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Suspended sediment (SS) has a wide range of negative effects such as increased water turbidity, altered habitat structures, sedimentation, and effects on hydraulic systems and environmental engineering projects. Nevertheless, the methods for accurately determining SS sources on a basin-scale are poorly understood. Herein, we used a simplified neural network analysis (NNA) model to identify the sources of SS in Japan's Oromushi River Catchment Basin. Fine soil samples were collected from different locations of the catchment basin, processed, and separately analysed using X-ray fluorescence (XRF) and X-ray diffraction (XRD). The sampling stations were grouped according to the type of soil cover, vegetation type and land-use pattern. The geochemical components of each group were fed into the same neural network layer, and a series of equations were applied to estimate the sediment contribution from each group to the downstream side of the river. Samples from the same sampling locations were also analysed by XRD, and the obtained peak intensity values were used as the input in the NNA model. SS mainly originated from agricultural fields, with regions where the ground is covered with volcanic ash identified as the key sources through XRF and XRD analysis, respectively. Therefore, based on the nature of the surface soil cover and the land use pattern in the catchment basin, NNA was found to be a reliable data analytical technique. Moreover, XRD analysis does not incorporate carbon, and also provides detailed information on crystalline phases. The results obtained in this study, therefore, do not depend on seasonal uncertainty due to organic matter. [ABSTRACT FROM AUTHOR]

River sediment load estimation poses a critical challenge for water engineers due to its complex and nonlinear hydrological processes. This study assessed the amount of suspended sediment at the Bagh-e-Kalayeh hydrometric station on the Alamut River in the Qazvin province of Iran using two hydrological and meteorological variables, including discharge and rainfall, by considering three scenarios (discharge, discharge + monthly rainfall, and discharge + monthly rainfall + daily rainfall). For modeling, kernel-based data-driven methods, including Gaussian process regression (GPR) and support vector regression (SVR), and tree models, including the M5 tree, random forest (RF), random tree (RT), extra trees, reduced error pruning tree (REPT), and multi-search methods, were used. The results showed that the best performance was achieved by the SVR, with r = 0.948, Wilmot index = 0.965, and RMSE = 0.011 in the first scenario (only discharge). Discharge had the most significant impact on sediment estimation compared to rainfall. It was determined that the suspended sediment load in the Alamut River can be successfully estimated by the SVR method, where only the discharge was used as the input parameter. Additionally, the results indicated that given its characteristics and inherent features, the multi-search method can be used as a complementary approach in sediment modeling, especially in situations where the data volume is not extensive. [ABSTRACT FROM AUTHOR]

Abstract The significance of suspended sediment in the context of nutrient cycling and distribution in reservoirs is rarely reported in the scientific literature. What’s more, suspended sediment (SS) have so far not been taken into account as a potential factor amplifying the degrading effects of nutrients on the functioning of reservoirs. In the experiment described here, the variability of SS concentrations and the content of total nitrogen (TN), total phosphorus (TP), total organic carbon (TOC) and organic matter (OM) were investigated to determine the potential and determinants of SS in the process of migration of these substances at the water-sediment interface in retention reservoirs. The results showed that SS significantly manipulate the distribution of values of total phosphorus and total organic carbon contained in bottom sediment. It was confirmed that the circulation and distribution of selected nutrients in reservoirs is closely related to suspended sediment. Thus, it was proven that SS significantly affect the quality of deposited sediment. Finally, it was concluded that suspended sediment in the water of reservoirs forms a kind of micro-ecosystem, in which it plays a hitherto undiscovered role of a catenary (hub) consolidating a number of phenomena and processes occurring between individual components in the water-bottom sediment system.

Deltas are reliant on upstream fluvial sediment sources for their survival. In the Rhine basin, the suspended sediment transport governs the supply of fines to the delta, a supply that has been dwindling for several decades. We investigate the changes in suspended sediment (SS) fluxes along the main Rhine branch and its tributaries since 1997 and link these to past and ongoing human activities in the basin. We demonstrate that the spatial pattern in the temporal change can allow us to discount and determine specific mechanisms of SS delivery, transport and trapping that are causing the recent decline. A clear spatial trend in the temporal change emerges: there is an increasing loss of SS from the upper basin towards the delta apex. For the last two decades, this is contributed to the introduction of retention basins, which increase the trapping of overbank fines during high flow. However, the declining sediment flux to the delta extends further in the past (at least since 1950); thus, we also examine potential historical mechanisms, which are different from the short‐term explanation. The longer term decline can be explained by the compound effects of dam construction and the decreasing fine sediment uptake by the river from its channel bed. The Rhine River has demonstrated incision in response to normalization works (finalized in the early 1920s). The incision rates declined over the second half of the 20th century corresponding to a declining SS supply from the bed to the delta apex. The importance of the channel bed as a source contributing to the total SS in human‐affected rivers and the contribution of fines through varying channel bed incision has not yet been identified or considered in global river basin studies. However, it may become increasingly relevant as more rivers are urbanized and controlled for flood protection purposes in the face of climate change. [ABSTRACT FROM AUTHOR]

Wildfires, and the sediment‐rich floods that commonly follow, increasingly threaten riverine ecosystems and water infrastructure. Suspended sediment exported throughout fire–flood sequences poses particular risks due to rapid transit times and direct impacts on water quality. However, opportunities to measure suspended‐sediment transport during and after post‐fire floods, and therefore to illuminate what controls the magnitude and timing of suspended‐sediment export from burned, flooded watersheds, are rare. A ~ 100‐year flood that occurred one year into a three‐year study monitoring suspended‐sediment response to wildfire in the Colorado Front Range provides a unique opportunity to (1) quantify how suspended‐sediment concentrations and loads change throughout a fire–flood sequence, and (2) infer what controls the timescale over which suspended‐sediment dynamics recover toward pre‐fire conditions. We find that suspended‐sediment concentrations (SSCs) during summer storms declined monotonically to background conditions over 3 years. Snowmelt SSCs peaked in the second year before declining to background levels. Sediment load calculations reveal that the flood exported ~35 years' worth of suspended sediment and triggered ~1.5 years of elevated SSCs and sediment loads. SSCs and sediment loads indicate a fairly short post‐fire recovery timescale of about 3 years. We suggest that the flood accelerated recovery by (1) exporting much of the available suspended sediment from this supply‐limited landscape and (2) facilitating the export of remaining sediment by making it more accessible to subsequent flows. Our results indicate that large post‐wildfire floods, though representing major geomorphic disturbances, may hasten post‐fire suspended‐sediment recovery to background conditions, at least in supply‐limited regions. [ABSTRACT FROM AUTHOR]

The characteristics of suspended sediments determine the water color, and remote sensing methods have been developed to leverage this physics to determine sediment concentration and size. However, current measurement practices rely on empirical correlations, which have only been tested for a limited range of particle conditions. This gap prevents their applicability in the field. To address the issue, this study analyzes hyperspectral spectra across various wavelength bands to characterize spectral signatures of different sediment sizes and concentrations. The results reveal inflection points of the light scattering of suspended sediment solution depending on particle concentration and sizes: the light scattering positively correlates with a low concentration but negatively correlates with a high concentration, while it negatively correlates with particle size for low concentrations but positively correlates for high concentrations. Sensitivity analyses indicate increased responsiveness to concentration changes at low concentrations and a higher sensitivity to particle size changes at both low and high concentrations. Machine learning models were tested for simulated satellite bands, and it was found that existing machine learning models are limited in reliably determining sediment characteristics, reaching an R-square of up to 0.8 for concentration and 0.7 for particle size. This research highlights the importance of selecting appropriate wavelength bands in the appropriate range of sediments and the need to develop advanced models for remote sensing measurements. This work underscores hyperspectral imaging's potential in environmental monitoring and remote sensing, revealing the complicated physics behind water color changes due to turbidity and informing next-generation remote sensing technology for turbidity measurements. [ABSTRACT FROM AUTHOR]

