Hydrology

Standard flood-frequency analysis methods rely on an assumption of stationarity, but because of growing understanding of climatic persistence and concern regarding the effects of climate change, the need for methods to detect and model nonstationary flood frequency has become widely recognized. In this study, a regional statistical method for estimating the effects of climate variations on annual maximum (peak) flows that allows for the effect to vary by quantile is presented and applied. The method uses a panel–quantile regression framework based on a location-scale model with two fixed effects per basin. The model was fitted to 330 selected gauged basins in the midwestern United States, filtered to remove basins affected by reservoir regulation and urbanization. Precipitation and discharge simulated using a water-balance model at daily and annual time scales were tested as climate variables. Annual maximum daily discharge was found to be the best predictor of peak flows, and the quantile regression coefficients were found to depend monotonically on annual exceedance probability. Application of the models to gauged basins is demonstrated by estimating the peak-flow distributions at the end of the study period (2018) and, using the panel model, to the study basins as-if-ungauged by using leave-one-out cross validation, estimating the fixed effects using static basin characteristics, and parameterizing the water-balance model discharge using median parameters. The errors of the quantiles predicted as-if-ungauged approximately doubled compared to the errors of the fitted panel model.

Flooding poses a significant hazard to urban areas, particularly under the pressures of climate change and rapid urbanization. This study evaluates the flood risk in the Wadi Ibrahim watershed, located in Makkah Al-Mukarramah City, Kingdom of Saudi Arabia (KSA), by analyzing the impacts of climate change on flood hazards. The analysis incorporates projections from the Coordinated Regional Climate Downscaling Experiment (CORDEX) regional climate model (RCM) for three climate scenarios: representative concentration pathway (RCP) 2.6, RCP 4.5 and RCP 8.5. A novel aspect of this study is the integration of 2D HEC-RAS rain-on-grid (RoG) hydrodynamic modeling with climate change projection analysis, which has not been previously applied in this watershed. Flood risk maps are generated for each scenario at three return periods: 50, 100, and 200 years. The results indicate an increasing flood volume and depth under future climate scenarios. The flood risk mapping shows an expansion of medium- and high-risk zones compared to current conditions. Under the current climate, the low-risk areas (0–0.5 m) slightly decrease from 13.9 km2 (50 years) to 13.8 km2 (200 years), while the medium- (0.5–2 m) and high-risk areas (>2 m) increase from 6.5 km2 to 7.0 km2 and from 7.2 km2 to 9.8 km2, respectively. Under RCP 2.6, the low-risk zones decline from 13.6 km2 to 13.0 km2, the medium-risk zones grow from 14.5 km2 to 16.2 km2, and the high-risk zones rise from 4.3 km2 to 6.5 km2. The higher emissions scenarios show greater risk increases, with the high-risk areas expanding from 5.3 km2 to 12.0 km2 under RCP 4.5, and from 9.5 km2 to 16.6 km2 under RCP 8.5. These findings underscore the escalating flood risks due to climate change and highlight the need for mitigation in the Wadi Ibrahim watershed.

This study assesses the functioning of the karst aquifer system located on the Croatian coast of the Adriatic Sea, where saltwater intrusion often presents a major problem for freshwater supply. We use two years of sensor data collected from two coastal springs to conduct a range of time-invariant and time-variant statistical analyses over various timescales. We perform separate analyses of the within-day and longer-term variation in the data as well as the interactions between the spring levels, salinity, rainfall, and sea levels. Such comprehensive analyses provide a greater understanding into the inner functioning of the intricate, heavily karstified aquifers. Time-invariant time-series analyses of the hourly data indicate that the spring levels and salinity are strongly controlled by sea levels. Furthermore, time-variant wavelet analyses demonstrate that the variation in spring levels in both springs has two modes defined by flow regime. Increases in the delay of the spring response to sea level indicate that aquifer diffusivity decreases in low flow conditions. Analyses facilitated the development of a conceptual model of the karst subsurface in the discharge zone. Using daily data, we constructed a linear mixed model of the spring levels. This model identified long-term sea level changes, rainfall from previous weeks, and seasonal recharge patterns as the primary factors influencing longer-term spring dynamics.

