Hydrology, Volume 12, Issue 9 (September 2025) – 23 articles

Climate change affects streamflow through changes in precipitation, temperature, and extreme weather events. These changes will impact water resource availability significantly. Thus, understanding the impacts of climate change on hydrology is essential for sustainable water management. This study investigated the potential effects of climate change on streamflow in the Olifants River basin under shared socioeconomic pathways (SSPs), utilizing the restructured version of the Soil and Water Assessment Tool (SWAT+) model. Projected precipitation and temperature (Tmax and Tmin) were analyzed for the near (2030–2060) and far (2070–2100) future to simulate and analyze streamflow variations under SSP245 and SSP585 scenarios using bias-corrected CMIP6 data and the SWAT+ model. The SWAT+ model was calibrated and validated successfully, with Nash–Sutcliffe efficiency (NSE) values of 0.76 and 0.77, and coefficient of determination (R²) values of 0.78 and 0.82 during the calibration and validation periods, respectively. Climate model ensemble projections show a consistent decline in precipitation and increases in Tmax and Tmin, with Tmin increasing more significantly. These changes are projected to reduce streamflow, with annual declines of 43.08% and 50.89% under SSP245 and 57.79% and 58.82% under SSP585 for the near and far future, respectively. Moreover, climate change reduces streamflow across all seasons in the Olifants River basin. Therefore, adopting water management strategies such as enhancing integrated water resource management and investing in climate-resilient infrastructure is essential for sustainable water resource management under changing climate conditions in the basin. Full article

The Yangtze River Basin is vital to China’s water security and flood management yet lacks a basin-wide quantitative assessment of long-term hydroclimatic changes. This study uses the high-resolution CMFD 2.0 dataset and the VIC model to evaluate spatiotemporal trends in precipitation and natural streamflow from 1951 to 2020. The results show a significant increase in annual precipitation in the upper basin (1.10 mm yr⁻¹, p < 0.05), particularly during the wet season, with spatially concentrated increases along the eastern Tibetan Plateau. The VIC model performed reliably across major stations, with NSE > 0.9 and PBIAS within ±10% during calibration. Natural streamflow trends are spatially heterogeneous: upper mainstream stations (e.g., Shigu, Panzhihua, Zhutuo) exhibit significant increases (6.25–14.58 m³/s per year), while lower stations remain stable or decline. Seasonally, wet-season streamflow increased in the upper basin, whereas dry-season streamflow decreased in the middle and lower reaches. At Yichang, natural simulations reveal growing seasonal extremes, with rising wet-season and declining dry-season flows (−19.06 m³/s yr⁻¹). Human interventions have partially offset these extremes. Since 1990, observed peak discharge at Yichang during the wet season has decreased by 10.04% compared to natural streamflow, while the dry-season minimum discharge has increased by 27.63%. This shows that large reservoirs help reduce flood peaks and increase low flows. These findings highlight the intensifying impacts of climate variability and human regulation on hydrological processes and provide a scientific basis for adaptive water resource management in large river basins. Full article

Based on long-term series of observations of water flow of 22 small and medium-sized rivers in the forest-steppe and steppe east of the East European Plain, an analysis of its trend changes in 1961–2022, i.e., in the time interval of the Anthropocene with the most progressive climate change in the study region, was carried out. The main quantitative hydrological parameters studied were annual average water discharge, annual maximum water discharge (Q_max), minimum water discharge (Q_min-CP) during the ice-covered period of the riverbed (cold period, mainly December–March), minimum water discharge (Q_min-WP) during the ice-free period of the riverbed (warm period, mainly April–November), as well as some of their ratios, which provide a clear idea of changes in the intra-annual variability of water flow. The principal methodological toolkit used was a standard set of statistical tests applied to time series analysis. A summary of the study results showed that statistically significant trend changes in the annual water flow of most of the analyzed rivers were not observed for the specified period. At the same time, statistically significant intra-annual changes in the flow were revealed: a significant reduction in Q_max (especially in the forest-steppe zone) and a significant increase in Q_min-CP and Q_min-WP. Thus, the ratio between Q_max and Q_min-CP (Q_min-WP) decreased between the baseline climatic periods 1961–1990 and 1991–2020 by an average of 4.1 (4.0) times in the rivers of the forest-steppe zone, and by 5.2 (5.3) times in the rivers of the steppe zone. Climate change is considered the leading cause of the observed intra-annual changes in river water flow in the study region, with the main factor being an increment in annual air temperature, especially during the cold season. Full article

