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Abstract The main objective of this study is to map and evaluate groundwater potential zones (GWPZs) using advanced ensemble machine learning (ML) models, notably Random Forest (RF) and Support Vector Machine (SVM). GWPZs are identified by considering essential factors such as geology, drainage density, slope, land use/land cover (LULC), rainfall, soil, and lineament density. This is combined with datasets used for training and validating the RF and SVM models, which consisted of 75 potential sites (boreholes and springs), 22 non‐potential sites (bare lands and settlement areas), and 20 potential sites (water bodies). Each dataset is randomly partitioned into two sets: training (70%) and validation (30%). The model's performance is evaluated using the area under the receiver operating characteristic curve (AUC‐ROC). The AUC of the RF model is 0.91, compared to 0.88 for the SVM model. Both models classified GWPZs effectively, but the RF model performed slightly better. The classified GWPZ map shows that high GWPZs are typically located within water bodies, natural springs, low‐lying regions, and forested areas. In contrast, low GWPZs are primarily found in shrubland and grassland areas. This study is vital for decision‐makers as it promotes sustainable groundwater use and ensures water security in the studied area.

Studies reviewed in this paper show anomaly for temperature pertaining to streamflow and rainfall showing different trends, especially in Ethiopia to support the research findings and interpretation. There are many hydrological models, including 54 physically distributed, lumped, and conceptual hydrological models, of which 28 have been used in Ethiopian river basins. The models include the most adaptable and commonly used SWAT model applicable from small areas up to large basins. It is indeed a challenge to use a single hydrological model as the data rely on consistency, limitation-free, and exactly fitted output. The overall performance of individual physically-based, conceptual, and machine learning (ML) models varied at different watersheds. Reasonably, ML performs very well, up to 0.99 for R2 and NSE and up to 0.001 for PBIAS. Inopportunely, using a single hydrological model has its limitations; ensemble multi-individual models, coupling or hybridization of physical or conceptual models with machine learning, combining evolutionary optimization algorithms with ML, and also comparisons of multi-single hydrological models, and selecting the best one are recommended options. No single hydrological model is indispensable and can be termed as better than the other for any watershed. Somewhat, ML outperforms SWAT but cannot be considered an absolute substitute. The size of the watershed, the number of data used, and the ratio between calibrations year to validation year do not have a clear correlation with the performance, particularly for the SWAT model accounted for in this review. Optimization algorithms explore multiple options and choosing the right one is a tedious task before a final decision is taken.

In smart cities, sustainable development depends on energy load prediction since it directs utilities in effectively planning, distributing and generating energy. This work presents a novel hybrid deep learning model including components of the Improved-convolutional neural network (CNN), bidirectional long short-term memory (Bi-LSTM), Graph neural network (GNN), Transformer and Fusion Layer architectures for precise energy load forecasting. Better feature extraction results from the Improved-CNN's dilated convolution and residual block accommodation of wide receptive fields reduced the vanishing gradient problem. By capturing temporal links in both directions, Bi-LSTM networks help to better grasp complicated energy use patterns. Graph neural networks improve predictive capacities across linked systems by characterizing the spatial relationships between energy-consuming units in smart cities. Emphasizing critical trends to guarantee reliable forecasts, transformer models use attention methods to manage long-term dependencies in energy consumption data. Combining CNN, Bi-LSTM, Transformer and GNN component predictions in a Fusion Layer synthesizes numerous data representations to increase accuracy. With Root Mean Square Error of 5.7532 Wh, Mean Absolute Percentage Error of 3.5001%, Mean Absolute Error of 6.7532 Wh and R 2 of 0.9701, the hybrid model fared better than other models on the ‘Electric Power Consumption’ Kaggle dataset. This work develops a realistic model that helps informed decision-making and enhances energy efficiency techniques, promoting energy load forecasting in smart cities.

