Understanding extreme drought dynamics in monsoon-dominated regions remains challenging due to strong seasonal contrasts and rainfall variability. In the northern region of Bangladesh, limited integration of drought indices and long-term spatiotemporal analysis has constrained comprehensive extreme drought characterization. This study addresses these gaps by conducting a comparative analysis of the Standardized Precipitation Index (SPI) and Rainfall Anomaly Index (RAI), combining optimised artificial neural network (ANN)-based forecasting, and spatial and seasonal variations of drought metrics. Drought dynamics were further assessed using Mann-Kendall (MK) trend tests and Sen’s slope estimations. The results indicate that ANN models perform better for RAI (correlation coefficient R = 0.89). SPI-based analysis showed that 46.07% of droughts were severe or extreme. Notably, 5.12% increase in extreme drought and 22.58% increase in post-monsoon drought onset are forecasted in the future (2024-2055). Trend analysis reveals statistically significant drying signals in SPI at stations such as Mymensingh (ZMK = −4.14) and Rajshahi (ZMK = −3.39). The northwestern region consistently emerges as more vulnerable to drought compared to the northeastern region. The findings highlight the complementary roles of SPI and RAI in capturing drought behavior and demonstrate the potential of data-driven forecasting to support proactive drought risk management in monsoon-dominated systems.
Drought is a recurring climate hazard in the Bilate Watershed of southern Ethiopia, affecting water availability, agricultural production, and ecosystem services. Yet, its variation across different time scales and locations within the watershed remains poorly understood. This study examines meteorological drought using the Standardized Precipitation Index (SPI) at 3-, 6-, and 12-month time scales. Monthly rainfall data from eight meteorological stations covering 1980–2022 were analyzed to assess drought frequency, severity, persistence, and spatial distribution. The results show that drought characteristics vary considerably across time scales and locations. Mild droughts were the most common, while severe and extreme droughts occurred less frequently but were associated with substantial rainfall deficits. Shorter SPI periods captured frequent short-term moisture fluctuations, whereas longer periods revealed more persistent deficits with greater implications for water-resource availability. Several widespread drought episodes affected multiple stations, although their timing and severity varied spatially. Spatial analysis further showed that the northern and northwestern highlands often experienced relatively severe drought conditions, highlighting the importance of geographic variability in drought assessment. Overall, this study provides a clearer understanding of drought dynamics in the Bilate Watershed and supports improved drought monitoring, targeted water-resource planning, catchment management, and climate adaptation in southern Ethiopia.
Rainfall is a fundamental component of hydrological systems, and its changing characteristics under a warming climate pose significant challenges for water security and climate adaptation. Despite receiving substantial annual precipitation, Bangladesh is increasingly exposed to rainfall uncertainty associated with changing seasonal distributions and long-term hydroclimatic shifts. This study aims to provide an integrated assessment of rainfall dynamics by examining precipitation concentration, long-term trends, and abrupt regime shifts. Forty years of rainfall records (1984-2023) from 25 meteorological stations were analyzed using the coefficient of variation (CV), precipitation concentration index (PCI), Mann-Kendall (MK) trend test, Sen’s slope estimator, and change-point detection methods. The key findings of this study are as follows: (1) Annual PCI analysis revealed irregular rainfall distribution, with the highest concentration at Teknaf (PCI = 21.52). (2) Trend analysis showed declining annual and monsoon rainfall, with the strongest decline at Madaripur (ZMK = −2.83, p < 0.005). (3) Change-point analysis identified abrupt rainfall regime shifts, particularly during the 1980s and recent decades. The spatially heterogeneous rainfall responses highlight the combined influence of monsoon variability, regional atmospheric processes, and coastal climatic controls. The findings provide evidence for climate adaptation and sustainable water-resource management in Bangladesh.
