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

==== 10.5.2.2 Findings ====

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===== 10.5.2.2.1 Disaster risk reduction =====

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Technological advances have enhanced the capabilities of Asian countries to monitor and prepare for climate-related hazards. Remote sensing technologies and GIS are widely used for DRR (Kato et al., 2017), for example, to assess and mitigate risks of an area to potential climate-related disasters ( [[#Wu--2018b|Wu et al., 2018b]] ). The potential impacts of different types of hazards can be visualised using interactive maps ( [[#Lee--2017|Lee, 2017]] ), which help local communities to understand risks and find appropriate evacuation areas ( [[#Cadiz--2018|Cadiz, 2018]] ). These maps provide a situational overview and instant risk assessment ( [[#Yang--2012b|Yang et al., 2012b]] ). As an emerging technology, artificial intelligence (AI) can identify conditioning factors of a landslide disaster ( [[#Hong--2019b|Hong et al., 2019b]] ). Mobile virtual reality is used for disaster mitigation training through a three-dimensional visualisation of a past disaster ( [[#Ghosh--2018|Ghosh et al., 2018]] ).

A community-based DRR system provides risk investigation, training and information analysis ( [[#Liu--2016b|Liu et al., 2016b]] ). Sharing information enhances establishment of such a system and contributes to disaster prevention ( [[#Nakamura--2017|Nakamura et al., 2017]] ). One example is an online mapping tool, which has been developed by volunteers ( [[#Sakurai--2017|Sakurai and Thapa, 2017]] ). Social media enables the population to access real-time information on a disaster ( [[#Ghosh--2018|Ghosh et al., 2018]] ), raises situation awareness ( [[#Yin--2012|Yin et al., 2012]] ) and empowers communities towards appropriate emergency actions ( [[#Leong--2015|Leong et al., 2015]] ). Among the various forms of social media, Twitter is widely used as a social sensor to detect what is happening in a disaster event ( [[#Sakaki--2013|Sakaki et al., 2013]] ). Accuracy of information on Twitter has been proved in collecting local details about floods ( [[#Shi--2019b|Shi et al., 2019b]] ); however, it has been noticed that Twitter generates rumours as well ( [[#Ogasahara--2019|Ogasahara et al., 2019]] ). Artificial intelligence is expected to reduce human error when they operate a decision-making system ( [[#Lin--2018|Lin et al., 2018]] ). Since technologies supporting DRR completely depend on electricity, the loss of power supply and communication constrains the recovery work in disaster-affected areas ( [[#Sakurai--2014|Sakurai et al., 2014]] ).

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===== 10.5.2.2.2 Urban sector =====

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In the urban sector, a wide variety of sensor technologies are being used to monitor urban land-use and climate changes over time, and to better understand the potential impacts of future changes. These sensors range from large optical–thermal–radar satellite instruments with (near) global coverage–for example, Landsat (US Geological Service), Sentinel (European Space Agency), ALOS (Japan Aerospace Exploration Agency) and MethaneSAT–to portable sensors embedded in mobile phones (e.g., phone cameras or temperature sensors) whose data are collected into centralised databases through crowdsourcing ( [[#Fenner--2017|Fenner et al., 2017]] ; [[#Meier--2017|Meier et al., 2017]] ). To combine and extract useful information from these heterogeneous sensor data–for example, for conducting climate risk assessments ( [[#Perera--2018|Perera and Emmanuel, 2018]] ; [[#Bechtel--2019|Bechtel et al., 2019]] ) and/or simulations of future land-use or climate changes in urban areas ( [[#Bateman--2016|Bateman et al., 2016]] ; [[#Iizuka--2017|Iizuka et al., 2017]] ; [[#Liu--2017c|Liu et al., 2017c]] )–AI technologies (e.g., machine-learning algorithms) are now being widely adopted ( [[#Johnson--2016|Johnson and Iizuka, 2016]] ; [[#Joshi--2016|Joshi et al., 2016]] ; [[#Mao--2017|Mao et al., 2017]] ). Thanks to advances in cloud-computing technology, which allows for online processing of massive volumes of remote sensing data, high-resolution (~30 m) global urban-area maps from the late 1990s to 2018 are now available from several different sources ( [[#Gong--2020|Gong et al., 2020]] ). Using these historical maps, researchers have been able to generate maps of future urban land-use changes at the global level to 2100 ( [[#Chen--2020a|Chen et al., 2020a]] ), which can help to elucidate the potential impacts of this future urban expansion and identify adaptation needs. Technology also plays a major role in urban planning and design in the context of adaptation. To mitigate rising urban temperatures and reduce the impacts of climate-related hazards, many new ‘grey’ infrastructure and ‘green’ infrastructure technologies are being adopted in urban areas in Asia, for example, cool (i.e., high solar reflectance) rooftops and pavements as well as green (i.e., vegetated) rooftops to mitigate high temperatures; and porous pavements to mitigate flooding ( [[#Akbari--2016|Akbari and Kolokotsa, 2016]] ).

