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Technical Assistance Consultant’s Report
Project Number: 40253-012
November 2013
Cambodia: Climate Change Impacts and Vulnerability
Assessments for Mondulkiri and Koh Kong Provinces
in Cambodia
(Financed by the Asian Development Bank)
Prepared by
J. Katzfey, R. Suppiah, P. Hoffmann and K.C. Nguyen
CSIRO, Marine & Atmospheric Research
Melbourne, Australia
X. Jiao and S. Poun
Department of Food and Resource Economics, University of Copenhagen
Copenhagen, Denmark
For: Asian Development Bank
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.)
Citation
Katzfey, J., Jiao, X., Suppiah, R., Hoffmann, P., Nguyen, K. C. and Poun, S. (2013) Climate Change Impacts
and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
Copyright and disclaimer
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.)
Important disclaimer
CSIRO advises that the information contained in this publication comprises general statements based on
scientific research. The reader is advised and needs to be aware that such information may be incomplete
or unable to be used in any specific situation. No reliance or actions must therefore be made on that
information without seeking prior expert professional, scientific and technical advice. To the extent
permitted by law, CSIRO (including its employees and consultants) excludes all liability to any person for
any consequences, including but not limited to all losses, damages, costs, expenses and any other
compensation, arising directly or indirectly from using this publication (in part or in whole) and any
information or material contained in it.
Climate Change Projections for Mondulkiri and Koh Kong Provinces in Cambodia
Contents
Figures ............................................................................................................................................. iii
Tables ............................................................................................................................................... v
Acknowledgements ........................................................................................................................ vii
Abbreviations ................................................................................................................................ viii
Project Overview .......................................................................................................................................... 1
Executive Summary -- Part 1 ........................................................................................................................ 3
1
Introduction to Part 1...................................................................................................................... 4
1.1 Key Climate Processes in Cambodia ..................................................................................... 4
1.2 Previous Climate Change Projections for Cambodia ............................................................ 7
2
Observational Datasets ................................................................................................................... 9
3
Global Climate Model Assessment................................................................................................ 10
4
GCM Selection from CMIP5 Simulations ....................................................................................... 15
5
Selection of Climatic Variables for Impact Studies ....................................................................... 16
6
Methodology ................................................................................................................................. 17
7
Results ........................................................................................................................................... 20
7.1 Temperature Changes......................................................................................................... 20
7.2 Rainfall Changes .................................................................................................................. 24
7.3 Extreme Temperatures ....................................................................................................... 26
7.4 Droughts ............................................................................................................................. 27
7.5 Extreme Rainfall, Consecutive Wet and Dry Days .............................................................. 32
7.6 Sea Level Changes ............................................................................................................... 35
8
Conclusions for Part 1 ................................................................................................................... 41
Executive Summary -- Part 2 ...................................................................................................................... 44
9
Introduction to Part 2.................................................................................................................... 45
10
Methodology ................................................................................................................................. 46
10.1 Literature Review ................................................................................................................ 46
10.2 Conceptual Framework and Key Terminology .................................................................... 47
10.3 Data Collection .................................................................................................................... 47
10.4 BCC Target Area and Selection of Survey Sites ................................................................... 48
11
Climate Change Vulnerability Assessment in Koh Kong ................................................................ 52
11.1 Climate Risks and Impacts on Local Livelihoods ................................................................. 52
11.2 Vulnerability of Key Livelihood Systems and Sectors ......................................................... 54
11.3 Adaptation Options and Priorities in Koh Kong .................................................................. 60
12
Climate Change Vulnerability Assessment in Mondulkiri ............................................................. 61
12.1 Climate Risks and Impacts on Local Livelihoods ................................................................. 61
12.2 Vulnerability of Key Livelihood Systems and Sectors in Mondulkiri ................................... 63
12.3 Adaptation Options and Priorities ...................................................................................... 70
i
13
Summary of Commune-Level Climate Change Vulnerability and Adaptation Priorities............... 71
References .................................................................................................................................................. 76
Appendix 1: Climate Change Vulnerability Matrix in Koh Kong ................................................................. 82
Appendix 2: Climate Change Vulnerability Matrix in Mondulkiri ............................................................... 85
ii
Climate Change Projections for Mondulkiri and Koh Kong Provinces in Cambodia
Figures
Figure 1. Schematic diagram showing the major climate drivers that influence the climate of Cambodia.
ITCZ: Intertropical Convergence Zone, EVS: East Vietnam Sea, ENSO: El Niño-Southern Oscillation
phenomenon. Source: Climate Change in Vietnam, 2013. .......................................................................... 5
Figure 2. Distribution of annual rainfall (mm) in Cambodia for the period 1981-2004. The Tonle Sap
River is to the west, and the Mekong River to the east. Source: Sao 2009. ................................................ 6
Figure 3 Names and boundaries of administrative provinces in Cambodia. Source: Atlas of Cambodia. ... 6
Figure 4. Comparison of CO2 concentrations (ppmv) from SRES (A1B, A1FI, A2, B1) and RCPs (3.0, 4.5,
6.0, 8.5) for the years 2000-2100. Source: Meinshausen, M. et al. (2011). ............................................... 10
Figure 5. Relationship between pattern correlation and RMSE values for annual mean sea level pressure
(top) and temperature (bottom) for the larger domain (60-160°E, 15°S-50°N) over Southeast Asia for
1975-2004. Note the best models tend to sit towards the top left corner and the worst models tend to
sit towards the bottom right. ..................................................................................................................... 11
Figure 6. Annual average global climate model temperature biases (°C) for Southeast Asia based on the
difference between model simulations and observed values for the period 1975-2004. ......................... 12
Figure 7. Relationship between pattern correlation and RMSE values for annual rainfall for ERA-Interim
(top) and CRU data (bottom) for the larger domain (60-160°E, 15°S-50°N) for 1975-2004. Note the best
models tend to sit towards the top left corner and the worst models tend to sit towards the bottom
right. ........................................................................................................................................................... 13
Figure 8. Annual rainfall bias (mm/day) between model simulations and ERA-Interim for the period
1975-2004................................................................................................................................................... 13
Figure 9. Seasonal rainfall bias (mm/day) between model simulations and observations for four seasons
for the period 1975-2004. (a) First Intermonsoon Season, (b) Southwest Monsoon Season, (c) Second
Intermonsoon Season and (d) Northeast Monsoon Season. ..................................................................... 14
Figure 10. CCAM grid used for downscaled simulations. Left: global, at 50 km, every 4th grid point;
Right: for South East Asia, at 10 km, every 2nd grid point. ....................................................................... 18
Figure 11. Projected increases by the end of this century in ensemble mean average annual temperature
(°C) relative to a 1980-1999 baseline for Southeast Asia from 24 global climate models for RCP 8.5. ..... 20
Figure 12. Ensemble mean of CCAM 10 km simulated maximum temperature increases (°C) for the 20
year period centred on 2055 relative to a 1980-1999 baseline for four seasons for RCP8.5. FIMS: First
Intermonsoon Season (April-May), SWMS: Southwest Monsoon Season (June-September), SIMS: Second
Intermonsoon Season (October-November) and NEMS: Northeast Monsoon Season (DecemberMarch). ....................................................................................................................................................... 21
Figure 13. Annual projected changes in rainfall (%) over Southeast Asia from an ensemble mean of 24
global climate models for the end of this century relative to the baseline period 1980-1999 under RCP
8.5. .............................................................................................................................................................. 24
Figure 14. Ensemble mean of CCAM-10 km simulated rainfall (mm/day) over Cambodia under RCP 8.5
for the 20 year period centred on 2055 relative to a 1980-1999 baseline for the four seasons. FIMS: First
Intermonsoon Season (April-May), SWMS: Southwest Monsoon Season (June-September), SIMS: Second
Intermonsoon Season and NEMS: Northeast Monsoon Season. ............................................................... 25
Figure 15. Ensemble mean of CCAM-simulated annual mean hot days (HDs, left) and number of days in a
heat wave based on the Heat Wave Duration Index (HWDI, right) for 1990 (top) and their change under
RCP 8.5 by 2055 (bottom) based upon 20 year averages. ......................................................................... 28
iii
Figure 16. Ensemble mean CCAM 10 km simulated average 3-month drought frequency (top left) and
duration in months (top right) for 1990, compared with their changes by 2055 (bottom) under RCP 8.5,
based upon 20 year averages. .................................................................................................................... 30
Figure 17. Ensemble mean CCAM 10 km simulation mean extreme rainfall indices for the baseline period
(20 years centred on 1990). Upper left: RX1 (mm/day); upper right: RX5 (mm/day); lower left:
consecutive wet days (CWDs, number of days); lower right: consecutive dry days (CDDs, number of
days). .......................................................................................................................................................... 33
Figure 18. Projected CCAM multi-model mean extreme rainfall indices under RCP 8.5 for the 20 year
period centred on 2055 relative to the baseline period. Upper left: RX1 (mm/day); upper right: RX5
(mm/day); lower left: consecutive wet days (CWDs, number of days); lower right: consecutive dry days
(CDDs, number of days). ............................................................................................................................. 34
Figure 19. Estimates of global mean sea-level rise from 1880 to 2012, updated from Church and White
(2011). ........................................................................................................................................................ 36
Figure 20. Map of stations with sea-level data near Cambodia. ................................................................ 37
Figure 21. Preliminary sea-level rise results (mm) for oceans near Cambodia under RCP 8.5 for the end of
the 21st century (2081-2100) compared to a 1986-2005 baseline. The star indicates the location of Vung
Tau, Vietnam, which is the sea-level station closest to Cambodia with data available. ............................ 39
Figure 22. Historical sea-level rise and future projections for Vung Tau, Vietnam from 1950 to 2100 (mm)
incorporating tide gauge and satellite data. The observed tide-gauge relative sea-level records (since the
late 1970s) are indicated in light blue, with the satellite record (since 1993) in green. Black lines are
multi-model mean projections of sea level for the RCP 8.5 and RCP 2.6 emissions scenarios, with the 5%
(lower) and 95% (upper) range of annual variability shown by the red and blue shaded regions from
1995 to 2100. Dashed lines are an estimate of interannual variability in sea level. The location of Vung
Tau is indicated by a star in Figure 21. ....................................................................................................... 39
Figure 23. Conceptual Framework of Climate Vulnerability. Source: Adapted from IPCC 2007............... 47
Figure 24. Data Collection and Survey Methods. ....................................................................................... 48
Figure 25. Biodiversity Conservation Corridors Initiative (BCI) and Communes in which village surveys
were conducted in Koh Kong...................................................................................................................... 50
Figure 26. Biodiversity Corridors and Communes in which village surveys were conducted in
Mondulkiri. ................................................................................................................................................. 51
Figure 27. Livelihood Strategy Portfolios of Surveyed BCC Communes in Koh Kong. Source: R-PPTA field
survey 2013. ............................................................................................................................................... 54
Figure 28. Climate Change Adaptation Capacity and Needs in Koh Kong. Source: R-PPTA field survey
2013. ........................................................................................................................................................... 60
Figure 29. Livelihood Strategy Portfolios of Surveyed BCC Communes in Mondulkiri. Source: R-PPTA field
survey 2013. ............................................................................................................................................... 64
Figure 30. Climate Change Adaptation Capacity and Needs in Mondulkiri. Source: R-PPTA field survey
2013 ............................................................................................................................................................ 70
iv
Climate Change Projections for Mondulkiri and Koh Kong Provinces in Cambodia
Tables
Table 1. List of CCAM runs analysed for this study, with their resolution, number of levels, and input
data ............................................................................................................................................................. 18
Table 2. Projected changes in maximum temperature and their ranges (°C) for Koh Kong Province by
2025 relative to 1990 for RCP 4.5 and RCP 8.5 based upon 20 year averages. FIMS: First Intermonsoon
Season, SWMS: Southwest Monsoon Season, SIMS: Second Intermonsoon Season and NEMS: Northeast
Monsoon Season. ....................................................................................................................................... 22
Table 3. Projected changes in maximum temperature and their ranges (°C) for Mondulkiri Province by
2025 relative to 1990 for RCP 4.5 and RCP 8.5 based upon 20 year averages. FIMS: First Intermonsoon
Season, SWMS: Southwest Monsoon Season, SIMS: Second Intermonsoon Season and NEMS: Northeast
Monsoon Season. ....................................................................................................................................... 22
Table 4. Projected changes in minimum temperature and their ranges (°C) for Koh Kong Province by
2025 relative to 1990 for RCP 4.5 and RCP 8.5 based upon 20 year averages. FIMS: First Intermonsoon
Season, SWMS: Southwest Monsoon Season, SIMS: Second Intermonsoon Season and NEMS: Northeast
Monsoon Season. ....................................................................................................................................... 22
Table 5. Projected changes in average minimum temperature and their ranges (°C) for Mondulkiri
Province by 2025 relative to 1990 for RCP 4.5 and RCP 8.5 based upon 20 year averages. FIMS: First
Intermonsoon Season, SWMS: Southwest Monsoon Season, SIMS: Second Intermonsoon Season and
NEMS: Northeast Monsoon Season. .......................................................................................................... 23
Table 6. Projected changes in average temperature and their ranges (°C) for Koh Kong Province by 2025
relative to 1990 for RCP 4.5 and RCP 8.5 based upon 20 year averages. FIMS: First Intermonsoon Season,
SWMS: Southwest Monsoon Season, SIMS: Second Intermonsoon Season and NEMS: Northeast
Monsoon Season. ....................................................................................................................................... 23
Table 7. Projected changes in average temperature and their ranges (°C) for Mondulkiri Province by
2025 relative to 1990 for RCP 4.5 and RCP 8.5 based upon 20 year averages. FIMS: First Intermonsoon
Season, SWMS: Southwest Monsoon Season, SIMS: Second Intermonsoon Season and NEMS: Northeast
Monsoon Season. ....................................................................................................................................... 23
Table 8. Projected percentage changes in mean seasonal and annual rainfall and their ranges for Koh
Kong Province by 2025 relative to 1990 for RCP 4.5 and RCP 8.5 based upon 20 year averages. FIMS:
First Intermonsoon Season, SWMS: Southwest Monsoon Season, SIMS: Second Intermonsoon Season
and NEMS: Northeast Monsoon Season. ................................................................................................... 26
Table 9. Projected percentage changes in mean seasonal and annual rainfall and their ranges for
Mondulkiri Province by 2025 relative to 1990 for RCP 4.5 and RCP 8.5 based upon 20 year averages.
FIMS: First Intermonsoon Season, SWMS: Southwest Monsoon Season, SIMS: Second Intermonsoon
Season and NEMS: Northeast Monsoon Season. ....................................................................................... 26
Table 10. Number of hot days (HDs, Tmax35 = number of days above 35°C) for Mondulkiri and Koh Kong
provinces at 1990 and 2025 for RCP 8.5 based upon 20 year averages. ................................................... 29
Table 11. Duration and frequency of heat waves, based on the Heat Wave Duration Index (HWDI), for
Mondulkiri and Koh Kong provinces at 1990 and 2025 for RCP 8.5 based upon 20 year averages. ......... 29
Table 12. Ensemble mean CCAM 10 km simulated drought duration and frequency for the baseline
period (20 year period centred on 1990) and projected changes by the 20 year period centred on 2025
relative to the baseline period for 3-, 6- and 12-month droughts for Mondulkiri and Koh Kong
provinces. ................................................................................................................................................... 31
Table 13. Percent changes in extreme rainfall amounts (mean annual one- (RX1) and five-day (RX5)
rainfall totals) by 2025 for Mondulkiri and Koh Kong provinces. Changes are relative to 1990 and are
based upon 20 year averages. .................................................................................................................... 35
v
Table 14. Changes in Consecutive Dry Days (CDDs) and Consecutive Wet Days (CWDs) by 2025 for
Mondulkiri and Koh Kong provinces. Changes are relative to 1990 and are based upon 20 year
averages. .................................................................................................................................................... 35
Table 15. Observed time series of average monthly and yearly sea-level measurements for stations near
Cambodia for various periods. See graph axes for periods and Figure 20 for locations. Note differing
vertical and horizontal axis on these various plots. ................................................................................... 38
Table 16. Selected BCC Communes for Field Surveys in Koh Kong and Mondulkiri .................................. 49
Table 17. Seasonal Distribution of Climate Risks and Livelihood Calendar in Koh Kong ........................... 53
Table 18. Climate Change Vulnerability of Agriculture in Koh Kong .......................................................... 56
Table 19. Climate Change Vulnerability of Fishery in Koh Kong................................................................. 57
Table 20. Climate Change Vulnerability of Water, Food and Health in Koh Kong ..................................... 58
Table 21. Climate Change Vulnerability of Infrastructure and Housing in Koh Kong................................. 59
Table 22. Seasonal Distribution of Climate Risks and Livelihood Calendar in Mondulkiri ......................... 62
Table 23. Climate Change Vulnerability of Agricultural Systems in Mondulkiri ......................................... 66
Table 24. Climate Change Vulnerability of Forest-based Activities in Mondulkiri ..................................... 67
Table 25. Climate Change Vulnerability of Water and Health Issues in Mondulkiri .................................. 68
Table 26. Climate Change Vulnerability of Housing and Infrastructure in Mondulkiri .............................. 69
Table 27. Climate Change Vulnerability of Selected Communes in Koh Kong (KKG) & Mondulkiri (MDK) 71
Table 28. Key Climate Change Adaptation Options and Priorities of Selected Communes in Koh Kong
(KKG) and Mondulkiri (MDK) ...................................................................................................................... 73
vi
Climate Change Projections for Mondulkiri and Koh Kong Provinces in Cambodia
Acknowledgements
This work was funded by the Asian Development Bank under the PPCR Grant to the Kingdom of Cambodia.
We gratefully acknowledge the assistance of the Cambodian government agencies, including the Forestry
Administration (FA), the BCC Executing Agency (EA) and Provincial Project management Units, which
facilitated the study team’s access to communes and villages for consultative participation as well as those
that supplied data and information or coordinated activities.
We acknowledge the provincial governments of Koh Kong and Mondulkiri for their help in carrying out the
project, as well as the support and active participation of the inhabitants of the communes surveyed for
this study.
The work of the authors draws upon research findings of many colleagues within CSIRO Marine and
Atmospheric Research and overseas research institutions. We acknowledge the modelling groups, the
Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on
Coupled Modelling for their roles in making available the WCRP CMIP5 multi-model dataset.
This work also draws upon information generated by CSIRO’s Climate and Adaptation Flagship.
Results from the High-resolution Climate Projections for Vietnam project, funded by the Australian AID
organisation, were also used in this study.
vii
Abbreviations
ADB
Asian Development Bank
ARCC
Adaptation and Resilience to Climate Change
BCC
Biodiversity Conservation Corridors
BCI
Biodiversity Conservation Corridors Initiative
CCSR
Center for Climate Systems Research
CIF
Climate Investment Funds
CIFOR
Center for International Forestry Research
CSIRO
Commonwealth Scientific and Industrial Research Organisation
DRR
Disaster Risk Reduction
EA
Executing Agency
FA
Forestry Administration
FS
Feasibility Study
GCMs
Global Climate Models
GMS
Greater Mekong Sub-region
IOM
International Organization for Migration
IPCC
Intergovernmental Panel on Climate Change
KKG
Koh Kong Province
LMB
Lower Mekong Basin
MAFF
Ministry of Agriculture, Forestry and Fisheries
MDK
Mondulkiri Province
MOE
Ministry of Environment
NAPA
National Adaptation Program of Action
NBCC
Non-BCC Target Communes
NCDM
National Committee for Disaster Management
NGO
Non-governmental Organization
NTFP
Non-timber Forest Products
PEN
Poverty and Environment Network
PF
Protected Forest
PPCR
Pilot Program for Climate Resilience
PPMU
Provincial Project Management Unit
PRA
Participatory Rural Appraisal
RGC
Royal Government of Cambodia
R-PPTA
Regional Project Preparatory Technical Assistance
RRP
Report and Recommendation to the President
SCF
Strategic Climate Fund
SNAP
Strategic National Action Plan
SPSS
Statistical Product and Service Solutions
USAID
United States Agency for International Development
viii
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
Project Overview
This study was undertaken to complement a larger project funded by the Asian Development Bank (ADB),
the Greater Mekong Sub-region (GMS) Biodiversity Conservation Corridors (BCC) Project (ADB-TA7459
40253-012) in Koh Kong and Mondulkiri provinces in Cambodia, which will be carried out over an eightyear period (2011 – 2018). Agriculture, forestry and fisheries are some of the sectors most vulnerable to
climate change, which potentially will alter temperature and rainfall patterns, as well as affect water
resources and the frequency and intensity of extreme events. Understanding the impacts of climate
change on agriculture and natural resources and identifying appropriate adaptation measures will be key
components in any poverty-reduction strategy in Cambodia.
There are two parts to this study:


High-resolution projections of future climate using the most current emissions scenarios and
modelling techniques;
Climate change vulnerability assessment for two Cambodian provinces, Koh Kong and Mondulkiri,
produced through a participatory approach.
Koh Kong is a coastal area, highly dependent on fishing and livelihoods associated with aquatic resources,
while Mondulkiri is located in an upland area. It is remote and rugged, with more dependence on
agriculture. Because of their different livelihood bases, the potential impacts of climate change will vary for
each province.
Previous simulations of future climate by Global Climate Models (GCMs) have identified this region of
Southeast Asia as one of the areas most vulnerable to the impacts of climate change (see, for example, IPCC
2007). However, their coarse horizontal resolution (about 200 km between data points) is not adequate to
capture the details of climate necessary for adaptation planning in a small, topographically complex area
such as Cambodia. To address this, new simulations have been produced at 10 km resolution for the period
1970 – 2099. Results are presented for mid- and end-of-century, with detailed analysis of projected
changes for 2025 that have been requested as inputs for infrastructure projects.
The key objectives of the vulnerability assessment are to assess local vulnerability to climate change, use of
coping mechanisms by households, and the level of adaptive capacity in the community. Combined with
the data from the high-resolution climate simulations, this can be used to identify the most effective
climate change adaptation options and priorities.
1
PART 1:
Climate Change Impact Modelling for
Koh Kong and Mondulkiri, Cambodia
2
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
Executive Summary -- Part 1
In order to assess climate change impacts for Koh Kong and Mondulkiri provinces in Cambodia, new highresolution projections of future climate have been produced. The simulations used bias-corrected sea
surface temperatures from six GCMs (global climate models, including atmosphere, ocean and ice) to drive
a global atmosphere-only model at 50 km horizontal resolution, and these 50 km simulations were then
used to drive regional models at about 10 km resolution, in a process known as downscaling. Two emissions
scenarios were considered: RCP 4.5 (lower greenhouse gas concentrations) and RCP 8.5 (higher greenhouse
gas concentrations).
The main findings, relative to the baseline period 1980-1999, are as follows:
 Temperature is expected to increase by between 0.7°C (RCP4.5) and 1.0°C (RCP8.5) by 2025 in both
provinces.
 Annual mean projected rainfall decreases by between -1% (RCP 4.5) and -2.5% (RCP 8.5) for Koh
Kong.
 Annual mean rainfall is not projected to change for either RCP 4.5 or RCP 8.5 for Mondulkiri.
 Rainfall is projected to decrease during the wet season and increase in other parts of the year.
 The number of days with extreme temperatures above 35°C is projected to more than double.
 The Heat Wave Duration Index (HWDI) is projected to increase in both provinces.
 Projected changes in number of short and medium duration droughts tend to decrease, but the
number of droughts of long duration tend to increase in the future.