Wastewater overflows (WOs) are a growing concern for water quality and ecological health in urban estuaries. This study provides a robust water quality and ecological assessment of WOs from four pump stations discharging into the Waimea Estuary, Aotearoa, New Zealand. Using overflow scenario modelling, baseline and event-based water quality sampling, and whole effluent toxicity testing, we assessed the potential impacts under conservative (2 h) and worst-case (24 h) overflow durations. Results showed that, even under worst-case conditions, the estuary’s natural dilution capacity exceeded the median dilution required to meet the 95% ecological protection level. Ecotoxicity was site- and season-specific, with amphipods and mussels showing sensitivity at some sites, while algal assays indicated nutrient enrichment rather than toxicity. Impacts were spatially limited and unlikely to persist beyond one or two tidal cycles. The estuary’s tidal exchange and resilient biota further mitigated risks. This method provides a transferable framework for assessing intermittent discharges in other coastal systems, especially those with high ecological value and infrequent discharge events. Full article

Climate change is expected to reduce water availability during cropping season, while growing populations and rising living standards will increase the global water demand. This creates an urgent need for national water management tools to optimize water allocation. In particular, agriculture requires targeted approaches to improve efficiency. Alongside field measurements and remote sensing, agro-hydrological models have emerged as a particularly valuable resource for assessing and managing agricultural water demand. This study introduces WaterCROPv2, a state-of-the-art agro-hydrological model designed to estimate national-scale irrigation water demand while effectively balancing accuracy with practical data requirements. WaterCROPv2 incorporates innovative features such as hourly time-step computations, advanced rainwater canopy interception modeling, detailed soil-dependent leakage dynamics, and localized daily evapotranspiration patterns based on meteorological data. Through comprehensive analyses, WaterCROPv2 demonstrates significantly enhanced reliability in estimating irrigation water needs across various climatic regions, particularly under contrasting dry and wet conditions. Validation against independent data from the Italian National Institute of Statistics (ISTAT) for maize cultivation in Italy in 2010 confirms the model’s accuracy and underscores its potential for broader international applications. A spatial analysis further reveals that the estimation errors align closely with regional precipitation patterns: the model tends to slightly underestimate irrigation needs in the wetter northern regions, whereas it somewhat overestimates demand in the drier southern areas. WaterCROPv2 has also been used to analyze irrigation water requirements for maize cultivation in Italy from 2005 to 2015, highlighting its significant potential as a strategic decision-support tool. The model identifies optimal cultivation areas, such as the Pianura Padana, where the irrigation requirements do not exceed 200 mm for the entire maize growing period, and unsuitable regions, such as Salentino, where over 500 mm per season are required due to the local climatic conditions. In addition, estimates of the water volumes required for the current extent of maize cultivation show that the Pianura Padana region demands nearly three times the amount of water used in the Salentino area. The model has also been used to identify regions where adopting efficient irrigation technologies could lead to substantial water savings. With micro-irrigation currently covering less than 18% of irrigated land, simulations suggest that a complete transition to this system could reduce the national water demand by 21%. Savings could reach 30–40% in traditionally water-rich regions that rely on inefficient irrigation practices but are expected to be increasingly exposed to temperature increases and precipitation shifts. The analysis shows that those regions currently lacking adequate irrigation infrastructure stand to gain the most from targeted irrigation system investments but also highlights how incentives where micro-irrigation is already widespread can provide further 5–10% savings. Full article

This study describes the challenges and solutions encountered when developing a high-resolution, process-based hydrological model of the Adige River Lowland Basin (ARLB), a flat, intensively managed agricultural region in northeastern Italy. The model was based on the Soil and Water Assessment Tool (SWAT) and simulates streamflow and nutrient dynamics. Using detailed local hydrological, agricultural, and point-source data, the model robustly reproduces current conditions and projects future scenarios under climate change. Streamflow calibration demonstrated strong performance (NSE up to 0.76), with simulated monthly average discharge (192 m³/s) closely matching observed values (218 m³/s) and capturing intra- and inter-annual variability. Nutrient simulations also aligned well with observations. Total nitrogen (TN) concentrations averaged 1.08 mg/L versus 1.09 mg/L observed. Spatial TN loads were satisfactorily predicted across the subbasins, without additional nutrient calibration to prevent overfitting. Spatial analysis revealed that point sources, notably wastewater treatment plants (WWTPs) along the main river, contribute approximately 65% of the total nitrogen loads, while diffuse agricultural runoff (though secondary in load magnitude) is concentrated in the northern subbasins and is sensitive to climate variability. Climate change projections under 2 °C and 3 °C warming scenarios indicate increases in TN loadings by about 150 and 300 t/y, respectively. Phosphorus loadings exhibited weaker and more variable responses to warming than TN, reflecting model and scenario uncertainties. Overall, this work demonstrates the capability of the proposed modeling approach, based on high-resolution spatio-temporal variables, to model complex lowland hydrology and nutrient fluxes. The model can be used as a decision-support tool for regional nutrient management and climate adaptation strategies. Full article

