Editing IPCC:AR6/WGII/Chapter-4 (section)

== 4.7 Benefits and Effectiveness of Water-Related Adaptations, Their Limits and Trade-Offs ==

<div id="h1-8-siblings" class="h1-siblings"></div>

The previous section documented adaptation responses in water use sectors we assess in this chapter ( [[#4.6|Section 4.6]] ), and noted that in many instances, effectiveness of those responses is not clear. While there are thousands of case studies of implemented adaptation responses (observed adaptation) to water insecurity, there is a lack of synthesised understanding about the effectiveness and benefits of adaptation ( [[#Berrang-Ford--2021a|Berrang-Ford et al., 2021a]] ) and whether or not those benefits also translate into climate risk reduction ( [[#Singh--2021|Singh et al., 2021]] ). In contrast, literature on the effectiveness of future projected adaptation in reducing climate risks is limited in number. Yet, even then, the findings are not synthesised across various options to make an overall assessment of the effectiveness of future projected adaptation. In this section, we draw on two meta-review protocols (see SM4.2 for a description of each protocol) and assess the benefits of current adaptation and effectiveness of future projected adaptation in reducing climate risks. We also assess limits to adaptation and trade-offs and synergies between adaptation and mitigation.

<div id="4.7.1" class="h2-container"></div>

<span id="current-water-related-adaptation-responses-benefits-co-benefits-and-maladaptation"></span>
=== 4.7.1 Current Water-Related Adaptation Responses, Benefits, Co-benefits and Maladaptation ===

<div id="h2-44-siblings" class="h2-siblings"></div>

AR5 ( [[#Jiménez%20Cisneros--2014|Jiménez Cisneros et al., 2014]] ) concluded that developing countries needed a larger share of adaptation investments for anticipatory adaptation in the water sector ( ''medium evidence, high agreement'' ) and that adaptive water management measures were critical in addressing climate-related uncertainty. [[#Noble--2014|Noble et al. (2014)]] listed various examples of adaptation options, and water-related adaptation featured prominently in almost all categories. They also discussed the challenges of developing metrics for measuring adaptation outcomes and stressed the importance of transformational adaptation instead of incremental adaptation. Finally, SR1.5 ( [[#de%20Coninck--2018|de Coninck et al., 2018]] ) made one of the first attempts to systematically assess the feasibility of adaptation options ( [[#Singh--2020|Singh and Basu, 2020]] ).

<div id="4.7.1.1" class="h3-container"></div>

<span id="current-water-related-adaptation-responses"></span>
==== 4.7.1.1 Current Water-Related Adaptation Responses ====

<div id="h3-7-siblings" class="h3-siblings"></div>

We define an adaptation response as a water-related adaptation if the hazard is water-related (e.g., floods, droughts, extreme rainfall events, groundwater depletion, melting and thawing of cryosphere, Figure 4.25) or the adaptation intervention is water-related (e.g., irrigation, rainwater harvesting, soil moisture conservation, etc.). Adaptation responses were implemented across all water use sub-sectors assessed in this chapter ( [[#4.6|Section 4.6]] , Figure 4.23). Given the overall interest in assessing adaptations that documents outcomes, we limited our analysis to a set of 359 unique articles that measure outcomes of adaptation across pre-defined outcome categories (SM4.2, Table SM4.5; [[#Berrang-Ford--2021a|Berrang-Ford et al., 2021a]] ; [[#Mukherji--2021|Mukherji et al., 2021]] ). A total of 1054 adaptation responses were documented in the 359 case studies; these were categorised into 16 categories (Figure 4.22). These adaptation responses are not always specific to long-term climate change impacts (that is, changes in annual mean fluxes), but rather respond to changes in variability in the water cycle and specific water hazards. Adaptation to internal variability is needed to increase the resilience to projected water cycle changes because water cycle changes primarily manifest as changes in variability ( [[#Douville--2021|Douville et al., 2021]] ).

<div id="_idContainer087" class="Figure"></div>

[[File:391331e2154d2f21d45b885a41439933 IPCC_AR6_WGII_Figure_4_023.png]]

'''Figure 4.23 |''' '''Sectoral distribution of documented water-related adaptation responses (observed adaptation) across the 16 categories derived from Figure''' '''4.''' '''22.''' The quantity of evidence is derived from the number of papers in a particular adaptation response category where high is &gt; 40 papers, medium is 10–40 papers and low is &lt; 10 papers. Confidence in evidence relates to the way the article links outcomes of adaptation with the adaptation response. Category 1: studies causally link adaptation outcomes to the adaptation response by constructing credible counterfactuals; category 2: studies correlate responses and outcomes without causal attribution; category 3: studies describe adaptation outcomes without making any causal or correlation claims between adaptation outcomes and adaptation responses. ''High confidence'' : more than 67% of the studies fall in categories 1 and 2; ''medium confidence'' : 50–67% of the studies are in categories 1 and 2, and ''low confidence'' is less than 50% of studies are in categories 1 and 2.

There is ''high confidence'' that a significant share of water-related adaptations is occurring in the agriculture sector. Agriculture accounts for 60–70% of total water withdrawals ( [[#Hanasaki--2018|Hanasaki et al., 2018]] ; [[#Burek--2020|Burek et al., 2020]] ; [[#Müller%20Schmied--2021|Müller Schmied et al., 2021]] ) and supports the livelihoods of a large majority of people in the developing countries. Within the agriculture sector, there is ''high confidence'' in the quality and quantity of evidence of adaptation responses such as improved cultivars and agronomic practices, on-farm irrigation and water management and water and soil moisture conservation, and ''medium confidence'' , derived from ''robust evidence'' , and ''medium agreement'' for other most other adaptation responses (Figure 4.23 and Figure 4.24). Most of these adaptation case studies are from Asia and Africa, and agriculture is the predominant sector where most of these adaptation responses are being implemented ( ''high confidence'' ) ( [[#4.6.2|Section 4.6.2]] ). However, the sectoral nature of adaptation responses varies across continents. Agriculture is the most important sector in all continents, except Europe and Australasia, where most adaptation occurs in the urban sector ( ''high confidence'' ) (Figure 4.24).

<div id="_idContainer089" class="Figure"></div>

[[File:dcdbd382d3fd70d5ef2b5207cebfb86e IPCC_AR6_WGII_Figure_4_024.png]]

'''Figure 4.24 |''' '''Location of case studies on water-related adaptation which measure adaptation outcomes (''' '''''n''''' '''= 359) and their sectoral distribution across all regions.''' Circles denote the number of case studies in a particular location in the continent. The pie chart shows the sectors in which adaptation is taking place. The sectors correspond to water use sectors described in Sections 4.3, 4.5 and 4.6 of this chapter.

The top four adaptation responses in terms of frequency of documentation are changes in the cropping pattern and crop systems (145 responses), improved crop cultivars and agronomic practices (139 responses), irrigation and water management practices (115 responses) and water and soil conservation measures (102 responses). These top four responses provide several benefits such as higher incomes and yields, better water use efficiencies and related outcomes ( ''high confidence'' ) (Table 4.9 and Figure 4.27). However, those benefits are incremental, that is, they help improve crop production and incomes, at least in the short run, but may not automatically lead to transformative outcomes and climate risk reductions ( [[#Pelling--2015|Pelling et al., 2015]] ; [[#Fedele--2019|Fedele et al., 2019]] ). One way to move from incremental to transformative adaptation could be to invest gains from incremental adaptation in education and capacity building to improve overall adaptive capacity ( [[#Vermeulen--2018|Vermeulen et al., 2018]] ). Responses such as migration, including spontaneous and planned relocation, are also relatively well documented ( ''medium confidence'' ), as are responses such as collective action, training and capacity building and economic and financial measures for increasing adaptive capacities ( ''medium confidence'' ). These categories of adaptation can potentially lead to transformative outcomes, such as a shift to livelihoods that are less exposed to climate hazards. However, transformative pathways are not always straightforward ( [[#Pahl-Wostl--2020|Pahl-Wostl et al., 2020]] ) (Table 4.8).

'''Table 4.8 |''' Illustrative examples of case studies of water-related adaptation responses where outcomes were measured ( ''n'' = 359). These cases include instances where adaptation benefits were positive, negative or neutral. Examples also include studies with or without causal and correlation links between adaptation response and outcomes (categories 1, 2 and 3 studies as described in the caption of Figure 4.23). The purpose of the table is to provide a list of illustrative examples to showcase the wide range of adaptation responses that are being implemented. Table 4.9 zooms into examples where adaptation had positive benefits on any of the selected parameters described in [[#4.7.1.2|Section 4.7.1.2]] .

{| class="wikitable"
|-
! Name of the adaptation response (number of documented responses in that category)

! Description of adaptation response

! Sources

|-
| rowspan="3"| Changes in the cropping pattern and crop systems

(145 responses)

| Changes in cropping pattern; e.g., the introduction of sugarcane and rice in Costa Rica; crop diversification in Ethiopia and Zimbabwe; crop diversification in Tanzania

| [[#Singh--2014|Singh et al. (2014)]] ; [[#Warner--2015|Warner et al. (2015)]] ; Asmare et al. (2019); [[#Lalou--2019|Lalou et al. (2019)]] ; [[#Makate--2019|Makate et al. (2019)]]

|-
| Changes in the timing of sowing and harvesting, e.g., in China; India and Pakistan

| [[#Yu--2014|Yu et al. (2014)]] ; Macchi et al. (2015)

|-
| On-farm diversification, e.g., an integrated crop-livestock system in France

| [[#Havet--2014|Havet et al. (2014)]]

|-
| rowspan="2"| Improved crop cultivars and agronomic practices (139 responses)

| Improved crop cultivars, e.g., short-duration paddy varieties in Nepal; saline-tolerant rice cultivar in Bangladesh; drought-tolerant maize varieties in Malawi, Nigeria, Zimbabwe and Uganda

| [[#Kabir--2016|Kabir et al. (2016)]] ; [[#Wossen--2017|Wossen et al. (2017)]] ; [[#Khanal--2018a|Khanal et al. (2018a)]] ; [[#Makate--2019|Makate et al. (2019)]]

|-
| Improved agronomic practices, e.g., conservation agriculture to conserve soil moisture in Malawi and Tanzania; climate-smart agricultural practices in Zambia; alternate wetting and drying and direct seeding of rice in India

| Thierfelder et al. (2015); [[#Kimaro--2016|Kimaro et al. (2016)]] ; [[#Traore--2017|Traore et al. (2017)]] ; [[#Kakumanu--2019|Kakumanu et al. (2019)]]

|-
| rowspan="2"| Irrigation and water management practices

(115 responses)

| Irrigation, e.g., construction of local irrigation infrastructure in Chile; funding of community wells in Canada; drilling of borewells in Thailand; irrigation in Ethiopia; spate irrigation in Sudan; night-time irrigation scheduling to reduce evaporative demand in the UK

| [[#Hurlbert--2014|Hurlbert and Pittman (2014)]] ; Ferchichi et al. (2017); Rey et al. (2017); [[#Pak-Uthai--2018|Pak-Uthai and Faysse (2018)]] ; Fadul et al. (2019); Lemessa et al. (2019); [[#Lillo-Ortega--2019|Lillo-Ortega et al. (2019)]] ; [[#Torres-Slimming--2020|Torres-Slimming et al. (2020)]]

|-
| On-farm water management and water-saving technologies, e.g., use of surface pipes for irrigation water conveyance in China; drip irrigation in China; and use of water-saving measures in India

| [[#Hong--2017|Hong and Yabe (2017)]] ; [[#Tan--2017|Tan and Liu (2017)]] ; [[#Deligios--2019|Deligios et al. (2019)]] ; Rouabhi et al. (2019)

|-
| rowspan="4"| Water and soil conservation (102 responses)

| On-farm water and soil conservation measures, e.g., in Burkina Faso; terraces and contour bunds in Ethiopia

| West Colin et al. (2016); [[#Kosmowski--2018|Kosmowski (2018)]]

|-
| Water harvesting through on-sand dams in Kenya; ''in situ'' and ''ex situ'' water harvesting in Uganda and India

| Ngigi et al. (2018); [[#Sullivan-Wiley--2018|Sullivan-Wiley and Short Gianotti (2018)]] ; [[#Kalungu--2021|Kalungu et al. (2021)]]

|-
| Watershed conservation programmes, e.g., in Ethiopia

| Siraw et al. (2018)

|-
| Revival of water bodies; e.g., creation of artificial lakes in Portugal

| [[#Santos--2018|Santos et al. (2018)]]

|-
| rowspan="5"| Collective action, policies and institutions (95 responses)

| Collective action and cooperation; e.g., grassroots-level collective action for conflict resolution in Guatemala; collective decision to reduce water withdrawals during drought in Japan

| [[#Hellin--2018|Hellin et al. (2018)]] ; [[#Tembata--2018|Tembata and Takeuchi (2018)]]

|-
| Community-based adaptation in Bangladesh, community-based management of rangelands in Mongolia

| [[#Fernández-Giménez--2015|Fernández-Giménez et al. (2015)]] ; [[#Roy--2018|Roy (2018)]]

|-
| Local institutions, e.g., multi-stakeholder platforms for disaster risk reduction and agriculture in Peru and several African countries; Adaptation Learning Programme.