 Maximum one-day rainfall tends to increase for both provinces, while maximum five-day totals tend
to increase in Mondulkiri and to decrease in Koh Kong in the future.
 The projected mean length of Consecutive Dry Days and their frequencies decrease slightly in both
provinces.
 The projected mean length of Consecutive Wet Days tends to decrease, while the frequencies of
these days tend to increase in both provinces.
 Local sea level at Koh Kong is projected to increase by approximately 10 cm above the 1986-2005
baseline value by 2025.
With the projected doubling in the number of hot days and increase in heat waves comes the potential for
enhanced evaporation. Changes in rainfall from year to year and from decade to decade are greater than
expected overall changes in the mean annual totals, so there is a need to consider interannual and
interdecadal rainfall variability when making decisions.
The trend to greater frequency of droughts of long duration (longer than 12 months) could be the feature
of climate change that has the most impact in Koh Kong and Mondulkiri for the next 20 years. This might
lead to less groundwater due to less recharge and greater extraction. In addition, more and longer
droughts for Koh Kong could lead to more salt water intrusion.
Projected increases in extreme rainfall amounts in Mondulkiri also suggest more likely risk of floods.
Sea-level rise is likely to be more important in winter (the time of the highest annual sea level), and storm
surges may also be affected. There is some suggestion of decreases in the summer monsoon; less rain will
possibly lead to fewer storms, though changes in the frequency of storms were not directly assessed in this
study. In Koh Kong, decreases in five-day extreme rainfall amounts indicate that there may possibly be
fewer storms, although this will be combined with a trend toward greater tidal and storm extremes in the
coastal regions.
The inherent uncertainty in the assumptions made when producing projections of future climate and the
differences in model dynamics highlights the need to consider a range of projections (low, middle and high
greenhouse gas concentration scenarios) and a range of model outputs in an ensemble technique, as was
done in this study, to make the best possible decisions for planning of public projects and climate change
adaptation measures.
3
1 Introduction to Part 1
In Part 1 of this report, we first briefly describe the current climate of Cambodia, focusing on seasonal
rainfall patterns and atmospheric circulation. Section 2 describes the various observational datasets
available for validation of current climate simulations. This is followed in Section 3 by discussion of the
methods of assessing global climate model (GCM) simulations over a broader region centred on Southeast
Asia, and in Section 4 by a description of the process used to select GCMs used in this study for dynamical
downscaling over the Indochina Peninsula. Section 5 discusses selection of climate variables for impact
studies, while Section 6 gives details of the downscaling methodology and experiments employed in this
study, which were primarily based on simulations using CSIRO’s Conformal Cubic Atmospheric Model
(CCAM). Analysis of results in Section 7 includes changes in mean climate, extreme indices and droughts
for the 20 year period centred on 2025 relative to the baseline period, 1980-1999. This is followed by
conclusions in the final section.
1.1 Key Climate Processes in Cambodia
Cambodia has a tropical climate controlled by the Southwest and Northeast Monsoons. The Southwest
Monsoon Season (SWMS, June-September) is the wet season, while the Northeast Monsoon Season
(NEMS; December-March) is the dry season. There are two transitional seasons: the First Intermonsoon
Season (FIMS, April-May) and the Second Intermonsoon Season (SIMS, October-November). Figure 1 shows
a schematic view of large-scale circulation features that influence the climate of Cambodia. Cambodia is
vulnerable to extreme weather and climatic events such as floods and droughts due to its location within
the tropics in the vicinity of the Indian Ocean. The complex topography of the country results in strong
spatial variations in temperature and rainfall, which are particularly evident during the Southwest Monsoon
and Northeast Monsoon seasons. El Niño-Southern Oscillation (ENSO) is the major phenomenon that
dominates the variability of the climate on the interannual (year-to-year) time scale, and cold wind surges
from Siberia and Mongolia during the Northeast Monsoon Season influence the region on a shorter time
scale, which ranges between 8 and 10 days (Suppiah and Wu, 1998). On average five to six tropical
cyclones that form over the Western Pacific influence Cambodia per year. Major droughts and forest fires
are associated with failure of Southwest Monsoon rain events which are influenced by the year-to-year
changes in ENSO.
Due to Cambodia’s location within the Asian monsoon region, its climate is predominantly controlled by the
Northern Hemisphere summer and winter monsoons, and the country also experiences a tropical wet and
dry climate as a result of marked seasonal differences (Ramage, 1971). In summer, the Southwest Monsoon
from the Indian Ocean affects the country, while during winter the Northeast Monsoon from the Central
Asian landmass is the primary influence. The Southwest Monsoon brings the rainy season from May to
September or early October, with rain occurring almost daily during much of this season, while the
Northeast Monsoon flow of drier and cooler air lasts from early December to March. From November to
February the weather is generally mild and dry, whereas the weather is hot from February until the onset
of the Southwest Monsoon (Sao, 2009).Transitional seasons, which are marked by some difference in
humidity, have negligible change in temperature.
Figure 2 shows the annual rainfall distribution for Cambodia. Rainfall distribution in Cambodia is strongly
influenced by the two monsoons and also by topography, so the spatial pattern of rainfall varies
throughout the country. The highest rainfall occurs in the southwest in coastal areas, with annual amounts
ranging from 2000 mm to 3400 mm. In this region about 80% of the annual rainfall is received during the
Southwest Monsoon season. The second highest rainfall occurs in the northeast plateau area, where the
annual rainfall amounts range from 1800 mm to over 2200 mm. A region of less rainfall stretches from the
4
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
Siberian High
Latitude
July - ITCZ
Cambodia
Pacific Ocean
ITCZ
Indian Ocean
Longitude
Figure 1. Schematic diagram showing the major climate drivers that influence the climate of Cambodia. ITCZ:
Intertropical Convergence Zone, EVS: East Vietnam Sea, ENSO: El Niño-Southern Oscillation phenomenon. Source:
Climate Change in Vietnam, 2013.
northwest to the southeast, including the Tonle Sap (the lake and river system between Siem Reap and
Pursat, Figure 2) which receives annual rainfall of less than 1400 mm.
Relative humidity is lowest in March and highest in September. Daily evaporation values range from 3.1
mm in October to 6.7 mm in March, and the sunshine duration ranges from 6.0 hours a day in August to 9.3
hours a day in January.
Temperatures are fairly uniform throughout Cambodia, with slightly cooler temperatures over the higher
elevations in the northeast and southwest. There are only small variations from the average annual mean
of about 25°C +/- 3°C. Maximum temperatures of higher than 32°C are common, however, and just before
the start of the rainy season, they can rise to more than 38°C. Minimum temperatures rarely fall below
10°C. January is the coolest month, and April is the warmest. For a more detailed description of Cambodia’s
climate, see Sao (2009).
5
Figure 2. Distribution of annual rainfall (mm) in Cambodia for the period 1981-2004. The Tonle Sap River is to the
west, and the Mekong River to the east. Source: Sao 2009.
Floods and droughts occur on a year-to-year time scale in a number of provinces. Two types of floods are
identified in Cambodia (Sao, 2009): (1) floods resulting from overflows of the Mekong and Tonle Sap Rivers
and (2) floods that are associated with extreme rainfall locally and in larger areas. Provinces such as Stung
Treng, Kratie, Kampong Cham, Kandal, Kampong Thom, Kampong Chhnang, Battambang, Siem Reap, Prey
Veng, Svay Rieng, and Takeo located along the Mekong and Tonle Sap tributaries are affected by the first
type of flood (Figure 3; note variant spellings of province names). Coastal flooding (affecting Koh Kong) has
increased since 2006, with the largest amount of flooding in 2011. More extreme storms have been noted
since 2011 in both provinces (Bobenreith et al., 2012).
Figure 3 Names and boundaries of administrative provinces in Cambodia. Source: Atlas of Cambodia.
The agriculture sector, particularly rice crop cultivation, is highly vulnerable to these extreme climatic
events. Severe droughts and floods also affect infrastructure and ecosystems of the country. Recently, a
6
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
number of adaptive measures were proposed to reduce drought risk in Cambodia (Nguyen et al., 2013).
Although tropical cyclones often affect Vietnam, their impact on Cambodia is minimal. On rare occasions
tropical cyclones cause damage along Cambodia’s coastline during the month of November. Tropical
cyclones that devastate coastal Vietnam rarely cause damage in Cambodia; however, two tropical cyclones
severely affected Cambodia in 2012.
1.2 Previous Climate Change Projections for Cambodia
To better understand the many climate hazards that affect Cambodia and to put the new projections
presented in this study in context, it is useful to review previous climate change projections for the region.
Globally measured atmospheric greenhouse gas concentrations have increased rapidly in the past century
and are almost certain to continue to increase in the future (IPCC, 2007). GCMs are the best available tools
for simulating future climates based on various greenhouse gas and aerosol emission scenarios. GCM
experiments indicate a global average warming of 1.1 to 6.4°C by the year 2100 under the B1 (low) and
A1FI (high) emissions scenarios, respectively, relative to 1990 (IPCC, 2007). This is likely to be associated
with changes to weather patterns, sea-level rise and impacts on ecosystems, water resources, agriculture,
forests, fisheries, industries, urban and rural settlements, energy usage, tourism and health. Changes in
climate will not be globally uniform. More warming is expected in polar regions and over land areas,
compared with the tropics and oceans. Although compared to industrialised countries, the amount of
greenhouse gases emitted from industry and transport in Cambodia may be negligible, greenhouse gases
emitted from biomass may have some impacts, and the country will be affected by global emission levels
due to large-scale atmospheric processes as well as regional levels. Most projections are derived from
multiple model results, using simple averages or weighted values based on statistical measures of model
reliability, such as the correlation between observed and simulated climate patterns. The underlying
assumption is that projections are likely to be more reliable from models that simulate the present climate
well.
Discussions of future climate projections for Cambodia and the likely impacts of climate change on various
sectors are often considered in the context of the wider Asia-Pacific region (see, for example, Hay and
Mimura, 2006). A few studies have focused on impacts of drought on various sectors in Cambodia and
proposed a number of strategies to cope with the risks associated with them (Nguyen et al., 2013). Climate
change projections for Cambodia were given by the Cambodian Ministry of Environment using simulations
performed for the IPCC Fourth Assessment Report (Ministry of Environment, 2001). Those climate change
projections were based on the previous Special Report on Emission Scenarios (SRES, IPCC 2000). Under a
high emission scenario (SRES A1FI), the mean air temperature in Cambodia was projected to increase by
about 0.5°C by 2020 and by 1.0°C by 2050. Under a low emission scenario (SRES B1), the mean air
temperature was projected to increase by about 0.3°C by 2020 and by about 0.7°C by 2050. The
projections also showed that wet season rainfall may increase, while dry season rainfall may decrease. A
previous study by EVS Environment Consultants (1996) in Stung Metoek, Russei Chrum, Stung Sala
Munthun and Stung Chhay Areng provinces suggests that the dry season river flow may decrease by up to 4
m3 s-1, while the wet season river flow may increase by up to 10 m3 s-1 with global warming. These findings
suggest an increase to the risk of droughts and floods in parts of Cambodia in the future. Almost all the
regions of Cambodia are among the hotspots or areas most vulnerable to climate change in Southeast Asia,
along with all the regions of the Philippines and the Mekong River Delta in Vietnam (Economy and
Environment Program for Southeast Asia, 2009).
High-resolution climate change projections for Cambodia produced by the Center for Climate Systems
Research (CCSR) and Commonwealth Scientific and Industrial Research Organisation (CSIRO) as part of a
United Nations Development Programme (2009) were analysed for the SRES A2 and SRES B1 emission
scenarios (see Figure 4, Section 3, for details of scenarios). Projected increases in temperature ranged from
1.35 to 2.5°C and increases in annual rainfall ranged from 3 to 35% by 2100. In Koh Kong Province, in
particular, the rainfall in four main river basins was predicted to increase between 2 and 15%, thereby
increasing water flow by 2-10 m3 s-1, and this, along with a 1 m rise in sea level, would put 44 km2 of the
7
province (0.4% of total provincial area) permanently under water and flood about 56% of the settlement
areas (Thoeun et al., 2001; UNDP, 2009).
Previous climate change projections for Cambodia were derived from the simulations based on SRES
emission scenarios that were produced for the World Climate Research Programme’s (WCRP’s) Coupled
Model Intercomparison Project phase 3 (CMIP3)and used in the IPCC Fourth Assessment Report (IPCC,
2007). More recent simulations produced by a new generation of climate models for CMIP5 that are
included in the IPCC Fifth Assessment Report are now available (IPCC, 2013). In the new simulations,
models were forced with improved greenhouse gas and aerosol estimates and land-use schemes, with
slightly finer horizontal resolution than the previous CMIP3 simulations. The new projections indicate a
global average warming of 0.3 to 4.8°C by the year 2100 under the RCP2.6 (low) and RCP8.5 (high)
emissions scenarios, respectively, relative to 1990 (IPCC, 2013). Selected CMIP5 models were used in this
study for future climate change projections for the wider Southeast Asian region, including Cambodia. They
were also used as boundary conditions or initial input to drive six fine-resolution simulations over the
Indochina Peninsula using the CCAM model developed in Australia at CSIRO’s Marine and Atmospheric
Research division (CMAR) for projections of changes in mean and extreme climate indices for Cambodia, as
described in the following sections of this report.
8
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
2 Observational Datasets
2.1.1 GLOBAL DATASETS AND VARIABLES
A number of global gridded datasets are commonly used for studying present climate and climate
variability and also for verification of global climate simulations produced for the IPCC Fifth Assessment
Report. Observed global atmospheric datasets include the European Centre for Medium Range Forecasts
(ECMWF) ERA-Interim reanalysis. Global precipitation data can be obtained from the Climate Research Unit
(CRU) dataset. The datasets listed below are used to assess the performance of the current generation of
global climate models over the domain of interest in this study. In addition, a brief description of some
other datasets is given to aid impact assessment researchers who may decide to incorporate them in their
analyses.
2.1.2 ERA-INTERIM DATASETS
The ERA-Interim reanalyses were produced by the European Centre for Medium-range Weather Forecasts
(ECMWF) in collaboration with other institutions. These reanalyses include meteorological observations of
many atmospheric variables on a daily time scale from September 1957 to August 2002 .The observing
system changed considerably over this reanalysis period, with data assimilation provided by a succession of
satellite-borne instruments from the 1970s onwards, supplemented by increasing numbers of observations
from aircraft, ocean buoys and other surface platforms, but with a declining number of radiosonde ascents
since the late 1980s. Details of the procedure for creating this dataset and variables are given by Uppala et
al. (2005).
2.1.3 THE CRU DATASET
The CRU (Climatic Research Unit) Global Climate Dataset, available through the University of East Anglia, UK
or the IPCC Data Distribution Centre, consists of a multi-variate 0.5° latitude by 0.5° longitude resolution
mean monthly climatology for global land areas, excluding Antarctica. Together with a mean climatology,
which is strictly constrained to the period 1961-1990, there is a monthly time series at the same resolution
for the period 1901-2000. The mean 1961-1990 climatology is comprised of a suite of eleven surface
variables: precipitation and wet-day frequency; mean, maximum and minimum temperature; vapour
pressure and relative humidity; sunshine percentage and cloud cover; frost frequency; and wind speed. The
time series component is comprised of all variables except sunshine percentage, frost frequency and wind
speed. Details of this dataset are provided by New et al. (1999) and Mitchell and Jones (2005).
9
3 Global Climate Model Assessment
Monthly mean sea level pressure, temperature and rainfall data from 31 GCM simulations from CMIP5
were obtained from the archives of CSIRO. They were originally extracted from the IPCC Model Output
website at http://pcmdi9.llnl.gov/esgf-web-fe/. Some of the models have single simulations for the 20th
and 21st centuries, while others have multiple simulations. For historical climate, only one simulation was
used to construct the average climatology for the years 1979 to 2004. For models with multiple simulations
of the future climate, the ensemble mean was computed to calculate future changes in climate. The
simulations of the 20th century climate were driven by observed changes in greenhouse gases and
aerosols. Some simulations included direct and indirect effects of aerosols, some included ozone depletion,
and some included volcanic aerosols and solar forcing. The 21st century simulations were driven by
Representative Concentration Pathways (RCPs; IPCC, 2008), which describe a wide range of potential
futures with different values of the main drivers of climate change: greenhouse gas and air pollutant
emissions and land use, in terms of their radiative forcing. Radiative forcings of 3.01 (low), 4.5, 6.0 and 8.5
(high) Watts m-2 were estimated by the end of the 21st century.
This study includes simulations driven by RCP 8.5, which is similar to the SRES A1FI emission scenario and
represents the upper end of the concentrations, and by RCP 4.5, which is similar to the SRES B1 emission
scenario and represents the lower end of the concentrations (Figure 4). Using available data, changes in
mean seasonal and annual temperatures by the end of the 21st century were analysed for 24 of the 31
CMIP5 simulations for RCP 8.5 and RCP 4.5, and changes in rainfall were analysed for 22 and 18 simulations,
respectively.
Figure 4. Comparison of CO2 concentrations (ppmv) from SRES (A1B, A1FI, A2, B1) and RCPs (3.0, 4.5, 6.0, 8.5) for
the years 2000-2100. Source: Meinshausen, M. et al. (2011).
The reliability of climate models over the Southeast Asian region has been tested by comparing observed
and simulated patterns of average temperature, precipitation and mean sea level pressure (MSLP).
Observed global surface air temperature and MSLP data were taken from ERA-Interim archives (available
1
Note that under emission scenario RCP 3.0-PD the peak radiative forcing of 3 Watts m-2 declines to 2.6 Watts m-2 by the end of the century.
10
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
through the ERA data server, http://data-portal.ecmwf.int/). The dataset covers land and ocean areas.
Observed land-only global rainfall data were taken from Climate Research Unit (CRU) and ERA-Interim
archives for land and ocean areas. Climatological averages for the MSLP, temperature and rainfall for a 30year period (1975-2004) for four seasons were computed. Statistical methods were used to test whether
the models adequately reproduce each of these fields over a broader region that covers Asia and Southeast
Asia (60-160°E, 15°S-50°N). A relatively smaller region excluding the mountainous regions of Asia was also
selected to test the reliability of the models. The domain for the smaller region covers Southeast Asia (90140°E, 0-40°N). A pattern correlation coefficient of 1.0 indicates a perfect match between the observed
and simulated spatial pattern, and a root mean square error (RMSE) of 0.0 indicates a perfect match
between the observed and simulated magnitudes.
The RMSE is based on all grid points in the considered domain, and is calculated in the units of hPa, °C and
mm/day for seasonal MSLP, temperature and rainfall, respectively. The seasons are demarcated as follows:
First Intermonsoon Season (FIMS, April-May), Southwest Monsoon Season (SWMS, June-September), Second
Intermonsoon Season (SIMS, October-November) and Northeast Monsoon Season (NEMS, December–
March). In this report we show results based on annual values. However, we discuss the results based on
seasonal values where necessary.
An assessment of the new generation of model simulations over Southeast Asia using the above-mentioned
statistical measures and visual judgements reveals that most of the models simulate the spatial patterns of
MSLP fairly well, except some models like HadGM2-CC, which has relatively weak correlations and large
RMSE values, as shown in Figure 5 (top). Results based on seasonal values indicate that many models fail to
capture the observed spatial patterns of mean sea level pressure during the Southwest Monsoon and
Second Intermonsoon seasons, with large RMSE values presumably due to complex topography over
Central Asia. However, the models capture the observed spatial patterns when the domain was reduced in
size. These results suggest that the new generation models still have difficulty simulating the correct
atmospheric patterns over topographically complex areas.
1.0
Pattern Correlation
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Annual
0.0
0
1
2
3
4
5
6
7
8
9
10
3.5
4.0
4.5
5.0
RMS Error (hPa)
Pattern Correlation
1.00
0.95
0.90
0.85
Annual
0.80
0.0
0.5
1.0
1.5
2.0
2.5
3.0
ACCESS1-0
ACCESS1-3
CanESM2
CCSM4
CNRM-CM5
CSIRO-Mk3-6-0
FGOALS-g2
FGOALS-s2
GFDL-CM3
GFDL-ESM2M
GISS-E2-H
HadCM3
HadGM2-CC
HadGM2-ES
inmcm4
IPSL-CM5A-LR
IPSL-CM5A-MR
MIROC4h
MIROC5
MIROC-ESM-CHEM
MIROC-ESM
MPI-ESM
MRI-CGCM3
NorESM1-M
RMS Error (oC)
Figure 5. Relationship between pattern correlation and RMSE values for annual mean sea level pressure (top) and
temperature (bottom) for the larger domain (60-160°E, 15°S-50°N) over Southeast Asia for 1975-2004. Note the best
models tend to sit towards the top left corner and the worst models tend to sit towards the bottom right.
11
Results for temperature simulations reveal that almost all the models capture the observed annual and
seasonal spatial patterns. However, Figure 5 (bottom) shows that there are strong model-to-model
variations in RMSE values, which range from 1°C to 3°C. The annual bias between CMIP5 models
simulations and observations is shown in Figure 6. The models tend to underestimate observed average
temperature. The underestimation over land areas is large during all seasons except the First Intermonsoon
Season.
Temperature °C
Figure 6. Annual average global climate model temperature biases (°C) for Southeast Asia based on the difference
between model simulations and observed values for the period 1975-2004.
Figure 7 depicts the pattern correlations and RMSE values for observed and simulated annual rainfall over
the broader region of Asia, particularly focused on Southeast Asia. Here we present results comparing
simulated data against two global data sets: the ERA-Interim dataset, which covers land and ocean areas,
and the CRU dataset, which covers only land areas. Pattern correlations are relatively strong (greater than
0.6), except for a few models. A cluster of models shows strong correlations, with RMSE values between 1.5
and 2.5 mm/day, indicating that these models are capturing the observed pattern well. Correlations are
relatively strong when compared with data from only over land, but tend to be weaker when using data
from ocean and land areas separately. There are differences in the performance of the models for different
seasons. During the Northeast Monsoon and Second Intermonsoon seasons the models perform better
than in the Southwest and First Intermonsoon seasons. This may be partly due to the ability of the models
to simulate rainfall in these seasons over topographically complex areas. RMSE values range from 1.5 to
3.0 mm/day for most of the models, with the exception of a few outliers.
The annual rainfall bias between CMIP5 models and observations for the historical period 1975-2004 is
shown in Figure 8. Overall the bias ranges between -6.0 and +4 mm per day. The models tend to
underestimate observed annual average rainfall over Southeast Asia, the northern Indian and the western
Pacific Oceans. In particular, areas in the northern part of the Indochina Peninsula show relatively stronger
underestimation. The models also overestimate observed annual rainfall over the islands along the equator
and also slightly over northern Asia. There are significant seasonal differences in rainfall bias and these
differences are shown in Figure 9. The models again tend to underestimate rainfall over the land and over
the Indian and western Pacific Oceans, and overestimate rainfall over the islands near the equator. The
biases are larger during the First Intermonsoon and Southwest Monsoon seasons.
