Mauritania: Human health

Climate change threatens the health and sanitation sector through more frequent incidences of heatwaves, floods, droughts and storms. Among the key health challenges in Mauritania are morbidity and mortality through vector-borne diseases such as malaria, waterborne diseases related to extreme weather events (e.g. flooding) such as diarrhoea and cholera, as well as tuberculosis, HIV and respiratory diseases [29]. Climate change is also likely to impact food and water supply, thereby increasing the risk of malnutrition, hunger and death by famine. Many of these challenges are expected to become more severe under climate change. According to the World Health Organization (WHO), Mauritania recorded around 174 000 cases of malaria in 2018 [30]. Climate change is likely to have an impact on malaria transmission periods and the geographic range of vector-borne diseases: In Mauritania, the general malaria risk is projected to fall due to rising temperatures [31]. However, some regions are likely to become more vulnerable to malaria, for instance, due to more frequent incidences of flooding [32]. Climate change also poses a threat to food security and malnutrition, particularly for subsistence farmers. While chronic malnutrition is generally high at 19.6 %, it could further increase due to the consequences of the COVID-19 pandemic [33]. According to the WHO, more than 900 000 people faced food insecurity in June 2020, which is an increase of 48 % within six months [33].

Exposure to heatwaves

Figure 18: Projections of population exposure to heatwaves at least once a year for Mauritania for different GHG emissions scenarios.

Rising temperatures will result in more frequent heatwaves in Mauritania, leading to increased heat-related mortality. Under RCP6.0, the population affected by at least one heatwave per year is projected to increase from 6 % in 2000 to 34 % in 2080 (Figure 18).

Heat-related mortality

Figure 19: Projections of heat-related mortality for Mauritania for different GHG emissions scenarios assuming no adaptation to increased heat.

Furthermore, under RCP6.0, heat-related mortality will likely increase from about 2 to about 7 deaths per 100 000 people per year by 2080, which translates to an increase by a factor of more than three towards the end of the century compared to year 2000 levels, provided that no adaptation to hotter conditions will take place (Figure 19). Under RCP2.6, heat-related mortality is projected to increase to about 4 deaths per 100 000 people per year in 2080.

References

[29] Republique Islamique de Mauritanie, “Rapport d’analyse de situation du secteur de la santé,” Nouakchott, Mauritania, 2011.
[30] WHO, “World Malaria Report 2019,” Rome, Italy, 2019.
[31] C. Caminade, A. E. Jones, R. Ross, and G. Macdonald, “Malaria in a Warmer West Africa,” Nat. Clim. Chang., vol. 6, no. November, pp. 984–985, 2016.
[32] R. Boyce, R. Reyes, M. Matte, M. Ntaro, E. Mulogo, J. P. Metlay, L. Band, and M. J. Siedner, “Severe Flooding and Malaria Transmission in the Western Ugandan Highlands: Implications for Disease Control in an Era of Global Climate Change,” J. Infect. Dis., vol. 214, pp. 1403–1410, 2016.
[33] WFP, “Mauritania Country Brief: June 2020,” Rome, Italy, 2020.

Mauritania: Ecosystems

Climate change is expected to have a significant influence on the ecology and distribution of tropical ecosystems, though the magnitude, rate and direction of these changes are uncertain [27]. With rising temperatures and increased frequency and intensity of droughts, wetlands and riverine systems are increasingly at risk of being converted to other ecosystems, with plant populations being succeeded and animals losing habitats. Increased temperatures and droughts can also impact succession in forest systems while concurrently increasing the risk of invasive species, all of which affect ecosystems. In addition to these climate drivers, low agricultural productivity and population growth might motivate further agricultural expansion resulting in increased deforestation, land degradation and forest fires, all of which will impact animal and plant biodiversity.

Species richness

Figure 16: Projections of the aggregate number of amphibian, bird and mammal species for Mauritania for different GHG emissions scenarios.

Model projections of species richness (including amphibians, birds and mammals) and tree cover for Mauritania are shown in Figure 16 and 17, respectively. The models applied for this analysis show particularly strong agreement on the development of species richness: Under RCP2.6, south-western Mauritania is expected to gain up to 30 % of animal species due to climate change. This trend will intensify under RCP6.0, in addition to a decrease of up to 50 % in the south-east of the country (Figure 16).

Tree cover

Figure 17: Tree cover projections for Mauritania for different GHG emissions scenarios.

With regard to tree cover, model results are very uncertain and of low magnitude under both RCPs (Figure 17), which could also relate to the fact that tree cover in Mauritania is generally sparse. Overall, no reliable estimations on the development of tree cover can be made.

