Assessment of sediment yield estimations for large watershed areas: a case study for the Seyhan,Demirköprü and Hirfanlı reservoirs in Turkey

Abstract

Analyses of data from reservoir surveys and sediment rating curves are compared to predict sediment yield in three large reservoir watershed areas in Turkey. Sediment yield data were derived from reservoir sedimentation rates and suspended sediment measurements at gauging stations. The survey data were analysed to provide the volume estimates of sediment, the time-averaged sediment deposition rates, the long-term average annual loss rates in the reservoir storage capacity, and the long-term sediment yield of the corresponding watershed areas. Four regression methods, including linear and nonlinear cases, were applied to rating curves obtained from gauging stations. Application of the efficiency test to a power function form of a rating curve with nonlinear regression yielded the highest efficiency values. Based on the analysis of the sediment rating curves, sediment load fluxes were calculated by using average daily discharge data at each gauging station. Comparison of these two sediment yield values for each reservoir showed that the sediment yields from the suspended sediment measurements, SY_GS, are 0.99 to 3.54 times less than those obtained from the reservoir surveys, SY_RS. The results from the reservoir surveys indicate that all three reservoirs investigated have lost significant storage capacity due to high sedimentation rates.     

Simulation of relationship between river discharge and sediment yield in the semi-arid river watersheds

Complex and variable nature of the river sediment yield caused many problems in estimating the long-term sediment yield and problems input into the reservoirs. Sediment Rating Curves (SRCs) are generally used to estimate the suspended sediment load of the rivers and drainage watersheds. Since the regression equations of the SRCs are obtained by logarithmic retransformation and have a little independent variable in this equation, they also overestimate or underestimate the true sediment load of the rivers. To evaluate the bias correction factors in Kalshor and Kashafroud watersheds, seven hydrometric stations of this region with suitable upstream watershed and spatial distribution were selected. Investigation of the accuracy index (ratio of estimated sediment yield to observed sediment yield) and the precision index of different bias correction factors of FAO, Quasi-Maximum Likelihood Estimator (QMLE), Smearing, and Minimum-Variance Unbiased Estimator (MVUE) with LSD test showed that FAO coefficient increases the estimated error in all of the stations. Application of MVUE in linear and mean load rating curves has not statistically meaningful effects. QMLE and smearing factors increased the estimated error in mean load rating curve, but that does not have any effect on linear rating curve estimation.     

Application of the honey-bees mating programming (HBMP) algorithm to sediment concentration modelling

Abstract

The honey-bees mating programming (HBMP) algorithm is introduced as a novel tool for predicting suspended sediment concentration for the Mad River catchment near Arcata, USA. The paper also applies gene expression programming (GEP) as a comparison and shows that these two approaches can the produce transparent, nonlinear relationships between the independent and dependent variables. Some modifications have been made to the HBMP algorithm to improve its capability and efficiency. The results achieved from this method and GEP are compared with two different sediment rating curves based on regression techniques. The findings show that the results from both the HBMP and GEP methods are promising and outperform the results obtained from the sediment rating curves.

Editor D. Koutsoyiannis; Associate editor L. See     

Estimation of suspended sediment concentration and yield using linear models,random forests and quantile regression forests

For sediment yield estimation, intermittent measurements of suspended sediment concentration (SSC) have to be interpolated to derive a continuous sedigraph. Traditionally, sediment rating curves (SRCs) based on univariate linear regression of discharge and SSC (or the logarithms thereof) are used but alternative approaches (e.g. fuzzy logic, artificial neural networks, etc.) exist. This paper presents a comparison of the applicability of traditional SRCs, generalized linear models (GLMs) and non‐parametric regression using Random Forests (RF) and Quantile Regression Forests (QRF) applied to a dataset of SSC obtained for four subcatchments (0·08, 41, 145 and 445 km²) in the Central Spanish Pyrenees. The observed SSCs are highly variable and range over six orders of magnitude. For these data, traditional SRCs performed inadequately due to the over‐simplification of relating SSC solely to discharge. Instead, the multitude of acting processes required more flexibility to model these nonlinear relationships. Thus, alternative advanced machine learning techniques that have been successfully applied in other disciplines were tested. GLMs provide the option of including other relevant process variables (e.g. rainfall intensities and temporal information) but require the selection of the most appropriate predictors. For the given datasets, the investigated variable selection methods produced inconsistent results. All proposed GLMs showed an inferior performance, whereas RF and QRF proved to be very robust and performed favourably for reproducing sediment dynamics. QRF additionally provides estimates on the accuracy of the predictions and thus allows the assessment of uncertainties in the estimated sediment yield that is not commonly found in other methods. The capabilities of RF and QRF concerning the interpretation of predictor effects are also outlined. Copyright © 2008 John Wiley & Sons, Ltd.     