12
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
1.0
Pattern Correlation
0.9
ERA-Interim
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
PR-ANNUAL
0.0
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0
RMS Error (mm/day)
1.0
Pattern Correlation
0.9
CRU
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
PR ANNUAL
0.0
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0
ACCESS1-0
ACCESS1-3
CanESM2
CCSM4
CNRM-CM5
CSIRO-Mk3-6-0
FGOALS-g2
FGOALS-s2
GFDL-CM3
GFDL-ESM2M
GISS-E2-H
HadCM3
HadGM2-CC
HadGM2-ES
inmcm4
IPSL-CM5A-LR
IPSL-CM5A-MR
MIROC4h
MIROC5
MIROC-ESM-CHEM
MIROC-ESM
MPI-ESM
MRI-CGCM3
NorESM1-M
RMS Error (mm/day)
Figure 7. Relationship between pattern correlation and RMSE values for annual rainfall for ERA-Interim (top) and
CRU data (bottom) for the larger domain (60-160°E, 15°S-50°N) for 1975-2004. Note the best models tend to sit
towards the top left corner and the worst models tend to sit towards the bottom right.
Rainfall bias (mm/day)
Figure 8. Annual rainfall bias (mm/day) between model simulations and ERA-Interim for the period 1975-2004.
13
(a)
(b)
(c)
(d)
Rainfall bias (mm/day)
Figure 9. Seasonal rainfall bias (mm/day) between model simulations and observations for four seasons for the period
1975-2004. (a) First Intermonsoon Season, (b) Southwest Monsoon Season, (c) Second Intermonsoon Season and (d)
Northeast Monsoon Season.
Deciding on acceptable performance of a model is not straightforward. A good performance at simulating
current climate does not guarantee that the enhanced greenhouse simulation is reliable. Nor do errors in
the current climate performance mean that the enhanced greenhouse simulated changes in climate are
necessarily unreliable. This means that focusing on the results of the very best performing models in
current climate conditions may inadequately represent the underlying uncertainty in projecting regional
climate change. Thus, our approach to validation has been to view a model as acceptable unless the current
climate errors are of a nature which, in our judgment, significantly reduces the likelihood that the enhanced
greenhouse simulation is reliable. Absence of key climate features (e.g. pressure patterns and seasonal
winds) in the region of interest is an example of an unacceptable failure. For this reason we have placed
emphasis on multi-variable assessment and on accuracy of capturing spatial patterns of climate variables.
14
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
4 GCM Selection from CMIP5 Simulations
To reflect the uncertainty of climate projections, ensemble simulations of GCMs are needed, but
downscaling the results of all CMIP5 GCMs is computationally too expensive at the moment. For this
project, we used data from a subset of six different CMIP5 GCMs as a compromise between computational
costs and the need to capture the range of different climate change signals that arise from model-to-model
variations and increase confidence in future projections. The decision on which models to downscale was
based on three different criteria.
First, the models should be able to capture the observed spatial patterns of the current climate, shown by
small RMSE values and higher pattern correlation coefficients. The observed climatology of atmospheric
variables such as mean sea level pressure, temperature and rainfall should be captured by the models, as
well as their observed trends. Although there are more than 30 evaluation studies available, they do not
provide a ranking or use just a small subset of the CMIP5 models. Based on the results of the last section,
the GCMs available for this study were ranked according to the RMSE values for annual temperature
(Figure 5) and rainfall (Figure 7) as well as the pattern correlation of the annual rainfall.
The second criterion relates to the downscaling method applied in this project. Since only the sea surface
temperatures (SSTs) and sea ice concentration from the GCM output were used to initialise the fineresolution simulations, oceanic features should be simulated well by the selected GCMs, especially larger
scale phenomena such as El-Niño Southern Oscillation (ENSO), since they have a large impact on the
climate of Cambodia as well as on the broader region of Southeast Asia. Grose et al. (2013) analysed the
GCM SSTs in the Pacific Ocean with a special focus on the two El-Niño regions (Central and Eastern Pacific).
They also investigated the observed and simulated frequency of extremes of the ENSO phenomenon, El
Niño and La Nina events. Kim and Yu (2012) correlated the spatial patterns of the two types of El Niño with
the observed patterns, while Kug et al. (2012) focused on the temporal correlation between both types.
Results of these three studies were ranked and used to help select models to downscale. In order to
correct some of the biases in the GCMs, the biases (monthly mean errors) and variances (interannual
variability) in SSTs of the GCMs were corrected before used in the downscaling.
The rankings of the individual studies of models were averaged to yield a final ranking of all CMIP5 models.
Not all the studies considered include the same number of GCMs. Therefore, the ranks were normalized by
the number of models in the individual studies. Consequently, the possible range of the averaged scores
can vary between 0.15 (best model of the 24 models) and 1 (worst model).
The final criterion used was that the models selected produce a range of possible future changes in SST.
Since only the climate change signal is used from the GCMs, selecting GCMs with different patterns of
climate change signals will result in range of downscaled results. Further details on the selection procedure
can be found in Katzfey et al. (2014).
Based on these analyses, we selected six GCMs for downscaling experiments using CCAM: CNRM-CM5,
CCSM4, NorESM1-M, ACCESS1.0, MPI-ESM-LR and GFDL-CM3. The high-resolution climate change
projections produced by this method can then be analysed for impact-oriented studies across Cambodia as
well as for two of its regions: Koh Kong and Mondulkiri.
15
5 Selection of Climatic Variables for Impact Studies
For climate impact assessment and future planning purposes, we have generated several climate indices for
the next 10 to 20 years (to 2025). Some of the indices, such as changes in average maximum, minimum and
mean temperatures and average rainfall for given periods, can be used to investigate changes in mean
climate. Indices such as the one and five day consecutive rainfall values of daily rainfall (RX1 and RX5,
respectively), consecutive dry and wet days, heat wave duration or Standardised Precipitation are used to
investigate changes in characteristics of extreme events. The impact of these extreme events on
agriculture, natural resources and ecosystems, rural infrastructure and human settlements can then be
assessed. Along with the data for the two provinces, maps of selected variables that cover a wider region
of Southeast Asia centred on Cambodia will also be provided to show spatial details of changes in some
important variables, such as changes in extreme rainfall and temperatures. The selected extreme events
indices are listed below:

RX1day: yearly maximum 1-day precipitation (mm/day)

RX5day: yearly maximum of 5-day consecutive precipitation (mm/day)

Tmax35: annual count of number of days with maximum daily temperature above 35°C

CDD: annual consecutive dry days, average annual maximum number of consecutive days with daily
rainfall less than 1 mm

CWD: annual consecutive wet days, average annual maximum number of consecutive days with daily
rainfall greater than or equal to 1 mm

HWDI: Heat Wave Duration Index, defined as the maximum number of consecutive days with daily
maximum temperature (Tmax) above the 95th percentile for a reference period

SPI: The Standardized Precipitation Index (SPI, McKee et al., 1993) was used to quantify the number
and duration of severe (SPI from -1.5 to -1.99) and extreme (SPI less than or equal to -2) droughts.
The intensity of a drought is defined by the minimum of the SPI between two zero crossings. The
duration of the drought is defined as the number of months between the zero crossings. Only
droughts with duration longer than 2 months are considered for the computation of the number of
events.
Additional variables can be assessed upon request.
16
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
6 Methodology
Data generated in the High-resolution Climate Projections for Vietnam project (Katzfey et al., 2014), funded
by the Australian Aid organisation and carried out by CSIRO, have been used to construct the projections
for Cambodia. Due to lack of capacity within Cambodia, there were no locally generated datasets available.
In this project, the CMIP5 GCM sea surface temperatures (after bias correction) and sea ice distributions
were used to drive CCAM at 50 km global horizontal resolution using two RCPs (future emission scenarios).
For each scenario, values for equivalent CO2 and ozone were input, as well as the aerosol emissions. No
atmospheric data was used from the GCMs. These CCAM 50 km simulations were then further downscaled
using CCAM to 10 km resolution over the Indochina Peninsula (see Figure 10 for representations of grids
used). The runs completed are summarised in Table 1.
CCAM is a variable-resolution global atmospheric model that has been developed at CSIRO (McGregor
2005b; McGregor and Dix 2001, 2008). CCAM includes a comprehensive set of physical parameterisations.
The updated GFDL parameterisations for long-wave and short-wave radiation (Schwarzkopf and
Ramaswamy, 1999; Freidenreich and Ramaswamy, 1999) are employed, with interactive cloud distributions
determined by the liquid- and ice-water scheme of Rotstayn (1997). The simulations also include the
scheme of Rotstayn and Lohmann (2002) for the direct and indirect effects of sulphate aerosols. The model
employs a stability-dependent boundary-layer scheme based on Monin–Obukhov similarity theory
(McGregor et al., 1993). The CABLE biosphere-atmosphere exchange model is included, as described by
Kowalczyk et al. (2006), having six layers for soil temperatures, six layers for soil moisture (solving Richards
equation), and three layers for snow. The cumulus convection scheme uses mass-flux closure as described
by McGregor (2003), and includes downdrafts and detrainment. CCAM also includes a simple
parameterisation to enhance sea surface temperatures under conditions of low wind speed and large
downward solar radiation, affecting the calculation of surface fluxes.
The dynamical formulation of CCAM includes a number of distinctive features. The model is nonhydrostatic, with two-time-level semi-implicit time differencing. It employs semi-Lagrangian horizontal
advection with bi-cubic horizontal interpolation (McGregor, 1993; McGregor, 1996), in conjunction with
total-variation-diminishing vertical advection. The grid is unstaggered, but the winds are transformed
reversibly to/from C-staggered locations before/after the gravity wave calculations, providing improved
dispersion characteristics (McGregor, 2005a). Three-dimensional Cartesian representation is used during
the calculation of departure points, and also for the advection or diffusion of vector quantities. CCAM may
be employed in quasi-uniform mode or in stretched mode by utilising the Schmidt (1977) transformation.
Further details of the model dynamical formulation are provided by McGregor (2005b).
17
Figure 10. CCAM grid used for downscaled simulations. Left: global, at 50 km, every 4th grid point; Right: for South
East Asia, at 10 km, every 2nd grid point.
Table 1. List of CCAM runs analysed for this study, with their resolution, number of levels, and input data
Model
Resolution/
vertical levels
Input GCMs
Input data
Period
simulated
Representative
Concentration
Pathway
CCAM
50 km/27
CNRM-CM5,
CCSM4,
ACCESS1.0,
NorESM1-M,
MPI-ESM-LR,
GFDL-CM3
Variance and
bias-corrected
SSTs and sea ice
1970-2099
RCP8.5,
CNRM-CM5,
CCSM4,
ACCESS1.0,
NorESM1-M,
MPI-ESM-LR,
GFDL-CM3
CCAM 50 km
1970-2099
CCAM
10 km/27
RCP4.5
RCP8.5,
RCP4.5
The results presented in the next section are all based upon 20 year averages. Changes are presented as
differences from the base period (1980-1999). The projections were analysed for three time periods:
2025, mid-century (the twenty year period centred on 2055), and the end of the 21st century (the twenty
year period centred on 2090). Although the complete set of simulations covers the period 1970-2099, data
for 2025 was unavailable at the time of this study, so the 2025 values for the provinces shown in the tables
in the following sections were computed by linearly scaling in time between the values at 2055 and 1990.
The figures for the GCMs in the Results section include annual average temperature and rainfall changes by
the end of the century (20 years centred on 2090) under RCP 8.5, calculated as ensemble means of
projections by 24 GCMS. GCM results are included to give a broader view that covers South and Southeast
Asia as well as the country itself, since Cambodia is strongly influenced by monsoons and climate drivers of
the tropics, such as El Niño-Southern Oscillation (ENSO) and sea-surface temperatures over the Indian and
Pacific oceans. They also give an indication of long-term trends.
The figures for the ensemble mean CCAM 10 km downscaled simulations in the Results section are
presented as spatial maps of projected climate changes by 2055 under the high emissions scenario (RCP
8.5) over a smaller region around Cambodia, compared to current climate (1980-1999, as a measure of
model validation). They give a more detailed picture of the regional changes across Cambodia.
18
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
The data in the tables for Koh Kong are based upon areal averages over the region 10-13N° and 102-104°E
and for Mondulkiri on areal averages over the region 11-14°N and 105-108°E. Although these regions are
larger than the provinces, the results will be more robust since more data is used in the calculations. For
the current (base) climate, averages for the 20 years centred on 1990 were used. Changes by 2025 are
presented for both emissions scenarios: lower (RCP 4.5) and highest (RCP 8.5). The low, mean and high
values for climate variables and indices are given in order to present the range of possible change signals
captured by the downscaled simulations.
The analysis of possible changes to sea level is based on data from nearby sea level observing sites, since no
observed sea-level data were available for Cambodia, as well as from simulations of 20th century and
projections of 21st century sea-level rise based on GCMs that were included in the World Climate Research
Programme CMIP5 project. The analysis also incorporated additional information, such as changes in
glaciers, gravitational changes, etc. (See Section7.6 for details.)
19
7 Results
In Sections 7.1-7.5, the climate change results from the GCMs and the CCAM 10 km downscaled simulations
are presented for mid-century and end-of-century. In addition, tables with change values of climate
variables and indices from the downscaled simulations for the two provinces, Mondulkiri and Koh Kong, are
given for 2025. Section 7.6 uses data from various sources to discuss possible future changes in sea level in
Cambodia and the surrounding region.
7.1 Temperature Changes
Almost all the global climate models (GCMs) simulate increases in temperature over the region of interest
by the end of this century. Figure 11 shows the ensemble mean average annual temperature increases
from 24 GCMs over a broad region of Asia for the high emission scenario (RCP 8.5). These models indicate
an annual warming of between 3 and 4°C over Cambodia by the end of the century. Greater warming is
evident further north and inland, with values of more than 5°C. Due to the relatively course horizontal
resolution of the GCMs (around 200 km), little spatial detail is evident over Cambodia.
Since the selected provinces in Cambodia are very small in the spatial scale of GCMs, the changes in mean
maximum temperatures for the high scenario (RCP 8.5) from the CCAM 10 km downscaled simulations for
the 20 year period centred on 2055 relative to the base period are shown in Figure 12 for the four seasons
in Cambodia. Generally for Cambodia, there is greater warming during the Northeast Monsoon Season
(more than 2°C) and less warming in the First Intermonsoon Season (less than 1.8°C). Note that these
values are about half of the GCM values by the end of the century. Also note that the spatial pattern of
warming varies from season to season.
Annual
(a)
Ann
(b)
Temperature change (°C)
Figure 11. Projected increases by the end of this century in ensemble mean average annual temperature (°C) relative
to a 1980-1999 baseline for Southeast Asia from 24 global climate models for RCP 8.5.
20
Rainfall
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
Figure 12. Ensemble mean of CCAM 10 km simulated maximum temperature increases (°C) for the 20 year period
centred on 2055 relative to a 1980-1999 baseline for four seasons for RCP8.5. FIMS: First Intermonsoon Season
(April-May), SWMS: Southwest Monsoon Season (June-September), SIMS: Second Intermonsoon Season (OctoberNovember) and NEMS: Northeast Monsoon Season (December-March).
Tables 2 to 7 show simulated seasonal and annual maximum, minimum and mean temperature changes by
2025 relative to a 1980-1999 baseline for RCP 4.5 and RCP 8.5 for Koh Kong and Mondulkiri Provinces. As
mentioned in the Methodology section, these are areal averaged values for regions covering these
provinces (see Methodology section). Temperatures are projected to increase by about 0.7°C for RCP 4.5
and 1.0°C for RCP 8.5 by 2025. However, there are small seasonal differences. Note that these values are
about half of those shown in Figure 12, which is about 65 years from 1990, while the table values are for
35 years from 1990.
21
Table 2. Projected changes in maximum temperature and their ranges (°C) for Koh Kong Province by 2025 relative to
1990 for RCP 4.5 and RCP 8.5 based upon 20 year averages. FIMS: First Intermonsoon Season, SWMS: Southwest
Monsoon Season, SIMS: Second Intermonsoon Season and NEMS: Northeast Monsoon Season.
Koh Kong Maximum Temperature
2025:RCP4.5
2025:RCP8.5
Season
Lowest
Highest
Mean
Lowest
Highest
Mean
FIMS
0.4
1.0
0.6
0.6
1.3
0.9
SWMS
0.6
1.1
0.7
0.9
1.4
1.0
SIMS
0.6
1.1
0.8
0.8
1.4
1.0
NEMS
0.6
1.2
0.8
0.9
1.4
1.0
ANNUAL
0.6
1.1
0.7
0.9
1.4
1.0
Table 3. Projected changes in maximum temperature and their ranges (°C) for Mondulkiri Province by 2025 relative
to 1990 for RCP 4.5 and RCP 8.5 based upon 20 year averages. FIMS: First Intermonsoon Season, SWMS: Southwest
Monsoon Season, SIMS: Second Intermonsoon Season and NEMS: Northeast Monsoon Season.
Mondulkiri Maximum Temperature
2025:RCP4.5
Highest
2025:RCP8.5
Season
Lowest
Mean
FIMS
0.4
1.1
0.7
SWMS
0.6
1.1
SIMS
0.7
NEMS
ANNUAL
Lowest
Highest
Mean
0.7
1.4
0.9
0.8
0.9
1.4
1.0
1.2
0.8
1.0
1.5
1.1
0.7
1.2
0.9
1.0
1.4
1.1
0.7
1.2
0.8
0.9
1.4
1.0
Table 4. Projected changes in minimum temperature and their ranges (°C) for Koh Kong Province by 2025 relative to
1990 for RCP 4.5 and RCP 8.5 based upon 20 year averages. FIMS: First Intermonsoon Season, SWMS: Southwest
Monsoon Season, SIMS: Second Intermonsoon Season and NEMS: Northeast Monsoon Season.
Koh Kong Minimum Temperature
2025:RCP4.5
2025:RCP8.5
Season
Lowest
Highest
Mean
Lowest
FIMS
0.5
1.1
0.7
0.7
1.3
1.0
SWMS
0.5
1.1
0.7
0.7
1.4
1.0
SIMS
0.5
1.0
0.7
0.6
1.4
0.9
NEMS
0.5
1.1
0.8
0.9
1.3
1.0
ANNUAL
0.5
1.1
0.7
0.8
1.3
1.0
22
Highest
Mean
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
Table 5. Projected changes in average minimum temperature and their ranges (°C) for Mondulkiri Province by 2025
relative to 1990 for RCP 4.5 and RCP 8.5 based upon 20 year averages. FIMS: First Intermonsoon Season, SWMS:
Southwest Monsoon Season, SIMS: Second Intermonsoon Season and NEMS: Northeast Monsoon Season.
Mondulkiri Minimum Temperature
2025:RCP4.5
2025:RCP8.5
Season
Lowest
Highest
Mean
Lowest
Highest
Mean
FIMS
0.5
1.1
0.7
0.7
1.3
1.0
SWMS
0.5
1.1
0.7
0.7
1.4
1.0
SIMS
0.5
1.1
0.7
0.6
1.4
0.9
NEMS
0.6
1.0
0.8
0.8
1.3
1.0
ANNUAL
0.5
1.1
0.7
0.7
1.3
1.0
Table 6. Projected changes in average temperature and their ranges (°C) for Koh Kong Province by 2025 relative to
1990 for RCP 4.5 and RCP 8.5 based upon 20 year averages. FIMS: First Intermonsoon Season, SWMS: Southwest
Monsoon Season, SIMS: Second Intermonsoon Season and NEMS: Northeast Monsoon Season.
Koh Kong Mean Temperature
2025:RCP4.5
2025:RCP8.5
Season
Lowest
Highest
Mean
Lowest
Highest
Mean
FIMS
0.5
1.0
0.6
0.7
1.3
0.9
SWMS
0.5
1.1
0.7
0.8
1.4
1.0
SIMS
0.6
1.0
0.7
0.8
1.4
1.0
NEMS
0.6
1.1
0.8
0.9
1.3
1.0
ANNUAL
0.6
1.1
0.7
0.8
1.4
1.0
Table 7. Projected changes in average temperature and their ranges (°C) for Mondulkiri Province by 2025 relative to
1990 for RCP 4.5 and RCP 8.5 based upon 20 year averages. FIMS: First Intermonsoon Season, SWMS: Southwest
Monsoon Season, SIMS: Second Intermonsoon Season and NEMS: Northeast Monsoon Season.
Mondulkiri Mean Temperature
2025:RCP4.5
2025:RCP8.5
Season
Lowest
Highest
Mean
Lowest
FIMS
0.5
1.1
0.7
0.7
1.3
0.9
SWMS
0.5
1.1
0.7
0.8
1.4
1.0
SIMS
0.6
1.1
0.7
0.7
1.4
1.0
NEMS
0.6
1.1
0.8
0.9
1.3
1.0
ANNUAL
0.6
1.1
0.7
0.8
1.4
1.0
23
Highest
Mean
7.2 Rainfall Changes
Rainfall changes simulated by both the global and regional climate models are more complex than
temperature changes since projections show both increases and decreases in rainfall in the future.
Simulated ensemble mean annual rainfall changes from 24 GCMs for RCP 8.5 by the end of the century
relative to a 1980-1999 baseline generally show increases over Southeast Asia (Figure 13), but there are
seasonal differences (not shown). Over Cambodia, there are annual rainfall increases of up to 10% by the
end of this century for RCP 8.5.
Annual
Annual
(b)
ature change (°C)
Rainfall change (%)
Figure 13. Annual projected changes in rainfall (%) over Southeast Asia from an ensemble mean of 24 global climate
models for the end of this century relative to the baseline period 1980-1999 under RCP 8.5.
The CCAM 10 km downscaled simulations generally project increases in rainfall in all seasons, except during
the Southwest Monsoon, which shows decreases in parts of Cambodia (Figure 14). Note these results are
in contrast to previous studies which suggested increases in the Southwest Monsoon rain. Annually, the
downscaled simulations show only small changes (not shown), broadly similar to the GCMs even though
they are for different time periods. But the differing changes seasonally will have significant impact on the
hydrological cycle of the provinces.
Projected percentage changes in mean seasonal and annual rainfall and the ranges for Koh Kong and
Mondulkiri provinces for 2025 relative to 1990 for RCP 4.5 and RCP 8.5 are shown in Table 8 and Table 9,
respectively. These are area-averaged values for these provinces. The ensemble mean results suggest
reduced rainfall during the Southwest and Second Intermonsoon seasons and increases during the
Northeast Monsoon and the First Intermonsoonal Season for both provinces. These results are consistent
with those shown in Figure 14. Annually, Koh Kong shows slight decreases in rainfall while Mondulkiri
shows little change.
24
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
Figure 14. Ensemble mean of CCAM-10 km simulated rainfall (mm/day) over Cambodia under RCP 8.5 for the 20
year period centred on 2055 relative to a 1980-1999 baseline for the four seasons. FIMS: First Intermonsoon Season
(April-May), SWMS: Southwest Monsoon Season (June-September), SIMS: Second Intermonsoon Season and NEMS:
Northeast Monsoon Season.
25
Table 8. Projected percentage changes in mean seasonal and annual rainfall and their ranges for Koh Kong Province
by 2025 relative to 1990 for RCP 4.5 and RCP 8.5 based upon 20 year averages. FIMS: First Intermonsoon Season,
SWMS: Southwest Monsoon Season, SIMS: Second Intermonsoon Season and NEMS: Northeast Monsoon Season.
Koh Kong Mean Seasonal and Annual Rainfall
2025:RCP4.5
2025:RCP8.5
Season
Lowest
Highest
Mean
Lowest
Highest
Mean
FIMS
-4
19
4
1
12
6
SWMS
-10
4
-4
-7
-5
-6
SIMS
-8
0
-2
-7
3
-2
NEMS
-1
21
6
-4
13
3
ANNUAL
-6
6
-1
-4
0
-2
Table 9. Projected percentage changes in mean seasonal and annual rainfall and their ranges for Mondulkiri
Province by 2025 relative to 1990 for RCP 4.5 and RCP 8.5 based upon 20 year averages. FIMS: First Intermonsoon
Season, SWMS: Southwest Monsoon Season, SIMS: Second Intermonsoon Season and NEMS: Northeast Monsoon
Season.
