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

=== 4.2.5 Observed Changes in Droughts ===

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There are different types of droughts, and they are interconnected in terms of processes ( [[#Douville--2021|Douville et al., 2021]] ). ''Meteorological droughts'' (periods of persistent low precipitation) propagate over time into deficits in soil moisture, streamflow and water storage, leading to a reduction in water supply ( ''hydrological drought'' ). Increased atmospheric evaporative demand increases plant water stress, leading to ''agricultural and ecological drought.''

Hydrological drought can result in shortages of drinking water and cause substantial economic damages. Agricultural drought threatens food production through crop damage and yield decreases (e.g., [[#4.3.1|Section 4.3.1]] ) ( ''high confidence'' ) and consequent economic impacts (Table 4.4). For example, drought in India in 2014 was reported to have led to an estimated USD 30 billion in losses ( [[#Ward--2018|Ward and Makhija, 2018]] ). Ecological drought increases the risks of wildfire (Table 4.4). Cascading effects of droughts can include health issues triggered by a lack of sanitation ( [[#4.3.3|Section 4.3.3]] ); can cause human displacements and loss of social ties, sense of place and cultural identity; and migration to unsafe settlements ( ''medium confidence'' ) ( [[#Serdeczny--2017|Serdeczny et al., 2017]] ) ( [[#4.3.7|Section 4.3.7]] ). Between 1970 and 2019, only 7% of all disaster events were drought-related, yet they contributed disproportionately to 34% of disaster-related death, mostly in Africa ( [[#WMO--2021|WMO, 2021]] ). Nevertheless, IK, TK and LK have increased drought resilience among crop and livestock farmers, for example, in South Africa ( [[#Muyambo--2017|Muyambo et al., 2017]] ), Uganda ( [[#Mfitumukiza--2020|Mfitumukiza et al., 2020]] ) and India ( [[#Patel--2020|Patel et al., 2020]] ) ( [[#4.8.4|Section 4.8.4]] ).

'''Table 4.4 |''' Selected major drought events from 2013 to 2020 and their societal impact. Studies were selected for presentation based on the availability scientific literature impacts information and do not necessarily represent the most severe events.Impactful events are included even if not found to have a component attributable to climate change. This is not a systematic assessment of event attributions studies and their physical science conclusions. ‘Sign of influence’ indicates whether anthropogenic climate change was found to have made the event more or less likely , and ‘mechanism/magnitude of influence’ quantifies the change in likelihood and the processes or quantities involved.

{| class="wikitable"
|-
! rowspan="2"| Year

! rowspan="2"| Country/region

! rowspan="2"| Impact

! colspan="2"| Influence of anthropogenic climate change on the likelihood of an event

! Reference

|-
! ''Sign of influence''

! ''Mechanism/magnitude of influence''

!
|-
| 2019/2020

| Australia

| Wildfires burning ~97,000 km 2 across southern and eastern Australia; 34 human fatalities; 5900 buildings destroyed; millions of people affected by hazardous air quality; between 0.5 and 1.5 billion wild animals and tens of thousands of livestock killed; at least 30% of habitat affected for seventy taxa, including 21 already listed as threatened with extinction, over USD 110 billion financial loss

| Increase

| Extreme high temperatures causing drying of fuel. The likelihood of extreme heat at least doubled due to the long-term warming trend, and the likelihood of Fire Weather Index as severe or worse as observed in 2019/2020 by at least 30%, despite no attributable increase in meteorological (precipitation) drought.

| [[#van%20Oldenborgh--2020|van Oldenborgh et al. (2020)]] ; [[#Ward--2020|Ward et al. (2020)]] ; [[#Haque--2021|Haque et al. (2021)]]

|-
| rowspan="5"| 2019

| Western Cape, South Africa

| Water supply was reduced to 20% of capacity in January 2018. Agricultural yields in 2019 declined by 25%.

