Precipitation Variability in West Africa in the Context of Global Warming and Adaptation Recommendations
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It is commonly accepted that the Earth’s climate is changing and will continue to change in the future. Rising temperatures are one of the direct indicators of global climate change. To investigate how the rising global temperature will affect the spatial pattern of rainfall in West Africa, the precipitation and potential evapotranspiration variables from ten Global Climate Models (GCMs) under the RCP8.5 scenario were driven by the Rossby Centre regional atmospheric model (RCA4) from the COordinated Regional Climate Downscaling EXperiment (CORDEX) and analyzed at four specific global warming levels (GWLs) (i.e., 1.5 °C, 2.0 °C, 2.5 °C, and 3.0 °C) above the preindustrial level. This study utilized three indices, the precipitation concentration index (PCI), the precipitation concentration degree (PCD), and the precipitation concentration period (PCP) over West Africa to explore the spatiotemporal variations in the characteristics of precipitation concentrations. Besides, the analysis of the effect of the specified GWLs on the Consecutive Dry Days (CDD), Consecutive Wet Days (CWD), and frequency of the intense rainfall events allowed to a better understanding of the spatial and temporal patterns of extreme precipitation in West Africa. Results reveal that, for the projections simulations and at each GWL, the rainfall onset starts one month earlier in the Gulf of Guinea in response to the control period. To encourage adaptation to the various changes in climate in general, and particularly in respect of rainfall, this study proposes several adaptation methods that can be implemented at the local (country) level, as well as some mitigation and adaptation strategies at the regional (West African) level.
KeywordsPrecipitation concentration index (PCI) Precipitation concentration degree (PCD) Precipitation concentration period (PCP) Adaptation strategy Global warming
Precipitation variability and concentration are two important climate factors with an impact on society, agriculture, and the environment, as increased precipitation variability can reduce agricultural yields (Rowhani et al. 2011). Also, precipitation variability is strongly connected to extreme wet and dry events, floods, and droughts, which pose threats to the environment and to society, and can also have devastating consequences on ecosystems, food supplies, and economies at the local, regional, and global scale (Easterling et al. 2000). In its 5th report, the Intergovernmental Panel on Climate Change (IPCC) stresses that there is an increase in the number of extreme weather events for the twenty first century due to climate change (IPCC 2014). As temperatures rise, the amount of water vapor in the atmosphere also increases and the spatiotemporal distributions of precipitation change, resulting in significant differences in precipitation across the world (Chou and Lan 2011; Gao et al. 2014). Obtaining real-time information and facilitating earlier predictions by decision makers can be an efficient tool to adapt and to mitigate the impacts of climate events. However, this is an especially challenging task over data-sparse regions as West Africa, where unreliable monitoring networks and generally low institutional capacity limits the spread of timely information (Sheffield et al. 2014).
To assess the spatial and temporal variability of precipitation, various methods were adopted and documented. Precipitation concentration plays an important role, firstly, with regard to the annual total precipitation amount days and, secondly, with regard to the time and degree of concentration of the yearly total precipitation within a year. This variable has the potential to cause floods and drought events, both of which are expected to put considerable pressure on water resources (Zhang and Qian 2003; Zhang et al. 2009). Several studies have been interested in the importance of precipitation concentration indices. The present study adopted three of them: (i) the precipitation concentration index (PCI), which was developed by Oliver (1980); and years after was modified by De Luis et al. (2011). It has been used by Shi et al. (2015) as an indicator of the rainfall concentration for annual and seasonal scales, and Ezenwaji et al. (2017) to investigate the implications of the concentration and variability of rainfall on flooding over Awka Urban Area (Nigeria). (ii) The precipitation concentration degree (PCD) and (iii) the precipitation concentration period (PCP), which were redefined by Zhang and Qian (2003) in a study of droughts and floods in the Yangtze River valley (China). The basic concept behind the PCD and PCP is that the monthly total precipitation is a vector quantity with both magnitude and direction (Li et al. 2011; Wang et al. 2013).
In order to investigate and understand future climates, scientists refer to climate scenarios to provide a plausible explanation of how the future may evolve with respect to a number of variables, including socioeconomic change, technological change, energy and land-use, and the emissions of greenhouse gasses (GHGs) and air pollutants (Van Vuuren et al. 2011). Many factors are taken into account in order to predict how future global warming will contribute to climate change. A key variable is future GHG emissions. Some studies over West Africa (e.g., Egbebiyi 2016; Haensler et al. 2013; Sylla et al. 2012) have shown that projected increases in global GHG concentrations will lead to an increase in the frequency and intensity of extreme rainfall events (Klutse et al. 2018). So, for the meeting by the Intergovernmental Panel on Climate Change (IPCC) in 2007, four RCP radiative forcing levels were retained (Moss et al. 2008). The Representative Concentration Pathway (RCP) is defined as the trajectories relating to GHG concentrations, and was projected by the IPCC in 2014. The four Representative Concentration Pathways (RCPs), namely, RCP2.6, RCP4.5, RCP6, and RCP8.5, are labeled after a possible range of radiative forcing values up to the year 2100 (2.6, 4.5, 6.0, and 8.5 W/m2, respectively).
