Extreme events like flooding, droughts and heatwave are among the factors causing huge socio-economic losses to Cameroonians. Investigating the potential response of rainfall and temperature extremes to global warming is therefore critically needed for tailoring and adjusting the country’s policies. Recent datasets have been developed for this purpose within the Coordinated Output for Regional Evaluations (CORDEX-CORE) initiative, at similar to 25 km grid spacing. These regional climate models were used to dynamically downscaled four global climate models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5), under the optimistic and pessimistic representative concentration pathways (RCPs) 2.6 and 8.5, respectively. These models were employed in this study for characterizing the response of Cameroon’s extreme precipitation and temperature events to global warming, using seven indices defined by the Expert Team on Climate Change Detection and Indices. Under global warming, the maximum number of consecutive dry (wet) days’ is expected to increase (decrease). However, the annual total rainfall amount is expected to increase, mainly due to the intensification of very wet days and daily rainfall intensity. Furthermore, the temperature-based indices reveal an increase (decrease) in the total annual hot (cold) days, and overall, changes intensify with increased radiative forcing. The high-mitigated low-emission pathway RCP2.6 features attenuated changes, and even sometimes adapts to reverse the sign of changes. Designing reliable policies to limit the risks associated with the above changes is required, as their socio-economic consequences are likely to include food insecurity, heat-related illness, population impoverishment, price rises and market instability.
The design of appropriate adaptation strategies to the impacts of climate change requires a contextual study of local perceptions due to the non-homogeneity of climate in a given agro-ecological area. The research objective of the current study aims to examine the evolution of climate parameters from 1983 to 2019 linked to the perceptions of local populations and appropriate adaptation measures in the Belabo-Diang Communal Forest of Cameroon. The methodological approach includes collecting and analyze climate data from 1983 to 2019; and surveying existing local perceptions and adaptive strategies among 540 households using semi-structured questionnaires. A significant increase in temperature of about 1 °C over 36 years (1983-2019) and a non-significant decrease in precipitation (95.36 mm) over the same period were observed. Local perceptions related to climate change vary according to the sector of activity and are mainly associated with more heat in the dry season (90%), late onset of rains (84%), drought recurrence (82%), less rainfall during the year (80%), and increase in the duration of drought (80%). For 82-100% of households, according to the activity sector, no appropriate adaptation measures to climate change were applied depending on activities. The adaptation measures used by less than 0-20% of respondents, include mainly the abandonment or change of activity, and modification of the agricultural calendar. With the lack of appropriate and adequate adaptation measures by the riparian populations, this study appears necessary to inform policy-makers of the need to develop and implement more appropriate strategies to enable the riparian people living in forest area of Cameroon to better adapt to these effects of climate changes.
BACKGROUND: Weather fluctuation affects the incidence of malaria through a network of causuative pathays. Globally, human activities have ultered weather conditions over time, and consequently the number of malaria cases. This study aimed at determining the influence of humidity, temperature and rainfall on malaria incidence in an inland (Muyuka) and a coastal (Tiko) settings for a period of seven years (2011-2017) as well as predict the number of malaria cases two years after (2018 and 2019). METHODS: Malaria data for Muyuka Health District (MHD) and Tiko Health District (THD) were obtained from the Regional Delegation of Public Health and Tiko District Health service respectively. Climate data for MHD was obtained from the Regional Delegation of Transport while that of THD was gotten from Cameroon Development Coorporation. Spearman rank correlation was used to investigate the relationship between number of malaria cases and the weather variables and the simple seasonal model was used to forecast the number of malaria cases for 2018 and 2019. RESULTS: The mean monthly rainfall, temperature and relative humidity for MHD were 200.38 mm, 27.05(0)C, 82.35% and THD were 207.36 mm, 27.57 °C and 84.32% respectively, with a total number of malaria cases of 56,745 and 40,160. In MHD, mean yearly humidity strongly correlated negatively with number of malaria cases (r = - 0.811, p = 0.027) but in THD, a moderate negative yearly correlation was observed (r = - 0.595, p = 0.159). In THD, the mean seasonal temperature moderately correlated (r = 0.599, p = 0.024) positively with the number of malaria cases, whereas MHD had a very weak negative correlation (r = - 0.174, p = 0.551). Likewise mean seasonal rainfall in THD moderately correlated (r = - 0.559, p = 0.038) negatively with malaria cases, contrary to MHD which showed a very weak positive correlation (r = 0.425, p = 0.130). The simple seasonal model predicted 6,842 malaria cases in Muyuka, for 2018 and same number for 2019, while 3167 cases were observed in 2018 and 2848 in 2019. Also 6,738 cases of malaria were predicted for MHD in 2018 likewise 2019, but 7327 cases were observed in 2018 and 21,735 cases in 2019. CONCLUSION: Humidity is the principal climatic variable that negatively influences malaria cases in MHD, while higher seasonal temperatures and lower seasonal rain fall significantly increase malaria cases in THD.