The primary aim of this research was to study the manufacturing process of bamboo charcoal briquettes following the principles of life cycle assessment. Charcoal briquettes produced from lowland bamboo exhibited good fuel characteristics, higher calorific value and fixed carbon, and low moisture content. The ash content was in compliance with the international acceptable standard. Bamboo charcoal briquettes from highland bamboo (Oldeania alpina) and lowland bamboo (Oxytenanthera abyssinica) had calorific values of 6785 cal/gm and 7106 cal/gm, respectively. Manufacturing the briquettes at an ideal moisture content for carbonization saves time and leads to a maximum yield. Bamboo bioenergy (briquettes) produced in regulated kilns using locally available materials have gained popularity. Bamboo biomass utilization contributes to reducing deforestation and creates employment opportunities in bamboo value chains, as well as and contributing to UN SDG 7. Developing sustainable bamboo biomass energy use requires informing entrepreneurs about suitable business ecosystems. This research should encourage the proper utilization of the biomass feedstock and should ensure a healthier environment through the supply of a renewable source of energy. The most relevant findings in the present work were the production of environmentally friendly charcoal briquettes and their characterization (proximate and calorific value analysis) within the context of life cycle assessment of bamboo charcoal briquette technology.

The research was conducted to assess the climate change impact on crop water requirements (CWRs) in the Woybo catchment of southern Ethiopia. The impact of climate change was assessed through climate models under RCP4.5 and RCP8.5 emission scenarios for the 2050s compared to 1971–2005. During the 2050s, the annual mean minimum temperature increased by 1.9–2.3 °C, while the maximum temperature over the catchment increased by 1.4–2.0 °C. The projected rainfall is expected to increase in autumn, summer, and annually by 3.7–16.8 and 8.7–12.2% in the 2050s under RCP4.5 and 8.5, respectively. But it is likely to decrease in winter and spring by 1.5–10.2% and 8.7–2.8%, respectively. The average annual potential evapotranspiration will rise by 0.1–6.8%. Under both RCPs, CWRs may increase from 6.2 to 17.2% during the growing season, while irrigation water requirements (IWRs) may rise from 4.6 to 16.4%. Future period projection results show that potential evapotranspiration and IWRs will increase for maize crops compared to the baseline period. The study will help water managers, users, and agricultural developers in preparing new water-saving strategies and achieving agricultural sustainability. HIGHLIGHTS Due to continuous change in climatic conditions, farmers are abruptly facing a huge loss due to erratic rainfall, increase in temperature, insufficient irrigated water, and many other causes.; An attempt has been made in this research to study the impact of climate change on maize production and, with different scenarios, the effects have been calculated and assessed.; The RCPs are well augmented.;

Spatial and seasonal evaluation and monitoring of groundwater (GW) quality is essential for the sustainable management of this priceless resource and the provision of safe drinking water. Nevertheless, GW quality appraisal was not given due attention in the current study area (flat terrain part of the Tana sub-basin). This study sought to assess the seasonal and regional physicochemical GW quality parameters for drinking appropriateness using the drinking water quality index (DWQI) and support vector machine (SVM). The main cations in both the dry and wet study seasons were, in decreasing order, Na+, Ca2+, Mg2+, K+, and Fe2+, according to the results. Conversely, the main anions were HCO3−, CO32−, Cl− or NO3−, SO42− and PO43−, ordered from higher to lower. During the two research seasons, Ca–HCO3 and Na–HCO3 were the predominant water types based on Piper diagram results. Reverse ion exchange and evaporation were the principal hydrogeochemical processes that control the hydrogeochemistry identified by Durov and Gibbs diagrams, respectively. Excellent GW quality class for drinking was demonstrated by the majority of geographical and seasonal DWQI readings over the two seasons. Nevertheless, during the rainy season, there was a noticeable decline in the GW quality condition around the northern shores of Lake Tana. Therefore, it is advised to implement comprehensive GW quality protection measures and improve system management to mitigate pollution to reduce health hazards in the examined region.

Abstract Multi‐input converters play an important role in integrating the independent energy sources utilized in the grid‐connected system and electric vehicle (EV) applications. In this scenario, several types of multi‐input converters are presented in the literature. Most of the MICs are operated using a time‐sharing scheme. This leads to a restricted duty cycle which limits the energy sources utilization and output voltage. To overcome the aforementioned limitations, a multi‐input single‐output (MISO) boost converter is proposed. It can improve the utilization of energy sources and output voltage with a reduction in the part count. To verify the feasibility of the proposed scheme, a 200 W prototype circuit is developed; simulation and experimental results are validated.

