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Reliable information of hydrological processes within a river basin is essentially required for developing an appropriate strategy for achieving sustainable development goals. The present study assesses the streamflow of a pro-glacial stream and also intends to estimate the contribution of suspended sediments, erosion rate, and the headwater contribution of the Panchachuli glacier. A field study during ablation period was carried out to measure streamflow and suspended sediment concentration (SSC). Further, HBV model was used to estimate the snowmelt. The average seasonal streamflow and SSC during the gauging period (July to October) for the basin were measured to be 7.17 m3/s, and 1.52 g/l in 2018, and 6.84 m3/s, and 1.21 g/l in 2019, respectively. Snowmelt contribution in total streamflow was 54.75% in 2018 which is reduced to 49.16% in 2019. Similarly, glacier melt contributes to 32.62% of its total runoff share in 2018 which was reduced to 28.73% in 2019. The rainfall runoff in total runoff increased to 12.62% from 2018 to 2019. Rainfall-runoff in its total runoff contribution showed an increased share of 22.13% in 2019. The streamflow, SSC, and suspended sediment load (SSL) showed a strong positive correlation for both the years. The suspended sediment yield (SSY), SSL, and erosion rate of the basin were found as high as compared to the other Himalayan basins in Himachal Pradesh, Jammu and Kashmir, and Ladakh and non-Himalayan regions that was found low when compared to other glaciers in Uttarakhand.

Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (N ES ), root mean square error (RMSE), Willmott’s index of agreement (WI), and Legates–McCabe’s index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Q t-n ) and suspended sediment concentration (S t-n ) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (S t-1 , S t-2 , S t-3 , S t-4 ) outperformed the other developed models with the lowest RMSE = 95.02 mg/l and the highest N ES  = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71 mg/l and the highest N ES  = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships.

Dams disrupt the natural flow of water and sediment along rivers. Reservoirs trap a significant amount of sediment, which substantially alters downstream hydrology, channel morphology, and sediment transport capacity. The longitudinal recovery of suspended sediment concentration (SSC) along rivers is potentially a new metric for estimating downstream responses to dams over space, rather than time, but is rarely quantified due to the lack of spatial SSC data. Satellites can estimate SSC along rivers where no field data exist and provide a high enough spatial resolution for assessing downstream recovery at the scale of tens to hundreds of kilometers. Here, we use a recently published database of spatially explicit SSC observations derived from Landsat to quantify if a river recovers or not, the SSC recovery percentage, and SSC recovery length downstream of large dams across the Contiguous United States (CONUS). Rivers recover SSC downstream of most dams (71%). The chance of a river recovering and the length of river required to recover SSC is associated primarily with the size of the reservoir (mean storage, km3) and the size of the river (mean discharge, m3/s). Rivers were more likely to recover SSC downstream of run‐of‐river, navigation dams compared to large storage or hydropower dams. Our results suggest that rivers typically recover suspended sediment downstream of dams, influenced by factors like dam storage, purpose, and river channel characteristics. Key Points Satellite‐derived suspended sediment concentration (SSC) shows rivers recovery to pre‐reservoir SSC downstream of 71% of dams in the US The length of river required to longitudinally recover SSC downstream of dams is most strongly associated with reservoir and river size Multidecadal satellite SSC observations provide a new metric for assessing the long‐term mean downstream response to dams

