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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 References Abarquez, I. and Z. 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Imperial College Press, London. 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 Australia is founding its future on science and innovation. 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