Particle size characteristics of suspended sediment in Southern Ontario rivers tributary to the great lakes

In spite of the important relationship between sediment particle size and the transport/deposition of adsorbed pollutants in fluvial systems, little information regarding the size characteristics of suspended sediment transported by southern Ontario Great Lakes tributaries is currently available. This paper examines long-term sediment and hydrometric data collected by the Water Resources Branch of Environment Canada in order to provide information on (1) typical particle size distributions of suspended sediment, (2) relationships between source material and particle size characteristics of suspended sediment, and (3) temporal variation in the particle size characteristics of suspended sediment from six southern Ontario rivers. Results illustrate the complex behaviour and variability of sediment particle size transport in these rivers and demonstrate the need for a better understanding of seasonal effects on sediment availability and conveyance processes in fluvial systems.     

Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis

ABSTRACT

Ensemble machine learning models have been widely used in hydro-systems modeling as robust prediction tools that combine multiple decision trees. In this study, three newly developed ensemble machine learning models, namely gradient boost regression (GBR), AdaBoost regression (ABR) and random forest regression (RFR) are proposed for prediction of suspended sediment load (SSL), and their prediction performance and related uncertainty are assessed. The SSL of the Mississippi River, which is one of the major world rivers and is significantly affected by sedimentation, is predicted based on daily values of river discharge (Q) and suspended sediment concentration (SSC). Based on performance metrics and visualization, the RFR model shows a slight lead in prediction performance. The uncertainty analysis also indicates that the input variable combination has more impact on the obtained predictions than the model structure selection.     

A semi-physical sediment yield model for estimation of suspended sediment in loess region

Sediment yield is a complex function of many environmental factors including climate,hydrology,vegetation,basin topography,soil types,and land cover.We present a new semi-physical watershed sediment yield model for the estimation of suspended sediment in loess region.This model is composed by three modules in slope,gully,and stream phases.For slope sediment yield,a balance equation is established based on the concept of hydraulic erosion capacity and soil erosion resistance capacity.According to the statistical analysis of watershed characteristics,we use an exponential curve to approximately describe the spatial variability of watershed soil erosion resistance capacity.In gully phase,the relationship between gully sediment concentration and flow velocity is established based on the Bagnold'stream power function.In the stream phase,we assume a linear dependence of the sediment volume in the reach on the weighted sediment input and output.The proposed sediment yield model is operated in conjunction with a conceptual hydrologic model,and is tested over 16 regions including testing grounds,and small,medium and large watersheds in the loess plateau region in the mid-reach of Yellow River.Our results indicate that the model is reasonable in structure and is able to provide a good simulation of sediment generation and transportation processes at both flood event scale and inter-annual time scale.The proposed model is generally applicable to the watersheds with soil texture similar to that of the loess plateau region in the Yellow River basin in China.     

River suspended sediment load prediction based on river discharge information: application of newly developed data mining models

ABSTRACT

Suspended sediment load (SSL) is one of the essential hydrological processes that affects river engineering sustainability. Sediment has a major influence on the operation of dams and reservoir capacity. This investigation is aimed at exploring a new version of machine learning models (i.e. data mining), including M5P, attribute selected classifier (AS M5P), M5Rule (M5R), and K Star (KS) models for SSL prediction at the Trenton meteorological station on the Delaware River, USA. Different input scenarios were examined based on the river flow discharge and sediment load database. The performance of the applied data mining models was evaluated using various statistical metrics and graphical presentation. Among the applied data mining models, the M5P model gave a superior prediction result. The current and one-day lead time river flow and sediment load were the influential predictors for one-day-ahead SSL prediction. Overall, the applied data mining models achieved excellent predictions of the SSL process.     

Particle size analysis of the sediment suspended in a proglacial stream: Glacier De tsidjiore Nouve,Switzerland

Hourly, at-a-point samples of suspended sediment taken from the outflow stream of Glacier de Tsidjiore Nouve, Switzerland, over a 60 day sampling period (n = 1440) are shown to be dominantly composed of silt-sized particles. Particle size, SEM, and XRD analyses indicate a subglacial provenance for the suspended sediment. Temporal variations in particle size and sorting correspond poorly to fluctuations in water discharge, being dominated by erratic hour-to-hour fluctuations and clockwise hysteresis over diurnal flow events. Examination of grain size and sorting dynamics over snowmelt- and icemelt-related ablation events, during precipitation events, and during glacier drainage events enables some inferences to be drawn regarding sediment source areas and supply regimes. We conclude that although the bulk of the suspended sediment in the proglacial stream of Glacier de Tsidjiore Nouve is derived directly from subglacial sources (with occasional contributions from the valley train during rapid snowmelt and heavy rainfall periods), a portion of the suspended load undergoes intermittent ‘flush-fall’ transfer through the proglacial zone, which acts as a sediment source during rising flows and as a sink during periods of waning flow.     

Adaptive neuro-fuzzy inference system for prediction of water level in reservoir

Accurate prediction of the water level in a reservoir is crucial to optimizing the management of water resources. A neuro-fuzzy hybrid approach was used to construct a water level forecasting system during flood periods. In particular, we used the adaptive network-based fuzzy inference system (ANFIS) to build a prediction model for reservoir management. To illustrate the applicability and capability of the ANFIS, the Shihmen reservoir, Taiwan, was used as a case study. A large number (132) of typhoon and heavy rainfall events with 8640 hourly data sets collected in past 31 years were used. To investigate whether this neuro-fuzzy model can be cleverer (accurate) if human knowledge, i.e. current reservoir operation outflow, is provided, we developed two ANFIS models: one with human decision as input, another without. The results demonstrate that the ANFIS can be applied successfully and provide high accuracy and reliability for reservoir water level forecasting in the next three hours. Furthermore, the model with human decision as input variable has consistently superior performance with regard to all used indexes than the model without this input.     