| [[#Mapfumo--2017|Mapfumo et al. (2017)]] ; [[#Lindsay--2018|Lindsay (2018)]]

|-
| Water dispute resolution; e.g., water conflict mitigation in Costa Rica.

| [[#Kuzdas--2016|Kuzdas et al. (2016)]]

|-
| Institutional and policy reforms; e.g., local water and land use planning instruments in Australia; the Dutch Delta Programme in the Netherlands; implementation of EU Flood Directives in Sweden

| Fallon and Sullivan (2014; Zevenbergen et al. (2015; [[#Hedelin--2016|Hedelin (2016)]]

|-
| rowspan="4"| Migration and off-farm diversification (92 responses)

| Spontaneous migration, e.g., voluntary relocation in the Solomon Islands and rural to urban migration in Ethiopia and Pakistan.

| [[#Birk--2014|Birk and Rasmussen (2014)]] ; [[#Iqbal--2018|Iqbal et al. (2018)]]

|-
| Employment and remittances, e.g., in Senegal.

| [[#Romankiewicz--2016|Romankiewicz et al. (2016)]]

|-
| Planned relocation; e.g., the Massive Southern Shaanxi Migration Programme in China; resettlement of flood-prone communities in Bangladesh.

| [[#Islam--2014|Islam et al. (2014)]] ; [[#Lei--2017|Lei et al. (2017)]]

|-
| Off-farm diversification; e.g., migration to towns and engaging in off-farm labour wage-earning in Niger, Ghana Bangladesh; shifting to non-pastoral livelihoods in Ethiopia

| Mussetta et al. (2016); Basupi et al. (2019)

|-
| rowspan="2"| Livestock and fishery-related (63 responses)

| Livestock related, e.g., livestock species diversification in Ethiopia and Kenya; insuring livestock in Pakistan; changes in range management practices in the USA.

| [[#Opiyo--2015|Opiyo et al. (2015)]] ; [[#Yung--2015|Yung et al. (2015)]] ; Wako et al. (2017); [[#Rahut--2018|Rahut and Ali (2018)]]

|-
| Fishery related, e.g., non-destructive fishery gears and techniques in Ghana and Tanzania

| [[#Yang--2019a|Yang et al. (2019a)]]

|-
| Training and capacity building (57 responses)

| Information, training and capacity building; e.g., climate information services in Kenya and Senegal; training contributed new learning about digging canals to avoid prolonged water logging in the Philippines; soil conservation training programme in Ethiopia

| [[#Bacud--2018|Bacud (2018)]] ; [[#McKune--2018|McKune et al. (2018)]] ; [[#Chesterman--2019|Chesterman et al. (2019)]]

|-
| rowspan="2"| Agroforestry and forestry-related responses (56 responses)

| Agroforestry-related measures in India, Kenya, Nigeria; farmer-managed natural regeneration (FMNR) in Ghana.

| [[#Weston--2015|Weston et al. (2015)]] ; [[#Pandey--2017|Pandey et al. (2017)]] ; [[#Fuchs--2019|Fuchs et al. (2019)]] ; [[#Okunlola--2019|Okunlola et al. (2019)]]

|-
| Forestry related; e.g., coastal afforestation by planting salinity-resistant trees in Bangladesh and Colombia

| Pandey et al. (2016); Barrucand et al. (2017); Barua et al. (2017)

|-
| rowspan="6"| Economic and financial incentives (54 responses)

| Insurance; rice crop insurance programme in Indonesia; agricultural insurance programme in South Africa.

| Dewi et al. (2018); Elum et al. (2018)

|-
| Micro-finance and credit programmes, e.g., in Bangladesh.

| Fenton et al. (2017b)

|-
| Social safety nets; e.g., food-based safety net programmes in Brazil, food for work programmes in Ethiopia.

| [[#Mesquita--2017|Mesquita and Bursztyn (2017)]] ; [[#Sain--2017|Sain et al. (2017)]] ; [[#Tesfamariam--2017|Tesfamariam and Hurlbert (2017)]] ; [[#Gao--2018|Gao and Mills (2018)]]

|-
| Subsidies and incentives, e.g., farm input subsidy programme in Malawi; financing programmes in Canada to help producers with resources to improve/maintain the quality of soil, water, biodiversity for drought mitigation.

| [[#Hurlbert--2014|Hurlbert (2014)]] ; [[#Kawaye--2018|Kawaye and Hutchinson (2018)]]

|-
| Water markets and tariffs; e.g., urban water tariffs in Zaragoza, Spain; informal groundwater markets in China.

| [[#Kayaga--2014|Kayaga and Smout (2014)]] ; [[#Zhang--2016b|Zhang et al. (2016b)]]

|-
| Payment for ecosystems services, e.g., in Mexico

| [[#Newsham--2018|Newsham et al. (2018)]]

|-
| IKLK based adaptations (41 responses)

| Use of TK of Konda Reddy’s in India to shift agroforestry practices; and among ''Khasia'' and Tripura communities in Bangladesh; use of local ecological knowledge is by small-scale fisher-farmers in the Amazon floodplains, Brazil; traditional water sharing system ‘ ''bethma'' ’ in Sri Lanka; indigenous methods of water harvesting in India

| [[#Sarkar--2015|Sarkar et al. (2015)]] ; [[#Burchfield--2016|Burchfield and Gilligan (2016)]] ; [[#Kodirekkala--2018|Kodirekkala (2018)]] ; [[#Ahmed--2019|Ahmed and Atiqul Haq (2019)]]

|-
| rowspan="5"| Flood risk reduction measures include (40 responses)

| Non-structural measures for flood management; e.g., changes in day-to-day practices in Indonesia; place-specific social structures in the UK.

| [[#Petzold--2018|Petzold (2018)]] ; [[#Bott--2019|Bott and Braun (2019)]]

|-
| Structural measures for flood management; improvement of the drainage system in Indonesia; flood walls in Beira, Mozambique; dredging and construction of culverts in Nigeria.

| [[#Bahinipati--2015|Bahinipati and Patnaik (2015)]] ; [[#Wijaya--2015|Wijaya (2015)]] ; Egbinola et al. (2017); [[#Spekker--2017|Spekker and Heskamp (2017)]]

|-
| Early warning systems; e.g., flood forecasting in Nepal, Indonesia, Nigeria.

| [[#Ajibade--2014|Ajibade and McBean (2014)]] ; Devkota et al. (2014); [[#Sari--2018|Sari and Prayoga (2018)]]

|-
| Flood-resilient housing; e.g., houses on stilts in Guyana, in Pakistan, Vietnam, Philippines.

| [[#Mycoo--2014|Mycoo (2014)]] ; [[#Ling--2015|Ling et al. (2015)]] ; [[#Abbas--2018|Abbas et al. (2018)]]

|-
| Wetland restoration; e.g., in the USA and Netherlands

| [[#Zevenbergen--2015|Zevenbergen et al. (2015)]] ; Pinto et al. (2018)

|-
| rowspan="3"| Urban water management (22 responses)

| Urban water management, e.g., incorporating low impact development and urban design features for sustainable urban drainage systems in Spain and Malaysia; demand management and tariff reforms in several European countries.

| [[#Flyen--2018|Flyen et al. (2018)]] ; [[#Rodríguez-Sinobas--2018|Rodríguez-Sinobas et al. (2018)]] ; Stavenhagen et al. (2018); [[#Chan--2019|Chan et al. (2019)]]

|-
| Green infrastructure; e.g., ecological stormwater management and re-naturalisation processes in Sweden; pavement watering in France, Ghana, India, Kenya, Bangladesh

| [[#Hendel--2015|Hendel and Royon (2015)]] ; [[#Wamsler--2016|Wamsler et al. (2016)]] ; [[#Tauhid--2018|Tauhid and Zawani (2018)]] ; Birtchnell et al. (2019)

|-
| Desalinisation for water supplies in Spain

| Martínez-Alvarez et al. (2016); Morote et al. (2019)

|-
| rowspan="2"| Energy-related adaptations (eight responses)

| Hydropower related; e.g., hydropower benefit-sharing in the Mekong basin and Nepal

| [[#Balasubramanya--2014|Balasubramanya et al. (2014)]] ; [[#Suhardiman--2014|Suhardiman et al. (2014)]] ; [[#Shrestha--2015|Shrestha et al. (2015)]]

|-
| Other renewable energy-related, e.g., “Raising Water and Planting Electricity project” in Taiwan province of China

| [[#Lin--2016|Lin and Chen (2016)]]

|-
| rowspan="2"| WaSH-related adaptations (five responses)

| Hand washing and hygiene, e.g., provision of latrines and washing hands with soap in Bangladesh

| [[#Dey--2019|Dey et al. (2019)]]

|-
| Safe drinking water and sanitation; e.g., piped water supply in China

| [[#Su--2017|Su et al. (2017)]]

|-
| Any other including coping strategies (20 responses)

| Reduction in consumption, selling off assets, etc.; e.g., selling of household property and livestock in Nigeria; consumption smoothing in Ghana; reducing consumption in Nepal

| [[#Musah-Surugu--2018|Musah-Surugu et al. (2018)]] ; [[#Rai--2019|Rai et al. (2019)]]

|}

'''Table 4.9 |''' Illustrative examples of adaptation responses and their benefits across different outcome indicators. All these studies are either category 1 or category 2 studies in that the link between adaptation response and the outcome is either causal or correlated with one another. These benefits notwithstanding, links of adaptation benefits to climate and associated risk reduction are not always clear. Some of these adaptation responses can have beneficial outcomes in one of the five parameters, but can have maladaptive outcomes in others.