Accurate rainfall–runoff modeling is essential for effective water resources management, particularly in data-scarce regions. This study evaluates HEC-HMS and five machine learning (ML) models, ANN, CNN, LSTM, XGBoost, and a hybrid XGBoost–LSTM–CNN, for rainfall–runoff simulation in the Gumara watershed, Upper Blue Nile Basin, Ethiopia.Daily rainfall from four meteorological stations and observed discharges at the watershed outlet were used. The dataset was divided into calibration (2001–2012) and validation (2013–2018) periods, with monthly simulations performed. Model performance was assessed NSE, R², and PBIAS. HEC-HMS showed very good performance (calibration: NSE = 0.81, R² = 0.83, PBIAS = −8.43%; validation: NSE = 0.82, R² = 0.83, PBIAS = 12.58%); however, it underestimated peak flows. The ML models improved simulation accuracy, with the hybrid XGBoost–LSTM–CNN model performing best (calibration: NSE = 0.98, R² = 0.98, PBIAS = 2.57%; validation: NSE = 0.89, R² = 0.88, PBIAS = −6.9%). For peak flows with exceedance probability below 10%, the hybrid model substantially outperformed HEC-HMS (NSE: 0.98 vs. 0.30; R²: 0.99 vs. 0.87; PBIAS: 0.10% vs. 18.12%). These findings demonstrate the potential of hybrid ML models to improve rainfall–runoff and peak-flow simulation in data-limited watersheds.
Lakes provide essential ecosystem services, yet many tropical lakes are increasingly threatened by anthropogenic pressures and environmental change. Despite its ecological importance and recent designation as a UNESCO Ecohydrology Demonstration Site, Lake Hayq lacks an integrated assessment linking ecosystem services, environmental threats, ecological change, and management. This study addressed this gap by integrating household surveys across six kebeles adjacent to Lake Hayq (the smallest administrative units in Ethiopia), focus group discussions, key informant interviews, field observations, and Landsat 8/9 remote sensing (2015–2024). Ecosystem services were identified based on respondents' reporting frequency. Fisheries, drinking water and grasses for livestock feed, ceremonial grasses were the most frequently reported ecosystem services. The lake is increasingly threatened by interacting pressures, including siltation, invasive aquatic plants, overfishing, pollution, and buffer-zone encroachment. Remote sensing detected a significant decline in lake surface area (0.70hayr⁻¹). The locally evaluated three-band difference algorithm (3BDA; R² = 0.64) estimated chlorophyll-a concentrations of 4.15–5.55μgL⁻¹, with localized enrichment near river inflows and shoreline areas, indicating increasing ecological stress. By integrating socio-economic evidence with Earth observation, this study provides the first holistic assessment of Lake Hayq and an evidence base for adaptive management of tropical lake ecosystems.
Overpopulation and unplanned rapid urbanization with industrial effluent have gradually decreased the river water quality of Dhaka remarkably. The focus of the study is to understand the restoration capacity of dissolved oxygen on the basis of the upstream-to-downstream flow of the rivers of Dhaka. Data were collected from 38 stations covering an area of 81.4km of the Turag, Buriganga, Dhaleswari, and Shitalakshya rivers along the flow paths. Due to the observation of oxygen deficiency, the Streeter-Phelps equation was used to make the DO sag curve and figure out the profile of oxygen restoration capacity. The DO level lies between 0.28-4.3mg/L, whereas the DO deficit is about 5.37-7.46mg/L. The recovery distances have been found around 294-727km the four rivers. Spearman’s rho correlation confirmed that both ultimate BOD (rho = 0.947, p < 0.01) and Total Dissolved Solids (TDS) are the primary drivers of oxygen depletion and restoration dynamics, significantly influencing the river's self-purification capacity. Furthermore, the linear regression (R² = 0.747) is used to explain the relationship between organic pollution and oxygen deficit. Overall, this study provides insights into the restoration capacity of Dhaka's rivers and will serve as a supporting tool for waste management and environmental restoration.