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The settling velocity (ws) in estuarine environments can impact whether a region is eroding or accreting sediment on the bed, yet determining this rate can be an indirect process requiring a number of assumptions. Accurate determination of ws is especially needed for numerical models to reproduce observed sediment concentrations at the appropriate timescale. We collected information on suspended sediment flocculation at a channel site (13 m deep) and a shallows site (4 m deep) within South San Francisco Estuary, alongside timeseries of flow, wave statistics, turbulent shear, and bottle samples analyzed for both ws and particle size. Using the measurements of floc size and settling velocity, we performed a sensitivity analysis on the unknown parameters in the general explicit formula for settling velocity. The collected particle size distribution data show that multiple classes of flocs are present; these are characterized as flocculi, microflocs, and macroflocs. We show that ws of flocculi is closest to ws for the full distribution. The determined parameter values lead to near‐bed mass‐weighted settling velocities (standard deviation) of 1.18 (0.55) and 0.22 (0.15) mm/s at the channel and shallows sites, respectively. Modeling efforts can use this work to help select an appropriate sediment model and parameter values. Plain Language Summary: Tides and waves pick up mud on the bottom of estuaries and transport it between the river and ocean or between the shallows and the deep channel. One important control on this transport of mud is how fast it settles out of the water column. Larger things typically settle faster, and as small particles stick together into larger aggregates, or "flocs", the flocs settle faster than the original small particles. We collected data in the channel and shallows of San Francisco Estuary, and compute settling velocity using a range of tuning parameters. We use the observations to narrow the values of these parameters, and we show how the choice of some parameter values can be more important than others in determining settling velocity. In locations where sediment flocculates, this work will help modelers select appropriate models and parameter values. Key Points: The parameters needed to compute settling velocity are constrained using settling column dataMultiple floc classes are common, suggesting a multiple class model is necessaryThe characteristic size of particles in San Francisco Estuary is smaller than the size commonly observed with camera setups [ABSTRACT FROM AUTHOR]

Enka Lake is a 25-ha reservoir in the Southern Appalachian mountains surrounded by the Biltmore Lake residential neighborhood. The aesthetic and recreational value of Enka Lake can be negatively affected by turbid water and sediment buildup, resulting from stormwater originating upstream in its 15.38 km2 watershed. In this study, we monitored streamflow and sediment loads for nearly 2 years to better understand sedimentation dynamics and evaluate management options. We collected nearly 500 water samples throughout the watershed, focusing on two tributaries that provide the majority of streamflow into Enka Lake. During baseflow conditions, those tributaries are very clear (turbidity ranging from 1 to 20 NTU), but sediment-laden stormflow (turbidity > 1000 NTU) from dirt roads, gravel driveways, and poorly vegetated yards is common and, during extreme storm events, causes lake discoloration. Discharge-sediment data pairs were used to construct sediment rating curves and estimate sediment fluxes through these tributaries. The predicted sediment fluxes were tested with high-frequency sampling during and after three storms in different seasons; predicted values underestimated the peak sediment fluxes (0–400%) but generally matched total measured sediment loads. Estimates of annual sediment load from the two tributaries (540–900 tonnes/year) suggest that a dredging effort commissioned by the neighborhood association in 2016 may have removed only about 1–3 years' worth of sediment. Installing sediment forebays or other retention structures is likely the preferred sediment management strategy moving forward (over a regular dredging schedule) since they can be maintained more easily without affecting lake levels. Designing these retention structures will benefit from the estimates of stormflow and sediment fluxes, and the study design presented here—including the help of community-based volunteer samplers—provides a model that could be used at other sites in the Southern Appalachians and elsewhere. [ABSTRACT FROM AUTHOR]

This study aims to clarify the characteristics of the riverbed deformation, bank erosion, and channel changes in the lower Sangu River basin using a depth-averaged 2-D flow model where suspended sediment transport is dominant and its flow characteristics are influenced by active tides. A 45 km long area including the river mouth is computed using the 2-D model, and the results are compared with the observed channel changes. The computation results show that bed deformation and channel change are mainly caused during the ebb tide period of the spring tide particularly in an area of about 10 km from the river mouth. The detail study domain calculation results show that the flow concentration at the bend area causes the bed erosion there, and the eddy separated from the main stream causes the sediment deposition at the inner bank, while this eddy is not developed at the outer bank, resulting in the bank erosion there. Through these investigations, the characteristics and mechanisms of the morphodynamics in the lower Sangu River reach, particularly the potential of the combination of tides and floods to enhance riverbed deformations and the associated bank shifts, have been clarified. [ABSTRACT FROM AUTHOR]

To reduce aquatic eutrophication, measurements of bioavailable phosphorus (BAP) rather than total phosphorus (TP) are deemed critical. However, current methods require much time to separate sediments from river water, which limits the routine measurement of BAP in rivers. Therefore, in this study, a simultaneous multisample ultrasonic extraction method is proposed to directly measure total BAP (TBAP) in river water without the separation of sediment and water. Spike-and-recovery assessments showed that at least three extractions are required to maintain efficiency. A process including 2-min extraction time and three extractions was suggested. The concentrations of TBAP extracted by this process showed no significant differences with the spike calculations. Furthermore, river water TBAP was quantified using the conventional and proposed method to examine the practicality of using the proposed method for simultaneous multisample ultrasonic extraction and to evaluate its adaptability to actual river water analysis. The extracted concentrations matched those obtained using the conventional method, in which total BAP is calculated as the sum of dissolved BAP and particulate BAP; no significant difference was observed between the concentrations. Ultrasonic extraction was considerably less time-consuming than the conventional method because more samples could be analyzed during a single run. Therefore, the simultaneous multisample ultrasonic extraction method proposed in this study can be used to directly quantify total BAP in river water. [ABSTRACT FROM AUTHOR]

Anthropogenic stressors such as agriculture and urbanization can increase river turbidity, which can negatively impact fish gill morphology and growth due to reduced oxygen in the benthic environment. We assessed the gill morphology, field metabolic rate (FMR), and two hypoxia tolerance metrics (oxygen partial pressure at loss of equilibrium, PO2 at LOE, and critical oxygen tension, Pcrit) of eastern sand darter (Ammocrypta pellucida), a small benthic fish listed as threatened under the Species at Risk Act in Canada, from rivers in southern Ontario. Field trials were conducted streamside in the Grand River (August 2019; mean NTU 8) and in the comparatively more turbid Thames River (August 2020; mean NTU 94) to test the effect of turbidity on each physiological endpoint. Gills were collected from incidental mortalities and museum specimens, and were assessed using hematoxylin and eosin and immunofluorescent staining. The between‐river comparison indicated that turbidity significantly increased interlamellar space and filament width but had no significant influence on other gill morphometrics or FMR. Turbidity significantly increased PO2 at LOE (i.e., fish had a lower hypoxia tolerance) but did not significantly impact Pcrit. Therefore, although turbidity influences hypoxia tolerance through LOE, turbidity levels were not sufficiently high in the study rivers to contribute to measurable changes in gill morphology or metabolism in the wild. Determining whether changes in gill morphology or metabolism occur under higherturbidity levels would help resolve the ecological importance of turbidity on species physiology in urban and agricultural ecosystems. [ABSTRACT FROM AUTHOR]

