Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized THAILAND CLIMATE RISK COUNTRY PROFILE COPYRIGHT © 2021 by the World Bank Group 1818 H Street NW, Washington, DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org © 2021 Asian Development Bank 6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, Philippines Tel +63 2 8632 4444; Fax +63 2 8636 2444 www.adb.org This work is a product of the staff of the World Bank Group (WBG) and the Asian Development Bank (ADB) and with external contributions. The opinions, findings, interpretations, and conclusions expressed in this work are those of the authors’ and do not necessarily reflect the views or the official policy or position of the WBG, its Board of Executive Directors, or the governments it represents or of ADB, its Board of Governors, or the governments they represent. The WBG and ADB do not guarantee the accuracy of the data included in this work and do not make any warranty, express or implied, nor assume any liability or responsibility for any consequence of their use. This publication follows the WBG’s practice in references to member designations, borders, and maps. ADB, however, recognizes “China” as the People’s Republic of China. The boundaries, colors, denominations, and other information shown on any map in this work, or the use of the term “country” do not imply any judgment on the part of the WBG or ADB, their respective Boards, or the governments they represent, concerning the legal status of any territory or geographic area or the endorsement or acceptance of such boundaries. The mention of any specific companies or products of manufacturers does not imply that they are endorsed or recommended by either the WBG or ADB in preference to others of a similar nature that are not mentioned. RIGHTS AND PERMISSIONS The material in this work is subject to copyright. Because the WB and ADB encourage dissemination of their knowledge, this work may be reproduced, in whole or in part, for noncommercial purposes as long as full attribution to this work is given. This work is licensed under the Creative Commons Attribution-NonCommercial 3.0 IGO License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/3.0/igo/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. This CC license does not apply to WBG or non-ADB copyright materials in this publication. If the material is attributed to another source, please contact the copyright owner or publisher of that source for permission to reproduce it. WBG or ADB cannot be held liable for any claims that arise as a result of your use of the material. Please cite the work as follows: Climate Risk Country Profile: Thailand (2021): The World Bank Group and the Asian Development Bank. Any queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: pubrights@worldbank.org. Cover Photos: © doronko, “Thailand” March 4, 2009 via Flickr, Creative Commons CC BY-NC-ND 2.0. © Sanchez Jalapeno, “Thailand” August 17, 2011 via Flickr, Creative Commons CC BY-NCND 2.0. Graphic Design: Circle Graphics, Inc., Reisterstown, MD. CLIMATE RISK COUNTRY PROFILE: THAILAND ii ACKNOWLEDGEMENTS This profile is part of a series of Climate Risk Country Profiles that are jointly developed by the World Bank Group (WBG) and the Asian Development Bank (ADB). These profiles synthesize the most relevant data and information on climate change, disaster risk reduction, and adaptation actions and policies at the country level. The profile is designed as a quick reference source for development practitioners to better integrate climate resilience in development planning and policy making. This effort is co-led by Veronique Morin (Senior Climate Change Specialist, WBG), Ana E. Bucher (Senior Climate Change Specialist, WBG) and Arghya Sinha Roy (Senior Climate Change Specialist, ADB). This profile was written by Alex Chapman (Consultant, ADB), William Davies (Consultant, ADB) and Ciaran Downey (Consultant). Technical review of the profiles was undertaken by Robert L. Wilby (Loughborough University). Additional support was provided by MacKenzie Dove (Senior Climate Change Consultant, WBG), Jason Johnston (Operations Analyst, WBG), Yunziyi Lang (Climate Change Analyst, WBG), Adele Casorla-Castillo (Consultant, ADB), and Charles Rodgers (Consultant, ADB). This profile also benefitted from inputs of WBG and ADB regional staff and country teams. Climate and climate-related information is largely drawn from the Climate Change Knowledge Portal (CCKP), a WBG online platform with available global climate data and analysis based on the latest Intergovernmental Panel on Climate Change (IPCC) reports and datasets. The team is grateful for all comments and suggestions received from the sector, regional, and country development specialists, as well as climate research scientists and institutions for their advice and guidance on use of climate related datasets. CLIMATE RISK COUNTRY PROFILE: THAILAND iii CONTENTS FOREWORD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 KEY MESSAGES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 COUNTRY OVERVIEW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 CLIMATOLOGY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Climate Baseline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Key Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Climate Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 CLIMATE RELATED NATURAL HAZARDS . . . . . . . . . . . . . . . . . . . . . . 13 Heatwaves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Drought . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Flood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Cyclones and Storm Surge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 CLIMATE CHANGE IMPACTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Natural Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Water . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 The Coastal Zone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Land and Soil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Economic Sectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Urban and Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Poverty, Inequality, and Disaster Vulnerability . . . . . . . . . . . . . . . . . . . . . . . . . 23 Human Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 POLICIES AND PROGRAMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 National Adaptation Policies and Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Climate Change Priorities of ADB and the WBG . . . . . . . . . . . . . . . . . . . . . . . . 26 CLIMATE RISK COUNTRY PROFILE: THAILAND iv FOREWORD Climate change is a major risk to good development outcomes, and the World Bank Group is committed to playing an important role in helping countries integrate climate action into their core development agendas. The World Bank Group (WBG) and the Asian Development Bank (ADB) are committed to supporting client countries to invest in and build a low-carbon, climate- resilient future, helping them to be better prepared to adapt to current and future climate impacts. Both institutions are investing in incorporating and systematically managing climate risks in development operations through their individual corporate commitments. For the World Bank Group: a key aspect of the World Bank Group’s Action Plan on Adaptation and Resilience (2019) is to help countries shift from addressing adaptation as an incremental cost and isolated investment to systematically incorporating climate risks and opportunities at every phase of policy planning, investment design, implementation and evaluation of development outcomes. For all International Development Association and International Bank for Reconstruction and Development operations, climate and disaster risk screening is one of the mandatory corporate climate commitments. This is supported by the World Bank Group’s Climate and Disaster Risk Screening Tool which enables all Bank staff to assess short- and long-term climate and disaster risks in operations and national or sectoral planning processes. This screening tool draws up-to-date and relevant information from the World Bank’s Climate Change Knowledge Portal, a comprehensive online ‘one-stop shop’ for global, regional, and country data related to climate change and development. For the Asian Development Bank (ADB): its Strategy 2030 identified “tackling climate change, building climate and disaster resilience, and enhancing environmental sustainability” as one of its seven operational priorities. Its Climate Change Operational Framework 2017–2030 identified mainstreaming climate considerations into corporate strategies and policies, sector and thematic operational plans, country programming, and project design, implementation, monitoring, and evaluation of climate change considerations as the foremost institutional measure to deliver its commitments under Strategy 2030. ADB’s climate risk management framework requires all projects to undergo climate risk screening at the concept stage and full climate risk and adaptation assessments for projects with medium to high risk. Recognizing the value of consistent, easy-to-use technical resources for our common client countries as well as to support respective internal climate risk assessment and adaptation planning processes, the World Bank Group’s Climate Change Group and ADB’s Sustainable Development and Climate Change Department have worked together to develop this content. Standardizing and pooling expertise facilitates each institution in conducting initial assessments of climate risks and opportunities across sectors within a country, within institutional portfolios across regions, and acts as a global resource for development practitioners. For common client countries, these profiles are intended to serve as public goods to facilitate upstream country diagnostics, policy dialogue, and strategic planning by providing comprehensive overviews of trends and projected changes in key climate parameters, sector-specific implications, relevant policies and programs, adaptation priorities and opportunities for further actions. We hope that this combined effort from our institutions will spur deepening of long-term risk management in our client countries and support further cooperation at the operational level. Bernice Van Bronkhorst Preety Bhandari Global Director Chief of Climate Change and Disaster Risk Management Thematic Group Climate Change Group concurrently Director Climate Change and Disaster Risk Management Division The World Bank Group Sustainable Development and Climate Change Department Asian Development Bank CLIMATE RISK COUNTRY PROFILE: THAILAND 1 KEY MESSAGES • Observations show temperature increases across Thailand since the mid-20th century and an increase in annual precipitation. Most of this increase occurs during the wet season. • By the 2090s, the average temperature is projected to