Editing IPCC:AR6/WGII/Chapter-12 (section)

==== 12.3.2.4 Impacts ====

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

An increase in the frequency of climate-related disasters has been reported ( ''high confidence'' ) ( [[#Huggel--2015a|Huggel et al., 2015a]] ; [[#Stäubli--2018|Stäubli et al., 2018]] ) (WGI AR6 Chapter 12) ( [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ). Scale studies indicate an increase of flood risk during the 21st century, consistent with more frequent floods, with the risk being worse in higher emission scenarios ( ''high confidence'' ) ( [[#Arnell--2013|Arnell and Gosling, 2013]] ; [[#Hirabayashi--2013|Hirabayashi et al., 2013]] ; [[#Alfieri--2017|Alfieri et al., 2017]] ; WGI AR6 Chapter 12, [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ). Those living on riverbanks and in slums built on steep slopes are among the most affected by floods of all kinds ( ''high confidence'' ) ( [[#Emmer--2016|Emmer et al., 2016]] ; [[#Emmer--2017|Emmer, 2017]] ). There is still uncertainty in relation to future drought intensity and frequency ( [[#Pabón-Caicedo--2020|Pabón-Caicedo et al., 2020]] ).

Increased SST, coupled with stronger ENSO events, will affect marine life and fisheries by loss of productive habitat, disruption of nutrient structure, productivity and alteration of species migration patterns, leading to changes in fishing rates, which will impact coastal livelihoods ( ''high confidence'' ) ( [[#Bayer--2014|Bayer et al., 2014]] ; [[#Cai--2015|Cai et al., 2015]] ; [[#Ding--2017|Ding et al., 2017]] ; Mariano [[#Gutiérrez--2017|Gutiérrez et al., 2017]] ; [[#Bertrand--2020|Bertrand et al., 2020]] ). Figure 12.8 shows other observed sensitivities in several ecosystems and in such places as the Galapagos and Malpelo islands and coastal economic exclusion zones (EEZs).

ENSO events coupled with climate change lead to warmer ocean temperatures, heavy rains, floods and heavy river discharges, which will continue to impact several activities, including small-scale fishery infrastructure ( ''very high confidence'' ). In Peru alone, wet extremes are estimated to be at least 1.5 times more likely to happen compared to pre-industrial times. The extremely wet ENSO event of 2017 resulted in 6–9 billion USD in monetary losses in that country, 1.7 million inhabitants affected and crops, roads, bridges, homes, schools and health service facilities damaged or destroyed. Distinct types of ENSO events can have differentiated impacts ( [[#French--2017|French and Mechler, 2017]] ; [[#Christidis--2019|Christidis et al., 2019]] ; [[#Takahashi--2019|Takahashi and Martínez, 2019]] ; [[#Bertrand--2020|Bertrand et al., 2020]] ; [[#Coayla--2020|Coayla and Culqui, 2020]] ).

Irrigation, potable water, health and education infrastructures, as well as roads, bridges, cities and residential constructions, are frequently damaged or destroyed by extreme precipitation events, which also impact sediment transport, river erosion and annual discharge ( ''very high confidence'' ) ( [[#Martínez--2017|Martínez et al., 2017]] ; [[#Morera--2017|Morera et al., 2017]] ; [[#Isla--2018|Isla, 2018]] ; [[#Rosales-Rueda--2018|Rosales-Rueda, 2018]] ; [[#Salazar--2018|Salazar et al., 2018]] ; [[#Puente-Sotomayor--2021|Puente-Sotomayor et al., 2021]] ). The increasing variability of precipitation has compromised rainfed agriculture and power generation, particularly in the dry season ( ''high confidence'' ) ( [[#Bradley--2006|Bradley et al., 2006]] ; [[#Bury--2013|Bury et al., 2013]] ; [[#Buytaert--2017|Buytaert et al., 2017]] ; [[#Carey--2017|Carey et al., 2017]] ; [[#Vuille--2018|Vuille et al., 2018]] ; [[#Orlove--2019|Orlove et al., 2019]] ). For the Amazon–Andes transition zone, the impacts of hydrological variability and transport of sediments have been noticed in riparian agriculture and biodiversity ( ''high confidence'' ) ( [[#Maeda--2015|Maeda et al., 2015]] ; [[#Espinoza--2016|Espinoza et al., 2016]] ; [[#Vauchel--2017|Vauchel et al., 2017]] ; [[#Ronchail--2018|Ronchail et al., 2018]] ; [[#Ayes%20Rivera--2019|Ayes Rivera et al., 2019]] ; [[#Armijos--2020|Armijos et al., 2020]] ; [[#Figueroa--2020|Figueroa et al., 2020]] ; [[#Pabón-Caicedo--2020|Pabón-Caicedo et al., 2020]] ). Changes in seasonality and rain patterns are affecting coffee producers ( [[#Lambert--2020|Lambert and Eise, 2020]] ).