Submerged vanes offer a promising solution for reducing scour depth around hydraulic structures such as bridge piers by modifying near-bed flow patterns. However, temporal changes in bed profiles around a cylindrical pier remain insufficiently quantified. This study employs three machine learning models (MLMs), gene expression programming (GEP), support vector regression (SVR), and an artificial neural network (ANN), to simulate the temporal evolution of the bed profile around a cylindrical pier under constant subcritical flow. We use a published laboratory flume dataset (106 observations) obtained for a pier of diameter $D=6\mathrm{cm}$ and uniform sediment with median size $D_{50}=0.43\mathrm{mm}$. Geometric/layout parameters of the submerged vanes (number n, transverse offset z, longitudinal spacing e, and distance from the pier base a) were fixed at their reported optima, and subsequent tests varied installation angles $\alpha$ to minimize scour. Models were trained on $70\%$ of the data and tested on $30\%$ using dimensionless inputs $(t/t_e,{\alpha }_1,{\alpha }_2,{\alpha }_3)$ with t the elapsed time from the start of the run and $t_e$ the equilibrium time at which scour growth becomes negligible and response $s/D$ with s the instantaneous scour depth at time t. The GEP model with a three-gene structure achieved the best accuracy. During training and testing, GEP attained $(\mathrm{RMSE}, \mathrm{MAE}, R^2, {(D_s/D)}_{\mathrm{DDR}\left(\max \right)})=(0.0864,0.0681,0.9237,4.25)$ and $(0.0729,0.0641,0.9143,4.94)$, respectively, where $D_s$ denotes scour depth at equilibrium state, D is the pier diameter, and $\mathrm{DDR}\left(\max \right)\equiv max(D_s/D)$ is the maximum dimensionless depth ratio observed/predicted. Full article

In Greece, the geodynamics of karst hypothermal–mineral springs, in which often shallow fresh groundwater is intermixed, is not well known. This study aims to investigate the monthly hydrochemical variability of three karst hypothermal mineral springs in Greece named Kyllini (southern Greece), Agiasma (northern Greece), and Voskina (northern Greece). Hence, monthly samples were collected and an analysis of major and trace elements, ion ratios, and saturation indices was performed, as well as statistical analysis and cross correlation. Elevated concentrations of Ca²⁺, Mg²⁺, and HCO₃⁻ are present in all springs, indicating that the dissolution of calcite and dolomite constitutes the main water–rock interaction process. Additionally, the mobilization and transport of Mn, Fe, and As are favored by the negative ORP values. However, there are also differences between the three springs. The Kyllini spring is characterized by high salinity and dominated by Na–Cl–HCO₃ water, while the Agiasma spring exhibits a mixed water type with moderate salinity. The Voskina spring reflects a fresher, bicarbonate-dominated aquifer system with modest trace element mobilization. This study provides the first comprehensive monthly assessment of the hydrochemical response of karst hypothermal–mineral springs in Greece, offering new insights into seasonal geochemical dynamics. Full article

High levels of phosphorus cause eutrophication, leading to water blooms and making the water undesirable in aquatic environments. Surface water pollution by phosphorus (P) is caused by both point and diffuse sources. Despite the recent technological advancements in wastewater phosphorus removal, this element persists in aquatic ecosystems, particularly in sediments, often in non-bioavailable forms (in the case of precipitation by aluminum salts) or within biomass associated with high concentrations of heavy metals, rendering it unsuitable for reuse. In this paper, we review the measures and methods commonly used for reducing or removing bioavailable phosphorus, with a focus on the strategies and methods for direct in situ phosphorus removal or reuse, including the use of microbial biofilms and aquatic macrophytes, natural and constructed wetlands, and biotised (biologically enhanced) solid-phase sorbents or woodchip bioreactors. This paper also highlights the significance of bioavailable phosphorus from both the hydrochemical perspectives, examining phosphorus speciation, solubility, and the geochemical interactions influencing mobility in water and sediments, and the biological perspectives, which consider phosphorus uptake, bioaccumulation in aquatic organisms, and the role of microbial and plant communities in modulating phosphorus cycling. This overview presents sustainable phosphorus management approaches that are key to reducing eutrophication and supporting ecosystem health. Full article

