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This study examined land use/land cover (LULC) changes in Chemoga watershed of the Upper Blue Nile Basin, comprising four distinct agroecological regions: Wet Wurch, Moist Dega, Moist Weyna Dega, and Moist Kolla. We used multi-temporal Landsat images from 1985 to 2020, a hybrid classification method and the Cellular Automata-Markov model to analyze historical and predict future (2020–2060) LULC changes under business-as-usual (BAU) and land conservation (LC) scenarios. Magnitudes and patterns of spaciotemporal LULC changes were analyzed using intensity analysis. Cropland expanded across all agroecologies from 1985 to 2020, with Moist Kolla experiencing the highest increase at the expense of woodland, due the introduction of commercial farming to this hotter, less populated and inaccessible area. Moist Dega exhibited the highest allocation changes within cropland and forest, attributable to farmers’ adoption of rotational land use to rehabilitate extensively degraded cultivated lands. Under the BAU scenario, projections suggest further cropland expansion at expense of woodland in Moist Kolla and built-up areas at the expense of cropland and grassland in Moist Dega. Under the LC scenario, forest cover is expected to increase at the expense of cropland across all agroecologies. The historical and projected BAU LULC change scenario substantially increased soil erosion and reduced ecosystem services. These effects can be minimized if LC scenario is properly implemented. The agroecology-based LULC intensity analysis reveals local drivers of change and associated impacts, providing vital insights for targeted land use planning in this study watershed and other watersheds facing similar challenges.

Flood susceptibility mapping plays a crucial role in flood risk assessment and management. Accurate identification of areas prone to flooding is essential for implementing effective mitigation measures and informing decision-making processes. In this regard, the present study used high-resolution remote sensing products, i.e., synthetic aperture radar (SAR) images for flood inventory preparation and integrated four machine learning models (Random Forest: RF, Classification and Regression Trees: CART, Support Vector Machine: SVM, and Extreme Gradient Boosting: XGBoost) to predict flood susceptibility in Metlili watershed, Morocco. Initially, 12 independent variables (elevation, slope angle, aspect, plan curvature, topographic wetness index, stream power index, distance from streams, distance from roads, lithology, rainfall, land use/land cover, and normalized vegetation index) were used as conditioning factors. The flood inventory dataset was divided into 70% and 30% for training and validation purposes using a popular library, scikit-learn (i.e., train_test_split) in Python programming language. Additionally, the area under the curve (AUC) was used to evaluate the performance of the models. The accuracy assessment results showed that RF, CART, SVM, and XGBoost models predicted flood susceptibility with AUC values of 0.807, 0.780, 0.756, and 0.727, respectively. However, the RF model performed better at flood susceptibility prediction compared to the other models applied. As per this model, 22.49%, 16.02%, 12.67%, 18.10%, and 31.70% areas of the watershed are estimated as being very low, low, moderate, high, and very highly susceptible to flooding, respectively. Therefore, this study showed that the integration of machine learning models with radar data could have promising results in predicting flood susceptibility in the study area and other similar environments.

Study region: Six small agricultural watersheds in three tropical climatic regions of Ethiopia. Three regions of contrasting climate: highland, midland, and lowland regions with respectively high, middle, and low elevations and rainfalls. Study focus: Lag time (TL) and time of concentration (TC) are two measures of how quickly a stream responds to runoff-producing rainfall. These parameters are the main inputs used to estimate peak flow under flood conditions in ungauged watersheds. Many empirical methods have been proposed to estimate TL and TC, but the validity of none of them has been tested. This study compared 10 commonly used methods by using measured TL and TC. New hydrological insights: Measured median values of TL and TC for 176 rainfall–runoff events were used to evaluate the performance of empirical methods. For individual watersheds, the estimates of TL and TC differed by up to 2.6 h and 4.4 h, respectively. Most of the empirical methods tended to substantially underestimate TL and TC, which would lead to overestimation of runoff volume. TL and TC computed by two methods that consider both overland and channel flow were closest to the measured values of TL and TC, because such mixed flow is typical of tropical climate regions. Our results show the need for caution when empirical methods developed in regions with a particular climatic and geomorphological conditions are applied elsewhere.