increase by 0.95°C–3.23°C above the 1986–2005 baseline, with the rate of warming dependent on the emissions pathway. • Projected temperature increases are strongest in the south, and in daily maximum and minimum temperatures. • Floods are by far the greatest natural hazard facing Thailand in terms of economic and human impacts. Thailand is cited as one of the ten most flood-affected countries in the world. Drought and cyclone impacts also represent major hazards. All may intensify in future climate scenarios. • The number of people affected by an extreme river flood could grow by over 2 million by 2035–2044, and coastal flooding could affect a further 2.4 million people by 2070–2100. • Projections suggest that Thailand’s agriculture sector could be significantly affected by a changing climate, due to its location in the tropics where agricultural productivity is particularly vulnerable to temperature rises. • The combination of rising seas and sinking land, as well as potential cyclone-induced storm surge resulted from the climate change impact, place the country’s capital Bangkok in a precarious position when the net, or relative, rate of sea-level rise. Large amounts of critical public and private infrastructure are in areas which are likely to be exposed under future climate change situation. • The aftermath of devastating floods in 2011 provides an example of how climate change can adversely affect poorer people in Thailand, with studies showing that post-flood, higher income groups received more government compensation than lower income groups. • The human impacts of climate change in Thailand remain dependent on the approach to adaptation adopted, but there is a significant risk that the poorest and marginalized groups will experience disproportionately greater loss and damage. COUNTRY OVERVIEW T hailand is the 20th most populous country in the world, located at the center of Southeast Asia with a land area of 513,120 km2. Thailand is categorized into key areas: the northern region is hilly and mountainous, the northeast region is a high plain, with the central region as a large, low plain, the eastern region has valleys and small hills, with the western region being hilly and mountainous. The southern end of the country is a peninsula with the Andaman Sea to the west and Gulf of Thailand. Located in the tropical region, Thailand’s climate is relatively warm all year round.1 By 2030, Thailand’s population is projected to reach about 71–77 million, with an increasing proportion living in urban areas. Thailand’s economy is 90% based on the industrial and service sector, with the agricultural sector accounting for only 10% (but 33% of the workforce).2 The latter half of the 20th century witnessed significant economic growth 1 Thailand (2018). Third National Communication to the UNFCCC. URL: https://unfccc.int/sites/default/files/resource/ Thailand%20TNC.pdf 2 CIA (2018). The World Factbook: Thailand. URL: https://www.cia.gov/library/publications/the-world-factbook/geos/th.html CLIMATE RISK COUNTRY PROFILE: THAILAND 2 of 7.5% a year between 1960 and 1996, such that Thailand is now considered a newly industrialized country. As a result, Thailand has reduced poverty significantly, improving the education and health circumstances for millions of its population. Economic growth has slowed in recent decades due to a number of national and global economic and political instabilities, and multidimensional poverty and undernourishment persist (Table 1).3 The country has experienced slower average growth after the 1997 Asian financial crisis and 2008 global sub-prime crisis. Thailand has experienced negative growth due to the impacts form the COIVD-19 pandemic, which has adversely affected Thailand’s small, open economy, its export and the country’s tourism sector. To counter this, Thailand has placed emphasis on self-reliance and resilience to external factors in its economic planning.1 Thailand submitted its Third National Communication to the UNFCCC in 2018, its Initial Nationally Determined Contribution in 2016 and its Updated Nationally Determined Contribution in 2020. Thailand is recognized as highly vulnerable to climate variability and change due to increasing natural hazards, such as heavy rainfall, floods, and droughts, as well as sea level rise impacts the country’s coasts. Thailand is focusing its adaptation efforts key sectors such as energy, water, transportation, agriculture, human settlements and public health.4 TABLE 1.  Key indicators Indicator Value Source Population Undernourished 5 9.3% (2017–2019) FAO, 2020 National Poverty Rate 6 9.9% (2018) ADB, 2020 Share of Income Held by Bottom 20%7 7.2% (2018) World Bank, 2019 Net Annual Migration Rate 8 0.03% (2015–2020) UNDESA, 2019 Infant Mortality Rate (Between Age 0 and 1) 9 0.8% (2015–2020) UNDESA, 2019 Average Annual Change in Urban Population 10 1.7% (2015–2020) UNDESA, 2018 Dependents per 100 Independent Adults11 42 (2020) UNDESA, 2019 Urban Population as % of Total Population12 51.4% (2020) CIA, 2020 External Debt Ratio to GNI 13 35.1% (2018) ADB, 2020 Government Expenditure Ratio to GDP 14 20.5% (2019) ADB, 2020 3 World Bank (2018). The World Bank in Thailand URL: https://www.worldbank.org/en/country/thailand/overview [accessed 12/12/2018] 4 Thailand (2018). Thailand’s Third National Communication. Ministry of Natural Resources and Environment. URL: https://unfccc.int/ sites/default/files/resource/Thailand%20TNC.pdf 5 FAO, IFAD, UNICEF, WFP, WHO (2020) The state of food security and nutrition in the world. Transforming food systems for affordable healthy diets. FAO. Rome. URL: http://www.fao.org/documents/card/en/c/ca9692en/ 6 ADB (2020). Basic Statistics 2020. URL: https://www.adb.org/publications/basic-statistics-2020 [accessed 27/01/21] 7 World Bank (2019). Income share held by lowest 20%. URL: https://data.worldbank.org/ [accessed 17/12/20] 8 UNDESA (2019). World Population Prospects 2019: MIGR/1URL: https://population.un.org/wpp/Download/Standard/Population/ [accessed 17/12/20] 9 UNDESA (2019). World Population Prospects 2019: MORT/1-1. URL: https://population.un.org/wpp/Download/Standard/Population/ [accessed 17/12/20] 10 UNDESA (2019). World Urbanization Prospects 2018: File 6. URL: https://population.un.org/wup/Download/ [accessed 17/12/20] 11 UNDESA (2019). World Population Prospects 2019: POP/11-A. URL: https://population.un.org/wpp/Download/Standard/Population/ [accessed 17/12/20] 12 CIA (2020). The World Factbook. Central Intelligence Agency. Washington DC. URL: https://www.cia.gov/the-world-factbook/ 13 ADB (2020). Key Indicators for Asia and the Pacific 2020. Asian Development Bank. URL: https://www.adb.org/publications/ key-indicators-asia-and-pacific-2020 14 ADB (2020). Key Indicators for Asia and the Pacific 2020. Asian Development Bank. URL: https://www.adb.org/publications/ key-indicators-asia-and-pacific-2020 CLIMATE RISK COUNTRY PROFILE: THAILAND 3 Green, Inclusive and Resilient Recovery The coronavirus disease (COVID-19) pandemic has led to unprecedented adverse social and economic impacts. Further, the pandemic has demonstrated the compounding impacts of adding yet another shock on top of the multiple challenges that vulnerable populations already face in day-to-day life, with the potential to create devastating health, social, economic and environmental crises that can leave a deep, long-lasting mark. However, as governments take urgent action and lay the foundations for their financial, economic, and social recovery, they have a unique opportunity to create economies that are more sustainable, inclusive and resilient. Short and long- term recovery efforts should prioritize investments that boost jobs and economic activity; have positive impacts on human, social and natural capital; protect biodiversity and ecosystems services; boost resilience; and advance the decarbonization of economies. This document aims to succinctly summarize the climate risks faced by Thailand. This includes rapid onset and long-term changes in key climate parameters, as well as impacts of these changes on communities, livelihoods and economies, many of which are already underway. This is a high-level synthesis of existing research and analyses, focusing on the geographic domain of Thailand, therefore potentially excluding some international influences and localized impacts. The core data presented is sourced from the database sitting behind the World Bank Group’s Climate Change Knowledge Portal (CCKP), incorporating climate projections from the Coupled Model Inter-comparison Project Phase 5 (CMIP5). FIGURE 1.  The ND-GAIN Index summarizes a This document is primarily meant for WBG and ADB country’s vulnerability to climate change and staff to inform their climate actions. The document also other global challenges in combination with its aims and to direct the reader to many useful sources readiness to improve resilience. It aims to help of secondary data and research. businesses and the public sector better prioritize investments for a more efficient response to Due to a combination of political, geographic, and the immediate global challenges ahead. social factors, Thailand is recognized as vulnerable 59 to climate change impacts, ranked 62nd out of 58 181 countries in the 2020 ND-GAIN Index.15 The ND-GAIN Index ranks 181 countries using a score 57 which calculates a country’s vulnerability to climate Score 56 change and other global challenges as well as their readiness to improve resilience. The more vulnerable 55 a country is the lower their score, while the more ready 54 a country is to improve its resilience the higher it will be. Norway has the highest score and is ranked 1st. 53 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 Figure 1 is a time-series plot of the ND-GAIN Index Thailand showing Thailand’s progress. 15 University of Notre Dame (2020). Notre Dame Global Adaptation Initiative. URL: https://gain.nd.edu/our-work/country-index/ CLIMATE RISK COUNTRY PROFILE: THAILAND 4 CLIMATOLOGY Climate Baseline Overview Thailand has a tropical climate influenced by seasonal monsoon winds. The southwest monsoon (May) brings a stream of warm moist air from the Indian Ocean towards Thailand, causing abundant rain over the country, especially the mountainous regions. This phenomenon is intensified by the Inter-Tropical Convergence Zone (ITCZ) in the months of May to October and tropical cyclones which produce large amounts of rainfall. The northeast monsoon, starting in October, brings cold and dry air from the anticyclone in China over major parts of Thailand, especially the northern and northeastern parts which are located at higher latitude areas. In the south, the monsoon causes mild weather and abundant rain along the eastern coast.16 Figure 2 provides an overview of Thailand’s seasonal climate cycle, but hides sub-national variations, across the latest climatology, 1991–2020. Thailand’s hottest months are April and May, with the coldest months experienced during December and January. The mean annual temperature is 26.3°C, with a seasonal temperature variation of 5.7°C (between lows of 23.2°C and highs of 28.9°C). The months with the highest rainfall are August and September, with approximately 255 mm recorded during these months. The months with the highest rainfall coincide with Thailand’s monsoon season, May to October. Mean annual rainfall is 1,542 mm. Figure 3 shows the spatial differences of observed historical temperature and rainfall in Thailand. Annual Cycle FIGURE 2.  