The US state of Iowa has experienced profound historical changes in its streamflow and baseflow. While several studies have noted increasing baseflow and baseflow index (BFI) values throughout the 20th century, analyses quantifying BFI trends in recent years or exploring spatial differences in watersheds marked by varying land use and geologic properties have not been conducted. This study calculated annual values for BFI (and several other hydrologic metrics) using flow records from 42 Iowa stream gauges containing at least 50 years of uninterrupted measurements. While BFI overwhelmingly rose throughout the mid-1900s, circa 1990 it began to level off. In some areas of Iowa (e.g., the southwest), BFI has continued to rise over the past 30 years—albeit at a slower rate; in other regions, it has become stationary or declined. One site failed to follow this trend (Walnut Cr), the only basin to experience large-scale urbanization. Furthermore, BFI demonstrated a strong negative correlation to streamflow flashiness, indicating that rising baseflow has also made Iowa streams less dynamic. BFI was largely independent of overall streamflow. These results may suggest the increased influence of conservation practices and the diminishing impacts of tile drainage on the delivery of water to Iowa’s rivers.

This study fosters tropical hydroclimatology research by implementing a computational modeling framework based on artificial neural networks and machine learning techniques. We evaluated two models, Multilayer Perceptron (MLP) and Support Vector Machine (SVM), in their ability to simulate 20-year monthly time series (2001-2021) of minimum and maximum river stage in the Itacaiúnas River Basin (BHRI), located in the eastern Brazilian Amazon. The models were configured using explanatory variables spanning meteorological, climatological, and environmental dimensions, ensuring representation of key local and regional hydrological drivers. Both models exhibited robust performance in capturing fluviometric variability, with a comprehensive multimetric statistical evaluation indicating MLP's superior accuracy over SVM. Notably, the MLP model reproduced the maximum river level during a sequence of extreme hydrological events linked to natural disasters (floods) across BHRI municipalities. These findings underscore the computational model's potential for refining hydrometeorological products, thus supporting water resource management and decision-making processes in the Amazon region.

The relationship between global warming and heatwaves contributes to environmental risks. We investigate lake heatwaves (LHWs) in the Eastern Mediterranean, where dust intrusions are frequently observed. The dust intrusions are characterized by the arrival of warm air masses containing dust pollution from the desert. In saline lakes, LHWs caused by dust intrusions have not been investigated in previous studies. In our study we focus on this point. It was found for the first time that, in the hypersaline Dead Sea, a severe dust intrusion (aerosol optical depth of over 3) caused the formation of LHWs, as appeared in September 2015. At the water surface, the LHWs were represented by abnormally high daily maximal and minimal surface water temperature (SWT) in comparison with their seasonally varied 90th percentile thresholds for 10 consecutive days (7–17 September). The surface LHWs’ intensity was up to 3 °C. Satellite (MODIS-Terra and METEOSAT) SWT did not detect the LHWs. Surface LHWs were accompanied by subsurface LHWs down to a depth of 20 m. The subsurface LHWs lasted longer (16 days) than the surface LHWs (10 days). There was a 4-day delay between the first date of the surface LHWs (7 September) and the start date of the subsurface LHWs (11 September). The maximal intensity of the subsurface LHWs decreased with depth from 1m (0.6 °C) down to 5 m (0.3 °C), followed by an increase (up to 0.6 °C) at the deeper layers (from 10 m to 20 m). Taking into account that, over the Eastern Mediterranean, desert dust has increased during the past several decades, one can expect frequent occurrence of dust-related intense persistent heatwaves in the Dead Sea in the coming years. This will contribute to additional water heating and further drying up of the Dead Sea.

