Linear spectral unmixing algorithm for modelling suspended sediment concentration of flash floods, upper Tekeze River, Ethiopia

2020 ◽  
Vol 35 (1) ◽  
pp. 79-90 ◽  
Author(s):  
Hagos G. Gebreslassie ◽  
Assefa M. Melesse ◽  
Kevin Bishop ◽  
Azage G. Gebremariam
Keyword(s):  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Flash Floods ◽  
Spectral Unmixing ◽  
Linear Spectral Unmixing

scholarly journals Element Geochemical Analysis of the Contribution of Aeolian Sand to Suspended Sediment in Desert Stream Flash Floods

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Xiaopeng Jia ◽  
Haibing Wang
Keyword(s):  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Flash Floods ◽  
Suspended Sediment Transport ◽  
Arid Zones ◽  
Stream Channels ◽  
Aeolian Sand ◽  
Aeolian Processes ◽  
Desert Stream

The interaction of wind and water in semiarid and arid areas usually leads to low-frequency flash flood events in desert rivers, which have adverse effects on river systems and ecology. In arid zones, many aeolian dune-fields terminate in stream channels and deliver aeolian sand to the channels. Although aeolian processes are common to many desert rivers, whether the aeolian processes contribute to fluvial sediment loss is still unknown. Here, we identified the aeolian-fluvial cycling process responsible for the high rate of suspended sediment transport in the Sudalaer desert stream in the Ordos plateau of China. On the basis of element geochemistry data analysis, we found that aeolian sand was similar to suspended sediment in element composition, which suggests that aeolian sand contributes to suspended sediment in flash floods. Scatter plots of some elements further confirm that aeolian sand is the major source of the suspended sediment. Factor analysis and the relation between some elements and suspended sediment concentration prove that the greater the aeolian process, the higher the suspended sediment concentration and the greater the contribution of aeolian sand to suspended sediment yield. We conclude that aeolian sand is the greatest contributor to flash floods in the Sudalaer desert stream.

Neural networks approached for modelling river suspended sediment concentration due to tropical storms

2013 ◽  
Vol 11 (4) ◽  
pp. 457-466
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Time Series Data ◽  
Water Discharge ◽  
Sediment Concentration ◽  
Series Data ◽  
Generalized Regression Neural Network ◽  
Event Based

Artificial neural networks are one of the advanced technologies employed in hydrology modelling. This paper investigates the potential of two algorithm networks, the feed forward backpropagation (BP) and generalized regression neural network (GRNN) in comparison with the classical regression for modelling the event-based suspended sediment concentration at Jiasian diversion weir in Southern Taiwan. For this study, the hourly time series data comprised of water discharge, turbidity and suspended sediment concentration during the storm events in the year of 2002 are taken into account in the models. The statistical performances comparison showed that both BP and GRNN are superior to the classical regression in the weir sediment modelling. Additionally, the turbidity was found to be a dominant input variable over the water discharge for suspended sediment concentration estimation. Statistically, both neural network models can be successfully applied for the event-based suspended sediment concentration modelling in the weir studied herein when few data are available.

scholarly journals Suspended sediment concentration field quantified from a calibrated MultiBeam EchoSounder

2021 ◽  
Vol 180 ◽  
pp. 108107
Author(s):  
Guillaume Fromant ◽  
Nicolas Le Dantec ◽  
Yannick Perrot ◽  
France Floc'h ◽  
Anne Lebourges-Dhaussy ◽  
...  
Keyword(s):  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Concentration Field ◽  
Sediment Concentration ◽  
Multibeam Echosounder

scholarly journals Insights on the Impacts of Hydroclimatic Extremes and Anthropogenic Activities on Sediment Yield of a River Basin

Earth ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 32-50
Author(s):  
Rocky Talchabhadel ◽  
Jeeban Panthi ◽  
Sanjib Sharma ◽  
Ganesh R. Ghimire ◽  
Rupesh Baniya ◽  
...  
Keyword(s):  
Suspended Sediment ◽  
River Basin ◽  
Extreme Precipitation ◽  
Suspended Sediment Concentration ◽  
Anthropogenic Activities ◽  
Sediment Concentration ◽  
Sediment Flux ◽  
Mountain River ◽  
Ecological Processes ◽  
Precipitation Indices

Streamflow and sediment flux variations in a mountain river basin directly affect the downstream biodiversity and ecological processes. Precipitation is expected to be one of the main drivers of these variations in the Himalayas. However, such relations have not been explored for the mountain river basin, Nepal. This paper explores the variation in streamflow and sediment flux from 2006 to 2019 in central Nepal’s Kali Gandaki River basin and correlates them to precipitation indices computed from 77 stations across the basin. Nine precipitation indices and four other ratio-based indices are used for comparison. Percentage contributions of maximum 1-day, consecutive 3-day, 5-day and 7-day precipitation to the annual precipitation provide information on the severity of precipitation extremeness. We found that maximum suspended sediment concentration had a significant positive correlation with the maximum consecutive 3-day precipitation. In contrast, average suspended sediment concentration had significant positive correlations with all ratio-based precipitation indices. The existing sediment erosion trend, driven by the amount, intensity, and frequency of extreme precipitation, demands urgency in sediment source management on the Nepal Himalaya’s mountain slopes. The increment in extreme sediment transports partially resulted from anthropogenic interventions, especially landslides triggered by poorly-constructed roads, and the changing nature of extreme precipitation driven by climate variability.

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