Editing IPCC:AR6/WGII/Chapter-10 (section)

== 10.4 Key Systems and Associated Impacts, Adaptation and Vulnerabilities ==

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=== 10.4.1 Energy Systems ===

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==== 10.4.1.1 Regional Diversity ====

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Energy consumption of Asia accounts for 36% of the global total at present. China, India and the ASEAN countries have largely contributed to the ever-growing global energy consumption. Asia is predicted to account for 80% of coal, 26% of natural gas and 52% of electricity consumption of the world by 2040 ( [[#IEA--2018|IEA, 2018]] ). The share of Asia in the global primary energy consumption will increase to 48% by 2050. China continues to be the world’s largest energy consumer, and the combined consumption of India and ASEAN will be similar to that of China by that time ( [[#IEEJ--2018|IEEJ, 2018]] ).

The current energy structure of Asia is dominated by fossil fuels. As the trend indicates, the share of coal in China’s primary energy consumption is forecasted to sharply decline from 60% in 2017 to around 35% in 2040 ( [[#BP--2019|BP, 2019]] ). In contrast, India and ASEAN rely more on coal since coal may meet their soaring energy demand. Accordingly, more than 80% of the global coal will be consumed in Asia by 2050. China will surpass the USA in about 10 years to become the world’s largest oil consumer. India will then replace the USA to be the second largest by the late 2040s ( [[#IEEJ--2018|IEEJ, 2018]] ).

Around 60% of the incremental electricity demand globally, predicted to double by 2050, will occur in Asia. By that time, the electrification rate will increase to 30%, but 40% of electricity demand will be still covered by coal ( [[#IEEJ--2018|IEEJ, 2018]] ). Asia accounts for almost half of the growth in global renewable power generation. It is hardly possible for Japan and Republic of Korea to develop additional nuclear power plants as planned, whereas nuclear generation continues to increase quickly in China and the scale will be similar to the entire generation of OECD by 2040 ( [[#BP--2019|BP, 2019]] ). India and Russia’s nuclear power sectors are also growing fast (e.g., the recent launch of the Akademik Lomonosov offshore nuclear power plant in Russia).

The rapid growth of energy demand in Asia reinforces the region’s position as the largest energy importer ( [[#BP--2019|BP, 2019]] ). Around 80% of energy traded globally will be consumed in Asia, and the rate of self-sufficiency will decrease from 72 to 63% by 2050. This tendency is especially remarkable for ASEAN, which will become a net importer in the early 2020s. The self-sufficiency rate of coal will be maintained at a level of 80%, while that of oil and natural gas will decline significantly. The additional oil imports of the emerging Asian economies will be from North America, the Middle East and North Africa. The main players in Asia for the liquefied natural gas imports will extend from Japan and Republic of Korea to China and India. ASEAN has been a net exporter of natural gas but starts to expand its importation due to the increased consumption and resource depletion ( [[#IEEJ--2018|IEEJ, 2018]] ).

The increase in energy demand at a rapid rate in these countries thus cannot be attributed only to population growth and rising living standards, but also to increasingly extreme temperature variations. The decrease in precipitation influences energy demand as well, as countries are becoming more dependent on energy-intensive methods (e.g., desalination, underground water pumping) to supply water. Similarly, energy systems are influenced by the way the agriculture sector, mainly in Al Mashrek, relies increasingly on energy-intensive methods (e.g., more fertilisers, different irrigation and harvesting patterns) ( [[#Farajalla--2013|Farajalla, 2013]] ).

Climate change has direct and indirect impacts on energy and industrial systems. It has a particularly wide and profound impact on energy systems (energy development, transportation, supply, etc.). With global warming, the energy consumption for heating in winter decreases, while the energy consumption for cooling in summer significantly increases, but the overall energy demand shows an upwards trend ( ''high confidence'' ) ( [[#Sailor--2001|Sailor, 2001]] ; [[#Szabo--2018|Szabo et al., 2018]] ). Such demands in summer seasons will by far exceed any energy savings from the decrease in heating demand due to warmer winters. Higher demand for cooling due to hotter temperatures has become a major challenge in the energy sector in all countries. Furthermore, decreased water levels due to lower precipitation reduces hydroelectric output. This is particularly the case for countries such as Syria and Iraq with large hydroelectric capacity ( [[#Hamid--2009|Hamid and Raouf, 2009]] ). Additionally, the decrease in water levels negatively affects low-carbon energy systems such as concentrated solar power and thermal-generation plants that require regular cooling and cleaning.

Climate change adds extra pressure to current energy infrastructures in most countries where systems failures and blackouts are already common ( [[#Assaf--2009|Assaf, 2009]] ). In the wake of extreme weather events (e.g., heatwaves), energy infrastructures remain inadequate to cope. This is particularly the case for countries such as Lebanon, Syria, Jordan and Palestine, with poor electricity infrastructures (Jordan, 2015). Extreme weather events could generate grave damage to power plants, most being located only a few metres above sea level, as well as power-transmission towers and lines. In Lebanon, a small country where there are no Indigenous energy resources, the disruption of shipping of fuel supplies due to extreme weather events is a major risk. Other extreme weather events, such as floods and sandstorms, expose energy and industrial systems in the coastal areas due to a rise in sea level. Countries of the Arabian Peninsula are projected to experience significant inland flooding as sea levels rise ( [[#Hamid--2009|Hamid and Raouf, 2009]] ). In East Asia wet snow accretion enhanced by global warming often causes damage to electric power lines ( [[#Sakamoto--2000|Sakamoto, 2000]] ; [[#Ohba--2020|Ohba and Sugimoto, 2020]] ).

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==== 10.4.1.2 Key Drivers to Vulnerability, with Observed and Projected Impacts ====

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Universal energy access is a big challenge for Asia ( [[#IEA--2018|IEA, 2018]] ). About 230 million Indian people lack access to electricity, and around 800 million still use solid fuels for cooking ( [[#Sharma--2019|Sharma, 2019]] ). The average electricity access rate in South Asia was 74%, the equivalent of 417 million people without electricity and accounting for more than a third of the global 1.2 billion lacking the access ( [[#Shukla--2017|Shukla et al., 2017]] ). With a total population of nearly 640 million in ASEAN, an estimated 65 million people remain without electricity and 250 million rely on solid biomass for cooking fuel ( [[#IEA--2017|IEA, 2017]] ). Universal access to electricity is expected to be achieved by 2030, while 1.6 billion people in Asia will still lack clean energy for cooking ( [[#UNESCAP--2018b|UNESCAP, 2018b]] ).

Asia faces an energy security problem even with the rapid growth in production and trade ( [[#IEEJ--2018|IEEJ, 2018]] ). Among 13 developing countries with large energy consumption in Asia, 11 are exposed to high energy security risk ( [[#WEC--2018|WEC, 2018]] ). This will be a major challenge for the sustainable development of Asia due to the vulnerability to global energy supplies and price volatility ( [[#Nangia--2019|Nangia, 2019]] ). Asia lacks natural energy resources and has the smallest oil reserve but largely relies on fossil fuels. The dependency on fossil fuels was as high as 88.3% in China, 72.3% in India, 89.6% in Japan and 82.8% in Republic of Korea in 2013 (BP, 2014). Many countries in South Asia rely on a single source to supply more than half of the electricity (i.e., 67.9% from coal for India, 99.9% from hydropower for Nepal, 91.5% from natural gas for Bangladesh and 50.2% from oil for Sri Lanka) ( [[#Shukla--2017|Shukla et al., 2017]] ). Additionally, cooperation in Asia to create the integrated energy systems needed for enhancing overall security is still at a very preliminary stage due to countries having different strategic plans and lack of cooperation among them on the common concerns ( [[#Kimura--2013|Kimura and Phoumin, 2013]] ).

Even though energy efficiency is improving, the deployment of low-carbon energy, such as renewables, is not sufficient in Asia. To be consistent with the temperature goal of the Paris Agreement, the share of renewables in total energy consumption needs to reach 35% in Asia by 2030. Moreover, the financing to deploy renewables presents another considerable challenge ( [[#UNESCAP--2018b|UNESCAP, 2018b]] ).

In order to cope with climate change, renewable energy has become the core of energy development and transformation. Since the 1960s, the total solar radiation on the ground in Asia has shown a downwards trend as a whole, which is consistent with the change in global total solar radiation on the ground, and has experienced a phased change process of ‘first darkening and then brightening’ ( ''high confidence'' ). This conclusion has been further confirmed by ground station observations, satellite remote sensing inversion data and model simulation research ( [[#Wang--2016|Wang and Wild, 2016]] ; [[#Qin--2018|Qin et al., 2018]] ; [[#Yang--2018a|Yang et al., 2018a]] ).

However, wind speed over most Asian regions is obviously decreasing ( ''high confidence'' ). Based on meteorological observation records or reanalysis data, many studies have analysed the variation of near-surface average wind speed in Asia. It is generally found that wind speed has declined since the 1970s, although the declining trend is different in different subregions. ( [[#Yang--2012c|Yang et al., 2012c]] ; [[#Lin--2013|Lin et al., 2013]] ; [[#Liu--2014b|Liu et al., 2014b]] ; [[#Zha--2016|Zha et al., 2016]] ; [[#Guo--2017a|Guo et al., 2017a]] ; [[#Torralba--2017|Torralba et al., 2017]] ; [[#Wu--2017a|Wu et al., 2017a]] ; [[#Ohba--2019|Ohba, 2019]] ). The decline of near-surface wind speed in Asia is consistent with the general decline of global land-surface wind speeds, among which the frequency of strong winds and the decline of wind speed are more prominent ( [[#McVicar--2012|McVicar et al., 2012]] ; [[#Jiang--2013|Jiang et al., 2013]] ; [[#Blunden--2017|Blunden and Arndt, 2017]] ; [[#Wu--2018c|Wu et al., 2018c]] ). Since the early 2010s, the average wind speed in the world and some parts of Asia has shown signs of increasing ( [[#Li--2018d|Li et al., 2018d]] ; [[#Wu--2018c|Wu et al., 2018c]] ; [[#Zeng--2019|Zeng et al., 2019]] ), which seems to be an inter-decadal variability. Whether this means a change in its trend needs the support of longer observation data.

At the same time, with the increase in the proportion of renewable energy in the power system, the power system will be more vulnerable to climate change and extreme weather and climate events, and the vulnerability and risk of the power system will greatly increase ( ''medium confidence'' ).

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==== 10.4.1.3 Adaptation Options ====

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The overall solution would be to develop a resilient energy system and avoid the risk of unsustainable energy growth in developing Asia. This requires that strategic planning be consistent with the long-term climate projection, impact and adaptation ( [[#EUEI-PDF--2017|EUEI-PDF, 2017]] ). Although no single policy package would be applicable for all the countries across the region, several measures could be addressed as the common options, including fortification of energy infrastructure and diversification of the sources by sufficient investment, improvement of energy efficiency for sector flexibility, and promotion of regional cooperation and integration for increasing energy security ( [[#UNESCAP--2018b|UNESCAP, 2018b]] ). Adaptation also includes promoting renewable energy resources, securing local natural gas resources, enhancing water production and adopting green-building technologies. These adaptation measures may help increase readiness for the anticipated impact of climate change.

The improvement of energy efficiency and demand-side management can alleviate supply constraints and thus lower overall required-energy capacity. Energy storage, smart grids for the electricity network as well as other flexible management measures enable this energy demand shifting. Regional integration of energy markets drives productivity increase, cost reduction, new investment, human capability and diversity of energy sources ( [[#WEC--2018|WEC, 2018]] ). For example, better interconnection of natural gas supply networks among the ASEAN countries enhances gas security in the region. The development of the long-planned regional power grid would make large-scale renewable projects more viable and aid the integration of rising shares of wind and solar power ( [[#IEA--2017|IEA, 2017]] ).

Providing enough investment in energy supply is a top priority to extend the connections to those without access to electricity and satisfy the soaring demand ( [[#IEA--2017|IEA, 2017]] ). The investment in non-fossil energies like renewables has been expanding to leverage the economic growth in China, India and Republic of Korea. According to the updated estimate of ADB, 14.7 trillion USD will be needed for the infrastructure development in the power sector of developing Asia over the 15 years from 2016 to 2030 ( [[#ADB--2017a|ADB, 2017a]] ). The cumulative investment needs of ASEAN for energy supply and efficiency up to 2040 is estimated at 2.7 to 2.9 trillion USD ( [[#IEA--2017|IEA, 2017]] ). Mobilising investment to such a scale will require significant participation from the private sector and international financial institutions.

Diversifying energy sources increases energy security and thus the resilience of the whole system. The deployment of renewable energy is widely recognised as a crucial measure for enhancing energy access and diversity. There remains huge potential for renewable sources in Asia (i.e., India has massive solar power potential) ( [[#Shukla--2017|Shukla et al., 2017]] ). Many renewable technologies (i.e., hydro- and wind power as well as solar photovoltaics) are becoming competitive, and their life-cycle costs may fall below those of coal and natural gas in the near term. Great progress has been made in enhanced geothermal systems (EGS), and in the conventional and unconventional fusion power that China is promoting. Conventional and underground pumped hydropower will level out supplies for intermittent renewable energy generation.

Substantial progress may be fulfilled by increasing the share of renewable energy in the overall energy consumption of Asia ( [[#ADB--2017a|ADB, 2017a]] ). Access to energy, particularly in rural areas, can reduce climate vulnerability of developing Asia. Due to the high cost of extending the electricity network to rural regions, an alternative way is to develop the off-grid renewable energy systems in these areas. The distributed, instead of centralised, energy systems can increase energy access and resilience ( [[#EUEI-PDF--2017|EUEI-PDF, 2017]] ).

Some countries in the Arabian Peninsula, such as the United Arab Emirates (UAE), are adopting an array of approaches to enhance the adaptive capacity of the energy infrastructure and diffuse the risk of climate change over a larger area (e.g., energy efficiency, demand management, storm planning for power plants). In Al Mashrek, building institutional capacity in the energy sector is a necessary first step to mainstream climate-change adaptation (CCA). Countries such as Lebanon and Jordan have already made progress in mainstreaming CCA into electricity infrastructure. In the UAE, buildings account for more than 80% of the total electricity consumption. There are currently a set of measures and regulations on building conditions and specifications that are being applied to increase energy efficiency in buildings, but the rehabilitation and upgrading of old buildings still require further efforts ( [[#Environment--2015|Environment, 2015]] ). In Kuwait, one adaptation measure to dust storms is through the reduction of the proportion of open-desert land from 75 to 51%, the increase in protected areas from 8 to 18% and greenbelt projects in desert areas ( [[#Kuwait--2015|Kuwait, 2015]] ). Addressing climate-change impact on energy systems in Lebanon, Jordan, Syria, Iraq and Palestine needs to simultaneously consider other interlinked challenges of population growth, rapid urbanisation, refugee influx, conflict and geopolitical location. To address these challenges and provide solutions for CCA, the promotion of multi-stakeholder partnerships is key to breaking the silo approach.

These CCA measures need to be broadened to fit the scope and depth of mitigation efforts by each country. Risk assessments and vulnerability assessments are in their early stages in the energy and industrial sectors, and are not currently based on a comprehensive plan of action. The first step is to undertake comprehensive national assessments of the risks associated with climate change based on existing studies on climate impacts and risks, and by making evidence-based decisions on adaptation actions.

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'''Box 10.2 | Migration and Displacement in Asia'''
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Migration is a key livelihood strategy across Asia and is driven by multiple factors such as socioeconomic changes, increasing climate variability and disaster incidence, and changing aspirations. Displacement denotes a more involuntary movement in reaction to climatic or non-climatic factors. There is ''robust evidence, medium agreement'' that increased climate variability and extreme events are already driving migration ( [[#Gemenne--2015|Gemenne et al., 2015]] ; [[#Rigaud--2018|]] [[#Rigaud--2018|Rigaud et al., 2018]] ; [[#IDMC--2019|IDMC, 2019]] ; [[#Jacobson--2019|Jacobson et al., 2019]] ; [[#Siddiqui--2019|Siddiqui et al., 2019]] ; [[#IDMC--2020|IDMC, 2020]] ; [[#Maharjan--2020|Maharjan et al., 2020]] ) and ''medium evidence, medium agreement'' projecting that longer-term climate change will increase migration flows across Asia ( [[#Abubakar--2018|Abubakar et al., 2018]] ; [[#Rigaud--2018|]] [[#Rigaud--2018|Rigaud et al., 2018]] ; [[#Hauer--2020|Hauer et al., 2020]] ; [[#Bell--2021|Bell et al., 2021]] ).

'''Detection and attribution: Does climate change drive migration?''' Ascertaining the role of climate change in migration is difficult and contested (see Cross-Chapter Box MIGRATE in [[IPCC:Wg2:Chapter:Chapter-7|Chapter 7]] and RKR-H in Chapter 16), with observation-based studies either linking extreme event incidence, weather anomalies and environmental change with migration numbers or drivers ( [[#McLeman--2014|McLeman, 2014]] ; [[#Singh--2019a|Singh et al., 2019a]] ; [[#Kaczan--2020|Kaczan and Orgill-Meyer, 2020]] ), and projection studies looking at particular risks such as SLR or drought by linking increasing warming (often through representative concentration pathways, RCPs) and population growth. Despite methodological disagreement on detection and attribution of migration due to climate change, there is medium confidence that higher warming and associated changes in frequency and intensity of slow-onset events (such as drought and sea level rise) and rapid-onset events (such as cyclones and flooding) will increase involuntary displacement in the future, especially under SSP3 and SSP4 pathways ( [[#Dasgupta--2014a|Dasgupta et al., 2014a]] ; [[#Davis--2018|Davis et al., 2018]] ; [[#Rigaud--2018|]] [[#Rigaud--2018|Rigaud et al., 2018]] ; [[#Hauer--2020|Hauer et al., 2020]] ). But its role is smaller than non-climatic socio-economic drivers of migration ( [[#Wodon--2014|Wodon et al., 2014]] ; [[#Adger--2021|Adger et al., 2021]] ).

''Current migration and displacement.'' One in three migrants comes from Asia and the highest ratio of outward migrants is seen from hazard-exposed Pacific countries ( [[#Ober--2019|Ober, 2019]] ). In 2019, approximately 1900 disasters triggered 24.9 million new displacements across 140 countries; in particular, Bangladesh, China, India and the Philippines each recorded more than 4 million disaster displacements ( [[#IDMC--2019|IDMC, 2019]] ). Tajikistan, Kyrgyzstan and Russia see significant disaster-associated displacements: for example, heavy rain-induced flooding in Khatlon (Tajikistan) triggered 5400 new displacements; landslides in the Jalal-Abad (Kyrgyzstan) saw 4700 new displacements; and floods in Altai, Tuva and Khakassia (Russia) displaced 1500 people. Iran reported the highest sub-regional figures with &gt;520,000 new disaster-related displacements in 2019 ( [[#IDMC--2019|IDMC, 2019]] ). In Southeast and East Asia, cyclones, floods and typhoons triggered internal displacement of 9.6 million people in 2019, almost 30% of total global displacements ( [[#IDMC--2019|IDMC, 2019]] ). With most migrants in the region being temporary migrant workers, loss of jobs and wages among them have been particularly severe due to adverse economic climate triggered by COVID-19 ( [[#ESCWA--2020|ESCWA, 2020]] ). It has also resulted in large-scale returns of migrant workers, and remittances have declined drastically ( [[#Khanna--2020|Khanna, 2020]] ; [[#Li--2021|Li et al., 2021]] ). Remittances to Eastern Europe and Central Asia are expected to decline 16.1% from 57 billion USD in 2019 to 48 billion USD in 2020. Remittances in East Asia and the Pacific are estimated to fall 10.5% over the same period, from 147 billion to 131 billion USD ( [[#United%20Nations--2020|United Nations, 2020]] ). The COVID-19 pandemic has had significant impacts on migrants ( [[#Rajan--2020|Rajan, 2020]] ) in the region, and some countries have targeted migrants in economic stimulus packages or income-support programmes; however, access to such support has been heterogeneous.

''Projected migration.'' Regional variation is significant across Asia. By one estimate, in South Asia, internal climate migrants (i.e., those migrating due to climate change and associated impacts such as water scarcity, crop failure, SLR and storm surges) are projected to be 40 million by 2050 (1.8% of regional population) under high warming ( [[#Rigaud--2018|]] [[#Rigaud--2018|Rigaud et al., 2018]] ). While methodological critiques remain on projected migration estimates, what is certain is that some countries will be more affected that others; it is estimated that in southern Bangladesh, SLR could displace 0.9–2.1 million people by direct inundation by 2050 ( [[#Jevrejeva--2016|Jevrejeva et al., 2016]] ; [[#Davis--2018|Davis et al., 2018]] ). In South Asia, migration hotspots include the Gangetic Plain and the Delhi–Lahore corridor, and coastal cities such as Chennai, Chittagong, Dhaka and Mumbai, which will be simultaneously exposed to climate-change impacts, major migration destinations and amplified rural–urban migration ( [[#Ober--2019|Ober, 2019]] ). Importantly, there is ''low agreement'' on projected numbers (see [[#Boas--2019|Boas et al., 2019]] ) with uncertainties around how local policies and individual behaviours will shape migration choices. Even in high-risk places, people might choose to stay or be unable to move, resulting in ‘trapped’ populations ( [[#Zickgraf--2019|Zickgraf, 2019]] ; [[#Ayeb-Karlsson--2020|Ayeb-Karlsson et al., 2020]] ). There is currently inadequate evidence to ascertain the nature and numbers of trapped populations currently or in the future.

'''Implications of migration for adaptation.''' The evidence on migration and its impacts on adaptive capacity and risk reduction are mixed ( [[#Upadhyay--2014|Upadhyay, 2014]] ; [[#Banerjee--2018|Banerjee et al., 2018]] ; [[#Szabo--2018|Szabo et al., 2018]] ; [[#Maharjan--2020|Maharjan et al., 2020]] ; [[#Singh--2020|Singh and Basu, 2020]] ). Financial remittances help vulnerable households spread risk through better incomes, expanded networks and improved assets such as housing, education and communication technology ( [[#Jha--2018|Jha et al., 2018]] ; [[#Szabo--2018|Szabo et al., 2018]] ; [[#Ober--2019|Ober, 2019]] ; [[#Maharjan--2020|Maharjan et al., 2020]] ). Benefits from international remittances across the Asia Pacific region were approximately 276 billion USD in 2017 ( [[#UN--2018|UN, 2018]] ), and in countries such as Kyrgyzstan, Tajikistan and Nepal remittances were ~25% of the national GDP in 2015. However, migration requires a minimum level of resources, and liquidity constraints impede internal migration by the poorest households often rendering them immobile ( [[#Ayeb-Karlsson--2020|Ayeb-Karlsson et al., 2020]] ; [[#Maharjan--2020|Maharjan et al., 2020]] ). Furthermore, migration does not necessarily mean that people move out of risk; in fact, often they might be subjected to new risks. Notably, migrants in South and Southeast Asia have been severely affected by the compounding crises of disasters and the COVID-19 pandemic, and there is emerging evidence that inclusion of universal safety-net provisions that embed adaptation planning can reduce vulnerabilities of migrants ( [[#Sengupta--2020|Sengupta and Jha, 2020]] ; [[#Cundill--2021|Cundill et al., 2021]] ; [[#Sultana--2021|Sultana, 2021]] ).

While there is ''robust evidence'' ( ''medium agreement'' ) that migration exacerbates gendered vulnerability and work burdens ( [[#Banerjee--2019|Banerjee et al., 2019]] ; [[#Singh--2019|Singh, 2019]] ; [[#Rao--2020|Rao et al., 2020]] ), it is well established that differential vulnerability of migrants intersects with ethnicity, age and gender; political networks and social capital; and livelihoods in destination areas ( [[#Maharjan--2020|Maharjan et al., 2020]] ; [[#Cundill--2021|Cundill et al., 2021]] ). Across Asia, international and internal migration are changing social norms and household structures, with significant implications for local adaptive capacity ( [[#Singh--2019|Singh, 2019]] ; [[#Evertsen--2020|Evertsen and van der Geest, 2020]] ; [[#Porst--2020|Porst and Sakdapolrak, 2020]] ; [[#Rao--2020|Rao et al., 2020]] ).

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=== 10.4.2 Terrestrial and Freshwater Ecosystems ===

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Sub-regional diversity of ecosystems is high in Asia (Section 10.2.2). Climate-impact drivers of Asian terrestrial ecosystems (ATS) change are global warming, precipitation and Asian monsoon alteration, permafrost thawing and extreme events like dust storms. Observed and projected changes in ATS are affected by several interacting factors. Non-climatic human-related drivers are change of land use, change of human use of natural resources, including species and ecosystems overexploitation as well as other non-sustainable use, socioeconomic changes and direct impacts of rising greenhouse gases (GHGs). Ecosystem vulnerability has resulted from complex interactions of CIDs and non-climate drivers. Species interaction and natural variability of organisms, species and ecosystems is currently poorly understood, and much more work still needs to be done to unravel these multiple stressors (i.e., [[#Berner--2013|Berner et al., 2013]] ; [[#Brazhnik--2015|Brazhnik and Shugart, 2015]] ).

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==== 10.4.2.1 Observed Impacts ====

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===== 10.4.2.1.1 Biomes and mountain treeline =====

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Changes in biomes in Asia are compatible with a response to regional surface air temperature increase ( [[#Arias--2021|Arias et al., 2021]] ) ( ''medium agreement, medium evidence'' ). Expansion of the boreal forest and reduction of the tundra area is observed for about 60% of latitudinal and altitudinal sites in Siberia ( [[#Rees--2020|Rees et al., 2020]] ). In Central Siberia, the changes in climate and disturbance regimes are shifting the southern taiga ecotone northward ( [[#Brazhnik--2017|Brazhnik et al., 2017]] ). In Taimyr, no significant changes in the forest boundary have been observed during the past three decades ( [[#Pospelova--2017|Pospelova et al., 2017]] ). For the Japanese archipelago, it is suggested that the change in tree community composition along the temperature gradient is a response to past and/or current climate changes ( [[#Suzuki--2015|Suzuki et al., 2015]] ).

Alpine treeline position in Asian mountains in recent decades either moves upwards in North Asia or demonstrates multi-directional shifts in Himalaya ( ''high confidence'' ). Since AR5, in North Asia new evidence has appeared of tree expansion into mountain tundra and steppe, of intensive reproduction and increase in tree stands productivity in the past 30–100 years at the upper treeline in the Ural Mountains ( [[#Shiyatov--2015|Shiyatov and Mazepa, 2015]] ; [[#Zolotareva--2017|Zolotareva and Zolotarev, 2017]] ; [[#Moiseev--2018|Moiseev et al., 2018]] ; [[#Sannikov--2018|Sannikov et al., 2018]] ; [[#Fomin--2020|Fomin et al., 2020]] ; [[#Gaisin--2020|Gaisin et al., 2020]] ), in the Russian Altai Mountains ( [[#Kharuk--2017a|Kharuk et al., 2017a]] ; [[#Cazzolla%20Gatti--2019|Cazzolla Gatti et al., 2019]] ) and in the Putorana Mountains ( [[#Kirdyanov--2012|Kirdyanov et al., 2012]] ; [[#Pospelova--2017|Pospelova et al., 2017]] ; [[#Grigor’ev--2019|Grigor’ev et al., 2019]] ). Lower treelines in the southernmost ''Larix sibirica'' forests in the Saur Mountains, eastern Kazakhstan, have suffered from increased drought stress in recent decades causing forest regeneration and tree growth decrease, and tree mortality increase ( [[#Dulamsuren--2013|Dulamsuren et al., 2013]] ). In Jeju Island, Republic of Korea, recent warming has enhanced ''Quercus mongolica'' growth at its higher distribution and has led to ''Abies koreana'' (ABKO) growth reduction at all elevations, except the highest locality. Thus, the combination of warming, increasing competition and frequent tropical cyclone disturbances could lead to population decline or even extinction of ABKO at Jeju Island ( [[#Altman--2020|Altman et al., 2020]] ). In the Himalaya, the treeline over recent decades either moves upwards ( [[#Schickhoff--2015|Schickhoff et al., 2015]] ; [[#Suwal--2016|Suwal et al., 2016]] ; [[#Sigdel--2018|Sigdel et al., 2018]] ; [[#Tiwari--2018|Tiwari and Jha, 2018]] ) or does not show upslope advance ( [[#Schickhoff--2015|Schickhoff et al., 2015]] ; [[#Gaire--2017|Gaire et al., 2017]] ; [[#Singh--2018c|Singh et al., 2018c]] ), or moves downwards ( [[#Bhatta--2018|Bhatta et al., 2018]] ). In the Tibetan Plateau, the treeline either shifted upwards or showed no significant upwards shift ( [[#Wang--2019c|Wang et al., 2019c]] ). This can be explained by site-specific complex interaction of positive effect of warming on tree growth, and negative effects of drought stress, change in snow precipitation, inter- and intraspecific interactions of trees and shrubs, land-use change (especially grazing) and other factors ( [[#Liang--2014|Liang et al., 2014]] ; [[#Lenoir--2015|Lenoir and Svenning, 2015]] ; [[#Tiwari--2017|Tiwari et al., 2017]] ; [[#Sigdel--2018|Sigdel et al., 2018]] ; [[#Tiwari--2018|Tiwari and Jha, 2018]] ; [[#Sigdel--2020|Sigdel et al., 2020]] ). It is largely unknown how broader-scale climate inputs, such as pre-monsoon droughts, interact with local-scale factors to govern treeline response patterns ( [[#Schickhoff--2015|Schickhoff et al., 2015]] ; [[#Müller--2016|Müller et al., 2016]] ; [[#Bhatta--2018|Bhatta et al., 2018]] ; [[#Singh--2019b|Singh et al., 2019b]] ).

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===== 10.4.2.1.2 Species ranges and biodiversity =====

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Since AR5, new evidence has appeared of alterations in terrestrial and freshwater species, populations and communities in line with climate change across Asia ( ''medium to high confidence'' ) ( [[#Arias--2021|Arias et al., 2021]] ). In North Asia, temperature increase and droughts have promoted spread northward of the current silk moth outbreak (has affected nearly 2.5 × 10 6 ha) in Central Siberia dark taiga since 2014 ( [[#Kharuk--2017b|Kharuk et al., 2017b]] ; [[#Kharuk--2020|Kharuk et al., 2020]] ). The climatic range of the Colorado potato beetle ( ''Leptinotarsa decemlineata'' ) in 1991–2010 expanded east- and northward in Siberia and the Russian Far East compared with the 1951–1970 range ( [[#Popova--2014|Popova, 2014]] ). The climatic range of ''Ixodes ricinus'' , a vector of dangerous human diseases, expanded into Central Asia and south of the Russian Far East ( [[#Semenov--2020|Semenov et al., 2020]] ). A butterfly ( ''Melanargia russiae'' ) in the Middle Urals moved northward ( [[#Zakharova--2017|Zakharova et al., 2017]] ). Thrush birds in West Siberia penetrated northward up to the limits of the sparse woodlands ( [[#Ryzhanovskiy--2019a|Ryzhanovskiy, 2019a]] ). The increase in the length of frost-free period observed in the Ilmen Nature Reserve, Middle Urals, during recent decades is supposed to be interlinked with changes in the amplitude and frequency of population waves of bank vole ( [[#Kiseleva--2020|Kiseleva, 2020]] ). In Katunskiy Biosphere Reserve, Russian Altai, in the period 2005–2015, alpine plant species have shifted towards higher altitudes by 5.3 m on average ( [[#Artemov--2018|Artemov, 2018]] ). Wild reindeer herds in Taimyr, north of Central Siberia, migrated northward to the Arctic Sea coast in hot summers between 1999–2003 and 2009–2016 because of an earlier massive emergence of bloodsucking insects ( [[#Pospelova--2017|Pospelova et al., 2017]] ). In Yakutia, the ranges of red deer, elk and the northern pika are expanding, and the winter survival of the mouse-like rodents has increased ( [[#Safronov--2016|Safronov, 2016]] ). In the Chukchi Sea, in recent decades the average duration polar bears spent onshore increased by 30 d ( [[#Rode--2015b|Rode et al., 2015b]] ) in line with global warming and the rapid decline of their sea ice habitat ( [[#Derocher--2013|Derocher et al., 2013]] ; [[#Jenssen--2015|Jenssen et al., 2015]] ; [[#Rode--2015a|Rode et al., 2015a]] ).

In Central Kazakh Steppe, in line with warming, in 2018 there were more ‘southern’ sub-arid species in the communities and fewer relatively ‘northern’ boreal and polyzonal species of ground beetles (Carabidae) and black beetles (Tenebrionidae) than in 1976–1978 ( [[#Mordkovich--2020|Mordkovich et al., 2020]] ). The present distribution of Asian black birch ( ''Betula davurica'' Pall.) in East and North Asia was formed as a result of northward expansion during post-Last Glacial Maximum global warming ( [[#Shitara--2018|Shitara et al., 2018]] ). Both upper and lower limits of avifauna of two New Guinean mountains, Mt. Karimui and Karkar Island, have been shifting upslope since 1965 ( [[#Freeman--2014|Freeman and Freeman, 2014]] ). In Republic of Korea, for the past 60 years, the northern boundary line of 63 southern butterfly species has moved further north ( [[#Bae--2020|Bae et al., 2020]] ). The change in the butterflies’ occurrence in this period has been influenced mostly by large-scale reforestation, not by climate change ( [[#Kwon--2021|Kwon et al., 2021]] ). Warming-driven geographic range shift was recorded in 87% of 124 endemic plant species studied in the Sikkim Himalaya in the periods 1849–1850 and 2007–2010 ( [[#Telwala--2013|Telwala et al., 2013]] ). In Darjeeling, India, significant change in lichen community structure was shown in response to climate change and anthropogenic pollution ( [[#Bajpai--2016|Bajpai et al., 2016]] ).

The observed loss of biodiversity and habitat of animals and plants has been linked to climate change in some parts of Asia ( ''high confidence'' ). Climate change, together with human disturbances, have caused local extinction of some large and medium-sized mammals during the past three centuries in China ( [[#Wan--2019|Wan et al., 2019]] ). Climate change has shown significant impacts on subalpine plant species at low altitudes and latitudes in Republic of Korea and may impose a big threat to these plant species ( [[#Adhikari--2018|Adhikari et al., 2018]] ; [[#Kim--2019c|Kim et al., 2019c]] ). Climate change has caused habitat loss of amphibians ( [[#Surasinghe--2011|Surasinghe, 2011]] ) and extinction of some endemic species in Sri Lanka ( [[#Kottawa-Arachchi--2017|Kottawa-Arachchi and Wijeratne, 2017]] ).