Background and Objectives Erosion and deposition cause irreparable damage to water construction projects. Among these damages, we can mention the accumulation of sediment behind the dams and the reduction of their useful volume, the destruction of structures, the reduction of capacity and the increase in the maintenance cost of irrigation channels. On the other hand, sediment transport affects-water quality indicators in terms of drinking and agriculture. Accurate estimation of river-suspended sediment load from various aspects of water resources engineering, environmental issues and water quality is of particular importance. In this regard, the hydrological models of the basin have not shown proper efficiency in estimating the amount of suspended sediment, due to the many influencing factors. Most of the simulation studies of suspended sediment estimation are based only on the flow rate of the basin outlet, and the obtained results are proof of their inefficiency. In the meantime, the development and use of new sediment estimation methods that are easy to use in addition to sufficient accuracy will play an important role. Nowadays, the fuzzy and neural intelligent conductor system has found many applications in various water engineering problems, including sedimentation, due to its ability to solve complex and nonlinear phenomena. Due to the great importance of sediment transport in the optimal use of water resources and the design of dams, it is very necessary to obtain an accurate method for estimating the amount of suspended sediment load in rivers. Methodology The purpose of this research is to evaluate and compare the two methods of support vector machine models (SVR) and group data control method (GMDH) for estimating the suspended sediment load of the Pol Doab station of Qarachai River in Markazi Province and comparing it with the results of the measuring curve. For this purpose, the daily data of discharge, sediment, temperature, and rainfall parameters of Shazand Pol Doab station were used. For this work, 13 scenarios and different combinations of parameters were defined. Then the results of the two methods were compared with each other and with the results of the measuring curve. Finally, the best method was suggested. For this purpose, library, and field studies and a review of related sources, statistics and information were collected. The statistics of temperature, rainfall, and daily average discharge of stream and sediment measured daily during a long-term statistical period of 40 years at Pol Doab Hydrometry Station were received from the Meteorology and Regional Water Department of Markazi Province. The received data were categorized and converted into the input format of the models. According to the discharge and corresponding sediment, the curve of the sediment gauge was drawn and its equation was obtained. Appropriate patterns of input variables were selected based on trial and error. Considering that the mentioned parameters have a historical course, therefore, the design of the input patterns of soft computing models should be done based on time delays (like what is discussed in the analysis and forecasting of time series). Then the model was taken for each input and output pattern. In the next step, the most appropriate time delay of the input parameters in the modeling, which had a higher R2 determination coefficient and a lower root mean square error (RMSE) was selected. In this research, 70% of the research data was used as training and 30% for validation and testing. Finally, two data-mining methods were compared with each other and also with sediment rating curve (SRC) and observational data. Findings The results obtained from this research indicated the acceptable performance of the methods used in predicting suspended sediment amounts. Comparing the results of GMDH, SVR, and SRC models shows the superiority of GMDH and SVR models in predicting suspended sediment values compared to Verdi model number 6. The results showed the acceptable performance of the GMDH model with the highest R2 determination coefficient of 0.99 and the lowest root mean square error of 83 tons per day. According to the obtained results, it can be said that the GMDH model as a powerful and high-speed model can be used to model suspended sediment. The results of the research showed that both data-mining methods have far better efficiency and accuracy in estimating the suspended load of river sediment than the sediment gauge curve. Data mining-based methods can be used as an alternative to estimate the river's suspended load. It should be noted that due to climate change and droughts, industrial development, colonization of land use, and changes in the morphology of watersheds, the obtained results cannot be used forever at any time, but should be used whenever the models need to be updated. Another weakness of the models is that with the increase in the number of developed layers, the accuracy of the produced answers increases, but the produced relationships between the input and output variables become very complicated. Conclusion The results of modeling with (SVR) showed that scenario number 6, which includes discharge at the current time and discharge and sediment with a time delay step, with the highest determination coefficient R² with a value of 0.98 and the lowest root mean square error RMSE equal to 185 tons per day. It performs better in other scenarios. In the next step, the best model selection model (SVR) was used as the input of the GMDH model. The results were compared with the model (SVR) and sediment gauge curve. The results show the acceptable performance of the GMDH model with the highest R2 determination coefficient of 0.99 and the lowest root mean square error of 83 tons per day compared to the other two methods. The obtained results showed that both investigated data mining methods provide much better results than the sediment gauge curve. The coefficients and functions used to calibrate the intelligent models used in this research can be very useful for estimating the suspended sediments of nearby stations without statistics having the same geological and hydrological conditions at the regional level. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) is often employed to estimate suspended sediment concentration (SSC) in rivers, and the availability of hyperspectral imagery enhances the effectiveness of RS-based water quality monitoring due to its high spectral resolution. Yet, the necessity of hyperspectral imagery for SSC estimation in rivers has not been fully validated. This study thus compares the performance of hyperspectral RS with that of multispectral RS by conducting field-scale experiments in shallow rivers. In the field experiments, we measured radiance from a water body mixed with suspended sediments using a drone-mounted hyperspectral sensor, with the sediment and riverbed types considered as controlling factors. We retrieved the SSC from UAV imagery using an optimal band ratio analysis, which successfully estimated SSC distributions in the sand bed conditions with both multispectral and hyperspectral data. In the vegetated bed conditions, meanwhile, the prediction accuracy decreased significantly due to the temporally varying bottom reflectance associated with the random movement of vegetation caused by near-bed turbulence. This is because temporally inhomogeneous bottom reflectance distorts the relationship between the SSC and total reflectance. Nevertheless, the hyperspectral imaging exhibited better prediction accuracy than the multispectral imaging, effectively extracting optimal spectral bands sensitive to back-scattered reflectance from sediments while constraining the bottom reflectance caused by the vegetation-covered bed. [ABSTRACT FROM AUTHOR]

This study investigates the heavy metal contamination of soils and suspended sediments in the Nurzec River catchment (Eastern Poland), focusing in particular on the effects of catchment land use. The fingerprinting technique has been combined with the classic, Igeo, and EF index-supported contamination analysis to identify heavy metals sources. A wide range of elements (31 chemical elements including heavy metals) allowed the author to analyse the pathways and to identify the pressures of heavy metal contamination. The developed statistical models of heavy metal source distribution displayed results at a statistically significant level. The results have revealed the significant impact of land use connected with urban areas (URBAN) and pastures (PAST), which together constitute less than 20% of the river catchment area. These results are relevant to the local authorities and stakeholders, as they highlight the significant impact of low-density urban areas that are not locally considered as the major sources of heavy metal pollution. These results will contribute to sustainable decisions in the field of contaminated catchment area remediation. [ABSTRACT FROM AUTHOR]

Elevated nutrient and suspended sediment concentrations often result in negative environmental impacts within freshwater environments. Studies that directly compare suspended sediment and bioavailable nutrients between predominantly agricultural and predominantly urban watersheds during baseflow conditions are largely lacking. The purpose of this study was to determine the impacts of land cover, stream discharge, and wastewater treatment plant (WWTP) discharge on nutrient and sediment concentrations, across a large land cover gradient in Southwest Ohio streams. Weekly baseflow samples were collected from eight streams over 1 year from November, 2016 through November, 2017. Total suspended sediment, nitrate, and phosphate concentrations were measured. Results indicate that agricultural land cover and WWTPs increase nitrate and phosphate concentrations in the study area. Total suspended sediment and nitrate concentrations increased with discharge, and discharge was a relatively weak predictor of phosphate concentrations. Seasonal water quality trends varied by parameter and land use also had unique impacts on seasonal water quality trends. Results suggest that to improve water quality in the study area, efforts should focus on improving WWTP effluent treatment and agricultural land management. [ABSTRACT FROM AUTHOR]

Sediment has the potential to influence the landscape, economy and way of life. If the estimation is accurate and the distribution characteristics are known, sediment can be used as a resource. In this brief review, we evaluated recent advances in suspended sediment estimation techniques between 2011 and 2022. The three most popular techniques are Sediment Rating Curves, Machine Learning Models and Physical and/or Numerical Models. Their potential and limitations are briefly explored. Our findings indicate that Machine Learning Models perform better, whereas Sediment Rating Curves offer flexibility in field applications. On the other hand, the paucity of necessary highresolution data for model calibration and validation hinders the performance of Physical and/or Numerical Models. The sampling frequency of input sediment data emerges as a pivotal factor influencing the performance of all methods. To overcome this issue, a semi-automatic surrogate method might be useful. Also, particle size could be included in the rating curves to obtain additional information. This is especially valuable for understanding sediment dynamics and the process of delta formation. [ABSTRACT FROM AUTHOR]