Gully erosion is one of the most extreme land degradation processes that exhibit spatial and temporal variation depending on topography, soil, climate, and land use and management characteristics. This study investigated the impact of changes in rainfall, land use/land cover (LULC), and land use management (LUM) practices on gully erosion in two midland watersheds (treated Kecha and untreated Laguna) in the Upper Blue Nile basin of Ethiopia by using the LANDPLANER model in combination with intensive field measurements and remote sensing products. We simulated gully erosion under past (in 2005), present (in 2021), and three potential future curve number conditions, each time under four rainfall scenarios (10, 30, 60, and 100 mm) using the dynamic erosion index (e), static topographic (esp), and erosion channel (esp_channel) thresholds. Density plot analyses showed that gullies frequently occur in low-lying gentle slope areas with relatively higher curve number values. The best predictions of gullies identified through true positive rates (TPR) and true negative rates (TNR) were achieved considering the static esp_channel > 1 for Kecha (TPR = 0.667 and TNR = 0.544) and the dynamic e > 0.1 for 60 mm of rainfall in Laguna (TPR = 0.769 and TNR = 0.516). Despite the 10 mm rainfall having negligible erosion-triggering potential in both watersheds, the 60 and 100 mm rainfall scenarios were 4–5 and 10–17 times, respectively, higher than the 30 mm rainfall scenario. While the LULC change in the untreated Laguna watershed increased the impact of rainfall on gully initiation by only 0–2% between 2005 and 2021, the combination of LULC and LUM significantly reduced the impact of rainfall in the treated Kecha watershed by 64–79%. Similarly, the gully initiation area in Kecha was reduced by 28% (from 33% in 2005 to 5% in 2021) due to changes in LULC and LUM practices, whereas Laguna showed little increment by only 1% (from 42% in 2005 to 43% in 2021) due to LULC change. In addition, the future predicted alternative land use planning options showed that gully initiation areas in Laguna could be reduced by 1% with only LULC conversion; 39% when only LUM practices were implemented; and 37% when both were combined. These results indicate that LUM practices outweigh the impact of LULC on gully erosion in the studied paired watersheds. Overall, LANDPLANER successfully simulated the spatio-temporal variation of gully erosion with scenario-based analyses and hence can be used to predict gullies in the study area and other data-scarce regions with similar agro-ecological settings.

Study region: Robit-Bata watershed, Upper Blue Nile basin, Ethiopia. Study focus: Stable isotopes of water (Oxygen-18 and Deuterium) were used as tracers to estimate the contribution of groundwater in shallow hillslope aquifers to streamflow in the Robit-Bata watershed. To assess the spatiotemporal variability of shallow groundwater and develop a hydrograph separation technique, we collected rainfall, shallow groundwater, and streamflow samples and analyzed their δ18O and δ2H isotopic compositions. The local meteoric water line (LMWL) and local evaporative line (LEL) of the study area were determined and compared with the global meteoric water line (GMWL). A standard unweighted two-component isotope-based hydrograph separation model was used to determine the percentage contribution of shallow groundwater to streamflow. New hydrological insights for the region: The LMWL (δ2H = 8.63·δ18O + 18.2) mostly showed heavy isotopic enrichment relative to GMWL, and the LEL (δ2H = 5.45·δ18O + 6.96) indicated isotopic enrichment compared to Ethiopian lakes. Shallow groundwater responded rapidly to rainfall, with good spatial correlation depending on topographic positions of wells. Pre-event water contributed 90% when the watershed reached maximum storage. This finding gives insight towards the predominant runoff generation process and has significant implications for sustainable dry season irrigation expansion in the area as the sub-surface flow drains out of the watershed from October onwards reducing water tables in the shallow wells.