To elucidate the recharge relationships and hydrological processes among different water bodies in the Qinghai Lake Basin, precipitation, river water, groundwater, and lake water samples were collected from April to November 2024. The hydrogen and oxygen stable isotope compositions (δ2H and δ18O) were determined to characterize their spatiotemporal variations, and the MixSIAR model was applied to quantitatively evaluate the recharge contributions among different water bodies. The results show significant differences in stable isotope compositions among the various water bodies. Overall, lake water exhibited the most enriched isotopic signatures, whereas groundwater was the most depleted and isotopically stable, while precipitation displayed the largest variability. Precipitation isotopes exhibited pronounced seasonal effects. River water showed a depletion–enrichment–depletion pattern, reflecting the important recharge contributions from wet season precipitation and frozen-soil meltwater. Groundwater exhibited relatively weak seasonal variations and a distinct smoothing effect. In contrast, the isotopic composition of lake water was jointly controlled by evaporative fractionation and multiple recharge sources, with the highest enrichment occurring in spring and gradual depletion during wet season and dry season. Spatially, significant isotopic differences were observed among rivers. Groundwater was relatively enriched in the western part of the basin and depleted in the eastern part, whereas lake water exhibited an opposite pattern, characterized by enrichment in the east and depletion in the west. The Local Meteoric Water Line (LMWL) of the Qinghai Lake Basin was defined as δ2H = 8.07δ18O + 37.48 (R2 = 0.96). Both the slope and intercept were higher than those of the Global Meteoric Water Line (GMWL), indicating that locally recycled evaporated moisture played an important role in regional precipitation formation. The river water line showed characteristics similar to those of the groundwater line, suggesting strong hydraulic connectivity between river water and groundwater. In contrast, the lake water line deviated markedly from the meteoric water line, indicating significant evaporative fractionation of lake water. The MixSIAR results indicate that the contributions of river water, groundwater, and precipitation to lake water were 37.5%, 33.9%, and 28.6%, respectively. These findings reveal the complex hydrological connections and transformation processes among multiple water bodies in the Qinghai Lake Basin and provide a scientific basis for water resource management and ecological environmental protection in inland basins of the Qinghai–Tibet Plateau.
This article presents a scoping review of the literature on digital twins (DTs) for sustainable groundwater resources management, which constitutes a very recent and rapidly expanding research field, with literature moving quickly from conceptual frameworks to application-oriented systems. The literature, selected through the PRISMA 2020 methodology, is organized into seven sub-topics: AI and ML foundations, digital twin architectures and frameworks, aquifer-scale DT applications, agricultural and water–energy–food (WEF) nexus DTs, basin and urban water DTs, sensing, monitoring and IoT infrastructures, and governance, resilience and socio-hydrology. This structure shows how the field is shifting from monitoring and prediction toward integrated decision support, where process-based models, machine learning surrogates, real-time sensing and optimization are combined to support drought mitigation, saltwater intrusion control, irrigation management, climate adaptation and basin-scale planning. Across the reviewed studies, the most recurrent contributions are the construction of hybrid model architectures, the use of DTs to close the loop between observation and control, and the growing recognition that groundwater management must incorporate governance, stakeholder decision-making and socio-hydrological feedbacks. At the same time, the literature still faces key limitations, especially uncertainty quantification, interoperability between models and data streams, transferability to data-scarce settings and limited validation under real operational conditions. Future research should therefore focus on physics-informed and explainable AI, federated and scalable DT architectures, stronger coupling with socio-hydrological and governance frameworks, and more field-tested implementations that can demonstrate robust performance across diverse hydrogeological and institutional contexts.
