Water Suspended Sediment Concentration Research Articles

Monitoring Suspended Sediment Transport in the Lower Yellow River using Landsat Observations

The spatiotemporal variations in suspended sediment concentration (SSC) in the lower reaches of the Yellow River exhibit significant variability and are influenced by reservoir operations. Understanding the spatiotemporal distribution characteristics of SSC in water holds crucial implications for environmental protection and reservoir operation management. Based on daily-scale SSC monitoring data from four hydrological stations in the lower Yellow River, this study established an SSC remote sensing model applicable to Landsat series satellite data. The independent variable of the model, Rrs(NIR)/(Rrs(G) + Rrs(R) + Rrs(SWIR)), demonstrated sensitivity to water bodies with different SSC values. Distinctive spatiotemporal characteristics in sediment transport were observed across the lower Yellow River. Spatially, the SSC values in the Sanmenxia and Xiaolangdi reservoirs were notably lower than those in other river sections, averaging 1008.42 ± 602.83 mg/L and 1177.89 ± 627.95 mg/L, respectively. Over time, the majority of the river sections (96%) exhibited decreasing trends in SSC during 1984–2022, particularly in the downstream Xiaolangdi reservoir, with average SSC values of 4265.58 ± 1101.77 mg/L in the 1980s and 1840.80 ± 2255.15 mg/L in the 2020s. Seasonal variations in SSC were prominent, with higher summer concentrations, averaging 5536.43 ± 2188.77 mg/L (2020s summer) and 814.11 ± 158.27 mg/L (2020s winter). Reductions in SSC during 1984–2022 primarily occurred in summer, weakening its seasonal variability in the lower Yellow River. Water discharge emerged as a critical factor influencing suspended sediment transport, with SSC increasing in high-water-flow months. Following the construction of the Xiaolangdi reservoir, the relationship between SSC and water discharge at different stations underwent notable alterations. This study enhances our understanding of the spatiotemporal dynamics of suspended sediment transport in the lower Yellow River, providing valuable insights for utilizing long-term Landsat series data in the dynamic monitoring of river sediment transport.

The Influence of the Internal Properties of River Ice on Ground Penetrating Radar Propagation

Ground penetrating radar (GPR) has proven to be a very effective method for examining ice thickness. However, two preconditions must be met for this approach to be useful; the round-trip travel time of electromagnetic (EM) waves and radar transmission speed in the ice must be known. These issues are problematic because many factors affect radar transmission speed in ice, including impurities, physical properties such as porosity and density, and temperature. Results show that if these factors are not taken into account and a signal velocity of 0.17 m/ns in pure ice is used to estimate thickness, overestimates will result. We carried out a series of GPR surveys using dual channel host 200 MHz shielded antennas at the Toudaoguai Hydrological Station on the Yellow River, China, and collected samples to analyze ice impurities and physical properties. The results show that the ice crystal types include frazil, granular, and column at the Toudaoguai Hydrologic Station section. Our analysis of ice gas bubble and sediment content showed that the gas bubble volume content is between 11.95 and 13.0% in the frazil ice and between 7.9% and 8.6% in granular and columnar ice. At the same time, the ice sediment content ranged between 0.11‰ and 0.57‰, and the average was 0.24‰ in granular and columnar ice, which was about one-tenth that of the suspended sediment concentration in water. Additionally, a combination of GPR data as well as ice impurities, porosity, density, and temperature enabled us to provide insights on the variability of radar transmission speed and the equivalent dielectric permittivity in river ice. Our extensive observations reveal that radar transmission speed falls between 0.141 m/ns and 0.164 m/ns and that the equivalent dielectric permittivity of river ice increases in concert with ice temperature.

Identifying the Driving Factors of Black Bloom in Lake Bay through Bayesian LASSO.

Black blooms are a serious and complex problem for lake bays, with far-reaching implications for water quality and drinking safety. While Fe(II) and S(−II) have been reported as the most important triggers of this phenomenon, little effort has been devoted in investigating the relationships between Fe(II) and S(−II) and the host of potentially important aquatic factors. However, a model involving many putative predictors and their interactions will be oversaturated and ill-defined, making ordinary least squares (OLS) estimation unfeasible. In such a case, sparsity assumption is typically required to exclude the redundant predictors from the model, either through variable selection or regularization. In this study, Bayesian least absolute shrinkage and selection operator (LASSO) regression was employed to identify the major influence variables from 11 aquatic factors for Fe(II), S(−II), and suspended sediment concentration (SSC) in the Chaohu Lake (Eastern of China) bay during black bloom maintenance. Both the main effects and the interactions between these factors were studied. The method successfully screened the most important variables from many items. The determination coefficients (R2) and adjusted determination coefficients (Adjust R2) showed that all regression equations for Fe(II), S(-II), and SSC were in good agreement with the situation observed in the Chaohu Lake. The outcome of correlation and LASSO regression indicated that total phosphorus (TP) was the single most important factor for Fe(II), S(-II), and SSC in black bloom with explanation ratios (ERs) of 76.1%, 37.0%, and 12.9%, respectively. The regression results showed that the interaction items previously deemed negligible have significant effects on Fe(II), S(−II), and SSC. For the Fe(II) equation, total nitrogen (TN) × dissolved oxygen (DO) and chlorophyll a (CHLA) × oxidation reduction potential (ORP), which contributed 10.6% and 13.3% ERs, respectively, were important interaction variables. TP emerged in each key interaction item of the regression equation for S(−II). Water depth (DEP) × Fe(II) (30.7% ER) was not only the main interaction item, but DEP (5.6% ER) was also an important single factor for the SSC regression equation. It also indicated that the sediment in shallow bay is an important source for SSC in water. The uncertainty of these relationships was also estimated by the posterior distribution and coefficient of variation (CV) of these items. Overall, our results suggest that TP concentration is the most important driver of black blooms in a lake bay, whereas the other factors, such as DO, DEP, and CHLA act in concert with other aquatic factors. There results provide a basis for the further control and management policy development of black blooms.

