Estimation Of Suspended Sediment Concentration Research Articles

Comparison between Hyperspectral and Multispectral Retrievals of Suspended Sediment Concentration in Rivers

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.

Application of deep learning in predicting suspended sediment concentration: A case study in Jiaozhou Bay, China

Previous research methodologies for quantifying Suspended Sediment Concentration (SSC) have encompassed in-situ observations, numerical simulations, and analyses of remote sensing datasets, each with inherent constraints. In this study, we have harnessed Convolutional Neural Networks (CNNs) to create a deep learning model, which has been applied to the remote sensing data procured from the Geostationary Ocean Color Imager (GOCI) spanning April 2011 to March 2021. Our research indicates that on a small time scale, wind and hydrodynamic forces both have a significant impact on the prediction results of CNNs model. Considering both wind and hydrodynamic forces can effectively improve the model's prediction efficiency for SSC. Moreover, we have employed CNNs to interpolate absent values within the remote sensing datasets, yielding enhancements superior to those attained via linear or multivariate regression techniques. Finally, the correlation coefficient between CNN-derived SSC estimates for Jiaozhou Bay (JZB) and its corresponding remote sensing data is 0.72. Correlation coefficient and root mean square error differ in different regions. In the shallow water of JZB, due to water level changes, there is limited data, and the correlation coefficient in this area is about 0.5–0.6. In the central region of JZB with sufficient data, the correlation coefficient is generally higher than 0.75. Therefore, we believe that this CNNs model can be used to predict the hourly variation of SSC. When juxtaposed with alternative methodologies, the CNN approach is found to economize computational resources and enhance processing efficiency.

Improved adaptive neuro-fuzzy inference system with bacterial foraging optimization algorithm for suspended sediment concentration estimation

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.

Suspended Sediment Concentration Estimation along Turbid Water Outflow Using a Multispectral Camera on an Unmanned Aerial Vehicle

Optical remote sensing using unmanned aerial vehicles (UAVs) is proposed to monitor changes in marine environments effectively. Optical measurements were performed using a UAV multispectral camera (RedEdge, five spectral wavelengths of 475, 560, 668, 717, and 842 nm) with high spatial (5 cm) and temporal (1 s) resolutions to monitor the rapidly changing suspended sediment concentration (SSC) in the Saemangeum coastal area on the western coast of Korea. To develop the SSC algorithm, optical field, and water sample measurements were obtained from outside (11 stations) and inside (three stations) regions separated by a seawall, accounting for 100 measurements from 2018 to 2020. Accordingly, the remote sensing reflectance (Rrs) was estimated at each sampling station and used to develop the SSC algorithm based on multiple linear regression. The algorithm reasonably estimated the SSC with an R2 and root mean square error of 0.83 and 4.27 (mg L−1), respectively. Continuous individual UAV measurements over the coastal area of Saemangeum were combined to generate a wider SSC map. For the UAV observational data, the atmospheric influence at each altitude was reduced to the surface altitude level using a relative atmospheric correction technique. The SSC map enabled front monitoring of SSC fluctuations caused by discharge water due to the sluice gate opening. These results demonstrated the usability of the UAV-based SSC algorithm and confirmed the possibility of monitoring rapid SSC fluctuations.

Sediment source and sink identification using Sentinel-2 and a small network of turbidimeters on the Vjosa River

