The West Sahel is facing significant threats to its vegetation and wildlife due to the land degradation and habitat fragmentation. It is crucial to assess the regional vegetation greenness dynamics in order to comprehensively evaluate the effectiveness of protection in the natural reserves. This study analyzes the vegetation greenness trends and the driving factors in the Dosso Partial Faunal Reserve in Niger and nearby unprotected regions—one of the most important habitats for endemic African fauna—using satellite time series data from 2001 to 2020. An overall vegetation browning trend was observed throughout the entire region with significant spatial variability. Vegetation browning dominated in the Dosso Reserve with 17.7% of the area showing a significant trend, while the area with significant greening was 6.8%. In a comparison, the nearby unprotected regions to the north and the east were found to be dominated by vegetation browning and greening, respectively. These results suggest that the vegetation protection practice was not fully effective throughout the Dosso Reserve. The dominant drivers were also diagnosed using the Random Forest model-based method and the Partial Dependence Plot tool, showing that water availability (expressed as soil moisture) and land use/land cover change were the most critical factors affecting vegetation greenness in the study region. Specifically, soil moisture stress and specific land management practices associated with logging, grazing, and land clearing appeared to dominate vegetation browning in the Dosso Reserve. In contrast, the vegetation greening in the central Dosso Reserve and the nearby unprotected region to the east was probably caused by the increase in shrubland/forest, which was related to the effective implementation of protection. These findings improve our understanding of how regional vegetation greenness dynamics respond to environmental changes in the Dosso Reserve and also highlight the need for more effective conservation planning and implementation to ensure sustainable socio-ecological development in the West Sahel.

Introduction: The vegetation dynamics of the Sahel-Sudan-Guinea region in Africa, one of the largest transition zones between arid and humid zones, is of great significance for understanding regional ecosystem changes. However, a time-unvarying trend based on linear assumption challenges the overall understanding of vegetation greenness evolution and of tracking a complex ecosystem response to climate in the Sahel-Sudan-Guinea region. Methods: This study first applied the ensemble empirical mode decomposition (EEMD) method to detect the time-varying trends in vegetation greenness based on normalized difference vegetation index (NDVI) data in the region during 2001–2020, and then identified the dominant climatic drivers of NDVI trends by employing explainable machine learning framework. Results: The study revealed an overall vegetation greening but a significant nonlinear spatio-temporal evolution characteristic over the region. Trend reversals, i.e., browning-to-greening and greening-to-browning, were dominant in approximately 60% of the study area. The browning-to-greening reversal was primarily observed in the southern Sahel, Congo Basin north of the Equator, and East Africa, with a breakpoint around 2008, while the greening-to-browning reversal was mainly observed in West Africa, with a breakpoint around 2011. The sustained greening primarily took place in northern Sahel, Central African Republic and South Sudan; while sustained browning clustered in central West Africa and Uganda, mainly in agricultural lands. Furthermore, the combination of Random Forest (RF) algorithm and the SHapley Additive exPlanations (SHAP) method could robustly model and reveal the relationships between the observed trends in NDVI and in climatic variables, also detected by applying EEMD. The results suggested that air temperature and precipitation were the most important climatic drivers controlling the NDVI trends across the Sahel-Sudan-Guinea region. The NDVI trends were more likely to have negative correlations with solar radiation and vapor pressure deficit in arid areas, while they could have positive correlations in humid areas. The study also found that large-scale climate changes induced by sea surface temperature (SST) anomalies had strong relationships with trend reversals in vegetation greenness at a sub-continental scale. These findings advanced the understanding of the impacts of climatic drivers on vegetation greenness evolution in the Sahel-Sudan-Guinea region.

Water depth, a fundamental characteristic of a lake, is important for understanding climatic, ecological, and hydrological processes. However, lake water depth data are still scarce due to the high cost of in-situ measurements and the limitations of remote sensing observations. In this study, a novel method was developed to estimate time series of pixel-wise water depths of lakes that have ever exposed their bottom by remote sensing observations. Lake water depths were calculated as the difference between the elevations of the dynamic water surface and the historical lakebed elevations using optical images and DEM data. The method was applied in the Sahel-Sudano-Guinean region of Africa where complex climatic conditions and rare in-situ measurements. Experiments showed that the proposed method could get consistent water depths compared with the HydroLAKES data, i.e. with a MAE of 0.86 m and a RMSE of 1.69 m, and water surface elevations similar to the estimates derived from ICESat/ICESat-2 measurements with a MAE of 3.79 m and a RMSE of 5.92 m. The method can provide pixel-wise information on lake water depth at high temporal frequency, and is expected to provide an efficient solution to gather essential information on lakes.

