Understanding the impacts of climate variability and change on terrestrial ecosystems in Africa remains a critical issue for ecology as well as for regional and global climate policy making. However, acquiring this knowledge can be useful for future predictions towards improved governance for sustainable development. In this study, we analyzed the spatial–temporal characteristics of vegetation greenness, and identified the possible relationships with climatic factors and vulnerable plant species across Africa. Using a set of robust statistical metrics on the Normalized Difference Vegetation Index (NDVI3g) for precipitation and temperature over 34 years from 1982 to 2015, relevant results were obtained. The findings show that, for NDVI, the annual rate of increase (0.013 y⁻¹) was less than that of decrease (−0.014 y⁻¹). In contrast, climate data showed a sharper increase than a marked decrease. Temperature is increasing while rainfall is decreasing, both at a sharp rate in central Africa. In Africa, tree cover, broadleaved, deciduous, closed to open (>15%) and shrubland plant species are critically endangered. The tropical vegetation devastated by the climate variability, causes different plant species to gradually perish; some were cleared out from the areas which experienced degradation, while others were from that of improvement. This study provides valuable information to African governments in order to improve environmental sustainability and development that will lead to the sustainability of natural resources.

The increasing demand for water results in the overexploitation of water resources. This situation calls for more effective water management alternatives including rainwater harvesting (RWH) systems. Due to the lack of biophysical data and infrastructure, the identification of suitable sites for various RWH systems is a challenging issue. However, integrating geospatial analysis and modeling approaches has become a promising tool to identify suitable sites for RWH. Thus, this study aimed at identifying suitable sites for RWH in the Nyabugogo catchment located in Rwanda by integrating a geo-information-based multi-criteria decision-making (MCDM) and SWAT (Soil and Water Assessment Tool) model. Moreover, the sediment yield was compared to the soil erosion evaluated using the Revised Universal Soil Loss Equation (RUSLE) owing to the lack of sediment concentration measured data. The results revealed that about 4.8 and 16.35% of the study area are classified as highly suitable and suitable areas for RWH, respectively. Around 6% of the study area (98.5 km2) was found to be suitable for farm ponds, whereas 1.6% (26.1 km2) suitable for check dams, and 25.9% (423 km2) suitable for bench terraces. Among 50 proposed sites for the RWH structures, 29 are located in the most suitable area for RWH. The results implicated that the surface runoff, sediment yield, and topography are essential factors in identifying the suitability of RWH areas. It is concluded that the integrated geospatial and MCDM techniques provide a useful and efficient method for planning RWH at a basin scale in the study area.

Streamflow impacts water supply and flood protection. Snowmelt floods occur frequently, especially in mountainous areas, and they pose serious threats to natural and socioeconomic systems. The current forecasting method relies on basic snowmelt accumulation and has geographic limitations that restrict the accuracy and timeliness of flood simulation and prediction. In this study, we clarified the precipitation types in two selected catchments by verifying accumulated and maximum temperatures’ influences on snow melting using a separation algorithm of rain and snow that incorporates with the temperatures. The new snow-melting process utilizing the algorithm in the soil and water assessment tool model (SWAT) was also developed by considering the temperatures. The SWAT model was used to simulate flooding and snowmelt in the catchments. We found that the contributions of snowmelt to the river flow were approximately 6% and 7% higher, according to our model compared to the original model, for catchments A and B, respectively. After the model improvement, the flood peaks increased by 49.42% and 43.87% in A and B, respectively. The contributions of snowmelt to stream flow increased by 24.26% and 31% for A and B, respectively. Generally, the modifications improved the model accuracy, the accuracy of snowmelt’s contributions to runoff, the accuracy of predicting flood peaks, the time precision, and the flood frequency simulations.