This study aims to predict streamflow at 50 gauging stations located within Canada’s southern provinces by utilizing the three most popular gradient boosting techniques including XGBoost, CatBoost, and LightGBM. A set of engineered features which previously proved successful in several ungauged catchments is employed as inputs of the models. While the models perform mainly close, among the gradient boosting models, LightGBM (Nash-Sutcliffe Efficiency range of 0.46–0.99 with an average of 0.79) proves to be the single best model in predicting streamflow or shares the best performance in 41 catchments of the study. XGBoost (NSE range of 0.45–0.99 with an average of 0.77) and CatBoost (NSE range of 0.17–0.99 with an average of 0.75) have the highest performance or share it respectively in 17 and 10 catchments. Thus, LightGBM displays strong performance in almost all the study’s catchments and proves promising for streamflow prediction in Canada’s southern provinces.

To implement effective reservoir management practices, critical assessments of sediment deposition are important. In this study, we developed an automated approach using the Google Earth Engine to assess reservoir sedimentation and validated it on two reservoirs: Koka and Gefersa I/II, which are located in the upper Awash River Basin (ARB), Ethiopia. Landsat 8 Operational Land Imager (OLI) and Landsat 5 Thematic Mapper (TM) data from the study period, along with reservoir water level, pre-impoundment reservoir capacity and recent bathymetry survey data, were used. Statistical validation confirmed a strong association between the obtained results and the bathymetric survey results: Koka Reservoir (Pearson correlation coefficient (r) = 0.999, regression coefficient (R2) = 0.976, Nash-Sutcliffe efficiency (NSE) = 0.997) and Gefersa I/II Reservoir (r = 0.997, R2 = 0.992, NSE = 0.955). This study highlights that GEE effectively estimates reservoir sedimentation, providing valuable insight for the active management of reservoirs, especially in resource-limited regions.

This study presents a comprehensive flood hazard assessment of a potential breach of the Attabad landslide dam in Gilgit-Baltistan, Pakistan, using the HEC-RAS 2D hydraulic model. Four breach scenarios – overtopping and piping failure modes – were simulated to analyze downstream flood dynamics. Peak outflows ranged from 33,129 to 235,611 m³/s, attenuating over a 121 km stretch of the Hunza River. Sensitivity analysis with the Morris method (240 simulations) identified breach width and formation time as the most critical parameters, while Manning’s coefficients significantly influenced downstream flow. Froehlich’s breach parameterization method was adopted for critical failure time predictions. Flood inundation mapping was carried out to visualize the spatial extent of potential flooding, highlighting areas at highest risk. Flood depth, velocity, and arrival time were quantified for key infrastructure, including Sarat and Gilgit bridges. Findings underscore scenario-based modeling and sensitivity analysis as essential for enhancing dam safety and emergency preparedness.

This study investigates streamflow components (baseflow and surface flow) during the recession period using the classical hydrograph recession technique and a two-component mixing model (salinograph and hydrograph). The methodologies were applied to the Mahabad and Zarrinehroud Rivers in northwestern Iran, using data on streamflow and salinity in 2016. Results indicated that baseflow indices from the mixing model (0.945 for Mahabad, 0.978 for Zarrinehroud) were higher than those from the hydrograph recession method (0.811 and 0.960, respectively), suggesting greater accuracy in capturing groundwater influence. While the classical hydrograph recession method yielded a constant groundwater recession coefficient (α g) of 0.006 d−1, the mixing model estimated a variable α g range of 0.004 to 0.010 d−1. Incorporating salinity into hydrograph analysis improved baseflow separation and captured the interaction between flow and water quality, enhancing estimates of groundwater contributions. These findings support improved hydrological modeling in semi-arid regions and underscore groundwater’s role in sustaining streamflow.

This study offers insights into the wave climate of the Gulf of Guinea (GoG) and its past and future trends under various climate change scenarios. Using linear regression and difference analysis, it examines changes in average and extreme wave heights (Hs), wave periods (Tm), and wave directions (Dm) across the GoG. The findings reveal a consistent increase in both average and extreme Hs, with a more pronounced increase in extreme Hs during the rainy season. An increase in extreme Hs of over 7 cm is accompanied by an increase in Tm, indicating stronger wave energy. A projected clockwise shift in wave direction of over 2° is expected for extreme wave conditions by the 2100. The study highlights the complex interplay of factors influencing wave climate, including distant swell systems. It concludes by emphasising the importance of proactive measures to mitigate potential risks associated with changing wave climate in the GoG.