{| class="wikitable"
|-
! Hazard

! Adaptation responses

! Outcome category

! Adaptation outcome

! Reference

|-
| Droughts, floods, and general climate impacts in Nepal

| Improved crop cultivars, agronomic practices, irrigation, soil water conservation measures

| rowspan="10"| Economic and financial outcomes

| Farming households that adapted produced about 33% more rice than households that did not adapt after controlling for all heterogeneity.

| [[#Khanal--2018a|Khanal et al. (2018a)]]

|-
| Increased rainfall variability in India

| Farmer’s training on agronomic measures, for example, alternate drying and wetting (ADW), modified system of rice intensification (MSRI) and direct-seeded rice (DSR)

| The capacity building and water saving increased crop yields by 960 kg ha –1 , 930 kg ha –1 and 770 kg kg –1 through the adoption of AWD, MSRI and DSR, respectively. The three practices have increased farmers’ income and decreased the cost of cultivation by up to USD 169 ha –1 .

| [[#Kakumanu--2019|Kakumanu et al. (2019)]]

|-
| Droughts and changes in the seasonality of rainfall in Pakistan

| Adjusting sowing time of wheat

| Household income and wheat yields were higher for households who adjusted the sowing time to cope with climate risks than those who did not, after controlling for other factors.

| [[#Rahut--2017|Rahut and Ali (2017)]]

|-
| Droughts in North China Plains

| Irrigation

| Adding one extra irrigation could increase wheat yield by up to 12.8% in a severe drought year.

| [[#Wang--2019a|Wang et al. (2019a)]]

|-
| Soil degradation; extreme rainfall events and high runoff causing erosion in Mali

| Soil and water conservation using contour ridges and improved millet and sorghum cultivars

| Millet grain yield during 2012–2014 was statistically higher in contour ridge terrace plots than the control, with yield differences ranging from 301 kg ha –1 in 2012 to 622 kg ha –1 in 2013. Improved varieties produced on average 55% more yield than the local ones.

| [[#Traore--2017|Traore et al. (2017)]]

|-
| Drought, floods, hailstorm and erratic rainfall, Ethiopia

| On-farm agricultural water management

| The net revenue from adopting a combination of agricultural water management and modern seeds or inorganic fertiliser is significantly higher by 7600 and 1500 Birr ha –1 , respectively, than adopting modern seeds or inorganic fertiliser alone. Birr is the Ethiopian currency.

| [[#Teklewold--2017|Teklewold et al. (2017)]]

|-
| Droughts and general climate impacts, South Africa

| Crop insurance and irrigation

| Farmers who insured their farm business and had access to irrigation had relatively higher net revenue than those who did not, but this link is not causal. Instead, it shows causality could go either way, including those farmers who were better off getting their business insured.

| Elum et al. (2018)

|-
| Droughts and floods in Kenya

| Migration

| Remittance income enables uptake of costlier adaptation measures such as a change in livestock species, which also have higher returns for households. Therefore, the study was not causal in its inference.

| [[#Ng’ang’a--2016|Ng’ang’a et al. (2016)]]

|-
| Droughts in Nigeria

| Drought-tolerant varieties

| Per capita, food expenditure of those who adopted drought-tolerant maize was significantly lower than those who did not after controlling for everything else and causal inference.

| [[#Wossen--2017|Wossen et al. (2017)]]

|-
| General climate impacts, including rainfall variability in Brazil

| Agroforestry systems as land use in rural municipalities

| The land value in the municipalities with agroforestry was higher than that of the municipalities where the agroforestry scheme was not implemented.

| [[#Schembergue--2017|Schembergue et al. (2017)]]

|-
| Water quality deterioration due to floods in Bangladesh

| Water, sanitation and health WaSH programme

| rowspan="5"| Outcomes for vulnerable people

| Children: prevalence of childhood diarrhoea reduced by 35% in midline prevalence, 8.9% and by 73% in end line prevalence, 3.6% compared to baseline prevalence 13.7%.. Inferences are causal.

| [[#Dey--2019|Dey et al. (2019)]]

|-
| Droughts in Zimbabwe

| Adoption of drought-tolerant maize varieties by smallholder farmers

| Smallholder farmers: Smallholder farmers practising conservation agriculture (CA) were as likely to adopt drought-tolerant maize varieties as other farmers and thus benefit from increased yields and incomes.

| [[#Makate--2019|Makate et al. (2019)]]

|-
| General climate impacts, including droughts in Niger

| Crop diversification

| Poor households: Crop diversification mainly benefits the most vulnerable households; the impact on the poorest group ranges from double to triple the impact on the wealthiest group.

| Asfaw et al. (2018)

|-
| Droughts and general climate impacts in Malawi and Zimbabwe

| Conservation agriculture; drought-tolerant maize and improved legume varieties

| Female farmers: Yield and income effects on the adoption of conservation agriculture and improved varieties of maize and legumes were both positive for men and women.

| [[#Makate--2019|Makate et al. (2019)]]

|-
| Historically widespread and severe droughts in Ethiopia in 1999, 2002, 2003, 2005 and 2008.

| Government safety net programme called Productive Safety Net Programme (PSNP)

| Poor households: PSNP transfers reduce chronic poverty level from 15.7% to 10.6% and increase the never poor share from 11.5% to 15.8%.

| [[#Gao--2018|Gao and Mills (2018)]]

|-
| Droughts in

Kenya

| Water harvesting structures, for example, sand dams

| rowspan="4"| Water-related outcomes

| Sand dams increase groundwater storage in riverbanks by up to 40%, which is maintained throughout the year.

| [[#Ryan--2016|Ryan and Elsner (2016)]]

|-
| Millennium drought in Australia

| Water trading

| Irrigation application rates fell in the dairy industry from 4.2 million litres ha –1 in 2000–2001 to 3.5 million litres ha –1 in 2005–2006

| [[#Kirby--2014|Kirby et al. (2014)]]

|-
| Droughts, floods and soil erosion and sediment load in a river basin in France

| Agreement signed between water and electricity utilities and farmers

| Agreement between water and electricity utilities to compensate farmers for reducing water use resulted in a decrease in water demand from 310 Mm 3 in 1997 to 220 Mm 3 in 2012 in the Durance Valley irrigation system in France.

| [[#Andrew--2017|Andrew and Sauquet (2017)]]

|-
| Drought in India

| The reducing area under irrigated rice crop

| Reduced rice irrigation resulted in over 60 mm ha –1 of water savings compared to irrigated rice crops on that land.

| [[#Hochman--2017b|Hochman et al. (2017b)]]

|-
| Floods due to cyclonic storms and tidal inundation in Bangladesh

| Planting of vetiver grass for stabilising coastal embankments

| rowspan="3"| Ecological and environmental outcomes

| Households that planted vetiver grass around their homestead and nearby road managed to save their houses and assets from the recent cyclonic storm and tidal inundation.

| Barua et al. (2017)

|-
| General climate impacts, including rainfall variability in Brazil

| Agroforestry systems as land use in rural municipalities

| Trees planted as a part of the agroforestry programme provide thermal comfort to both animals and humans.

| [[#Schembergue--2017|Schembergue et al. (2017)]]

|-
| Drought in 2015 in Ethiopia

| Contour ridge terraces as soil water conservation measure

| Contour ridge terraces primarily controlled water runoff and soil erosion and acted as a buffer during the 2015 Ethiopian drought.

| [[#Kosmowski--2018|Kosmowski (2018)]]

|-
| Drought and rainfall variability in Pakistan

| Climate-smart agricultural practices

| rowspan="3"| Institutional and sociocultural outcomes

| Farmers who adopted climate-smart practices also tended to form a better relationship with local extension agents and reached out to them more frequently. Again, however, causality might as well lie the other way round.

| [[#Imran--2019|Imran et al. (2019)]]

|-
| Droughts, Mexico

| Strengthening of local water users’ associations through external assistance programmes

| Local water user’s associations were able to reduce water abstractions during years of severe droughts.

| [[#Villamayor-Tomas--2017|Villamayor-Tomas and García-López (2017)]]

|-
| Rainfall variability in Niger

| Community-based adaptation and through adaptation learning programmes

| More robust social networks where women were able to take important decisions

| [[#Vardakoulias--2015|Vardakoulias and Nicholles (2015)]]

|}

Droughts, followed by precipitation variability and extreme precipitation, are the two most common hazards against which adaptation responses are forged. The other three top hazards are general climate impacts, heat-related hazards and inland and riverine flooding (Figure 4.25). The majority of the adaptation responses across all categories were introduced by individuals and households, followed by the civil society, and hence autonomous (Figure 4.26). The private sector (defined as profit-making companies and distinct from individual farmers and households) has played a relatively minor role in initiating adaptation responses. However, the low participation of the private sector in initiating adaptation responses could be partly an artefact of the nature of documentation.

<div id="_idContainer092" class="Figure"></div>

[[File:47fa182350f4d34e352a3fb66f9d0911 IPCC_AR6_WGII_Figure_4_025.png]]

'''Figure 4.25 |''' '''Water-related adaptations and climate hazards against which adaptation responses are forged.''' Evidence and confidence are derived in the same way as in Figure 4.23.

<div id="_idContainer094" class="Figure"></div>

[[File:f4536d09789b8e31d9247a0d3ab2feb3 IPCC_AR6_WGII_Figure_4_026.png]]

'''Figure 4.26 |''' '''Water-related adaptations and their initiators.''' The initiator of adaptation is defined broadly and includes the entities who initiate a response, implement that response or engage in that response in any way, including leading, financing or enabling. Evidence and confidence are derived in the same way as in Figure 4.23.

<div id="4.7.1.2" class="h3-container"></div>

<span id="benefits-including-co-benefits-of-water-related-adaptation-responses-and-resulting-maladaptation"></span>
==== 4.7.1.2 Benefits, Including Co-benefits of Water-related Adaptation Responses and Resulting Maladaptation ====

<div id="h3-8-siblings" class="h3-siblings"></div>

There is no consensus in the literature about ways of measuring the effectiveness of current adaptation responses in reducing climate-related impacts ( [[#Singh--2021|Singh et al., 2021]] ). However, various methodologies, including feasibility assessment, have been deployed ( [[#Williams--2021|Williams et al., 2021]] ). Given the methodological challenges in defining and measuring the effectiveness of adaptation in reducing climate risks, in this section, we focus on outcomes of water-related adaptation across several dimensions. A total of 359 studies were identified to contain sufficiently ''robust evidence'' of documented adaptation outcomes to form the basis of this assessment (SM4.2, Table SM4.5; [[#Berrang-Ford--2021a|Berrang-Ford et al., 2021a]] ; [[#Mukherji--2021|Mukherji et al., 2021]] ). Positive outcomes denote benefits of adaptation, while negative outcomes may mean that adaptation was not effective in bringing any benefits or that it was maladaptive ( [[#Schipper--2020|Schipper, 2020]] ).

We assess outcomes across five indicators: (a) economic and financial indicators, such as improvements in crop yields and resulting incomes; increase in profits, higher savings or lesser losses from hazards; (b) impacts on vulnerable people, for example, on women, children and Indigenous Peoples; (c) water-related impacts, for example, improved water use efficiency, water saving, reduction in water withdrawals and application; (d) ecological and environmental impacts such as lesser energy use, better soil structures and better thermal comfort.; (e) institutional and sociocultural impacts such as improved social capital and stronger communities of practice, equity; and strengthening of local institutions or national policies. Of these 359 studies, 319 documented beneficial outcomes across one or more indicators, while the remaining 40 presented no beneficial outcomes. Illustrative examples are shown in Table 4.9, while the distribution of these responses with positive outcomes is shown in Figure 4.27, and indicates that economic benefits of adaptation are more common in developing countries, while benefits along ecological dimensions are more common in the developed countries,

<div id="_idContainer097" class="Figure"></div>

[[File:2518c328ae4e704d76634c50e1050e2f IPCC_AR6_WGII_Figure_4_027.png]]

'''Figure 4.27 |''' '''Top panel: location of case studies of water-related adaptation responses (996 data points from 319 studies).''' In these 996 data points, at least one positive outcome was recorded in one of the five outcome indicators. These outcome indicators are economic/financial, outcomes for vulnerable people, ecological/environmental, water-related, and sociocultural and institutional. Middle panel: the top six documented adaptation options per region as a fraction of the total of reported studies, with grey bars containing the share of all other adaptation responses. In most instances, the top six adaptation categories include nearly 3/4 of the studies. Bottom panel: The spider diagrams show the number of studies reporting beneficial outcomes for one or more dimensions for the top six adaptation options identified in each region. Due to a small number of studies in small island states, a spider diagram was not generated for the small island states.