This study examines the impact of recent climatic trends on the preservation of submerged wooden structures at the Gran Carro archaeological site in Lake Bolsena, Italy. Climatic data from the Bolsena Meteorological Station were analysed alongside in situ water quality measurements collected near the archaeological remains at a depth of 4 m. The key parameters included water temperature (Tw), redox potential (Eh), dissolved oxygen (DO), and total dissolved solids (TDS). Trend analyses using the Mann–Kendall test and Sen’s slope revealed significant increases in air and water temperatures, which were strongly correlated. Although precipitation exhibited an upward trend, its negative correlation with temperature suggests greater variability rather than a stable water supply. Despite increased rainfall, lake levels showed a significant decline, likely due to intensified evaporation and water extraction for irrigation. UAV surveys confirmed recent lowering of the lake’s water surface during drought periods. Among the limnological parameters, dissolved oxygen saturation declined significantly, while redox potential increased, indicating shifts toward more anaerobic conditions. These environmental changes could promote the activity of erosive bacteria that degrade submerged wood. Conversely, increased evaporation might also enhance oxygen penetration at depth, potentially activating decay agents such as soft rot fungi and wood-boring bacteria. Overall, the findings suggest that ongoing climatic changes are adversely affecting the preservation of submerged wooden structures, highlighting the need for adaptive management strategies to protect both the lake ecosystem and its archaeological heritage. Full article

This study presents an assessment of hydrological diversity (hydrodiversity) in Rio Grande do Norte, Brazil, aiming to identify potential correlations between hydrodiversity and hydrological features of geoheritage. The methodology applied a quantitative approach based on mean annual precipitation, river discharge, reservoir distribution, and stream order. These variables were analyzed within a 5.5 km grid using GIS tools. The four resulting sub-indices were normalized through the Maximum Possible Value method to ensure equal weighting in the final Hydrodiversity Index, which classifies areas into four levels: low, medium, high, and very high. Results show the highest hydrodiversity values in the eastern region and along the Apodi–Mossoró River, where rainfall and drainage density are greatest. The Hydrodiversity Index map was examined alongside land use data and the distribution of 22 previously identified hydrological sites (hydrosites). A greater concentration of anthropogenic land use was noted in areas with medium to high hydrodiversity, especially in the east and along the northern coast, emphasizing the role of water resources in territorial dynamics. The findings indicate that no hydrosites are located within areas of Very High Hydrodiversity; however, more than 50% of the hydrosites correspond to areas classified as High Hydrodiversity. While further research is required to better elucidate the relationship between geodiversity and geoheritage, these results underscore both the complexity of the link between hydrodiversity and water-related geoheritage, as well as the importance of employing an index to inform conservation and management strategies. Full article

The environmental characterization of a given region is fundamental for decision-making by public administrators and, consequently, for sustainable development. Regarding water quality, establishing the use of this resource is a priority, as specific parameters must be defined for each type of usage, to determine water quality in that region. The objective of this study was to propose a water quality index for supplying rural communities, given that the most commonly used indices relate to urban water after treatment. To construct the index, water samples were collected over 12 months from 29 sampling points across seven rural centers without the governmental service of treated water. Principal component analysis was used to identify the most representative parameters, and final weights were assigned considering Brazilian regulatory standards. The results obtained revealed a very simplified index with five variables and five usage classes, with scores ranging from simplified to advanced treatment. It is hoped that the proposed index will better guide rural communities and generate improved policies for water resource management in the Brazilian Federal District and support public policy development in rural areas with limited water treatment infrastructure. Full article