The decline in groundwater storage (GWS) poses a critical threat to water security in semi-arid regions where increasing agricultural water demand and climate variability are increasing pressure on aquifers. This study presents a novel hybrid modeling framework integrating multi-source satellite and climate data (GRACE, GLDAS, TerraClimate, and MODIS) with machine learning and explanatory artificial intelligence techniques for the long-term assessment and interpretation of GWS anomalies in the data-poor Iğdır Basin. Three different modeling approaches were developed: XGBoost, Long Short-Term Memory (LSTM) networks, and their combined model, and interpreted using the Shapley Additive Explanations (SHAP) method. The results showed a significant long-term decreasing trend in groundwater storage anomalies at a rate of −0.87 mm per month during the 2002–2016 period, indicating continuous depletion. The LSTM model demonstrated the best performance with R2 of 0.59, RMSE of 19.5 mm, and MAE of 15.1 mm, revealing the dominant role of temporal dependencies in groundwater systems. SHAP analysis identified lagged groundwater anomalies (especially GWS_lag3) as the most effective predictors; this may reflect the memory effect and lagged response specific to semi-arid aquifer systems, but this interpretation needs to be validated in different study areas. Snow water equivalent and total water storage anomalies also emerged as significant determinants, while the direct effect of instantaneous precipitation was found to be limited. This study addresses significant gaps in the literature by combining sequence-based modeling with model interpretability in a semi-arid closed basin. The findings highlight the necessity of using system memory and explainable artificial intelligence together for reliable groundwater prediction. While the proposed hybrid approach has the potential for application in other semi-arid regions, its broader usability needs to be supported by independent validation studies under different hydrogeological and climatic conditions.
Seasonal hydrological variation can reorganize fluorescent dissolved organic matter (FDOM) in floodplain lakes; yet, its expression in Poyang Lake remains uncertain. We assessed campaign differences in FDOM composition by comparing 32 excitation–emission matrices from 16 fixed sites sampled during the dry and wet periods of 2024 using parallel factor analysis (PARAFAC), fluorescence indices, paired tests with Benjamini–Hochberg correction, and principal component analysis. Rank 4 fitted better and resolved two humic-like and two protein-like regions, although incomplete validation limited component-specific interpretation. In the wet period, C2 and combined humic-like maximum fluorescence intensity (Fmax) decreased (q = 0.00836), whereas bulk total organic carbon (TOC) and total Fmax did not differ. Mean protein-like contribution rose from 25.0% to 37.5% (q = 0.00322); the biological index increased, the humification index decreased, and the fluorescence index was unchanged. The first two principal components explained 78.11% of the variance, and an exact paired multivariate test detected an overall period difference (p = 3.05 × 10−5). No component–environment correlation survived correction or differed between periods. FDOM composition therefore underwent a campaign-specific reorganization without a corresponding change in bulk carbon, but the two-campaign design and incomplete validation preclude causal or source-specific inference.
Recent efforts to address flooding have explored incorporating flow-reduction capabilities into existing infrastructure. Roadside ditches have historically been viewed as an underutilized component of flood reduction, and a two-stage design has been proposed that modifies a conventional trapezoidal ditch by incorporating bench insets along the main channel. While it is expected that this second stage becomes inundated during storm events, resulting in flow attenuation, the exact impacts of this design are unknown. This study quantified the impact of the two-stage design by modeling a roadside ditch corridor in eastern Iowa. An existing single-stage ditch was converted to a two-stage design, and a HEC-RAS model was constructed to investigate the ditch’s impacts for four design storms (1-year, 2-year, 5-year, and 10-year). In the modeled results, peak flow rates were reduced by 22%, 21%, 7.5%, and 4.3%, respectively, while water volume reductions were near 6%. Maximum velocities throughout the ditch corridor also decreased, with reductions spanning 32% (1-year)–45% (10-year). These results indicate that increased travel times and infiltration associated with the two-stage design provide hydrologic and hydraulic benefits by lessening flood and erosion risk. While further study is needed to verify this behavior through monitoring and modeling at other locations, our findings suggest that two-stage ditches can be a useful best management practice for the transportation community.