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===== 10.5.2.2.3 Water and agriculture =====

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The majority of the Asian region is experiencing water stress in terms of both quantity and quality, due to poor management systems and governance. This has dire consequences for the national GDP as the majority of the population belongs to agrarian communities and their water-dependent agriculture system. Despite a substantial investment and progress in research and development, and capacity building in the recent past, the majority of developing countries in Asia are struggling to manage both water resources and agriculture sectors heavily ''dependent'' on water resources, in response to rapid global changes. Considering the frequent extreme weather conditions, progress in management tasks is even more consequential; hence, critically important for these countries, to achieve better CCA, are advanced science and technology viz. smart agriculture, robust EWS using downscaled meteorological information, a participatory approach, IWRM and so forth.

Having scientific knowledge relevant at the local scale through placed knowledge is important to identify climate-change risk and vulnerability. Moreover, once integrated with socioeconomic attributes, it can be useful for natural resource management, agriculture and so forth ( [[#Leith--2017|Leith and Vanclay, 2017]] ). Role of big data and data mining is undeniably very huge to get reliable climatic information and hence for designing appropriate adaptation measures for natural resource measurement. For example, use of big data in terms of EWS and real-time observation data provides more accurate information on hydro-meteorological extreme weather conditions or hazards like drought and flood, and will help farmers and local governments to improve their perception and hence preparedness for better adaptation ( [[#Hou--2017|Hou et al., 2017]] ; Ong and G.L.B.L., 2017). Using big data, different adaptation measures, such as new cultivar breeding, cropping-region adjustment, irrigation-pattern change, crop rotation and cropping-practice optimisation, are being designed in the agriculture sector, and these practices have greatly increased crop yield, leading to higher resource-use efficiency as well as greatly increased soil organic carbon content with reduced GHG emissions. It results in a win–win situation in terms of enhancing food security and mitigating climate warming ( [[#Deng--2017|Deng et al., 2017]] ). However, usability and application of this technology are still not common, especially in data-scarce regions. Integrated numerical simulations are efficient tools for estimating the current status and predicting the risk and efficiency of the adaptive capabilities of different countermeasures for sustainable natural resource management such as with water ( [[#Kumar--2019|Kumar, 2019]] ). Similarly, the agent-based model is commonly used to estimate risk of food-borne diseases due to climate change, using tunable parameters such as hygiene level, the microorganism’s growth rate and the number of consumers, and hence it has the potential to be a useful tool for optimising decision making and urban planning strategies related to health and climate change (Gay [[#Garcia--2017|Garcia et al., 2017]] ). The integrated assessment model under the shared-socioeconomic pathway (SSP) framework is effectively used to estimate future energy development and possible mitigation strategies to reduce GHG emissions related to the energy sector ( [[#Bauer--2017|Bauer et al., 2017]] ). Sound understanding of different drivers, pressures and stress factors, such as abnormal temperature, rainfall, insect pests or pathogens and their interaction pattern with the genetic makeup of crops, is the key to produce high-yielding varieties of wheat with better nutritional quality and resistance to major diseases ( [[#Goel--2017|Goel et al., 2017]] ). Another critical point to address this water security is inclusive, polycentric and adaptive governance. Polycentric governance is a means by which water management plans and policies should be framed and agreed by all relevant stakeholders. For adaptive governance, more emphasis will be on finding the best pathways to make robust water management plans amid rapid global changes. The benefit of such plans should reach the end users in terms of providing clean water, protection from hydrological hazards and maintaining the health of the ecosystem. In addition, there is urgent need for co-management, which includes the cycle of co-design, co-implementation and co-delivery throughout the whole water cycle. The best suitable example is using the circulating and ecological sphere (CES) approach. The CES is a concept that complements and supports regional resources by building broader networks, which are composed of natural connections (connections among forests; city and countryside; groundwater, rivers and the sea) and economic connections (human resources, funds and others), thus complementing each other and generating synergy ( [[#Mavrodieva--2020|Mavrodieva and Shaw, 2020]] ). Another suitable example for managing water resources is the participatory watershed land-use management (PWLM) approach. The PWLM is another very innovative and successful approach for more robust water resource management as explained by [[#Kumar--2020|Kumar et al. (2020)]] . It helps to make land-use and CCA policies more effective at the local scale. This is an integrative method using both participating tactics and computer-simulation modelling for water resource management at the regional scale.

