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

=== 4.2.4 Observed Changes in Floods ===

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AR6 WGI [[IPCC:Wg2:Chapter:Chapter-11|Chapter 11]] ( [[#Seneviratne--2021|Seneviratne et al., 2021]] ) assessed with ''high confidence'' the increase in the extreme precipitation and associated increase in the frequency and magnitude of river floods. However, there is ''low confidence'' in changes in the river flooding regionally, which is strongly dependent upon complex catchment characteristics and land use patterns. SROCC ( [[#Hock--2019b|Hock et al., 2019b]] ) summarised with ''high confidence'' that changes in the cryosphere have led to changes in frequency, magnitude and location of rain-on-snow floods, snowmelt floods and glacier-related floods.

There is ''high confidence'' that the frequency and magnitude of river floods have changed in the past several decades in some regions mentioned below (and in WGI 11.5.2; SM4.1) with impacts across human and natural systems ( [[#4.3|Section 4.3]] ). A global flood database based on ''in situ'' measurement and satellite remote-sensing during 1985–2015 show that floods have increased 4-fold and 2.5-fold in the tropics and northern mid-latitudes, respectively ( [[#Najibi--2018|Najibi and Devineni, 2018]] ). Estimates of flood exposure using satellite-derived inundation area and high-resolution population data showed a 20–24% increase during 2000–2018 ( [[#Tellman--2021|Tellman et al., 2021]] ). Analyses of ''in situ'' streamflow measurement showed both increases and decreases in the frequency of river floods for 1960–2010 in Europe ( [[#Berghuijs--2017a|Berghuijs et al., 2017a]] ; [[#Blöschl--2019a|Blöschl et al., 2019a]] ) and the USA ( [[#Berghuijs--2017a|Berghuijs et al., 2017a]] ), an overall increase in China, Brazil and Australia ( [[#Berghuijs--2017a|Berghuijs et al., 2017a]] ) but decrease in some areas in the Mediterranean ( [[#Tramblay--2019|Tramblay et al., 2019]] ) and southern Australia ( [[#Ishak--2013|Ishak et al., 2013]] ; [[#Do--2017|Do et al., 2017]] ). Warming in the last 40–60 years has led to a 1–10-d earlier per decade spring flood occurrence depending on the location (the most frequent being 2–4 d per decade) ( ''high confidence'' ) (Yang L. et al., 2015; [[#Blöschl--2017|Blöschl et al., 2017]] ; [[#Dudley--2017|Dudley et al., 2017]] ; [[#Solander--2017|Solander et al., 2017]] ; [[#Rokaya--2018|Rokaya et al., 2018]] ; [[#Kireeva--2020|Kireeva et al., 2020]] ).

Between 1970 to 2019, 44% of all disasters and 31% of all economic losses were flood related ( [[#WMO--2021|WMO, 2021]] ). Observed flood risks changes in recent decades are often caused by human factors such as increased urbanisation and population growth rather than climate change alone ( [[#Tramblay--2019|Tramblay et al., 2019]] ). There is ''medium confidence'' that flood vulnerability varies among various regions and countries ( [[#Jongman--2012|Jongman et al., 2012]] ; [[#Scussolini--2016|Scussolini et al., 2016]] ; [[#Tanoue--2016|Tanoue et al., 2016]] ) (Figure 4.8), reflecting differences in GDP, severity and characteristics of hazard and political and social conditions ( [[#Rufat--2015|Rufat et al., 2015]] ). Flood vulnerability has decreased with economic development in many regions, while increased exposure has elevated risk in some places ( [[#Mechler--2016|Mechler, 2016]] ; [[#Tanoue--2016|Tanoue et al., 2016]] ). Global annual mean expected damage considering the current flood protection standard is estimated to be USD 54 million under the climate of 1976–2005 and unevenly distributed ( [[#Alfieri--2017|Alfieri et al., 2017]] ). Similar estimation using different models shows an increase of flood exposure in the past (USD 31 million for 1971–1990 and USD 45 million for 1991–2010 without population change as fixed in 2010) ( [[#Tanoue--2016|Tanoue et al., 2016]] ) ( [[#4.7.5|Section 4.7.5]] ).

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[[File:6ae64e0f771d823153bf40630200917f IPCC_AR6_WGII_Figure_4_008.png]]

'''Figure 4.8 |'''

'''(a)''' Modelled mean global fluvial flood water depth ( [[#Tanoue--2016|Tanoue et al., 2016]] ; [[#Tanoue--2021|Tanoue et al., 2021]] ) based on a land surface model and a river and inundation model driven by reanalysis climate forcing of five CMIP5 GCMs (metres). The annual maximum daily river water was allocated along elevations, and inundation depth was calculated for each year and averaged for the target period.

