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

==== 12.3.1.4 Impacts ====

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The countries in the region are consistently ranked highest in the world by risk of being impacted by extreme events ( ''high confidence'' ). The economic costs of climate-change impacts in 2010 were estimated as being from 2.9% of GDP for Guatemala to 7.7% for Belize ( [[#ECLAC--2015|ECLAC et al., 2015]] ). For the period 1992–2011, Honduras, Nicaragua and Guatemala were among the 10 most impacted countries in the world by extreme weather events ( [[#UNISDR%20and%20CEPREDENAC--2014|UNISDR and CEPREDENAC, 2014]] ). The number of these events has increased 3% annually in the last 30 years ( [[#Bárcena--2020a|Bárcena et al., 2020a]] ).

Human and economic losses, changing water availability and increasing food insecurity are the most studied impacts of climate change in CA (Figure 12.9) ( [[#Harvey--2018|Harvey et al., 2018]] ; [[#Hoegh-Guldberg--2019|Hoegh-Guldberg et al., 2019]] ). Hydro-meteorological events, such as storm surges and TCs, are the most frequent extreme events and have the highest impact ( ''high confidence'' ) ( [[#Reyer--2017|Reyer et al., 2017]] ). From 2005 to 2014, the cumulative impacts were over 3410 people dead, hundreds of thousands displaced and damages estimated around USD 5.8 billion ( [[#Ishizawa--2016|Ishizawa and Miranda, 2016]] ). One standard deviation in the intensity of a hurricane windstorm leads to a decrease in both the growth of total GDP per capita (0.9% to 1.6%) and total income and labour income by 3%, whereas it increases moderate and extreme poverty by 1.5% in CA ( [[#Ishizawa--2016|Ishizawa and Miranda, 2016]] ).

Food insecurity is a serious impact of climate change in a region where 10% of the GDP depends on agriculture, livestock and fisheries ( ''very high confidence'' ) ( [[#ECLAC--2015|ECLAC et al., 2015]] ; [[#CEPAL--2018|CEPAL et al., 2018]] ; [[#Harvey--2018|Harvey et al., 2018]] ; [[#BCIE--2020|BCIE, 2020]] ). Crop losses largely result from highly variable rainfall and seasonal droughts, which have increased significantly in recent decades (Table 12.3) ( [[#CEPAL%20and%20CAC-SICA--2020|CEPAL and CAC-SICA, 2020]] ), particularly the observed changes in the MSD that reduces rainfall at the onset of the rainy season (May–June) ( [[#Anderson--2019|Anderson et al., 2019]] ). Small and subsistence farmers experience the highest impact because they practice rainfed agriculture ( [[#Imbach--2017|Imbach et al., 2017]] ), along with poor neighbourhoods, which face socioeconomic and physical barriers for adapting to climate change ( [[#Kongsager--2017|Kongsager, 2017]] ). In 2015, precipitation diminished between 50% and 70% of its historic average, causing a loss of up to 80% of beans and 60% of maize, leaving 2.5 million people food insecure, 1.6 million of whom were in the Dry Corridor of CA ( [[#ECLAC--2015|ECLAC et al., 2015]] ; [[#FAO--2016a|FAO, 2016a]] ). In 2019, the region entered its fifth consecutive drought year, with 1.4 million people in need of food aid. Seasonal-scale droughts are projected to lengthen by 12–30%, intensify by 17–42% and increase in frequency by 21–42% in RCP4.5 and RCP8.5 scenarios by the end of the century ( [[#Depsky--2021|Depsky and Pons, 2021]] ).

Studies have shown that the incidence of some vector-borne and zoonotic diseases in CA is correlated to climatic variables, particularly temperature and rainfall ( ''high confidence'' ) (Figure 12.4; Table 12.1). In Honduras, rainfall and relative humidity were positively correlated with the occurrence of haemorrhagic dengue cases ( [[#Zambrano--2012|Zambrano et al., 2012]] ). In Costa Rica, temperature and rainfall were correlated to cattle rabies outbreaks and mortality during 1985–2016 ( [[#Hutter--2018|Hutter et al., 2018]] ); incidence of leishmaniasis showed cycles of 3 years related to temperature changes ( [[#Chaves--2006|Chaves and Pascual, 2006]] ); and snakebites were more likely to occur at high temperatures and were significantly reduced after the rainy season for the period 2005–2013 ( [[#Chaves--2015|Chaves et al., 2015]] ). In Panama, rainfall was associated with an increased number of malaria cases among the Gunas, an Indigenous People with high vulnerability living in poverty conditions on small islands affected by SLR ( [[#Hurtado--2018|Hurtado et al., 2018]] ). These correlations point to a possible change in disease incidence with climate change; evidence of that change is yet to be reported in the literature because longitudinal studies are lacking in the region.

Heat stress is another health concern in this already warm and humid part of the world ( ''high confidence'' ) (Table 12.2); it is an increasing occupational health hazard with potential impacts on kidney disease ( [[#Sheffield--2013|Sheffield et al., 2013]] ; [[#Dally--2018|Dally et al., 2018]] ; [[#Johnson--2019|Johnson et al., 2019]] ). SLR exacerbating wave-driven flooding is expected to impact infrastructure and freshwater availability in small islands and atolls off the coast of Belize ( [[#Storlazzi--2018|Storlazzi et al., 2018]] ). Observed and expected impacts in the coastal and ocean ecosystems of the sub-region are described in Figure 12.9.

