Search Results (2,248)

Contamination with potentially toxic elements (PTEs) frequently occurs in surface water in coal mining areas. This study analyzed 34 surface water samples collected from the Yunnan–Guizhou Plateau for their hydrochemical characteristics, spatial distribution, source apportionment, and human health risks. Our statistical analysis showed that the average concentrations of PTEs in the surface water ranked as follows: Fe > Al > Zn > Mn > Ba > B> Ni > Li > Cd > Mo > Cu > Co > Hg > Se > As > Pb > Sb. The spatial analysis revealed that samples with high concentrations of Fe, Al, and Mn were predominantly distributed in the main stream, Xichong River, and Yangchang River. Positive matrix factorization (PMF) identified four sources of PTEs in the surface water. Hg, As, and Se originated from wastewater discharged by coal preparation plants and coal mines. Mo, Li, and B originated from the dissolution of clay minerals in coal seams. Elevated concentrations of Cu, Fe, Al, Mn, Co, and Ni were attributed to the dissolution of kaolinite, illite, chalcopyrite, pyrite, and minerals associated with Co and Ni in coal seams. Cd, Zn, and Pb were derived from coal melting and traffic release. The deterministic health risks assessment showed that 94.12% of the surface water samples presented non-carcinogenic risks below the health limit of 1. Meanwhile, 73.56% of the surface water samples with elevated As posed level III carcinogenic risk to the local populations. Special attention to drinking water safety for children is warranted due to their lower metabolic capacity for detoxifying PTEs. This study provides insight for PTE management in sustainable water environments. Full article

Estrogens in river systems can significantly impact aquatic ecosystems. This study aimed to investigate the multiphase partitioning of estrogens in Wulo Creek, Taiwan, which receives animal feedlot wastewater, to understand their distribution and potential environmental implications. Water samples were separated into suspended particulate matter (SPM), colloidal, and soluble phases using centrifugation and cross-flow ultrafiltration. Concentrations of estrone (E1), 17β-estradiol (E2), and estriol (E3) in each phase were analyzed using LC/MS/MS. Partition coefficients were calculated to assess estrogen distribution among phases. Estrogens were predominantly found in the soluble phase (85.8–87.3%). The risk assessment of estrogen equivalent (EEQ) values suggests that estrogen concentration in water poses a higher risk compared to SPM, with a majority of the samples indicating a high risk to aquatic organisms. The colloidal phase contained 12.7–14.2% of estrogens. The log K_COC values (4.72–4.77 L/kg-C) were significantly higher than the log K_OC and log K_POC values (2.02–3.40 L/kg-C) for all estrogens. Colloids play a critical role in estrogen distribution in river systems, potentially influencing their fate, transport, and biotoxicity. This finding highlights the importance of considering colloidal interactions in assessing estrogen behavior in aquatic environments. Full article

Here, we investigate the use of rolling-windowed L-moments (RWLMs) and L-moment ratio diagrams (LMRDs) combined with a Multiple Linear Regression (MLR) machine learning algorithm to model non-stationary low-flow hydrological extremes with the potential to simultaneously understand time-variant shape, scale, location, and probability distribution (PD) shifts under climate change. By employing LMRDs, we analyse changes in PDs and their parameters over time, identifying key environmental predictors such as lagged precipitation for September 5-day low-flows. Our findings indicate a significant relationship between total August precipitation L-moment ratios (LMRs) and September 5-day low-flow LMRs (${\tau }_2$-Precipitation and ${\tau }_2$-Discharge: R² = 0.675, p-values < 0.001; ${\tau }_3$-Precipitation and ${\tau }_3$-Discharge: R² = 0.925, p-value for slope < 0.001, intercept not significant with p = 0.451, assuming $\alpha$ = 0.05 and a 31-year RWLM), which we later refine and use for prediction within our MLR algorithm. The methodology, applied to the Goat River near Creston, British Columbia, aids in understanding the implications of climate change on water resources, particularly for the yaqan nuʔkiy First Nation. We find that future low-flows under climate change will be outside the Natural Range of Variability (NROV) simulated from historical records (assuming a constant PD). This study provides insights that may help in adaptive water management strategies necessary to help preserve Indigenous cultural rights and practices and to help sustain fish and fish habitat into the future. Full article