Imprudent extraction of groundwater tends to undue stress and portends its sustainability. Spatiotemporal analysis of groundwater storage anomaly (GWSA) is imperative for the judicious use, management, and sustainable development of a region. This study aimed to examine the changes in groundwater storage over the past 20 years in the Tana sub-basin using Gravity Recovery and Climate Experiment (GRACE) assimilated into Global Land Data Assimilation Systems (GLDAS). Validation analysis was carried out to evaluate the accuracy of GWSA against anomalies of Lake Tana water level, precipitation, and in-situ groundwater level. Modified Mann-Kendal test and Sen's slope estimator were applied for trend analysis of the GWSA. The results exhibited that GWSA strongly correlated (Pearson's correlation coefficient, R ranges from 0.75 to 0.96) with the three validation above variables, which elucidated in general, credible GWSA estimation. The net annual GWSA curve showed a non-significant (p > 0.05) decreasing trend from 2003 to 2012. However, years including 2005, 2006, and 2009 were drought periods, which caused 0.49 billion cubic meters (BCM) groundwater loss. In the entire study period (2003–2022), on the other hand, the net annual GWSA revealed a significant increasing trend (p

Understanding the sources of precipitation and their impacts is crucial for basin-wide water balance research. Previous research concentrated on the sources of moisture in Ethiopia. The southern part’s moisture sources, however, were not investigated. The primary objective of this study is to trace the source of atmospheric moisture in the Abaya-Chamo sub-basin of southern Ethiopia using numerical water vapor tracers like Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Exploring the possible regions of atmospheric vapor roots and the path of moist air initiating rainfall that reaches the basin was feasible for the year 2018–2020. The anticyclone from the Arabian High, which is positioned in the Arabian and Mediterranean seas, was the primary source of moisture supply in the study area during the Belg (March to May) season, according to the back trajectory cluster analysis results. Additionally, the Indian Ocean adds moisture resulting from Mascarene highs brought by equatorial easterlies. Furthermore, during Kiremt (June to September), air masses from the Congo basin were the potential moisture source region for the study areas in combination with air masses originating from the Mascarene highs, located in the South Indian Ocean, and the St. Helena high, centered in the subtropical southern Atlantic Ocean. This study primarily focuses on the complex dynamics of atmospheric moisture sources around Abaya-Chamo sub-basin of southern Ethiopia, offering insight into seasonal fluctuations and contributing various components. These findings contribute to basin-specific water balance research by filling gaps in the previous studies.

The research aims at determining the optimal release rule to increase the capacity of Rib reservoir. The reservoir inflow using HBV-light hydrological model embracing optimal reservoir operation through HEC-ResSim model were used to prepare an optimum operational plan. The potential of the river for hydropower generation prioritise the demand at a specified level regarding storage capacity (m3), level of reservoir (m), and the relation between inflow and outflow of the reservoir. From the model performance features, the coefficient of correlation (R2) and Nash Sutcliffe Efficiency (NSE) were determined to be, respectively, 0.77 and 0.73 for calibration and 0.72 and 0.70 for validation. The Sobol approach was used for detailed sensitivity analysis of DROP model parameters based on the performance of C2M on outflows and volumes. The results suggest that the threshold coefficient characterizing the demand-controlled release level is the most significant parameter. According to the simulation's findings, the reservoir's average regulated release is calculated to be 22.86 m3/s, and its average monthly hydropower output is 6.73 MW. Average annual hydropower energy was estimated as 58.955 GW h/year and mean annual inflow of reservoir volume of water to be 223.54 Mm3. This volume of water is adequate to accommodate total annual irrigation demand, environmental obligation, and other respective requirements in the downstream. The demand for hydropower and irrigation and supply from reservoir capacity can be counterbalanced from the simulated result without any hindrance.

Long-term and sustainable agricultural practices can be achieved through monitoring and evaluation of groundwater quality for irrigation operations. However, less attention was given to irrigation water quality in the Tana sub-basin, Ethiopia. The present study is aimed to assess the seasonal and spatial groundwater suitability for irrigation uses. The groundwater quality parameters measured in 40 samples in each dry and wet season were the pH, electrical conductivity (EC), Na+, Ca2+, Mg2+, K+, Fe2+, HCO3-, CO32-, Cl−, and NO3-. The groundwater suitability for irrigation was assessed using the irrigation water quality index (IWQI) and support vector machine (SVM). The results showed high and medium irrigation suitability classes in the dry and wet seasons. The proportion of groundwater samples in the medium irrigation suitability class in the dry and wet seasons, respectively, was 72.5 and 67.5%. The groundwater in the wet season is comparatively more suitable than that in the dry season, which is attributed to the leaching of accumulated salts during the wet season. To avoid a salinity threat, vigilance should be exercised when using groundwater during the dry seasons. The groundwater quality map developed here for irrigation may aid in locating better-quality groundwater sources for irrigation. HIGHLIGHT At the headwaters of the Blue Nile, in the Lake Tana sub-basin, a coupled strategy for evaluating groundwater quality for irrigation has been researched during the dry and wet seasons. This study concludes that the rainy season's water quality is superior to the dry, suggesting that using GW in the dry season should be done with prudence.; The groundwater in the wet season occurs with better quality than that in the dry season since there is water quality improvement due to leaching in the wet season.;