The interlinked issues of climate change and increasing water demand are creating high pressure on water resources. In Slovakia, groundwater is a principal resource for human consumption. Consequently, an analysis was conducted of the current water yields of three springs, river discharges and precipitation trends, from several points of view. As a case study, we selected the area around Slatina nad Bebravou (Slovakia), which has the most relevant database. Descriptive statistics, the Mann-Kendall test and Sen's slope were used for the trend analysis. The findings indicate that the current capacity of springs is sufficient to meet the present needs of water users. However, the downward trend in abundance, the increasing trend in water withdrawal, and the current poor state of infrastructure point to an early tipping point. Data analysis revealed a problem with the yield of springs, especially in the summer and autumn months.

The internal nutrient load of natural and artificial lakes is a worldwide problem. To minimize its potential risks, the dredging of the highly eutrophic shallow first reservoir of Kis-Balaton (Lake Hídvégi) is planned in the near future. Our study aimed to evaluate the potential effects of dredging and desiccation on water and sediment quality. Experimental dredging was carried out in the northernmost part of Lake Hídvégi (2023). The physical and chemical characteristics of the sediment and nutrient loss during desiccation were examined in a column experiment. The relationships between the properties of leachate and sediment were identified using principal component analysis (SPSS). Spatial variations in sediment particle size distribution, nutrient content, and other chemical parameters (e.g., organic matter) suggest that deeper core sampling than the depth of preliminary dredging is necessary for a more comprehensive assessment of potential impacts. We found that spatiotemporally varying the dominance of chemical and biological processes affects the amount of and changes in phosphorus fractions under lake-/sediment-specific conditions. The readily available calcium- and iron-bound phosphorus, texture, and organic matter content of the sediment play an important role in phosphorus fixation/release. Based on our results, dredging and desiccation are feasible within the intended operating parameters. The sediment’s composition does not preclude potential agricultural disposal.

This research addresses the challenge of bias in Remotely Sensed Rainfall (RSR) datasets used for hydrological planning in Uganda’s data-scarce, ungauged catchments. Four bias correction methods, Quantile Mapping (QM), Linear Transformation (LT), Delta Multiplicative (DM), and Polynomial Regression (PR), were evaluated using daily rainfall data from four gauged stations (Gulu, Soroti, Jinja, Mbarara). QM consistently outperformed other methods based on statistical metrics, e.g., for National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA_CPC) RSR data at Gulu, Root-Mean-Square Error (RMSE) was reduced from 29.20 mm to 19.00 mm, Mean Absolute Error (MAE) reduced from 22.44 mm to 12.84 mm, and Percent Bias (PBIAS) reduced from −19.23% to 1.05%, and improved performance goodness-of-fit tests (KS = 0.03, p = 1.00), while PR, though statistically strong, failed due to overfitting. A bias correction framework was developed for ungauged catchments, using predetermined bias factors derived from observed station data. Validation at Arua (tropical savannah) and Fort Portal (tropical monsoon) demonstrated significant improvements in RSR data when the bias correction framework was applied. At Arua, bias correction of Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data reduced RMSE from 49.14 mm to 21.41 mm, MAE from 45.74 mm to 17.38 mm, and PBIAS from −59.83% to −8.18%, while at Fort Portal, bias correction of the CHIRPS dataset reduced RMSE from 28.35 mm to 15.02 mm, MAE from 25.28 mm to 11.35 mm, and PBIAS from −46.2% to 4.74%. Our research concludes that QM is the most effective method, and that the framework is a tool for improving RSR data in ungauged catchments. Recommendations for future work includes machine learning integration and broader regional validation.