The Mississippi River Delta Front (MRDF) is a subaqueous apron of rapidly deposited and weakly consolidated sediment extending from the subaerial portions of the Birdsfoot Delta of the Mississippi River, long characterized by mass-wasting sediment transport. Four (4) depositional environments dominate regionally (an undisturbed topset apron, mudflow gully, mudflow lobe, and prodelta), centering around mudflow distribution initiated by a variety of factors (hurricanes, storms, and fluid pressure). To better understand the spatiotemporal scales of the events as well as the controlling processes, eight cores (5.8–8.0 m long) taken offshore from the South Pass (SP) and the Southwest Pass (SWP) were analyzed for gamma density, grain size, sediment fabric (X-radiography), and geochronology (²¹⁰Pb/¹³⁷Cs radionuclides). Previous work has focused on the deposition of individual passes and has been restricted to <3 m core penetration, limiting its geochronologic completeness. Building on other recent studies, within the mudflow gully and lobe cores, the homogeneous stepped profiles of ²¹⁰Pb activities and the corresponding decreased gamma density indicate the presence of gravity-driven mass failures. ²¹⁰Pb/¹³⁷Cs indicates that gully sedimentary sediment accumulation since 1953 is greater than 580 cm (sediment accumulation rate [SAR] of 12.8 cm/y) in the southwest pass site, and a lower SAR of the South Pass gully sites (2.6 cm/y). This study shows that (1) recent dated mudflow deposits are identifiable in both the SWP and SP; (2) SWP mudflows have return periods of 10.7 y, six times more frequent than at the SP (66.7 y); (3) ²¹⁰Pb inventories display higher levels in the SWP area, with the highest focusing factors in proximal/gully sedimentation, and (4) submarine landslides in both study areas remain important for sediment transport despite the differences in sediment delivery and discharge source proximity. Full article

Evaluating flood risk though numerical simulations in areas where hydrometric and bathymetric data are scarcely available is a challenge. This is, however, of paramount importance, particularly in urban areas, where huge losses of human life and extensive damage can occur. This paper focuses on the 2–3 June 2023 event at Léogâne in Haiti, where the Rouyonne River partly flooded the city. Water depths in the river have been recorded since April 2022, and a few discharges were measured manually, but these were not sufficient to produce a reliable rating curve. Using a uniform-flow assumption combined with the Bayesian rating curve (BaRatin) method, it was possible to extrapolate the existing data to higher discharges. From there, a rainfall–runoff relation was developed for the site using a distributed hydrological model, which allowed the discharge of the June 2023 event to be determined, which was estimated as twice the maximum conveying capacity of the river in the measurement section. Bathymetric data were obtained using drone-based photogrammetry, and two-dimensional simulations were carried out to represent the flooded area and the associated water depths. By comparing the water depths of 21 measured high-water marks with the simulation results, we obtained a Kling–Gupta Efficiency (KGE) and Nash–Sutcliffe Efficiency (NSE) values of 0.890 and 0.882, respectively. This allows us to conclude that even when only scarce official data are available, it is possible to use field data acquired by low-cost methodologies to build a model that is sufficiently accurate and that can be used by flood managers and decision makers to assess flood risk and vulnerability in Haiti. Full article

This study focuses on a complex Brazilian Neoproterozoic karst (hydro)geological and geomorphological area, consisting of metapelitic–carbonate sedimentary rocks of ~740–590 Ma, forming the largest carbonate sequence in the country. At the center of the area lies the Lagoa Santa Karst Environmental Protection Area (LSKEPA), located near the Minas Gerais’ state capital, Belo Horizonte, and presents a series of lakes associated with the large fluvial system of the Velhas river under the influence, locally, of carbonate rocks. The hydrodynamics of carbonate lakes remain enigmatic, and various factors can influence the behavior of these water bodies. This work analyzed the hydrological behavior of 129 lakes within the LSKEPA to understand potential connections with the main karst aquifer, karst-fissure aquifer, and porous aquifer, as well as their evolution patterns in the physical environment. Pluviometric surveys and satellite image analysis were conducted from 1984 to 2020 to observe how the lakes’ shorelines behaved in response to meteorological variations. The temporal assessment for understanding landscape evolution proves to be an effective tool and provides important information about the interaction between groundwater and surface water. The 129 lakes were grouped into eight classes representing the hydrological connection patterns with the aquifers in the region, with classes defined for perennial lakes: (1) constantly connected, (2) seasonally disconnected, and (3) disconnected; for intermittent lakes: (4) disconnected during the analyzed time interval, (5) seasonally connected, (6) disconnected, (7) extremely disconnected, and (8) intermittent lakes that connected and stopped drying up. The patterns observed in the variation of lakes’ shorelines under the influence of different pluviometric moments showed a positive correlation, especially in dry periods, where these water bodies may be functioning as recharge or discharge zones of the karst aquifer. These inputs and outputs are conditioned to the well-developed karst tertiary porosity, where water flow in the epikarst moves according to the direction of enlarged karstified fractures, rock foliation planes, and lithological contacts. Other factors may condition the hydrological behavior of the lakes, such as rates of evapotranspiration, intensity of rainfall during rainy periods, and excessive exploitation of water. Full article