AbstractGroundwater is the primary source of water supply in Ethiopia. The study area was challenged due to increasing water demand, uneven water resource distribution, and noticeable changes in groundwater levels. The study focused on the examining of existing abstraction and future water demand scenarios on groundwater balance in the Weyib watershed using the WetSpass-M and MODFLOW-2005 models. The input datasets, such as aquifer properties, observed groundwater heads, hydrogeology, groundwater recharge, the Digital Elevation Model (DEM), and hydrological data were used. Datasets were prepared to better represent subsurface hydrology and its future demand effects evaluated using calibrated steady-state numerical groundwater modeling. The WetSpass-M and MODFLOW-2005 models depicted good performances during the simulations of groundwater recharge and groundwater budget under existing abstractions and estimated demand scenarios, respectively. The mean annual groundwater recharge estimated was 177.66 mm/year. The existing groundwater abstraction was 34,686.39, estimated short-term and long-term water demand scenarios were 72,113.61 and 93,795.57 m3/day, respectively. The upstream area has the highest groundwater head and recharge, while decreasing as it approaches the Weyib watershed outlet. During the outflow groundwater budget, the groundwater abstractions increased as expenses of river leakage and head dept. bounds increased. Moreover, the increasing groundwater withdrawal would reduce groundwater heads, and the estimated future water demand scenarios would substantially impact the groundwater budget, which would also have an impact on the watershed hydrology and ecosystem.

AbstractEvaluating and comparing two different irrigation scheduling methods like soil moisture (SM) and evapotranspiration (ET)-based irrigation scheduling methods were assessed scientifically to maximize tomato yield, especially in areas where restricted irrigation facilities are available. The experimental design was arranged in a Randomized Complete Block Design (RCBD) with 100%, 75%, and 50% water requirements with three replications. The water was applied to furrows using Randomized Block Design (RBC) flume with an inflow rate of 1.62 l/s. The total amount of water applied in SM- and ET-based methods were 229.1 mm and 280 mm, respectively. The collected data were analyzed using ANOVA (statistical software) at 5% significance level. Values on agronomic characteristics, yield attributes, and yield were determined for those two methods. T1–T6 are the total treatments used in this research. Out of which, T1, T2, and T3 are taken as SM-based represented for 100%, 75%, and 50% of requirements and T4, T5, and T6 as ET-based represented for the same percentage of requirements. The results did not illustrate a huge difference in yield potential. The highest net benefit on yield was obtained at T2 (SM-based), whereas the lowest in T6 (ET-based). It was also observed that SM-based method saved around 18.2% of irrigation water compared to ET-based approach. Therefore, to cultivate high water demanding crops such as tomato in water scarce areas, SM-based irrigation scheduling method is comparatively better than ET-based as far as yield is concerned.

This research aims to map flood inundated areas under changing climate in the Boyo watershed of Southern Ethiopia. A semi-distributed physically based Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) and Hydrologic Engineering Center-River Analysis System (HEC-RAS) were used to simulate the flood events and maps, respectively, for climate scenarios. The bias-corrected data of four climate models were used for the baseline (1976–2005), mid-term (2041–2070) and long-term (2071–2100) cycles under RCP4.5 and RCP8.5 scenarios. The 50- and 100-year return period flood events were generated from the baseline and future period streamflow data. The HEC-RAS model was used to simulate the inundation areas and depths from the flood events. The result exhibited that the average annual rainfall and maximum and minimum temperatures of the catchment will increase in the future with an increase in annual runoff. The severity of annual floods would increase in the future under RCP4.5 and RCP8.5 scenarios. Approximately, 193 ha of the study may be flooded with flood events having a return period of 100 years under the RCP8.5 scenario in the long-term period, which is an extreme case. The result is a benchmark to reduce the flood risk and management of floodplains in this watershed. HIGHLIGHTS The result is a benchmark to reduce the flood risk and management of floodplains in this watershed.; This study will be helpful for cities, farming communities nearby the river bank, local community and government bodies concerned with risk management.;

