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

== 4.3 Observed Sectoral Impacts of Current Hydrological Changes ==

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The intensification of the hydrological cycle due to anthropogenic climate change has multifaceted and severe impacts for cultural, economic, social and political pathways. In this section, we assess burgeoning evidence since AR5 which shows that environmental quality, economic development and social well-being have been affected by climate-induced hydrological changes since many aspects of the economy, environment and society are dependent upon water resources. We advance previous IPCC reports by assessing evidence on the impacts of climate change-induced water insecurity for energy production ( [[#4.3.2|Section 4.3.2]] ), urbanisation ( [[#4.3.4|Section 4.3.4]] ), conflicts ( [[#4.3.6|Section 4.3.6]] ), human mobility ( [[#4.3.7|Section 4.3.7]] ) and cultural usage of water ( [[#4.3.8|Section 4.3.8]] ).

Integrating qualitative and quantitative data, we show that it is evident that societies heightened exposure to water-induced disasters—such as floods and droughts—and other hydrological changes have increased vulnerability across most sectors and regions, with few exceptions. Through the assessment of literature relying on IK, we are also able to present evidence on how observed changes impact particularly Indigenous Peoples, local communities and marginalised groups, such as women, people without social protections and minorities.

Importantly, we note that climate change-induced hydrological changes are, for most sectors, one of the several factors, often coupled with urbanisation, population growth and heightened economic disparities, that have increased societal vulnerability and required communities across the globe to alter their productive and cultural practices.

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=== 4.3.1 Observed Impacts on Agriculture ===

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AR5 concluded with ''high confidence'' that agricultural production was negatively affected by climate change, with droughts singled out as a major driver of food insecurity. In contrast, evidence of floods on food production was ''limited'' ( [[#Porter--2014|Porter et al., 2014]] ).

Globally, 23% of croplands are irrigated, providing 34% of global calorie production. Of these lands, 68% experience blue water scarcity at the least one month yr –1 and 37% up to five months yr –1 . Such agricultural water scarcity is experienced in mostly drought-prone areas in low-income countries ( [[#Rosa--2020a|Rosa et al., 2020a]] ). Approximately three quarters of the global harvested areas (~454 million hectares) experienced drought-induced yield losses between 1983 and 2009, and the cumulative production losses corresponded to USD 166 billion ( [[#Kim--2019|Kim et al., 2019]] ). Globally, droughts affected both harvested areas and yields, with a reported cereal production loss of 9–10% due to weather extremes between 1964 and 2007. Yield losses were greater by about 7% during recent droughts (1985–2007) due to greater damage—reducing harvested area—compared to losses from earlier droughts (1964–1984), with 8–11% greater losses in high-income countries than in low-income ones ( [[#Lesk--2016|Lesk et al., 2016]] ). Globally, between 1961 and 2006, it has been estimated that 25% yield loss occurred, with yield loss probability increasing by 22% for maize, 9% for rice and 22% for soybean under drought conditions ( [[#Leng--2019|Leng and Hall, 2019]] ). Mean climate and climate extremes are responsible for 20–49% of yield anomalies variance, with 18–45% of this variance attributable to droughts and heatwaves ( [[#Vogel--2019|Vogel et al., 2019]] ). Drought has been singled out as a major driver of yield reductions globally ( ''high confidence'' ) ( [[#Lesk--2016|Lesk et al., 2016]] ; [[#Meng--2016|Meng et al., 2016]] ; [[#Zipper--2016|Zipper et al., 2016]] ; [[#Anderson--2019|Anderson et al., 2019]] ; [[#Leng--2019|Leng and Hall, 2019]] ).

Yields of major crops in semiarid regions, including the Mediterranean, sub-Saharan Africa, South Asia and Australia, are negatively affected by precipitation declines in the absence of irrigation ( [[#Iizumi--2018|Iizumi et al., 2018]] ; [[#Ray--2019|Ray et al., 2019]] ), but this trend is less evident in wetter regions ( [[#Iizumi--2018|Iizumi et al., 2018]] ). Precipitation and temperature changes reduced global mean yields of maize, wheat and soybeans by 4.1, 1.8 and 4.5%, respectively ( [[#Iizumi--2018|Iizumi et al., 2018]] ). Of the global rice yield variability of ~32%, precipitation variability accounted for a larger share in drier South Asia than in wetter East and Southeast Asia ( [[#Ray--2015|Ray et al., 2015]] ). Between 1910 and 2014 agro-climatic conditions became more conducive to maize and soybean yield growth in the American Midwest due to increases in summer precipitation and cooling due to irrigation ( [[#Iizumi--2016|Iizumi and Ramankutty, 2016]] ; [[#Mueller--2016|Mueller et al., 2016]] ) (Box 4.3). In Australia, between 1990 and 2015, the negative effects of reduced precipitation and rising temperature led to yield losses, but yield losses were partly avoided because of elevated CO 2 atmospheric concentration and technological advancements ( [[#Hochman--2017a|Hochman et al., 2017a]] ). Overall, temperature-only effects are stronger in wetter regions like Europe and East and Southeast Asia, and precipitation-only effects are stronger in drier regions ( [[#Iizumi--2018|Iizumi et al., 2018]] ; [[#Ray--2019|Ray et al., 2019]] ) ( ''medium evidence, high agreement'' ). In Asia, the gap between rain-fed and irrigated maize yield widened from 5% in the 1980s to 10% in the 2000s ( [[#Meng--2016|Meng et al., 2016]] ). In North America, yields of maize and soybeans have increased (1958–2007), yet meteorological drought has been associated with 13% of overall yield variability. However, yield variability was not a concern where irrigation is prevalent ( [[#Zipper--2016|Zipper et al., 2016]] ). However, when water scarcity has reduced irrigation, yields have been negatively impacted ( [[#Elias--2016|Elias et al., 2016]] ). In Europe, yields have been affected negatively by droughts ( [[#Beillouin--2020|Beillouin et al., 2020]] ), with losses tripling between 1964 and 2015 ( [[#Brás--2021|Brás et al., 2021]] ). In West Africa, between 2000 and 2009, drought, among other altered climate conditions, led to millet and sorghum yield reductions between 10 and 20% and 5 and 15%, respectively ( [[#Sultan--2019|Sultan et al., 2019]] ). Between 2006 and 2016, droughts contributed to food insecurity and malnutrition in northern, eastern and southern Africa, Asia and the Pacific. In 36% of these nations—mainly in Africa—where severe droughts occurred, undernourishment increased ( [[#Phalkey--2015|Phalkey et al., 2015]] ; [[#Cooper--2019|Cooper et al., 2019]] ). An attribution study showed that anthropogenic emissions increased the chances of October–December droughts over the region by 1.4–4.3 times and resulted in below-average harvests in Zambia and South Africa ( [[#Nangombe--2020|Nangombe et al., 2020]] ). Root crops, a staple in many tropics and subtropical countries, and vegetables are particularly prone to drought, leading to smaller fruits or crop failure ( [[#Daryanto--2017|Daryanto et al., 2017]] ; [[#Bisbis--2018|Bisbis et al., 2018]] ). Livestock production has also been affected by changing seasonality, increasing frequency of drought, rising temperatures and vector-borne diseases and parasites through changes in the overall availability, as well as reduced nutritional value, of forage and feed crops ( [[#Varadan--2014|Varadan and Kumar, 2014]] ; [[#Naqvi--2015|Naqvi et al., 2015]] ; [[#Zougmoré--2016|Zougmoré et al., 2016]] ; [[#Henry--2018|Henry et al., 2018]] ; [[#Godde--2019|Godde et al., 2019]] ) ( ''medium confidence'' ).

Floods have led to harvest failure and crop and fungal contamination ( [[#Liu--2013|Liu et al., 2013]] ; [[#Uyttendaele--2015|Uyttendaele et al., 2015]] ). Globally, between 1980 and 2018, excess soil moisture has reduced rice, maize, soybean and wheat yields between 7 and 12% ( [[#Borgomeo--2020|Borgomeo et al., 2020]] ). Changes in groundwater storage and availability, which are affected by the intensity of irrigated agriculture, also negatively impacted crop yields and cropping patterns ( [[#4.2.6|Section 4.2.6]] , Box 4.3, 4.7.2). Moreover, extreme precipitation can lead to increased surface flooding, waterlogging, soil erosion and susceptibility to salinisation ( ''high confidence'' ). For example, in Bangladesh, in March and April 2017, floods affected 220,000 ha of a nearly harvest-ready summer paddy crop and resulted in almost a 30% year-on-year increase in paddy prices. An attribution study of those pre-monsoon extreme rainfall events in Bangladesh concluded that anthropogenic climate change doubled the likelihood of the extreme rainfall event ( [[#Rimi--2019|Rimi et al., 2019]] ). Moreover, floods, extreme weather events and cyclones have led to animal escapes and infrastructure damage in aquaculture (Beveridge et al., 2018; [[#Islam--2018|Islam and Hoq, 2018]] ; [[#Naskar--2018|Naskar et al., 2018]] ; [[#Lebel--2020|Lebel et al., 2020]] ) (see [[IPCC:Wg2:Chapter:Chapter-5#5.9.1|Section 5.9.1]] ).

Worldwide, the magnitudes of climate-induced water-related hazards and their impact on agriculture are differentiated across populations and genders (Sections 4.3.6; 4.8.3). Evidence shows that hydroclimatic factors pose high food insecurity risks to subsistence farmers, whose first and only source of livelihood is agriculture, and who are situated at low latitudes where the climate is hotter and drier ( [[#Shrestha--2016|Shrestha and Nepal, 2016]] ; [[#Sujakhu--2016|Sujakhu et al., 2016]] ). Historically, they have been the most vulnerable to observed climate-induced hydrological changes ( [[#Savo--2016|Savo et al., 2016]] ). Indigenous and local communities, often heavily reliant on agriculture, have a wealth of knowledge about observed changes. These are important because they shape farmers’ perceptions, which in turn shape the adaptation measures farmers will undertake ( [[#Caretta--2015|Caretta and Börjeson, 2015]] ; [[#Savo--2016|Savo et al., 2016]] ; [[#Sujakhu--2016|Sujakhu et al., 2016]] ; [[#Su--2017|Su et al., 2017]] ) ( [[#4.8.4|Section 4.8.4]] ) ( ''high confidence)'' .