This study was conducted on Manoka Island (Littoral Region of Cameroon) with the aim of analyzing climate change vulnerability and local adaptation strategies based on the local community’s perceptions and biophysical evidence. We used household surveys, focus group discussions, field observation, GIS, and remote sensing to collect data on variables of exposure, sensitivity, and adaptive capacity. Historical changes in rainfall and temperature, mangrove cover, and the occurrence of extreme climatic events were used as indicators of exposure. Property losses and income structure were used as indicators of sensitivity, while human, natural, social, financial, and physical assets represented adaptive capacity. 89 households were interviewed in the nine settlements of the island. Results show that Manoka Island is experiencing irregular rainfall patterns (with average annual values deviating from the mean by -1.9 to +1.8 mm) and increasing temperature (with annual values deviating from the mean by -1.2 to +3.12). The dynamics of the coastline between 1975 and 2017 using EPR show average setbacks of more than ±3 m/year, with erosion levels varying depending on the period and location. The number of households perceiving extreme climatic events like seasonal variability, flood, and rain storm was higher. From respondents’ perception, housing and health are the sectors most affected by climate change. The reported high dependence of households on fishing for income, their overall low livelihood diversification, and their poor access to climate information reported by 65% of respondents portray their poor adaptive capacity. Local response initiatives are ineffective and include among others constructing buildings on stilts and using car wheels to counter the advancement of seawater inland. The study concludes that households on Manoka Island are vulnerable to the impacts of climate change. Income diversification, mangrove reforestation, the development of sustainable supply chains for wood fuel, and sustainable fish smoking devices are the main pathways for adaptation planning in this area.
BACKGROUND: Epidemiological data of heart failure (HF) decompensation from the northern hemisphere suggests higher rates during winter. OBJECTIVES: We aimed to explore the seasonal variation in decompensated HF admission and mortality rates in a country with equatorial climate. METHODS: We conducted a retrospective cross-sectional study by chart review of the admission, discharge registries and patient files from 2016 to 2018 in the cardiology unit of the Yaound?? Central Hospital, Cameroon. Data was collected on HF morbidity and mortality from the registers and patients’ files. Corresponding seasonal climatic data was obtained from the meteorology office of the Cameroonian ministry of transports. Analysis of variance and Chi-square test were respectively used to compare the continuous and categorical data between across seasons. Correlation between continuous data was assess with the Spearman correlation. RESULTS: Decompensated HF accounted for 636 (36.2%) out 1755 cardiology unit admission and an 18% lethality rate. Decompensated HF admission, mortality and lethality rates were respectively 38.2%, 6.7% and 17.9% higher during the long rainy season (all P values >0.05). We observed a borderline-to-significant inverse linear continuous correlation between monthly temperatures and admission rate (r=-0.301; P=0.070), lethality rate (r=-0.361; P=0.030) and mortality rate (r=-0.385; P=0.020). There was no significant difference of the distribution of precipitating factors between seasons. CONCLUSION: Although statistically insignificant, decompensated HF admissions and mortality increase in rainy season where the temperature is lower in an equatorial climate.
Malaria is a critical health issue across the world and especially in Africa. Studies based on dynamical models helped to understand inter-linkages between this illness and climate. In this study, we evaluated the ability of the VECTRI community vector malaria model to simulate the spread of malaria in Cameroon using rainfall and temperature data from FEWS-ARC2 and ERA-interim, respectively. In addition, we simulated the model using five results of the dynamical downscaling of the regional climate model RCA4 within two time frames named near future (2035-2065) and far future (2071-2100), aiming to explore the potential effects of global warming on the malaria propagation over Cameroon. The evaluated metrics include the risk maps of the entomological inoculation rate (EIR) and the parasite ratio (PR). During the historical period (1985-2005), the model satisfactorily reproduces the observed PR and EIR. Results of projections reveal that under global warming, heterogeneous changes feature the study area, with localized increases or decreases in PR and EIR. As the level of radiative forcing increases (from 2.6 to 8.5 W.m(-2)), the magnitude of change in PR and EIR also gradually intensifies. The occurrence of transmission peaks is projected in the temperature range of 26-28 °C. Moreover, PR and EIR vary depending on the three agro-climatic regions of the study area. VECTRI still needs to integrate other aspects of disease transmission, such as population mobility and intervention strategies, in order to be more relevant to support actions of decision-makers and policy makers.