Mondulkiri Mean Seasonal and Annual Rainfall
2025:RCP4.5
2025:RCP8.5
Season
Lowest
Highest
Mean
Lowest
Highest
Mean
FIMS
0
23
8
3
18
9
SWMS
-8
7
-3
-5
4
-3
SIMS
-5
3
-2
-6
4
-0
NEMS
-1
13
4
-4
10
3
ANNUAL
-3
8
0
-3
7
0
7.3 Extreme Temperatures
A Hot Day (HD) is defined as a day above 35°C. The HD climate index is the number of days per year above
this temperature. Since HDs are defined on an absolute threshold, there will be variations related to the
terrain (higher elevations have cooler temperatures and therefore fewer HDs). In this study, the definition
of heatwave, based on the Heat Wave Duration Index (HWDI) of Perkins et al. (2012), is a period of at least
six consecutive days with maximum temperature (Tmax) over the 95th percentile of daily maximum
temperature over each year. Since the percentile is based upon the distribution of Tmax over the year at
each grid point, it will be less affected by elevation than HD, and will primarily capture events during the
warm season.
Figure 15 shows the ensemble mean of CCAM 10 km simulated annual HDs and the number of days in a
heat wave (based upon the HWDI) for the 21 year baseline period (1980-2000) and the projected changes
for the 21 year period centred on 2055 over Southeast Asia. As expected, there is a significant influence of
terrain on the number of HDs, with higher elevations having no HDs since the maximum temperature never
reaches 35°C. Also, there are few HDs in southern and coastal areas as the maximum temperatures in
26
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
these areas do not go above 35°C often, if at all. The current climate distribution of the HWDI shows a
relatively even distribution across Cambodia. The average numbers of hot days observed for the period
1980 to 2000 for Mondulkiri and Koh Kong provinces are 7 and 5, respectively (Table 10). The CCAM 10 km
ensemble mean results show significant increases in the number of hot days to 17 days per year in
Mondulkiri and 15 days per year in Koh Kong, a more than doubling of the number per year.
Changes in the average duration of the heatwaves and the frequency of heatwaves by 2025 are given in
Table 11. Both the duration and the frequency of heat waves are projected to increase substantially. The
increase in maximum temperature is the primary cause for the increase in the heat wave index, but other
factors such as rainfall and wind speed, which influence the temperature and its variations over time, may
also change the characteristics of heat waves. For example, the decrease in rainfall during the summer
Southwest Monsoon Season may cause an increase in the number of heat waves. The average heat wave
duration in Mondulkiri for the 1980-2000 baseline period is 9.4 days. This is expected to increase to 10.1
days by 2025, and the frequency of such events is projected to increase by four times compared to the
baseline period. The baseline period heat wave index for Koh Kong is 8.7 days, and is projected to be 9.6
days by 2025. The frequency of these events, currently about 0.4 per year, is projected to increase to 2.3
per year by 2025.
7.4 Droughts
Droughts in Cambodia are caused by failure of the monsoon or intermonsoonal seasonal rains, particularly
during the Southwest Monsoon Season. Such a failure of the monsoon rains is often linked to the coupled
ocean-atmosphere phenomenon, ENSO. ENSO is the major driver that modulates the variability of tropical
climate on a year-to-year time scale. El Niño events, which are associated with warmer sea surface
temperature anomalies over the eastern tropical Pacific and cooler sea surface temperature anomalies
over the western Pacific, are often correlated with droughts over Southeast and South Asia. The opposite
phase, a La Niña event, is associated with above-normal rainfall and sometimes extreme flood events over
these regions.
Drought intensity and frequency are categorized using a widely used drought index, the Standardization
Precipitation Index (SPI, McKee et al., 1993). This a versatile method which can be used to calculate drought
at various time scales. In this study, droughts of 3, 6 and 12 month duration are investigated. Droughts are
defined based on the definitions given by McKee et al. (1993). The 3-month period is considered to be
agricultural drought, which affects agricultural activities such as rice cultivation in Cambodia. The 6-month
drought is considered to be drought of medium duration that affects plantation and perennial crops. The
12-month period is considered to be hydrological drought, which affects water resources such as
groundwater and dam capacity on a longer time scale.
Changes in extreme drought intensity and frequency for the 20 years centered on 2055 relative to the
baseline period are shown in Figure 16 for rainfall simulated in the CCAM 10 km simulations. There is a
tendency for short-term agricultural (3-month) drought to decline in both duration and frequency, but for
12-month drought to increase in both duration and frequency. Six-month droughts show a slight increase
in duration and a decrease in frequency. Area-averaged values of these indices for these provinces are
given in Table 12.
27
Figure 15. Ensemble mean of CCAM-simulated annual mean hot days (HDs, left) and number of days in a heat wave
based on the Heat Wave Duration Index (HWDI, right) for 1990 (top) and their change under RCP 8.5 by 2055
(bottom) based upon 20 year averages.
28
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
Table 10. Number of hot days (HDs, Tmax35 = number of days above 35°C) for Mondulkiri and Koh Kong provinces
at 1990 and 2025 for RCP 8.5 based upon 20 year averages.
Mondulkiri and Koh Kong HD
Index
Mondulkiri
Koh Kong
Tmax35
No. of days
No. of days
HD-1990
7
5
HD-2025
17
15
Table 11. Duration and frequency of heat waves, based on the Heat Wave Duration Index (HWDI), for Mondulkiri
and Koh Kong provinces at 1990 and 2025 for RCP 8.5 based upon 20 year averages.
Mondulkiri and Koh Kong HWDI
Index
Heat Wave
Duration Index
Mondulkiri
Koh Kong
Mean duration
(days)
Frequency
(No./year)
Mean duration
(days)
Frequency
(No./year)
HWDI-1990
9.4
0.5
8.7
0.4
HWDI-2025
10.1
1.9
9.6
2.3
29
Figure 16. Ensemble mean CCAM 10 km simulated average 3-month drought frequency (top left) and duration in
months (top right) for 1990, compared with their changes by 2055 (bottom) under RCP 8.5, based upon 20 year
averages.
30
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
Table 12. Ensemble mean CCAM 10 km simulated drought duration and frequency for the baseline period (20 year
period centred on 1990) and projected changes by the 20 year period centred on 2025 relative to the baseline
period for 3-, 6- and 12-month droughts for Mondulkiri and Koh Kong provinces.
Mondulkiri and Koh Kong Drought Duration and Frequency
Index
Drought Indices
Mondulkiri
Duration
(months)
Change
in
duration
(months)
No. of
events
Koh Kong
Change
in
Events
(no.)
Duration
(months)
Change
in
duration
(months)
No. of
events
Change in
Events
(no.)
1990
Severe and
extreme 3month
droughts
5.81
13.00
5.72
12.56
6.50
10.66
6.95
9.70
20.47
2.64
20.57
2.50
1990
Severe and
extreme 6month
droughts
1990
Severe and
extreme 12month
droughts
2025
Severe and
extreme 3month
droughts
-0.14
-0.12
-0.09
-0.41
0.12
-0.51
0.19
-0.58
0.74
0.02
1.28
0.12
2025
Severe and
extreme 6month
droughts
2025
Severe and
extreme 12month
droughts
31
7.5 Extreme Rainfall, Consecutive Wet and Dry Days
The average annual maximum one day rainfall (RX1) and five consecutive day rainfall (RX5) were calculated
from ensemble CCAM experiments for RCP 8.5 for the 20 year baseline period centred on 1990 (Figure 17).
The upper panels of Figure 17 show that the RX1 for the Koh Kong region is 160-200 mm/day, while over
the Mondulkiri region it is around 100-120 mm/day. The RX5 index is above 160 mm/day over most parts of
Cambodia. The lower panels of Figure 17 show the ensemble mean number of consecutive wet days
(CWDs) and consecutive dry days (CDDs) for the baseline period. Consecutive dry days (CDDs) are
calculated as the average annual maximum number of days with daily rainfall less than 1 mm. Consecutive
wet days (CWDs) are calculated as the average annual maximum number of days with daily rainfall greater
than or equal to 1 mm. Both CDDs and CWDs are around 20-50 days over the entire country for the current
climate, with slightly more CWDs and fewer CDDs along the coast.
The spatial patterns of the changes in the extreme rainfall indices are shown in Figure 18. The upper panels
of Figure 18 indicate increases of more than 10-20 mm/day for RX1 over Cambodia by 2055. However, the
RX5 index shows a decrease of around 10-20 mm/day over most of the Koh Kong region but an increase of
20-40 mm/day over Mondulkiri. The changes in RX1 and RX5 for 2025 are summarised in Table 13. The two
measures of extreme rainfall show increases for both provinces, except Koh Kong for the 5-day annual
rainfall maximum (RX5).
The lower panels of Figure 18 show the spatial patterns of projected CDDs and CWDs for 2055 for RCP 8.5
simulated by CCAM. By 2055, both the mean number of days and the frequency of CDDs are expected to
decrease for both provinces, but for CWDs the mean days are expected to decrease, while the frequency is
expected to increase (Table 14).
In summary, the decrease in CDDs is consistent with the projected increase in rain in the non-monsoon
period, and the large decrease in CWDs is consistent with the projected decrease in the summer monsoon
during the summer wet season.
32
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
Figure 17. Ensemble mean CCAM 10 km simulation mean extreme rainfall indices for the baseline period (20 years
centred on 1990). Upper left: RX1 (mm/day); upper right: RX5 (mm/day); lower left: consecutive wet days (CWDs,
number of days); lower right: consecutive dry days (CDDs, number of days).
33
Figure 18. Projected CCAM multi-model mean extreme rainfall indices under RCP 8.5 for the 20 year period centred
on 2055 relative to the baseline period. Upper left: RX1 (mm/day); upper right: RX5 (mm/day); lower left:
consecutive wet days (CWDs, number of days); lower right: consecutive dry days (CDDs, number of days).
34
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
Table 13. Percent changes in extreme rainfall amounts (mean annual one- (RX1) and five-day (RX5) rainfall totals)
by 2025 for Mondulkiri and Koh Kong provinces. Changes are relative to 1990 and are based upon 20 year averages.
Mondulkiri and Koh Kong Extreme Rainfall
Index
Mondulkiri
Koh Kong
Percentage change in amount
Percentage change in amount
RX1-2025
3.25
0.9
RX5-2025
2.15
-1.35
Table 14. Changes in Consecutive Dry Days (CDDs) and Consecutive Wet Days (CWDs) by 2025 for Mondulkiri and
Koh Kong provinces. Changes are relative to 1990 and are based upon 20 year averages.
Mondulkiri and Koh Kong Consecutive Dry Days
Index
Mondulkiri
Koh Kong
Change in mean
number of days
Percentage
change in
frequency
Change in mean
number of days
Percentage
change in
frequency
CDD-2025
-1.75
-1.8
-1.7
-1.3
CWD-2025
-7.3
3.4
-8.05
3.8
7.6 Sea Level Changes
Although no direct observed sea-level data were available for Cambodia, a number of sea-level observing
sites are available in nearby countries. The analysis in this section includes data from these sites as well as
from simulations of 20th century and projections of 21st century sea-level rise based on GCMs from CMIP5.
The projections provide estimates of changes in ocean temperatures (and hence ocean thermal expansion)
in response to changing greenhouse gas concentrations and changes in air-sea fluxes of heat, freshwater
and momentum and atmospheric temperature and precipitation patterns. The air-sea fluxes and internal
variability determine ocean circulation and the ocean sea surface height. The changing temperature (and
sometimes precipitation) patterns are used to model changes in glacier mass (Radic and Hock, 2011;
Slangen et al., 2011; Marzeion et al., 2012). Changes in ocean temperatures and atmospheric temperature
and precipitation patterns are used to drive ice sheet models. Sea level changes are a result of changes in
the density of the ocean (due to ocean thermal expansion or contraction, obtained from GCMs) and/or
changes in the mass of the ocean through exchanges with the cryosphere (glaciers and ice sheets) and the
terrestrial environment (soil moisture, terrestrial reservoirs, lakes, ground water, etc.). In addition to the
globally averaged change, sea level changes regionally relative to the land as a result of changes in ocean
dynamics (changes in ocean currents related to changes in surface winds, air-sea fluxes of heat and
freshwater and internal variability), and changes in the Earth’s gravitational field, with changes in the
distribution of water on the Earth and vertical motion of the land (Church et al., 2011). The total sea level
changes regionally are a combination of all these factors.
Multi-century station sea-level time series indicate an increase in the rate of sea-level rise (Woodworth,
1999), perhaps starting as early as the 18th century (Jevrejeva et al., 2008), although this latter assessment
depends on only three records for the early part of the series. Large-scale spatial correlations estimated
35
from satellite altimeter data have been combined with tide gauge data in an attempt to improve estimates
of 20th (and late 19th) century sea-level rise (Church et al., 2004, 2006, 2011, Figure 19; Ray and Douglas,
2011). These analyses, and a third dependent on tide gauge data alone (Jevrejeva et al., 2006), agree on a
20th century rate of sea-level rise of 1.7 ± 0.2 mm yr-1. All of these analyses indicate significant changes in
the rate of rise during the 20th century, with the largest rate of increase since 1993. This larger rate of rise
since 1993 is also confirmed by the satellite altimeter data (Church et al., 2011; Masters et al., 2012 and the
references therein). The Church et al. and the Jevrejeva et al. analyses indicate a small (about 0.01 mm yr-2)
increase in the rate of sea-level rise from the start of the analysis in the late 19th century. However, Ray and
Douglas (2011) found no significant acceleration for the 20th century alone. Paleo data from a number of
locations around the world indicate an increase in the rate of rise in sea level from several tenths of a mm
yr-1 to modern rates on the order of 2 mm yr-1 from the late 19th to the early 20th centuries (Gerhels and
Woodworth, 2013).
Figure 19. Estimates of global mean sea-level rise from 1880 to 2012, updated from Church and White (2011).
An attempt was made to obtain sea-level and tidal data from Cambodia. However, no long-term qualitycontrolled dataset was obtained, so data from a number of nearby observing sites were examined. The
locations of the stations selected for this study are shown in Figure 20 (obtained from
http://www.psmsl.org/data/obtaining/). Monthly and annual time series of sea-level measurements from
these stations are shown in Table 15. Although there are strong variations in the annual cycle in sea level
at various locations due to the monsoonal influence, an increase of between 5 and 15 cm is observed at
several stations over the last several decades. Only Ko Mattaphon, Thailand has shown little increase.
These increases could be partly influenced by natural variability, as most increases are not statistically
significant.
36
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
Figure 20. Map of stations with sea-level data near Cambodia.
To determine the regional changes in sea level for Cambodia, the approach of Church et al. (2011) and
Slangen et al. (2012) was used. This involved combining the dynamic ocean sea-level distribution with the
regional changes (associated with contemporary changes in mass distribution on the Earth) and an ongoing
glacial isostatic adjustment from changes in mass distribution since the last glacial maximum.
The preliminary regional distribution of sea level change for 2081-2100 compared with 1986-2005 for the
RCP 8.5 scenario is shown in Figure 21. The analysis is based on based upon the latest CMIP5 model
simulations as well as the latest findings on other factors affecting sea level. These regional sea-level
projections combine several globally averaged sea-level projections incorporating thermal expansion,
glacier, ice sheets and land storage. The amount of regional sea-level rise is largest (600-950 mm) for the
RCP 8.5 scenario and smallest (300-600 mm) for the RCP 2.6 scenario by the end of the century, with RCP
4.5 and 6.0 being in between (not shown). The patterns are similar across the scenarios, with the largest
rise east of southern China, extending westward and then southward into the South China Sea. The
projections indicate slightly lower sea-level rise for the coast of Cambodia than offshore as a result of uplift
of the coastline by glacial isostatic adjustment and a small net contribution (a maximum around 10-15 mm)
from fingerprint patterns associated with redistribution of mass from glaciers and ice sheets. This
contribution is positive in the South China Sea and slightly negative in the Gulf of Tonkin and Gulf of
Thailand. It is the sum of a larger-than-global-averaged rise from the Greenland contribution and a smallerthan-global-averaged rise from the smaller glacier fingerprint, because of the proximity of the region of
glacier mass loss in the Asian interior. Note that local geodetic effects (such as compaction of sediments)
need to be added to the large-scale sea-level changes considered here.
37
Table 15. Observed time series of average monthly and yearly sea-level measurements for stations near Cambodia
for various periods. See graph axes for periods and Figure 20 for locations. Note differing vertical and horizontal
axis on these various plots.
Station number
and name (increase
in sea level, in cm)
Monthly sea-level values (mm)
1495: Vung Tau,
Vietnam (+10 cm)
449: Ko Sichang,
Thailand (+5 cm)
444: Pom Phrachun,
Thailand (+10 cm)
174: Ko Lak, Thailand
(+15 cm)
1792: Ko Mattaphon,
Thailand (Little
change)
1703: Getting,
Malaysia (+10 cm)
38
Yearly sea-level values (mm)
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
Figure 21. Preliminary sea-level rise results (mm) for oceans near Cambodia under RCP 8.5 for the end of the 21st
century (2081-2100) compared to a 1986-2005 baseline. The star indicates the location of Vung Tau, Vietnam, which
is the sea-level station closest to Cambodia with data available.
The observed and projected time series of relative sea-level changes at Vung Tau, Vietnam (the closest sealevel observing station with projections) are given in Figure 22. Although results are preliminary, the
dashed lines, which give an estimate of interannual variability in sea level under RCP 8.5 and RCP 2.5, show
the large range of potential changes in sea level in the future, particularly by the end of the century.
Figure 22. Historical sea-level rise and future projections for Vung Tau, Vietnam from 1950 to 2100 (mm)
incorporating tide gauge and satellite data. The observed tide-gauge relative sea-level records (since the late 1970s)
are indicated in light blue, with the satellite record (since 1993) in green. Black lines are multi-model mean
projections of sea level for the RCP 8.5 and RCP 2.6 emissions scenarios, with the 5% (lower) and 95% (upper) range
of annual variability shown by the red and blue shaded regions from 1995 to 2100. Dashed lines are an estimate of
interannual variability in sea level. The location of Vung Tau is indicated by a star in Figure 21.
39
In Cambodia, port authorities like Sihanoukville collect data on tide and meteorological stations collect
necessary meteorological and surface water level data. Although attempt was made to obtain this data,
data was not made available during this study. Therefore, detailed analysis of sea-level rise for the
Cambodian coastline was not possible because of the absence of easily accessible high-quality, highfrequency (hourly) sea-level data. Therefore, results from other studies were used to derive conclusions for
this region. The higher sea levels in the 21st century will result in an increase in the frequency of sea-level
extremes. Hunter (2012) demonstrated that even for a 0.5 m sea-level rise, what is currently a 1-in-100year flooding event at many locations could be occurring annually by 2100. This effect will mean more
frequent flooding events for coastal regions and adjacent low-lying areas that are currently subject to
occasional flooding events. Most studies to date have argued that changes in mean sea level are the
dominant factor in projected changes in the frequency of extreme sea levels (Sterl et al, 2009; Lowe et al.,
2010; Seneviratne et al., 2012; Colberg and McInnes, 2012), but changes in storminess (frequency or
intensity) would also lead to changes in the frequency of extreme sea levels.
There remains uncertainty in the range of sea-level projections for Cambodia. Assuming small changes in
storminess, Hunter et al. (2013) have combined this uncertainty with the currently observed frequency of
extreme sea level to estimate an “allowance” for sea-level rise. If infrastructure was raised by this
allowance, the current risk of coastal flooding would be maintained rather than rising with time. Again,
such studies have not been completed for the Cambodian coast.
Significant uncertainty in sea-level projections comes from the uncertainty surrounding the future of the
dynamic response of the ice sheets and the regional distribution of sea-level rise. There is currently
significant progress occurring in these fields and a revised assessment of sea-level rise for the Cambodian
coasts should be undertaken incorporating new information from the Intergovernmental Panel on Climate
Change’s 5th Assessment Report, published in September 2013. Such a study should also examine the
interaction between mean sea-level rise and extreme events.
40
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
8 Conclusions for Part 1
On the basis of climate modelling and observational studies included in the IPCC 5th Assessment Report
(2013), human influence has been detected in warming of the atmosphere and the ocean, in changes in the
global water cycle, in reductions in snow and ice, in global mean sea level rise, and in changes in some
climate extremes. It is extremely likely that human influence has been the dominant cause of the observed
warming since the mid-20th century. Continued increases in greenhouse gas concentrations imply that
their impact on future climate is now inevitable. Therefore, we need the best possible estimates of regional
changes to future climate and of the sensitivity of natural and socio-economic sectors to such changes for
better future planning to mitigate climate change risks.
In order to better estimate Cambodia’s future climate on a large scale, we assessed a number of GCM
simulations produced for the latest IPCC report, using statistical measures to assess the simulations over
South and Southeast Asia. All of the global climate models show an increase in temperature. Both
increases and decreases in rainfall were projected, with more complex patterns than for temperature.
These GCMs capture spatial patterns of rainfall for current climate over complex topographic regions
poorly. Therefore, we used dynamical downscaling methods to get local and regional-scale information for
Southeast Asia. The fine-resolution climate simulations have been produced through the end of the 21st
century (1970-2099), but changes in mean climate and extremes for Koh Kong and Mondulkiri provinces of
Cambodia have been analysed for the 20 year period centred on 2025, for use in sensitivity studies to
explore various options for hydrological applications.
Due to the location of Cambodia within the tropics, spatial and temporal variability in temperature is small,
but variability of rainfall is greater at various time scales, such as intraseasonal, interannual and
interdecadal. Variations in the monsoon rainfall associated with Pacific and Indian Ocean temperature and
ENSO are the primary factors that affect the country’s agriculture, industrial activities, and ecosystems.
Annual temperature in Mondulkiri is projected to increase by +0.7°C for RCP 4.5 and by about +1.0 °C for
RCP 8.5 by 2025, with greater increases by the end of the century. Maximum temperature is projected to
increase by about 2°C by mid-century and by 4.0°C by the end of the century for RCP 8.5. Minimum
temperature is projected to increase by about 1.9°C by mid-century and by 3.6°C by the end of the century
for RCP 8.5. These increases are uniform throughout the year.
Annual rainfall in Mondulkiri shows no change (near 0%) by 2025, with a range of -3% to +8% for the RCP
4.5 (lower) and a range of -3% to +7% for the RCP 8.5 (higher) scenario. However, there are seasonal
differences in rainfall changes: increases in the Northeast Monsoon Season (NEMS) and the First
Intermonsoon Season (FIMS) and decreases in the Southwest Monsoon Season (SWMS) and the Second
Intermonsoon Season (SIMS). Extreme rainfall amounts are projected to increase by +3% for maximum
annual 1-day rainfall and by +2% for maximum annual 5-day rainfall. Consecutive dry days per year
decrease by -2 days in mean length and by -2% in frequency. Consecutive wet days decrease by -7 days per
year in mean length, but with a +3% increase in frequency.
