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Abstract Promoting renewable energy sources, particularly in the solar industry, has the potential to address the energy shortfall in Central Africa. Nevertheless, a difficulty occurs due to the erratic characteristics of solar irradiance data, which is influenced by climatic fluctuations and challenging to regulate. The current investigation focuses on predicting solar irradiance on an inclined surface, taking into consideration the impact of climatic variables such as temperature, wind speed, humidity, and air pressure. The used methodology for this objective is Artificial Neural Network (ANN), and the inquiry is carried out in the metropolitan region of Douala. The data collection device used in this research is the meteorological station located at the IUT of Douala. This station was built as a component of the Douala sustainable city effort, in partnership with the CUD and the IRD. Data was collected at 30-min intervals for a duration of around 2 years, namely from January 17, 2019, to October 30, 2020. The aforementioned data has been saved in a database that underwent pre-processing in Excel and later employed MATLAB for the creation of the artificial neural network model. 80% of the available data was utilized for training the network, 15% was allotted for validation, and the remaining 5% was used for testing. Different combinations of input data were evaluated to ascertain their individual degrees of accuracy. The logistic Sigmoid function, with 50 hidden layer neurons, yielded a correlation coefficient of 98.883% between the observed and estimated sun irradiation. This function is suggested for evaluating the intensities of solar radiation at the place being researched and at other sites that have similar climatic conditions.

Flooding constitutes a major problem for the inhabitants of Douala City in general and those of the Tongo Bassa watershed (TBW) in particular. Faced with this situation, public authorities need to put in place measures to mitigate the vulnerability of populations to these disasters. This article aims to map flooding risk areas in the TBW using the geographic information system, field data (historical flood points), remote sensing data (Sentinel II image) and the frequency ratio model. The map produced shows that 1.41, 8.88, 28.51, 33.86 and 27.33% of the basin area are respectively delimited into very low, low, medium, high and very high flood vulnerability classes. High and very high flooding risk areas (those where flooding is most likely to occur) occupy more than half of the basin (61.19%). These areas are characterized by significant imperviousness, low altitudes, weak slopes, significant proximity to watercourses and clayey soils. Most of the houses in the basin (66.92%) are located in areas affected by these two levels of exposure (high and very high). With respective success and prediction accuracy rates of 89 and 96.78%, a certain confidence deserves to be placed on the map of flooding risk areas produced. HIGHLIGHTS Flood susceptibility mapping is addressed through the geographic information system, remote sensing and the frequency ratio model.; A flooding risk areas map with five levels of exposure is proposed.; The number of houses included in each flood exposure level is summarized.;

Study region: Mbakaou and Bamendjing basins (Sanaga River sub-basins). Study focus: In this study, the availability of water resources was assessed over the period 2002–2019, based on the SWAT (Soil and Water Assessment Tool) hydrological model and certain meteorological and spatial reference data available for the region (Merra2, Landsat, etc.). Forecasts of its evolution were then made with the same tool (SWAT) over two futures periods (near 2024–2035 and medium: 2036–205) based on data from four (04) regional climate models (RCMs) (CCCma, HIRHAM5, RCA4 and REMO) and future land use and land cover (LULC) data simulated using the CA-Markov procedure. To separate the impact of climate variability (CV) and land use and use and land cover changes (LULCCs) on future water resources, two evolution scenarios (experiments) were established: (1) the impact of the CV, by associating future climate data with LULC from the historical period; (2) the impact of LULCCs, by combining future LULC maps with climate data from the historical period. New hydrological insights for the region: The performances of the SWAT model are satisfactory in calibration and validation on the two basins with R2, NSE and KGE greater than 0.68. Two models (CCCma and REMO) predict a decline in water resources in these basins, and two others (HIRHAM5 and RCA4) the opposite. The REMO model seems the most reliable. It predicts a drop in precipitation and runoff (SURQ) in the two basins that do not respectively exceed –19% and –31%. CV is the only forcing whose impact will be visible in the dynamics of future water resources, given the insignificant changes expected in the evolution of LULC patterns. The results of this study could contribute to improving the management of water resources in the studied basins and the region.