Suspension is the key mechanism by which fine‐grained sediment (≤125 μm) is winnowed and transported across shallow‐water carbonate platforms into adjacent deep waters. Unlike sliding and saltation, which deliver sedimentary structures via bedload, the sedimentological signature of suspended sediment is more cryptic. This study focuses on suspended sediment, and its drivers – wind, waves and tides – to better constrain the processes responsible for the transport of fine‐grained sediments. By building forward from remote sensing algorithms developed for deep‐waters, sediment suspension in the shallow water column can be mapped from satellite. By applying machine learning to Moderate Resolution Imaging Spectroradiometer data for Great Bahama Bank, this study demonstrates how the drivers of sediment suspension change over 18 years across this 100 000 km2 carbonate platform. Through time, both seasonal patterns of suspension, as well as those induced by El Niño‐Southern Oscillation and, more subtly, the Atlantic Meridional Overturning Circulation were tracked. El Niño‐Southern Oscillation modulates wind‐induced currents, while Atlantic Meridional Overturning Circulation affects local sea level. Across space, this study shows how the eastern margin of Great Bahama Bank, which is traditionally considered to be wind‐dominated, primarily owes its suspended sediment to tidal currents focused between islands. Sediment suspension across the leeward margin of Great Bahama Bank, meanwhile, can be attributed to wind‐induced currents and waves. The authors consider this work a step towards delivering a quantitative, reproducible, process‐based understanding of sediment suspension atop carbonate platforms using Earth observation data. [ABSTRACT FROM AUTHOR]

Discharged floods containing a large amount of suspended sediment (SS) can lead to total dissolved gas (TDG) supersaturation downstream of dams. The swimming ability of fish may be affected by TDG supersaturation and excessive SS. However, previous studies focused only on the separate effects of TDG and SS, ignoring their combined effects. In this study, juvenile rock carp and grass carp were selected to assess the interactive effects of TDG and SS on fish swimming ability. A totally crossed two‐factorial design (six TDG levels: 100%, 115%, 120%, 125%, 130% and 135%; four SS concentrations: 0, 50, 100 and 150 mg/L) was set, and the critical swimming speed (Ucrit) and the burst swimming speed (Uburst) were measured. The findings indicated that the swimming abilities (Ucrit and Uburst) of both fish species decreased with increasing TDG levels and SS concentrations. Separate exposure to TDG significantly decreased the swimming abilities of the two fish species, whereas separate exposure to SS decreased only the swimming abilities of juvenile grass carp. Juvenile rock carp exhibited weaker swimming abilities than juvenile grass carp in TDG‐supersaturated water. Additionally, the interaction between TDG and SS reduced the swimming abilities of juvenile rock carp and grass carp compared with a separate effect, but this reduction was significant only for the Ucrit of juvenile grass carp. This study demonstrates that TDG‐supersaturated water containing SS during flooding may result in fish failure through fishways. [ABSTRACT FROM AUTHOR]

The Sikkim–Darjeeling Himalaya region receives the highest amount of rainfall along the whole southern Himalayan margin and is known for the occurrence of extreme hydrometeorological and geomorphological events. The massive amounts of water and sediment transported each year through the mountain part of the Teesta River drainage system (∼8,150 km2)––the largest river in the region––have been severely impacted by dam construction in recent decades. The aim of the current study was to determine, for the first time in this part of the Himalaya region, the dynamics of suspended sediment transfer at a number of points distributed through the mountainous part of the Teesta River catchment prior to dam construction and preliminarily assess the impact of dam operations on the suspended sediment. Sediment sources were identified using a database of landslide inventories from 1965 to 2019, combined with visual interpretation of satellite imagery from the U.S. Corona programme and Google Earth. Hydrological and sediment data up to the second half of the 1990s were used to reconstruct the discharge and suspended sediment dynamics before direct human intervention in the river channels. The beginning and end of the construction of the reservoirs was determined by analyzing satellite images. The impact of dam operations on the suspended sediment was compiled from the available literature. The results of the current study indicate that the primary sources of sediment are landslides caused by the interaction of rainfall and road undercutting of slopes as well as channel erosion. During extreme rainfall events, the influence of deforested areas in the mobilization and delivery of sediment to the river network increases. The current analysis reveals that reconstruction of the suspended sediment dynamics should take into account the course of extreme events responsible for supplying material to the river network, as well as the long-term remobilization of already deposited sediment in the river channel. It was found that the mean suspended sediment load (SSL) following extreme rainfall, flooding, and landslides in the Teesta River catchment can be up to four times higher than its average values for the same catchment unaffected by such an event, and the effects can be observed for more than a decade afterwards. Under these conditions, the mean suspended sediment yield can reach 12,000 and up to 20,000 t/(km2·y) in individual years, which is among the highest in the Himalaya region and, indeed, the world. The construction of 13 dams in the last 30 years has disrupted the hydrological regime and sediment transport in the Teesta River catchment along 70% of its main course and largest tributaries, and this has resulted in the selective retention of coarser material in the reservoirs and a reduction in the SSL in the Himalayan piedmont. The high density of the dams suggests that further transport of suspended sediment will depend on the efficiency of the water and sediment management at the reservoirs, which may be affected by irregular natural extreme events. [ABSTRACT FROM AUTHOR]

Flood events, whose number and intensity are predicted to increase in the Mediterranean region, are difficult to monitor. This causes the number of observations of suspended sediment and total phosphorus concentration (|SS| and |TP|, respectively) during their occurrence to be still scarce. Non-perennial or temporary water bodies, which react more promptly to rainfall events, represent ideal natural observatories. In this study, observations of streamflow, |SS| and |TP|, carried out during some flood events, in the Celone river basin, a temporary river located in south-eastern Italy, are presented. The research examined the correlations between flows, concentrations and loads of sediment and phosphorus and investigated factors that influence sediment and phosphorous dynamics in the river basin. The results show no relationship between the time of the year and the precipitation quantity of each event. The high coefficient of determination of the |SS|–|TP| correlations (R2 = 0.67 on average) proves the importance of soil erosive processes in the delivery of phosphorus to the river. More than 73% of the total suspended sediment load and 83% of total phosphorus load in the period 2010–2011 were transported during the 11 monitored events. In addition to the discharge, |SS| and |TP| also depend on numerous other factors related to land management, such as soil cover and fertilizations. The study, thanks to the improved understanding of the mechanisms governing sediment and phosphorus losses, represents a useful contribution for river basin authorities who have to draw up management plans aimed at preventing eutrophication phenomena and soil fertility reduction. [ABSTRACT FROM AUTHOR]

IntroductionBest management practices (BMPs) are important tools for mitigating the impact of non-point source pollutants on water quality. Drivers of the high variance observed in BMP performance field tests are not well documented and present challenges for planning BMP construction and forecasting water quality improvements.MethodsWe conducted a systematic review of published nonpoint source water quality BMP studies conducted in the United States and used a meta-analysis approach to describe variance in pollutant removal performance. We used meta-regression to explore how much BMP pollutant removal process, influent pollutant concentration, and aridity effected BMP performance.ResultsDespite high variance, we found the BMPs on average were effective at reducing fecal indicator bacteria (FIB), total nitrogen (TN), total phosphorus (TP), and total suspended sediment (TSS) concentrations. We found that influent concentration and interaction effect between the BMP pollutant removal process and aridity explained a substantial amount of variance in BMP performance in FIB removal. Influent concentration explained a small amount of variability in BMP removal of TP and orthophosphate (PO4). We did not find evidence that any of our chosen variables moderated BMP performance in nitrogen or TSS removal. Through our systematic review, we found inadequate spatial representation of BMP studies to capture the underlying variability in climate, soil, and other conditions that could impact BMP performance.

Anoxic conditions within reservoirs related to thermal stratification and oxygen depletion lead to methylmercury (MeHg) production, a key process governing the uptake of mercury in aquatic food webs. Once formed within a reservoir, the timing and magnitude of the biological uptake of MeHg and the relative importance of MeHg export in water versus biological compartments remain poorly understood. We examined the relations between the reservoir stratification state, anoxia, and the concentrations and export loads of MeHg in aqueous and biological compartments at the outflow locations of two reservoirs of the Hells Canyon Complex (Snake River, Idaho-Oregon). Results show that (1) MeHg concentrations in filter-passing water, zooplankton, suspended particles, and detritus increased in response to reservoir destratification; (2) zooplankton MeHg strongly correlated with MeHg in filter-passing water during destratification; (3) reservoir anoxia appeared to be a key control on MeHg export; and (4) biological MeHg, primarily in zooplankton, accounted for only 5% of total MeHg export from the reservoirs (the remainder being aqueous compartments). These results improve our understanding of the role of biological incorporation of MeHg and the subsequent downstream release from seasonally stratified reservoirs and demonstrate that in-reservoir physical processes strongly influence MeHg incorporation at the base of the aquatic food web.

