Spatial and seasonal groundwater quality assessment for drinking suitability using index and machine learning approach.

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.

Groundwater recharge estimation using WetSpass-M and MTBS leveraging from HydroOffice and WHAT tools for baseflow in Weyib watershed, Ethiopia.

WetSpass-M model and multi-technique baseflow separation (MTBS) were applied to estimate spatio-temporal groundwater recharge (GWR) to be used to comprehend and enhance sustainable water resource development in the data-scarce region. Identification of unit Hydrographs And Component flows from Rainfall, Evaporation, and Streamflow (IHACRES) techniques outperform the existing 13 MTBS techniques to separate baseflow depending on the correlation matrix; mean baseflow was 5.128 m3/s. The WetSpass-M model performance evaluated by Nash-Sutcliff Efficiency (NSE) was 0.95 and 0.89; R2 was 0.90 and 0.85 in comparison to observed and simulated mean monthly baseflow and runoff (m3/s), respectively. The estimated mean annual water balance was 608.2 mm for actual evapotranspiration, 221.42 mm for the surface runoff, 87.42 mm for interception rate, and 177.66 mm for GWR, with an error of - 3.29 mm/year. The highest annual actual evapotranspiration was depicted in areas covered by vegetation, whereas lower in the settlement. The peak annual interception rates have been noticed in areas covered with forests and shrublands, whereas the lowest in settlement and bare land. The maximum annual runoff was depicted in settlement and bare land, while the lowest was in forest-covered areas. The annual recharge rates were low in bare land due to high runoff and maximum in forest-covered areas due to low surface runoff. The watershed's downstream areas receive scanty annual rainfall, which causes low recharge and drought. The findings point the way ahead in terms of selecting the best approach across multi-technique baseflow separations.

Comparative analysis in selecting best irrigation method to maximize tomato yield from various irrigation approaches in water scarce regions.

It is now essential for farmers to evaluate and compare different irrigation scheduling strategies in order to operate their irrigation systems in accordance with their resources and achievements. For tomato crops, a rigorous comparison was made between the yield of soil moisture and irrigation scheduling systems based on evapotranspiration. The experimental setup used a randomized complete block design (RCBD) with three replications, with water requirements of 100%, 75%, and 50%. Using a ReplogleBos-Clemmens (RBC) flume with an input rate of 1.62 l/s, water was applied to the furrows. The total applied amount in the Soil Moisture (SM) and Evapotranspiration (ET) based methods was 229.1 mm, and 280 mm, respectively. The collected data were assessed using ANOVA at a 5% significance level. For every technique, yield values, yield attributes, and agronomic traits were computed. The result indicates that there is no appreciable variation among the factors. The net yield advantage was highest at 75% SM and lowest at 25% SM. Furthermore, compared to the ET-based approach, it was demonstrated that the SM-based method conserved approximately 18.2% of irrigation water. As a result, when producing crops that require a lot of water, like tomatoes, in areas with limited water resources, SM-based irrigation scheduling is preferable to ET-based irrigation scheduling.

Long-term spatiotemporal dynamics of groundwater storage in the data-scarce region: Tana sub-basin, Ethiopia.

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 < 0.05) with a rate of 0.333 cm/year. Generally, the Tana sub-basin was nurtured with a net 4.87 BCM groundwater gain in the study period. The most sensitive parts of the study area to large fluctuations of groundwater storage were mainly the nearby southern and eastern directions of Lake Tana.

Multi-purpose reservoir operation oncomitant with estimating hydropower potential using multifarious hydrological models.

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.

Groundwater potential delineation using geodetector based convolutional neural network in the Gunabay watershed of Ethiopia.

Groundwater potential delineation is essential for efficient water resource utilization and long-term development. The scarcity of potable and irrigation water has become a critical issue due to natural and anthropogenic activities in meeting the demands of human survival and productivity. With these constraints, groundwater resource is now being used extensively in Ethiopia. Therefore, an innovative convolutional neural network (CNN) is successfully applied in the Gunabay watershed to delineate groundwater potential based on the selected major influencing factors. Groundwater recharge, lithology, drainage density, lineament density, transmissivity, and geomorphology were selected as major influencing factors during the groundwater potential of the study area. For dataset training, 70% of samples were selected and 30% were used for serving out of the total 128 samples. The spatial distribution of groundwater potential has been classified into five groups: very low (10.72%), low (25.67%), moderate (31.62%), high (19.93%), and very high (12.06%). The area obtains high rainfall but has a very low amount of recharge due to lack of proper soil and water conservation structures. The major outcome of the study showed that moderate and low potential is dominant. Geodetoctor results revealed that the magnitude influences on groundwater potential have been ranked as transmissivity (0.48), recharge (0.26), lineament density (0.26), lithology (0.13), drainage density (0.12), and geomorphology (0.06). The model results showed that using a convolutional neural network (CNN), groundwater potentiality can be delineated with higher predictive capability and accuracy. CNN based AUC validation platform showed that, 81.58% and 86.84% were accrued from the accuracy of training and testing values, respectively. Based on the findings, the local government can receive technical assistance for groundwater exploration, and sustainable water resource development in the Gunabay watershed. Finally, the use of a detector-based deep learning algorithm can provide a new platform for industrial sectors, groundwater experts, scholars, and decision-makers.