Time‐varying,nonlinear suspended sediment rating curves to characterize trends in water quality: An application to the Upper Hudson and Mohawk Rivers,New York

This work proposes two modelling frameworks for diagnosing temporal variations in nonlinear rating curves that describe suspended sediment–discharge relationships. A variant of the weighted regression on time, discharge, and season model is proposed and is compared against dynamic nonlinear modelling, a newly developed nonlinear time series filter based on sequential Monte Carlo sampling. Both approaches estimate a time series of rating curve parameters, with uncertainty, that can be used to diagnose variability in the sediment–discharge relationship over time. We evaluate the models with a variety of synthetic scenarios to highlight their ability to estimate signals of known rating curve change. Results reveal important bias‐variance trade‐offs unique to each approach, and in general, suggest that dynamic nonlinear modelling is better suited for rapid rating curve changes, whereas the weighted regression on time, discharge, and season variant more precisely estimates slow change. The techniques are then applied in two case studies in the Upper Hudson and Mohawk Rivers in New York. We conclude with a discussion of the implications of dynamic rating curves for the management of water quality in riverine and estuary systems.     

Suspended sediment yield estimation using genetic algorithm-based artificial intelligence models: case study of Mahanadi River,India

The estimation of sediment yield is important in design, planning and management of river systems. Unfortunately, its accurate estimation using traditional methods is difficult as it involves various complex processes and variables. This investigation deals with a hybrid approach which comprises genetic algorithm-based artificial intelligence (GA-AI) models for the prediction of sediment yield in the Mahanadi River basin, India. Artificial neural network (ANN) and support vector machine (SVM) models are developed for sediment yield prediction, where all parameters associated with the models are optimized using genetic algorithms simultaneously. Water discharge, rainfall and temperature are used as input to develop the GA-AI models. The performance of the GA-AI models is compared to that of traditional AI models (ANN and SVM), multiple linear regression (MLR) and sediment rating curve (SRC) method for evaluating the predictive capability of the models. The results suggest that GA-AI models exhibit better performance than other models.     

Bayesian analysis of stage–fall–discharge models for gauging stations affected by variable backwater

Parsimonious stage–fall–discharge rating curve models for gauging stations subject to backwater complications are developed from simple hydraulic theory. The rating curve models are compounded in order to allow for possible shifts in the hydraulics when variable backwater becomes effective. The models provide a prior scientific understanding through the relationship between the rating curve parameters and the hydraulic properties of the channel section under study. This characteristic enables prior distributions for the rating curve parameters to be easily elicited according to site‐specific information and the magnitude of well‐known hydraulic quantities. Posterior results from three Norwegian and one American twin‐gauge stations affected by variable backwater are obtained using Markov chain Monte Carlo simulation techniques. The case studies demonstrate that the proposed Bayesian rating curve assessment is appropriate for developing rating procedures for gauging stations that are subject to variable backwater. Copyright © 2009 John Wiley & Sons, Ltd.     

The need for operational reasoning in data‐driven rating curve prediction of suspended sediment

The use of data‐driven modelling techniques to deliver improved suspended sediment rating curves has received considerable interest in recent years. Studies indicate an increased level of performance over traditional approaches when such techniques are adopted. However, closer scrutiny reveals that, unlike their traditional counterparts, data‐driven solutions commonly include lagged sediment data as model inputs, and this seriously limits their operational application. In this paper, we argue the need for a greater degree of operational reasoning underpinning data‐driven rating curve solutions and demonstrate how incorrect conclusions about the performance of a data‐driven modelling technique can be reached when the model solution is based upon operationally invalid input combinations. We exemplify the problem through the re‐analysis and augmentation of a recent and typical published study, which uses gene expression programming to model the rating curve. We compare and contrast the previously published solutions, whose inputs negate their operational application, with a range of newly developed and directly comparable traditional and data‐driven solutions, which do have operational value. Results clearly demonstrate that the performance benefits of the published gene expression programming solutions are dependent on the inclusion of operationally limiting, lagged data inputs. Indeed, when operationally inapplicable input combinations are discounted from the models and the analysis is repeated, gene expression programming fails to perform as well as many simpler, more standard multiple linear regression, piecewise linear regression and neural network counterparts. The potential for overstatement of the benefits of the data‐driven paradigm in rating curve studies is thus highlighted. Copyright © 2011 John Wiley & Sons, Ltd.     