Decreasing water availability is another impact of climate change ( ''high confidence'' ). Under a climate-change scenario of 3.5°C warming and a 30% reduction in rainfall, a reduction in the production and export of crops and livestock is projected, affecting the wages and decreasing the GDP of Guatemala by 1.2%, thereby increasing food insecurity ( [[#Vargas--2018b|Vargas et al., 2018b]] ). By 2100, water availability per capita is projected to decrease 82% and 90% on average for the region under B2 (low emissions) and A2 (high emissions) scenarios respectively (Figure 12.3) ( [[#CEPAL--2010|CEPAL, 2010]] ).

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[[File:408541c0d0dc7dd04b0db72c155d851d IPCC_AR6_WGII_Figure_12_003.png]]

'''Figure 12.3 |''' '''Reduction of water availability per capita projected to 2100 without climate change (baseline scenario) and with two climate-change scenarios ( [[#CEPAL--2010|CEPAL, 2010]] ).'''

Impacts on rural livelihoods, particularly for small and medium-sized farmers and Indigenous Peoples in mountains, include an overall reduction in production, yield (Table 12.4), suitable farming area and water availability ( ''high confidence'' ) ( [[#Walshe--2016|Walshe and Argumedo, 2016]] ; [[#Bouroncle--2017|Bouroncle et al., 2017]] ; [[#Hannah--2017|Hannah et al., 2017]] ; [[#Imbach--2017|Imbach et al., 2017]] ; [[#Harvey--2018|Harvey et al., 2018]] ; [[#Batzín--2019|Batzín, 2019]] ; [[#Donatti--2019|Donatti et al., 2019]] ). Bean production in El Salvador, Nicaragua, Honduras and Guatemala, is projected to decrease, using the Decision Support for Agro-Technology Transfer (DSSAT) under the A2 scenario, by 19% for 2050, whereas maize production, depending on the water retention capacity of soils, will drop between 4% and 21% by 2050 ( [[#CEPAL--2018|CEPAL et al., 2018]] ). In Guatemala, the yield of rainfed maize is expected to decrease by 16% by 2050 under RCP8.5 using the Global Gridded Crop Model Intercomparison GGCMI; yields for rainfed sugarcane are expected to drop by 44% and irrigated sugarcane by 36% under the same modelling conditions ( [[#Castellanos--2018|Castellanos et al., 2018]] ). Rice production is expected to decrease by 23% under scenario A2 by 2050 ( [[#CEPAL%20and%20CAC/SICA--2013|CEPAL and CAC/SICA, 2013]] ).

The extent and quality of suitable areas for basic grains are expected to contract ( ''high confidence'' ). The suitable area for maize will experience a 35% reduction of cultivated area expected by 2100 under the A2 scenario. The area suitable for beans is expected to shrink by 2050. Projections show that suitable areas with excellent capacity under current conditions will decrease by 14%, mainly in Panama (41%) Costa Rica (21%) and El Salvador (20%). The Species Distribution Model, using the IPSL GCM, projects that the suitable zones for cacao and coffee will shrink between 25% and 75% under RCP6.0 ( [[#Fernandez-Manjarrés--2018|Fernandez-Manjarrés, 2018]] ; [[#Fernández%20Kolb--2019|Fernández Kolb et al., 2019]] ). Warmer and dryer lower areas will become unsuitable for coffee and will drive its production to higher land ( [[#Läderach--2013|Läderach et al., 2013]] ; [[#Bunn--2015|Bunn et al., 2015]] ). Under the A2 climate-change scenario, areas with excellent capacity for Arabica coffee will decrease by 12% in CA; coffee yield will decrease in suitable zones whereby the extent of high yield (&gt;0.8 T ha −1 ) zones is projected to shrink from 34% to 12%, whereas low-yield (&lt;0.3 T ha −1 ) zones will expand from 14% to 36% by 2100 under the A2 scenario ( [[#CEPAL%20and%20CAC/SICA--2014|CEPAL and CAC/SICA, 2014]] ).

Mesoamerica, a biodiversity hotspot spanning across CA and southern Mexico, is a global priority for terrestrial biodiversity conservation, and it is projected to be negatively impacted by climate change, especially through the contraction of distribution of native species as the area becomes increasingly dryer ( ''high confidence'' ) (Section [https://www.ipcc.ch/chapter/12#CCP1.2.2 CCP1.2.2] ) ( [[#Feeley--2013|Feeley et al., 2013]] ; [[#Manes--2021|Manes et al., 2021]] ). A significant reduction in net primary productivity in tropical forests is expected under both RCP4.5 and RCP8.5 as a result of temperature increase, precipitation reduction and droughts ( [[#Lyra--2017|Lyra et al., 2017]] ; [[#Castro--2018|Castro et al., 2018]] ; [[#Stan--2020|Stan et al., 2020]] ). Aridity index models show that the dry, sub-humid vegetation of the dry corridor will expand to neighbouring areas and replace the humid forests in the Pacific lowlands and the northern parts of Guatemala by 2050 under RCP4.5 and RCP8.5 scenarios ( [[#Pons--2018|Pons et al., 2018]] ; [[#CEPAL%20and%20CAC-SICA--2020|CEPAL and CAC-SICA, 2020]] ). A warming of 3°C would shrink the tropical rainforest and replace it with savannah grassland. Wetlands are also expected to be highly affected by climate change in the region ( [[#Hoegh-Guldberg--2019|Hoegh-Guldberg et al., 2019]] ).

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