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Understanding land use / land cover (LULC) dynamic is of great importance to sustainable development in Africa where deforestation is a common problem. This study aimed to assess the historical and future dynamics of LULC in the Nakambé River Basin. Landsat images were used to determine LULC dynamics for the years 1990, 2005 and 2020 using Random Forest classification system in Google Earth Engine while the predicted LULC of 2050 was simulated using the Markov Chain and Multi-Layer-Perceptron neural network in Land Change Modeler. The findings showed significant changes in LULC patterns. From 1990 to 2020, woodland and shrubland decreased by -45% and -68% respectively while water body, cropland and bare land/built-up increased by 233%, 51%, and 75%, correspondingly. From 2020 to 2050, the results revealed that under the Business-as-usual scenario, bare land/built-up and water bodies could continue to increase by 99% and 1% respectively. However, cropland, shrubland, and woodland could decrease by -32.61%, -33.91%, and -46.86%, respectively. Under the afforestation scenario, the contrary of Business-as-usual could occur. While woodland, shrubland, and cropland would increase by 22.24%, 51.57%, and 18.13%, correspondingly, between 2020 and 2050, the area covered by water bodies and bare land/built-up will decrease by -6.16% and -39.04%, respectively. The results of this research give an insight into past and future LULC dynamics in the Nakambé River Basin and suggest the need to strengthen the policies and actions for better land management in the region.

Understanding rainwater dispersion in a spatiotemporal context is invaluable toward resourceful water management and a food-secure society. This study, therefore, assessed the variations in rainfall at a spatiotemporal scale in the Oti River Basin of West Africa for observed (1981–2010) and future periods (2021–2050) under the representative concentration pathways (RCPs) 4.5 and 8.5 emission scenarios. Rainfall data from meteorological stations and Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) were used. The percentage changes in rainfall for the peak month as well as for rainy and dry seasons under the two climate scenarios were determined. The coefficient of variation (CV) and the standardized anomaly index (SAI) were used to assess annual variations in rainfall. In general, under both emission scenarios, rainfall is projected to decrease over the study area. However, the amount of rainfall during the peak month (August) for RCP4.5 and RCP8.5 could increase by 0.26 and 9.3%, respectively. The highest SAIs for the observed period were +1.58 (2009) and −2.29 (1983) with the latter showing a relationship with historic drought in the basin. The projected SAI under RCP4.5 and RCP8.5 indicated extremely wet (+2.12) and very wet (+1.91) periods for the years 2037 and 2028, respectively. The study provides relevant information and a chance to aid the design of innovative adaptation measures toward efficient water management and agricultural planning for the basin.

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