and sluice gate system.

Risks We measured agricultural and livelihood vulnerability by combining data and information from the Table 18. Risk from Climate Change in the Agriculture and Livelihood Sector. district and sectoral surveys, including: human assets (occupations, access to employment, adults at Agriculture Livelihoods working age etc); natural assets (water, land, aquatic etc.); economic (sectoral productivity, GDP and productive assets); and financial capital (household wealth characteristics) together with water Climate Change Impact Rice Based System Rice-Shrimp System Aquaculture Household GDP reliant livelihood strategies. Temperature Definite Risk Definite Risk Definite Risk Minor Risk The overall distribution of agricultural and livelihood vulnerability for Kien Giang and Ca Mau province was assessed as a function of the above key indicators and the existing and projected Sea Level Rise Definite Risk Minor Risk Minor Risk Minor Risk climate exposure and hazard for sea level rise, inundation and salinity. The assessment is based on Flooding & Inundation Definite Risk Minor Risk Minor Risk Definite Risk the assumption that the current demonstrable vulnerability in the agricultural sector is the best available basis for assessing the future climatic risks for that sector. Salinity Minor Risk Minor Risk Negligible Risk Negligible Risk We ranked the districts in the study area according to their relative exposure to flooding, inundation, Storm Surge Minor Risk Definite Risk Definite Risk Minor Risk salinity and storm surge and found that: Typhoons Definite Risk Definite Risk Definite Risk Definite Risk The level of exposure to climate hazards such as flooding, inundation and salinity, together with a heavy dependence on natural resources for their livelihoods make rural communities in both Kien Hotspots Giang and Ca Mau vulnerable. U Minh: A high rural population with low incomes is offset by a high number of possible income sources and available land. Exposure to However the overall agricultural and livelihood vulnerability for all districts were assessed as being inundation salinity and storm surge leads to high vulnerability in the future. low to medium – primarily because of the level of control, adaptation and resilience exhibited in all districts except Ngoc Hien; Dam Doi: A high number of income streams reduce the current vulnerability, but the high population decreases the availability of land for primary production thus increasing vulnerability. Further to this, it is expected that this situation will change by 2030, and by 2050 the rating for all mainland districts is expected to increase from medium to high, primarily due to the increase in the Tran Van Thoi: A high number of rural households and a moderate income increase vulnerability as the area impacted by inundation increases to 80% in the future. level of exposure to flooding and inundation, and the heavy reliance on water based livelihood and agricultural systems.

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CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

Ca Mau Province Energy and Industry Vulnerability 30 Industry & Energy Vulnerability Industry and Energy Vulnerability: refers to the vulnerability of industrial and energy generation and transmission infrastructure and services to the effects of climate change, and recognises that industry and energy generation are important drivers for the economic development, 25 growth and sectoral transition in the delta necessary to build resilience and adaptive capacity into the future. In this context, industry and energy infrastructure and services are considered to be vulnerable if there is a high probability of loss or damage from climate change from 20 which there is a high probability of it not recovering quickly or fully because the effects are either irreversible or the opportunities of recouping the losses are negligible. We measured industry and energy vulnerability by combining data and information from the district and sectoral 15 surveys, including: human assets (% of population working in Industry, households reliant on industry); natural assets (diversity of industrial development, power generation capacity); economic (sectoral productivity Average Annual GDP per household contribution from Industry); 10 and financial capital (investment levels, household connections, levels of service etc.) together the nature, location and extent of industrial zones, energy generation and power transmission infrastructure. 5 Map of Provincial Energy and Industry Vulnerability- A2 Scenario (Baseline, 2030, 2050)

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Accordingly, we ranked overall vulnerability of each district according to the indices and found that in 2010, industry and energy vulnerability for all districts in Ca Mau was considered to be low to medium. However, this is expected to increase by 2030, and by 2050 the rating for six districts in Ca Mau Ca Mau City, Tran Van Thoi, Phu Tan, Thoi Binh, Ngoc Hien and Dam Doi, are expected to increase to medium and high, primarily due to the exposure of surface water resources to sea level rise, and the combined effects of flooding, saline intrusion and storm surge. The most vulnerable districts are those with a large number of households that are highly dependent on local industry for employment or income, and are most exposed to SLR, flooding, inundation and extreme events and their effects on industrial areas, factories, and power generation and supply infrastructure and services. The coastal districts, whilst being adversely affected by salinity and storm surge were assessed as less vulnerable, primarily dyke to the higher level of control and or protection afforded by the sea dyke and sluice gate system.

Hotspots

Risks Dam Doi: While industry has a low contribution to the district economy the high population means that a large number of households are reliant on a few industries The primary risks to industry and energy infrastructure in the study area relate to the combined effects of SLR, flooding and inundation Combined with a poor electrical connection rate but a large amount of electrical and the impacts associated with extreme events. The risks associated with salinity and temperature again are minor in comparison. infrastructure potentially effected by inundation, salinity and storm surge this vulnerability increases in the future. Table 19. Risk from Climate Change in the Energy and Industry Sector. Tran Van Thoi: A large contribution to GDP from industry and a lot of energy

Industry Type Power Infrastructure infrastructure leads to high vulnerability which increases with inundation and storm surge. Primary Industry Manufacturing & Services Power Generation Power Transmission Climate Change Impact Construction Industry Facilities Ca Mau: The aggregation of industry and electricity infrastructure in the city and the reliance of house hold incomes on industry mean that Ca Mau city is vulnerable in this Temperature Definite Risk Minor Risk Negligible Risk Minor Risk Negligible Risk sector, especially to extreme events. The extent increases in the future particularly due to Sea Level Rise Definite Risk Minor Risk Minor Risk Minor Risk Negligible Risk flooding. Flooding & Inundation Definite Risk Minor Risk Minor Risk Minor Risk Minor Risk Salinity Minor Risk Minor Risk Negligible Risk Negligible Risk Negligible Risk Storm Surge Minor Risk Minor Risk Minor Risk Minor Risk Negligible Risk Typhoons Definite Risk Definite Risk Minor Risk Definite Risk Definite Risk

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Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

Ca Mau Province Urban Settlements and Provincial Urban Settlements and Transport Vulnerability- A2 Scenario (Baseline, 2030, 205 Transportation Vulnerability

Urban settlements and transportation vulnerability refers to the vulnerability of urban settlements and transportation to the effects of climate change, and recognises the need to protect people and property, and the importance of the transport system to support and promote regional development and economic growth in the Mekong Delta. In this context, urban settlement and transportation infrastructure are considered to be vulnerable if there is a high probability of loss or damage from climate change from which there is a high probability of it not recovering quickly or fully because the effects are either irreversible or the opportunities of recouping the losses are negligible. We measured urban settlement and transportation vulnerability by combining data and information from the district and sectoral surveys, including: human assets (% of urban population); natural assets (% urban area); economic (value of goods shipped); and financial capital (urban infrastructure and levels of service) together with the nature, location and extent of the transport network and infrastructure.

