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.

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.

scholarly journals ANALISIS PENGARUH PERUBAHAN TUTUPAN LAHAN TERHADAP HIDROLISIS DAS PAMUKKULU SULAWESI SELATAN

2020 ◽  
Vol 4 (1) ◽  
pp. 62-76
Author(s):  
Andi Rasti Serastiwati ◽  
St. Subaedah ◽  
Netty Syam
Keyword(s):  
Land Cover ◽  
River Discharge ◽  
Water Discharge ◽  
Secondary Data ◽  
Climate Data ◽  
Discharge Data ◽  
The Poor ◽  
Land Cover Data ◽  
Performance Report ◽  
Major River

The Pamukkulu watershed is one of the Jeneberang-Kelara Sub-watersheds, which is one of the 108 Priority Watersheds in Indonesia determined based on the 2017 Ministry of Environment and Forestry Performance Report which is prioritized as a location for Forest and Land Rehabilitation activities. The purpose of this study was to analyze changes in land cover in the Pamukkulu watershed in 2008 and 2017, the effect of land cover changes in the Pamukkulu watershed on fluctuations in major river flows and analyze the health level of the Pamukkulu watershed based on analysis of major river discharge and changes in land cover. The study was conducted in February to April 2018. Data collection was carried out by taking secondary data in the form of land cover data in 2008 and 2017, climate data and Pamukkulu River discharge data. The results showed that based on the results of the analysis of the Land Cover Index (IPL), the condition of Pamukkulu watershed land cover in 2008 was at 19.38% and 16.96% in 2017 so that it was categorized as bad. The results of the hydrological analysis (river water discharge) on the River Regime Coefficient in 2008 were 125 and in 2017 amounted to 119.6 so that the KRS is also categorized as bad. While the results of the analysis of the Variant Coefficient (CV) in 2008 amounted to 144.90% and in 2017 amounted to 87.5% then the CV was categorized as poor. Based on the analysis of the value of the Land Cover Index, River Regime Coefficient and River Regime Coefficient in the Pamukkulu Watershed in 2008 and 2017 which are in the poor category, the performance of the Pamukkulu Watershed is in the poor category.

Kajian Debit Sungai di DAS Wae Ruhu, Kota Ambon

2018 ◽  
Vol 1 (1) ◽  
pp. 18-23
Author(s):  
Matheus Souisa ◽  
Paulus R. Atihuta ◽  
Josephus R. Kelibulin
Keyword(s):  
River Discharge ◽  
River Flow ◽  
Secondary Data ◽  
Data Retrieval ◽  
Sediment Discharge ◽  
Flood Prevention ◽  
Retrieval Processes ◽  
Water Catchment ◽  
Steep Slopes ◽  
Station Point

Ambon City is a region consisting of hilly areas and steep slopes with diverse river characteristics. Research has been carried out in the Wae Ruhu watershed in Ambon City which starts from upstream (water catchment) to downstream. This study aims to determine the magnitude of river discharge and sediment discharge in the Wae Ruhu watershed. This research was conducted in several stages including, secondary data collection, research location survey, preparation of research tools and materials as well as field data retrieval processes which included tracking coordinates at each station point and entire watershed, calculation of river flow velocity, river geometry measurements, and sampling sediment. The results showed that the average river discharge in the Wae watershed in the year 2018 was 1.24 m3 / s, and the average sediment discharge was 6.27 kg / s. From the results of this study and the field observations proposed for flood prevention and the rate of sediment movement are the construction of cliffs with sheet pile and gabions.

scholarly journals Pembuatan Model Kendali Mutu Data Sedimen

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Sri Mulat Yuningsih ◽  
Asep Ferdiansyah ◽  
Muhammad Fauzi
Keyword(s):  
Quality Control ◽  
Sediment Transport ◽  
Data Quality ◽  
River Flow ◽  
Control Model ◽  
Special Treatment ◽  
Data Quality Control ◽  
Discharge Data ◽  
Sediment Rating Curve ◽  
Sediment Data

Special treatment for watershed management was needed due to severe of watershed condition in most regions in Indonesia. The treatment should be directed to comprehensive changes of management paradigm for all aspects in it. Those were indicated by the increasing of disasters around the watershed, such as floods, droughts, landslides, erosion and increased of sediment transported by the river basin. The increasing of sedimentation which occurs in the river flow will disrupt the performance of existing hydraulic structure in the river. The event could be monitored by hydrological data, especially with the continuously and accurately of discharge and sediment data. In order to solve the problem, sediment data quality control model was needed. The purpose of this research is to determined suspended sediment data quality control model, in order to have continuous and quality guaranteed of sediment transport data. The scopes of this sediment data quality control were making criteria and sub, determining rank priority between criteria and sub, arranging scoring form, trial and error, finalization. The model consists of three main stages, there are measurement of discharge and taking sediment sample (QC1), drawing of sediment rating curve (QC2), and conversion of discharge data to sediment transport (QC3).