Annual temperature in Koh Kong is projected to increase by +0.7°C for RCP 4.5 and by about +1.0 °C for RCP
8.5 by 2025, with greater increases by the end of the century. Maximum temperature is projected to
increase by about 2°C by mid-century and by 4.0°C by the end of the century for RCP 8.5. Minimum
temperature is projected to increase by about 1.9°C by mid-century and by 3.6°C by the end of the century
for RCP 8.5. These increases are uniform throughout the year.
Annual rainfall in Koh Kong shows little change (-1%), with a range of-6% to +6% for the RCP 4.5 (lower)
scenario and a change of-2%, with a range of -4% to +0%, for the RCP8.5 (higher) scenario by 2025.
However, seasonal rainfall changes do show differences: increases in NEMS and FIMS and decreases in
SWMS and SIMS. Small changes in extreme rainfall are projected: +1% for maximum annual 1-day rainfall
amounts and -1% for maximum annual 5-day rainfall amounts. Consecutive dry days per year decrease by 41
2 days in mean length and by -1% frequency. Consecutive wet days per year decrease by -8 days in mean
length and by +4% in frequency.
Partly due to the increase in temperature, the number of Hot Days (days above 35°C per year) will increase
in both areas by about +7 days each year. Heat waves (periods of more than five consecutive days of
extreme temperatures) are projected in increase in average length by 10-15 days and become 3-5 times
more frequent.
Most types of droughts show increases by 2025: 3-month droughts decrease in frequency and duration for
both provinces, 6-month droughts increase in duration but decrease in frequency for both provinces, and
12-month droughts increase in frequency and duration for both provinces (slightly greater for Koh Kong).
The observed mean sea–level rise shows an annual cycle with highest levels in January and lowest levels in
July. A 10 cm rise has been observed already in the last 40 years.
It is estimated that sea level in Koh Kong will rise by 40 to 60 cm by the end of the century. Previous
studies have indicated that a one-meter sea-level rise would lead to loss of 44 km2 of coastline in Koh Kong
and significantly raise the risk of severe flooding in Koh Kong City.
In summary, temperature is projected to increase for both provinces in the future. A large increase in the
number of hot days and heat waves is projected. With this comes also the potential also for enhanced
evaporation. Annual rainfall changes are more complex, with the models showing both increases and
decreases in the future. There is some indication of decreases in summer rainfall and increases in winter
rainfall. Changes in rainfall from year to year and from decade to decade are greater than expected overall
changes in the mean annual totals, so there is a need to consider interannual variability as well as changes
due to climate change in future planning for the provinces.
The trend to greater frequency of long-term droughts could be the feature of climate change that has the
most impact in Koh Kong and Mondulkiri for the next 20 years. This might lead to less groundwater due to
less recharge and greater extraction. In addition, more and longer droughts for Koh Kong could lead to
more salt water intrusion.
Projected increases in extreme rainfall amounts in Mondulkiri suggest more likely risks of floods. However,
only small changes in extreme rainfall amounts are projected for Koh Kong.
Sea level will continue to rise. Sea-level rise is likely to be more important in winter (the time of highest
annual sea level), and storm surges may also be affected. The decrease in summer monsoon activity will
possibly lead to fewer storms, though changes in the frequency of storms were not directly assessed in this
study. In Koh Kong, only small changes (or possibly decreases) in extreme rainfall amounts indicate that
there may possibly be fewer storms, although this will be combined with a trend toward greater tidal and
storm extremes in the coastal regions of Koh Kong.
Dynamically downscaled simulations do have some limitations, since this method uses information from
GCMs, which do not fully capture the intra-seasonal and interannual variability of climate associated with
sea surface temperature, ENSO, and other factors. Different GCMs produce different ranges of future
climates due to their differing internal dynamics and model physics. The uncertainty associated with
considering only a few model simulations may be reduced by including multiple simulations, as in the
ensemble technique employed in this study. Because there is uncertainty associated with estimation of
future greenhouse gases, aerosols and human activities that will impact on climate change, we have
included simulations for both a higher (RCP 8.5) and lower (RCP 4.5) emission scenario. In future studies,
the use of more than one model for downscaling GCMs would help further to reduce uncertainty.
The next section of this report, Part 2, deals with community / village based vulnerability assessment at
household level in the GMS BCC project sites of Koh Kong and Mondulkiri, for which there is additional
financing provided to Cambodia by PPCR. The projected changes to future climate in these provinces,
including increased temperatures, alterations in rainfall patterns and frequency and duration of extreme
events and sea-level rise discussed in Part 1 of this study are some of the factors that will potentially
increase vulnerability and need to be considered in future planning for Koh Kong and Mondulkiri.
42
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
PART 2:
Climate Change Vulnerability Assessment
for Koh Kong and Mondulkiri, Cambodia
43
Executive Summary -- Part 2
Cambodia is highly vulnerable to impacts of climate change and variability due to the high dependence of
its economy on climate-sensitive sectors and the low adaptive capacity of its populations
(http://www.climatechange.searca.org). The National Adaptation Program of Action (RGC, 2006) has
determined that agriculture and water resources, the most important sectors of the economy, are highly
vulnerable to climate change, with around 57.6% of the population relying on agriculture for their
livelihoods. Fisheries and forest extraction are also adversely affected by changes in climate. Increased
rainfall variability impacts surface and ground water availability, including potable water supply, flood
protection and irrigation. Likewise, rural infrastructure, including roads, water supply and sanitation, suffers
from the impacts of floods and cyclones. The poorer households, especially, are the most at risk and least
able to recover from the impact of shocks such as flooding, drought and climatic extremes. While rural
livelihood strategies are dynamic and able to respond to changing pressures and opportunities, the speed
and magnitude of climate-induced changes as predicted in Part 1 may seriously challenge the adaptive
capacity of some or all livelihood strategies.
Part 2 provides an overall assessment of current community vulnerability to climate change in the
Biodiversity Conservation Corridors (BCC) target communes in Koh Kong and Mondulkiri, including coping
mechanisms and adaptive capacity. Both quantitative and qualitative methods have been applied (see
Figure 24).
In Koh Kong, the coastal area is threatened by impacts of climate change such as (1) seawater intrusion,
high tides and sea-level rise; (2) storms and storm surges; (3) heavy rain and flooding; and (4) drought and
water stress. Increased storm frequency and intensity damages rural housing and infrastructures, and
associated surges speed up coastline erosion and potentially mangrove forest loss. The number of fishing
days and income is also highly affected by more frequent storms. Flash flooding or sometimes river floods
due to heavy rainfall destroy crops in low-lying areas. Irregularities in rainfall patterns such as a dry spell
early in the rainy season or a shortened rainy season reduce crop productivity; hence the food security
situation in the area could be further aggravated. Prolonged and increased drought conditions together
with high temperatures pose another stress on water supply and increase health-related problems.
Mondulkiri has been identified as one of the climate change hotspots in the Lower Mekong Basin (ARCC,
2013). Over the past few decades it has become increasingly vulnerable to the hazards of drought, flood,
storms and other climate variability. Drought affects the agricultural outputs of communities, the health of
villagers and animals alike, as well as the availability of water resources for personal consumption and
household use. Heavy rainfall and flash flooding frequently impact on crop cultivation, and cause loss of
livestock and damage to housing and rural infrastructure. Communities also experience an increase in
outbreaks of water-borne diseases (diarrhoea, malaria) and dizziness as a result of flooding. More frequent
storms and thunderstorm events pose a threat to housing and human lives.
Adaptation options and priorities in both provinces were identified through community participatory
consultation and grouped into five broad categories: (1) irrigation schemes, (2) water storage and supply,
(3) coastal protection (e.g. mangrove restoration, sea/land dykes), (4) climate-resilient agricultural technical
assistance, and (5) improving infrastructure and accessibility and health care capacity (especially during the
wet season).
Local-level climate change adaptation is more effective if incorporated with other strategies such as
livelihood enhancement, water and other resource management, land use planning and entitlement.
44
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
9
Introduction to Part 2
Cambodia is highly vulnerable to impacts of climate change and variability due to high dependence of its
economy on climate-sensitive sectors, and low adaptive capacity of its populations. Agriculture and water
resources are the most important sectors of the Cambodian economy, with around 57.6% of the population
relying on agriculture for their livelihoods. The National Adaptation Program of Action (RGC, 2006)
determined that both sectors are highly vulnerable to climate change. Rainfall variability on year-to-year
and within-season time scales, as projected in the high-resolution simulations in Part 1 of this study,
impacts the availability of surface and ground water resources including potable water supply, flood
protection and irrigation. Likewise, rural infrastructure including roads, water supply and sanitation suffers
from impacts of floods and storms. Temperature increases are projected for both Mondulkiri and Koh Kong
provinces in the future, along with a large increase in the number of hot days and heat waves and the
potential for enhanced evaporation. Over the next twenty years, there is a trend to greater frequency of
long-term drought in both provinces (see Part 1 results for details.) These changes are likely to lead to
more adverse impacts on agriculture, fishing and aquatic resources, infrastructure and rural livelihoods.
The Royal Government of Cambodia (RGC) and the Asian Development Bank (ADB) have signed a Grant
Agreement in December 2010 to implement the Greater Mekong Subregion (GMS) Biodiversity
Conservation Corridors (BCC) Project in Koh Kong and Mondulkiri provinces over an eight-year (2011 –
2018) period to protect and sustain forest ecosystems and productivity in the larger agricultural landscape.
Implementation of this agreement has commenced under the Ministry of Environment (MOE) and Ministry
of Agriculture, Forestry and Fisheries (MAFF). Recognising that agriculture, forestry and fisheries are some
of the most vulnerable sectors to climate change and that agriculture and natural resources are key
components in any poverty-reduction strategy in Cambodia, the Strategic Climate Fund (SCF) of the multidonor supported Climate Investment Fund (CIF) and its Pilot Program for Climate Resilience (PPCR) subcommittee endorsed on 28 June 2011 a project labelled “Promoting Climate Resilient Forestry, Water
Supply and Coastal Resources in Koh Kong and Mondulkiri.” This is additional financing for the BCC project
in Cambodia.
The PPCR funding administered under the TA 7459-REG: GMS Biodiversity Conservation Corridors Project
(R-PPTA) was in part for a feasibility study for preparation of climate change adaptation activities
supplementing the Biodiversity Conservation Corridors (BCC) Project in Koh Kong and Mondulkiri provinces
of Cambodia. This is aimed at improving adaptive capacity of local communities to climate change impacts
and thereby reducing vulnerability. This report was prepared as part of the PPCR component of the R-PPTA
for BCC supplementary project design.
The key objectives of the community climate change vulnerability assessment are to:
1. assess local vulnerability to climate change, use of coping mechanisms by households, and
the level of adaptive capacity prevalent currently among the beneficiary communities and
households;
2. identify climate change adaptation options and priorities for the local community through a
participatory consultation process at commune/village level; and
3. provide inputs into the preparation of the Feasibility Study (FS) report to address all aspects
of the proposed Additional Financing of the BCC Project in Cambodia.
This report provides an overall assessment of current vulnerability, including coping mechanisms and
adaptive capacity in BCC target communes in Koh Kong and Mondulkiri in relation to flooding, drought and
other climatic extremes.
45
10 Methodology
10.1 Literature Review
The main economic activities of BCC target communities in Cambodia, such as subsistence agriculture,
fisheries, and logging and forest product extraction are likely to be significantly affected by climate change
due to high dependence on climate-sensitive resources such as local water, agricultural land and nontimber forest products. At the same time, communities commonly have low adaptive capacity to anticipate,
cope with, resist, and recover from the impact of shocks. Thus they are vulnerable to livelihood shocks,
including those induced by climate change; the poorer households, especially, are the most at risk (Ellis,
2000; Pretzsch, 2003; Hunter, 2007; Heltberg et al., 2009). While rural livelihood strategies are also
dynamic and have some ability to respond to changing pressures and opportunities (Dorward et al., 2001;
IPCC, 2001), the speed and magnitude of climate-induced changes may seriously challenge the adaptive
capacity of some or all livelihood strategies (Brown et al., 2006; Smit and Wandel, 2006).
Vulnerability to climate change impacts varies widely across individual people, sectors, and regions because
factors that determine vulnerability are usually embedded in broader social, cultural, political, and
economic structures. Vulnerability is defined by IPCC (2001, 2007) as the degree to which a system is
susceptible to, and unable to cope with, adverse effects of climate change, including climate variability and
extremes. It is a function of the character, magnitude, and rate of climate change and variation to which a
system is exposed, its sensitivity, and its adaptive capacity. Assessing vulnerability to climate change
impacts has become critically important to understanding how climate change influences the development
process.
Over the past few years, vulnerability, in general, has been well studied in development circles by, for
example, scholars like Bohle and Watts (1993) and Chambers (1989). Methods of vulnerability assessment
have been developed in the fields of natural hazards, food security, sustainable livelihoods and other
related fields (Downing et al., 2002). With specific regard to climate change, literature addressing
vulnerability has grown enormously through the studies of Bohle et al. (1994), Downing (1992), Kasperson
and Kasperson (2001), Adger and Kelly (1999) and others. Downing et al. (2002) introduced a structured
approach including task-analysis techniques for vulnerability assessment. In 2009, along with a toolkit for
designing climate change adaptation, the UNDP published a guide book on mapping climate change
vulnerability and impact scenarios at sub-national level. Vulnerability was considered as one of the key
elements to understand the likely effects of climate change in a specific environment and on society in
general. There is an increasing demand for policy-relevant decision support information at micro levels
derived from evidence-based assessments of local-level vulnerability and adaptive capacity to climate
change. A general conceptual model of vulnerability has emerged in the climate change-related literature
(Kelly and Adger, 2000; Downing, 2001; Turner et al., 2003; Smit and Pilifosova, 2003; Yohe et al., 2003;
Adger, 2006). However, empirical studies at local level are limited and inadequate (e.g. Gay et al., 2006;
Wehbe et al., 2006).
The concept of community-based climate change adaptation has been actively developed and
implemented in various countries (prominent are coastal and riverine communities, e.g. Bangladesh,
Vietnam, and the Philippines (Huq and Reid, 2007; Raihan et al., 2010; Shaw, 2006; Capili et al., 2005).
Community-based vulnerability assessments have been employed in climate change adaptation, disaster
management, poverty and food security fields that are aimed at contributing to practical adaptation
initiatives (Jones, 2001; Lim et al., 2004; Turner et al., 2003; DFID, 2004; FAO/WFP, 2005). These “bottomup” participatory vulnerability assessments require active involvement of stakeholders to allow for the
recognition of multiple stimuli beyond those related to climate, such as political, cultural, economic,
institutional and technological factors (Smit and Wandel, 2006).
46
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
10.2 Conceptual Framework and Key Terminology
In this assessment, a simple framework is used, considering “vulnerability” as a function of exposure,
sensitivity and adaptive capacity (see Figure 23 below). Key terminology is from the IPCC 2001 and IPCC
2007, AR4 Working Group II – Impacts, Adaptation, and Vulnerability report.
Figure 23. Conceptual Framework of Climate Vulnerability. Source: Adapted from IPCC 2007.
Exposure is the degree of climate stress upon a particular unit of analysis; it may be represented as either
long-term changes in climate conditions, or by changes in climate variability, including the magnitude and
frequency of extreme events.
Sensitivity is the degree to which a system is affected, either adversely or beneficially, by climate variability
or change. The effect may be direct (e.g. a change in crop yield in response to a change in the mean, range
or variability of temperature) or indirect (e.g. damage caused by an increase in the frequency of coastal
flooding due to sea-level rise).
Adaptive capacity (in relation to climate change impacts) is the ability of natural or human systems to
respond to and adjust to actual or expected climatic stimuli or their effects (including climate variability and
extremes) to moderate potential damage, to take advantage of beneficial opportunities, or to cope with
the consequences. Various types of adaptation can be distinguished, including anticipatory, autonomous
and planned adaptation.
10.3 Data Collection
In this assessment, both quantitative and qualitative methods of data collection have been applied (Figure
24). Primary data collection methods were (1) participatory focus group discussions; (2) structured
household questionnaire surveys; (3) key informant interviews; and (4) secondary literature and statistical
data collection through online resources and local government offices. Data was collected in mid-2013.
The team consisted of one international and one national vulnerability assessment consultant, together
with a group of survey assistants and enumerators experienced in conducting field surveys and household
interviews from the Faculty of Agricultural Economics and Rural Development, Royal University of
Agriculture, Cambodia.
47
Figure 24. Data Collection and Survey Methods.
Importance was given to a community-based participatory approach in this assessment so as to engage
stakeholders and decision-makers in discussing possible interventions to enhance local adaptive capacity.
Participatory hazard mapping incorporated historical data on temperature, precipitation, floods and
droughts, and data on land use of the village area to identify high-risk hotspots and dominant hazards.
Focus group discussions were conducted with a group of villagers with diversified livelihood strategies
considering gender and age balance, to assess exposure, sensitivity and adaptive capacity to cope with
identified climate risks and vulnerable systems, and to identify potential adaptation options.
The household survey questionnaire focused on climate change perception and observation,
income/livelihood strategies, asset holdings, existing coping mechanisms and adaptation behaviours, and
additional data on changes in natural resources. Recall and hypothetical questions were formulated to
assess existing coping and adaptation capacity. The design and structure of the questionnaire were adapted
from a CIFOR Poverty and Environment Network (PEN) prototype questionnaire, with focus on climate
change vulnerability and adaptive capacity. A select group of enumerators received training on carrying out
the household interviews and field surveys, and participated in pre-testing of the questionnaire and data
collection process. Household survey data was coded, tabulated, and analysed using statistical software
(e.g. SPSS). In total, over 700 households from both Koh Kong and Mondulkiri provinces participated in the
household questionnaire surveys.
10.4 BCC Target Area and Selection of Survey Sites
In Cambodia, one of the BCC focal areas is located in the Cardamom and Elephant Mountains landscape in
Koh Kong Province, and another is situated in the Eastern Plains landscape in Mondulkiri Province. There
are 10 communes in Koh Kong and 12 communes in Mondulkiri covered by the GMS BCC project (see Figure
25 for BCC target communes in Koh Kong and Figure 26 for BCC target communes in Mondulkiri).
Three of these communes in Koh Kong Province (Andoung Tuek Commune, Ta Tai Kraom Commune, and
Peam Krasaob Commune) and four communes in Mondulkiri Province (Romonea Commune, Srae Khtum
Commune, Srae Huy Commune, and Bu Chri Commune) were selected in consultation with local authorities
for conducting detailed household surveys and participatory consultations (see Table 16).
48
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
The selection criteria for the choice of survey communes were (1) exposure to climate risks (i.e. drought,
storms, floods, and saltwater intrusion); (2) sensitivity of affected systems/sectors; (3) administrative and
geographic features.
Table 16. Selected BCC Communes for Field Surveys in Koh Kong and Mondulkiri
No.
Commune
District
No. of Households
Major Climate Risks
Koh Kong Province
- Seawater intrusion (***)
1
Andoung Tuek
Botum Sakor
1198
- Storm (**)
- Drought/water stress (**)
- Seawater intrusion (**)
2
Ta Tai Kraom
Koah Kong
265
- Flood/heavy rain (**)
- Drought/water stress (*)
- Seawater intrusion (**)
3
Peam Krasaob
Mondol Seima
443
- Storm/surge (***)
- Drought/water stress (***)
Mondulkiri Province
- Drought (**)
1
Romonea
Senmonorom
677
- Storm/thunderstorm (**)
- Flood (flash flood) (*)
- Drought (***)
2
Srae Khtum
Keo Seima
2635
- Flood (flash flood) (**)
- Storm/thunder (*)
- Flood (flash/river flood)(***)
3
Srae Huy
Koh Nheak
386
- Drought (**)
- Storm/thunderstorm (*)
4
Bu Chri
Pech Chreada
1296
- Drought (***)
- Storm/thunderstorm (**)
Source: R-PPTA field survey 2013 (Note: “***” = very high risk, “**” = high risk, “*” = moderate risk)
49
Figure 25. Biodiversity Conservation Corridors Initiative (BCI) and Communes in which village surveys were
conducted in Koh Kong.
The vulnerability assessment team has consulted and worked closely with the Forestry Administration (FA),
the BCC Executing Agency (EA) and Provincial Project Management Units (PPMU), and other local
government authorities, as well as the Regional Project Preparatory Technical Assistance (R-PPTA) Team
Leader (TL) from the BCC project in the selection of the survey sites for vulnerability assessment.
50
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
Figure 26. Biodiversity Corridors and Communes in which village surveys were conducted in Mondulkiri.
51
11 Climate Change Vulnerability Assessment in Koh
Kong
11.1 Climate Risks and Impacts on Local Livelihoods
One of the most vulnerable areas in Cambodia in terms of climate change is the coastal zone (CCCA, 2012).
The coastal zone’s climate is defined as tropical monsoon with an annual rainfall between 2000 and 4000
mm. Mean daily temperature ranges from 26.5 to 28.5oC with minimum temperatures of between 21 and
23oC and maximum temperatures of between 31.5 and 34.6oC. Relative humidity ranges from 79% to 82%.
New, fine-resolution CCAM 10 km simulations for Cambodia undertaken for Part 1 of this study better
represent factors such as complex topography and land-sea interface than is possible at the coarse
resolution of GCMs. The downscaled simulations project annual temperature increases for Koh Kong of
about +1.0°C by 2025 for the higher emissions scenario, with greater increases by end-of-century. A large
increase in the number of hot days and heat waves is projected, with the potential for enhanced
evaporation. Projected rainfall changes are more complex, with the models showing little change in annual
rainfall. Unlike the previous GCM simulations, there is some is suggestion of decreases in summer (wet
season) monsoonal rainfall and increases in winter (dry season) rainfall, as well as small increases in
intensity but not frequency of extreme rainfall events. Over the next 20 years, the trend to greater
frequency and duration of 12-month (long duration) droughts identified in the high-resolution projections
could be the feature of climate change that has the most impact in Koh Kong. These projected changes in
temperature and rainfall, along with associated changes to groundwater resources and coastal inundation
due to sea-level rise, have the potential for large impacts on livelihoods in the province.
The Royal Government of Cambodia has identified the coastal zone as a focal point in Cambodia’s work to
adapt to existing and future impacts of climate change. In Koh Kong, the coastal area is threatened by
impacts of climate change such as (1) seawater intrusion and sea level rise, (2) storms and storm surges, (3)
heavy rain and flooding, and (4) drought and water stress. During the dry season, especially from
November to February, seawater intrusion and high tides pose serious threats to land and freshwater
sources in the coastal communes in Koh Kong. Salinisation of the surface and the groundwater has
detrimental effects for the local communities along the coast. It also results in a severe shortage of fresh
water and potential contamination of groundwater sources as saltwater has a higher mineral content and
higher water pressure than freshwater. Soil salinity of the coastal lands increases due to salt accumulation,
which also has a severe impact on the fertility of the areas used for farming.
Storm frequency and intensity have increased since 2001 as observed by local communities, damaging
roofs in Peam Krasaob, Ta Tai Kraom and Andoung Tuek communes, and associated surges speed up
coastline erosion and mangrove forest loss. Fisheries are the predominant livelihood in some of the coastal
communities (i.e. Peam Krasaob Commune), and the number of days when fishing is possible and income is
produced are also highly affected by more frequent storms.
During the southwest summer monsoon season (May to October), abundant rain allows for the cultivation
of a wide variety of crops. However, during July to September, there is increased potential for flash
flooding or occasional river floods due to heavy rainfall to destroy crops in low-lying areas in the coastal
zone, where most agriculture is concentrated, and pests often break out after flood events.