Poyang Lake is not only a globally important stopover for migratory birds and a habitat for fish, but also one of the main sand mining areas in China. However, sand mining activities significantly affect the suspended sediment concentration in Poyang Lake, thereby impacting water quality, altering lake bed topography, and potentially disturbing the living environment of plants and animals. Therefore, understanding the suspended sediment concentration and the spatiotemporal patterns of sand mining activities in Poyang Lake holds vital significance for effective lake management and biodiversity conservation. Utilizing surface reflectance (SR) data derived from Landsat satellite imagery sourced via Google Earth Engine spanning from 1989 to 2018, coupled with SSC data recorded by daily observation at Hukou hydrological stations, we conducted a comparative analysis of five distinct methods: Linear Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Classification and Regression Trees (CART), and Back Propagation Neural Network (BPNN). Moreover, the applicability of machine learning methods was analyzed and compared across four scenarios (#01 Landsat 5, #02 Landsat 7, #03 Landsat 8, and #04, Landsat 5, 7, and 8). Using the optimal SSC estimation model, we investigated the spatial and temporal trends of sand mining activities in Poyang Lake from 1989 to 2021, along with their potential impact on suspended sediment levels in the water body. The results showed that the prediction accuracy of machine learning is better than that of linear regression models, and the RF model has the best performance. The RF model generated R $^{2} \gt 0.9$ for four scenarios (#01 Landsat 5, #02 Landsat 7, #03 Landsat 8, and #04 Landsat 5, 7, and 8) and showed little to no overfitting. The generated SSC map can clearly show the distribution of SSC in Poyang Lake, unveiling the impact of sand mining activities on suspended sediment, especially around sand dredgers where SSC can exceed 0.15 g/L. Sand mining activities in Poyang Lake emerged after 2000 and gradually shifted southward and expanded, reaching a peak in 2016. Fortunately, under government regulation, illegal sand mining has been effectively controlled and is currently concentrated near Songmenshan Island. Despite the fact that sand mining has been brought under control, there remains a necessity for heightened oversight to prevent any impact on national nature reserves. The Random Forest (RF) model demonstrates significant potential in utilizing Landsat satellite data to predict SSC in Poyang Lake, as well as to monitor sand mining activities in the area.

Suspended sediment (SS) has a wide range of negative effects such as increased water turbidity, altered habitat structures, sedimentation, and effects on hydraulic systems and environmental engineering projects. Nevertheless, the methods for accurately determining SS sources on a basin-scale are poorly understood. Herein, we used a simplified neural network analysis (NNA) model to identify the sources of SS in Japan’s Oromushi River Catchment Basin. Fine soil samples were collected from different locations of the catchment basin, processed, and separately analysed using X-ray fluorescence (XRF) and X-ray diffraction (XRD). The sampling stations were grouped according to the type of soil cover, vegetation type and land-use pattern. The geochemical components of each group were fed into the same neural network layer, and a series of equations were applied to estimate the sediment contribution from each group to the downstream side of the river. Samples from the same sampling locations were also analysed by XRD, and the obtained peak intensity values were used as the input in the NNA model. SS mainly originated from agricultural fields, with regions where the ground is covered with volcanic ash identified as the key sources through XRF and XRD analysis, respectively. Therefore, based on the nature of the surface soil cover and the land use pattern in the catchment basin, NNA was found to be a reliable data analytical technique. Moreover, XRD analysis does not incorporate carbon, and also provides detailed information on crystalline phases. The results obtained in this study, therefore, do not depend on seasonal uncertainty due to organic matter.

1- Introduction Water erosion is the result of interactions between various environmental factors such as topography, soil characteristics, climate characteristics, runoff and land use and management. Sediment production is highly dependent on runoff, so doubling the speed of runoff increases its leaching capacity and transportability up to five and six times. Knowing the effective factors in sediment production plays an important role in determining the amount of sedimentation of a watershed and understanding the phenomenon of erosion and its consequences, and it can be used in prioritizing the sub-basins of a watershed. The purpose of this research is to investigate the relationship between morphological characteristics and the amount of sediment production in the catchment area of the Gharasu River in Ardabil using principal component analysis and multivariate regression, in this regard, using 16 independent variables and 1 dependent variable in 13 sub-basins of the Gharasu River. 2- Methodology As one of the sub-basins of the Aras catchment basin, Gharasu catchment is located in the geographical coordinates of 47°31' to 48°47' east longitude and 37°47' to 38°52' north latitude. In this study, 1:100,000 scale geological maps, 1:25,000 scale topographic maps, and discharge and suspended sediment statistics and information of 13 hydrometric stations of Gharasu River sub-basin were used in the 50-year period from 1350 to 1399. In order to check the correlation between independent and dependent variables and to test the normality and normal distribution of data, Shapiro-Wilk and Kolmogorov-Smirnov tests were used in SPSS software. Also, in this research, the OLI Landsat 8 satellite images were used to extract the vegetation cover index (NDVI). For this purpose, step-by-step linear regression is used to determine the most effective variables and to determine the most appropriate statistical relationship between suspended sedimentation and the variables used, and principal component analysis is used to determine the most effective factors of suspended sediment production in the watershed. 3- Results In this study, the average annual suspended sediment of the basin was considered as dependent variable and other parameters as independent variables, and Pearson correlation method was used to check the correlation between independent variables and dependent variable. The variables of time of concentration, length of the main waterway, area, Slenderness ratio, perimeter and slope have a higher correlation with the amount of sediment production in the basin than other variables. Based on the models obtained from sediment correlation analysis, the amount of sediment produced with the concentration time variable had a positive correlation and was significant at the 5% level. The results of the analysis of the principal components show that the four factors of area, length of the main waterway, concentration time and slenderness ratio of the basin have an eigenvalue greater than 1, and the number of extracted factors is 4 factors. The first factor (area) has been able to explain 37.230 percent of the variance of all research variables. This value is 24.735, 16.950 and 9.849 percent for the second factor (length of the main waterway), the third factor (concentration time) and the fourth factor (slenderness ratio) respectively. 4- Discussion & Conclusions The present research was conducted with the aim of investigating the relationship between geomorphic parameters, vegetation and erodible formations of selected sub-basins of Gharasu basin with the average annual sediment. For this purpose, the statistics and information of 17 variables (16 independent variables and 1 dependent variable) were obtained for 13 sub-basins of Gharasu basin from the regional water of Ardabil province. The relationship between geomorphic parameters and annual precipitation was determined using stepwise multivariate regression method. The research results indicate that geomorphic parameters have a high correlation with the amount of annual sediment production and can be used in sediment prediction. Meanwhile, the variables of concentration time, length of the main waterway, area, slenderness ratio, perimeter and slope have a higher correlation with the amount of sediment production in the basins than other variables. Among these variables, the basin concentration time variable was used in the final step-by-step regression model and was selected as the sediment predictor variable. This variable alone can predict 98% of annual precipitation. In order to ensure the appropriateness of the data, the KMO coefficient was used to analyze the main components. The value of KMO = 0.78, as a result, the data will be suitable for factor analysis. The results showed that the four factors of area, main waterway length, concentration time and elongation factor could explain 88.754% of the variance of all research variables. [ABSTRACT FROM AUTHOR]

