Sediment Rating Curves Research Articles

On the complexities of sediment load modeling using integrative machine learning: Application of the great river of Loíza in Puerto Rico

Sediment transportation in water bodies may cause many problems for the water resources projects and damage the environment. Hence, modeling sediment load components, including suspended sediment load (SSL) and bedload (BL) in rivers is of prime importance. Effective modeling of SSL and BL remains a challenging task due to their complex hydrological process. On this account, this study aims to appraise the potential of conventional machine learning (ML) models including adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and their integrative version with nature optimization algorithm called genetic algorithm (GA-ANFIS and GA-SVR) for SSL and BL prediction. Two traditional models are developed for modeling verification including the sediment rating curve (SRC) and multiple linear regression (MLR). The modeling results are assessed using four statistical measures (e.g., root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe Efficiency (NSE), and coefficient of determination (R2)), diagnostic analysis (scatter plots and Taylor diagram), and evaluation of the dependence of the state of the river flow-sediment system (hysteresis analysis). Based on the attained predictability performance, the integrative ML models reveal a superior prediction capacity in comparison with the standalone ANFIS, SVR, and the traditional models. In quantitative evaluation, the proposed integrative ML models indicate a remarkable prediction enhancement approximately 44% mean magnitude based on the MAE metric against the SRC traditional model for both the SSL and BL predicted values. Overall, the current investigation evidences the potential of the nature-inspired algorithm as a hyper-parameter optimizer for ML models that produce a reliable and robust predictive model for sediment concentration quantification.

Evaluating estimated sediment delivery by Revised Universal Soil Loss Equation (RUSLE) and Sediment Delivery Distributed (SEDD) in the Talar Watershed, Iran

The performance of the Revised Universal Soil Loss Equation (RUSLE) as the most widely used soil erosion model is a challenging issue. Accordingly, the objective of this study is investigating the estimated sediment delivery by the RUSLE method and Sediment Delivery Distributed (SEDD) model. To this end, the Talar watershed in Iran was selected as the study area. Further, 700 paired sediment-discharge measurements at Valikbon and Shirgah-Talar hydrometric stations between the years 1991 and 2011 were collected and used in sediment rating curves. Nine procedures were investigated to produce the required RUSLE layers. The estimated soil erosion by RUSLE was evaluated using sediment rating curve data by two methods including least squares and quantile regression. The average annual suspended sediment load was calculated for each sub-watershed of the study area using the SEDD model. Afterwards, a sediment rating curve was estimated by least squares and quantile regression methods using paired discharge-sediment data. The average annual suspended sediment load values were calculated for two hydrometric stations and were further evaluated by the SEDD model. The results indicated that the first considered procedure, which utilized 15-min rainfall measurements for the rainfall factor (R), and the classification method of SENTINEL-2 MSI image for the cover management factor (C), offered the best results in producing RUSLE layers. Furthermore, the results revealed the advantages of utilizing satellite images in producing cover management layer, which is required in the RUSLE method.

Annual suspended sediment load of the Yenisei river

Field studies, including high-frequency sampling for suspended sediment flux estimation, were conducted in 20142016 in the city of Igarka, at the outlet of the Yenisei River. During spring freshet of each year, multiple suspended sediment concentration (SSC) peaks were observed, irrelated to water discharge fluctuations. The form of hysteresis loops evidences the importance of input from local in-channel sediment sources, i.e. banks and bedforms, and scouring of bed material, deposited during winter, in observed sharp SSC peaks. On the falling stage of the freshet, longer peaks are related to sediment waves from major tributaries, notably the Nizhnyaya Tunguska River. Annual sediment load was calculated based on the daily water discharge and observed SSC data, using sediment rating curves and LOADEST models as two reference methods. Mean annual suspended sediment load of the Yenisei River in Igarka is estimated at 8.1 0.5 mln t., which significantly exceeds previously published values for the 19702001 period, from 4.6 to 5.9 mln t. Cumulative sediment load for 20142016 totals 24.2 2.1 mln t.