Evaluation of groundwater potential and its quality assessment for drinking and irrigation has recently become a major concern, especially in developing countries due to various constraints. The primary aim of this study is to evaluate the quality of groundwater and establish whether they are safe for domestic and agricultural usage. 78 samples were collected during dry and wet seasons from 39 locations in the Gunabay district of the upper Blue Nile, Ethiopia. The following physicochemical parameters were evaluated successfully (T, pH, EC, TDS, Na+, K+, Ca2+, Mg2+, Fe, Cl−, F−, SO42−, PO43−, CO32−, HCO3−, and NO3−-N). Then, Entropy Weight Water Quality Index (EWQI) and irrigation water quality indices (SAR, %Na, MAR, RSC, PS, KI, PI, and IWQI) were used to assess the distribution of groundwater quality in the study area. The Piper diagram used to characterize the groundwater types revealed that Ca–HCO3 is dominant in the area and rock-water interaction regulates the chemical characteristics of groundwater. Wilcox diagram was used to analyze the salinity level in the groundwater. The findings showed that the groundwater had higher nitrate levels relative to the permissible level of WHO standards due to excessive use of fertilizers in rural areas. Depending on the EWQI approach, the study area was categorized as excellent, good, and medium zones, covering 84.6%, 12.8%, and 2.6%, respectively. The results depict that high-quality drinking water was available in rural areas, n high to medium in the urban regions. The comparative irrigation water indices record 85% of water wells are suitable for irrigation, but some well sites are unsuitable due to higher salinity hazards and deep rock interaction. These integrated water quality indices were effective in validating drinking and irrigation water quality in the study area.

Appropriate quantification and identification of the groundwater distribution in a hydrological basin may provide necessary information for effective management, planning and development of groundwater resources. Groundwater potential assessment and delineation in a highly heterogeneous environment with limited Spatiotemporal data derived from Gelana watershed of Abaya Chamo lake basin is performed, using integrated multi-criteria decision analysis (MCDA), water and energy transfer between soil and plant and atmosphere under quasi-steady state (WetSpass) models. The outputs of the WetSpass model reveal a favorable structure of water balance in the basin studied, mainly using surface runoff. The simulated total flow and groundwater recharge are validated using river measurements and estimated baseflow at two gauging stations located in the study area, which yields a good agreement. The WetSpass model effectively integrates a water balance assessment in a geographical information system (GIS) environment. The WetSpass model is shown to be computationally reputable for such a remote complex setting as the African rift, with a correlation coefficient of 0.99 and 0.99 for total flow and baseflow at a significant level of p-value

Sensible and judicious utilization of water for agriculture in conjunction with prediction techniques increases the crop yield. The Ethiopian economy relies on and is exclusively dependent on agricultural-based activities. Different soil compositions (nitrogen, phosphorous, and potassium), crop alternation, soil dampness, and climate conditions play an imperative contribution in cultivation. The primary purpose of this study was to conduct a machine learning approach which can be practiced dynamically for efficient farming at a low cost. The support vector machine (SVM) was applied as a machine learning procedure, whereas long short-term memory (LSTM) and the recurrent neural network (RNN) were considered as deep learning procedures. The research comprised a model that is combined with machine learning procedures (ANN, random forest, and decision tree) to know efficient and appropriate crop types. The planned model is improved through conducting deep learning methods incorporated to the existing practice for different crop condition. Pure data and related evidence are attained concerning the quantities of soil constituents desired through their expenditures distinctly. It delivers well precision as compared to the current model examining the specified documents and assisting the local agronomists in forecasting different types of crop and gain benefits. In RNN, LSTM, and SVM algorithms, the accuracy is determined as 96% which is comparatively preferable as compared to other machine learning procedures under different feature and crop types. The techniques are evaluated in terms of percentage in prediction accuracy. The results generated are important for agrarians, experts, researchers, and local farmers to maximize the crop productivity and help to enhance agriculture and climate change-related decisions, especially in low-to-middle-income countries.