| Increase

| Anthropogenic greenhouse forcing at least doubled the likelihood of drought levels seen in 2015–2019, offsetting anthropogenic aerosol forcing.

| [[#Kam--2021|Kam et al. (2021)]]

|-
| Yunnan, southwestern China

| Water scarcity affected nearly 7 million residents and resulted in crop failure over at least 1.35 × 10 4 km 2 cropland. More than 94% of the total area in the province was drought-stricken, and around 2 million people faced drinking water shortages, with a direct economic loss of about 6.56 billion RMB.

| Increase

| Anthropogenic influence increased the risk of 2019 March–June hot and dry extremes over Yunnan province in southwestern China by 123–157% and 13–23%, respectively.

| Wang et al. (2021b)

|-
| Southwestern China

| Over 640,100 hectares of crops with rice, corn and potatoes were extensively damaged. Over 100 rivers and 180 reservoirs dried out. Over 824,000 people and 566,000 head of livestock experienced a severe lack of drinking water, with a direct economic loss of 2.81 billion Chinese yuan (USD 400 million).

| Increase

| Anthropogenic forcing has likely increased the likelihood of the May–June 2019 severe low-precipitation event in southwestern China by approximately 1.4 to 6 times.

| [[#Lu--2021|Lu et al. (2021)]]

|-
| South China

| A lightning-caused forest fire in Muli County killed 31 firefighters and burned about 30 ha of forest.

| Increase

| Anthropogenic global warming increased the weather-related risk of extreme wildfire by 7.2 times. In addition, the El Niño event increased risk by 3.6 times.

| Du et al. (2021)

|-
| Middle and lower reaches of the Yangtze River, China

| Reduced agriculture productivity and increased load on power system supplies and transportations, and on human health

| Decrease

| Anthropogenic forcing reduced the probability of rainfall amount in the extended rainy winter of 2018/2019 by ~19%, but exerted no influence on the excessive rainy days.

| Hu et al. (2021)

|-
| 2018

| South China

| Shrinking reservoirs, water shortages. Area and yield for early rice reduced by 350 thousand hectares and 1.28 million tons relative to 2017

| Increase

| Likelihood increased by 17 times in the HadGEM3-A model. However, the event did not occur without human influence in the CAM5 model.

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

|-
|
| China (Beijing)

| A record 145 consecutive dry days (CDD), severe drought, increased risk of wildfires

| Increase

| The likelihood of the record 145 CDD was increased by between 1.29 and 2.09 times by anthropogenic climate change and between 1.43 and 4.59 times by combining the La Niña event and a weak Arctic polar vortex.

| Du et al. (2021)

|-
| rowspan="2"| 2017

| USA (Northern Great Plains)

| “billion-dollar disaster”; widespread wildfires (one of Montana’s worst wildfire seasons on record) compromised water resources, destruction of property, livestock sell-offs, reduced agricultural production, agricultural losses of USD 2.5 billion

| Increase

| 1.5 times more likely due to increased ET (minimal anthropogenic impact on precipitation)

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

|-
| East Africa

| Extensive drought across Tanzania, Ethiopia, Kenya and Somalia contributed to extreme food insecurity approaching near-famine conditions.

| Increase

| Likelihood doubled

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

|-
| 2016

| Southern Africa

| Millions of people were affected by famine, disease and water shortages. In addition, a 9-million-tonne cereal deficit resulted in 26 million people in need of humanitarian assistance.

| Increase

| Anthropogenic climate change ''likely'' increased the intensity of the 2015/2016 El Niño, and a drought of this severity would have been very unlikely (probability ~9%) in the pre-industrial climate.

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

|-
| 2016

| Brazil

| Três Marias, Sobradinho, and Itaparica reservoirs reached 5% of volume capacity. Ceará registered 39 (of 153) reservoirs empty. Another 42 reached inactive volume; 96 (of 184) Ceará municipalities experienced water supply interruption.

| Not found

| Not found

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

|-
| 2016

| Thailand

| Severe drought affected 41 Thai provinces, had devastating effects on major crops, such as rice and sugar cane, and incurred a total loss in the agricultural production of about half a billion USD.

| Increase

| The record temperature of April 2016 in Thailand would not have occurred without the influence of both anthropogenic forcings and El Niño. Anthropogenic forcing has contributed to drier Aprils, but El Niño was the dominant cause of low rainfall.