Climate change has intensified extreme rainfall events, increasing the risk of overflow in agricultural reservoirs originally designed without flood control. This study presents a practical approach for assessing the overflow risk by incorporating both hydrological and structural factors. The key evaluation indicators used for analysis were freeboard (height from the flood level to the embankment level), height from the full water level to the embankment level, inflow–outflow relationship, and flood management capacity relative to the watershed area. Based on the design standards, reservoirs in Jeollanam-do were selected for assessment, and those with a high overflow risk were identified. Rainfall runoff simulations were conducted to evaluate the reservoirs, and the results indicated that considering all four indicators allowed an effective assessment of overflow risk. Multiple regression analysis yielded an R2 value of 0.79, suggesting that the relationships among the selected indicators were suitable and had high explanatory power. The findings of this study are expected to provide a practical method for rapidly assessing reservoir overflow risks and developing effective flood response strategies.

This study presents a comprehensive analysis of hydrological alterations and environmental flow components in the Beht River basin in northwest Morocco, using a coupled approach involving the Soil and Water Assessment Tool (SWAT) for hydrological modeling, the Indicators of Hydrologic Alteration (IHA) for flow regime assessment, and the Standardized Precipitation Index (SPI) for drought characterization. The SWAT model, run on a daily time step, showed satisfactory performance in terms of statistical criteria for both calibration and validation periods, despite encountering limitations, and proved its ability to simulate and reproduce the hydrological behavior of the basin. Using the IHA, we investigated changes in the hydrological regime over two distinct periods, revealing significant hydrological alteration. The SPI analysis supported these findings by highlighting the variable impacts of dry and wet periods on the hydrological regime, thus validating the observed changes in river flow indicators. As a preliminary step toward establishing environmental flows in the Beht River, this study provides foundational insights into the temporal evolution of its hydrology. These findings offer a valuable basis for better water resource management and conservation in the region.

Flood risk management involves managing the consequences of flooding as well at its hazard characteristics. This means attending to the human effects of floods and flood risk reduction. These effects are many and varied and are often poorly understood by those who make the relevant decisions. This short piece addresses this issue and calls for a greater awareness of what measures those at risk are motivated likely to undertake to reduce the risks that they personally face.

This study presents a multi-temporal analysis of vegetation cover changes in the Guayepo stream watershed (Cartagena de Indias, Colombia) for 2000, 2010, and 2020 and their impact on surface runoff generation. Hydrological data from 1974 to 2019 were processed to model intensity–duration–frequency (IDF) curves and simulate heavy rainfall events using six storms of nine-hour duration. Following the Soil Conservation Service guidelines, these were used to estimate runoff flows for return periods of 25, 50, and 100 years via the curve number method in HEC-HMS. Vegetation cover was assessed using the CORINE land cover methodology applied to official land use maps. The analysis revealed a significant loss of natural vegetation: dense forest cover declined dramatically from 14.38% in 2000 to 0% in 2020, and clean pastures were reduced by 46%. In contrast, weedy pastures and pasture mosaics with natural areas increased by 299% and 136%, respectively, reflecting a shift towards more degraded land cover types. As a result of these changes, total runoff flows of the model increased by 9.7% and 4.3% under antecedent moisture conditions I and II, respectively, for the 100-year return period. These findings reveal ongoing degradation of the watershed’s natural cover, linked to expanding agricultural uses and changes in vegetation structure. The decline in forested areas has increased surface runoff, elevating flood risk and compromising the watershed’s hydrological regulation. The study suggests that integrated land management and ecological restoration strategies could be key in preserving hydrological ecosystem services and reducing the negative impacts of land use change.