A spectral mixing algorithm for quantifying suspended sediment concentration in the Yellow River: a simulation based on a controlled laboratory experiment

ABSTRACTThis study investigated the upper limit of suspended sediment concentration (SSC) with respect to the relationship between SSC and reflectance to develop an SSC remote-sensing model for the highly turbid Yellow River. An SSC quantification model was generated by using the spectral mixing index of sediments in water and sediment mixtures. In this study, laboratory experiments were made to measure the spectral curves of sediment-laden water with a high-resolution spectroradiometer. River-bed deposited sediments from two sites, one on the Yellow River at Huayuankou (HYK) and the other on the Wei River at Yangling (YL), and their sand, silt and clay particle groups were used for laboratory experiments to measure the spectral responses of sediment-laden water. The correlation analysis depicted stable correlation between SSC and reflectance at wavelengths ranging from 450 to 1000 nm, in which Spearman rank correlation coefficient (rs) for all sediments was above 0.7 while rs for the HYK natural sediment exceeded 0.9. Experimental results revealed the curves of the relationship between SSC and reflectance, up to 40 g l−1. A physical-based exponential model ( > 0.9) at each simulated Landsat band effectively interpreted the relationship between SSC and reflectance. The highest upper limit SSCs at 21 and 15 g l−1 in natural YL and HYK sediments, respectively, were observed in Landsat Band 4. A spectral mixing algorithm was used to build the model and estimate the SSC from reflectance at correlated wavelength bands. The spectral mixing algorithm can generate a uniform model that disregards the effects of sediment type by adopting the reflectance curve at the upper-limit SSC to represent the standard reflectance of sediment. This study is useful in understanding the spectral characteristic of high SSC in water and in applying remote-sensing techniques to monitor SSC in the Yellow River.

An exploratory study of using external fluid loading on a vibrating tube for measuring suspended sediment concentration in water

This paper presents an exploratory study of using external fluid loading on a vibrating tube for measuring the suspended sediment concentration (SSC) in bodies of water such as rivers and reservoirs. This new measuring concept provides an opportunity for an automated on-site monitoring of the conditions in a body of water by taking the fluid sample instantaneously in the area surrounding the vibrating tube. The physical properties of the fluid sample are those of the fluid that naturally flows around the tube, and are more representative of those of the water with SSC to be measured. The theoretical analysis presented in this paper shows that the resonance frequencies of an immersed vibrating tube change significantly with mass density variations that normally occur in bodies of water with suspended sediment. These changes are sensitive enough to have a possible 1% resolution of the measured fluid density. The signal processing issues are discussed, and a schematic of a conceptual measuring setup is proposed. Based on the theoretical analyses and other measurement issues presented in the paper, using the loading by external fluid on a vibrating tube is feasible for measuring the SSC in water bodies.

Remote Sensing Analysis in Yangtzi River Estuary and the Inshore Area

Yangtzi River is the largest river in China.It brings 4.245×108 ton soil and sands into the ocean each year,which greatly influences the suspended sediment concentration(SSC) in the area of Yangtzi River estuary and continental shelf area of East Chinese Sea.Remote sensing technology is one of useful methods to monitor the SSC in the surface water.In this sea area,there is a good relativity between surface water SSC and vertical average SSC.In this article,the author uses MODIS data to reverse the SSC in the area around the Yangtzi River estuary and analyzes the characteristic of its distribution and movement as well as the influence to peripheral sea.

Estimation of suspended sediment concentration in water using integrated surface reflectance

The reflectance spectra of suspended sediments derived from a silty soil were analyzed in an effort to measure suspended sediment concentration (SSC) in surface waters. Twenty levels of SSC (50 to 1000 mg/l) were created in a large tank filled with 7510 liters of clear water. Reflectance measurements were made using a high resolution spectroradiometer. The spectroradiometer data were integrated into the band widths of Landsat‐TM. The simulated TM bands were then used to develop regression models for estimating SSC. We found that the spectroradiometer data, integrated into the band width of Landsat‐TM 4, when used in second‐order regression models, is the best estimator of SSC.