Time‐varying suspended sediment‐discharge rating curves to estimate climate impacts on fluvial sediment transport

This study presents time‐varying suspended sediment‐discharge rating curves to model suspended‐sediment concentrations (SSCs) under alternative climate scenarios. The proposed models account for hysteresis at multiple time scales, with particular attention given to systematic shifts in sediment transport following large floods (long‐term hysteresis). A series of nested formulations are tested to evaluate the elements embedded in the proposed models in a case study watershed that supplies drinking water to New York City. To maximize available data for model development, a dynamic regression model is used to estimate SSC based on denser records of turbidity, where the parameters of this regression are allowed to vary over time to account for potential changes in the turbidity‐SSC relationship. After validating the proposed rating curves, we compare simulations of SSC among a subset of models in a climate change impact assessment using an ensemble of flow simulations generated using a stochastic weather generator and hydrologic model. We also examine SSC estimates under synthetic floods generated using a peaks‐over‐threshold model. Our results indicate that estimates of extreme SSC under new climate and hydrologic scenarios can vary widely depending on the selected model and may be significantly underestimated if long‐term hysteresis is ignored when simulating impacts under sequences of large storm event. Based on the climate change scenarios explored here, average annual maximum SSC could increase by as much as 2.45 times over historical values.     

Suspended sediment delivery from small catchments to the Bay of Biscay. What are the controlling factors?

The transport and yield of suspended sediment (SS) in catchments all over the world have long been topics of great interest. This paper addresses the scarcity of information on SS delivery and its environmental controls in small catchments, especially in the Atlantic region. Five steep catchments in Gipuzkoa (Basque Country) with areas between 56 and 796 km² that drain into the Bay of Biscay were continuously monitored for precipitation, discharge and suspended sediment concentration (SSC) in their outlets from 2006 to 2013. Environmental characteristics such as elevation, slope, land‐use, soil depth and erodibility of the lithology were also calculated. The analysis included consideration of uncertainties in the SSC calibration models in the final suspended sediment yield (SSY) estimations. The total delivery of sediments from the catchments into the Bay of Biscay and its standard deviation was 272 200 ± 38 107 t yr.^?1, or 151 ± 21 t km^?2 yr.^?1, and the SSYs ranged from 46 ± 0.48 to 217 ± 106 t km^?2 yr.^?1. Hydroclimatic variables and catchment areas do not explain the spatial variability found in SSY, whereas land‐use (especially non‐native plantations) and management (human impacts) appear to be the main factors that control this variability. Obtaining long‐term measurements on sediment delivery would allow for the effects of environmental and human induced changes on SS fluxes to be better detected. However, the data provided in this paper offer valuable and quantitative information that will enable decision‐makers to make more informed decisions on land management while considering the effects of the delivery of SS. Copyright © 2016 John Wiley & Sons, Ltd.     

Total least square method applied to rating curves

The ordinary least square method (OLS) has been the most frequently used least square method in hydrological data analysis. Its computational algorithm is simple, and the error analysis is also simple and clear. However, the primary assumption of the OLS method, which states that the dependent variable is the only error‐contaminated variable and all other variables are error free, is often violated in hydrological data analyses. Recently, a matrix algorithm using the singular value decomposition for the total least square (TLS) method has been developed and used in data analyses as errors‐in‐variables model where several variables could be contaminated with observational errors. In our study, the algorithm of the TLS is introduced in the evaluation of rating curves between the flow discharge and the water level. Then, the TLS algorithm is applied to real data set for rating curves. The evaluated TLS rating curves are compared with the OLS rating curves, and the result indicates that the TLS rating curve and the OLS rating curve are in good agreement. The TLS and OLS rating curves are discussed about their algorithms and error terms in the study. Copyright © 2013 John Wiley & Sons, Ltd.     

Suspended sediment yield modeling in Mahanadi River,India by multi-objective optimization hybridizing artificial intelligence algorithms

River sediment produced through weathering is one of the principal landscape modification processes on earth.Rivers are an integral part of the hydrologic cycle and are the major geologic agents that erode the continents and transport water and sediments to the oceans.Estimation of suspended sediment yield is always a key parameter for planning and management of any river system.It is always challenging to model sediment yield using traditional mathematical models because they are incapable of handling the complex non-linearity and non-stationarity.The suspended sediment modeling of the river depends on the number of factors such as rock type,relief,rainfall,temperature,water discharge and catchment area.In this study,we proposed a hybrid genetic algorithm-based multi-objective optimization with artificial neural network(GA-MOO-ANN)with automated parameter tuning model using these factors to estimate the suspended sediment yield in the entire Mahanadi River basin.The model was validated by comparing statistically with other models,and it appeared that the GA-MOO-ANN model has the lowest root mean squared error(0.009)and highest coefficient of correlation(0.885)values among all comparative models(traditional neural network,multiple linear regression,and sediment rating curve)for all stations.It was also observed that the proposed model is the least biased(0.001)model.Thus,the proposed GA-MOOANN is the most capable model,compared to other studied models,for estimating the suspended sediment yield in the entire Mahanadi river basin,India.The results also suggested that the proposed GA-MOO-ANN model is unable to estimate suspended sediment yield satisfactorily at gauge stations having very small catchment areas whereas performing satisfactorily on locations having moderate to the large catchment area.The models provide the best result at Tikarapara,the gauge station location in the extreme downstream,having the largest catchment area.     