Terrestrial water storage (TWS) in the Qaidam Basin in western China is highly sensitive to climate change. The GRACE mascon products provide variations of TWS anomalies (TWSAs), greatly facilitating the exploration of water storage dynamics. However, the main meteorological factors affecting the TWSA dynamics in this region need to be comprehensively investigated. In this study, variations in TWSAs over the Qaidam Basin from 2002 to 2024 were analyzed using three GRACE mascon products with CSR, JPL, and GSFC. The groundwater storage anomalies (GWAs) were extracted through GRACE and GLDAS products. The impact of meteorological elements on TWSAs and GWAs was identified. The results showed that the GRACE mascon products showed a significant increasing trend with a rate of 0.51 ± 0.13 mm per month in TWSAs across the entire basin from 2003 to 2016. The groundwater part accounted for the largest proportion and was the main contributor to the increase in TWS for the entire basin. In addition to the dominant role of precipitation, other meteorological elements, particularly air humidity and solar radiation, were also identified as important contributors to TWSA and GWA variations. This study highlighted the climatic effect on water storage variations, which have important implications for local water resource management and ecological conservation under ongoing climate change.

Predictive uncertainty analysis focuses on defensible variability in model projected values after estimation of the posterior parameter distribution. Inverse-style parameter estimation selects posterior parameters through history matching where parameters are varied and resulting model simulation values are compared to observations, and parameters are selected balancing goodness-of-fit between simulated and observed values and expert knowledge. When inverse-style parameter estimation approaches are used, parameter sensitivity, which is the change in simulated outputs relative to the change in parameter values, is an important consideration. Variation in null space parameters has a limited impact on history matching skill; however, these parameters become important when they impact predictions. A new null space sensitivity analysis for ensemble methods of data assimilation (DA) using observation error models is developed and implemented for an integrated hydrological model. Empirical parameter sensitivity is estimated by comparing the spreads of prior and posterior parameter distributions. Sensitivity analysis is generated by an ensemble of models with insensitive parameters varying across the prior parameter distribution and sensitive parameters fixed to best-fit model values. The result is identification of insensitive aquifer storage parameters that change storage-related model predictions by as much as two times. This null space analysis describes uncertainty from data insufficiency. Ensemble methods using observation error models also describe predictive uncertainty from noisy measurements and imperfect models.

Accurate and prompt flood forecasting is essential for effective decision making in flood control to help minimize or prevent flood damage. We propose a new custom deep learning model, IF-CNN-GRU, for multi-step-ahead flood forecasting that incorporates the flood index (IF) to improve the prediction accuracy. The model integrates convolutional neural networks (CNNs) and gated recurrent neural networks (GRUs) to analyze the spatiotemporal characteristics of hydrological data, while using a custom recursive neural network that adjusts the neural unit output at each moment based on the flood index. The IF-CNN-GRU model was applied to forecast floods with a lead time of 1–5 d at the Baihe hydrological station in the middle reaches of the Han River, China, accompanied by an in-depth investigation of model uncertainty. The results showed that incorporating the flood index IF improved the forecast precision by up to 20%. The analysis of uncertainty revealed that the contributions of modeling factors, such as the datasets, model structures, and their interactions, varied across the forecast periods. The interaction factors contributed 17–36% of the uncertainty, while the contribution of the datasets increased with the forecast period (32–53%) and that of the model structure decreased (32–28%). The experiment also demonstrated that data samples play a critical role in improving the flood forecasting accuracy, offering actionable insights to reduce the predictive uncertainty and providing a scientific basis for flood early warning systems and water resource management.

Global water demand due to population growth and agricultural development has led to widespread overexploitation of groundwater, particularly in semi-arid regions. The traditional hydrochemistry monitoring system still suffers from limited laboratory accessibility and high costs. This study aims to predict the major ions of groundwater, including Ca2+, Mg2+, Na+, SO42−, Cl−, K+, HCO3−, and NO3−, utilizing two fieldmeasurable parameters (i.e., total dissolved solids (TDS) and mineralization (MIN)) in the Aflou syncline region, Algeria. A multilayer perceptron (MLP) model optimized with Levenberg–Marquardt backpropagation (LMBP) provided the greatest predictive accuracy for the different ions of SO42−, Mg2+, Na+, Ca2+, and Cl− with R2 = (0.842, 0.980, 0.759, 0.945, 0.895), RMSE = (53.660, 12.840, 14.960, 36.460, 30.530) (mg/L), and NSE = (0.840, 0.978, 0.754, 0.941, 0.892) in the testing phase, respectively. However, the predictive accuracy for the remaining ions of K+, HCO3−, and NO3− was supplied as R2 = (0.045, 0.366, 0.004), RMSE = (6.480, 41.720, 40.460) (mg/L), and NSE = (0.003, 0.361, −0.933), respectively. The performance of our model (LMBP-MLP) was validated in adjacent and similar geological locations, including Aflou, Madna, and Ain Madhi. In addition, LMBP-MLP showed very promising results, with performance similar to that in the original research region.

