Understanding the influence of land use/land cover (LULC) on water quality is pertinent to sustainable water management. This study aimed at assessing the spatio-seasonal variation of water quality in relation to land use types in Lake Muhazi, Rwanda. The National Sanitation Foundation Water Quality Index (NSF-WQI) was used to evaluate the anthropogenically-induced water quality changes. In addition to Principal Components Analysis (PCA), a Cluster Analysis (CA) was applied on 12-clustered sampling sites and the obtained NSF-WQI. Lastly, the Partial Least Squares Path Modelling (PLS-PM) was used to estimate the nexus between LULC, water quality parameters, and the obtained NSF-WQI. The results revealed a poor water quality status at the Mugorore and Butimba sites in the rainy season, then at Mugorore and Bwimiyange sites in the dry season. Furthermore, PCA displayed a sample dispersion based on seasonality while NSF-WQI’s CA hierarchy grouped the samples corresponding to LULC types. Finally, the PLS-PM returned a strong positive correlation (+ 0.831) between LULCs and water quality parameters in the rainy season but a negative correlation coefficient (− 0.542) in the dry season, with great influences of cropland on the water quality parameters. Overall, this study concludes that the lake is seasonally influenced by anthropogenic activities, suggesting sustainable land-use management decisions, such as the establishment and safeguarding protection belts in the lake vicinity.

Examining the drivers of landscape ecological risk can provide scientific information for planning and landscape optimization. The landscapes of the Amu Darya Delta (ADD) have recently undergone great changes, leading to increases in landscape ecological risks. However, the relationships between landscape ecological risk and its driving factors are poorly understood. In this study, the ADD was selected to construct landscape ecological risk index (ERI) values for 2000 and 2015. Based on a geographically weighted regression (GWR) model, the relationship between each of the normalized difference vegetation index (NDVI), land surface temperature (LST), digital elevation model (DEM), crop yield, population density (POP), and road density and the spatiotemporal variation in ERI were explored. The results showed that the ERI decreased from the periphery of the ADD to the centre and that high-risk areas were distributed in the ADD’s downstream region, with the total area of high-risk areas increasing by 86.55% from 2000 to 2015. The ERI was spatially correlated with Moran’s I in 2000 and 2015, with correlation of 0.67 and 0.72, respectively. The GWR model indicated that in most ADD areas, the NDVI had a negative impact on the ERI, whereas LST and DEM had positive impacts on the ERI. Crop yield, road density and POP were positively correlated with the ERI in the central region of the ADD, at road nodes and in densely populated urban areas, respectively. Based on the findings of this study, we suggest that the ecological constraints of the aforementioned factors should be considered in the process of delta development and protection.

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.

Long-term satellite-based precipitation estimates (LSPE) play a significant role in climatological studies like drought monitoring. In this study, three popular LSPEs (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), Rainfall Estimates from Rain Gauge and Satellite Observations (CHIRPS) and Multi-Source Weighted-Ensemble Precipitation (MSWEP)) were evaluated on a monthly scale using ground-based stations for capturing drought event characteristics over northwestern China from 1983 to 2013. To reflect dry or wet evolution, the Standardized Precipitation Index (SPI) was adopted, and the Run theory was used to identify drought events and their characteristics. The conventional statistical indices (relative bias (RB), correlation coefficient (CC), and root mean square error (RMSE)), as well as categorical indices (probability of detection (POD), false alarm ratio (FAR), and missing ratio (MISS)) are used to evaluate the capability of LSPEs in estimating precipitation and drought characteristics. We found that: (1) three LSPEs showed generally satisfactory performance in estimating precipitation and characterizing drought events. Although LSPEs have acceptable performance in identifying drought events with POD greater than 60%, they still have a high false alarm ratio (>27%) and a high missing ratio (>33%); (2) three LSPEs tended to overestimate drought severity, mainly because of an overestimation of drought duration; (3) the ability of CHIRPS to replicate the temporal evolution of precipitation and SPI values is limited; (4) in severe drought events, PERSIANN-CDR tends to overestimate precipitation, and drought severity, as well as drought area; (5) among the three LSPEs, MSWEP outperformed the other two in identifying drought events (POD > 66%) and characterizing drought features. Finally, we recommend MSWEP for drought monitoring studies due to its high accuracy in estimating drought characteristics over northwestern China. In drought monitoring applications, the overestimation of PERSIANN-CDR for drought peak value and area, as well as CHIRPS’s inferiority in capturing drought temporal evolution, must be considered.