Average monthly temperature and rainfall in Thailand (1991–2020)17 32 300 Temperature (°C) 28 200 Rainfall (mm) 24 100 20 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Rainfall Temperature 16 Thailand (2018). Third National Communication to the UNFCCC. URL: https://unfccc.int/sites/default/files/resource/ Thailand%20TNC.pdf 17 WBG Climate Change Knowledge Portal (CCKP, 2021). Thailand Climate Data: Historical. URL: https://climateknowledgeportal. worldbank.org/country/thailand/climate-data-historical CLIMATE RISK COUNTRY PROFILE: THAILAND 5 Spatial Variation FIGURE 3.  (Left) annual mean temperature (°C), and (right) annual mean rainfall (mm) in Thailand over the period 1991–2020.18 Key Trends Temperature Various studies report temperature increases across Thailand since mid-20th century. Manton et al. (2001) report a significant increase in minimum temperatures at meteorological stations located in Thailand between 1961–1998, as well as an increase in the number of warm nights.19 Atsamon (2011) observed increases in daily maximum, mean and minimum temperatures at 65 meteorological stations between 1970–2006 (0.12–0.59°C, 0.10–0.40°C and 0.11–0.55°C per decade, respectively).20 18 WBG Climate Change Knowledge Portal (CCKP, 2021). Thailand Climate Data: Projections. URL: https://climateknowledgeportal. worldbank.org/country/thailand/climate-data-projections 19 Manton, M.J. & Della-Marta, Paul & Haylock, M.R. & Hennessy, K & Nicholls, Neville & Chambers, Lynda & Collins, D.A. & Daw, G & Finet, A & Gunawan, Dodo & Inape, Kasis & Isobe, H & Kestin, T.S. & Lefale, Penehuro & Leyu, C.H. & Lwin, T & Maitrepierre, Luc & Ouprasitwong, N & Page, C.M. & Yee, D. (2001). Trends in extreme daily rainfall and temperature in Southeast Asia and The South Pacific: 1961–1998. International Journal of Climatology. 21. 269 - 284. URL: https://rmets.onlinelibrary.wiley.com/doi/10.1002/ joc.610 20 Limsakul, Atsamon & Limjirakan, Sangchan & Sriburi, Thavivongse & Boochub Suttamanuswong, and. (2011). Trends in Temperature and Its Extremes in Thailand. Thai Environmental Engineering Journal. 25. 9–16. URL: https://www.researchgate.net/publication/ 230692853_Trends_in_Temperature_and_Its_Extremes_in_Thailand CLIMATE RISK COUNTRY PROFILE: THAILAND 6 The Berkeley Earth dataset21 provides historical temperature change estimates for 1° × 1° grid cells, and can be used to estimate warming over the 20th century. In general, it should be noted that estimates of warming over grid cells with larger proportions of ocean cover are less reliable, but also generally show less warming. Estimated warming around Bangkok between 1851 and 2017 (average) is 1°C. Observations show a warming of 1.4°C over the same period in the southern town of Nakhon A Precautionary Approach Si Thammarat, while there was an observed increase of 1.2°C in the northern town of Lampang. Studies published since the last iteration of the IPCC’s report (AR5), such as Gasser Precipitation et al. (2018), have presented evidence which Studies observe an increase in annual precipitation, with an suggests a greater probability that earth will increase in precipitation during the wet season contributing experience medium and high-end warming most to this increase.22 Variability of precipitation in Thailand scenarios than previously estimated.25 Climate over the 20th century was driven particularly by El Niño change projections associated with the highest Southern Oscillation, with years of strong El Niño correlated emissions pathway (RCP8.5) are presented with moderate and severe drought.23 A 2016 study found here to facilitate decision making which is that while precipitation events have been less frequent robust to these risks. across the country, they have intensified.24 Climate Future Overview The main data source for the World Bank Group’s Climate Change Knowledge Portal (CCKP) is the Coupled Model Inter-comparison Project Phase 5 (CMIP5) models, which are utilized within the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), providing estimates of future temperature and precipitation. Four Representative Concentration Pathways (i.e. RCP2.6, RCP4.5, RCP6.0, and RCP8.5) were selected and defined by their total radiative forcing (cumulative measure of GHG emissions from all sources) pathway and level by 2100. In this analysis, RCP2.6 and RCP8.5, the extremes of low and high emissions pathways, are the primary focus where RCP2.6 represents a very strong mitigation scenario and RCP8.5 assumes business- as-usual scenario. For more information, please refer to the RCP Database. 21 Carbon Brief (2018). Mapped: How every part of the world has warmed – and could continue to. Infographics, Berkeley Dataset. [26 September 2018]. URL: https://www.carbonbrief.org/mapped-how-every-part-of-the-world-has-warmed-and-could-continue- to-warm 22 Lacombe, Guillaume & Hoanh, Chu & Smakhtin, Vladimir. (2012). Multi-year variability or unidirectional trends? Mapping long-term precipitation and temperature changes in continental Southeast Asia using PRECIS regional climate model. Climatic Change. 113. URL: https://wle.cgiar.org/multi-year-variability-or-unidirectional-trends-mapping-long-term-precipitation-and-temperature-0 23 Lyon, B. (2004). The strength of El Nino and the spatial extent of tropical drought. Advances in Geosciences, 31. URL: https:// agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2004GL020901 24 Limsakul, A. and Singhruck, P. (2016). Long-term trends and variability of total and extreme precipitation in Thailand. Atmospheric Research, 169, pp. 301–317. URL: https://tdri.or.th/wp-content/uploads/2015/11/1-long-term-trends-main.pdf 25 Gasser, T., Kechiar, M., Ciais, P., Burke, E. J., Kleinen, T., Zhu, D., . . . Obersteiner, M. (2018). Path-dependent reductions in CO2 emission budgets caused by permafrost carbon release. Nature Geoscience. URL: http://pure.iiasa.ac.at/id/eprint/15453/ CLIMATE RISK COUNTRY PROFILE: THAILAND 7 For Thailand, these models show a trend of consistent warming, which will increase towards the end of the century. While rainfall projections are less certain and vary by both RCP scenario as well as models, projected precipitation trends show a likely slight increase in rainfall. Tables 2 and 3 below, provide information on temperature projections and anomalies for the four RCPs over two distinct time horizons; presented against the reference period of 1986–2005. TABLE 2. Projected anomaly (changes °C) for maximum, minimum, and average daily temperatures in Thailand for 2040–2059 and 2080–2099, from the reference period of 1986–2005 for all RCPs. The table is showing the median of the CCKP model ensemble and the 10–90th percentiles in brackets26 Average Daily Maximum Average Daily Minimum Temperature Average Daily Temperature Temperature Scenario 2040–2059 2080–2099 2040–2059 2080–2099 2040–2059 2080–2099 RCP2.6 1.0 (−0.6, 2.9) 1.1 (−0.6, 3.0) 1.0 (−0.3, 2.4) 1.1 (−0.2, 2.5) 1.0 (−0.1, 2.2) 1.1 (−0.2, 2.4) RCP4.5 1.3 (−0.5, 3.3) 1.8 (0.0, 3.9) 1.4 (0.0, 2.8) 1.9 (0.4, 3.5) 1.4 (0.0, 2.7) 2.0 (0.6, 3.5) RCP6.0 1.2 (−0.7, 3.0) 2.2 (0.4, 4.5) 1.2 (−0.4, 2.5) 2.3 (0.6, 4.1) 1.2 (−0.2, 2.4) 2.4 (0.7, 4.0) RCP8.5 1.7 (0.0, 3.6) 3.6 (1.6, 6.1) 1.8 (0.4, 3.2) 3.8 (2.0, 5.8) 1.9 (0.5, 3.2) 3.9 (2.2, 5.9) TABLE 3.  Projections of average temperature change (°C) in Thailand for different seasons (3-monthly time slices) over different time horizons and emissions pathways, showing the median estimates of the full CCKP model ensemble and the 10th and 90th percentiles in bracket20 2040–2059 2080–2099 Scenario Jun–Aug Dec–Feb Jun–Aug Dec–Feb RCP2.6 1.0 1.0 1.0 1.1 (0.2, 2.0) (−0.6, 2.6) (0.1, 2.0) (−0.4, 2.6) RCP4.5 1.4 1.4 1.8 1.9 (0.5, 2.5) (−0.4, 2.8) (0.9, 3.0) (0.2, 3.7) RCP6.0 1.2 1.0 2.3 2.1 (0.3, 2.3) (−0.8, 2.2) (1.2, 3.7) (0.2, 4.0) RCP8.5 1.6 1.9 3.5 3.8 (0.6, 2.8) (0.1, 3.4) (2.4, 5.4) (1.4, 6.1) 26 WBG Climate Change Knowledge Portal (CCKP, 2021). Thailand Climate Data: Projections. URL: https://climateknowledgeportal. worldbank.org/country/thailand/climate-data-projections CLIMATE RISK COUNTRY PROFILE: THAILAND 8 Model Ensemble Climate projections presented in this document are derived from datasets available through the CCKP, FIGURE 4.  ‘Projected average temperature unless otherwise stated. These datasets are processed anomaly’ and ‘projected annual rainfall outputs of simulations performed by multiple General anomaly’ in Thailand. Outputs of 16 models Circulation Models (GCM) (for further information see within the ensemble simulating RCP8.5 Flato et al., 2013).27 Collectively, these different GCM over the period 2080–2099. Models shown simulations are referred to as the ‘model ensemble’. represent the subset of models within the Due to the differences in the way GCMs represent ensemble which provide projections across the key physical processes and interactions within all RCPs and therefore are most robust for the climate system, projections of future climate comparison.20 Three outlier models are labelled. conditions can vary widely between different GCMs, 6 miroc_esm_chem csiro_mk3_6_0 this is particularly the case for rainfall related variables Average temperature anomaly (°C) 5 and at national and local scales. The range of projections 4 from 16 GCMs for annual average temperature change and annual precipitation change in Thailand under 3 giss_e2_r RCP8.5 is shown in Figure 4. Spatial variation 2 of future projections of annual temperature and Median, 1 10th and 90th precipitation for mid and late century under RCP8.5 Percentiles are presented in Figure 5. 0 –10% 0% 10% 20% 30% 40% 50% Average annual precipitation anomaly (%) 27 Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W., . . . Rummukainen, M. (2013). Evaluation of Climate Models. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 741–866. URL: http://www.climatechange2013.org/images/report/WG1AR5_ALL_ FINAL.pdf CLIMATE RISK COUNTRY PROFILE: THAILAND 9 Spatial Variation FIGURE 5.  