Rainfall–runoff modeling is a key challenge in hydrological research. Despite the extensive application of long short-term memory (LSTM) networks in rainfall–runoff modeling, our understanding of the influence of different hyperparameter configurations on various hydrograph components, as well as the linkages between hydrological concepts and LSTM architectures, remains elusive. Here, we integrated Multi-Objective Particle Swarm Optimization (MOPSO) with LSTM hyperparameter optimization by targeting the root mean square error of the overall hydrograph (RMSEall), high-flow (RMSEhigh) and low-flow (RMSElow) dynamics, and water volume deviation (Dv). The MOPSO-LSTM framework was applied to the upstream catchments of the Miyun Reservoir in Beijing, China. At a lead time of 1d, the optimal solution achieved an NSE of 0.920 in the Chaohe River Basin, with a minimum RMSEall of 0.848 m3/s, RMSEhigh of 2.081 m3/s, RMSElow of 0.382 m3/s, and Dv of 0.002%. However, as the lead time increased to 3 and 7 days, the maximum NSE declined to 0.747 and 0.560, respectively, with process-related metrics deteriorating more substantially than water balance-related metrics. The Baihe River Basin performed better, with maximum NSE and KGE values of 0.949 and 0.970 at a lead time of 1d. Clear trade-offs among different evaluation objectives were further identified, particularly the competitive relationship between RMSEhigh and RMSElow, as well as the coupling between RMSElow and Dv. SHAP (Shapley additive explanation) and partial dependence plots (PDPs) were used to quantify and interpret the effects of hyperparameters on model performance, and the results showed that learning rate, number of units, and lookback window served as the most influential hyperparameters. Moreover, optimization preferences resulted in distinct hyperparameter configurations, where Dv-oriented solutions favored smaller learning rates, longer lookback windows, and larger batch sizes than RMSE-oriented solutions. Compared with the Chaohe River Basin, the larger Baihe River Basin favored LSTM configurations with longer lookback windows, more hidden units, higher learning rates, and lower dropout rates, which was associated with the hydrological memory of the catchment. Overall, this study provides a novel multi-objective LSTM optimization framework, improving the understanding of LSTM hyperparameters and offering practical guidance for hydrological prediction and water resource management.
This study investigated the effectiveness and accuracy of the analytic hierarchy process (AHP) and frequency ratio (FR) models, which integrated remote sensing (RS) and geographic information systems (GIS), for delineating and validating groundwater potential zones (GWPZs) in Northern Mozambique. For both methods, six key factors influencing groundwater potential were selected from which thematic maps were generated for each. In the AHP method, the influencing factors (IFs) were subjected to a hierarchy of criteria and sub-criteria. Then, using a pairwise comparison matrix based on professional assessment and literature review, relative weights were assigned to each factor. Analysis of the AHP ultimately resulted in the integration of the various thematic layers using the weighted sum tool in ArcGIS 10.8 to produce a composite GWPZ map. In the FR method, 1026 borehole locations for the area were randomly divided into two sets: 718 boreholes (70%) were used as a training dataset, and the remaining 308 boreholes (30%) were kept as a testing dataset for validation purposes. From the training dataset, the ratio of the probability of a groundwater event (wells) occurring in a class of influencing factors to the overall probability of that event happening in the study area was calculated as the FR for that class, representing the weight assigned to each factor. The overall FR was also computed using the weighted sum tool to produce a GWPZ map. The map produced from each model delineated the area into five zones: “very low”, “low”, “moderate”, “high” and “very high”. The testing dataset was then used to validate the GWPZ maps using the field data overlay technique. The respective validation results revealed the AHP model outperformed the FR model in terms of accuracy for delineating GWPZs.