The largest inhabited river island in the world, Majuli, has undergone ongoing geomorphological degradation, but there are still few thorough studies that combine long-term river planform dynamics with changes in land use. Addressing this critical research gap, the present study combines riverbank erosion–accretion analysis with multi-temporal Land Use/Land Cover (LULC) assessment using Landsat imagery for 2005, 2015, and 2024. The study is important for learning about the interaction between the highly dynamic Brahmaputra River and the anthropogenic pressure through which Majuli’s delicate landscape is reshaped. The main contribution of the present work is its up-to-date integrated approach whereby fluvial instability and landscape transformation are concurrently quantified and their combined implications for geomorphic resilience are evaluated. The present study, addressing this critical research gap, mixes riverbank erosion–accretion analysis with multi-temporal Land Use/Land Cover (LULC) assessment via Landsat imagery for the years 2005, 2015, and 2024. The findings indicate a worrying net land loss scenario, with erosion (219.25 sq. km and 223.10 sq. km) continuously surpassing accretion during both the study periods. The forest cover and waterbody extent declines as well as substantial rises in bare soil, agricultural land, and settlements are revealed by the LULC results that are consequences of increased sediment deposition, riparian vegetation loss, and human encroachment onto unstable floodplains. Such transformations all signal increased channel migration and the rundown of natural buffers that are crucial to island stability. The study provides great geospatial evidence that will support long-term resilience planning, ecological restoration, targeted riverbank protection, and land-use regulation to maintain Majuli's socio-ecological sustainability.
The alteration of land use and land cover (LULC) is a key driver of hydrologic change that is often overlooked in high Himalayan basins, where studies typically emphasize climate and glaciers controls over the poorly quantified role of LULC as an independent variable. This study quantifies the LULC-induced component of the streamflow in the Tamor River basin, Nepal, using an experimental approach by imposing a constant meteorological forcing from 2009 and varying the LULC data for 2009, 2014, and 2019 obtained from ICIMOD. The Spatial Processes in Hydrology (SPHY) model was first calibrated (NSE = 0.78, PBIAS = 2.11%) and then validated (NSE = 0.61, PBIAS = -13.32%) against observed discharge at Majitar station. Contraction of cropland and bare-rock cover between 2009 and 2019 lowered the area-weighted crop coefficient, reducing evapotranspiration and increasing both baseflow and rainfall runoff by more than one third. The annual energy generation potential of a representative run-of-river hydropower project with a head of 100m increased by 17%, and the firm energy projection increased by 51%. These findings demonstrate that LULC change produced a hydrological signal of sufficient magnitude to materially affect both the dry-season energy reliability and monsoon-season operational risks of run-of-river hydropower projects.
Land suitability assessment is critical for sustainable agricultural intensification, but remains limited in data-sparse regions of sub-Saharan Africa. This study evaluates surface irrigation suitability in Ethiopia’s Abaya Chamo sub-basin (18,023 km²) by integrating geospatial techniques with the Analytic Hierarchy Process (AHP). Eleven biophysical and socio-economic factors, encompassing soil, terrain, and accessibility variables, were classified following FAO guidelines and weighted through expert-informed pairwise comparison. Soil depth (24%), land use/land cover (19%), slope (13%), and soil texture (11%) were the most influential parameters, collectively accounting for 67% of overall suitability. The results indicate that 7.5% of the sub-basin is highly suitable (S1) for surface irrigation, while 48.5% is moderately suitable (S2). Approximately 42% of the area is classified as marginally suitable (S3) or constrained due to steep slopes and unfavourable soil properties, whereas only 2.0% is permanently unsuitable (N). Existing irrigated land accounts for just 2.85% of the total suitable area (S1–S3), highlighting substantial untapped potential for irrigation expansion. Model validation yielded a consistency ratio of 0.071 and an AUC of 0.78, indicating good predictive performance. Overall, the study provides a spatial decision-support framework to guide irrigation development, optimize resource allocation, and support sustainable agricultural transformation in Ethiopia.