It is important to keep in mind that the model projections exclude any impacts on biodiversity loss from human activities such as land use, which have been responsible for significant losses of global biodiversity in the past, and are expected to remain its main driver in the future [28]. In recent years, Mauritania’s vegetation has experienced profound disturbances due to population pressure and increasing demand for pastures, agricultural land and firewood, leading to high rates of deforestation [25]. The country has lost 86 000 ha of forest cover in the period from 2001 to 2016, which is equivalent to a 28 % decrease [2].

References

[2] World Bank, “World Bank Open Data,” 2019. Online available: https://data.worldbank.org [Accessed: 31-Jan-2020].
[25] N. K. Dia, A. A. Bayod-Rújula, N. Mamoudou, M. Diallo, C. S. Ethmane, and B. O. Bilal, “Energy Context in Mauritania,” Energy Sources, Part B Econ. Plan. Policy, vol. 12, no. 2, pp. 182–190, 2017.
[27] T. M. Shanahan, K. A. Hughen, N. P. McKay, J. T. Overpeck, C. A. Scholz, W. D. Gosling, C. S. Miller, J. A. Peck, J. W. King, and C. W. Heil, “CO2 and Fire Influence Tropical Ecosystem Stability in Response to Climate Change,” Nat. Publ. Gr., no. July, pp. 1–8, 2016.
[28] IPBES, “Report of the Plenary of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services on the Work of Its Seventh Session,” n.p., 2019.

Mauritania: Infrastructure

Climate change is expected to significantly affect Mauritania’s infrastructure through extreme weather events. High precipitation amounts can lead to the flooding of roads, while high temperatures can cause roads, bridges and protective structures to develop cracks and degrade more quickly. This will require earlier replacement and lead to higher maintenance and replacement costs. The near-total absence of passenger railways and limited airport facilities increase Mauritania’s reliance on road transportation [23]. The country has only 2 743 km of paved roads, which is one of the lowest densities on the continent [23]. While some roads become impassable during the rainy season, cutting off villages and rural communities, others are obscured by drifting sand during the dry season [23]. Investments will have to be made to build climate-resilient and safe road networks.

Extreme weather events will also have devastating effects on human settlements and economic production sites, especially in urban areas with high population densities like Nouakchott or Nouadhibou. Informal settlements are particularly vulnerable to extreme weather events: Makeshift homes are often built in unstable geographical locations including steep slopes or river banks, where flooding can lead to loss of housing, contamination of water, injury or death. Dwellers usually have low adaptive capacity to respond to such events due to high levels of poverty and a lack of risk-reducing infrastructures. For example, heavy precipitation events during the 2019 rainy season caused flooding in the region of Guidimakha in southern Mauritania, affecting 33 600 people [24]. The city of Sélibaby was hit particularly hard, recording damages to houses, markets and infrastructure as well as disruptions to water and energy supplies. Flooding and droughts will also affect hydropower generation: Together with Senegal and Mali, Mauritania shares the Manantali Dam, which has a total installed capacity of 200 MW and which is located on the Bafing River in Mali, a tributary of the Senegal River [25]. However, variability in precipitation and climatic conditions could severely disrupt hydropower generation in the whole region.

Despite the risk of infrastructure damage being likely to increase, precise predictions of the location and the extent of exposure are difficult to make. For example, projections of river flood events are subject to substantial modelling uncertainty, largely due to the uncertainty of future projections of precipitation amounts and their spatial distribution, affecting flood occurrence (see also Figure 4). In the case of Mauritania, median projections show almost no change in national road exposure to river floods (Figure 13). In the year 2000, 0.4 % of major roads were exposed to river floods at least once a year. By 2080, this value is projected to not change under RCP2.6 and to decrease to 0.3 % under RCP6.0. In a similar way, exposure of urban land area to river floods is projected to change only marginally under RCP6.0 from 0.05 % in 2000 to 0.14 % in 2080, with no change under RCP2.6 (Figure 14).

Figure 13: Projections of major roads exposed to river floods at least once a year for Mauritania for different GHG emissions scenarios.
Figure 14: Projections of urban land area exposed to river floods at least once a year for Mauritania for different GHG emissions scenarios.

With the exposure of the GDP to heatwaves projected to increase dramatically from around 6 % in 2000 to 25 % (RCP2.6) and 35 % (RCP6.0) by 2080 (Figure 15), it is recommended that policy planners start identifying heat-sensitive economic production sites and activities, and integrating climate adaptation strategies such as improved solar-powered cooling systems, “cool roof” isolation materials or switching the operating hours from day to night [26].