The site suitability assessment of Bamboo in Kenya was studied for diverse agroecological zones (AEZs) comprising one indigenous and fifteen exotic bamboo species in nine different AEZs of Kenya. Three bamboo clumps from each species that were at least five years old were assessed to accurately capture data on growth performance and yield. Soil samples collected at 0–30 cm depth from different areas varied considerably. The proportion of soil varied across the bamboo planting sites (F(1,11) = 24.94; p

Characteristics of ungauged catchments can be studied from the hydrological model parameters of gauged catchments. In this research, discharge prediction was carried out in ungauged catchments using HEC-HMS in the central Omo-Gibe basin. Linear regression, spatial proximity, area ratio, and sub-basin mean were amalgamated for regionalization. The regional model parameters of the gauged catchment and physical characteristics of ungauged catchments were collated together to develop the equations to predict discharge from ungauged catchments. From the sensitivity analysis, crop coefficient (CC), storage coefficient (R), constant rate (CR), and time of concentration (TC) are found to be more sensitive than others. The model efficiency was evaluated using Nash–Sutcliffe Efficiency (NSE) which was greater than 0.75, varying between −10% and +10% and the coefficient of determination (R2) was approximated to be 0.8 during the calibration and validation period. The model parameters in ungauged catchments were determined using the regional model (linear regression), sub-basin mean, area ratio, and spatial proximity methods, and the discharge was simulated using the HEC-HMS model. Linear regression was used in the prediction where p-value ≤ 0.1, determination coefficient (R2) = 0.91 for crop coefficient (CC) and 0.99 for maximum deficit (MD). Constant rate (CR), maximum storage (MS), initial storage (IS), storage coefficient (R), and time of concentration (TC) were obtained. The result is that an average of 30 m3/s and 15 m3/s as the maximum monthly simulated flow for ungauged sub-catchments, i.e. Denchiya and Mansa of the main river basin.

AbstractLack of irrigated water poses a threat in arid and semi-arid areas. Hybrid irrigation system has become an obligatory to save water and increase productivity. Deficit irrigation scheduling is a recent innovative approach of water-saving method. An experiment was conducted to estimate the irrigation interval and find an amicable solution by optimum deficit irrigation level at Arba Minch of southern Ethiopia using conventional furrow irrigation system. The treatments comprised 75%, 50% and 25% of irrigation requirement and four growth stages (initial, development, bulb formation, and late), which were considered in random complete block design with three replications. Refilling was commenced when 25% of Readily Available Water (RAW) depletion at the full irrigation was observed. The irrigation intervals at growth stages were 4, 6, 4, and 3 days, respectively, with an overall irrigation interval of 3 days. The net and gross irrigation of the crop were 311.80 mm and 445.42 mm, respectively. The highest crop phenology and yield were obtained by applying Irrigation Requirement (IR) of a maximum 100% and minimum at 25%. The maximum yield of 25.88 ton/ha was obtained from full irrigation which hardly affects the yield of 25.70 ton/ha obtained from 75%IR at late growth stages. Therefore, application of 100%IR at 3 days interval was assessed as economically productive. However, 50%IR at 3 days interval can be an option when there is a constraint of water supply. The findings will be useful for irrigation water management to maximize crop yields with limited water and unlimited land resources.

Floods have a terrible impact on people's lives and property all around the world. In this study, we evaluated the modeling capabilities of two Machine Learning (ML) approaches such as Naive Bayes Tree (NBT) and Naive Bayes (NB) and four Multi-Criteria Decision-Making (MCDM) analysis techniques MABAC (multi-attributive border approximation area comparison), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (Vise Kriterijumska Optimizacijaik Ompromisno Resenje), and SAW (Simple Additive Weighting) were applied in Abela-Abaya floodplain of Ethiopia. Sixteen flood influencing factors such as elevation, land use/cover (LULC), soil, aspect, geomorphology, normalized difference vegetation index (NDVI), rainfall, distance from river (DR..), topographic wetness index (TWI), sediment transport index (STI), stream power index (SPI), geology, curvature, flow accumulation (FA), slope and flow direction (FD) were used as input parameters. Area Under the Receiver Operating Characteristic Curve was used to assess and validate the models' predictive power (AUC). The NB model performed the best (AUC = 0.92), indicating that it is a viable strategy for determining flood-prone locations in order to properly plan and control flood hazards. Face to face interactive sessions were conducted with 160 respondents to find a coordinated analysis between the impact issues and the community's apprehension of flood danger and to analyze the socioeconomic impact of flood risk. The findings revealed that respondents' socio-demographic traits, flood experience, flood awareness, flood prevention responsibility, and government confidence building were all cohesively related to their opinion of flood danger. The estimated damage for households and farmland were $6249 and $5326 respectively by 2016 flood. The compilation of the findings of this study on flood susceptibility mapping and socioeconomic impact of flood risk seeks to enhance human perceptions of flood risk to minimize flood risk by enhancing communication about the issues and inspiring people of flood-prone areas to take measures for mitigating flood damage.