In 2010, the sixteenth session of the Conference of the Parties (COP16) adopted the Cancun Agreement for keeping the global temperature rise below 2.0 °C (UNFCCC 2010). A few years later, in 2015, at Paris, the COP21 proposed an ambitious target, which is to keep the increase in global average temperature to well below 2.0 °C above preindustrial levels, while pursuing efforts to limit the temperature increase to 1.5 °C. The rate of climate change is an important issue for mitigation and/or adaptation policies (Watkiss et al. 2015). Many studies have recently examined regional risks for the whole of Africa, as well some of its regions, such as Southern Africa and West Africa, at various global warming levels (GWLs). Recently, Déqué et al. (2017) suggested a classification of global warming for all the RCPs and for GWLs from 1.5 °C to 4 °C. Based on Déqué et al.’s (2017) suggestion, several studies (Abiodun et al. 2019; Klutse et al. 2018; Kumi and Abiodun 2018; Maúre et al. 2018; Nikulin et al. 2018) were done to assess the impact of different GWLs on various climate variables.
Mitigation and adaptation are two fundamental societal response options to deal with the problem of climate change (IPCC 2001; Füssel 2007; Locatelli 2011). The community of climate change scientists has focused mainly on mitigation rather than adaptation; for a long time, the latter has been ignored in the debate on climate change (Füssel 2007; De Perthuis et al. 2010). Mitigation policies have already been adopted at the international level, since it is clear that the global climate is already changing and will continue to change in the future due to anthropogenic GHG and aerosol emissions; it is thus important to make adjustments all the more urgently, especially at a local level (Füssel 2007; De Perthuis et al. 2010). In addition, though, there is a need to focus the debate on adaptation measures to climate change in order to prepare communities, especially the most vulnerable ones, to cope with its impacts (Kpadonou et al. 2012).
Identification of a suitable methodology to assess the spatiotemporal precipitation distribution is vital for planning effective adaptation and mitigation strategies to overcome potential drought and flood situations. Selecting the optimal methodology or tools may be useful for planning disaster management and preparedness to respond to droughts and floods, and to mitigate their effects on the activities in different sectors of the economy. Therefore, this study aims to use the analyses of PCI, PCD, PCP, consecutive dry days (CDD), and consecutive wet days (CWD) to explain the spatial and temporal variability of precipitation in West Africa in the context of GWLs 1.5 °C, 2.0 °C, 2.5 °C, and 3.0 °C.
Description of the Study Areas and Methodology
Study Area and Data Sources
The PCI is calculated in the section “Calculation of Precipitation Concentration Index (PCI)” to classify how the rainfall is spatially distributed, and the PCD and PCP in the section “Computation of Precipitation Concentration Degree (PCD) and Precipitation Concentration Period (PCP)” were designed to determine, respectively, the period (months) and the degree of the concentration of the precipitation during the year.
Calculation of Precipitation Concentration Index (PCI)
Equation 1 was used for annual PCI, while Equations 2 and 3 were utilized for seasonal scales (rainy and dry seasons, respectively); nw and nd represent, respectively, the number of rainy and dry season months, and P = precipitation of the ith month. In order to investigate changes in the PCI, a 30-year period was considered both for historical and future periods. Table 1 shows the projection periods used for each GWL; the historical 30-year period is 1971–2000.
Computation of Precipitation Concentration Degree (PCD) and Precipitation Concentration Period (PCP)
The next section discusses the results obtained with regard to the three indices above mentioned.
This section presents the results obtained from the study. Based on the variability between the historical period (1971–2000, also referred to as the control period, CTL) and the various GWLs periods the section “Annual Precipitation Concentration” shows the annual precipitation concentration patterns, while the section “Seasonal Precipitation Concentration” focuses on the seasonal PCI variabilities. The section “Evaluation of the Models’ Robustness” investigates the robustness of the models to assess the PCI over West Africa. The period and the degree of the concentration of the rainfall are evaluated in the section “Variability of PCD and PCP”, while the section “Daily Precipitation Variability” shows the daily consecutive precipitation variability. Finally, the adaptation processes proposed are explained in section “Adaptation Strategies.”