30 Urban Settlements & Transport Vulnerability

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10 The most vulnerable districts are those with high levels of urban infrastructure, buildings and urban households, which are highly exposed to flooding, inundation and storm surge. The coastal districts, whilst being adversely affected by salinity were assessed as less vulnerable, primarily due to the higher level of control and or protection afforded by the sea dyke and sluice gate system. - Risk Table 20. Risk from Climate Change in the Urban Settlements and Transport Sector. Settlements Transportation

Building Water Supply Waste & Road Transport Waterway Ports & Harbors Climate Change Impact Sanitation Transport

Temperature Negligible Risk Negligible Risk Negligible Risk Minor Risk Negligible Risk Negligible Risk Sea Level Rise Definite Risk Minor Risk Minor Risk Minor Risk Minor Risk Definite Risk We ranked the districts in the study area according to their relative exposure to flooding, Flooding & Inundation Definite Risk Minor Risk Minor Risk Minor Risk Minor Risk Negligible Risk inundation, salinity and storm surge and found that: Salinity Minor Risk Minor Risk Negligible Risk Minor Risk Negligible Risk Negligible Risk The high levels of exposure to climate hazards such as flooding, inundation and salinity, together with high sensitivities in relation to high urban populations and densities, mean Storm Surge Definite Risk Negligible Risk Negligible Risk Minor Risk Minor Risk Definite Risk that from an urban settlement perspective Ca Mau city and the semi-urban hinterlands of Typhoons Definite Risk Minor Risk Minor Risk Minor Risk Definite Risk Definite Risk Tran Van Thoi, and Cai Nuoc are vulnerable; The overall urban settlement and transportation vulnerability for all districts were Hotspots assessed as being low to medium – primarily because of the level of control, adaptation The most vulnerable districts in Ca Mau are: and resilience exhibited in all districts; Ca Mau: The High population, the large area of the urban area and the concentration of transport hubs increases vulnerability in this Further to this, it is expected that this situation will change by 2030, and by 2050 the sector. rating for all mainland districts is expected to increase from medium to high, primarily Cai Nuoc: A moderate urban population and poor sewage connections leads to high vulnerability that increases with extensive flooding in due to the increase in the level of exposure to flooding and inundation, and the heavy the future. reliance on water based livelihood and agricultural systems. Tran Van Thoi: A high urban population in two towns, one of which is on the coast, leads to high vulnerability limited infrastructure and

protection of the urban centre from inundation and storm surge means that vulnerability is low, which increases as inundation and storm surge affect larger areas.

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CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

Ca Mau Province Regional synthesis Vulnerability 30 Ca Mau Provincial Vulnerability Synthesis

The first phase of the vulnerability assessment began with an evaluation of how specific systems, both natural and human, such as roadways, water resources, and industrial areas etc., were “exposed” to climate hazards and impacts. To this end a composite of 20 ‘vulnerability indicators’ for the main sectoral areas and hazard categories were used to assess the vulnerability of both the natural and human systems in the study area in terms of exposure and sensitivity, and for measuring risk and adaptive capacity. The 10 vulnerability rankings for each of the district are based on a standard set of indicators so that we can compare the vulnerability not only between districts, but also across sectors. The assessment is supplemented by expert judgment and the feedback from the key - stakeholder groups and government agencies. Vulnerability rankings were averaged across the five sectors to produce a vulnerability synthesis ranking, which is displayed below.

Map of the Vulnerability Synthesis - A2 Scenario (Baseline, 2030, 2050)

Comparison of the rankings for the different districts clearly shows that current overall vulnerability to climate change for the majority of districts is low to medium. However, into the future many districts were assessed as being medium to high.The vulnerability assessment identifies three districts as being highly vulnerable to the impacts of climate change by 2050.

Highly Vulnerable Districts

In Ca Mau province, the following districts have been identified as being expected to have a high level of overall vulnerability to climate change across the various sectors in Summary of Sector Control Measures this study: Table 21. Summary of sector Control Measures. Ca Mau Province has only medium control measures in place Impacts  Tran Van Thoi Overall to protect settlements and transport infrastructure and good Sector  Ca Mau Erosion & Flooding & Salinity Storm Surge Protection protection across the other sectors Sedimentation Inundation  Dam Doi Settlements & Population • •• •• • Medium • Minor and/or well controlled Poverty & Income • •• ••• • High Intermediate and/or partly controlled •• Agriculture & Livelihoods •• ••• ••• • High ••• Major but largely controlled The vulnerability ratings for all the mainland districts Industry & Energy • • •• • High •••• Major and largely uncontrolled would have been much higher in the absence of the Transportation • • •• • High sea-dyke flood control system.

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Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

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CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

Ca Mau Province – District Summaries

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Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

Ca Mau City Vulnerability Index Ranking Total Area (Ha) Population 90,000.0 250000 Total Population High 80,000.0 Population Density High 70,000.0 200000

Average Family Size Low 60,000.0 150000 Number of Households High 50,000.0 40,000.0 Population at working age High 100000 30,000.0

Population Average Natural Population High 20,000.0 50000 Growth Rate 10,000.0 - 0 Annual Average Income per Low Dam U Minh Ngoc Tran Thoi Nam Phu Cai Ca Mau Ca Mau Tran Dam Thoi Cai Phu Tan U Minh Ngoc Nam Doi Hien Van Binh Can Tan Nuoc Van Doi Binh Nuoc Hien Can Capita Thoi Thoi

Number of Poor Households Low % of Poor Households Low Population Density (Persons/Ha) Poor Households (%) Number of Teachers Medium 10 25 Poverty Number of Doctors Low 9 Agricultural Land per person High 8 20 % Ethnic Households Low 7 6 15 Number of Rural Households Low 5 4 10 Number of Livelihood Streams High

3 Administrative Provincial Capital Streams Employing > 10,000 or Low 2 5 producing >250 Billion VND 1 Center 0 0 Average Annual GDP per Low Ca Mau Cai Tran Phu Tan Dam Doi Thoi Nam U Minh Ngoc U Minh Ngoc Dam Doi Tran Cai Nam Phu Tan Thoi Ca Mau Land Area (ha) 24,929 Nuoc Van Binh Can Hien Hien Van Nuoc Can Binh

Livelihoods Household Thoi Thoi Rice Crop Land per Person High Population 218,148 Aquaculture Land per Person High Households reliant on Industry High Average Income per Capita (VND) Land Use Average Annual GDP per High 60000000 Urban Population Density 8.75 9% Household contributed by 50000000 (person/ha) Industry Low 40000000 No. of Households 53,229 Households Connected to National Grid Medium 30000000 Length of High/Medium Voltage 20000000 Average Family 4.10 Power Lines High 10000000 Size Number of Power Plants/High Agriculture 0 91% Energy & IndustryEnergy & Average Annual 50,035,000 VND Voltage Substations High Ca Mau Nam Dam Phu Cai Ngoc Tran U Minh Thoi % off-farm Income High Can Doi Tan Nuoc Hien Van Binh Household Income Thoi Number of Factories Medium GDP contribution 11,529,938 VND Number of Different Industries from Industry (HH) Urban Population Low Inundation Total 2000 2030 2050 Unemployment No Data Urban Households Low Rate Urban Area High Roads (Km) 64.4 9.1 23.2 36.3 % Urban High Education 9.4 Sewer/Septic Tank Low Power (Km) 347 70 172 234 (Teachers/1000p.) Water Supply Low Agriculture Area (Km2) 117.11 18.72 60.95 82.35 Health 1.34 Major Waterways - (Doctors/1000p.) Major Roads Medium Aquaculture Area (Km2) 61.68 13.28 33.56 45.16 District Roads Low Residential & Commercial Land (Km2) 14.90 2.79 7.45 10.84 Ethnicity (% 99.1/0.9 Transport Settlements & Transport Hubs High Kinh/non-Kinh)

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CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

Vulnerability Profile Population: A high existing population that is at high Population 40 density combined with large in migration results in high vulnerability for this sector 30 Poverty: High incomes, a low number of poor 20 Settlements & Poverty households and good access to health and education Transport 10 lead to low vulnerability in this sector. - Livelihoods: A low number of rural households keeps the present vulnerability low, but exposure to inundation and saline intrusion increases vulnerability in the future especially by 2050. Energy & Livelihoods Energy & Industry: The aggregation of industry and Industry electricity infrastructure in the city and the reliance of Current 2030 A2 2050 A2 house hold incomes on industry mean that Ca Mau city is vulnerable in this sector, especially to extreme events. The extent increases in the future particularly due to flooding. Settlements & Transport: The High population, the large area of the urban area and the concentration of transport hubs increases vulnerability in this sector. In all sectors except poverty, vulnerability increases in the future due to population growth and inward migration which emphasises the current susceptibility to impacts.

