Search Results (118)

Monitoring sediment concentration in water bodies is crucial for assessing water quality, ecosystems, and environmental health. However, physical sampling and sensor-based approaches are labor-intensive and unsuitable for large-scale, continuous monitoring. This study employs machine learning models to estimate suspended sediment concentration using images captured in natural light, named RGB, and near-infrared (NIR) conditions. A controlled dataset of approximately 1300 images with SSC values ranging from 1000 mg/L to 150,000 mg/L was developed, incorporating temperature, time of image capture, and solar irradiance as additional features. Random forest regression and gradient boosting regression were trained on mean RGB values, red reflectance, time of captured, and temperature for natural light images, achieving up to 72.96% accuracy within a 30% relative error. In contrast, NIR images leveraged gray-level co-occurrence matrix texture features and temperature, reaching 83.08% accuracy. Comparative analysis showed that ensemble models outperformed deep learning models like Convolutional Neural Networks and Multi-Layer Perceptrons, which struggled with high-dimensional feature extraction. These findings suggest that using machine learning models and RGB and NIR imagery offers a scalable, non-invasive, and cost-effective way of sediment monitoring in support of water quality assessment and environmental management. Full article

The remote sensing inversion of the Suspended Sediment Concentration (SSC) at the Yellow River estuary is crucial for regional sediment management and the advancement of monitoring techniques for highly turbid waters. Traditional in situ methods and low-resolution imagery are no longer sufficient for high-accuracy studies. Using SSC data from the Longmen Hydrological Station (2019–2020) and Sentinel-2 imagery, multiple models were compared, and the random forest regression model was selected for its superior performance. A non-parametric regression model was developed based on optimal band combinations to estimate the SSC in high-sediment rivers. Results show that the model achieved a high coefficient of determination (R² = 0.94) and met accuracy requirements considering the maximum SSC, MAPE, and RMSE. The B4, B7, B8A, and B9 bands are highly sensitive to high-concentration sediment rivers. SSC exhibited significant seasonal and spatial variation, peaking above 30,000 mg/L in summer (July–September) and dropping below 1000 mg/L in winter, with a positive correlation with discharge. Spatially, the SSC was higher in the gorge section than in the main channel during the flood season and higher near the banks than in the river center during the dry season. Overall, the random forest model outperformed traditional methods in SSC prediction for sediment-laden rivers. Full article

Water color remote sensing is vital for the monitoring and quantification of marine suspended sediment dynamics and their distributions. Yet validations of these observables in coastal regions and deltaic estuaries, including the Hangzhou Bay in the East China Sea, remain challenging, primarily due to the pronounced complex oceanic dynamics that exhibit high spatiotemporal variability in the signals of the suspended sediment concentration (SSC) in the ocean. Here, we integrate satellite images from the sun-synchronous satellites, China’s Huanjing (Chinese for environmental, HJ)-1A/B (charged couple device) CCD (30 m), and from Korea’s Geostationary Ocean Color Imager GOCI (500 m) to the spatiotemporal scale effects to validate SSC remote sensing-retrieved data products. A multi-scale validation framework based on coefficient of variation (CV)-based zoning was developed, where high-resolution HJ CCD SSC data were resampled to the GOCI scale (500 m), and spatial variability was quantified using CV values within corresponding HJ CCD windows. Traditional validation, comparing in situ point measurements directly with GOCI pixel-averaged data, introduces significant uncertainties due to pixel heterogeneity. The results indicate that in regions with high spatial heterogeneity (CV > 0.10), using central pixel values significantly weakens correlations and increases errors, with performance declining further in highly heterogeneous areas (CV > 0.15), underscoring the critical role of spatial averaging in mitigating scale-related biases. This study enhances the quantitative assessment of uncertainties in validating medium-to-low-resolution water color products, providing a robust approach for high-dynamic oceanic environment estuaries and bays. Full article

Accurately predicting suspended sediment concentrations (SSC) is vital for effective reservoir planning, water resource optimization, and ecological restoration. This study proposes a hybrid ensemble model—VMD-MGGP-NGO-BiLSTM-NGO—which integrates Variational Mode Decomposition (VMD) for signal decomposition, Multi-Gene Genetic Programming (MGGP) for feature filtering, and a double-optimized NGO-BiLSTM-NGO (Northern Goshawk Optimization) structure for enhanced predictive learning. The model was trained and validated using daily discharge and SSC data from the Tangnaihai Hydrological Station on the upper Yellow River. The main findings are as follows: (1) The proposed model achieved an NSC improvement of 19.93% over the Extreme Gradient Boosting (XGBoost) and 15.26% over the Convolutional Neural Network—Long Short-Term Memory network (CNN-LSTM). (2) Compared to GWO- and PSO-based BiLSTM ensembles, the NGO-optimized VMD-MGGP-NGO- BiLSTM-NGO model achieved superior accuracy and robustness, with an average testing-phase NSC of 0.964, outperforming the Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) counterparts. (3) On testing data, the model attained an NSC of 0.9708, indicating strong generalization across time. Overall, the VMD-MGGP-NGO-BiLSTM-NGO model demonstrates outstanding predictive capacity and structural synergy, serving as a reliable reference for future research on SSC forecasting and environmental modeling. Full article