Waterlogging simulation is an important non-structural measure for flood-risk management; however, the heterogeneity of urban surfaces complicates reliable simulation. Urban drainage inlets and buildings strongly influence runoff routing, yet the effects of alternative modeling treatments remain uncertain. This study evaluated the impact of three inlet treatments and three building treatments on urban waterlogging simulation under different storms. Results show that (1) under rainfall pattern 1, the grate inlet produced 4–5.8% higher peak drainage discharge than curb-opening treatments, and point-scale water-level differences reached 0.49 m at hydraulically sensitive locations. Compared with the roof-to-drainage method, the roof-to-surface discharge method increased flood volume, flooded area, and average water depth by 43.5%, 21.3%, and 15.6%, respectively. (2) The effects of the two representation types responded differently to rainfall characteristics. Drainage inlet rankings were strongly rainfall-dependent: under rainfall pattern 2 at a 100-year return period, the hierarchy reversed, with the depressed curb-opening inlet slightly outperforming the grate inlet by 0.6%. By contrast, the building treatment methods (BTMs) ranking remained consistent across all rainfall scenarios, with the roof-to-surface discharge method producing the largest flood volume and extent regardless of rainfall pattern or return period. Overall, this study identifies urban drainage inlet and building representations as important sources of structural uncertainty, providing practical guidance for urban flood modeling and drainage planning.
Accurate land use/land cover (LULC) classification in monsoon-driven and heterogeneous landscapes is challenged by strong seasonal variability and inconsistencies between dynamic satellite observations and static reference datasets. This study proposes a time-series-based framework integrating MODIS-derived Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII) with unsupervised K-means clustering and a temporally consistent refinement strategy. Multi-temporal NDVI (23 composites year−1) and NDII (46 composites year−1) data from 2010–2021 were used to derive spectral clusters and aggregate them into five land use classes using percentile-based temporal signatures and RMSE-based similarity with Land Development Department (LDD) data. To reconcile discrepancies between dynamic satellite observations and static reference datasets, a refinement procedure combining spatial agreement and temporal similarity was applied to reassign misclassified pixels. Initial classifications achieved Overall Accuracies (OA) of 57.35% for NDII and 51.27% for NDVI, increasing to 87.28% and 86.24% after refinement, with Kappa coefficients of 0.82 and 0.81, respectively. NDII consistently outperformed NDVI, highlighting the value of moisture-sensitive indices for distinguishing vegetation classes in tropical environments. The modular Python-based version 3.11 implementation ensures reproducibility and transferability, providing a robust and scalable framework for LULC classification in dynamic landscapes.
This paper explores the intricate relationship between novel paleo-hydrological settings and the sustainability of ancient human societies in the Sahara Desert, focusing on the sandstone massifs of Tassili n’Ajjer, Tadrart Acacus, and Messak Settafet. While this region is currently hyper-arid, archeological evidence reveals a history of significant human settlement facilitated by the African Humid Period (AHP). The core of the research is the idea that the natural geological and hydrogeological settings worked to magnify rainfall in a manner that is analogous to modern techniques in water systems engineering. Serendipitous features of geology, structural settings, and stream networks are presented, along with illustrative calculations to suggest how this system functioned as an accidental rainwater harvesting system, concentrating runoff into conveniently located lakes. On the Messak Settafet, the archaeologic evidence points to a rising water table and more robust groundwater flow as runoff infiltrated. We conceptualize this behavior as a managed aquifer recharge system. This natural system worked effectively by storing ephemeral surface water in a sandstone aquifer, shielded from the high evaporation rates of the Sahara. These “natural technologies” created perennial water sources such as lakes, ponds, and springs that supported hunter-gatherers and pastoralist societies. Long after the end of the Holocene AHP, the Garamantian Empire arose with the help of qanat technology that was able to produce the stored groundwater. This paper illustrates how an unlikely array of components worked to create natural technologies able to provide “livable niches.” These findings offer instructive lessons for modern sustainability, demonstrating how integrated landscape management can secure water resources in water-stressed environments.