Villagers have also observed and experienced prolonged and increased drought conditions, normally quite
serious during the dry season. Together with high temperature, drought poses another stress on water
supply and causes health related problems (i.e. diarrhoea, fever, dehydration, etc.) Irregularities in rainfall
patterns, such as dry spells at the beginning of the rainy season or a shortened rainy season cause crop
losses and reduced productivity; hence the food security situation in the area could be further aggravated.
52
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
Furthermore, some basic infrastructure (i.e. roads, bridges and dykes) in the coastal zone is under pressure
from impacts of climate change, which can lead to an increased vulnerability in accessibility and potential
loss in income from tourism and service sectors.
Table 17 below presents the seasonal distribution of key climate risks such as climate variability and
extreme events (top) and the livelihood calendar of coastal communities in Koh Kong (middle), with
associated stresses (bottom). The table may not be applicable to some BCC communes located in the
Central Cardamom Protected Forests (i.e. Ta Tey Leu, Ruessei Chrum, and Thma Doun Pov), which are less
vulnerable to climate change due to higher elevation and being surrounded by healthy forests and fertile
land. Potential climate risks in those communes are associated with some minor flooding and droughts.
Table 17. Seasonal Distribution of Climate Risks and Livelihood Calendar in Koh Kong
Seawater
Intrusion
Climate Factors
Seawater Intrusion
High temp. and very dry
Prolonged dry season
Temp- shifts
Dry spell
Drought
Flood/heavy rain
Storm
Jan
Abnormal rainfall
Storm/storm surge
Feb
Mar
Apr
May
Jun
Jul
Aug
Soil salinity
Storm
Sep
Oct
Nov
Dec
Paddy
Cassava (harvesting)
Rice, cash crops (e.g. corn, sugarcane) and fruit trees
Vegetable garden
Vegetable garden
Livelihoods
Livestock raising
Mud crab
Sea crab/fish/shrimp
Aquaculture (i.e green mussel, snail, fish)
Wild mushroom
Rattan collection
Resin collection
Resin collection
Impacts
Food shortage
Water stress/shortage
Health: diseases
Infra. Damages
Health: disease
Health: disease
Damages: housing, mangrove, infra.
Infra. Damages
Source: R-PPTA field survey 2013
Nevertheless, to fully understand the vulnerability of local communities to climate change, non-climate
concerns in the target area should also be considered, such as (1) decline in coastal and marine resources,
(2) weak fisheries resource management and the introduction of modern fishing equipment, (3) sand
dredging and mining, (4) deforestation, especially mangrove forests, (5) market fluctuations, and (6) other
changes in economic development over time (i.e. economic land concessions, hydropower development).
53
11.2 Vulnerability of Key Livelihood Systems and Sectors
The key livelihood strategies in the coastal communities in Koh Kong are (1) agriculture (including paddy
rice and cash crop cultivation and livestock raising); (2) fisheries (i.e. mud crab, sea crab, fish, shrimp,
shellfish etc.); (3) non-farm activities such as small businesses, tourism-related service sectors (i.e.
transportation, restaurants, guest houses etc.), and (4) forest-based livelihoods.
Agriculture remains the predominant livelihood strategy for most of the households living in the coastal
area; survey results show that 54% of households in Andoung Tuek Commune and 57% of households in Ta
Tai Kraom Commune are practicing agriculture as a primary livelihood activity. Fishery is another major
pillar of local livelihoods, especially for communities located close to the coast with limited suitable
agricultural lands. For instance, almost 60% of all villagers living in Peam Krasaob Commune rely on fishing
as their main source of livelihood, while only 7 ha of paddy land is available for the whole commune.
Figure 27 illustrates the detailed livelihood strategy portfolios of the three surveyed BCC communes in Koh
Kong. With rapid economic development and market opportunities in the area, some households have
shifted their primary livelihood strategy to non-farm based activities, running small businesses/trades, or
providing tourism-related services such as inland and boat transportation, food and accommodation, etc. A
limited number of households still rely on forest-based livelihoods, especially for communities located close
to the Cardamom Mountains (e.g. Ta Tai Kraom). Increasing numbers of households have diversified their
major livelihood strategies (shown as ‘other’ in the pie charts).
Figure 27. Livelihood Strategy Portfolios of Surveyed BCC Communes in Koh Kong. Source: R-PPTA field survey 2013.
11.2.1 AGRICULTURE
The most popular crop in the surveyed coastal communities in Koh Kong is paddy rice wherever land is
available and suitable. Figure 27 shows that 46% of households in Ta Tai Kraom and 30% of households in
Andoung Tuek practice paddy rice cultivation as a primary livelihood strategy. The average yield is 1.5-2
tons per hectare, achieved without use of much fertilizer. Growth of short-term rice varieties has become
more common, while growing medium-term and long-term varieties is still experimental. Other crops
grown in the BCC communes are upland dry rice, cash crops (i.e. cassava, corn, and sugarcane), fruit trees,
and vegetables. These crops are grown throughout the year as presented in the crop calendar (see Table 17
above). The average household paddy land holding is relatively low (0.5ha in Andoung Tuek, 0.6ha in Ta Tai
54
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
Kraom), and the percentage of households with less than 1 ha of farmland remains very large (65.4% in
Andoung Tuek, 47.2% in Ta Tai Kraom, and nearly 99% in Peam Krasaob). Livestock raising is another key
component of subsistence livelihood, as well as a source of cash income and savings for emergency use.
Table 18 presents a detailed climate change vulnerability assessment of agriculture in the surveyed BCC
communes in Koh Kong Province. The analysis consists of three dimensions of the vulnerability framework:
exposure, sensitivity and adaptive capacity. Paddy rice is the most vulnerable system in the area as it is
prone to many climatic risks with high sensitivity, especially seawater intrusion, storms and potential
flooding and droughts during the rainy season. Farmers have tried to adapt to the situations by using shortterm and/or salt-tolerant varieties, replanting after crop failure, and shifting the crop calendar according to
the rainfall patterns, etc. Existing community dykes were partially able to prevent impacts from seawater
intrusion; however, protection remains insufficient, with limited capacity for maintenance. Livestock raising
is threatened by disease and death of stock during long periods of very dry and high temperature days as
well as sudden changes in the patterns of temperature and rainfall during April to June. Vaccinations have
been provided to the livestock; yet the situation is not under control and lack of technical support is a main
concern among the local communities.
11.2.1 AQUACULTURE
The majority of villagers in Peam Krasaob are highly dependent on fishing and coastal resources. The main
products are mud crabs, sea crabs, fish, shrimp, snails and shellfish. Local fishermen normally own small
fishing boats, which can only operate in fair weather conditions. They still practice traditional fishing
methods, although modern equipment has been employed to some extent. Quite a few larger boats that
operate in the area are from Thailand using modern technology. The pressure on marine life has also
resulted in a decline in fishery resources, and increased costs of fishing operations.
Climate risks of highest concern for the fishermen if there are future increases are storms and heavy rainfall
conditions during June to September, as fishermen cannot sail out for fishing during days with bad weather,
which directly affects the number of fishing days and the yield of crabs and fish. Households without
income on storm days have to find other methods to support their livelihood; well-off people can afford to
dig into their savings, while some poor households have to find other alternatives (i.e. wage labour) or
borrow food or money from relatives or neighbours, or even reduce food intake. Local fishermen have
traditional knowledge and skills to read weather changes, and they have an effective radio network for
communication among the fishing groups to spread the information or for emergency rescue.
Sea crab fishing (i.e. Blue Sea Crab) and aquaculture in the open sea (i.e. Green Mussels, snails and fish) are
highly sensitive to high temperature and extended periods of rainfall. Loss due to these factors is difficult
for people to manage, as no effective measures are so far available to cope with the situation. Other nonclimate factors are also significant concerns of the local fishing communities, such as sand mining, price
fluctuations, and fisheries management. Details of the climate change vulnerability of each fishery system
are summarised in Table 19 below.
55
Table 18. Climate Change Vulnerability of Agriculture in Koh Kong
Exposure
Sensitivity
Adaptive capacity (AC)
Seawater intrusion and high tides
November-February,
- Flooded paddy land and other
croplands up to 40%
- Build up dykes to prevent
intrusion (limited capacity)
4-5 days per month, annually
- Soil salinity/reduced soil fertility
- Use salinity-tolerant rice
and/or crop varieties
Extreme cases up to 15 days per
month (in Ta Tai Kraom)
(High exposure)
Heavy rain and potential flooding, and
related pests
July-September, every few years
(Medium exposure)
- Destruction of paddy rice, crops
and vegetable gardens 30%-100%
(High sensitivity)
- Loss of livestock 5-10%
- Keep livestock at higher/
safer places
(Low sensitivity)
(Medium AC)
- Paddy rice productivity loss of 5080% during serious flooding events
(High sensitivity)
- No particular measures in
regard to productivity loss
after flood events, except
finding other alternative
livelihood sources to cope
- Upland dry rice, cash crop/fruit
tree productivity loss during flooding
(Medium sensitivity)
- Livestock loss during flooding (Low
sensitivity)
Storms/surges
June-September, annually
It can last 1 to 2 weeks per month
(High exposure)
(Low to Medium AC)
- Destruction of paddy rice and/or
other crops, productivity loss of 30%
- Damage/erosion to paddy dykes 12 meters length per paddy field due
to associated rainfall and flash floods
(Medium sensitivity)
- Apply fertilizer and
pesticide (lack of technical
support, not so effective)
(Low AC)
- Apply local knowledge of
weather to forecasts
(unpredictable nowadays)
- Lack of early warning
system
- Repair paddy dykes
(Low AC)
Drought/Irregular rainfall patterns
- Prolonged dry season and potential
dry spell in the early rainy season
May-July, every 1-3 years
- Shortened rainy season and lack of
rainfall at the end of the rainy season
September-October, every few years
(Low to medium exposure)
- Crop failure (lack of water for
seedlings in early part of the rainy
season, or for crop growing during
September-October)
- Shift crop calendar
according to the rainfall
(Medium sensitivity)
- Replant after crop failure
(if dry spell happens early in
the rainy season)
- Plantation/vegetable death during
dry season (Low sensitivity)
- Change to short-term crop
variety
(Medium AC)
- Livestock disease and death (due to
water shortage or disease, or sudden
change of temperature/rainfall)
- Livestock vaccination (lack
of technical support so not
so effective)
(High sensitivity)
(Low AC)
N/A
November-December, every 4-5
years)
- Affects paddy rice harvesting and
other cash crops, productivity loss
up to 50%
(Low exposure)
(Medium sensitivity)
- High temperature and very dry
weather
April - mid-May, annually
(High exposure)
Abnormal rainfall
56
(No AC)
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
Table 19. Climate Change Vulnerability of Fishery in Koh Kong
Exposure
Sensitivity
Adaptive capacity
Storms/surges
- Difficult to carry out fishing
activities in open sea during strong
storm events, 80%-100% income
loss
- Find other alternatives for
income generation or borrow
from others
Wet season: June–September,
annually, It can last 1 to 2 weeks per
month
(High exposure)
(High sensitivity)
- Local knowledge for
weather forecasting
- Moor the boat in safe places
Dry season: November-February,
annually
- Loss of fishing gear/equipment
(Medium sensitivity)
(Medium AC)
(Low exposure)
Heavy rainfall
July-August, annually
- Affects mud crab fishing
(productivity loss 80%)
(High sensitivity)
(High exposure)
- Affects aquaculture (e.g. snails die
in the rain water, fishes get disease
etc.)
(Medium sensitivity)
High temperature and high tides
- Radio communication for
information
- Spend savings or borrow
from others to cope with
food shortage
- Apply traditional medicinal
plants for rescuing
aquaculture
(Low AC)
- Affects sea crabs (i.e. Blue
Swimmer Crab) and other fishery
resources
- Find other alternatives for
income generation or borrow
from others
- Difficult to catch during high tide
days - loss of income
- Local knowledge for
weather forecasting
(High exposure)
(High sensitivity)
(Medium AC)
Abnormal rainfall and high
temperatures
- Affects aquaculture (i.e. Green
Mussels and snails die, and fish get
diseases)
N/A
January–March, annually
November-December, every 4-5
years
(Medium sensitivity)
(No AC)
(Low exposure)
11.2.2 WATER AND HEALTH ISSUES
Lack of safe freshwater for drinking and other domestic usage during the dry season is a persistent concern
of many coastal villages in Koh Kong (i.e. Village I and II in Peam Krasaob Commune, Ta Meah and Ta Ok
Villages in Andung Tuek Commune). Over 50% of all surveyed households in Koh Kong reported water
shortages, especially during January to June. The main water sources for household consumption in the
surveyed communities are rainwater harvesting and water wells; however, seawater intrusion poses a high
risk of salinity of the surface and groundwater, and together with high temperatures and extended dry
periods makes the water stress even worse. Well-off households can afford to buy water for consumption,
while the poor have to travel far to find water sources or even reduce family water consumption.
Health problems are often associated with weather conditions in the local communities. People commonly
are prone to diseases (i.e. malaria, diarrhoea) and dehydration during very hot and dry days in April and
57
May; anomalies in the usual patterns of temperature and rainfall also cause increased sickness. Health care
facilities at the local level are considered relatively poor. See Table 20 below for details.
Table 20. Climate Change Vulnerability of Water, Food and Health in Koh Kong
Exposure
Sensitivity
Adaptive capacity
Seawater intrusion
- Water stress/shortage of
freshwater resources
- Reduce water
consumption
- Water contamination/not
suitable for drinking
- Purchase water
November–February, annually
(High exposure)
High temperature and prolonged dry
season
- Health problems: disease
- Get water from reservoirs
and ponds
(High sensitivity)
(Low AC)
- Water shortage
- Find water from far away
(High sensitivity)
- Reduce water
consumption
April-May, annually
(High exposure)
June-July, every few years
(Low exposure)
- Health problems: disease (i.e.
malaria, diarrhoea), and people
get sick easily
- Purchase water
- Access to health centre
(Medium AC)
(Medium sensitivity)
Abnormal rainfall and sharp
temperature shifts
- Health problems: catch cold
or get sick more often
- Access to health centre
(limited capacity)
(Medium sensitivity)
(Medium AC)
Storms/surges
- Accidents during fishing
- Reduce food consumption
June–September, annually
- Food shortage due to lack of
income-generation sources
during stormy weather, and
before crop harvesting
- Borrow from others
November–December, quite often in
recent years
(Medium exposure)
Can last 1 to 2 weeks per month
- Find other alternative
livelihoods
- Migration/wage labour
(High exposure)
(Medium sensitivity)
(Medium AC)
11.2.3 INFRASTRUCTURE AND HOUSING
Koh Kong is well connected to the rest of the country by National Highway 4 and Highway 48. However,
inter-commune and village roads can be still poor, although this is rapidly improving due to emerging
development opportunities such as tourism, hydropower, economic land concessions for large-scale
agricultural plantations, and establishment of special economic zones. Over 40% of surveyed households
stated that it is difficult to access the market and other services during the wet season. Storms and floods
cause damage to the infrastructure (i.e. roads, drainage systems, bridges, and dykes) nearly every year,
although the damage is normally minor, and most of the infrastructure facilities are under annual
maintenance.
58
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
Coastal land and mangrove forests are also under threat of storms and surges. Villagers from Peam Krasaob
have observed that a large portion of the old resettlement area on islands, where some families still own
houses and live, is gone. Part of the coastal mangrove forests surrounding the islands and pine tree
plantations on the islands were also destroyed by big waves. The commune and village area was relocated
to the mainland in 2004, and each household was allocated some land for a homestead and some home
garden plots. Many households have sold their land to outsiders during recent years, mainly for economic
reasons. A land dyke to prevent seawater intrusion was built, which also functions as an inter-commune
road, but it faces annual erosion from sea intrusion and storms.
Most of the households have their own housing in the surveyed communes in Koh Kong, and the majority
(82%) of the houses are made of wood. Another 14% of the houses are built of bamboo or thatch grass, or
mixed with some wood material. These houses are under high risk from storms. Households try to build
lower houses and plant some trees around the houses to avoid damage from storms. Table 21 summarises
these vulnerabilities
Table 21. Climate Change Vulnerability of Infrastructure and Housing in Koh Kong
Exposure
Sensitivity
Adaptive capacity
Storms/surges
- Damage to the roofs and walls of
housing
- Repair roofs/walls, build
lower houses and plant trees
around
Wet season: June–September,
annually
It can last 1 to 2 weeks per
month
(High exposure)
Dry season: November-February,
annually
(Medium exposure)
(High sensitivity)
- Destruction of fishing boats
during strong storm events (Low
sensitivity)
- Local knowledge of weather
forecasting, get information on
TV
(High sensitivity)
- Mangrove restoration
(effective in general, but
difficult in some coastal areas
prone to storms/surges)
- Damage to infrastructure (i.e. sea
dyke in Andoung Tuek)
- Relocate the villagers to the
inland area (Peam Krasaob)
(Low sensitivity)
- Repair the dykes
- Coastal erosion/loss of
land/damage to mangroves
(Medium AC)
Heavy rainfall and flash flooding
- Rural road damage/erosion
July-August, annually
- Damage to drainage systems
- Repair and maintenance
- Damage to channel (erosion)
(High exposure)
- Damage to bridges
- Damage to land dykes (Peam
Krasaob)
(Medium AC)
(Medium sensitivity)
River flooding (Ta Tai Kroam)
July-September, every few years
- Flooding of some houses, 1-3
meters
- Damage to household assets
(Low exposure)
(Medium sensitivity)
Seawater intrusion / high tides
November–February, annually
-Road and dyke damage / erosion
by seawater flood
(High exposure)
(Low sensitivity)
- Escape to higher/safer places
during flooding
- Build disaster observation
tower/cottage and get
prepared for flooding events
(Medium to high AC)
N/A
(No AC)
59
11.3 Adaptation Options and Priorities in Koh Kong
During the PRA consultations, adaptation options and priorities were identified and discussed with regard
to specific climate risk exposures and affected systems in all surveyed communes (for details of survey
results see Appendix 1 for Koh Kong and Appendix 2 for Mondulkiri). In Koh Kong, high priority adaptation
options at community level are (1) water storage and supply facilities (i.e. reservoirs, ponds, wells, water
supply from urban water distribution system, etc.); (2) construction of land and sea dykes to prevent
seawater intrusion; (3) mangrove restoration and other plantations for coastal protection and storm
prevention; (4) technical support on livestock raising and disease prevention, and other agricultural
production (i.e. climate-resilient crops, soil fertility improvement, water management etc.); and (5) an early
warning system and climate information-sharing mechanism.
At household level, survey results (Table 28) also show that the most popular adaptation needs among the
respondents in Koh Kong are (1) an early warning system and climate information dissemination; (2) water
storage and supply facilities; (3) awareness raising and climate change knowledge education; and (4)
community disaster management enhancement. Improvement of local infrastructure and health care
capacity and facilities were also highly in demand during the PRA consultations. It is a widely acknowledged
lesson that local-level climate change adaptation is more effective if incorporated with other strategies
such as livelihood enhancement, water and other resource management, land use planning and
entitlement, etc. (Kelly and Adger, 2000; Smit and Wandel, 2006). Integrating climate change adaptation
approaches and mainstreaming climate change adaptation into local development planning are highly
preferable for local communities. Over 90% of respondents in Koh Kong think such approaches work well to
reduce vulnerability and enhance their adaptive capacity.
Figure 28. Climate Change Adaptation Capacity and Needs in Koh Kong. Source: R-PPTA field survey 2013.
60
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
12 Climate Change Vulnerability Assessment in
Mondulkiri
12.1 Climate Risks and Impacts on Local Livelihoods
Mondulkiri has been identified as one of the climate change hotspots in the Lower Mekong Basin (LMB) in a
recent study on Mekong Adaptation and Resilience to Climate Change (ARCC, 2013) sponsored by USAID.
Though not included in the Strategic National Plan for Disaster Risk Reduction for 2008-2013 (known as
SNAP, Cambodia’s national plan on DRR; RGC,2008) or the National Adaptation Programme for Action on
Climate Change (RGC, 2006), the province has become, over the past few decades, increasingly vulnerable
to natural hazards of droughts, floods, storms and climate variability.
The high-resolution simulations for Cambodia undertaken for Part 1 of this study project annual
temperature increases for Mondulkiri of about +1.0°C for the higher emissions scenario by 2025. Maximum
temperature will increase by +2.0°C by mid-century, and by +4.0°C by end-of-century. There is a large
increase in the number of hot days and heat waves as well. As for Koh Kong, annual rainfall shows little
change, but there is an indication of a decrease in summer (wet season) monsoonal rainfall and an increase
in winter (dry season) monsoonal rainfall. Consistent with this, there is a decrease in the frequency of
Consecutive Dry Days, but an increase in the frequency of Consecutive Wet Days. Extreme rainfall amounts
are projected to increase, unlike Koh Kong, suggesting a more likely risk of flooding. Over the next 20
years, 3- and 6-month droughts are projected to decrease, but 12-month (long-term) droughts will
increase. These changes have the potential to have large impacts on local livelihoods.
The seasonal distribution of climate risks and livelihood calendar for Mondulkiri is given in Table 22.
Mondulkiri has the lowest population density of all provinces in Cambodia, but has the largest land area
(14,682 km2) and is characterised as remote, rugged and diverse in natural resources. The province has
become increasingly important to the economic development of Cambodia, and its location in the northeast section of the country, bordered by Vietnam and Lao PDR, also makes it of interest to international
partners in trade. Recently, the integrity of Mondulkiri’s resources has been threatened by rampant logging
and mining activities, leaving the landscape of the province fragmented. The communities that live in the
province, mainly comprised of ethnic minorities and indigenous groups, are strongly reliant upon these
resources, making these villages increasingly vulnerable to the impacts of more frequent hazards and
further environmental degradation.
A comprehensive study conducted by the International Organization for Migration (IOM, 20092) pointed
out that Mondulkiri faces two types of drought: agricultural drought during the wet season3 and
hydrological drought4 during the dry season. Typical rainfall occurs from May to October, with heavy
rainfall from August to October, and a short dry period of about one to three weeks during the wet season.
Longer dry spells can lead to agricultural drought, as experienced in Mondulkiri between May and June, July
to August, or November to December, lasting from 15 days to a few months. These droughts are marked by
unpredictable and erratic variations in the onset of rainfall during the wet season, a decreased amount and
duration of rain, and an early ending of the rainy season, with the result that farmers are unprepared to
undertake activities in their livelihood calendars (see Table 22). Drought affects the agricultural outputs of
communities and the health of villagers and animals alike, as well as the availability of water resources for
personal consumption and household use.
Mondulkiri has historically experienced annual flooding events, with findings from the IOM research
surveys (2009) highlighting risks of both slow onset and flash flooding. Three broad types of flooding were
identified to be affecting Mondulkiri: (1) flash floods triggered by torrential rains resulting in the
2
The study covered 26 villages in 14 communes in Mondulkiri, providing some detailed data to this assessment since our field survey team could
not reach all target communes during this rainy season.
3
This corresponds to 3-month drought discussed in Part 1.
4
In Part 1, hydrological drought is 12-month drought, which affects water resources and dam capacity on a longer time scale.
61
overflowing of streams and tributaries; (2) flooding caused by seasonal water-level variations and, where
relevant, the operation of the hydropower dam in the Sre Pok River in Vietnam; and (3) seasonal flooding
resulting from heavy rainfall during the wet season. Most floods described by communities are
characteristic of flash floods, in line with The National Committee for Disaster Management’s (NCDM)
definition, resulting in part from the province’s landscape of mountainous regions and streams and small
tributaries inside valleys. These floods often occur swiftly and last for short periods; however, they have the
potential to cause severe damage to village crops and infrastructure.