Variability of PCI
Annual Precipitation Concentration
Seasonal Precipitation Concentration
There are two major seasons over the study area. For the purposes of this analysis, the rainy season is assumed to last from early May to the end of September (MJJAS). The PCI computed for the seasonal scale over West Africa indicates complex spatial patterns of precipitation distribution. Therefore, Fig. 2f–j shows that precipitation is uniformly distributed (i.e., almost the same amount of precipitation occurs in each month) over the Gulf of Guinea and the Savanna. For the studied GWLs, when compared with the historical period, it can be noticed that the uniform precipitation distribution extends a little toward the Sahel. So, the precipitation concentration is irregularly distributed (i.e., PCI ∈ [16, 20] according to Oliver 1980) over the northern region of West Africa during the rainy season.
Figure 2k–o presents the PCI for the dry season. It illustrates an irregular precipitation concentration distribution over the Gulf of Guinea. This is obvious, because there is almost no rain during this period of the year, and the irregular distribution noticed might be due to some isolate rains recorded after some particular meteorological phenomena. The precipitation in the Savanna and Sahel is strongly irregularly distributed (i.e., PCI > 20 during the rainy period, according to Oliver 1980), which means that the total of significant precipitation occurs within a short period (1–2 months).
The analysis of simulations presented in Fig. 2 (with regard to annual and seasonal evaluation) establishes that in West Africa, the precipitation is uniformly well distributed during the period May–September (MJJAS) in the Gulf of Guinea and the Savanna. A northward gradient is well noticed because the highest values of the PCI are located in the Sahel, whereas the lowest are identified around the Gulf of Guinea. Despite the global warming effect for all levels, the precipitation concentration does not change over the Gulf of Guinea and the Savanna; on the contrary, it extends toward the northern region of the study domain.
Evaluation of the Models’ Robustness
Variability of PCD and PCP
Daily Precipitation Variability
In contrast to the CDD analysis over West Africa, the CWD did not show important variability. Its variation is very slight and ranges between 0 ± 3 days. Nonetheless, some high variations could be observed at several specific points. On the projected simulations illustrated in Fig. 5e–h), the CWD decreases about 10 ± 2 over the South Benin and Nigeria. A slight augment in CWD of up to 2 days is likely to be noticed in the Sahel.
Since the spectacular drought events of the 1970s, it has become clear that the high variability in precipitation constitutes one of the major challenges faced by the West African region. Agriculture is one of the major economic activities of West Africa, and thus significant changes in rainfall due to climate change will negatively affect the entire region. These concerns have generated ongoing scientific, social, and political debate. Moreover, some parts of West Africa (mostly along the Guinean Coast) have recorded recurrent flood events since 2000. Thus, both climate variability and increasing trends in droughts and floods and other severe weather events pose a challenge for the primarily rain-fed agriculture systems in West Africa (Sultan and Gaetani 2016). Therefore, any adaptations must enable inhabitants to cope successfully with short-term climate variability as well as to reduce the long-term negative impacts of climate change (Lobell 2014; Saba et al. 2013). Households and communities must become accustomed to and able to respond creatively and effectively to disruptions of their livelihoods. Indeed, in order to be successful, adaptations must be anchored in all processes affecting life. Some of the possible adaptation strategies, especially relating to floods, droughts, and food crops, are illustrated in this study.
According to the results above, at increasing GWLs, precipitation over the Gulf of Guinea and the Savanna will shift and start earlier (in May) and that the highest precipitation concentration will occur in May–June over that area. In addition, intense rainfall events and consecutive wet days will increase in frequency, which can expose the Gulf of Guinea and Savanna to flood events from June onwards. This confirms the results from Donat et al. (2016), which showed that the intensification of the hydrological cycle both in recent decades and in future projections will lead to an increased risk of flooding in dry regions as the climate warms. Groundnut, cassava, and maize are important crops for the Gulf of Guinea, especially for Nigeria, southern Mali, Benin, Ivory Coast, Burkina Faso, Ghana, and Senegal, while over the Savanna, the main crops are yam, millet, and sorghum. Therefore, to inform farmers about short-term coping and adaptation practices, scientists are encouraged to simulate crop models and to assess their uncertainties according to the shift in the times of the projected precipitation distribution and the increase in the soil temperature. Alternative strategies, such as constructing infrastructure or irrigation systems, could also be used to mitigate the impact of exposure.