In‐situ observations of hydrodynamics and suspended sediment concentrations (SSCs) were conducted on an abandoned lobe in the northern part of the modern Yellow River Delta, China. The SSC record at the site is found to be the superposition of a general trend (fast increase and slow decrease cycle) caused by storm waves (SubSSC1) and relatively smaller fluctuations caused by tidal currents (SubSSC2). Physically, this indicates that storm waves eroded the bottom sediments while tidal currents then re‐suspended and advected the suspended sediments in the study area. To further obtain the suspended sediment transport parameters, first, SubSSC1 is modeled with significant wave height which incorporates a “memory curve” to consider the remaining impacts of historical waves. It is detected that waves in the past 75 hr still influence the present SSC which is reasonable because 75 hr is roughly the typical duration of a normal storm. Second, SubSSC2 is modeled with tidal excursion and trigonometric functions with measured periodicities. Finally, some sediment transport parameters, for example, the background SSC, the horizontal SSC gradient, the tidal constituents that advect it, and their relative time lags are optimized from the best fits of the measured and modeled SSC time series. The proposed framework for model construction and parameter optimization can be extended to other sea areas for inferring sediment transport parameters from field SSC time series at a specific station. Plain Language Summary The evolution of our coastal zone fundamentally depends on the transport volume and direction of sediment, and understanding them is very beneficial for our coastal engineering construction and long‐term planning. Conducting in situ observations on‐site is one of the most reliable methods, but its observation cost is expensive. Therefore, we hope to extract as much information as possible from as few observation points as possible. This article successfully extracted the above information from the observation data of a station. First, we analyzed the data to preliminarily clarify that the suspended sediment in the area mainly comes from local erosion resuspension and advection transport from other regions. Furthermore, we conducted data modeling (i.e., constructed mathematical expressions of suspended sediment components from different sources, but with undetermined coefficients). Finally, we adjusted the model parameters to approximate the measured results, determined the undetermined coefficients, and explained the practical significance of the undetermined coefficients in physics. The analysis method we proposed can be extended to other sea areas. Key Points Storm wave‐induced suspended sediment concentration (SSC) variation is modeled with a “memory curve” of wave height Tidally‐induced SSC variation is modeled with tidal excursion and trigonometric functions Sediment transport parameters are estimated from the optimal matching of measured and modeled SSC time series

To monitor sediment variations in the Chilika Lake, the Landsat-8 OLI data was used to calibrate suspended sediment concentration (SSC) model. The relationship between remote sensing reflectance of OLI bands and in-situ measured SSC were used to develop new site-specific algorithms. Four different models were calibrated in this study for retrieval of SSC using in-situ observation and remote sensing reflectance of OLI data. The multiband linear regression model provided better result (R2 = 0.6) as compared to the single-band regression model (R2 = 0.45, polynomial; R2 = 0.38, exponential and R2 = 0.39, linear). The Landsat-8 OLI image shows spatiotemporal variations of SSC during pre and post-monsoon season (2013–15) in the lake. It is observed that the SSC variation is predominantly influenced by three factors: monsoon effect, wind-induced re-suspension of bottom sediments and influx of river water into the lake. It is also observed that due to the impact of severe tropical cyclone Phailin, there was a rapid increase of SSC in the lake.

The processes of erosion and suspended sediment delivery are important components of runoff originating from a glacier-fed basin. The magnitude of suspended sediments has important implications for channel morphology, material flux, geochemical cycling, and water quality of the river. This study investigates the dynamics of suspended sediment and its mineralogical and chemical characteristics with meltwater draining from Gangotri Glacier during the ablation season of 2012 in relation to hydrology and geology of the basin. Significant variations in suspended sediment concentrations (SSC) and Suspended sediment load (SSL) were observed. The results indicate that there may be a major source (or a few similar sources) of suspended sediment contributing to river discharge over the study site. The concentration of contributing sediment is either diluted by mixing with relatively clean snowmelt water or concentrated by stormwater after interaction with lithology. The major possible source(s) of suspended sediment lies beneath the glacier. In addition, significant variations in relative mineralogy (fine fraction of chlorite, quartz, K-feldspar, and plagioclase) and chemical analyses of alkali and heavy metal (K, Na, Fe, As, Mn, Cu) in the sediment suggest a significant role of subglacial meltwater in controlling the chemical composition of suspended sediment during the ablation season. Snow and glacier melt are the main factors controlling river discharge near the snout of Gangotri glacier. Further, the variability of snow and glacier meltwater due to temperature variation controls the dynamics of suspended sediment concentration.