The accuracy of predicting river-suspended sediment concentration (SSC) is crucial for evaluating the functional lifespan of reservoirs, analyzing river geomorphological evolution, and assessing riverbed stability. In this study, we aim to develop new models for SSC prediction at two hydrological stations near Puerto Rico, USA, by integrating the bacterial foraging optimization algorithm and adaptive neural fuzzy inference network (ANFIS). The models comprise ANFIS with grid partition (ANFIS-GP), ANFIS with subtractive clustering (ANFIS-SC), and ANFIS with fuzzy c-means clustering (ANFIS-FCM). Additionally, we employ an artificial neural network (ANN) and the sediment rating curve (SRC) for predicting daily series data of flow discharge-suspended sediment concentration (SSC). Different scenarios are considered based on varying input and output variables, leading to predictions for four distinct scenarios. At the Rio Valenciano Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-GP, ANFIS-SC, ANN, and SRC are 2.2172, 2.5389, 2.6627, 2.7549, 2.7994, and 3.7882, respectively. For the Quebrada Blanca Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-SC, ANFIS-GP, ANN, and SRC are 0.8295, 0.8664, 0.8964, 0.9110, 0.9684, and 1.6742, respectively. It can be inferred that ANFIS-BFO exhibits superior prediction results compared to all other models. Furthermore, ANFIS-SC and ANFIS-FCM demonstrate slightly better prediction performance than ANFIS-GP. In comparison to ANN, ANFIS-GP, ANFIS-SC, and ANFIS-FCM exhibit slightly superior prediction performance. [ABSTRACT FROM AUTHOR]

The physicochemical properties of suspended sediment affect the adsorption of heavy metals. Based on scanning electron microscopy-energy dispersive X-ray spectrometry and Image Pro Plus, we detected the physicochemical properties of suspended sediment in Yangtze Estuary. The following results were observed: (1) The graphical diameter (GD), roundness (RO) and fractal dimension (FD) of suspended sediment all presented normal distribution. According to Kolmogorov-Smirnov (K-S) test, the RO of suspended sediment of the ShiDongkou station at sewage outlet was 1.31, which was significantly higher than that of other points. (2) According to the K-S test, there was no significant difference in elements among various points in the Yangtze River Estuary. (3) Compared to the chemical properties of suspended sediment, its physical characteristics had a greater impact on heavy metal absorption. The physical characteristics had the least impact on the Cu adsorption, while the chemical properties had the least impact on the Cr absorption. This study deepens the understanding of suspended sediment in the Yangtze Estuary and provides comprehensive data for the numerical simulation of suspended sediment dynamics. [ABSTRACT FROM AUTHOR]

Stream corridor erosion can majorly contribute to the overall sediment and phosphorus load in urbanizing watersheds. However, the relative contribution of stream bed and bank erosion, compared with upland watershed sources and the potential for stream restoration to mitigate total contaminant loads, is poorly understood. In this study, a new method was developed, using the percent impervious cover (PIC) indicator of urbanization to evaluate the relative contribution of the stream corridor versus upland watershed contributions to observed total sediment and phosphorus loads in the receiving watercourse. This method was used to develop a cost-optimized mitigation plan, including implementing low-impact development (LID) stormwater infrastructure for urban areas and agricultural best management practices (BMPs) for rural areas in the watershed and stream restoration for the degraded stream reaches. A new cost–benefit analysis methodology is developed and used to assess the relative benefits of the mitigation measures for the case study of the Tannery Creek sub-watershed of the East Holland River in Ontario, Canada. The novel contributions of this study include the development of three relationships to estimate sediment and associated phosphorus loading based on contributing catchment area and land use, as well as a method to optimize the costs and benefits of planned mitigation measures. The results support stream restoration as an essential and cost-effective part of a comprehensive water quality watershed management plan to help maintain healthy streams in urbanized watersheds. [ABSTRACT FROM AUTHOR]

A significant amount of the damages caused by floods is usually due to suspended sediments in the flood and dredging costs due to their subsidence in natural, residential and industrial areas. Therefore, sediment monitoring is very important when the water discharges. On the other hand, the non-linear nature of sediment data have made it difficult to predict this parameter. Wavelet theory is one of the pre-processing methods that can help we lead to a better resolution of the internal relationships of non-linear data by breaking down the main time series into sub-signals. I this research, the sediment data values in two stations of Abnama and Minab from Hormozgan River watershed were broken through wavelet conversion into sub-signals, and then the prediction process was carried out by the artificial neural network. Moreover, in order to investigate the impact of wavelet transform on the performance of the neural network model, the results obtained from this combined model were compared with the results obtained from the single neural network model, and their efficiency was evaluated using multi-part validation method, correlation, and root-mean-square error. The results showed that the artificial neural network in the two studied stations is able to simulate the sediment discharge with a correlation of 0.89 and 0.68 as well as the wavelet neural network with a correlation of 0.9 and 0.8. Moreover, the normalized root-mean-square error statistics were 0.104 and 0.35 in artificial neural networks and 0.124 and 0.18 in combined networks, respectively. The results showed that the impact of the wavelet on identifying sub-signals and thus improving the performance of the model compared to individual neural networks on predicting the amount of sediments in floods is clearly significant. [ABSTRACT FROM AUTHOR]

Identifying potential sources of suspended sediment (SS) in headwater catchments is crucial for water quality management. To differentiate these potential SS sources, we investigated the distribution of two fallout radionuclides (FRNs), 137Cs and 210Pbex, using gamma spectrometry along with soil organic matter (SOM) analysis in a headwater catchment with five potential SS sources: cultivated land, non-harvested forest floor, eroded hillslope, harvested forest floor, and stream bank. The 137Cs and 210Pbex concentrations and the SOM content were considerably higher in the harvested forest floor materials than in the other four potential SS source materials. FRN concentrations revealed distinct properties according to the type of potential SS sources. Specifically, the combination of FRNs (with the effect of SOM content removed) associated with the mineral fraction and SOM showed distinguishable differences among the potential SS sources, except for no difference between cultivated land and eroded hillslope. Therefore, SOM and FRNs, or their combination, can be effective indices to differentiate or trace potential SS sources on various land use/land cover types within a catchment. Further field tests will allow the tracing techniques that bind FRNs with SOM to contribute to understanding SS transport from non-point sources within a catchment. [ABSTRACT FROM AUTHOR]

Aquatic vegetation has the potential to increase suspended sediment capture while also increasing sediment resuspension and bedload transport. Suspended sediment can induce density stratification, which modulates the turbulence in the water column. We derive a Rouse‐based formulation for suspended sediment concentration (SSC) including the effect of sediment‐induced density stratification. We perform Large Eddy Simulations of vegetated and non‐vegetated channels to explicitly highlight the effect of stratification on SSC profiles. We found that the impact of stratification is dominant in the near‐bed region within the bottom boundary layer, affecting both sediment resuspension and bedload transport. Stratification reduces the likelihood of both dominant sweep and ejection events in the near the bed region which may affect sediment entrainment and bedload transport. Modifications to existing models of sediment entrainment and bedload transport are suggested to account for the effects of sediment induced stratification in vegetated and non‐vegetated channels. Plain Language Summary: One of the primary functions of rivers is to transport sediments due to the movement of water. Sediments come in different sizes‐ larger sediments are heavy and slowly move along the riverbed, while smaller lighter sediments are suspended in the water and move with the flow. Suspension of fine sediments in river is directly related to the amount of turbulence in the flow. Suspended sediments tend to settle under their weight, while the turbulence keeps them from settling. The balance of gravitational settling and turbulent mixing leads to an equilibrium suspended sediment concentration profile in the water column. This causes a variation in the density of the water‐sediment mixture along the river's depth, which reduces the turbulence in the river. However, aquatic vegetation which is a natural part of the river increases turbulence in the flow. In this work, we study the effect of density stratification in channels with aquatic vegetation which has implications for sediment transport in natural waterways. Key Points: We present a Rouse‐type formulation including effects of sediment‐induced stratification to predict suspended sediment concentration in vegetated channel flowsStratification effects are explicitly revealed using Large Eddy Simulations of vegetated and non‐vegetated channelsStratification is relevant for near bed processes such as sediment entrainment and bedload transport [ABSTRACT FROM AUTHOR]