There is evidence that climate change can alter species interaction or spatial distribution of invasive species in Asia ( ''high confidence'' ). Climate warming has enhanced the competitive ability of the native species ( ''Sparganium angustifolium'' ) against the invasive species ( ''Egeria densa'' ) in China under a mesocosm experiment in a greenhouse ( [[#Yu--2018e|Yu et al., 2018e]] ). It has also increased the non-target effect on a native plant ( ''Alternanthera sessilis'' ) by a biological control beetle ( ''Agasicles hygrophila'' ) in China due to range expansion of the beetle and change of phenology of the plant ( [[#Lu--2015|Lu et al., 2015]] ). Climate warming has expanded the distribution of invasive bamboos ( ''Phyllostachys edulis'' and ''P. bambusoides'' ) northward and upslope in Japan ( [[#Takano--2017|Takano et al., 2017]] ), while soil dry-down rates have been a key driver of invasion of dwarf bamboo ( ''Sasa kurilensis'' ) in central Hokkaido above and below the treeline ( [[#Winkler--2016|Winkler et al., 2016]] ).

Climate change along with land-use and land-cover change influences soil organic carbon content, microbial biomass C, microbial respiration and the soil carbon cycle in the Hyrcanian forests of Iran ( [[#Soleimani--2019|Soleimani et al., 2019]] ; [[#Francaviglia--2020|Francaviglia et al., 2020]] ). In the fir forest ecosystems of the Tibetan Plateau, winter warming affects the ammonia-oxidising bacteria and archaea, thus altering the nitrogen cycle ( [[#Huang--2016|Huang et al., 2016]] ). Ecosystem carbon pool in the spruce forests of the northeast Tibetan Plateau was reduced by about 25% by deforestation due to recent decades of climate warming as well as wood pasture and logging ( [[#Wagner--2015|Wagner et al., 2015]] ). In Mongolia’s forest steppe, recent decades of drought- and land-use-induced deforestation has reduced the ecosystem carbon stock density by about 40% ( [[#Dulamsuren--2016|Dulamsuren et al., 2016]] ). In Inner Mongolia, the predicted decreases in precipitation and warming for most of the temperate grassland region could lead to a pH change, which would contribute to a soil C-N-P decoupling that could reduce plant growth and production in arid ecosystems ( [[#Jiao--2016|Jiao et al., 2016]] ).

In Central Asia, in the Vakhsh, Kafirnigan and Kyzylsu river basins, Tajikistan, it has been shown that temperature stimulates algal species diversity, while precipitation and altitude suppress it ( [[#Barinova--2015|Barinova et al., 2015]] ). In line with the warming of Lake Baikal, Russia, since the 1990s in the lake’s south basin, there have been shifts in diatom community composition towards higher abundances of the cosmopolitan ''Synedra acus'' and a decline in endemic species, mainly ''Cyclotella minuta'' and ''Stephanodiscus meyerii'' , and to a lesser extent ''Aulacoseira baicalensis'' and ''A. skvortzowii'' ( [[#Roberts--2018|Roberts et al., 2018]] ). In Gonghai Lake, North China, diatom biodiversity has increased remarkably from 1966, but began to decline after 1990 presumably in response to rapid climate warming ( [[#Yan--2018|Yan et al., 2018]] ).

<div id="10.4.2.1.3" class="h4-container"></div>

<span id="wildfires"></span>
===== 10.4.2.1.3 Wildfires =====

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Climate change, human activity and lightning determine increases in wildfire severity and area burned in North Asia (high detection with medium-to-low attribution to climate change). In North Asia, the extent of fire-affected areas in boreal forest can be millions of hectares in a single extreme fire year ( [[#Duane--2021|Duane et al., 2021]] ) and nearly doubled between 1970 and 1990 ( [[#Brazhnik--2017|Brazhnik et al., 2017]] ). During recent decades, the number, area and frequency of forest fires increased in Putorana Plateau (north of Central Siberia), in larch-dominated forests of Central Siberia and in Siberian forests as a whole. This increase is in line with an increase in the average annual air temperature, air temperature anomalies, droughts and the length of fire season ( [[#Ponomarev--2016|Ponomarev et al., 2016]] ; [[#Kharuk--2017|Kharuk and Ponomarev, 2017]] ; [[#Pospelova--2017|Pospelova et al., 2017]] ). The number of forest fires and damaged areas in Gangwon Province and the Yeongdong area in the 2000s increased by factors of 1.7 and 5.6, respectively, compared with the 1990s ( [[#Bae--2020|Bae et al., 2020]] ). Climate change is not the sole cause of the increase in forest fire severity ( [[#Wu--2014|Wu et al., 2014]] ; [[#Wu--2018d|Wu et al., 2018d]] ). Ignition is often facilitated by lightning ( [[#Canadell--2021|Canadell et al., 2021]] ), and over 80% of fires in Siberia are ''likely'' anthropogenic in origin (e.g., ( [[#Brazhnik--2017|Brazhnik et al., 2017]] ). Gas field development and Indigenous tundra burning practices that may get out of control contribute to fire frequency in the forest–tundra of West Siberia ( [[#Adaev--2018|Adaev, 2018]] ; [[#Moskovchenko--2020|Moskovchenko et al., 2020]] ). Climate change in combination with socioeconomic changes has resulted in an increase in fire severity and area burned in South Siberia, and illegal logging increases fire danger in forest–steppe Scots pine stands ( [[#Ivanova--2010|Ivanova et al., 2010]] ; [[#Schaphoff--2016|Schaphoff et al., 2016]] ).

<div id="10.4.2.1.4" class="h4-container"></div>

<span id="phenology-growth-rate-and-productivity"></span>
===== 10.4.2.1.4 Phenology, growth rate and productivity =====

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In East and North Asia, satellite measurements and ground-based observations in recent decades demonstrate either an increase in the length of plant growth season over sub-regions or in some territories in line with climate warming, or do not show any significant trend in other territories ( ''high confidence'' ). In recent decades in China, there has been an increasing trend in annual mean grassland net primary production (NPP), average leaf area index and lengthening of the local growing season ( [[#Piao--2015|Piao et al., 2015]] ; [[#Zhang--2017b|Zhang et al., 2017b]] ; [[#Xia--2019|Xia et al., 2019]] ). Nevertheless, phenology patterns vary across different studies, species and parts of China. In most regions of Northeast China, start date and length of land surface phenology from 2000 to 2015 had advanced by approximately 1 d yr −1 , except in the needle-leaf and cropland areas ( [[#Zhang--2017d|Zhang et al., 2017d]] ). For Inner Mongolia, it has been shown that neither the start of growing season (SOS) nor the end of growing season (EOS) presented detectable progressive patterns at the regional level in 1998–2012, except for the steppe–desert (6% of the total area) ( [[#Sha--2016|Sha et al., 2016]] ). In the Tianshan Mountains in China, the NPP of only 2 out of 12 types of vegetation increased in spring, and the NPP of only one type increased in autumn from 2000–2003 to 2012–2016 ( [[#Hao--2019|Hao et al., 2019]] ). In Republic of Korea, from 1970 to 2013, the SOS has advanced by 2.7 d per decade, and the EOS has been delayed by 1.4 d per decade ( [[#Jung--2015|Jung et al., 2015]] ). During the past decade, leaf unfolding has accelerated at a rate of 1.37 d yr −1 , and the timing of leaf fall has been delayed at a rate of 0.34 d yr −1 ( [[#Kim--2019d|Kim et al., 2019d]] ). Cherry blossoms are predicted to flower 6.3 and 11.2 d earlier after 2090 according to scenarios RCP4.5 and RCP8.5, respectively ( [[#Bae--2020|Bae et al., 2020]] ). On the Tibetan Plateau, it was found that the SOS has advanced and the EOS has been delayed over the past 30–40 years ( [[#Yang--2017|Yang et al., 2017]] ). Using normalised difference vegetation index (NDVI) datasets and ground-based Budburst data ( [[#Wang--2017c|Wang et al., 2017c]] ) found no consistent evidence that the SOS has been advancing or delaying over the Tibetan Plateau during the past two to three decades. The discrepancies among different studies in the trends of spring phenology over the Tibetan Plateau could be largely attributed to the use of different phenology retrieval methods. An uncertainty exists with the relationship between land-surface phenology and climate change estimated by satellite-derived NDVI because these indices are usually composite products of a number of days (e.g., 16 d) that could fail to capture more details. Besides, due to lack of ''in situ'' observations, the SOS and EOS at large areas cannot be easy defined ( [[#Zhang--2017d|Zhang et al., 2017d]] ).

In North Asia, in Central Siberia and south of West Siberia, the growth index of Siberian larch based on tree-ring width increased with the onset of warming and changed in antiphase with aridity in the 1980s ( [[#Kharuk--2018|Kharuk et al., 2018]] ). In Mongolia and Kazakhstan, the temperature increase over the previous decade promoted radial stem increment of the Siberian larch. However, the simultaneous influence of increased temperature, decreased precipitation and increased anthropogenic pressure resulted in widespread declines in forest productivity and reduced forest regeneration, and increased tree mortality ( [[#Dulamsuren--2013|Dulamsuren et al., 2013]] ; [[#Lkhagvadorj--2013a|Lkhagvadorj et al., 2013a]] ; [[#Lkhagvadorj--2013b|Lkhagvadorj et al., 2013b]] ; [[#Dulamsuren--2014|Dulamsuren et al., 2014]] ; [[#Khansaritoreh--2017|Khansaritoreh et al., 2017]] ). In Eastern Taimyr, growing season, the number of flowering shoots, annual increment, success of seed ripening and vegetation biomass have increased considerably in recent decades ( [[#Pospelova--2017|Pospelova et al., 2017]] ). In Vishera Nature Reserve, northern Ural Mountains, annual temperature has increased in recent decades in parallel with a summer temperature drop and an increase in summer frost numbers. As a result, trends in vegetation change are mostly unreliable ( [[#Prokosheva--2017|Prokosheva, 2017]] ).

In Asia, the date of arrival of migrant birds to nesting areas and the date of departure from winter areas are changing consistently with climate change ( ''medium confidence'' ). Time of arrival of the grey crow to the Lower Ob river region, northwest Siberia, shifted to earlier dates in the period 1970–2017, which is consistent with an increase in the daily average temperatures on the day of arrival ( [[#Ryzhanovskiy--2019b|Ryzhanovskiy, 2019b]] ). In Ilmen Nature Reserve, Urals, an earlier arrival of the majority of nesting bird species has not been observed in recent decades. This is explained by the fact that other factors, such as the weather of each spring month of particular years, population density in the previous nesting period, the seed yield of the main feeding plants and migration of wintering species from adjacent areas, determinate the long-term dynamics of bird arrival ( [[#Zakharov--2016|Zakharov, 2016]] ; [[#Zakharov--2018|Zakharov, 2018]] ). In Yokohama, Japan, observations since 1986 have revealed that the arrival of six winter bird species came later and the departure earlier than in the past, due to warmer temperatures ( [[#Kobori--2012|Kobori et al., 2012]] ; [[#Cohen--2018|Cohen et al., 2018]] ). Some papers corroborate that earlier start and later end of phenological events in Asia are associated with global warming; however, other papers do not confirm such a connection. Comparison and synthesis of results is impeded by usage of different metrics, measurement methods and models (e.g., [[#Hao--2019|Hao et al., 2019]] ). Relative contribution of climatic stress and other factors to phenology and plant growth trends are poorly understood (e.g., [[#Andreeva--2019|Andreeva et al., 2019]] ).

<div id="10.4.2.2" class="h3-container"></div>

<span id="projected-impacts"></span>
==== 10.4.2.2 Projected Impacts ====

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<div id="10.4.2.2.1" class="h4-container"></div>

<span id="biomes-and-mountain-treeline-1"></span>
===== 10.4.2.2.1 Biomes and mountain treeline =====

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Across Asia, under a range of representative concentration pathways (RCPs) and other scenarios, rising temperatures are expected to contribute to a northward shift of biome boundaries and an upwards shift of mountain treeline ( ''medium confidence'' ). Northward shift and area change of bioclimatic zones in Siberia ( [[#Anisimov--2017|Anisimov et al., 2017]] ; [[#Torzhkov--2019|Torzhkov et al., 2019]] ) and northeast Asia ( [[#Choi--2019|Choi et al., 2019]] ) are projected. Projected changes in vegetation in China at the end of the 21st century reveal that the area covered by cold–dry potential vegetation decreases as the area covered by warm–humid potential vegetation increases ( [[#Zhao--2017a|Zhao et al., 2017a]] ). Forest expansion into mountain tundra of the northern Urals is expected ( [[#Sannikov--2018|Sannikov et al., 2018]] ). In Republic of Korea, projected under RCP4.5 and RCP8.5 in the 2070s, suitable area loss of six subalpine tree species, namely, Korean fir, Khingan fir, Sargent juniper, Yeddo spruce, Korean yew and Korean arborvitae, range from 17.7 ± 20.1% to 65.2 ± 34.7%, respectively ( [[#Lee--2021b|Lee et al., 2021b]] ). Korean fir forests would be replaced by temperate forests at lower elevations, while they would continuously persist at the highest elevations on Mt. Halla, Jeju Island and Republic of Korea ( [[#Lim--2018|Lim et al., 2018]] ). Himalayan birch at its upper distribution boundary either is projected to move upwards ( [[#Schickhoff--2015|Schickhoff et al., 2015]] ; [[#Bobrowski--2018|Bobrowski et al., 2018]] ) or considered to downslope as a response to global-change-type droughts ( [[#Liang--2014|Liang et al., 2014]] ). Upwards shift in elevation of bioclimatic zones, decreases in area of the highest elevation zones and large expansion of the lower tropical and sub-tropical zones can be expected by the year 2050 throughout the transboundary Kailash Sacred Landscape of China, India and Nepal, and ''likely'' within the Himalayan region more generally ( [[#Zomer--2014|Zomer et al., 2014]] ).

In North Asia, a shift is projected in the dominant biomes from conifers to deciduous species across Russia after 20 years of altered climate conditions ( [[#Shuman--2015|Shuman et al., 2015]] ). In South Siberia, [[#Brazhnik--2015|Brazhnik and Shugart (2015)]] projected a shift from the boreal forest to the steppe biome. [[#Rumiantsev--2013|Rumiantsev et al. (2013)]] also project a positive northward shift of vegetation boundaries for the greater part of West Siberia in line with warming; however, no shift for the north of West Siberia and negative shift for the southern Urals and northwest Kazakhstan are projected for 2046–2065. The replacement of forest–steppe with steppe at the lower treeline in South Siberia is projected ( [[#Brazhnik--2015|Brazhnik and Shugart, 2015]] ), and retreat of larch forests from the southernmost strongholds of boreal forest in eastern Kazakhstan is expected as part of a global process of forest dieback in semiarid regions ( [[#Dulamsuren--2013|Dulamsuren et al., 2013]] ). In North Asia, tree growth is intertwined with permafrost, snowpack, insect outbreaks, wildfires, seed dispersal and climate (e.g., [[#Klinge--2018|Klinge et al., 2018]] ). It is challenging to isolate the affects of individual factors, particularly since they can interact on one another in unanticipated ways because the underlying mechanisms are not well understood ( [[#Berner--2013|Berner et al., 2013]] ; [[#Brazhnik--2015|Brazhnik and Shugart, 2015]] ). The accuracy of treeline-shift projections is limited because projections are based on vegetation models which do not consider all the factors ( [[#Tishkov--2020|Tishkov et al., 2020]] ). The regional vegetation model structure and parameterisation can affect model performance, and the corresponding projections can differ significantly ( [[#Shuman--2015|Shuman et al., 2015]] ).

<div id="10.4.2.2.2" class="h4-container"></div>

<span id="species-ranges-and-biodiversity-1"></span>
===== 10.4.2.2.2 Species ranges and biodiversity =====

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Considerable changes in plant and animal species distribution under warming stress are expected in Asia until 2100 ( ''high confidence'' ). In East Asia, ''Cunninghamia lanceolate'' , a fast-growing and widely distributed coniferous timber species in China, is projected to increase distribution, to decrease the establishment probability and to reduce total NPP by the 2050s ( [[#Liu--2014c|Liu et al., 2014c]] ). In the monsoon regions of Asia, by the end of the 21st century, NPP is projected to increase by 9–45% ( [[#Ito--2016|Ito et al., 2016]] ). Under climate change on the Korean Peninsula (KP), the potential habitat for ''Abies nephrolepis'' is the northern part of KP, and ''A. koreana'' will disappear from Jeju Island and shrink significantly in the KP ( [[#Yun--2018|Yun et al., 2018]] ), while evergreen forests will expand to the northern part of KP ( [[#Koo--2018|Koo et al., 2018]] ; [[#Lim--2018|Lim et al., 2018]] ). It is expected that under projected warming, fig species in China will expand to higher latitudes and altitudes ( [[#Chen--2018c|Chen et al., 2018c]] ). In Japan, under the A1B scenario, 89% of the area currently covered by the ''Fagus crenata'' -dominant forest type will be replaced by ''Quercus'' spp.-dominant forest types ( [[#Matsui--2018|Matsui et al., 2018]] ). Current trends of climate change will reduce distribution of tall (2–2.5 m high) herb communities in Japan, and will increase suitably for them in the Russian Far East ( [[#Korznikov--2019|Korznikov et al., 2019]] ). A range expansion of ''Lobaria pindarensis'' , an endemic epiphytic lichen in the HKH region, is projected to move to the northeast and to higher altitudes in response to climate change, although the species’ low dispersal abilities and the local availability of trees as a substratum will considerably limit latitudinal and altitudinal shifts ( [[#Devkota--2019|Devkota et al., 2019]] ).

The climatic range of Italian locust ( ''Calliptamus italicus'' L.) under RCP4.5 will expand north- and east-ward to Siberia, the Russian Far East and Central Asia ( [[#Popova--2016|Popova et al., 2016]] ). In Krasnoyarsk Krai, Siberia, it is projected that the needle cast disease caused by fungi from the genus ''Lophodermium'' Chevall. in the Scots pine nurseries would shift northward up to 2080 under A2 and B1 scenarios ( [[#Tchebakova--2016|Tchebakova et al., 2016]] ). All four RCP scenarios showed north-ward expansion of vulnerable regions to pine wilt disease in China, Republic of Korea, the Russian Far East and Japan under climate conditions in 2070 ( [[#Hirata--2017|Hirata et al., 2017]] ), and during 2026–2050 in Japan ( [[#Matsuhashi--2020|Matsuhashi et al., 2020]] ). It is noteworthy that disease expansion depends not only on climatic factors but also on the dispersal capacity of insect vectors, the transportation of infected logs to non-infected regions and the susceptibility of host trees (e.g., [[#Gruffudd--2016|Gruffudd et al., 2016]] ). The suitable habitat area of the snow leopard ''Panthera uncia'' is projected to increase by 20% under the IPCC Scenario A1B by 2080: for the seven northernmost snow leopard range states (Afghanistan, Tajikistan, Uzbekistan, Kyrgyzstan, Kazakhstan, Russia and Mongolia) the suitable habitat area will increase, while habitat loss is expected on the southern slope of the Himalaya and the southeast Tibetan Plateau ( [[#Farrington--2016|Farrington and Li, 2016]] ). Climate change projected under four RCP scenarios will not affect the distribution patterns of Turkestan Rock Agama ''Paralaudakia lehmanni'' (Nikolsky 1896; [[#Sancholi--2018|Sancholi, 2018]] ). In Iran, among 37 studied species of plants and animals, the ranges of 30 species are expected to shrink and ranges of 7 species are expected to increase between 2030 and 2099 under climate-change stress ( [[#Yousefi--2019|Yousefi et al., 2019]] ).

Future climate change would cause biodiversity and habitat loss in many parts of Asia using modelling approaches ( ''high confidence'' ). [[#Warren--2018|Warren et al. (2018)]] projected that extirpation risks to terrestrial taxa (plants, amphibians, reptiles, birds and mammals) from 2°C to 4.5°C global warming in 12 ‘priority places’ in Asia, under the assumption of no adaptation (i.e., dispersal) by the 2080s, is from 12.2–26.4% to 29–56% (Table 10.1; Figure 10.4). Under different scenarios, future climate change could reduce the extent of a suitable habitat for giant pandas ( [[#Fan--2014|Fan et al., 2014]] ), moose ( ''Alces alces'' ) ( [[#Huang--2016|Huang et al., 2016]] ), black muntjac ( ''Muntiacus crinifrons'' ) ( [[#Lei--2016|Lei et al., 2016]] ) and the Sichuan snub-nosed monkey ( ''Rhinopithecus roxellana'' ) ( [[#Zhang--2019d|Zhang et al., 2019d]] ) in China; the Persian leopard ( ''Panthera pardus saxicolor'' ) in Iran ( [[#Ashrafzadeh--2019a|Ashrafzadeh et al., 2019a]] ); the Bengal tiger ( [[#Mukul--2019|Mukul et al., 2019]] ) in India; and four tree-snail species ( ''Amphidromus'' ) in Thailand ( [[#Klorvuttimontara--2017|Klorvuttimontara et al., 2017]] ). However, climate change would have little impact on the habitats of the Asian elephant, but would cause extinction of the Hoolock gibbon in Bangladesh by 2070 ( [[#Alamgir--2015|Alamgir et al., 2015]] ). Climate change would increase the distribution of the Mesopotamian spiny-tailed lizard ( ''Saara loricate'' ) in Iran ( [[#Kafash--2016|Kafash et al., 2016]] ). Future climate change would reduce the suitable habitat of certain protected plants ( [[#Zhang--2014|Zhang et al., 2014]] ) including ''Polygala tenuifolia'' Wild ( [[#Lei--2016|Lei et al., 2016]] ); relict species in East Asia ( [[#Tang--2018|Tang et al., 2018]] ); tree ''Abies'' ( [[#Ran--2018|Ran et al., 2018]] ) in China; two threatened medicinal plants ( ''Fritillaria cirrhosa'' and ''Lilium nepalense'' ) in Nepal ( [[#Rana--2017|Rana et al., 2017]] ); a medicinal and vulnerable plant species ''Daphne mucronata'' ( [[#Abolmaali--2018|Abolmaali et al., 2018]] ) and ''Bromus tomentellus'' in Iran ( [[#Sangoony--2016|Sangoony et al., 2016]] ); a valuable threatened tree species, ''Dysoxylum binectariferum'' , in Bangladesh ( [[#Sohel--2016|Sohel et al., 2016]] ); and plant diversity in Republic of Korea ( [[#Lim--2018|Lim et al., 2018]] ).

'''Table 10.1 |''' Projected extirpation risks: percentage of taxa (plants, amphibians, reptiles, birds and mammals) for 2°C and 4.5°C global warming in ‘priority places’ in Asia, without adaptation by the 2080s. (From [[#Warren--2018|Warren et al., 2018]] ).

{| class="wikitable"
|-
! Priority places

! At 2°C (%)

! At 4.5°C (%)

|-
| Mekong

| 26.4

| 55.2

|-
| Baikal

| 22.8

| 49.5

|-
| Yangtze

| 20

| 42.6

|-
| Coral Triangle

| 19.2

| 41.8

|-
| Western Ghats

| 18.8

| 41.67

|-
| New Guinea

| 19.8

| 41.2

|-
| Atlai-Syan

| 18.6

| 37

|-
| Sumatra

| 16.8

| 37

|-
| Borneo

| 17.6

| 36.8

|-
| Amur

| 14.2

| 35.6

|-
| Eastern Himalayas

| 12.2

| 29

|-
| Black sea

| 26.2

| 56

|}

<div id="_idContainer012" class="Figure"></div>

[[File:a091579abc41f0455382430f112766ce IPCC_AR6_WGII_Figure_10_004.png]]

'''Figure 10.4 |''' '''Location of ‘priority places’ in Asia.''' (Modified from [[#Warren--2018|Warren et al., 2018]] ).

The impact of future climate change on invasive species may be species- or region specific ( ''medium confidence'' ). Climate change would promote invasion of a highly invasive aquatic plant ''Eichhornia crassipes'' ( [[#You--2014|You et al., 2014]] ), ''Ambrosia artemisiifolia'' ( [[#Qin--2014|Qin et al., 2014]] ), alligator weed ( ''Alternanthera philoxeroides'' ) ( [[#Wu--2016|Wu et al., 2016]] ), invasive alien plant ''Solidago canadensis'' ( [[#Xu--2014|Xu et al., 2014]] ), three invasive woody oil-plant species ( ''Jatropha curcas, Ricinus communis'' and ''Aleurites moluccana'' ) ( [[#Dai--2018|Dai et al., 2018]] ), and 90 of ~150 poisonous plant species ( [[#Zhang--2017a|Zhang et al., 2017a]] ) in China; six mostly highly invasive species ( ''Ageratum houstonianum'' Mill., ''Chromolaena odorata'' (L.) R.M. King &amp; H. Rob., ''Hyptis suaveolens'' (L.) Poit., ''Lantana camara'' L ''.'' , ''Mikania micrantha'' Kunth and ''Parthenium hysterophorus'' L.) in Nepal (Shrestha et al. 2018); 11 invasive plant species in the western Himalaya ( [[#Thapa--2018|Thapa et al., 2018]] ); alien plants in Georgia ( [[#Slodowicz--2018|Slodowicz et al., 2018]] ); the invasive green anole ( ''Anolis carolinensis'' ) in Japan ( [[#Suzuki-Ohno--2017|Suzuki-Ohno et al., 2017]] ); the Giant African Snail in India ( [[#Sarma--2015|Sarma et al., 2015]] ); and a major insect vector ( ''Monochamus alternatus'' ) of the pine wilt disease ( [[#Kim--2016b|Kim et al., 2016b]] ) and melon thrips ( ''Thrips palmi'' Karny) ( [[#Park--2014|Park et al., 2014]] ) in Republic of Korea. In contrast, a few studies have projected that climate change would inhibit the invasion of one exotic species ( ''Spartina alterniflora'' ) ( [[#Ge--2015|Ge et al., 2015]] ), alien invasive weeds ( [[#Wan--2017|Wan et al., 2017]] ), an invasive plant ( ''Galinsoga parviflora'' ) ( [[#Bi--2019|Bi et al., 2019]] ) and an invasive species ( ''Galinsoga quadriradiata'' ) ( [[#Yang--2018b|Yang et al., 2018b]] ) in China; and two invasive plants ( ''Chromolaena odorata'' and ''Tridax procumben'' s) in India ( [[#Panda--2019|Panda and Behera, 2019]] ).

Five of 15 endemic freshwater fish species in Iran will lose some parts of their current suitable range under climate change by 2070 ( [[#Yousefi--2020|Yousefi et al., 2020]] ). In line with projected large increases in mean water temperature, the strongest increase is projected in exceeded frequency and magnitude of maximum temperature tolerance values for freshwater minnow ( ''Zacco platypus'' ) in East Asia for 2031–2100 ( [[#Van%20Vliet--2013|Van Vliet et al., 2013]] ). Climate change under the A1B scenario is projected to decrease diversity (–0.1%) along with increased local richness (+15%) and range size (+19%) of stream macroinvertebrates in the Changjiang River catchment, southeast China, for the period 2021–2050, while land-use change is predicted to have the strongest negative impact ( [[#Kuemmerlen--2015|Kuemmerlen et al., 2015]] ). The Asian clam ''Corbicula fluminea'' Müller, an invasive species native to southeast China, the Republic of Korea and southeast Russia, is projected to invade Southeast Asia under all four RCP scenarios for the 2041–2060 and 2061–2080 periods ( [[#Gama--2017|Gama et al., 2017]] ). Projected SLR, related aquatic salinisation and alteration in fish species composition may have a negative impact on poor households in southwest coastal Bangladesh ( [[#Dasgupta--2017a|Dasgupta et al., 2017a]] ).

<div id="10.4.2.2.3" class="h4-container"></div>

<span id="wildfires-1"></span>
===== 10.4.2.2.3 Wildfires =====

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Under regional projections for North Asia, warmer climate will increase forest fire severity by the late 21st century ( ''medium confidence'' ). For the southern taiga in Tuva Republic, Central Siberia, in a warmer climate, both the annual area burned and fire intensity will increase by 2100. For the central taiga in the Irkutsk region, the annual area burned as well as crown fire-to-ground fire ratiowill increase by the late 21st century compared with the historical (1960–1990) estimate. This moves forest composition towards greater contribution of hardwoods (e.g., ''Betula'' spp., ''Populus'' spp.) ( [[#Brazhnik--2017|Brazhnik et al., 2017]] ). This shifting was also proved by observations in northern Mongolia, where boreal forest fires ''likely'' promote the relative dominance of ''B. platyphylla'' and threaten the existence of the evergreen conifers, ''Picea obovata'' and ''Pinus sibirica'' ( [[#Otoda--2013|Otoda et al., 2013]] ). For Tuva Republic, warming ambient temperatures increase the potential evapotranspiration demands on vegetation, but if no concurrent increase in precipitation occurs, vegetation becomes stressed and either dies from temperature-based drought stress or more easily succumbs to insects, fire, pathogens or wind throw ( [[#Brazhnik--2017|Brazhnik et al., 2017]] ). Although [[#Torzhkov--2019|Torzhkov et al. (2019)]] also projected fire risk (FR) increase in Tuva Republic, they expect FR decrease in the Irkutsk region and Yakutia under RCP8.5, and FR decrease in major parts of Central and East Siberia under RCP4.5 for 2090–2099. This discrepancy is due to differences in models, climate projections, fire severity metrics and other assumptions. According to global projections, FR will increase in Central Asia, Russia, China and India under a range of scenarios ( [[#Sun--2019|Sun et al., 2019]] ).

<div id="10.4.2.3" class="h3-container"></div>

<span id="vulnerabilities-to-key-drivers"></span>
==== 10.4.2.3 Vulnerabilities to Key Drivers ====

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Both natural and managed ecosystems, ecosystem services and livelihoods in Asia will potentially be substantially impacted by changing climate ( [[#Wu--2018d|Wu et al., 2018d]] ). There will be increased risk for biodiversity, particularly many endemic and threatened species of fauna and flora already under environmental pressure from land-use change and other regional and global processes ( [[#Zomer--2014|Zomer et al., 2014]] ; [[#Rashid--2015|Rashid et al., 2015]] ; [[#Choi--2019|Choi et al., 2019]] ). Biomes shift not only serves as a signal of climate change but also provides important information for resources management and ecotone ecosystem conservation. A widespread upwards encroachment of subalpine forests would displace regionally unique alpine tundra habitats and possibly cause the loss of alpine species ( [[#Schickhoff--2015|Schickhoff et al., 2015]] ). In North Asia, emissions from fires reduce forests’ ability to regulate climate. A warmer and longer growing season will increase vulnerability to fires, although fires can be attributed both to climate warming and to other human and natural influences. Recent field-based observations revealed that the forests in South Siberia are losing their ability to regenerate after fire and other landscape disturbances under a warming climate ( [[#Brazhnik--2017|Brazhnik et al., 2017]] ). Data support the hypothesis of a climate-driven increase in fire frequency in boreal forests with the possible turning of boreal forests from a carbon sink to a carbon source ( [[#Ponomarev--2016|Ponomarev et al., 2016]] ; [[#Schaphoff--2016|Schaphoff et al., 2016]] ; [[#Brazhnik--2017|Brazhnik et al., 2017]] ; [[#Ponomarev--2018|Ponomarev et al., 2018]] ); however, warming resulting from forest fire is partly offset by cooling in response to increased surface albedo of burned areas in a snow-on period ( [[#Chen--2018|Chen and Loboda, 2018]] ; [[#Chen--2018a|Chen et al., 2018a]] ; [[#Jia--2019|Jia et al., 2019]] ; [[#Lasslop--2019|Lasslop et al., 2019]] ).

<div id="10.4.2.4" class="h3-container"></div>

<span id="adaptation-options-1"></span>
==== 10.4.2.4 Adaptation Options ====

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Modelling of the interactions between climate-induced vegetation shifts, wildfire and human activities can provide keys to how people in Asia may be able to adapt to climate change ( [[#Kicklighter--2014|Kicklighter et al., 2014]] ; [[#Tian--2020|Tian et al., 2020]] ). Conservation and sustainable development would benefit from being tailored and modified considering the changing climatic conditions and shifting biomes, mountain belts and species ranges ( [[#Pörtner--2021|Pörtner et al., 2021]] ). Expanding the nature reserves would help species conservation; to facilitate species movements across climatic gradients, an increase in landscape connectivity can be elaborated by setting up habitat corridors between nature reserves and along elevational and other climatic gradients ( [[#Brito-Morales--2018|Brito-Morales et al., 2018]] ; [[#D’Aloia--2019|D’Aloia et al., 2019]] ; [[#United%20Nations%20Climate%20Change%20Secretariat--2019|United Nations Climate Change Secretariat, 2019]] ). Assisted migration of species should be considered for isolated habitats as mountain summits or where movements are constrained by poor dispersal ability. Introducing seeds of the species to new regions will help to protect them from the extinction risk caused by climate change ( [[#Mazangi--2016|Mazangi et al., 2016]] ). In Asian boreal forests, a strategy and integrated programmes should be developed for adaptation of the forests to global climate change, including sustainable forest management, firefighting infrastructure and forest fuel management, afforestation, as well as institutional, social and other measures in line with Sustainable Development Goal (SDG) 15 ‘Life on Land’ ( [[#Isaev--2013|Isaev and Korovin, 2013]] ; [[#Kattsov--2014|Kattsov and Semenov, 2014]] ; [[#Bae--2020|Bae et al., 2020]] ). Improvements in forest habitat quality can reduce the negative impacts of climate change on biodiversity and ecosystem services ( [[#Choi--2021|Choi et al., 2021]] ). Adaptation options for freshwater ecosystems in Asia include increasing connectivity in river networks, expanding protected areas, restoring hydrological processes of wetlands and rivers, creating shade to lower temperatures for vulnerable species, assisted translocation and migration of species ( [[#Hassan--2020|Hassan et al., 2020]] ; Chapter 2). Reduction of non-climate anthropogenic impacts can enhance the adaptive capacity of ecosystems ( [[#Tchebakova--2016|Tchebakova et al., 2016]] ).