Figure 15: Exposure of GDP in Mauritania to heatwaves for different GHG emissions scenarios.

References

[23] Logistics Cluster and WFP, “Mauritanie Infrastructures Logistiques,” 2020. Online available: https://dlca.logcluster.org/display/public/DLCA/2+Mauritania+Infrastructures+Logistiques [Accessed: 14-Jul-2020].
[24] IFRC, “Emergency Plan of Action (EPoA): Mauritania – Floods in Guidimakha,” Geneva, Switzerland, 2019.
[25] N. K. Dia, A. A. Bayod-Rújula, N. Mamoudou, M. Diallo, C. S. Ethmane, and B. O. Bilal, “Energy Context in Mauritania,” Energy Sources, Part B Econ. Plan. Policy, vol. 12, no. 2, pp. 182–190, 2017.

Mauritania: Agriculture

Smallholder farmers in Mauritania are increasingly challenged by the uncertainty and variability of weather caused by climate change [16], [17]. Since crops are predominantly rainfed, yields highly depend on water availability from precipitation and are prone to drought. However, the length and intensity of the rainy season is becoming increasingly unpredictable and the use of irrigation facilities remains limited: In 2004, less than 10 % of the estimated irrigation potential of 250 000 ha (0.6 % of total national crop land) were irrigated [6]. The main irrigated crop is rice, in addition to maize, sorghum and vegetables [22]. Especially in central and northern Mauritania, soils are sandy and poor in nutrients, which complicates irrigation and crop production [22].

Crop land exposure to drought

Figure 11: Projections of crop land area exposed to drought at least once a year for Mauritania for different GHG emissions scenarios.

Currently, the high uncertainty of projections regarding water availability (Figure 10) translates into high uncertainty of drought projections (Figure 11). According to the median over all models employed for this analysis, the national crop land area exposed to at least one drought per year will increase from 6 % in 2000 to 10 % in 2080 under RCP6.0 and decrease to 5 % under RCP2.6. Under RCP6.0, the likely range of drought exposure of the national crop land area per year widens from 0.3–19 % in 2000 to 0.6–36 % in 2080. The very likely range widens from 0–20 % in 2000 to 0.01–44 % in 2080. This means that some models project a doubling of drought exposure over this time period, while others project no change.

Crop yield projections

Figure 12: Projections of crop yield changes for major staple crops in Mauritania for different GHG emissions scenarios assuming constant land use and agricultural management, relative to the year 2000.

In terms of yield projections, model results indicate high uncertainty (Figure 12)6. Compared to the year 2000, yields of cow peas are projected to decrease by 6 % under RCP2.6 and increase by 4 % under RCP6.0. A possible explanation for the positive results under RCP6.0 is that cow peas are so-called C3 plants, which follow a different metabolic pathway than maize, millet and sorghum (C4 plants), and benefit more from the CO2 fertilisation effect under higher concentration pathways. For maize, the trend is reversed: Under RCP2.6, yields are projected to slightly increase by 3 % and decrease by 11 % under RCP6.0. Millet and sorghum are projected to gain 8 % under RCP2.6 and 6 % under RCP6.0. The higher increases under RCP2.6 can be explained by higher precipitation projections under RCP2.6 (Figure 5). Finally, yields of rice are projected to not change under either RCP, however, some models project an increase of up to 200 %. Although some yield changes may appear small at the national level, they will likely increase more strongly in some areas and, conversely, decrease more strongly in other areas as a result of climate change impacts.

Overall, adaptation strategies such as switching to improved varieties in climate change-sensitive crops need to be considered, yet should be carefully weighed against adverse outcomes, such as a resulting decline of agro-biodiversity and loss of local crop types.

6 Modelling data is available for a selected number of crops only. Hence, the crops listed on page 2 may differ. Maize, millet and sorghum are modelled for all countries, except for Madagascar.

References

[6] FAO, “AQUASTAT Database.” Online available: http://www.fao.org/nr/water/aquastat/data/query/index.html?lang=en [Accessed: 07-May-2020].
[16] K. Sissoko, H. van Keulen, J. Verhagen, V. Tekken, and A. Battaglini, “Agriculture, Livelihoods and Climate Change in the West African Sahel,” Reg. Environ. Chang., vol. 11, no. 1, pp. 119–125, 2011.
[17] P. Ozer, Y. C. Hountondji, J. Gassani, B. Djaby, and D. L. F, “Évolution récente des extrêmes pluviométriques en Mauritanie (1933–2010),” XXVIIeme Colloq. l’Association Int. Climatol., pp. 394–400, 2014.
[22] Y. M. Bachir and A. Ould Hamadi Sherif, “Mauritania Livelihood Zoning Plus,” Washington, D.C. and Madrid, Spain, 2013.