In summary, ongoing climate change in temperate climates has some positive impacts on agricultural production. In subtropical/tropical climates, climate-induced hazards such as floods and droughts negatively impact agricultural production ( ''high confidence'' ). People living in deprivation and Indigenous Peoples have been disproportionally affected. They often rely on rain-fed agriculture in marginal areas with high exposure and high vulnerability to water-related stress and low adaptive capacity ( ''high confidence)'' .

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=== 4.3.2 Observed Impacts on Energy and Industrial Water Use ===

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AR5 ( [[#Jiménez%20Cisneros--2014|Jiménez Cisneros et al., 2014]] ) concluded with ''medium evidence'' and ''high agreement'' that hydropower negatively impacts freshwater ecosystems. SROCC ( [[#IPCC--2019a|IPCC, 2019a]] ) concluded with ''medium confidence'' that climate change has led to both increases and decreases in annual/seasonal water inputs to hydropower plants.

Water is a crucial input for hydroelectric and thermoelectric energy production, which together account for 94.7% of the world’s current electricity generation ( [[#Petroleum--2020|Petroleum, 2020]] ). Climate change impacts hydropower production through changes in precipitation, evaporation, volume and timing of runoff; and impacts cooling of thermoelectric power plants through reduced streamflow and increased water temperatures ( [[#Yalew--2020|Yalew et al., 2020]] ). In addition, extreme weather events, like tropical cyclones, landslides and floods, damage energy infrastructure ( [[#MCTI--2020|MCTI, 2020]] ; [[#Yalew--2020|Yalew et al., 2020]] ), while high temperature and humidity increase the energy requirement for cooling ( [[#Maia-Silva--2020|Maia-Silva et al., 2020]] ).

With 1308 GW installed capacity in 2019, hydropower became the world’s largest single source of renewable energy ( [[#IHA--2020|IHA, 2020]] ) (also see Figure 6.12, WGIII). While hydropower reduces emissions relative to fossil fuel-based energy production, hydropower reservoirs are being increasingly associated with GHG emissions caused by submergence and later re-emergence of vegetation under reservoirs due to water level fluctuations ( [[#Räsänen--2018|Räsänen et al., 2018]] ; [[#Song--2018|Song et al., 2018]] ; [[#Maavara--2020|Maavara et al., 2020]] ). A recent global study concluded that reservoirs might emit more carbon than they bury, especially in the tropics ( [[#Keller--2021|Keller et al., 2021]] ) ( ''medium confidence'' ).

In Ghana, between 1970 and 1990, rainfall variability accounted for 21% of interannual variations in hydropower generation ( [[#Boadi--2019|Boadi and Owusu, 2019]] ). In Brazil’s São Francisco River, following drought events in 2016 and 2017, hydropower plants operated with an average capacity factor of only 23% and 17%, respectively ( [[#de%20Jong--2018|de Jong et al., 2018]] ). In Switzerland, increased glacier melt contributed to 3–4% of hydropower production since 1980 ( [[#Schaefli--2019|Schaefli et al., 2019]] ) ( [[#4.2.2|Section 4.2.2]] ). In the USA, hydropower generation dropped by nearly 27% for every standard deviation increase in water scarcity. Equivalent social costs of loss in hydropower generation between 2001 and 2012 were approximately USD 330,000 (at 2015 value) per month for every power plant that experienced water scarcity ( [[#Eyer--2018|Eyer and Wichman, 2018]] ). Globally, for the period 1981–2010, the utilisation rate of hydropower was reduced by 5.2% during drought years compared to long-term average values ( [[#van%20Vliet--2016a|van Vliet et al., 2016a]] ). Thus, there is a growing body of evidence of negative impacts of extreme events on hydropower production ( ''high confidence'' ).

Impacts of water scarcity on thermoelectric plants are more unequivocal than hydropower plants. For example, a scenario-based simulation study showed that 32% of the world’s coal-fired power plants (CFPPs) plants are currently experiencing water scarcity for at least five months or more in a year ( [[#Rosa--2020c|Rosa et al., 2020c]] ). In the UK, almost 50% of freshwater thermal capacity is lost on extreme high-temperature days, causing losses in the range of average GBP 29–66 million yr –1 . For ~20% of particularly vulnerable power plants, these losses could increase to GBP 66–95 million yr –1 annualised over 30 years ( [[#Byers--2020|Byers et al., 2020]] ). Globally, for the period 1981–2010, the utilisation rate of thermoelectric power was reduced by 3.8% during drought years compared to long-term average values ( [[#van%20Vliet--2016a|van Vliet et al., 2016a]] ), and none of the studies reported increases in thermoelectric power production as a consequence of climate change ( ''high confidence'' ).

In the energy sector, a large number of studies document the impact of extreme climate events (e.g., droughts or extreme temperature days) on production of hydropower and thermoelectric power, yet there are limited studies that measure trends in energy production due to long-term climate change. This remains a knowledge gap.

Mining in regions already vulnerable to climate change-induced water scarcity is under threat, leading some countries like El Salvador to ban metal mining completely ( [[#Odell--2018|Odell et al., 2018]] ). Likewise, food and agro-processing companies are aware of water-related threats to their operations, with 77% of 35 publicly traded companies evaluated in 2019 explicitly citing water as a risk factor in their annual reports, up from 59% in 2017 ( [[#CDP--2018|CDP, 2018]] ; [[#CERES--2019|CERES, 2019]] ). Changes in water availability affect the mining, electrical, metal and agro-processing sectors ( [[#UNIDO--2017|UNIDO, 2017]] ; [[#Odell--2018|Odell et al., 2018]] ; [[#Frost--2019|Frost and Hua, 2019]] ), but these impacts are less understood due to the lack of studies.

In summary, there is ''high confidence'' that climate change has had negative impacts on hydro and thermal power production globally due to droughts, changes in the seasonality of river flows, and increasing ambient water temperatures.

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=== 4.3.3 Observed Impacts on Water, Sanitation and Hygiene (WaSH) ===

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AR5 showed that local changes in temperature and rainfall had altered the distribution of some water-related diseases ( ''medium confidence'' ), and extreme weather events disrupt water supplies, impacting morbidity, mortality and mental health ( ''very high confidence'' ) ( [[#Field--2014b|Field et al., 2014b]] ). In addition, melting and thawing of snow, ice and permafrost ( [[#4.2.2|Section 4.2.2]] ) have also adversely impacted water quality, security and health ( ''high confidence'' ) ( [[#IPCC--2019a|IPCC, 2019a]] ) ( [[#4.2.7|Section 4.2.7]] ).

Literature since AR5 confirms that temperature, precipitation and extreme weather events are linked to increased incidence and outbreaks of water-related and neglected tropical diseases ( [[#Colón-González--2016|Colón-González et al., 2016]] ; [[#Levy--2016|Levy et al., 2016]] ; [[#Azage--2017|Azage et al., 2017]] ; [[#Harp--2021|Harp et al., 2021]] ) ( ''high confidence'' ). For example, the rainy season in Senegal has been associated with an 84% increase in relative risk of childhood diarrhoea, and an additional wet day per week was associated with up to 2% increases in diarrhoeal disease in Mozambique ( [[#Thiam--2017|Thiam et al., 2017]] ; [[#Horn--2018|Horn et al., 2018]] ). In Ecuador, increases of 1.5 cases of diarrhoea per 1000 were associated with heavy rainfall after dry periods, while a decrease of one case per 1000 was associated with heavy rain after wet periods ( [[#Carlton--2014|Carlton et al., 2014]] ). Floods have been associated with 22% increases in relative risk of diarrhoea in China ( [[#Liu--2018c|Liu et al., 2018c]] ). In addition, higher levels of faecal contamination of drinking water and hands (i.e., lack of WaSH) has been statistically significantly associated with increased child diarrhoea ( [[#Goddard--2020|Goddard et al., 2020]] ).

In 2020, 2 billion people lacked access to uncontaminated water, while 771 million lacked basic sanitation services, primarily in sub-Saharan Africa and rural areas ( [[#WHO%20and%20UNICEF--2021|WHO and UNICEF, 2021]] ). Even in high-income countries, poor-quality drinking water can be a health issue ( [[#Murphy--2014|Murphy et al., 2014]] ). For example, in a sampled population in Canada, reported exposure to exposure routes for waterborne illness included 7% from private wells and 71.8% from municipal water ( [[#David--2014|David et al., 2014]] ). Drinking water treatment can be compromised by degraded source water quality and extreme weather events, including droughts, storms, ice storms and wildfires that overwhelm or cause infrastructure damage ( [[#Sherpa--2014|Sherpa et al., 2014]] ; [[#Khan--2015|Khan et al., 2015]] ; [[#Howard--2016|Howard et al., 2016]] ; [[#White--2017|White et al., 2017]] ) ( ''high confidence'' ). Adverse health effects are exacerbated due to the absence of adequate WaSH, particularly in poorer households ( [[#Khan--2015|Khan et al., 2015]] ; [[#Kostyla--2015|Kostyla et al., 2015]] ; [[#Cissé--2016|Cissé et al., 2016]] ), WaSH infrastructure failure ( [[#Khan--2015|Khan et al., 2015]] ; [[#Wanda--2017|Wanda et al., 2017]] ) or inadequate WaSH facilities in emergency shelters ( [[#Alam--2014|Alam and Rahman, 2014]] ). For example, WaSH coverage decreased from 65% to 51% due to damage from floods and earthquakes in Malawi ( [[#Wanda--2017|Wanda et al., 2017]] ). Loss of electricity also impacts WaSH service delivery ( [[#Cashman--2014|Cashman, 2014]] ), and infrastructure damage caused by climate hazards may reverse progress on universal access to WaSH ( [[#Kohlitz--2017|Kohlitz et al., 2017]] ) ( ''limited evidence, high agreement'' ). In addition, wastewater outflows have been associated with a 13% increased relative risk of gastrointestinal illness through contaminated drinking water sources ( [[#Jagai--2015|Jagai et al., 2015]] ) ( ''limited evidence, high agreement'' ). Harmful algal blooms represent an emerging health risk, but lack of monitoring and reporting prevent risk exposure assessments ( [[#Carmichael--2016|Carmichael and Boyer, 2016]] ; [[#Nichols--2018|Nichols et al., 2018]] ) ( ''limited evidence, high agreement'' ). Chemical contaminants (e.g., nitrates, arsenic) have been linked to non-communicable diseases, including neurological disorders, liver and kidney damage, and cancers ( [[#Jones%20Rena--2016|Jones Rena et al., 2016]] ), and to some water-related diseases (e.g., schistosomiasis) ( ''low evidence, medium agreement'' ).