Predictability of river flow and suspended sediment transport in the Mississippi River basin: a non‐linear deterministic approach

As the Mississippi River plays a major role in fulfilling various water demands in North America, accurate prediction of river flow and sediment transport in the basin is crucial for undertaking both short‐term emergency measures and long‐term management efforts. To this effect, the present study investigates the predictability of river flow and suspended sediment transport in the basin. As most of the existing approaches that link water discharge, suspended sediment concentration and suspended sediment load possess certain limitations (absence of consensus on linkages), this study employs an approach that presents predictions of a variable based on history of the variable alone. The approach, based on non‐linear determinism, involves: (1) reconstruction of single‐dimensional series in multi‐dimensional phase‐space for representing the underlying dynamics; and (2) use of the local approximation technique for prediction. For implementation, river flow and suspended sediment transport variables observed at the St. Louis (Missouri) station are studied. Specifically, daily water discharge, suspended sediment concentration and suspended sediment load data are analysed for their predictability and range, by making predictions from one day to ten days ahead. The results lead to the following conclusions: (1) extremely good one‐day ahead predictions are possible for all the series; (2) prediction accuracy decreases with increasing lead time for all the series, but the decrease is much more significant for suspended sediment concentration and suspended sediment load; and (3) the number of mechanisms dominantly governing the dynamics is three for each of the series. Copyright © 2005 John Wiley & Sons, Ltd.     

Properties of suspended sediment in the estuarine turbidity maximum of the highly turbid Humber Estuary system, UK

Measurements are presented of the properties of suspended particulate matter (SPM) in the estuarine turbidity maximum (ETM) of the upper Humber and Ouse estuaries during transient, relatively low freshwater inflow conditions of September 1995. Very high concentrations of near-bed SPM (more than 100 g l⁻¹) were observed in the low-salinity (less than 1), upper reaches. SPM within the ETM consisted largely of fine sediment (silt and clay) that existed as microfloc and macrofloc aggregates and individual particles. Primary sediment particles were very fine grained, and typically, about 20–30% was clay-sized at high water. The clay mineralogy was dominated by chlorite and illite. There was a pronounced increase in particle size in the tidal river, up-estuary of the ETM. The mean specific surface area (SSA) of near-bed SPM within the ETM was 22 m² g⁻¹ on a spring tide and 24 m² g⁻¹ on a neap tide. A tidal cycle of measurements within a near-bed, high concentration SPM layer during a very small neap tide gave a mean SSA of 26 m² g⁻¹. The percentage of silt and clay in surficial bed sediments along the main channel of the estuary varied strongly. The relatively low silt and clay percentage of surficial bed sediments (about 10–35%) within the ETM’s region of highest near-bed SPM concentrations and their low SSA values were in marked contrast to the overlying SPM. The loss on ignition (LOI) of near-bed SPM in the turbid reaches of the estuary was about 10%, compared with about 12% for surface SPM and more than 40% in the very low turbidity waters up-estuary of the ETM. Settling velocities of Humber–Ouse SPM, sampled in situ and measured using a settling column, maximized at 1.5 mm s⁻¹ and exhibited hindered settling at higher SPM concentrations.     

Theoretical/numerical model for the transport of non-uniform suspended sediment in open channels

In this paper, we address the transport of multi-disperse suspended sediment mixtures in open channels, via the use of the two-fluid model. To that end, we extend previously developed frameworks for the dilute and non-dilute transport of suspended sediment. Within the scope of the Reynolds-averaged Navier-Stokes (RANS) equations, these modeling frameworks comprise mass and momentum equations for both phases (water and sediment). Here, we calculate the distribution of total volumetric concentration of sediment using two approaches: (1) by considering the mixture as represented by a single size; we call this approach Partial two-fluid model for uniform sediments (PTFMU); and (2) by combining the volumetric concentration of the sediment corresponding to several particle size classes; we call this approach Partial two-fluid model for non-uniform sediments (PTFMNU). In the second approach, we propose a methodology for the computation of the overall velocity of the disperse phase as a function of the velocities of each size class. k-ε type closures to account for the turbulence in the carrier phase (water) are applied. We also consider the coupling between the two phases through the drag force. Velocities of the carrier and disperse phases, and concentrations for each sediment class size are numerically solved by integrating the differential equations over control volumes. In order to validate our models, we compare numerical results to experimental data of Einstein and Chien [H.A. Einstein, N. Chien, Effects of heavy sediment concentration near the bed on velocity and sediment distribution, MRD sediment series report, University of California, Berkley, 1955] and Taggart et al. [W.C. Taggart, C.A. Yermoli, S. Montes, A. Ippen, Effects of sediment size and gradation on concentration profiles for turbulent flow, Massachusetts Institute of Technology, 1972]. Results of mean velocity of the carrier phase are in close agreement with the experimental data. For the prediction of sediment concentrations, we observe that there is a difference in the results using the two approaches mentioned above. We additionally obtain values of the Schmidt number needed to improve the agreement between predictions of the distribution of suspended sediment and the experimental data, and discuss the effect of sediment size and increasing sediment concentration on the values of the Schmidt number.