<div id="box-10.3" class="h2-container box-container"></div>

'''Box 10.3 | Case Study on Sand and Dust Storm, Climate Change in West Asia’s Iranian Region'''
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The West Asia region, especially the Tigris–Euphrates alluvial plain, has been recognised as one of the most important dust-source areas in the world ( [[#Cao--2015|Cao et al., 2015]] ). The inhabitants of each of these settlements have experienced a decline in dust storms in recent decades, since the late 1980s at Nouakchott, since 2004 at Zabol and since the late 1970s at Minqin. Iran is mostly arid or semiarid, with deserts making up at least 25 million hectares of the country (NASA, 2018). Iran is experiencing unprecedented climate-related problems such as drying of lakes and rivers, dust storms, record-breaking temperatures, droughts and floods ( [[#Vaghefi--2019|Vaghefi et al., 2019]] ). There are three key factors responsible for the generation of sand and dust storms: strong wind, lack of vegetation and absence of rainfall (EcoMENA, 2020). It seems that all of this is closely related to the heating surface and the occurrence of local dry instabilities ( [[#Ghasem--2012|Ghasem et al., 2012]] ). According to EcoMENA (2020), sand and dust storms cause significant negative impacts on society, the economy and the environment at the local, regional and global levels. The seasonality of the numbers of dusty days (NDD) in Iran shows the highest frequency in summer followed by spring and autumn ( [[#Modarres--2018|Modarres and Sadeghi, 2018]] ). In the past decade, West Asia has witnessed more frequent and intensified dust storms affecting Iran and other Persian Gulf countries ( [[#Nabavi--2016|Nabavi et al., 2016]] ). In terms of long-term frequency of dust events, observational analyses show an overall rising trend of the frequency of Iran’s dust events in recent years ( [[#Alizadeh-Choobari--2016|Alizadeh-Choobari et al., 2016]] ). Results show that there has been a direct relationship between dust event, drought and years of intensive drought ( [[#Dastorani--2019|Dastorani and Jafari, 2019]] ). Compared with the period 1980–2004, in the period 2025–2049, Iran is ''likely'' to experience more extended periods of extreme maximum temperatures in the southern part of the country, more extended periods of dry (for ≥120 d: precipitation &lt;2 mm, T max ≥30°C) as well as wet (for ≤3 d: total precipitation ≥110 mm) conditions and a higher frequency of floods ( [[#Vaghefi--2019|Vaghefi et al., 2019]] ). The slope of precipitation in West Asia shows that during the period 2016–2045 in January, February, July and August, precipitation would increase and decrease in other months of the year ( [[#Ahmadi--2018|Ahmadi et al., 2018]] ). Temperatures in Central Asia have risen significantly within recent decades, whereas mean precipitation remains almost unchanged ( [[#Haag--2019|Haag et al., 2019]] ); however, climatic trends can vary greatly between different sub-regions, across altitudinal levels and within seasons ( [[#Haag--2019|Haag et al., 2019]] ).

<div id="10.4.3" class="h2-container"></div>

<span id="ocean-and-coastal-ecosystems"></span>
=== 10.4.3 Ocean and Coastal Ecosystems ===

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Coastal habitats of Asia are diverse, and the impacts of climate change, including rising temperatures, ocean acidification and SLR, are known to affect the services and livelihoods of the people depending on them. The risk of irreversible loss of many marine and coastal ecosystems increases with global warming, especially at 2°C or more ( ''high confidence'' ) ( [[#IPCC--2018b|IPCC, 2018b]] ). In the South China Sea, coral growth and sea surface temperature (SST) have shown regional long-term trends and inter-decadal variations, while coral growth is predicted to decline by the end of this century ( [[#Yan--2019|Yan et al., 2019]] ). Increasing human impacts have also been found to reduce coral growth ( [[#Yan--2019|Yan et al., 2019]] ). In the South China Sea, nearly 571 coral species have been severely impacted by global climate changes and anthropogenic activities ( [[#Huang--2015a|Huang et al., 2015a]] ).

The 2014–2017 global-scale coral bleaching event (GCBE) resulted in very high coral mortality on many reefs, rapid deterioration of reef structures and far-reaching environmental impacts ( [[#Eakin--2019|Eakin et al., 2019]] ). The thermally tolerant Persian Gulf corals ( [[#Coles--2013|Coles and Riegl, 2013]] ) are facing an increasing frequency of mass bleaching ( [[#Riegl--2018|Riegl et al., 2018]] ) and each event leaves a substantial long-term impact on coral communities (Burt, 2014) with low capacity for recovery indicating a bleak future for Persian Gulf reefs ( [[#Burt--2019|Burt et al., 2019]] ).

One of the probable results of global warming is high SLR. Scientists believe that increasing GHGs (the Earth’s temperature controllers) is the cause of this global warming, and by using satellite measurements, these scientists have forecasted on average 1–2 mm for SLR ( [[#Jafari--2016|Jafari et al., 2016]] ). The level of thermal stress (based on a degree heating month index, DHMI) at these locations during the 2015–2016 El Niño was unprecedented and stronger than previous ones ( [[#Lough--2018|Lough et al., 2018]] ). In the Persian Gulf, the reef-bottom temperatures in 2017 were among the hottest on record, with mean daily maxima averaging 35.9 ± 0.10°C across sites, with hourly temperatures reaching as high as 37.7°C ( [[#Riegl--2018|Riegl et al., 2018]] ). About 94.3% of corals were bleached, and about 66% perished, in 2017 ( [[#Burt--2019|Burt et al., 2019]] ). In 2018 coral cover averaged just 7.5% across the southern basin of the Persian Gulf. This mass mortality did not cause dramatic shifts in community composition as earlier bleaching events had removed the most sensitive taxa. An exception was the already rare ''Acropora'' spp. which were locally extirpated in summer 2017 ( [[#Burt--2019|Burt et al., 2019]] ). During 2008–2011 also the coral communities of Musandam and Oman showed changes depending on the stress-tolerance levels of the species and the local environmental disturbance level ( [[#Bento--2016|Bento et al., 2016]] ).

The health and resilience of corals have been found to be associated with beneficial microorganisms of coral (BMC) which alter during environmental stress. Increasing seawater temperatures have been found to affect the functioning of the symbiotic algae of corals ( [[#Lough--2018|Lough et al., 2018]] ; [[#Gong--2019|Gong et al., 2019]] ) and its bacterial consortia leading to coral bleaching and mortality ( [[#Bourne--2016|Bourne et al., 2016]] ; [[#Peixoto--2017|Peixoto et al., 2017]] ; [[#Bernasconi--2019|Bernasconi et al., 2019]] ; ( [[#Motone--2020|Motone et al., 2020]] ).

Coral reefs were found to be affected differentially during bleaching episodes, and those species which survived had more stress-tolerant symbionts and higher tolerance to thermal changes ( [[#Majumdar--2018|Majumdar et al., 2018]] ; [[#Thinesh--2019|Thinesh et al., 2019]] ; [[#van%20der%20Zande--2020|van der Zande et al., 2020]] ). Rare thermally tolerant algae and host species-specific algae may play important roles in coral bleaching ( [[#van%20der%20Zande--2020|van der Zande et al., 2020]] ). Along the Indian coast, in the coral reefs of Palk Bay (Bay of Bengal), varied bleaching and recovery patterns among coral genera was observed during the 2016 bleaching episode ( [[#Thinesh--2019|Thinesh et al., 2019]] ). Bleaching was high in ''Acropora'' spp. (86.36%), followed by ''Porites'' (65.45%), while moderate to no bleaching was observed in ''Favites Symphyllia, Favia, Platygyra'' and ''Goniastrea'' .

The presence of stress-tolerant symbiont Durusdinium (Clade D) during the post-bleach period indicated the high adaptive capacity of ''Acropora'' spp. in tropical waters ( [[#Thinesh--2019|Thinesh et al., 2019]] ). Also ''Porites'' spp. were found to have higher thermal thresholds and showed better resilience to bleaching than species like ''Fungiid'' spp. ( [[#Majumdar--2018|Majumdar et al., 2018]] ). In the Philippines, during the 2010 bleaching event, the size structure of the mushroom coral was found to be affected ( [[#Feliciano--2018|Feliciano et al., 2018]] ). In Indonesia, it was found that branching coral diversity may decrease relative to massive, more resilient corals ( [[#Hennige--2010|Hennige et al., 2010]] ). This would have large-scale impacts upon reef biodiversity and ecosystem services, and reef metabolism and net reef accretion rates, since massive species are typically slow growers ( [[#Hennige--2010|Hennige et al., 2010]] ).

Macro-tidal coral reefs are particularly sensitive to medium- to long-term changes in sea-level Andaman trenches ( [[#Simons--2019|Simons et al., 2019]] ). Data compiled from 11 cities throughout East and Southeast Asia, with particular focus on Singapore, Jakarta, Hong Kong and Naha (Okinawa), highlights several key characteristics of urban coral reefs, including ‘reef compression’ (a decline in bathymetric range with increasing turbidity and decreasing water clarity over time and relative to shore), dominance by domed coral growth forms and low reef complexity, variable city-specific inshore–offshore gradients, early declines in coral cover with recent fluctuating periods of acute impacts and rapid recovery, and colonisation of urban infrastructure by hard corals ( [[#Heery--2018|Heery et al., 2018]] ).

In Taiwan, Province of China, the calcification rate of the model reef coral ''Pocillopora damicornis'' was higher in coral reef mesocosms featuring seagrasses under ocean acidification conditions at 25°C and 28°C. The presence of seagrass in the mesocosms helped to stabilise the metabolism of the system in response to simulated climate change ( [[#Liu--2020a|Liu et al., 2020a]] ).

An increase in host susceptibility, pathogen abundance or virulence has led to higher prevalence and severity of coral diseases and to decline and changes in coral reef community composition ( [[#Maynard--2015|Maynard et al., 2015]] ). Relative risk has been found to be high in the province of Papua in Indonesia, the Philippines, Japan, India, northern Maldives, the Persian Gulf and the Red Sea. For the combined disease-risk metric, relative risk was considered lower for locations where anthropogenic stress was low or medium, a condition found for some locations in Thailand ( [[#Maynard--2015|Maynard et al., 2015]] ).

Degradation and loss of coral reefs can affect about 4.5 million people in Southeast Asia and the Indian Ocean ( [[#Lam--2019|Lam et al., 2019]] ). In the coral reef fisheries sector, there are about 3.35 million fishers in Southeast Asia and 1.5 million fishers in the Indian Ocean ( [[#Teh--2013|Teh et al., 2013]] ). The economic loss under different climate-change scenarios and fishing efforts were estimated to range from 27.78 to 31.72 million USD annually in Nha rang Bay, Vietnam. A survey conducted in Taiwan, Province of China, showed that the average annual amount that people were personally willing to pay was 35.75 USD and the total amount was 0.43 billion USD. These high values indicate the need to preserve these coral reef ecosystems ( [[#Tseng--2015|Tseng et al., 2015]] ). In Bangladesh, the coral reef of St. Martin’s Island contributes 33.6 million USD yr –1 to the local economy, but climate change, along with other anthropogenic activities, has been identified as a threat these habitats ( [[#Rani--2020a|]] [[#Rani--2020|Rani et al., 2020]] a ).

Mitigation of global warming has been identified to be essential to maintain healthy coral reef ecosystems in Asia ( [[#Comte--2018|Comte and Pendleton, 2018]] ; [[#Heery--2018|Heery et al., 2018]] ; [[#Lam--2019|Lam et al., 2019]] ; [[#Yan--2019|Yan et al., 2019]] ). Restoration of reefs ( [[#Nanajkar--2019|Nanajkar et al., 2019]] ) and building resilience through multiple mechanisms, such as innovative policy combinations, complemented by environmental technology innovations and sustained investment ( [[#Hilmi--2019|Hilmi et al., 2019]] ; [[#McLeod--2019|McLeod et al., 2019]] ), are suggested.

An ecosystem-based approach to managing coral reefs in the Gulf of Thailand is needed to identify appropriate marine protected area (MPA) networks and to strengthen marine and coastal resource policies in order to build coral reef resilience ( [[#Sutthacheep--2013|Sutthacheep et al., 2013]] ). Broadening the scope to develop novel mitigation approaches towards coral protection through the use of symbiotic bacteria and their metabolites ( [[#Motone--2018|Motone et al., 2018]] ; [[#Motone--2020|Motone et al., 2020]] ) has been suggested. Coral culture and transplantation within the Gulf are feasible for helping maintain coral species populations and preserving genomes and adaptive capacities of Gulf corals that are endangered by future thermal-stress events ( [[#Coles--2013|Coles and Riegl, 2013]] ). Greater focus on understanding the flexibility and adaptability of people associated with coral reefs, especially in a time of rapid global change ( [[#Hoegh-Guldberg--2019|Hoegh-Guldberg et al., 2019]] ), and a well-designed research programme for developing a more targeted policy agenda ( [[#Lam--2019|Lam et al., 2019]] ), is also recommended. Cutting carbon emissions ( [[#Bruno--2016|Bruno and Valdivia, 2016]] ) and limiting warming to below 1.5°C is essential to preserving coral reefs worldwide and protecting millions of people ( [[#Frieler--2013|Frieler et al., 2013]] ; [[#Hoegh-Guldberg--2017|Hoegh-Guldberg et al., 2017]] ). Many visitors to coral reefs have high environmental awareness, and reef visitation can both help to fund and encourage coral reef conservation ( [[#Spalding--2017|Spalding et al., 2017]] ).

The largest mangrove forests are in Asia contributing to about 42% of the world’s mangroves. This includes Sundarbans, the world’s largest remaining contiguous mangrove forest ( [[#Dasgupta--2020|Dasgupta et al., 2020]] ). Mangrove ecosystems are rich in biodiversity. The ecosystems are supported and maintained by both flora and a large array of living things, which include mammals, birds, fish, crustaceans, shrimps, insects and microbes [[#footnote-010|3]] . Contemporary rates of mangrove deforestation are lower than in the late 20th century ( [[#Gandhi--2019|Gandhi and Jones, 2019]] ; [[#Friess--2019|Friess et al., 2019]] ); however, some areas in Asia continue the trend. Myanmar is the primary mangrove-loss hotspot in Asia, exhibiting 35% loss from 1975 to 2005 and 28% from 2000 to 2014. Rates of loss in Myanmar were four times the global average from 2000 to 2012. The Philippines is additionally identified as a loss hotspot, with secondary hotspots including Malaysia, Cambodia and Indonesia ( [[#Gandhi--2019|Gandhi and Jones, 2019]] ).

Mangrove deforestation is expected to increase as many tropical nations utilise mangrove areas for economic security. Increased river damming would reduce fluvial sediment sources to the coast making mangroves more vulnerable to SLR, and uncertain climate with extreme oscillations can create unstable conditions for survival and propagation of mangrove ( [[#Friess--2019|Friess et al., 2019]] ).

Valuation of ecosystem services of mangroves have indicated that they prevent more than 1.7 billion USD in damages for extreme events (i.e., one event in 50 years) in the Philippines ( [[#Menéndez--2018|Menéndez et al., 2018]] ). They reduce flooding to 613,500 people yr –1 , 23% of whom live below the poverty line and avert damages up to 1 billion USD yr –1 in residential and industrial property. Mangroves have also become a very popular source of livelihood in Asia through tourism ( [[#Dehghani--2010|Dehghani et al., 2010]] ; [[#Kuenzer--2013|Kuenzer and Tuan, 2013]] ; [[#Spalding--2019|Spalding and Parret, 2019]] ; [[#Dasgupta--2020|Dasgupta et al., 2020]] ) and they also support fisheries ( [[#Hutchison--2014|Hutchison et al., 2014]] ).

Mangroves, tidal marshes and seagrass meadows (collectively called coastal blue carbon ecosystems) have sequestered carbon dioxide from the atmosphere continuously over thousands of years, building stocks of carbon in biomass and organic rich soils. Carbon dynamics in mangrove-converted aquaculture in Indonesia indicate that the mean ecosystem carbon stocks in shrimp ponds are less than half of the relatively intact mangroves ( [[#Arifanti--2019|Arifanti et al., 2019]] ). Conversion of mangroves into shrimp ponds in the Mahakam Delta have resulted in a carbon loss equivalent to 226 years of soil carbon accumulation in natural mangroves. In the Philippines, abandoned fishpond reversion to former mangrove has been found to be favourable for enhancing climate change mitigation and adaptation ( [[#Duncan--2016|Duncan et al., 2016]] ). Integrated mangrove-shrimp farming, with deforested areas not exceeding 50% of the total farm area, has been suggested to support both carbon sequestration as well as livelihood ( [[#Ahmed--2018|Ahmed et al., 2018]] ).

Globally, the extent of the blue carbon ecosystem has been estimated at 120,380 km 2 , with the highest spread by mangroves at 114,669 km 2 (95.3%), followed by seagrass meadows at 2,201 km 2 (1.8%) and salt marshes at 3510 km 2 (2.9%) ( [[#Himes-Cornell--2018|Himes-Cornell et al., 2018]] ). In Asia, the total extent of these three ecosystems is 33,224 km 2 , forming 27.6% of the global total with the highest spread of mangrove at 32,767 km 2 , which forms 28.6% of the global mangrove coverage. The area of seagrass meadows spread in Asia has been estimated as 236 km 2 and salt marsh 220 km 2 ,which forms 10.8 and 6.03% of the respective ecosystems globally ( [[#Himes-Cornell--2018|Himes-Cornell et al., 2018]] ). Found at the land–sea interface, seagrasses provide varied services apart from acting as ecosystem engineers providing shelter and habitat for several marine fauna which are fished in several Asian countries ( [[#Jeyabaskaran--2018|Jeyabaskaran et al., 2018]] ; [[#Nordlund--2018|Nordlund et al., 2018]] ; [[#Unsworth--2019b|Unsworth et al., 2019b]] ) thereby providing livelihood to millions across the continent ( [[#UNEP--2020|UNEP, 2020]] ).

The seagrass meadows are also good sinks of carbon ( [[#Fourqurean--2012|Fourqurean et al., 2012]] ) capable of storing 19.9 petagrams (pg) of organic carbon, but with very high regional and site- and species variability ( [[#Ganguly--2017|Ganguly et al., 2017]] ; [[#Stankovic--2018|Stankovic et al., 2018]] ; [[#Gallagher--2019|Gallagher et al., 2019]] ; [[#Ricart--2020|Ricart et al., 2020]] ). As highly efficient carbon sinks, they store up to 18% of the world’s oceanic carbon, and they also reduce the impacts of ocean acidification ( [[#UNEP--2020|UNEP, 2020]] ).

The deterioration of this ecosystem is fast, 7% yr –1 since 1990 ( [[#Waycott--2009|Waycott et al., 2009]] ), which has led to development of restoration protocols across Asia ( [[#Paling--2009|Paling, 2009]] ; [[#van%20Katwijk--2016|van Katwijk et al., 2016]] ). In Vietnam, the loss of seagrass has been estimated as above 50% and in some regions complete loss has been observed ( [[#Van%20Luong--2012|Van Luong et al., 2012]] ). The seagrass meadows of Indonesia are fast deteriorating, and the need for increased local autonomy for the management of marine resources and restoration has been highlighted ( [[#Unsworth--2018|Unsworth et al., 2018]] ). Development of science-based policies for conservation, including participatory methods ( [[#Fortes--2018|Fortes, 2018]] ; [[#Ramesh--2019|Ramesh et al., 2019]] ; [[#Unsworth--2019a|Unsworth et al., 2019a]] ) and large-scale planting ( [[#van%20Katwijk--2016|van Katwijk et al., 2016]] ), has been recommended to preserve the ecosystem services of these habitats.

Globally, the diversity of the plankton community has been predicted to be affected by warming and related changes ( [[#Ibarbalz--2019|Ibarbalz et al., 2019]] ), and these changes are expected in Asia also. Combined effects of high temperature, ocean acidification and high light exposure would affect important phytoplankton species in the SCS, ''Thalassiosira pseudonana'' ( [[#Yuan--2018|Yuan et al., 2018]] ) and ''Thalassiosira weissflogii'' ( [[#Gao--2018b|Gao et al., 2018b]] ). Also in the SCS the phytoplankton-assemblage responses to rising temperatures and CO 2 levels were found to differ between coastal and offshore waters and the predicted increases in temperature and ''p'' CO 2 may not boost surface-phytoplankton primary productivity ( [[#Zhang--2018|Zhang et al., 2018]] ).

Ocean warming and acidification can affect the functioning and ecological services of sedentary molluscs like the bivalves ( [[#Guo--2016|Guo et al., 2016]] ; [[#Zhao--2017b|Zhao et al., 2017b]] ; [[#Cao--2018|Cao et al., 2018]] ; [[#Zhang--2019c|Zhang et al., 2019c]] ; [[#Liu--2020b|Liu et al., 2020b]] ) and gastropods ( [[#Leung--2020|Leung et al., 2020]] ), and also sea urchins ( [[#Zhan--2020|Zhan et al., 2020]] ). The oyster ''Crassostrea gigas'' becomes more vulnerable to disease when exposed to acidification conditions and pathogen challenge indicating incapability for supporting long-term viability of the population ( [[#Cao--2018|Cao et al., 2018]] ). More tolerance and benefits to rising ''p'' CO 2 was observed in clam species like ''Paphia undulate'' which has been attributed to adaptation to its acidified sediment habitat ( [[#Guo--2016|Guo et al., 2016]] ). Warming boosted the energy budget of marine calcifiers like the gastropod ''Austrocochlea concamerata'' , by faster shell growth and greater shell strength, making them more mechanically resilient while acidification negatively affected shell building thereby impacting the physiological adaptability ( [[#Leung--2020|Leung et al., 2020]] ). It is expected that there will be transgenerational acclimation to changes in ocean acidification in marine invertebrates ( [[#Lee--2020b|Lee et al., 2020b]] ).

Assessment of the potential impacts and the vulnerability in marine biodiversity in the Persian Gulf under climate change has suggested a reduction of up to 35% of initial species richness and habitat loss for hawksbill turtles in the southern and southwest parts of the Persian Gulf ( [[#Wabnitz--2018|Wabnitz et al., 2018]] ).

Seaweeds are an important biotic resource capable of capturing carbon and used widely as food, medicine and as raw material for industrial purposes. Warming and altered pH can affect seaweeds in different ways ( [[#Gao--2016|Gao et al., 2016]] ; [[#Gao--2017|Gao et al., 2017]] ; [[#Gao--2018a|Gao et al., 2018a]] ; [[#Wu--2019b|Wu et al., 2019b]] ). Outbreak of intense blooms of species like ''Ulva rigida'' ( [[#Gao--2017|Gao et al., 2017]] ) and ''Ulva prolifera'' (Zhang et al., 2019 f) have increased due to varied factors including climate change. These blooms have created huge economic losses in the Yellow Sea affecting local mariculture, tourism and the functioning of the coastal and marine ecosystems (Zhang et al., 2019 f). Increased temperature was found to enhance the dark respiration and light compensation point of ''Ulva conglobate'' , which thrives in the mid-intertidal to upper subtidal zones, while the altered pH showed a limited effect ( [[#Li--2020|Li et al., 2020]] ). Elevated temperature significantly enhanced growth, photosynthetic performances and carbon-use efficiency of ''Sargassum horneri'' in both elevated and ambient CO 2 levels suggesting that the present greenhouse effect would benefit the golden tide blooming macroalgae ''Sargassum horneri'' , which might enhance both the frequency and scale of golden tide ( [[#Wu--2019b|Wu et al., 2019b]] ).

<div id="10.4.3.1" class="h3-container"></div>

<span id="key-drivers-to-vulnerability"></span>
==== 10.4.3.1 Key Drivers to Vulnerability ====

<div id="h3-10-siblings" class="h3-siblings"></div>

The vulnerabilities to disaster in coastal regions with high population densities are reported in several studies ( [[#Sajjad--2018|Sajjad et al., 2018]] ) that have assessed the vulnerabilities of coastal communities along the Chinese coast and shown that roughly 25% of the coastline, and more than 5 million residents, are in highly vulnerable coastal areas of mainland China, and these numbers are expected to double by 2100. [[#Husnayaen--2018|Husnayaen et al. (2018)]] assessed the Semarang coast in Indonesia and showed that 20% of the total coastline (48.7 km) is very highly vulnerable. Mangroves continue to face threats due to pollution, conversion for aquaculture, agriculture, apart from climate-based threats like SLR and sea erosion ( [[#Richards--2016|Richards and Friess, 2016]] ; [[#Romañach--2018|Romañach et al., 2018]] ; [[#Wang--2018b|Wang et al., 2018b]] ; [[#Friess--2019|Friess et al., 2019]] ). Hypersalinity, storm effects on sediment deposition, fishery development and land erosion are responsible for most of the Sunderban mangrove degradations leading to loss of livelihood (Uddin, 2014; Paul, 2017). In the Sunderbans of Asia, climate change is expected to increase river salinisation, which in turn could significantly negatively impact the valued timber species, ''Heritiera fomes'' ( [[#Dasgupta--2017b|Dasgupta et al., 2017b]] ). Augmented potential for honey production is also predicted, which could increase the conflict between humans and wildlife ( [[#Dasgupta--2017b|Dasgupta et al., 2017b]] ).

Destruction by natural hazards was found to remove the above-ground C pool, but the sediment C pool was found to be maintained ( [[#Chen--2018b|Chen et al., 2018b]] ). In the Andaman and Nicobar Islands, the 2004 Indian Ocean tsunami severely impacted mangrove habitats at the Nicobar Islands ( [[#Nehru--2018|Nehru and Balasubramanian, 2018]] ), although new inter-tidal habitats suitable for mangrove colonisation did develop. Mangrove species with a wide distribution and larger propagules showed high colonisation potential in the new habitats compared with other species ( [[#Nehru--2018|Nehru and Balasubramanian, 2018]] ). Mangrove sites in Asia are predominantly minerogenic, so continued sediment supply is essential for the long-term resilience of Asia’s mangroves to SLR ( [[#Lovelock--2015|Lovelock et al., 2015]] ; [[#Balke--2016|Balke and Friess, 2016]] ; [[#Ward--2016a|Ward et al., 2016a]] ; [[#Ward--2016b|Ward et al., 2016b]] ).

<div id="10.4.3.2" class="h3-container"></div>

<span id="observed-impacts-1"></span>
==== 10.4.3.2 Observed Impacts ====

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Primary production in the western Indian Ocean showed a reduction by 20% during the past six decades, attributed to rapid warming and ocean stratification which restricted nutrient mixing ( [[#Roxy--2016|Roxy et al., 2016]] ). Variation in secondary-production zooplankton densities and biomass in the East Asian Marginal Seas affected the recruitment of fishes due to mismatch in spawning period and larval-feed availability during the last three climate regime shifts (CRS) in the mid-1970s, late 1980s and late 1990s, which were characterised by the North Pacific index and the Pacific Decadal Oscillation index (Kun [[#Jung--2017|Jung et al., 2017]] ). In the western North Pacific, climate change has affected recruitment and the population dynamics of pelagic fishes, such as sardine and anchovy ( [[#Nakayama--2018|Nakayama et al., 2018]] ), and also shifts in the spawning ground and extension of the spawning period of the chub mackerel ''Scomber japonicas'' ( [[#Kanamori--2019|Kanamori et al., 2019]] ).

Varied responses to CRS in the China seas have been observed for small pelagic fishes ( [[#Ma--2019|Ma et al., 2019]] ) and cephalopods ( [[#Ichii--2017|Ichii et al., 2017]] ). The winter and summer SSTs have shown evidence of decadal variability with abrupt changes from cold to warm in substantial association with climate indices to which coastal cephalopods in the China seas respond differentially, with some benefiting from warmer environments while others respond negatively ( [[#Pang--2018|Pang et al., 2018]] ). In the western and eastern North Pacific marine ecosystem, it is indicated that groundfish may suffer more than pelagic fish ( [[#Yati--2020|Yati et al., 2020]] ). Habitat Suitability index models using SST, chlorophyll- ''a'' , sea surface height anomaly (SSHA) and sea surface salinity (SSS), as well as fishing effort, strongly indicate that Neon flying squid is affected by interannual environmental variations and undertakes short-term migrations to suitable habitat, affecting the fisheries ( [[#Yu--2015|Yu et al., 2015]] ). The 2015–2016 El Niño was found to impact coral reefs of shallower regions (depth of 5–15 m) in South Andaman, India, more than those beyond 20 m ( [[#Majumdar--2018|Majumdar et al., 2018]] ). On the southeast coast of India, with bleaching largely mediated by the SST anomaly and during the recovery period, macroalgae outgrowth has been observed (2.75%) indicating impacts on the benthic community ( [[#Ranith--2019|Ranith and Kripa, 2019]] ). In the South China Sea, the increase in SST was found to be higher than predicted in recent decades, while the pH decreased at a rate of 0.012–0.014 yr –1 , more than the predicted level, due to high microbial respiratory processes releasing CO 2 ( [[#Yuan--2019|Yuan et al., 2019]] ). Simulation experiments have shown differential adaptation capacity of common species (Zheng, 2019; [[#Yuan--2019|Yuan et al., 2019]] ).

The UN’s (2019) report on climate action and support trends highlights that the impacts of climate change on coastal ecosystems are mainly increased risks due to flooding, inundation due to extreme events, coastal erosions, ecosystem processes and, in the case of fisheries, variations in population or stock structures due to ocean circulation pattern, habitat loss degradation and ocean acidification. Analysis of data on the occurrence of varied natural hazards from 1900 to 2019 has shown that tropical cyclones, riverine floods and droughts have increased significantly, and the impacts of these events on coastal communities are also severe and destructive. The UN’s average score for SDG Goal 14 (Life Under Water) for Asia was estimated as 46 among the scores of 40 nations, and the Ocean Health Biodiversity index was comparatively high (average 87.9); however, the indices show that more region-specific action plans are required to achieve the UN 2030 goal for Life Under Water.

Apart from the human impacts, the ecology and resource abundance of coastal waters have been found to be impacted by extreme events. During tropical cyclones ecological variations, like lowering of SST, an increase in chlorophyll- ''a'' and a decrease in oxygen ( [[#Chacko--2019|Chacko, 2019]] ; [[#Girishkumar--2019|Girishkumar et al., 2019]] ) have been observed. Global analyses of such events have indicated that they may have an impact on the fishery directly by creating unfavourable ecological conditions and destruction of critical habitats indirectly by affecting the eggs and larvae as well as subsequent fishery recruitment ( [[#McKinnon--2003|McKinnon et al., 2003]] ; [[#Bailey--2016|Bailey and Secor, 2016]] ). In the South China Sea in July 2000, during a 3-day cyclone period, an estimated thirtyfold increase in surface chlorophyll- ''a'' concentration was observed ( [[#Lin--2003|Lin et al., 2003]] ). The estimated carbon fixation resulting from this event alone is 0.8 Mt, or 2–4% of the SCS’s annual new production ( [[#Lin--2003|Lin et al., 2003]] ). Since an average of 14 cyclones pass over this region annually, the contribution of cyclones to the annual new production has been estimated to be as high as 20–30% ( [[#Lin--2003|Lin et al., 2003]] ).

<div id="10.4.3.3" class="h3-container"></div>

<span id="projected-impacts-1"></span>
==== 10.4.3.3 Projected Impacts ====

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Water pollution and climate stressors have been considered major challenges to ecosystem sustainability, and now it has been shown that the combined effect these two stressors would be more damaging ( [[#Buchanan--2019|Buchanan et al., 2019]] ). For seagrass beds the pollution stress was found to increase by 2.6% (from 39.7 to 42.3%) when climate factors were added. Assuming the pollution levels remain at the 2014 levels, different scenarios including RCP2.6 and RCP8.5 were worked out for the Bohai Sea, and the results indicated amplification of the impacts on the ecosystem. Pollutants like petroleum hydrocarbons, dissolved inorganic nitrogen and soluble reactive phosphorus were the major pollution stressors ( [[#Lu--2018|Lu et al., 2018]] ). In the future, policies that focus strictly on pollution control should be changed and take into account the interactive effects of climate change for better forecast and management of potential ecological risks ( [[#Lu--2018|Lu et al., 2018]] ).

Projected changes in catch potential (in percent) by 2050 and 2100 relative to 2000 under RCP2.6 and RCP8.5, based on outputs from the dynamic bioclimate envelop model and the dynamic size-based food-web models, indicate that the marine and coastal resources of most Asian countries will be impacted with varying intensity ( [[#FAO--2018b|FAO, 2018b]] ).

Better management of resources through projections of resource distribution, abundance and catch is required; however, lack of data (e.g., oceanographic surveys) and scientific knowledge is a constraint to this aim ( [[#Maung%20Saw%20Htoo--2017|Maung Saw Htoo et al., 2017]] ). Effective forecasts of areas of resource abundance based on habitat preference have to be worked out for Asian regions.

Modelling and assessment of the vulnerability and habitat suitability of the Persian Gulf for 55 species to climate change indicated that there is a high rate of risk of local extinction in the southwest part of the Persian Gulf, off the coast of Saudi Arabia, Qatar and the United Arab Emirates (UAE). Likelihood of reduced catch was observed, and Bahrain and Iran were found to be more vulnerable to climate change ( [[#Wabnitz--2018|Wabnitz et al., 2018]] ). Projected changes in fish catches can impact the supply of fish available for local consumption (i.e., food security) and exports (i.e., income generation) ( [[#Wabnitz--2018|Wabnitz et al., 2018]] ). As per ( [[#UNESCAP--2018a|UNESCAP, 2018a]] ), over 40% of coral reefs and 60% of coastal mangroves in the Asia-Pacific region have already been lost, and approximately 80% of the region’s coral reefs are currently at risk.

Regionally, the escalation in thermal stress estimated for the different global warming scenarios is greatest for Southeast Asia and least for the Pacific Ocean ( [[#Lough--2018|Lough et al., 2018]] ). For the 100 reef locations examined here and given current rates of warming, the 1.5°C global warming target represents twice the thermal stress they experienced in 2016 ( [[#Lough--2018|Lough et al., 2018]] ).