Using data‐driven methods to explore the predictability of surface soil moisture with FLUXNET site data

Soil moisture (SM) is a key variable of land surface‐atmosphere interactions. Data‐driven methods have been used to predict SM, but the predictability of SM has not been well evaluated. This study investigated what variables and methods can be used to better predict SM for leading times of 7 days or longer with a global coverage of FLUXNET site data for the first time. Three machine‐learning models, that is, Bayesian linear regression, random forest, and gradient boosting regression tree, are used for the prediction. Variables including atmospheric forcing, surface soil temperature, time variables (year, day of year, and hour), the Fourier transformation of time variables, and lagged SM (7‐ to 14‐day lagged) were sequentially added into models. A framework with five experiments is designed for factorial exploration of SM predictability. A stepwise method was used to build the best models for each site. The performance of regression models became better when adding more explaining variables in most cases. The results showed that from 50 to 95% of variation of the best models can be explained. The important explaining variables are lagged surface SM, followed by day of year, year, soil temperature, and atmospheric forcing. The predictability of SM depends highly on SM memory characteristics and the persistence of seasonality. The effect of SM memory characteristics on SM prediction as an initial condition question has been widely discussed in this paper. Our results also provide an insight that mechanisms of seasonality effects on SM should be also paid more attention to.     

Modelling daily suspended sediment of rivers in Turkey using several data-driven techniques / Modélisation de la charge journalière en matières en suspension dans des rivières turques à l'aide de plusieurs techniques empiriques

Abstract

The transport of sediment load in rivers is important with respect to pollution, channel navigability, reservoir filling, longevity of hydroelectric equipment, fish habitat, river aesthetics and scientific interest. However, conventional sediment rating curves cannot estimate sediment load accurately. An adaptive neuro-fuzzy technique is investigated for its ability to improve the accuracy of the streamflow—suspended sediment rating curve for daily suspended sediment estimation. The daily streamflow and suspended sediment data for four stations in the Black Sea region of Turkey are used as case studies. A comparison is made between the estimates provided by the neuro-fuzzy model and those of the following models: radial basis neural network (RBNN), feed-forward neural network (FFNN), generalized regression neural network (GRNN), multi-linear regression (MLR) and sediment rating curve (SRC). Comparison of results reveals that the neuro-fuzzy model, in general, gives better estimates than the other techniques. Among the neural network techniques, the RBNN is found to perform better than the FFNN and GRNN.     

Estimating river discharge from very high‐resolution satellite data: a case study in the Yangtze River,China

The measurement of river discharge is necessary for understanding many water‐related issues. Traditionally, river discharge is estimated by measuring water stage and converting the measurement to discharge by using a stage–discharge rating curve. Our proposed method for the first time couples the measurement of water‐surface width with river width–stage and stage–discharge rating curves by using very high‐resolution satellite data. We used it to estimate the discharge in the Yangtze (Changjiang) River as a case study. The discharges estimated at four stations from five QuickBird‐2 images matched the ground observation data very well, demonstrating that the proposed approach can be regarded as ancillary to traditional field measurement methods or other remote methods to estimate river discharge. Copyright © 2004 John Wiley & Sons, Ltd.     

Stage‐discharge uncertainty derived with a non‐stationary rating curve in the Choluteca River,Honduras

Uncertainty in discharge data must be critically assessed before data can be used in, e.g. water resources estimation or hydrological modelling. In the alluvial Choluteca River in Honduras, the river‐bed characteristics change over time as fill, scour and other processes occur in the channel, leading to a non‐stationary stage‐discharge relationship and difficulties in deriving consistent rating curves. Few studies have investigated the uncertainties related to non‐stationarity in the stage‐discharge relationship. We calculated discharge and the associated uncertainty with a weighted fuzzy regression of rating curves applied within a moving time window, based on estimated uncertainties in the observed rating data. An 18‐year‐long dataset with unusually frequent ratings (1268 in total) was the basis of this study. A large temporal variability in the stage‐discharge relationship was found especially for low flows. The time‐variable rating curve resulted in discharge estimate differences of ? 60 to + 90% for low flows and ± 20% for medium to high flows when compared to a constant rating curve. The final estimated uncertainty in discharge was substantial and the uncertainty limits varied between ? 43 to + 73% of the best discharge estimate. Copyright © 2010 John Wiley & Sons, Ltd.     