Co-benefits are defined as mitigation benefits resulting from an adaptation response ( [[#Deng--2017|Deng et al., 2017]] ). Around a quarter of papers that documented positive adaptation outcomes also reported mitigation co-benefits. Agroforestry, community forests and forest-based adaptations are the most oft-cited examples of mitigation co-benefits ( [[#Bhatta--2015|Bhatta et al., 2015]] ; [[#Etongo--2015|Etongo et al., 2015]] ; [[#Weston--2015|Weston et al., 2015]] ; [[#Pandey--2017|Pandey et al., 2017]] ; [[#Sain--2017|Sain et al., 2017]] ; [[#Sánchez--2017|Sánchez and Izzo, 2017]] ; [[#Wood--2017|Wood et al., 2017]] ; [[#Adhikari--2018a|Adhikari et al., 2018a]] ; [[#Hellin--2018|Hellin et al., 2018]] ; [[#Aniah--2019|Aniah et al., 2019]] ; [[#Quandt--2019|Quandt et al., 2019]] ; also see Box 5.11). Other examples include mitigation benefits of climate-smart agricultural practices that reduce input intensity and help in carbon sequestration ( [[#Arslan--2015|Arslan et al., 2015]] ; [[#Somanje--2017|Somanje et al., 2017]] ), retrofitting buildings in urban areas with energy-efficient devices for lowering electricity bills and emissions ( [[#Fitzgerald--2016|Fitzgerald and Lenhart, 2016]] ) and reuse of treated wastewater for irrigation and urban uses ( [[#Morote--2019|Morote et al., 2019]] ) (Box 4.5, 4.7.6).

Not all adaptation responses reduce risks, and some may have long-term maladaptive outcomes, even if they are beneficial in the short term. Maladaptation often stems from poor planning and implementation of adaptation responses and because of not addressing the root causes of vulnerability ( [[#Schipper--2020|Schipper, 2020]] ; [[#Eriksen--2021|Eriksen et al., 2021]] ). Of the 319 case studies where adaptation response was found to have some beneficial outcomes, around one third of them also mentioned the possibility of maladaptation. Migration can often have maladaptive outcomes because migration can exacerbate the inherent vulnerabilities of migrants ( [[#4.6.8|Section 4.6.8]] ). For example, slum dwellers in cities may earn higher incomes, but their quality of life worsens ( [[#Ayeb-Karlsson--2016|Ayeb-Karlsson et al., 2016]] ). In some instances, even wage rates in migration hotspots can remain low due to the high volume of the migrant population ( [[#Fenton--2017b|Fenton et al., 2017b]] ); as such, it does not help buffer consumption against rainfall shocks ( [[#Gao--2018|Gao and Mills, 2018]] ). Migration also has gendered impacts, with girls from migrating families being taken out of school ( [[#Gioli--2014|Gioli et al., 2014]] ) or interrupting children’s education overall ( [[#Warner--2014|Warner and Afifi, 2014]] ). In planned relocation from vulnerable urban slums, relocation sites can be far from job sites and increase social conflicts ( [[#Tauhid--2018|Tauhid and Zawani, 2018]] ).

Adaptation responses that focus on improving incomes through production intensification can have maladaptive outcomes. An oft-cited example of this is groundwater overuse as a result of irrigation intensification. There is widespread evidence of groundwater overuse in many countries in Africa ( [[#Mapfumo--2017|Mapfumo et al., 2017]] ), in the Middle East and North Africa ( [[#Petit--2017|Petit et al., 2017]] ; [[#Daly-Hassen--2019|Daly-Hassen et al., 2019]] ), in Asia ( [[#Burchfield--2016|Burchfield and Gilligan, 2016]] ; [[#Zhang--2016b|]] [[#Zhang--2016|Zhang et al., 2016]] b ; [[#Kattumuri--2017|Kattumuri et al., 2017]] ), in Spain ( [[#Petit--2017|Petit et al., 2017]] ) and in Australia ( [[#Kirby--2014|Kirby et al., 2014]] ) (Sections 4.2.6, 4.6.2, Box 4.3). Intensification-based approaches also increase costs of cultivation ( [[#Mussetta--2016|Mussetta et al., 2016]] ; [[#Wang--2018|Wang and Chen, 2018]] ; [[#Quan--2019|Quan et al., 2019]] ), and can lead to more use of fertilisers and herbicides ( [[#Thierfelder--2015|Thierfelder et al., 2015]] ; [[#Sujakhu--2016|Sujakhu et al., 2016]] ; [[#Khanal--2018a|Khanal et al., 2018a]] ; [[#Yamba--2019|Yamba et al., 2019]] ). Diversification away from food crops can also compromise domestic food security ( [[#Kloos--2014|Kloos and Renaud, 2014]] ; [[#Brüssow--2017|Brüssow et al., 2017]] ).

Even interventions that have positive carbon co-benefits like forestry and agroforestry can have maladaptive consequences on land and water resources, especially if inappropriate species ( [[#Etongo--2015|Etongo et al., 2015]] ) with higher water demands are grown ( [[#Krishnamurthy--2019|Krishnamurthy et al., 2019]] ) ( [[#4.7.6|Section 4.7.6]] ).

In summary, current adaptation responses have benefits across several dimensions. In developing countries, most adaptation measures improve economic outcomes ( ''high confidence'' ). Adaptation responses also have benefits in terms of water outcomes and environmental and ecological parameters, and these benefits are more commonly manifested in developed countries ( ''high confidence'' ). Of the papers assessed for water-related adaptation, roughly one fourth reported adaptation co-benefits ( ''high confidence'' ). In contrast, one third of studies reported maladaptive outcomes, now or in the future ( ''high confidence'' ), emphasizing the importance of looking at synergies and trade-offs. Despite many adaptation case studies, there is a knowledge gap in understanding if the benefits of adaptation also translate into a reduction of climate impacts, and if so, to what extent, and under what conditions ( ''high confidence'' ). In view of this critical knowledge gap, our assessment is limited to benefits of current adaptation responses.

<div id="4.7.2" class="h2-container"></div>

<span id="projections-of-future-effectiveness-of-adaptation-responses"></span>
=== 4.7.2 Projections of Future Effectiveness of Adaptation Responses ===

<div id="h2-45-siblings" class="h2-siblings"></div>

Several adaptation options have been shown to have beneficial effects on societally relevant outcomes under current climate conditions ( [[#4.7.1.2|Section 4.7.1.2]] ) and will remain critical to adapt to future climate change. However, there is limited quantitative information on the future viability of available responses to reduce projected climate impacts effectively. However, the context-specific nature of adaptation on the ground and the uncertainties associated with future climate outcomes, both in terms of policy decisions around mitigation and model-inherent uncertainties, make long-term projections of adaptation effectiveness of limited use for decision-making on the ground. However, such projections are still needed to understand the efficacy of current technical and managerial solutions to reduce climate risk. Consequently, an increasing body of literature focuses on the effectiveness of specific interventions to reduce projected climate risks in a local to regional setting.

This section provides a quantitative aggregate assessment of effectiveness of projected water-related climate adaptations at different levels of GWLs (SM4.2). Effectiveness is defined as the potential of a given adaptation measure to address projected changes in climate and return the system under analysis to baseline conditions. If the measure cannot fully compensate for the projected climate risk, residual risks remain, defined as the fraction of risk remaining after adaptation. For example, in many regions, projected temperature-driven yield loss can be reduced by shifting to or increasing irrigation. However, yields often do not always fully return to baseline conditions without climate change, leaving residual risk after adaptation. Assessed options are limited to technical solutions, which have quantitative entry points to global climate impact models.

Most adaptation projections focus on water-related interventions in the agricultural sector, including irrigation-related responses, shifting planting dates, changing crops and cultivars, and water and soil conservation. Sectoral projections of adaptation effectiveness are limited in forestry- and agroforestry-related responses, flood protection measures (excluding here options that are solely related to effects of sea level rise), urban water-related adaptation and energy-related responses. The majority of assessed studies focus on comparing different variations of one or several response options in terms of timing or duration, for example, a shift in planting dates of 10 d and 20 d, relative to present-day practice and provide results for a range of scenarios and (or) timeframes.

A total of 45 studies were identified for this assessment, based on their quantitative assessment of the effects of adaptation on projected impacts (SM4.2 for the method of future projected effectiveness assessment). From each study, the distinct combinations of specific variations of adaptations, scenarios and timeframes assessed were considered as individual data points, providing a total of 450 unique data points for the assessment (Table SM4.6). The study-specific temperature increase was classified relative to the 1850–1900 baseline for each data point, based on the model and scenario specifications provided and grouped into outcomes at 1.5°C, 2°C, 3°C and 4°C. The effectiveness is assessed based on the fraction of risk that an option can reduce. Co-benefits are defined as a situation where outcomes improve relative to baseline conditions, whereas maladaptive outcomes describe a situation where risks increase after adaptation has been implemented.

Several studies assess the future effectiveness of improved cultivars and agronomic practices, such as changing fertiliser application or switching to drought-resistant crops (five studies; 85 data points). Results show a range of effectiveness levels across regions and warming levels and vary depending on the tested response options ( [[#Qin--2018|Qin et al., 2018]] ) (Figure 4.29), with moderate to small effectiveness, large residual impacts or potential maladaptive outcomes, as well as decreasing effectiveness with increasing warming (Figure 4.28) ( ''high confidence'' ). For studies testing results across a range of scenarios, approaches show increasingly mixed ( [[#Qin--2018|Qin et al., 2018]] ) and limited effects ( [[#Amouzou--2019|Amouzou et al., 2019]] ) with higher warming, with overall reductions across warming levels for most tested responses ( [[#Qin--2018|Qin et al., 2018]] ).

<div id="_idContainer099" class="Figure"></div>

[[File:fb73afe89b1a992add26191873a13d61 IPCC_AR6_WGII_Figure_4_028.png]]

'''Figure 4.28 |''' '''Projected effectiveness of adaptation options in returning the system to a study-specific baseline state relative to the projected climate impact; and level of residual risk retained after adaptation, relative to baseline conditions.''' Regional summaries are based on IPCC regions. Warming levels refer to the global mean temperature (GMT) increase relative to an 1850–1900 baseline. For each data point, the study-specific GMT increase was calculated to show effectiveness at 1.5°C, 2°C, 3°C and 4°C. Based on the ability of an implemented option to return the system to its baseline state, the effectiveness is classified based on the share of risk the option can reduce: large (&gt;80%); moderate (80–50%); small (&lt;50–30%); insufficient (&lt;30%). Where the system state is improved relative to baseline, co-benefits are identified. Residual impacts show the share of remaining impacts after adaptation has been implemented: negligible (&lt;5%); small (5 to &lt;20%); moderate (20 to &lt;50); large (≥50%). Where risks increase after adaptation, data points are shown as maladaptation. All underlying data is provided in Table SM4.6.