A permeable reactive barrier (PRB) containing zero-valent iron (ZVI) is an in situ groundwater remediation technology that passively intercepts and treats contaminated groundwater plumes. Over time, secondary mineral precipitation within the PRB diminishes porosity and hydraulic conductivity, altering flow paths, residence times, and sometimes causing bypass of the reactive zone. This study utilizes the THMC software to simulate porosity reduction in a PRB, capturing the coupled effects of fluid flow and geochemical interactions. The simulation results indicate that porosity loss is most significant at the PRB entrance and stabilizes beyond 0.2 m. Porosity reduction is primarily caused by aragonite, siderite, and ferrous hydroxide precipitating in pore spaces. The model further elucidates the influence of groundwater chemistry, demonstrating that variations in bicarbonate concentrations significantly impact mineral precipitation processes, thereby leading to porosity reduction. Furthermore, the study highlights reaction kinetics, with anaerobic iron corrosion rates being critical in controlling porosity reduction via mineral precipitation. THMC software effectively simulates porosity reduction in PRBs, identifies key factors driving clogging, and informs design optimization for long-term remediation. Full article

This study aims to analyse the effect of partially clogged inlets on the behaviour of urban drainage systems at the city scale, particularly regarding intercepted volumes and flood depths. The main challenges were to represent the inlet network in detail at a rather large scale and to avoid the effect of sewer network surcharging on the draining capacity of inlets. This goal has been achieved through a 1D/2D coupled hydraulic model of the whole urban drainage system in La Almunia de Doña Godina (Zaragoza, Spain). The model focuses on the interaction between grated drain inlets and the sewer network under partial clogging conditions. The model is fed with data obtained on field surveys. These surveys identified 948 inlets, classified into 43 types based on geometry and grouped into 7 categories for modelling purposes. Clogging patterns were derived from field observations or estimated using progressive clogging trends. The hydrological model combines a semi-distributed approach for micro-catchments (buildings and courtyards) and a distributed “rain-on-grid” approach for public spaces (streets, squares). The model assesses the impact of inlet clogging on network performance and surface flooding during four rainfall scenarios. Results include inlet interception volumes, flooded surface areas, and flow hydrographs intercepted by single inlets. Specifically, the reduction in intercepted volume ranged from approximately 7% under a mild inlet clogging condition to nearly 50% under severe clogging conditions. Also, the model results show the significant influence of the 2D mesh detail on flood depths. For instance, a mesh with high resolution and break lines representing streets curbs showed a 38% increase in urban areas with flood depths above 1 cm compared to a scenario with a lower-resolution 2D mesh and no curbs. The findings highlight how inlet clogging significantly affects the efficiency of urban drainage systems and increases the surface flood hazard. Further novelties of this work are the extent of the analysis (city scale) and the approach to improve the 2D mesh to assess flood depth. Full article

Stream temperatures are expected to increase with warming air temperatures, yet the extent and aquatic health impacts vary significantly across heterogeneous landscapes. This study was conducted in a 3360-ha multi-land-use watershed in the Pacific Northwest region of the USA to assess and compare the driving factors for stream temperature heating, cooling, and cool-water refugia along a 12-km mainstem stream longitudinal profile. Study objectives were to (1) determine yearlong stream temperature variability along the entire stream longitudinal profile, and (2) assess stream-environment relationships influencing stream temperature dynamics across forest, agriculture, and urban landscapes within the watershed. Stream and riparian air temperatures, solar radiation, shade, and related stream-riparian characteristics were measured over six years at 21 stations to determine changes, along the longitudinal profile, of thermal sensitivity, maximum and minimum stream temperatures, and correlation between solar radiation and temperature increases, and potential causal factors associated with these changes. Solar radiation was a primary heating factor for an exposed agricultural land use reach with 57% effective shade, while southern stream aspects and incoming tributary conditions were primary factors for forested reaches with greater than 84% effective shade. Potential primary cooling factors were streambank height, groundwater inflows, and hyporheic exchange in an urban reach with moderate effective shade (79%) and forest riparian width (16 m). Combining watershed-scale analysis with on-site stream-environmental data collection helps assess primary temperature heating factors, such as solar radiation and shade, and potential cooling factors, such as groundwater and cool tributary inflows, as conditions change along the longitudinal profile. Full article

This work aims at improving the accuracy of ensemble streamflow forecasts at short-to-medium ranges with the conditional bias-penalized regression (CBPR)-aided Meteorological Ensemble Forecast Processor (MEFP) and streamflow data assimilation (DA). To assess the potential impact of the CBPR-aided MEFP and streamflow DA, or CBPR-DA, 20-yr hindcast experiments were carried out using the Global Ensemble Forecast System version 12 reforecast dataset for 46 locations in the service areas of 11 River Forecast Centers of the US NWS. The results show that, relative to the current practice of using the MEFP and no DA, or MEFP-NoDA, CBPR-DA improves the accuracy of ensemble forecasts of 3-day flow over lead times of 0 to 3 days by over 40% for 4 RFCs and by over 20% for 9 of the 11 RFCs. The margin of improvement is larger where the predictability of precipitation is larger and the hydrologic memory is stronger. As the lead time increases, the margin of improvement decreases but still exceeds 10% for the prediction of 14-day flow over lead times of 0 to 14 days for all but 3 RFCs. Full article