Water insecurity and inadequate WaSH have been associated with increased disease risk ( ''high confidence'' ), stress and adverse mental health ( ''limited evidence, medium agreement'' ), food insecurity and adverse nutritional outcomes, and poor cognitive and birth outcomes ( ''limited evidence, medium agreement'' ) ( [[#Workman--2017|Workman and Ureksoy, 2017]] ; [[#Sclar--2018|Sclar et al., 2018]] ; [[#Boateng--2020|Boateng et al., 2020]] ; [[#Rosinger--2020|Rosinger and Young, 2020]] ; [[#Wutich--2020|Wutich et al., 2020]] ). Climate-induced water scarcity and supply disruptions disproportionately impact women and girls. The necessity of water collection takes away time from income-generating activities, child care and education ( [[#Yadav--2018|Yadav and Lal, 2018]] ; [[#Schuster--2020|Schuster et al., 2020]] ) ( ''medium evidence, medium agreement'' ). Consumption of larger volumes of water is essential for healthy women during pregnancy, lactation and caregiving, which increases the amount of water that has to be fetched. Fetching of water is associated with increased risk of sexual abuse, demand for sexual favours at controlled water collection points, physical injuries (e.g., musculoskeletal or from animal attacks), domestic violence for not completing daily water-related domestic tasks ( ''limited evidence, high agreement'' ), and poorer maternal and child health (Mercer and [[#Hanrahan--2017|Hanrahan, 2017]] ; [[#Pommells--2018|Pommells et al., 2018]] ; [[#Anwar--2019|Anwar et al., 2019]] ; [[#Collins--2019a|Collins et al., 2019a]] ; [[#Geere--2020|Geere and Hunter, 2020]] ; [[#Venkataramanan--2020|Venkataramanan et al., 2020]] ) ( ''medium evidence, high agreement'' ). Menstrual hygiene management is a public health issue but poorly linked to climate change, despite relationships between lack of adequate WaSH, poor menstrual hygiene, and urinary tract infections ( [[#Ellis--2016|Ellis et al., 2016]] ; [[#Pouramin--2020|Pouramin et al., 2020]] ). Water insecurity also affects emotional, spiritual and cultural relationships that are often critical to Indigenous health ( [[#Wilson--2019|Wilson et al., 2019]] ) ( ''limited evidence, high agreement'' ).

There are gaps in data on climate-driven water-related disease burden for both infectious and non-communicable diseases. Increased demands for water and WaSH services for infectious diseases, such as HIV/AIDS and COVID-19 (Box 4.4) exacerbate existing vulnerabilities and inequities ( [[#Stanley--2017|Stanley et al., 2017]] ; [[#Armitage--2020a|Armitage and Nellums, 2020a]] ; [[#Rodriguez-Lonebear--2020|Rodriguez-Lonebear et al., 2020]] ). Additionally, limited research has been undertaken to quantify the effects of climate-compromised WaSH on health and well-being.

In summary, WaSH-related household water insecurity and disease incidence are products of geography, politics, social and environmental determinants, vulnerability and climate change ( [[#Bardosh--2017|Bardosh et al., 2017]] ; [[#Stoler--2021|Stoler et al., 2021]] ).

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=== 4.3.4 Observed Impacts on Urban and Peri-Urban Sectors ===

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All previous IPCC reports have focused on future water-related risks to urban areas due to climate change rather than documented observed impacts.

Climate extremes have profound implications for urban and peri-urban water management, particularly in an increasingly urbanised world ( ''high confidence'' ). Over half (54%) of the global population currently lives in cities ( [[#WWAP--2019|WWAP, 2019]] ), and global urbanisation rates continue to increase across all SSPs ( [[#Jiang--2017|Jiang and O’Neill, 2017]] ). Using observed station data for 217 urban areas worldwide, [[#Mishra--2015|Mishra et al. (2015)]] noted that 17% of cities experienced statistically significant increases ( ''p'' value &lt; 0.05) in the frequency of daily precipitation extremes from 1973 to 2012 and hypothesised that such observed climate changes in urban areas were largely due to large-scale changes rather than local land cover changes.

Since AR5, factors such as rapid population growth, urbanisation, ageing infrastructure and changes in water use have also magnified climate risks, such as drought and flooding, and contributed to urban and peri-urban water insecurity ( ''medium agreement, medium evidence'' ) ( [[#4.1.2|Section 4.1.2]] ). For example, despite an increase in flooding events from 1.1 flood events yr –1 (1986–2005) to five flood events yr –1 (2006–2016) in Ouagadougou (Burkina Faso), analyses of rainfall indices showed few have significant trends at a 5% level over the period 1961–2015 and that the generalised extreme value distribution fit the time series of annual maximum daily rainfall (Tazen et al., 2019). On the other hand, long-term annual variations of maximum hourly precipitation in Shanghai (China) increased significantly during 1916–2014, especially from 1981.

Advances in the attribution of extreme weather events have made it possible to determine the causal relationship between droughts, floods and climate change for some cities, particularly those with long hydro-meteorological records ( [[#Bader--2018|Bader et al., 2018]] ; [[#Otto--2020|Otto et al., 2020]] ). Attribution analysis shows that urbanisation contributed to the increase in both frequencies of local and abrupt heavy rainfall events in the city, at a rate of 1.5 and 1.8 10 yr –1 , respectively ( [[#Liang--2017|Liang and Ding, 2017]] ). A multi-method attribution showed that the likelihood of prolonged rainfall deficit in Cape Town (South Africa) during 2015–2017 was made more likely by a factor of 3.3 (1.4–6.4) due to anthropogenic climate change ( [[#Otto--2018|Otto et al., 2018]] ). These results show that climate change has impacted the return time of extreme droughts in the Western Cape, exceeding the capacity of the existing water supply system to cope ( [[#Otto--2018|Otto et al., 2018]] ) (Box 9.4; 9.8.2). In Baton Rouge (USA), a rapid attribution study showed that the probability of an event such as the intense precipitation and flash flooding of August 2016 has increased by at least a factor of 1.4 due to radiative forcing (USA) ( [[#van%20der%20Wiel--2017|van der Wiel et al., 2017]] ). In Houston (USA), a study found that the combination of urbanisation and climate change nearly doubled peak discharge (84%) during Hurricane Harvey (August 2017), suggesting that land use change magnified the effects of climate change on catchment response to extreme precipitation events ( [[#Sebastian--2019|Sebastian et al., 2019]] ) (14.4.3.1; Box 14.5 The Economic Consequences of Climate Change in North America, Cross-Chapter Box DISASTER in Chapter 4). According to a multi-method approach, the 2014/2015 drought event in Sao Paulo (Brazil) was more likely to have been driven by water use changes and population growth than climate change ( [[#Otto--2015|Otto et al., 2015]] ) (Cross-Chapter Box DISASTER in Chapter 4).

The science of weather event attribution requires high-quality observational data and climate models that are currently available only in highly developed countries ( [[#Otto--2020|Otto et al., 2020]] ). In addition, further research is necessary to determine the impacts of climate change on water-related extremes in the urban areas of developing countries ( [[#Bai--2018|Bai et al., 2018]] ). For example, a combination of observational analysis and global coupled climate models showed that the 2015 flooding event in Chennai (India) could not be attributed to anthropogenic climate change, with the effects of that being relatively small in the region due to the impact of GHG increases being largely counteracted by those of aerosols ( [[#van%20Oldenborgh--2017a|van Oldenborgh et al., 2017a]] ) ( [[#4.2.5|Section 4.2.5]] ). Further research is also required to determine the impacts of climate change on water-related extremes in informal settlements where vulnerability to water insecurity is high due to poverty, overcrowding, poor-quality housing and lack of basic infrastructure ( [[#Scovronick--2015|Scovronick et al., 2015]] ; [[#Grasham--2019|Grasham et al., 2019]] ; [[#Williams--2019|Williams et al., 2019]] ; [[#Satterthwaite--2020|Satterthwaite et al., 2020]] ).

In summary, water-related hazards such as drought and flooding have been exacerbated by climate change in some cities ( ''high confidence'' ). Further research is necessary to determine the extent and nature of water-related climate change impacts in the urban areas of developing countries ( ''high confidence'' ).

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'''Cross-Chapter Box DISASTER | Disasters as the Public Face of Climate Change'''
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Authors: Aditi Mukherji (India, Chapter 4), Guéladio Cissé (Mauritania/Switzerland/France, Chapter 7), Caroline Zickgraf (Contributing Author), Paulina Aldunce (Chile, Chapter 7), Liliana Raquel Miranda Sara (Peru, Chapter 12), William Solecki, (USA, Chapter 17), Friederike Otto (UK, WGI), François Gemenne (France, WGI), Martina Angela Caretta (Sweden, Chapter 4);, Richard Jones (UK, WGI); Richard Betts (UK, Chapter 4), Maarten van Aalst (the Netherlands, Chapter 16), Jakob Zscheischler (Switzerland), Kris Murray (UK), Mauro E. González (Chile).

Introduction

Some extreme weather events are increasing in frequency and (or) severity as a result of climate change ( [[#Seneviratne--2021|Seneviratne et al., 2021]] ) ( ''high confidence'' ). These include extreme rainfall events ( [[#Roxy--2017|Roxy et al., 2017]] ; [[#Myhre--2019|Myhre et al., 2019]] ; [[#Tabari--2020|Tabari, 2020]] ); extreme and prolonged heat leading to catastrophic fires ( [[#Bowman--2017|Bowman et al., 2017]] ; [[#Krikken--2019|Krikken et al., 2019]] ; [[#van%20Oldenborgh--2020|van Oldenborgh et al., 2020]] ); and more frequent and stronger cyclones/hurricanes and resulting extreme rainfall ( [[#Griego--2020|Griego et al., 2020]] ). These extreme events, coupled with high vulnerability and exposure in many parts of the world, turn into disasters and affect millions of people every year. New advances enable the detection and attribution of these extreme events to climate change ( [[#Otto--2016|Otto et al., 2016]] ; [[#Seneviratne--2021|Seneviratne et al., 2021]] ), with the most recent study saying that heavy rains leading to devastating floods in western Europe that captured the world’s attention in July 2021 were made more likely due to climate change ( [[#Kreienkamp--2021|Kreienkamp et al., 2021]] ). Most WGII chapters (this volume) report various extreme event-induced disasters and their societal impacts. This cross-chapter box brings together authors from WGI and WGII to emphasise that disasters following extreme events have become the most visible and public face of climate change ( [[#Solecki--2014|Solecki and Rosenzweig, 2014]] ). These disasters reflect immediate societal and political implications of rising risks ( ''high confidence'' ), but also provide windows of opportunity to raise awareness about climate change and to implement disaster-reduction policies and strategies ( ''high confidence'' ) ( [[#Albright--2020|Albright, 2020]] ; [[#Boudet--2020|Boudet et al., 2020]] ).