Increases in vector-borne diseases can be related to increases in rainfall and minimum temperatures during ENSO events ( [[#Stewart-Ibarra--2013|Stewart-Ibarra and Lowe, 2013]] ) and the expansion of the diseases’ altitudinal distribution ( ''high confidence'' ) ( [[#Lowe--2017|Lowe et al., 2017]] ; [[#Lippi--2019|Lippi et al., 2019]] ; [[#Portilla%20Cabrera--2020|Portilla Cabrera and Selvaraj, 2020]] ). ENSO events have been related to such diseases as dengue and leptospirosis ( [[#Quintero-Herrera--2015|Quintero-Herrera et al., 2015]] ; [[#Sánchez--2017|Sánchez et al., 2017]] ; [[#Arias-Monsalve--2019|Arias-Monsalve and Builes-Jaramillo, 2019]] ); they can also lead to an increased incidence of chikungunya (Sections 7.2.2.1 and 7.3.1.3). Precipitation, relative humidity and temperature have influenced dengue incidence in recent years ( [[#Mattar--2013|Mattar et al., 2013]] ) (Table 12.1). Dengue cases are predicted to increase in the 1.5°C and the 3.7°C warming scenarios by 2050 and 2100, with increases ranging from 28,900 to 88,800 in Peru, 34,600 to 110,000 in Ecuador, and 97,400 to 317,000 in Colombia, although these scenarios do not consider the potential effects of vaccines or socioeconomic trajectories ( [[#Colón-González--2018|Colón-González et al., 2018]] ). Other studies found that ''Aedes aegypti'' (arbovirus vector) will shift into higher elevations, increasing the populations at risk (Figure 12.5) ( [[#Lippi--2019|Lippi et al., 2019]] ). Climate change will contribute to increased malaria vectorial capacity ( ''high confidence'' ) ( [[IPCC:Wg2:Chapter:Chapter-7#7.2.2|Section 7.2.2.1]] ) ( [[#Laporta--2015|Laporta et al., 2015]] ). Increases in minimum temperature were associated with historical malaria transmission when taking into consideration disease control interventions and climate factors ( [[#Fletcher--2020|Fletcher et al., 2020]] ). Figure 12.4 shows mixed changes in the number of months suitable for malaria transmission, with low-lying areas in coastal regions becoming more suitable. Zoonotic tick-borne diseases and the epidemiology of tuberculosis are also influenced ( [[#Garcia-Solorzano--2019|Garcia-Solorzano et al., 2019]] ; [[#Rodriguez-Morales--2019|Rodriguez-Morales et al., 2019]] ).

<div id="_idContainer010" class="Figure"></div>

[[File:e1ad2ef05a6338c971f7a248296bd08a IPCC_AR6_WGII_Figure_12_004.png]]

'''Figure 12.4 |''' '''Change in average number of months in a given year suitable for malaria transmission by''' '''Plasmodium falciparum''' ''', from 1950–1959 to 2010–2019.''' The threshold-based model used incorporates precipitation accumulation, average temperature and relative humidity ( [[#Grover-Kopec--2006|Grover-Kopec et al., 2006]] ; [[#Romanello--2021|Romanello et al., 2021]] ).