Mauritania: Water resources

Mauritania has experienced strong seasonal and annual variation in precipitation as well as recurring droughts, all of which present major constraints to agricultural production [16], [17]. The country was hit by recurring droughts in the 1970s and 1980s as precipitation amounts decreased during that time [18]. This decrease in precipitation led to critical reductions in water resources and vegetation, increased land degradation and desertification, which resulted in loss of arable land and reduced agricultural production, as well as loss of pastures and livestock depletion [18]. Poverty rates soared in already vulnerable rural communities and created a mass exodus to urban centres [18]. While in 1980, only 27 % of Mauritania’s population was urban, this figure increased more than twofold to 55 % in 2019 [2]. Furthermore, the effects of drought sparked conflicts between farmers and herders in the Senegal River Valley, leading to the Senegal-Mauritania Conflict in 1989 with thousands of people killed and hundreds of thousands displaced [19], [20]. Although annual precipitation sums recovered in the 1990s, they remain below the national average of the past century with further droughts recorded in 2005, 2008, 2010 and 2012 [17], [21]. Overall, Mauritania’s freshwater resources are very unevenly distributed with concentrations along the southern border, leaving the country’s growing population under water stress and in competition over limited water resources.

Per capita water availability

Figure 9: Projections of water availability from precipitation per capita and year with (A) national population held constant at year 2000 level and (B) changing population in line with SSP2 projections for different GHG emissions scenarios, relative to the year 2000.

Current projections of water availability in Mauritania display high uncertainty under both GHG emissions scenarios. Assuming a constant population level, multi-model median projections suggest only little change in per capita water availability over Mauritania by the end of the century under either RCP (Figure 9A). Yet, when accounting for population growth according to SSP2 projections5, per capita water availability for Mauritania is projected to decline by 71 % under RCP2.6 and 77 % under RCP6.0 by 2080 relative to the year 2000 (Figure 9B). While this decline is primarily driven by population growth rather than climate change, it highlights the urgency to invest in water saving measures and technologies for future water consumption after 2030.

Spatial distribution of water availability

Figure 10: Water availability from precipitation (runoff) projections for Mauritania for different GHG emissions scenarios.

Projections of future water availability from precipitation vary depending on the region and scenario (Figure 10). In line with precipitation projections, water availability is projected to increase in parts of western, central and north-eastern Mauritania under RCP2.6. Under RCP6.0, however, model agreement is low with precipitation decreases of up to 30 % projected for the south of Mauritania. The projected increase in water availability under RCP2.6 is based on a constant population level. Hence, water saving measures are likely to become important for Mauritania’s rapidly growing population.

5 Shared Socio-economic Pathways (SSPs) outline a narrative of potential global futures, including estimates of broad characteristics such as country level population, GDP or rate of urbanisation. Five different SSPs outline future realities according to a combination of high and low future socio-economic challenges for mitigation and adaptation. SSP2 represents the “middle of the road”-pathway.

References

[17] P. Ozer, Y. C. Hountondji, J. Gassani, B. Djaby, and D. L. F, “Évolution récente des extrêmes pluviométriques en Mauritanie (1933–2010),” XXVIIeme Colloq. l’Association Int. Climatol., pp. 394–400, 2014.
[18] Islamic Republic of Mauritania, “National Adaptation Programme of Action to Climate Change,” Nouakchott, Mauritania, 2004.
[19] R. Parker, “The Senegal–Mauritania Conflict of 1989: a Fragile Equilibrium,” J. Mod. Afr. Stud., vol. 29, no. 1, pp. 155–171, Mar. 1991.
[20] A. Nicolaj, “The Senegal Mauritanian Conflict,” Africa Riv. Trimest. di Stud. e Doc. dell’Instituto Ital. per l’Africa e l’Oriente, vol. 45, no. 3, pp. 464–480, 1990.
[21] USAID, “Climate Change Risk Profile: West African Sahel,” Washington, D.C., 2017.

Mauritania: Climate

Temperature

Figure 2: Air temperature projections for Mauritania for different GHG emissions scenarios.4

In response to increasing greenhouse gas (GHG) concentrations, air temperature over Mauritania is projected to rise by 2.0 to 4.5 °C (very likely range) by 2080 relative to the year 1876, depending on the future GHG emissions scenario (Figure 2). Compared to pre-industrial levels, median climate model temperature increases over Mauritania amount to approximately 2.1 °C in 2030, 2.3 °C in 2050 and 2.5 °C in 2080 under the low emissions scenario RCP2.6. Under the medium / high emissions scenario RCP6.0, median climate model temperature increases amount to 2.1 °C in 2030, 2.7 °C in 2050 and 3.8 °C in 2080.