Here, we document eight catastrophic climate-related disasters that took place between 2017 and 2021. These disasters resulted in the loss of lives and livelihoods and had adverse impacts on biodiversity, health, infrastructure and the economy. These disasters provided important rallying points for discussions around climate change, equity and vulnerability in some cases. These disasters also offer valuable lessons about the role of effective climate change adaptation in managing disaster risks and the importance of Loss and Damage mechanisms in global negotiation processes ( [[#Jongman--2014|Jongman et al., 2014]] ; [[#Mechler--2014|Mechler et al., 2014]] ; [[#Cutter--2015|Cutter and Gall, 2015]] ).

Case 1. Compounded events and impacts on human systems: Cyclones Idai and Kenneth in Mozambique in 2019

While individual events alone can lead to major disasters, when several events occur in close spatial and temporal proximity, impacts get compounded, with catastrophic results ( [[#Zscheischler--2018|Zscheischler et al., 2018]] ; [[#Zscheischler--2020|Zscheischler et al., 2020]] ). In March 2019, Cyclone Idai (category 2) was the deadliest storm on record to strike the African continent, with the coastal city of Beira in Mozambique being particularly hard hit with at least 602 deaths ( [[#CRED--2019|CRED, 2019]] ; [[#Zehra--2019|Zehra et al., 2019]] ; [[#Phiri--2020|Phiri et al., 2020]] ). Nationally, Idai caused massive housing, water supply, drainage and sanitation destruction, but its impact extended to South Africa through disruption of the regional electricity grid ( [[#Yalew--2020|Yalew et al., 2020]] ). In April 2019, amidst heightened vulnerabilities in the aftermath of cyclone Idai, cyclone Kenneth (category 4) hit the country, affecting 254,750 people and destroying more than 45,000 homes ( [[#Kahn--2019|Kahn et al., 2019]] ). These circumstances caused the rapid spread of cholera, which triggered a massive vaccination programme to control the epidemic ( [[#Kahn--2019|Kahn et al., 2019]] ; [[#Lequechane--2020|Lequechane et al., 2020]] ). While there were no specific detection and attribution studies for Idai and Kenneth, overall, there is ''high confidence'' that the rainfall associated with tropical cyclones is more intense because of global warming. However, there remain significant uncertainties about the impact of climate change on the numbers and strength of tropical cyclones per se ( [[#Walsh--2019|Walsh et al., 2019]] ; Zhang G. et al., 2020).

Case 2. COVID-19 as the compounding risk factor: Cyclone Amphan in India and Bangladesh, 2020

Cyclone Amphan hit coastal West Bengal and Bangladesh on 20 May 2020. It was the first supercyclone to form in the Bay of Bengal since 1999 and one of the fiercest to hit West Bengal, India, in the last 100 years. The cyclone intensified from a cyclonic storm (category 1) to a supercyclone (category 5) in less than 36 hours ( [[#Balasubramanian--2020|Balasubramanian and Chalamalla, 2020]] ). Several hours before and on 20 May, extreme rain events resulted in heavy cumulative rainfall, flash flooding and landslides in several adjoining districts ( [[#Mishra--2020|Mishra and Vanganuru, 2020]] ). As per the initial estimates, about 1600 km 2 area in the mangrove forests of ''Sundarbans'' were damaged, and over 100 lives were lost. Earlier cyclones in the region have shown that impacts of these events are gendered ( [[#Roy--2019|Roy, 2019]] ). The cyclone damage was somewhat lessened due to the delta’s mangroves ( [[#Sen--2020|Sen, 2020]] ). The estimated damage was USD 13.5 billion. Cyclone Amphan was the largest source of displacement in 2020, with 2.4 million displacements in India alone, of which 800,000 were pre-emptive evacuations by authorities ( [[#IDMC--2020|IDMC, 2020]] ). Because it happened amidst the COVID-19 crisis, evacuation plans were constrained due to social distancing norms ( [[#Baidya--2020|Baidya et al., 2020]] ). Social media played an important role in disseminating pre-cyclone warnings and information on post-cyclone relief work ( [[#Crayton--2020|Crayton et al., 2020]] ; [[#Poddar--2020|Poddar et al., 2020]] ).

Case 3. Further exacerbating inequities in human systems: Hurricane Harvey, USA, 2017

Hurricane Harvey, a category 4 hurricane, made landfall on Texas and Louisiana in August 2017, causing catastrophic flooding and 80 deaths and inflicting $125 billion (2017 USD) in damage, of which $67 billion (2017 USD) was attributable to climate change ( [[#Frame--2020|Frame et al., 2020]] ). Several studies estimated the return period of the rainfall associated with this event and assessed that human-induced climate change increased the likelihood by a factor of approximately three using a combination of observations and climate models ( [[#Risser--2017|Risser and Wehner, 2017]] ; [[#van%20Oldenborgh--2017b|van Oldenborgh et al., 2017b]] ). The impacts of Hurricane Harvey were exacerbated by extensive residential development in flood-prone locations. A study showed that urbanisation increased the probability of such extreme flood events several folds (Zhang W. et al., 2018) through the alteration of ground cover and disruption and redirection of water flow. Water quality in cities also deteriorated ( [[#Horney--2018|Horney et al., 2018]] ; [[#Landsman--2019|Landsman et al., 2019]] ), and 85% of flooded land subsided at a rate of 5 mm yr –1 following the event ( [[#Miller--2019|Miller and Shirzaei, 2019]] ). Notably, the impacts of Harvey were unequally distributed along racial and social categories in the greater Houston area. Neighbourhoods with larger Black, Hispanic and disabled populations were the worst affected by the flooding following the storm and rainfall ( [[#Chakraborty--2018|Chakraborty et al., 2018]] ; [[#Chakraborty--2019|Chakraborty et al., 2019]] ; [[#Collins--2019b|Collins et al., 2019b]] ). In addition, racial and ethnic disparities were shown to impact post-disaster needs, ranging from household damage to mental health and recovery ( [[#Collins--2019b|Collins et al., 2019b]] ; [[#Flores--2020|Flores et al., 2020]] ; [[#Griego--2020|Griego et al., 2020]] ).

Case 4. Impacts worsened due to sociocultural and political conditions: The “Coastal Niño” in Peru, 2017

The Coastal Niño event of 2017 led to extreme rainfall in Peru, which was made more likely by at least 1.5 times as compared to pre-industrial times due to anthropogenic climate change and Coastal Niño ( [[#Christidis--2019|Christidis et al., 2019]] ) and comparable to the El Niño events of 1982–1983 and 1997–1998 ( [[#Poveda--2020|Poveda et al., 2020]] ). This event showed evidence of larger anomalies in flood exposure ( [[#Muis--2018|Muis et al., 2018]] ; [[#Christidis--2019|Christidis et al., 2019]] ; [[#Rodríguez-Morata--2019|Rodríguez-Morata et al., 2019]] ) and sediment transport ( [[#Morera--2017|Morera et al., 2017]] ). In Peru, this Niño event led to USD 6 to 9 billion of monetary losses, more than a million inhabitants were affected, 6614 km of roads were damaged, 326 bridges were destroyed, 41,632 homes were damaged or became uninhabitable and 2150 schools and 726 health posts were damaged ( [[#French--2017|French and Mechler, 2017]] ; [[#French--2020|French et al., 2020]] ), leaving half of the country in a state of emergency ( [[#Christidis--2019|Christidis et al., 2019]] ). Furthermore, institutional and systemic sociocultural and political conditions at multiple levels significantly worsened disaster risk management which hampered response and recovery ( [[#French--2020|French et al., 2020]] ). Citizens and zero-order responders proved to be more effective and quicker than national disaster risk management response ( [[#Briones--2019|Briones et al., 2019]] ).

Case 5. Triggering institutional response for future preparedness: Mega-fires of Chile, 2017

The mega-fire that occurred in Chile in January 2017 had the highest severity recorded on the planet ( [[#CONAF--2017|CONAF, 2017]] ), burning in three weeks an area close to 350,000 hectares in south-central Chile. These events have been associated with the prolonged ongoing drought that has persisted for more than one decade and with the increase in heat waves ( [[#González--2018|González et al., 2018]] ; [[#Miranda--2020|Miranda et al., 2020]] ). This extreme drought and the total burned area of the last decades have been attributed to anthropogenic climate change in at least 25% and 20% of their severity, respectively ( [[#Boisier--2016|Boisier et al., 2016]] ). The mega-fire of summer 2017 resulted in 11 deaths, more than 1500 houses burned and the destruction of the small town of Santa Olga. The smoke from these fires exposed 9.5 million people to air pollution, causing an estimated 76 premature deaths ( [[#Bowman--2017|Bowman et al., 2017]] ; [[#González--2020|González et al., 2020]] ). The direct costs incurred by the State exceeded USD 360 million ( [[#González--2020|González et al., 2020]] ). The 2017 mega-fires led to a series of institutional responses such as management plans that include preventive forestry techniques, regulatory plans containing rural–urban interface areas, an emergency forest fire plan, and promotion of native species ( [[#González--2020|González et al., 2020]] ).

Case 6. Loss of human lives and biodiversity: Bushfires in Australia, 2019/2020

In the summer of 2019/2020, bushfires in Australia killed 417 people due to smoke and killed between 0.5 and 1.5 billion wild animals and tens of thousands of livestock ( [[#van%20Oldenborgh--2020|van Oldenborgh et al., 2020]] ). These fires also destroyed approximately 5900 buildings and burnt 97,000 km 2 of vegetation, which provided habitat for 832 species of native vertebrate fauna. Seventy taxa had more than 30% of their habitat impacted, including 21 already identified as threatened with extinction ( [[#Ward--2020|Ward et al., 2020]] ). In addition, millions of people experienced levels of smoke 20 times higher than the government-identified safe level. The year 2019 had been Australia’s warmest and driest year on record. In the summer of 2019/2020, the seasonal mean and mean maximum temperatures were the hottest by almost 1°C above the previous record. Eight of the 10 hottest days on record for national mean temperatures occurred in December 2019. While the prevailing weather conditions were strongly influenced by the Indian Ocean Dipole pressure pattern, with a contribution from weakly positive ENSO conditions in the Pacific, the fact that Australia is approximately 1°C warmer than the early 20th century demonstrates links to anthropogenic climate change. Eight climate models using event attribution methodologies (comparison of simulations with present-day and pre-industrial forcings) indicates that anthropogenic climate change made the heat conditions of December 2019 more than twice as likely ( [[#van%20Oldenborgh--2020|van Oldenborgh et al., 2020]] ).