In the Southeast Asia region threats from both warming and acidification has indicated that by 2030, 99% of reefs will be affected, and by 2050, 95% are expected to be in the highest levels of the ‘threatened’ category ( [[#Burke--2011|Burke et al., 2011]] ), similar to global corals ( [[#Frieler--2013|Frieler et al., 2013]] ; [[#Bruno--2016|Bruno and Valdivia, 2016]] ). Modelling results indicate that even under RCP scenarios, the functional traits of coral reefs can be affected ( [[#van%20der%20Zande--2020|van der Zande et al., 2020]] ) and coral communities will mainly consist of small numbers of temperature-tolerant and fast-growing species ( [[#Kubicek--2019|Kubicek et al., 2019]] ). Increases in temperature (+3°C) and ''p'' CO2 (+400 matm) projected for this century can reduce the sperm availability for fertilisation, which along with adult population decline either due to climate change or anthropogenic impacts ( [[#Hughes--2017|Hughes et al., 2017]] ) can affect coral reproductive success thereby reducing the recovery of populations and their adaptation potential ( [[#Albright--2013|Albright and Mason, 2013]] ; [[#Hughes--2018|Hughes et al., 2018]] ; [[#Jamodiong--2018|Jamodiong et al., 2018]] ). In the southern Persian Gulf, increased disturbance frequency and severity has caused progressive reduction in coral size, cover and population fecundity ( [[#Riegl--2018|Riegl et al., 2018]] ), and this can lead to functional extinction. Connectivity required to avoid extinctions has increased exponentially with disturbance frequency and correlation of disturbances across the metapopulation. In the Philippines experiments have also proved that scleractinian corals, such as ''A. tenuis, A. millepora'' and ''F. colemani'' , which spawn their gametes directly into the water column, may experience limitations from sperm dilution and delays in initial sperm–egg encounters that can impact successful fertilisation ( [[#de%20la%20Cruz--2020|de la Cruz and Harrison, 2020]] ).

Apart from these threats, natural hazards have also been found to affect coral reefs of Asia. The extensive and diverse coral reefs of Muscat, Oman, in the northeast Arabian Peninsula were found to have long-term effects from Cyclone Gonu, which struck the Oman coast in June 2007, more than coastal development ( [[#Coles--2015|Coles et al., 2015]] ).

Sandy beaches are subject to highly dynamic hydrological and geomorphological processes, giving them more natural adaptive capacity to climate hazards ( [[#Bindoff--2019|Bindoff et al., 2019]] ). Progress is being made towards models that can reliably project beach erosion under future scenarios despite the presence of multiple confounding drivers in the coastal zone (Chapter 3). Assuming minimal human intervention and projected impacts of SLR by 2100 under RCP8.5-like scenarios, 57–72% of Thai beaches (Ritphring, 2018), at least 50% loss of area on around a third of Japanese beaches (Mori, 2018) will disappear.

Marine heatwaves (MHWs) in Asia have been making changes to the structure and functioning of coastal and marine ecosystems ( [[#Kim--2017|Kim and Han, 2017]] ; [[#Oliver--2017|Oliver et al., 2017]] ; [[#Frölicher--2018|Frölicher and Laufkötter, 2018]] ; [[#Oliver--2019|Oliver et al., 2019]] ; [[#Smale--2019|Smale et al., 2019]] ), affecting resources like copepods ( [[#Doan--2019|Doan et al., 2019]] ) and coral reefs ( [[#Zhang--2017c|Zhang et al., 2017c]] ). Coral reefs of the southeast Indian Ocean have been affected by MHWs ( [[#Zhang--2017c|Zhang et al., 2017c]] ).

Simulation of RCP scenarios have shown that continued warming can drive a poleward shift in distribution of the seaweed ''Ecklonia cava'' of Japan, and under the lowest-emissions scenario (RCP2.6) most populations may not be impacted, but under the highest-emissions scenario (RCP8.5) the existing habitat may become unsuitable and it can also increase predation by herbivorous fishes ( [[#Takao--2015|Takao et al., 2015]] ).

<div id="10.4.3.4" class="h3-container"></div>

<span id="adaptation-options-2"></span>
==== 10.4.3.4 Adaptation Options ====

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The UN (2019) has identified establishment of protected areas, restoring ecosystems like mangroves and coral reefs, integrating coastal-zone management practices, sand banks and structural technologies, and implementing local monitoring networks for increasing adaptive capacity and protecting the biodiversity of the coastal ecosystem. In Asia, management of marine sites by earmarking protected areas (SDG 14) has been found to be low with only 27% of areas being protected. In India, detailed CCA guidelines for coastal protection and management has been prepared considering various environmental and social aspects ( [[#Black--2017|Black et al., 2017]] ). The Ocean Health index for clean waters was also low (54.6), and the threat to the ecosystem due to the combined effects of pollution and climate change was high. Table 10.2 shows the ocean and MPAs.

'''Table 10.2 |''' Status of ocean health and mean of marine protected areas (MPA) a

{| class="wikitable"
|-
!
! Ocean Health index: Clean waters (0–100)

! Fish stocks overexploited or collapsed (%)

! Ocean Health index: Fisheries (0–100)

! Fish caught by trawling (%)

! Ocean Health index: Biodiversity (0–100)

! Mean MPA (%)

|-
| Eastern Asia

| 54.0

| 29.1

| 49.5

| 39.8

| 89.6

| 32.5

|-
| Southeast Asia

| 54.1

| 28.5

| 54.9

| 34.7

| 84.6

| 25.0

|-
| Western Asia

| 54.3

| 28.3

| 46.2

| 20.4

| 89.4

| 18.3

|-
| Southern Asia

| 50.3

| 17.4

| 51.0

| 15.1

| 88.3

| 41.2

|-
| Northern Asia

| 91.6

| 55.4

| 57.6

| 60.0

| 93.4

| 30.0

|-
| Asia (whole)

| 54.6

| 26.9

| 50.3

| 27.3

| 87.9

| 27.0

|}

(a) Data are from [[#Sachs--2018|Sachs et al. (2018)]] .

Conservation and restoration of mangroves were found to be effective tools for enhancing ecosystem carbon storage and an important part of Reducing Emissions from Deforestation and forest Degradation plus (REDD+) schemes and climate-change mitigation ( [[#Ahmed--2016|Ahmed and Glaser, 2016]] ). In East Asia, restoration success has been attributed to choosing the right geomorphological locations ( [[#Van%20Cuong--2015|Van Cuong et al., 2015]] ; [[#Balke--2016|Balke and Friess, 2016]] ) and co-management models ( [[#Johnson--2016|Johnson and Iizuka, 2016]] ; [[#Veettil--2019|Veettil et al., 2019]] ).

In South Asia, restoration programmes have been largely successful ( [[#Jayanthi--2018|Jayanthi et al., 2018]] ) but in some regions partly a failure due to inappropriate site selection, poor post-planting care and other issues ( [[#Kodikara--2017|Kodikara et al., 2017]] ). Using remote sensing it has been observed that there are high recovery rates of mangroves in a relatively short period (1.5 years) after a powerful typhoon, indicating that natural recovery and regeneration would be a more economically and ecologically viable strategy. Better mangrove management through mapping is suggested ( [[#Castillo--2018|Castillo et al., 2018]] ; [[#Gandhi--2019|Gandhi and Jones, 2019]] ). Statistical tools developed for modelling biomass and timber volume ( [[#Phan--2019|Phan et al., 2019]] ), and allometric models to estimate above-ground biomass and carbon stocks ( [[#Vinh--2019|Vinh et al., 2019]] ), will be useful in estimating stocks in mangroves. Future mangrove loss may be offset by increasing national and international conservation initiatives that incorporate mangroves, such as the SDGs, Blue Carbon, and Payments for Ecosystem Services ( [[#Friess--2019|Friess et al., 2019]] ). Since seagrass meadows and marine macroalgae are important habitats capable of combating impacts of climate change, the need for a global networking system with participation of stakeholders has been suggested ( [[#Duffy--2019|Duffy et al., 2019]] ).

<div id="10.4.4" class="h2-container"></div>

<span id="freshwater-resources"></span>
=== 10.4.4 Freshwater Resources ===

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In Asia, freshwater resources, an important component of ecosystem services, are widely used for agriculture, domestic, irrigation, navigation, energy and industry. Freshwater availability is changing at the global scale because of unsustainable use of surface water and groundwater, pollution and other environmental changes. These changes in space and time, directly or indirectly, affect water-use sectors and services ( [[#Wheater--2015|Wheater and Gober, 2015]] ; [[#Rodell--2018|Rodell et al., 2018]] ). About 82% of the global population served by freshwater provisions from upstream areas are exposed to high threat ( [[#Green--2015|Green et al., 2015]] ). Given that some of the fastest-growing economies in the world are in Asia, and the geographies of development are highly uneven, both CIDs and non-climate drivers, such as socioeconomic changes, have contributed to water stress conditions in both water supply and demand in diverse sub-regions of Asia. In the case of Asia, therefore, the entanglement between the non-climate drivers and CIDs makes it difficult to attribute environmental changes—both present and projected—neatly and exclusively to CIDs.

[[#Immerzeel--2020|Immerzeel et al. (2020)]] have ranked all mountain-dependent water towers according to their water-supplying role and the downstream dependence of ecosystems, societies and economies. The resulting Global Water Tower index indicates that the upper Indus basin is both the most important and the most vulnerable water tower unit (WTU) in the world. A WTU is defined as ‘the intersection between major river basins and a topographic mountain classification based on elevation and surface roughness’. Whereas all important transboundary WTUs in Asia remain highly vulnerable, it is the Indus WTU (inhabited by approximately 235 million people in the basin in 2016 (which is projected to increase by 50% by the middle of 21st century) where the average annual temperature is projected to increase by 1.9°C between 2000 and 2050, with wide-ranging consequences and trans-sectoral spillovers. The Indus WTU faces a deep risk produced by a combination of factors including water stress, ineffective governance, hydropolitical tensions, population growth and density, urbanisation and social transformations, with a significant bearing on SDG 6 on water, SDG 2 on food and SDG 7 on energy.

<div id="10.4.4.1" class="h3-container"></div>

<span id="key-drivers"></span>
==== 10.4.4.1 Key Drivers ====

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Across Asia and its various sub-regions, the key drivers behind an increasingly inadequate supply of freshwater resources, affecting the livelihood security of millions, are varied, complex and intersect with multiple social, cultural, economic and environmental stressors ( [[#Luo--2017|Luo et al., 2017]] ; [[#Tucker--2015|Tucker et al., 2015]] ; [[#Kongsager--2016|Kongsager et al., 2016]] ). Water stress has been defined as the situation ‘when the demand for water exceeds its supply, during a certain period of time, or when poor quality restricts its use’( [[#Felberg--1999|Felberg et al., 1999]] ; see also Figure 4.32 in Lee et al., 2021a).

Freshwater resources in Asia, which include both surface water and groundwater, are considerably strained, and changing climate is ''likely'' to act as a major stress multiplier ( [[#Dasgupta--2015|]] [[#Dasgupta--2015|Dasgupta et al., 2015]] ; [[#Fant--2016|Fant et al., 2016]] ; [[#Gao--2018c|Gao et al., 2018c]] ; [[#Mack--2018|Mack, 2018]] ). In Southern and Eastern Asia (SEA) nearly 200 million people are at risk of serious water-stressed conditions. Effective mitigation might reduce the additional population under threat by 30% (60 million people), but still there is a 50% chance that 100 million people across SEA might face a 50% increase in water stress and a 10% chance that water stress will almost double in the absence of wide-ranging, multi-scalar adaptive measures ( [[#Gao--2018c|Gao et al., 2018c]] ). With Millennium Development Goal 7c, which aimed to halve the population that had no sustainable access to water and basic sanitation before 2015, not having been fully realised, and Sustainable Development Goal 6 on water and sanitation not having been effectively operationalised, the water stress is ''likely'' to increase by the end of 2030 ( [[#Weststrate--2019|Weststrate et al., 2019]] ).

In Asia and elsewhere the interplay between the challenge of sustainability and climate change poses major policy challenges ( [[#von%20Stechow--2016|von Stechow et al., 2016]] ). The pursuit of SDG 6—protection and restoration of water-related ecosystems, universal and equitable access to safe and affordable drinking water for all, improvement in water quality by reducing pollution, elimination of dumping and significant reduction in release of hazardous chemicals and materials, and treatment of waste water through recycling and safe reuse globally—could be directly or indirectly challenged and undermined by climate change ( [[#Parkinson--2019|Parkinson et al., 2019]] ). Dissolved organic materials from sewage can enhance CO 2 emissions, especially in rapidly urbanising river systems which receive untreated waste water and/or sewage across developing countries ( [[#Kim--2019b|Kim et al., 2019b]] ). Conversely, policy interventions aimed at significant augmentation in water-use efficiency across all sectors, ensuring sustainable withdrawals and supply of freshwater to address water scarcity and a significant reduction in the number of victims of water scarcity, especially the poor and marginalised, could mitigate vulnerabilities caused by climate change. More interdisciplinary research is needed on highly precarious future pathways and the intersection between CIDs and non-climate drivers in order to anticipate and mitigate diverging and uncertain outcomes.

<div id="10.4.4.2" class="h3-container"></div>

<span id="sub-regional-diversity"></span>
==== 10.4.4.2 Sub-regional Diversity ====

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According to a quantitative scenario assessment for future water supply and demand in Asia to 2050, based on global climate change and socioeconomic scenarios ( [[#Satoh--2017|Satoh et al., 2017]] ), water demand in sectors such as irrigation, industry and households will increase by 30–40% around 2050 in comparison with 2010. Water stress is ''likely'' to be more pronounced in Pakistan, and northern parts of India and China. By mid-21st Century, the international transboundary river basins of Amu Darya, Indus, Ganges could face severe water scarcity challenges due to climatic variability and changes acting as stress multipliers ( ''high confidence'' ). Within a country as well, the water scarcity could be exacerbated, such as in India and China, due to various drivers like population increase and climate change. Research on the differentiated impacts of climate change on freshwater sources across the Asian sub-regions remains inconclusive and requires assessment at the sub-regional scale ( [[#IPCC--2014b|IPCC, 2014b]] ; [[#Wester--2019|Wester et al., 2019]] ).

<div id="10.4.4.3" class="h3-container"></div>

<span id="observed-impact"></span>
==== 10.4.4.3 Observed Impact ====

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The climate-change impact on different parts of freshwater ecosystems ( [[#10.4.2|Section 10.4.2]] ) has affected water supply in various sub-regions of Asia. While headwater zones are susceptible to change in snow cover, permafrost and glaciers, the downstream plain areas of these river systems are vulnerable to the increasing high demand of freshwater which will affect water availability in space and time. The observed impact of climate change has also been seen in direct physical losses such as precipitation (Mekong Delta), floods (Vietnam) and saltwater intrusion leading to low agricultural productivity ( [[#Mora--2018|Mora et al., 2018]] ; [[#Almaden--2019a|Almaden et al., 2019a]] ; [[#Pervin--2020|Pervin et al., 2020]] ).

The HKH region extends 3,500 km from Afghanistan in the west to Myanmar in the east. It is a source of major river systems originating in Asia, supporting livelihoods, energy, agriculture and the ecosystem for 240 million people in the mountains and hills and 1.65 billion people in the plains ( [[#Sharma--2019|Sharma et al., 2019]] ). The HKH region stores about half of the ice mass in HMA, provisioning freshwater to almost 869 million people in the Indus, Tarim, Ganges and Brahmaputra river basins. While the warming climate increases the melt-water runoff enhancing water supply, it is indeed at the cost of glacier-mass reduction that would eventually reduce melt water and impact the people’s livelihood downstream in the future ( [[#Nie--2021|Nie et al., 2021]] ). The melt runoff from the region plays an important role in downstream agriculture such as in the case of Indus where two-thirds of total irrigation withdrawal is from melt runoff in the pre-monsoon season ( [[#Biemans--2019|Biemans et al., 2019]] ). Changes in cryosphere and other environmental changes have already impacted people living in high-mountain areas and are ''likely'' to introduce new challenges for water, energy and food security in the future ( [[#Borodavko--2018|Borodavko et al., 2018]] ; [[#Adler--2019|Adler et al., 2019]] ; [[#Bolch--2019|Bolch, 2019]] ; [[#Hoelzle--2019|Hoelzle et al., 2019]] ; [[#Rasul--2019|Rasul and Molden, 2019]] ; [[#Shen--2020|Shen et al., 2020]] ).

With climate-change impacts resulting in the shrinking and melting of snow, ice, glacier and permafrost, and correspondingly causing an increase in melt water, the incidences of flash floods, debris flow, landslides, snow avalanches, livestock diseases and other disasters in the HKH region have become more frequent and intense. Some of the key factors that get in the way of assigning confidence levels to climate-change impacts include lack of sufficient observed data on factors such as river discharges, precipitation and glacier melt ( [[#You--2017|You et al., 2017]] ). Climate-change impacts cryospheric water sources in the Hindu Kush, Karakoram and Himalayan ranges which, in turn, carry consequences for the Indus, Ganges and Brahmaputra basins.

The combined impacts of climate change and non-climate drivers on hydrological processes and water resources in transboundary rivers in diverse regions of Asia were well noted in AR5. In Central Asia, withdrawal is approximately equal to water availability, with Turkmenistan and Uzbekistan as the most water-stressed countries in the region ( [[#Karthe--2017|Karthe et al., 2017]] ; Russell, 2018). A study on water availability in mainland South Asia has pointed in the direction of decreasing precipitation trends in recent years, which have also contributed to the increasing incidence and severity of droughts ( [[#Liu--2018b|Liu et al., 2018b]] ). There are reports of increase in occurrence and severity of different forms of droughts in the Koshi River basin (Central Himalaya) ( [[#Wu--2019a|Wu et al., 2019a]] ; [[#Hamal--2020|Hamal et al., 2020]] ; [[#Dahal--2021|Dahal et al., 2021]] ; [[#Nepal--2021|Nepal et al., 2021]] ). Figure 10.5 shows the water stress in the HKH region. The water stress is relatively higher in the western region compared with the central and eastern regions.

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[[File:136ae2baf51f825017c0165043125ac4 IPCC_AR6_WGII_Figure_10_005.png]]

'''Figure 10.5 |''' '''Water stress in the Hindu Kush Himalaya (HKH) region according to Wester et al.''' '''(2019) and [[#Hu--2018|Hu and Tan (2018)]] .'''

Climate change is also having an impact on stream flows. The changes in snowmelt water can explain 19% of the variations in rivers of arid regions like Xinjiang, China ( [[#Bai--2018|Bai et al., 2018]] ) ( ''medium confidence'' ), and the 10.6% of the runoff of the upper Brahmaputra River was contributed by snow during 2003–2014 ( [[#Chen--2017c|Chen et al., 2017c]] ) ( ''medium confidence'' ). A recent study (Chen et al., 2018 f) has shown that with the average temperature after 1998 being 1.0°C higher than that during 1960–1998 in the Tienshan Mountains, the process of glacier shrinkage and decreases in snow cover are causing earlier peak runoff and aggravated extreme hydrological events, affecting regional water availability and adding to the future water crisis in Central Asia. The magnitude and frequency of flooding have increased across the Himalayan region, such as in the Tarim basin in China ( [[#Zhang--2016c|Zhang et al., 2016c]] ) and the higher Indus, Ganges and Brahmaputra, in the past six decades ( [[#Elalem--2015|Elalem and Pal, 2015]] ). The latter also reported the highest number of flood disasters and greater spatial coverage in recent decades as compared with previous decades. In the Middle Yellow River basin, which has become much warmer and drier, climate variability accounts for 75.8% of streamflow decrease during 1980–2000, whereas during 2001–2016, change in land use and cover was the main factor in streamflow decrease, accounting for 75.5% of the decline ( [[#Bao--2019|Bao et al., 2019]] ). The changes in hydrological regime and extreme floods cause changes in river morphology and the river channel system which impact water availability.

In China, a quantitative assessment based on a multi-model dataset (six global hydrological models driven by three observation-based global forcings) during 1971–2010 suggested that climate variability dominated the changes in streamflow in more the 80% of river segments, while direct human impact dominated changes mostly in northern China ( [[#Liu--2019b|Liu et al., 2019b]] ). In the Lancang-Mekong River basin, climate variability would have contributed 45% more flood occurrences in the middle of the basin, while reservoir operation reduced it by 36% during 2008–2016 as compared with 1985–2007 ( [[#Yun--2020|Yun et al., 2020]] ).

In western China, the total annual snow mass declines at a rate of 3.3 × 10 9 pg per decade ( ''p'' &lt; 0.05), which accounts for approximately 0.46% of the mean of annual snow mass (7.2 × 10 11 pg). The loss could be valued in terms of replacement cost at 0.1 billion CN¥ (at the present value) every year (1 USD = 7 CN¥) compounded over the past 40 years ( [[#Wu--2021|Wu et al., 2021]] ). In the Mekong River Delta in Vietnam, climate-change impacts include a 30% annual increase in rainfall, shifting rainfall patterns, an average temperature increase of 0.5°C over the past 30 years and an average SLR of 3 mm yr –1 over the past three decades, resulting in a greater flooding threat ( [[#Wang--2021a|Wang et al., 2021a]] ).

A recent study ( [[#Wang--2021b|Wang et al., 2021b]] ) has shown that during 1936–2019, due largely to intensified precipitation induced by a warming climate, the streamflow of the Ob, Yenisei and Lena rivers has increased by ∼ 7.7, 7.4 and 22.0%, respectively. While rising temperatures can reduce streamflow via evapotranspiration, it can enhance groundwater discharge to rivers due to permafrost thawing. In permafrost-developed basins, the thawing permafrost will continue to result in increased streamflow. However, with further permafrost degradation in the future, the positive effect of permafrost thaw on streamflow would probably be offset by the negative effect of the increase in basin evapotranspiration. This could result in a situation where runoff reaches threshold level and then declines. This is clearly marked in the Ob River basin, which is characterised by the highest precipitation, whereas in the case of the Yenisei and Lena rivers, further research is needed.

The HKH region is susceptible to floods and related hazards caused by a cloud burst and other landscape-based processes such as glacial lake outburst floods, which can seriously damage property, lives and infrastructure ( [[#Shrestha--2010|Shrestha et al., 2010]] ). The likely increased frequency of hazards caused by abnormal glacier changes, such as the glacier collapses happened on two glaciers in western Tibetan Plateau in 2016 ( [[#Kääb--2018|Kääb et al., 2018]] ), and also surges which were frequently found in this vast region (e.g. [[#Bhambri--2017|Bhambri et al., 2017]] ; [[#Mukherjee--2017|Mukherjee et al., 2017]] ; [[#Ding--2018|Ding et al., 2018]] ), threatening the security of the local and down streaming societies (high confidence).The total amount and area of glacier lakes increased during last decade ( [[#Zhang--2015|Zhang et al., 2015]] ; [[#Chen--2017c|Chen et al., 2017c]] ) ( ''high confidence'' ). Himalayan rivers are frequently hit by catastrophic floods caused by the failure of glacial lakes ( [[#Cook--2018|Cook et al., 2018]] ; [[#Ahluwalia--2016|Ahluwalia et al., 2016]] ). In Kedarnath, India (western Himalaya), a flash flood was triggered by glacier lake outburst flood (GLOF) released from the Chorabari glacial lake in June 2013 which caused extensive flooding, erosion of riverbanks and damage to downstream villages and towns, as well as the loss of several thousand lives ( [[#Rafiq--2019|Rafiq et al., 2019]] ; [[#Das--2015|Das et al., 2015]] ). Nepal has experienced 24 GLOF events which have caused considerable loss of life and damage to property and infrastructure (Icimod, 2011). There is ''high confidence'' that current glacier shrinkages have caused more glacial lakes to form in most mountainous regions, including HMA, but there is limited evidence that the frequency of GLOF has changed ( [[#Hock--2019|Hock et al., 2019]] ). [[#Veh--2018|Veh et al. (2018)]] reported no clear trend of increasing GLOF events in the Himalayan region, although the southern Himalaya was identified as a hotspot region compared with the western Himalaya. Research has shown a decrease in glacier area of 24% in Nepal between 1980 and 2010 ( [[#Bajracharya--2014|Bajracharya et al., 2014]] ).

Climate-change impacts on both the quantity and quality of freshwater resources will hinder the attainment of SDG-6 ( [[#Water--2020|Water, 2020]] ). Contamination of drinking water is caused by wildfires and drought that contribute to elevated levels of nutrients (nitrogen, phosphorus and sulphates), heavy metals (lead, mercury, cadmium and chromium), salts (chloride and fluorides), hydrocarbons, pesticides and even pharmaceuticals. Heavy rains and flooding also increase nutrients, heavy metals and pesticides, as well as turbidity and faecal pathogens in water supplies–especially when sewage treatment plants are overwhelmed by runoff ( [[#Mora--2018|Mora et al., 2018]] ). Pharmaceuticals and personal-care products (from source to disposal) are contributing to the vulnerability of urban waters. A study of vulnerability assessment of urban waters in highly populated cities in India and Sri Lanka, through analysing the concurrence of Pharmaceuticals and Personal Care Products (PPCPs), enteric viruses, antibiotic-resistant bacteria, metals, faecal contamination and antibiotic resistance genes (ARGs), also underlines the need for a resilience strategy and action plan ( [[#Rafiq--2019|Rafiq et al., 2019]] ).

Adequate water supply for various uses is crucial for millions of people living in the mountains of Asia. Particularly in the HKH region, mountain springs play an important role in generating stream flow for non-glaciated catchments and in maintaining dry-season flows across many watersheds ( [[#Scott--2019|Scott et al., 2019]] ; [[#Stott--2014|Stott and Huq, 2014]] ). There is a good deal of evidence that the springs are drying up or yielding less discharge ( [[#Tambe--2012|Tambe et al., 2012]] ; Tiwari and [[#Joshi--2014|Joshi, 2014]] ; [[#Sharma--2016|Sharma et al., 2016]] ), threatening local communities who depend on spring water for their lives and livelihoods. Some of the main reasons for drying springs include anthropogenic impacts (deforestation, exploitative land use), infrastructure (road construction), socioeconomic changes (increasing demand and modernisation of facilities) and climatic changes (changes in rainfall regime and higher temperature) ( [[#Stott--2014|Stott and Huq, 2014]] ; Tiwari and [[#Joshi--2014|Joshi, 2014]] ; [[#Sharma--2016|Sharma et al., 2016]] ).

The Ganges–Brahmaputra region also faces the threat increased frequency of flood events ( [[#Lutz--2019|Lutz et al., 2019]] ). Floods and extreme events can impact river channel systems ( [[#Grainger--2014|Grainger and Conway, 2014]] ). One of the challenges in South Asia is the shifting boundaries of river channels. For instance, the major floods on the Indus in July 2010 altered the river’s course in Pakistan, moving it closer to the Indian district of Kutch ( [[#Grainger--2014|Grainger and Conway, 2014]] ). In the eastern tributary of the Ganges system, the alluvial fan of the Koshi River basin has shifted by more than 113 km to the west in the past two centuries ( [[#Chakraborty--2010|Chakraborty et al., 2010]] ), which may be due to heavy sediment load from the Himalayan rivers in which about 50 million tons of sediment is deposited annually in the alluvial plains ( [[#Sinha--2019|Sinha et al., 2019]] ; [[#Chakraborty--2010|Chakraborty et al., 2010]] ).

Asia is no exception to the global trend of lake ecosystems, which provide drinking water to millions of people, being degraded ( [[#Jenny--2020|Jenny et al., 2020]] ) and severely threatened at the same time by climate change ( [[#Mischke--2020|Mischke, 2020]] ). Lake surface conditions, such as ice cover, surface temperature, evaporation and water level react dramatically to this threat, and there are negative implications for water quantity and quality, food provisioning, recreational opportunities and transportation ( [[#Woolway--2020|Woolway et al., 2020]] ). Due to substantial regional variability, the quantum of future changes in lake water storage remains uncertain. A recent study ( [[#Liu--2019a|Liu et al., 2019a]] ) using Moderate Resolution Imaging Spectroradiometer 500-m spatial resolution global water product data, and applying the least squares method to analyse changes in the area of 14 lakes in Central Asia from 2001 to 2016, has shown that the area-shrinkage changes for all plains lakes in the study region could be attributed to climate change and human activities.

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<span id="projections"></span>
==== 10.4.4.4 Projections ====

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Asian and global water demands for irrigation, despite geographic variation in terms of water availability, are ''very likely'' to surpass supply by 2050 ( [[#Chartres--2014|Chartres, 2014]] ). A regional quantitative assessment ( [[#Lutz--2019|Lutz et al., 2019]] ) of the impacts of 1.5°C versus 2°C global warming for a major global climate-change hotspot–the Indus, Ganges and Brahmaputra river basins (IGB) in South Asia–shows adverse impacts of climate change on agricultural production, hydropower production and human health. A global temperature increase of 1.5°C with respect to pre-industrial levels would imply a ≈ 2.1°C temperature increase for IGB, whereas under a 2.0°C global temperature increase scenario, these river basins would warm by ≈ 2.7°C. Future warming is expected to further increase rain-on-snow events that can cause snowmelt flood during winter ( [[#Ohba--2020|Ohba and Kawase, 2020]] ), affecting hydropower and resulting in river flooding, avalanches and landslides.

In the Mekong River Delta (in Vietnam), with an area of 40,500 km 2 and home to 17.8 million people in 2018, climate change is projected to increase the average temperature by 1.1–3.6°C, and the maximum and minimum monthly flow are projected to increase and decrease, respectively, and are ''likely'' to result in a high risk of food during the wet season and water shortages during the dry season ( [[#Wang--2021a|Wang et al., 2021a]] ).

Researchers have found that the southern Tibetan Plateau has been consistently melting from 1998–2007 and is projected to continue melting until 2050 ( [[#Lutz--2014b|Lutz et al., 2014b]] ) ( ''high confidence'' ). In HMA, glacier ice is projected to decrease by 49 ± 7% and 64 ± 5% by the end of the century under RCP4.5 and RCP8.5 scenarios, respectively ( [[#Kraaijenbrink--2017|Kraaijenbrink et al., 2017]] ). Local- and regional-scale projections suggest that peak water will generally be reached around the middle of the century, followed by steadily declining glacier runoff thereafter ( [[#Hock--2019|Hock et al., 2019]] ). A global-scale projection suggests that a decline in glacier runoff by 2100 (RCP8.5) may reduce basin runoff by about 10% for at least 1 month of the melt season ( [[#Huss--2018|Huss and Hock, 2018]] ). Significantly, research on climate change and its impact across Asia remains inconclusive and requires an assessment at the sub-regional scale ( [[#IPCC--2014a|IPCC, 2014a]] ; [[#Wester--2019|Wester et al., 2019]] ).

There is a projection of an increase in runoff until the 2050s mainly due to an increase in precipitation in the upper Ganges, Brahmaputra, Salween and Mekong basins, where it could be due to accelerated melting in the upper Indus basin. The runoff could increase in the range of 3–27% (7–12% in Indus, 10–27% in Ganges and 3–8% in Brahmaputra) by mid-century compared with the reference period (1998–2008) for Himalayan river basins depending on the different RCP scenarios ( [[#Lutz--2014a|Lutz et al., 2014a]] ). Likewise, [[#Khanal--2021|Khanal et al. (2021)]] suggest contrasting responses to climate change for HMA rivers in which, on the seasonal scale, the earlier onset of melting causes a shift in magnitude and peak of water availability, whereas on the annual scale, total water availability increases for the headwaters. The future flow would increase in Nepal’s Central Himalaya region ( [[#Nepal--2016|Nepal, 2016]] ; [[#Ragettli--2016|Ragettli et al., 2016]] ; [[#Bajracharya--2018|Bajracharya et al., 2018]] ). These changes in water availability in space and time will have serious consequences in downstream water availability for various sectoral uses and ecosystem functioning in Asia ( [[#Nepal--2014|Nepal et al., 2014]] ; [[#Green--2015|Green et al., 2015]] ; [[#Arfanuzzaman--2018|Arfanuzzaman, 2018]] ; [[#Wijngaard--2018|Wijngaard et al., 2018]] ; [[#Rasul--2019|Rasul and Molden, 2019]] ); however, future water availability is largely uncertain due to significant variation in climate-change projections among different global climate models ( [[#Nepal--2015|Nepal and Shrestha, 2015]] ; [[#Lutz--2016|Lutz et al., 2016]] ; [[#Li--2019a|Li et al., 2019a]] ).

A recent study ( [[#Didovets--2021|Didovets et al., 2021]] ) covering eight river catchments having diverse natural conditions within Central Asia, where water availability or scarcity is also a major developmental concern, and using the eco-hydrological model SWIM (including scenarios from five bias-corrected GCMs under RCP4.5 and RCP8.5) has show an increase in mean annual temperature in all catchments for both RCPs to the end of the 21st century. The projected changes in annual precipitation indicate a clear trend to increase in the Zhabay and decrease in the Murghab catchments, and for other catchments, they were smaller. Both the projected trends for river discharge and precipitation show an increase in the northern and decrease in the southern parts of the study region, whereas seasonal changes include a shift in the peak of river discharge up to one month, a shortening of the snow accumulation period and a reduction in discharge during the summer months.

The intensity and frequency of extreme discharges are ''very likely'' to increase towards the end of the century. The future of the upper Indus basin water availability is highly uncertain in the long term due to uncertainty surrounding precipitation projections ( [[#Lutz--2016|Lutz et al., 2016]] ). The future hydrological extremes of the upper Indus, Ganges and Brahmaputra river basins suggest an increase in the magnitude of extremes towards the end of the 21st century by applying RCP4.5 and RCP8.5 scenarios, mainly due to an increase in precipitation extremes ( [[#Wijngaard--2017|Wijngaard et al., 2017]] ). In the Brahmaputra, Ganges and Meghna, including the downstream component, the runoff is projected to increase by 16, 33 and 40%, respectively, under the climate-change scenarios by the end of the century during which the changes in runoff are larger in the wet seasons than the dry seasons ( [[#Masood--2015|Masood et al., 2015]] ). In the Mekong River basin also, extremely high-flow events are ''likely'' to increase in both magnitude and frequency, which can exacerbate flood risk in the basin ( [[#Hoang--2016|Hoang et al., 2016]] ); however, uncertainty is high regarding future hydrological response due to large variation in precipitation projections, modelling approaches and bias-correction methods ( [[#Nepal--2015|Nepal and Shrestha, 2015]] ; [[#Lutz--2016|Lutz et al., 2016]] ; [[#Li--2019a|Li et al., 2019a]] ).

Current research on the adverse relationship between climate change and river flows suggests that there is a high possibility that some of the river basins affected by floods could be Brahmaputra, Congo, Ganges, Lena and Mekong, with a return period of 10 years ( [[#Best--2018|Best, 2018]] ).