Arid regions are often characterized by exceptionally high rates of fluvial sediment transport, but the processes responsible for this apparent connection between climate and sediment transport remain unclear. We examined decades of continuous flow records and suspended sediment concentrations from 71 rivers across the United States by comparing the suspended sediment rating curve behavior, quantified using power law coefficients and exponents, to an aridity index. Results indicate that higher aridity correlates with both greater overall suspended sediment concentration and lower sensitivity of concentration to changes in discharge, demonstrating that rivers in arid locations on average have greater suspended sediment transport efficiency across most discharges, and achieve high transport rates at a relatively lower discharge than rivers in temperate climates. Furthermore, based on additional analyses of the Normalized Difference Vegetation Index, specific suspended sediment yield, and a hydrograph flashiness index, we attribute the relationships between sediment transport and climate primarily to differences in vegetation density, precipitation, and runoff, variables that all influence both sediment supply and riverbed grain sorting. Finally, we note that the observed contrasts in sediment transport behavior likely represent climate‐driven differences in the magnitude and frequency of sediment supply rather than annual suspended sediment load, which does not depend significantly on climate. This study highlights a critical connection between multiple interrelated climatic factors and sediment transport, an important finding for future hazard mitigation in a changing climate with rapidly shifting vegetation patterns and hydrology. Plain Language Summary: There is a wide variation in the appearance and behavior of rivers throughout the world, which often relates to how rivers move sediment. For example, the degree of erosion and sediment deposition following large floods does not depend solely on the size of the flood but also on the amount of sediment it contains. To this point, some evidence suggests that rivers in dry climates carry larger volumes of sediment at a given flow than rivers in temperate climates, but this connection has not been sufficiently explored on a large scale. We addressed this scientific gap by examining how sediment concentrations in rivers respond to changes in flow. Our results from dozens of rivers across the United States indicate that streams in more arid climates often carry higher concentrations of sediment at a given flow rate than their temperate counterparts, especially during smaller flood events. This behavior may relate to differences in vegetation density and rainfall patterns, as both of these climate‐dependent factors influence the quantity of sediment entering streams and the organization of sediment on the streambed. These findings reveal an important link between climate and sediment transport, which is important to consider as we prepare for future climate change. Key Points: Flow records and suspended sediment data from 71 rivers in the US reveal a significant relationship between aridity and sediment transportIn arid climates, suspended sediment concentration is on average higher and less sensitive to changes in discharge than in humid climatesClimate‐dependent factors like vegetation density and runoff variability likely explain observed differences in sediment transport behavior [ABSTRACT FROM AUTHOR]

The majority of sediment transported from rivers to the global oceans is moved in suspension as fine particles. Thereby, the transported sediment shapes the physical environment regarding erosive and accumulative processes. Temporal variations in sediment supply and transport lead to unquantified uncertainties in annual load estimates, requiring high‐resolution data sets and a sound understanding of site‐specific catchment characteristics. We investigate the temporal variability of suspended sediment transport in four catchments in Germany with highly different discharge regimes and catchment sizes (<1,000 km2 to >100,000 km2). The data set consists of high‐resolution 15‐min turbidity measurements with daily discharge and frequent manual sampling. Utilizing a bootstrap approach based on the 15‐min time series, we assessed the impact of the sampling interval on annual load estimates for less frequent data sets. We use the sediment load exceedance time (Ts80%) as a measure of variability and relate it to uncertainties in annual load estimates. Since low‐frequency data sets rely on sediment rating curves, we performed a sensitivity analysis of the rating parameters a, b, and ε. Our results indicate a negative exponential relationship between Ts80% and uncertainties in annual load estimates. Based on the Ts80%, we can derive the shortest sampling frequency necessary to obtain annual load estimates with an error of <20% over varying discharge regimes. Additionally, Ts80% is linked to rating exponent b, with low b‐values indicating high Ts80%‐values and lower variability, and high b‐values indicating higher variability. Plain Language Summary: On their way to the global oceans, rivers transport sediment. The transported sediment can be divided into two main fractions: fine particles and coarse sediment. Fine particles are distributed over the entire water column and represent most of the mass (sediment load) that is transported by rivers. The sediment load for rivers is not constant and varies significantly over time. Thereby, it is challenging to determine (annual) sediment loads with high accuracy. Through the combination of specialized sensors, which measure continuously, and manual water samples it is possible to reduce uncertainties in the calculation of sediment loads. First, we investigate how much data is necessary to calculate annual sediment loads with as little uncertainty as possible. Second, we investigate how much time is necessary to transport the majority (80%) of the annual sediment load. Third, we investigate how interpolation and extrapolation of small/infrequent data sets affects uncertainties in the calculation of annual sediment loads. Our results help planners and users of sediment transport data to refine (existing) sampling schemes and interpret low‐resolution data sets. Key Points: Negative exponential relationship between sediment load exceedance time and uncertainties in annual load estimatesOptimal sampling frequency can be derived from uncertainty‐sediment load exceedance time functionSediment rating exponent b is directly linked to sediment load exceedance time [ABSTRACT FROM AUTHOR]

Most hydrological and water resources researchers prioritize the development of an accurate sediment prediction model. Several conventional techniques have failed to accurately predict suspended sediment. Because of the intricacy, non-stationarity, and nonlinearity of sediment movement behavior in streams and rivers, many techniques fall short. Over the last several years, there have been meaningful theoretical improvements in the understanding of machine learning approaches, vis a vis strategy for the implementation of their processes and uses of the technique for practical and hydrological issues. To produce the desired output, machine learning models and other algorithms have been employed to predict complicated nonlinear connections and patterns of huge input parameters. This paper examines a few key works of the literature on sediment transport prediction while focusing on a variety of machine learning applications. Sediment transport models aided by machine learning have attracted a growing number of researchers in recent years. As a result, they must gain in-depth knowledge of their theory and modeling methodologies. Furthermore, this chapter includes an overview of the machine learning technique and other developing hybrid models that have produced promising outcomes. This overview also includes various examples of successful machine learning applications in sediment prediction. [ABSTRACT FROM AUTHOR]

When dams discharge floodwaters, the river downstream often becomes supersaturated in total dissolved gases (TDG) and contains high volumes of suspended sediments (SS). Supersaturated TDG and high SS concentrations in water may affect fish physiologically in ways that manifest in swimming performance. Despite increasing awareness of the separate effects of TDG supersaturation and SS, knowledge about their synergistic effects remains unknown. To explore the interactive effects of TDG and SS on the swimming performance of bighead carp, the juveniles were exposed to 100, 110, 115, 120, 125, 130, 135, and 140% of TDG‐supersaturated water with SS concentrations of 0, 50, 100, and 150 mg/L, respectively, and the critical swimming ability speed (Ucrit) and burst swimming ability speed (Uburst) were measured. The results indicated that the swimming ability (Ucrit and Uburst) decreased when TDG levels and SS concentrations increased. TDG and SS did not interact significantly to decrease both Ucrit and Uburst. In contrast, exposure to TDG alone significantly decreased both Ucrit and Uburst, whereas exposure to SS alone decreased only Uburst. In addition, our results suggested that there was a negative linear relationship between TDG and fatigue time. Swimming ability can decline significantly due to high TDG levels (>130%). Therefore, high TDG levels (>130%) should be restricted during reservoir operation to prevent the stress caused by TDG. [ABSTRACT FROM AUTHOR]