<div id="_idContainer046" class="Box_Header-continued"></div>

Cross-Chapter Box DISASTER

Case 7. Improved preparedness reduced mortality: Heatwave in Europe, 2019

In 2019, Europe experienced several record-breaking heatwaves. In June, the first one featured record heat for that time in early summer, with temperatures of 6°C–10°C above normal over most of France and Germany, northern Spain, northern Italy, Switzerland, Austria and the Czech Republic (Climate, 2019). The second heatwave also resulted in all-time records for Belgium, Germany, Luxembourg, the Netherlands and the UK in July. Attribution studies ( [[#Vautard--2020|Vautard et al., 2020]] ) demonstrated that these would have had extremely small odds in the absence of human-induced climate change or would have been 1.5°C–3°C colder without human-induced climate change. This study concluded that state-of-the-art climate models underestimate the trends in local heat extremes compared to the observed trend. Since the 2003 heatwave, which resulted in tens of thousands of deaths across Europe, many European countries have adopted heatwave plans, including early warning systems. Therefore, mortality in 2019 was substantially lower than it might have been. Unfortunately, mortality is not registered systematically across Europe, and therefore, comprehensive analyses are missing. But even based on the countries that provide the numbers, more specifically France, Belgium and the Netherlands, the European heatwave of 2019 resulted in over 2500 deaths ( [[#CRED--2019|CRED, 2019]] ). Despite their deadliness and the fact that climate change increases the frequency, intensity and duration of heatwaves globally ( [[#Perkins-Kirkpatrick--2020|Perkins-Kirkpatrick and Lewis, 2020]] ), heatwaves are not consistently reported in many countries ( [[#Harrington--2020|Harrington and Otto, 2020]] ), rendering it currently impossible to estimate climate change impacts on lives and livelihoods comprehensively.

Case 8. Loss of human lives and property: Floods in Europe in 2021

From 12 to 15 July 2021, extreme rainfall in Germany, Belgium, Luxembourg and neighbouring countries led to severe flooding. The severe flooding was caused by very heavy rainfall over a period of 1–2 d, wet conditions prior to the event and local hydrological factors. The observed rainfall amounts in the Ahr/Erft region and the Belgian part of the Meuse catchment substantially exceeded previous records for observed rainfall. An attribution study ( [[#Kreienkamp--2021|Kreienkamp et al., 2021]] ) focused on the heavy rainfall rather than river discharge and water levels, because sufficient hydrological data was not available, partly because hydrological monitoring systems were destroyed by the event. Considering a larger region of western Europe between the northern side of the Alps and the Netherlands, in any given location, one such event can be expected every 400 years on average in the current climate. The floods resulted in least 222 fatalities and substantial damage to houses, roads, communication infrastructure, motorways, railway lines and bridges.

'''Table Cross-Chapter Box DISASTER.1 |''' Summarising impacts, losses and damages, displacement and climate change detection and attribution of these seven disaster case studies.

{| class="wikitable"
|-
! Name of the disaster event

! Impacts, losses and damages; and displacement

! Climate change detection and attribution

|-
| Cyclones Idai and Kenneth, March and April 2019, Mozambique, Africa

| 254,750 affected people, and more than 45,000 houses were destroyed. Sparked cholera outbreaks that resulted in 6600 cases and over 200 deaths. More than 500,000 people were displaced in 2019. As of 31 December 2019, more than 132,000 people were internally displaced in Mozambique ( [[#IDMC--2020|IDMC, 2020]] ).

| There are no detection and attribution studies on Idai and Kenneth, but it is known that rainfall associated with tropical cyclones are now more intense because of global warming, but there remain significant uncertainties concerning changes in the number and strength of the cyclones themselves ( [[#Walsh--2019|Walsh et al., 2019]] ; Zhang G. et al., 2020).

|-
| Cyclone Amphan, May 2020, West Bengal, India and Bangladesh

| About 1600 km 2 area in the mangrove forests of Sundarbans were damaged. The city of Kolkata lost a substantial portion of its green cover due to Amphan. The estimated damage was USD 13.5 billion. Cyclone Amphan was the largest source of displacement in 2020, with 2.4 million displacements in India and a similar number in Bangladesh. Out of these 2.4 million, roughly 800,000 were pre-emptive evacuations or organised by the authorities ( [[#IDMC--2020|IDMC, 2020]] ).

| The combined decline of both aerosols (due to COVID-19-related lockdowns) and clouds may have contributed to the increased sea surface temperature, further compounding the climate change-related warming of the oceans ( [[#Vinoj--2020|Vinoj and Swain, 2020]] ). However, there are no attribution studies on tropical cyclones in the Indian Ocean.

|-
| Hurricane Harvey, 2017, USA

| Catastrophic flooding and many deaths inflicted $125 billion (2017 USD). In addition, economic costs due to the rainfall are estimated at $90 billion, of which $67 billion are attributed to climate change ( [[#Frame--2020|Frame et al., 2020]] ).

| Several attribution studies found that the rainfall associated with Harvey has increased by a factor of three, while intensity in rainfall and wind speed also increased due to human-induced climate change ( [[#Emanuel--2017|Emanuel, 2017]] ; [[#Risser--2017|Risser and Wehner, 2017]] ; [[#Patricola--2018|Patricola and Wehner, 2018]] ; [[#van%20Oldenborgh--2020|van Oldenborgh et al., 2020]] ).

|-
| Coastal Niño 2017, Peru

| USD 6–9 billion monetary losses with 114 deaths, 414 injuries and 1.08 million inhabitants affected. In addition, 6614 km of improved roads were damaged, 326 bridges destroyed, 41,632 homes destroyed or uninhabitable, and 242,433 homes, 2150 schools and 726 health centres damaged.

| Clear anthropogenic climate change fingerprint detected. For example, while the anomalously warm ocean favoured extreme rainfall of March 2017 in Peru, the human influence was estimated to make such events at least 1.5 times more likely ( [[#Christidis--2019|Christidis et al., 2019]] ).

|-
| Mega-fires in Chile, January 2017

| The mega-fire that occurred in Chile in January 2017 burned in three weeks an area close to 3500 km 2 in south-central Chile. As a result, thousands of people were displaced.

| There is no attribution study on the fires in Chile (yet). Still, there is an increasing number of attribution studies on wildfires worldwide, finding that because climate change has increased the likelihood of extreme heat, which is part of the fire weather, the likelihood of wildfire weather conditions has increased too ( [[#Krikken--2019|Krikken et al., 2019]] ; [[#van%20Oldenborgh--2020|van Oldenborgh et al., 2020]] ).

|-
| Australian bushfires of 2019/2020

| Killed 417 people due to smoke, and between 0.5 and 1.5 billion wild animals and tens of thousands of livestock. Destroyed ~5900 buildings and burnt 97,000 km 2 of vegetation that provided habitat for 832 species of native vertebrate fauna.

| Anthropogenic climate change made the extreme heat condition of December 2019 more than twice as likely ( [[#van%20Oldenborgh--2020|van Oldenborgh et al., 2020]] ).

|-
| Heatwaves of Europe, 2019

| Record heat in several European countries, and deadliest global disaster of 2019, with over 2500 deaths ( [[#CRED--2019|CRED, 2019]] )

| There have been many attribution studies on heatwaves in Europe, finding that human-induced climate change is increasing the frequency and intensity of heatwaves. In the case of 2019, the observed heat would have been extremely unlikely without climate change. The studies also find that climate models underestimate the increase in heat waves in Europe compared to observed trends ( [[#Vautard--2020|Vautard et al., 2020]] ).

|-
| Floods in western Europe (Germany, Belgium), July 2021

| Severe flooding resulting in at least 222 fatalities and substantial damage to houses, roads, communication infrastructure, motorways, railway lines and bridges. Some communities were cut off for days due to road closures, inhibiting emergency responses, including evacuation.

| Climate change was found to have increased the intensity of the maximum 1-d rainfall event in the summer season in this large region by about 3–19% compared to a global climate 1.2°C cooler than at the present day. The increase was similar for the 2-d event. The likelihood of such an event today was found to have increased by a factor between 1.2 and 9 for both the 1-d and 2-d events in the large region ( [[#Kreienkamp--2021|Kreienkamp et al., 2021]] ).

|}

Disaster risk reduction needs to be a central component of adaptation and mitigation for meeting Sustainable Development Goals and for a climate-resilient future

Disasters resulting from extreme events are increasingly experienced by a large section of human population ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ). Disasters expose inequalities in natural and managed systems and human systems as they disproportionately affect poor and marginalised communities like ethnic minorities, people of colour, Indigenous Peoples, women and children. Therefore, disaster risk reduction is fundamental for climate justice and climate resilient development ( [[#UNISDR--2015|UNISDR, 2015]] ). Far from being disconnected policy objectives, disaster risk reduction and climate change mitigation/adaptation are two sides of the same coin as recognised explicitly by the Paris Agreement and Sendai Framework of 2015. There can be no sustainable development without disaster risk reduction, as explicitly recognised by the SDGs of 2015. Furthermore, disaster events can increase awareness among citizens and provide a platform for all important stakeholders, including climate activists, to come together, and give a clarion call for the urgency of climate action.

In summary, disasters are a stark illustration of the potential for extreme weather events to impact people and other species. With the frequency, severity and (or) likelihood of several types of extreme weather increasing, disasters can increasingly be regarded as ‘the public face of climate change’ ( ''high confidence'' ). Detection and attribution studies make the climate change fingerprint of several types of disasters increasingly clear ( ''high confidence'' ). Moreover, existing vulnerabilities and exposures play an important role in turning extreme events into disasters, further exacerbating existing racial, gender and social inequalities ( ''high confidence'' ). Therefore, disaster risk reduction needs to be central to adaptation and mitigation efforts to meet the SDGs and the Paris Agreement for a climate-resilient future.