In most parts of the upper Ganges and Brahmaputra rivers, the 50-year return level flood is ''likely'' to increase and to a lesser degree in Indus River. Similarly, the extreme precipitation events are also expected to increase to a higher degree in the Indus than the Ganges and Brahmaputra basins ( [[#Wijngaard--2017|Wijngaard et al., 2017]] ). Increase in extreme precipitation events is ''likely'' to cause more flash-flood events in the future ( ''medium confidence'' ). In the case of the Indus, increasing temperature trend in the future may lead to accelerated snow and ice melting which may increase the frequency and intensity of floods in the downstream areas ( [[#Hayat--2019|Hayat et al., 2019]] ). The Ganges–Brahmaputra region also faces the threat of increased frequency of flood events ( [[#Lutz--2019|Lutz et al., 2019]] ). Additionally, the Ganges basin also shows a higher sensitivity to changes in temperature and precipitation ( [[#Mishra--2016|Mishra and Lilhare, 2016]] ).

Assessing the impact of climate change on water resources in nine alpine catchments in arid and semiarid Xinjiang of China ( [[#Li--2019a|Li et al., 2019a]] ), it has been noted that even though the total discharge revealed an overall increasing trend in the near future, the impact of climate change on different hydrological components indicated significant spatio-temporal heterogeneity in terms of the area, elevation and slope of catchments, which could be usefully factored into climate-adaptation strategies.

It was noted early on ( [[#Singh--2011|Singh et al., 2011]] ) that the main drivers that influence the provisioning of ecosystem services and human well-being in the HKH region are a mix of environmental change in general and climate change in particular, but much more data and knowledge on the HKH region are needed in order to develop either a regional or global understanding of climate-change processes.

Climate change impacts cryospheric water sources in the Hindu Kush, Karakoram and Himalayan ranges which in turn carry consequences for the Indus, Ganges and Brahmaputra basins. The impact of climate change on spring-fed rivers in the HKH is under-researched and therefore makes projections difficult. Further research is needed for understanding the impact of deforestation, urbanisation, development and introduction of water infrastructures, such as tube wells, in the hill region ( [[#Aayog--2017|Aayog, 2017]] ). This in turn calls for greater investment in research and development for the HKH by both the national and regional organisations. There is ''high confidence'' that due to global warming, Asian countries could experience an increase in drought conditions (5–20%) by the end of this century ( [[#Prudhomme--2014|Prudhomme et al., 2014]] ; [[#Satoh--2017|Satoh et al., 2017]] ).

Soil erosion in high-mountain areas is particularly sensitive to climate change. A recent study ( [[#Wang--2020|Wang et al., 2020]] ) that focused on the mid-Yarlung Tsangpo River, located in the southern part of the Tibetan Plateau, has revealed dramatic land surface environment changes due to climate change during recent decades. It has further shown that increasing precipitation and temperature would lead to increasing soil-erosion risk in ~2050 based on the Coupled Model Intercomparison Project (CMIP5) and RUSLE models.

High-resolution climate-change simulations suggest that due to deadly heatwaves projected in some of the densely populated agricultural regions of South Asia (i.e., the Ganges and Indus river basins), those regions are ''likely'' to exceed the critical threshold of wet-bulb temperature of 35°C under the business-as-usual scenario of future GHG emissions ( [[#Im--2017|Im et al., 2017]] ).

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<span id="climate-vulnerability-and-adaptation-interfaces-and-interventions"></span>
==== 10.4.4.5 Climate Vulnerability and Adaptation: Interfaces and Interventions ====

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In Asia and its diverse sub-regions, the challenge of adaptation to climate change at diverse sectors, sites and scales of vulnerability in the domain of freshwater resources is compounded by the nexus between long-standing non-climatic vulnerabilities and climatic impacts, both observed and projected. Water insecurities in Asia are increasing due to excessive freshwater withdrawals ( [[#Satoh--2017|Satoh et al., 2017]] ), economic and population growth ( [[#Gleick--2018|Gleick and Iceland, 2018]] ), urbanisation and peri-urbanisation ( [[#Roth--2019|Roth et al., 2019]] ), food insecurity ( [[#Demin--2014|Demin, 2014]] ) and lack of access to clean and safe drinking water ( [[#Cullet--2016|Cullet, 2016]] ), which mostly affects the health of the most vulnerable members of society.

Significantly, climate change will add to already existing vulnerabilities. In the case of the Yellow River basin in China, underlining the interface between future water scarcity and hydroclimatic and anthropogenic drivers, a recent study expects moderate-to-severe water scarcity over six Yellow River sub-catchments under the RCP4.5 scenario, and anticipates that human influences on water scarcity will be worse than that of climate change, with water availability in the downstream being impacted by concurrent changes in land use and high temperature ( [[#Omer--2020|Omer et al., 2020]] ). Nearly 8% of internationally shared or transboundary aquifers (TBAs), ensuring livelihood security for millions of people through sustaining drinking water supply and food production, are currently overstressed due to human overexploitation ( [[#Wada--2013|Wada and Heinrich, 2013]] ). The Asia Pacific region has the highest annual water withdrawal due to its geographic size, growing population and irrigation practices, and water for agriculture continues to consume 80% of the region’s resources ( [[#Taniguchi--2017b|Taniguchi et al., 2017b]] ; [[#Visvanathan--2018|Visvanathan, 2018]] ).

In South Asia, surface water and groundwater resources are already under stress (both in terms of quality and quantity) due to population growth, economic development, poor governance and management, and poor efficiency of use in economic production. In the past 40 years, there has been an increasing reliance on groundwater in South Asia for irrigation ( [[#Rodell--2009|Rodell et al., 2009]] ; [[#Tiwari--2009|Tiwari et al., 2009]] ; [[#Surie--2015|Surie and Prasai, 2015]] ; [[#Bhanja--2016|Bhanja et al., 2016]] ; [[#Shrestha--2016|Shrestha et al., 2016]] ; [[#Mukherjee--2018|Mukherjee, 2018]] ; [[#Shah--2018|Shah et al., 2018]] ). It is noteworthy that India, Bangladesh, Pakistan and China together account for more than 50% of the world’s groundwater withdrawals ( [[#Scott--2019|Scott et al., 2019]] ). A study conducted in the Shahpur and Maner districts of Bihar, India, in which drinking water sourced from the groundwater of 388 households was tested, showed that 70–90% of the sampled household’s drinking water contained either arsenic or iron, or both ( [[#Thakur--2019|Thakur and Gupta, 2019]] ). Given the nexus between CIDs and non-climate drivers, an effective adaptation to the impacts of climate change would also demand sustainable development and management of shared aquifer resources, which in turn require reliable TBA inventories and improved knowledge production and knowledge sharing on the shared groundwater systems ( [[#Lee--2018a|Lee et al., 2018a]] ).

A study of peri-urban spaces involving four South Asian cities, Khulna (Bangladesh) ( [[#Pervin--2020|Pervin et al., 2020]] ), Gurugram and Hyderabad (India), and Kathmandu (Nepal), has shown the nexus between intensifying use and deteriorating quality of water and the impact of climate change, resulting in peri-urban water insecurity and conflict ( [[#Roth--2019|Roth et al., 2019]] ). The challenge of ensuring access to water resources and their (re)allocation and prioritisation for marginalised communities remains on the agenda of policy-oriented interdisciplinary research and demands effective implementation of its findings at the grassroots level by the administrative agencies. Taking water security as a key CCA goal at the urban-city scale of Bangkok, a study ( [[#Babel--2020|Babel et al., 2020]] ) has shown the usefulness of a generic framework with 5 dimensions, 12 indicators and a set of potential variables to support national-level initiatives and plans in diverse climatic and socioeconomic conditions across various sub-regions of Asia.

In the Kathmandu valley in Nepal, where groundwater resources are under immense pressure from multiple stresses, including overextraction and climate change, mapping groundwater resilience to climate change has been demonstrated as a useful tool to understand the dynamics of groundwater systems, and thereby facilitate the development of strategies for sustainable groundwater management ( [[#Shrestha--2020|Shrestha et al., 2020]] ).

In the Mekong Delta, the groundwater storage is projected to decline by more than 120 and 160 million m 3 under RCP4.5 and RCP8.5 scenarios, respectively, by the end of the 21st century, in conjunction with land subsidence and SLR. This in turn calls for proactive planning and implementation of adaptation strategies that address multiple stresses in order to ensure sustainable utilisation of groundwater resources in the Mekong Delta in the context of future climatic conditions and associated uncertainties ( [[#Wang--2021a|Wang et al., 2021a]] ). Proposed CCA strategies for the Mekong River basin include a better understanding of the complex linkages between climate change, technological interventions, land-use change, water-use change and socioeconomic developments both in the upstream and downstream riparian countries ( [[#Evers--2018|Evers and Pathirana, 2018]] ).

While South Asian countries have done well in attaining Goal 6 of Sustaining Development Goals, access to safe and clean drinking water remains a challenge. Taking Indian rivers as an example, it is suggested that participatory river protection and rehabilitation, based on comprehensive knowledge of the river-system dynamics, and local awareness at the community level, may act as a multiplier for river conservation measures ( [[#Nandi--2016|Nandi et al., 2016]] ).

Hydroclimatic extremes in the HKH region could adversely impact the Ganga, Brahmaputra and Meghna basins ( [[#Wijngaard--2017|Wijngaard et al., 2017]] ; [[#Acharya--2019|Acharya and Prakash, 2019]] ). Studies have recommended watershed or basin analysis to address the challenge of adaptation in urban spaces ( [[#Lele--2018|Lele et al., 2018]] ). A study of northern Bangladesh that focused on encouraging traditional ways of cultivation suggests that rural women have Indigenous knowledge and their participation can play a useful role ( [[#Kanak%20Pervez--2015|Kanak Pervez et al., 2015]] ). The knowledge pertains to agriculture, soil conservation, fish and animal production, irrigation and water conservation. There has also been a focus on gendered construction of local flood-forecasting knowledge in rural communities in India living in the Gandak River basin ( [[#Acharya--2019|Acharya and Prakash, 2019]] ). While designing the adaptation options, understanding the water–energy–food (WEF) nexus among different water-use sectors is crucial ( [[#10.5.3|Section 10.5.3]] ). Understanding of the WEF nexus could be beneficial for achieving water security in developing countries in Asia ( [[#Nepal--2019|Nepal et al., 2019]] ).

AR5 identified a number of adaptation challenges and options facing the stakeholders in the wake of climate-change-induced vulnerabilities, uncertainties and risks in the freshwater sector, and underlined the importance of an integrated management approach as well as acknowledging diverse socioeconomic contexts, differentiated capacities and the uneven pace of impacts. Further validated by recent research in terms of their usefulness, these adaptation options include building and improving capital-intensive physical water infrastructure such as irrigation channels, flood-control dams and water storage ( [[#Nüsser--2017|Nüsser and Schmidt, 2017]] ). Drawing upon customary institutions and combining Indigenous knowledge systems with scientific knowledge, innovative structures, including artificial glaciers, ice stupas and snow barrier bands, have been built by local communities in Ladakh, Zanskar and Himachal Pradesh in India ( [[#Hock--2019|Hock et al., 2019]] ; [[#Nüsser--2019|Nüsser et al., 2019]] ). Communities in Solukhumbu, Nepal, in response to depleting water flow in snow-fed rivers, have chosen adaptation through changing practices by collecting water from distant sources for domestic consumption ( [[#McDowell--2013|McDowell et al., 2013]] ). Taking the IPCC concept of climate risk as a basis for adaptation planning, a pilot study of flood risk in Himachal Pradesh, India ( [[#Allen--2018|Allen et al., 2018]] ), integrating assessment of hazard, vulnerability and exposure in the complementary domains of CCA and DRR, has identified stakeholder consultation, knowledge exchange and institutional capacity building as key steps in adaptation planning. Aquifer storage and recovery has been proposed as an ‘alternative climate-proof freshwater source’ for deltaic regions in Asia, particularly those with a history of saline groundwater aquifers ( [[#Hoque--2016|Hoque et al., 2016]] ). It is further argued ( [[#Hadwen--2015|Hadwen et al., 2015]] ) that water, sanitation and hygiene objectives would need to be addressed as a component of a wider integrated water resource management (IWRM) framework.

Ensuring sustainability of the rivers and ecosystems requires coordinated and collaborative action on the part of all countries, with the long-term goal of synergising political, social, cultural and ecological facets associated with the riverine system. Daunting as this challenge is, evidence suggests that a long-term view of transboundary basins is not very optimistic as big rivers of Asia contribute heavily towards urban and agricultural activities, and are experiencing challenges of increasing sedimentation, large-scale damming and pollution, among others ( [[#Best--2018|Best, 2018]] ). In the case of China, [[#Sun--2016|Sun et al. (2016)]] have shown that the localised vulnerabilities within the Yangtze River basin prompt an ‘integrated basin-wide approach’ that is able to account for the specific needs of each of its sub-basins.

In HMA, factors that undermine effective adaptation to climate change include both sudden-onset and slow-paced disasters along with the knowledge deficit regarding cryospheric change and its adverse impacts on water resources and also the agriculture and hydropower sectors. Other key barriers include a sectoral approach, overemphasis on structural approaches and the lack of context-sensitive, community-centric understanding of how these changes influence perceptions, options and decisions about migration, relocation and resettlement ( [[#Rasul--2020|Rasul et al., 2020]] ; [[#Hock--2019|Hock et al., 2019]] ). More interdisciplinary research is needed on highly precarious future pathways and the intersection between CIDs and non-climate drivers in order to anticipate and mitigate diverging and uncertain outcomes.

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'''Box 10.4 | Case Study on Climate Vulnerability and Cross-Boundary Adaptation in Central Asia'''
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In Central Asia, water scarcity has been ranked in the top five global risks ( [[#Gleick--1993|Gleick, 1993]] ; [[#Zhupankhan--2018|Zhupankhan et al., 2018]] ). Cross-boundary adaptation remains critically important in this region with abundant glaciers in the Pamir Plateau of Tajikistan ( [[#Hu--2017|Hu et al., 2017]] ) and areas with severe glacier retreat in the Tianshan Mountains ( [[#Liu--2015|Liu and Liu, 2015]] ). The spatial variations of glacier and other climate variables have added to uncertainty related to the dynamic of the water cycle. The headwater regions, such as Pamir area, would be significantly affected by the climate parameters, such as the stronger rainfall intensity, more frequent rainfall and higher temperature ( [[#Luo--2019|Luo et al., 2019]] ). The water resources in the Pamir Plateau will range from −0.48 to 5.6% ( [[#Gulakhmadov--2020|Gulakhmadov et al., 2020]] ), and the crop phenological period in Tajikistan and Kyrgyzstan will be about 1–2 weeks earlier. The threat of agricultural water stress is increasing as well. The oasis in downstream areas will face more complex water resource fluctuations, water crisis and desertification. In particular, rain-fed agriculture in northern Kazakhstan, Uzbekistan and western Turkmenistan is particularly dependent on water resources. Under the RCP2.6 and RCP4.5 scenarios, considering CO 2 fertilisation effects and land-use projections, the increase in CO 2 atmospheric concentration and accumulated temperature can contribute to a 23% increase in cotton yield in Central Asia ( [[#Tian--2019|Tian and Zhang, 2019]] ), but extreme climate, such as drought, heatwaves and rainstorms, will have a 10% negative impact on agricultural production and the ecological environment ( [[#Zhang--2017|Zhang and Ren, 2017]] ). High-efficiency water-saving technology will help the upstream and downstream water resource management in Central Asian countries to adapt to the variation in water resources quantity, frequency and spatial pattern.

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<span id="agriculture-and-food"></span>
=== 10.4.5 Agriculture and Food ===

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Asia accounts for 67% of global agricultural production ( [[#Mendelsohn--2014|Mendelsohn, 2014]] ) and employs a large portion of the population in many developing countries and regions ( [[#Briones--2013|Briones and Felipe, 2013]] ; [[#ADB--2017b|ADB, 2017b]] ; [[#ILO--2017a|ILO, 2017a]] ). Since the release of IPCC AR5, more studies have reinforced the earlier findings on the spatio-temporal diversity of climate-change impacts on food production in Asia depending on the geographic location, agroecology and crops grown ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#Ahmad--2019|Ahmad et al., 2019]] ), recognising that there are winners and losers associated with the changing climate across scales ( [[#Dasgupta--2013a|Dasgupta et al., 2013a]] ; [[#Yong-Jian--2013|Yong-Jian et al., 2013]] ; [[#Bobojonov--2014|Bobojonov and Aw-Hassan, 2014]] ; [[#Hijioka--2014|Hijioka et al., 2014]] ; [[#Li--2014a|Li et al., 2014a]] ; [[#Prabnakorn--2018|Prabnakorn et al., 2018]] ; [[#Trisurat--2018|Trisurat et al., 2018]] ; [[#Matsumoto--2019|Matsumoto, 2019]] ). Despite the observed increase in total food production in terms of crops and food yields from 1990 to 2014 in Asia ( [[#FAO--2015|FAO, 2015]] ), there is ''high confidence'' that overall, at the regional level, the projected total negative impacts will far outweigh the expected benefits, with India emerging as the most vulnerable nation in terms of crop production (Figure 10.6). Recent evidence also indicates that climate-related risks to agriculture and food security in Asia will progressively escalate as global warming reaches 1.5°C and higher above pre-industrial levels ( [[#IPCC--2018b|IPCC, 2018b]] ) with differentiated impacts across the Asian continent.

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[[File:21ea5ce9b67eb804507e3dbedebe590e IPCC_AR6_WGII_Figure_10_006.png]]

'''Figure 10.6 |''' '''Projected impacts of climate change to agriculture and food systems in sub-regions of Asia based on post-IPCC-AR5 studies.''' The figure illustrates the spatio-temporal diversity of projected future impacts on food production highlighting that there are winners and losers associated with the changing climate at different scales. AGRI: agriculture; E: east; N: north; NRCP: no RCP analysis; Pre: precipitation; PY: production yield; RCP: representative concentration pathway; S: south; Temp: temperature; W: west. (Refer to Table SM10.2 for details and supporting references.)

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<span id="observed-impacts-2"></span>
==== 10.4.5.1 Observed Impacts ====

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There remains a paucity of data for observed climate-change impacts on Asian agriculture and food systems since the release of IPCC AR5. Most of these impacts have been associated with drought, monsoon rain and oceanic oscillations, the frequency and severity of which have been linked with the changing climate ( [[#Heino--2018|Heino et al., 2018]] ; [[#Heino--2020|Heino et al., 2020]] ). In general, major impacts to agricultural production, such as those observed by the farmers in the Philippines and Indonesia, include among others delays in crop harvesting, declining crop yields and quality of produce, increasing incidence of pests and diseases, stunted growth, livestock mortality and low farm income ( [[#Stevenson--2013|Stevenson et al., 2013]] ). In South Asia, the series of monsoon floods from 2005 to 2015 contributed to a high level of loss in agricultural production with peaks in 2008 and 2015 ( [[#FAO--2018a|FAO, 2018a]] ). Similarly, in Pakistan, farmers are experiencing a decline in crop yields and increasing incidence of crop diseases as a result of climate extremes, particularly floods, droughts and heatwaves ( [[#Fahad--2018|Fahad and Wang, 2018]] ; [[#Ahmad--2019|Ahmad et al., 2019]] ).

Limited studies have quantified the actual impacts of climate change on agricultural productivity and the economy. In a study in the Mun River basin, northeast Thailand, yield losses of rice due to past climate trends covering the period 1984–2013 was determined to be in the range of &lt; 50 kg ha –1 per decade or 3% of actual average yields with a high possibility of more serious yield losses in the future ( [[#Prabnakorn--2018|Prabnakorn et al., 2018]] ). Likewise, in China, an economic loss of 595–858 million USD for the corn and soybean sectors was computed from 2000 to 2009 ( [[#Chen--2016b|Chen et al., 2016b]] ). On the other hand, the intensive wheat–maize system in China seems to have benefited from climate change with the northward expansion of the northern limits of maize and multi-cropping systems brought about by the rising temperatures ( [[#Li--2014|Li and Li, 2014]] ).

There is ''high agreement'' in more recent studies that linked the frequency and extent of the El Niño phenomenon with global warming ( [[#Thirumalai--2017|Thirumalai et al., 2017]] ; [[#Wang--2017a|Wang et al., 2017a]] ; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) that can trigger substantial loss in crop and fishery production. The 2004 El Niño caused the Philippines an 18% production loss during the dry season and a 32% production loss during the wet season ( [[#Cruz--2017|Cruz et al., 2017]] ). In the 2015 El Niño event, the Indian oil sardine fishery declined by more than 50% of previous years ( [[#Kripa--2018|Kripa et al., 2018]] ) severely impacting coastal livelihoods and economies ( [[#Shyam--2017|Shyam et al., 2017]] ). The 2015–2016 El Niño also inflicted adverse impacts on agricultural productivity and food security, especially affecting the rural poor in middle- and lower-income countries in Southeast and South Asia ( [[#UNDP%20ESCAP%20OCHA%20RIMES%20APCC--2017|UNDP ESCAP OCHA RIMES APCC, 2017]] ).

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<span id="projected-impacts-2"></span>
==== 10.4.5.2 Projected Impacts ====

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<span id="fisheries-and-aquaculture"></span>
===== 10.4.5.2.1 Fisheries and aquaculture =====

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The fisheries and aquaculture production from Asia in 2019 was estimated at 159.67 mmt contributing to 74.7% of the global production ( [[#FAO--2020|FAO, 2020]] ). This sector provides employment to an estimated 50.46 million people where fishing and aquaculture are important socioeconomic activities and fish products are a substantial source of animal protein ( [[#Bogard--2015|Bogard et al., 2015]] ; [[#Azad--2017|Azad, 2017]] ; [[#FAO--2018c|FAO, 2018c]] ). The economic contribution could be as high as 44% of the coastal communities’ GDP as in the case of Sri Lanka ( [[#Sarathchandra--2018|Sarathchandra et al., 2018]] ). Five Asian countries (i.e., China, Indonesia, India, Vietnam and Japan) are in the top ten of global fish producers, representing a cumulative share of 36% in 2018 ( [[#FAO--2020|FAO, 2020]] ). As a top producer with 15% global share, China also remains a top exporter of fish and fish products with 14% global market share.

There is ''high agreement'' in the literature that Asian fisheries and aquaculture, including the local communities depending on them for livelihoods, are highly vulnerable to the impacts of climate change. Asia has been impacted by SLR ( [[#Panpeng--2017|Panpeng and Ahmad, 2017]] ), a decrease in precipitation in some parts ( [[#Salik--2015|Salik et al., 2015]] ) and an increase in temperature ( [[#Vivekanandan--2016|Vivekanandan et al., 2016]] ), all of which have drastic effects on fisheries and aquaculture ( [[#FAO--2018c|FAO, 2018c]] ). Its coastal fishing communities is exposed to disasters, which are predicted to increase ( [[#Esham--2018|Esham et al., 2018]] ). Fisheries in most of South Asia and Southeast Asia involve small-scale fishers who are more vulnerable to climate-change impacts compared with commercial fishers (Sönke [[#Kreft--2016|Kreft et al., 2016]] ; [[#Blasiak--2017|Blasiak et al., 2017]] ), although there is a general decreasing trend in the number of small units ( [[#Fernandez-Llamazares--2015|Fernandez-Llamazares et al., 2015]] ; [[#ILO--2015|ILO, 2015]] ). A regional study of South Asia forecast large decreases in potential catch of two key commercial fish species (hilsa shad and Bombay duck) in the Bay of Bengal ( [[#Fernandes--2016|Fernandes et al., 2016]] ), which forms a major fishery and food source for coastal communities. About 69% of the commercially important species of the Indian marine fisheries were found to be impacted by climate change and other anthropogenic factors ( [[#Dineshbabu--2020|Dineshbabu et al., 2020]] ). Likewise, water salinisation brought about by SLR is expected to impact the availability of freshwater fish in southwest coastal Bangladesh with adverse implications to poor communities ( [[#Dasgupta--2017a|Dasgupta et al., 2017a]] ). Analysis of fishery has indicated that there will be a continued decrease in catch impacting the seafood sector in the Philippines, Thailand, Malaysia and Indonesia ( [[#Nong--2019|Nong, 2019]] ). Climate change is predicted to decrease total productive fisheries potential in South and Southeast Asia, driven by a temperature increase of approximately 2°C by 2050 ( [[#Barange--2014|Barange et al., 2014]] ).

Like fisheries, Asian aquaculture is highly vulnerable to climate change. Shrimp farmers and fry catchers of Bangladesh are frequently affected by extreme climatic disruptions like cyclones and storm surges that severely damage the entire coastal aquaculture ( [[#Islam--2016a|Islam et al., 2016a]] ; [[#Kais--2018|Kais and Islam, 2018]] ). The majority of shrimp farmers also observed that weather has changed abruptly during the past 5 years and that high temperature is most detrimental because it lowers growth rate, increases susceptibility to diseases, including deformation, and affects production ( [[#Islam--2016a|Islam et al., 2016a]] ). Low production in shrimp farming is also attributed to variation and intensity of rainfall perceived by the majority of farmers as part of climate-change impacts ( [[#Ahmed--2015|Ahmed and Diana, 2015]] ; [[#Islam--2016a|Islam et al., 2016a]] ; [[#Henriksson--2019|Henriksson et al., 2019]] ). In Vietnam, small-scale shrimp farmers are likewise vulnerable to climate change, although those who practise an extensive type of farming with low inputs are more vulnerable compared with those who practise a more intensive type with more capital investment ( [[#Quach--2015|Quach et al., 2015]] ; [[#Quach--2017|Quach et al., 2017]] ). Seaweed farming in Asia is very popular, and the significance of seaweed aquaculture beds in capturing carbon is recognised, but most of the farmed seaweeds are susceptible to climate change ( [[#Chung--2017a|Chung et al., 2017a]] ; [[#Duarte--2017|Duarte et al., 2017]] ).

Marine heatwaves are a new threat to fisheries and aquaculture ( [[#Froehlich--2018|Froehlich et al., 2018]] ; [[#Frölicher--2018|Frölicher and Laufkötter, 2018]] ) including disease spread ( [[#Oliver--2017|Oliver et al., 2017]] ), live feed culture (copepods) ( [[#Doan--2018|Doan et al., 2018]] ) and farming of finfishes like Cobia ( [[#Le--2020|Le et al., 2020]] ). Predicting MHWs is considered a prerequisite for increasing the preparedness of farmers ( [[#Frölicher--2018|Frölicher and Laufkötter, 2018]] ). In Southeast Asian countries more than 30% of aquaculture areas are predicted to become unsuitable for production by 2050–2070 and aquaculture production is predicted to decrease 10–20% by 2050–2070 due to climate change ( [[#Froehlich--2018|Froehlich et al., 2018]] ).

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<span id="crop-production"></span>
===== 10.4.5.2.2 Crop production =====

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Since IPCC AR5, more studies have been done on different scales from local to global that focus on the differentiated projected impacts of climate change on the production and economics of various crops with rice, maize and wheat among the major crops receiving more attention. New research findings affirm that climate-change impacts, and will continue to significantly affect, crop production in diverse ways in particular areas all over Asia (Figure 10.6). An increasing number of sub-regional and regional studies using various modelling tools provide significant evidence on the overall projected impacts of climate change on crop production at the sub-regional and regional scales with clear indications of winners and losers among and within nations (see, for instance, [[#Mendelsohn--2014|Mendelsohn, 2014]] ; [[#Cai--2016|Cai et al., 2016]] ; [[#Chen--2016b|Chen et al., 2016b]] ; [[#Schleussner--2016|Schleussner et al., 2016]] ).

Beyond the usual research interest in crop yields which has dominated the current literature, recent studies, such as those in Japan, focus on the impacts of climate change on the ''quality'' of crops (see, for instance, [[#Sugiura--2013|Sugiura et al., 2013]] , for apple; as well as [[#Morita--2016|Morita et al., 2016]] , and [[#Masutomi--2019|Masutomi et al., 2019]] , for rice). A large-scale evaluation by [[#Ishigooka--2017|Ishigooka et al. (2017)]] shows that the increased risk in rice production brought about by temperature increase may be avoided by selecting an optimum transplanting date considering both yield and quality. More studies of this nature have to be conducted for other crops in different locations to better understand and adapt to the negative impacts of the changing climate on the quality of crops ( [[#Ahmed--2016|Ahmed and Stepp, 2016]] ).

New studies have projected the ''likely'' negative impact of pests in Asian agriculture. The golden apple snail ( ''Pomacea canaliculate'' ), which is among the world’s 100 most notorious invasive alien species, threatens the top Asian rice-producing countries, including China, India, Indonesia, Bangladesh, Vietnam, Thailand, Myanmar, the Philippines and Japan, with the predicted increase in climatically suitable habitats in 2080 ( [[#Lei--2017|Lei et al., 2017]] ). Similarly, a study by ( [[#Shabani--2018|Shabani et al., 2018]] ) in Oman projected that the pest of date palm trees, Dubas bug ( ''Ommatissus lybicus'' Bergevin), could reduce the crop yield by 50% under future climate scenarios.

While there is general agreement that CO 2 promotes growth and productivity of plants through enhanced photosynthesis, there remains uncertainty on the extent to which carbon fertilisation will influence agricultural production in Asia as it interacts with increasing temperatures, changing water availability and the different adaptation measures employed ( [[#Ju--2013|Ju et al., 2013]] ; [[#Jat--2016|Jat et al., 2016]] ; [[#ADB--2017b|ADB, 2017b]] ). As global warming compounds beyond 1.5°C, however, the likelihood of adverse impacts on agricultural and food security in many parts of developing Asia increases ( [[#Mendelsohn--2014|Mendelsohn, 2014]] ; [[#IPCC--2018b|IPCC, 2018b]] ). There is a growing trend towards more integrated studies and modelling that combines biophysical and socioeconomic variables (including management practices) in the context of changing climate to reduce uncertainty associated with future impacts of climate change on the agriculture sector (see, for instance, [[#Mason-D’Croz--2016|Mason-D’Croz et al., 2016]] ; [[#Smeets%20Kristkova--2016|Smeets Kristkova et al., 2016]] ; [[#Gaydon--2017|Gaydon et al., 2017]] ).

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<span id="livestock-production"></span>
===== 10.4.5.2.3 Livestock production =====

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There is hardly any mention about the impacts of climate change on livestock production in the Asia chapter of AR5 due to limited studies on this area. This scarcity of information persists to the current assessment with very scant information on the projected impacts and adaptation aspects of livestock production ( [[#Escarcha--2018a|Escarcha et al., 2018a]] ). The use of scenarios and models to determine alternative futures with participatory engagement processes has been recommended for informed policy and decision making with potential application in the livestock sector ( [[#Mason-D’Croz--2016|Mason-D’Croz et al., 2016]] ). Of the limited assessment available, a study on the smallholders’ risk perceptions of climate change impacts on water-buffalo production systems in Nueva Ecija, the Philippines, identified feed availability and animal health as the production aspects most severely affected by multiple weather extremes ( [[#Escarcha--2018b|Escarcha et al., 2018b]] ). In the Mongolian Altai Mountains, early snowmelt and an extended growing season have resulted in reduced herder mobility and prolonged pasture use, which has in turn initiated grassland degradation ( [[#Lkhagvadorj--2013a|Lkhagvadorj et al., 2013a]] ). Furthermore, reduced herder mobility has increased the pressure on forests resulting in increased logging for fuel and construction wood and reduced regeneration due to browsing damage by increasing goat populations ( [[#Khishigjargal--2013|Khishigjargal et al., 2013]] ; [[#Dulamsuren--2014|Dulamsuren et al., 2014]] ).

In terms of direct impacts, climate-change-induced heat stress and reduced water availability are ''likely'' to generally have negative effects on livestock ( [[#ADB--2017b|ADB, 2017b]] ). In the HKH region, climate change has induced severe impacts on livestock through degradation of rangelands, pastures and forests ( [[#Hussain--2019|Hussain et al., 2019]] ). However, indirect effects may be positive such as in Uzbekistan and South Asia where alfalfa and grassland productivity is expected to improve under warming conditions, which have beneficial effects on livestock production ( [[#Sutton--2013|Sutton et al., 2013]] ; [[#Weindl--2015|Weindl et al., 2015]] ).

At the global level, analysis involving 148 countries in terms of the potential vulnerability of their livestock sector to climate and population change shows that some Asian nations, particularly Mongolia, are ''likely'' to be the most vulnerable while South Asia is the most vulnerable region ( [[#Godber--2014|Godber and Wall, 2014]] ).

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<span id="farming-systems-and-crop-areas"></span>
===== 10.4.5.2.4 Farming systems and crop areas =====

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There is new evidence since AR5 that farming systems and crop areas will change in many parts of Asia in response to climate change. In South Asia, a study in Nepal showed that farmers are inclined to change practices in cropland use to reduce climate-change risk ( [[#Chalise--2016|Chalise and Naranpanawa, 2016]] ). In India, climate change is also predicted to lead to boundary changes in areas suitable for growing certain crops (Srinivasa [[#Rao--2016|Rao et al., 2016]] ). A study in Bangladesh revealed a shift in crop choices among farmers, implying changes in the future rice-cropping pattern. Specifically, a temperature increase will compel farmers to choose irrigation-based Boro, Aus and other crops in favour of the rain-fed Aman rice crop ( [[#Moniruzzaman--2015|Moniruzzaman, 2015]] ).

In the coastal area of Odisha in India, adverse impact on the agriculture sector is anticipated considering the increasing temperature trends over the past 30 years for all the seasons ( [[#Mishra--2014|Mishra and Sahu, 2014]] ). In a national study that groups Bangladesh into 16 sub-regions with similar farming areas, simulations of a 62 cm rise in mean sea level project damages to production because of area loss in excess of 31% in sub-region 15 and nearly 40% in sub-region 16 ( [[#Ruane--2013|Ruane et al., 2013]] ). Also in Bangladesh, a study on predicting the design of water requirements for winter paddy rice under climate change conditions shows that agricultural water resource management will help minimise drought risk and implement future agricultural water resource policies (Islam et al., 2018) that may have important implications for crop areas and production.