Accurate precipitation information is essential for hydrological studies, hazard monitoring, and water resource management. This study assesses the performance of CHIRPS v2.0 and CHIRPS v3.0 against observations from 505 quality-controlled rain gauges for the period 1981–2024, aiming to identify systematic biases, quantify improvements across temporal scales, and evaluate the applicability of CHIRPS v3.0. Daily, 3-day, 5-day, and monthly accumulated precipitation were analyzed using a comprehensive set of statistical performance metrics. Differences between CHIRPS versions were assessed using paired t-tests and Wilcoxon signed-rank tests for statistical significance, Cohen’s d for effect size, and paired bootstrap confidence intervals for uncertainty and robustness. For 3-day and 5-day aggregations, both versions saw reduced data dispersion, but outliers persisted in mountain regions. CHIRPS v3.0 detected rainfall events more frequently across all subregions, though CHIRPS v2.0 had lower average error indices at shorter time scales. At the monthly scale, both versions showed the highest agreement with rain gauge observations. A total of 59% of all rain gauges had KGE scores above 0.7 for CHIRPS v3.0, compared to 38% for CHIRPS v2.0. Paired tests indicated significant differences in several metrics, while Cohen’s d showed meaningful improvements for correlation (|d|>0.60) and Probability of Detection (|d|>0.85) across subregions and time scales. Overall, CHIRPS v3.0 improves rainfall occurrence detection and monthly precipitation representation, supporting its use for regional hydroclimatic monitoring, although local validation remains necessary, especially for short-scale error behavior in complex coastal and mountainous environments.
Streams, Uyun, and rivers once flowed across the Arabian Peninsula; subsequently, as the region transformed into an arid desert, inhabitants sought new groundwater resources and methods for collecting, conveying, and distributing water. Foremost among these were Water-Uyun ("Ain" singular), which spread widely across the Arabian Peninsula and assumed great significance. This system later spread to many arid countries, with varying nomenclature due to environmental and cultural differences, yet sharing common objectives. With the succession of generations, the need for a legally just and practically feasible allocation led to unique conveyance and distribution systems. Despite their rudimentary nature, they were precise and equitable, applied according to legal principles and refined engineering calculations, with some still in use today. This study addresses the history of the emergence of Water-Ain systems, their areas of distribution, nomenclature, and types, as well as their engineering and environmental aspects, in addition to systems and techniques for distributing their waters. To this end, an integrated approach was adopted, combining the inductive method with a social field survey, and culminating in the deductive method to derive precise scientific conclusions. The findings reveal that the engineering and hydraulic characteristics of the Ain system are highly comparable and consistent with modern standardized systems utilized for groundwater collection, conveyance, and distribution.
Heavy metals in riverbed sediments and surface waters pose significant environmental and health risks due to their toxicity and bioaccumulation. This study analyzed sediment and surface water samples to evaluate heavy metal concentrations, sources, and associated health risks. Results showed that sediment concentrations followed the order Mn > Cu > Cr > Ni > Pb > Zn > Cd, while in water, the order was Cr > Cu > Ni > Zn > Mn > Pb > Cd. Sediment quality assessments using enrichment factor (EF), geo-accumulation index (Igeo), pollution load index (PLI), and contamination factor (CF) revealed notable accumulation of Cd. Water quality, evaluated by the heavy metal pollution index (HPI), indicated significant impairment by Cd and Ni. Health risk assessments, including hazard index (HI) and hazard quotient (HQ), suggested negligible non-carcinogenic risk (HI < 1), but dermal exposure to Cr, Cd, and Ni posed carcinogenic risks exceeding the acceptable threshold (10⁻⁴). Statistical analysis highlighted positive correlations among Cu, Zn, and Cr in both water and sediment. Field surveys identified local pollution sources, including metallurgical activities, fishing boats, and ship oil leaks. These findings are crucial for regional environmental monitoring and for protecting human health.
Nepal’s topographic complexity and intense monsoon precipitation make it one of the most flood-prone nations in South Asia. Most small tributaries in Nepal are ungauged, making flood analysis challenging; however, the flashy nature of these rivers often results in severe and sudden flood damage. Roshi River Basin, situated 30–40 km southeast of Kathmandu in Kavrepalanchok District, faces recurrent monsoon flooding that severely impacts settlements, farmland, and road networks, yet until now has lacked a rigorous, model-based flood hazard assessment. These growing impacts highlight the need for scientifically rigorous and model-based flood hazard assessment to support effective risk reduction and planning. This study couples two-dimensional hydraulic modelling in HEC-RAS with geospatial analysis in ArcGIS to produce flood inundation and hazard maps for four design return periods: 10, 50, 100, and 200 years. The flood inundation results indicate that the total affected area increases from 45.10 km² during the 10-year return period to 52.42 km² during the 200-year return period. Similarly, extreme hazard zones expand from 6.28 km² to 17.35 km² across the same return periods. The inundation maps confirm that riverside settlements face life-threatening inundation depths of 1.5–3.0 m, while agricultural floodplains sustain widespread economic losses. The resulting hazard maps provide a spatially explicit decision-support framework for land-use regulation, structural flood mitigation, early warning systems, and disaster preparedness in the Roshi River Basin.