CMIP5 ensemble projected change (32 GCMs) in annual temperature (top) and precipitation (bottom) by 2040–2059 (left) and by 2080–2090 (right) relative to 1986–2005 baseline under RCP8.5.28 28 WBG Climate Change Knowledge Portal (CCKP 2021). Thailand. Climate Data. Projections. URL: https://climateknowledgeportal. worldbank.org/country/thailand/climate-data-projections CLIMATE RISK COUNTRY PROFILE: THAILAND 10 Temperature Projections of future temperature change are presented in three primary formats. Shown in Table 2 are the changes in daily maximum and daily minimum temperatures over the given time period, as well as changes in the average temperature. Figures 6 and 7 display the annual and monthly average temperature projections. While similar, these three indicators can provide slightly different information. Monthly/annual average temperatures are most commonly used for general estimation of climate change, but the daily maximum and minimum can explain more about how daily life might change in a region, affecting key variables such as the viability of ecosystems, health impacts, productivity of labor, and the yield of crops, which are often disproportionately influenced by temperature extremes. FIGURE 6.  Historic and projected average FIGURE 7.  Projected change (anomaly) annual temperature in Thailand under in monthly temperature, shown by month, RCP2.6 (blue) and RCP8.5 (red) estimated for Thailand for the period 2080–2099 by the model ensemble. Shading represents under RCP8.5. The value shown represents the the standard deviation of the model median of the model ensemble with the shaded ensemble29. areas showing the 10th–90th percentiles23. 32 8 31 7 30 6 Temperature (°C) Temperature (°C) 29 5 28 4 27 3 26 2 25 1 1980 2000 2020 2040 2060 2080 2100 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Year Historical RCP 2.6 RCP 4.5 RCP 6.0 RCP 8.5 Under the RCP8.5 emissions pathway, average temperatures are projected to increase by 3.8°C by the 2080s, approximately 0.5°C less than the global average, and 1.1°C by the 2080s under the RCP2.6 emissions pathway, similar to the projected global average. Under all emissions scenarios, annual average of monthly maximum and monthly minimum temperatures are projected to increase considerably greater than projected increases in the average temperature (Table 2). For example, under RCP8.5 emissions pathway, by the 2090s annual average monthly maximum is projected at 3.8°C, minimum at 3.9°C compared to the annual average of 3.2°C. As shown in Table 3 and Figure 7, there is relatively little seasonal variation in projected temperature rises, across all emissions pathways. What is evident in Figure 7 is the high degree of uncertainty surrounding these projections. 29 WBG Climate Change Knowledge Portal (CCKP 2021). Thailand. Climate Data. Projections. URL: https://climateknowledgeportal. worldbank.org/country/thailand/climate-data-projections CLIMATE RISK COUNTRY PROFILE: THAILAND 11 Precipitation A majority of the ensemble models project increases in annual precipitation rates (Figure 4 and 8). However, FIGURE 8.  Boxplots showing the projected uncertainty remains high as reflected in the range average annual precipitation for Thailand of model estimates, and in and between emissions in the period 2080–209923. pathways (Figure 8). This uncertainty is also seen in 2800 2600 studies applying downscaling techniques to assess 2400 precipitation changes.30 Downscaling studies in the Precipitation (mm) 2200 upper Ping River Basin in the north of the country 2000 project rainfall extent and frequency to vary across the 1800 1600 catchment, with wet days increasing in frequency and 1400 extent during the wet season for some areas, and in 1200 the dry season for the central areas of the catchment.31 1000 Historical RCP 2.6 RCP 4.5 RCP 6.0 RCP 8.5 For the Bangkok region, one study suggests an increase in precipitation during the rainy season by 2100.32 Downscaling studies in the upper Ping River Basin in the north of the country project rainfall extent and frequency to vary across the catchment, with wet days increasing in frequency and extent during the wet season for some areas, and in the dry season for the central areas of the catchment. The poor performance of global climate models in consistently projecting precipitation trends has been linked to their poor simulation of the El Niño phenomenon,33,34 an important area for future development. While considerable uncertainty surrounds projections of local long-term future precipitation trends (see Figure 8) some global trends are evident. The intensity of sub-daily extreme rainfall events appears to be increasing with temperature, a finding supported by evidence from different regions of Asia.35 30 Lacombe, G., Hoanh, C. T., & Smakhtin, V. (2012). Multi-year variability or unidirectional trends? Mapping long-term precipitation and temperature changes in continental Southeast Asia using PRECIS regional climate model. Climatic Change, 113(2), 285–299. URL: http://publications.cirad.fr/une_notice.php?dk=593587 31 Saengsawang, S., Pankhao, P., Kaprom, C. and Sriwongsitanon, N., 2017. Projections of future rainfall for the upper Ping River Basin using regression-based downscaling. Advances in Climate Change Research, 8(4), pp. 256–267. URL: https://www.sciencedirect. com/science/article/pii/S1674927817300084 32 Vu, M.T., Aribarg, T., Supratid, S., Raghavan, S.V. and Liong, S.Y., 2016. Statistical downscaling rainfall using artificial neural network: significantly wetter Bangkok?. Theoretical and applied climatology, 126(3–4), pp. 453–467. URL: https://www.tib.eu/en/ search/id/BLSE%3ARN379732731/Statistical-downscaling-rainfall-using-artificial/ 33 Yun, K.S., Yeh, S.W. and Ha, K.J. (2016).. Inter-El Niño variability in CMIP5 models: Model deficiencies and future changes. Journal of Geophysical Research: Atmospheres, 121, 3894–3906. URL: https://ui.adsabs.harvard.edu/abs/2016JGRD..121.3894Y/abstract 34 Chen, C., Cane, M.A., Wittenberg, A.T. and Chen, D. 2017. ENSO in the CMIP5 simulations: life cycles, diversity, and responses to climate change. Journal of Climate, 30, 775–801. URL: https://journals.ametsoc.org/jcli/article/30/2/775/96236/ENSO-in-the- CMIP5-Simulations-Life-Cycles 35 Westra, S., Fowler, H. J., Evans, J. P., Alexander, L. V., Berg, P., Johnson, F., Kendon, E. J., Lenderink, G., Roberts, N. (2014). Future changes to the intensity and frequency of short-duration extreme rainfall. Reviews of Geophysics, 52, 522–555. URL: https:// agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2014RG000464 CLIMATE RISK COUNTRY PROFILE: THAILAND 12 CLIMATE RELATED NATURAL HAZARDS Thailand faces high exposure to natural hazard risks and is ranked 81st out of 191 countries by the 2019 Inform Risk Index36 (Table 4). Thailand has extremely high exposure to flooding (ranked 9th), including, riverine, flash, and coastal flooding. Thailand also has exposure to tropical cyclones and their associated hazards (ranked 27th). Drought exposure is also significant (ranked 29th). Thailand’s overall ranking on the INFORM risk index is somewhat mitigated by its coping capacity and the levels of social vulnerability in its population, both of which are scored higher than most other countries in the region. The section which follows analyses climate change influences on the exposure component of risk in Thailand. The following section focuses on the climate change implications for the natural hazard exposure component of risk in Thailand. As seen in Figure 1, the ND-GAIN Index presents an overall picture of a country’s vulnerability and capacity to improve its resilience. In contrast, the Inform Risk Index identifies specific risks across a country to support decisions on prevention, preparedness, response and a country’s overall risk management. TABLE 4.  Selected indicators from the INFORM 2019 index for risk management for Thailand. For the sub-categories of risk (e.g. “Flood”) higher scores represent greater risks. Conversely the most at-risk country is ranked 1st. Global average scores are shown in brackets. Lack of Overall Tropical Coping Inform Flood Cyclone Drought Vulnerability Capacity Risk Level Rank (0–10) (0–10) (0–10) (0–10) (0–10) (0–10) (1–191) 8.8 [4.5] 4.9 [1.7] 5.7 [3.2] 3.1 [3.6] 3.9 [4.5] 4.1 [3.8] 81 Heatwaves Thailand regularly experiences high maximum FIGURE 9.  Projected changes in the probability temperatures, with an average monthly maximum of of observing a heat wave in Thailand for the around 31.6°C and an average April maximum of period 2080–2099. A ‘Heat Wave’ is defined 35.1°C. The current median probability of a heat wave as a period of 3 or more days where the (defined as a period of 3 or more days where the daily daily temperature is above the long-term temperature is above the long-term 95th percentile 95th percentile of daily mean temperature23. of daily mean temperature) is around 3%23. Under all 0.8 emissions pathways, the likelihood of experiencing a 0.7 heat wave increases considerably by 2080–2099, 0.6 up to 18% under the RCP6.0 pathway and 31% Daily probability 0.5 under the RCP8.5 pathway (see Figure 9). 0.4 0.3 There is considerable spatial variation in projected 0.2 0.1 likelihood of experiencing heatwave: in the southern 0 areas of the country, the probability of heat wave per Historical RCP 2.6 RCP 4.5 RCP 6.0 RCP 8.5 annum is as high as 73% by the 2090s (under RCP8.5 36 European Commission (2019). INFORM Index for Risk Management. Thailand Country Profile. URL: https://drmkc.jrc.ec.europa.eu/ inform-index/INFORM-Risk/Country-Profile/moduleId/1767/id/386/controller/Admin/action/CountryProfile CLIMATE RISK COUNTRY PROFILE: THAILAND 13 emissions pathway) but as low as 17% in the northern regions under the same scenario (Figure 9). However, these changes need to be interpreted with regard to the baseline (1986–2005) against which changes are measured. Historically stable environments, as found in many tropical regions (particularly Southern Thailand), will see more significant increases in heatwave simply due to long-term warming which moves ambient temperatures away from the baseline. Another measure of future heat-hazard risk is the number of days each year in which temperatures reach levels dangerous to human life. By the 2080s, Thailand is projected to experience very significant increases in the number of days in which Heat Index exceeds 35°C, particularly under higher emissions pathways (RCP6.0 and 8.5) (Figure 10). FIGURE 10.  Projected changes in the number of days with a Heat Index above 35°C by 2080–2099 under RCP8.5 emissions pathways.20 One study suggests climate change made a 29% contribution to the extreme temperatures experienced across Southeast Asia in April 2016, while ENSO contributed 49%.37 The contribution of general global warming to extreme temperatures has been growing (Figure 11), while the contribution of climate change through its impact on the ENSO process is poorly understood. 37 Thirumalai, K., DiNezio, P. N., Okumura, Y., & Deser, C. (2017). Extreme temperatures in Southeast Asia caused by El Niño and worsened by global warming. Nature Communications: 8: 15531. URL: https://www.nature.com/articles/ncomms15531 CLIMATE RISK COUNTRY PROFILE: THAILAND 14 Drought Two primary types of drought may affect Thailand, meteorological (usually associated with a FIGURE 11.  