In East Asia, the observed changes in agricultural flooding in different parts of China could influence farming systems and crop areas ( [[#Zhang--2016b|Zhang et al., 2016b]] ) as extreme events intensify in the context of changing climate. Agricultural management practice in China may also change to optimise soil organic carbon sequestration ( [[#Zhang--2016a|Zhang et al., 2016a]] ). A study on projected irrigation requirements under climate change using a soil-moisture model for 29 upland crops in the Republic of Korea showed that water scarcity is a major limiting factor for sustainable agricultural production ( [[#Hong--2016|Hong et al., 2016]] ). In terms of drought, despite increasing future precipitation in most scenarios, crop-specific agricultural drought is expected to be a significant risk due to rainfall variability ( [[#Lim--2019a|Lim et al., 2019a]] ). On the other hand, a projected rise in water availability in the Korean Peninsula using multiple regional climate models and evapotranspiration methods indicates that it will ''likely'' increase agricultural productivity for both rice and corn, but would decrease significantly in rain-fed conditions ( [[#Lim--2017b|Lim et al., 2017b]] ). Thus, irrigation and soil-water management will be a major factor in determining future farming systems and crop areas in the country.

Global studies on climate-change-induced hotspots of heat stress on agricultural crops show that large suitable cropping areas in Central and Eastern Asia, and the northern part of the Indian subcontinent, are under heat stress risk under the A1B emissions scenario ( [[#Teixeira--2013|Teixeira et al., 2013]] ) and hence may reduce cropping areas in these regions. In Japan, the projected decline in rice yield in some areas suggests that the current rice-producing regions would be divided into suitable and unsuitable areas as temperatures increases ( [[#Ishigooka--2017|Ishigooka et al., 2017]] ), with important implications regarding the possible shift in cropping area. Similarly, it has been shown that there will be change in the geographic distribution of the occurrence of poor skin colour of table-grape berries ( [[#Sugiura--2019|Sugiura et al., 2019]] ) and suitable areas for cultivation of subtropical citrus ( [[#Sugiura--2014|Sugiura et al., 2014]] ) in Japan by the middle of the 21st century.

There is emerging evidence from modelling and field experimentation that designing future farming systems and crop areas that will promote sustainable development in Asia in the context of climate change would have to incorporate not only productivity and price considerations but also how to moderate temperature increase, enhance water conservation and optimise GHG mitigation potential ( [[#Sapkota--2015|Sapkota et al., 2015]] ; [[#Zhang--2016a|Zhang et al., 2016a]] ; [[#Ko--2017|Ko et al., 2017]] ; [[#Lim--2017b|Lim et al., 2017b]] ). The effects of agricultural landscape change on ecosystem services also need to be understood and taken into account in designing farming systems and allocating farm areas ( [[#Lee--2015b|Lee et al., 2015b]] ; [[#Zanzanaini--2017|Zanzanaini et al., 2017]] ).

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<span id="food-security"></span>
==== 10.4.5.3 Food Security ====

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[[#FAO--2001|FAO (2001)]] defines food security as ‘a situation that exists when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life’. There is significant evidence that climate change significantly undermines both agricultural production and food security in Asia ( [[#ADB--2017b|ADB, 2017b]] ). Increasing evidence from sub-regions and individual countries suggests that climate-related hazards, such as increasing temperature, changing rainfall, SLR, drought, flooding and the more frequent and intense occurrences of El Niño–Southern Oscillation events, all impact agricultural production with significant effects on food security. All these hazards interact with non-climatic factors, such as competing demand for scarce water resources, rural–urban migration, food prices and increasing food demand in the long term, and poor governance, among other things, that may worsen food insecurity in the region ( [[#Montesclaros--2021|Montesclaros and Teng, 2021]] ).

In West Asia, particularly in Saudi Arabia and Yemen, increasing water scarcity brought about by temperature rise is anticipated to have a severe impact on agriculture and food production that undermines food security ( [[#Al-Zahrani--2019|Al-Zahrani et al., 2019]] ; [[#Baig--2019|Baig et al., 2019]] ). Saudi Arabia, for instance, was forced to phase out its wheat production starting in 2016 and fully rely on importation to conserve its drying fossil water resources ( [[#Al-Zahrani--2019|Al-Zahrani et al., 2019]] ), a situation which is also linked to a water governance issue.

In Central Asia, a study using a bioeconomic farm model shows very large differences in climate change impacts across farming systems at the subnational level. Large-scale commercial farms in the northern regions of Kazakhstan will have positive income gains, while small-scale farms in arid zones of Tajikistan will experience a negative impact with ''likely'' effects on farm income security ( [[#Bobojonov--2014|Bobojonov and Aw-Hassan, 2014]] ). Impacts on farmers’ income in western Uzbekistan will also significantly vary and could fall by as much as 25% depending on the extent of temperature increase and water-use efficiency ( [[#Bobojonov--2016|Bobojonov et al., 2016]] ).

In a regional study among South Asian countries using an integrated assessment modelling framework, changes in rice and wheat productions brought about by climate change are anticipated to engender wild price volatilities in the markets ( [[#Cai--2016|Cai et al., 2016]] ). Price spikes are projected for 2015–2040 in all South Asian regions with India, Pakistan and Sri Lanka predicted to experience increasingly much higher rice and wheat prices than under the baseline scenario, creating major concerns about food affordability and food security. This will ''likely'' severely affect the overall economic growth of these countries since they are mainly agriculture-driven economies.

A study on mapping global patterns of drought risk projected an increase in drought frequency and intensity in the populated areas of South to Central Asia extensively used for crop and livestock production with serious repercussion to food security and potential civil conflict in the medium to long term ( [[#Carrão--2016|Carrão et al., 2016]] ). In Southeast Asia, a Philippine study on the relationship between seasonal rainfall, agricultural production and civil conflict suggests that the projected change towards wetter rainy seasons and drier dry seasons in many parts of the country will lead to more civil conflict ( [[#Crost--2018|Crost et al., 2018]] ) with negative implications for food and human security. Similarly, floods and higher food prices are also associated with higher risks of social unrest in Asia that may undermine food security ( [[#Hendrix--2015|Hendrix and Haggard, 2015]] ; [[#Ide--2021|Ide et al., 2021]] ).

Food insecurity will be localised across Asia where one part of the country or sub-region will be more food secured while the others, more insecure. This will require in-country or sub-regional trade and development cooperation to minimise the adverse impacts of food insecurity associated with the changing climate ( [[#Li--2014a|Li et al., 2014a]] ; [[#Abid--2016|Abid et al., 2016]] ).

<div id="10.4.5.4" class="h3-container"></div>

<span id="key-drivers-to-vulnerability-1"></span>
==== 10.4.5.4 Key Drivers to Vulnerability ====

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There is ''high confidence'' that agriculture will continue to be among the most vulnerable sectors in Asia in light of the changing climate ( [[#Mendelsohn--2014|Mendelsohn, 2014]] ; [[#ADB--2017b|ADB, 2017b]] ). Among the more vulnerable areas include mountain agriculture where fluctuation in crop production ( [[#Poudel--2016|Poudel and Shaw, 2016]] ; [[#Hussain--2019|Hussain et al., 2019]] ), and food insufficiency, is more widespread than in lowland areas ( [[#Poudel--2015|Poudel and Shaw, 2015]] ; [[#Kohler--2009|Kohler and Maselli, 2009]] ). Also vulnerable are flood-prone areas like the Vietnam Mekong River Delta where 39% of the total rice area is exposed to sustained flood risks ( [[#Wassmann--2019a|Wassmann et al., 2019a]] ). Increasing temperatures and changing precipitation levels will persist to be important vulnerability drivers that will shape agricultural productivity particularly in South Asia, Southeast Asia and Central Asia as well as in selected areas of the region. With the increasing likelihood of extreme weather events, such as strong typhoons in the Philippines, the agriculture sector in the typhoon-prone areas of Southeast and East Asia, as well as the Indus Delta, will be more vulnerable to crop destruction (Mallari and Ezra, 2016). Projections on increasing SLR and flooding, such as those in Bangladesh and the Mekong Delta, will submerge and decrease crop production areas and severely affect agriculture and fishery sectors, but will also trigger outmigration from these areas ( [[#ADB--2017b|ADB, 2017b]] ).

Vulnerability of aquaculture-related livelihoods to climate change was assessed at the global scale using the MAGICC/SCENGEN climate modelling tools, and Vietnam and Thailand were identified as most vulnerable in brackish-water aquaculture production ( [[#Handisyde--2017|Handisyde et al., 2017]] ). China, Vietnam and the Philippines are also ranked highly vulnerable in marine production. Moreover, a recent vulnerability assessment of Korean aquaculture based on predicted changes in seawater temperature and salinity according to RCP8.5 indicated that vulnerability was highest for seaweed, such as laver and sea mustard, while fish, shrimp and abalone are relatively less vulnerable as they are less sensitive to high water temperature and their farming environments are controllable to a large extent ( [[#Kim--2019a|Kim et al., 2019a]] ). In Indonesia, farming of whiteleg shrimp ( ''Litopenaeus vannamei'' ) has been found to be vulnerable to increased rainfall and temperature decrease ( [[#Puspa--2018|Puspa et al., 2018]] ).

Climate-change-induced vulnerability, however, is complicated by non-climate drivers. In Thailand, for instance, a 38% reduction (from 21,486 to 13,328 million at the present value (1 USD = 33.54 THB) in the export values of rice and products in the last quarter of 2011 has been attributed not only to the impact of tropical cyclone Nock-Ten on Thai rice export but also the economic slowdown in Thailand during 2011–2012 ( [[#Nara--2014|Nara et al., 2014]] ).

Considering the high vulnerability of Asia to climate change as a whole, there is a need to look at the drivers of vulnerability in an integrated and comprehensive manner. The increasing interest on nexus studies that links climate-change impacts on agriculture with the other sectors like water, energy, land-use change, urbanisation, poverty, economic liberalisation and others (see, for example, [[#Takama--2016|Takama et al., 2016]] ; [[#Aich--2017|Aich et al., 2017]] ; [[#Eslamian--2017|Eslamian et al., 2017]] ; [[#Duan--2019b|Duan et al., 2019b]] ) could contribute to a systemwide vulnerability reduction and an important initial step towards a more climate-resilient future.

<div id="10.4.5.5" class="h3-container"></div>

<span id="adaptation-options-3"></span>
==== 10.4.5.5 Adaptation Options ====

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Since AR5, there has been a surge in the volume of literature that documents and assesses the different adaptation practices already employed in Asian agriculture as well as those that provide future adaptation options. There is ''robust evidence'' that a variety of adaptation practices already employed in agriculture and fisheries are valuable in reducing the negative effects of current climate anomalies but may not be sufficient to fully offset the adverse impacts of future climate scenarios. Recent literature, therefore, focuses on how to build on current adaptation initiatives and processes to improve current and future outcomes ( [[#Iizumi--2019|Iizumi, 2019]] ).

Asian farmers and fishers already employ a variety of adaptation practices to minimise the adverse impacts of climate change. In a recent systematic and comprehensive review of farmers’ adaptation practices in Asia, Shaffril et al. (2018) categorised these practices into different forms such as crop management, irrigation and water management, farm management, financial management, physical infrastructure management and social activities. ‘Climate-smart agriculture’–an integrated approach for developing agricultural strategies that address the intertwined challenges of food security and climate change–is increasingly being promoted in many parts of the region, especially in Southeast and South Asia, with potentially promising outcomes ( [[#Chandra--2017|Chandra et al., 2017]] ; [[#Khatri-Chhetri--2017|Khatri-Chhetri et al., 2017]] ; [[#Shirsath--2017|Shirsath et al., 2017]] ; [[#Westermann--2018|Westermann et al., 2018]] ; [[#Wassmann--2019b|Wassmann et al., 2019b]] ). Site-specific adaptations, such as those in Pakistan, include farmers’ utilisation of several adaptation techniques which include changing crop type and variety, and improving seed quality; fertiliser application and use of pesticides, and planting of shade trees; and water storage and farm diversification ( [[#Fahad--2018|Fahad and Wang, 2018]] ), as well as the implementation of comprehensive climate information services for farming communities ( [[#World%20Meteorological%20Organization--2017|World Meteorological Organization, 2017]] ).

Adaptation measures are also beneficial to small-scale fishers and fish farmers ( [[#Miller--2018|Miller et al., 2018]] ), and through fisheries management plans (FMP) and Early warning systems, the Asian region is reducing climate impact ( [[#FAO--2018c|FAO, 2018c]] ). The most common FMPs adopted in different Asian countries are limits to fishing gear, licensing schemes and seasonal closures ( [[#ILO--2015|ILO, 2015]] ), protection of nursery grounds, providing alternative livelihoods ( [[#Azad--2017|Azad, 2017]] ), limiting fish aggregating devices (FADs) and introduction of monitoring and control tools ( [[#Department%20of%20Fisheries%20(Thailand)--2015|Department of Fisheries (Thailand), 2015]] ). Fishers’ strong sense of belonging to their place of residence and the sense of responsibility to protect the vulnerable fish stock has been advantageously used for developing cooperatives and starting community-based fisheries management (FAO, 2012; [[#ILO--2015|ILO, 2015]] ; [[#Shaffril--2017|Shaffril et al., 2017]] ), and these initiatives have yielded positive results.

In aquaculture, most households in shrimp communities rely on process-oriented multiple coping mechanisms such as consumption smoothing, income smoothing and migration that enhance farmers’ resilience to climate anomalies ( [[#Kais--2018|Kais and Islam, 2018]] ). Diversification and integration of varied resources and interventions in feed and husbandry are seen to help the aqua farmers increase their profits and overcome the impacts of climate change ( [[#Henriksson--2019|Henriksson et al., 2019]] ). Strategies like polyculture, integrated multitrophic aquaculture (IMTA) and recirculating aquaculture systems (RAS) have been suggested to increase aquaculture productivity, environmental sustainability and climate change adaptability ( [[#Ahmed--2019c|Ahmed et al., 2019c]] ; [[#Tran--2020|Tran et al., 2020]] ). In Bangladesh, several adaptation measures, such as integrated community-based adaptation strategies ( [[#Akber--2017|Akber et al., 2017]] ) and integrated coastal zone management ( [[#Ahmed--2015|Ahmed and Diana, 2015]] ), have been recommended to increase climate resilience among shrimp farmers.

More recently, nature-based solutions (NbS) have gained attention globally to enhance climate adaptation. In the context of agriculture, NbS are seen as cost-effective interventions that can increase resilience in food production while advancing climate mitigation and improving the environment ( [[#Iseman--2021|Iseman and]] [[#Miralles-Wilhelm--2021|Miralles-Wilhelm, 2021]] ). Experiences in implementing NbS in agricultural landscapes have been documented both in agriculture and fisheries sectors that promote production while providing co-benefits such as environmental protection and sustainability ( [[#Miralles-Wilhelm--2021|Miralles-Wilhelm, 2021]] ).

Despite the numerous adaptation measures already employed, there is sufficient evidence that farmers’ current adaptation practices are inadequate to offset the worsening climate change impacts. A more comprehensive approach that integrates economic and social strategies with other measures is seen to reduce climate vulnerability. For instance, agriculture insurance is viewed as a promising adaptation approach to reduce risks and increase the financial resilience of farmers and herders in many Asian countries ( [[#Prabhakar--2018|Prabhakar et al., 2018]] ; [[#Matheswaran--2019|Matheswaran et al., 2019]] ; [[#Nguyen--2019|Nguyen et al., 2019]] ; [[#Stringer--2020|Stringer et al., 2020]] ). Similarly, participation of multiple stakeholders from all relevant sectors at different levels in adaptation planning and decision making is seen as an important factor in improving outcomes ( [[#Arunrat--2017|Arunrat et al., 2017]] ; [[#Hochman--2017|Hochman et al., 2017]] ; [[#Chandra--2018|Chandra and McNamara, 2018]] ). Moreover, while adaptation is local and context specific, the following general adaptation-related strategies are distilled from the current literature, based on the Asian experience, to enhance current and future adaptations (see Figure 10.7 for details or examples of each strategy):

<div id="_idContainer022" class="Figure"></div>

[[File:e2ea5dbb8d6801ed8dc68e775a652df1 IPCC_AR6_WGII_Figure_10_007.png]]

'''Figure 10.7 |''' '''Adaptation-related strategies in Asian agriculture to enhance current and future adaptations.'''

<ul>
<li>Create enabling policies ( [[#Chen--2018d|Chen et al., 2018d]] ) and enhance institutional capacity ( [[#Wang--2014|Wang et al., 2014]] ; [[#Hirota--2019|Hirota and Kobayashi, 2019]] )</li>
<li>Improve adaptation planning and decision making ( [[#Xu--2014|Xu and Grumbine, 2014]] ; [[#Asmiwyati--2015|Asmiwyati et al., 2015]] ; [[#Dissanayake--2017|Dissanayake et al., 2017]] ; [[#Hochman--2017|Hochman et al., 2017]] ; [[#Qiu--2018|Qiu et al., 2018]] ; [[#Shuaib--2018|Shuaib et al., 2018]] ; [[#Aryal--2020b|Aryal et al., 2020b]] ; [[#Ruzol--2021|]] [[#Ruzol--2021|Ruzol and Pulhin, 2021]] ; [[#Ruzol--2021|Ruzol et al., 2021]] )</li>
<li>Promote science-based adaptation measures ( [[#Alauddin--2014|Alauddin and Sarker, 2014]] ; [[#Sapkota--2015|Sapkota et al., 2015]] ; [[#Lim--2017b|Lim et al., 2017b]] )</li>
<li>Adopt an integrated approach to improve adaptation ( [[#Teixeira--2013|Teixeira et al., 2013]] ; [[#Yamane--2014|Yamane, 2014]] ; [[#Abid--2016|Abid et al., 2016]] ; [[#Sakamoto--2017|Sakamoto et al., 2017]] ; [[#Sawamura--2017|Sawamura et al., 2017]] ; [[#Trinh--2018|Trinh et al., 2018]] )</li>
<li>Invest in critical infrastructure ( [[#Cai--2016|Cai et al., 2016]] ; [[#Rezaei--2018|Rezaei and Lashkari, 2018]] )</li>
<li><p>Address farmers’ adaptation barriers ( [[#Alauddin--2014|Alauddin and Sarker, 2014]] ; [[#Pulhin--2016|Pulhin et al., 2016]] ; [[#Fahad--2018|Fahad and Wang, 2018]] ; [[#Gunathilaka--2018|Gunathilaka et al., 2018]] ; [[#Almaden--2019b|Almaden et al., 2019b]] )</p>
<span id="cities-settlements-and-key-infrastructures"></span>
=== 10.4.6 Cities, Settlements and Key Infrastructures ===
</li></ul>

<div id="10.4.6" class="h2-container"></div>

<span id="cities-settlements-and-key-infrastructures-1"></span>
=== 10.4.6 Cities, Settlements and Key Infrastructures ===

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Cities across Asia have large populations exposed to climate risks but also present an opportunity for concerted climate action ( [[#Revi--2014|Revi et al., 2014]] ; [[#Chu--2017|Chu et al., 2017]] ; [[#Revi--2017|Revi, 2017]] ; [[#Khosla--2019|Khosla and Bhardwaj, 2019]] ) and report numerous examples of adaptation actions at various stages of planning and implementation ( [[#Dulal--2019|Dulal, 2019]] ; [[#Singh--2021b|Singh et al., 2021b]] ). However, challenges specific (though not exclusive) to Asian cities such as uneven economic development, rapid land-use changes, increasing inequality, growing exposure to extreme events and environmental change, such as land subsidence (with antecedent impacts on people and infrastructure), and large, socially differentiated vulnerable populations, remain key concerns as Asian cities simultaneously tackle challenges of sustainable development and equitable climate action.

<div id="10.4.6.1" class="h3-container"></div>

<span id="sub-regional-diversity-1"></span>
==== 10.4.6.1 Sub-regional Diversity ====

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By 2050, urban areas are expected to add 2.5 billion people, 90% of whom will be in Asia and Africa ( [[#UNDESA--2018|UNDESA, 2018]] ). Critically, this urban population increase will be concentrated in India, China and Nigeria, with India and China adding 416 million and 255 million urban dwellers, respectively, between 2018 and 2050 ( [[#UNDESA--2018|UNDESA, 2018]] ).

Asia is home to 54% of the world’s urban population, and by 2050, 64% of Asia’s 3.3 billion people will be living in cities. Asia is also home to the world’s largest urban agglomerations: Tokyo (37 million inhabitants), New Delhi (29 million) and Shanghai (26 million) are the top three with Cairo, Mumbai, Beijing and Dhaka home to nearly 20 million people each ( [[#UNDESA--2018|UNDESA, 2018]] ). By 2028, New Delhi is projected to become the most populous city in the world. In certain parts of Asia (e.g., some cities in Japan and the Republic of Korea), a steep decline in urban population is projected, mainly due to declining birth rates ( [[#Hori--2020|Hori et al., 2020]] ). Within Asia, rates of urbanisation differ sub-regionally. Eastern Asia has seen the most rapid urban growth with the percentage of urban population having more than tripled from 18 to 60% between 1950 and 2015, while rates of urbanisation have decreased in West Asia and remained steady in Central Asia ( [[#UNDESA--2018|UNDESA, 2018]] ).

Asian cities are seeing growing income inequality, with rural poverty being replaced by urban poverty ( [[#ADB--2013|ADB, 2013]] ). Regional studies show high and growing inequality within Indian and Chinese urban areas and decreasing rural–urban income gaps in Thailand and Vietnam ( [[#Baker--2017|Baker and Gadgil, 2017]] ; [[#Imai--2018|Imai and Malaeb, 2018]] ). Critically, East Asia and the Asia–Pacific in general continue to house the world’s largest population of slum dwellers at 250 million, with most of them in China, Indonesia and the Philippines, and the highest rates of urban poverty in Papua New Guinea, Vanuatu, Indonesia and the Lao PDR ( [[#McIlreavy--2015|McIlreavy, 2015]] ; [[#Baker--2017|Baker and Gadgil, 2017]] ). A lot of urbanisation, especially in South Asia, is also ‘hidden’ due to poor, competing definitions of what is ‘urban’ and limited data ( [[#Ellis--2016|Ellis and Roberts, 2016]] ).

<div id="10.4.6.2" class="h3-container"></div>

<span id="key-drivers-of-vulnerabilities"></span>
==== 10.4.6.2 Key Drivers of Vulnerabilities ====

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In Asian cities, climatic hazards such as changes in precipitation and, during the Asian monsoon, SLR, cyclones, flooding, dust storms, heatwaves and permafrost thawing ( [[#Byers--2018|Byers et al., 2018]] ; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#Rogelj--2018|Rogelj et al., 2018]] ; Shiklomanov, 2019), as well as non-climatic vulnerabilities such as non-climatic hazards (e.g., seismic hazards), inadequate infrastructure and services, unplanned urbanisation, socioeconomic inequalities and existing adaptation deficits ( [[#Johnson--2013|Johnson et al., 2013]] ; [[#Araos--2016|Araos et al., 2016]] ; [[#de%20Leon--2017|de Leon and Pittock, 2017]] ; [[#Meerow--2017|Meerow, 2017]] ; [[#Dulal--2019|Dulal, 2019]] ) interact to shape overall urban risk ( [[#Shaw--2016a|Shaw et al., 2016a]] ; [[#Rumbach--2017|Rumbach and Shirgaokar, 2017]] ; [[#Dodman--2019|Dodman et al., 2019]] ). Caught at the intersection of high exposure, socioeconomic vulnerability and low adaptive capacities, informal settlements in urban and peri-urban areas are particularly at risk ( ''robust evidence, high agreement'' ) ( [[#Meerow--2017|Meerow, 2017]] ; [[#Rumbach--2017|Rumbach and Shirgaokar, 2017]] ; [[#Byers--2018|Byers et al., 2018]] ).

<div id="10.4.6.3" class="h3-container"></div>

<span id="observed-and-projected-impacts"></span>
==== 10.4.6.3 Observed and Projected Impacts ====

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<div id="10.4.6.3.1" class="h4-container"></div>

<span id="multi-hazard-risk"></span>
===== 10.4.6.3.1 Multi-hazard risk =====

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Of the multi-hazard global average annual loss (AAL) [[#footnote-009|4]] of 293 billion USD, 170 billion USD (58%) is in the Asia Pacific region ( [[#UNISDR--2017|UNISDR, 2017]] ). Of the top ten highest AALs associated with multi-hazards, six are in Asia (Japan, China, Republic of Korea, India, the Philippines and Taiwan, Province of China) ( [[#UNISDR--2017|UNISDR, 2017]] ). As per [[#Gu--2015|Gu et al. (2015)]] , 56% of cities with populations greater than 300,000 in 2014 are exposed to at least one of the six physical hazards (cyclones, floods, droughts, earthquakes, landslides and volcanic eruptions). Cities in areas highly exposed and vulnerable to multiple hazards were also the ones that grew rapidly in population between 1950 and 2014, implying greater infrastructural investments in climate-sensitive areas. Among 27 cities highly exposed to multiple disasters, 13 cities had a population of 1 million or more in 2014. Among them were three megacities, Tokyo (Japan), Osaka (Japan) and Manila (the Philippines), with more than 10 million inhabitants exposed to three or more hazards. Seven other cities with 1 million inhabitants or more in Asia were at high risk of three or more types of disaster. Manila is highly vulnerable to economic losses and disaster-related mortality from all six types of disasters. Moscow (Russia) is the only megacity not exposed to the risk of any of the six types of physical hazards analysed (cyclones, floods, droughts, earthquakes, landslides and volcano eruptions). Of the eight megacities most vulnerable to disaster-related mortality, seven–Tokyo, Osaka, Karachi, Kolkata, Manila, Tianjin and Jakarta, totalling 143 million people–are in Asia ( [[#Gu--2015|Gu et al., 2015]] ).

<div id="10.4.6.3.2" class="h4-container"></div>

<span id="extreme-temperatures-and-heatwaves"></span>
===== 10.4.6.3.2 Extreme temperatures and heatwaves =====

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Urbanisation and climate change interact to drive an urban heat island (UHI) effect across Asian cities ( [[#Hauck--2016|Hauck et al., 2016]] ; [[#Chapman--2017|Chapman et al., 2017]] ; also see Figure 6.4 in Chapter 6). Three regions which are expected to see higher maximum wet-bulb temperature than global averages are southwest Asia around the Persian Gulf and Red Sea, South Asia in the Indus and Ganges river valleys, and eastern China ( [[#Im--2017|Im et al., 2017]] ; Perkins-Kirkpatrick et al., 2020).

Impacts of heatwaves at 1.5°C and 2°C in cities are substantially larger than under the present climate ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ). In South Asia particularly, more intense heatwaves of longer durations and occurring at a higher frequency are projected with ''medium confidence'' over India ( [[#Murari--2015|Murari et al., 2015]] ) and Pakistan (IPCC AR6, WGI, Table 11.5; [[#Ali--2018|Ali et al., 2018]] ; [[#Nasim--2018|Nasim et al., 2018]] ; [[#Ali--2020|Ali et al., 2020]] ; [[#IPCC--2021|IPCC, 2021]] ). At the city level, these projections could translate into significant impacts: at 1.5°C, on average, every year Kolkata will experience heat equivalent to the 2015 record heatwaves; Karachi about once every 3.6 years; and under 2°C warming, both regions could expect such heat annually ( [[#Matthews--2017|Matthews et al., 2017]] ). In Pakistan, Hyderabad is ''likely'' to be the hottest city by 2100 with the highest average temperature reaching 29.9°C (RCP4.5) to 32°C (RCP8.5) followed by Jacobabad, Bahawalnagar and Bahawalpur cities ( [[#Ali--2020|Ali et al., 2020]] ). The frequency of heatwave days (HWF) will increase by 22.8, 22.3 and 26.5 d yr –1 in northern, eastern and western Japan, respectively, with megacities such as Tokyo, Osaka and Nagoya seeing large increases in HWF and related deaths ( [[#Nakano--2013|Nakano et al., 2013]] ).

In China’s urban agglomerations, an increase in the global warming from 1.5°C to 2°C is ''likely'' to exacerbate the intensity of extreme maximum temperature 4.1 times ( [[#Yu--2018d|Yu et al., 2018d]] ). From 1995 to 2014 China’s urban agglomerations (Beijing–Tianjin–Hebei, Yangtze River Delta, Middle Yangtze River, Chongqing–Chengdu and Pearl River Delta) experienced no more than three heat danger d yr –1 , which is projected to increase to 3–13 d by 2041–2060 and 8–67 d by 2081–2100 under high-emissions shared socioeconomic pathways SSP3-7.0 and SSP5-8.5, resulting in approximately 260 million people (19% of the total population of China) and 310 million people (39% of the total population), respectively, facing more than three heat-danger days annually ( [[#Zhang--2021|Zhang et al., 2021]] ). This projected risk exposure is reduced under low-emissions pathways (SSP1-2.6 and SSP2-4.5), where annual heat-danger days will remain similar to current levels or increase slightly ( [[#Zhang--2021|Zhang et al., 2021]] ).

Critically, these projections of higher temperatures will have a significant impact on heat-related morbidity and mortality, labour productivity, mental health, and health and well-being outcomes across all sub-regions of Asia ( ''medium evidence, high confidence'' ) ( [[#Pal--2016|Pal and Eltahir, 2016]] ; [[#Im--2017|Im et al., 2017]] ; [[#Arifwidodo--2019|Arifwidodo et al., 2019]] ; [[#Arshad--2020|Arshad et al., 2020]] ). In West Asia and the North China Plain especially, extreme wet-bulb temperatures are expected to approach, and possibly exceed, the physiological threshold for human adaptability (35°C) ( [[#Pal--2016|Pal and Eltahir, 2016]] ; [[#Kang--2018|Kang and Eltahir, 2018]] ). By the end of the century, under higher projections (RCP8.5), the daily maximum wet-bulb temperature is expected to exceed the survivability threshold across most of South Asia ( [[#Im--2017|Im et al., 2017]] ). City-specific studies articulate what these regional projections will mean for urban populations. For example, at 1.5°C warming, without adaptation, annual heat-related mortality in 27 major cities across China is projected to increase from 32.1 per million inhabitants annually in 1986–2005 to 48.8–67.1 per million. This number increases to 59.2–81.3 per million for 2°C warming ( [[#Wang--2019a|Wang et al., 2019a]] ). In the Republic of Korea, deaths from heat disorders are expected to increase approximately fivefold under the RCP4.5 and 7.2-fold under RCP8.5 by 2060 compared with the current baseline value of ~23 people per summer ( [[#Kim--2016a|Kim et al., 2016a]] ). Importantly, heat exposure is differentiated within cities: it disproportionally affects the poorest populations (Lohrey et al., 2021) and those with lesser access to green spaces ( [[#Arifwidodo--2020|Arifwidodo and Chandrasiri, 2020]] ).

<div id="10.4.6.3.3" class="h4-container"></div>

<span id="precipitation-extremes-excess-rainfall-drought-and-water-scarcity"></span>
===== 10.4.6.3.3 Precipitation extremes: excess rainfall, drought and water scarcity =====

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Warming from 1.5°C to 2°C will increase extreme precipitation events across Asia especially over East and South Asia ( ''medium evidence, high agreement'' ) (Zhang et al. 2018; [[#Supari--2020|Supari et al., 2020]] ; [[#Zhang--2020b|Zhang et al., 2020b]] ). In East and Central Asia, under 1.5°C warming, extreme 1- and 5-d precipitation will increase by 28 and 15% relative to 1971–2000 ( [[#Zhang--2020b|Zhang et al., 2020b]] ). In China’s urban agglomerations, an increase in global warming from 1.5°C to 2°C is ''likely'' to increase the intensity of total precipitation of very wet days 1.8 times and double maximum 5-d precipitation ( [[#Yu--2018d|Yu et al., 2018d]] ). Extreme rainfall has direct and increasing consequences on urban flooding risk ( [[#Dasgupta--2013b|Dasgupta et al., 2013b]] ), which is further exacerbated by urbanisation trends that reduce permeability, divert water flow and disrupt watersheds ( [[#Chen--2015b|Chen et al., 2015b]] ; [[#Duan--2016|Duan et al., 2016]] ).

Urban extent in drylands in expected to increase from 2000 to 2030 with large expansions in West Asia, Central Asia, South Asia and China and antecedent impacts on exposure to drought and water scarcity ( [[#Güneralp--2015|Güneralp et al., 2015]] ). Urban dryland extent in West Asia will increase from 19,400 to 67,400 km 2 ( [[#Güneralp--2015|Güneralp et al., 2015]] ). In the Haihe River basin in China, the proportion of people exposed to droughts at 1.5°C (without accounting for population growth) is projected to decrease by 30.4% but increase by 74.8% at 2°C relative to people exposed in 1986–2005 (339.65 million) ( [[#Sun--2017|Sun et al., 2017]] ). About 411 million people living in 330 cities above 300,000 population are exposed to drought risk, which include three Asian megacities Delhi (India), Karachi (Pakistan) and Kolkata (India). Drought-related economic losses are also high in Dhaka (Bangladesh) ( [[#Pervin--2020|Pervin et al., 2020]] ), Istanbul (Turkey), Manila (the Philippines) and Shenzhen (China), and Manila is also highly vulnerable to drought-related mortality ( [[#Gu--2015|Gu et al., 2015]] ).

Increasing urban drought risk will also have cascading impacts on regions from where water is imported, exacerbating drought exposure beyond urban settlements and limiting water availability in certain regions (Chuah et al., 2018; [[#Garrick--2019|Garrick et al., 2019]] ; [[#Zhang--2020c|Zhang et al., 2020c]] ; [[#Zhao--2020|Zhao et al., 2020]] ). There is ''medium evidence'' ( ''high agreement'' ) that urban water insecurity is experienced differentially based on income, risk exposure, and assets, and that urban drought and water scarcity is causing material and non-material losses and damage ( [[#Singh--2021a|Singh et al., 2021a]] ). Importantly, in several Asian cities, flood and drought risk is expected to occur concurrently, especially in South Asia which is projected to see the largest increase in urban land exposed to both floods and droughts (25–32% increase in flood and drought risk between 2000 and 2030).