Cross-Chapter Box DISASTER

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<span id="observed-impacts-on-freshwater-ecosystems"></span>
=== 4.3.5 Observed Impacts on Freshwater Ecosystems ===

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The loss and degradation of freshwater ecosystems have been widely documented, and SRCCL assessed with ''medium confidence'' the loss of wetlands since the 1970s (Olsson et al., 2020).

The links between air and water temperatures and ecological processes in freshwater ecosystems are well recognised. Increasing temperatures affect wetlands by influencing biophysical processes, affecting feeding and breeding habits and species’ distribution ranges, including their ability to compete with others. Increased temperatures can also cause deoxygenation in the lower depths of the water columns and throughout the entire water column if heating destabilises the water column. Under extreme heat, often associated with minimal rainfall or water flows, the drying of shallower areas and the migration or death of individual organisms can occur ( [[#Dell--2014|Dell et al., 2014]] ; [[#Miller--2014|Miller et al., 2014]] ; [[#Scheffers--2016|Scheffers et al., 2016]] ; [[#Szekeres--2016|Szekeres et al., 2016]] ; [[#Myers--2017|Myers et al., 2017]] ; [[#FAO--2018a|FAO, 2018a]] ) ( ''high confidence'' ). A global systematic review of studies since 2005 shows that climate change is a critical direct driver of freshwater ecosystems impacts through increasing temperatures or declining rainfall, for example, by causing physiological stress or death (thermal stress, dehydration or desiccation), limiting food supplies, or resulting in migration of animals to other feeding or breeding areas, and possibly increased competition with animals already present in those migrating locations {Diaz et al. 2019; Dziba et al. 2018} . Other drivers include land use changes, water pollution, extraction of water, drainage and conversion, and invasive species, which to varying extents interact synergistically with climate change or are exacerbated due to climate change ( [[#Finlayson--2017|Finlayson et al., 2017]] ; [[#Ramsar%20Convention--2018|Ramsar Convention, 2018]] ).

The Global Wetland Outlook ( [[#Ramsar%20Convention--2018|Ramsar Convention, 2018]] ) reported that between 1970 and 2015, the area of freshwater wetlands declined by approximately 35% ( [[#Davidson--2018|Davidson and Finlayson, 2018]] ), with high levels of the overall percentage of threatened species recorded in Madagascar and Indian Ocean islands (43%); in Europe (36%); in the tropical Andes (35%); and New Zealand (41%) ( [[#Ramsar%20Convention--2018|Ramsar Convention, 2018]] ). Where long-term data are available, only 13% of the wetlands recorded in and around the year 1700 remained by 2000. However, these data may overestimate the rate of loss ( [[#Davidson--2014|Davidson, 2014]] ) ( ''limited evidence, medium agreement'' ). Many wetland-dependent species have seen a long-term decline, with the Living Planet Index showing that 81% of populations of freshwater species are in decline and others being threatened by extinction ( [[#Davidson--2018|Davidson and Finlayson, 2018]] ; [[#Darrah--2019|Darrah et al., 2019]] ; [[#Diaz--2019|Diaz et al., 2019]] ) ( ''high confidence'' ).

Temperature changes lead to changes in the distribution patterns of freshwater species. Poleward and up-elevation range shifts due to warming temperatures tend to ultimately lead to reduced range sizes. Freshwater species in the tropics are particularly vulnerable ( [[#Jezkova--2016|Jezkova and Wiens, 2016]] ; [[#Sheldon--2019|Sheldon, 2019]] ). Systematic shifts towards higher elevation and upstream were found for 32 stream fish species in France ( [[#Comte--2013|Comte and Grenouillet, 2013]] ). In North America, for the bull trout ( ''Salvelinus confluentus'' ) a reduction in the number of occupied sites was documented in a watershed in Montana ( [[#Eby--2014|Eby et al., 2014]] ). Other impacts include disruption of seasonal movements of migratory waterbirds that regularly visit freshwater ecosystems, with adverse impacts on their feeding and breeding ( [[#Finlayson--2006|Finlayson et al., 2006]] ; [[#Bussière--2015|Bussière et al., 2015]] ). Keystone species, such as the beaver ( ''Caster Canadensis'' ) in North America, have been moving into new areas as the vegetation structure has changed in response to higher temperatures enabling shrubs to establish in the Arctic and alpine tundra ecosystems ( [[#Jung--2016|Jung et al., 2016]] ). Increased occurrence and intensity of algal blooms have occurred due to the interactive effects of thermal extremes and low dissolved oxygen concentrations in water ( [[#Griffith--2020|Griffith and Gobler, 2020]] ) ( [[#4.2.7|Section 4.2.7]] ). A global review found that almost 90% of all studies reviewed documented a decline in salmonid populations in North America and Europe, and identified knowledge gaps elsewhere ( [[#Myers--2017|Myers et al., 2017]] ). Another review ( [[#Pecl--2017|Pecl et al., 2017]] ) found declines in Atlantic salmon in Finland and poleward shift in coastal fish species, while another review ( [[#Scheffers--2016|Scheffers et al., 2016]] ) noted hybridisation between freshwater species like invasive rainbow trout ( ''Oncorhynchus mykiss'' ) and native cutthroat trout ( ''O. clarkia'' ).

Lakes have been warming, as shown by an increasing trend of summer surface water temperatures between 1985 and 2009 of 0.34°C per decade ( [[#O’Reilly--2015|O’Reilly et al., 2015]] ). However, responses of individual lakes to warming were very dependent on local characteristics ( [[#O’Reilly--2015|O’Reilly et al., 2015]] ), with warming enhancing the impacts of eutrophication in some instances ( [[#Sepulveda-Jauregui--2018|Sepulveda-Jauregui et al., 2018]] ). For example, temperature increases led to lower oxygen concentrations in eutrophic coastal wetlands due to phytoplankton and microbial respiration ( [[#Jenny--2016|Jenny et al., 2016]] ) and stimulated algal blooms ( [[#Michalak--2016|Michalak, 2016]] ) and affected the community structure of fish and other biotas ( [[#Mantyka-Pringle--2014|Mantyka-Pringle et al., 2014]] ; [[#Poesch--2016|Poesch et al., 2016]] ).

Rising temperatures have a strong impact in the arctic zone, where the southern limit of permafrost is moving north and leading to changes in the landscape ( [[#Arp--2016|Arp et al., 2016]] ; [[#Minayeva--2018|Minayeva et al., 2018]] ). Thawing of the permafrost leads to increased erosion and runoff and changes in the geomorphology and vegetation of arctic peatlands ( [[#Nilsson--2015|Nilsson et al., 2015]] ; [[#Sun--2018b|Sun et al., 2018b]] ). Permafrost thawing has led to the expansion of lakes in the Tibetan Plateau ( [[#Li--2014|Li et al., 2014]] ). As northern high-latitude peatlands store a large amount of carbon, permafrost thawing can increase methane and carbon dioxide emissions ( [[#Schuur--2015|Schuur et al., 2015]] ; [[#Moomaw--2018|Moomaw et al., 2018]] ). This represents a major gap in our understanding of the rates of change and their consequences for freshwater ecosystems.

The extent of past degradation due to multiple drivers is important, as climate change is expected to interact synergistically and cumulatively with these ( [[#Finlayson--2006|Finlayson et al., 2006]] ), exacerbate existing problems for wetland managers and potentially increase emissions from carbon-rich wetland soils ( [[#Finlayson--2017|Finlayson et al., 2017]] ; [[#Moomaw--2018|Moomaw et al., 2018]] ). Freshwater ecosystems are also under extreme pressure from changes in land use and water pollution, with climate change exacerbating these, such as the further decline of snow cover ( [[#DeBeer--2016|DeBeer et al., 2016]] ) and increased consumptive use of fresh water, and leading to the decline, and possibly extinction, of many freshwater-dependent populations ( ''high confidence'' ). Thus, differentiating between the impacts of multiple drivers is needed, especially given the synergistic and cumulative nature of such impacts, which remains a knowledge gap.

In summary, climate change is one of the key drivers of the loss and degradation of freshwater ecosystems and the unprecedented decline and extinction of many freshwater-dependent populations. The predominant key drivers are changes in land use and water pollution ( ''high confidence'' ).

<div id="4.3.6" class="h2-container"></div>

<span id="observed-impacts-on-water-related-conflicts"></span>
=== 4.3.6 Observed Impacts on Water-Related Conflicts ===

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According to AR5, violent conflict increases vulnerability to climate change ( [[#Field--2014a|Field et al., 2014a]] ) ( ''medium evidence, high agreement'' ). Furthermore, the IPCC SRCCL ( [[#Hurlbert--2019|Hurlbert et al., 2019]] ) concluded with ''medium confidence'' that climatic stressors can exacerbate the negative impacts of conflict.

Since AR5, only a few studies focused specifically on the association between observed changes in the hydrological cycle linked to climate change and conflicts ( [[#Zografos--2014|Zografos et al., 2014]] ; [[#Dinar--2015|Dinar et al., 2015]] ) ''.'' Some studies associate conflicts with local abundance of water ( [[#Salehyan--2014|Salehyan and Hendrix, 2014]] ; [[#Selby--2014|Selby and Hoffmann, 2014]] ; [[#de%20Juan--2015|de Juan, 2015]] ), mainly because of political mobilisation around abundant waters and the need for developing new rules of allocation among competing users. Others provide evidence that the increase in water availability in some areas compared with a decrease in other surrounding areas can affect the risk of a conflict in a region ( [[#de%20Juan--2015|de Juan, 2015]] ) ( ''low to medium confidence'' ). However, the large majority acknowledges reduction of water availability due to climate change as having the potential to exacerbate tensions ( [[#de%20Stefano--2017|de Stefano et al., 2017]] ; [[#Waha--2017|Waha et al., 2017]] ), especially in regions and within groups dependent on agriculture for food production ( [[#von%20Uexkull--2016|von Uexkull et al., 2016]] ; [[#Koubi--2019|Koubi, 2019]] ) ( ''high confidence'' ). Particularly representative is the case of Syria, where drought aggravated existing water and agricultural insecurity ( [[#Kelley--2015|Kelley et al., 2015]] ). However, whether drought caused civil unrest in Syria remains highly debated ( [[#Gleick--2014|Gleick, 2014]] ; [[#Kelley--2017|Kelley et al., 2017]] ; [[#Selby--2017|Selby et al., 2017]] ; [[#Ash--2019|Ash and Obradovich, 2019]] ). Additionally, there is no consensus on the causal association between observed climate changes and conflict ( [[#Hsiang%20Solomon--2013|Hsiang Solomon et al., 2013]] ; [[#Burke--2015|Burke et al., 2015]] ; [[#Selby--2019|Selby, 2019]] ). However, evidence suggests that changes in rainfall patterns amplify existing tensions ( [[#Abel--2019|Abel et al., 2019]] ); examples include Syria, Iraq ( [[#Abbas--2016|Abbas et al., 2016]] ; [[#von%20Lossow--2016|von Lossow, 2016]] ) and Yemen ( [[#Mohamed--2017|Mohamed et al., 2017]] ) ( ''medium confidence'' ). There is also ''medium evidence'' that in some regions of Africa (e.g., Kenya, Democratic Republic of the Congo), there are links between observed water stress and individual attitude for participating in violence, particularly for the least resilient individuals ( [[#von%20Uexkull--2020|von Uexkull et al., 2020]] ) ( ''medium confidence'' ). A reverse association from conflict to climate impacts has also been observed ( [[#Buhaug--2016|Buhaug, 2016]] ). For example, conflict-affected societies cannot address climate-change impacts due to other associated vulnerabilities such as poverty, food insecurity and political instability.