The Vietnamese Mekong Delta (VMD) faces increasing challenges due to upstream hydrological fluctuations and climate change, necessitating optimized water management strategies. Sluice gates play a critical role in regulating water levels, yet their effectiveness under different operational modes remains insufficiently assessed. This study examines water level fluctuations under three sluice gate operation scenarios implemented along the West Sea dike in the Long Xuyen Quadrangle, Kien Giang Province, using the MIKE 11 hydrodynamic model. The model was calibrated and validated using the observed data, yielding high accuracy at key sluice gates, including Kien River and Ba Hon. Three sluice gate management scenarios were tested: (1) the current automatic and partially forced operation, (2) fully automatic gate control, and (3) fully forced hydraulic operation. The simulation results indicate that Scenario 3 maintained water levels above +0.6 m more frequently, ensuring better water availability for irrigation and domestic use, while Scenarios 1 and 2 resulted in lower water levels at certain locations. Additionally, forced operation led to higher gate opening and closing frequencies at key sluices, allowing for more adaptive control over water levels. These findings emphasize the benefits of proactive sluice gate management in improving water regulation and mitigating the water scarcity risks. This study is among the first to provide empirical, scenario-based evidence comparing fully forced, automatic, and mixed sluice gate strategies under varying hydrological conditions in the Long Xuyen Quadrangle.

Tributaries to meandering rivers rarely join the river on the interior of bends. The limited drainage area on bend interiors explains why tributaries seldom form there, but not why existing tributaries are redirected as meanders develop. Other relevant factors include flow dynamics at junctions, runoff partitioning on inner vs. outer bends, and tributary deflection as the main channel migrates laterally. This study investigated whether the lack of confluences on bend interiors applies to lower coastal plain rivers in South and North Carolina, USA, where the factors above are not necessarily active, and if so how tributaries at sites of developing meanders are redirected. Of the 121 confluences examined using GIS data supplemented with field observations, none occurred on meander bend interiors. A total of 17 cases of potentially deflected tributaries were identified. Of these, 11 had sufficient evidence for a confident interpretation of how redirection occurred. In all 11 cases, pre-bend river paleochannels were involved in redirecting the tributaries away from the bend interior. This is explained by a model showing that the local slope gradient and mean depth advantages of the paleochannels provide velocity, stream power, and shear stress advantages over extension of the tributary channel into the bend interior. The results illustrate the importance of local hydraulic selection, and the influence of antecedent morphology on river hydrology and geomorphology.