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

|-
| 2015

| Washington state, USA

| USD 335 million loss for the agricultural industry

| Increase

| Snowpack drought resulted from exceedingly high temperatures despite normal precipitation

| Fosu et al. (2016)

|-
| 2014

| São Paulo, Brazil

| In January 2015, the largest water supply system used for Sao Paulo, Cantareira, sank to a water volume of just 5% of capacity, and the number of people supplied fell from 8.8 million people to 5.3 million people, with other systems taking over supplies for the remainder.

| No impact

| Anthropogenic climate change is not found to be a major influence on the hazard, whereas increasing population and water consumption increased vulnerability.

| [[#Otto--2015|Otto et al. (2015)]]

|-
| 2014

| Southern Levant, Syria

| While the extent to which the 2007/2008 drought in the Levant region destabilised the Syrian government was not clear, ‘there is no questioning the enormous toll this extreme event took on the region’s population. The movement of refugees from both the drought and war-affected regions into Jordan and Lebanon ensured that the anomalously low precipitation in the winter of 2013/2014 amplified impacts on already complex water and food provisions.’

| Increase

| The persistent drought in the 2014 rainy season

was unprecedented for the critical January–February period in the observational

record, and was made ~45% more likely by anthropogenic climate change.

| [[#Bergaoui--2015|Bergaoui et al. (2015)]]

|-
| 2013–2014

| Mediterranean coastal Middle East, northward through Turkey and eastward through Kazakhstan, Uzbekistan and Kyrgyzstan

| The eastern (main) basin of the Aral Sea dried up for the first time in modern history.

| Unclear

| High western Pacific sea surface temperatures (SSTs) linked to drought in the Middle East and central-southwest Asia, and the SSTs in that region showed a strong warming trend.

| [[#Barlow--2015|Barlow and Hoell (2015)]]

|-
| 2014

| East Africa

| Some isolated food security crises

| Increase

| Anthropogenic warming contributed to the 2014 East African drought by increasing East African and west Pacific temperatures, and increasing the gradient between standardised western and central Pacific SST, causing reduced rainfall, ET and soil moisture.

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

|}

When hazard, vulnerability and exposure are considered together, drought risk is lower for sparsely populated regions, such as tundra and tropical forests, and higher for populated areas and intensive crop and livestock farming regions, such as southern and central Asia, southeastern South America, central Europe and the southeastern USA (Figure 4.9). Dynamics in exposure and vulnerability are rarely addressed ( [[#Jurgilevich--2017|Jurgilevich et al., 2017]] ; [[#Hagenlocher--2019|Hagenlocher et al., 2019]] ). Quantifying economic vulnerability to drought in terms of damages as a percentage of exposed GDP, [[#Formetta--2019|Formetta and Feyen (2019)]] show a disproportionate burden of drought impact on low-income countries, but with a clear decrease in global economic drought vulnerability between 1980–1989 and 2007–2016, including a convergence between lower-income and higher-income countries due to stronger vulnerability reduction in less-developed countries. Nevertheless, during 2007–2016, economic vulnerability to drought was twice as high in lower-income countries compared to higher-income countries ( [[#Formetta--2019|Formetta and Feyen, 2019]] ).

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[[File:81039aa792ccfc2cbcb954f48dfc8b65 IPCC_AR6_WGII_Figure_4_009.png]]

'''Figure 4.9 |''' '''Current global drought risk and its components.'''

'''(a)''' Drought hazard computed for the events between 1901 and 2010 by the probability of exceedance the median of global severe precipitation deficits, using precipitation data from the Global Precipitation Climatology Centre (GPCC) for 1901–2010.

