Erosion thresholds and suspended sediment yields, Waipaoa River Basin, New Zealand

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.

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Variability of suspended sediment yields within the Loire river basin (France)

Journal of Hydrology ◽

10.1016/j.jhydrol.2014.08.045 ◽

2014 ◽

Vol 519 ◽

pp. 1225-1237 ◽

Cited By ~ 19

Author(s):

A. Gay ◽

O. Cerdan ◽

M. Delmas ◽

M. Desmet

Keyword(s):

Suspended Sediment ◽

River Basin ◽

Sediment Yields ◽

Loire River

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Concentrations and loads of suspended sediment and nutrients in surface water of the Yakima River basin, Washington, 1999-2000 [electronic resource] : with an analysis of trends in concentrations

10.3133/wri034026 ◽

2003 ◽

Keyword(s):

Surface Water ◽

Suspended Sediment ◽

River Basin ◽

Electronic Resource ◽

Yakima River

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Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

Sustainability ◽

10.3390/su13020542 ◽

2021 ◽

Vol 13 (2) ◽

pp. 542

Author(s):

Tarate Suryakant Bajirao ◽

Pravendra Kumar ◽

Manish Kumar ◽

Ahmed Elbeltagi ◽

Alban Kuriqi

Keyword(s):

Time Series ◽

Wavelet Transform ◽

Suspended Sediment ◽

River Basin ◽

Time Series Data ◽

Series Data ◽

Discrete Wavelet ◽

Original Time Series ◽

Inference System ◽

Original Time

Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.

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

Earth ◽

10.3390/earth2010003 ◽

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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