Controls on suspended‐sediment transfer at a high‐arctic glacier,determined from statistical modelling

Simple linear regression models have been widely employed in the analysis of suspended‐sediment concentration (SSC) time series from glacierized catchments, although they have many limitations. This paper builds regression models which address these shortcomings and permit inferences concerning the controls on suspended‐sediment transfer from a glacier at 78°N in the Svalvard archipelago. A bivariate regression model, deterministically predicting SSC from discharge alone, explained less than 15 per cent of the variance in SSC. A multivariate model, incorporating additional potentially explanatory variables, offered little improvement. Diurnal hysteresis in the data gives rise to quasi‐autocorrelation in the residual series from regression models. This was effectively removed by incorporating dummy diurnal variables into the multivariate model. The presence of a first‐order autoregressive, stochastic process gives rise to true autocorrelation in the residual series from regression models. This was accommodated by incorporating an ARIMA (1,0,0) term into a multivariate autoregression model. The model‐building process yielded a systematic progression in the explanation of variance in SSC, stripping away pattern in the autocorrelation function of the residual series; mean model error was reduced from 54 per cent to 6 per cent. The dependence of SSC on the magnitude of discharge is weak and highly variable, whereas the dependence of current SSC on recent values of SSC, revealed through the stochastic term, is an order of magnitude greater and relatively constant during the melt season. The dominant control on SSC throughout the melt season is therefore short‐term sediment availability. The simple and largely unchanging stochastic process generally responsible for generating the observed SSC series implies a simple and unchanging glacier drainage system. Copyright © 1999 John Wiley & Sons, Ltd.     

Using sediment rating parameters to evaluate the changes in sediment transport regimes in the middle Yellow River basin,China

This study analyses the changes in sediment transport regimes in the middle Yellow River basin (MYRB) using sediment rating parameters. Daily streamflow and suspended sediment concentration data were collected at 35 hydrological stations from the 1950s to 2016, which can be divided into three periods based on the type and intensity of human activities: the base stage before 1970, the restraining stage from 1971 to 1989, and the restoration stage after 2002. Data within each period were fitted by log‐linear sediment rating curves and the sediment rating parameters were utilized to analyse the spatial and temporal variations in sediment transport regimes. The results show that sediment rating parameters are indicative of sediment transport regimes. In the base stage and the restraining stage, the hydrological stations can be categorized into four groups based on their locations on the rating parameter plot. The stations with small drainage basins were characterized by the highest sediment transport regime, followed by those located in the coarse‐particle zone, the loess zone, and the mountainous/forest zone. In the restoration stage, the difference in sediment transport regimes between different geomorphic zones became less distinguishable than in previous stages. During the transition from the base stage to the restraining stage, sediment rating parameters showed no significant changes in sediment transport regimes in all four geomorphic groups. During the transition from the restraining stage to the restoration stage, significant changes were observed in the coarse‐particle zone and the mountain/forest zone, indicating that the revegetation programme and large reservoirs imposed a stronger influence on sediment transport regimes in these two zones than in the rest of the MYRB. This study provides theoretical support for evaluating sediment transport regimes with sediment rating parameters.     

Predicting suspended sediment concentrations in the Meuse river using a supply‐based rating curve

A rating curve provides a reasonable estimate of the suspended sediment concentration at a given discharge. However, analysis of a detailed 9‐year time‐series of suspended sediment concentration (SSC) and discharge Q of the Meuse River in The Netherlands indicates that SSC is (besides discharge) controlled by exhaustion and replenishment of different sediment sources. Clockwise hysteresis and other effects of sediment exhaustion can be observed during and after flood events, and the effects of stockpiling of sediment in the river bed during low‐discharge periods are obvious in the SSC of the next flood. In a single regression equation we have implemented a parameter that represents the presence or absence of stock for sediment uptake. In comparison with a rating curve of SSC and Q, adding this parameter is shown to be a more reliable and comprehensive method to predict SSCs at all discharge regimes with all preceding discharge conditions, for single‐peaked and multi‐peaked runoff events as well as for low flow conditions. The method is probably applicable to other small‐ to medium‐scaled river basins. Copyright © 2007 John Wiley & Sons, Ltd.