Exposure, Risk and Control Elements

Exposure and Risk Time Period Inundation Salinity Storm Surge Hazard Current 19 100 0 2030 25 100 0 (% of Total Area) 2050 71 100 0 Current 3 10 0 Risk Rating 2030 6 10 0 2050 9 10 0 < 5 Low risks 5 – 12 Medium risks See Table 12 pg. 16 for detailed descriptions. While exposure to flooding is currently low it is projected to increase with an associated increase in risk by 2050. The district has complete exposure to salinity with medium risk.

Control Elements

Infrastructure Flooding Salinity Storm Surge Agricultural • ••• • Major Industry ••• •• • Major Energy ••• • • Urban •••• • • Transport •• • • • Adequate •• Change-long term ••• Improvement-medium term •••• Rehabilitation urgent Improvements of the salinity control measures for agricultural infrastructure are required in the medium term in the areas of dyke strengthening, improvements to sluice gates, and improvements to aquaculture techniques

and calendars. Flood protection measures in the energy and industry sectors are required in the medium term and urban infrastructure requires immediate protection. These are mainly related to raising structures above flood levels and improving water and sanitation.

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Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

Cai Nuoc Vulnerability Index Ranking Total Area (Ha) Population Total Population High 90,000.0 250000 80,000.0

Population Density High 70,000.0 200000 Average Family Size Low 60,000.0 150000 Number of Households High 50,000.0 40,000.0 Population at working age Medium 100000 30,000.0 Population Average Natural Population 20,000.0 50000 Growth Rate High 10,000.0 Annual Average Income per Capita - 0 Dam U Minh Ngoc Tran Thoi Nam Phu Cai Ca Mau Ca Mau Tran Dam Thoi Cai Phu Tan U Minh Ngoc Nam Number of Poor Households Medium Doi Hien Van Binh Can Tan Nuoc Van Doi Binh Nuoc Hien Can Thoi Thoi % of Poor Households Medium Number of Teachers Medium

Number of Doctors Low Population Density (Persons/Ha) Poor Households (%) Poverty Agricultural Land per person Low 10 25 % Ethnic Households High 9 Medium 8 20 7 Number of Rural Households Medium 6 15 Number of Livelihood Streams High 5

Streams Employing > 10,000 or 4 10 3 producing >250 Billion VND Medium 2 5 Administrative Cai Nuoc Average Annual GDP per 1 Center 0 0 Livelihoods Household Medium Ca Mau Cai Tran Phu Tan Dam Doi Thoi Nam U Minh Ngoc U Minh Ngoc Dam Tran Cai Nam Phu Tan Thoi Ca Mau Land Area (ha) 41,700 Rice Crop Land per Person Medium Nuoc Van Binh Can Hien Hien Doi Van Nuoc Can Binh Thoi Thoi Aquaculture Land per Person High Households reliant on Industry Low Population 137,846 Average Annual GDP per Average Income per Capita (VND) Land Use Household contributed by Industry Medium 60000000 2% Households Connected to National Population Density 3.31 50000000 Grid (person/ha) Length of High/Medium Voltage Medium 40000000 Agriculture No. of Households 32,441 Power Lines 30000000 Forest Number of Power Plants/High High 20000000 Voltage Substations Urban Average Family 4.25 10000000 Unused Energy & IndustryEnergy & % off-farm Income Medium Size 0 Number of Factories Low 98% Ca Mau Nam Dam Phu Cai Ngoc Tran U Minh Thoi Average Annual 15,399,600 VND Number of Different Industries High Can Doi Tan Nuoc Hien Van Binh Thoi Household Income High Urban Population High GDP contribution 398,660 VND Urban Households High Inundation Total 2000 2030 2050 from Industry (HH) Urban Area Medium Unemployment 3.2% % Urban High Roads (Km) 73.1 28.3 49.9 62.3 Rate Sewer/Septic Tank Medium Power (Km) 390 181 273 325 Education 11.5 Water Supply Medium (Teachers/1000p.) Major Waterways - Agriculture Area (Km2) 86.33 38.58 57.79 68.13 Major Roads Medium Health 0.68 District Roads Low Aquaculture Area (Km2) 305.53 137.14 212.69 249.18 (Doctors/1000p.) Transport Settlements & Transport Hubs Low Residential & Commercial Land (Km2) 0.23 0.08 0.15 0.19 Ethnicity (% 99.1/0.9

Kinh/non-Kinh)

32 .

CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

Population: An increasing area of the district will be Vulnerability Profile subject to inundation affecting greater numbers of Population families in the future. 25 20 Poverty: An intermediate % of poor households and 15 good access to health and education ameliorates Settlements & 10 Poverty the effects of increased inundation. Transport 5 Livelihoods: The major increase in the area flooded - occurs in 2030 increasing vulnerability. Energy & Industry: The level of industrialisation

means there is low vulnerability in this sector. Energy & Livelihoods Industry Settlements & Transport: A moderate urban Current 2030 A2 2050 A2 population and poor sewage connections leads to high vulnerability that increases with extensive flooding in the future.

Exposure, Risk and Control Elements

Exposure and Risk Time Period Inundation Salinity Storm Surge Hazard Current 47 100 0 2030 70 100 0 (% of Total Area) 2050 82 100 0 Current 6 10 0 Risk Rating 2030 9 10 0 2050 9 10 0 < 5 Low risks 5 – 12 Medium risks See Table 12 pg. 16 for detailed descriptions. While exposure to flooding is currently moderate it is projected to increase with an associated increase in risk. The district has complete exposure to salinity with medium risk.

Control Elements

Infrastructure Flooding Salinity Storm Surge Agricultural •• • • Major Industry •• • • Major Energy •• • • Urban •• •• • Transport • • •

• Adequate •• Change-long term ••• Improvement-medium term •••• Rehabilitation urgent The overall control measures are good across all of the sectors. Improvements against flooding will be required for most sectors and against salinity in the urban sector in the long term. These are mainly related to; dyke strengthening and improvements to sluice gates and improvements in aquaculture techniques and calendars in order to account for climate change, raising structures above flood levels and improving water and sanitation.

33

Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

Dam Doi Vulnerability Index Ranking Total Area (Ha) Population Total Population High 90,000.0 250000 80,000.0

Population Density Medium 70,000.0 200000 Average Family Size High 60,000.0 150000 Number of Households Medium 50,000.0 40,000.0 Population at working age High 100000 30,000.0 Population Average Natural Population 20,000.0 50000 Growth Rate Low 10,000.0 Annual Average Income per Low - 0 Dam U Minh Ngoc Tran Thoi Nam Phu Cai Ca Mau Ca Mau Tran Dam Thoi Cai Phu Tan U Minh Ngoc Nam Capita Doi Hien Van Binh Can Tan Nuoc Van Doi Binh Nuoc Hien Can Thoi Thoi Number of Poor Households High % of Poor Households High

Number of Teachers High Population Density (Persons/Ha) Poor Households (%) Poverty Number of Doctors Medium 10 25 Agricultural Land per person Medium 9 % Ethnic Households Medium 8 20 7 Number of Rural Households High 6 15 Number of Livelihood Streams Medium 5

Streams Employing > 10,000 or 4 10 3 producing >250 Billion VND Low 2 5 Administrative Center Dam Doi Average Annual GDP per 1 0 0

Livelihoods Household Low Ca Mau Cai Tran Phu Tan Dam Doi Thoi Nam U Minh Ngoc U Minh Ngoc Dam Doi Tran Cai Nam Phu Tan Thoi Ca Mau Land Area (ha) 83,415 Rice Crop Land per Person Medium Nuoc Van Binh Can Hien Hien Van Nuoc Can Binh Thoi Thoi Aquaculture Land per Person Medium Households reliant on Industry Population 182,332 Average Annual GDP per High Average Income per Capita (VND) Land Use

Household contributed by Medium 60000000 Forest Population Density 2.19 Industry 11% Unused 50000000 1% Urban (person/ha) Households Connected to High 1% National Grid 40000000 No. of Households 39,724 Length of High/Medium High 30000000

Voltage Power Lines 20000000 Average Family Size 4.59 Number of Power Plants/High Medium 10000000 Energy & IndustryEnergy & Voltage Substations Agriculture 87% % off-farm Income Low 0 Average Annual 18,156,000 VND Ca Mau Nam Dam Phu Cai Ngoc Tran U Minh Thoi Number of Factories Low Household Income Can Doi Tan Nuoc Hien Van Binh Thoi Number of Different Industries Low GDP contribution 435,111 VND Urban Population Low

from Industry (HH) Urban Households Low Inundation Total 2000 2030 2050 Unemployment Rate 19.1% Urban Area High % Urban Low Roads (Km) 190.1 22.6 45.9 68.0 Education 7.1 Sewer/Septic Tank Medium Power (Km) 578 84 196 266 (Teachers/1000p.) Water Supply Medium 2 Health 0.37 Major Waterways - Agriculture Area (Km ) 110.17 15.33 31.73 41.02 (Doctors/1000p.) Major Roads Medium 2 District Roads High Aquaculture Area (Km ) 607.86 57.27 148.94 200.49

Ethnicity (% Kinh/ 96.4/3.6 Transport Settlements & Transport Hubs High Residential & Commercial Land (Km2) - - - - non-Kinh)

34 .

CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

Population: A large family size and a relatively high population mean the initially moderate Vulnerability Profile Population vulnerability increases in the future due to the 30 increased exposure to inundation and storm surge. 20 Poverty: A high number of poor household is Settlements & 10 Poverty only slightly ameliorated by a moderate access Transport to health and education. Low population growth reduces the effect of increases in - inundation by 2050. Livelihoods: A high number of income streams reduce the current vulnerability, but the high Energy & population decreases the availability of land for Livelihoods primary production thus increasing Industry Current 2030 A2 2050 A2 vulnerability. Energy & Industry: While industry has a low contribution to the district economy the high population means that a large number of households are reliant on a few industries Combined with a poor electrical connection rate but a large amount of electrical infrastructure potentially effected by inundation, salinity and storm surge this vulnerability increases in the future. Settlements & Transport: A low urban population and protection of the urban centre from inundation and storm surge results in little change in vulnerability in the future. Exposure, Risk and Control Elements Exposure and Risk Time Period Inundation Salinity Storm Surge Hazard Current 13 100 0 2030 28 100 0 (% of Total Area) 2050 36 100 1 Current 3 10 4 Risk Rating 2030 6 10 4 2050 6 10 4 < 5 Low risks 5 – 12 Medium risks See Table 12 pg. 16 for detailed descriptions. The district has complete exposure to salinity and while exposure to flooding is currently low it is projected to increase. The risk from saline intrusion is high and the risk from inundation increases in 2030. Exposure to storm surge is projected to increase by 2050. Storm surge along the coast may cause extensive damage. Storm surge risk is low due to it being confined to the coast.

Control Elements Infrastructure Flooding Salinity Storm Surge Agricultural •• • ••• Major Industry •• • • Major Energy •• • •• Urban •• •• ••• Transport • • •• • Adequate •• Change-long term ••• Improvement-medium term •••• Rehabilitation urgent All sectors will require improvements in flooding control measures in the long term. Improvements in the control measures for the agricultural infrastructure are required in the medium to long term to protect from storm surge. Protection measures required are in the areas of; dyke strengthening and improvements to sluice gates, improved crop handling and processing as well as in aquaculture techniques and calendars in order to account for climate change. In the urban sector improvements are required in measures to control storm surge in the medium term and to control flooding and salinity in the long term. Control measures to protect the energy and transport sectors from storm surge will also be required in the long term.

35

Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

Vulnerability Index Ranking Nam Can Total Area (Ha) Population Total Population Low 90,000.0 250000 80,000.0 Population Density Low 70,000.0 200000 Average Family Size Low 60,000.0 150000 Number of Households Low 50,000.0 40,000.0 Population at working age Low 100000

Population Average Natural Population 30,000.0 20,000.0 50000 Growth Rate Medium 10,000.0 Annual Average Income per - 0 Dam U Minh Ngoc Tran Thoi Nam Phu Cai Ca Mau Ca Mau Tran Dam Thoi Cai Phu Tan U Minh Ngoc Nam Capita Low Doi Hien Van Binh Can Tan Nuoc Van Doi Binh Nuoc Hien Can

Number of Poor Households Low Thoi Thoi

% of Poor Households Medium Number of Teachers Medium Poverty Number of Doctors Low Population Density (Persons/Ha) Poor Households (%) Agricultural Land per person Medium 10 25 9 % Ethnic Households Medium 8 20 Number of Rural Households Low 7 Number of Livelihood Streams Low 6 15 5 Streams Employing > 10,000 or 4 10 producing >250 Billion VND Medium 3 Administrative Nam Can Average Annual GDP per 2 5 Center 1

Livelihoods Household Low 0 0 Land Area (ha) 50,789 Rice Crop Land per Person Low Ca Mau Cai Tran Phu Tan Dam Doi Thoi Nam U Minh Ngoc U Minh Ngoc Dam Doi Tran Cai Nam Phu Tan Thoi Ca Mau Nuoc Van Binh Can Hien Hien Van Nuoc Can Binh Aquaculture Land per Person Medium Thoi Thoi Households reliant on Industry Medium Population 66,261 Average Annual GDP per Average Income per Capita (VND) Land Use Household contributed by Low Population Density 1.30 Industry 60000000 1% 2% Households Connected to 50000000 (person/ha) National Grid Low 40000000 No. of Households 16,565 Length of High/Medium Voltage Agriculture 30000000 Forest Power Lines Medium 41% 20000000 Average Family Size 4.00 Number of Power Plants/High 56% Urban Energy & IndustryEnergy & Voltage Substations Low 10000000 Unused % off-farm Income Low 0 Average Annual 22,656,000 VND Number of Factories Medium Ca Mau Nam Dam Phu Cai Ngoc Tran U Minh Thoi Household Income Can Doi Tan Nuoc Hien Van Binh Number of Different Industries Medium Thoi

GDP contribution 175,584 VND Urban Population Medium

from Industry (HH) Urban Households Medium Urban Area Low Inundation Total 2000 2030 2050 Unemployment 2.5% % Urban High Roads (Km) 63.1 27.1 31.7 33.6 Rate Sewer/Septic Tank Low Education 8.6 Water Supply Low Power (Km) 364 122 160 194 Major Waterways - (Teachers/1000p.) 2 Major Roads Low Agriculture Area (Km ) 31.82 14.68 17.69 20.50 Health 0.54 District Roads Medium 2 (Doctors/1000p.) Aquaculture Area (Km ) 369.02 107.79 153.95 199.67 Settlements & Transport Settlements & Transport Hubs Low 2 Ethnicity (% 96.7/3.3 Residential & Commercial Land (Km ) 1.13 0.35 0.55 0.56 Kinh/non-Kinh)

36 .

CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

Population: A low population growth rate Vulnerability Profile reduces the vulnerability in this sector but an Population increase in inundation from 36 to 62% and 20 exposure to storm surge increases vulnerability 15 in the future. 10 Settlements & Poverty: A low number of poor and ethnic Poverty Transport 5 households results in low vulnerability. - Livelihoods: A range of off farm income sources ameliorates the effects of a high rural population but vulnerability increases as the

area affected by inundation and storm surge Energy & Livelihoods increases. Industry Current 2030 A2 2050 A2 Energy & Industry: A high reliance of household incomes on industry combined with a low rate of connection to the national grid means that vulnerability in this sector also increases. Settlements & Transport: A low urban population and protection of the urban centre from inundation and storm surge means that current vulnerability is low. Vulnerability increases with exposure.