Abstract. Measurement of suspended sediment concentration (SSC) at a basin outlet yields a basin-integrated picture of sediment fluxes; however, it does not give a full spatial perspective on possible sediment pathways within the catchment. Spatially resolved estimates of SSC along river networks are needed to identify sediment sources and sinks, to track erosion gradients, and quantify anthropogenic effects on catchment-scale sediment production, e.g., by dam construction or erosion control. Here we explore the use of high-resolution Sentinel-2 satellite images for this purpose in narrow and morphologically complex mountain rivers, combined with ground station turbidity sensing for calibration and supported by a Lagrangian kayak-derived river profile measurement. The study is carried out on the Vjosa River in Albania, which is one of the last intact large river systems in Europe. We developed a workflow to estimate river turbidity profiles from Sentinel-2 images including atmospheric, cloud cover, and deepwater corrections for the period May 2019 to July 2021 (106 images). In situ turbidity measurements from four turbidity sensors located along the Vjosa River provided ground truthing. A multivariate linear regression model between turbidity and reflectance was fitted to this data. The extracted longitudinal river turbidity profiles were qualitatively validated with two descents of the river with a turbidity sensor attached to a kayak. The satellite-derived river profiles revealed variability in turbidity along the main stem with a strong seasonal signal, with the highest mean turbidity in winter along the entire length of the river. Most importantly, sediment sources and sinks could be identified and quantified from the river turbidity profiles, both for tributaries and within the reaches of the Vjosa. The river basin and network acted as a sediment source most of the time and significant sediment sinks were rare. Sediment sources were mostly tributaries following basin-wide rainfall, but within-reach sources in river beds and banks were also possible. Finally, we used the data to estimate the mean annual fine sediment yield at Dorez at ∼2.5±0.6 Mt yr−1, in line with previous studies, which reveals the importance of the Vjosa River as an important sediment source of the Adriatic Sea. This work presents a proof of concept that open-access high-resolution satellite data have potential for suspended sediment quantification not only in large waterbodies but also in smaller rivers. The potential applications are many, including identifying erosion hotspots, sediment activation processes, local point sources, glacial sediment inputs, and sediment fluxes in river deltas, with a necessary future research focus on improving accuracy and reducing uncertainty in such analyses.

Enhancing suspended sediment concentration retrieval by integrating thermal infrared and optical bands of Landsat-8 and machine learning algorithms

ABSTRACT Suspended Sediment Concentration (SSC) variabilities sizably affect the bio-optical conditions in water bodies. Remote sensing plays a key role in spatiotemporal monitoring of the SSC in inland and coastal waters. However, existing studies on remote sensing of SSC focus on optical domains, i.e. visible and near-infrared bands. Here, we assume thermal infrared bands can potentially contribute to retrieving SSC, inspired by sediments’ heat-trapping effect demonstrated in aquatic research. Thus, we propose to incorporate the thermal infrared bands of Landsat-8 along with the optical bands for the estimation of SSC using machine learning models. In this study, 85 Landsat-8 images collected from the Google Earth Engine (GEE), coupled with in-situ observations from the United States Geological Survey (USGS) gauging stations, were employed to estimate SSC variabilities in Missouri-Mississippi Rivers. The relationship between in-situ SSC and water temperature revealed a promising correlation (R = 0.7), motivating our assumption of incorporating thermal infrared bands. Therefore, we employed both optical and thermal infrared bands of Landsat-8 imagery as predictors for the first time to estimate SSC. Three machine learning algorithms of Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) were considered. The results demonstrated that incorporating thermal infrared bands improved the performance of the models (average improvements in R2 for ~ 9.6% and in RMSE for ~ 18.9%) with respect to the use of optical bands only.

Quantifying Flow Velocities in River Deltas via Remotely Sensed Suspended Sediment Concentration

AbstractDeltas are fragile ecosystems threatened by sea‐level rise, sediment starvation, and subsidence. Erosional/depositional processes in these systems mainly depend on the sediment supply and the spatial divergence in bed shear stress induced by hydrodynamic forces. Thus, quantifying the spatiotemporal variability of the flow velocity field is essential for forecasting their fate. To calibrate/validate models, field measurements alone are not sufficient because such data only characterize the hydrodynamic conditions in localized areas. Remote sensing has potential to fill this data gap. We developed a methodology to estimate flow velocities from a map of suspended sediment concentration (SSC) measured by the NASA airborne spectrometer AVIRIS‐NG within the Wax Lake Delta, Louisiana. We extracted streaklines from remotely sensed SSC estimates, and quantified water fluxes and velocities based on the distance between them. Our study demonstrates that the velocity field in deltas can be estimated by leveraging the synoptic information offered by remote sensing.

Applying the C-Factor of the RUSLE Model to Improve the Prediction of Suspended Sediment Concentration Using Smart Data-Driven Models

