The spatio-temporal characteristic of rainfall in the Beles Basin of Ethiopia is poorly understood, mainly due to lack of data. With recent advances in remote sensing, satellite derived rainfall products have become alternative sources of rainfall data for such poorly gauged areas. The objectives of this study were: (i) to evaluate a multi-source rainfall product (Climate Hazards Group Infrared Precipitation with Stations: CHIRPS) for the Beles Basin using gauge measurements and (ii) to assess the spatial and temporal variability of rainfall across the basin using validated CHIRPS data for the period 1981–2017. Categorical and continuous validation statistics were used to evaluate the performance, and time-space variability of rainfall was analyzed using GIS operations and statistical methods. Results showed a slight overestimation of rainfall occurrence by CHIRPS for the lowland region and underestimation for the highland region. CHIRPS underestimated the proportion of light daily rainfall events and overestimated the proportion of high intensity daily rainfall events. CHIRPS rainfall amount estimates were better in highland regions than in lowland regions, and became more accurate as the duration of the integration time increases from days to months. The annual spatio-temporal analysis result using CHIRPS revealed: a mean annual rainfall of the basin is 1490 mm (1050–2090 mm), a 50 mm increase of mean annual rainfall per 100 m elevation rise, periodical and persistent drought occurrence every 8 to 10 years, a significant increasing trend of rainfall (~5 mm year⁻¹), high rainfall variability observed at the lowland and drier parts of the basin and high coefficient of variation of monthly rainfall in March and April (revealing occurrence of bimodal rainfall characteristics). This study shows that the performance of CHIRPS product can vary spatially within a small basin level, and CHIRPS can help for better decision making in poorly gauged areas by giving an option to understand the space-time variability of rainfall characteristics.

Climate predictions using recent and high-resolution climate models are becoming important for effective decision-making and for designing appropriate climate change adaptation and mitigation strategies. Due to highly variable climate and data scarcity of the upper Blue Nile Basin, previous studies did not detect specific unified trends. This study discusses, the past and future climate projections under CMIP6-SSPs scenarios for the basin. For the models’ validation and selection, reanalysis data were used after comparing with area-averaged ground observational data. Quantile mapping systematic bias correction and Mann–Kendall trend test were applied to evaluate the trends of selected CMIP6 models during the 21st century. Results revealed that, ERA5 for temperature and GPCC for precipitation have best agreement with the basin observational data, MRI-ESM2-0 for temperature and BCC-CSM-2MR for precipitation were selected based on their highest performance. The MRI-ESM2-0 mean annual maximum temperature for the near (long)-term period shows an increase of 1.1 (1.5) °C, 1.3 (2.2) °C, 1.2 (2.8) °C, and 1.5 (3.8) °C under the four SSPs. On the other hand, the BCC-CSM-2MR precipitation projections show slightly (statistically insignificant) increasing trend for the near (long)-term periods by 5.9 (6.1)%, 12.8 (13.7)%, 9.5 (9.1)%, and 17.1(17.7)% under four SSPs scenarios.

Water quality degradation of freshwater bodies is a concern worldwide, particularly in Africa, where data are scarce and standard water quality monitoring is expensive. This study explored the use of remote sensing imagery and machine learning (ML) algorithms as an alternative to standard field measuring for monitoring water quality in large and remote areas constrained by logistics and finance. Six machine learning (ML) algorithms integrated with Landsat 8 imagery were evaluated for their accuracy in predicting three optically active water quality indicators observed monthly in the period from August 2016 to April 2022: turbidity (TUR), total dissolved solids (TDS) and Chlorophyll a (Chl-a). The six ML algorithms studied were the artificial neural network (ANN), support vector machine regression (SVM), random forest regression (RF), XGBoost regression (XGB), AdaBoost regression (AB), and gradient boosting regression (GB) algorithms. XGB performed best at predicting Chl-a, with an R² of 0.78, Nash–Sutcliffe efficiency (NSE) of 0.78, mean absolute relative error (MARE) of 0.082 and root mean squared error (RMSE) of 9.79 µg/L. RF performed best at predicting TDS (with an R² of 0.79, NSE of 0.80, MARE of 0.082, and RMSE of 12.30 mg/L) and TUR (with an R² of 0.80, NSE of 0.81, and MARE of 0.072 and RMSE of 7.82 NTU). The main challenges were data size, sampling frequency, and sampling resolution. To overcome the data limitation, we used a K-fold cross validation technique that could obtain the most out of the limited data to build a robust model. Furthermore, we also employed stratified sampling techniques to improve the ML modeling for turbidity. Thus, this study shows the possibility of monitoring water quality in large freshwater bodies with limited observed data using remote sensing integrated with ML algorithms, potentially enhancing decision making.