Accurately determining the hydraulic properties of soilless growing media is essential for optimizing water management in container-based horticulture and agriculture. The very rapid estimation of hydraulic properties using a Mini Disk Infiltrometer has great potential for practical use compared to the very time-consuming standard methods. The objectives of this study were (1) to calibrate simulated cumulative stepwise infiltration under different suctions with the measured data from Mini Disk Infiltrometer, (2) to evaluate the efficiency of the Hydrus-2D inverse model to predict water dynamics through substrates, (3) to compare the substrate hydraulic parameters obtained through the numerical inversion model to those obtained via laboratory methods, and (4) to provide recommendations on how to effectively use the MDI-based method for practical applications. This study employs numerical inversion of Mini Disk Infiltrometer (MDI) data to estimate the hydraulic parameters of three different growing media, namely white peat, thermally treated wood fibre (WF4), and Seedling substrate. Infiltration experiments were conducted under suction-controlled conditions using varying initial moisture contents, followed by numerical simulations using the Hydrus-2D model and the Van Genuchten equation to describe the hydraulic parameters. The results demonstrated strong agreement between observed and simulated infiltration data, particularly under moistened conditions, with high R2 > 0.9 values indicating the model’s effectiveness. However, discrepancies were observed for substrates in their initial dry state, suggesting limitations in capturing early-stage infiltration dynamics. The findings highlighted the potential of numerical inversion methods for estimating substrate hydraulic properties but also revealed the need for methodological refinements. Modifying the Van Genuchten model or exploring alternative approaches such as the Brooks and Corey model may enhance accuracy. Extending the suction range of measurement techniques is also recommended to improve parameter estimation. This study provides important evidence that the inverse method based on MDI is an effective tool for rapidly determining the hydraulic functions of substrates, which are important in promoting sustainable horticultural practices. Future research should focus on refining parameter estimation methods and addressing model limitations to enhance the reliability of hydraulic property assessments in soilless growing media.

Water resources are considered to be of paramount importance to the natural world on a global scale, being critical for the sustenance of ecosystems, the support of life, and the achievement of sustainable development. However, these resources are under threat from climate change, population growth, urbanization and pollution. This necessitates the development of robust and effective assessment methods to ensure their sustainable use. Although assessing the ecological footprint (EF) of urban water systems plays a critical role in advancing sustainable cities and managing water assets, existing research has largely overlooked the application of geospatial visualization techniques in evaluating resource allocation strategies within karst mountain watersheds, an oversight this study aims to correct through innovative methodological integration. This research establishes an evaluation framework for predicting water resource availability in Guizhou through the synergistic application of three methodologies: (1) the water-based ecological accounting framework (WEF), (2) ecosystem service thresholds defined by the water ecological carrying capacity of water resources (WECC) thresholds, and (3) composite sustainability metrics, all correlated with contemporary hydrological utilization profiles. Spatiotemporal patterns were quantified across the province’s nine administrative divisions during the 2013–2022 period through time-series analysis, with subsequent WEF projections for 2023–2027 generated via Long Short-Term Memory (LSTM) temporal forecasting techniques.

Ensuring access to clean water for drinking, agriculture, and recreational activities remains a global challenge. Groundwater, supplying approximately 50% of domestic water and 40% of agricultural irrigation, faces increasing threats from climate change, population growth, and unsustainable agricultural practices. These factors contribute to groundwater contamination, notably nitrate pollution resulting from excessive fertilizer use, which poses risks to water quality and public health. Addressing this issue demands innovative, efficient, and sustainable remediation technologies. Permeable reactive barriers (PRBs) have emerged as promising solutions for in situ groundwater treatment, using reactive media to transform contaminants into less toxic forms. PRBs offer advantages like low energy consumption and minimal maintenance. This study uses bibliometric analysis to explore the scientific production of PRBs for nitrate remediation, revealing research trends, key focus areas, and significant contributions. It included 141 articles published from 1975 to 2023. Early research focused on basic mechanisms and materials like zero-valent iron (ZVI), while recent studies emphasize sustainability and cost-effectiveness using low-cost materials such as agricultural byproducts. The findings highlight a growing focus on the circular economy and the need for more in situ studies to assess PRB performance under varying conditions. PRBs show significant potential for enhancing groundwater management and long-term water quality in agricultural contexts.