<div id="_idContainer015" class="Figure"></div>

[[File:caa24597389a053e20ffda3909aa6e1e IPCC_AR6_WGII_Figure_12_005.png]]

'''Figure 12.5 |''' '''Predicted thermal suitability for transmission of dengue by''' '''Aedes aegypti''' '''mosquitoes, mapped as the number of months of the year suitable under baseline or current conditions (2015), and in 2030, 2050 and 2080 under RCP4.''' '''5 and RCP8.5.''' Adapted from [[#Ryan--2019|Ryan et al. (2019)]] . See SM12.8 for additional data on population at risk for dengue and Zika in the sub-regions and methodological details.

Accelerated warming is reducing tropical glaciers. Glacier volume loss and permafrost thawing will continue in all scenarios ( ''high confidence'' ) ( [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ). On average, the tropical Andes have lost about 30% and more of their area since the 1980s ( [[#Basantes-Serrano--2016|Basantes-Serrano et al., 2016]] ; [[#Mark--2017|Mark et al., 2017]] ; [[#Thompson--2017|Thompson et al., 2017]] ; [[#Rabatel--2018|Rabatel et al., 2018]] ; [[#Vuille--2018|Vuille et al., 2018]] ; [[#Reinthaler--2019a|Reinthaler et al., 2019a]] ; [[#Seehaus--2019|Seehaus et al., 2019]] ; [[#Masiokas--2020|Masiokas et al., 2020]] ). In a low-emissions scenario, by the end of the 21st century, Peru will lose about 50% of its present glacier surface, while in a high-emission scenario there will remain very small areas of only about 3–5% on the highest peaks ( [[#Schauwecker--2017|Schauwecker et al., 2017]] ).

Changing glaciers, snow and permafrost (Figure 12.13), in synergy with land use change, have implications for the occurrence, frequency and magnitude of derived floods and landslides ( ''high confidence'' ) ( [[#Huggel--2007|Huggel et al., 2007]] ; [[#Iribarren%20Anacona--2015|Iribarren Anacona et al., 2015]] ; [[#Emmer--2017|Emmer, 2017]] ; [[#Mark--2017|Mark et al., 2017]] ), as well as for landscape transformation through lake formation or drying and for alterations in hydrological dynamics, with impacts on water for human consumption, agriculture, industry, hydroelectric generation, carbon sequestration and biodiversity ( ''high confidence'' ) ( [[#Michelutti--2015|Michelutti et al., 2015]] ; [[#Carrivick--2016|Carrivick and Tweed, 2016]] ; [[#Kronenberg--2016|Kronenberg et al., 2016]] ; [[#Emmer--2017|Emmer, 2017]] ; [[#Mark--2017|Mark et al., 2017]] ; [[#Milner--2017|Milner et al., 2017]] ; [[#Polk--2017|Polk et al., 2017]] ; [[#Reyer--2017|Reyer et al., 2017]] ; [[#Young--2017|Young et al., 2017]] ; [[#Vuille--2018|Vuille et al., 2018]] ; [[#Cuesta--2019|Cuesta et al., 2019]] ; [[#Drenkhan--2019|Drenkhan et al., 2019]] ; [[#Hock--2019|Hock et al., 2019]] ; [[#Motschmann--2020a|Motschmann et al., 2020a]] ).