Climate change and irrigation expansion are expected to substantially alter hydrological processes and agricultural productivity in the semi-arid watersheds of northern Ethiopia; however, their combined impacts remain insufficiently quantified. This study evaluated the effects of future climate change and irrigation management on watershed hydrology and crop yield in the Geba watershed using the Soil and Water Assessment Tool Plus (SWAT+). The model was calibrated and validated using observed daily streamflow data for the 2006–2020 period and driven by an ensemble of five bias-corrected CORDEX Africa regional climate models (RCMs) under the RCP 4.5 and RCP 8.5 scenarios for the mid-century (2046–2060) and late-century (2086–2100) periods. Two agricultural management systems, namely rainfed and irrigation-rainfed integrated management, were evaluated. Model performance was satisfactory for streamflow simulation, with NSE values of 0.56 and 0.50 and KGE values of 0.64 and 0.54 during calibration and validation, respectively. The results indicate a progressive shift toward an evapotranspiration-dominated hydrological regime under future climate conditions. Under rainfed management, surface runoff and evapotranspiration increased by up to 60% and 30%, respectively, whereas groundwater recharge and lateral flow declined substantially. Irrigation scenarios intensified hydrological stress by reducing percolation, lateral flow, and water yield by up to 80%, 60%, and 55%, respectively. Statistical analyses revealed that climate forcing, management type, and their interactions significantly affected hydrological responses (p < 0.001), with emission pathways representing the dominant driver of variability. Crop responses varied considerably among management systems and crop types. Rainfed maize and wheat exhibited moderate yield increases under mid-century conditions, whereas teff consistently showed negative responses under most climate scenarios, indicating high vulnerability to warming and moisture stress. Under irrigation management, most crops experienced substantial yield reductions during late-century periods, although tomatoes showed localized gains under high-emission scenarios. Overall, the findings demonstrate that irrigation expansion alone may not provide sustainable adaptation under increasing climate stress because it intensifies evapotranspiration and reduces groundwater recharge. Integrated watershed management, climate-resilient crop selection, efficient irrigation practices, and soil-moisture conservation strategies are therefore essential for sustaining agricultural productivity and water availability in semi-arid Ethiopian watersheds.
Seawater intrusion presents a significant risk to coastal aquifers, particularly in low-lying locations where groundwater resources are intensively exploited. This study assesses the vulnerability of the Keta Strip aquifer in Southeastern Ghana to seawater intrusion using the GALDIT model; a widely applied index-based approach that evaluates seawater intrusion risk based on six key hydrogeological indicators: groundwater occurrence (G), aquifer hydraulic conductivity (A), groundwater level above sea level (L), distance from the shoreline (D), impact of existing intrusion (I), and aquifer thickness (T). These parameters were analyzed using data from 105 monitoring wells within a Geographic Information System (GIS) environment. The resulting vulnerability index was spatially grouped into four categories: low, moderate, high, and very high vulnerability. Results indicate that very high and high vulnerability regions are predominantly clustered along the coastal margins and central portions of the study area, driven mainly by low hydraulic gradients, proximity to the shoreline, and high hydraulic conductivity. Moderate vulnerability zones dominate inland areas, while low vulnerability zones are limited and confined to northern sections. Sensitivity analysis reveals that hydraulic head (L) and distance from shoreline (D) are the most influential parameters, whereas TDS exhibits relatively low contribution to overall vulnerability. The findings highlight the critical role of hydrogeological controls and anthropogenic pressures in shaping seawater intrusion risk and provide a scientific basis for sustainable groundwater management in the Keta Strip and similar coastal environments.