For transboundary waters, the probability of inter-state conflict can both increase and decrease ( [[#Dinar--2019|Dinar et al., 2019]] ) depending on climatic variables (e.g., less precipitation) and other socioeconomic and political factors, such as low levels of economic development and political marginalisation ( [[#Koubi--2019|Koubi, 2019]] ). Climate change concerns also play a role in stimulating cooperative efforts, as in the case of the Ganges-Brahmaputra-Meghna River Basin ( [[#Mirumachi--2015|Mirumachi, 2015]] ; [[#Link--2016|Link et al., 2016]] ) ( ''medium confidence'' ). More generally, there is some evidence that when hydrological conditions change in transboundary river basins, formal agreements (e.g., water treaties or river basin organisations) can enhance cooperation ( [[#de%20Stefano--2017|de Stefano et al., 2017]] ; [[#Dinar--2019|Dinar et al., 2019]] ) ( ''medium evidence, high agreement'' ). Still, more cooperation does not necessarily reduce the risk of conflict, especially when water variability increases beyond a certain threshold ( ''low evidence, medium agreement'' ) ( [[#Dinar--2015|Dinar et al., 2015]] ; [[#Dinar--2019|Dinar et al., 2019]] ).

In summary, there is no consensus on the causal association between observed climate change and conflicts. Still, evidence exists that those tensions can be amplified depending on climatic variables and other concomitant socioeconomic and political factors.

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<span id="observed-impacts-on-human-mobility-and-migration"></span>
=== 4.3.7 Observed Impacts on Human Mobility and Migration ===

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AR5 ( [[#Adger--2014|Adger and Pulhin, 2014]] ) found links between climate change and migration in general ( ''medium evidence, high agreement'' ), but provided no assessment of climate-induced hydrological changes and migration specifically. Likewise, SRCCL ( [[#Mirzabaev--2019|Mirzabaev et al., 2019]] ; Olsson et al., 2020) and SROCC ( [[#Hock--2019b|Hock et al., 2019b]] ) noted that migration is complex and that migration decisions and outcomes are influenced by a combination of social, demographic, economic, environmental and political factors and contexts (see Cross-Chapter Box MIGRATE in Chapter 7). This chapter confirms this evidence, focusing on climate-induced hydrological changes.

Climate-induced hydrological changes can, through slow-onset (e.g., drought) or rapid-onset (e.g., flood) events, influence human mobility and migration through effects on the economy and livelihoods ( [[#Adger--2018|Adger et al., 2018]] ). There is ''medium confidence'' that climate-induced hydrological changes have affected bilateral migration ( [[#Backhaus--2015|Backhaus et al., 2015]] ; [[#Cattaneo--2016|Cattaneo and Peri, 2016]] ; [[#Falco--2019|Falco et al., 2019]] ). However, there is ''medium evidence'' and ''low agreement'' on the effects on the movements of refugees globally ( [[#Missirian--2017|Missirian and Schlenker, 2017]] ; [[#Owain--2018|Owain and Maslin, 2018]] ; [[#Abel--2019|Abel et al., 2019]] ; [[#Schutte--2021|Schutte et al., 2021]] ) ''.''

There is ''robust evidence'' that floods and droughts have, mainly through adverse impacts on agriculture ( [[#Mastrorillo--2016|Mastrorillo et al., 2016]] ; [[#Nawrotzki--2017|Nawrotzki and Bakhtsiyarava, 2017]] ; [[#Bergmann--2021|Bergmann et al., 2021]] ; [[#Zouabi--2021|Zouabi, 2021]] ) ( [[#4.6.2|Section 4.6.2]] ), both increased and decreased the risk of temporary or permanent migration ( [[#Obokata--2014|Obokata et al., 2014]] ; [[#Afifi--2016|Afifi et al., 2016]] ; [[#Thiede--2016|Thiede et al., 2016]] ; [[#Murray-Tortarolo--2021|Murray-Tortarolo and Salgado, 2021]] ; [[#Wesselbaum--2021|Wesselbaum, 2021]] ). However, migration effects depend on the nature of the hydrological change, for example, whether it is a slow-onset or rapid-onset event ( [[#Kaczan--2020|Kaczan and Orgill-Meyer, 2020]] ), the perception of change ( [[#Koubi--2016|Koubi et al., 2016]] ; [[#de%20Longueville--2020|de Longueville et al., 2020]] ) or the socioeconomic situation of the affected communities ( [[#Ocello--2015|Ocello et al., 2015]] ; [[#Afifi--2016|Afifi et al., 2016]] ; [[#Thiede--2016|Thiede et al., 2016]] ) ( ''robust evidence; medium agreement'' ).

The Internal Displacement Monitoring Centre (IDMC) estimates that an average of 12 million new displacements happen each year due to droughts and floods alone. By the end of 2020, there were 7 million people displaced due to natural disasters, including drought and floods ( [[#IDMC--2020|IDMC, 2020]] ). Furthermore, household water insecurity has also been singled out as a driver of migration, given its physical, mental health and socioeconomic effects ( [[#Stoler--2021|Stoler et al., 2021]] ) ( ''medium confidence'' ).

More research is needed to understand better the contexts in which climate-induced hydrological changes affect the likelihood of migration or alter existing patterns ( [[#Obokata--2014|Obokata et al., 2014]] ; [[#Gray--2016|Gray and Wise, 2016]] ; [[#Cattaneo--2019|Cattaneo et al., 2019]] ).

In summary, climate-induced hydrological changes can increase and decrease the likelihood of migration ( ''robust evidence, medium agreement'' ). The outcome is determined mainly by the socioeconomic, political and environmental context ( ''medium confidence'' ).

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<span id="observed-impacts-on-the-cultural-water-uses-of-indigenous-peoples-local-communities-and-traditional-peoples"></span>
=== 4.3.8 Observed Impacts on the Cultural Water Uses of Indigenous Peoples, Local Communities and Traditional Peoples ===

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AR5 concluded with ''high confidence'' that the livelihoods and cultural practices of the diverse Indigenous Peoples of the Arctic have been impacted by climate change ( [[#Larsen--2014|Larsen et al., 2014]] ). SROCC found with ''high confidence'' that cryospheric and associated hydrological changes have affected culturally significant terrestrial and freshwater species and ecosystems in high-mountain and polar regions, thus impacting residents’ livelihoods and cultural identity, including Indigenous Peoples ( [[#Hock--2019b|Hock et al., 2019b]] ; [[#IPCC--2019a|IPCC, 2019a]] ; [[#Meredith--2019|Meredith et al., 2019]] ). SROCC also concluded that IKLK are vital in determining community responses to environmental risk. The report further noted that IKLK helps increase adaptive capacity and reduces long-term vulnerability, but did not assess climate-related impacts on cultural water uses on low-lying islands ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ).

Freshwater (including ice and snow) has diverse meanings and symbolic representations, as well as associated practices, management and reciprocal responsibilities for many Indigenous Peoples, local communities and traditional peoples ( [[#Cave--2016|Cave and McKay, 2016]] ; [[#Craft--2018|Craft, 2018]] ; [[#Hansen--2018|Hansen and Antsanen, 2018]] ; [[#Ngata--2018|Ngata, 2018]] ; Chiblow 2019; [[#Wilson--2019|Wilson et al., 2019]] ; [[#Moggridge--2021|Moggridge and Thompson, 2021]] ). Climate-driven hydrological changes are affecting culturally significant terrestrial and freshwater species and ecosystems, particularly for Indigenous Peoples, local communities and traditional peoples in the Arctic, high mountain areas, and small islands ( ''high confidence'' ). These climate impacts on cultural water uses are influencing travel, hunting, herding, fishing and gathering practices, which have negative implications for livelihoods, cultural traditions, economies and self-determination (Table 4.5).

'''Table 4.5 |''' Selected observed impacts on cultural water uses of Indigenous Peoples (also see Figure 4.6).

{| class="wikitable"
|-
! '''Region'''

! '''Indigenous Peoples'''

! '''Climate hazard'''

! '''Water-related impact'''

! '''Situated knowledge'''

! '''Reference'''

|-
| Asia

| Manangi

| Increased temperatures; increased precipitation

| Glacier retreat; decreased permanent snow cover

| Manangi villagers reported a deep sense of spiritual loss associated with the decline of mountain snows and the receding glacier, which some attributed to a lack of spiritual devotion.

| [[#Konchar--2015|Konchar et al. (2015)]] ; [[#Mukherji--2019|Mukherji et al. (2019)]]

|-
| Asia

| Gurung

| Increased temperatures

| Decreasing snow; increased snowmelt

| Indigenous Gurung herders reported water scarcity in traditional water sources such as streams and wells along traditional livestock migration routes. As a result of these changes, they have altered their routes and camp locations.

| [[#Popular--2016|Popular and Rik (2016)]]

|-
| Asia

| Dokpa

| Increased temperatures

| Decreasing snowfall

| Dokpa herders reported that pasture conditions have deteriorated due to shallower snowpack, shorter winters and erratic rainfall, which has impacted sheep populations. As a result of these changes, Dokpa herders are replacing traditionally important sheep with yaks, which are more tolerant to poor-quality pasturage.

| [[#Ingty--2017|Ingty (2017)]]

|-
| Asia

| Jagshung pastoralists

| Increased temperatures

| Glacier melt

| Due to the expansion of the majority of large lakes on the Tibetan Plateau, herders in Jagshung Village have lost large areas of pastures to inundation. As a result, the quality of nearby feed has also deteriorated, which has led to reduced livestock populations and productivity.

| Nyima and Hopping (2019)

|-
| Central and South America

| Aymara

| Increased temperatures

| Glacier loss

| Decreasing rain and snow have led to degraded and dry peatland pastures ( ''bofedales'' ). This reduction of pasture contributes to out-migration, over-grazing and the loss of ancestral practices and community commitment to pasture management (Table 12.5).