Observations: The relative precipitation deficit) and hydrological (usually contribution of El Niño (green bars) versus the associated with a deficit in surface and subsurface long-term warming trend (red bars) towards water flow, potentially originating in the region’s the 15 hottest April SATs (>80th percentile) in wider river basins). Local soil and land management the GISTEMP record of Southeast Asia (MSA; practices can also interact with the hydrological 1940–2016) using a regression model. The conditions to result in agricultural drought. At present residual of the observed anomaly and the Thailand faces an annual median probability of regression fit is termed as ‘other’ variability severe meteorological drought of around 4%23, as (yellow bars). The years in red on the x-axis defined by a standardized precipitation evaporation indicate the eight hottest extreme April events index (SPEI) of less than −2. This is projected to (>90th percentile), from Thirumalai et al. (2017)30 double by 2080–2099 under RCP6.0 and RCP8.5 a Observations 2.5 emissions pathways, but uncertainty in the model Warming trend El Niño estimates is high (see Figure 12). Other Regression-based contribution to hot 2.0 Naumann et al. (2018) provide a global overview April SAT anomaly (°C) of changes in drought conditions under different 1.5 warming scenarios. In comparison to West and Central Asia, South East Asia is less likely to 1.0 experience extreme increases in drought intensity. 38 Nevertheless, it is likely to experience more 0.5 prolonged periods of drought. In Western Thailand, 39 El Niño-related droughts have become more frequent 0 80 83 90 91 92 95 98 01 03 04 05 10 13 14 16 and severe concurrently with increasing CO2 levels 20 20 20 20 20 20 20 20 19 19 19 19 19 19 19 Year of hot April and as such likely to increase under all RCP emissions pathways. 40 With increased drought conditions, as well as increases in temperature, Thailand is at risk from heightened air pollution, particularly for major urban areas. These conditions are also likely to increase the country’s risk for forest fires, which will impact air quality, particularly for harmful particulate matter (PM2.5), population health and can impact revenue from the tourism sector. 38 Naumann, G., Alfieri, L., Wyser, K., Mentaschi, L., Betts, R. A., Carrao, H., . . . Feyen, L. (2018). Global Changes in Drought Conditions Under Different Levels of Warming. Geophysical Research Letters, 45(7), 3285–3296. URL: https://agupubs.onlinelibrary.wiley.com/ doi/10.1002/2017GL076521 39 Nock, Charles & Baker, Patrick & Wanek, Wolfgang & Leis, Albrecht & Grabner, Michael & Bunyavejchewin, Sarayudh & Hietz, Peter. (2011). Long-term increases in intrinsic water-use efficiency do not lead to increased stem growth in a tropical monsoon forest in Thailand. Global Change Biology. 17(2). pp1049-1063. URL: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-2486.2010.02222.x 40 Thailand (2018). Third National Communication to the UNFCCC. URL: https://unfccc.int/sites/default/files/resource/ Thailand%20TNC.pdf CLIMATE RISK COUNTRY PROFILE: THAILAND 15 Flood The World Resources Institute’s AQUEDUCT Global Flood Analyzer can be used to establish a baseline FIGURE 12.  Annual probability of level of flood exposure to large-scale river flooding. experiencing a ‘severe drought’ in Thailand As of 2010, assuming protection for up to a 1 in (−2 SPEI Index) in 2080–2099 under four 25-year event, the population annually affected by emissions pathways23. river flooding in Thailand is estimated at 1.1 million 0.7 people and expected annual urban damage is 0.6 estimated at $1.6 billion. Development and climate 0.5 Probability change are both likely to increase these figures. The 0.4 climate change component can be isolated and by 0.3 2030 is expected to increase the annually affected 0.2 population by 500,000 people, and urban damage 0.1 by $6.9 billion under the RCP8.5 emissions pathway 0 Historical RCP 2.6 RCP 4.5 RCP 6.0 RCP 8.5 (AQUEDUCT Scenario B). 41 Paltan et al. (2018) demonstrate that even under lower emissions pathways coherent with the Paris Climate Agreement almost all Asian countries face an increase in the frequency of extreme river flows. What would historically have been a 1 in 100-year flow, could become a 1 in 50-year or 1 in 25-year event in most of South, Southeast, and East Asia.42 There is good agreement among models on this trend. Floods are by far the major natural hazard facing Thailand in terms of frequency and damage – the country is cited as one of the ten most flood-affected in the world.43 According to the UNISDR,44 the average annual loss associated with flooding in Thailand is approximately US$2.6 billion, which represents almost 100% of losses associated with hazards. Studies suggest flooding incidence across the country are likely to increase as a result of climate change, with higher frequency of intense rainfall events contributing to irregular riverbank overflow, flash floods in urban areas and landslides and flash floods in mountain areas. Coastal areas are also likely to experience more flooding from sea-level rise (see The Coastal Zone section).45,46 41 WRI (2018). AQUEDUCT Global Flood Analyzer. URL: https://floods.wri.org/# [Accessed: 22/11/2018] 42 Paltan, H., Allen, M., Haustein, K., Fuldauer, L., & Dadson, S. (2018). Global implications of 1.5°C and 2°C warmer worlds on extreme river flows Global implications of 1.5°C and 2°C warmer worlds on extreme river flows. Environmental Research Letters, 13, 094003. URL: https://iopscience.iop.org/article/10.1088/1748-9326/aad985/meta 43 Loo, Yen Yi & Billa, Lawal & Singh, Ajit. (2014). Effect of climate change on seasonal monsoon in Asia and its impact on the variability of monsoon rainfall in Southeast Asia. Geoscience Frontiers. 36 (6), 817–823. URL: https://www.sciencedirect.com/science/article/ pii/S167498711400036X 44 UNISDR (2014). PreventionWeb: Basic country statistics and indicators. Available at: https://www.preventionweb.net/countries 45 Lebel, Louis & Manuta, Jesse & Garden, Po. (2010). Institutional traps and vulnerability to changes in climate and flood regimes in Thailand. Regional Environmental Change. 11. 45–58. URL: https://link.springer.com/article/10.1007/s10113-010-0118-4 46 Promchote, Parichart & Wang, Shih-Yu & Johnson, Paul. (2015). The 2011 Great Flood in Thailand: Climate Diagnostics and Implications from Climate Change. Journal of Climate. 29 (1) 367–379. URL: https://pdfs.semanticscholar.org/a47b/ 2acca30c2039169a040e4583a6cc7467078f.pdf CLIMATE RISK COUNTRY PROFILE: THAILAND 16 Willner et al. (2014)47 suggest that the median increase in the population affected by an extreme (90th percentile) flood by 2035–2044 is approximately 2.3 million people (this estimation based on fixed present-day distribution of population) (see Table 5). This represents an increase of 258% from the population exposed to extreme flooding in 1971–2004. TABLE 5.  Estimated number of people in Thailand affected by an extreme river flood (extreme flood is defined as being in the 90th percentile in terms of numbers of people affected) in the historic period 1971–2004 and the future period 2035–2044. Figures represent an average of all four RCPs and assume present day population distributions.40 Population Exposed Population Exposed Increase to Extreme Flood to Extreme Flood in Affected Estimate (1971–2004) (2035–2044) Population 16.7 Percentile 312,568 1,194,555 881,987 Median 886,335 3,177,190 2,290,855 83.3 Percentile 2,184,124 4,941,744 2,757,620 Cyclones and Storm Surge Climate change is expected to interact with cyclone hazard in complex ways which are currently poorly understood. Known risks include the action of sea-level rise to enhance the damage caused by cyclone-induced storm surges, and the possibility of increased windspeed and precipitation intensity. Modelling of climate change impacts on cyclone intensity and frequency conducted across the globe points to a general trend of reduced cyclone frequency and increased intensity and frequency of the most extreme events.48 Further research is required to better understand potential changes in cyclone seasonality and routes, and the potential for cyclone hazards to be experienced in unprecedented locations. Studies suggest that the frequency of extreme rainfall events (greater than 100mm in one day) are likely to become more commonplace as result of climate warming.49 Thailand’s Second National Communication to the UNFCC expects an increase in typhoons reaching Thailand between 2013 and 2043, while the number of monsoon storms are projected to stay relatively stable during the same time-period. Higher sea levels and wetter pre-monsoon conditions increase the risk of large-scale flooding, as experienced in 2011.50 47 Willner, S., Levermann, A., Zhao, F., Frieler, K. (2018). Adaptation required to preserve future high-end river flood risk at present levels. Science Advances: 4:1. URL: https://advances.sciencemag.org/content/4/1/eaao1914 48 Walsh, K., McBride, J., Klotzbach, P., Balachandran, S., Camargo, S., Holland, G., Knutson, T., Kossin, J., Lee, T., Sobel, A., Sugi, M. (2015). Tropical cyclones and climate change. WIREs Climate Change: 7: 65–89. URL: https://onlinelibrary.wiley.com/doi/full/ 10.1002/wcc.371 49 USAID (2014). Thailand Climate Change Vulnerability Profile. URL: http://cmsdata.iucn.org/downloads/thailand_country_profile___ june2014_press.pdf 50 Promchote, P., Wang, S.Y.S. and Johnson, P.G. (2016). The 2011 great flood in Thailand: Climate diagnostics and implications from climate change. Journal of Climate, 29(1), pp. 367–379. URL: https://journals.ametsoc.org/jcli/article/29/1/367/35049/ The-2011-Great-Flood-in-Thailand-Climate CLIMATE RISK COUNTRY PROFILE: THAILAND 17 CLIMATE CHANGE IMPACTS Natural Resources Water Thailand’s NC2 describes the country’s water resources: 25 watershed areas, 6.4 million hectares irrigated, 14.6 million hectares rain-fed, with approximately a quarter of its 800 billion m3/year rainfall becoming utilizable surface water and a total water storage capability of 74 billion cum., of which 90% is made of large and medium- sized reservoirs. It is predicted water demand could rise to 120 billion m3/year as a result of population and economic growth, threatening socio-economic development.1 Two river systems account for most water flows over Thailand’s land surface: namely the Mekong River in the east, and Chao Phraya in the north and central regions. Both systems have been significantly influenced by human development impacts on land cover. Issues such as deforestation and agricultural intensification have reduced water retention and increased flood potential. Under climate change, most studies suggest flow volumes are likely to increase under most emissions pathways and time horizons. One study showed a particularly