Effective and sustainable groundwater management necessitates a thorough understanding of the spatial and temporal patterns of groundwater recharge, particularly in data-scarce regions such as the Jewuha watershed in tropical Northern Ethiopia. This study applied the GIS-based WetSpass-M model to quantify key water balance components, namely actual evapotranspiration (AET), surface runoff, and groundwater recharge. A comprehensive set of spatial input datasets including land use, slope, elevation, soil texture, temperature, potential evapotranspiration, wind speed, and groundwater depth were systematically prepared and converted into ASCII format using GIS processing techniques. Model simulations revealed that the long-term mean annual rainfall of 889.8 mm was partitioned into AET of 436.0 mm (49.0%), surface runoff of 299.5 mm (36.0%), and groundwater recharge of 124.0 mm (15.0%). The results demonstrate the WetSpass-M model's capacity to reliably simulate spatial and temporal variability in water balance components, establishing it as an effective tool for hydrological assessment in data-limited environments. The spatial distribution of these components is primarily governed by land use and land cover characteristics, topography, and prevailing hydroclimatic variables. This study represents a novel application of the WetSpass-M model within the Jewuha watershed, providing a robust framework for characterizing water balance dynamics and generating critical insights to support groundwater management and long-term resource planning. The findings further affirm the model's value in guiding hydrological decision-making across comparable data-scarce basins including delineating optimal sites for groundwater extraction, prioritizing recharge zones, and informing land use and water conservation strategies.
Groundwater serves as drinking water in many educational institutions, yet its safety from potentially toxic elements remains poorly characterized. This preliminary study assessed the physicochemical, microbiological and elemental quality of groundwater at six institutions in Kumasi, Ghana. Exposure through oral ingestion and dermal contact was used to estimate the health risks for children and adults. Statistical techniques were used to determine the relationship between selected parameters and probable sources of elements, although these are indicative, given the small sample size and dry-season sampling. Physiochemical parameters were all within the WHO guideline values, except for Mn, which exceeded the limit in 33.3% of samples. Water quality index (WQI) indicated excellent quality across all sites. However, the estimated cancer risk value was above the USEPA acceptable threshold (1.00E-04) at three sites for children and two sites for adults. Non-carcinogenic risk was projected only in children at one site, with ingestion as the main exposure pathway. Results emphasizes that the WQI may not account for toxic elements adequately. Principal components analysis suggested that most controlling variables were of geological origin, although anthropogenic influences cannot be excluded. Findings provide a baseline for future studies and call for further investigation and treatment of groundwater.
A biomonitoring study of Rawasan Stream, located in Pauri Garhwal district of Uttarakhand, India, was conducted for the first time. The study aimed to evaluate the water quality of the stream at different sites using aquatic insects, especially macroinvertebrates, as bioindicators. Macroinvertebrate samples were collected monthly from selected sites throughout the stream from 2021 to 2022. A total of 87 genera, and 8 orders, were recorded, dominated by the three-intolerant order: Trichoptera (25.09%), Plecoptera (24.77%), and Ephemeroptera (22.02%), and two tolerant Acariformes (2.67%) and Hemiptera (2.47%) of collected 2435 individuals with Ephemeroptera, Plecoptera, and Trichoptera percentage (EPT%) range of 25–47.04% and Pollution Tolerance Index (PTI) value 22. These biotic indices indicate relatively good and clean water quality in the upper reaches of the Rawasan Stream than in the downstream reaches. Thirteen environmental variables were analysed, and water quality varied across all study sites, reflecting local environmental conditions and minor anthropogenic influences in the streams. Still, their values fell within the permissible limits set by the Bureau of Indian Standards (BIS) and the World Health Organisation (WHO). These parameters serve as reliable indicators of Rawasan Stream health and exhibit significant associations with the presence/absence and abundance patterns of key aquatic groups.