Investigation of Temporal Phenomena of Sediment Rating Curve and Comparison of it with the Some Statistical Methods for Estimating Suspended Sediment Load (Case Study: Gamasiab Watershed)

Investigation of Temporal Phenomena of Sediment Rating Curve and Comparison of it with the Some Statistical Methods for Estimating Suspended Sediment Load (Case Study: Gamasiab Watershed)

Characterizing braided rivers in two nested watersheds in the Source Region of the Yangtze River on the Qinghai-Tibet Plateau

Large and complex braided channels (>1 km wide) in the Source Region of the Yangtze River (SRYR) on the Qinghai-Tibet Plateau, China, are characterized by a unique alpine environment with very high elevations and glacier sources. Yet, little is known about the morphological complexity and changes of large braided channels owing to the scarcity of hydrological and topographical data. In this study, we provided the first insight into these issues using compiled long-term (several decades) climatic and suspended sediment data, and Landsat images of braided channels in two nested headwater watersheds (i.e., Tuotuo and Tongtian river watersheds) in the SRYR. Analysis of annual and monthly mean temperature, precipitation, and water discharges illustrated the nature of the hydrological regime and their response to glacier melting in the two watersheds. Examination of annual mean suspended sediment loads and concentrations (C) led to two sediment rating curves and generally similar C values between the two watersheds. Among a suite of morphological indices for braided rivers, braiding intensity may be quantified using four different ones. After comparing the indices, we showed that they had similar ability of characterizing braiding patterns and adopted branch count index (BIT3) for further analysis. We found that BIT3 was linearly correlated with active valley width (WR) during both flood and dry seasons for the 17 selected braided reaches distributed within the two nested watersheds. In addition, braiding density, defined as the ratio of BIT3 to WR, remained approximately unchanged irrespective of elevations and locations of these reaches, indicating clearly that morphological structures of braided channels are spatially uniform in the SRYR. Along the main channels of the two nested watersheds over the entire study period, BIT3 always reached the maximum as RW, defined as the ratio of water body to exposed active riverbed area, was around 0.43. Furthermore, RW was always positively correlated with the associated water discharge (Q). Variable degrees of braiding adjustment in response to changes of hydrological regime were represented by hysteresis loops between BIT3 and Q. These results revealed relatively homogeneous morphologic properties of braided channels across the two nested watersheds and provided an important benchmark for future morphodynamic research on braided rivers in the SRYR.

Developing a Sediment Rating Curve Model Using the Curve Slope

Developing a Sediment Rating Curve Model Using the Curve Slope

Sediment assessment for a watershed in arid region via neural networks

In the present study, the estimation of suspended sediment load is computed by four Artificial Neural Networks (ANNs) algorithms, Cascade Forward Back Propagation (CFBP), Feed Forward Back Propagation (FFBP), Radial Basis Function (RBF), and Recurrent Neural Network (RNN). Five cases of model input are calibrated to establish the relationship among precipitation, discharge and suspended sediment load. While discharge and rainfall up to four previous days as employed for input, model gives pre-eminent performance. Sensitivity of all models is appraised concerning Nash-Sutcliffe coefficient (ENS) and coefficient of determination (R2) for predicting sediment load. Among all ANNs, MMF (Morgan-Morgan-Finney) model when trained with stream flow as the input in RNN, gives best result with coefficient of determination, R2 as 0.9474, while the values for FFBP, CFBP and RBF are 0.9115, 0.8766 and 0.8511, respectively. Performance of all results show that an MMF model is superior to conventional SRC (Sediment Rating Curve) and MLR (Multiple Linear Regression) models in determining the complex relationship between discharge and suspended sediment load.

Development of reservoir capacity loss model using bootstrapping of sediment rating curves

ABSTRACT Significant developments have been made in understanding the process of reservoir sedimentation. However, predicting reservoir capacity loss (RCL) is still a complex problem. In estimating RCL, a number of challenges are encountered. These are majorly associated with the prediction of sediment load, trap efficiency and deposited-sediment bulk-density. The aim of the present study is to develop an RCL model. RCL experienced by the reservoir, for a longer time period, is estimated using the sediment rating curve (SRC), developed from discharge and sediment concentration, observed over a shorter time frame. The developed model is based on non-parametric bootstrapping of SRC and the inverse relationship of the coefficients of the rating curves. The established RCL loss model is applied to Shetrunji Reservoir, India. The model calibration and validation suggest that its efficiency to predict sediment concentration, sediment load, RCL and gross storage capacity is satisfactory. The present research methodology can be used for any other reservoir to compute RCL. The proposed model is important for field use as dam operators can easily predict the impact of different hydrological events on reservoir capacity and can plan the operation of flushing, sluicing or sediment bypasses.