Water flow has decreased in several basins, such as the Shullcas River in the Cordillera Huaytapallana in Peru, and is expected to decrease in the near future in places such as the Cordillera Blanca in Peru ( ''very high confidence'' ) ( [[#Baraer--2012|Baraer et al., 2012]] ; [[#Vuille--2018|Vuille et al., 2018]] ; [[#Somers--2019|Somers et al., 2019]] ; [[#Molina--2020|Molina et al., 2020]] ). Disruptions in water flows will significantly degrade or eliminate high-elevation wetlands ( ''high confidence'' ) ( [[#Bury--2013|Bury et al., 2013]] ; [[#Dangles--2017|Dangles et al., 2017]] ; [[#Mark--2017|Mark et al., 2017]] ; [[#Polk--2017|Polk et al., 2017]] ; [[#Cuesta--2019|Cuesta et al., 2019]] ). Impacts on wetlands are affecting the wild vicuña and the domesticated alpaca ( [[#Duchicela--2019|Duchicela et al., 2019]] ). New lakes represent a source of future hazards and water scarcity, as well as opportunities to serve as water reservoirs ( [[#Colonia--2017|Colonia et al., 2017]] ; [[#Drenkhan--2019|Drenkhan et al., 2019]] ). The timing and extent of peak water due to glacier shrinkage is spatially highly variable and has passed for a large number of tropical Andes glaciers ( [[#Hock--2019|Hock et al., 2019]] ). Cities dependent on glacier melt have experienced high variability in domestic water supply ( [[#Chevallier--2011|Chevallier et al., 2011]] ; [[#Soruco--2015|Soruco et al., 2015]] ; [[#Mark--2017|Mark et al., 2017]] ), as shown in Case Study 2.7.3, but an increase in demand may also have an effect ( [[#Buytaert--2012|Buytaert and De Bièvre, 2012]] ). Water provision is related to socioeconomic issues ( [[#Drenkhan--2015|Drenkhan et al., 2015]] ). Glacier retreat impacts Andean pastoralists ( ''high confidence'' ), as shown in Case Study 2.6.5.4.

NWS houses several global priority areas for biodiversity conservation, including the Tropical Andes and Tumbes-Chocó-Magdalena terrestrial biodiversity hotspots (Section [https://www.ipcc.ch/chapter/12#CCP1.2.2 CCP1.2.2] ; [[#Manes--2021|Manes et al., 2021]] ). Biodiversity in the Tropical Andes and Tumbes-Chocó-Magdalena is projected to suffer negative impacts ( ''medium confidence: medium evidence, high agreement'' ) (Figure 12.9). Invasive plant species might benefit from climate change in these hotspots ( [[#Wang--2017a|Wang et al., 2017a]] ). Species distribution is changing upslope due to increasing air temperature, leading to range contraction and local extinctions of highland species, whereas lowland species are experiencing range contractions at the rear end and expansions in the front end, including vectors of diseases ( ''high confidence'' ) ( [[#Crespo-Pérez--2015|Crespo-Pérez et al., 2015]] ; [[#Duque--2015|Duque et al., 2015]] ; [[#Morueta-Holme--2015|Morueta-Holme et al., 2015]] ; [[#Moret--2016|Moret et al., 2016]] ; [[#Aguirre--2017|Aguirre et al., 2017]] ; [[#Cuesta--2017a|Cuesta et al., 2017a]] ; [[#Seimon--2017|Seimon et al., 2017]] ; [[#Fadrique--2018|Fadrique et al., 2018]] ; [[#Tito--2018|Tito et al., 2018]] ; [[#Zimmer--2018|Zimmer et al., 2018]] ; [[#Cauvy-Fraunié--2019|Cauvy-Fraunié and Dangles, 2019]] ; [[#Cuesta--2019|Cuesta et al., 2019]] ; [[#Moret--2020|Moret et al., 2020]] ; [[#Rosero--2021|Rosero et al., 2021]] ). Vegetation in summits of the northern Andes is particularly vulnerable because of a high abundance of endemic species with narrow thermal niches and lowland dispersal capacity in comparison to the central Andes ( [[#Cuesta--2020|Cuesta et al., 2020]] ).