Floods in Germany have become increasingly frequent and severe over recent decades, with notable events in 2002, 2013, and 2021. This study examines the trends and drivers of flood occurrences in Germany from 1990 to 2024, focusing on the influence of climate-change-related variables, such as temperature, precipitation, and river discharge. Using a comprehensive time series analysis, including Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) models and correlation and regression analyses, we identify significant correlations between these climatic variables and flood events. Our findings indicate that rising temperatures (with a mean of 8.46 °C and a maximum of 9 °C) and increased precipitation (averaging 862.26 mm annually)are strongly associated with higher river discharge (mean 214.6 m³/s) and more frequent floods (mean 197.94 events per year). The ANN model outperformed the ARIMA model in flood forecasting, showing lower error metrics (e.g., RMSE of 10.86 vs. 18.83). The analysis underscores the critical impact of climate change on flood risks, highlighting the necessity of adaptive flood-management strategies that incorporate the latest climatic and socio-economic data. This research contributes to the understanding of flood dynamics in Germany and provides valuable insights into future flood risks. Combining flood management with groundwater recharge could effectively lower flood risks and enhance water resources’ mitigation and management. Full article

Fish farming is increasingly important globally and nationally, playing a crucial role in fish production for human consumption. Monitoring microbiological and chemical contaminants from water discharge is essential to mitigate the risk of contaminating water and fish for human consumption. This study analyzes the physicochemical and E. coli parameters of water and tambacu fish muscles (Colossoma macropomum × Piaractus mesopotamicus) in Western Maranhão, Brazil. It also includes a qualitative characterization of zooplankton in the ponds. Samples were collected from tambacu ponds in a dam system fed by natural watercourses from the Tocantins River tributaries, located at the connection of the Brazilian savanna and Amazon biomes. The physicochemical and E. coli parameters of water did not meet national standards. The zooplankton community included Rotifera, Cladocera, Copepoda, and Protozoa representatives, with no prior studies on zooplankton in the region, making these findings unprecedented. The biological quality of freshwater is crucial in fish farming, as poor quality can lead to decreased productivity and fish mortality, raising significant food safety concerns. The water quality studied is related to the potential influence of untreated wastewater as a source of contamination, leaving the studied region still far from safe water reuse practices. The findings on chemical and E. coli contamination of fish farming waters concern human health and emphasize the need for appropriate regulations. Full article

River discharge is an essential input to hydrosystem projects. This paper aimed to modify the group method of data handling (GMDH) to create a new artificial intelligent forecast model (abbreviated as MGMDH) for predicting discharges at river cross-sections (CSs). The basic idea was to optimise the weights for selected hydrometric and meteorological predictors. One novelty of this study was that MGMDH could take the discharge observed from a neighbouring CS as a predictor when observations from the CS of interest had ceased. Another novelty was that MGMDH could include meteorological parameters as extra predictors. The model was validated using data from natural rivers. For given lead times, MGMDH automatically determined the best forecast equations, consistent with physical river hydraulics laws. This automation minimised computing time while improving accuracy. The model gave reliable forecasts, with a coefficient of determination greater than 0.978. For lead times close to the advection time from upstream to the CS of interest, the forecast had the highest reliability. MGMDH results compared well with some other machine learning models, like neural networks and the adaptive structure of the group method of data handling. It has potential applications for efficiently forecasting discharge and offers a tool to support flood management. Full article

Ensuring the protection of the aquatic environment and addressing the water scarcity and degradation of water quality in the Mediterranean region pose significant challenges. This study specifically aims to assess the impact of urban and industrial pollution on the ZAT River water quality. The study exploits a combination of field measurements and mathematical simulations using the PEGASE model. The objective is to evaluate how water quality changes throughout the different seasons and to determine whether olive oil factories discharge industrial wastewater into the river. The study reveals that the river water quality remains relatively stable along its course, up to km 64 in winter and km 71.77 in summer, where poor water quality is recorded. This degradation can be attributed to multiple factors. One of these factors is the discharge of industrial wastewater, which accounts for 47% of the COD pollution load. This industrial wastewater is released into the river without treatment during the production period (January–February) and inactivity period (March–May). The combined impact of urban and industrial wastewater is also associated with the decrease in water flow resulting from water withdrawals due to irrigation canals and groundwater recharge, which both contribute to the observed changes in river water quality. Importantly, field measurements combined with results obtained from the calibrated model provide compelling evidence of unauthorized wastewater discharges from the olive oil factories into the river. These results emphasize the need for stricter regulation, such as developing water quality monitoring strategies based on the use of modeling methodologies. They also emphasize the importance of improving wastewater management practices, such as setting up treatment plants for different sources of pollution or developing a co-treatment plant to mitigate the adverse impact of industrial pollution on river water quality. Full article