Water hyacinth is a well-known invasive weed in lakes across the world and harms the aquatic environment. Since 2011, the weed has invaded Lake Tana substantially posing a challenge to the ecosystem services of the lake. The major factors which affect the growth of the weed are phosphorus, nitrogen, temperature, pH, salinity, and lake depth. Understanding and investigating the hotspot areas is vital to predict the areas for proper planning of interventions. The main objective of this study is therefore to predict the hotspot areas of the water hyacinth over the surface of the lake using the geographical information system (GIS)-based multi-criteria evaluation (MCE) technique. The main parameters used in the multi-criteria analysis were total phosphorus (>0.08 mg L⁻¹), total nitrogen (>1.1 mg L⁻¹), temperature (<26.2 °C), pH (<8.6), salinity (<0.011%), and depth (<6 m). These parameters were collected from 143 sampling sites on the lake in August, December (2016), and March (2017). Fuzzy overlay spatial analysis was used to overlay the different parameters to obtain the final prediction map of water hyacinth infestation areas. The results indicated that 24,969 ha (8.1%), 21,568.7 ha (7.1%), and 24,036 ha (7.9%) of the lake are susceptible to invasion by the water hyacinth in August, December, and March, respectively. At the maximum historical lake level, 30,728.4 ha will be the potential susceptible area for water hyacinth growth and expansion at the end of the rainy season in August. According to the result of this study, the north and northeastern parts of the lake are highly susceptible for invasion. Hence, water hyacinth management and control plans shall mainly focus on the north and northeastern part of Lake Tana and upstream contributing watersheds.

Gully expansion in the Ethiopian Highlands dissects vital agricultural lands with the eroded materials adversely impacting downstream resources, for example as they accumulate in reservoirs. While gully expansion and rehabilitation have been more extensively researched in the semiarid region of Ethiopia, few studies have been conducted in the (sub)humid region. For that reason, we assessed the severity of gully erosion by measuring the expansion of 13 selected permanent gullies in the subhumid Debre Mawi watershed, 30 km south of Lake Tana, Ethiopia. In addition, the rate of expansion of the entire drainage network in the watershed was determined using 0.5 m resolution aerial imagery from flights in 2005 and 2013. About 0.6 Mt (or 127 t ha⁻¹ yr⁻¹) of soil was lost during this period due to actively expanding gullies. The net gully area in the entire watershed increased more than 4-fold from 4.5 ha in 2005 to 20.4 ha in 2013 (> 3 % of the watershed area), indicating the growing severity of gully erosion and hence land degradation in the watershed. Soil losses were caused by upslope migrating gully heads through a combination of gully head collapse and removal of the failed material by runoff. Collapse of gully banks and retreat of headcuts was most severe in locations where elevated groundwater tables saturated gully heads and banks, destabilizing the soils by decreasing the shear strength. Elevated groundwater tables were therefore the most important cause of gully expansion. Additional factors that strongly relate to bank collapse were the height of the gully head and the size of the drainage area. Soil physical properties (e.g., texture and bulk density) only had minor effects. Conservation practices that address factors controlling erosion are the most effective in protecting gully expansion. These consist of lowering water table and regrading the gully head and sidewalls to reduce the occurrence of gravity-induced mass failures. Planting suitable vegetation on the regraded gully slopes will in addition decrease the risk of bank failure by reducing pore-water pressures and reinforcing the soil. Finally, best management practices that decrease runoff from the catchment will reduce the amount of gully-related sediment loss.

Water hyacinth originated from the Amazon Basin and has expanded to other parts of the world since the 1800s. In Ethiopia, the weed is affecting the socio-economic activities of the people whose livelihood is directly or indirectly dependent on Lake Tana. Still, the area covered by water hyacinth and the impact of water level fluctuation on the expansion of water hyacinth has not been known clearly. Therefore, the main objective of this study was to determine the spatiotemporal distribution of water hyacinth and relation with lake-level fluctuation. The area covered by water hyacinth was determined using monthly Sentinel-2 images, which were collected from November 2015 to December 2019. The impact of water level fluctuation on the expansion of water hyacinth was evaluated using hourly water level data converted to a monthly average to correlate with the area covered by the water hyacinth. In addition, MOD13Q1.006 data was used to evaluate the trend of the Normalized Difference Vegetation Index (NDVI) and its linkage with the weed. The maximum areas covered by water hyacinth were 278.3, 613.6, 1108.7, 2036.5, and 2504.5 ha in Feb 2015, October 2016, September 2017, December 2018, and in December 2019, respectively. Its areal coverage was declining from the northern corridors and increasing in eastern shores of the lake. The lake-level fluctuation was observed in the range of 1.5 to 3.98 m in this study. The annual mean maximum spatial values of the NDVI were in the range of 0.27 and 0.47. The area covered by water hyacinth was increasing significantly (P < 0.05) and positively correlated with the seasonal lake-level fluctuation. High water level enabled the expansion of the weed by extending its suitable habitat of shallow water to the flood plain. Based on the results of this study, lake-level fluctuations can have an adverse impact on the expansion of the weed.