'''(b)''' Drought vulnerability is derived from an arithmetic composite model combining social, economic and infrastructural factors proposed by the United Nations International Strategy for Disaster Risk Reduction ( [[#UNISDR--2004|UNISDR, 2004]] ).

'''(c)''' Drought exposure computed at the sub-national level with the non-compensatory Data Envelopment Analysis (DEA) model ( [[#Cook--2014|Cook et al., 2014]] ).

'''(d)''' Drought risk based on the above components of hazard, vulnerability and exposure, scored on a scale of 0 (lowest risk) to 1 (highest risk) with the lowest and highest hazard, exposure and vulnerability ( [[#Carrão--2016|Carrão et al., 2016]] ).

AR6 WGI ( [[#Douville--2021|Douville et al., 2021]] ; [[#Seneviratne--2021|Seneviratne et al., 2021]] ) found that increasing agricultural and ecological droughts trends are more evident than increasing trends in meteorological drought in several regions due to increased evaporative demand. Therefore, WGI concluded with ''high confidence'' that the increased frequency and the severity of agricultural/ecological droughts over the last decades in the Mediterranean and western North America can be attributed to anthropogenic warming.

In addition, there is ''high confidence'' in anthropogenic influence on increased meteorological drought in southwestern Australia and ''medium confidence'' that recent drying and severe droughts in southern Africa and southwestern South America can be attributed to human influence. Increased agricultural/ecological and (or) meteorological and (or) hydrological drought is also seen with either ''medium confidence'' or ''high confidence'' in the trend but with ''low confidence'' on attribution to anthropogenic climate change in western, northeastern and central Africa; central, eastern and southern Asia; eastern Australia; southern and northeastern South America and the South American monsoon region; and western and central Europe. Finally, decreased drought in one or more categories is seen with ''medium confidence'' in western and eastern Siberia; northern and central Australia; southeastern South America; central North America and northern Europe, but with ''low confidence'' in attribution to anthropogenic influence, except in northern Europe, where anthropogenic influence on decreased meteorological drought is assessed with ''medium confidence'' .

Major drought events worldwide have had substantial societal and ecological impacts, including reduced crop yields, shortages of drinking water, wildfires causing deaths of people and very large numbers of animals, impacting the habitats of threatened species, and widespread economic losses (Table 4.4, Cross-Chapter Box DISASTER in Chapter 4). In addition, anthropogenic climate change was found to have increased the likelihood or severity of most such events examined in event attribution studies.

Although long-term drought trends are clearer for agricultural or ecological drought compared to meteorological droughts ( [[#Douville--2021|Douville et al., 2021]] ; [[#Seneviratne--2021|Seneviratne et al., 2021]] ), most attribution studies for individual extreme events focus on meteorological (precipitation) drought and sometimes also consider temperature anomalies. A complete examination of drought relevant to societal impacts often requires consideration of hydrological and agricultural drought, so extreme event attribution conclusions relating to precipitation alone may not fully capture the processes leading to societal effects. There is, therefore, a critical knowledge gap in the attribution of changes in drought indicators more closely related to societal impacts such as soil moisture and the availability of fresh water supplies.

In summary, droughts can have substantial societal impacts ( ''virtually certain'' ), and agricultural and ecological drought conditions in particular have become more frequent and severe in many parts of the world but less frequent and severe in some others ( ''high confidence'' ). Drought-induced economic losses relative to GDP are approximately twice as high in lower-income countries compared to higher-income countries, although the gap has narrowed since the 1980s, and at the global scale there is a decreasing trend of economic vulnerability to drought ( ''medium confidence'' ). Nevertheless, anthropogenic climate change has contributed to the increased likelihood or severity of drought events in many parts of the world, causing reduced agricultural yields, drinking water shortages for millions of people, increased wildfire risk, loss of lives of humans and other species and loss of billions of dollars of economic damages ( ''medium confidence'' ).

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