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

|-
| Central and South America

| Quelcaya pastoralists

| Increased temperatures; reduced rainfall; increasing precipitation variability

| Decreased snow and ice

| Pastoralists reported water scarcity in traditional water sources along migration routes. As a result, women pastoralists had to herd livestock farther to find water. Pastoralists also reported the deterioration of pasture due to decreasing water availability (Table 12.5).

| [[#Postigo--2020|Postigo (2020)]]

|-
| Europe

| Saami

| Increased winter temperature; Increased summer precipitation

| Harder and deeper snow cover; increased ice formation; flooding rivers and wet ground

| Changes in the quality of winter pastures (especially decreased access to forage and the amount of forage) have increased the number of working hours and altered reindeer herding practices.

Rainy summers increase the difficulty of gathering and moving reindeer to round-up sites and limit hay production for supplementary winter feed (13.8.1.2).

| [[#Forbes--2019|Forbes et al. (2019)]] ; [[#Rasmus--2020|Rasmus et al. (2020)]]

|-
| North America

| Kashechewan First Nation

| Increased temperatures

| Flooding

| The timing and extent of spring flooding have changed, which, combined with inadequate infrastructure, have increased the frequency and risk of flooding for the Kashechewan community.

Earlier snowmelt has also affected the migration patterns of migratory birds and reduced the duration of traditional hunting and harvesting camps for culturally important species (14.4.6.7, 14.4.7.1).

| Khalafzai et al. (2019)

|-
| North America

| Inuit

| Increased temperatures (an average of 2.18°C from 1985 to 2016)

| Changing ice conditions

| Trail access models showed that overall land and water trail access in the Inuit Nunangat had been minimally affected by temperature increase between 1985 and 2016. However, these findings illustrate that although Inuit are developing new trails and alternative forms of transport, these changes could negatively impact cultural identity and well-being (14.4.6.7, 14.4.7.1).

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

|-
| North America

| Inuit

| Increased temperatures; increased precipitation

| Early snowmelt

| Inuit in Labrador, Canada, are grieving the rapid decline of culturally significant caribou, which is partly due to rising temperatures in the circumpolar north and the associated changes to caribou habitat and migration. In addition, the decline of this species is negatively affecting their sense of cultural identity because of the importance of hunting and cultural continuity (14.4.6.7, 14.4.7.1).

| [[#Cunsolo--2020|Cunsolo et al. (2020)]]

|-
| North America

| Alaskan Natives

| Increasing temperatures

| Increasing temperature of freshwater lakes; permafrost melt; thinning ice

| In Alaska, permafrost melting and the shorter ice season make it more difficult for hunters to access traditional hunting grounds. Increased temperatures are changing the habitats and migration patterns of culturally important freshwater species. Declining fish health and populations threaten requirements of treaty rights and tribal shares of harvestable fish populations 14.4.6.7, 14.4.7.1.

| [[#Albert--2018|Albert et al. (2018)]] ; Norton-Smith et al. (2016)

|-
| Small islands

| iTaukei

| Sea level rise

| Flooding, inundation and salt-water intrusion

| The village of Vunidogola was relocated in response to inundation, storm surges and flooding, which villagers found emotionally and spiritually distressing. Although the village was relocated as a single unit and on customary lands, the shift away from the coast has impacted spiritual relationships, as the ocean is an integral part of village culture (15.6.5).

| Charan et al. (2017); [[#Piggott-McKellar--2019a|Piggott-McKellar et al. (2019a)]]

|-
| Small islands

| iTaukei

| Sea level rise

| Coastal erosion; inundation

| Villagers of Viti Levu reported their grief at the potential loss of their traditions and livelihoods. In addition, they are concerned as to how climate change is affecting their cosmology and cultural traditions and understand possible relocation as another source of cultural loss (15.6.5).

| [[#du%20Bray--2017|du Bray et al. (2017)]] ; McNamara et al. (2021)

|-
| Small islands

| Funafuti

| Sea level rise

| Coastal erosion; inundation

| In addition to climate impacts and stresses affecting Tuvalu, the potential for further environmental hardships in the future exacerbated worry and distress for local people, who are anxious about future cultural loss arising from sea level rise (15.6.5).

| Gibson et al. (2019); [[#Yates--2021|Yates et al. (2021)]] ; McNamara et al. (2021)

|}

Some of these losses may be classified as non-economic losses and damages, such as loss of culture and traditions ( [[#Thomas--2018b|Thomas and Benjamin, 2018b]] ; [[#McNamara--2021|McNamara et al., 2021]] ). The vulnerability of these cultural uses to climate change is exacerbated by historical and ongoing processes of colonialism and capitalism, which dispossessed Indigenous Peoples and disrupted culturally significant multi-species relationships ( [[#Whyte--2017|Whyte, 2017]] ; [[#Whyte--2018|Whyte, 2018]] ; [[#Wilson--2019|Wilson et al., 2019]] ; [[#Whyte--2020|Whyte, 2020]] ; [[#Rice--2021|Rice et al., 2021]] ) (14.4.7.3; 9.13.2.4). Despite these significant structural barriers, there is ''medium confidence'' that some Indigenous Peoples, local communities and traditional peoples are adapting to the risks of climate-driven hydrological changes to cultural water uses and practices ( [[#4.6.9|Section 4.6.9]] ).

There is ''high confidence'' that the prospect of loss (anticipatory grief) due to climate-related hydrological change, such as inundation or relocation, affects Indigenous Peoples, local communities and traditional peoples. These communities are especially susceptible to detrimental mental health impacts because of the implications of climate change for their cultural, land-based practices ( [[#du%20Bray--2017|du Bray et al., 2017]] ). For example, fears of cultural loss in Tuvalu ( [[#Gibson--2019|Gibson et al., 2019]] ) are resulting in worry, anxiety and sadness among local people, with similar responses reported in Fiji and other Pacific islands ( [[#du%20Bray--2017|du Bray et al., 2017]] ; [[#Yates--2021|Yates et al., 2021]] ) (Box 15.1).

There is ''high confidence'' that glacier retreat and increasing glacier runoff variability are negatively affecting cultural beliefs and practices in high-mountain areas. For example, the loss of glaciers threatens the ethnic identity of the Indigenous Manangi community of the Annapurna Conservation Area of Nepal ( [[#Konchar--2015|Konchar et al., 2015]] ; [[#Mukherji--2019|Mukherji et al., 2019]] ). Likewise, ice loss in the Cordillera Blanca in the Peruvian Andes has challenged traditional approaches of interacting with the glaciers ( [[#Motschmann--2020|Motschmann et al., 2020]] ) ( [[#4.2.2|Section 4.2.2]] ). There is ''high confidence'' that cryospheric changes in high-mountain areas also impact traditional pastoral practices by altering seasonal conditions, pasture quality and water availability. For example, pasture quality in India ( [[#Ingty--2017|Ingty, 2017]] ); Tibet Autonomous Region, People’s Republic of China ( [[#Nyima--2019|Nyima and Hopping, 2019]] ); and Bolivia ( [[#Yager--2019|Yager et al., 2019]] ) has been negatively impacted by climate-related hydrological changes, leading some Indigenous herders to diversify livestock, while herders in Nepal ( [[#Popular--2016|Popular and Rik, 2016]] ) and Peru ( [[#Postigo--2020|Postigo, 2020]] ) have altered their routes in response to local water scarcity. Local communities in high-mountain areas understand these hydrological changes through cultural and spiritual frameworks ( ''medium evidence, high agreement'' ). For instance, in the Peruvian Andes and the Hindu Kush Himalaya, changing ice is attributed to a lack of spiritual devotion ( [[#Drenkhan--2015|Drenkhan et al., 2015]] ; [[#Konchar--2015|Konchar et al., 2015]] ; [[#Scoville-Simonds--2018|Scoville-Simonds, 2018]] ). Communities in the Peruvian Andes also interpret climate impacts in the broader context of socioeconomic and political injustice and inequality ( [[#Drenkhan--2015|Drenkhan et al., 2015]] ; [[#Paerregaard--2018|Paerregaard, 2018]] ).

In polar areas, there is ''high confidence'' that the appearance of land previously covered by ice, changes in snow cover, and thawing permafrost are contributing to changing seasonal activities. These include changes in accessibility, abundance and distribution of culturally important plant and animal species. These changes are harming the livelihoods and cultural identity of Indigenous Peoples, local communities and traditional peoples. In northern Fennoscandia, for example, reindeer herders reported experiences of deteriorated foraging conditions due to changes in the winter climate ( [[#Forbes--2019|Forbes et al., 2019]] ; [[#Rasmus--2020|Rasmus et al., 2020]] ). In addition, Inuit and First Nations communities in Canada ( [[#Ford--2019|Ford et al., 2019]] ; [[#Khalafzai--2019|Khalafzai et al., 2019]] ) and Alaskan Natives and Native American communities in the USA (Norton-Smith et al., 2016) identified disruption to access routes to traditional hunting grounds and climate-related stresses to culturally important species.

Further research is needed to provide culturally informed integrated assessments of climate change impacts on Indigenous Peoples’, local communities’ and traditional uses of water in the context of multiple stresses, disparities and inequities ( [[#Yates--2021|Yates et al., 2021]] ). In the Arctic, for example, increased rates of development and resource extraction, including hydropower dams, mining, fisheries and sport hunting, all threaten water quality, habitat condition and the ecosystem services provided by Arctic freshwaters ( [[#Mustonen--2016|Mustonen and Mustonen, 2016]] ; [[#Knopp--2020|Knopp et al., 2020]] ).

In summary, the cultural water uses of Indigenous Peoples, local communities and traditional peoples are being impacted by climate change ( ''high confidence'' ), with implications for cultural practices and food and income security, particularly in the Arctic, high-mountain areas and small low-lying islands.

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