Shifts of sediment transport regime caused by ecological restoration in the Middle Yellow River Basin

Sediment in rivers is the dominant material source for ecosystems in lower reaches and estuaries, and it is undergoing large variations globally in recent decades. Though we have knowledge that human activities are greatly affecting land surface ecosystem processes and functions, the relationships between sediment transport regime and the intensifying human activities, are still poorly understood. This study was to investigate the changes of sediment transport regime due to the large-scale ecological restoration in the Middle Yellow River Basin (MYRB). In this study, we examined the change of the sediment rating curves using daily sediment load and streamflow data from 30 stations during time periods including pre- and post-ecological restoration in this region. We found the pair-relationship of the rating parameters (coefficient and exponent), denoted as coefficient-exponent pair-line, is a critical indicator that can detect the shift of sediment transport regime due to disturbed land surface conditions, though the changed hydrometeorological condition may just influence the absolute values of the rating parameters. Our analysis indicates there was a significant and interesting change of the sediment transport regime in the MYRB characterized by the consistent shift of the coefficient-exponent pair-line, together with an increasing exponent and a decreasing coefficient. This changed sediment transport regime can tell that sediment delivery would become lower for normal discharge conditions but potentially higher for extreme discharge conditions, and this phenomenon seems more distinct in relatively smaller watersheds, suggesting a higher risk of the potential high sediment delivery for extreme rainfall conditions especially for small watersheds. Our study would be informative and valuable to decision makers for sustainable watershed management in the MYRB when considering the changed sediment delivery.

Effect of Sediment Load Boundary Conditions in Predicting Sediment Delta of Tarbela Reservoir in Pakistan

Setting precise sediment load boundary conditions plays a central role in robust modeling of sedimentation in reservoirs. In the presented study, we modeled sediment transport in Tarbela Reservoir using sediment rating curves (SRC) and wavelet artificial neural networks (WA-ANNs) for setting sediment load boundary conditions in the HEC-RAS 1D numerical model. The reconstruction performance of SRC for finding the missing sediment sampling data was at R2 = 0.655 and NSE = 0.635. The same performance using WA-ANNs was at R2 = 0.771 and NSE = 0.771. As the WA-ANNs have better ability to model non-linear sediment transport behavior in the Upper Indus River, the reconstructed missing suspended sediment load data were more accurate. Therefore, using more accurately-reconstructed sediment load boundary conditions in HEC-RAS, the model was better morphodynamically calibrated with R2 = 0.980 and NSE = 0.979. Using SRC-based sediment load boundary conditions, the HEC-RAS model was calibrated with R2 = 0.959 and NSE = 0.943. Both models validated the delta movement in the Tarbela Reservoir with R2 = 0.968, NSE = 0.959 and R2 = 0.950, NSE = 0.893 using WA-ANN and SRC estimates, respectively. Unlike SRC, WA-ANN-based boundary conditions provided stable simulations in HEC-RAS. In addition, WA-ANN-predicted sediment load also suggested a decrease in supply of sediment significantly to the Tarbela Reservoir in the future due to intra-annual shifting of flows from summer to pre- and post-winter. Therefore, our future predictions also suggested the stability of the sediment delta. As the WA-ANN-based sediment load boundary conditions precisely represented the physics of sediment transport, the modeling concept could very likely be used to study bed level changes in reservoirs/rivers elsewhere in the world.

Hydraulic Parameters for Sediment Transport and Prediction of Suspended Sediment for Kali Gandaki River Basin, Himalaya, Nepal