large increase (>20%) in runoff in the central province of Nakhon Sawan.51 The net change in runoff from the northeastern region of Thailand which feeds the Mekong River is less clear, with models disagreeing on the direction of change. However, there is convincing evidence that peak flows could increase, by 5–10% by 2036–2065.52 Future flows in the Mekong River are also likely to be affected by the operation of hydropower dams.53 While overall annual precipitation is projected to increase, rainfall during some periods may decrease, such as between September and October. This, alongside a less stable runoff regime, may have consequences for rice agriculture, increasing water stress and requiring greater irrigated water requirements54 (see Agriculture section). The Coastal Zone Sea-level rise threatens significant physical changes to coastal zones around the world. Global mean sea-level rise was estimated in the range of 0.44–0.74m by the end of the 21st century by the IPCC’s Fifth Assessment Report but some studies published more recently have highlighted the potential for greater rises (Table 6).55 51 Kotsuki, S., Tanaka, K., & Watanabe, S. (2014). Projected hydrological changes and their consistency under future climate in the Chao Phraya River Basin using multi-model and multi-scenario of CMIP5 dataset. Hydrological Research Letters, 8(1), 27–32. URL: https://www.jstage.jst.go.jp/article/hrl/8/1/8_27/_article 52 Hoang, L. P., Lauri, H., Kummu, M., Koponen, J., Vliet, M. T. H. Van, Supit, I., . . . Ludwig, F. (2016). Mekong River flow and hydrological extremes under climate change. Hydrology and Earth System Sciences, 20, 3027–3041. URL: https://hess.copernicus.org/articles/ 20/3027/2016/ 53 Räsänen, T.A., Someth, P., Lauri, H., Koponen, J., Sarkkula, J. and Kummu, M. (2017). Observed river discharge changes due to hydropower operations in the Upper Mekong Basin. Journal of hydrology, 545, pp. 28–41. URL: https://research.aalto.fi/en/ publications/observed-river-discharge-changes-due-to-hydropower-operations-in- 54 Boonwichai, Siriwat & Shrestha, Sangam & Babel, Mukand & Weesakul, Sutat & Datta, Avishek. (2018). Climate change impacts on irrigation water requirement, crop water productivity and rice yield in the Songkhram River Basin, Thailand. Journal of Cleaner Production. 198, 1–1652. URL: https://www.x-mol.com/paper/744044?recommend 55 Church, J. a., Clark, P. U., Cazenave, A., Gregory, J. M., Jevrejeva, S., Levermann, A., . . . Unnikrishnan, A. S. (2013). Sea level change. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 1137–1216). Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press. URL: https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_Chapter13_FINAL.pdf CLIMATE RISK COUNTRY PROFILE: THAILAND 18 TABLE 6.  Estimates of global mean sea-level rise by rate and total rise compared to 1986–2005 including likely range shown in brackets, data from Chapter 13 of the IPCC’s Fifth Assessment Report with upper-end estimates based on higher levels of Antarctic ice-sheet loss from Le Bars et al. 2017.56 Rate of Global Mean Sea-Level Global Mean Sea-Level Rise in Scenario Rise in 2100 2100 Compared to 1986–2005 RCP2.6 4.4 mm/yr (2.0–6.8) 0.44 m (0.28–0.61) RCP4.5 6.1 mm/yr (3.5–8.8) 0.53 m (0.36–0.71) RCP6.0 7.4 mm/yr (4.7–10.3) 0.55 m (0.38–0.73) RCP8.5 11.2 mm/yr (7.5–15.7) 0.74 m (0.52–0.98) Estimate inclusive of high-end Antarctic ice-sheet loss 1.84 m (0.98–2.47) Thailand’s First Biennial Update Report describes how coastal inundation and seawater intrusion are likely to increase as a result of climate change, however specific studies are limited.57 Studies have explored the impacts of extreme land subsidence on sea levels along Thailand’s coast.58 A 2013 study found relative sea level rise in the Gulf of Thailand ranging from 1.4–12.7mm/year between 1985 and 2009 and that the largest contribution to this rise was land subsidence at the river mouths.59 A combination of rising seas and sinking land, as well as potential cyclone-induced storm surge, place the country’s capital Bangkok in a precarious position when the net, or relative, rate of sea-level rise is considered.60 Land loss from sea-level rise will also affect sustainable land use for economic activities in the tourism, import and export sectors and industrial zones. Large amounts of critical public infrastructure is located in areas which are likely to be exposed under future climate change scenarios.61 As shown in Table 7, under the RCP8.5 emissions pathway, by 2070–2100, up to 2.5 million people in Thailand are potentially exposed to flooding from sea-level rise. However, with investment in effective adaptation, including balancing of trade-offs between hard infrastructural approaches (e.g. dykes and sea-walls) and nature-based approaches (e.g. habitat restoration), this number may be very significantly reduced. 56 Le Bars, D., Drijhout, S., de Vries, H. (2017) A high-end sea level rise probabilistic projection including rapid Antarctic ice sheet mass loss. Environmental Research Letters: 12:4. URL: https://iopscience.iop.org/article/10.1088/1748-9326/aa6512 57 Thailand (2018). Third National Communication to the UNFCCC. URL: https://unfccc.int/sites/default/files/resource/ Thailand%20TNC.pdf 58 Saramul, S. and Ezer, T., 2014. Spatial variations of sea level along the coast of Thailand: Impacts of extreme land subsidence, earthquakes and the seasonal monsoon. Global and Planetary Change, 122, pp. 70–81. URL: http://www.ccpo.odu.edu/∼tezer/ PAPERS/2014_GPC_GOT_SeaLev.pdf 59 Sojisuporn, Pramot & Sangmanee, Charmrat & Wattayakorn, Gullaya. (2013). Recent estimate of sea-level rise in the Gulf of Thailand. Maejo International Journal of Science and Technology. 7. 106–113. URL: https://www.researchgate.net/publication/260166201_ Recent_estimate_of_sea-level_rise_in_the_Gulf_of_Thailand 60 Fuchs, Roland & Mostafanezhad, Mary & Louis, Elizabeth. (2011). Climate Change and Asia’s Coastal Urban Cities. Environment and Urbanization Asia. 2. 13–28. https://doi.org/10.1177%2F097542531000200103 61 Duangyiwa, C., Yu, D., Wilby, R., Aobpaet, A. (2015) Coastal Flood Risks in the Bangkok Metropolitan Region, Thailand: Combined Impacts of Land Subsidence, Sea Level Rise and Storm Surge. AGU Fall Meeting, San Francisco, 14th–18th December 2015. URL: https://ui.adsabs.harvard.edu/abs/2015AGUFMNH33C1927D/abstract CLIMATE RISK COUNTRY PROFILE: THAILAND 19 TABLE 7.  The average number of people experiencing flooding per year in the coastal zone in the period 2070–2100 under different emissions pathways (assumed medium ice-melt scenario) and adaptation scenarios for Thailand.62 Scenario Without Adaptation With Adaptation RCP2.6 491,270 570 RCP8.5 2,451,250 1,370 Land and Soil Thailand’s NC2 describes ‘problematic land’ which impacts land designated as agricultural, with over half of this land possessing saline, sandy, shallow or acidic soils (see Table 8). Having low productivity, these soils have reduced ecological resilience to climate change, and limit options when looking to adapt to a changing climate1. Increasing temperatures and possible (though uncertain) increases in drought incidence, may drive desertification, but land-use and land management practices, particularly agricultural intensification,63 remain the dominant process contributing to land degradation in Thailand.64 Historical deforestation65 has also exposed Thailand’s soils to erosion and degradation and ultimately impacted negatively on biodiversity.66 TABLE 8.  Land with problematic soils in Thailand, 2004. Source: Thailand’s second national communication Problematic Land Area (HA) 1.  Saline Soils 721,920 2.  Sandy Soils 2,043,173 3.  Shallow Soils 6,938,499 4.  Acid Sulfate Soils 881,623 5.  Organic Soils 42,456 6.  Slope Complex 15,361,117 7.  Acid Soils 15,749,199 62 UK Met Office (2014). Human dynamics of climate change: Technical Report. Met Office, UK Government. URL: https://www. metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/weather/learn-about/climate/human-dynamics-of-climate-change/ hdcc_alternative_version.compressed.pdf 63 Bruun, T. B., de Neergaard, A., Burup, M. L., Hepp, C. M., Larsen, M. N., Abel, C., . . . Mertz, O. (2017). Intensification of Upland Agriculture in Thailand: Development or Degradation? Land Degradation & Development, 28(1), 83–94. URL: https://onlinelibrary. wiley.com/doi/full/10.1002/ldr.2596 64 Wijitkosum, S. (2016). The impact of land use and spatial changes on desertification risk in degraded areas in Thailand. Sustainable Environment Research, 26(2), 84–92. URL: https://www.sciencedirect.com/science/article/pii/S246820391630019X 65 Leinenkugel, P., Wolters, M. L., Oppelt, N., & Kuenzer, C. (2015). Tree cover and forest cover dynamics in the Mekong Basin from 2001 to 2011. Remote Sensing of Environment, 158, 376–392. URL: https://www.sciencedirect.com/science/article/pii/S0034425714004313 66 Akber, M. A., & Shrestha, R. P. (2015). Land use change and its effect on biodiversity in Chiang Rai province of Thailand. Journal of Land Use Science, 10(1), 108–128. URL: https://www.tandfonline.com/doi/abs/10.1080/1747423x.2013.807315 CLIMATE RISK COUNTRY PROFILE: THAILAND 20 Economic Sectors Agriculture Climate change may influence food production via direct and indirect effects on crop growth processes. Direct effects include alterations to carbon dioxide availability, precipitation and temperatures. Indirect effects include through impacts on water resource availability and seasonality, soil organic matter transformation, soil erosion, changes in pest and disease profiles, the arrival of invasive species, and decline in arable areas due to the submergence of coastal lands and desertification. On an international level, these impacts are expected to damage key staple crop yields, even on lower emissions pathways. Tebaldi and Lobell (2018) estimate 5% and 6% declines in global wheat and maize yields respectively even if the Paris Climate Agreement is met and warming is limited to 1.5°C.67 Shifts in the optimal and viable spatial ranges of certain crops are also inevitable, though the extent and speed of those shifts remains dependent on the emissions pathway. Projections suggest that Thailand’s agriculture sector could be significantly affected from a changing climate, due to its location in the tropics where agricultural productivity is particularly vulnerable.68 Boonwichai (2018) found that decreases in rainfall during rice productive phase (September and October) and increases in temperature could influence rice yield. Their study projects rain-fed rice yields to reduce 10% by 2080 under RCP 8.5 emissions pathway and crop water productivity reducing 29% by 2080 under the same emissions pathway.47 Other work suggests that Thailand could experience a 5.3% decrease in rice yield by 2041–2050 compared to the 1991–2000 baseline under the RCP 4.5 emissions pathway and a 6.1% decrease for the same time period under the RCP 8.5 emissions pathway.69 Increased temperatures, which result in more very hot (>35°C) days, indeed a potential 160% rise in FIGURE 13.  