scholarly journals DEVELOPING AN EXCELLENT SEDIMENT RATING CURVE FROM ONE HYDROLOGICAL YEAR SAMPLING PROGRAMME DATA: APPROACH

2008 ◽  
Vol 2 (1) ◽  
pp. 21-27 ◽  
Author(s):  
Preksedis Ndomba ◽  
Felix Mtalo ◽  
Ånund Killingtveit
Keyword(s):  
Rating Curve ◽  
Sediment Rating Curve ◽  
Sampling Programme

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 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

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 Effect of Adaptive Cluster Sampling Design on Accuracy of Sediment Rating Curve Estimation

2010 ◽  
Vol 15 (2) ◽  
pp. 142-151 ◽  
Author(s):  
Mahmood Arabkhedri ◽  
F. S. Lai ◽  
Noor-Akma Ibrahim ◽  
Mohamad-Roslan Mohamad-Kasim
Keyword(s):  
Sampling Design ◽  
Cluster Sampling ◽  
Adaptive Cluster Sampling ◽  
Rating Curve ◽  
Curve Estimation ◽  
Sediment Rating Curve

scholarly journals Scale breaks of suspended sediment rating in large rivers in Germany induced by organic matter

2020 ◽  
Vol 8 (3) ◽  
pp. 661-678
Author(s):  
Thomas O. Hoffmann ◽  
Yannik Baulig ◽  
Helmut Fischer ◽  
Jan Blöthe
Keyword(s):  
Organic Matter ◽  
Suspended Sediment ◽  
Suspended Matter ◽  
Monitoring Network ◽  
Suspended Sediments ◽  
Low Flow ◽  
Rating Curve ◽  
Organic Fraction ◽  
Sediment Rating Curve ◽  
Aquatic Biomass

Abstract. Understanding the transport of suspended sediment and associated nutrients is of major relevance for sustainable sediment management aiming to achieve healthy river systems. Sediment rating curves are frequently used to analyze the suspended sediments and their potential sources and sinks. Here we use more than 750 000 measurements of suspended sediment concentrations (SSCs) and discharge (Q) collected at 62 gauging stations along 19 waterways in Germany based on the suspended sediment monitoring network of the German water and shipping authority, which started in the 1960s. Furthermore, we analyze more than 2000 measurements of the loss on ignition (LOI) of suspended matter at two stations along the rivers Moselle and Rhine to provide a proxy for the relative contributions of mineral load and organic matter. SSC and LOI are analyzed in terms of the power-law rating curve to identify discharge-dependent controls of suspended matter. Our results indicate that for most studied gauging stations, rating coefficients are not constant over the full discharge range, but there is a distinct break in the sediment rating curve, with specific SSC–Q domains above and below this break. The transition of the rating exponent likely results from increased supply of mineral suspended sediments from hillslope erosion at high flow and a shift of the organic matter sources from aquatic biomass-derived organic matter (i.e., high % LOI) at low flow, to mineral-associated organic matter with low % LOI eroded from hillslopes at higher flow. Based on these findings we developed a conceptual rating model for large (>10 000 km2) and low-turbidity (SSC < 1000 mg L−1) rivers separating the mineral and organic fraction of the suspended matter in German waterways. This model allows evaluating the sources of the mineral and organic fraction of the suspended matter and facilitates new insights into the first-order control of discharge on the quality and quantity of suspended sediments.

scholarly journals A Sediment Rating-curve Method to Determine Sediment Discharge for Rainy Season in Micro-scale Watersheds

2019 ◽  
Vol 2 (1) ◽  
pp. 21-27 ◽  
Author(s):  
Atikah Sitorus ◽  
Edi Susanto
Keyword(s):  
River Discharge ◽  
Secondary Data ◽  
Sediment Discharge ◽  
Climate Data ◽  
Rating Curve ◽  
Concentration Data ◽  
Discharge Data ◽  
Sediment Rating Curve ◽  
Curve Method ◽  
Sediment Data

This research was carried out to overcome the problem of the lack of sediment data available in several watersheds in North Sumatra, the lack of available sediment data is caused by the requirement of a large amount of time, cost and risk to obtain such data. Purpose of this study was to obtain the equation of sediment rating curve. The sediment rating curve is an equation that connects the river discharge with sediment discharge, so that to obtain the sediment discharge, it is enough to use the river discharge data. This research used the descriptive method using the primary (sediment discharge and concentration data) and secondary data (climate data). Result of the study obtained the equation of the sediment rating curve of Qs = 14.115 Q2.2736 and the value of R2 of 0.711. The sediment discharge obtained has exceeded the limit set by the Ministry of Forestry regarding the criteria for determination of watersheds.

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