Climate change-induced sea level rise, shoreline changes, and coastal erosion are projected to drive massive population displacement and mobility in Africa. This study was conducted to examine the pattern of shoreline changes, coastal erosion, land use/land cover dynamics, projections, and their implications on internal migration in Senegal, Kenya, and Tanzania, representing West and East Africa. The digitized shoreline was mapped into erosion, accretion, and trend analysis, which further explains the vulnerability and physical processes that could trigger human displacement within the context of environmental/climate migration. Analysis of land use and land cover dynamics was obtained from Landsat 5 TM of 1986, Landsat 7 ET of 2006, Landsat 8 OLI/TIRS of 2016, and Landsat 9 OLI/TIRS of 2022 and computed using ArcGIS 10.7 for land-use change and percentage change in square kilometers was conducted to examine land use/land cover dynamics and their contributions to the risk of coastal erosion in the study regions. The outcome of the shoreline analysis reveals that 972.03 sqkm of land has been lost to coastal erosion in Senegal from 1986 to 2022 with 2016–2022 described as the period with the highest in terms of land loss. In Kenya, −463.30 sqkm of land has also been lost to coastal erosion and agents of wave processes, with 1986–2006 recording the highest share of −87.74% loss of valuable land, while in Tanzania, −1033.35 sqkm of valuable land has been lost from 1986 to 2022 to coastal erosion, with 2006–2016 alone recording −10.4634% of land loss. The result of the land use/land cover percentage change analysis indicates a massive loss of vegetation cover with a significant increase in settlement representing urbanization. The scenario analysis of the shoreline at 10, 20, and 30 m indicates that 567 persons per sqkm at 10 m, 25,904.6 persons per sqkm at 20 m, and 25,904.5 persons per sqkm will be displaced in Senegal at 30 m. In Kenya, 57,746 persons per sqkm are projected to be displaced at 10 m while 1210.5 persons per sqkm will be displaced at 20 m and 7737.32 persons per sqkm will be displaced at 30 m. In Tanzania, the maximum population density projected to be displaced at 10, 20, and 30 m is 10,260.97 per sqkm. Structured questionnaires were administered to elicit responses from coastal dwellers on their perception of coastal erosion and climate migration as part of ground truthing and the result of the survey affirms that coastal erosion and its exposure are the major drivers of climate migration in the study area.

This study used an ERA5 reanalysis SST dataset re-gridded to a common grid with a 0.25° × 0.25° spatial resolution (latitude × longitude) for the historical (1940–2014) and projected (2015–2100) periods. The SST simulation under the SSP5-8.5 scenario was carried out with outputs from eight General Circulation Models (GCMs). The bias-corrected dataset was developed using Empirical Quantile Mapping (EQM) for the historical (1940–2015) and future (2030–2100) periods while the CMIP6 model simulation was evaluated against the ERA5 monthly observed reanalysis data for temperatures over the Gulf of Guinea. Overall, the CMIP6 models’ future simulations in 2030–20100 based on the SSP5-8.5 scenario indicate that SSTs are projected, for the Gulf of Guinea, to increase by 4.61 °C, from 31 °C in the coast in 2030 to 35 °C in 2100, and 2.6 °C in the Western GOG (Sahel). The Linux-based Ncview, Ferret, and the CDO (Climate Data Operator) software packages were used to perform further data re-gridding and assess statistical functions concerning the data. In addition, ArcGIS was used to develop output maps for visualizing the spatial trends of the historical and future outputs of the GCM. The correlation coefficient (r) was used to evaluate the performance of the CMIP6 models, and the analysis showed ACCESS 0.1, CAMS CSM 0.2, CAN ESM 0.3, CMCC 0.3, and MCM 0.4, indicating that all models performed well in capturing the climatological patterns of the SSTs. The CMIP6 bias-corrected model simulations showed that increased SST warming over the GOG will be higher in the far period than the near-term climate scenario. This study affirms that the CMIP6 projections can be used for multiple assessments related to climate and hydrological impact studies and for the development of mitigation measures under a warming climate.

Spatial pattern and variability of sea surface temperature SST and their teleconnections with rainfall dynamics in the Gulf of Guinea (GOG) were examined in this study. SST and rainfall data of 50 years (1970–2022) were obtained from ERA5 and NOAA CPC at 0.25° x × 0.25° and 0.5° × 0.5° spatial resolution from longitudes 10°W and 8°W, 6^oW, 4^oW, and 2^oW and latitudes 15°N, 5^oN, 3^oN, 15^oS, 5^oS, and 3^oS, distributed along the Gulf of Guinea (GOG) respectively. Analysis was further carried out on twelve rainfall gridded stations distributed along the Gulf of Guinea (GoG) for the characterization of the Rainfall-SST teleconnection across the region while the relationship between the rainfall-SST anomalies, seasonal, inter-annual, and decadal scales was carried out using correlation analyses and composites. Interpolation of the meteorological variables was carried out using Inverse Distance Weight (IDW) from the ArcGIS Spatial Analyst Tool, Ferret, and CDO which were further employed to generate the seasonal and decadal rainfall and SST maps and statistical analysis of the study area. The result of the decadal and seasonal analysis of SST variability from 1970–1980,1980–1990, 1990–2000,2000–2010,2010–2020 and 2022 indicate that SST was highest from 2010 to 2020 at 28.91 °C and fluctuated between (28.49 °C) in the 1970–1980 and (28.08 °C) for the 1980–1990 decade While the seasonal pattern of SST showed marked variability with March–April and May(MAM) recording 29.34 °C with the lowest being in June-July–August(JJA) at 28.7 °C. In terms of decadal analysis of rainfall, the period 2010–2020 recorded the highest amount of rainfall along the coast (3,145.5 mm-3,928.3 mm while 1970–1980 recorded the lowest amount of rainfall (2,650–3.310 mm. To investigate the teleconnection between of SST and rainfall dynamics, statistical analysis was used where the SST values were plotted against seasonal rainfall in 11 stations namely Abidjan, Banjul, Accra, Guinea, Conakry, Cotonou, Dakar Doula, Freetown, Lagos, Lome, and Monrovia. The outcome of the statistical analysis and Standardized Anomaly Index used indicate that Banjul, Cotonou, Dakar, and Doula exhibited statistically insignificant correlation at 0.05 confidence level while Abidjan, Accra, Lagos, Lome, Freetown, and Monrovia showed positive and statistically significant correlation. The spatial pattern of seasonal rainfall climatology categorized into DJF, MAM, JJA, and SON reveals that JJA and SON produced 80% of rainfall in the Coastal GOG followed by MAM. The study affirmed that warm and cold tongues exist in the GOG alongside positive teleconnection and that the spatial variability of SST observed in this study corresponds positively with the decadal and seasonal variability of rainfall.

