Frequency analyses of peak discharge and suspended sediment concentration in the United States

2019 ◽  
Vol 20 (2) ◽  
pp. 1157-1168 ◽  
Author(s):  
Tong Li ◽  
Shuai Wang ◽  
Bojie Fu ◽  
Xiaoming Feng
Keyword(s):  
United States ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
The United States ◽  
Peak Discharge

scholarly journals Integration of ANFIS with PCA and DWT for daily suspended sediment concentration prediction

Water SA ◽  
2021 ◽  
Vol 47 (2 April) ◽  
Author(s):  
Nguyen Mai Dang ◽  
Duong Tran Anh
Keyword(s):  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Fuzzy Inference ◽  
Ecological Integrity ◽  
Principal Component ◽  
Sediment Concentration ◽  
The United States ◽  
Discrete Wavelet ◽  
Coupled Models ◽  
Inference System

Quantifying sediment load is vital for aquatic and riverine biota and has been the subject of various environmental studies since sediment plays a key role in maintaining ecological integrity, river morphology and agricultural productivity. However, predicting sediment concentration in rivers is difficult because of the non-linear relationships of flow rates, geophysical characteristics and sediment loads. It is thus very important to propose suitable statistical methods which can provide fast, accurate and robust prediction of suspended sediment concentration (SSC) for management guidance. In this study, we developed coupled models of discrete wavelet transform (DWT) with adaptive neuro-fuzzy inference system (ANFIS), named DWT-ANFIS, and principal component analysis (PCA) with ANFIS, named PCA-ANFIS, for SSC time-series modeling. The coupled models and single ANFIS model were trained and tested using long-term daily SSC and river discharge which were measured on the Schuylkill and Iowa Rivers in the United States. The findings showed that the PCA-ANFIS performed better than the single ANFIS and the coupled DWT-ANFIS. Further applications of the PCA-ANFIS should be considered for simulation and prediction of other indicators relating to weather, water resources, and the environment.

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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