scholarly journals A rating-curve method for determining debit for dry season in micro-scale watersheds

2019 ◽  
Vol 260 ◽  
pp. 012029
Author(s):  
E Susanto ◽  
N Hatta ◽  
Sumono
Keyword(s):  
Dry Season ◽  
Rating Curve ◽  
Micro Scale ◽  
Curve Method

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.

scholarly journals Classifications of runoff and sediment data to improve the rating curve method

2017 ◽  
Vol 48 ◽  
Author(s):  
Hossein Khaledian ◽  
Homayoun Faghih ◽  
Ata Amini
Keyword(s):  
Suspended Sediment ◽  
River Flow ◽  
Data Classification ◽  
Suspended Sediments ◽  
Sediment Concentration ◽  
Low Flow ◽  
Rating Curve ◽  
Sediment Rating Curve ◽  
Curve Method ◽  
Sediment Data

In this study, data classification method was evaluated to increase accuracy of estimating suspended sediment load. To achieve this objective, suspended sediment in Chehelgazi and Khalifeh-Tarkhan rivers in Kurdistan, Iran, were estimated using Sediment Rating Curve (SRC) method in three different approaches of data classification. At first, measured data were modeled without classification. Then, data based on flow statues were divided into two series as high and low flow. Eventually, based on sediment concentration, the data were divided into low and high sediment concentration. Long-term runoff and sediment data were used to calibrate rating curve model. The estimated values were compared with recorded data and the performances of these models were evaluated using statistical criteria. The results indicated an effective role of data classification to improve estimating sediment transportation by rating curve method. In one of the stations, it was observed that due to classification based on river flow and sediment concentration, model efficiency was increased about 45% and 28%, respectively. Furthermore, in case of improving efficiency of SRC method, classifying data based on flow statues was found to be more effective than sediment concentration. The results of this study can be used to improve the management of the basin by more accurately estimating the amount of suspended sediments transporting in the rivers draining to reservoirs.

Discharge of the river Rhine from multi-sensor data from empirical and physical methods

2021 ◽  
Author(s):  
Luciana Fenoglio-Marc ◽  
Elena Zahkavova ◽  
Matthias Gärtner ◽  
Bahtiyor Zohidov ◽  
Salvatore Dinardo ◽  
...  
Keyword(s):  
Water Level ◽  
River Discharge ◽  
Water Surface ◽  
Water Discharge ◽  
River Rhine ◽  
Rating Curve ◽  
Physically Based ◽  
Curve Method ◽  
Semi Empirical

<p>River discharge is a key variable to quantify the water cycle and its flux.  This study focuses on the river Rhine, of width between 200 and 500 meters. River discharge is evaluated in this paper from the Sentinel-3 altimeter water level using various approches, which are the empirical rating curve method, the semi-empirical Bjerklie method and the physically-based method based on hydraulic equations.</p><p>The Sentinel-3 GPOD ESA products from the SAMOSA+ retracker perform better than the standard Copernicus products that use the OCOG and ocean retrackers. Root-mean-square errors (RMSEs) between altimetry and in-situ stations are between 0.10 m and 0.30 m at 10 of the 17 virtual tide gauge locations. The empirical rating curve method applied to the altimetric water level and in-situ discharge provides estimates of the water discharge with accuracy of 3-7% (expressed as RMSE normalized with the mean of the discharge).</p><p>The performance of the semi-empirical Bjerklie method and of the physically-based Manning algorithm to estimate the river discharge is assessed from water surface slope, elevation and top width data for different part of the river and flow conditions. Firstly, daily synthetic water surface slopes and elevations are generated from selected in-situ gauges and mean top river widths. Secondly the input to the discharge algorithm comes from the 1D-hydrodynamic model Sobek. Various choises for reach lengths and for number of observed time-series are considered. Different time sampling are used to study the effect of the repeat cycle of nadir altimeter and SWOT missions. The effect of the priori information on the accuracy of the flow water discharge is investigated.</p>

scholarly journals Uncertainty in river discharge observations: a quantitative analysis

2009 ◽  
Vol 13 (6) ◽  
pp. 913-921 ◽  
Author(s):  
G. Di Baldassarre ◽  
A. Montanari
Keyword(s):  
River Discharge ◽  
River Flow ◽  
Flow Data ◽  
Rating Curve ◽  
Po River ◽  
Discharge Data ◽  
Model Code ◽  
Curve Method ◽  
Different Sources ◽  
Extrapolation Error

Abstract. This study proposes a framework for analysing and quantifying the uncertainty of river flow data. Such uncertainty is often considered to be negligible with respect to other approximations affecting hydrological studies. Actually, given that river discharge data are usually obtained by means of the so-called rating curve method, a number of different sources of error affect the derived observations. These include: errors in measurements of river stage and discharge utilised to parameterise the rating curve, interpolation and extrapolation error of the rating curve, presence of unsteady flow conditions, and seasonal variations of the state of the vegetation (i.e. roughness). This study aims at analysing these sources of uncertainty using an original methodology. The novelty of the proposed framework lies in the estimation of rating curve uncertainty, which is based on hydraulic simulations. These latter are carried out on a reach of the Po River (Italy) by means of a one-dimensional (1-D) hydraulic model code (HEC-RAS). The results of the study show that errors in river flow data are indeed far from negligible.

A rating-curve method for determining silt-discharge of streams

1940 ◽  
Vol 21 (2) ◽  
pp. 603 ◽  
Author(s):  
F. B. Campbell ◽  
H. A. Bauder
Keyword(s):  
Rating Curve ◽  
Curve Method
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