The earth's natural water and energy systems rely on actual evapotranspiration (AET). Climate change plays a crucial role in affecting the hydrologic processes of Abayya-Chamo lake basin in Ethiopia's Rift Valley, resulting into a distributed actual evapotranspiration (DAET) system. Various studies have already been undertaken on the effects of climate change (CC) on AET but forecasted precipitation and temperature to determine space-time distribution of AET across the basin have not been studied yet. Estimates for precipitation and temperature were acquired from the Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa platform, using RCP4.5 and RCP8.5 scenarios, during 1986–2015, 2041–2070, and 2071–2100 periods. WetSpass-M model was employed to investigate seasonal and annual DAET under varied climate amplitude and distribution. For the baseline period (1986–2015), the maximum annual AET was predicted to be 2815.8 mm/yr. For 2041–2070, and 2071–2100 periods, the estimated maximum annual AET for RCP4.5 scenarios was 3019.2 and 3212.1 mm/yr, respectively, while for RCP8.5 scenarios, it was 3116 and 3352.2 mm/yr, respectively. The baseline annual AET was 516.6 mm/yr, while the mid-term (RCP4.5 and RCP8.5) and long-term (RCP4.5 and RCP8.5) models predicted mean annual AETs of 423.8 and 432 mm/yr and 429.6, and 438.5 mm/yr, respectively. Between 2041 and 2070, the RCP4.5 and RCP8.5 scenarios predicted a 92.8 and 84.6 mm/yr decrease in mean annual AET, respectively. The model predicted a decline in mean annual AET of 87 and 78.2 mm/yr for both scenarios in 2071 and 2100, respectively. With the exception of the basin's maximum AET, the mean annual AET for both RCP4.5 and RCP8.5 emission scenarios may decline during 2041–2070 and 2071–2100. As rainfall declines and temperature rises and the projected AET in the basin gets disrupted in the future decades. This research may add information to the water management and utilization, and a better knowledge of how climate change directly affects AET systems.

Climate changes significantly cause the precipitation deficiency and in turn reduce the inflow amount in reservoir affecting hydroelectric power generation. The primary objective of this study was to evaluate hydropower generation and reservoir operation under climate change from Kesem reservoir. Recent Representative Pathway (RCP) scenarios were used to evaluate the impact of climate change on power generation. Power transformation equation and variance scaling approach were amalgamated to adjust the bias correction of precipitation and temperature, respectively. Bias, root mean square error, and coefficient of variation were used to check the accuracy of projected rainfall. The base and future precipitation, temperature, and evaporation trend was analysed using the Mann–Kendall test. The flow calibration and validation were carried out by the Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS), and hydropower generation was evaluated with reservoir simulation model (MODSIM 8.1) under climate scenarios. The performance of the model was found good with Nash–Sutcliffe coefficient (NSE) of 0.72 and coefficient of determination (R2) of 0.73 for calibration and NSE of 0.74 and R2 of 0.75 for validation. Projected future climate scenarios predicted increasing and decreasing trend of temperature and precipitation, respectively. For RCP4.5 climate scenario, the average energy generation is likely to decrease by 0.64% and 0.82% in both short-term (2021–2050) and long-term (2051–2080), respectively. In case of RCP8.5 climate scenario, the average energy generation will be decreased by 1.06% and 1.35% for short-term and long-term, respectively. Remarkable reduction of energy generation was revealed in RCP8.5 with relation to RCP4.5 scenario. This indicates that there will be high energy fluctuation and decreasing trend in the future energy generation. The research finding is crucial for decision-makers, power authorities, governmental and nongovernmental organizations, and watershed management agencies to take care for sustainability in the future hydropower generation in the Kesem reservoir.