Sediment yield is a complex phenomenon of weathering, land sliding, and glacial and fluvial erosion. It is highly dependent on the catchment area, topography, slope of the catchment terrain, rainfall, temperature, and soil characteristics. This study was designed to evaluate the key hydraulic parameters of sediment transport for Kali Gandaki River at Setibeni, Syangja, located about 5 km upstream from a hydropower dam. Key parameters, including the bed shear stress (τb), specific stream power (ω), and flow velocity (v) associated with the maximum boulder size transport, were determined throughout the years, 2003 to 2011, by using a derived lower boundary equation. Clockwise hysteresis loops of the average hysteresis index of +1.59 were developed and an average of 40.904 ± 12.453 Megatons (Mt) suspended sediment have been transported annually from the higher Himalayas to the hydropower reservoir. Artificial neural networks (ANNs) were used to predict the daily suspended sediment rate and annual sediment load as 35.190 ± 7.018 Mt, which was satisfactory compared to the multiple linear regression, nonlinear multiple regression, general power model, and log transform models, including the sediment rating curve. Performance indicators were used to compare these models and satisfactory fittings were observed in ANNs. The root mean square error (RMSE) of 1982 kg s−1, percent bias (PBIAS) of +14.26, RMSE-observations standard deviation ratio (RSR) of 0.55, coefficient of determination (R2) of 0.71, and Nash–Sutcliffe efficiency (NSE) of +0.70 revealed that the ANNs’ model performed satisfactorily among all the proposed models.

Development and testing of the Revised Morgan-Morgan-Finney (RMMF) soil erosion model under different pedological datasets

ABSTRACTThe study aims to investigate the effect of soil properties delineation on erosion modelling. To that end, the soil attributes of the Venetikos River catchment, northwestern Greece, are described using two pedological datasets, i.e. field samples and classification maps. The goal is to select the most appropriate for the accurate estimation of erosion. The Revised Morgan-Morgan-Finney (RMMF) model is developed per base map (annual or multi-annual), keeping all other parameters unchanged. Modelled sediment yield (SY) values are validated against “observed” ones, calculated utilizing the sediment rating curve methodology. Overall, the classification maps approach (164.35 t km-2 year‒1) performed better than the soil samples one (82.97 t km-2 year‒1), displaying higher convergence to the synthetic SY (548.9 t km-2 year‒1). The discrepancy among approaches is attributed to the different computation methodologies (thus pedological background) used. Both approximations successfully identified the high-risk erosion areas. The same conclusions arose from the multi-annual application of the model.

Analysing the land-use change effects on soil erosion and sediment in the North of Iran; a case study: Talar watershed

This research aims at predicting the future land-use and examining the effects of the land-use changes on the present and future soil erosion and sediment delivery. Then, Land Change Modeler (LCM) and Geo-spatial Water Erosion Prediction Project (GeoWEPP) were applied. Annual suspended sediment derived from sediment rating curves in Talar Watershed, Iran was used to evaluate the GeoWEPP. The predicted sediment load values of 2016 by GeoWEPP in the likely zone of 0.5 for Shirgah-Talar and Valikbon hydrometric stations were achieved respectively as 756094 and 62082.8 ton. Moreover, the mean annual sediment delivery per unit area of 2000, 2010, 2016 and 2030 were calculated respectively as 37.5, 43, 49.4 and 53.2 (t/ha.yr). The reason for the acquired incremental trend of the soil loss and sediment delivery was the land-use change. The results proved the acceptable performance of LCM combined by GeoWEPP to predict the nearby land-use and related sediment delivery.

Evaluating the performance of four different heuristic approaches with Gamma test for daily suspended sediment concentration modeling.

Accurate prediction of suspended sediment concentration (SSC) carried by a river or watershed basin is essential for understanding the hydrology of basin in terms of water quality, river bed sustainability and aquatic habitats. In this study, four heuristic methods, namely, radial basis neural network (RBNN), self-organizing map neural network (SOMNN), least square support vector regression (LSSVR), and multivariate adaptive regression spline (MARS) were employed for daily SSC modeling at Ashti, Bamini, and Tekra stations located in Godavari River basin, Andhra Pradesh, India. The Gamma test (GT) was utilized for identifying the most significant input variables for the applied heuristic approaches. The results obtained by RBNN, SOMNN, LSSVR, and MARS models were compared with those of the traditional sediment rating curve (SRC). The performance of the models was evaluated based on the root mean square error (RMSE), coefficient of efficiency (COE), Pearson correlation coefficient (PCC), Willmott index (WI), and pooled average relative error (PARE) indices, as well as the visual inspection using line diagram, scatter diagram, and Taylor diagram (TD). The results of comparison revealed that the four heuristic methods gave higher accuracy than the SRC model. Among the heuristic models, the RBNN-3 (RMSE = 0.045, 0.062, 0.131g/l; COE = 0.884, 0.883, 0.914; PCC = 0.955, 0.961, 0.958; and WI = 0.970, 0.963, 0.976) outperformed the other models in simulating daily SSC records in the studied stations.