'''(b)''' Local flood protection standard (return period) at sub-country scale ( [[#Scussolini--2016|Scussolini et al., 2016]] ) based on published reports and documents, websites and personal communications with experts. Note that the vulnerability of this map reflects local flood protection such as complex infrastructure and does not fully reflect the other source of vulnerabilities, including exposure.

'''(c)''' Population distribution per 30 arc second grid cell ( [[#Klein%20Goldewijk--2010|Klein Goldewijk et al., 2010]] ; [[#Klein%20Goldewijk--2011|Klein Goldewijk et al., 2011]] ).

'''(d)''' Population exposed to flood (number of people where inundation occurs) per 30 arc-second grid cell. Population under inundation depth &gt; 0 m (a) was counted when the return period of annual maximum daily river water exceeds the flood protection standard (c) calculated by the authors. All values are averages for the period 1958–2010 for the past and 2050–2070 for the future.

The link between rainfall and flooding is complex. While observed increases in extreme precipitation have increased the frequency and magnitude of pluvial floods and river floods in some regions, floods could decrease in some regions due to other factors. These factors could include soil wetness condition, cryospheric change, land cover change and river system management, adaptation measures or water usage within the river basin (WGI FAQ8.2). For example, in the USA and Europe, a study indicated that major (e.g., 25–100-year return period) floods did not show significant long-term trends ( [[#Hodgkins--2019|Hodgkins et al., 2019]] ). Nevertheless, anthropogenic climate change increased the likelihood of a number of major heavy precipitation events and floods that resulted in disastrous impacts in southern and eastern Asia, Europe, North America and South America (Table 4.3) ( ''high confidence'' ). Davenport et al. (2021) demonstrated that anthropogenic changes in precipitation extremes had contributed one third of the cost of flood damages (from 1988 to 2017) in the USA. Anthropogenic climate change has altered 64% (eight out of 22 events increased, eight decreased) of floods events with significant losses and damages during 2010–2013 ( [[#Hirabayashi--2021a|Hirabayashi et al., 2021a]] ). [[#Gudmundsson--2021|Gudmundsson et al. (2021)]] attributed observed change in extreme river flow trends to anthropogenic climate change ( [[#4.2.3|Section 4.2.3]] ). Although there is growing evidence on the effects of anthropogenic climate change on each event, given the relatively poor regional coverage and high model uncertainty, there is ''low confidence'' in the attribution of human-induced climate change to flood change on the global scale.

'''Table 4.3 |''' Selected major heavy-precipitation events from 2014 to 2021 that led to flooding and their impacts. Studies were selected for presentation based on the availability of scientific literature with 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"| Anthropogenic climate change influence on the likelihood of an event

! rowspan="2"| Reference

|-
! Sign of influence

! Mechanism/magnitude of influence

|-
| 2021

| Germany, Belgium, Luxembourg and neighbouring countries

| At least 222 fatalities, substantial damage to transport and communications infrastructure and houses, severe disruption to businesses and livelihoods.,

| Increase

| One-day rainfall intensity increased by 3–19%, the likelihood of event increased by a factor between 1.2 and 9.

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

|-
| rowspan="2"| 2019

| Canada (Ottawa)

| Thousands of people evacuated, extended states of emergency, and about $200 million in insured losses

| Increase

| Spring maximum 30-d rainfall accumulation in 2019 was three times as likely with anthropogenic forcing.

| Kirchmeier-Young et al. (2021)

|-
| Southern China

| Over 6 million people across several southern China provinces were affected by heavy rains, floods and landslides. These extremes caused at least 91 deaths, collapsed over 19,000 houses, damaged around 83,000 houses and affected 419,400 ha of crops (China Ministry of Emergency Management 2020). The direct economic loss was estimated to be more than 20 billion RMB (equivalent to 3 billion USD)

| Decrease

| Anthropogenic forcings have reduced the likelihood of heavy precipitation in southern China like the 2019 March–July event by about 60%.

| [[#Li--2021b|Li et al. (2021b)]]

|-
| rowspan="5"| 2018

| USA (Mid-Atlantic)

| One fatality, $12 million damages

| Increase

| 1.1 to 2.3 times more likely

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

|-
| Central western China

| Persistent heavy rain led to floods, landslides and house collapse affecting 2.9 million people. The direct economic loss of over USD 1.3 billion.

| Decrease

| ~47% reduction in the probability

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

|-
| Northwestern China

| Extreme flooding in the Upper Yellow River basin affected about 1.4 million people and led to 30 deaths and disappearances.