The accurate forecasts and estimations of the amount of sediment transported by rivers are critical concerns in water resource management and soil and water conservation. The identification of appropriate and applicable models or improvements in existing approaches is needed to accurately estimate the suspended sediment concentration (SSC). In recent decades, the utilization of intelligent models has substantially improved SSC estimation. The identification of beneficial and proper input parameters can greatly improve the performance of these smart models. In this regard, we assessed the C-factor of the revised universal soil loss equation (RUSLE) as a new input along with hydrological variables for modeling SSC. Four data-driven models (feed-forward neural network (FFNN); support vector regression (SVR); adaptive neuro-fuzzy inference system (ANFIS); and radial basis function (RBF)) were applied in the Boostan Dam Watershed, Iran. The cross-correlation function (CCF) and partial autocorrelation function (PAFC) approaches were applied to determine the effective lag times of the flow rate and suspended sediment, respectively. Additionally, several input scenarios were constructed, and finally, the best input combination and model were identified through trial and error and standard statistics (coefficient of determination (R2); root mean square error (RMSE); mean absolute error (MAE); and Nash–Sutcliffe efficiency coefficient (NS)). Our findings revealed that using the C-factor can considerably improve model efficiency. The best input scenario in which the C-factor was combined with hydrological data improved the NS by 16.4%, 21.4%, 0.17.5%, and 23.2% for SVR, ANFIS, FFNN, and RBF models, respectively, compared with the models using only hydrological inputs. Additionally, a comparison among the different models showed that the SVR model had about 4.1%, 13.7%, and 23.3% (based on the NS metric) higher accuracy than ANFIS, FFNN, and RBF for SSC estimation, respectively. Thus, the SVR model using hydrological data along with the C-factor can be a cost-effective and promising tool in SSC prediction at the watershed scale.

Assessment of the potential for quantifying multi-period suspended sediment concentration variations using satellites with different temporal resolution

Suspended sediment concentration (SSC) is a crucial indicator for coastal health and geomorphological evolution, featured by complex periodic processes on multiple timescales in response to different cyclic forcing factors. Although remote sensing has functioned as an important means for SSC estimation with sufficient spatio-temporal coverage, the low effective sampling rates and resulting unevenly spaced characteristics of the retrieved time series would hamper the extraction of the representative SSC portrayal (amplitude and phase) on multiple timescales, especially for low-resolution satellites. Here, we retrieved a 9-year hourly GOCI SSC time series (January 2012 to December 2020) at two coastal sites in China (Haimen and Haizhou Bay) as reference cases, and utilized them to obtain MODIS, Sentinel and Landsat sequences with average temporal resolutions of 0.5, 5.6 and 11.2 days as preliminary investigations into amplitude and phase extractions. Furthermore, we generated GOCI-based hypothetical satellite time series with temporal resolutions ranging from 1 to 16 days (1088 subsets) and their mutual combination (591,328 subsets) to explore general laws when extracting amplitudes and phases from satellites with different temporal resolutions by application of the Lomb-Scargle Periodogram and phase-folded diagram methods. The amplitude and phase deviations were found to increase with decreasing temporal resolution on seasonal and fortnightly timescales at Haimen and in Haizhou Bay, while by mutual combination of satellites the errors could be reduced as more data were utilized for the extraction. It is shown that larger amplitude and phase deviations occur on the seasonal timescale in comparison to the fortnightly timescale at Haimen, whereas the situation reverses in the case of Haizhou Bay. These results demonstrate that temporal resolution, data characteristics on the target timescale and absolute SSC amplitude codetermine the extraction accuracy. This further indicates that satellites with lower temporal resolutions can potentially be used on a global scale for extracting the feature changes of multi-period SSC variations, in particular as continuous improvements in data quantity and quality can be expected in the future.

Measurements of suspended ashes concentration in turbulent flow with acoustic doppler velocimeter

Measuring and estimating dispersion coefficients for ash particles released in watercourses is required for a broad understanding of their transport and behaviour in aquatic ecosystems. Calibration of a high-frequency acoustic backscattering system and estimates of its sensitivity to particulates of known size distribution and concentration are presented. Laboratory experiments were conducted to derive the relationship between suspended sediment concentration (SSC) of biomass ashes and the signal-to-noise ratio (SNR) measured by a 10 MHz ADV. Small portions of ashes were added in steps (0.006–20 gl−1) into a stirred water tank while ADV was recording changes in SNR, with various combinations of ADV settings, which included power (LOW, LOW+), transmit length (TL = 0.3 mm, 0.6 mm) and frequencies of data acquisition (25 Hz, 100 Hz). Samples were collected for gravimetric and particle size distribution (PSD) analysis. As expected, the salinity and pH of the collected samples increased with SSC. Different backscatter responses were detected depending on the ADV settings and SSC. To maximize the calibration range, the ADV settings power LOW+, TL = 0.3 mm and sample frequency 25 Hz were combined, resulting in a calibrated SSC range of 10–500 mgl−1, where a linear relationship was obtained. The results suggest that it may be possible to apply a single-frequency acoustic technique to the detection of suspended ashes. The particle type and size distribution changed with a concentration above 0.5 gl−1, which may lead to errors in the acoustic estimates of SSC in ash-laden flows.