Presenting a New Approach to Increase the Efficiency of the Sediment Rating Curve Model in Estimating Suspended Sediment Load in Watersheds (Case Study: Mahabad-Chai River, Lake Urmia Basin, West Azarbayejan Province, Iran)

Presenting a New Approach to Increase the Efficiency of the Sediment Rating Curve Model in Estimating Suspended Sediment Load in Watersheds (Case Study: Mahabad-Chai River, Lake Urmia Basin, West Azarbayejan Province, Iran)

Development of the FCM-SVR Hybrid Model for Estimating the Suspended Sediment Load

The accurate estimation of suspended sediment load (SSL) carried by a river is one of the primary issues in river engineering, water resources, and environment projects. Boundary condition and simplification of some important parameters lead to some limitation on the empirical sediment equations which are based on flow and sediment properties. In this study, the potential of a developed Fuzzy C-mean clustering-support vector regression model, as a FCM-SVR hybrid model, was investigated in comparison with sediment rating curve (SRC), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector regression (SVR) models for estimating the daily SSL in Sistan River in Iran. Root mean square error (RMSE), mean absolute error (MAE), determination coefficient (R2), and Nash-Sutcliffe efficiency coefficient (NSE) statistics were utilized to evaluate the accuracy of the models. The result showed that FCM-SVR model estimates SSL (with RMSE, MAE, R2, and NSE equal to 34,415.52 ton/day, 12,256.28 ton/day, 0.922, and 0.918 respectively in testing period) more accurate than other models. The RMSE value, which was 50% lower compared to other models, reveals that this model possesses the lowest error than the other models. Also, the obtained results indicated that the estimated SSLs, using the best FCM-SVR, were in good agreement and linear dependence with observed values. Unlike other models, FCM-SVR model appropriately estimates extreme values of SSL.

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

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.

Performance comparison between genetic programming and sediment rating curve for suspended sediment prediction

The accurate prediction of suspended sediment flowing into reservoirs is very important during their design and operation stage. It is also of great significance in environmental engineering, hydroelectric equipment longevity, river aesthetics, pollution and channel navigability. Hence the development of methods that can accurately forecast the suspended sediment loads from historical water data sets and infrequently or non-sampled rivers will greatly help in the management of water resources. One of the widely used methods employing empirical models are rating curves. Therefore, in this study, 12 sediment rating curves (SRCs) were developed from 14 years’ historical data, one for each month of the year, to show the relationship between stream and suspended sediment flowing into Inanda Dam, Durban, South Africa. The developed monthly SRCs were evaluated using two performance assessment criteria, specifically RMSE and R2. The performance of these SRC models was compared with those of the corresponding monthly developed genetic programming (GP) models for each month of the year. The results indicate that the predictions from the GP models are better than the predictions from the SRC models, especially in predicting large quantities of suspended sediment load during high streamflow such as during flood events.

Estimation of Suspended Sediment Load in the Ressoul Watershed, Algeria

Sediment load and its response to the variations of the hydrological elements are important to understand the phenomena of erosion. This study was fulfilled with the aim of developing a model to predict sediment load using sediment rating curve for the Ain Berda gauging station. The model was developed based on the available streamflow discharge and suspended sediment concentration data during sampled storm events over 39 year-period in the Ressoul watershed. Relationships between sediment concentration and water discharge were used according to single and rising-falling stage ratings to determine the best model for sediment load prediction. Additionally, a technique was devised to correct for log-transform bias on the sediment rating curves. The mean annual sediment yield during high and medium high flood events was 302 T km-2 yr-1. The high sediment loads in the study basin could be explained by the intensity of rain, the aggressiveness of the flows, the topography and the availability of sediments from hillslopes. The sediment load was dominated by winter and spring seasons accounting for 89% of the annual load. A high sediment supply in winter might confirm the intense geomorphic action caused by high intensity rainfall, low vegetation cover, and heavy machine activity in the agricultural fields. Following watershed management for local communities may bring multiple benefits. The adoption of suitable measures for soil conservation should reduce soil erosion and improve the livelihoods of the inhabitants. This study can serve as a reference for policymakers and planners.

Pembuatan Model Kendali Mutu Data Sedimen

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).