| Decrease

| 34% reduction in the probability

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

|-
| Japan

| 237 fatalities, more than 6000 buildings destroyed by floods and landslides

| Increase

| 7% increase in total precipitation

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

|-
| Australia (Tasmania)

| $100 million in insurance claims

| Unknown

| Unknown

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

|-
| rowspan="4"| 2017

| Peru

| Widespread flooding and landslides affected 1.7 million people, 177 fatalities, estimated total damage of $3.1 billion

| Increase

| At least 1.5 times more likely

| Christidis et al. (2019)

|-
| Uruguay and Brazil

| Direct economic loss in Brazil of USD 102 million, displacement of more than 3500 people in Uruguay

| Increase

| At least double, with a most likely increase of about fivefold

| [[#de%20Abreu--2019|de Abreu et al. (2019)]]

|-
| North-East Bangladesh

| Flash flood affected ~850,000 households, ~220,000 ha of nearly harvestable Boro rice damaged. Crop failure contributed to a record 30% rice price hike compared to the previous year.

| Increase

| Doubled the likelihood of the 2017 pre-monsoon extreme 6-d rainfall event

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

|-
| China

| 7.8 million people affected 34 fatalities, about 0.8 million people displaced, 605,000 hectares of crops affected, 116,000 hectares without harvest. 32,000 houses collapsed, 41,000 were severely damaged. Direct economic loss 24.12 billion Chinese Yuan (~ USD 3.6 billion)

| Increase

| Doubled the probability from 0.6% to 1.2%

| [[#Sun--2019b|Sun et al. (2019b)]]

|-
| rowspan="4"| 2016

| South China

| Widespread severe flooding, waterlogging, and landslides in the Yangtze–Huai region.

| Increase

| 1.5-fold (0.6 to 4.7) increase in the probability

| [[#Sun--2018|Sun and Miao (2018)]]

|-
| China (Wuhan)

| 237 fatalities, 93 people missing, at least USD 22 billion in damage

| Increase

| Approximately 60% of the risk

| Zhou et al. (2018a)

|-
| China (Yangtze River)

| Direct economic loss of about USD 10 billion

| Increase

| Increased probability by 38% (± 21%)

| Yuan et al. (2018)

|-
| Australia

| Flooding and wild weather impacted some agriculture and power generation.

| None

| Minimal

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

|-
| 2015

| India (Chennai)

| City declared a disaster area. Damages estimated as $3 billion.

| None

| None

| [[#van%20Oldenborgh--2017a|van Oldenborgh et al. (2017a)]]

|-
| 2014

| Indonesia (Jakarta)

| 26 reported deaths, thousands of buildings flooded, much infrastructure damaged. Losses up to USD 384 million

| Unclear

| 2-d rain event approximately 2.4 times more likely compared to 1900, but cause not established

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

|}

In snow-dominated regions, 1~10 d earlier spring floods per decade due to warmer temperature are reported for the last decades ( ''high confidence'' ), such as in Europe ( [[#Morán-Tejeda--2014|Morán-Tejeda et al., 2014]] ; [[#Kormann--2015|Kormann et al., 2015]] ; [[#Matti--2016|Matti et al., 2016]] ; [[#Vormoor--2016|Vormoor et al., 2016]] ; [[#Blöschl--2017|Blöschl et al., 2017]] ), the European part of Russia ( [[#Frolova--2017a|Frolova et al., 2017a]] ; [[#Frolova--2017b|Frolova et al., 2017b]] ; [[#Kireeva--2020|Kireeva et al., 2020]] ), Canada (Yang L. et al., 2015; [[#Burn--2016|Burn et al., 2016]] ; [[#Rokaya--2018|Rokaya et al., 2018]] ) and the USA ( [[#Mallakpour--2015|Mallakpour and Villarini, 2015]] ; [[#Solander--2017|Solander et al., 2017]] ).

There is a knowledge gap in how ice-related floods, including glacier-related and ice-jam floods, respond to ongoing climate change. Despite the increase in the number of glacial lake studies ( [[#Wang--2017|Wang and Zhou, 2017]] ; [[#Harrison--2018|Harrison et al., 2018]] ; [[#Begam--2019|Begam and Sen, 2019]] ; [[#Bolch--2019|Bolch et al., 2019]] ), changes in the frequency of occurrence of glacier-related floods associated with climate change remain unclear ( ''medium confidence'' ). Studies show that the compound occurrence of high surges and high river discharge has increased in some regions (WGI Chapter 11), but few studies quantify changes and impacts. Increases in precipitation from tropical cyclones (WGI Chapter 11) and associated high tide are expected to exacerbate coastal flooding. However, more studies are required to quantify their impacts. In addition, limitations in the duration of data hinder the assessment of trends in low-likelihood high-impact flooding (WGI BOX 11.2).

In summary, the frequency and magnitude of river floods have changed in the past several decades with high regional variations ( ''high confidence'' ). Anthropogenic climate change has increased the likelihood of extreme precipitation events and the associated increase in the frequency and magnitude of river floods ( ''high confidence'' ). There is ''high confidence'' that the warming in the last 40–60 years has led to a maximum of 10 days earlier spring floods per decade, shifts in timing and magnitude of ice-jam floods and changes in frequency and magnitude of snowmelt floods.

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