Changes in cropping patterns and crop systems (Figure 4.28) (five studies; 31 data points) indicate limited potential to reduce projected climate risks, with the majority of studies providing results of up to 1.5°C of warming and limited evidence for higher warming levels. At 1.5°C, effectiveness in Africa is mostly insufficient, with substantial maladaptive potential ( [[#Brouziyne--2018|Brouziyne et al., 2018]] ). Over Asia, effectiveness is mostly small at 1.5°C with substantial residual impacts, further reducing to insufficient effectiveness at large residual risks at 4°C (Figure 4.28 Projected effectiveness) ( ''robust evidence; medium agreement'' ) ( [[#Boonwichai--2019|Boonwichai et al., 2019]] ; [[#Dai--2020|Dai et al., 2020]] ; [[#Mehrazar--2020|Mehrazar et al., 2020]] ). Amongst the options related to changes in cropping patterns and crop systems, shifting planting dates is projected to retain moderate to high residual risks under some specifications in Iran ( [[#Paymard--2018|Paymard et al., 2018]] ) and Morocco ( [[#Brouziyne--2018|Brouziyne et al., 2018]] ), while high effectiveness is reported for similar specifications in Thailand ( [[#Boonwichai--2019|Boonwichai et al., 2019]] ), Australia ( [[#Luo--2016|Luo et al., 2016]] ), Morocco (( [[#Brouziyne--2018|Brouziyne et al., 2018]] ) and Iran ( [[#Mehrazar--2020|Mehrazar et al., 2020]] ). Of the assessed adaptation options, changes in cropping patterns and cropping systems appear least effective in reducing climate risk, with decreasing effectiveness at higher levels of warming.

Studies assessing the future effectiveness of irrigation-related responses (Figure 4.28) focus on a range of specific approaches, including increasing irrigation efficiency, deficit irrigation, irrigated area expansion or shifting from rain-fed to irrigated agriculture, as well as specific types of irrigation (21 studies; 103 data points). As a frequently implemented option with direct entry points to agricultural models, this option provides the most robust set of data points across regions and warming levels. For all regions, a reduction in effectiveness is apparent from 1.5°C to higher levels of warming, leading to increased residual risk with increasing warming ( ''high confidence'' ). Irrigation can increase yield relative to present day, showing co-benefits for some regions, though the share of co-benefits decreases with higher warming ( ''high confidence'' ) (Figure 4.28). However, since many of these studies rely on global agricultural models which do not fully represent the actual availability of water, further expansion of irrigation at the scale assumed in those studies may not be realistic (Sections 4.3.1.2. 4.3.1.3) ( [[#Elliott--2014|Elliott et al., 2014]] ).

A wide range of water and soil management-related options (Figure 4.28), including mulching, no tilling or contour farming, has been assessed for future effectiveness (eight studies; 49 data points). Results underline the context-specific nature and need to carefully adjust the specific options to a regional setting, with variations of options leading to effective outcomes or residual impacts within individual studies ( [[#Qiu--2019|Qiu et al., 2019]] ) and across regions and warming levels.

Similar to observed adaptation, studies assessing combinations of the agricultural adaptation options outlined above (11 studies; 36 data points) show the highest effectiveness across agricultural adaptation outcomes and generally project moderate to high effectiveness with the potential for co-benefits (Figure 4.28). Though maladaptive outcomes are also documented, residual risks are limited, also at higher levels of warming. Therefore, developing integrated plans of synergistic options linked to adequate monitoring and evaluation approaches and designed to adjust to changing conditions continuously is desirable to minimise climate risk and ensure food security ( [[#Babaeian--2021|Babaeian et al., 2021]] ).

Globally, agroforestry-related adaptation (four studies; 18 data points) is moderately to highly effective, with the potential for substantial co-benefits at 1.5° and 2°C of warming, with a sharp decline in effectiveness at 3°C and 4°C and a substantial increase in residual risk and maladaptive outcomes (Figure 4.28).

Flood risk-related adaptation (four studies; 47 data points) is associated with the potential for substantial co-benefits relative to present-day flood risk, indicating a current adaptation gap larger than for other impact areas. These co-benefits decline with increasing warming. Limits to the tested options become increasingly apparent at 3°C and 4°C of warming, where residual risks increase for most assessed cases (Figure 4.28).

Adaptation projections for urban water risks as well as the energy sector are limited to one study each, with one data point for urban adaptation ( [[#Rosenberger--2021|Rosenberger et al., 2021]] ) and 80 data points for different variations of adaptation outcomes across regions and scenarios for the energy sector ( [[#van%20Vliet--2016c|van Vliet et al., 2016c]] ). Sustainable stormwater management, focusing on a combination of nature-based solutions, is shown to be highly effective and yields co-benefits at 3°C. However, these results were gained in a specific case study setting in a European city with limited generalizability (Figure 4.28).

The assessment of adaptation in the hydropower and thermoelectric power-generation sector indicates high effectiveness and co-benefits across all regions for 1.5°C, with decreasing effectiveness and increasing residual risks for 2°C and 3°C of warming and highest reductions in effectiveness for Central and South America (Figure 4.28).

Quantitative projections of future adaptation depend on available impact models to analyse the effect of specific adaptation interventions. However, since not all possible future adaptation responses can be incorporated in climate impact models, this is a major limitation to assessing the full scope of options available in the future. For example, many frequently implemented measures showing effective outcomes, such as behavioural and capacity building-focused responses or migration and off-farm diversification ( [[#4.7.1.2|Section 4.7.1.2]] ), are not incorporated in quantitative water-related climate impact projection models. In addition, projections of future adaptation depend on currently available technologies or approaches, but new methods and technologies will probably emerge. Thus, improving the representation of adaptation in future projections is a significant knowledge gap that remains to be addressed.

Whether specific adaptation responses are shown to be effective and even lead to co-benefits or are associated with residual impacts is highly contextually, location- and crop-specific. In addition, the specific climate-impact-scenario combinations play an important role in determining assessed outcomes.

In practice, responding to increasing climate risk will need to be context-specific and sufficiently agile to respond to ever-changing realities on the ground. The adaptive pathways approach underline that a sequence of different options responding to climate change over time may be most effective ( [[#Babaeian--2021|Babaeian et al., 2021]] ). In addition, impact models generally underestimate or underrepresent climate extremes ( [[#Schewe--2019|Schewe et al., 2019]] ), limiting the ability of the present analysis to reflect adaptation requirements to extremes, which are likely to push systems to their limits ( [[#4.7.4|Section 4.7.4]] ). While currently known structural adaptation responses can reduce some of the projected risks across sectors and regions, residual impacts remain at all levels of warming, and effectiveness decreases at higher levels of warming. Adaptation generally performs more effectively at 1.5°C, though residual damages are projected at this warming level across sectors and regions ( ''high confidence'' ). A range of options also shows the potential for further increasing negative effects (maladaptation) across sectors, regions and warming levels, further underlining the need for contextualised approaches.

<div id="4.7.3" class="h2-container"></div>

<span id="comparing-current-and-future-water-related-adaptation-responses"></span>
=== 4.7.3 Comparing Current and Future Water-Related Adaptation Responses ===

<div id="h2-46-siblings" class="h2-siblings"></div>

Water-related adaptation is being observed across sectors and regions ( [[#4.6|Section 4.6]] ), and beneficial outcomes are documented across different dimensions ( [[#4.7.1|Section 4.7.1]] ). A limited set of frequently documented adaptation responses is also represented in quantitative projections of adaptation effectiveness ( [[#4.7.2|Section 4.7.2]] , Figure 4.29). However, due to the largely different assessment methodologies for measuring beneficial outcomes for current adaptations and effectiveness to reduce impacts for future adaptations, comparing current and future adaptation outcomes is not straightforward. For current adaptation responses, beneficial outcomes may or may not translate to climate risk reduction, making risk reduction potential of observed adaptation a significant gap in our current understanding. The large diversity of outcomes across regions and assessed options becomes apparent for future adaptation options, with the group of ‘inconclusive’ outcomes indicating a large spread of results across regions. This underlines the contextual nature of adaptation and boundary conditions for implementation that can determine the success of adaptation outcomes, now or in the future.

<div id="_idContainer101" class="Figure"></div>

[[File:3d5cf45a262a42aca2f793e279456f11 IPCC_AR6_WGII_Figure_4_029.png]]

'''Figure 4.29 |''' '''The panel on the left side shows observed benefits of adaptation.''' Observed outcomes are reported across five dimensions of benefits, co-benefits and maladaptation outcomes. Benefits are measured across five dimensions. Strength of evidence is high if &gt;80% of adaptation responses in that category have at least one beneficial outcome; medium if between 50 and 80% of adaptation responses in that category have at least one beneficial outcome, and low if &lt;50% of adaptation responses have at least one beneficial outcome. Confidence in evidence relates to the way the article links outcomes of adaptation with the adaptation response. Category 1: studies causally link adaptation outcomes to the adaptation response by constructing credible counterfactuals; category 2: studies correlate responses and outcomes without causal attribution; category 3: studies describe adaptation outcomes without making any causal or correlation claims between adaptation outcomes and adaptation responses. ''High confidence'' : more than 67% of the studies fall in categories 1 and 2; ''medium confidence'' : 50–67% of the studies are in categories 1 and 2; ''low confidence'' is less than 50% of studies are in categories 1 and 2. The panel on the right-hand side shows the effectiveness of future adaptations. Future outcomes are assessed in terms of their effectiveness to reduce climate impacts at 1.5°C, 2°C, 3°C and 4°C of global temperature increase relative to 1850–1900. Effectiveness is defined as the fraction of adaptation that the option is able to reduce; residual risk is the fraction of risk remaining after adaptation. If &gt;66% of assessed data points agree on the effectiveness class, a response–temperature combination is shown as belonging to that class. Where results diverge, the result is inconclusive, with studies showing high and low effectiveness across regions and studies. Confidence is based on the number of data points available for each response–temperature combination with ''high confidence'' : 5 or more data points; ''medium confidence'' : 2–4 data points; ''low confidence'' : 1 data point. Also, see Figure 4.28 for further explanations, and Tables SM4.5 and SM4.6 provide underlying data.

Documented implemented adaptations show several beneficial outcomes, with most studies (319 of 356) documenting positive rather than negative outcomes. However, there may be a positive reporting bias in the literature, as positive outcomes are more likely to be reported than negative ones. Also, positive outcome in one parameter does not preclude negative outcomes in others, so maladaptation is still possible even when an adaption has some positive benefits ( [[#4.7.1.2|Section 4.7.1.2]] ). In addition, much of the adaptation happening on the ground may not be published in peer-reviewed publications and, therefore, not covered by the literature assessed in this report. Further, there is limited knowledge about the effectiveness of current adaptation in reducing climate-related risks due to documentation and methodological challenges elaborated in [[#4.7.1.2|Section 4.7.1.2]] (SM4.2).

In contrast, evaluating the effectiveness for future projected adaptations is methodologically possible ( [[#4.7.2|Section 4.7.2]] , and SM4.2), but every adaptation that is happening now cannot be modelled for the future. Therefore, projections of future adaptation effectiveness are limited to those options that can be incorporated into (global) quantitative climate impact models. Unfortunately, an extensive range of options, such as capacity building or training, migration and employment, which are essential building blocks in the portfolio of available (water-related) adaptation options, are currently not quantitatively represented in adaptation projections. In addition, the future will probably bring further development in technical solutions, which are currently also not modelled. While implementing the modelled technical options may be feasible in general, several barriers and constraints ( [[#4.7.4|Section 4.7.4]] ) and enabling conditions, which influence adaptation action in practice, are not included in current modelling studies. Therefore, the modelling studies may present optimistic assessments of adaptation effectiveness for the future.

Adaptations that are beneficial now (e.g., crop- and water-related ones) are also projected to be effective to varying extents in reducing future risks, with the degree of effectiveness strongly depending on future GWLs. For example, beyond a certain level of warming (2°C and upwards), the effectiveness of most options is projected to reduce, and residual impacts are projected to increase. Reduction in the effectiveness of future adaptation at higher global warming levels emphasises the need for limiting warming to 1.5°C, as space for adaptation solution starts to shrink beyond that for most options for which future projections exists ( ''high confidence'' ).