The potential drivers of vegetation changes in the Sudano-Sahelian region of Africa remain poorly understood due to complex interactions between climatic and anthropogenic processes. In this study, we analyzed the vegetation greenness trends in relation to rainfall variability that we considered the essence of climatic effects on vegetation in a well-known water-limited environment by using time series of satellite data in the Sudano-Sahelian region during 2001–2020. We quantified in more detail the relative contributions of rainfall variability (climatic factor), land use/land cover (LULC) change, and fire occurrence change (non-climatic factors) to vegetation greenness trends in selected sub-regions. The results showed that vegetation greening was widespread (26.9% of the total study area), while vegetation browning was more clustered in central West Africa (5% of the total study area). About half of the vegetation greening area can be explained by long-term rainfall variability during 2001–2020, but most of the area characterized by a browning trend was unrelated to rainfall variability. An analysis of the relative importance showed that LULC changes had significant local effects on vegetation greenness and that these changes were characterized by a strong spatial heterogeneity in specific sub-regions. Gains in cropland and natural vegetation related to positive land management were probably the dominant drivers of greening in Senegal and Ethiopia. Also, the combined impacts of rainfall variability and LULC changes contributed to greening trends in the arid zone, particularly in Mali and Sudan. In contrast, vegetation browning in central West Africa appeared to be driven by cropland gain and natural vegetation loss associated with extensive agricultural production activities. Furthermore, we found that repeated fires for agricultural expansion in central West Africa intensified vegetation browning. These results advanced our understanding of vegetation dynamics in response to climatic and non-climatic factors in Sudano-Sahelian drylands characterized by increasing pressures on land resources.

Study region: Senegal river (SRB), Niger river (NRB), and Lake Chad basins (LCB). Study focus: We investigated the impacts of land use/land cover change (LULC) and climate variability on the water balance components from 1990 to 2020. We applied the Soil and Water Assessment Tool (SWAT) coupled with remote sensing retrievals of actual evapotranspiration (ETa) and surface soil moisture (SSM). To separate the impacts of the two aforementioned factors, two numerical experiments were designed: (i) climate variability effects by applying frozen LULC while changing the climate; (ii) LULC change impacts by applying frozen climate while changing LULC. New hydrological insights for the region: Overall, at the basin level, the results indicated that climate variability had the dominant role in increasing groundwater recharge, surface runoff, groundwater return flow and lateral flow in LCB and SRB. These increases triggered the recovery of lake area and higher water table in LCB and increased in SRB streamflow, while water scarcity increased in NRB. In contrast, the separate effect of LULC change, specifically natural vegetation expansion, increased actual ET and decreased the surface runoff, which could be a reason for lake area depletion in LCB and decreasing SRB and NRB streamflow. At the sub-basin level, LULC change, i.e. a gain in cropland and urban areas at the expense of forests in some sub-basins in NRB, led to a local increase in surface runoff. This implies a better redistribution of water in downstream and compensates the deficit in surface runoff caused by natural vegetation expansion in some other catchments. These changes, simultaneously with high intensity and long-duration precipitation, may increase the likelihood of inundation in some small catchments in the Niger river basin. These outcomes give useful hydrological insights into water and land management by emphasizing the crucial role of water recycling.

