Demesew A. Mhiret

Flood occurrence in tropical regions is intensifying due to climate variability and land-use change, increasing the need for reliable flood response time estimation. Accurate prediction of flood lag time (TL)—the interval between the centroid of excess rainfall and peak runoff—is critical for flood early warning and water resource planning. However, TL estimation remains challenging in data-scarce regions because of complex interactions among watershed morphology, rainfall characteristics, and runoff generation processes. This study evaluates four machine learning (ML) algorithms—Random Forest (RF), Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)—for predicting TL across twenty gauged watersheds in the Blue Nile Basin of Ethiopia. Fourteen physiographic and hydro-climatic watershed characteristics were used as predictors. Among the tested models, XGBoost achieved the highest training performance (R² = 0.98, NSE = 0.96), while RF showed better generalization in the test dataset (R² = 0.77, NSE = 0.70, KGE = 0.71). SVM produced the lowest prediction errors (MAE = 0.95; RMSE = 2.25) but had lower explanatory power (R² = 0.49). To enhance interpretability and practical applicability, ML-based feature importance was used to develop a parsimonious empirical model: TL = 0.8 + 0.011A − 0.023RI, where A is watershed area and RI is rainfall intensity. This model explained 51% of TL variability and retained much of the predictive skill of more complex ML models. The proposed hybrid ML–empirical framework provides a transparent and operational approach for flood response time estimation in tropical highland watersheds. Its broader applicability remains subject to additional watershed-level validation and regional calibration.

Water hyacinth (Eichhornia crassipes), native to South America, is a highly invasive aquatic plant threatening freshwater ecosystems worldwide. Its rapid proliferation negatively impacts water quality, biodiversity, and navigation. Remote sensing offers an effective means to monitor such aquatic environments by providing extensive spatial and temporal coverage with improved resolution. This systematic review examines remote sensing applications for monitoring water hyacinth and water quality in studies published from 2014 to 2024. Seventy-eight peer-reviewed articles were selected from the Web of Science, Scopus, and Google Scholar following strict criteria. The research spans 25 countries across five continents, focusing mainly on lakes (61.5%), rivers (21%), and wetlands (10.3%). Approximately 49% of studies addressed water quality, 42% focused on water hyacinth, and 9% covered both. The Sentinel-2 Multispectral Instrument (MSI) was the most used sensor (35%), followed by the Landsat 8 Operational Land Imager (OLI) (26%). Multi-sensor fusion, especially Sentinel-2 MSI with Unmanned Aerial Vehicles (UAVs), was frequently applied to enhance monitoring capabilities. Detection accuracies ranged from 74% to 98% using statistical, machine learning, and deep learning techniques. Key challenges include limited ground-truth data and inadequate atmospheric correction. The integration of high-resolution sensors with advanced analytics shows strong promise for effective inland water monitoring.

Water hyacinth (Eichhornia crassipes), native to South America, is a highly invasive aquatic plant threatening freshwater ecosystems worldwide. Its rapid proliferation negatively impacts water quality, biodiversity, and navigation. Remote sensing offers an effective means to monitor such aquatic environments by providing extensive spatial and temporal coverage with improving resolution. This systematic review examines remote sensing applications for monitoring water hyacinth and water quality in studies published from 2014 to 2024. Seventy-eight peer-reviewed articles were selected from the Web of Science, Scopus, and Google Scholar following strict criteria. Research spans 25 countries across five continents, focusing mainly on lakes (61.5%), rivers (21%), and wetlands (10.3%). Approximately 49% of studies addressed water quality, 42% focused on water hyacinth, and 9% covered both. The Sentinel-2 Multispectral Instrument (MSI) was the most used sensor (35%), followed by the Landsat 8 Operational Land Imager (OLI) (26%). Multi-sensor fusion, especially combining Sentinel-2 MSI with Unmanned Aerial Vehicles (UAVs), was frequently applied to enhance monitoring capabilities. Detection accuracies ranged from 74% to 98% using statistical, machine learning, and deep learning techniques. Key challenges include limited ground-truth data and inadequate atmospheric correction. The integration of high-resolution sensors with advanced analytics shows strong promise for effective inland water monitoring.

Understanding the relationship between topography, hydrological processes, and runoff source areas is essential in engineering design, such as predicting floods and implementing effective watershed management practices. This relationship is not well defined in the highlands with a monsoon climate and needs further study. The objective of this study is to relate topographic position and hydrological response in tropical highlands. The research was conducted in the Debre Mawi watershed in the northwest sub-humid Ethiopian highlands. In the monsoon rain phase of 2017 and 2018, groundwater depth, infiltration rate, and surface runoff were monitored at the upslope, midslope, and downslope positions. Surface runoff rates were measured in farmer fields through distributed V-notch weirs as estimates of positional runoff. Average water table depths were 30 cm deep in the downslope regions and 95 cm in the upslope position. The water table depth affected the steady-state infiltration rate in the rain phase. It was high upslope (350 mm h⁻¹), low midslope (49 mm h⁻¹), and zero downslope. In 2017, the average runoff coefficients were 0.29 for the upslope and midslope and 0.73 downslope. Thus, topographic position affects all aspects of the watershed hydrology in the humid highlands and is critical in determining runoff response.