To sum up, there are two significant knowledge gaps in our understanding of water-related adaptations. First, the nature of literature on current adaptation makes it challenging to infer their effectiveness in reducing climate risks, even though the benefits of adaptation are clear ( ''high confidence'' ). Second, not all adaptation responses that are possible in the future can be modelled because of inherent limitations to what can be modelled. Thus, advancement in tools and metrics for measuring the effectiveness of current adaptation in reducing climate risks and suitable downscaled climate and impact models that incorporate economic, social, cultural and management aspects for an extensive range of future adaptation options is needed.

<div id="4.7.4" class="h2-container"></div>

<span id="limits-to-adaptation-and-losses-and-damages"></span>
=== 4.7.4 Limits to Adaptation and Losses and Damages ===

<div id="h2-47-siblings" class="h2-siblings"></div>

The core constraints identified in AR5 ( [[#Klein--2014|Klein et al., 2014]] ) for freshwater-related adaptation were lack of governance, financial resources and information, while water availability was singled out as a core constraint to diversifying options for water-dependent sectors. SR1.5 showed that increasing aridity and decreased freshwater availability, including limited groundwater supply in fossil aquifers in conjunction with rising sea levels may pose hard limits to adaptation for small islands ( [[#Roy--2018|Roy et al., 2018]] ). SR1.5 also shows that water-related risks can be reduced substantially by limiting warming to 1.5°C ( ''high confidence'' ) ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ), thereby also reducing the potential to reach hard limits to adaptation. SROCC highlighted that several barriers and limits to adapt to reduced water availability in mountain areas, such as lack of finance and technical knowledge ( [[#Hock--2019b|Hock et al., 2019b]] ). The SRCCL further highlighted the critical importance of water-related climate change adaptation and potential limits to adaptation in the land sector when extreme forms of desertification lead to a complete loss of land productivity ( ''high confidence'' ) ( [[#Mirzabaev--2019|Mirzabaev et al., 2019]] ).

Institutional constraints, including path dependency and lengthy decision-making processes, remain major limitations to successful adaptation globally ( ''high confidence'' ) ( [[#Barnett--2015|Barnett et al., 2015]] ; [[#Oberlack--2017|Oberlack, 2017]] ), as well as for the water sector ( [[#Kingsborough--2016|Kingsborough et al., 2016]] ; [[#Oberlack--2017|Oberlack, 2017]] ; [[#Azhoni--2018|Azhoni and Goyal, 2018]] ). For example, a lack of institutional support has limited the ability of farmers to implement adaptation, even if information about the benefits is acknowledged ( [[#Nambi--2015|Nambi et al., 2015]] ). A lack of inter-sectoral coordination and communication within institutions and conflicting interests between water sectors limit the potential for integrated policies. For all water-related adaptation options, which have shown to be effective in one or more dimensions ( [[#4.7.1.2|Section 4.7.1.2]] ), governance and institutional constraints were identified to be the most commonly encountered to a moderate or significant extent (Figure 4.30). Water–energy–food nexus approaches can help overcome these inter-sectoral barriers (Box 4.8) ( [[#Rasul--2016|Rasul and Sharma, 2016]] ; [[#Ernst--2017|Ernst and Preston, 2017]] ). In addition, trade-offs between different policy goals must be considered to ensure the broader significance of the implemented adaptation strategies, such as water quality implication of adaptation efforts in the agricultural or energy sectors ( [[#4.7.6|Section 4.7.6]] ) ( [[#Fezzi--2015|Fezzi et al., 2015]] ).

<div id="_idContainer103" class="Figure"></div>

[[File:003d344b5fe79bfaa476275a43faf4c5 IPCC_AR6_WGII_Figure_4_030.png]]

'''Figure 4.30 |''' '''Adaptation constraints manifest across a range of dimensions and here are assessed based on a meta-review of water-related adaptation (Section 4''' '''.''' '''7.1, SM4.2, and Table SM4.5).''' Where less than five articles are available for assessment, data is insufficient to assess the extent to which a constraint is present. Where less than 20% of the articles reporting on the respective adaptation option identify the presence of a constraint, it is classified as ‘limited’, where 20 to 50% report on a specific constraint it is considered as ‘moderate’. Where more than 50% of articles report on the presence, the constraint is considered ‘significant’. This assessment is based on the available peer-reviewed literature assessing adaptation benefits in the water sector—in practice, these or other constraints may still be significant, but have not have been identified in peer-review sources.

The lack of financial and technological resources constrains adaptation implementation ( [[#Castells-Quintana--2018|Castells-Quintana et al., 2018]] ; [[#Iglesias--2018|Iglesias et al., 2018]] ) and was identified as significant or moderate across all water-related adaptation responses, with significant constraints especially present in options related to the agricultural sector (Figure 4.30). For example, financial resources were significant constraints to implementing Climate Smart Agriculture in Guatemala, a relevant adaptation strategy to improve food security, resilience, and low emission development ( [[#Sain--2017|Sain et al., 2017]] ).

While financial barriers played an important role in adopting new technologies at the farm level in Spain, acceptance, common understanding and awareness were amongst the most frequently identified barriers across different adaptation options ( [[#Esteve--2018|Esteve et al., 2018]] ). Limitations in knowledge and understanding of complex processes, feedback effects and interconnections in the water sector pose challenges to effective adaptation and adaptation decision-making ( [[#Kundzewicz--2018|Kundzewicz et al., 2018]] ). Such constraints are identified as moderate across the range of options assessed in this chapter (Figure 4.30). For tropical and mountainous regions and the African continent, in particular, significant uncertainties in available data and a lack of reliable climate projections remain one of the biggest obstacles in long-term adaptation planning ( [[#Antwi-Agyei--2015|Antwi-Agyei et al., 2015]] ), especially in the water sector ( [[#Watson--2017|Watson et al., 2017]] ; [[#Azhoni--2018|Azhoni and Goyal, 2018]] ; [[#Hirpa--2018|Hirpa et al., 2018]] ; [[#González-Zeas--2019|González-Zeas et al., 2019]] ). There is also often a discrepancy between the level of awareness among different stakeholders, for example, between affected farmers whose agency is limited by the lack of knowledge by local authorities ( [[#Chu--2017|Chu, 2017]] ).

For some regions of the world, such as small islands ( [[#Karnauskas--2016|Karnauskas et al., 2016]] ; [[#Karnauskas--2018|Karnauskas et al., 2018]] ) (Box 4.2) and the Mediterranean (Cross-Chapter Paper 4) ( [[#Schleussner--2016|Schleussner et al., 2016]] ), aridity increases have the potential to pose hard adaptation limits. In mountain and polar regions, changes in the cryosphere (Sections 4.2.2, 4.4.2) may limit water availability for irrigation systems that depend on melt-water ( [[#4.5.1|Section 4.5.1]] ) ( [[#Qin--2020|Qin et al., 2020]] ). Biophysical limits may also be reached through impacts of hydrological extremes, such as crop loss as a consequence of extreme precipitation events ( [[#Huggel--2019|Huggel et al., 2019]] ; [[#van%20der%20Geest--2019|van der Geest et al., 2019]] ). Such limits are reported to a limited to moderate extent across all adaptation options assessed (Figure 4.30). However, knowledge gaps remain about physical and biological constraints to adaptation in the water sector. Climate impacts, such as droughts in East Africa or glacier melt in the cryosphere, indicate that biophysical limits to adaptation may exist, even under current climate conditions (Figure 4.31) ( [[#Warner--2013|Warner and van der Geest, 2013]] ; [[#Huggel--2019|Huggel et al., 2019]] ; [[#van%20der%20Geest--2019|van der Geest et al., 2019]] ). A lack of investment in relevant infrastructure, such as dikes for example, as well as maladaptive effects of certain measures could increase existing risks and exacerbate impacts ( [[#van%20der%20Geest--2019|van der Geest et al., 2019]] ).

<div id="_idContainer105" class="Figure"></div>

[[File:f60e6cd6d41e33b337699435acdee49a IPCC_AR6_WGII_Figure_4_031.png]]

'''Figure 4.31 |''' '''Examples of regional studies where communities experienced negative impacts despite or beyond implemented adaptation have been documented.''' Panels indicate the climate hazard that leads to the need for adaptation, the adaptation option implemented and the recorded impacts per region (A – Arctic ( [[#Landauer--2019|Landauer and Juhola, 2019]] ), B – Africa ( [[#van%20der%20Geest--2019|van der Geest et al., 2019]] ), C – Caribbean ( [[#Lashley--2015|Lashley and Warner, 2015]] ), D – South Asia ( [[#Kusters--2013|Kusters and Wangdi, 2013]] ; [[#van%20der%20Geest--2016|van der Geest and Schindler, 2016]] ; [[#Bhowmik--2021|Bhowmik et al., 2021]] ), E – Southeast Asia ( [[#Acosta--2016|Acosta et al., 2016]] ; [[#Beckman--2016|Beckman and Nguyen, 2016]] ), F – Pacific the Small Island States ( [[#Gawith--2016|Gawith et al., 2016]] ; [[#Handmer--2019|Handmer and Nalau, 2019]] ), G – Global effect: Mountain Cryosphere ( [[#Huggel--2019|Huggel et al., 2019]] )). Presented examples are limited to the available peer-reviewed literature that focuses explicitly on impacts that have been documented despite documented evidence that adaptation in relation to water hazards had previously been implemented. [[#4.3|Section 4.3]] provides a full assessment of observed impacts across sectors and regions.

Integrated approaches, such as linking land use and water policies ( [[#Mehdi--2015|Mehdi et al., 2015]] ), inter-institutional networks ( [[#Azhoni--2017|Azhoni et al., 2017]] ), nexus approaches (Box 4.8) ( [[#Conway--2015|Conway et al., 2015]] ) as well as consideration of linkages to the SDGs ( [[#4.8|Section 4.8]] ) ( [[#Gunathilaka--2018|Gunathilaka et al., 2018]] ) are crucial to overcoming constraints in water adaptation. In addition, monitoring and evaluating the effectiveness of adaptation measures, policies and actions can contribute to knowledge, awareness and data to support adaptation implementation in the future (Sections 4.7.1; 4.8) ( [[#Klostermann--2018|Klostermann et al., 2018]] ). Although the information on climate change adaptation that has beneficial impacts, including enabling conditions and success factors specific to the water sector, is emerging, significant knowledge gaps remain ( [[#4.7.1.2|Section 4.7.1.2]] ) ( [[#Gotgelf--2020|Gotgelf et al., 2020]] ). Further understanding the constraints and limits that exist with regard to adaptation in the water sector is becoming urgent in light of increasing slow (e.g., droughts) and rapid (e.g., floods) onset impacts associated with climate change.

Taking action towards adaptation critically determines the outcomes and impacts of climate change processes across space and time. Where efforts to reduce risk do not effectively occur, losses and damages occur as a consequence of climate change, some of which can have irreversible and existential effects ( [[#van%20der%20Geest--2015|van der Geest and Warner, 2015]] ; [[#Page--2016|Page and Heyward, 2016]] ; [[#Thomas--2018a|Thomas and Benjamin, 2018a]] ; [[#Mechler--2019|Mechler et al., 2019]] ). Water-related impacts that occurred despite implemented adaptation have been documented across all world regions ( ''high confidence'' ) (Figure 4.31).

Advances in climate change attribution ( [[#4.2|Section 4.2]] ; SM4.3; Figure 4.20) show the direct effects of anthropogenic climate change, also with regard to climate extremes. These advances also provide the basis for climate litigation ( [[#Marjanac--2018|Marjanac and Patton, 2018]] ) to hold countries/companies accountable for climate change impacts, for example, concerning risks of glacial lake outburst in Peru ( [[#Frank--2019|Frank et al., 2019]] ).