Regional provisions and strategies that include all West African countries should be developed to meet the challenge of combating GHG production. The framework agreements must link and bind countries to ensure strict compliance with community-based adaptation measures. At the local level, moreover, each country will have to develop precautionary flood and drought warning systems to limit the loss of human life. Scientific community frameworks need to be developed at the local level to improve the seasonal prediction of rainfall models that must be updated frequently in order to generate reliable information. Better research findings are needed to increase knowledge of how information structures could be framed and used to reduce the power of parochial conflicting benefits and overcome inertia, apathy, and lack of political drive. Finally, communication systems geared toward achieving specific targets (e.g., to assist farmers) will have to be developed. Informing media platforms about climate sciences and adaptation strategy policies and discussions, for instance, would educate the public about the impacts of global warming, the importance of reducing GHG emissions, and the need for developing and implementing mitigation and adaptation strategies.
Discussion and Conclusion
It is widely demonstrated in the literature that West Africa is vulnerable to climate change due to its high climate variability and high reliance on rain-fed agriculture. It does not have an institutional capacity to respond and to adapt climate variability and climate change (Quenum et al. 2019). In order to reduce risks and suggest reliable adaptation strategies for predicted climate change, this study focused on determining the variability of precipitation both in the present time and under various future scenarios. The findings obtained with regard to the PCI showed that in West Africa the period summarizing the main rainfall activity is between May and September. For the historical period, simulations show over the Gulf of Guinea and the Savanna a uniform precipitation distribution, and in the Sahel both moderate and irregular distribution of precipitation. Under future scenarios, i.e., at all the GWLs (1.5 °C, 2.0 °C, 2.5 °C, and 3.0 °C in this study), the magnitude of the simulated PCIs over West Africa reduced and became close to PCI<10. This demonstrates that under the selected GWLs, the precipitation concentration of West African becomes more uniformly distributed except for some countries in the north-eastern areas (Niger and Chad), which are least dry at all four GWLs. To get further details on the temporal patterns of the precipitation concentration in the study domain, the PCP variable is computed, which reveals that the magnitude of the PCI increases northwardly materializing a south-north precipitation gradient. The highest precipitation concentrations during the control period occur in July–August and cover the Gulf of Guinea and the Savanna regions, whereas the Sahel records the peaks of its precipitation concentration in September. The PCP too changed in response to increasing GWLs. Therefore, the precipitation for the projected period becomes more concentrated in June–July over the Gulf of Guinea and the Savanna, and in August for the Sahel region. Thus, the rainfall concentration starts one month earlier in the future period compared to the historical (which is very important and needed information for the agricultural sector). Globally, it also noticed that the Savanna-Sahel region recorded a high magnitude of PCD. This means that the total yearly precipitation recorded in this region occurs in a short time, due to the WAM system, which is led by the back-and-forth movement of the Inter-Tropical Discontinuity (ITD) between south and north. Indeed, this movement creates an increased precipitation concentration in the Savanna-Sahel area, which is immediately followed by the southward movement of the ITD. This establishes a long time record of precipitation concentration, which is highlighted by the PCD values over this area. A significant reduction in the CDD is recorded over the north-east (i.e., Niger and Chad), and a slight increase in the number of CWD over the Sahel. Additionally, based on the results from PCI and PCP, Niger and Chad are projected to experience more wet condition under increasing GWLs. The 5-day cumulative rainfall variable shows that the Gulf of Guinea is projected to experience more intense, very intense, and heavy rainfall events under increasing GWLs. All these results together show how much West Africa will be exposed to higher variability of climate change and also to future heavier rainfall and wet conditions.
To cope with such changes, reduce loss of life, and better manage the impact on inhabitants of this region, some adaptation strategies are necessary under continual climate change. Two types of strategies are required: a regional framework agreement and local coping strategies. The regional agreement is very important because it forces each stakeholder (country) to respect the framework agreement; this is mostly in line with mitigation of climate change at the regional scale. Local strategies are important in two ways: Firstly, local strategies should enable West African countries to respect the regional framework agreement. Secondly, they should encourage countries to look for and develop adequate adaptation possibilities, by responding to the contributions of scientists and decision makers, which represent an important factor in development. They are the ones who have to provide reliable information to the population, and particularly to farmers, for better management of crops in order to ensure food security.
In summary, this study has investigated precipitation variability and change in West Africa under increasing GWLs, and identified an earlier onset of rainfall, especially over the Gulf of Guinea. It has found that the variability of CDDs and CWDs under increasing GWLs is considerable, and that even the intensity of rainfall has increased. Such significant information is useful for farmers and decision makers to ensure the survival and prosperity of the population.
This work was funded by the German Federal Ministry of Education and Research (BMBF) through the West African Science Service Center on Climate Change and Adapted Land Use (WASCAL), which provided a research fund to Gandome Mayeul L.D. Quenum. The Centre for High-Performance Computing (CHPC, South Africa) provided the computing facility used for the study.
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