Climate model ensemble estimate the number of very hot days by 2080–2099 under of the annual number of very hot (Tmax >35°C) the highest emissions pathways (Figure 13), are days in 2080–2099 under four emissions projected to have detrimental impacts on agricultural pathways in Thailand23 productivity. Increasing temperatures could affect 300 other key agricultural products, such as lychee in the 250 north, which is vulnerable to temperature change as 200 witnessed in December 2009, where above average Days 150 temperatures saw lychee crop productivity fall by 100 more than half.42 The impacts on agriculture are projected to have regional variation: western, north- 50 central and north-western areas are likely to suffer less 0 Historical RCP 2.6 RCP 4.5 RCP 6.0 RCP 8.5 negative impacts compared to eastern, south-central 67 Tebaldi, C., & Lobell, D. (2018). Differences, or lack thereof, in wheat and maize yields under three low-warming scenarios. Environmental Research Letters: 13: 065001. URL: https://iopscience.iop.org/article/10.1088/1748-9326/aaba48 68 Hughes, J. (2007). The Impact of Climate Change on Tropical Agriculture. ICRISAT, 4(1). URL: https://www.omicsonline.org/open-access/ review-on-impacts-of-climate-change-on-vegetable-production-and-its-management-practices-2329-8863-1000330-99188.html 69 Li, S., Wang, Q., & Chun, J. A. (2017). Impact assessment of climate change on rice productivity in the Indochinese Peninsula using a regional-scale crop model. International Journal of Climatology, 37(April), 1147–1160. URL: https://rmets.onlinelibrary.wiley.com/doi/ full/10.1002/joc.5072 CLIMATE RISK COUNTRY PROFILE: THAILAND 21 and north-eastern areas.70 When placed in a global context, aggregate agriculture production in Southeast Asian countries are projected to suffer a greater decline than most other regions.71 Urban and Energy Research has established a reasonably well constrained relationship between heat stress and labor productivity, household consumption patterns, and (by proxy) household living standards.72 In general terms the impact of an increase in temperature on these indicators depends on whether the temperature rise moves the ambient temperature closer to, or further away from, the optimum temperature range. The optimum range can vary depending on local conditions and adaptations. The effects of temperature rise and heat stress in urban areas are increasingly compounded by the phenomenon of Urban Heat Island (UHI). Dark surfaces, residential and industrial sources of heat, an absence of vegetation, and air pollution73 can push temperatures higher than those of the rural surroundings, commonly anywhere in the range of 0.1°C–3°C in global mega-cities.74 As well as impacting human health, the temperature peaks that result from combined UHI and climate change, as well as future urban expansion, are likely to damage the productivity of the service sector economy, both through direct impacts on labor productivity, but also through the additional costs of adaptation. Studies have shown the presence of an UHI in Bangkok and how it is intensifying. Indeed, the UHI severity is higher compared to other cities considered to have UHI problems such as Shanghai and San Diego, with average annual temperatures 0.8°C higher than surrounding areas between 2008–2012.75 Research suggests that on average a one degree rise in ambient temperature can result in a 0.5%–8.5% increase in electricity demand.76 Notably this serves business and residential air-cooling systems. This increase in demand places strain on energy generation systems which is compounded by the heat stress on the energy generation system itself, commonly due to its own cooling requirements, which can reduce its efficiency.77 70 Attavanich, Witsanu (2013): The Effect of Climate Change on Thailand’s Agriculture. Published in: 7th International Academic Conference Proceedings No. ISBN: 978-80-905241-7-0: pp. 23–40. URL: https://mpra.ub.uni-muenchen.de/84005/ 71 Kurukulasuriya, Pradeep & Rosenthal, Shane. (2003). Climate Change and Agriculture: A Review of Impacts and Adaptations. Climate Change Series 91. Environment Department Papers, World Bank, Washington, D.C. URL: https://openknowledge.worldbank. org/bitstream/handle/10986/16616/787390WP0Clima0ure0377348B00PUBLIC0.pdf?sequence=1&isAllowed=y 72 Mani, M., Bandyopadhyay, S., Chonabayashi, S., Markandya, A., Mosier, T. (2018) South Asia’s Hotspots: The Impact of Temperature and Precipitation changes on living standards. South Asian Development Matters. World Bank, Washington DC. URL: https:// openknowledge.worldbank.org/bitstream/handle/10986/28723/9781464811555.pdf?sequence=5&isAllowed=y 73 Cao, C., Lee, X., Liu, S., Schultz, N., Xiao, W., Zhang, M., & Zhao, L. (2016). Urban heat islands in China enhanced by haze pollution. Nature Communications, 7, 1–7. URL: https://www.nature.com/articles/ncomms12509 74 Zhou, D., Zhao, S., Liu, S., Zhang, L., & Zhu, C. (2014). Surface urban heat island in China’s 32 major cities: Spatial patterns and drivers. Remote Sensing of Environment, 152, 51–61. URL: https://chunxxu.github.io/zhaolab/assets/paper/201405.pdf 75 Arifwidodo, Sigit & Tanaka, Takahiro. (2015). The Characteristics of Urban Heat Island in Bangkok, Thailand. Procedia – Social and Behavioral Sciences, 195, 423–428. URL: https://www.sciencedirect.com/science/article/pii/S1877042815039634 76 Santamouris, M., Cartalis, C., Synnefa, A., & Kolokotsa, D. (2015). On the impact of urban heat island and global warming on the power demand and electricity consumption of buildings—A review. Energy and Buildings, 98, 119–124. URL: https://www.sciencedirect.com/ science/article/abs/pii/S0378778814007907 77 ADB (2017). Climate Change Profile of Pakistan. Asian Development Bank. URL: https://www.adb.org/publications/climate-change- profile-pakistan CLIMATE RISK COUNTRY PROFILE: THAILAND 22 With average summer temperatures projected to rise throughout Thailand’s urban areas, particularly at the FIGURE 14.  Historic and projected annual levels projected by higher emissions pathways, there cooling degree days in Thailand (cumulative is a major risk to human and ecosystem health, as well degrees above 65°F) under RCP2.6 (blue) as economic productivity. As shown in Figure 14 a and RCP8.5 (red). The values shown represent substantial increase in the amount of building cooling the median of 30+ GCM model ensemble required is projected, placing demands either on with the shaded areas showing the energy systems or on health systems depending on 10–90th percentiles23. the efficacy of the response. Infrastructure will likely 9000 8500 come under pressure, both from temperatures and the 8000 increased risk of riverine (fluvial) and surface water Temperature (°F) 7500 (pluvial) flooding. 7000 6500 6000 5500 Communities 5000 4500 1980 2000 2020 2040 2060 2080 2100 Year Historical RCP 2.6 RCP 4.5 RCP 6.0 RCP 8.5 Poverty, Inequality, and Disaster Vulnerability Many of the climate changes projected are likely to disproportionately affect the poorest groups in society. Vulnerability to climate change is differentiated across social groups, the result of embedded inequalities and uneven power structures.78 For instance, heavy manual labor jobs are commonly among the lowest paid whilst also being most at risk of productivity losses due to heat stress.79 Poorer businesses are least able to afford air conditioning, an increasing need given the projected increase in cooling days. Poorer farmers and communities are least able to afford local water storage, irrigation infrastructure, and technologies for adaptation.80 Poor people are less able to invest in prevention and mitigation against adverse effects of environment change and natural hazards.81 Studies have also shown that Thailand’s richer households are more likely to engage in adaptation activities in advance of disaster than poorer counterparts.82 The aftermath of 2011’s devastating flood provides an example of how climate change can adversely affect poorer people in Thailand. Post-flood, higher income groups received more government compensation than lower income groups, 500 Bahts compared to 200 Bahts. 78 Plan International. (2018). Climate change, young women and girls: vulnerability, impacts and adaptations in northern Thailand. URL: https://plan-international.org/publication/climate-change-girls-thailand 79 Kjellstrom, T., Briggs, D., Freyberg, C., Lemke, B., Otto, M., Hyatt, O. (2016) Heat, human performance, and occupational health: A key issue for the assessment of global climate change impacts. Annual Review of Public Health: 37: 97–112. URL: https:// pubmed.ncbi.nlm.nih.gov/26989826/ 80 Hallegatte, Stéphane & Fay, Marianne & Barbier, Edward. (2018). Poverty and climate change: Introduction. Environment and Development Economics. 23. 217–233. URL: https://agris.fao.org/agris-search/search.do?recordID=US202000034159 81 Noy I and Patel P (2014). Floods and Spillovers: Households After the 2011 Great Flood in Thailand. Working Paper Series No. 3609. Wellington: Victoria University of Wellington, School of Economics and Finance. URL: https://www.cesifo.org/en/publikationen/2019/ working-paper/floods-and-spillovers-households-after-2011-great-flood-thailand 82 Hallegatte, Stéphane & Bangalore, Mook & Bonzanigo, Laura & Fay, Marianne & kane, tamaro & narloch, ulf & Rozenberg, Julie & Treguer, David & Vogt-Schilb, Adrien. (2015). Shock Waves: Managing the Impacts of Climate Change on Poverty. URL: https:// openknowledge.worldbank.org/handle/10986/22787 CLIMATE RISK COUNTRY PROFILE: THAILAND 23 Thailand is one of the most flood-prone countries in the world. As mentioned above, flooding accounts for nearly 100% of average annual loss associated with hazards. However, according to the UNISDR, in terms of mortality and loss of life, earthquakes pose a more significant (non-climate related) risk.83 In 2011, a record-breaking flood caused widespread destruction and served as example of the country’s vulnerability to climate-related disaster. The flood, the result of an exceptionally heavy monsoon season and the landfall of Tropical Storm Nock-ten, caused 815 deaths, affected 13.6 million people and damaged 20,000 km2 of farmland. Economically, the flood led to $45 billion of damages and resulted in the most expensive insurance loss ever recorded in global history from a flood at $15 billion.36,84 This flood also had significant impacts on global supply chains.85 Disasters are made more likely by the persistence of multidimensional poverty and malnourishment (Table 1). As is often the case, marginalized groups, including ethnic minorities, remote communities, and the disabled, are typically the most vulnerable to natural hazards in Thailand86, and key determinants of resilience are