We analyzed the impacts of islands on suspended sediment concentration (SSC) in Zhoushan Coastal waters based on data from HY-1C, which was launched in September 2018 in China, carrying Coastal Zone Imager (CZI) and Chinese Ocean Color and Temperature Scanner (COCTS) on it for offshore observation. A new SSC retrieved model was established based on the relationship between in situ SSC and the reflectance in red and near infrared bands of CZI image. Fifteen CZI images obtained from October to December 2019 were applied to retrieve SSC in Zhoushan coastal waters. The results show that SSC in study area is 100–1600 mg·L−1. The SSC near islands changes obviously. Upstream of the islands, SSC is lower than downstream. During the flood and ebb, when the current passes through the islands, circumfluence will appear, under certain geophysical factors, generating Karman vortex streets downstream of the islands. The sediments were stirred by the fast speed current at the outer side of vortex street to the sea surface inducing higher SSC at the outer side of the vortex street, while the central sediments of the vortex street were lower. In the direction of ocean currents, the SSC of the vortex street downstream of islands is changing regularly, i.e., increasing, then decreasing and increasing again and then decreasing in a snaking vortex street whose length downstream is between 1000 and 8000 m long.

PurposeSuspended sediment transport, which represents the majority of the sediment load, has been studied across very different scales and in a wide variety of regions and climates. Despite numerous studies, data for European watersheds are generally limited and correspond to large rivers systems. Especially, in Belgium, little data is available outside the Belgian loess belt. Moreover, the high heterogeneity of soil erosion and sediment transport makes it difficult to measure or model at watershed scale. The purpose of this research is to estimate the median sediment yields in different geographical regions and to detect their explanatory variables.Materials and methodsGathering data from 1994 to 2016, more than 2000 measurements of suspended sediment concentration at 72 river stations mainly located in South-Belgium were sampled according to a flood-event-based manual methodology. This allowed fast acquisition of data in watersheds ranging from 7 to 3600 km2 in different geographical regions. Median area-specific sediment yields (SSY) were calculated at watershed scale while looking for regional differences.Results and discussionMedian area-specific sediment yields computed for the period 1996–2018 show regional differences: 19.2 t km−2 year−1 on sandy substrate (Lorraine), 24.9 t km−2 year−1 on schisto-sandstone substratum (Ardenne), and up to 119 t km−2 year−1 in the loamy Brabant plateau, with a link to the agricultural land cover and, to a lesser extent, to the watershed slope. The high temporal and spatial variability of rainfall has great effects on the SSY, necessitating the gathering of more than 20 years of data to smooth the high variability of SSY. A multiple correlation of land cover variables and the average slope of the watershed with SSY managed to explain 48% of the variance within the SSY observations.ConclusionsThe agricultural land cover has an important effect on median SSY values. While the regionalization of Belgium is largely based on lithology, soils, and altitude, the land use resulting from these physical and climatic characteristics explains the differences in SSY. Field values of clogged dams and waterways confirm the matching of the SSY computation from discrete samples, despite the high temporal variability of sediment transport.

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

This study presents a novel methodology for estimating Suspended Sediment Concentration (SSC) in the Mula Dam reservoir, Maharashtra, by integrating in-situ hyperspectral reflectance with Sentinel-2 satellite imagery. While conventional remote sensing techniques or field-based spectroscopy have been employed independently for SSC monitoring, this research introduces a spectral integration framework that bridges these two data sources through a transitive relation model. Field data collection was conducted from October 2021 to February 2022, during which 121 surface water samples were obtained and their spectral signatures recorded using an SVC HR-1024i Spectroradiometer. Simultaneously, Sentinel-2 MSI Level 2A images were processed to extract spectral reflectance at corresponding sampling points. Strong correlations were observed between SSC and reflectance in the Green, Red, and Red Edge 1 bands. Multiple spectral indices and band ratios were evaluated to identify optimal SSC estimators, with the combination (Green × Red Edge 1)/Red demonstrating the highest predictive capability. A spectral integration function was developed using a two-stage regression approach: first, linking observed SSC to Spectroradiometer-derived indices; second, connecting these indices to Sentinel-2 reflectance data. The resulting models were validated using linear regression, Student’s t-test, residual analysis, and k-fold cross-validation. Among all models, the (Green × Red Edge 1)/Red function achieved superior performance with an R 2 of 0.80, RMSE of 8.58 mg/L, and MAPE of 19.41%. The approach was further tested for temporal SSC mapping using past Sentinel-2 imagery, revealing seasonal sediment trends. The study concludes that the proposed spectral integration method provides a robust, scalable, and transferable framework for accurate SSC monitoring in large water bodies. This advancement holds significant implications for sediment management, water quality assessment, and hydrological research.