Estuarine benthos, among other lifeforms of interest to water quality, can be sensitive to size-distributed suspended cohesive flocs. In such a context, tide-dependent floc mass distributions in the Tamar Estuary in the UK are revisited. At the field site close to maximum turbidity, time-series of the water level, current velocity, salinity, and suspended sediment concentration (SSC) were recorded in 1998 over several tidal cycles. Concurrently, at selected times and elevation, floc mass distributions were derived from in situ observations of the SSC, floc diameters, and settling velocities. A previously developed time-dependent model, revised to account for both multiclass floc aggregation/disaggregation and bed sediment exchange by erosion and deposition, is applied to simulate mass distributions during ebb/flood cycles on 24 June and 5 August. Although the model does not account for the density effects of salinity or sediment advection, limited comparisons between simulated and observed mass distributions indicate generally good agreement in median diameter prediction on both days. This concurrence is due to the primary role of suspended floc dynamics and only a secondary contribution from bed sediment exchange in governing floc properties. For a better prediction of the SSC variation with the tide, the effects of salinity and advection can be incorporated by coupling the modeled floc dynamics with a suitable multi-dimensional hydrodynamic code. Full article

Many coastal coal-fired power plants utilize seawater flue gas desulfurization (SWFGD) systems, which may pose risks of heavy metal attachment on suspended sediments. Understanding variations in suspended sediment concentration (SSC) is therefore useful for controlling marine pollution. We studied two power plants as examples of discharging SSC using continuous measurement techniques. Monitoring sites at intake and discharge points and the surrounding coastal areas of the power plants was conducted across seasons. The first case study, Linkou Power Plant, is located in a high-SSC region influenced by monsoon winds and wave activity. Results indicate that SSC levels at all the monitoring sites are correlated with environmental factors of wind and wave conditions, with strong positive correlations observed between the intake and discharge points. In contrast, the Dalin Power Plant is located within an international harbor, where the SSC levels are generally low; however, sudden increases in SSC are observed at the intake point due to disturbances from vessel activities. These sudden increases are not evident at the discharge point, suggesting a sink of SSC may occur within the system. These results demonstrate that the two studied power plants have limited effects on the increase in SSC; the SSC in the discharge point is mainly related to the SSC input at the intake point. Effective management of SSC at the intake may help mitigate coastal pollution caused by SSC discharge and reduce the risk of harmful substances adhering to suspended solids in the discharging wastewater. Full article

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is one of the most urbanized and industrialized coastal regions in China, where intense human activities contribute to substantial terrestrial sediment discharge into the adjacent marine environment. However, complex hydrodynamic conditions and high spatiotemporal variability pose challenges for accurate suspended sediment concentration (SSC) retrieval. Developing water quality retrieval models based on different classifications of water bodies could enhance the accuracy of SSC inversion in coastal waters. Therefore, this study classified the coastal waters of the GBA into clear and turbid zones based on Hue angle α, and established retrieval models for SSC using a single-scattering approximation model for clear zones and a secondary-scattering approximation model for turbid zones based on radiative transfer processes. Model validation with in-situ data shows a coefficient of determination (R²) of 0.73, a root mean square error (RMSE) of 8.30, and a mean absolute percentage error (MAPE) of 42.00%. Spatial analysis further reveals higher SSC in the waters around Qi’ao Island in the Pearl River Estuary (PRE) and along the coastline of Guanghai Bay, identifying these two areas as priorities for attention. This study aims to offer valuable insights for SSC management in the coastal waters of the GBA. Full article

In forested watersheds, suspended sediment concentration (SSC) is an important parameter that impacts water quality and beneficial use. Water quality also has impacts beyond the stream channel, as elevated SSC can violate Indigenous sovereignty, treaty rights, and environmental law. To address elevated SSC, watershed partners must understand the dynamics of the sediment regime in the basins they steward. Collection of additional data is expensive, so this study presents modeling and analysis techniques to leverage existing data on SSC. Using data from the South Fork Clearwater River in Idaho County, Idaho, USA, we modeled SSC over water years 1986–2011 and we applied regression techniques to evaluate correlations between SSC and natural disturbances (channel-building flow events) and anthropogenic disturbances (timber harvesting, hazardous fuel management, controlled burns, and wildfire). Analysis shows that SSC did not change over the period of record. This study provides a monitoring program design to support future decision making leading to reductions in SSC. Full article