<div id="10.4.6.3.4" class="h4-container"></div>

<span id="sea-level-rise-and-coastal-flooding"></span>
===== 10.4.6.3.4 Sea level rise and coastal flooding =====

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Global assessments identify Asia as the most exposed region to SLR (see Section [https://www.ipcc.ch/chapter/10#CCP2.2 CCP2.2.1] ) in terms of the number of people living in low-elevation coastal zones and the number of people exposed to flooding from 1-in-100-years storm surge events ( [[#Neumann--2015|Neumann et al., 2015]] ; [[#Jevrejeva--2016|Jevrejeva et al., 2016]] ; [[#Kulp--2019|Kulp and Strauss, 2019]] ; [[#Abadie--2020|Abadie et al., 2020]] ; [[#Haasnoot--2021|Haasnoot, 2021]] ). Twelve of the top 20 countries exposed to SLR and associated flood events are in Asia, and of these, China, India, Bangladesh, Indonesia and Vietnam are estimated to have the highest total coastal population exposure ( [[#Neumann--2015|Neumann et al., 2015]] ; [[#Edmonds--2020|Edmonds et al., 2020]] ). Critically, regardless of the emissions scenario, 70% of the global population exposed to SLR and land subsidence are in eight Asian countries: China, Bangladesh, India, Vietnam, Indonesia, Thailand, the Philippines and Japan ( [[#Kulp--2019|Kulp and Strauss, 2019]] ). This is particularly worrisome since in highly populated low-lying coastal cities across Asia, it is estimated that land subsidence could be as influential as climate-induced SLR over the 21st century ( [[#Cao--2021|Cao et al., 2021]] ; [[#Nicholls--2021|Nicholls et al., 2021]] ). In East Asia and the Asia–Pacific in general (expected to see 0.2–0.5 m SLR), without adaptation, 1 million people (range of 0.3–2.2 million) are projected to be affected by submergence under RCP8.5 by 2095. Limiting warming will reduce this risk, and under RCP4.5, these numbers of people at risk will be reached by 2140. However, continuing on RCP8.5 increases risk exposure to 7 million (estimated range of 2–24 million people) ( [[#Haasnoot--2021|Haasnoot, 2021]] ). Notably, assuming present-day population and adaptation (in the form of existing protection standards), East and South Asia already have a large number of people at risk of a 1-in-100-years flooding event (63 million) because of relatively lower flood protection (except in China and Malaysia).

These global scenarios will have significant impacts on national and subnational populations. For example, in Bangladesh, under 0.44 and 2 m mean SLR, direct inundation is estimated to drive migration of 0.73–2.1 million people by 2100 ( [[#Davis--2018|Davis et al., 2018]] ). Such migration will have direct development implications: for example, destination locations could see additional demands on jobs (594,000), housing (197,000) and food (783 × 10 9 calories) by mid-century as a result of those displaced by SLR ( [[#Davis--2018|Davis et al., 2018]] ).

Among the 20 largest coastal cities with the highest flood losses by 2050, 13 are in Asia [[#footnote-008|5]] , with a regional concentration in South, Southeast and East Asia ( [[#Hallegatte--2013|Hallegatte et al., 2013]] ). Furthermore, 9 of these cities (Guangzhou, Kolkata, Tianjin, Ho Chi Minh City, Jakarta, Zhanjiang, Bangkok, Xiamen, Nagoya) also have an additional risk of subsidence due to SLR and flooding ( [[#Hallegatte--2013|Hallegatte et al., 2013]] ). Guangzhou, China, is estimated to be the most economically vulnerable city in the world to SLR by 2050, with estimated losses of 254 million USD yr –1 under 0.2 m SLR ( [[#Jevrejeva--2016|Jevrejeva et al., 2016]] ). With a 2°C warming, Guangzhou is expected to see SLR of 0.34 m; under 5°C warming, this number would rise to 1.93 m. A more recent estimate calculates expected damage in Guangzhou due to SLR under RCP8.5 to reach 331 billion USD by 2050 and 420 billion USD under the high-end scenario with figures doubling by 2070. By 2100, expected damage could reach 1.4 trillion USD under RCP8.5 and 1.8 trillion USD under the high-end scenario. Similarly, in Mumbai (India) SLR damages amount to US$ 112–162 billion by 2050 and could increase by a factor of 2.8–2.9 by 2070 ( [[#Abadie--2020|Abadie et al., 2020]] ). In coastal cities such as Bangkok and Ho Chi Minh City, projected land subsidence rates, mainly due to excessive groundwater extraction, are comparable to, or exceed, expected rates of SLR, resulting in an additional 0.2 m SLR by 2025 ( [[#Jevrejeva--2016|Jevrejeva et al., 2016]] ). In Shanghai, current annual damage by coastal inundation is estimated at 0.03% of local GDP; under RCP4.5, this increases to 0.8% by 2100 (uncertainty range of 0.4–1.4%) and is further exacerbated by land subsidence and socioeconomic development ( [[#Du--2020|Du et al., 2020]] ). It is important to note that these projections assume (a) no adaptation and (b) that damage repairs are undertaken and completed annually. Given these assumptions, while these estimates communicate the scale of projected impacts, they are indicators of possible damages in the absence of adaptation and ''not'' actual projections.

The SLR affects economic growth, its drivers and welfare outcomes ( [[#Hallegatte--2012|Hallegatte, 2012]] ; [[#Pycroft--2016|Pycroft et al., 2016]] ; [[#Lee--2020|Lee and Asuncion, 2020]] ) through (a) permanent loss of land and natural capital, (b) loss of infrastructure and physical capital, (c) loss of social capital and migration, (d) temporary floods, food insecurity and loss of livelihoods and (e) added expenditure for coastal protection. Without adaptation, direct damage to the GDP by 2080 due to SLR would be highest in Asia ( ''robust evidence, medium agreement'' ), with China losing between 64.2 billion USD (under A1B of 2.4°C by the 2050s and 3.8°C by the 2090s at 0.47 m), 95.8 billion USD (under the RAHM scenario of 1.4 m SLR by 2100 at 1.12 m) and 118.4 billion USD (at a high SLR of 2 m by 2100 at 1.75 m) in direct damages, and an additional 5.7, 4.5 and 4.5 billion USD, respectively, due to migration ( [[#Pycroft--2016|Pycroft et al., 2016]] ). Closely after China will be India, the Republic of Korea, Japan, Indonesia and Russia. Overall, Asia can experience direct losses of about 167.6 billion USD (at 0.47 m), 272.3 billion USD (at 1.12 m) or 338.1 billion USD (at 1.75 m), and an additional 8.5, 24 or 15 billion USD at the respective SLR projections, due to migration [[#footnote-007|6]] .

<div id="10.4.6.3.5" class="h4-container"></div>

<span id="tropical-cyclones"></span>
===== 10.4.6.3.5 Tropical cyclones =====

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Globally, there is ''high confidence'' that the proportion of intense tropical cyclones is expected to increase despite the total number of tropical cyclones being expected to decrease or remain unchanged ( [[#Arias--2021|Arias et al., 2021]] ), especially in Southeast and East Asia ( [[#Knutson--2015|Knutson et al., 2015]] ; [[#Yamamoto--2021|Yamamoto et al., 2021]] ). Historical trends from South Asia indicate that more lives are lost due to storm surge levels than the intensity of the cyclone (Niggol and Bakkensen, 2017). The number of people exposed to 1-in-100-years storm surge events is highest in Asia. China, India, Bangladesh, Indonesia and Vietnam have the highest numbers of coastal populations exposed ( [[#Neumann--2015|Neumann et al., 2015]] ) with Guangzhou, Mumbai, Shenzen, Tianjin, Ho Chi Minh City, Kolkata and Jakarta incurring losses of 1520 million USD due to coastal flooding in 2005 alone ( [[#Dulal--2019|Dulal, 2019]] ), although Jakarta is exposed to monsoonal storm surge. It is projected that by 2050, without adaptation, the annual losses incurred in these cities will increase to approximately 32 billion USD ( [[#Dulal--2019|Dulal, 2019]] ).

Globally, six of the top ten countries/places with the highest AAL associated with tropical cyclones are in Asia (Japan, Republic of Korea, the Philippines, China, Taiwan, Province of China, and India) ( [[#Mori--2021a|Mori et al., 2021a]] ). The AAL associated with storm surge is primarily concentrated in Japan, China, Hong Kong SAR of China, and India. The AAL associated with wind and storm surge relative to the existing capital stock in the country is highest in New Caledonia, Tonga, Vanuatu, Palau, the Philippines, Fiji and the Solomon Islands, indicating less resilience. For example, in Ise Bay, Japan, the current storm surges are estimated to lead to property and business damage of approximately 100.04 billion JPY with current adaptation (protective sea wall), but this can more than double to 236.49 billion JPY under climate-change-induced increases in storm surge intensity ( [[#Jiang--2016|Jiang et al., 2016]] ).

<div id="10.4.6.3.6" class="h4-container"></div>

<span id="riverine-floods"></span>
===== 10.4.6.3.6 Riverine floods =====

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Over one-third of Asian cities and about 932 million urban dwellers live in areas with high risk of flooding ( [[#Gu--2015|Gu et al., 2015]] ). Of 437 cities at low risk of flood exposure but highly vulnerable to flood-related economic losses, approximately half are in Asia ( [[#Gu--2015|Gu et al., 2015]] ).

Globally, China and India have the highest AALs associated with riverine floods, with a magnitude of 13 and 6 billion USD, respectively. Other countries from Asia among the top ten of absolute AALs are Japan, Bangladesh and Thailand. There is an increased flood risk for habitations on the deltas influenced by both riverine and coastal drivers of flooding ( [[#Szabo--2016a|Szabo et al., 2016a]] ), globally exposing 9.3% more people annually to riverine flooding than otherwise estimated without the compounded influence ( [[#Eilander--2020|Eilander et al., 2020]] ). Simultaneously, SLR and subsidence are also expected to increase the risk due to frequent flood events for these delta regions than the longer-return periods otherwise associated with SLR ( [[#Yin--2020|Yin et al., 2020]] ).

<div id="10.4.6.3.7" class="h4-container"></div>

<span id="permafrost-thawing-and-associated-risks"></span>
===== 10.4.6.3.7 Permafrost thawing and associated risks =====

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In Northern Eurasia, observed and projected climate-change impacts are especially pronounced. On land, the presence of permafrost, which occupies substantial areas of eastern Russia, Mongolia and mountain regions of China, creates specific challenges for economic development and human activities. By 2050, it is likely that 69% of fundamental human infrastructure in the Pan Arctic will be at risk (RCP 4.5 scenario)( ''medium confidence'' ), including more than 1200 settlements ( [[#Hjort--2018|Hjort et al., 2018]] ). The majority of the population and the absolute majority (85%) of large settlements on permafrost are located in Russia, and 44% of those are expected to be profoundly affected by permafrost thaw by 2050 ( [[#Streletskiy--2019|Streletskiy et al., 2019]] ; Ramage et al., 2021). Under RCP8.5, the climate-induced decrease of bearing capacity and, in regions with ice-rich permafrost, thaw subsidence, is projected to affect 54% of all residential buildings on permafrost with a combined worth of 20.7 billion USD; 20% of commercial and industrial structures and 19% in critical infrastructure with a total worth of 84.4 billion USD (Streletskiy, 2019). Transport infrastructure in Russia and China are impacted by thaw subsidence and, to a lesser degree, from frost heave, which add significant operational costs and limit accessibility to remote settlements (Porfiriev et al., 2019; Ni et al., 2021).

Especially in Russia, significant populations and fixed infrastructure assets are located in urban centres on permafrost that is degrading significantly. Two major risks associated with permafrost degradation are loss of permafrost bearing capacity and ground subsidence ( [[#Streletskiy--2015|Streletskiy et al., 2015]] ). The former determines the ability to support foundations of buildings and structures and is a vital characteristic of sustainability of the economic centres, while the latter impacts the ability of critical infrastructure (roads, railroads) to provide transportation and support accessibility of remote populations and economic centres on permafrost. The proximity of some settlements to the coasts or areas with uneven topography may further increase risks associated with permafrost degradation as ice-rich coasts characterised by high rates of coastal erosion, while settlements located on slopes may experience higher rates of mass wasting processes.

Changes in climate have resulted in permafrost warming and increased thaw depth in undisturbed locations ( [[#Biskaborn--2019|Biskaborn et al., 2019]] ), but in built up areas these transformations have been exacerbated by human activities ( [[#Grebenets--2012|Grebenets et al., 2012]] ). Norilsk, the largest city built on permafrost above the Arctic Circle ( [[#Shiklomanov--2017b|Shiklomanov et al., 2017b]] ), was found to have one of the highest trends of near-surface permafrost warming ( [[#Streletskiy--2012|Streletskiy et al., 2012]] ). Anomalous high temperatures and earlier snowmelt in 2020 may have contributed to oil storage collapse and the resulting spill of 20,000 tons of diesel fuel in Norilsk area (Rajendan et al., 2021). The ability of foundations to support structures has decreased by 10–40% relative to the 1960s in the majority of settlements on permafrost in Russia ( [[#Streletskiy--2012|Streletskiy et al., 2012]] ) and is expected to further decrease by 20–33% by 2050–2059 relative to 2006–2015 ( [[#Streletskiy--2019|Streletskiy et al., 2019]] ).

<div id="10.4.6.3.8" class="h4-container"></div>

<span id="risks-and-impacts-on-infrastructure"></span>
===== 10.4.6.3.8 Risks and impacts on infrastructure =====

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South Asia and Africa bear the highest losses from unreliable infrastructure, and climate change will increase these losses due to hazards and necessitate additional infrastructure investments to address new risks ( [[#Hallegatte--2019|Hallegatte et al., 2019]] ; [[#Lu--2019|Lu, 2019]] ) [[#footnote-006|7]] . Specifically, power generation and transport infrastructure incur losses of 30 billion USD a year on average from hazards (about 15 billion USD each), with low- and middle-income countries shouldering about 18 billion USD of the total amount ( [[#Koks--2019|Koks et al., 2019]] ; [[#Nicholls--2019|Nicholls et al., 2019]] ).

Among the top 20 countries that are rapidly expanding their infrastructure stock while facing high disaster risk and low infrastructure quality, the Asian countries are Lao PDR, the Philippines, Bangladesh, Cambodia, Kyrgyzstan, Bhutan and Vietnam. ( [[#UNISDR--2017|UNISDR, 2017]] ; [[#WEF--2018|WEF, 2018]] ). The losses are due to direct damage to infrastructure, disruption in services and affected supply chains ( [[#Hallegatte--2019|Hallegatte et al., 2019]] ). East Asia and the Pacific and South Asia have the highest adaptation deficits in coastal protection with 75 billion USD in the former and 49 billion USD in the latter ( [[#Nicholls--2019|Nicholls et al., 2019]] ). If overall damages are minimised, low- and middle-income countries may need to invest 0.1–0.5% of their GDP annually up to 2030 for protection against both coastal and river floods, varying based on level of acceptable risks, construction costs, urbanisation and climate uncertainties [[#footnote-005|8]] .

* '''Power disruption:''' Contrasting with high-income, countries such as the USA, where hazards, particularly storms, are responsible for 50% of power outages, this share is much lower in countries like Bangladesh or India, because system failures due to unnatural causes are very frequent. However, outages caused by hazards tend to be longer and geographically more widespread than other outages ( [[#Rentschler--2019|Rentschler et al., 2019]] ). Climate-change-induced SLR is expected to impact power infrastructure, even necessitating power plant relocation ( [[#Hallegatte--2019|Hallegatte et al., 2019]] ). In Bangladesh, to avoid inundations caused by SLR (SSP2, RCP8.5), approximately one-third of power plants may need to be relocated by 2030. An additional 30% of power plants are ''likely'' to be affected by increased salinity of cooling water and increased frequency of flooding, while power plants in the northern region will probably see a decrease in output because of droughts ( [[#Hallegatte--2019|Hallegatte et al., 2019]] ). In 2013 in Chittagong ( [[#Pervin--2020|Pervin et al., 2020]] ), users experienced about 16 power outages due to storms alone ( [[#Hallegatte--2019|Hallegatte et al., 2019]] ). Furthermore, low-carbon technology diffusion might make certain infrastructures redundant, leading to stranded assets.

Across Asia, infrastructure impacts are mixed: net importers, such as China and India, will see GDP gains, while extreme examples include Russia, a net exporter, which could see steep declines in fossil fuel production ( [[#Mercure--2018|Mercure et al., 2018]] ). In low- and middle-income countries globally, disruption in power supply can impact firms directly (up to 120 billion USD yr –1 ), with coping costs (up to 65 billion USD yr –1 ) and other indirect impacts. Similarly, for households, the direct impact and cost of coping could be between 2.3 and 190 billion USD yr –1 . Although all power outage is not due to natural hazards, there is a significant number that is attributed to disasters. Besides, outages caused by natural hazards tend to be longer and geographically larger than other causes ( [[#Hallegatte--2019|Hallegatte et al., 2019]] ).

<ul>
<li>'''Transportation disruption:''' Of the 20 countries in which the road and railway infrastructure is expected to be most affected in absolute terms due to multi-hazards, half are Asian ( [[#Koks--2019|Koks et al., 2019]] ). In low- and middle-income countries globally, the direct losses to firms on account of transportation disruption are about 107 billion USD yr –1 , excluding the costs due to sales losses or delayed supplies and deliveries alone ( [[#Hallegatte--2019|Hallegatte et al., 2019]] ). In the transport sector, floods and other hazards disrupt traffic and cause congestion, taking a toll on people and firms in rich and poor countries alike.</li>
<li><p>'''Water supply and disposal infrastructure disruption:''' In low- and middle-income countries, disruption of water supply could lead to direct losses of about 6 billion USD yr –1 for firms, and between 88 and 153 billion USD yr –1 for households (due to willingness to pay to avoid disruption). Additionally, there are second-order costs associated with finding alternate sources of water and also health issues (on the order of 6–9 billion USD yr –1 accounting for medical bills and missed income) ( [[#Hallegatte--2019|Hallegatte et al., 2019]] ). In China, climate models project that an increasing number of wastewater-treatment-plant assets face climate-induced flood hazards in both the near and distant future, potentially affecting as many as 208 million users by 2050 ( [[#Hu--2019|Hu et al., 2019]] ).</p>
<span id="adaptation-in-cities-across-asia"></span>
==== 10.4.6.4 Adaptation in Cities Across Asia ====
</li></ul>

A review of urban adaptation in South, East and Central Asia found examples of 180 adaptation activities across 74 cities ( [[#Dulal--2019|Dulal, 2019]] ). Most adaptation actions in Asia are in the initial stages ( [[#Araos--2016|Araos et al., 2016]] ) with 57% focused on preparatory actions, such as capacity building and vulnerability assessment, and 43% focused on implemented adaptation (see also SM10.4). Most adaptation actions were focused on disaster risk management ( [[#Dulal--2019|Dulal, 2019]] ), although the proportion of climate finance spent on disaster preparedness is not very high (as [[#Georgeson--2016|Georgeson et al., 2016]] , show in the megacities of Beijing, Mumbai and Jakarta). Although key port cities across Asia are at high risk of climate impacts, it is estimated that adaptation interventions constitute only a small proportion of cities’ climate efforts ( [[#Blok--2015|Blok and Tschötschel, 2015]] ). Figure 10.8 shows risks and key adaptation options in select cities across Asia.

<div id="_idContainer024" class="Figure"></div>

[[File:d1da2e524a9884942611e779d1a12b91 IPCC_AR6_WGII_Figure_10_008.png]]

'''Figure 10.8 |''' '''Risks and key adaptation options in select cities across Asia.''' Cities were chosen to ensure coverage of different sub-regions of Asia, represent different risk profiles, different city sizes (based on current population and projected growth) and reported progress on different adaptation strategies (infrastructural, institutional, ecosystem based and behavioural). There is a full line of sight in SM10.4.

Critically, most urban adaptation in South, East and Central Asia is reactive in nature ( [[#Dulal--2019|Dulal, 2019]] ; [[#Singh--2021b|Singh et al., 2021b]] ), raising questions on preparedness, proactive building of adaptive capacities and whether present actions can lock certain cities or sectors into maladaptive pathways ( [[#Friend--2014|Friend et al., 2014]] ; [[#Gajjar--2018|Gajjar et al., 2018]] ; [[#Salim--2019|Salim et al., 2019]] ; [[#Chi--2020|Chi et al., 2020]] ). China, India, Thailand and the Republic of Korea record the most number of urban adaptation initiatives, driven mainly by supportive government policies ( [[#Lee--2015|Lee and Painter, 2015]] ; [[#Dulal--2019|Dulal, 2019]] ). The number of actors working on urban adaptation is growing: in addition to national governments and local municipalities, civil society, private-sector actors ( [[#Shaw--2019|Shaw, 2019]] ) and transnational municipal networks ( [[#Fünfgeld--2015|Fünfgeld, 2015]] ) are emerging as important for knowledge brokering, capacity building and financing urban adaptation ( [[#Karanth--2014|Karanth and Archer, 2014]] ; [[#Chu--2017|Chu et al., 2017]] ; [[#Bazaz--2018|Bazaz et al., 2018]] ).

Adaptation options include: (a) infrastructural measures such as building flood protection measures and sea walls, and climate-resilient highways and power infrastructure ( [[#Shaw--2016b|Shaw et al., 2016b]] ; [[#Ho--2017|Ho et al., 2017]] ); (b) sustainable land-use planning through zoning, developing building codes ( [[#Knowlton--2014|Knowlton et al., 2014]] ; [[#Nahiduzzaman--2015|Nahiduzzaman et al., 2015]] ; [[#Rahman--2016|Rahman et al., 2016]] ; [[#Ahmed--2019b|Ahmed et al., 2019b]] ); (c) ecosystem-based adaptation measures such as protecting urban green spaces, improving permeability, mangrove restoration in coastal cities, etc. ( [[#Brink--2016|Brink et al., 2016]] ; [[#Fink--2016|Fink, 2016]] ; [[#Yu--2018d|Yu et al., 2018d]] ); (d) relocation and migration out of risk-prone areas ( [[#McLeman--2019|McLeman, 2019]] ; [[#Hauer--2020|Hauer et al., 2020]] ; [[#Maharjan--2020|Maharjan et al., 2020]] ); and (e) disaster management and contingency planning such as through Early warning systems (EWS), improved awareness and preparedness measures ( [[#Shaw--2016a|Shaw et al., 2016a]] ). Asian cities are also focusing on institutional adaptation measures which cut across the five categories mentioned above such as through building capacity and local networks ( [[#Anguelovski--2014|Anguelovski et al., 2014]] ; [[#Friend--2014|Friend et al., 2014]] ; [[#Knowlton--2014|Knowlton et al., 2014]] ), improving awareness ( [[#Knowlton--2014|Knowlton et al., 2014]] ), and putting local research and monitoring mechanisms in place ( [[#Lee--2015|Lee and Painter, 2015]] ) to enable adaptation. Figure 10.9 shows the effectiveness of select adaptation options in cities across Asia.

<div id="_idContainer028x" class="Figure"></div>

[[File:31408e3b142daa24a2a2ceaffe41ad00 IPCC_AR6_WGII_Figure_10_009.png]]

'''Figure 10.9 |''' '''Effectiveness of select adaptation options in cities across Asia.''' Effectiveness is assessed based on the option’s ability to reduce risk as reported in the literature.

<div id="10.4.6.4.1" class="h4-container"></div>

<span id="infrastructural-adaptation-options"></span>
===== 10.4.6.4.1 Infrastructural adaptation options =====

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The challenge of adapting infrastructure to climate change across Asia is twofold: there are significant infrastructure deficiencies, especially in low-income countries, and key infrastructures are at high risk due to climate change ( [[#Hallegatte--2019|Hallegatte et al., 2019]] ; [[#Lu--2019|Lu, 2019]] ). Infrastructural adaptation options in cities attempt to enable networked energy, water, waste and transportation systems to prepare for, and deal with, climate risks better ( [[#Meerow--2017|Meerow, 2017]] ) through interventions such as improved highways and power plants, climate-resilient housing, improved water infrastructure and so forth ( [[#ADB--2014|ADB, 2014]] ).

* '''Power infrastructure:''' Adaptations in electricity systems include climate-resilient power infrastructure, particularly essential for coastal megacities such as Manila, Mumbai, Bangkok and Ho Chi Minh City ( [[#Meerow--2017|Meerow, 2017]] ; [[#Duy--2019|Duy et al., 2019]] ), which double as regional economic hubs and are home to tens of millions of people. In the Philippines, solar panels at water pumping stations are installed to operate and maintain a minimal capacity to pump water if the electricity grid were to break down ( [[#Stip--2019|Stip et al., 2019]] ).
* '''Water infrastructure:''' Sustainable water supply and resource management are key to urban adaptation through improved water service delivery, wastewater recycling and storm-water diversion ( [[#Deng--2015|Deng and Zhao, 2015]] ; [[#Xie--2017|Xie et al., 2017]] ; [[#Yu--2018d|Yu et al., 2018d]] ). Infrastructure-based adaptation options in urban water management include building water storage facilities, storm-water management and enhancing water quality improving permeability, managing runoff and enabling groundwater recharge. One example is of Shanghai (China), where infrastructural and policy incentives come together to enable adaptation: the city has been divided into 14 water conservancy zones, including 348 polder areas with 2517 km of dykes, 1499 pump stations and 2203 sluices ( [[#Yu--2018d|Yu et al., 2018d]] ). It also depends on a regional inundation control system, flood Early warning system and an emergency plan to deal with flood risk and mitigate waterlogging ( [[#Chen--2018e|Chen et al., 2018e]] ; [[#Yu--2018d|Yu et al., 2018d]] ). Another example is Ho Chi Minh City (Vietnam), where, given significant increases in area at risk of flooding under climate change, the city has invested in storm sewer upgradation, dike works, improving drainage and increasing the height of road embankments and minor bridges ( [[#Storch--2011|Storch and Downes, 2011]] ; [[#ADB--2014|ADB, 2014]] ; [[#Ho--2017|Ho et al., 2017]] ). These infrastructural interventions were complemented by designing an Early warning system to initiate flood mitigation procedures, such as isolating critical electrical and mechanical operating systems from water.
* '''Built infrastructure:''' Current built-infrastructure adaptation interventions are mostly reactive (e.g., strengthening housing units, using sandbags during flooding, storing of food, evacuation) rather than preventive (e.g., relocation, building multi-storey and stronger housing units), mainly due to limited resources within most vulnerable households for investing in proactive measures ( [[#Francisco--2019|Francisco and Zakaria, 2019]] ). For cities in North Asia seeing permafrost thawing, adequate land-use practices, permafrost monitoring, maintenance of infrastructure and engineering solutions (e.g., using thermosiphons) may temporarily offset the negative effects of permafrost degradation in small, economically vital areas, but are unlikely to have an effect beyond the immediate areas ( [[#Shiklomanov--2017b|Shiklomanov et al., 2017b]] ; Streletskiy, 2019). Importantly, thawing permafrost and GHG emissions create feedbacks where emissions amplify warming and drive additional thaw. Reducing these impacts through mitigation will reduce the need for adaptation significantly ( [[#Schaefer--2014|Schaefer et al., 2014]] ).
* '''Infrastructure and technology:''' Several infrastructural options employ technology, such as smart meters, to monitor water usage and service delivery, but these are differentially adopted across Asian sub-regions with higher adoption across East Asia. Examples include: the Yokohama smart city project in Japan, which has been smart eco-urbanism interventions since 2011 (e.g., energy saving and storage infrastructure, wastewater management, behavioural change towards renewable energy and low-carbon transportation) (IUC, 2019); the Tianjin Eco-city mega-project in China, which is testing a range of measures to meet urban sustainability goals in partnership with Singapore ( [[#ICLEI--2014b|ICLEI, 2014b]] ; [[#Blok--2015|Blok and Tschötschel, 2015]] ); development in New Songdo (Republic of Korea), which is experimenting with interventions, such as embedded smart waste management ( [[#Anthopoulos--2017|Anthopoulos, 2017]] ), and national policy initiatives such as the Smart Cities Mission covering 100 cities in India (e.g. technology-enabled water, energy and land management for urban agriculture in Nashik city) ( [[#ICLEI--2014a|ICLEI, 2014a]] ). However, the efficacy of such measures, especially for larger sustainability and climate-change goals, remains to be seen ( [[#ICLEI--2014a|ICLEI, 2014a]] ; [[#ICLEI--2014b|ICLEI, 2014b]] ; [[#Caprotti--2015|Caprotti et al., 2015]] ; [[#Anthopoulos--2017|Anthopoulos, 2017]] ).

Infrastructural measures alone are seldom effective in building urban resilience as seen in the examples of the 2011 floods in Bangkok and the 2005 typhoon in Manila ( [[#Duy--2019|Duy et al., 2019]] ), or projected estimates by [[#Pervin--2020|Pervin et al. (2020)]] who found that structural interventions in existing drainage systems reduce flooding risk by 7–19% in Sylhet (Bangladesh) and Bharatpur (Nepal); however, without proper solid waste management, areas under flood risk could increase to 18.5% in Sylhet and 7.6% in Bharatpur in five years, rendering the infrastructural interventions ineffective over time. While in some cities it is estimated that infrastructural adaptation through ‘hard’ flood protection strategies (e.g., storm surge barriers and floodwalls) is more effective than institutional or ecosystem-based adaptation by 2100, for example, Shanghai. A hybrid approach where hard strategies protect from flood risk, and soft strategies reduce residual risk from hard strategies, is suggested ( [[#Du--2020|Du et al., 2020]] ). In Japan, without adaptation, estimated damage costs of floods (caused by tropical cyclones and altered precipitation) by 2081–2100 under RCP2.6 will be 28% higher (compared with 1981–2000), rising to 57% higher under RCP8.5 ( [[#Yamamoto--2021|Yamamoto et al., 2021]] ). With a combination of adaptation measures (such as land-use control, piloti building and flood control measures), estimated damage costs can be reduced even below the 1981–2000 levels, and with a combination of mitigation and these adaptation measures, an estimated 69% reduction in flood damage costs are expected–demonstrating the importance of concerted and immediate climate action in reducing damage.

Infrastructural interventions can sometimes be maladaptive when assessed over longer time periods: for example, the Mumbai Coastal Road (MCR) project aimed at reducing flood risk and protecting against SLR will potentially cause damages to intertidal fauna and flora and local fishing livelihoods ( [[#Senapati--2017|Senapati and Gupta, 2017]] ); and Jakarta’s Great Garuda project aimed at reducing flood risk is expected to ''increase'' flood risk for the poorest urban dwellers ( [[#Salim--2019|Salim et al., 2019]] ).

<div id="10.4.6.4.2" class="h4-container"></div>

<span id="sustainable-land-use-planning-and-regulation"></span>
===== 10.4.6.4.2 Sustainable land-use planning and regulation =====

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Land use in cities impacts resource use (e.g., water, energy), risk (a function of population density, service provision and hazard exposure) and adaptive capacity, all of which influence the efficacy of urban adaptation ( [[#de%20Coninck--2018|de Coninck et al., 2018]] ). Locally suited land-use planning and regulation (such as appropriate zoning or building codes and safeguarding land rights) can have adaptation co-benefits ( [[#Mitchell--2015|Mitchell et al., 2015]] ; [[#Dhar--2016|Dhar and Khirfan, 2016]] ): for example, strict building regulations can protect urban wetlands and associated ecosystem services ( [[#Jiang--2015|Jiang et al., 2015]] ); appropriate land zoning can safeguard green spaces, ensure improvements in permeability and obviate new development in risk-prone locations ( [[#Duy--2019|Duy et al., 2019]] ); and ensuring tenurial security or regularising informal settlements can incentivise improvements to housing quality, thereby alleviating vulnerability of the most marginal people ( [[#Mitchell--2015|Mitchell et al., 2015]] ).

Land tenure arrangements strongly shape urban dwellers’ vulnerability and their adaptive capacities ( [[#Roy--2013|Roy et al., 2013]] ; [[#Michael--2018|Michael et al., 2018]] ). For example, in Khulna (Bangladesh), Roy et al. (2013) found significant differences between the adaptive strategies of homeowners and renters in low-income settlements, a finding echoed in Bangalore (India) ( [[#Deshpande--2018|Deshpande et al., 2018]] ) and Phnom Penh (Cambodia) ( [[#Mitchell--2015|Mitchell et al., 2015]] ). In Riyadh (Saudi Arabia), land-based adaptation strategies include land zoning to control population and building density, demarcating environmental protection zones, and sub-urbanisation ( [[#Nahiduzzaman--2015|Nahiduzzaman et al., 2015]] ; [[#Rahman--2016|Rahman et al., 2016]] ). In many Asian cities, land subsidence control can serve as an adaptation strategy since it is estimated to significantly reduce relative SLR ( ''high confidence'' ). This has an important implication in that subsidence control would be a good and complementary measure to climate mitigation and climate adaptation in many coastal urban settings in Asia ( [[#Cao--2021|Cao et al., 2021]] ; [[#Nicholls--2021|Nicholls et al., 2021]] ). Urban land-use planning, if used proactively, can incentivise adaptation–mitigation synergies and obviate unintended negative consequences of urbanisation as [[#Xu--2019|Xu et al. (2019)]] have shown in Xiamen.

<div id="10.4.6.4.3" class="h4-container"></div>

<span id="ecosystem-based-adaptation"></span>
===== 10.4.6.4.3 Ecosystem-based adaptation =====

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The literature on urban ecosystem-based adaptation (EbA) [[#footnote-004|9]] , especially across Asia, has grown significantly since AR5 (Demuzere and al., 2014; [[#Yao--2015|Yao et al., 2015]] ; [[#Brink--2016|Brink et al., 2016]] ; [[#Bazaz--2018|Bazaz et al., 2018]] ; [[#de%20Coninck--2018|de Coninck et al., 2018]] ; Ren, 2018). This growing literature reflects the wide recognition that infrastructural adaptation can often have ecological and social trade-offs ( [[#Palmer--2015|Palmer et al., 2015]] ) and need to be complemented by ecosystem-based actions to manage risk more effectively ( [[#Du--2020|Du et al., 2020]] ), build adaptive capacity, and in some cases, meet mitigation and SDGs ( [[#Huang--2020|Huang et al., 2020]] ).

Illustrative examples of EbA in Asian cities include sponge cities in China for sustainable water management, flood mitigation and minimising heatwave impact ( [[#Jiang--2018|Jiang et al., 2018]] ; [[#Yu--2018d|Yu et al., 2018d]] ; [[#Wang--2019a|Wang et al., 2019a]] ; [[#Zhanqiang--2019|Zhanqiang et al., 2019]] ), Singapore’s Active, Beautiful, Clean Waters (ABC Waters) Programme, which uses bio-engineering approaches to protect river channels and prevent localised flooding, improve water quality and create community spaces, and Dhaka’s green roofs and urban agriculture ( [[#Zinia--2018|Zinia and McShane, 2018]] ).