Nutrient stoichiometry, particularly the balance of carbon (C), nitrogen (N), and phosphorus (P), plays a fundamental role in regulating freshwater ecosystem dynamics, primary production, and biogeochemical cycling. This study presents one of the first dedicated reviews to combine bibliometric mapping with ecological synthesis of C:N:P ratios in inland waters, drawing on 1004 publications indexed in the Web of Science Core Collection (2000–2025), comprising peer-reviewed articles and review articles refined by document type, language, and research area. Bibliometric mapping using VOSviewer (version 1.6.20) identified exponential growth in publications after 2010, with phosphorus dynamics and eutrophication emerging as the most-cited themes, while recent years have shown increasing attention to C:P ratios as reliable ecological indicators. Four dominant thematic clusters were identified: Nutrient Cycling and Biogeochemistry; Phytoplankton and Food Web Dynamics; Eutrophication and Water Quality; and Climate Change and Ecosystem Responses. Ecological synthesis demonstrated substantial deviations from the canonical Redfield ratio (106C:16N:1P), with pronounced stoichiometric variability across trophic states, latitudes, and ecosystem types. Case comparisons revealed high C:P ratios in Arctic and alpine lakes linked to dissolved organic carbon inputs, low N:P ratios in tropical waters that promote cyanobacterial dominance, and stable, low phosphorus concentrations in deep African lakes. These findings emphasize the significance of flexible stoichiometry in predicting ecosystem tipping points, managing harmful algal blooms (HABs), and guiding nutrient restoration strategies. By integrating bibliometric and ecological evidence, this study identifies C:P ratios as a promising candidate indicator that merits further field validation for freshwater management, while underscoring persistent research gaps in microbial stoichiometry, cross-scalar modeling, and policy uptake in the Global South.
To further improve prediction accuracy, a VMD-VPPSO-LSTM model is proposed in this study, which combines Variational Mode Decomposition (VMD) for signal decomposition, Velocity-Pause Particle Swarm Optimization (VPPSO) for parameter optimization, and Long Short-Term Memory (LSTM) for runoff prediction. The model was evaluated at Huangtaiqiao station in the Xiaoqing River Basin, Dawenkou station in the Dawen River Basin, and Tangnaihai station in the source region of the Yellow River Basin. The proposed model achieved the best overall performance among all comparison models, with Nash–Sutcliffe Efficiency (NSE) values of 0.970, 0.962, and 0.994 and Root Mean Square Error (RMSE) values of 1.357, 0.989, and 46.804 at the three stations, respectively. Compared with VMD-LSTM, VPPSO further reduced the RMSE at all stations and maintained training-test NSE gaps below 0.006, indicating strong generalization performance. The model also achieved the lowest Peak Percent Standard Deviation (PPSD) values for high-flow events, reaching 9.03%, 14.42%, and 3.88% at the three stations, respectively. These results demonstrate that VMD-VPPSO-LSTM is a reliable and effective model for daily runoff prediction.
River avulsion, the sudden relocation of a river channel to a new course from the parent channel, is a geomorphic process with direct implications for floodplain evolution, ecosystem dynamics, and infrastructure vulnerability. This review article discusses how sandbar migration acts as a precursor to avulsion by altering hydraulic geometry, redirecting flow paths, modifying sediment transport patterns, and affecting the development of incipient channels. The morphodynamic evolution of sandbars, influenced by sediment supply, flow regime, vegetation, and anthropogenic influences, such as dams and sand mining, plays a central role in creating avulsion. Different methods, such as field measurements, remote sensing imagery (including multispectral, SAR, LiDAR and UAV), physics-based numerical models, machine learning, and deep learning techniques, which are used to evaluate river sandbar and river avulsion, are also thoroughly evaluated for efficacy and fit for purpose. Future research should focus on combining different data sources and creating a model that understands vegetation–sediment–flow feedbacks, sediment sorting process, anthropogenic impacts and extreme climate change impacts on channel evolution.