This study addresses the limitations of and the common challenges faced by one-dimensional river-routing methods in hydrological models, including the National Water Model (NWM), in accurately representing coastal regions. We developed a two-way coupling between the NWM and the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM). The approach demonstrated improvements in modeling coastal river dynamics, particularly during extreme events like Hurricane Matthew. The coupled model successfully captured tidal influences, storm surge effects, and complex river–river interactions that the standalone NWM missed. The approach revealed more accurate representations of peak discharge timing and magnitude as well as water storage and release in coastal floodplains. However, we also identified challenges in reconciling variable representations between hydrological and hydraulic models. This work not only enhances the understanding of coastal–riverine interactions but also provides valuable insights for the development of next-generation hydrological models. The improved modeling capabilities have implications for flood forecasting, coastal management, and climate change adaptation in vulnerable coastal areas. Full article

Due to the changing climate and human activity, more and more researchers started to focus on non-stationarity in hydrology. In the Wei River Basin, which is the largest tributary of the Yellow River, there is a significant reduction in the total amount of water resources which has been found in past decades. Additionally, the distribution of water resources within the basin is unbalanced, with the lower reaches and southern regions having relatively abundant water resources and other regions lacking these resources. Within this situation, it is important to consider the spatial aspect of water resource management. Four non-stationarity detection methods have been applied to investigate variation in seasonal discharge series. Two meteorological factors have also been analyzed. Based on test results and Köppen Geiger Climate classification, the water resource management has been investigated spatially. As for results, the Baojixia Channel has significant impact on the abrupt change of discharge, while the precipitation and temperature may have an impact on the discharge trend change. In addition, there was no clear evidence to prove that the climate zones impact spatially on the non-stationarity of discharge. Full article

The area of Sia Kouanza in the Sahel of southwestern Niger is a potential location for expanding agriculture through irrigation with groundwater. Agriculture is key to supporting smallholders and promoting food security. As plans proceed, questions include how much water is available, how is groundwater replenished, many hectares to develop, and where to locate the wells. While these questions can be addressed with a model, it is difficult to find detailed procedures, especially when data are scarce. How can we use existing information to develop a model of a natural system where groundwater development will take place? We describe an approach that can be employed in data-scarce areas where similar questions are being asked. The approach includes setting details; conceptual model development; water balance; numerical code MODFLOW; model construction, calibration, and statistics; and result interpretation. Conceptual model component estimates are derived from field data: recharge, evapotranspiration, wetlands discharge, existing extraction, and river stages. When field data are not available or scarce, we employ other sources and describe how they are validated with field data or analog sites. The calibrated steady-state model gives a water balance of 22 × 10⁶ m³/yr with inflows (recharge 22 × 10⁶ m³/yr) and outflows (extraction 7.2 × 10⁵ m³/yr, wetlands 5.7 × 10⁶ m³/yr, evapotranspiration 11.9 × 10⁶ m³/yr). The model is a point of departure; approaches for transient and predictive models, which can be used to simulate changes in irrigation pumping volumes and drought, for example, will be described subsequently. Full article

Improved navigability can enhance inland waterway transportation efficiency, contributing to synchro-modal logistics and promoting sustainable development in regions that can benefit from the presence of considerable waterways. Modern technological solutions, such as digital twins in corridor management systems, must integrate functions of navigability forecasts that provide timely and reliable information for safe trip planning. This information needs to account for the type of vessel and for the environmental and geomorphological characteristics of each navigation trait. This paper presents a case study, within the EU project CRISTAL, focusing on the Italian Po River, of which the navigability forecast requirements of a digital twin are illustrated. Preliminary results to deliver navigability risk information were obtained. In particular, the statistical correlation of water discharge and water depth, computed from historical data, suggested that efficient forecast models for navigability risk, given some water discharge forecasts, could be built. To this aim, the LSTM (long-short-term-memory) technique was used on the same data to provide models linking water discharge and water depth predictions. Future work involves further testing these models with updated real data and integrating outcomes with climatic and infrastructure management information to enhance the accuracy of the risk information. Full article

Most Andean lakes and lagoons are used as reservoirs to manage hydropower generation and cropland irrigation, which, in turn, alters river flow patterns through processes of storage and discharge. The Carhuacocha and Vichecocha lagoons, fed by glaciers, are important aquatic ecosystems regulated by dams. These dams increase the flow of the Mantaro River during the dry season, supporting both energy production and irrigation for croplands. Water quality in the Carhuacocha and Vichecocha lagoons was assessed between storage and discharge events by using the Canadian Council of Ministers of the Environment Water Quality Index (CCME _WQI) and multivariate statistical methods. The quality of both lagoons is excellent during the storage period; however, it decreases when they are discharged during the dry season. The most sensitive parameters are pH, dissolved oxygen (DO), and biochemical oxygen demand (BOD). This paper details the changes in water quality in the Carhuacocha and Vichecocha lagoons during storage and discharge events. Full article