This paper deals with an approach for hybrid and coupled modeling of the design of fixed and suspended biomass reactors for organic matter removal from sanitary landfill leachates. Five bioreactors, comprising Rotating Biological Contactors, Trickling Filter, Upflow Anaerobic Sludge Blanket, Upflow Anaerobic Filter in Two Separated Stages (UAF-2SS) and Upflow Anaerobic Filter in Three Separated Stages (UAF-3SS) were evaluated by treating the organic matter contained in the leachates produced in two sanitary landfills. With respect to the hybrid and coupled models, the best performance in the approximation of the measured values in the Se/Si ratio to the predicted results of models were the equations for UAF-2SS and UAF-3SS reactors. Hybrid models were made up of a combination of mass conservation law and equations for estimating transformation process. Coupled models consisted of a combination of transport process models and transformation models based on the substrate utilization rate by the microorganisms.

ABSTRACT The Sesan River, a major tributary of the Lower Mekong River, is renowned for its diverse biomes and is an important resource for its nearby inhabitants. Hydropower dams have proliferated in the basin, yet their hydrologic impacts remain poorly understood, particularly when combined with the influence of climate change. This study evaluates hydrologic alterations resulting from the operation of hydropower dams and projected climate change in the basin. Climate change projections are estimated under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5), and daily streamflow is simulated using the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS). The degree of hydrologic alteration is assessed using the Indicators of Hydrologic Alteration (IHA) under dam operation, climate change, and dams and climate change combined. Dam operations induce moderate alteration, while climate change alone causes low alteration. The combined scenario causes higher alteration than the individual scenarios, but the alteration falls within the moderate category.

Water temperature is an important component for river water quality. Good knowledge of river thermal regime is critical for aquatic resources management and environmental impact studies. This study aims to predicting surface water temperature at a given site of Sebou estuary (Morocco) from air temperature, using artificial intelligence applied to neural networks (NN) and linear regression (LR). The models used were applied to an hourly temperature database obtained by simulating hydraulic and thermal regime of the estuary using HEC-RAS model. Temperature data (1560) was divided into training and validation series. The results showed that coefficient of determination for training was 86.26%(NN) and 79.94%(LR). For validation it was 91.23%(NN) and 80.3%(LR). Moreover the residuals generally vary between −1.5°C and +1.5°C, no trend was noted as function of estimated temperature. Hence, it is possible to predict water temperature of estuary using air temperature. Additionally, NN model gave slightly results compared to LR.

Lake Chivero, Harare’s main potable raw water source, is eutrophic. This has complicated water treatment at Morton Jaffray Water Treatment Works (MJWTW) leading to frequent filter backwashing and reduced plant output. This study investigated the use of dual media filters (DMF) as an alternative to single media filters (SMF) currently employed using pilot filters. Key operational parameters (headloss and flow rate) and water quality parameters [electrical conductivity (EC), pH, total dissolved solids (TDS), turbidity and temperature] were monitored hourly. The change in EC, pH, TDS, and turbidity across the SMF and DMF was 6.3% and 6.3%; 0.5%. and 0.2%; 5.5% and 4.5%; 68% and 69%, respectively thus showing no significant difference. However, headloss development for the SMF was significantly higher reaching 247.3 m compared to 134.6 m for the DMF. Use of DMF could increase the filter run and production at MJWTW.

The aim of this research is the modelling and mapping of flood risk zones in the M’zab valley of Ghardaia city by combining the hazard map and the flood vulnerability map. The undertaken research utilizes the hydrologic engineering centre-hydrologic modelling system as the modelling tool to simulate the runoff in the watershed and hydrologic engineering centre – river analysis system to model the water levels of different river locations based on steady flow analysis and to define flood-prone areas. The flood risk map allows the identification of five classes ranging from a low to a very high risk of flooding. Thus, the low to very low-risk areas extend over roughly 70.58% of the total surface area of Ghardaia city. These areas, which are located in the southern part of the alluvial plain, are considered as low vulnerability regions, while areas with high-risk cover more than 20% of the city.