Very hot days

Figure 3: Projections of the annual number of very hot days (daily maximum temperature above 35 °C) for Mauritania for different GHG emissions scenarios.

In line with rising mean annual temperatures, the annual number of very hot days (days with daily maximum temperature above 35 °C) is projected to rise substantially and with high certainty, in particular over western Mauritania (Figure 3). Under the medium / high emissions scenario RCP6.0, the multi-model median, averaged over the whole country, projects 18 more very hot days per year in 2030 than in 2000, 27 more in 2050 and 49 more in 2080. In some parts, especially in south-western Mauritania, this amounts to about 300 days per year by 2080.

Sea level rise

Figure 4: Projections for sea level rise off the coast of Mauritania for different GHG emissions scenarios, relative to the year 2000.

In response to globally increasing temperatures, the sea level off the coast of Mauritania is projected to rise (Figure 4). Until 2050, very similar sea levels are projected under both emissions scenarios. Under RCP6.0 and compared to year 2000 levels, the median climate model projects a sea level rise by 10 cm in 2030, 19 cm in 2050 and 36 cm in 2080. This threatens Mauritania’s coastal communities and may cause saline intrusion in coastal waterways and groundwater reservoirs.

Precipitation

Figure 5: Annual mean precipitation projections for Mauritania for different GHG emissions scenarios, relative to the year 2000.

Future projections of precipitation are less certain than projections of temperature change due to high natural year-to-year variability (Figure 5). Out of the four climate models underlying this analysis, two models project no change in mean annual precipitation over Mauritania and two models project a decrease under RCP6.0. Under RCP2.6, one model projects an increase, one a decrease and two no change. Median model projections show a slight precipitation increase of 6 mm per year by 2080 under RCP2.6, while median model projections for RCP6.0 show a decrease of 11 mm by 2080 compared to year 2000.

Heavy precipitation events

Figure 6: Projections of the number of days with heavy precipitation over Mauritania for different GHG emissions scenarios, relative to the year 2000.

In response to global warming, heavy precipitation events are expected to become more intense in many parts of the world due to the increased water vapour holding capacity of a warmer atmosphere. At the same time, the number of days with heavy precipitation events is expected to increase. However, this tendency is not reflected in climate projections for Mauritania (Figure 6), with climate models projecting a decrease in the number of days with heavy precipitation events, from 7 days per year in 2000 to 6 days per year in 2080 under RCP6.0. Under RCP2.6, no change is projected.

Soil moisture

Figure 7: Soil moisture projections for Mauritania for different GHG emissions scenarios, relative to the year 2000.

Soil moisture is an important indicator for drought conditions. In addition to soil parameters and management, it depends on both precipitation and evapotranspiration and therefore also on temperature, as higher temperatures translate to higher potential evapotranspiration. Annual mean top 1-m soil moisture projections for Mauritania show a minimal increase under RCP2.6 and a decrease of 5 % under RCP6.0 by 2080 compared to the year 2000 (Figure 7). However, looking at the different models underlying this analysis, there is large year-to-year variability and modelling uncertainty, with some models projecting an increase and others projecting a decrease in soil moisture. Hence, a clear trend cannot be identified.

Potential evapotranspiration

Figure 8: Potential evapotranspiration projections for Mauritania for different GHG emissions scenarios, relative to the year 2000.

Potential evapotranspiration is the amount of water that would be evaporated and transpired if sufficient water was available at and below land surface. Since warmer air can hold more water vapour, it is expected that global warming will increase potential evapotranspiration in most regions of the world. In line with this expectation, hydrological projections for Mauritania indicate a stronger rise of potential evapotranspiration under RCP6.0 than under RCP2.6 (Figure 8). Under RCP6.0, potential evapotranspiration is projected to increase by 2.3 % in 2030, 3.6 % in 2050 and 6.2 % in 2080 compared to year 2000 levels.

4 Changes are expressed relative to year 1876 temperature levels using the multi-model median temperature change from 1876 to 2000 as a proxy for the observed historical
warming over that time period.