Past Land Use Land Cover (LULC) transitions analysis at the sub-continental scale of West Africa revealed spatial reallocation, i.e., simultaneous losses and gains of the LULC categories at different locations. We applied the component analysis approach to separate the total change into three major components, i.e., quantity (net change), exchange and shift (allocation change) as a way to analyse such spatial reallocation and identify the paired categories that accounted for the largest exchange and shift through time. Quantity change is the absolute value of the category's gross gains minus the category's gross losses. An exchange occurs when for example, a natural vegetation patch evolves to cropland at a location concurrently with an equal extent of cropland evolving into natural vegetation at a different location. A shift occurs when the LULC categories involved in the exchange are more than two. The amount of exchange and shift and locations that these exchanges occurred are very useful information for land policies appraisal and the long term contested re-greening of Africa as it may signal simultaneous regrowth and degradation of natural vegetation at different locations in the same landscape and also possible misclassification errors. The results revealed large exchanges in the landscape of West Africa between 1975 and 2000 for arid and humid eco-regions in West Africa. Overall, the exchange and shift components between wetland, water bodies and some other LULC categories such as forestland, other vegetation and cropland were the highest. The exchange between natural vegetation and cropland was considerable, which confirms regrowth despite the massive degradation revealed by the previous studies. Here, the large exchange in 1975–2000 highlighted large spatial reallocation of the LULC categories. The highest net change was experienced in the period between 2000 and 2013 at all spatial aggregations. Settlement and cropland experienced the highest positive net change whilst forestland and other vegetation experienced the highest negative net change. Shift was absent in the category of settlements indicating persistence over time. This analysis provided useful information on the contested re-greening of West Africa.

Model calibration and validation are challenging in poorly gauged basins. We developed and applied a new approach to calibrate hydrological models using distributed geospatial remote sensing data. The Soil and Water Assessment Tool (SWAT) model was calibrated using only twelve months of remote sensing data on actual evapotranspiration (ETa) geospatially distributed in the 37 sub-basins of the Lake Chad Basin in Africa. Global sensitivity analysis was conducted to identify influential model parameters by applying the Sequential Uncertainty Fitting Algorithm–version 2 (SUFI-2), included in the SWAT-Calibration and Uncertainty Program (SWAT-CUP). This procedure is designed to deal with spatially variable parameters and estimates either multiplicative or additive corrections applicable to the entire model domain, which limits the number of unknowns while preserving spatial variability. The sensitivity analysis led us to identify fifteen influential parameters, which were selected for calibration. The optimized parameters gave the best model performance on the basis of the high Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), and determination coefficient (R²). Four sets of remote sensing ETa data products were applied in model calibration, i.e., ETMonitor, GLEAM, SSEBop, and WaPOR. Overall, the new approach of using remote sensing ETa for a limited period of time was robust and gave a very good performance, with R² > 0.9, NSE > 0.8, and KGE > 0.75 applying to the SWAT ETa vs. the ETMonitor ETa and GLEAM ETa. The ETMonitor ETa was finally adopted for further model applications. The calibrated SWAT model was then validated during 2010–2015 against remote sensing data on total water storage change (TWSC) with acceptable performance, i.e., R² = 0.57 and NSE = 0.55, and remote sensing soil moisture data with R² and NSE greater than 0.85.

The occurrence of natural vegetation at a given time is determined by interplay of multiple drivers. The effects of several drivers, e.g., geomorphology, topography, climate variability, accessibility, demographic indicators, and changes in human activities on the occurrence of natural vegetation in the severe drought periods and, prior to the year 2000, have been analyzed in West Africa. A binary logistic regression (BLR) model was developed to better understand whether the variability in these drivers over the past years was statistically significant in explaining the occurrence of natural vegetation in the year 2000. Our results showed that multiple drivers explained the occurrence of natural vegetation in West Africa at p < 0.05. The dominant drivers, however, were site-specific. Overall, human influence indicators were the dominant drivers in explaining the occurrence of natural vegetation in the selected hotspots. Human appropriation of net primary productivity (HANPP), which is an indicator of human socio-economic activities, explained the decreased likelihood of natural vegetation occurrence at all the study sites. However, the impacts of the remaining significant drivers on natural vegetation were either positive (increased the probability of occurrence) or negative (decreased the probability of occurrence), depending on the unique environmental and socio-economic conditions of the areas under consideration. The study highlights the significant role human activities play in altering the normal functioning of the ecosystem by means of a statistical model. The research contributes to a better understanding of the relationships and the interactions between multiple drivers and the response of natural vegetation in West Africa. The results are likely to be useful for planning climate change adaptation and sustainable development programs in West Africa.