Annual Plutonium Budget for the Rio Grande

1995 ◽  
Author(s):  
William L. Graf
Keyword(s):  
National Laboratory ◽  
Rio Grande ◽  
Suspended Sediments ◽  
River System ◽  
Sediment Discharge ◽  
Concentration Data ◽  
Discharge Data ◽  
Mean Values ◽  
Plutonium Concentration ◽  
Rio Chama

A mean annual plutonium budget for the Northern Rio Grande provides an accounting of the amounts of plutonium moving into and out of various reaches of the river during a typical year. Such a budget is a basis for assessing the rates of plutonium transport and the location of storage along the river. The budget presented in the following pages is for bedload and suspended sediments. It does not include plutonium in water because water-borne plutonium is such a small portion of the total in the system (as discussed in Chapter 7). The budget as calculated here requires data concerning sediment and plutonium concentrations in the sediment. The sediment discharge data that are available from U. S. Geological Survey gaging sites (Chapter 4) define the overall framework for budget construction. A reasonably detailed picture is possible for the river system from the Rio Grande at Embudo and the Rio Chama at Chamita southward to the Rio Grande at San Marcial (for locations, see Figure 3.9) where the river empties into Elephant Butte Reservoir. Data collected by Los Alamos National Laboratory and published in the annual surveillance reports by the laboratory’s Environmental Studies Group and later by the Environmental Surveillance Group provide plutonium concentrations for bedload and suspended sediments. The calculations for each site in this study used mean values of plutonium concentrations from all measurements at or near the site. Table 8.1 reviews the sources of plutonium concentration data for each of the sediment-gaging sites in the regional budget calculations. Unfortunately, the sites for collecting the plutonium data were not always colocated with the gaging sites that produced the sediment discharge data. In addition, most of the plutonium concentration data are for bedload sediments because of the manner in which the workers collected samples. In some cases, the best estimates of plutonium concentrations in suspended load for gaging sites are from concentrations found in sediments of the nearest reservoir downstream because those sediments are likely to have been in suspension before their emplacement on reservoir floors. The assumption that the mean concentration is a useful representative value seems reasonable given that in those reaches with relatively large amounts of data, concentration values do not show temporal or geographic trends.

scholarly journals Copula-Based Infilling Methods for Daily Suspended Sediment Loads

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1701
Author(s):  
Jenq-Tzong Shiau ◽  
Yu-Cheng Lien
Keyword(s):  
Suspended Sediment ◽  
Conditional Distribution ◽  
Sediment Load ◽  
Distribution Model ◽  
Rating Curve ◽  
Similar Frequency ◽  
Sediment Rating Curve ◽  
Sediment Loads ◽  
Recorded Data ◽  
Sediment Data

Less-frequent and inadequate sampling of sediment data has negatively impacted the long and continuous records required for the design and operation of hydraulic facilities. This data-scarcity problem is often found in most river basins of Taiwan. This study aims to propose a parsimonious probabilistic model based on copulas to infill daily suspended sediment loads using streamflow discharge. A copula-based bivariate distribution model of sediment and discharge of the paired recorded data is constructed first. The conditional distribution of sediment load given observed discharge is used to provide probabilistic estimation of sediment loads. In addition, four different methods based on the derived conditional distribution of sediment load are used to give single-value estimations. The obtained outcomes of these methods associated with the results of the traditional sediment rating curve are compared with recorded data and evaluated in terms of root mean square error (RMSE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE), and modified Nash-Sutcliffe efficiency (MNSE). The proposed approach is applied to the Janshou station located in eastern Taiwan with recorded daily data for the period of 1960–2019. The results indicate that the infilled sediments by the sediment rating curve exhibit better performance in RMSE and NSE, while the copula-based methods outperform in MAPE and MNSE. Additionally, the infilled sediments by the copula-based methods preserve scattered characteristics of observed sediment-discharge relationships and exhibit similar frequency distributions to that of recorded sediment data.

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 The Rise of Climate-Driven Sediment Discharge in the Amazonian River Basin

Atmosphere ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 208 ◽  
Author(s):  
Nazzareno Diodato ◽  
Naziano Filizola ◽  
Pasquale Borrelli ◽  
Panos Panagos ◽  
Gianni Bellocchi
Keyword(s):  
River Basin ◽  
Amazon Basin ◽  
Global Climate ◽  
Climate Extremes ◽  
Temporal Variations ◽  
Sediment Discharge ◽  
Storm Events ◽  
Discharge Data ◽  
Magdalena River ◽  
Sediment Export

The occurrence of hydrological extremes in the Amazon region and the associated sediment loss during rainfall events are key features in the global climate system. Climate extremes alter the sediment and carbon balance but the ecological consequences of such changes are poorly understood in this region. With the aim of examining the interactions between precipitation and landscape-scale controls of sediment export from the Amazon basin, we developed a parsimonious hydro-climatological model on a multi-year series (1997–2014) of sediment discharge data taken at the outlet of Óbidos (Brazil) watershed (the narrowest and swiftest part of the Amazon River). The calibrated model (correlation coefficient equal to 0.84) captured the sediment load variability of an independent dataset from a different watershed (the Magdalena River basin), and performed better than three alternative approaches. Our model captured the interdecadal variability and the long-term patterns of sediment export. In our reconstruction of yearly sediment discharge over 1859–2014, we observed that landscape erosion changes are mostly induced by single storm events, and result from coupled effects of droughts and storms over long time scales. By quantifying temporal variations in the sediment produced by weathering, this analysis enables a new understanding of the linkage between climate forcing and river response, which drives sediment dynamics in the Amazon basin.

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