Rapid changes to global river suspended sediment flux by humans.

Rivers support indispensable ecological functions and human health and infrastructure. Yet limited river sampling hinders our understanding of consequential changes to river systems. Satellite-based estimates of suspended sediment concentration and flux for 414 major rivers reveal widespread global change that is directly attributable to human activity in the past half-century. Sediment trapping by dams in the global hydrologic north has contributed to global sediment flux declines to 49% of pre-dam conditions. Recently, intensive land-use change in the global hydrologic south has increased erosion, with river suspended sediment concentration on average 41 ± 7% greater than in the 1980s. This north-south divergence has rapidly reconfigured global patterns in sediment flux to the oceans, with the dominant sources of suspended sediment shifting from Asia to South America.

Seagrass Meadows Reduce Wind-Wave Driven Sediment Resuspension in a Sheltered Environment

Seagrass meadows are prominent in many coastal zones worldwide and significant contributors to global primary production. The large bottom roughness (or canopy) created by seagrass meadows substantially alters near-bed hydrodynamics and sediment transport. In this study, we investigate how a seagrass meadow in a low-energy environment (forced by local winds) modifies near-bed mean and wave-driven flows and assess how this relates to suspended sediment concentration (SSC). A two-week field study was conducted at Garden Island in southwestern Australia, a shallow and sheltered coastal region subjected to large diurnal sea-breeze cycles, typical of many low-energy environments where seagrasses are found. The mean and turbulent flow structure, along with optical estimates of SSC, were measured within both a seagrass canopy and over an adjacent bare bed. Near-bed mean current velocities within the seagrass canopy were on average 35% of the velocity above the canopy. Oscillatory wave velocities were less attenuated than mean current velocities, with near-bed values on average being 83% of those above the canopy. Mean and maximum shear velocities inferred from currents and waves above the canopy frequently exceeded the threshold for sediment resuspension, but no significant variation was observed in the SSC. However, a significant correlation was observed between SSC and bed shear stress estimated using near-bed velocities inside the canopy. When sediment was resuspended, there were substantial differences between the SSCs within and above the canopy layer, with higher levels confined within the canopy. This study demonstrates the importance of measuring near-bed hydrodynamic processes directly within seagrass canopies for predicting the role seagrass meadows play in regulating local rates of sediment resuspension.

Improving Flow Discharge-Suspended Sediment Relations: Intelligent Algorithms versus Data Separation

Information on the transport of fluvial suspended sediment loads (SSL) is crucial due to its effects on water quality, pollutant transport and transformation, dam operations, and reservoir capacity. As such, adopting a reliable method to accurately estimate SSL is a key topic for watershed managers, hydrologists, river engineers, and hydraulic engineers. One of the most common methods for estimating SSL or suspended sediment concentrations (SSC) is sediment rating curve (SRC), which has several weaknesses. Here, we optimize the SRC equation using two main approaches. Firstly, three well recognized metaheuristic algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and imperialist competitive algorithm (ICA)) were used together with two classical approaches (food and agriculture organization (FAO) and non-parametric smearing estimator (CF2)) to optimize the coefficients of the SRC regression model. The second approach uses separation of data based on season and flow discharge (Qw) characteristics. A support vector regression (SVR) model using only Qw as an input was employed for SSC estimation and the results were compared with the SRC and its optimized versions. Metaheuristic algorithms improved the performance of the SRC model and the PSO model outperformed the other algorithms. These results also indicate that the model performance was directly related to the temporal separation of data. Based on these findings, if data are more homogenous and related to the limited climatic conditions used in the estimation of SSC, the estimations are improved. Moreover, it was observed that optimizing SRC through metaheuristic models was much more effective than separating data in the SCR model. The results also indicated that with the same input data, SVR was superior to the SRC model and its optimized version.