The water environment in estuaries is a crucial factor affecting the biodiversity and self-purification capacity of coastal zones. This study focuses on Sanmen Bay as an example to study the characteristics and temporal variations of the water environment in the turbid coastal waters on the East China Sea coast. The field data of hydrodynamics and water environment from 2018 to 2023 including different seasons in the bay were collected and analyzed. We analyzed the correlation between water environmental factors and sediment and explored the impact of sediment mixing layers on the water environment. Field data indicate that water temperature, dissolved oxygen content, and suspended sediment concentration (SSC) vary seasonally. In summer, the water temperature and SSC are the highest; in autumn, the dissolved oxygen content is the highest. Salinity and pH values showed little variation from 2018 to 2023. The concentration of oils in sediments across the entire area within Sanmen Bay varied from 2018 to 2023, which decreased from (30.6–92.2) × 10⁻⁶ mg/L to below 10⁻⁶ mg/L. Correlational analysis indicates that dissolved oxygen concentration and heavy metal content were correlated with sediment in 2018, with correlation coefficients of approximately 0.5. Sediments impact the water environment through changing stratification and mixing due to suspended particulate matter and through changing water environment parameters (e.g., heavy metal) due to bed sediment erosion. The bulk Richardson number in most areas is larger than 0.25. These results indicate that sediment impacts heavy metals in Sanmen Bay. In highly turbid waters, sediments are more likely to adsorb heavy metals and other pollutants, thereby impacting water quality. Full article

Predicting suspended sediment concentration (SSC) profiles with high accuracy remains a critical challenge for environmental and engineering applications. This study presents a novel, data-driven expert system that leverages a knowledge-based framework to select optimal SSC models based on diverse flow conditions. The system utilizes model function ranges and spatial relationships between data points as key decision factors. This methodology is applied to study vertical velocity profiles and SSC distribution in steady and uniform river flows. The system systematically extracts and categorizes influencing parameters, generating weighted averages to interpolate and extrapolate profiles where single models exhibit limitations. Two weight calculation methods are implemented: (1) a spatial conditional probability approach utilizing a uniform distribution within control cells, and (2) an isolated point analysis based on distances to cell centers. This approach exhibits some similarities to Voronoi tessellations and associated Laplace and Sibson weights, offering a robust and innovative method for SSC modeling. The proposed expert system empowers hydrologists and engineers by selecting and applying the most suitable SSC models for different scenarios, leading to enhanced prediction accuracy and reliability. This work represents a significant advancement in the field of sediment transport modeling, providing a valuable tool for improved water resource management and environmental protection. Full article

Sediment transport in urban streams is a critical environmental issue, with significant implications for water quality, ecosystem health, and infrastructure management. Accurately estimating suspended sediment concentration (SSC) is essential for effective long-term environmental management. This study investigates the relationships between streamflow, turbidity, and SSC in Dry Creek, South Australia, to understand sediment transport dynamics in urbanised catchments. We collected grab samples from the field and analysed them in the laboratory. We employed statistical modelling to develop a sediment rating curve (SRC) that provides insights into the sediment transport dynamics in the urban stream. The grab sample measurements showed variations in SSC between 3.2 and 431.8 mg/L, with a median value of 77.3 mg/L. The analysis revealed a strong linear relationship between streamflow and SSC, while turbidity exhibited a two-regime linear relationship, in which the low-turbidity regime demonstrated a stronger linear relationship compared to the high-turbidity regime. This is attributed to the urbanised nature of the catchment, which contributes to a first-flush effect in turbidity. This contributes to sediment hysteresis, resulting in non-proportional turbidity and SSC responses to streamflow changes. The findings demonstrate the effectiveness of a streamflow-based SRC for accurately predicting sediment discharge, explaining 97% of the variability in sediment discharge. The sediment discharge predicted using the SRC indicated a sediment load of 341.8 tonnes per year along the creek. The developed sediment rating curve provides a valuable tool for long-term sediment management in Dry Creek, enabling the assessment of downstream environmental risks. By addressing data limitations, this study contributes to a deeper understanding of sediment transport dynamics in urbanized environments, offering insights for informed decision-making and effective sediment management strategies. Full article