Chad: Human health

Climate change threatens the health and sanitation sector through more frequent incidences of heatwaves, floods, droughts and storms. Among the key health challenges in Chad are morbidity and mortality through vector-borne diseases such as malaria, waterborne diseases related to extreme weather events (e.g. flooding) such as diarrhoea and cholera, respiratory diseases, measles and meningitis [38], [39]. Climate change can impact food and water supply, which can increase the risk of malnutrition and hunger. Many of these challenges are expected to become more severe under climate change. According to the World Health Organization (WHO), more than 2.5 million cases of malaria including 8 693 deaths were reported in 2018 [38]. Climate change is likely to have an impact on malaria transmission periods and the geographic range of vector-borne diseases: In Chad, like in other Sahel countries, the general malaria risk could decrease due to rising temperatures, but some regions are likely to become more vulnerable, for instance, due to more frequent incidences of flooding [39], [40]. Temperature increases and humidity decreases due to climate change have the potential to significantly increase the number of meningitis cases and prepone the seasonal onset of meningitis [41], [42]. Southern Chad is part of the so-called Meningitis Belt, which largely coincides with the Sahel region and which is where the majority of meningitis epidemics occur. Food insecurity and malnutrition present another major health problem: Between June and August 2020, 1.1 million people are expected to be severely food insecure with more than 460 000 cases of severe acute malnutrition [43].

Exposure to heatwaves

Figure 17: Projections of population exposure to heatwaves at least once a year for Chad for different GHG emissions scenarios.

Rising temperatures will result in more frequent heatwaves in Chad, leading to increased heat-related mortality. Under RCP6.0, the population affected by at least one heatwave per year is projected to increase from 2.5 % in 2000 to 14 % in 2080 (Figure 17).

Heat-related mortality

Figure 18: Projections of heat-related mortality for Chad for different GHG emissions scenarios assuming no adaptation to increased heat.

Furthermore, under RCP6.0, heat-related mortality will likely increase from approximately 4 to about 12 deaths per 100 000 people per year (Figure 18). This translates to an increase by a factor of more than three towards the end of the century compared to year 2000 levels, provided that no adaptation to hotter conditions will take place. Under RCP2.6, heat-related mortality is projected to increase to about 8 deaths per 100 000 people per year in 2080.

References

[38] WHO, “World Malaria Report 2019,” Rome, Italy, 2019.
[39] R. Boyce et al., “Severe Flooding and Malaria Transmission in the Western Ugandan Highlands: Implications for Disease Control in an Era of Global Climate Change,” J. Infect. Dis., vol. 214, pp. 1403–1410, 2016.
[40] C. Caminade, A. E. Jones, R. Ross, and G. Macdonald, “Malaria in a Warmer West Africa,” Nat. Clim. Chang., vol. 6, no. November, pp. 984–985, 2016.
[41] A. F. Abdussalam et al., “The Impact of Climate Change on Meningitis in Northwest Nigeria: An Assessment Using CMIP5 Climate Model Simulations,” Weather. Clim. Soc., vol. 6, no. 3, pp. 371–379, 2014.
[42] A. M. Molesworth, L. E. Cuevas, S. J. Connor, A. P. Morse, and M. C. Thomson, “Environmental Risk and Meningitis Epidemics in Africa,” Emerg. Infect. Dis., vol. 9, no. 10, pp. 1287–1293, 2003.
[43] OCHA, “Chad: Humanitarian Situation Overview (February 2020),” N’Djamena, Chad, 2020.

Chad: Ecosystems

Climate change is expected to have a significant influence on the ecology and distribution of tropical ecosystems, though the magnitude, rate and direction of these changes are uncertain [34]. With rising temperatures and increased frequency and intensity of droughts, wetlands and riverine systems are increasingly at risk of being converted to other ecosystems with plants being succeeded and animals losing habitats. Increased temperatures and droughts can also influence succession in forest systems while concurrently increasing the risk of invasive species, all of which affect ecosystems.

Species richness

Figure 15: Projections of the aggregate number of amphibian, bird and mammal species for Chad for different GHG emissions scenarios.

Model projections of species richness (including amphibians, birds and mammals) and tree cover for Chad are shown in Figure 15 and 16, respectively. The models applied for this analysis show similar patterns of change in species richness across both RCPs, with higher modelling uncertainty under RCP2.6. Under RCP6.0, models project increases in the number of species of up to 40 % for north-eastern Chad and decreases of up to 20 % for the western and southern parts of the country by 2080.

Tree cover

Figure 16: Tree cover projections for Chad for different GHG emissions scenarios.

With regards to tree cover, model projections vary depending on the scenario (Figure 16). Under RCP2.6, models project a decrease in tree cover of 2 % for the very south of Chad, while under RCP6.0, tree cover is projected to increase by 2 % in the south of the country by 20807.