Exposure, Risk and Control Elements

Exposure and Risk

Ngoc Hien Time Period Inundation Salinity Storm Surge Hazard Current 22 100 60 2030 29 100 90 (% of Total Area) 2050 39 100 100 Current 3 10 8 Risk Rating 2030 6 10 10 2050 6 10 10

< 5 Low risks 5 – 12 Medium risks See Table 12 pg. 16 for detailed descriptions. Exposure to salinity is complete and while exposure to flooding and is moderate it is projected to increase. The risk from saline intrusion is high and the risk from inundation is projected to increase by 2050. Storm surge along the coast may cause extensive and widespread damage. Control Measures

Infrastructure Flooding Salinity Storm Surge Agricultural •• •• •••• Major Industry • • •••• Major Energy •• • •••• Urban •• •• •••• Transport • • ••••

• Adequate •• Change-long term ••• Improvement-medium term •••• Rehabilitation urgent The high exposure to storm surge requiring improvements in protection across all of the sectors in the medium to long term. The agriculture sector will require improvements in the control measures in the medium to long term in the areas of; dyke strengthening and improvements to sluice gates, improved crop handling and processing as well as in aquaculture techniques and calendars in order to account for climate change. The control measures in the urban areas will also require improvements in the long term to protect the town infrastructure from flooding and salinity. The energy infrastructure will also require protection from salinity in the long term.

37

Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

Ngoc Hien Vulnerability Index Ranking Total Area (Ha) Population Total Population Low 90,000.0 250000 80,000.0

Population Density Low 70,000.0 200000 Average Family Size Low 60,000.0 150000 Number of Households Low 50,000.0 40,000.0 Population at working age Low 100000 30,000.0 Population Average Natural Population 20,000.0 50000 Growth Rate High 10,000.0 Annual Average Income per - 0 Dam U Minh Ngoc Tran Thoi Nam Phu Cai Ca Mau Ca Mau Tran Dam Thoi Cai Phu Tan U Minh Ngoc Nam Capita Medium Doi Hien Van Binh Can Tan Nuoc Van Doi Binh Nuoc Hien Can Thoi Thoi Number of Poor Households Medium % of Poor Households High

Number of Teachers High Population Density (Persons/Ha) Poor Households (%) Poverty Number of Doctors High 10 25 Agricultural Land per person Low 9 % Ethnic Households Low 8 20 7 Number of Rural Households Low 6 15 Number of Livelihood Streams Medium 5

Streams Employing > 10,000 or 4 10 3 producing >250 Billion VND Low 2 5 Administrative Center Viên An Đông Average Annual GDP per 1 0 0

Livelihoods Household Medium Ca Mau Cai Tran Phu Tan Dam Doi Thoi Nam U Minh Ngoc U Minh Ngoc Dam Doi Tran Cai Nam Phu Tan Thoi Ca Mau Rice Crop Land per Person High Nuoc Van Binh Can Hien Hien Van Nuoc Can Binh Land Area (ha) 73,517 Thoi Thoi Aquaculture Land per Person Low Households reliant on Industry Medium Population 78,420 Average Annual GDP per Average Income per Capita (VND) Land Use Household contributed by Low 60000000 Unused, 5720.5 Population Density 1.07 Industry 50000000 (person/ha) Households Connected to National Grid Low 40000000 No. of Households 19,221 Length of High/Medium Voltage 30000000 Agriculture, Power Lines Low 28917.2 Average Family Size 4.08 20000000 Number of Power Plants/High 10000000 Forest, Energy & IndustryEnergy & Voltage Substations Low 39078.3 Average Annual 13,500,000 VND % off-farm Income Low 0 Ca Mau Nam Dam Phu Cai Ngoc Tran U Minh Thoi Household Income Number of Factories Low Can Doi Tan Nuoc Hien Van Binh Number of Different Industries Medium Thoi GDP contribution from 363,971 VND Urban Population Low Industry (HH) Urban Households Low Inundation Total 2000 2030 2050 Unemployment Rate 4.4% Urban Area Low % Urban Low Roads (Km) 72.0 8.3 14.1 25.7 Education 5.9 Sewer/Septic Tank Medium Power (Km) 245 44 55 86 (Teachers/1000p.) Water Supply High Agriculture Area (Km2) 0.30 0.20 0.20 0.22 Major Waterways - 2 Health 0.3 Major Roads Low Aquaculture Area (Km ) 421.71 59.82 90.40 136.96 (Doctors/1000p.) District Roads High Residential & Comercial Land (Km2) 0.00 0.08 0.09 0.19 Ethnicity (% Kinh/ non- 97.3/2.7 Transport Settlements & Transport Hubs Medium Kinh)

38 .

CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

Population: A small low density population Vulnerability Profile means leads to low vulnerability despite Population exposure to salinity and increasing exposure to 30 storm surge. Poverty: A very low income and high number of 20

poor household and limited access to health and Settlements & 10 Poverty education lead to increasing vulnerability as Transport exposure to inundation and storm surge - increases. The very high population growth increases the effect. Livelihoods: A range of income streams and availability of land for aquaculture reduce the Energy & Livelihoods effect of the high population growth on Industry vulnerability. Current 2030 A2 2050 A2 Energy & Industry: A moderate reliance on only a few industries increases vulnerability as the population grows and inundation and storm surge increase in the future. Settlements & Transport: A low and rural population reduces the increase in vulnerability in the future.

Exposure, Risk and Control Elements

Exposure and Risk

Ngoc Hien Time Period Inundation Salinity Storm Surge Hazard Current 22 100 60 2030 29 100 90 (% of Total Area) 2050 39 100 100 Current 3 10 8 Risk Rating 2030 6 10 10 2050 6 10 10 < 5 Low risks 5 – 12 Medium risks See Table 12 pg. 16 for detailed descriptions. This island district has a complete exposure to salinity and high exposure to storm surge that is expected to increase to 100%. Exposure to flooding is moderate. The risk from saline intrusion is at the high end of the

medium ranking and the risk from inundation increases in 2030. The low elevation and extensive coastline means that the exposure to storm surge along the coast may cause extensive and widespread damage and the risk from storm surge increases in 2030.

Control Measures All sectors require urgent upgrades to the control measures to protect from storm surge. The overall resilience is low Infrastructure Flooding Salinity Storm Surge and some improvement to controls in the agriculture sector for flooding and salinity are required in the long term in Agricultural •• •• •••• the areas of; dyke strengthening and improvements in aquaculture techniques and calendars in order to account for Major Industry • • •••• climate change. As well as protection from storm surge, the urban sector requires and improvements in control Major Energy •• • •••• measures to protect the town infrastructure from flooding and salinity in the long term. Required improvements are Urban •• •• •••• mainly related to raising structures above flood levels and improving water and sanitation. Resilience of the industry Transport • • •••• and transport sectors to salinity and flooding is good and control measures for these hazards are considered to be • Adequate •• Change-long term ••• Improvement-medium term •••• Rehabilitation urgent adequate.

39

Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

Phu Tan Vulnerability Index Ranking Total Area (Ha) Population Total Population Medium 90,000.0 250000 80,000.0

Population Density Medium 70,000.0 200000 Average Family Size High 60,000.0 150000 Number of Households Low 50,000.0 40,000.0 Population at working age Medium 100000 30,000.0 Population Average Natural Population 20,000.0 50000 Growth Rate Low 10,000.0 Annual Average Income per - 0 Dam U Minh Ngoc Tran Thoi Nam Phu Cai Ca Mau Ca Mau Tran Dam Thoi Cai Phu Tan U Minh Ngoc Nam Capita Medium Doi Hien Van Binh Can Tan Nuoc Van Doi Binh Nuoc Hien Can Thoi Thoi Number of Poor Households Low % of Poor Households Low

Number of Teachers Low Population Density (Persons/Ha) Poor Households (%) Poverty Number of Doctors High 10 25 Agricultural Land per person Medium 9 % Ethnic Households Low 8 20 7 Number of Rural Households Medium 6 15 Number of Livelihood Streams Medium 5 4 10 Streams Employing > 10,000 or 3 producing >250 Billion VND Medium 2 5 Administrative Center Cai Doi Vam Average Annual GDP per 1 Household Medium 0 0 Ca Mau Cai Tran Phu Tan Dam Doi Thoi Nam U Minh Ngoc U Minh Ngoc Dam Doi Tran Cai Nam Phu Tan Thoi Ca Mau Livelihoods Rice Crop Land per Person High Nuoc Van Binh Can Hien Hien Van Nuoc Can Binh Land Area (ha) 46,433 Thoi Thoi Aquaculture Land per Person Medium