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===== 10.5.2.2.4 Forests and biodiversity =====

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Technologies and their applications to identify habitat degradation, ecosystem functions and biodiversity conservation are increasing in Asia, with many countries looking to new and improved means for forest and biodiversity monitoring and conservation. In particular, there has been an impressive use of temporal satellite data, particularly from the Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS) series for widespread monitoring of forests and ecological resources. These series have provided reliable information on forests and ecosystem services at the country level, in difficult terrains, such as the mountains, cross-boundaries and otherwise inaccessible areas. For instance, [[#Yin--2017|Yin et al. (2017)]] estimated cross-boundary forest resources in Central Asia, a region which traditionally suffered from lack of reliable forest data, using remote sensing techniques. In a separate study, [[#Reddy--2020|Reddy et al. (2020)]] used long-term MODIS forest fire data from 2003 to 2017 to characterise fire frequency, density and hotspots in South Asia. Archives of scientific data, backed by state-of-the art modelling techniques, advanced-computing methods and innovations in big-data analysis, particularly helped the provisioning of scientific research. A number of studies simulated forest futures from the local to the continent scale under different socioeconomic and climate scenarios. For instance, at the local scale, Dasgupta et al. (2018) projected the future extent of mangroves in the Sundarban Delta under four local scenarios, while [[#Estoque--2019|Estoque et al. (2019)]] modelled and developed spatial maps of regional forest futures in Southeast Asia using the five SSP scenarios. Science and technology also helped the monitoring of species diversity and abundance, pivotal for sustaining an ecosystem and ecosystem-based adaptation. Digital camera traps and radio-collaring methods have largely replaced old film cameras and labour-intensive methods of photo screening to count target species ( [[#Pimm--2015|Pimm et al., 2015]] ). This enhanced scientific capacities to monitor biodiversity and facilitate better conservation in difficult terrains, control poachers and maintain steady ecological balance. [[#Umapathy--2016|Umapathy et al. (2016)]] , for example, used VHF radio collars and satellite-based tracking tools to monitor the movement of Bengal tigers over hostile island terrain. Photo recognition and other non-invasive techniques for individual identification have been rising in Asia. For example, a study by [[#Gray--2014|Gray et al. (2014)]] used faecal-DNA samples to estimate the population density of the Asian elephant in Cambodia. Furthermore, the advancement of citizen science programmes has greatly facilitated better monitoring of forest resources, including invasive floral and faunal species ( [[#Chandler--2017|Chandler et al., 2017]] ; [[#Johnson--2020|Johnson et al., 2020]] ). In Asia, citizen science has been used effectively in India ( [[#Chandler--2017|Chandler et al., 2017]] ), and also in Malaysia for the monitoring urban bird abundance ( [[#Puan--2019|Puan et al., 2019]] ).

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