Ce travail porte sur l’évaluation du potentiel des images multi-temporelles et multi-capteurs (optique et radar) des satellites Sentinel 1 et 2 pour la cartographie de l’occupation du sol et le suivi de l’évolution du trait de côte dans un écosystème tropical sur la période 2015 à 2020. La zone d’étude choisie est l’Estuaire du Cameroun. Cette zone représente un milieu de transition écologique majeur dans la sous-région avec la présence de réserves naturelles protégées (mangroves, forêt dense, zones humides) mais aussi une forte activité anthropique (constructions, agriculture, forêt dégradée). L’approche méthodologique a consisté en une chaine de prétraitements et d’analyses visuelles d’images, suivie d’une combinaison des bandes de chaque capteur, d’une classification supervisée Random Forest pour ébaucher une cartographie de l’occupation du sol et enfin une numérisation du trait de côte. Globalement, les résultats montrent que la classification, avec les images Sentinel 2 en utilisant 10 bandes et en ajoutant 4 indices de végétation, s’avère légèrement plus précise (95.75%) que celle issue des 13 bandes initiales (91.78%). La classification avec les seules images Sentinel 1A double polarisation (VV, VH) conduit à une précision de 78.44%. La combinaison des bandes Sentinel 2A (10 bandes et 4 indices) et Sentinel 1 améliore les résultats et conduit à une précision de 98.76%. Nos résultats montrent aussi que l’utilisation des séries chronologiques d’images multi-temporelles améliore considérablement la précision de classification par rapport à l’usage d’une seule image (mono-date), et cela pour les deux capteurs, soit un gain supplémentaire de 13% et 10% respectivement pour Sentinel-2 et Sentinel-1. Néanmoins ce gain reste faible pour les classes temporellement stables. Les résultats d’analyse de l’évolution du trait de côte montrent que l’estuaire du Cameroun est perturbé selon différents niveaux d’érosion (Cap Cameroun, Partie Nord île Manoka, embouchure de la Sanaga), et d’accrétion (Limbé, et Partie Sud île Manoka) et aussi par de faible variations internes. La fusion des données de télédétection optique et radar dans la discrimination des classes d’occupation du sol, a permis de montrer que les zones de constructions sont les plus vulnérables à l’érosion côtière. Par contre, la présence de la végétation (mangrove, forêt) stabilise et protège la côte d’éventuels risques de cette nature.

The Wouri estuary is located in the Gulf of Guinea on the Atlantic coast of Cameroon’s coastline plain (3°49′ and 4°04′ north latitude and 9°20′ to 9°40′ east longitude), and is strongly influenced by coastal dynamics that have remained unquantified over a long period of time. This study analyzed the historical evolution of the Wouri estuarine coastline between 1948 and 2012. Variations in the estuarine evolution of the Wouri were studied from (i) minute topographic extracts from 1948, (ii) 1996−1999 nautical charts, and (iii) 2012 spatial map vectors. The net temporal spatial variation rates were calculated using the statistical methods of the Digital Shoreline Analysis System (DSAS). These change rates were also calculated over two time intervals (1948−1996 and 1996−2012) and over a 64-year period (1948−2012). The study reveals highly disparate results. Indeed, kinematics show that the Wouri estuary was dominated by erosion in its downstream section, with 262.83 ha for −3.2 m/year and 110.56 ha for −5.8 m/year between 1948−1996 and 1996−2012 respectively, and by accretion on the other hand in its upstream section, with 239.17 ha for 4.3 m/year in zone 5 between 1948−1996 and 150.82 ha for 12.6 m/year in zone 4 between 1996−2012. Thus, over the 64-year period (1948−2012), we have a dominance of variation by erosion downstream and conversely by accretion upstream, marked by the presence of amplifying factors (anthropogenic pressure and climate change) of the rate of variation of morphological evolution at the beginning of the 21st century, as compared to the middle-20th century. The observed development of sediment loss and accumulation, both influences and will influence, the sediment regime along the Wouri estuarine coastline. There is a need to develop a systematic sub-regional coastal surveillance activity to effectively manage Cameroon’s coastline system.

Radar altimetry is now commonly used for the monitoring of water levels in large river basins. In this study, an altimetry-based network of virtual stations was defined in the quasi ungauged Ogooué river basin, located in Gabon, Central Africa, using data from seven altimetry missions (Jason-2 and 3, ERS-2, ENVISAT, Cryosat-2, SARAL, Sentinel-3A) from 1995 to 2017. The performance of the five latter altimetry missions to retrieve water stages and discharges was assessed through comparisons against gauge station records. All missions exhibited a good agreement with gauge records, but the most recent missions showed an increase of data availability (only 6 virtual stations (VS) with ERS-2 compared to 16 VS for ENVISAT and SARAL) and accuracy (RMSE lower than 1.05, 0.48 and 0.33 and R² higher than 0.55, 0.83 and 0.91 for ERS-2, ENVISAT and SARAL respectively). The concept of VS is extended to the case of drifting orbits using the data from Cryosat-2 in several close locations. Good agreement was also found with the gauge station in Lambaréné (RMSE = 0.25 m and R2 = 0.96). Very good results were obtained using only one year and a half of Sentinel-3 data (RMSE < 0.41 m and R2 > 0.89). The combination of data from all the radar altimetry missions near Lamabréné resulted in a long-term (May 1995 to August 2017) and significantly improved water-level time series (R² = 0.96 and RMSE = 0.38 m). The increase in data sampling in the river basin leads to a better water level peak to peak characterization and hence to a more accurate annual discharge over the common observation period with only a 1.4 m3·s−1 difference (i.e., 0.03%) between the altimetry-based and the in situ mean annual discharge.