Accurate and up-to-date morphometric data on lakes are crucial for hydrological modeling, ecosystem monitoring, and sustainable water resource management. This study presents the first centimeter-scale, high-resolution bathymetric model of Lake Markakol (eastern Kazakhstan), generated using advanced hydroacoustic and geospatial techniques. The primary objective was to reassess key morphometric parameters—surface area, depth, volume, and shoreline configuration—more than six decades after the only existing survey from 1962. High-density depth data were acquired with a Lowrance HDS-12 Live echo sounder, achieving vertical precision of ±0.17 m, and processed using ReefMaster and ArcGIS to produce a three-dimensional, hydrologically correct model of the lake basin. Compared with archival data, results show that while the surface area (455.365 ± 0.005 km²), length (38.304 ± 0.002 km), and width (19.138 ± 0.002 km) have remained stable, the maximum depth is lower (24.14 ± 0.17 m vs. 27 m), and the total water volume is slightly higher (6.667 ± 0.025 km³ vs. 6.37 km³). These differences highlight both the limitations of historical lead-line surveys and the enhanced accuracy of modern hydroacoustic and GIS-based methods. The workflow developed here is transferable to other remote alpine lakes, providing an invaluable baseline for limnological research, ecological assessment, hydrodynamic modeling, and long-term water resource management strategies in data-scarce mountain regions. Full article

In the arid and semi-arid climate of Southern Kazakhstan, groundwater is the primary and most resilient source of water for pasture irrigation. This study provides an integrated assessment of the predicted, natural, and operational groundwater resources across five administrative regions—Almaty, Zhetysu, Zhambyl, Kyzylorda, and Turkestan—considering water quality (total dissolved solids, TDS), potential well yield, and aquifer depth. Hydrogeological maps at 1:200,000 and 1:1,000,000 scales, a regional well inventory, and GIS-based spatial analysis were combined to classify resource availability and identify surplus and deficit zones. Results show that 92.5% of predicted exploitable resources (totaling 1155.2 m³/s) have TDS ≤ 3 g/L, making them suitable for domestic and livestock use. Regional disparities are pronounced: Zhetysu, Almaty, and Zhambyl exhibit resource surpluses, Kyzylorda approaches balance, while Turkestan faces a marked deficit. The developed groundwater availability map integrates mineralization, well productivity, and recommended drilling depth, enabling the design of water intake systems without costly field exploration. This decision-support tool has practical value for optimizing water allocation, reducing operational costs, and improving the sustainability of pasture management under the constraints of limited surface water resources. Full article

The proper development of groundwater resources is very important in many parts of the world. Its planning requires mathematical simulation of groundwater flows. Simulation can be either analytical or numerical. Analytical tools, when available, require fewer computational resources, but they are usually based on more assumptions, at the conceptual level, which restrict their applicability. In this paper, we aim to check the applicability of one-dimensional analytical solutions for groundwater flows through non-homogeneous aquifers, which are bound by two constant head and two impermeable boundaries and bear many zones of different transmissivities. These solutions are based on the stepwise inclusion of neighboring zones to larger ones, with equivalent transmissivity coefficients. We compare analytical results with numerical ones, obtained from a two-dimensional numerical model. We have selected the boundary element method (BEM) for this task. BEM is very versatile in solving steady-state groundwater flow problems, since discretization is restricted to external and internal field boundaries only. This feature fits perfectly with our research, which requires flow velocities at the boundaries only. Our research shows that analytical results can serve as upper and lower limits of total inflow. If the differences between the transmissivities of adjacent zones are small, they can be used in preliminary calculations too. Full article

