scholarly journals Developing a Sediment Rating Curve Model Using the Curve Slope

2020 ◽  
Vol 29 (2) ◽  
pp. 1151-1159
Author(s):  
Hamed Benisi Ghadim ◽  
Meysam Salarijazi ◽  
Iman Ahmadianfar ◽  
Mohammad Heydari ◽  
Ting Zhang
Keyword(s):  
Rating Curve ◽  
Sediment Rating Curve ◽  
Curve Model

Regionalization of sediment rating curve for sediment yield prediction in ungauged catchments

2013 ◽  
Vol 46 (1) ◽  
pp. 26-38 ◽  
Author(s):  
Sokchhay Heng ◽  
Tadashi Suetsugi
Keyword(s):  
Sediment Yield ◽  
Model Performance ◽  
Data Uncertainty ◽  
Spatial Proximity ◽  
Yield Prediction ◽  
Rating Curve ◽  
Ungauged Catchments ◽  
Probability Curve ◽  
Sediment Rating Curve ◽  
Almost All

The main objective of this research is to regionalize the sediment rating curve (SRC) for subsequent sediment yield prediction in ungauged catchments (UCs) in the Lower Mekong Basin. Firstly, a power function-based SRC was fitted for 17 catchments located in different parts of the basin. According to physical characteristics of the fitted SRCs, the sediment amount observed at the catchment outlets is mainly transported by several events. This also indicates that clockwise hysteretic phenomenon of sediment transport is rather important in this basin. Secondly, after discarding two outlier catchments due to data uncertainty, the remaining 15 catchments were accounted for the assessment of model performance in UCs by means of jack-knife procedure. The model regionalization was conducted using spatial proximity approach. As a result of comparative study, the spatial proximity approach based on single donor catchment provides a better regionalization solution than the one based on multiple donor catchments. By considering the ideal alternative, a satisfactory result was obtained in almost all the modeled catchments. Finally, a regional model which is a combination of the 15 locally fitted SRCs was established for use in the basin. The model users can check the probability that the prediction results are satisfactory using the designed probability curve.

scholarly journals Prediction of sediment load by sediment rating curve and neural network (ANN) in El Kebir catchment, Algeria

2013 ◽  
Vol 122 (5) ◽  
pp. 1303-1312 ◽  
Author(s):  
Z A BOUKHRISSA ◽  
K KHANCHOUL ◽  
Y LE BISSONNAIS ◽  
M TOURKI
Keyword(s):  
Neural Network ◽  
Sediment Load ◽  
Rating Curve ◽  
Sediment Rating Curve

Impact of Chelicorophium curvispinum on the concentration-discharge response of suspended sediment in the Rhine River

2021 ◽  
Author(s):  
Marcel van der Perk
Keyword(s):  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Sudden Increase ◽  
Rhine River ◽  
Rating Curve ◽  
Independent Evidence ◽  
Sediment Rating Curve ◽  
Suspended Sediment Concentrations

<p>In an ongoing study to the decline in suspended sediment concentrations and loads in the Rhine river since the mid-1950s, the temporal changes in the power-law sediment rating curve parameters were examined. This revealed that the rating exponent of the rating curve increased substantially between the early and late 1980s. Until the early 1980s, the ratings curves were relatively flat with values of the rating exponent b varying around 0.2. In the mid-1980s, the exponent suddenly increased to a value between 0.4 and 0.6 and since then has remained within this range. This change in the rating exponent was mainly caused by a decrease in suspended sediment concentrations during low discharges. During high discharges, the suspended sediment concentration initially increased during the late 1980s, but this increase was nullified soon afterwards due to the declining trend in suspended sediment concentration.</p><p>The sudden increase of the rating exponent coincided with the period that the Ponto-Caspian <em>Chelicorophium curvispinum</em> (Caspian mud shrimp) invaded the Rhine river basin. This suggests that this suspension-feeder species bears the prime responsibility for this increase, although this hypothesis requires further independent evidence. The sudden increase in the rating exponent does however not manifest itself in the long-term gradual trend of declining suspended sediment concentrations and vice versa. Apparently, the sequestration of sediment by <em>Chelicorophium curvispinum</em> is only temporary: the suspended sediment sequestered during periods of relatively low discharges is likely remobilised again during periods of high discharge. This implies that the invasion of <em>Chelicorophium curvispinum</em> has not played a significant role in the decline of suspended sediment concentrations. The precise reasons for the gradual long-term decline in suspended sediment concentration remain yet unknown.</p>

scholarly journals Suspended Sediment Concentration and Sediment Loading of Bernam River (Perak, Malaysia)

2020 ◽  
Vol 22 (2) ◽  
pp. 1-14
Author(s):  
Sumayyah Aimi Mohd Najib ◽  
Syazwani Aliah ◽  
Husna Nabilah Hamidon
Keyword(s):  
Suspended Sediment Concentration ◽  
Sampling Period ◽  
Sediment Concentration ◽  
High Flow ◽  
Low Flow ◽  
Sediment Loading ◽  
Land Uses ◽  
Rating Curve ◽  
Sediment Rating Curve ◽  
Rain Events