Flood susceptibility in Pakistan is strongly influenced by hydro-climatic variability, land-surface conditions, topography, and recurrent floodplain exposure; however, national-scale studies often lack a comprehensive assessment that captures both spatial patterns and temporal flood-risk dynamics within a single framework. This study is one of Pakistan’s first national efforts to address the gap between flood risk assessment and prioritization through a unified geospatial assessment. This study assesses flood susceptibility across Pakistan for 2002, 2012, and 2022 using a GIS-based AHP approach by integrating climatic, environmental, topographic, hydrological, soil, LULC, and anthropogenic indicators. The study results were further analyzed through district-level assessments, risk change analysis, persistence mapping, LULC exposure assessments, and the Comprehensive Flood Risk Priority Index (FRPI). The results show that high and very high flood susceptibility zones are primarily concentrated along the Indus River corridor, lower floodplains, and coastal Sindh, accounting for more than 7% of the total land area of Pakistan. Persistent flood hotspots are identified in Rann of Kutch (66.6%), Jacobabad (65.0%), and Jafarabad (61.1%), indicating strong temporal stability of flood-prone conditions. LULC exposure analysis reveals that cropland is the dominant exposed class, with the highest district-level exposure observed in Badin (17.1%) and Larkana (10.1%). The FRPI further identifies priority flood-risk zones where susceptibility, persistence, risk change, and exposure converge, with the highest FRPI values observed in Jacobabad (0.742), Rann of Kutch (0.738), and Badin (0.711). Model validation demonstrates strong predictive performance, with susceptibility ROC-AUC values ranging from 0.85 to 0.87 and FRPI AUC reaching 0.85. The proposed framework provides a robust decision-support tool for targeted flood-risk management and climate-resilient land-use planning in Pakistan.
Access to safe and sustainable water resources remains a major challenge in many sub-Saharan African urban centers, particularly in rapidly growing towns characterized by insufficient hydraulic infrastructure. This study investigates household water supply modes and evaluates spring productivity in the Foumban locality, situated in the Bamoun Plateau of the Western Highlands of Cameroon. It combines socio-economic surveys, hydrometric monitoring, geostatistical techniques, and multivariate statistical analyses to better understand the dependence of local populations on groundwater resources and the hydrodynamic behavior of springs in fractured basement aquifers. More than 500 households distributed across 13 localities were surveyed between 2014 and 2016 to assess water accessibility, consumption patterns, and socio-economic constraints related to water supply. In parallel, monthly discharge measurements were conducted on ten representative springs over one hydrological year using the volumetric gauging method. The results reveal that groundwater constitutes the principal source of domestic water supply in Foumban. During the dry season, 50% of households depend on springs, whereas 22.8%, 16%, 6.7%, and 4.5% rely respectively on CAMWATER, wells, boreholes, and rivers. In the rainy season, spring water remains dominant (39.7%), followed by CAMWATER (22.1%), wells (14.4%), rainwater (12.2%), boreholes (7.7%), and rivers (3.9%). Most households travel considerable distances to fetch water, reflecting the inadequacy of the public distribution network and the precarious socio-economic conditions of the population. Water-related diseases, notably typhoid and malaria, remain recurrent and are associated with the consumption of untreated water. Spring discharges vary significantly both spatially and temporally, ranging from 0.11 to 8.40 m³/h, with the most productive springs generally located along major fracture. Spring discharge variations closely follow seasonal rainfall patterns, although delayed recharge responses indicate heterogeneous aquifer behavior. Principal Component Analysis, Hierarchical Cluster Analysis, and semi-variogram modeling reveal the coexistence of shallow weathered aquifers and deep fractured aquifers characterized by strong spatial heterogeneity. These findings provide valuable scientific information for sustainable groundwater management, urban water planning, and socio-economic development in Foumban and other basement regions of tropical Africa.
