Developing a Sediment Rating Curve Model Using the Curve Slope

Abstract

There are different ways to estimate suspended sediment load of a river. The conventional sediment rating curve model has been used widely due to its simplicity and required parameters. The most important limitation of the conventional SRC model is its relatively low precision and underestimation of the suspended sediment load in most studies. However, in this study, the concept of SRC model segmentation is introduced based on the curve slope under the title of developed SRC-S model. The most important feature is the simplicity of the presented application. To compare the conventional SRC and the developed SRC-S models, data from two hydrometry stations in northern Iran were selected. Graphical study of the models shows that the developed SRC-S model enjoys more fitting precision in comparison with the conventional SRC model, and also has improved underestimation error of suspended sediment load in higher rates of river flow discharge. Six numerical criteria for model accuracy (Nash-Sutcliffe, root-mean-square error, and mean absolute error, difference ratio, efficiency ratio improved and index of agreement) are used for quantitative comparison of the results of conventional and developed models. Accordingly, we found that the mentioned criteria have improved significantly compared to the conventional model.

Estimation of soil mobilization rates by a rainy period and intense tillage practices in vineyards—A case study in the Maule region (Chile)

“…A higher slope increases the concentration of flows and thus erosion. Some authors indicate that erosive processes usually start from 10°, that is, a slope of 17.6% (Armstrong et al, 2011; Benisi Ghadim et al, 2020; Bryan & Wijdenes, 1992). Thus, the slope values present in the study area are enough for instigating erosive processes.…”

A brief review of recent global trends in suspended sediment estimation studies

“…The traditional SRC tends to over (under) estimate (Benisi Ghadim et al, 2020;Üneş et al, 2021), which limits its use as an all-purpose technique. A correction factor called the Ferguson correction factor (Ferguson, 1986) can be applied to improve underestimation.…”

“…A correction factor called the Ferguson correction factor (Ferguson, 1986) can be applied to improve underestimation. Different approaches have been applied to find all-purpose correction factors (Khaleghi and Varvani, 2018;Benisi Ghadim et al, 2020). The latest attempt includes the slope curve method (SCM) to improve estimation accuracy (Benisi Ghadim et al, 2020).…”

“…Different approaches have been applied to find all-purpose correction factors (Khaleghi and Varvani, 2018;Benisi Ghadim et al, 2020). The latest attempt includes the slope curve method (SCM) to improve estimation accuracy (Benisi Ghadim et al, 2020). This strategy needs to be further validated using large amounts of datasets and diverse hydrogeophysical features.…”

Modelagem temporal e espacial da concentração superficial de sedimentos utilizando sensoriamento remoto orbital e aprendizado de máquina

“…A maneira mais comum de monitorar a concentração superficial de sedimentos é por meio da obtenção da curva chave de sedimentos que, em geral, relaciona a vazão diária com a concentração de sedimentos (ZHENG, 2018;JAIYEOLA;ADEYEMO, 2019;KEOGH et al, 2019;BENISI GHADIM et al, 2020). Portanto, esperava-se que a vazão diária apresentasse boa relação com a CSS na área de estudos.…”