There is an increasing need globally to establish relationships among flow, ecology, and livelihoods to make informed decisions about environmental flows. This paper aimed to establish the ecological foundation for a holistic environmental flow assessment method in the Gumara River that flows into Lake Tana in Ethiopia and the Blue Nile River. First, the ecological conditions (fish, macro-invertebrate, riparian vegetation, and physicochemical) of the river system were characterized, followed by determining the hydrological condition and finally linking the ecological and hydrological components. The ecological data were collected at 30 sites along the Gumara River on March 2016 and 2020. River hydrology was estimated using the SWAT model and showed that the low flow decreased over time. Both physico-chemical and macroinvertebrate scores showed that water quality was moderate in most locations. The highest fish diversity index was in the lower reach at Wanzaye. Macroinvertebrate diversity was observed to decrease downstream. Both the fish and macroinvertebrate diversity indices were less than the expected maximum, being 3.29 and 4.5, respectively. The normalized difference vegetation index (NDVI) for 30 m and 60 m buffer distances from the river decreased during the dry season (March–May). Hence, flow conditions, water quality, and land-use change substantially influenced the abundance and diversity of fish, vegetation, and macroinvertebrate species. The pressure on the ecology is expected to increase because the construction of the proposed dam is expected to alter the flow regime. Thus, as demand for human water consumption grows, measures are needed, including quantification of environmental flow requirements and regulating river water uses to conserve the ecological status of the Gumara River and Lake Tana sub-basin.

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.

The sustainable development of water resources includes retaining some amount of the natural flow regime in water bodies to protect and maintain aquatic ecosystem health and the human livelihoods and wellbeing dependent upon them. Although assessment of environmental flows is now occurring globally, limited studies have been carried out in the Ethiopian highlands, especially studies to understand flow-ecological response relationships. This paper establishes a hydrological foundation of Gumara River from an ecological perspective. The data analysis followed three steps: first, determination of the current flow regime—flow indices and ecologically relevant flow regime; second, naturalization of the current flow regime—looking at how flow regime is changing; and, finally, an initial exploration of flow linkages with ecological processes. Flow data of Gumara River from 1973 to 2018 are used for the analysis. Monthly low flow occurred from December to June; the lowest being in March, with a median flow of 4.0 m³ s⁻¹. Monthly high flow occurred from July to November; the highest being in August, with a median flow of 236 m³ s⁻¹. 1-Day low flows decreased from 1.55 m³ s⁻¹ in 1973 to 0.16 m³ s⁻¹ in 2018, and 90-Day (seasonal) low flow decreased from 4.9 m³ s⁻¹ in 1973 to 2.04 m³ s⁻¹ in 2018. The Mann–Kendall trend test indicated that the decrease in low flow was significant for both durations at α = 0.05. A similar trend is indicated for both durations of high flow. The decrease in both low flows and high flows is attributed to the expansion of pump irrigation by 29 km² and expansion of plantations, which resulted in an increase of NDVI from 0.25 in 2000 to 0.29 in 2019. In addition, an analysis of environmental flow components revealed that only four “large floods” appeared in the last 46 years; no “large flood” occurred after 1988. Lacking “large floods” which inundate floodplain wetlands has resulted in early disconnection of floodplain wetlands from the river and the lake; which has impacts on breeding and nursery habitat shrinkage for migratory fish species in Lake Tana. On the other hand, the extreme decrease in “low flow” components has impacts on predators, reducing their mobility and ability to access prey concentrated in smaller pools. These results serve as the hydrological foundation for continued studies in the Gumara catchment, with the eventual goal of quantifying environmental flow requirements.

The Ethiopian highlands suffer from severe land degradation, including erosion. In response, the Ethiopian government has implemented soil and water conservation practices (SWCPs). At the same time, due to its economic value, the acreage of eucalyptus has expanded, with croplands and pastures converted to eucalyptus plantations. The impact of these changes on soil loss has not been investigated experimentally. The objective of this study, therefore, is to examine the impacts of these changes on stream discharge and sediment load in a sub-humid watershed. The study covers a nine-year period that included installation of SWCPs, a three-fold increase from 1.5 ha in 2010 to 5 ha in 2018 in eucalyptus, and the upgrading of an unpaved to the paved road. Precipitation, runoff, and sediment concentration were monitored by installing weirs at the outlets of the main and four nested watersheds. A total of 867 storm events were collected in the nine years. Runoff and sediment concentration decreased by more than half in nine years. In the main watershed W5, we estimated that evapotranspiration by eucalyptus during the dry phase (November to May) increased approximately from 30 mm a⁻¹ in 2010 to 100 mm a⁻¹ in 2018. In watershed W3 it increased from 2 mm a⁻¹ to 400 mm a⁻¹, requiring more rainfall before saturation excess runoff began in the rain phase. The reduction in runoff led to a decreased sediment load from 70 Mg ha⁻¹ a⁻¹ in 2010 to 2.8 Mg ha⁻¹ a⁻¹ in 2018, though the reduction in discharge may have negative impacts on ecology and downstream water resources. SWCPs became sediment-filled and minimally effective by 2018. This indicates that these techniques are either inappropriate for this sub-humid watershed or require improved design and maintenance.