Efficient irrigation water management is critical for optimizing crop productivity and ensuring the sustainable use of limited water resources, particularly in semi-arid regions. This study was conducted during the 2022/23 and 2023/24 cropping seasons at Kombolcha District, East Hararghe Zone, Oromia regional state, Ethiopia. The purpose of the activity was to create optimal irrigation regimes (when and how much to irrigate) for onions and evaluate the effect of different irrigation timings on the water productivity and yield of the onion crop. The results showed that irrigation water, maximum irrigation frequency, and short irrigation intervals were achieved by scheduling irrigation at 60% of the ASMDL treatment. The next maximum irrigation frequency was obtained by scheduling irrigation at 80% of ASMDL treatment. Minimum irrigation frequency and minimum water consumed by scheduling irrigation at 140% ASMDL treatment. The results show that the maximum onion yield was obtained by scheduling irrigation at 60% of ASMDL treatment, followed by 80% ASMDL treatment. Statistically, there is no significant difference between 60% ASMDL and 80% ASMDL treatments in terms of onion yield. Maximum water productivity was obtained by scheduling irrigation at 80% ASMD treatment followed by 60% ASMDL treatment. Statistically, there is no significant difference between 60%, 100%, and 120% ASMDL treatments in terms of onion water productivity. The minimum water productivity was reached by scheduling irrigation at 140% ASMDL treatment. So, scheduling irrigation at 80% ASMDL is recommended for onion with 5-day, 6-day, 7-day, and 10-day irrigation intervals at initial, development, mid, and maturity stages of onion, respectively.
Rainfall is essential in hydrologic and hydraulic analyses, serving as critical parameter in water resource studies. Hydraulic structures are designed to manage flooding triggered by extreme rainfall events. One common approach to analyze these extreme occurrences is through probability distribution or frequency analysis. This study evaluates various methods of rainfall frequency analysis. Rainfall data was sourced from the Ethiopian Meteorological Agency (EMA), specifically the Addis Ababa Observatory. Before conducting frequency analyses, data quality was assessed for outliers, with findings within acceptable limits. The frequency analysis utilizes four different distribution methods: Gumbel Extreme Value I, Lognormal, Pearson II and Log-Pearson III. Moreover, these distribution methods were fitted using RMC BestFit software to select a method that fits best for the dataset. The fitted distribution methods were also calibrated with non-probability Intensity-Duration-Frequency (IDF) models. Results indicated that while all methods performed satisfactorily, the Gumbel EVI displayed the best balance between model fit and error reduction in this IDF analysis. The study underscores the importance of selecting appropriate statistical methods for accurate rainfall modeling, which is vital for the design and operation of hydraulic structures. Future research could investigate the applicability of these findings in other regions or integrate climate change variables into rainfall frequency analysis for enhanced flood risk management. Additionally, employing advanced techniques, like machine learning algorithms, may improve prediction accuracy and provide deeper understanding of rainfall variability and trends.
Gravity Recovery and Climate Experiment (GRACE) satellites primarily monitor changes in land water storage, including groundwater, soil moisture, lake and river surface water, and canopy and snow water. However, its coarse spatial resolution of 0.25 degrees limits its ability to observe smaller basins. To assess aquifer depletion and evaluate a long-term water resource management framework, GRACE data are crucial. It remains rare for GRACE-focused studies to be conducted in great depth. A comprehensive review of 80 articles published between 2011 and 2025 was conducted using the Scopus and Web of Science databases. These articles focused on downscaling GRACE data using machine learning (ML) methods. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines were used in this review. This study highlights the attributes of ML models, the input variables used, the evaluation metrics, and the output resolution. Based on the analysis of the articles, random forest (RF) methods were used in the majority of the papers. Gradient boosting (GB), artificial neural networks (ANN), support vector machines (SVM), support vector regression (SVR), and long short-term memory (LSTM) were the most widely used ML methods. As input variables, rainfall (Pr), soil moisture (SM), and runoff (Qs) are essential. In 2011, there were very few journal articles; since 2021, the number has increased. The number of published studies from China was the highest (24), followed by the USA (12) and Iran (9). A total of 38 journals published reviewed articles. In terms of articles, Remote Sensing generates 19%, Journal of Hydrology has 10%, and Journal of Hydrology: Regional Studies has 8%. The paper also discusses limitations, challenges, recommendations, and potential future directions for improving the accuracy of the GWS change prediction model.