LULC change detection using support vector machines and cellular automata-based ANN models in Guna Tana watershed of Abay basin, Ethiopia.

Recurrent changes recorded in LULC in Guna Tana watershed are a long-standing problem due to the increase in urbanization and agricultural lands. This research aims at identifying and predicting frequent changes observed using support vector machines (SVM) for supervised classification and cellular automata-based artificial neural network (CA-ANN) models for prediction in the quantum geographic information systems (QGIS) plugin MOLUSCE. Multi-temporal spatial Landsat 5 Thematic Mapper (TM) imageries, Enhanced Thematic Mapper plus 7 (ETM+), and Landsat 8 Operational Land Imager (OLI) images were used to find the acute problem the watershed is facing. Accuracy was assessed using the confusion matrix in ArcGIS 10.4 produced from ground truth data and Google Earth Pro. The results acquired from kappa statistics for 1991, 2007, and 2021 were 0.78, 0.83, and 0.88 respectively. The change detection trend indicates that urban land cover has an increasing trend throughout the entire period. In the future trend, agriculture land may shoot up to 86.79% and 86.78% of land use class in 2035 and 2049. Grassland may attenuate by 0.03% but the forest land will substantially diminish by 0.01% from 2035 to 2049. The increase of land specifically was observed in agriculture from 3128.4 to 3130 km2. Judicious planning and proper execution may resolve the water management issues incurred in the basin to secure the watershed.

Evaluation of groundwater quality for drinking and irrigation purposes using proxy indices in the Gunabay watershed, Upper Blue Nile Basin, Ethiopia.

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-, SO4 2-, PO4 3-, CO3 2-, 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.

Potential risk assessment due to groundwater quality deterioration and quantifying the major influencing factors using geographical detectors in the Gunabay watershed of Ethiopia.

Groundwater quality has become deteriorated due to natural and anthropogenic activities. Poor water quality has a potential risk to human health and the environment. Therefore, the study aimed to assess the potential risk of groundwater quality contamination levels and public health risks in the Gunabay watershed. For this task, seventy-eight groundwater samples were collected from thirty-nine locations in the dry and wet seasons during 2022. The groundwater contamination index was applied to assess the overall quality of groundwater. Six major driving forces (temperature, population density, soil, land cover, recharge, and geology) and their quantitative impact of each factor on groundwater quality deterioration were demonstrated using Geodetector. The results showed that low groundwater quality was detected in urban and agricultural land. Especially nitrate contamination was highly linked to groundwater quality deterioration and public health risks, and a medium contamination level was observed in the area. This indicates that the inappropriate application of fertilizer on agricultural land and wastewater from urban areas has a great impact on shallow aquifers in the study area. Furthermore, the major influencing factors are ranked as soil type (0.33-0.31) > recharge (0.17-0.15) > temperature (0.13-0.08) > population density (0.1-0.08) > land cover types (0.07-0.04) > lithology (0.05-0.04). The interaction detector revealed that the interaction between soil ∩ recharge, soil ∩ temperature, and soil ∩ land cover, temperature ∩ recharge is more influential to deteriorate groundwater quality in both seasons. Identification and quantification of the major influencing factors may provide new insight into groundwater resource management.

Proxy modeling approach to evaluate groundwater recharge potentiality zones in the data scarce area of upper Blue Nile Basin, Ethiopia.

Prioritization of groundwater recharge potentiality evaluation is critical for sustainable water resources management. Since recharge is a main source for enhancing groundwater availability. Water scarcity is extremely severe in the upper Blue Nile Basin (i.e., Gunabay watershed). Therefore, this study emphasizes groundwater recharge delineating and mapping 3920.25 km2 in the data-limited area of the upper Blue Basin using proxy modeling (i.e., WetSpass-M model and geodetector model) and tools. The driving/influencing factors are rainfall, temperature, wind speed, evapotranspiration, elevation, slope, land cover, soil, groundwater depth, drainage density, geomorphology, and geology that control the movement of groundwater recharge. However, the first nine factors were used as inputs in the WetSpass-M model to evaluate groundwater recharge. To validate the groundwater recharge availability, water table fluctuation was established based on recorded groundwater levels. Furthermore, the major influencing factors and their interaction have been quantified using geodetector model. Spatiotemporal recharge distribution (in mm) is classified as very low (0-6), low (6-30), moderate (30-51), high (51-83), and very high (83-508) comprising 21%, 20%, 20%, 20%, and 19% of the total area, respectively. Very high groundwater recharge zone has been found in the northwest part of the area. The geodetector results showed that soil (0.841) and temperature (0.287) had larger individual contributions, but the interaction between soil and temperature (0.962) was more significant. It indicates that the interaction between climate and soil has the largest influence on groundwater recharge variability. Generally, the overall approach of this study can be applied to water sectors, policymakers, and decision-makers to overcome future water scarcity.

Envisaging the actual evapotranspiration and elucidating its effects under climate change scenarios on agrarian lands of bilate river basin in Ethiopia.

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.