Measurement of suspended sediment concentration in open channel flows based on hyperspectral imagery from UAVs

Since conventional in situ measurements of suspended sediments in the river system are labor-intensive and time-consuming, remote sensing approaches using multi- or hyper-spectral cameras have widely been applied to obtain high-resolution suspended sediment concentration (SSC) distributions in rivers and streams. However, in nature, the properties of heterogeneous sediment, such as the mineral content and particle size distribution, induce a strong variability in the optical images of the suspended sediments. For this reason, the robust estimation of the suspended sediment using the remote sensing technique is challenging due to the optical variability of the suspended sediment. Thus, it is necessary to deal with this variability of the optical images to improve the accuracy of remote sensing-based SSC measurements and extend them to the global estimator. In this study, a robust Machine Learning (ML) model for SSC estimation based on hyperspectral images was developed by considering the optical variability of the suspended sediment in water bodies. A series of field-scale tracer experiments were conducted in open channels with three different sediment types in order to obtain both the SSC using laser diffraction sensors and hyperspectral images using a UAV camera. The experimental results showed that the optical characteristics of SSC were critically heterogeneous due to the properties of the sediment. Using these experimental dataset, four explicit regression models and two implicit ML regression models were developed and compared to select an optimal estimator. Consequently, a Support Vector Regression (SVR) model using relevant spectral bands in a wide wavelength range yielded the most accurate results, with an R2 of 0.90 for the whole dataset. However, linear regression models, which could not consider various spectral bands and the nonlinear effect of the optical variability of SSC, were limited in their ability to retrieve SSC from hyperspectral images. Furthermore, the SVR model accurately reproduced the spatio-temporal SSC distributions in all study cases, including low-visibility suspended sediments, thus successfully resolved the optical variability of SSC with widely selected spectral bands from recursive feature elimination (RFE). The SVR model also successfully retrieved the SSC distribution in uncalibrated rivers. The results of this study demonstrated that the proposed ML regression models based on the hyperspectral imagery achieved a significant improvement in SSC estimation in terms of accuracy and global applicability.

Changes in suspended sediments in the Yangtze River Estuary from 1984 to 2020: Responses to basin and estuarine engineering constructions

Suspended sediments in the estuary play an important role in regulating the erosion-accretion and shaping the geomorphological patterns. Yet, it can be strongly impacted by the human activities. Constructions of basin and estuarine engineering in the Yangtze River Estuary (YRE), including constructions of Three Gorges Dam (TGD) further upstream of the Yangtze River and Deep Waterway Project (DWP) in the estuary, have greatly modified the distribution of estuarine suspended sediment concentration (SSC). In this study, we retrieved the surface water SSC in the YRE from a total of 149 Landsat-TM/ETM+/OLI images (from 1984 to 2020) and 515 GOCI images (from 2011 to 2020) to obtain its long-term dynamics. The results indicate that the SSC estimation model performed well with a mean absolute percentage error of 12.83% and a root mean square error of 0.027 g/L. The SSC in the YRE demonstrated an overall declining pattern over the past 37 years, particularly during the flood season, which was related to the decrease in the sediment discharge of Yangtze River. The SSC in different sub-regions across the entire YRE responded differently to the reduced sediment discharge caused by the impoundment of the TGD. The SSC in the inner estuary responded promptly with a 40.3% decline noted immediately after the impoundment of the TGD, whereas hysteresis of SSC changes was found in the estuarine turbidity maximum zone (TMZ) and have begun to decline by 20.0–30.0% in the recent 5 years. Constructions of local estuarine engineering, such as the DWP also exert important controls on the estuarine SSC dynamics by blocking the lateral transport of suspended sediments between the shoal and the river channel. Our results highlight the strong influence of engineering constructions on the SSC dynamics in the world large river-influenced estuaries.

Acoustic Backscatter and Attenuation Due to River Fine Sediments: Experimental Evaluation of Models and Inversion Methods

AbstractThe hydroacoustic monitoring of suspended sediment concentration (SSC) in rivers is based on the inversion of backscatter and attenuation models. To evaluate such models, acoustic backscatter and attenuation were measured from a homogeneous suspension of fine river sediments (clay) in a laboratory tank at various concentrations in the range 1–18 g/l. Agreement between the modeled and measured acoustic backscatter and attenuation values was found to be relatively poor. The results are highly sensitive to particle size and shape which come with large measurement uncertainties and they can be significantly improved by adjusting plausible particle parameters. Various inversion methods combining single or multiple frequencies, analysis of backscatter and/or attenuation, spherical or oblate shape hypothesis for particles and fixed or estimated lognormal grain size distribution are tested. The most promising inversion methods using both backscatter and attenuation information led to accurate SSC estimates.

Estimation of suspended sediment concentration in Guamá River in the Amazon region.