Abstract This paper presents some of our preliminary results on the sediment discharge and load based on weekly sampling starting from Oct 2017 to January 2018. Results show that sediment rating curve of Bernam River was R2 = 0.86 high flow and R2 = 0.5 low flow. Average sediment loading throughout this sampling period is 1,144 t. Land use activity is expected to be the main contribution for the highest sediment concentration during rain events. The amount of annual sediment yield was estimated at 23 t/km2/year and is comparable to other studies having similar land uses in the catchment area.

scholarly journals Comparison of Sediment Rating Curves Developed on Load and on Concentration

1997 ◽  
Vol 28 (3) ◽  
pp. 189-200 ◽  
Author(s):  
Margareta B. Jansson
Keyword(s):  
Linear Regression ◽  
Bias Correction ◽  
Water Discharge ◽  
Regression Equations ◽  
Rating Curve ◽  
Weighted Mean ◽  
Data Set ◽  
Sediment Rating Curve ◽  
Rating Curves ◽  
Linear Regressions

A sediment rating curve developed as a linear regression on logged values which is back-transformed must be corrected for the bias introduced by the log transformation. This article shows that the variances are identical for linear regressions based on values of logged load and logged concentration from the same data set. This means that the bias correction factoss 101.1513σ2 for the back-transformed regressions are equivalent. Therefore a back-tranoformed log regression based on loads corrected for bias gives identical sedimett discharges to a back-transformed log regression on concentrations corrected for bias. Regression equations from gauging stations in two neighbouring basins in Costa Rica confirm this conclusion. Mean loads for individual discharge classes were plotted on diagrams in log scales to find the points where the sedimett rating curve changes direction. When sediment rating curves were developed on logged mean concentrations, water discharge weighted mean concentrations had to be determined in order to produce equations comparable to those on logged mean loads. Consequently, discharge weighted mean concentrations must be used in a plot to determine the change in direction of a sedimett rating curve and to check the goodnsss of fit of a regression developed by any model employing concentration as the dependent variabe.

scholarly journals An expanded rating curve model to estimate river discharge during tidal influences across the progressive-mixed-standing wave systems

PLoS ONE ◽  
2019 ◽  
Vol 14 (12) ◽  
pp. e0225758 ◽  
Author(s):  
Allan E. Jones ◽  
Amber K. Hardison ◽  
Ben R. Hodges ◽  
James W. McClelland ◽  
Kevan B. Moffett
Keyword(s):  
Standing Wave ◽  
River Discharge ◽  
Rating Curve ◽  
Curve Model

scholarly journals Consistency assessment of rating curve data in various locations using Bidirectional Reach (BReach)

2017 ◽  
Vol 21 (10) ◽  
pp. 5315-5337
Author(s):  
Katrien Van Eerdenbrugh ◽  
Stijn Van Hoey ◽  
Gemma Coxon ◽  
Jim Freer ◽  
Niko E. C. Verhoest
Keyword(s):  
Critical Issue ◽  
Data Consistency ◽  
Data Series ◽  
Rating Curve ◽  
Discharge Data ◽  
Reliable Assessment ◽  
Observational Uncertainty ◽  
Curve Model ◽  
Definition Of ◽  
The Uk

Abstract. When estimating discharges through rating curves, temporal data consistency is a critical issue. In this research, consistency in stage–discharge data is investigated using a methodology called Bidirectional Reach (BReach), which departs from a (in operational hydrology) commonly used definition of consistency. A period is considered to be consistent if no consecutive and systematic deviations from a current situation occur that exceed observational uncertainty. Therefore, the capability of a rating curve model to describe a subset of the (chronologically sorted) data is assessed in each observation by indicating the outermost data points for which the rating curve model behaves satisfactorily. These points are called the maximum left or right reach, depending on the direction of the investigation. This temporal reach should not be confused with a spatial reach (indicating a part of a river). Changes in these reaches throughout the data series indicate possible changes in data consistency and if not resolved could introduce additional errors and biases. In this research, various measurement stations in the UK, New Zealand and Belgium are selected based on their significant historical ratings information and their specific characteristics related to data consistency. For each country, regional information is maximally used to estimate observational uncertainty. Based on this uncertainty, a BReach analysis is performed and, subsequently, results are validated against available knowledge about the history and behavior of the site. For all investigated cases, the methodology provides results that appear to be consistent with this knowledge of historical changes and thus facilitates a reliable assessment of (in)consistent periods in stage–discharge measurements. This assessment is not only useful for the analysis and determination of discharge time series, but also to enhance applications based on these data (e.g., by informing hydrological and hydraulic model evaluation design about consistent time periods to analyze).

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