scholarly journals Precipitation and streamflow data from the Fort Carson Military Reservation and precipitation, streamflow, and suspended-sediment data from the Piñon Canyon Maneuver Site, Southeastern Colorado, 2008-2012

2014 ◽  
Author(s):  
Christopher R. Brown
Keyword(s):  
Suspended Sediment ◽  
Streamflow Data ◽  
Sediment Data

scholarly journals Multivariate event time series analysis using hydrological and suspended sediment data

2021 ◽  
Vol 593 ◽  
pp. 125802
Author(s):  
Ali Javed ◽  
Scott D. Hamshaw ◽  
Byung Suk Lee ◽  
Donna M. Rizzo
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Suspended Sediment ◽  
Event Time ◽  
Series Analysis ◽  
Sediment Data

Fluvial Sedimentation in Southern Ontario

1975 ◽  
Vol 12 (11) ◽  
pp. 1813-1819 ◽  
Author(s):  
W. T. Dickinson ◽  
A. Scott ◽  
G. Wall
Keyword(s):  
Suspended Sediment ◽  
Drainage Density ◽  
Low Flow ◽  
Southern Ontario ◽  
Frequency Distributions ◽  
Sediment Yields ◽  
Sediment Loads ◽  
Fluvial Sedimentation ◽  
Sediment Data ◽  
Suspended Loads

Suspended sediment data for Southern Ontario stream have been analyzed with regard to areal and temporal patterns, characteristics of sediment concentrations, and relationships between sediment loads and watershed parameters. The range of annual suspended sediment yields for seven river basins is determined as 5250 to 175 000 kg/year/km2. The daily suspended loads exhibit highly skewed frequency distributions, with mean loads being equalled or exceeded less than 20% of the time and 50% of the annual load occurring during the months of March and April. It is demonstrated that sample loads collected during periods of low flow, and stream rankings determined from low flow data, are not indicative of the annual suspended sediment load picture. No simple model between suspended loads and such basin indices as relief ratio and drainage density is found to be satisfactory for prediction purposes.

scholarly journals Geo(Im)pulse | River Meuse suspended sediment yield: a new estimate and past estimates revisited

2008 ◽  
Vol 87 (2) ◽  
pp. 189-193 ◽  
Author(s):  
P.J. Ward
Keyword(s):  
Land Use ◽  
Time Series ◽  
Suspended Sediment ◽  
20Th Century ◽  
Sediment Yield ◽  
Rainfall Erosivity ◽  
Late 20Th Century ◽  
Suspended Sediment Yield ◽  
River Meuse ◽  
Sediment Data

AbstractDespite increasing research into changes in the discharge of the River Meuse, estimates of the river’s sediment yield are less forthcoming. Three published studies (in 1883, 1982, and 1987) have estimated suspended sediment yield at the Belgian-Dutch border; the latter two studies surmise that this increased substantially between the late 19th and 20th Centuries. In this paper a more recent and longer time-series of observed discharge and suspended sediment data (1995 – 2005) is used to estimate mean annual suspended sediment yield (ca. 386,000 Mg.a−1), and the results of the previous studies are revisited. New insights suggest that those studies do not in themselves provide evidence of increased sediment yield: the higher estimates in the late 20th Century could equally be due to interannual variability or methodological differences. Furthermore, there has been no significant increase in rainfall erosivity between the late 19th and 20th Centuries, and the effect of land use change over that time would have been to cause a decrease in suspended sediment yield, rather than an increase.

Suspended sediment prediction using two different feed-forward back-propagation algorithms

2007 ◽  
Vol 34 (1) ◽  
pp. 120-125 ◽  
Author(s):  
Mehmet Ardıçlıoğlu ◽  
Özgür Kişi ◽  
Tefaruk Haktanır
Keyword(s):  
Suspended Sediment ◽  
Back Propagation ◽  
Model Performance ◽  
Nonlinear Problems ◽  
Back Propagation Neural Network ◽  
Multilinear Regression ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Monthly Streamflow ◽  
Sediment Data

IIn this paper the capability of two different feed-forward back-propagation neural network algorithms, namely Levenberg-Marquardt and gradient-descent, in solving complex nonlinear problems is utilized for suspended sediment prediction. The monthly streamflow and suspended sediment data from two stations, Palu and Çayağzi, in the Firat Basin in Turkey are used as case studies. The first part of the study involves the prediction of sediment data for the two stations. The second part of the study focuses on the prediction of the downstream station sediment data using upstream data. The effect of the periodicity on model performance is also investigated in each application.Key words: suspended sediment, neural networks, multilinear regression, prediction.

Determination of Settling Velocity of Suspended Sediment in Estuary

2014 ◽  
Vol 670-671 ◽  
pp. 805-808 ◽  
Author(s):  
Hui Ming Huang ◽  
Da Ke Chen ◽  
Wei Na Zhang ◽  
Cheng Chen
Keyword(s):  
Suspended Sediment ◽  
Settling Velocity ◽  
Sediment Concentration ◽  
Fine Sediment ◽  
Cohesive Sediments ◽  
The Changjiang River ◽  
Sediment Particle Size ◽  
Logarithmic Distribution ◽  
Sediment Data

The settling velocity of sediment is a hot issue and a basic problem in study of sediment transport and estuarine engineering. According to field hydrodynamics and sediment data around the South Passage of the Changjiang River in China, this paper detected the characteristics of sediment particle size and vertical distribution pattern of suspended sediment concentration, and further estimated the settling velocity of suspended sediment in three methods. The results show that the sediments including suspended and bed load can be categorized into cohesive sediments and the sediment concentration profile agree well with logarithmic distribution. Furthermore, by comparison, it is found that the Rouse formula is more reasonable for estimating the settling velocity of fine sediment, but the Zhang Ruijin and Stokes formula obviously underestimate the values of settling velocity, caused by do not taking into account the flocculation of fine sediment in estuary.

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