A further increase in the frequency and/or intensity of water-related extremes ( [[#4.4|Section 4.4]] ) will also increase consequent risks and associated losses and damages ( [[#4.5|Section 4.5]] ), primarily for exposed and vulnerable communities globally ( [[#Bouwer--2019|Bouwer, 2019]] ). After assessing the future potential of currently available technologies to reduce projected water-related climate impacts, there is evidence that residual impacts will remain after adaptation for most adaptation options and levels of warming, with increasing residual risks at higher warming levels ( [[#4.7.2|Section 4.7.2]] ). Financial, technical and legal support will be needed when hard limits are transgressed and loss and damage occurs ( [[#Mechler--2020|Mechler et al., 2020]] ). Knowledge gaps remain regarding quantified information on limits and constraints to adaptation in the water sector.

In summary, institutional constraints (governance, institutions, policy), including path dependency and financial and information constraints, are the main challenge to adaptation implementation in the water sector ( ''high confidence'' ). Water-related losses and damages that manifest despite or beyond implemented adaptation have been observed across world regions, primarily for exposed and vulnerable communities ( ''high confidence'' ). Hard limits to adaptation due to limited water resources will emerge for small islands ( ''medium evidence, high agreement'' ) and regions dependent on glacier- and snowmelt ( ''medium evidence, high agreement'' ).

<div id="4.7.5" class="h2-container"></div>

<span id="costs-of-adaptation-and-losses-due-to-non-adaptation"></span>
=== 4.7.5 Costs of Adaptation and Losses due to Non-Adaptation ===

<div id="h2-48-siblings" class="h2-siblings"></div>

Estimating adaptation costs for climate change impacts on the various water use sectors is vital for decision-making, budgeting, and resource allocation ( [[#Chambwera--2014|Chambwera et al., 2014]] ). However, in AR5, studies on adaptation costs for water were deemed to have ‘limited coverage’ and mainly focused on ‘isolated case studies’; costs in agriculture were ‘extremely limited’ ( [[#Chambwera--2014|Chambwera et al., 2014]] ).

One estimate on observed losses due to climate change from the UK notes that almost 50% of freshwater thermal capacity is lost on extreme high-temperature days, causing losses in the range of average GBP 29–66 million/year ( [[#Byers--2020|Byers et al., 2020]] ). However, global estimates of current losses because of climate change impacts on water resources remain few. Most of the evidence is focused on projected damages rather than actual ones ( [[#World%20Bank--2016|World Bank, 2016]] ; [[#Rozenberg--2019|Rozenberg and Fay, 2019]] ).

Without adaptation, water-related impacts of climate change are projected to reduce global GDP by 0.49% in 2050 under SSP3, with significant regional variations for the Middle East (14%); Sahel (11.7%); Central Asia (10.7%), and East Asia (7%) ( [[#World%20Bank--2016|World Bank, 2016]] ). In Asia, water-related impacts of climate change on all sectors of the economy are projected to reduce GDP by 0.9% (in high-income Asia) to 2.7% (in low-income Asia) by 2050 without adaptation or mitigation. Under the A1B scenario, real GDP is projected to fall by 0.78% by 2030 in South Asia ( [[#Ahmed--2014|Ahmed and Suphachalasai, 2014]] ). In Sub-Saharan Africa, damages from floods in 2100 are projected at 0.5% of GDP under a 2°C temperature rise without adaptation; and will be non-uniformly spread across countries ( [[#Markandya--2017|Markandya, 2017]] ; [[#Dottori--2018|Dottori et al., 2018]] ). In Europe, annual damages due to coastal flooding are projected at €93 billion by 2100 under RCP8.5-SSP3 ( [[#Ciscar--2018|Ciscar et al., 2018]] ). Global direct damages from fluvial floods are projected to rise to €1250 billion yr –1 under a 3°C global warming level and SSP5 socioeconomic scenario ( [[#Dottori--2018|Dottori et al., 2018]] ). A model-based study of selected water-related sectors like fluvial and coastal flooding, agricultural productivity of major crops, hydroelectric power generation, and thermal power generation provides much conservative estimates of GDP loss ( [[#Takakura--2019|Takakura et al., 2019]] ). The study shows that without adaptation, loss of global GDP could be 0.094% under RCP8.5 and SSP5 and 0.013% under RCP2.6 and SSP1 scenarios in 2090 (2080–2099), with regional values for Africa (0.017 to 0.286%), Asia (0.015 to 0.104%), Australasia (-0.012 to 0.003%), North America (-0.002 to 0.005%) and South and Central America (0.011 to 0.055%) ( [[#Takakura--2019|Takakura et al., 2019]] ). So, while there is general agreement about negative impacts on GDP due to water-related risks in the future, the magnitude of GDP loss estimates varies substantially and depends on various model assumptions ( ''high confidence'' ). Updating costs while improving the modelling of uncertainties is essential for evidence-based decision-making ( [[#Ginbo--2020|Ginbo et al., 2020]] ).

Costs of water-related infrastructure in adaptation have received attention at the global and regional level to bridge the ‘adaptation gap’ ( [[#Hallegatte--2018|Hallegatte et al., 2018]] ; [[#UNEP--2018|UNEP, 2018]] ; [[#Dellink--2019|Dellink et al., 2019]] ; [[#GCA--2019|GCA, 2019]] ). For example, ( [[#Rozenberg--2019|Rozenberg and Fay, 2019]] ) estimated that subsidising capital costs to extend irrigation to its full potential would cost 0.13% of the GDP per year of low-and middle-income countries between 2015 and 2030. The coastal and riverine protection cost was between 0.06% and 1% of these countries’ GDP per year over the same period. Projected economic damage due to coastal inundation was USD 169–482 billion in 2100 under RCP8.5-SSP3 without adaptation, but USD 43–203 billion cost to raise dike height will reduce 40% of the total damage ( [[#Tamura--2019|Tamura et al., 2019]] ). Hard infrastructure for river floods, costing $4–9 billion yr –1 , can reduce damage by USD 22–74 billion yr –1 ( [[#Tanoue--2021|Tanoue et al., 2021]] ). Damages are estimated to be up to six-time larger than the cost of implementing efficient adaptation measures (H2020., 2014). ( [[#GCA--2019|GCA, 2019]] ) reported that investing USD 1.8 trillion globally, for example, in early warning systems, climate-resilient infrastructure; dryland crop production; mangrove protection; and improving the resilience of water resources between 2020 and 2030 could generate USD 7.1 trillion in benefits.

Comparatively, less attention has been paid to low-regret options, especially at the national and local levels. Conservation agriculture and integrated production systems, early-warning systems, restoration of wetlands, and zoning are postulated to have lower investment and lock-in costs than engineering-based options ( [[#Mechler--2016|Mechler, 2016]] ; [[#Cronin--2018|Cronin et al., 2018]] ; [[#Johnson--2020|Johnson et al., 2020]] ). However, they require regular maintenance and high technical and human capacity, which are likely to vary by scale, location, and context ( [[#Chandra--2018|Chandra et al., 2018]] ; [[#Khanal--2019|Khanal et al., 2019]] ; [[#Mutenje--2019|Mutenje et al., 2019]] ; [[#Rahman--2019|Rahman and Hickey, 2019]] ). Global studies suggest improvements in returns on adaptation investments by delivering better services and reducing water wastage through appropriate water pricing and regulations ( [[#Damania--2017|Damania et al., 2017]] ; [[#Bhave--2018|Bhave et al., 2018]] ). For example, under scenarios SSP1 and SSP3, water pricing and regulation are projected to reverse losses in expected 2050 global GDP of 0.49% to gains of 0.09%. GDP losses are projected to drastically reduce in the Middle East, eliminated in the Sahel and Central Africa, and reversed into gains in Central Asia and East Africa, with benefits concentrated in worst-affected regions ( [[#World%20Bank--2016|World Bank, 2016]] ). More local and national studies are needed to identify low regret options and their benefits and actual costs ( [[#Blackburn--2018|Blackburn and Pelling, 2018]] ; [[#Abedin--2019|Abedin et al., 2019]] ; [[#Brown--2019|Brown et al., 2019]] ; [[#Momblanch--2019|Momblanch et al., 2019]] ; [[#Page--2020|Page and Dilling, 2020]] ) ( ''limited evidence, high agreement'' ).

In summary, climate change impacts on water resources are projected to lower GDP in many low-and middle-income countries without adequate adaptation measures ( ''high confidence'' ). However, estimating the exact quantum of future GDP loss due to water-related impacts of climate change is fraught with several methodological challenges. Adaptation measures that focus on reducing water-related impacts of climate change will help stem losses further. Still, more work needs to be done on actual benefits and costs of adaptation strategies and residual impacts and risks of delaying adaptation action ( ''medium confidence'' ). In addition, better evidence on the costs and benefits of low-regret solutions, such as water pricing, increasing water use efficiency through technology and service improvements, and enhanced support for autonomous adaptation, is also needed for informed decision-making ( ''high confidence'' ).

<div id="box-4.8" class="h2-container box-container"></div>

'''Box 4.8 | Water-Energy-Food (WEF) Nexus Approaches for Managing Synergies and Trade-Offs'''
<div id="h2-65-siblings" class="h2-siblings"></div>

The WEF nexus is an approach that recognises that water, energy and food are linked in a complex web of relationships in the hydrological, biological, social, and technological realms ( [[#D’Odorico--2018|D’Odorico et al., 2018]] ; [[#Liu--2018b|Liu et al., 2018b]] ; [[#Märker--2018|Märker et al., 2018]] ). For instance, agricultural production requires significant energy inputs due to intensive groundwater pumping ( [[#Siddiqi--2013|Siddiqi and Wescoat, 2013]] ; [[#Gurdak--2018|Gurdak, 2018]] ; [[#Putra--2020|Putra et al., 2020]] ). Similarly, hydropower production often has trade-offs with irrigation, affecting food production, carbon emission and forest protection ( [[#Meng--2020|Meng et al., 2020]] ). New technologies, such as desalination plants for urban water supply against future climate change and drought, are also very energy-intensive ( [[#Caldera--2018|Caldera et al., 2018]] ) (Box 4.5). Quantifying the complex interdependencies among food, energy and water is critical to achieving the SDGs and reducing trade-offs ( [[#Liu--2018a|Liu et al., 2018a]] ; [[#Liu--2018b|Liu et al., 2018b]] ; [[#UN--2019|UN, 2019]] ). A key benefit of the nexus approach is to leverage the interconnection of WEF and achieve the most efficiency in the overall systems. Hence, this approach allows for widening the set of salient stakeholders and, therefore, solution possibilities that may otherwise not be possible in single-domain efforts and helps connect these stakeholders to achieve synergistic goals ( [[#Ernst--2017|Ernst and Preston, 2017]] ; [[#Mercure--2019|Mercure et al., 2019]] ).

The WEF nexus approach thus opens up possibilities for strategic interventions across sectors through a better understanding of trade-offs ( [[#Albrecht--2018|Albrecht et al., 2018]] ). Policies and strategies aiming to cope with climate change may amplify rather than reduce negative externalities and trade-offs within the nexus: low carbon transition, the shift to non-conventional water resources, and agricultural intensification, all implemented to mitigate and adapt to climate change, are not always nexus-smart. Hence, a nexus approach that integrates management and governance across these three sectors can enhance WEF security by minimising trade-offs and maximising synergies between sectors. At the same time, renewable energy offers the opportunity to decouple water and food production from fossil fuel supply, leading to several advantages from both a socioeconomic and environmental point of view ( [[#Cipollina--2015|Cipollina et al., 2015]] ; [[#Pistocchi--2020|Pistocchi et al., 2020]] ). WEF nexus approaches can achieve overall system efficiency when maximising the use and recovery of water, energy, nutrients and materials ( [[#Pistocchi--2020|Pistocchi et al., 2020]] ; [[#Tian--2021|Tian et al., 2021]] ). These types of holistic system thinking of WEF show promising strategies to catalyse transformative changes. Suppose that the specific types and extent of WEF linkages in a region are well understood. In that case, it becomes possible to intervene through one element to cause an effect on another connected component that may have proven difficult for direct intervention ( [[#Mukherji--2020|Mukherji, 2020]] ).