Population 105,599 Households reliant on Industry Low Average Income per Capita (VND) Land Use Average Annual GDP per 60000000

Population Density 2.27 Household contributed by High 50000000 Industry Forest, (person/ha) Households Connected to 40000000 6533 No. of Households 23,537 National Grid Low 30000000 Length of High/Medium Voltage 20000000 Power Lines Low Average Family Size 4.49 10000000 Agriculture, Number of Power Plants/High 34685 0 Energy & IndustryEnergy & Voltage Substations Low Ca Mau Nam Dam Phu Cai Ngoc Tran U Minh Thoi Average Annual 15,490,000 % off-farm Income High Can Doi Tan Nuoc Hien Van Binh Household Income Thoi Number of Factories High Number of Different Industries Medium GDP contribution from 1,572,365 VND Industry (HH) Urban Population Medium Urban Households Medium Inundation Total 2000 2030 2050 Unemployment Rate 3% Urban Area Low % Urban Medium Roads (Km) 80.4 38.4 46.6 61.5 Education 10.4 Sewer/Septic Tank High (Teachers/1000p.) Power (Km) 287 120 147 189 Water Supply Low 2 Health (Doctors/1000p.) 0.14 Major Waterways - Agriculture Area (Km ) 54.17 21.51 26.72 34.51 Major Roads Low Aquaculture Area (Km2) 298.62 102.66 136.04 185.93 District Roads Medium Ethnicity (% Kinh/non- 95/5 2 Kinh) Transport Settlements & Transport Hubs Low Residential & Commercial Land (Km ) 0.49 0.0948 0.1086 0.1595

40 .

CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

Population: A low population growth rate reduces the vulnerability in this sector but an increase in Vulnerability Profile Population inundation from 36 to 62% and exposure to storm 20 surge increases vulnerability in the future. 15 Poverty: A low number of poor and ethnic 10 households results in low vulnerability. Settlements & Poverty Livelihoods: A range of off farm income sources Transport 5 ameliorates the effects of a high rural population - but vulnerability increases as the area affected by inundation and storm surge increases. Energy & Industry: A high reliance of household Energy & incomes on industry combined with a low rate of Livelihoods connection to the national grid means that Industry vulnerability in this sector also increases. Current 2030 A2 2050 B2 Settlements & Transport: A low urban population and protection of the urban centre from inundation and storm surge means that current vulnerability is low. Vulnerability increases with exposure. Exposure, Risk and Control Elements Exposure and Risk Time Period Inundation Salinity Storm Surge Hazard Current 36 100 1 2030 46 100 1 (% of Total Area) 2050 62 100 2 Current 6 10 4 Risk Rating 2030 6 10 4 2050 6 10 4 < 5 Low risks 5 – 12 Medium risks See Table 12 pg. 16 for detailed descriptions. While exposure to flooding and is moderate it is projected to increase but the risk remains medium. Exposure to salinity is complete and the risk is at the high end of the medium ranking. The low elevation and extensive coastline means that the exposure and risk from storm surge increase.

Control Measures Infrastructure Flooding Salinity Storm Surge Agricultural •• • ••• Major Industry ••• • •• Major Energy • • •• Urban •• •• ••• Transport • • ••

• Adequate •• Change-long term ••• Improvement-medium term •••• Rehabilitation urgent The district is exposed to storm surge requiring improvements in protection across most of the sectors in the medium to long term. Improvements in the flood control measures for agricultural infrastructure are required in the medium to long term in the areas of; dyke strengthening and improvements to sluice gates, improved aquaculture techniques and calendars in order to account for climate change. The overall resilience in the energy and transport sectors is very good with some improvements in control measures for storm surge required in the medium term. In the industry sector some improvements in control measures for flooding are required in the short term and for storm surge in the medium term. These are mainly related to raising industry structures above flood levels and strengthening sea dykes. Some improvements in control measures for flooding and salinity required in the medium term and for storm surge in the short term. These are mainly related to; raising urban structures above flood levels, improving water and sanitation, improving urban drainage and strengthening sea dykes.

41

Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

Thoi Binh Vulnerability Index Ranking Total Area (Ha) Population Total Population Medium 90,000.0 250000 80,000.0

Population Density Medium 70,000.0 200000 Average Family Size High 60,000.0 150000 Number of Households Medium 50,000.0 40,000.0 Population at working age Medium 100000 30,000.0 Population Average Natural Population 20,000.0 50000 Growth Rate Low 10,000.0 Annual Average Income per - 0 Dam U Minh Ngoc Tran Thoi Nam Phu Cai Ca Mau Ca Mau Tran Dam Thoi Cai Phu Tan U Minh Ngoc Nam Capita High Doi Hien Van Binh Can Tan Nuoc Van Doi Binh Nuoc Hien Can Thoi Thoi Number of Poor Households Medium % of Poor Households Low Number of Teachers Medium Population Density (Persons/Ha) Poor Households (%) Poverty Number of Doctors Medium 10 25 Agricultural Land per person Low 9 % Ethnic Households High 8 20 7 Number of Rural Households High 6 15 Number of Livelihood Streams High 5

Streams Employing > 10,000 or 4 10 3 producing >250 Billion VND Low 2 5 Administrative Center Thoi Binh Average Annual GDP per 1 0 0

Livelihoods Household High Ca Mau Cai Tran Phu Tan Dam Doi Thoi Nam U Minh Ngoc U Minh Ngoc Dam Doi Tran Cai Nam Phu Tan Thoi Ca Mau Rice Crop Land per Person Medium Nuoc Van Binh Can Hien Hien Van Nuoc Can Binh Land Area (ha) 64,131 Thoi Thoi Aquaculture Land per Person Low Households reliant on Industry Low Population 140,600 Average Annual GDP per Land Use Average Income per Capita (VND) Household contributed by Medium 60000000 Forest, 1,927 Industry Population Density 2.19 Households Connected to 50000000 (person/ha) National Grid Medium 40000000

No. of Households 31,885 Length of High/Medium Voltage 30000000 Power Lines Medium 20000000 Number of Power Plants/High

Average Family Size 4.40 IndustryEnergy & Voltage Substations Low 10000000 Agriculture, 54,894 % off-farm Income Medium 0 Number of Factories Low Ca Mau Nam Dam Phu Cai Ngoc Tran U Minh Thoi Average Annual 10,080,000 VND Can Doi Tan Nuoc Hien Van Binh Household Income Number of Different Industries Medium Thoi Urban Population Medium GDP contribution 786,358 VND Urban Households Medium Inundation Total 2000 2030 2050 from Industry (HH) Urban Area Medium Unemployment Rate 9.5% % Urban Medium Roads (Km) 117.2 7.0 20.7 37.4 Sewer/Septic Tank High Education 8.1 Water Supply Medium Power (Km) 352 28 71 117 Major Waterways - (Teachers/1000p.) 2 Major Roads High Agriculture Area (Km ) 257.72 7.97 32.35 72.61 Health 0.31 District Roads Low 2 (Doctors/1000p.) Transport Settlements & Transport Hubs Low Aquaculture Area (Km ) 294.92 19.82 64.28 112.60 Ethnicity (% Kinh/ 94.1/5.9 Residential & Commercial Land (Km2) 4.01 0.09 0.36 1.04 non-Kinh)

42 .

CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

Population: Moderate population density and growth Vulnerability Profile rate combined with low current exposure to Population inundation results in low vulnerability. An increased 20 exposure to flooding increases vulnerability. 15 Poverty: A low number of poor households and 10 reasonable access to health and education leads to low Settlements & Poverty vulnerability. A high number of ethnic households Transport 5 increase vulnerability as inundation increases. - Livelihoods: A high number of rural households and a low income are offset by a large number of potential income sources and availability of land for aquaculture. A high growth rate and increasing impacts of Energy & Livelihoods inundation increases vulnerability in the future. Industry Energy & Industry: A moderate reliance on industry for Current 2030 A2 2050 A2 income and good connection to the national grid leads to low vulnerability which increases with exposure to inundation. Settlements & Transport: A low urban population, limited infrastructure and protection of the urban centre from inundation and storm surge means that vulnerability is low.