Climate change increases the possibility in varying rainfall and temperature that needs a detail study to estimate flood frequency under changing climate for the Woybo catchment in the Omo River basin of Ethiopia. The impact on flood frequency was evaluated using multiple climate models under RCP4.5 and RCP8.5 emission scenarios for 2020s, 2050s, and 2080s compared to 1976–2005. Hydrologic Engineering Center of Hydrologic Modeling System (HEC-HMS) was used to simulate streamflow after sensitivity analysis, calibration, and validation of the model for the catchment. Flood frequency analysis was initiated after discharge through a longer section followed by frequency analysis by partial duration series approach which provides a better result even though threshold selection is cumbersome. The results from the nonparametric Mann–Kendall test illustrate a slight increase in annual rainfall. The projected flow is expected to increase in autumn, summer, and annually by 8.34, 13.54, and 5.02% in the 2050s and 12.20, 18.06, and 11.87% in 2080s, respectively, under RCP4.5, while it is likely to increase in summer and annually by 15.66 and 5.82% in 2050s and 10.55 and 29.51% in 2080s, respectively, under RCP8.5. Flood frequency was analyzed by using Gumbel’s method. The flood magnitude predicts a positive change for 10, 50, 100, 200, 500, and 1000 years recurrent periods under both scenarios. This research paves way to reduce the negative impacts of flooding and plays a vital role in providing estimates of recurrence floods which are used in designing roads, dams, and bridges for sustainable water resources management.

Due to uninterrupted erosion and transportation, huge volume of sediments carried away by streams and rivers are finally deposited on the meanders, lakes, and reservoirs when the velocity of the surface water flow decreases. Kulfo River in the southern part of Ethiopia faces a challenge due to massive deposit of sediments. Hydrometeorological and spatial data of Kulfo watershed from the observed stream flow data series near Kulfo bridge and four meteorological station data were used to assess the depositional environment of Kulfo watershed. The data length covers the period from 2000 to 2019. Geomorphic parameters of the watershed were developed by using a 30m × 30m digital elevation model (DEM). The spatial distribution of sediment yield of the study area was estimated using SWAT, the soil and water assessment tool. Scenarios were developed to assess the effectiveness of watershed management interventions provided at the watershed and critical subwatershed level. The model genuinely replicated the observed discharge and sediment with an overall performance of 0.75 as measured by NSE. Twenty-one subbasins were created, and the observed average sediment yield was calculated as 11.9 ton/ha/y. The observed average sediment yield reduction at the hotspot subwatershed level postapplication of contouring, filter strip, terracing, and strip cropping were 40.79%, 57.94%, 66.02%, and 62.93%, respectively. By intricately analyzing, it can be referred that terracing is the best conservation measure to be incorporated into the affected subbasins.

The alteration of spatial patterns of landscape interrupts water balance components in Bilate basin of Ethiopia. The aim is to characterize the spatio-temporal variation of surface-subsurface hydrological water balance using the WetSpass model comprising of soil type, topography, groundwater depth, and slope. Environment for Visualizing Images (ENVI) and Arc-GIS software were assimilated for the classification of Landsat images from 1989 to 2019 replicating the forest, shrub, and grasslands which decrease by 4.0%, 9.41%, and 14.87%, respectively, and agricultural land increasing by 27.06% from 1989 to 2019. The goodness of fit in surface runoff and subsurface flow for the two model outputs with the square of regression (R2) of 0.79 and 0.81, while the root mean square errors (RMSEs) 8.26 mm and 8.39 mm for 1989 and 2019, respectively, were calculated. Average annual interception, groundwater recharge, surface runoff, and actual-evapotranspiration were 36.4 mm, 127.34 mm, 614.95 mm, and 517.59 mm, respectively, revealing that WetSpass works remarkably in simulating the components of the hydrological water balance.

Borehole samples drilled up to a depth of 10 m provide a clear understanding whether a foundation is safe for any structure. The main objective of the present study reconnoitered the soil bearing capacity and foundation settlement characteristics using the standard penetration test (SPT) data obtained from 3 boreholes at 1 m, 2 m, and 3 m depths to correlate soil properties and deterrents, if any, created by groundwater. The methodology of the research is to collect soil samples, and ensuing subsoil analysis was performed in order to obtain concrete information to optimize the foundation system within the safe bearing capacity of soil and its allowable settlement. The scope of the work encompasses conducting detailed soil investigation from drilling logs, laboratory testing, and conducting and estimating safe bearing capacity. The result of the research aims at providing safety to the foundation from the investigations of conclusive recommendation to be adopted which would be economically feasible and structurally secured.