Black bloom is a very serious water pollution phenomenon in eutrophic lakes, with Fe(II) and S(−II) being the key limiting factors for this problem. In this paper, three different machine learning methods, namely, Random Forest (RF), Gaussian Mixture Model (GMM), and Bayesian Network (BN), were used to explore the complex interactions among Fe(II), S(−II), and other aquatic factors in the estuary of Chaohu Lake to better characterize and monitor water degradation by black bloom. The results of RF showed that total nitrogen (TN), ammonia, total phosphorous (TP), suspended sediment concentration (SSC), and oxidation–reduction potential (ORP), which were chosen from 11 factors, had the most important relationships with Fe(II) and S(−II). The 69 sampling sites were divided in three groups identified as worst, worse, and bad according to the observed values of seven factors using the GMM. Then, the BN model was applied to three observation groups. The results showed that the structures of the interaction networks were different between the groups. S(−II) controlled only SSC production in the bad and worse group sites, while SSC was determined by both S(−II) and Fe(II) in the worst group. Ammonia and TN exhibited the most direct importance for S(−II) and Fe(II) production in all observation groups. According to the indications from the BNs, potential management strategies for different water pollution conditions were developed. Finally, the threshold values of Fe(II), S(−II), TP, ammonia, TN, SSC, and ORP, which were 0.80 mg/L, 0.04 mg/L, 0.45 mg/L, 3.44 mg/L, 4.15 mg/L, 55 mg/L, and 135 mv, respectively, were determined on the basis of the BN models. These values will be helpful to develop accurate strategies of oxygenation to quickly eliminate black bloom in the lake. Full article

Suspended sediment plays a major role in estuary morphological change and shoal erosion and deposition. The impact of storm waves on sediment transport and resuspension in the Yangtze River Estuary (YRE) was investigated using a 3D coupling hydrodynamic-wave model with a sediment transport model during Typhoon Ampil. This model has been validated in field observations of water level, current, wave, and sediment concentration. The model was run for tide only, tide + wind, tide + wind and wave forcing conditions. It was found that: (1) typhoons can increase the suspended sediment concentration (SSC) by enhancing bed shear stress (BSS), especially in the offshore area of the YRE, and there is hysteresis between SSC and BSS variation; (2) exponential and vertical-line types are the main vertical profile of the SSC in the YRE and typhoons can strengthen vertical mixing and reconstruct the vertical distribution; and (3) waves are the dominating forcing factor for the SSC in the majority of the YRE through wave-induced BSS which releases sediment from the seabed. This study comprehensively investigates the spatio-temporal variation in SSC induced by Typhoon Ampil in the main branch of the YRE, which provides insights into sediment transport and resuspension during severe storms for estuaries around the world. Full article

The Jiangsu Coast (JC), China, is an area susceptible to the impact of tropical cyclones (TCs). However, due to the lack of available on-site observation data, nearshore sedimentary dynamic processes under the impact of TCs have not been fully explored. This study developed a 3D wave–current–sediment numerical model for the JC based on the Finite Volume Community Ocean Model (FVCOM) to investigate sediment dynamic responses to TCs under various scenarios, including different tracks, intensities of TCs and tidal conditions. The validation results demonstrated the model’s satisfactory performance. According to the simulation results, typhoons can significantly impact the hydrodynamics and sediment dynamics. During Typhoon Lekima in 2019, strong southeasterly winds substantially increased the current velocity, bottom stress, wave height, and suspended sediment concentration (SSC). Three typical landfall-type typhoons, with prevailing southeasterly winds, brought significant sediment flux from southeast to northwest along the coast, while the typhoon that moved northward in the Yellow Sea induced a relatively small sediment flux from north to south. Typhoons could also induce stripe-like erosion and deposition, which is closely related to seafloor topography, resulting in seabed thickness variations of up to ±0.3 m. Additionally, strengthening typhoon wind fields can lead to increased sediment flux and seabed morphological changes. Typhoon Winnie, particularly at spring tide, had a greater impact on sediment dynamics compared to other landfall typhoons. Numerical simulations showed that the typhoon-induced net sediment flux within the spring tidal cycle could increase by 80% to 100% compared to the neap tidal cycle, indicating the significant influence of tidal conditions on sediment transport during TC events. Full article

This article focuses on developing models that estimate suspended sediment concentrations (SSCs) for the Lower Brazos River, Texas, U.S. Historical samples of SSCs from gauge stations and satellite imagery from Landsat Missions and Sentinel Mission 2 were utilized to develop models to estimate SSCs for the Lower Brazos River. The models used in this study to accomplish this goal include support vector machines (SVMs), artificial neural networks (ANNs), extreme learning machines (ELMs), and exponential relationships. In addition, flow measurements were used to develop rating curves to estimate SSCs for the Brazos River as a baseline comparison of the models that used satellite imagery to estimate SSCs. The models were evaluated using a Taylor Diagram analysis on the test data set developed for the Brazos River data. Fifteen of the models developed using satellite imagery as inputs performed with a coefficient of determination R² above 0.69, with the three best performing models having an R² of 0.83 to 0.85. One of the best performing models was then utilized to estimate the SSCs before, during, and after Hurricane Harvey to evaluate the impact of this storm on the sediment dynamics along the Lower Brazos River and the model’s ability to estimate SSCs. Full article