Exposure, Risk and Control Elements

Exposure and Risk Time Period Inundation Salinity Storm Surge Hazard Current 6 100 0 2030 19 100 0 (% of Total Area) 2050 35 100 0 Current 3 10 0 Risk Rating 2030 3 10 0 2050 6 10 0 < 5 Low risks 5 – 12 Medium risks See Table 12 pg. 16 for detailed descriptions. Exposure to flooding is very low but is projected to increase and exposure to salinity is complete The risk from saline intrusion risk is at the high end of the medium ranking and the risk from inundation increases from low to moderate.

Control Measures

Infrastructure Flooding Salinity Storm Surge Agricultural ••• •• • Major Industry •• • • Major Energy • • • Urban •• •• • Transport • • •

• Adequate •• Change-long term ••• Improvement-medium term •••• Rehabilitation urgent Improvements in flood control measures for agriculture and aquaculture infrastructure are required in the medium term in the areas of; dyke strengthening and improvements to sluice gates, improved crop handling and processing, improved crop handling and processing, improvements in rice varieties and cultivation methods as well as in aquaculture techniques and calendars. The overall resilience in the energy and industry sectors is very good and control measures are considered adequate. Some improvements in the control measures against flooding are required for the transport sectors in the long term. These are mainly related to raising structures above flood water levels. The urban sector requires improvements in the control measures against flooding and salinity in the medium term. These are; raising structures above flood levels, improved urban drainage and improved water and sanitation.

43

Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

Tran Van Thoi Vulnerability Index Ranking Total Area (Ha) Population Total Population High 90,000.0 250000 80,000.0 Population Density High 70,000.0 200000

Average Family Size Medium 60,000.0 150000 Number of Households High 50,000.0 40,000.0 Population at working age High 100000 30,000.0

Population Average Natural Population 20,000.0 50000 Growth Rate Low 10,000.0 - 0 Annual Average Income per Capita Dam U Minh Ngoc Tran Thoi Nam Phu Cai Ca Mau Ca Mau Tran Dam Thoi Cai Phu Tan U Minh Ngoc Nam Doi Hien Van Binh Can Tan Nuoc Van Doi Binh Nuoc Hien Can Number of Poor Households High Thoi Thoi

% of Poor Households High Number of Teachers Medium Population Density (Persons/Ha) Poor Households (%) Number of Doctors Low

Poverty 10 25 Agricultural Land per person High 9 % Ethnic Households High 8 20 High 7 6 15 Number of Rural Households High 5 Number of Livelihood Streams High 4 10

Streams Employing > 10,000 or 3 producing >250 Billion VND Low 2 5 Administrative Tran Van Thoi 1 Average Annual GDP per 0 0 Center Ca Mau Cai Tran Phu Tan Dam Doi Thoi Nam U Minh Ngoc U Minh Ngoc Dam Doi Tran Cai Nam Phu Tan Thoi Ca Mau

Livelihoods Household Low Nuoc Van Binh Can Hien Hien Van Nuoc Can Binh Land Area(ha) 70,942 Rice Crop Land per Person Low Thoi Thoi Aquaculture Land per Person High Households reliant on Industry High Population 187,132 Average Income per Capita (VND) Land Use Average Annual GDP per Urban, 835.5 60000000 Unused, 55.4 Household contributed by Industry High Forest, 7,708 Population Density 2.64 Households Connected to National 50000000 (person/ha) Grid 40000000 Length of High/Medium Voltage High 30000000 No. of Households 44,555 Power Lines Number of Power Plants/High High 20000000 Average Family Size 4.20 Voltage Substations 10000000 Agriculture,

Energy & IndustryEnergy & 53,215 % off-farm Income Medium 0 Number of Factories High Ca Mau Nam Dam Phu Cai Ngoc Tran U Minh Thoi Average Annual 13,000,000 VND Can Doi Tan Nuoc Hien Van Binh Number of Different Industries Low Household Income Thoi Low GDP contribution 1,750,976 VND Urban Population High from Industry (HH) Urban Households High Inundation Total 2000 2030 2050 Urban Area High Unemployment Rate 4% Roads (Km) 124.2 44.1 64.8 93.1 % Urban Medium Sewer/Septic Tank High Power (Km) 581 278 378 494 Education 10.9 Water Supply High 2 (Teachers/1000p.) Major Waterways - Agriculture Area (Km ) 511.32 229.76 230.17 230.27 2 Health 0.13 Major Roads High Aquaculture Area (Km ) 80.71 34.12 101.42 188.74 District Roads Medium (Doctors/1000p.) 2 Settlements & Transport Settlements & Transport Hubs medium Residential & Commercial Land (Km ) 0.06 0.01 0.68 1.60 Ethnicity (% 95/5 Kinh/non-Kinh)

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CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

Population: With high population and inward Vulnerability Profile migration, a coastal town subject to storm surge and Population with 46% of the areas subject to inundation the initial 40 high vulnerability increases as inundation and storm 30 surge affect larger areas. 20 Poverty: A lower average income, more poor Settlements & Poverty households and less access to health than the other Transport 10 urbanised districts results in a high vulnerability. The - concentration of households in towns reduces the effect of increased inundation. Livelihoods: A high number of rural households and a moderate income increase vulnerability as the area Energy & Livelihoods impacted by inundation increases to 80% in 2050. Industry Energy & Industry: A large contribution to GDP from Current 2030 A2 2050 A2 industry and a lot of energy infrastructure leads to high vulnerability which increases with inundation and storm surge. Settlements & Transport: A high urban population in two towns, one of which is on the coast, leads to high vulnerability limited infrastructure and protection of the urban centre from inundation and storm surge means that vulnerability is low, which increases as inundation and storm surge affect larger areas. Exposure, Risk and Control Elements Exposure and Risk Time Period Inundation Salinity Storm Surge Hazard Current 42 100 1 2030 58 100 1 (% of Total Area) 2050 79 100 1 Current 6 10 4 Risk Rating 2030 6 10 4 2050 9 10 4 < 5 Low risks 5 – 12 Medium risks See Table 12 pg. 16 for detailed descriptions. While exposure to flooding and is moderate it is projected to increase. Exposure to salinity is complete and exposure to storm surge may cause extensive damage particularly to industry on the coast. The risk from inundation increases by 2050 and the risk from saline intrusion is at the upper end of medium. Storm surge risk is low as it is confined to the coast.

Control Measures Infrastructure Flooding Salinity Storm Surge •• • ••• Agricultural Major Industry ••• • •••• Major Energy • • •• Urban •• •• •••• Transport • • •• • Adequate •• Change-long term ••• Improvement-medium term •••• Rehabilitation urgent The exposure to storm surge requires improvements in control measures across most of the sectors. The agriculture sector needs improvements in flood and storm surge control measures in the medium to long term in the areas of; dyke strengthening and improvements to sluice gates, improved crop handling and processing as well as in aquaculture techniques and calendars. The control measures in the energy and transport sectors are good with some improvements in storm surge required in the long term. The industry sector requires immediate improvements in storm surge control measures and improvements in flood control measures in the medium term. These are mainly related to raising industry structures above flood levels and strengthening sea dykes. The urban sector needs immediate improvements in control measures for storm surge and in the medium term for flooding and salinity; strengthening sea dykes and protecting Song Doc town and port, raising urban structures above flood levels, improving water and sanitation, and improving urban drainage.

