Variability of suspended sediment yield in the Pra River Basin, Ghana

2021 ◽  
Author(s):  
Ebenezer Boakye ◽  
F. O. K. Anyemedu ◽  
Emmanuel A. Donkor ◽  
Jonathan A. Quaye-Ballard
Keyword(s):  
Suspended Sediment ◽  
River Basin ◽  
Sediment Yield ◽  
Suspended Sediment Yield ◽  
Pra River

scholarly journals Suspended sediment yield at upper Guaporé River basin, Central-West Brazil

2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Albert Teixeira Cardoso ◽  
José Alexandre Pinto Coelho Filho
Keyword(s):  
Suspended Sediment ◽  
River Basin ◽  
Sediment Yield ◽  
Suspended Sediment Yield

scholarly journals The main regularities of the ratio between riverbed and basin components of erosion and suspended sediment yield in river basins of the USA

2019 ◽  
pp. 3-24
Author(s):  
A. V. Gusarov ◽  
L. F. Maksyutova
Keyword(s):  
Suspended Sediment ◽  
River Basin ◽  
Sediment Yield ◽  
Suspended Sediments ◽  
River Basins ◽  
Tropical Zone ◽  
Suspended Sediment Yield ◽  
The Us ◽  
The Usa ◽  
Floodplain Sediments

Suspended sediment yield is one of the objective and sufficiently accurate measures of erosion intensity in river basins. In first approximation, it can be divided into the riverbed component –r(rb), the products of vertical and horizontal riverbed deformations), and basin component – r(bas), the products of soil and gully erosion. An attempt was made to distinguish this erosion structure in the USA river basins based on the partition of suspended sediments of 224 rivers (based on the data from the US Geological Service on the average monthly water discharges and suspended sediment yields) according to the method proposed by one of the authors of the paper, as well as an assessment of its factor dependence. The average r(rb) value for the analyzed rivers of the USA is 7.9±1.1%: for lowland rivers – 10.6±1.7%, for low-mountain (including uplands) rivers – 5.7±1.5%, for mid-mountain rivers – 4.3±1.5%. The geomorphic factor, landscape and climatic conditions within the river basins have a major impact on the suspended sediments flux ratio r(rb)/r(bas). Thus, in the USA plains, the largest average r(rb) portion is in the forest landscapes (taiga, mixed and broadleaf forests of the temperate zone, subtropical forests) – 10–15%. On the contrary, in the arid landscapes (semi-deserts) this value does not exceed 1%. Within these general trends, there are quite strong variations in the r(rb)/r(bas) ratios due to the changes in high river basin areas, agricultural activities and lithologic composition of the riverbed and floodplain sediments. There is an inverse hyperbolic relationship between the actual suspended sediment yield of rivers and the riverbed sediment portion (r(rb)), which is most manifested in the plains and low-mountains of the USA. It is also shown that a composition of the river basin parent (surficial) rocks does not play a significant role in the variability of the r(rb)/r(bas) at this scale of the study. A comparison of the r(rb)/r(bas)-estimates and their factor dependence on the US rivers with the rivers of Northern Eurasia (the territory of the former Soviet Union) makes it possible to reveal good convergence of the results obtained in these parts of the Earth, and to suggest the universal nature of the revealed regularities (in total for 684 river basins) for the whole temperate (partly for subtropical and tropical) zone of the Northern hemisphere of our planet.

scholarly journals Evaluation of sediment yield predicting models of Ghana

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
John Manyimadin Kusimi ◽  
Bertha Ansaah Kusimi ◽  
Barnabas A. Amisigo
Keyword(s):  
Suspended Sediment ◽  
River Basin ◽  
Sediment Yield ◽  
Water Resource Management ◽  
Drainage Basin ◽  
Suspended Sediments ◽  
Yield Data ◽  
Sediment Yields ◽  
Alternative Approach ◽  
Pra River

Fluvial sediment transport data is a very important data for effective water resource management. However, acquiring this data is expensive and tedious hence sediment yield modeling has become an alternative approach in estimating river sediment yields. In Ghana, several sediment yield predicting models have been developed to estimate the sediment yields of ungauged rivers including the Pra River Basin. In this paper, 10 months sediment yield data of the Pra River Basin was used to evaluate the existing sediment yield predicting models of Ghana. A regression analysis between predicted sediment yield data derived from the models and the observed suspended sediment yields of the Pra Basin was done to determine the extent of estimation of observed sediment yields. The prediction of suspended sediment yield was done for 4 out of 5 existing sediment yield predicting models in Ghana. There were variations in sediment yield between observed and predicted suspended sediments. All predicted sediment yields were lower than observed data except for equation 3 where the results were mixed. All models were found to be good estimators of fluvial sediments with the best model being equation 4. Sediment yield tends to increase with drainage basin area. 

scholarly journals Contemporary erosion and suspended sediment yield within river basins in the steppe of the Southeastern part of the Russian Plain: A case study of the Samara River basin

2019 ◽  
pp. 37-51 ◽  
Author(s):  
A. V. Gusarov ◽  
A. G. Sharifullin
Keyword(s):  
Suspended Sediment ◽  
River Basin ◽  
Sediment Yield ◽  
Soil Freezing ◽  
Russian Plain ◽  
Accumulation Rates ◽  
Small Catchment ◽  
Suspended Sediment Yield ◽  
Erosion Intensity

The paper presents the results of contemporary trend assessment in general erosion intensity within the southeastern steppe sector of the Russian Plain, a case study of the Samara River (the upper reaches) basin (22,800 km2, Orenburg oblast, European part of Russia), based on the long-term studying of river suspended sediment yield dynamics. The assessment is supplemented by accumulation rates field study of the soil-rill-gully erosion products in a typical small catchment (the catchment area is 1.92 km2) of the river basin using environmental radioactive caesium-137 (incl. Chernobyl-derived 137Cs) as a chronomarker. The results obtained clearly show that the Samara River’s suspended sediment yield has been reduced at least twice over the last 30 years compared with 1940–1960s. The marked decreasing trend in the erosion intensity in the Samara River basin is confirmed by a decrease (by 3.0–3.6 times as a minimum) in accumulation rates of the erosion products over the past 60 years within the dry valley bottom of the studied small catchment. The main reason for such significant erosion rates reduction was a decrease in surface snowmelt runoff within the basin area since the early 1980s, associated with a reduction in a soil freezing depth and a general increase in air temperature during spring months.

scholarly journals Estimation of Suspended Sediment Yield using Artificial Neural Network Model

2020 ◽  
Vol 9 (3) ◽  
pp. 3249-3253
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Suspended Sediment ◽  
River Basin ◽  
Sediment Yield ◽  
Sediment Load ◽  
Water Discharge ◽  
Suspended Sediment Yield ◽  
Godavari River ◽  
Artificial Neural

Estimation of the suspended sediment yield is important for the planning and management of water resources and protection of the environment. Environmental change influences sediment generation and the transport and the consequent sediment load in river. In this study, artificial intelligence-based technique like the artificial neural network (ANN) is proposed for sediment yield estimation in the Godavari river basin, India. The ANN is one of the appropriate data-mining techniques that help model the complex phenomenon of sedimentation. In this study the prediction of the suspended sediment load is done using the ANN techniques by using the water discharge and water level data from 1970 to 2015 as inputs at Polavaram gauge station in Godavari river basin, India. The results demonstrate that the ANN shows a satisfactory performance based on the root mean squared error (RMSE), mean square error (MSE), mean absolute error (MAE) and correlation coefficient (r) error statistics and provided more accurate results.

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