This study employs a scenario-based analysis using the WEAP model to examine the combined effects of population growth, reservoir sedimentation, groundwater depletion, irrigation expansion, and climate variability on the water security of Gondar City. The existing water supply system, which depends primarily on the Angereb Reservoir (AR) and groundwater sources, currently meets less than 50% of demand. Planned infrastructure developments include the Megech Dam (anticipated by 2028) and the LTWPS. Eight scenarios were developed from both supply- and demand-side perspectives to assess impacts on the water supply–demand gap from 2025 to 2045. The model was calibrated using data from 2015 to 2018 and validated with data from 2020 to 2023, achieving high accuracy (NSE = 0.878; PBIAS = −0.095%; RMSE = 0.0237 MCM yr⁻¹). Under the RS, cumulative UWD could reach 374.8 million cubic meters (MCM) by 2045. Although the Megech Dam offers temporary relief, its long-term effectiveness is constrained by sedimentation (3.0–3.9% annual storage loss) and competition with irrigation. The LTWPS substantially mitigates shortages but does not fully eliminate deficits. An integrated urban water management approach combining supply diversification, sediment control, groundwater management, climate adaptation, and demand-side measures is essential for sustainable water security.
This study evaluates the spatio-temporal sensitivity and field applicability of the Hindu Kush Himalayan Biotic Score in subtropical stream ecosystems of southwest Bhutan. Macroinvertebrate samples were collected monthly over ten months from six sites representing undisturbed, agricultural, settlement, and erosion-impacted stream conditions. Sampling followed the Assessment System to Evaluate the Ecological Status of Rivers in the Hindu Kush Himalayan Region protocol, and biotic scores were computed using the Ecological Data Application Tool system. Results indicated that water quality ranged from reference to moderate conditions, with undisturbed sites showing the highest ecological integrity and settlement-impacted sites exhibiting reduced scores. Although spatial differences in biotic scores were not statistically significant (p = .158), temporal variation across months was significant (p = .021), indicating sensitivity to seasonal hydrological variability. No clear monotonic seasonal trend was observed, although higher variability occurred during monsoon periods. Biotic scores were positively related to habitat variables such as stream depth and width, while relationships with physicochemical variables were weak. An inverse relationship between taxa richness and biotic scores suggested the influence of tolerant taxa within structurally diverse assemblages. Despite incomplete taxonomic scoring and reduced sampling effort, the index demonstrated sensitivity and moderate field applicability under current analytical conditions.
Biogas production through Anaerobic Digestion (AD) is a prominent and sustainable pathway for energy recovery from organic waste; however, AD performance is governed by complex, nonlinear interactions among substrate properties, microbial activity, and operating conditions. Conventional mechanistic models like Anaerobic Digestion Model No. 1 (ADM1) necessitate extensive parameter calibration and reliable monitoring, which constrains their applicability for real-time prediction and process optimization. Machine learning (ML) has recently emerged as a powerful data-driven approach capable of effectively capturing nonlinear system behavior without relying on explicit mechanistic formulations. However, most ML-based approaches still inadequately represent complex microbial kinetics. Therefore, this review critically investigates and identifies gaps in the literature, synthesizing advances in ML-based prediction and optimization of biogas production, along with key influencing parameters reported between 2020 and 2026. Existing studies are often fragmented and predominantly limited to laboratory- and pilot-scale investigations that incorporated only limited parameters, for optimizing biogas production. The findings highlight persistent challenges in ML implementation, including data insufficiency, limited model transferability, overfitting, and the lack of real-time deployment. Future research should prioritize explainable ML models, low-cost sensors, and real-time integrated prediction–optimization–control frameworks to enable scalable smart biogas systems.