Several challenges remain for sound operationalisation of the nexus, notably insufficient data, information and knowledge in understanding the WEF inter-linkages and lack of systematic tools to address trade-offs involved in the nexus and to generate future projections ( [[#Liu--2017a|Liu et al., 2017a]] ; [[#Liu--2018b|Liu et al., 2018b]] ). There are recent signs of progress in developing models and tools for addressing the nexus trade-offs, for example, the bioenergy–water nexus ( [[#Ai--2020|Ai et al., 2020]] ). There is a need to move beyond viewing the WEF nexus as a way of problem identification to seek integrated solutions to interconnected problems.

<div id="4.7.6" class="h2-container"></div>

<span id="trade-offs-and-synergies-between-water-related-adaptation-and-mitigation"></span>
=== 4.7.6 Trade-Offs and Synergies between Water-Related Adaptation and Mitigation ===

<div id="h2-49-siblings" class="h2-siblings"></div>

In AR5, there was ''medium evidence'' and ''high agreement'' that some adaptation and mitigation measures can lead to maladaptive outcomes, such as a rise in GHG emissions, while further exacerbating water scarcity leading to increased vulnerability to climate change, now or in the future ( [[#Noble--2014|Noble et al., 2014]] ). In addition, SR1.5 ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#IPCC--2018a|IPCC, 2018a]] ) and SRCCL ( [[#IPCC--2019b|IPCC, 2019b]] ) reiterated the challenge of trade-offs that may undermine sustainable development. Conversely, adaptation, when framed and implemented appropriately, can synergistically reduce emissions and enhance sustainable development.

Different mitigation pathways can either increase or decrease water withdrawals or water consumption (or both, or either) depending on the specific combination of mitigation technologies deployed ( ''high confidence'' ) ( [[#Fricko--2016|Fricko et al., 2016]] ; [[#Jakob--2016|Jakob and Steckel, 2016]] ; [[#Mouratiadou--2016|Mouratiadou et al., 2016]] ; [[#Fujimori--2017|Fujimori et al., 2017]] ; [[#Parkinson--2019|Parkinson et al., 2019]] ). For example, the impacts of climate change mitigation on future global water demand depend largely on assumptions regarding socioeconomic and water policy conditions and range from reduction of 15,000 km 3 to an increase of more than 160,000 km 3 by the end of century ( [[#Mouratiadou--2016|Mouratiadou et al., 2016]] ). This section assesses some of the mitigation and adaptation measures from a water trade-off and synergy lens.

Solar pumps for irrigation are increasingly introduced where conventional energy is not available ( [[#Senthil%20Kumar--2020|Senthil Kumar et al., 2020]] ) or supply is intermittent or expensive ( [[#Shah--2018|Shah et al., 2018]] ), for example, in Africa ( [[#Schmitter--2018|Schmitter et al., 2018]] ), Europe ( [[#Rubio-Aliaga--2016|Rubio-Aliaga et al., 2016]] ) and South Asia ( [[#Sarkar--2017|Sarkar and Ghosh, 2017]] ). Solar pumps can replace diesel and electric pumps ( [[#Rajan--2020|Rajan et al., 2020]] ), potentially reduce 8–11% of India’s carbon emissions (~45.3–62.3 MMT of CO 2 ) attributable to groundwater pumping while also boosting agricultural productivity ( [[#Gupta--2019|Gupta, 2019]] ). However, in the absence of incentives to deter groundwater over-exploitation ( [[#Shah--2018|Shah et al., 2018]] ), solar pumps may exacerbate groundwater depletion ( [[#Closas--2017|Closas and Rap, 2017]] ; [[#Gupta--2019|Gupta, 2019]] ) ( ''low evidence, medium agreement'' ).

In many places, treatment and reuse of wastewater from urban residential and industrial sources may be the principal supply option under acute water scarcity ( [[#US%20EPA--2017|US EPA, 2017]] ) and help reduce other freshwater withdrawals ( [[#Tram%20Vo--2014|Tram Vo et al., 2014]] ; [[#Diaz-Elsayed--2019|Diaz-Elsayed et al., 2019]] ). While reuse may recover valuable nutrients, capture energy as methane, and save water, effluent containing heavy metals may degrade land and surface and groundwater quality and pose a salinisation risk in semiarid regions ( ''medium evidence, high agreement'' ). Agricultural reuse of poor-quality wastewater will become increasingly necessary, but treatment is energy-intensive and may contribute to further GHG emissions ( [[#Qadir--2014|Qadir et al., 2014]] ; [[#Salgot--2018|Salgot and Folch, 2018]] ) (Box 4.5).

Desalination of seawater or brackish water is an adaptation measure in many coastal water-scarce regions ( [[#Hanasaki--2016|Hanasaki et al., 2016]] ; [[#Jones--2019|Jones et al., 2019]] ). Solar desalination is developing rapidly, and it lessens the carbon footprint of conventional, fossil-fuel-powered desalinisation plants ( [[#Pouyfaucon--2018|Pouyfaucon and García-Rodríguez, 2018]] ) (also see Box 4.5). However, the desalinisation process is energy-intensive ( [[#Caldera--2018|Caldera et al., 2018]] ); it ejects brine that is difficult to manage inland, has high salinity and other contaminants ( [[#Wilder--2016|Wilder et al., 2016]] ) ( ''medium evidence, high agreement'' ) (Box 4.5).

Negative-emission technologies, such as direct air capture (DAC) of CO 2 , could reduce emissions up to 3 GtCO 2 /year by 2035, equivalent to 7% of 2019 global emissions. However, they can increase net water consumption by 35 km 3 yr –1 in 2050 ( [[#Fuhrman--2020|Fuhrman et al., 2020]] ) under the low-overshoot emissions scenario. According to other estimates, capturing 10 Gt CO 2 could translate to water losses of 10–100 km 3 , depending on the technology deployed and climatic conditions (temperate vs. tropical) (Chapter 12, WGIII). Some DAC technologies that include solid sorbents also produce water as a by-product, but not in quantities that can offset total water losses ( [[#Beuttler--2019|Beuttler et al., 2019]] ; [[#Fasihi--2019|Fasihi et al., 2019]] ) ( ''medium confidence'' ).

Developing countries are projected to witness the highest increase in future energy demand under 2°C global warming leading to significant increases in water use for energy production ( [[#Fricko--2016|Fricko et al., 2016]] ) ( [[#4.5.2|Section 4.5.2]] ). Results from a simulation study on retrofitting coal-fired power plants built after 2000 with carbon capture and storage (CCS) technologies show an increase in global water consumption, currently at 9.66 km 3 yr –1 , by 31–50% (to 12.66 km 3 yr –1 and 14.47 km 3 yr –1 , respectively) depending on the cooling and CCS technology deployed, and hence are best deployed in locations which are not water scarce ( [[#Rosa--2020c|Rosa et al., 2020c]] ) ( ''medium confidence'' ). In Asia, the near-term mitigation scenario with high CCS deployment increases the average regional water withdrawal intensity of coal generation by 50–80% compared to current withdrawals ( [[#Wang--2019b|Wang et al., 2019b]] ). Carbon can be ‘scrubbed’ from thermoelectric power plant emissions and injected for storage in deep geological strata ( [[#Turner--2018|Turner et al., 2018]] ), but this can lead to pollution of deep aquifers ( [[#Chen--2021|Chen et al., 2021]] ) and have health consequences ( ''low confidence'' ).

Bio-energy crop with carbon capture and storage (BECCS) involves CO 2 sequestration as biofuel or forest bioenergy ( [[#Creutzig--2015|Creutzig et al., 2015]] ). BECCS has profound implications for water resources ( [[#Ai--2020|Ai et al., 2020]] ), depending on factors including the scale of deployment, land use, and other local conditions. Evaporative losses from biomass irrigation and thermal bioelectricity generation are projected to peak at 183 km 3 yr –1 in 2050 under a low overshoot scenario ( [[#Fuhrman--2020|Fuhrman et al., 2020]] ). ( [[#Senthil%20Kumar--2020|Senthil Kumar et al., 2020]] ) projected that while BECCS strategies like irrigating biomass plantations can limit global warming by the end of the 21st century to 1.5°C, this will double the global area and population living under severe water stress compared to the current baseline. Both BECCS ( [[#Muratori--2016|Muratori et al., 2016]] ) and DAC can significantly impact food prices via demand for land and water ( [[#Fuhrman--2020|Fuhrman et al., 2020]] ). The direction and magnitude of price movement will depend on future carbon prices, while vulnerable people in the Global South will be most severely affected ( ''medium evidence, high agreement'' ).

Afforestation and reforestation are considered one of the most cost-effective ways of storing carbon. An additional 0.9 billion ha of canopy cover in suitable locations could store 205 Gt of carbon ( [[#Bastin--2019|Bastin et al., 2019]] ), but this estimate is deemed unrealistic. Aggressive afforestation and reforestation efforts can result in trade-offs between biodiversity, carbon sequestration, and water use ( [[#Smith--2008|Smith et al., 2008]] ). In northern China, ecological restoration by regreening drylands resulted in several environmental and social benefits ( [[#Mirzabaev--2019|Mirzabaev et al., 2019]] ) but also led to increased freshwater use in some pockets ( [[#Zhao--2020|]] [[#Zhao--2020|Zhao et al., 2020]] ). Afforestation and reforestation with appropriate broad-leaf species in temperate Europe ( [[#Schwaab--2020|Schwaab et al., 2020]] ) can offer water quality and quantity-related benefits, mitigate extreme heat, and buffer against drought ( [[#Staal--2018|Staal et al., 2018]] ). A global assessment on forest and water showed that forests influence the overall water cycle, including downstream water availability via rainfall-runoff dynamics and downwind water availability via recycled rainfall effects ( [[#Creed--2018|Creed and van Noordwijk, 2018]] ). The study concluded that afforestation and reforestation should be concentrated ( [[#Ellison--2017|Ellison et al., 2017]] ) in water-abundant locations (to offset downstream impacts) and where transpiration can potentially be captured downwind as precipitation ( [[#Creed--2019|Creed et al., 2019]] ) (Cross-Chapter Box NATURAL in Chapter 2). Overall, extensive BECCS and afforestation/reforestation deployment can alter the water cycle at regional scales ( ''high confidence'' ) (Cross-Chapter Box 5.1 in Chapter 5, WGI, ( [[#Canadell--2021|Canadell et al., 2021]] )).

On the other hand, demand-side mitigation options, such as dietary changes to more plant-based diets, reduced food waste ( [[#Aleksandrowicz--2016|Aleksandrowicz et al., 2016]] ; [[#Springmann--2018|Springmann et al., 2018]] ; [[#Kim--2020|Kim et al., 2020]] ), can reduce water use ( ''medium evidence, high agreement'' ).

In summary, many adaptation and mitigation measures have synergistic or maladaptive consequences for water use, depending on associated incentives, policies, and governance that guide their deployment. Many mitigation measures have a considerable water footprint ( ''high confidence'' ), which must be managed in socially and politically acceptable ways to reduce the water intensity of mitigation while increasing synergies with sustainable development ( ''medium evidence, high agreement'' ).

<div id="4.8" class="h1-container"></div>

<span id="enabling-principles-for-achieving-water-security-sustainable-and-climate-resilient-development-through-systems-transformations"></span>