Although these results paint a rather positive picture for climate change impacts on tree cover, it is important to keep in mind that the model projections exclude any impacts on biodiversity loss from human activities such as land use, which have been responsible for significant losses of global biodiversity in the past, and are expected to remain its main driver in the future [35]. For example, population influxes in affected areas, need for pasture and agricultural land and logging have resulted in high rates of deforestation [36]: Chad has lost 1.54 million ha of forest cover in the period from 2001 to 2016, which is equivalent to a 25 % decrease [37].

7 Due to the low starting values of tree cover in most parts of Chad, even small actual changes can lead to high percentage changes, which is why tree cover projections should be considered with caution.

References

[34] T. M. Shanahan et al., “CO² and Fire Influence Tropical Ecosystem Stability in Response to Climate Change,” Nat. Publ. Gr., no. July, pp. 1–8, 2016.
[35] IPBES, “Report of the Plenary of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services on the Work of Its Seventh Session,” n.p., 2019.
[36] FAO and UNHCR, “Rapid Woodfuel Assessment 2017 Baseline for the Area Around the City of Goré, Chad,” Rome, Italy and Geneva, Switzerland, 2018.
[37] Global Forest Watch, “Chad.” Online available: https://www.globalforestwatch.org [Accessed: 27-Apr-2020].

Chad: Infrastructure

Climate change is expected to significantly affect Chad’s infrastructure sector through extreme weather events, such as flooding and heatwaves. High precipitation amounts can lead to flooding of roads, while high temperatures can cause roads, bridges and protective structures to develop cracks and degrade more quickly. The absence of railways, seasonal navigability of rivers and limited airport facilities increase Chad’s reliance on road transportation [30]. The country’s road density ranges from 40.5 km per 1 000 km2 in the south to only 6.4 km per 1 000 km2 in the north, making it one of the lowest on the continent [30]. Many unpaved roads become impassable during the rainy season, cutting off villages and rural communities [30]. Investments will have to be made to build climate-resilient road networks.

Extreme weather events will also have devastating effects on human settlements and economic production sites, especially in urban areas with high population densities like N’Djamena, Moundou or Sarh. Informal settlements are particularly vulnerable to extreme weather events: Makeshift homes are often built in unstable geographical locations including river banks, where flooding can lead to loss of housing, contamination of water, injury or death. Dwellers usually have low adaptive capacity to respond to such events due to high levels of poverty and a lack of risk-reducing infrastructures. In 2012, heavy floods in southern Chad affected up to 700 000 people [31], with the most affected regions being Tandjilé, Mayo-Kebbi Est, Mayo-Kebbi Ouest and Sila [32]. At least 255 000 hectares of cropland and 96 000 houses were destroyed [32].

Despite the risk of infrastructure damage being likely to increase, precise predictions of the location and the extent of exposure are difficult to make. For example, projections of river flood events are subject to substantial modelling uncertainty, largely due to the uncertainty of future projections of precipitation amounts and their spatial distribution, affecting flood occurrence (see also Figure 4). In the case of Chad, projections show an increase in the exposure of major roads to river floods from 1.4 % in 2000 to 2.2 % by 2080 under RCP6.0. Under RCP2.6, projections indicate an increase towards mid-century but no overall change by 2080 (Figure 12). Exposure of urban land area to floods is projected to not change under RCP2.6 and to increase slightly under RCP6.0, from 0.2 % in 2000 to 0.4 % in 2080 (Figure 13).

Figure 12: Projections of major roads exposed to river floods at least once a year for Chad for different GHG emissions scenarios.
Figure 13: Projections of urban land area exposed to river floods at least once a year for Chad for different GHG emissions scenarios.

While three out of four models project an increase in the exposure of the GDP to heatwaves, its magnitude is uncertain, with one model projecting strong and two models projecting more moderate increases. Median model projections for RCP2.6 show an increase from 2.2 % in 2000 to 8.0 % by 2080. Under RCP6.0, exposure is projected to rise to 14 % over the same period (Figure 14). It is recommended that policy planners start identifying heat-sensitive economic production sites and activities, and integrating climate adaptation strategies, such as improved, solar-powered cooling systems, “cool roof” isolation materials or switching the operating hours from day to night [33].

Figure 14: Exposure of GDP in Chad to heatwaves for different GHG emissions scenarios.

References

[30] Logistics Cluster and WFP, “Chad Logistics Infrastructure,” 2020. Online available: https://dlca.logcluster.org/display/public/DLCA/2+Chad+Logistics+Infrastructure [Accessed: 27-Apr-2020].
[31] OCHA, “Humanitarian Bulletin: Chad (January 2013),” N’Djamena, Chad, 2013.
[32] OCHA, “Chad: Humanitarian Snapshot (24 September 2012),” N’Djamena, Chad, 2012.
[33] M. Dabaieh, O. Wanas, M. A. Hegazy, and E. Johansson, “Reducing Cooling Demands in a Hot Dry Climate: A Simulation Study for Non-Insulated Passive Cool Roof Thermal Performance in Residential Buildings,” Energy Build., vol. 89, pp. 142–152, 2015.