Monitoring of the suspended sediment concentration (SSC) is important for the analysis of morphological processes in watersheds and rivers, river habitats, and human activities associated with river management. Acoustic Doppler current profilers (ADCP) are used for measurements of flow and velocity in rivers through acoustic waves, which can also be converted into sediment concentrations. In this context, the objective is to verify the applicability of the use of ADCPs (Sontek YSI, model M9 River Surveyor) as an alternative for SSC estimates, this being a recent application of the equipment, mainly in rivers in the Amazon region rich in sediments in suspension. Thus, the Gartner method was used, calibrating its equation with the concentrations obtained by the point sampling method, collected with a bottle of Van Dorn in the Guamá River, Amazon, Brazil. The method used to obtain the SSC with data measured via ADCP resulted in a satisfactory estimate of the concentrations. The correlations between the measured and estimated sediment concentrations had R2 = 0.6827 for point sampling method and R2 = 0.8103 for vertically averaged SSC. These satisfactory results demonstrate the potential of using ADCP to estimate sediments suspended in Amazon basin.

Indirect Estimation of Flow and Suspended-sediment Concentration and Load in Small Mountain Streams: an Exploratory Study in Ribeira Seca Stream, Madeira Island

The aim of this study was to assess the applicability of indirect techniques for streamflow and suspended sediment concentration estimation and their use in the calculation of suspended sediment transport rate in the small mountain watersheds of Madeira Island, Portugal. Emphasis was given to the application of salt dilution gauging to the indirect determination of the flow rate and the use of water turbidity data to estimate the concentration of suspended sediments. The field and laboratory work carried out are briefly described, and the main experimental results and the field data from the short measurement campaign performed in the Ribeira Seca stream in Faial, on the north side of the island, are presented and discussed. Whilst the measurement campaign carried out was temporally and spatially limited, it was pioneering for Madeira and allowed to verify the applicability of the indirect hydrometric and sedimentometric techniques used in this exploratory study.

EVALUATION OF SENTINEL-2/MSI IMAGERY PRODUCTS LEVEL-2A OBTAINED BY THREE DIFFERENT ATMOSPHERIC CORRECTIONS FOR MONITORING SUSPENDED SEDIMENTS CONCENTRATION IN MADEIRA RIVER, BRAZIL

Abstract. Data provided by spatial sensors combined with remote sensing techniques and analysis of the optical properties of waters allow the mapping of the suspended sediment concentration (SSC) in aquatic bodies. For this, estimation models require data with the lowest possible amount of atmospheric artifacts. In this study we compared the water remote sensing reflectance (Rrs) of the Santo Antônio Hydroelectric Power Plant reservoir in Porto Velho-RO, Brazil, after applying three different atmospheric corrections algorithms in Sentinel-2/MSI imagery products. The atmospheric corrected reflectances of the MODIS sensor were also used for reference. SSC was calculated with models based on the red and near-infrared (NIR) bands over three distinct regions of the reservoir. Reflectance data showed significant variations for Sentinel-2, bands 4 and 8a, and MODIS, bands RED and IR, when different atmospheric correction algorithms were used. SSC maps and estimates were produced to show sediment load variation as a function of hydrological regime. The analyzes showed that the SSC estimates done with Sentinel-2 / MSI satellite images using GRS (Glint Remove Sentinel) atmospheric correction presented an average difference of 27.3% and were the closest to the in situ measurements. SSC estimates from MODIS products were around 34.6% different from estimates made using the GRS atmospheric correction applied to Sentinel-2 / MSI products.

Application of soft-computing techniques in forecasting sediment load and concentration

ABSTRACT This paper investigates conventional and soft-computing methods for the estimation of suspended sediment concentration (SSC) and load (SSL) in rivers. Frequently used methods of sediment rate curve (SRC) and multi-nonlinear regression, and soft-computing methods of multi-layer perceptron, multi-linear regression and adaptive neuro-fuzzy inference system are implemented using various hydrological and hydraulic parameters for the Little Kickapoo Creek Watershed, Illinois, USA. All methods performed equally well in the estimation of SSL, without any noticeable outperformance from any from the methods. However, the application of soft-computing methods decreased SSC estimation errors considerably as compared to the results of SRC. The results are significant in the way they reconcile traditionally used hydrological parameters into the soft-computing methods. Overall, soft-computing methods are recommended for the estimation of SSC in rivers because of their reasonably better performance and ease of implementation.