Villagers also mentioned that more frequent storms and thunderstorm events pose a threat to their
housing and lives, along with more unpredictable climate variability including abnormal patterns of
temperature and changes in regular rainfall patterns during the wet season. The irregularity of rainfall has
been affecting the availability of groundwater for drinking as well as household and livestock use, and also
the soil moisture of cultivated lands.
Table 22. Seasonal Distribution of Climate Risks and Livelihood Calendar in Mondulkiri
Delayed rainy season/dry spell
Very dry climate
Drought/lack of rainfall
High temp.
Very dry climate
River Floods (Srea Huy)
Heavy rain/flash flooding
Irregular rainfall
Storms
Thunderstorms
Sharp shifts in temp.
Jan
Feb
Sharp shifts in temp.
Mar
Apr
May
Warmer
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Paddy
Upland dry rice
Land preparation
Harvesting
Peanut/Soybean/mung bean (Crop 1)
Land preparation
Peanut/Soybean/mung bean (Crop 2)
Cassava
Har.
Livestock raising
Forest products collection (e.g. resin)
Honey collection
Food shortage
Water stress / shortage
Limited accessibility to services
Health: disease
Damage to housing and infrastructure
Source: R-PPTA field survey 2013
Mondulkiri is rich in forests and wildlife, which have been traditionally managed and used by indigenous
communities, but it has the lowest human development indicators in Cambodia, with most communities
62
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
having very poor access to infrastructure and social services. Findings from the IOM project highlighted
significant changes in resource availability, land use, migration patterns, and livelihood and food security,
often due to increasing economic and environmental pressures faced across the province. Deforestation
activities were identified by the communities as having an impact on water resources, as well as on hazard
frequency and severity. As more forest area is coming under stress through land use change and rapid
development, it is anticipated that rainfall may be adversely affected, with a decline in precipitation.
Moreover, hydropower facilities are under construction in Mondulkiri, which will need sound watershed
management and downstream linkages to irrigation and water supply. Appropriate adaptation measures,
therefore, are needed to avoid serious consequences in terms of food security and to maintain livelihoods
for a growing population vulnerable to the impacts of climate change.
12.2 Vulnerability of Key Livelihood Systems and Sectors in Mondulkiri
Traditional indigenous livelihood is based on upland farming (also known as chamkar). Traditional upland
rice production has been gradually shifting to more cash crop farming during recent years due to increasing
market opportunities and high cash income demand. Local communities in Mondulkiri are highly
dependent on agricultural land, and Figure 29 shows that the major livelihood strategies in all the surveyed
communes are mostly based on crop production, such as paddy rice, upland dry rice and cash crops.
However, the composition of livelihood strategy portfolios varies from commune to commune. Nearly 80%
of households in Bu Chri Commune nowadays depend on cash crop production as a major livelihood
strategy. Main cash crops in the province include peanuts, cashew nuts, cassava, mung beans and
soybeans. These crops are commonly grown at small family scale on land previously used for upland dry
rice. Indigenous communities located in lowland areas rely more on subsistence rain-fed paddy rice
cultivation (e.g. 92% of households grow paddy rice as a primary livelihood strategy in Srea Huy Commune,
and 52% in Romanea Commune).
Non-timber forest product (NTFP) collection (i.e. resin tapping and honey collection) remains important as
a source of cash income for the rural population in Mondulkiri, though it is not the primary livelihood
strategy. However, resin production has been continuously decreasing in the local communities despite the
improvement in market price and technical support from local NGOs. Large areas of forest land around the
province have been cleared for economic land concessions, which has also limited villagers’ access to forest
resources and products. According to official government sources, there are over 150,000 ha of land
currently under economic land concession in Mondulkiri (OpenDevelopment, 2013). Fishing is another key
livelihood activity from which households were forced to shift away due to declining resources.
12.2.1 AGRICULTURE
Rice productivity is relatively low in Mondulkiri compared to other provinces, with an average yield for
paddy rice of around 1-1.5 tons per hectare and of 0.7-1 ton/ha for upland dry rice. Families of the
surveyed communes in Mondulkiri own on average 2.33 ha of agricultural land (including both paddy and
upland), which is much larger than households in Koh Kong, though many of the villagers inherited the land
from their ancestors without land certificates. Nevertheless, some 27.6% of households own less than 1 ha
of agricultural land, and around 76% of these households do not own any land. Farming activities are
undertaken in Mondulkiri throughout the year, beginning with clearing of new upland fields and ploughing
of paddies, followed by planting of rice and other crops, with harvesting often taking place during the dry
season (for details see crop calendar in Table 22 and the list of vulnerabilities of agricultural systems in
Table 23). Some short-term cash crops (i.e. peanuts, soybeans, mung beans) can be cultivated as two crops
annually.
63
Figure 29. Livelihood Strategy Portfolios of Surveyed BCC Communes in Mondulkiri. Source: R-PPTA field survey
2013.
Rice cultivation is highly vulnerable to agricultural drought as it is entirely rain-fed. There are very few
alternative sources of water in the area and villagers do not have water pumps to draw water from natural
water sources, such as rivers, streams and ponds. This affects rice yields annually, contributing to
prolonged food shortages. Cash crops, such as corn, soybeans, green beans and peanuts, although more
resistant to drought than rice, are intercropped with rice in chamkar; these become damaged, leading to
lower yields and a loss of income for farmers. There are some old existing irrigation systems built during the
Pol Pot regime; most of them are broken, although a few are being rebuilt. Functioning irrigation systems
are only able to serve a limited number of paddy fields, and villagers have to pump water into the rice
fields. Households try to adapt to irregularity of rainfall patterns by shifting the crop calendar, changing to
short-term or drought-tolerant crop varieties, and diversifying their livelihood activities to mitigate the risk.
Heavy rainfall and flash flooding frequently impact on paddy rice and cash crop (especially peanut)
cultivation, particularly when crops are planted in lowland areas. Flooding also causes loss of livestock due
to mortality of pigs, chickens, and even larger livestock during times of more severe flooding. Villages most
prone to flooding are those located in lowland areas near the Sre Pok River and streams. Flooding can
affect these areas several times during a single rainy season, especially during the peak rainfall months of
August and September. Overflowing of streams surrounding the village destroys rice seedlings as well as
64
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
other agricultural crops. During serious flooding events, water levels reach up to 1.5 meters in the village,
inundating houses and killing livestock, including hundreds of chickens, a few cows and several pigs. Many
old dykes are either broken or require maintenance and upgrading but local people lack the capacity to do
so. Some households plant bamboo trees along the river bank to reduce or check soil erosion, and to
prevent or lessen water overflows during the rainy season. Stormy weather also causes damage to cash
crops (i.e. cassava) and other plantations. Abnormal rainfall occurring at the end of the year may also pose
a risk to crop harvesting.
Hot and dry weather in April followed by sharp shifts in temperature and rainfall during May and June often
causes rapid spreading of animal diseases, with links of cattle, pig, duck, and chicken mortality to rapid
changes in weather (i.e. from rain to heat). Buffaloes and cows remain the most important tools used by
most farmers in rice cultivation. Therefore, loss of these animals can seriously affect farmers who often
have only one cow or buffalo to assist with their farming. Very few indigenous villagers have agricultural
machines that can help them perform farm work. Vaccinations are often provided to prevent a specific
disease at a cost of 10,000 Riel (approx. US$2.5) per injection on average. Most villages have veterinary
workers and services, but they do not have sufficient capacity or medicine to treat the animals.
65
Table 23. Climate Change Vulnerability of Agricultural Systems in Mondulkiri
Exposure
Sensitivity
Adaptive capacity (AC)
Drought/Irregular rainfall patterns
- Upland dry rice and paddy crop
failure (lack of water for
seedlings/planting in early part of
the rainy season, or lack of rain for
crop growing during SeptemberOctober)
- Existing irrigation system (old and
limited capacity)
(High sensitivity)
- Shift planting season (if there are rainy
season onset delays)
- Shifts in rainy season/shortened
- Delayed rainy season and potential dry
spell in the early rainy season May-July,
every 1-2 years
- Shortened rainy season and lack of
rainfall at end of the rainy season AugustOctober, every few years
(High exposure)
Heavy rain and flash flooding
June-September, nearly every year
A serious flooding event happened in 2007
(Medium exposure)
- Cash crop failure (i.e. cassava,
peanut, soybean, etc.)
- Pump water for paddy area if
river/stream nearby
- Change to short-term crop variety
- Diversify livelihood activities
(Medium sensitivity)
(Medium AC)
- Destruction of short-term cash
crops (especially peanut, soybean)
during harvesting, up to 70%
- Existing old flood dykes
(Medium to high sensitivity)
- Destruction of paddy fields and
rice productivity loss
- Livestock loss and deaths
- Find other alternative livelihood
sources to cope
-Abandon land prone to flooding or
move to other areas
(Low AC)
(Medium sensitivity)
River flooding (Srae Huy)
- Flooded paddy rice fields
August-October, every 3 years
(1.5-1.7 meters, 200ha),
productivity lost is more than 50%
Water runoff from the Ou Koah Nhek to
Ou Chbar, depth of flood approx. 2-3 m
(Medium exposure)
(High sensitivity)
- Road built along the river can prevent
part of the flooding
- Replant after flood events
- Plant bamboo trees along river bank to
decrease soil erosion
(Medium AC)
Very dry climate and high temperature
- Livestock disease and death
April - mid-May, annually
(due to water shortage or
waterborne disease, or sudden
change in temperature/rainfall)
(High exposure)
- Livestock vaccination and treatment
(lack of technical support, not so
effective)
Sharp shifts in temperature and rainfall
May-June, more often than before
(Medium to high exposure)
Abnormal rainfall
November-December, every 4-5 years
(High sensitivity)
(Low AC)
- Affects paddy rice harvesting
and other cash crops (peanut,
soybean and cassava)
N/A
(Low exposure)
(Medium sensitivity)
Storm
- Destruction of cassava crops (70%)
in Bu Chri 2011
May–September, 2-4 times every year
(No AC)
- Replant
(High sensitivity)
(Medium exposure)
- Destruction of some parts of the
plantations or fruit trees
(Medium sensitivity)
66
(Low AC)
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
12.2.2 FOREST-BASED ACTIVITIES
Forest product collection is not only an important source of cash income to support local livelihoods, but
also an effective coping strategy that is used in times of hazards. When food or cash is needed during
difficult times, families traditionally used available forests to hunt wild animals and harvest resin and other
NTFPs. However, the loss of forest land has reduced village access to these resources. Where communities
still had access to forestry resources, this access was often prevented during flooding events, making it
difficult for villagers to collect products (see Table 24).
Household survey results show that 96.8% of respondents observed a decline in forest resources and
products in the area; 82.2% of households report that they have to spend more time in collecting firewood
for consumption compared to five years ago. Deforestation, considered by villagers to be the primary
contributing factor to drought, has reportedly accelerated because of the conversion of forest land to largescale agro-industrial plantations and logging activities. It also makes the area more prone to storm and
flood events due to loss of valuable ecosystem services (i.e. micro-climate and water regulation, soil
erosion protection, etc.). A loss of forest cover has allowed water to flow faster across landscapes leading
to further soil degradation, erosion of river banks and hills and, at times, triggering landslides.
Table 24. Climate Change Vulnerability of Forest-based Activities in Mondulkiri
Exposure
Sensitivity
Adaptive capacity (AC)
Heavy rain and flash flooding
- Resin collection not possible during
rainy weather
- Find other alternative
livelihood sources to cope
June-September, nearly every year
(High sensitivity)
(Low AC)
Storm
May–September, 2-4 times every year
- Difficult to access forest area/
products
(High sensitivity)
(Medium exposure)
- Destruction of some parts of
forests/trees
(Low sensitivity)
Declining land/forest resources
Due to land concessions and
expansion of agriculture areas,
logging activities and other forest
exploitation
- Enhances impacts of climate
change (i.e. drought, rainfall,
temperature, and storms), loss of
natural adaptation measures to
flood and storms
- Shift to more agricultural
activities nowadays
- Try to cultivate high
market-demand cash crops
(i.e. cassava)
- Reduced income from forest
products (NTFPs)
(High exposure)
- Loss of land for agriculture to land
concessions
- Soil degradation, river bank
erosion, and potential landslides
(Medium sensitivity)
67
(Medium AC)
12.2.3 WATER AND HEALTH ISSUES
The second type of drought, hydrological drought, is experienced annually during the dry season when
natural water sources, such as streams, ponds and canals, become shallow or dry up, resulting in a shortage
of groundwater. Major streams where most villagers collect their drinking water are ‘emptied’ during the
drought period, as well as many wells, which become unusable during this period as they do not contain
enough water, or the water quality becomes unsuitable for drinking. Household survey results show that
34% of interviewed families experience different levels of water stress, ranging from a few days up to
nearly eight months of water shortage.
Apart from water shortages, the quality of water from the hand pumps may be poor. Many incidences of
water-borne diseases like diarrhoea, stomach aches and typhoid are reported during drought periods.
Health is also affected by rapid weather and temperature changes. Children are especially affected and
frequently fall ill because of temperature fluctuations. Communities experience an increase in outbreaks of
water-borne diseases (diarrhoea, malaria) and dizziness as a result of flooding. Aside from the increased
risk of diseases carried by mosquitoes, such as dengue fever and malaria, villagers are also at risk of
diseases caused by drinking contaminated flood waters. Distance and the lack of available medical staff are
also major obstacles for villagers in seeking medical services. When a family member gets sick, families
must spend a considerable amount of their income on medication, which is compounded by a loss of labour
that can contribute to farming and household income. See Table 25 below for more information.
Table 25. Climate Change Vulnerability of Water and Health Issues in Mondulkiri
Exposure
Sensitivity
Adaptive capacity (AC)
Very dry climate and high
temperature/
- Water shortage, especially during
March and April
- Existing wells (not sufficient)
January-May, annually
- Water-borne diseases like
diarrhoea, stomach ache and
typhoid
(High exposure)
(High sensitivity)
Heavy rain and flash flooding
June-September, nearly every year
(Medium exposure)
- Access water source far from
home
- Reduce water consumption
(Low AC)
- Increased risk of diseases carried by
mosquitoes (i.e. dengue fever and
malaria) and from drinking flood
waters (i.e. diarrhoea, typhoid)
(Medium sensitivity)
High temperatures
April - mid-May, annually
(High exposure)
Sharp shifts in temperature and
rainfall
- Health issues: disease, getting sick
more often, especially for children
(e.g. tuberculosis, fever, malaria and
blisters.)
(Medium to high sensitivity)
May-June, more often
- Go to health centre in the
commune (limited capacity)
- If not getting better, they
have to go to provincial or
private health care facilities
(Medium AC)
(Medium to high exposure)
Thunderstorms
May-July, annually, stronger in recent
years: 2011-2013
(Low to medium exposure)
- Threat to human and livestock life
(3 people killed in Srea Khtum, 1
person in Bu Chri, and 4 cattle in
Romanea)
- Stay at safe places
(High sensitivity)
(Medium AC)
68
- Turn off the phone, etc.
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
12.2.4 INFRASTRUCTURE AND HOUSING
In terms of accessibility, there is only one main road connecting Mondulkiri province with the rest of the
country. Many communities still have very poor access to physical infrastructure and social services,
especially during the rainy season, which also made it difficult for the field survey team to reach some
target communities. During times of heavy rainfall and flooding, some villages also experience damage to
infrastructure, including roads, which blocks the flow of traffic to the commune and district centres. Flash
flooding may also cover paths used to cross streams, limiting village access to services and external
assistance (Table 26).
Many villagers tend to have two houses, one located at the village settlement and one near their paddy
land or upland fields. During the farming season, villagers move to their houses in the fields, taking with
them food and household materials for the duration of their cultivation. Many houses are still made of
small young tree posts, thatched roofs and bamboo walls; the structural integrity of houses in some villages
is observed to be weak and vulnerable to damage in the event of storms and flooding. Villagers from
communities (i.e. Nang Khi Loek Commune) residing along the Sre Pok River experience damage to their
household assets, including small power generators and rice barns, during serious flooding events (i.e.
2007). Villagers have slowly been investing, whenever possible, in building safer houses with stronger
structures and better materials. Villagers who are not able to afford new and more structurally sound
homes try to restore what remains of their old homes after damage from storm or flood.
Table 26. Climate Change Vulnerability of Housing and Infrastructure in Mondulkiri
Exposure
Sensitivity
Adaptive capacity (AC)
Storm
- Damage to the housing (roof, walls)
- Repair the house
May–September, 2-4 times every year
(Medium sensitivity)
- Use better materials
- Some strong storms can blow away
the whole house structure
- Get weather forecast (radio,
TV) to prepare in advance
(High sensitivity)
- Get some support from
community and neighbours
(Medium exposure)
- Limited accessibility to services
during storm days
(Medium AC)
(Medium sensitivity)
Heavy rain and flash flooding
- Damage to the road/houses
June-September, nearly every year
- Damage to irrigation facilities
(High exposure)
- Limited accessibility
(Medium sensitivity)
River flooding
- Damage to the road along the river
August-October, every 3 years
- Limited accessibility
(Medium exposure)
- Flooded housing, assets and other
structures (i.e. rice barns)
(Medium sensitivity)
69
- Repair the road and
maintenance
- Use boats for transportation
(Medium AC)
- Listen to forecasts of heavy
rains to prepare
- Repair and maintain the road
- Build bamboo rafts
(Medium AC)
12.3 Adaptation Options and Priorities
Participatory consultations at community level were carried out in four surveyed communes in Mondulkiri.
Adaptation needs and community-level priorities identified are focused on rehabilitation and development
of irrigation systems and facilities/schemes (i.e. channels, reservoirs, dam/dykes etc.), which will not only
mitigate the risks and impacts of potential dry spells during the rainy season but will also enable local
farmers to cultivate dry season paddy rice and other crops. Other major adaptation priority needs are (1)
water storage and supply facilities (i.e. ponds, reservoirs, dug wells and tube wells, etc.), and other water
harvesting technology; (2) technical assistance on agricultural practice both for crops and livestock raising
(i.e. disease prevention/control, veterinary support); (3) improvement of local infrastructure and other
physical assets (including roads, bridges, schools, health centres, and boats); and (4) an early warning
system and climate information-sharing mechanism. Many villagers also believe that forest restoration and
fruit tree plantations can improve their adaptive capacity in preventing risks and impacts from storms and
floods.
At household level (see Figure 30), respondents place the highest priority on technical assistance in
improving agricultural adaptive capacity, particularly on climate-resilient crop varieties. Villagers also
showed great interest and need for establishing an early warning system and disseminating weather
forecast information, improving awareness of and education on climate change, and enhancing community
disaster management capacity. Increasing adaptive capacity to climate change also requires improving
household access to resources, assets and services and diversifying their economic activities. Similar to Koh
Kong, 94% of respondents in Mondulkiri agree that it is important to integrate climate change adaptation
measures into overall community development planning.
Figure 30. Climate Change Adaptation Capacity and Needs in Mondulkiri. Source: R-PPTA field survey 2013
70
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
13 Summary of Commune-Level Climate Change
Vulnerability and Adaptation Priorities
The overall commune-level climate change vulnerability assessment of key climatic risks (i.e. floods/heavy
rainfall, drought/water shortage, seawater intrusion, and storm/storm surges) in selected communes of
both provinces is presented in Table 27 below. Telephone interviews with commune chiefs and/or
members have been carried out for those communes where the team was unable to conduct detailed field
surveys. Some of the non-BCC target communes are also highly vulnerable to climate change risks and
impacts, such as Koh Kapik, Kandol, and Chi Kha Kraom communes in Koh Kong located inside the Peam
Krasaob Wildlife Sanctuary, Botum Sakor National Park, and Dong Peng Multiple Use Area, and Ou Buon
Leu Commune in the northern part of Mondulkiri Protected Forest. Data from these communes is at the
bottom of table (dark purple), while Koh Kong provinces are at the top (light purple) and Mondulkiri
communes are in middle of the table (green).
Table 27. Climate Change Vulnerability of Selected Communes in Koh Kong (KKG) & Mondulkiri (MDK)
No.
Commune
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Andoung Tuek (KKG)
Ta Tai Kraom (KKG)
Trapeang Rung (KKG)
Bak Khlang (KKG)
Peam Krasaob (KKG)
Chi Kha Leu (KKG)
Ta Tey Leu (KKG)
Ruessei Chrum(KKG)
Chi Phat(KKG)
Thma Doun Pov (KKG)
Romonea (MDK)
Dak Dam (MDK)
Chong Phlah (MDK)
Srae Chhouk (MDK)
15
16
17
18
19
20
Srae Khtum (MDK)
Srae Preah (MDK)
Nang Khileuk (MDK)
Sok San (MDK)
Srae Huy (MDK)
KrangTeh (MDK)
Flood/ Heavy
rain
x
xx
xx
Drought/ water
shortage
xx
x
x
x
xxx
xx
x
x
x
xx
x
x
x
Saltwater
Intrusion
xxx
xx
Storm/ Storm
surge
xx
x
xx
xx
x
xx
xxx
xx
x
xx
xx
xx
xx
xxx
xx
x
xxx
xx
xxx
x
xxx
xx
x
xx
xx
xxx
21 Bu Chri (MDK)
x
xxx
22 Bu Sra (MDK)
xx
xx
23 Koh Kapic (KKG: non-BCC)
xxx
x
24 Kandol (KKG: non-BCC )
xxx
xxx
25 Chroy Svay (KKG: non-BCC)
xx
xxx
26 Toul Korki (KKG: non-BCC)
xx
xxx
27 Ou Buon Leu (MDK: non-BCC)
xxx
xx
Notes: “x” = moderate vulnerability, “xx” = high vulnerability, “xxx” = very high vulnerability
Source: PRA discussion, interviews with commune chiefs.
71
x
xx
x
x
x
x
x
x
x
xx
x
xxx
xx
x
Main climate change adaptation options and priorities are grouped into five broad categories: (1) irrigation
schemes, (2) water storage and supply, (3) coastal protection (e.g. mangrove restoration, sea/land dykes),
(4) climate-resilient agricultural technical assistance, and (5) improving infrastructure and accessibility
(especially during the wet season). Table 28 below presents the commune level adaptation priority needs
determined through participatory consultations and interviews with commune chiefs in selected BCC
communes and some non-BCC target communes in Koh Kong and Mondulkiri Provinces.
72
Table 28. Key Climate Change Adaptation Options and Priorities of Selected Communes in Koh Kong (KKG) and Mondulkiri (MDK)
No.
Commune
Irrigation
schemes
Water storage
and supply
√
√
√
√
√
√
Coastal protection (i.e. mangrove
restoration, sea/land dykes)
Climate resilient
Agriculture TA
Improving
Accessibility
√
√
√
√
√
1
Andoung Tuek (KKG)
√
2
Ta Tai Kraom (KKG)
3
Trapeang Rung (KKG)
4
Bak Khlang (KKG)
5
Peam Krasaob (KKG)
6
Chi Kha Leu (KKG)
7
Ta Tey Leu (KKG)
8
Ruessei Chrum(KKG)
9
Chi Phat(KKG)
√
10 Thma Doun Pov (KKG)
11 Romonea (MDK)
√
√
12 Dak Dam (MDK)
√
13 Chong Phlah (MDK)
14 Srae Chhouk (MDK)
√
√
15 Srae Khtum (MDK)
√
√
16 Srae Preah (MDK)
√
17 Nang Khileuk (MDK)
√
√
18 Sok San (MDK)
√
19 Srae Huy (MDK)
√
√
20 KrangTeh (MDK)
√
21 Bu Chri (MDK)
√
√
22 Bu Sra (MDK)
23 Koh Kapic (KKG: non-BCC)
√
24 Kandol (KKG: non-BCC )
√
25 Chroy Svay (KKG: non-BCC)
√
√
26 Toul Korki (KKG: non-BCC)
√
27 Ou Buon Leu (MDK: non-BCC)
√
√
Source: PRA discussions, interviews with commune chiefs, and secondary data.