The upper limit of alpine vegetation (paramo) shifted upslope 500 m in the Chimborazo ( [[#Morueta-Holme--2015|Morueta-Holme et al., 2015]] ), yet the upper forest limit (the ecotone between forest and alpine vegetation) is migrating at slower rates or not at all ( [[#Harsch--2009|Harsch et al., 2009]] ; [[#Rehm--2015b|Rehm and Feeley, 2015b]] ), so it is expected to be a major barrier to migration to several montane species, leading to population reductions and biodiversity losses ( [[#Lutz--2013|Lutz et al., 2013]] ; [[#Rehm--2015a|Rehm and Feeley, 2015a]] ). Shifts in tree species distribution may result in decreased above-ground carbon stocks and productivity in tropical mountain forests ( ''high confidence'' ) ( [[#Feeley--2011|Feeley et al., 2011]] ; [[#Duque--2015|Duque et al., 2015]] ; [[#Fadrique--2018|Fadrique et al., 2018]] ; [[#Duque--2021|Duque et al., 2021]] ), a biomass loss that will only be partially offset through increased recruitment and growth of lowland species migrating upslope. Water scarcity can enhance tree mortality and decrease above-ground carbon stocks ( [[#Álvarez-Dávila--2017|Álvarez-Dávila et al., 2017]] ; [[#McDowell--2020|McDowell et al., 2020]] ). The agricultural frontier of crops, such as potatoes or maize, is moving upwards ( ''high confidence'' ), following the freezing level height upward displacement ( [[#Morueta-Holme--2015|Morueta-Holme et al., 2015]] ; [[#Skarbø--2016|Skarbø and VanderMolen, 2016]] ; [[#Schauwecker--2017|Schauwecker et al., 2017]] ; [[#Vuille--2018|Vuille et al., 2018]] ). Modelling exercises agree with the observed impacts in species, ecosystem processes, crop impacts and related pests and diseases ( ''high confidence'' ) ( [[#Cernusak--2013|Cernusak et al., 2013]] ; [[#Tovar--2013|Tovar et al., 2013]] ; [[#Ramirez-Villegas--2014|Ramirez-Villegas et al., 2014]] ; [[#Ovalle-Rivera--2015|Ovalle-Rivera et al., 2015]] ; [[#van%20der%20Sleen--2015|van der Sleen et al., 2015]] ; [[#Lowe--2017|Lowe et al., 2017]] ). Agricultural options are changing as a result of intra-seasonal temperature variation ( [[#Ponce--2020|Ponce, 2020]] ). Changes in the timing and amount of precipitation are also impacting agriculture (Table 12.4) ( [[#Heikkinen--2017|Heikkinen, 2017]] ; [[#Altea--2020|Altea, 2020]] ).

Species distribution is changing in dry lowland forests, where deforestation is the more intense driver and climate change is intensely acting ( [[#Aguirre--2017|Aguirre et al., 2017]] ; [[#Manchego--2017|Manchego et al., 2017]] ). Extinctions in amphibians have been related to temperature rises acting in synergy with diseases ( [[#Catenazzi--2014|Catenazzi et al., 2014]] ). The fungus ''Batrachochytrium dendrobatidis'' successfully accompanied and caused disease in high-elevation Andean frogs as they expanded their ranges to 5200–5400 m ( [[#Seimon--2017|Seimon et al., 2017]] ). Several groups of freshwater species of the tropical Andes represent 35% of threatened freshwater species in the world ( [[#Gardner--2018|Gardner and Finlayson, 2018]] ). Potential impacts of species turnover in key areas for biodiversity conservation have been identified ( [[#Cuesta--2017b|Cuesta et al., 2017b]] ).

Climate-change-related hazards could foster rural poverty, and its impacts have led to the modification of agriculture calendars and irrigation adjustments ( [[#Postigo--2014|Postigo, 2014]] ). Livestock populations are diminishing due to rising temperatures, changing water flows and shrinkage of pastures, particularly cattle and pig production ( [[#Bayer--2014|Bayer et al., 2014]] ; [[#Tapasco--2015|Tapasco et al., 2015]] ; [[#Bergmann--2021|Bergmann et al., 2021]] ). In some cases farmers respond to extreme temperatures by increasing use of land and crop intensity ( [[#Aragón--2021|Aragón et al., 2021]] ). Climate change has prompted and will continue to prompt internal and international migrations ( [[#Løken--2019|Løken, 2019]] ; [[#Bergmann--2021|Bergmann et al., 2021]] ). A change in fire regimes and fire risk is expected in highland ecosystems, although it is difficult to determine the influence of human activities and climate change influence on fire patterns ( [[#Oliveras--2014|Oliveras et al., 2014]] , 2018; [[#Armenteras--2020|Armenteras et al., 2020]] ).

<div id="12.3.3" class="h2-container"></div>

<span id="northern-south-america-sub-region"></span>