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Ineffective urban stormwater management causes increased surface runoff, and impacts on infrastructure and human life. However, urban stormwater condition is not quantified in Arba Minch area, which cause significant damage on the infrastructures as residence and roads. Therefore, the main objective of this study is to evaluate the condition of urban stormwater under different runoff conditions using Stormwater Management Model (SWMM) in Arba Minch town, specifically Sekela sub city. A combination of field, satellite, observed and laboratory test-based data on drainage systems, infiltration and % imperviousness was collected. The catchment is divided into 47 sub-catchments depend on the building block, flow direction, and drainage networks. The sub-catchments with the average surface runoff revealed 0.07 ​m3/s, 0.14 ​m3/s, 0.25 ​m3/s, 0.65 ​m3/s, and 0.75 ​m3/s over a 2-year, 5-year, 10-year, 25-year, and 30-year return period, respectively. The maximum nodes flooded occur at junctions’ number (J7, J8, J24, and J25), as well as conduits (C14, C18, and C27), according to simulation findings of 2-year return period design rainfall which align with the major cause area. For return periods of 2, 5, 10, 25, and 30 years, the peak runoff result was greatest in sub-catchments SC-1, SC-6, SC-8, SC-9, and SC-30. This depicts sub-catchments in the area are flooded, hence resizing of canals and other drainage structures are necessary to mitigate the stormwater. The SWMM simulated results for 2-year, 5-year, 10-year, 25-year, and 30-year return periods showed peak runoff was greatest in sub-catchments No.8, 1, 6, 30, and 9 because of the slope of the sub-catchment and their large area with high percent imperviousness that results in less infiltration into the soil. The finding of this study shows a rapid increase in surface runoff, which mainly related to impermeable surface and inadequate urban drainage systems leading to damage to infrastructures and properties. This study suggests, a mitigation measures should be adopted to minimize the effect of stormwater using stormwater various intervention approaches following multi program combined with various runoff condition which is very important for road protection authorities, decision makers, and the scientific community to support future water related researches.

The research focuses on modelling the surface water potential using Arc SWAT and Multi-criteria decision-making (MCDM) for selecting the potential hydropower sites in the Megecha watershed. The tenure for stream flow and meteorological data used for SWAT model simulation is from 1990 to 2019 and 1988–2019 respectively. Geospatial method is used to generate gross head and respective potential hydropower. Multi criteria decision method is adopted to select the best site for hydropower generation. Curve number (CN2), saturated hydraulic conductivity (SOL_K), moist bulk density (SOL_BD), and available moisture in the soil layer (SOL_AWC) were among the most sensitive parameters assessed during the research. The performance of SWAT model obtained for R2, NSE and PBIAS were 0.84, 0.78 and −4.6 and 0.81, 0.72 and −4.8 in percentages during calibration and validation periods, respectively. Based on the flow duration curve, the minimum flow of the river for the percent of exceedance of 90 % (Q90) for the sites 1, 2, and 3 are 0.37 m3/s, 0.45 m3/s, and 0.48 m3/s, respectively. The available heads and corresponding hydropower potential on the identified sites were 15, 25, and 20 m and 54.45 KW, 110.36 KW, and 94.18 KW at site1, 2, and 3, respectively. From the GIS based MCDM analysis, the best alternative site for hydropower generation is at site 2, in comparison to identified sites. The total watershed area was 1176 km2 of which, 308.65 million m3 of runoff was generated by the model annually. The watershed has high surface water potential, and the rivers have enough surface water that may be used for hydropower development.

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.

The primary purpose of this study was to evaluate the hydraulic coefficient of coarse aggregate grain size beds and hydraulic parameters under random and perpendicular bed configurations, as well as to explore the discharge coefficient for rectangular weirs. The research objectives were to compare flow resistance coefficients, evaluate the discharge coefficient for rectangular weirs, investigate the relationship between roughness coefficient and hydraulic parameters, and validate the theoretical hydraulic equation for the rectangular weir. This was achieved by analysing different bed configurations, bed slopes, and 20 and 30-mm bed materials. Sieve analysis was conducted on bed materials using American-standard sieves to determine their particle size distribution. The experiment was performed in a rectangular flume measuring 12 m in length, 0.31 m in width, and 0.45 m in depth. In a laboratory experiment, water was pumped into a flume using centrifugal pumps, and a rectangular weir was attached downstream for discharge measurement. The experiment investigated factors such as Manning roughness coefficient, bed material geometry, bed slope, and weir shapes. Approximately 1680 tests were conducted to analysed the impact of these factors on discharge and the coefficient of discharge. The average Manning's roughness coefficients for a grain size of 20 mm were 0.019 (with weir) and 0.019 (without weir) in a random bed configuration, and 0.028 (with weir) and 0.027 (without weir) in a perpendicular flow bed configuration. For a grain size of 30 mm, the coefficients were 0.023 (with weir) and 0.022 (without weir) in a random bed configuration, and 0.033 (with weir) and 0.026 (without weir) in a perpendicular flow bed configuration. The presence of a weir has affected Manning's roughness coefficients and discharge coefficients. With a weir, the roughness coefficients have generally been higher compared to without a weir, indicating increased roughness in the channel. The discharge coefficient for a rectangular weir with a grain size of 20 mm ranged from 0.39 to 0.84 (random bed) and 0.27 to 0.68 (perpendicular flow bed), while for a grain size of 30 mm it ranged from 0.31 to 0.81 (random bed) and 0.23 to 0.48 (perpendicular flow bed). The discharge coefficients have varied depending on the grain size and bed configuration, reflecting different flow efficiencies over the weir. Rough particles influenced flow and Manning's roughness coefficient value, then reduced discharge and velocity values. Under two bed configurations and slopes, beds with a grain size of 30 mm have higher roughness coefficients compared to those with a grain size of 20 mm. The models have shown that the roughness coefficient is inversely proportional to the discharge and directly proportional to the tailgate water levels. The coefficient of roughness and discharge coefficient are mainly influenced by the channel slopes, bed roughness, bed grain size, and bed configuration. A randomly configured bed with a 20 mm grain size gravel bed is preferred over a perpendicular bed configuration to handle high discharges. Using a 20 mm grain-size gravel bed in open-channel flow is more suitable than a 30 mm grain-size bed. Relying on the constant friction factor, Manning's n, is not recommended as it may result in design errors. These findings have the potential to improve hydraulic engineering design practices, enhancing the accuracy and efficiency of open-channel flow systems.