Accurate precipitation data are essential for hydrological planning in mountainous regions with sparse opportunities for observation, such as the Ambato River basin in Ecuador. In this study, CHIRPS and IMERG satellite precipitation products were compared against six automatic rain gauges from 2014 to 2023, using both categorical metrics (to assess daily rainfall detection skill) and continuous validation (to evaluate rainfall amount), complemented by bias decomposition and spatiotemporal analysis. Our results show that IMERG demonstrated higher skill in detecting daily rainfall, while CHIRPS delivered a more stable performance during dry conditions, with fewer false alarms. Both products capture the main seasonal precipitation patterns but differ in bias behavior: CHIRPS tends to under-estimate daily rainfall less, whereas IMERG provides more reliable volumetric estimates overall. These findings suggest that IMERG may be best suited for flood risk and hydrological modelling, while CHIRPS could be preferred for drought monitoring and climatological studies in Andean catchments. Full article

This manuscript introduces a river segmentation method and explores the impact of human interventions through a long-term study of total nitrogen, total phosphorus, chemical oxygen demand, and biochemical oxygen demand. An indicator linking parameter concentrations to the river’s flow rate was used to assess the development of the examined parameters. The analysis spanned from 2011 to 2022, considering both seasonal and yearly variations. Normal probability plots served as statistical tools to evaluate whether the data followed normal distributions and identify outliers. The proposed segmentation divided the Bahlui River into four segments, each defined by anthropogenic stressors. It was found that, due to human activity, each river segment could be viewed as an “independent” river. This supports the idea that river segments can be analyzed separately as distinct components. The proposed segmentation approach represents an alternative approach in river water quality research, moving from traditional continuous system models to fragmented system analysis, which better reflects the reality of heavily modified river systems. The study’s findings are important for understanding how anthropogenic modifications affect river ecosystem functioning in the long term. Full article

Floods are among the most devastating natural disasters; predicting their depth and extent remains a global challenge. Machine Learning (ML) models have demonstrated improved accuracy over traditional probabilistic flood mapping approaches. While previous studies have developed ML-based models for specific local regions, this study aims to establish a methodology for estimating flood depth on a global scale using ML algorithms and freely available datasets—a challenging yet critical task. To support model generalization, 45 catchments from diverse geographic regions were selected based on elevation, land use, land cover, and soil type variations. The datasets were meticulously preprocessed, ensuring normality, eliminating outliers, and scaling. These preprocessed data were then split into subgroups: 75% for training and 25% for testing, with six additional unseen catchments from the USA reserved for validation. A sensitivity analysis was performed across several ML models (ANN, CNN, RNN, LSTM, Random Forest, XGBoost), leading to the selection of the Random Forest (RF) algorithm for both flood inundation classification and flood depth regression models. Three regression models were assessed for flood depth prediction. The pixel-based regression model achieved an R² of 91% for training and 69% for testing. Introducing a pixel clustering regression model improved the testing R² to 75%, with an overall validation (for unseen catchments) R² of 64%. The catchment-based clustering regression model yielded the most robust performance, with an R² of 83% for testing and 82% for validation. The developed ML model demonstrates breakthrough computational efficiency, generating complete flood depth predictions in just 6 min—a 225× speed improvement (90–95% time reduction) over conventional HEC-RAS 6.3 simulations. This rapid processing enables the practical implementation of flood early warning systems. Despite the dramatic speed gains, the solution maintains high predictive accuracy, evidenced by statistically robust 95% confidence intervals and strong spatial agreement with HEC-RAS benchmark maps. These findings highlight the critical role of the spatial variability of dependencies in enhancing model accuracy, representing a meaningful approach forward in scalable modeling frameworks with potential for global generalization of flood depth. Full article

Global climate change, combined with the construction of impermeable urban elements, tends to increase runoff, which might cause flooding and reduce groundwater recharge. Moreover, the first flash of these areas might accumulate pollutants that might deteriorate groundwater quality. A digital elevation model (DEM) describes urban landscapes by representing the watershed relief at any given location. While, in concept, finer DEMs and land use classification (LUC) are yielding better hydrological models, it is suggested that over-accuracy overestimates minor tributaries that might be redundant. Optimal DEM resolution with integrated spectral and feature-based LUC was found to reflect the hydrological network’s significant tributaries. To cope with the karstic urban watershed complexity, ModClark Transform and SCS Curve Number methods were integrated over a GIS-HEC-HMS platform to a nominal urban watershed sub-basin analysis procedure, allowing for detailed urban runoff modeling. This precise urban karstic terrain modeling procedure can predict runoff volume and discharge in urban, mountainous karstic watersheds, and may be used for water-sensitive design or in such cities to control runoff and prevent its negative impacts. Full article