The EbA approaches to manage floods, capture and store rainwater, restore urban lakes and rivers, and reduce surface runoff often blend infrastructural and ecosystem-based approaches. For example, in Tokyo, stormwater management is done by sophisticated underground infrastructure and an artificial infiltration stormwater system ( [[#Saraswat--2016|Saraswat, 2016]] ; [[#Mishra--2019|]] [[#Mishra--2019|Mishra et al., 2019]] ). China’s Sponge City Programme aims to reduce the impacts of flooding through low-impact development measures, urban greenery and drainage infrastructure, such that 80% of urban areas reuse 70% of rainwater by 2020, which would help ensure the resilience of these cities to floods ( [[#Li--2016b|Li et al., 2016b]] ; [[#Stip--2019|Stip et al., 2019]] ).

Case studies on urban EbA also raise equity concerns ( ''medium evidence, medium agreement'' ) such as interventions biased towards suburban areas in Haizhu District, Guangzhou (China) ( [[#Zhu--2019|Zhu et al., 2019]] ); inadequate consideration of low-income, vulnerable populations ( [[#Blok--2015|Blok and Tschötschel, 2015]] ; [[#Meerow--2017|Meerow, 2017]] ; [[#Mabon--2021|Mabon and Shih, 2021]] ); and low familiarity with interventions such as artificial wetlands, water retention ponds as well as green façades and walls can restrict inclusiveness ( [[#Zinia--2018|Zinia and McShane, 2018]] ). Furthermore, urban EbA is constrained by a range of factors such as inadequate institutional structures and processes for connecting different remits and knowledge systems along with trade-offs in land use for different purposes ( [[#Mabon--2021|Mabon and Shih, 2021]] ; [[#Singh--2021b|Singh et al., 2021b]] ).

The EbA interventions are not uniform across Asian cities: in a global study on urban EbA, [[#Brink--2016|Brink et al. (2016)]] found that Eastern Asia, India and Israel report most EbA interventions and that there is variable and ''limited evidence'' on effectiveness and scalability (SM10.5). Using a risk framing (i.e., the extent to which an option reduces risk), urban EbA options in Asian cities score as being ‘low to medium’ effective (see SM10.5); however, when the assessment is expanded to include the ecosystem benefits, economic impacts and human well-being co-benefits of EbA, effectiveness increases. Figure 10.10 shows evidence of the effectiveness of EbA.

<div id="_idContainer028" class="Figure"></div>

[[File:68388bfd6f5d105824b1a71661789e47 IPCC_AR6_WGII_Figure_10_010.png]]

'''Figure 10.10 |''' '''Evidence of the effectiveness of ecosystem-based adaptation (EbA) using examples of four commonly used EbA options''' '''[[#footnote-000|10]]''' '''.''' Effectiveness is assessed qualitatively based on the evidence (for a full line of sight see SM10.5) and is examined through four framings: potential to reduce risk (e.g., reduced exposure to hazard means to reduce risk); benefits to ecosystems (through improved ecosystem health and high biodiversity); economic benefits (e.g., improved incomes, fewer man-days lost, better livelihoods); and human well-being outcomes (e.g., health, quality of life, etc.). The darker shading signifies high effectiveness and the lightest shade signifies low effectiveness of an EbA option (i.e., the option scores low on the indicator).

-----

<div id="footnote-000" class="_idFootnote"></div>

[[#footnote-000-backlink|1]] 10 Assessing the effectiveness of adaptation actions is challenging because of the lack of a clear goal that signifies effective adaptation, varied conceptual framings and metrics used to assess effectiveness, and low empirical evidence on the effectiveness of implemented adaptation actions ( [[#Owen--2020|Owen, 2020]] ; [[#Singh--2021a|Singh et al., 2021a]] ).

For example, urban agriculture is identified as offering multiple benefits such as mitigating emissions associated with food transportation from rural to urban areas, improving food and nutritional security, strengthening local livelihoods and economic development, improved microclimate, soil conservation, improved water and nutrient recycling, and efficient water management ( [[#Padgham--2015|Padgham and Dietrich, 2015]] ; [[#Patil--2019|Patil et al., 2019]] ). However, it can potentially undermine ecosystem services through land-use changes, water overextraction or applying chemical fertilisers ( [[#Ackerman--2014|Ackerman et al., 2014]] ), exposure of smallholders to volatile markets and crops that are not consumed by farming households themselves (thus undermining food security) or increasing the work burdens on women, as well as health externalities (e.g. through use of untreated wastewater, or rearing poultry and livestock in unsanitary conditions). There remain gaps in understanding the differential impacts of urban agriculture at different scales as well as its effectiveness in improving adaptive capacity at scale.

<div id="10.4.6.4.4" class="h4-container"></div>

<span id="migration-and-planned-relocation"></span>
===== 10.4.6.4.4 Migration and planned relocation =====

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There is ''medium evidence'' with ''high agreement'' that climatic risks are exacerbating internal and international migration across Asia (see Box 10.2; [[#IDMC--2019|IDMC, 2019]] ; [[#Maharjan--2020|Maharjan et al., 2020]] ). In coastal cities, formal ‘retreat’ measures, such as forced displacement and planned relocation ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ), are commonly considered ‘last resort’ adaptation strategies once other infrastructural and ecosystem-based protect-and-accommodate strategies are exhausted ( [https://www.ipcc.ch/chapter/10#CCP2.3 CCP2.3] ) ( [[#Haasnoot--2019|Haasnoot et al., 2019]] ). In contrast, migration (which can take various forms from seasonal, temporary mobility to circular or permanent movement) is a regular feature across Asian urban settlements (Box 10.2, CCB MIGRATE, [[#Maharjan--2020|Maharjan et al., 2020]] ).

There is ''robust evidence'' ( ''medium agreement'' ) that across Asia, migration (and increasingly planned relocation) will continue to be a key risk management strategy, especially in low-lying flood-prone cities (e.g., in Southeast and South Asia) and across drylands (e.g., in South and Central Asia) ( [[#Davis--2018|Davis et al., 2018]] ; [[#Ajibade--2019|Ajibade, 2019]] ; [[#Lincke--2021|Lincke and Hinkel, 2021]] ). While there is insufficient evidence to project migration numbers under different warming levels, it is well established that migration as an adaptation strategy is not equally available to all ( [[#Ayeb-Karlsson--2020|Ayeb-Karlsson, 2020]] ), and climatic risks might reduce vulnerable populations’ ability to move due to loss of assets, thus reinforcing existing inequalities and differential adaptive capacities ( [[#Blondin--2019|Blondin, 2019]] ; [[#Zickgraf--2019|Zickgraf, 2019]] ; [[#Singh--2020|Singh and Basu, 2020]] ; [[#Cundill--2021|Cundill et al., 2021]] ; [[#Gavonel--2021|Gavonel et al., 2021]] ).

There is ''medium evidence'' ( ''low agreement'' ) about the effectiveness of migration and planned relocation in reducing risk exposure. Evidence on climate-driven internal migration shows that moving has mixed outcomes on risk reduction and adaptive capacity. On one hand, migration can improve adaptive capacity by increasing incomes and remittances as well as diversifying livelihoods ( [[#Maharjan--2020|Maharjan et al., 2020]] ); on the other, migration can expose migrants to new risks. For example, in Bangalore (India), migrants often face high exposure to localised flooding, insecure and unsafe livelihoods, and social exclusion, which collectively shape their vulnerability ( [[#Michael--2018|Michael et al., 2018]] ; [[#Singh--2020|Singh and Basu, 2020]] ). In greater Manila (the Philippines) and Chennai (India), planned relocations to reduce disaster risk have often exacerbated vulnerability, due to relocation sites being in environmentally sensitive areas, inadequate livelihood opportunities and exposure to new risks ( [[#Meerow--2017|Meerow, 2017]] ; [[#Ajibade--2019|Ajibade, 2019]] ; [[#Jain--2021|Jain et al., 2021]] ).

<div id="10.4.6.4.5" class="h4-container"></div>

<span id="disaster-management-and-contingency-planning"></span>
===== 10.4.6.4.5 Disaster management and contingency planning =====

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There is rich case-based evidence across Asia on urban adaptation to extreme events with relatively more evidence on rapid-onset events such as cyclones and flooding than slow-onset disasters such as drought (see Box 10.6; [[#Ray--2019|Ray and]] [[#Shaw--2019|Shaw, 2019]] ; [[#UNESCAP--2019|UNESCAP, 2019]] ; [[#Singh--2021a|Singh et al., 2021a]] ). Overall, there has been a growing emphasis on ‘build back better’ interventions ( [[#Mannakkara--2013|Mannakkara and Wilkinson, 2013]] ; [[#Hallegatte--2018|Hallegatte et al., 2018]] ) that approach disaster management holistically through infrastructural solutions such as climate-resilient housing or sea walls and soft approaches such as strengthening livelihoods, developing EWS 11 , [[#footnote-003|10]] increasing awareness about disaster risks and impacts, and building local capacities to deal with them ( [[#Bhowmik--2021|Bhowmik et al., 2021]] ). Notably, urban disaster management is effective when land-use planning processes, including greenfield development, zoning and building codes, and urban redevelopment, are leveraged to reduce and/or obviate risk, thereby averting potential maladaptation ( [[#Kuhl--2021|Kuhl et al., 2021]] ).

There is relatively lower empirical evidence on how microenterprises and businesses are adapting to increased risk, but recent examples in Mumbai, India ( [[#Schaer--2018|Schaer and Pantakar, 2018]] ), and Kratie, Cambodia ( [[#Ngin--2020|Ngin et al., 2020]] ), suggest that businesses primarily adopt temporary and reactive responses rather than long-term, anticipatory adaptation measures.

A review of innovative DRR approaches notes the use of geographic information system (GIS) and drone-based technologies for mapping risk exposure and impacts, mobile-based payments for post-disaster compensation, and transnational initiatives and learning networks to promote urban resilience ( [[#Izumi--2019|Izumi et al., 2019]] ). Furthermore, technology-based innovations, such as using big data ( [[#Yu--2018b|Yu et al., 2018b]] ), improved warnings through mobile phones or mobilising relief through social media ( [[#Carley--2016|Carley et al., 2016]] ), are proving effective for disaster preparedness, relief and recovery. Community-based DRR is consistently ranked as most effective for its role in transforming DRR towards being more context relevant and inclusive. Ecosystem-based DRR (EbDRR) is also gaining prominence and includes strategies such as mangrove plantation and rejuvenation in vulnerable coastal areas. Nature-based solutions for flood protection and reducing drought incidence have emerged as an alternative to costlier ‘hard’ infrastructure ( [[#UN-Water--2018|UN-Water, 2018]] ; [[#Zevenbergen--2018|Zevenbergen et al., 2018]] ; [[#Rozenberg--2019|Rozenberg and Fay, 2019]] ). Some cities are also reporting adaptation to heat risk. For example, Ahmedabad (India) has pioneered preparedness for extreme temperatures and heatwaves by developing annual Heat Action Plans, building regulations to minimise trapping heat, advisories about managing heat stress and instituting a cool-roofs policy ( [[#Ahmedabad%20Municipal--2018|Ahmedabad Municipal, 2018]] ).

Financing, regulations and institutional processes play a significant role in incentivising DRR and resilience in large-scale, city-level built infrastructure by the private sector and other actors. Currently there are gaps in these mechanisms, leading to infrastructure development in disaster-prone areas, increasing exposure to people, property, economy and systems ( [[#Jain--2013|Jain, 2013]] ). Both firms and governments need to take disaster risks into consideration in supply-chain management to avoid disruptions and subsequent negative effects ( [[#Abe--2013|Abe and Ye, 2013]] ). There are several institutional challenges faced during DRR and CCA implementation including overlapping efforts and inefficient use of scarce resources due to inappropriate funding mechanisms, a lack of coordination and collaboration, a lack of implementation and mainstreaming, scale mismatches, poor governance, the social–political–cultural structure, competing actors and institutions, and lack of information, communication, knowledge sharing, and community involvement, as well as policy gaps ( [[#Seidler--2018|Seidler et al., 2018]] ; [[#Islam--2020|Islam et al., 2020]] ).

<div id="10.4.6.5" class="h3-container"></div>

<span id="enabling-urban-adaptation-across-asia"></span>
==== 10.4.6.5 Enabling Urban Adaptation Across Asia ====

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There is growing empirical evidence of conditions enabling and constraining urban adaptation (Table 10.3) with relatively more literature from South, Southeast and East Asia. Governance and capacity-related deficits are repeatedly identified as significant barriers to urban adaptation ( ''robust evidence, high agreement'' ) and interact with financial and informational constraints to mediate adaptation action.

'''Table 10.3 |''' Barriers and enablers to climate adaptation across Asian cities

{| class="wikitable"
|-
! Indicator

! As an enabler

! As a barrier

|-
| Governance and planning

| National policy directives to adapt: for example, strong national climate commitments in China, India and Thailand ( [[#Dulal--2019|Dulal, 2019]] ); and dedicated public–private councils on climate change in Seoul, Republic of Korea ( [[#Lee--2015|Lee and Painter, 2015]] )

Participatory planning, co-producing solutions and engaging multiple stakeholders: for example, Surat ( [[#Anguelovski--2014|Anguelovski et al., 2014]] ; [[#Karanth--2014|Karanth and Archer, 2014]] ; [[#Chu--2017|Chu et al., 2017]] ) and Guwahati ( [[#Archer--2014|Archer et al., 2014]] ) in India; Bandar Lampung and Semarang in Indonesia ( [[#Archer--2014|Archer et al., 2014]] ); and Seoul, Republic of Korea ( [[#Lee--2015|Lee and Painter, 2015]] )

Devolving decision making to city governments ( [[#ADB--2013|ADB, 2013]] ) and strong political leadership helps to institutionalise adaptation programmes ( [[#Anguelovski--2014|Anguelovski et al., 2014]] ; [[#Friend--2014|Friend et al., 2014]] ; [[#Lee--2015|Lee and Painter, 2015]] ): for example, in Moscow where the city mayor has spearheaded climate action ( [[#van%20der%20Heijden--2019|van der Heijden et al., 2019]] ).

Mainstreaming climate adaptation in city plans ( [[#UN-HABITAT%20and%20UNESCAP--2018|UN-HABITAT and UNESCAP, 2018]] )

| Low accountability and transparency in planning processes with inadequate spaces for public dialogue ( [[#Friend--2014|Friend et al., 2014]] ) and limited accountability to the most economically and politically marginalised people within cities ( [[#Garschagen--2019|Garschagen and Marks, 2019]] )

Of 180 urban adaptation interventions across Asia, 65% are reactive in nature ( [[#Dulal--2019|Dulal, 2019]] ), thus missing opportunities for risk prevention and preparedness ( [[#Francisco--2019|Francisco and Zakaria, 2019]] ).

Lack of forward-looking, learning-oriented processes constrain adaptation with short-term development priorities often overshadowing long-term climate-action needs ( [[#Friend--2014|Friend et al., 2014]] ; [[#de%20Leon--2017|de Leon and Pittock, 2017]] ; [[#Gajjar--2018|Gajjar et al., 2018]] ; [[#Khaling--2018|Khaling et al., 2018]] ; [[#Garschagen--2019|Garschagen and Marks, 2019]] ; [[#Jain--2021|Jain et al., 2021]] ).

Fragmented governance, lack of mainstreaming between CCA and DRR ( [[#Fuhr--2018|Fuhr et al., 2018]] ; [[#Khaling--2018|Khaling et al., 2018]] ): for example, in Vietnam, Thailand, Indonesia ( [[#Friend--2014|Friend et al., 2014]] ) and greater Manila ( [[#Meerow--2017|Meerow, 2017]] )

|-
| Information

| Knowledge sharing through transnational municipal networks such as C40, ACCRN and A-PLAT ( [[#Fünfgeld--2015|Fünfgeld, 2015]] )

City-level knowledge creation and knowledge-transfer institutions ( [[#Lee--2015|Lee and Painter, 2015]] )

| Data gaps on projected climate risks and impacts in certain sub-regions and small settlements ( [[#Revi--2014|Revi et al., 2014]] )

Numerous tools for assessing vulnerability and adaptation planning ( [[#Nordgren--2016|Nordgren et al., 2016]] )

|-
| Technology and infrastructure

| Early warning systems, climate information and modelling studies inform adaptation decision making ( [[#Reed--2015|Reed et al., 2015]] ; [[#Singh--2018a|Singh et al., 2018a]] )

| Inadequate regional downscaled data at the city scale ( [[#ADB--2013|ADB, 2013]] ; [[#Khaling--2018|Khaling et al., 2018]] );

inadequate cost–benefit analyses of different adaptation strategies (Khaling et al. 2018)

|-
| Capacity and awareness

| A focus on learning, experimentation, awareness and capacity building leads to more sustained, legitimate and inclusive adaptation ( [[#ADB--2013|ADB, 2013]] ; [[#Anguelovski--2014|Anguelovski et al., 2014]] ; [[#Reed--2015|Reed et al. (2015)]] .

| Limited access to, and capacity to use, risk assessment tools ( [[#ADB--2013|ADB, 2013]] ; [[#Shaw--2016b|Shaw et al., 2016b]] )

|-
| Finance

| Dedicated adaptation financing (e.g., in Beijing, adaptation spending is 0.33% of the city’s GDP) ( [[#Georgeson--2016|Georgeson et al., 2016]] ); steering international and local funding to leverage adaptation benefits in urban development programmes such as in Surat (India) ( [[#Cook--2019|Cook and Chu, 2019]] ); mainstreaming climate adaptation into development programming to leverage developmental finance for adaptation action ( [[#Cuevas--2016|Cuevas et al., 2016]] ; [[#Narender--2018|Narender and Sethi, 2018]] )

| Inadequate adaptation funding and lack of financial devolution to city governments ( [[#Fuhr--2018|Fuhr et al., 2018]] ; [[#Garschagen--2019|Garschagen and Marks, 2019]] )

|}

<div id="10.4.7" class="h2-container"></div>

<span id="health-and-well-being"></span>
=== 10.4.7 Health and Well-Being ===

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Climate change is increasing risks to human health in Asia by increasing exposure and vulnerability to extreme weather events such as heatwaves, flooding and drought, and air pollutants, increasing vector- and water-borne diseases, undernutrition, mental disorders and allergic diseases ( ''high confidence'' ). Sub-regional diversity in socioeconomic and demographic contexts (e.g., ageing, urban compared with agrarian society, increasing population compared with reduced birth rate, high income compared with low to middle income), and geographic characteristics, largely define the differential vulnerabilities and impacts in Asia ( ''high confidence'' ).

<div id="10.4.7.1" class="h3-container"></div>

<span id="observed-impacts-3"></span>
==== 10.4.7.1 Observed Impacts ====

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High temperatures affect mortality and morbidity in Asia ( ''high confidence'' ). In addition to all-cause mortality ( [[#Dang--2016|Dang et al., 2016]] ; [[#Chen--2018e|Chen et al., 2018e]] ), deaths related to circulatory, respiratory, diabetic ( [[#Li--2014b|Li et al., 2014b]] ) and infectious diseases ( [[#Ingole--2015|Ingole et al., 2015]] ), as well as infant mortality ( [[#Son--2017|Son et al., 2017]] ), are increased with high temperature ( ''high confidence'' ). Increased hospital admissions ( [[#Giang--2014|Giang et al., 2014]] ; [[#Lin--2019|Lin et al., 2019]] ) and ambulance transport ( [[#Onozuka--2015|Onozuka and Hagihara, 2015]] ) coincide with increased ambient temperature ( ''high confidence'' ). Heatwaves are particularly detrimental to all-cause and cause-specific mortality ( [[#Chen--2015a|Chen et al., 2015a]] ; [[#Lee--2016|Lee et al., 2016]] ; [[#Guo--2017b|Guo et al., 2017b]] ; [[#Yin--2018|Yin et al., 2018]] ). Both rural and urban populations are vulnerable to heat-related mortality ( [[#Ma--2015|Ma et al., 2015]] ; [[#Chen--2016a|Chen et al., 2016a]] ; [[#Wang--2018a|Wang et al., 2018a]] ). Individuals with lower degrees of education and socioeconomic status, older individuals and individuals living in communities with less green space are more susceptible to heat-related mortality ( ''high confidence'' ) ( [[#Yang--2012a|Yang et al., 2012a]] ; [[#Huang--2015b|Huang et al., 2015b]] ; [[#Seposo--2015|Seposo et al., 2015]] ; [[#Son--2016|Son et al., 2016]] ; [[#Kim--2017|Kim and]] [[#Kim--2017|Kim, 2017]] ). These heat effects have been attenuating over recent decades in East Asian countries, although the driving force behind this remains unknown ( ''high confidence'' ) ( [[#Chung--2017c|Chung et al., 2017c]] ; [[#Chung--2018|Chung et al., 2018]] ).

Rising ambient temperature accelerates pollutant formation reactions and may modify air-pollution-related health effects ( ''medium confidence'' ). Higher temperatures are associated with the increased effects of ozone on mortality ( [[#Shi--2020|Shi et al., 2020]] ). Climate change causes intensified droughts and greater wind erosion resulting in increased intensity and frequency of sand and dust storms ( [[#Akhtar--2018|Akhtar et al., 2018]] ). Mortality and hospital admissions for circulatory and respiratory diseases are increased after exposures to Asian dust events ( ''high confidence'' ) ( [[#Hashizume--2020|Hashizume et al., 2020]] ). El Niño has a major influence on weather patterns in various regions. For example, it causes dry conditions that sometimes result in forest fires and transboundary haze that increased all-cause mortality in children by 41% in Malaysia ( [[#Sahani--2014|Sahani et al., 2014]] ).

Ambient temperature is associated with the risk of an outbreak of mosquito-borne disease in South and Southeast Asia ( ''high confidence'' ) ( [[#Servadio--2018|Servadio et al., 2018]] ). Warmer climates are associated with a higher incidence of malaria ( [[#Xiang--2018|Xiang et al., 2018]] ). Moderate rainfall also promotes malaria infection, while excessive rainfall decreases the risk of malaria ( [[#Wu--2017b|Wu et al., 2017b]] ). El Niño intensity is positively associated with malaria incidence in a single year in India ( [[#Dhiman--2017|Dhiman and Sarkar, 2017]] ). The duration and survival rate of dengue mosquito development, mosquito density, mosquito biting activity, mosquito spatio-temporal range and distribution, and mosquito flying distance are all affected by temperature ( ''high confidence'' ) ( [[#Li--2018b|Li et al., 2018b]] ). Temperature, precipitation, humidity and air pressure are major weather factors associated with dengue fever transmission ( ''high confidence'' ) ( [[#Sang--2014|Sang et al., 2014]] ; [[#Choi--2016|Choi et al., 2016]] ; [[#Xu--2017|Xu et al., 2017]] ).

Climate change alters the hydrological cycle by increasing the frequency of extreme weather events such as excess precipitation, storm surges, floods and droughts ( ''high confidence'' ). Water-borne diseases, such as diarrhoea, leptospirosis and typhoid fever, can increase in incidence following heavy rainfall, tropical cyclones and flooding events ( ''high confidence'' ) ( [[#Deng--2015|Deng et al., 2015]] ; [[#Levy--2016|Levy et al., 2016]] ; [[#Li--2018b|Li et al., 2018b]] ; [[#Matsushita--2018|Matsushita et al., 2018]] ; [[#Zhang--2019b|Zhang et al., 2019b]] ). Droughts can cause increased concentrations of pathogens, which overwhelm water-treatment plants and contaminate surface water. A positive association between ambient temperature and bacterial diarrhoea has been reported, compared with a negative association with viral diarrhoea ( [[#Carlton--2016|Carlton et al., 2016]] ; [[#Wang--2018c|Wang et al., 2018c]] ).

Asia has the highest prevalence of undernourishment in the world, which was 11.4% in 2017, representing more than 515 million people. Southeast Asia has been affected by adverse climate conditions such as floods and cyclones, with impacts on food availability and prices ( [[#FAO--2018d|FAO, 2018d]] ). Crop destruction due to tropical cyclones can include salt damage from tides blowing inland ( ''medium confidence'' ) ( [[#Iizumi--2015|Iizumi and Ramankutty, 2015]] ). Sea level rises result in intrusion of saline water into the coastal area of Bangladesh and people living in this area face an increased risk of hypertension resulting from high salt consumption ( [[#Scheelbeek--2016|Scheelbeek et al., 2016]] ).

Weather conditions have been linked to mental health. High temperatures increase the risk of mental problems including mental disorders, depression, distress and anxiety in Vietnam ( [[#Trang--2016|Trang et al., 2016]] ), Hong Kong SAR of China ( [[#Chan--2018|Chan et al., 2018]] ) and the Republic of Korea ( [[#Lee--2018d|Lee et al., 2018d]] ). In addition, high temperatures are reported to increase the risk of mortality from suicide in Japan, the Republic of Korea, Taiwan, Province of China ( [[#Kim--2016c|Kim et al., 2016c]] ), India ( [[#Carleton--2017|Carleton, 2017]] ) and China ( [[#Luan--2019|Luan et al., 2019]] ). Extreme weather events, such as storms, floods, hurricanes and cyclones, increase injuries and mental disorders (post-traumatic stress disorder and depressive disorders) ( [[#Rataj--2016|Rataj et al., 2016]] ), thereby negatively affecting well-being ( ''high confidence'' ).

Higher temperatures and increased CO 2 elevate the level of allergens such as pollen, which can result in increased allergic diseases, such as asthma and allergic rhinosinusitis. The association between variations in ambient temperature and the occurrence of asthma has been reported in several Asian countries/regions such as Japan ( [[#Yamazaki--2015|Yamazaki et al., 2015]] ), the Republic of Korea ( [[#Kwon--2016|Kwon et al., 2016]] ), China ( [[#Li--2016a|Li et al., 2016a]] ) and Hong Kong SAR of China ( ''medium confidence'' ) ( [[#Lam--2016|Lam et al., 2016]] ).

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==== 10.4.7.2 Projected Impacts ====

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Climate change is associated with significantly increased mortality ( ''high confidence'' ). Figure 10.11 shows projected health impacts due to climate change in Asia. The global estimates of excess deaths due to malnutrition, malaria, diarrhoea and heat stress will be approximately 250,000 deaths per year in 2030–2050 under the medium-to-high emissions scenario, assuming no adaptation ( [[#World%20Health%20Organization--2014|World Health Organization, 2014]] ). The impacts are expected to be greatest in South, East and Southeast Asia. Another projection showed that the change in heat-related deaths is largest in Southeast Asia, which was a 12.7% increase at the end of the century under a high-emissions scenario ( [[#Gasparrini--2017|Gasparrini et al., 2017]] ). As the proportion of older individuals in the population rises, the number of years lost due to disability increases more steeply ( [[#Chung--2017b|Chung et al., 2017b]] ). In the 2080s, the number of annual temperature-related deaths is estimated to reach twice that in the 1980s in China ( [[#Li--2018c|Li et al., 2018c]] ). Over a 20-year period in the mid-21st century (2041–2060), the incidence of excess heat-related mortality in 51 cities in China is estimated to reach 37,800 (95% CI: 31,300–43,500) deaths per year under RCP8.5 ( [[#Bazaz--2018|Bazaz et al., 2018]] ).

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[[File:4d652143d309f8c5a27a059188fa080a IPCC_AR6_WGII_Figure_10_011.png]]

'''Figure 10.11 |''' '''Projected health impacts due to climate change in Asia.'''

Increased concentrations of fine particulate matter and ozone influenced by extreme events such as atmospheric stagnations and heatwaves are projected to result in an additional 12,100 and 8,900 deaths per year due to fine particulate matter and ozone exposure, respectively, in China in the mid-century under RCP4.5 ( [[#Hong--2019a|Hong et al., 2019a]] ). Excess ozone-related future premature deaths is noticeable in 2030 in East Asia and India for RCP8.5 (over 95% of global excess mortality) ( [[#Silva--2016|Silva et al., 2016]] ).

The global estimates for increases in malaria and dengue deaths (annual estimates) are approximately 32,700 and 280 additional deaths, respectively, in 2050 under the medium-to-high emissions scenario ( [[#World%20Health%20Organization--2014|World Health Organization, 2014]] ). Among these additional deaths, 9,300 and 200 deaths, respectively, are projected to occur in South Asia. The population at risk of malaria infection is estimated to increase by 134 million by 2030 in South Asia under the medium-to-high emissions scenario, considering socioeconomic development. If no actions are taken, malaria incidence in northern China is projected to increase by 69–182% by 2050 ( [[#Song--2016|Song et al., 2016]] ). Another study suggested a decrease in climate suitability for malaria in northern and eastern India, southern Myanmar, southern Thailand, the Malaysia border region, Cambodia, eastern Borneo and Indonesia by 2050 ( [[#Khormi--2016|Khormi and Kumar, 2016]] ). By contrast, climate suitability for malaria is projected to increase in the southern and southeast mainland of China and Taiwan, Province of China ( [[#Khormi--2016|Khormi and Kumar, 2016]] ).

Dengue incidence is projected to increase to 16,000 cases per year by 2100 in Dhaka, Bangladesh, if ambient temperatures increase by 3.3°C without any adaptation measures or changes in socioeconomic conditions ( [[#Banu--2014|Banu et al., 2014]] ). This would represent an increase in incidence of over fortyfold compared with 2010. Higher numbers of dengue fever cases are projected to occur under RCP8.5 than RCP2.6 in China ( [[#Song--2017|Song et al., 2017]] ). Compared with the average numbers in 1997–2012, the annual number of days suitable for dengue fever transmission in the 2020s, 2050s and 2080s will increase by 15, 25 and 40 d, respectively, in southern China under RCP8.5. In addition, areas in which year-round dengue fever epidemics occur will ''likely'' increase by 4500, 8800 and 20,700 km 2 in the 2020s, 2050s and 2080s, respectively, under RCP8.5 ( [[#Nahiduzzaman--2015|Nahiduzzaman et al., 2015]] ).

The global estimates for increases in deaths due to diarrhoeal disease (annual estimates) in children under 15 years in 2030 and 2050 are approximately 48,000 and 33,000 additional deaths, respectively, under the medium-to-high emissions scenario ( [[#World%20Health%20Organization--2014|World Health Organization, 2014]] ). Among these additional deaths, 14,900 and 7,700 deaths, respectively, are projected to occur in South Asia. An updated projection with a pathogen-specific approach estimated 25,000 additional annual diarrhoeal deaths in Asia in 2080–2095 under the high-emissions scenario ( [[#Chua--2021|Chua et al., 2021]] ), while in some countries, such as Japan, net reductions in temperature-induced infectious diarrhoeal cases were estimated, because viral infections are dominant in these countries during the cold season ( [[#Onozuka--2019|Onozuka et al., 2019]] ).

South and Southeast Asia are projected to be among the highest-risk regions for reduced dietary iron intake among women of childbearing age and children under five years due to elevated CO 2 concentrations ( ''medium confidence'' ) ( [[#Smith--2018|Smith and Myers, 2018]] ). The estimated number of additional deaths due to climate change in children aged under five years attributable to moderate and severe stunting in 2030 and 2050 are approximately 20,700 and 16,500, respectively, in South Asia, under the medium-to-high emissions scenario ( [[#World%20Health%20Organization--2014|World Health Organization, 2014]] ). In Bangladesh, due to climate change, river salinity is projected to be increased in coastal and freshwater fishery communities leading to significant shortages of drinking water in the coastal urban areas ( [[#Dasgupta--2014c|Dasgupta et al., 2014c]] ).

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==== 10.4.7.3 Adaptation Options/Co-benefits ====

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The health co-benefits of GHG mitigation measures in energy generation have been reported to reduce disease burden. In China, the implementation of GHG policies would reduce the air-pollution-associated disease burden by 44% in 2020 under the Integrate Carbon Reduction scenario compared with the business-as-usual scenario ( [[#Liu--2017b|Liu et al., 2017b]] ). Transition to a half-decarbonised power supply for the residential and transport sectors would prevent 55,000–69,000 deaths in 2030 compared with the business-as-usual scenario ( [[#Peng--2018|Peng et al., 2018]] ). A shift in travel modes from private motor vehicles to the use of mass rapid-transit lines is estimated to reduce CO 2 -equivalent emissions by 6% in greater Kuala Lumpur and bring important health co-benefits to the population ( [[#Kwan--2017|Kwan et al., 2017]] ). The 25 measures developed for reducing air pollution levels in Asia and the Asia–Pacific in general would reduce CO 2 emissions in 2030 by almost 20% relative to baseline projections and decrease warming by 0.3°C by 2050, which could eventually reduce heat-related excess deaths in the region ( [[#UNEP--2019|UNEP, 2019]] ). The 25 measures include conventional emissions controls focusing on emissions that lead to the formation of fine particulate matter (PM2.5), next-stage air-quality measures for reducing emissions that lead to the formation of PM2.5 and are not yet major components of clean-air policies in many parts of the region, and measures contributing to development-priority goals with benefits for air quality. Health co-benefits outweigh mitigation costs in the Republic of Korea up to 2050 ( [[#Kim--2020|Kim et al., 2020]] ). Low-carbon pathways consistent with the 2°C and 1.5°C long-term climate targets defined in the Paris Agreement are associated with the largest health co-benefits when coordinated with stringent air pollution controls in Asia followed by Africa and Middle East ( [[#Rafaj--2021|Rafaj et al., 2021]] ).

Strategies to increase energy efficiency in urban environments by compact urban design and circular-economy policies can reduce GHG emissions and reap ancillary health benefits; for example, compared with conventional single-sector strategies, national CO 2 emissions can be reduced by 15–36%, and the annual deaths from 25,500 to 57,500 are avoidable from air pollution reduction in 637 Chinese cities ( [[#Ramaswami--2017|Ramaswami et al., 2017]] ). In a city in China, the existing mitigation policies (e.g., promotion of tertiary and high-tech industry) and the one-adaptation policy (increasing resilience) increased the co-benefits for well-being ( [[#Liu--2016a|Liu et al., 2016a]] ).

Changing dietary patterns, particularly reducing red meat consumption and increasing fruit and vegetable consumption, contributes to reduced GHG emissions as well as reduced premature deaths. The adoption of global dietary guidelines was estimated to prevent 5.1 million deaths per year relative to the reference scenario in which the largest number of avoidable deaths occurred in East Asia and South Asia, and GHG emissions would be reduced most in East Asia ( [[#Springmann--2016|Springmann et al., 2016]] ). In China, dietary shifts to meet national dietary reference intakes reduced the daily carbon footprint by 5–28% depending on the scenario ( [[#Song--2017|Song et al., 2017]] ). In India, the optimised healthy diets (e.g., lower amounts of wheat and increased amounts of legumes) could help reduce up to 30% water use per person for irrigation and reduce diet-related GHG emissions. This would result in 6800 life-years gained per 100,000 population in 2050 ( [[#Milner--2017|Milner et al., 2017]] ).

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