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.

Climate analysis at relevant time scales is important for water resources management, agricultural planning, flood risk assessment, ecological modeling, and climate change adaptation. This study analyzes the spatiotemporal variability of climate on rainfall distribution for the Hare catchment of Ethiopia. Numerous hydroclimatic variables and scenarios were developed to assess the pattern of rainfall during different seasons. The average annual precipitation varies between −37.3%, +33.1%, and −38.2%, +61.2%, for RCP 4.5 and RCP 8.5, respectively. The anticipated declines in mean seasonal rainfall changes for the Bega and Belg seasons range from −69.6% to 88.4% and from −60.6% to 15.2% for RCP 4.5 and RCP 8.5, respectively. Climate models predict that the average periodic precipitation considered for the Kiremt season will vary from −12.1% to 1.33%. The Belg, Kiremt, and Bega seasons will likely see a 28.2%, 12.2%, and 22.6% drop in mean seasonal precipitation, respectively. The decrease in stream flow accompanied by the aforementioned climate scenarios (RCP 4.5 and RCP 8.5) can be as high as 19.6% and 6.7%, respectively. Also, the amount of discharge will reduce in the near future because of a substantial reduction in rainfall and a rise in evapotranspiration in the catchment. This decline in stream flow has its own effect on the future availability of water resources. The research finding is vital to environmental protection authority, decision makers, and scientific community to undertake climate change adaption techniques for rain scare areas. A program combined with multi-RCMs to evaluate climate change effects on hydrometeorology generated a novel approach to this research with appropriate adaptation mechanisms.

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.

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.

A new pyridine-fused selenodiazolium salt, 3-(phenylselanyl)[1,2,4]selenadiazolo[4,5-a]pyridin-4-ylium chloride dichloromethane 0.352-solvate, C12H9N2Se2+·Cl-·0.352CH2Cl2, was obtained from the reaction between 2-pyridylselenenyl chloride and phenylselenocyanate. Single-crystal structural analysis revealed the presence of C--H⋯N, C--H⋯Cl-, C--H⋯Se hydrogen bonds as well as chalcogen-chalcogen (Se⋯Se) and chalcogen-halogen (Se⋯Cl-) interactions. Non-covalent interactions were explored by DFT calculations followed by topological analysis of the electron density distribution (QTAIM analysis). The structure consists of pairs of selenodiazolium moieties arranged in a head-to-tail fashion surrounding disordered dichloromethane molecules. The assemblies are connected by C--H⋯Cl- and C--H⋯N hydrogen bonds, forming layers, which stack along the c-axis direction connected by bifurcated Se⋯Cl-⋯H--C interactions. [ABSTRACT FROM AUTHOR]

The molecular and crystal structures of the title compound, [Cu2I2(C18H12N2)2], were examined by single-crystal X-ray diffraction and Hirshfeld surface analysis. The Cu atom is coordinated in a distorted tetra­hedral geometry by two N atoms from the 2,2′-bi­quinoline ligands and the two μ2-bridging iodide ligands. The mol­ecules are in contact via π-π-stacking inter­actions. Hirshfeld surface analysis showed that the most important contributions to the inter­molecular inter­actions are H⋯H (39.7%), H⋯I/I⋯H (17.8%), C⋯H/H⋯C (17.5%), C⋯C (16.5%), N⋯C/C⋯N (3.9%) and N⋯H/H⋯N (3.5%). [ABSTRACT FROM AUTHOR]

The title compound, C23H19BrN4O2, is a product of an azo coupling reaction between 3,4-bis(4-methoxyphenyl)imidazole and 4-bromophenyldiazonium tetrafluoroborate. Its crystal structure was determined using data collected at 120 K. The molecule adopts a trans configuration with respect to the N=N double bond. The imidazole and aryl rings attached to the azo linkage are coplanar within 12.73 (14)°, which indicates significant electron delocalization within the molecule. In the crystal, the molecules form centrosymmetric dimers via pairs of N--H…O hydrogen bonds. [ABSTRACT FROM AUTHOR]

The title compound, C21H23NO4S, obtained by alkaline treatment of 1,5-bis­(1-phen­oxy)-3-aza­pentane at moderate heating, is a N-tosyl­ated secondary vinyl­amine. An intra­molecular S=O⋯H—C hydrogen bond generates a 13-membered ring. The benzalacetone moiety adopts a trans conformation with respect to the C=C double bond, which is slightly longer than usual due to the conjugation with a neighbouring acetyl group. Theoretical predictions of potential biological activities were performed, suggesting that the title compound can inhibit gluconate 2-de­hydrogenase (85% probability), as well as to act as a mucomembranous protector (73%). [ABSTRACT FROM AUTHOR]