In October 2003, an artificial canal was dug across the Langue de Barbarie sand spit at the mouth of the Senegal River to prevent the city of Saint-Louis (Senegal) from being submerged by floods. This study aimed to explore the multiple facets of this sudden environmental change to provide a holistic overview of the situation and a better understanding of man-made alterations of coastal features, a crucial step for implementing efficient management of such situations and developing appropriate mitigation and adaptation policies. Satellite imagery from the US Geological Survey was used to show the historical evolution of the breach, and a comprehensive overview of the existing literature was conducted to explore its hydrological, geomorphological, ecological, and socioeconomic impacts. Although the canal facilitated the rapid evacuation of floodwaters and saved the city from a major flooding event, the breach widened considerably, becoming the new river mouth and resulted in unforeseen adverse consequences. Environmental consequences included the partial dismantling of the spit, increased tidal range, salinization of land and water, and loss of habitat and local biodiversity. Socioeconomic consequences were severe, including the loss of agricultural land and reduced yields, declining fishing productivity, the destruction of villages, the displacement of entire communities, and the forced migration of many young people. Affected communities developed resilience strategies, with women playing a leading role in these adaptive responses. This study highlights the need for integrated coastal management and policies that consider both environmental and human factors, as well as for future research that will help improve the management of coastal ecosystem alterations.

Classifying dams is important to ensure proper management, safety, and maintenance based on their size, purpose, and risk level. This helps in planning for emergency responses, structural inspections, and efficient water resource utilization. This study used the analysis of variance (ANOVA) technique to categorize the main Tunisian dams according to their precipitation to potential evapotranspiration (P/PET) ratio. The data were obtained from the NASA POWER platform, with potential evapotranspiration estimated using the Oudin model. Despite the violation of the normality assumption, the robustness of the ANOVA test for classification purposes remained unaffected. A comparison between Duncan’s test (parametric) and the Kruskal–Wallis test (non-parametric) revealed similar class structures, although Duncan’s test provided greater precision. The analysis identified four primary dam classes, reflecting regional differences in water availability and evaporative demand, and included dams in north-west Tunisia, considered the ‘water tower’ of the country, and those in semi-arid and arid regions.

This study examines runoff variations and their drivers in the Buha and Shaliu Rivers of the Qinghai Lake Basin (1960–2016), a key ecological area in China. Abrupt changes were detected using the Mann–Kendall and cumulative anomaly methods, while the Budyko framework attributed runoff variations to dominant factors. Correlation and grey relational analyses assessed multicollinearity, and a lake water balance model with climate elasticity theory quantified the effects of climate and land surface changes on runoff components and lake levels. Results indicate that the Buha River experienced an abrupt runoff change in 2004, while the Shaliu River exhibited a change beginning in 2003. Based on the trends and abrupt change points of each factor, the study period was divided into four segments: 1960–1993, 1994–2016, 1960–2003, and 2004–2016. The correlation coefficients are significantly different in different periods. The climate elasticity coefficients were as follows: P (precipitation), 1.98; ET0 (potential evapotranspiration), −0.98; Rn (net radiation), 0.66; T (average temperature), 0.02; U2 (wind speed at 2 m height), 0.16; RHU (relative umidity), −0.56. The elasticity coefficient of runoff with respect to precipitation is significantly higher than that for other climate variables. Net radiation and relative humidity contribute equally to runoff, while wind speed and temperature have relatively smaller effects. In the Qinghai Lake Basin, runoff is sensitive to precipitation (0.38), potential evapotranspiration (−0.07), and the underlying surface parameter ω (−98.32). Specifically, a 1 mm increase in precipitation raises runoff by 0.38 mm, while a 1 mm rise in potential evapotranspiration reduces it by 0.07 mm. A one-unit increase in ω leads to a significant runoff decrease of 98.32 mm. According to the lake water balance model, climate contributes 88.43% to groundwater runoff, while land surface changes contribute −11.57%. Climate change and land surface changes contribute 93.02% and 6.98%, respectively, to lake water levels. This study quantitatively evaluates the impacts of climate and land surface changes on runoff, providing insights for sustainable hydrological and ecological management in the Qinghai Lake Basin.