Estimation of the sediment load in rivers is one of the important issues in studies related to water quality and transport of pollutants, construction and operation of hydraulic structures, maintenance of reservoirs, water transmission networks, and water resources management .An accurate understanding of the sedimentation of a watershed can provide a correct understanding of soil erosion and its consequences. Since sediment changes in the river are often a function of flow discharge changes; therefore, methods of measuring suspended sediment load based on the suspended sediment concentration and flow discharge will be useful in estimating the amount of sediment load. The sediment rating curve is one of the methods that is based on flow discharge and sediment discharge and expresses the relationship between these two parameters in the form of power regression (Eq 1). QS=aQbw where 𝑄𝑠 is the suspended sediment discharge (in tons per day), 𝑄𝑤is the flow discharge (in cubic meters per second), and a and b are the coefficients of the equation. Rating curves can be drawn in different ways according to the way of data separation. Among these methods, we can refer to one-line, multi-line, mean of categories, seasonal, monthly, annual models, etc. The presence of bias in the sediment discharge relationship makes this relationship unable to show the exact sediment concentration in different flow discharges. This bias causes the amount of sediment to be underestimated. Various researchers have proposed some statistical correction factors to achieve the minimum error, which are applied in the sediment rating equation. In this research, in order to increase the accuracy of sediment estimation by using a sediment rating curve, at first, different types of rating curves were drawn for the station and, finally, correction factors consisting of QMLE, Smearing, MVUE, and (Beta) β were applied for the selected curve. Also, an attempt was made to separate the data into three categories of dry, normal and wet by using the percentage of normal precipitation and to draw the sediment rating curve for each. At the end, the results obtained from the statistical model (SRC) were compared with artificial intelligence models including two models of multilayer perceptron (MLP) and radial basis set (RBF) neural networks. In this research, the flow and sediment discharge data from 1350 to 1397 for the Jelogir station in Khuzestan province located on the main Karkhe River were prepared from the Khuzestan Regional Water Organization. Sediment rating curve models, including common linear curve (USBR), mean of categories, monthly, seasonal, bilinear, trilinear, dry, normal and wet models were drawn for the station. Then, for the drawn curves, evaluation criteria including RMSE, ME and P were checked and, finally, by ranking these criteria, the curve with the least error was selected. In determining the rank of each model, the values of the evaluation indices were compared with each other. In this way, the closest P and ME index value to 1 and the closest RMSE index value to zero, which indicates the least difference between the estimated and observed sediment values, was assigned the first rank. In order to investigate the effect of skew correction coefficients on the accuracy of sediment rating curves, coefficients including MVUE, FAO, QMLE and Smearing were applied on the rating curve which was selected as the optimal model in the previous step. The data were processed using neural network models. For this purpose, different structures of neural networks with different layers, neurons and functions were investigated through trial and error. 3- Results According to the obtained results, the mean categories method has the highest correlation coefficient (0.85). The RMSE in rainy and flooding months (April and March) and also in high flow discharge rates (in bilinear, and trilinear models, at flow discharge greater than 201 and 114 cubic meters per second, respectively), has allocated the largest amount. The lowest value of RMSE is related to the months of August and September, which is reasonable due to the lack of rainfall and flooding in these months and as a result of low erosion of sediments. According to the ranking values, the periods of low rainfall, including summer and July, August and September are in the first ranks, and as a result, the sediment rating curve has more accuracy in estimating sediments. Finally, the rating curve of August, which has the lowest total ranking value, was chosen as the optimal curve. According to the ranking of the correction coefficients, it can be seen that the sediment rating curve without applying the correction coefficients (the highest rank) has the highest amount of error and by applying the coefficients, the error of sediment flow estimation can be reduced. Finally, MVUE with the lowest total ranking was chosen as the optimal correction coefficient, and by applying it, the accuracy of the model in estimating the sediment discharge increases. In the neural network model, Lunberg-Marquardt optimization algorithm was used and the number of hidden layer neurons in the best MLP and RBF structure was obtained as 5 and 6, respectively. Also, the activator function in the hidden layer in MLP was selected as sigmoid tangent and Gaussian function in RBF. The results show that by using neural networks of multilayer perceptron, it is possible to predict the amount of suspended sediment with higher accuracy, and the accuracy of the results obtained from the artificial neural network method is far higher than the accuracy of the rating curve method with and without data classification. According to the results, the MLP model has shown a lower error value than the RBF radial base model. 4- Discussion & Conclusions In this article, in order to estimate the suspended sediment in the Jelogir station, the data were separated into different forms and the sediment rating curves were drawn into linear curve (USBR), mean of categories, monthly, seasonal, dry, normal, wet, bilinear, and trilinear types. The obtained results showed that the accuracy of the relationship obtained for the classification of data based on August (R2= 0.785) and the total rating of 9 (the lowest value) was more than the other models. And at high flow discharge, the accuracy of the models decreases. It was found that the correction coefficients are effective in increasing the accuracy of the models, and the lowest amount of error for the optimal model is obtained by using MVUE. Comparing the results of statistical methods and neural networks showed that neural network models are more accurate in estimating daily sediment. The better performance of artificial neural networks compared to statistical methods can be expressed in the nonlinear approximation capability of neural networks. [ABSTRACT FROM AUTHOR]

The Western Pacific Coasts (WPC) are the outlets of many large Asian rivers. In recent years, the interplay of climate changes and human activities has persistently altered the suspended sediment concentrations (SSC) in the WPC, triggering substantial shifts in coastal ecosystems. However, the scarcity of coastal observation stations hampered comprehensive investigations at large scales. This study employed three representative SSC retrieval models and utilized Landsat images acquired from 1990 to 2020 to estimate the SSC in the WPC with a focused endeavor to dissect the intricate spatial and temporal variability of SSC in the region. The findings revealed the following insights: (1) The outcomes derived from the three distinct SSC models consistently manifested a discernible decreasing pattern in SSC changes over the past three decades across all six major estuaries (Liao River Estuary, Yellow River Estuary, Yangtze River Estuary, Hangzhou Bay, Pearl River Estuary, and Mekong River Estuary). (2) The seasonal attributes of the six major estuaries differed, primarily due to distinct dominant influencing factors like precipitation, upstream sediment load, wind, and tides. (3) Collectively, SSC tends to be relatively higher in the Yangtze River Estuary, Hangzhou Bay, and Yellow River Estuary, while the Pearl River and Mekong River Estuaries exhibit relatively lower levels. Notably, the SSC exhibited distinct spatial traits along the coastlines of different estuaries. (4) SSC in the non-estuarine regions along the WPC, a similar significant declining trend in SSC is observed as in the estuaries, albeit the rate of decline generally appeared to be less pronounced. Furthermore, regions with faster rates of SSC reduction are typically concentrated near major estuaries in the northern part of the Coasts. The decline in estuarine SSC plays an important role in the overall decrease in SSC across the WPC. These study outcomes held substantial significance for advancing the stability and sustainable evolution of the WPC. [ABSTRACT FROM AUTHOR]

This paper was elaborated in order to estimate suspended sediment in Isser watershed located in North-West of Algeria. Power functions models by least squares regression were developed to establish rating curves. In total, 2026 pairs of instantaneous water discharge (Q) and instantaneous suspended sediment concentration (C) from the period 1988-1989 to 2003-2004 were used. In order to reveal the temporal impact, four subdivision data are elaborated: all, annual, seasonal and monthly scales. Better estimation of the total suspended sediment yield was obtained by application of the power linear model in monthly division data (-0.16%). When considering efficiency, both models with seasonal scale offered coefficients in order of 0.95%. The approach of correction for models did not improve accuracy of the estimation. The results have indicated for the 16 years of the available data, that the Isser watershed has drained a total of 194.37 million m3 of water and 1.4 million tons of suspended sediments, with a specific degradation of 77 tons/km²/year. [ABSTRACT FROM AUTHOR]

Understanding the physical processes as well as the hydrological and morphological factors that influence channel bank erosion is important for river restoration and the management of the floodplain and associated ecosystems. In this study, we introduced an innovative approach to quantify river bank erosion and its contribution to a reach fine sediment budget by combining repeat bank erosion surveys using a jetboat‐mounted LiDAR scanner with concurrent high‐frequency suspended sediment load measurements into and out of the surveyed reach. Using this information, we established a sediment budget for a 5.5‐km‐long study reach of the lower Ōreti River, Southland, New Zealand. A total of three surveys were conducted along the study reach to understand changes in the bank erosion contribution to suspended sediment load at different time scales. The first two surveys were separated by a short period of 8 weeks, and the third survey followed 2.5 years later. The measured volumes of fine sediment rendered from bank erosion equated to 25% and 29% of the measured outflowing suspended load over these two inter‐survey epochs, respectively. By comparison, the net contribution of measured bank erosion and derived fine sediment deposition on the riverbed to the outflowing suspended load was 12% over the first, shorter epoch and 25% for the second, 2.5‐year epoch. These results highlight the important role of in‐channel sediment deposition in the variability of net suspended sediment exports from channel reaches experiencing bank erosion. The approach used in this study has a unique capability to accurately monitor bank erosion and obtain high‐resolution topography data capturing changes in river banks over different time periods. [ABSTRACT FROM AUTHOR]