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
73
Conclusions
Almost all the regions of Cambodia are among the hotspots or areas most vulnerable to climate change in
Southeast Asia, along with all the regions of the Philippines and the Mekong River Delta in Vietnam
(Economy and Environment Program for Southeast Asia, 2009).
Since Cambodia has been identified as a country that is very vulnerable to potential impacts of climate
change, there have been many previous studies of the area and interventions by aid agencies, but more
work remains to be done. A recent study funded by the EU and implemented by the International Union
for Conservation of Nature (Bobenrieth et al., 2012) found that absence of technical knowledge to
understand and scale down climate information to regional and local scale was a weakness identified in the
area. In the household survey results summarised in Part 2 of this report, awareness raising and climate
change knowledge education also rated highly as adaptation needs and priorities for both Koh Kong and
Mondulkiri.
This problem is partially addressed in the current project, commissioned by the Asian Development Bank
(ADB) and carried out by CSIRO/CMAR to improve understanding of projected climate change by 2025 for
two provinces, Koh Kong and Mondulkiri, and to aid in assessment of risks due to these changes. It
combines a bottom-up participatory approach to determining climate vulnerabilities and adaptation
capacity with utilisation of high-resolution regional climate simulations for the two provinces. Improved
understanding of both climate change and real local needs will aid development of effective adaptation
strategies.
The Cambodian climate is strongly influenced by two monsoons, the summer (wet) Southwest and the
winter (dry) Northeast monsoon. In between the monsoon seasons, weather phenomena that originate
within the Inter Tropical Convergence Zone (ITCZ) influence the weather patterns over Cambodia. The
spatial distribution of rainfall reveals marked variations due to the influence of the monsoons and complex
topography of the region. Floods and droughts associated with the interannual variability of the
monsoonal rain significantly affect the agricultural sector. Tropical cyclones rarely significantly affect the
country.
The CCAM 10 km downscaled climate simulations produced for this study indicated potential changes to
the monsoons and weather patterns. In some cases, these differ from results from previous simulations,
most likely due to higher resolution and use of more up-to-date models and emissions scenarios.
Key findings of the projected changes by 2025 are:
1. Increases in annual temperature, as well as maximum and minimum temperatures, for both
provinces.
2. No change in annual mean rainfall for Mondulkiri but a slight decrease for Koh Kong.
3. Decrease in rainfall during the wet season and increases in other parts of the year.
4. Increase in the number of days of extreme temperatures (days above 35°C) in both provinces.
5. Increase in the Heat Wave Duration Index (HWDI) in both provinces.
6. Decrease in short- and medium-duration droughts, but increase in long-duration droughts.
7. Increase in maximum one-day extreme rainfall for both Mondulkiri and Koh Kong, while maximum
five-day totals tend to increase in Mondulkiri and to decrease in Koh Kong.
8. Increase in local sea level at Koh Kong by approximately 10 cm above the 1986-2005 baseline by
2025, and possibly by 60 cm by the end of the century.
The projected changes are based on 20 year periods relative to a 1980-1999 baseline. More details are
given in the Conclusions to Part 1.
In Part 2 of this study, the main climate risks identified for Koh Kong, which is a coastal province highly
dependent on fishery and sea resources as well as paddy rice, are seawater intrusion and storms. Seawater
intrusion and high tides flood paddy land and destroy crops, as well as causing water stress by reducing the
74
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
supply of fresh water. Storms prevent fishing activities and destroy houses and infrastructure. Heavy
summer rainfall also destroys crops and reduces productivity of aquaculture. Drought due to high
temperature and lack of rainfall in April to May further reduces water supplies, affecting crops and animal
health.
Mondulkiri is in a more rugged upland area, with diverse natural resources. Main livelihoods are growing
paddy rice, upland dry rice and cash crops, along with some collection of non-timber forest products.
Major climate risks identified for this province are drought, flooding and storms. Because rice cultivation is
rain-fed, it is vulnerable to drought, but flooding due to heavy rain and overflowing of streams also affects
crops. As in Koh Kong, hot and dry conditions in April and May reduce available water and cause livestock
disease and death. Storms in summer also destroy crops and cut off access to forest areas and health
services.
The trend to greater frequency of long-term droughts could be the feature of climate change that has the
most impact in Koh Kong and Mondulkiri for the next 20 years. In both provinces, water shortages are seen
as climate change vulnerabilities with high exposure or sensitivity for which there is low adaptive capacity.
With the large projected increase in the number of hot days and heat waves comes the potential for
enhanced evaporation. This might lead to less groundwater due to less recharge and greater extraction. In
addition, more and longer droughts for Koh Kong could lead to more salt water intrusion.
Both provinces are vulnerable to the high temperatures in April and May that damage crops and reduce
human and animal wellbeing. Projected temperature increases will only make this worse.
Projected changes to annual mean rainfall for Koh Kong and Mondulkiri are not large, but there is some
suggestion of decreases in the summer monsoon activity; less rain will possibly lead to fewer storms,
though changes in the frequency of storms were not directly assessed in this study. Although it was noted
in the vulnerability assessment that changing rainfall patterns and shifting seasons have negative impacts,
there is some adaptive capacity to deal with interannual rainfall variability through shifting of the planting
calendar and planting of different crop varieties.
In Koh Kong, only small changes (or possibly decreases) in extreme rainfall amounts indicate that there may
possibly be fewer storms, although this will be combined with a trend toward greater tidal (due to sea-level
rise) and storm extremes (extreme 1-day rainfall amount increases) in the coastal regions. Sea-level rise is
likely to be more important in winter (the time of highest annual sea level), and storm surges may also be
affected. Projected increases in extreme rainfall amounts in Mondulkiri also suggest more likely risk of
floods.
In addition to factors related to climate change, other non-climate factors such as politics and legislation
may have great effects on future livelihood and quality of life in Cambodia. These include the absence of
law enforcement, migration putting pressure on resource bases, especially in Koh Kong, where poachers
and better equipped fishers from Thailand are perceived to be depleting fish stocks, and sand dredging,
which affects breeding of sea life and for which there is no local control (Bobenrieth et al., 2012). In
Mondulkiri, where operation of the hydropower dam in the Sre Pok River may contribute to flooding,
consultation and co-ordination between responsible agencies are also important.
The climate change data from the high-resolution simulations of future climate from Part 1 of this study,
when combined with perceived vulnerabilities and adaptation priorities at household, community and
commune levels from Part 2, can aid integrated community development planning. However, because of
the inherent uncertainty in the assumptions made when producing projections of future climate and when
dealing with assessment of human behaviour, it is important to consider the appropriateness of data
presented in this study for the intended purpose in order to make the best possible decisions when
planning public projects and climate change adaptation measures. In addition, the projections for the
provinces are for 2025; longer term projections will show greater changes which should be considered,
especially for projects which have longer lifetimes, planned or otherwise.
75
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81
Appendix 1: Climate Change Vulnerability Matrix in Koh
Kong
No.
Commune
Climatic Risks
Affecting Systems
Adaptive Capacity
(Exposure)
(Sensitivity)
(Existing measures)
Seawater intrusion
NovemberDecember,
annually)
(high exposure)
Drought
- Dry spells during
rainy season
June-October
(medium
exposure)
1
Andoung
Tuek
- Very dry and high
temperatures
mid-April – May,
annually
(high exposure)
Storm/
heavy rain
June-September
annually, very
seriously in 2013
(medium
exposure)
Adaptation priorities
Agriculture
- destruction of paddy rice
up to 100%
- soil salinity/ reduced
fertility (up to 40% of land
flooded)
- paddy dyke erosion
(high sensitivity)
Water supply
- freshwater shortage (in Ta
Meakh and Ta Ok villages)
(high sensitivity)
- existing dykes in
Chimeal and Tameak
village (insufficient)
Agriculture
- crop failure/ productivity
loss
(high sensitivity)
- change crop variety
Health
- diseases (diarrhoea)
/sickness
(medium sensitivity)
- existing health centre
(limited capacity)
- improve health care
capacity and facilities
Water supply
- water shortage for
domestic use
- buy water
- travel far to other
villages/source
- water saving
- dig wells
- technical assistance
on water management
- water storage
(high priority)
(high sensitivity)
Fishery
- loss of fishing days
(high sensitivity)
Housing
- damage to houses & assets
(low sensitivity)
Infrastructure
- damage to dykes, roads,
bridges, channels
(medium sensitivity)
Human life
- accidents during storm
events
(low sensitivity)
82
- salinity tolerant crop
varieties
- buy water
- travel far to other
villages/sources
- water saving
- sea dykes and land
dykes
- water drainage
- canal irrigation system
(high priority)
- dig wells
- technical assistance
on water management
- water storage
(high priority)
- build reservoir
- technical assistance
on agricultural practice
(high priority)
- find other alternative
livelihood sources
- repair houses
- assistance from Red
Cross
- mangrove and forest
restoration
(high priority)
- repair roads
- infrastructure upgrade
and maintenance
- improve drainage
system
- early warning system
and awareness raising
- radio communication
for information and
rescue
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
No.
Commune
Climatic Risks
Affecting Systems
Adaptive Capacity
(Exposure)
(Sensitivity)
(Existing measures)
Drought
- Very dry and High
temperature
mid-April – May,
annually
(high exposure)
- Dry spells during
rainy season
June-October
(medium
exposure)
Saltwater
intrusion and high
tide
NovemberDecember,
annually
2
Peam
Krasaob
(high exposure)
Storm/
heavy rain
June-August
(high exposure)
Water supply
- domestic water shortage
(esp. potable water) associated with seawater
intrusion
(high sensitivity)
Agriculture
- vegetables/plantations die
- livestock diseases/death
(medium sensitivity)
Health
- Diseases
(low sensitivity)
Agriculture
- damage to crop
production (incl. fruit trees
and vegetable gardens)
- soil salinity
(high sensitivity)
- loss of livestock
(medium sensitivity)
Fishery
- affects sea crab fishing
(high sensitivity)
Infrastructure
- road and dyke erosion
(medium sensitivity)
Fishery
- loss of fishing days
(high sensitivity)
- loss of fishing gear
- affects aquaculture
(medium sensitivity)
Infrastructure and
housing/assets
- damage to houses/boats
- damage to roads/dykes
(low sensitivity)
Storm surge
June–September
old villages on the
island
(high exposure)
3
Heavy rain/ flash
flooding
Ta Tai Kroam
July–August,
annually
Coastal erosion
- mangrove destruction
- loss of land/ erosion
(high sensitivity)
Infrastructure/housing
- damage to roads/ bridges/
drainage systems/ channels
- flooded houses/assets
(high sensitivity)
83
- buying water (60% of
HH)
- existing water supply
system (bad water
quality)
- rain water harvesting
and storage
Adaptation priorities
- water supply from Koh
Kong City water
distribution system
- water harvesting and
storage facilities
(high priority)
- technical support on
livestock raising
- go to health centre
- drink boiled water
- old land dyke (low
capacity, could not
prevent intrusion
entirely, and prone to
erosion
- keep livestock in
higher cages
- repair and upgrade
the dyke
- technical assistance
on agricultural
production and climateresilient crops
(high priority)
- technical assistance
on livestock protection
and disease prevention
- find alternative
income sources
- road maintenance
- repair and upgrade
the dyke/road
- find alternative
income sources (rather
difficult)- face food shortage,
have to borrow
food/money
- mangrove restoration
- build climate-resistant
housing (e.g. lower
structures)
- mangrove restoration
- technical assistance
on aquaculture (snail,
fish raising)
(high priority)
- relocation of villagers
to mainland (some
households remain
living on the island and
many households sold
newly allocated land on
the mainland)
- coastal area
protection and
mangrove restoration
in the coastal area
- repair roads and
maintenance
- improve drainage
system
- upgrade and build
new roads and bridges
- improve drainage
system
(high priority)
- early warning
system/information
sharing
- improve water
drainage system
(high priority)
No.
Commune
Climatic Risks
Affecting Systems
Adaptive Capacity
(Exposure)
(Sensitivity)
(Existing measures)
(high exposure)
River flooding
(Very low
exposure)
Saltwater
intrusion
December–March,
annually)
15 days per month
Agriculture
- destruction of paddies and
other crops
- pests/insects
- loss of livestock
Adaptation priorities
- build channel to
divert/discharge
flooding water
- technical assistance
on crop production and
livestock raising and
disease prevention
(high sensitivity)
Agriculture
- damage to paddy fields
(saltwater intrusion covers a
large area of abandoned
paddy rice since it is located
in the protected area)
(medium sensitivity)
- harvest before
saltwater intrusion
Water supply
- domestic water shortage
(esp. potable water)associated with seawater
intrusion
(high sensitivity)
Agriculture
- affects crop production(i.e.
cash crops, fruit trees)
- affects livestock raising,
lack of water, animals get
diseases/deaths
(low sensitivity)
- existing water well
(not so good quality)
(high priority)
- build land dyke
few households remain
practicing paddy rice
cultivation on their land
(18 HH)
(high exposure)
Drought
- Very dry and high
temperatures
mid-April – May,
annually
- Dry spells during
rainy season
June-October
(medium
exposure)
- more water wells
(high priority)
- technical assistance
on livestock raising
(high priority)
- technical assistance
on agricultural
production (esp. on
seed variety)
(high priority)
Storms
June-August
(low to medium
exposure)
Health
- disease/sickness
(medium sensitivity)
Housing
- damage to houses
(low sensitivity)
84
- repair housing
- local knowledge for
weather prediction
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
Appendix 2: Climate Change Vulnerability Matrix in
Mondulkiri
No.
Commune
Climatic Risks
(Exposure)
Drought
- Delayed/shortened
rainy season and dry
spells during rainy
season
May-October, every 1-2
years
- High temperatures and
prolonged dry season
April–May
Affecting Systems
(Sensitivity)
Agriculture
-Paddy
loss of paddy rice
productivity due to water
shortage
(low sensitivity)
-Cash crops: cassava, soy
bean etc.
loss of productivity
(low sensitivity)
- Livestock
death because of disease.
(medium sensitivity)
Adaptive Capacity
(Existing measures)
- there is an existing
dyke and irrigation
system established
since Khmer Rouge.
Water supply
- domestic water shortage
(esp. potable water)
- use unclean water
from the channels
Housing/infrastructure
- damage to roofs/walls
- limited accessibility during
rainy season
(low sensitivity)
Agriculture
- destruction of short-term
cash crops and upland dry
rice
- livestock loss/death
(medium sensitivity)
Housing
-7 houses fell down and
were damaged
- destruction of roofs and
walls.
(high sensitivity)
Infrastructure
- damage to school
building, roofs and walls.
(medium sensitivity)
- repair housing
Human life
3 people were killed by
thunderstorm events
(high sensitivity)
- be cautious and
stay at safe places
when storm and
thunder occur.
- replant after crop
failure
- vaccinate livestock
(but low
effectiveness)
Adaptation priorities
- establish irrigation
system/facilities
(high priority)
-technical assistance
on livestock/
agricultural practice
(high priority)
(high exposure)
Flood /heavy rain
1
Srea Khtum
It normally lasts 1-2
days. Serious event in
Ou Rona village 2012
(high exposure)
Storms
Very strong in 2013 in
Or Arm and 2011 in
Chhneng village.
(medium exposure)
Thunderstorms
2011-2013 very strong.
(low exposure)
85
- digging pond for
water harvesting and
storage
(high priority)
- replant
- housing repair by
individual
households
- local authority and
villagers repaired the
school and students
could go back to
school.
- early warning
system/weather
forecasting
information on TV or
radio.
(high priority)
- early warning system
No.
Commune
Climatic Risks
(Exposure)
Drought
- High temperatures and
prolonged dry season
April–May
happens every year
(high exposure)
- Delayed/shortened
rainy season and dry
spells during rainy
season
May–October, every 1-2
years
In 2012 there was a
serious drought/dry
spell during the rainy
season from September
to November.
2
Bu Chri
Affecting Systems
(Sensitivity)
Agriculture
- loss of rice productivity
50-70%
- loss of peanut productivity
50-70%
Adaptive Capacity
(Existing measures)
- rehabilitate the
well/pond but not
sufficient with high
demand.
- livestock disease and
deaths
- provide vaccination
for disease
prevention.
(high sensitivity)
Water supply
- shortage for domestic use
and most of villagers are
facing water shortage
(medium sensitivity)
Storm / thunderstorm
Housing
- 23 houses were damaged
(medium to high
exposure)
- reservoir for water
harvesting and storage
- irrigation system for
agriculture.
(high priority)
-technical assistance
on agriculture
technical training.
(high priority)
- build new wells and
rehabilitate old well in
the village.
(high priority)
(medium sensitivity)
Human health
- Disease (i.e. malaria) and
getting sick more often
(high exposure)
Happens every year but
very strong in 2011
- existing old wells in
the village
Adaptation priorities
(medium sensitivity)
Agriculture
- cash crop: 70% of cassava
destroyed in the village.
(2011)
(medium sensitivity)
- 100 fruit trees fell down
(2011).
(low sensitivity)
- 20%-30% of young rubber
trees affected
- go to nearby clinic/
commune health
centre (limited
capacity)
- strengthen local
health care service.
- repair houses with
support from
community
- plant fruit trees
around homestead
area to prevent storm
impact.
(medium priority)
- replant
(medium sensitivity)
Heavy rain/flood
June–September
(medium exposure)
Human life
- 1 person was killed by a
thunderstorm in 2011.
(low sensitivity)
Agriculture
- destruction of short-term
cash crops during
harvesting (especially
peanut)
(medium sensitivity)
86
- replant
- technical assistance
on agriculture
technical training.
(high priority)
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
No.
Commune
Climatic Risks
(Exposure)
Flood/heavy rain
River flooding
every 2-3 years
water runoff from the
Ou Koah Nhek to Ou
Chbar, depth of flood
approx. 2 - 3 m
(low to medium
exposure)
Heavy rainfall
June-September
(high exposure)
Drought
- Delayed/shortened
rainy season, lack of
rainfall
May-October,
serious in 1994
(high exposure)
3
Srea Huy
- High temperatures and
prolonged dry season
April–May)
happens every year
(high exposure)
Affecting Systems
(Sensitivity)
Agriculture
- 200ha
- rice productivity lost more
than 50% in 2009.
- loss of livestock and
animal deaths
(high sensitivity)
Adaptive Capacity
(Existing measures)
- replant after flood
event
- road built along the
river could prevent
part of the flooding
- plant bamboo along
the river bank
- damage to
roads/irrigation facilities
- limited accessibility
- build bamboo rafts
- annual maintenance
(medium sensitivity)
Agriculture
- loss of rice productivity
up to 70%
(high sensitivity)
- cash crops loss of
productivity (1993-1995)
(medium sensitivity)
- livestock disease and
deaths (60-70% in Chhloul
village in 2012)
(high sensitivity)
Water supply
- lack of clean water for
domestic use, 7 areas in the
commune facing water
shortage
Storms
Not very strong or often
(low exposure)
Thunder/lightning
It happens during rainy
season and comes with
storms, but is not
frequent.
(low exposure)
Drought
4
Romanea
- Very dry climate and
high temperatures
April - mid-May,
annually)
(high sensitivity)
Housing/infrastructure
- 5 houses fell down/roofs
were destroyed
- limited accessibility to
roads/services
(medium sensitivity)
Forest and plantation
- 2-3% of fruit trees
destroyed
- difficult/dangerous to
access forest area/products
(low sensitivity)
Water Supply
- water shortage affecting
more than 40% of villagers
during dry season.
(high sensitivity)
- replant
- there are some
existing dykes for
water storage but
still insufficient to
supply to the paddy
area
- pump water from
nearby water source
if available and
affordable
- vaccination and
medicine for
livestock (low
effectiveness)
-collect water from
river or steam far
away from the
village.
- buy water for
drinking
Adaptation priorities
- rehabilitate/
construct river dyke
(4km)
(medium priority)
- rehabilitate the
existing reservoir and
- establish new
reservoir with
irrigation system for
water supply to paddy
land.
(high priority)
- technical assistance
on agriculture technical training.
(high priority)
- provide more wells in
the village
- rehabilitate the
existing pond or basin
for water
harvesting/storage.
(high priority)
- rebuild/ repair
- try to get
information on TV or
radio about weather
forecast.
-save/reduce water
usage
- collect water form
river or canal far
from home.
- establish water
harvesting and storage
system (reservoir/
basin)
- provide more wells
for water supply.
(high Priority)
87
No.
Commune
Climatic Risks
(Exposure)
(high exposure)
- Prolonged dry season /
Dry spells in rainy
season
May-July, not so often
- Shortened rainy
season and lack of
rainfall at end of the
rainy season
August-October, every
few years
- Temperature increases
during planting season
Affecting Systems
(Sensitivity)
Human Health
- malaria and other diseases
(heat/high temperature)
(medium sensitivity)
Agriculture
- lack of water for cash crop
cultivation (cassava and
bean, loss of 60%
productivity.)
(high sensitivity)
- Paddy crop failure due to
dry spells during rainy
season (150ha of paddy
area in Srea Ey village)
(medium sensitivity)
- Livestock disease/deaths
50% of buffalos got sick and
30 died (heat/rainfall)
(medium exposure)
(high sensitivity)
Heavy rain and flash
flooding
June-September
(medium exposure)
Storm/thunderstorm
May–September
Serious storm events in
2013, not very often in
this commune
(medium exposure)
Normally thunderstorms
happen with storm and
heavy rain.
Stronger since 2000
Infrastructure
- limited accessibility during
rainy season
(medium sensitivity)
Housing
- damage to houses/home
gardens
(low sensitivity)
Agriculture
- Livestock deaths
(4
cattle died)
- Plantations (A few trees
were destroyed
- Cash crops (5% of cassava
plantation destroyed)
(high exposure)
(medium sensitivity)
88
Adaptive Capacity
(Existing measures)
- go to health centre
near village or/and in
the province.
- replant, but not so
effective.
Adaptation priorities
- improve health care
capacity.
(medium priority)
- establish irrigation
system
- lowland farms could
survive.
(high priority)
- treatment by
veterinary volunteer
in village.
- TA on agriculture
technical training for
disease prevention.
(high priority)
- road maintenance
(low capacity)
- improve/upgrade
road (esp. for Srae Ey
village)
(medium priority)
- repair of roofs/walls
by individual
households
- get information
about weather
forecast on TV or
radio.
- awareness raising
- Early warning system
Climate Change Impacts and Vulnerability Assessments for Mondulkiri and Koh Kong Provinces in Cambodia
CONTACT US
FOR FURTHER INFORMATION
t 1300 363 400
Climate Adaptation Flagship
+61 3 9545 2176
Jack. Katzfey
e [email protected]
t +61 3 9239 4562
w www.csiro.au
e [email protected]
YOUR CSIRO
w www.csiro.au/org/ClimateAdaptationFlagshipOverview
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89