The increasing problems associated with global climate change due to the use of fossil fuels and greenhouse gases in the atmosphere call for serious concerns. This scenario has necessitated the need for a shift from the use of fossil fuels to renewable energy to curtail the impact of climate change on the environment. The study was conducted to assess the potential and variability of climatic and other natural resources for renewable energy and evaluation of public perception of the energy transition process in Rivers State Nigeria. The climate data was obtained from the Nigeria Meteorological Agency (NIMET) from 2000 and 2023 and analyzed using the Linear Regression Model while data on public perception was generated from structured questionnaires and analyzed using descriptive statistics. The outcome of the Regression and ANOVA analysis were generally significant for both the natural resources and residents of the study area. Also, 65.7% of the respondents affirmed that the available natural resources are viable and suitable for energy transition. The significant and positive relationship of the different climatic elements in the study signposts huge potential for the renewable energy transition in the study area. The perception of residents was positive and showed excellent knowledge of the abundance of natural resources for the energy transition. It is therefore recommended that the Rivers State government should partner with the private sector to increase investment in energy infrastructure for improved environmental sustainability.

Climate change-induced Sea level rise, shoreline changes, and coastal erosion are projected to drive massive population displacement and mobility in Africa. This study was conducted to examine the pattern of shoreline changes, coastal erosion, land use/Land cover dynamics their projections, and its implications on internal migration in Senegal, Kenya and Tanzania representing West and East Africa. The digitized shoreline was mapped into both erosion, accretion and trend analysis which further explains the vulnerability and physical processes that could trigger human displacement within the context of environmental/climate migration. Analysis of landuse and land cover dynamics was obtained from Landsat 5 TM of 1986, Landsat 7 ET of 2006, Landsat 8 OLI/TIRS of 2016 and Landsat 9 OLI/TIRS 2022 and computed using ArcGIS 10.7 for Land-use change and percentage change in square kilometers was conducted to examine landuse/landcover dynamics and their contributions to the risk of coastal erosion in the study regions. The outcome of the shoreline analysis reveals that 972.03sqkm of land has been lost to coastal erosion in Senegal from 1986-2022 with 2016-2022 described as the period with the highest in terms of land loss. In Kenya, -463.30sqkm of land has also been lost to coastal erosion and agents of wave processes with 1986-2006 recording the highest share of -87.74% loss of valuable land while In Tanzania, -1033.35sqkm of valuable land has been lost from 1986-2022 to coastal erosion with 2006-2016 alone recording -10.4634% of land loss. The result of the land use/land cover percentage change analysis indicates a massive loss of vegetation cover with a significant increase in settlement representing urbanization. The scenario modeling of the shoreline on 10,20 and 30ms indicates that 567 persons per sq, 25904.5 persons per sqm and 25904.5 persons per sq km for both 20 and 30ms will be displaced in Senegal while in Kenya 577.5 persons per sq km will be displaced at 10m followed by 1210.3 persons per sq km at 20m and 7737.3 sq km at 30m. In, Tanzania, 702001.9 persons per sq km are projected to be displaced at 10m shift in the shoreline. Structured Questionnaires were administered to elicit responses from coastal dwellers on their perception of coastal erosion and environmental migration as part of ground truthing and the result of the survey affirms that coastal erosion and its exposure are the major drivers of environmental migration in the study area.

The study used ERA5 reanalysis SST dataset re-gridded to a common grid of 0.25° X 0.25° spatial resolution (latitude × longitude) for the historic (1940–2014) and projected (2015–2100). The SST simulation under the SSP5-8.5 scenarios was carried out with output from 8 General Circulation Models (GCMs). The bias-corrected dataset was developed using Empirical Quantile Mapping EQM for the historical (1940-2015) and the future (2030-2100) while the CMIP6 model simulation was evaluated against the ERA5 monthly rainfall observed data for monthly and annual sea surface temperatures (SST) over the Gulf of Guinea. ). The CMIP6 models simulation for SST projection from 2030-20100 based on SSPS 8.5 SST is projected to increase by 4.61o (31oC) in 2030 to 35oC in 20100 in the coastal GOG and 2.6oC in the Western GOG (Sahel). The correlation coefficient (r) was used to evaluate the performance of the CMIP6 models and the analysis showed ACCESS 0.1,CAMS CSM 0.2, CAN ESM 0.3, CMCC, 0.3 and MCM, 0.4 indicating that all models performed well in capturing the climatological pattern of the SST. The.CMIP6 bias corrected model simulations showed increased SST warming over the GOG will be higher in the Far period end than the Near-term climate. The study affirmed that the CMIP6 projections can be used for multiple assessments related to climate and hydrological impact studies and for the development of mitigation measures under a warming climate