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Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

U Minh Vulnerability Index Ranking Total Area (Ha) Population Total Population Low 90,000.0 250000 80,000.0

Population Density Low 70,000.0 200000 Average Family Size Medium 60,000.0 150000 Number of Households Medium 50,000.0 40,000.0 Population at working age Low 100000 30,000.0 Population Average Natural Population 20,000.0 50000 Growth Rate Medium 10,000.0 Annual Average Income per High - 0 Dam U Minh Ngoc Tran Thoi Nam Phu Cai Ca Mau Ca Mau Tran Dam Thoi Cai Phu Tan U Minh Ngoc Nam Capita Doi Hien Van Binh Can Tan Nuoc Van Doi Binh Nuoc Hien Can Thoi Thoi Number of Poor Households High % of Poor Households High

Number of Teachers High Population Density (Persons/Ha) Poor Households (%) Poverty Number of Doctors Medium 10 25 Agricultural Land per person Low 9 % Ethnic Households High 8 20 7 Number of Rural Households Medium 6 15 Number of Livelihood Streams High 5

Streams Employing > 10,000 or 4 10 3 producing >250 Billion VND Low Administrative 2 5 U Minh Average Annual GDP per 1 Center 0 0

Livelihoods Household Medium Ca Mau Cai Tran Phu Tan Dam Doi Thoi Nam U Minh Ngoc U Minh Ngoc Dam Doi Tran Cai Nam Phu Tan Thoi Ca Mau Land Area (ha) Rice Crop Land per Person High Nuoc Van Binh Can Hien Hien Van Nuoc Can Binh 77,462 Thoi Thoi Aquaculture Land per Person Low Households reliant on Industry Medium Population 102,215 Average Annual GDP per Average Income per Capita (VND) Land Use Household contributed by Low 60000000 Urban, 825.9 Population Density 1.32 Industry (person/ha) 50000000 Households Connected to National Grid Medium 40000000 No. of Households 23,771 Length of High/Medium Voltage 30000000 Power Lines Medium Forest, Average Family Size 20000000 Agriculture, 4.30 34,352 Number of Power Plants/High 36,064 10000000 Energy & IndustryEnergy & Voltage Substations High Average Annual 10,500,000 VND % off-farm Income Low 0 Household Income Ca Mau Nam Dam Phu Cai Ngoc Tran U Minh Thoi Number of Factories Medium Can Doi Tan Nuoc Hien Van Binh GDP contribution Number of Different Industries Low Thoi 256,289 VND from Industry (HH) Urban Population Low Urban Households Low Unemployment Rate 2% Urban Area Medium Inundation Total 2000 2030 2050 % Urban Low Education 7 Roads (Km) 176.6 26.8 59.6 90.6 (Teachers/1000p.) Sewer/Septic Tank Low Water Supply Medium Power (Km) 143 27 53 81 Health 0.43 Major Waterways - 2 (Doctors/1000p.) Major Roads High Agriculture Area (Km ) 180.43 19.58 45.69 87.92 Ethnicity (% District Roads High Aquaculture Area (Km2) 126.15 16.19 39.13 70.81

95.4/4.6 Transport Settlements & Kinh/non-Kinh) Transport Hubs Low Residential & Commercial Land (Km2) 2.89 0.06 0.44 0.99

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CLIMATE CHANGE IMPACT AND ADAPTATION STUDY IN THE MEKONG DELTA (PART A ) – CA MAU ATLAS

Population: Vulnerability is low due to a low Vulnerability Profile population density and small area of current and Population projected inundation. 25 Poverty: A high number of poor households and low 20 GDP per head, a high number of ethnic households 15 Settlements & 10 and poor access to education lead to high vulnerability Poverty Transport which increases due exposure to inundation and storm 5 surge. The smaller area affected keeps this to fewer - than 18 in 2050. Livelihoods: A high rural population with low incomes is offset by a high number of possible income sources Energy & and available land. Exposure to inundation salinity and Livelihoods storm surge leads to high vulnerability in the future. Industry Current 2030 A2 2050 A2 Energy & Industry: With only a quarter of the population dependant on industry vulnerability is low. A large amount of energy infrastructure exposed to impacts increases vulnerability in the future. Settlements & Transport: A low urban population and only a moderate area affected means that vulnerability remains low. Exposure, Risk and Control Elements

Exposure and Risk Time Period Inundation Salinity Storm Surge Hazard Current 9 100 0 2030 22 100 1 (% of Total Area) 2050 42 100 1 Current 3 10 4 Risk Rating 2030 3 10 4 2050 6 10 4 < 5 Low risks 5 – 12 Medium risks See Table 12 pg. 16 for detailed descriptions. Exposure to flooding and is low and is projected to increase. Exposure to salinity is complete. Exposure to storm surge along the coast may cause extensive damage. The risk from inundation increases from low to moderate by 2050 and the risk from saline intrusion is at the upper end of medium. Storm surge risk is low as it is confined to the coast.

Exposure and Risk Infrastructure Flooding Salinity Storm Surge Agricultural •• •• ••• Major Industry •••• • • Major Energy •••• • •• Urban •• •• ••• Transport • • •• • Adequate •• Change-long term ••• Improvement-medium term •••• Rehabilitation urgent Improvements in the control measures for agricultural infrastructure are required in the medium to long term in the areas of; dyke strengthening and improvements to sluice gates, improved crop handling and processing as well as in aquaculture techniques and calendars. The industry and energy sectors require immediate improvements in control measures for protecting the energy generation and associated infrastructure from flooding. The measures are mainly related to raising structures above flood levels and strengthening sea dykes. The urban sector needs improvements in control measures for storm surge in the medium term and for flooding and salinity in the long term. These are mainly related to; strengthening sea dykes, raising urban structures above flood levels, improving water and sanitation, and improving urban drainage. The transport sector has good control measures in place with some improvements for storm surge required in the long term.

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Climate Change Impact and Adaptation Study in THE Mekong Delta (Part A ) – CA MAU ATLAS

About This Atlas

The Asian Development Bank (ADB) engaged Sinclair Knight Merz (SKM) in association with; the Center for Environmental Research (CENRE) under the Vietnam Institute of Meteorology, Hydrology and Environment (IMHEN); Acclimatise and University of Newcastle, Australia to undertake ‘Part A’ of the AusAID funded Climate Change Impact and Adaptation Study in the Mekong Delta (TA 7377 – VIE) that focuses on the two most southern ; Ca Mau and Kien Giang. This Atlas is one of two products that represent the culmination of Part A of the Climate Change Prediction and Impact Assessment study. The atlas presents information from two sources; modelling and GIS outputs from IMHEN; and intensive survey information and data from staff interviews provided through the Provincial Peoples Committee. The PPC and district departments contributed to the inception and mid project workshops. Each district in the province was considered in the scope of this assessment through stakeholder consultations and field surveys at all levels of government. This Atlas is a companion to the final report of Part A of the project: ‘Climate Change Vulnerability & Risk Assessment Study for Ca Mau and Kien Giang Provinces, Vietnam’. The atlas presents three key outputs: an identification of future climate conditions in the region; an assessment of the effects of future climate scenarios on natural, social, and economic systems in the region; and an analysis of the vulnerability of each district. It provides practical measures that provincial and district administrations can take to inform and strengthen their programs. It is expected that the maps of projected impacts of climate change and of present and projected vulnerability will be an important tool for policy makers at national, provincial and district level.

This atlas is based on an original idea by Peter Mackey and was compiled by Michael Russell, Ben Robinson, Sonya Sampson and Peter Mackay. GIS maps were produced by CENRE and Michael Russell. The information presented incorporates insights from the project report by Peter Mackay and Michael Russell and from working papers produced by the international assessment experts; Michael Russell, Ronny Venegas Carbonell, Ian Hamilton, Anthony Kiem and Frank Pool.

Cite as;

IMHEN, Ca Mau PPC (2011). Climate Change Impact and Adaptation Study in the Mekong Delta Ca Mau Atlas. Institute of Meteorology, Hydrology and Environment (IMHEN), Ha Noi, Vietnam.

Ca Mau Provincial Peoples Kien Giang Provincial Peoples Centre for Environmental Research Asian Development Bank Australian Government Overseas AID Sinclair Knight Merz Committee Committee (CENRE) Program Vietnam Institute of Meteorology, Hydrology and Environment

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