Chad: Agriculture

Smallholder farmers in Chad are increasingly challenged by the uncertainty and variability of weather that climate change causes [17], [18]. Since crops are predominantly rainfed, they depend on water availability from precipitation. However, the length and intensity of the rainy season is becoming increasingly unpredictable and the use of irrigation facilities remains limited due to high costs of initial investment, inefficient use of water resources and a lack of water storage and delivery techniques [27]. In 2002, less than 8 % of the estimated irrigation potential of 335 000 ha (0.7 % of the total national crop land) was irrigated [6]. Especially in central and northern Chad, soils are poor in nutrients, sandy and shallow, which has a negative effect on water retention, making soils vulnerable to drying and erosion [28].

Crop land exposure to drought

Figure 10: Projections of crop land area exposed to drought at least once a year for Chad for different GHG emissions scenarios.

Currently, the high uncertainty of projections regarding water availability (Figure 9) translates into high uncertainty of drought projections (Figure 10). According to the median over all models employed for this analysis, the national crop land area exposed to at least one drought per year will hardly change in response to global warming. However, there are models that project a strong increase in drought exposure. Under RCP6.0, the likely range of drought exposure of the national crop land area per year widens from 0.6–5.5 % in 2000 to 0.5–12.7 % in 2080. The very likely range widens from 0.1–15.8 % in 2000 to 0.1–25.0 % in 2080. This means that some models project up to a twofold increase in drought exposure over this time period, while others project no change.

Crop yield projections

Figure 11: Projections of crop yield changes for major staple crops in Chad for different GHG emissions scenarios assuming constant land use and agricultural management.

Climate change will have a negative impact on yields of maize, millet and sorghum (Figure 11)6. While maize is sensitive to hot temperatures above 35 °C, millet and sorghum have higher tolerance for hot temperatures and dry periods [29]. Still, model results indicate a negative yield trend for all three crops under both RCPs with a stronger decrease under RCP6.0. Compared to the year 2000, amounts are projected to decline by 7.4 % for maize and 9.6 % for millet and sorghum by 2080 under RCP6.0. Under RCP2.6, yields of maize are projected to decline by 2.9 % and yields of millet and sorghum by 6.5 %. Yields of rice, on the contrary, are projected to gain from climate change. Under RCP6.0, projections show an increase by 3.8 % by 2080 relative to the year 2000. These positive results under RCP6.0 can be mainly explained by the CO2 fertilisation effect, which benefits plant growth. Rice is a so-called C3 plant, which follows a different metabolic pathway than maize, millet and sorghum (C4 plants), and benefits more from higher concentration pathways. Yields of groundnuts are projected to decrease under RCP2.6 and increase under RCP6.0. The decrease under RCP2.6 can be explained by non-temperature related parameters such as changes in precipitation, while the increase under RCP6.0 can be explained by the CO2 fertilisation effect.

Overall, adaptation strategies such as switching to improved varieties in climate change sensitive crops should be considered, yet carefully weighed against adverse outcomes, such as a resulting decline of agro-biodiversity and loss of local crop types.

6 Modelling data is available for a selected number of crops only. Hence, the crops listed on page 2 may differ. Maize, millet and sorghum are modelled for all countries, except for Madagascar.

References

[6] AQUASTAT, “Irrigation and Drainage Development,” 2002. Online available: http://www.fao.org/nr/water/aquastat/data/query/resultshtml. [Accessed: 17-Apr-2020].
[17] B. Sarr et al., “Adapting to Climate Variability and Change in Smallholder Farming Communities: A Case Study From Burkina Faso, Chad and Niger (CVCADAPT),” J. Agric. Ext. Rural Dev., vol. 7, no. 1, pp. 16–27, 2015.
[18] P. Maharana, A. Y. Abdel-Lathif, and K. C. Pattnayak, “Observed Climate Variability Over Chad Using Multiple Observational and Reanalysis Datasets,” Glob. Planet. Change, vol. 162, pp. 252–265, 2018.
[27] FAO and Lake Chad Basin Commission, “Adaptive Water Management in the Lake Chad Basin,” Rome, Italy and N’Djamena, Chad, 2009.
[28] A. Jones et al., Soil Atlas of Africa. Luxembourg, Luxembourg: European Commission, Publications Office of the European Commission, 2013.