assets, and diversified income sources.87 Gender An increasing body of research has shown that climate-related disasters have impacted human populations in many areas including agricultural production, food security, water management and public health. The level of impacts and coping strategies of populations depends heavily on their socio-economic status, socio-cultural norms, access to resources, poverty as well as gender. Research has also provided more evidence that the effects are not gender neutral, as women and children are among the highest risk groups. Key factors that account for the differences between women’s and men’s vulnerability to climate change risks include: gender-based differences in time use; access to assets and credit, treatment by formal institutions, which can constrain women’s opportunities, limited access to policy discussions and decision making, and a lack of sex-disaggregated data for policy change.88 Human Health Nutrition The World Food Programme estimate that without adaptation the risk of hunger and child malnutrition on a global scale could increase by 20% by 205089. Springmann et al. (2016) assessed the potential for excess, climate- related deaths associated with malnutrition90. The authors identify two key risk factors that are expected to be the 83 UNISDR (2014). PreventionWeb: Basic country statistics and indicators. URL: https://www.preventionweb.net/countries [accessed 14/08/2018] 84 Zbigniew W. Kundzewicz, Shinjiro Kanae, Sonia I. Seneviratne, John Handmer, Neville Nicholls, Pascal Peduzzi, Reinhard Mechler, Laurens M. Bouwer, Nigel Arnell, Katharine Mach, Robert Muir-Wood, G. Robert Brakenridge, Wolfgang Kron, Gerardo Benito, Yasushi Honda, Kiyoshi Takahashi & Boris Sherstyukov (2014) Flood risk and climate change: global and regional perspectives, Hydrological Sciences Journal, 59:1, 1–28. URL: https://www.tandfonline.com/doi/full/10.1080/02626667.2013.857411 85 Haraguchi, M. and Lall, U. 2015. Flood risks and impacts: A case study of Thailand’s floods in 2011 and research questions for supply chain decision making. International Journal of Disaster Risk Reduction, 14, 256–272. URL: https://trid.trb.org/view/1377177 86 Lebel, L., Manuta, J. B., & Garden, P. (2011). Institutional traps and vulnerability to changes in climate and flood regimes in Thailand. Regional Environmental Change, 11(1), 45–58. URL: https://link.springer.com/article/10.1007/s10113-010-0118-4 87 Willroth, P., Revilla Diez, J., & Arunotai, N. (2011). Modelling the economic vulnerability of households in the Phang-Nga Province (Thailand) to natural disasters. Natural Hazards, 58(2), 753–769. URL: https://link.springer.com/article/10.1007/s11069-010-9635-1 88 World Bank Group (2016). Gender Equality, Poverty Reduction, and Inclusive Growth. URL: http://documents1.worldbank.org/curated/ en/820851467992505410/pdf/102114-REVISED-PUBLIC-WBG-Gender-Strategy.pdf 89 WFP (2015). Two minutes on climate change and hunger: A zero hunger world needs climate resilience. The World Food Programme. URL: https://docs.wfp.org/api/documents/WFP-0000009143/download/ 90 Springmann, M., Mason-D’Croz, D., Robinson, S., Garnett, T., Godfray, H. C. J., Gollin, D., . . . Scarborough, P. (2016). Global and regional health effects of future food production under climate change: a modelling study. The Lancet: 387: 1937–1946. URL: https://pubmed.ncbi.nlm.nih.gov/26947322/ CLIMATE RISK COUNTRY PROFILE: THAILAND 24 primary drivers: a lack of fruit and vegetables in diets, and health complications caused by increasing prevalence of people underweight. The authors’ projections suggest there could be approximately 44.68 climate-related deaths per million population linked to lack of food availability in Thailand by the year 2050 under RCP8.5. Heat-Related Mortality Research has placed a threshold of 35°C (wet bulb ambient air temperature) on the human body’s ability to regulate temperature, beyond which even a very short period of exposure can present risk of serious ill-health and death.91 Temperatures significantly lower than the 35°C threshold of ‘survivability’ can still represent a major threat to human health. Climate change will push global temperatures more towards the temperature ‘danger zone’ both through slow-onset warming and intensified heat waves. Bangkok already faces potentially lethal combinations of high temperatures and humidity on approximately 8 days per year.92 Honda et al. (2014), which utilized the A1B emissions scenario from CMIP3 (most comparable to RCP6.0), estimates that without adaptation, annual heat-related deaths in the South-Eastern Asian region, could increase 295% by 2030 and 691% by 2050.93 Under the RCP8.5 emissions pathway, heat-related deaths for 65+ year-olds are projected to increase considerably by 2080, from a baseline of 3 per 100,000 in 1961–1990 to 58 per 100,000.94 The potential reduction in heat-related deaths achievable by pursuing lower emissions pathways is significant, as demonstrated by Mitchell et al. (2018).95 Disease Climate change projections suggest a rise in infectious and vector-borne diseases in Thailand.81 Thailand’s Initial National Communication conducted the first study on climate change impacts on health in Thailand, exploring the relationship between temperature and mosquito growth rate. It found that increased temperatures could contribute to the greater spread of malaria by 2050. However, further research on the relationship of malaria and dengue diseases with climate factors has not established clear relationships and requires further study.1 Hydrological changes may also enhance disease transmission in Thailand. Relationships between flood, drought and diarrheal disease have been established,96 as has a relationship between flood and leptospirosis97. 91 Im, E. S., Pal, J. S., & Eltahir, E. A. B. (2017). Deadly heat waves projected in the densely populated agricultural regions of South Asia. Science Advances, 3(8), 1–8. URL: https://advances.sciencemag.org/content/3/8/e1603322 92 Matthews, T., Wilby, R.L. and Murphy, C. 2017. Communicating the deadly consequences of global warming for human heat stress. Proceedings of the National Academy of Sciences, 114, 3861–3866. URL: https://www.pnas.org/content/114/15/3861 93 Honda, Y., Kondo, M., McGregor, G., Kim, H., Guo, Y-L, Hijioka, Y., Yoshikawa, M., Oka, K., Takano, S., Hales, S., Sari Kovats, R. (2014) Heat-related mortality risk model for climate change impact projection. Environmental Health and Preventive Medicine 19: 56–63. URL: https://pubmed.ncbi.nlm.nih.gov/23928946/ 94 World Health Organisation (2015) Climate And Health Country Profile – 2015, Thailand. URL: http://www.searo.who.int/thailand/ areas/phe-country-profile-thailand.pdf 95 Mitchell, D., Heaviside, C., Schaller, N., Allen, M., Ebi, K. L., Fischer, E. M., . . . Vardoulakis, S. (2018). Extreme heat-related mortality avoided under Paris Agreement goals. Nature Climate Change, 8(7), 551–553. URL: https://www.nature.com/articles/ s41558-018-0210-1?WT.ec_id=NCLIMATE-201807&spMailingID=56915405&spUserID=ODE0MzAwNjg5MAS2&spJobID= 1440158046&spReportId=MTQ0MDE1ODA0NgS2 96 Wu, X., Lu, Y., Zhou, S., Chen, L., & Xu, B. (2016). Impact of climate change on human infectious diseases: Empirical evidence and human adaptation. Environment International, 86, 14–23. URL: https://www.sciencedirect.com/science/article/pii/S0160412015300489 97 Lau, C. L., Smythe, L. D., Craig, S. B., & Weinstein, P. (2010). Climate change, flooding, urbanisation and leptospirosis: Fuelling the fire? Transactions of the Royal Society of Tropical Medicine and Hygiene, 104(10), 631–638. URL: https://pubmed.ncbi.nlm.nih.gov/ 20813388/ CLIMATE RISK COUNTRY PROFILE: THAILAND 25 POLICIES AND PROGRAMS National Adaptation Policies and Strategies TABLE 9.  Key national adaptation policies, plans and agreements Policy/Strategy/Plan Status Document Access National Communications to the UNFCCC Three submitted Latest: August, 2018 Nationally Determined Contribution (NDC) to Paris Climate Agreement Submitted September, 2016 National Disaster Risk Management Plan Enacted March, 2015 Technology Needs Assessments (TNA) Report for Climate Change Completed July, 2012 12th National Economic and Social Development Plan (NESDP) Enacted 2017 National Adaptation Plan (2018–2037) Under Development Climate Change Master Plan (2015–2050) Approved July, 2016 Energy Efficiency Plan 2015 2011 Power Development Plan 2015 Enacted 2015 Alternative Energy Development Plan 2015 Enacted 2015 Natural Water Resources Management Strategies (2015–2026) Enacted 2015 Master Plan for Integrated Biodiversity Management B.E. 2558–2564 Enacted 2015 (2015–2021 Transport Infrastructure Development Plan (2015–2022) Enacted 2015 4th National Strategic Plan on Chemical Management (2012–2021) Enacted March, 2012 Bangkok Climate Change Master Plan 2013–2023 Enacted 2012 Climate Change Priorities of ADB and the WBG ADB – Country Partnership Strategy ADB’s most recent Country Partnership Strategy (CPS) with Thailand ran between 2013–2016. The strategy included issues of environmental sustainability as a core focus, stating that ADB will support the government’s environmentally sustainable development and green economy agenda by (i) helping to strengthen community- based integrated water and flood risk management projects; (ii) providing non sovereign investments in renewable energy and energy efficiency projects; (iii) conducting a study on green city development in the southern city of Songkhla; (iv) strengthening management of biodiversity conservation corridors; (v) pilot testing energy-saving technologies and the reduction of carbon emissions; and (vi) assisting in drawing on climate change funds and other financing modalities.98 ADB and the Government of Thailand implements a Country Operations Business Plan (2019–2021) to serve as a bridge between the current CPS and the forthcoming CPS planned for 2020–2024. 98 ADB (2013). Country Partnership Strategy, Thailand 2013–2016. URL: https://www.adb.org/sites/default/files/institutional-document/ 33990/files/cps-tha-2013-2016.pdf CLIMATE RISK COUNTRY PROFILE: THAILAND 26 WBG – Country Partnership Framework The World Bank Group have agreed a Country Partnership Framework (CPF) with Thailand covering the period 2019–2022. The Framework contains two focus areas, the first of which ‘promoting resilient and sustainable growth’ includes six objectives. Objective 4 aims to address climate change and improve water resource management. Specifically, under this objective. the WBG will partner with Thailand to develop resilience against floods and droughts in the face of climate change, and promote sustainable and equitable economic development.99 99 WBG (2019). Thailand – World Bank Group Country Partnership Framework 2019–2022. URL: https://elibrary.worldbank.org/doi/ abs/10.1596/30977 CLIMATE RISK COUNTRY PROFILE: THAILAND 27 CLIMATE RISK COUNTRY PROFILE THAILAND