Estimation of suspended sediment concentration in the Saint John River using rating curves and a machine learning approach

ABSTRACT

Sedimentation in navigable waterways and harbours is of concern for many water and port managers. One potential source of variability in sedimentation is the annual sediment load of the river that empties in the harbour. The main objective of this study was to use some of the regularly monitored hydro-meteorological variables to compare estimates of hourly suspended sediment concentration in the Saint John River using a sediment rating curve and a model tree (M5?) with different combinations of predictors. Estimated suspended sediment concentrations were multiplied by measured flows to estimate suspended sediment loads. Best results were obtained using M5? with four predictors, returning an R² of 0.72 on calibration data and an R² of 0.46 on validation data. Total load was underestimated by 1.41% for the calibration period and overestimated by 2.38% for the validation period. Overall, the model tree approach is suggested for its relative ease of implementation and constant performance.

EDITOR M.C. Acreman; ASSOCIATE EDITOR B. Touaibia     

Analysis of suspended sediment yields after low impact forest harvesting

Disturbances to forest catchments have profound effects on the environment of headwater streams and have an impact on suspended sediment (SS) management. Forest harvesting is a dominant factor in increasing SS yields. Road construction, skidder activity and ploughing associated with harvesting cause serious soil disturbance that results in SS increases. However, few studies have shown whether harvesting itself increases SS yields. This study examined how harvesting influenced SS yields in a steep forested area. During harvesting, soil surface disturbance was prevented as much as possible by using skyline logging treatments and piling branches and leaves at selected locations in the watershed. Using these methods, the representative SS rating curve did not change significantly after harvesting. The results also show that the characteristics of SS transport were related to the SS source area, and reveal that the riparian zone/stream bank was a dominant SS source area at the study site. Annual SS yields did not increase despite increasing annual water yields after harvesting. The limited water capacity of the soil at the study site likely led to only slight differences in pre‐ and post‐harvest water discharge from heavy rainfall events. Most SS was transported during heavy rainfall events, and increases in SS yields were not detected after harvesting. We concluded that it is possible to prevent post‐harvest SS increases by performing careful, low‐impact harvesting procedures. Copyright © 2007 John Wiley & Sons, Ltd.     

Modelling suspended sediment concentration using artificial neural networks for Gangotri glacier

The dynamics of suspended sediment involves inherent non‐linearity and complexity because of existence of both spatial variability of the basin characteristics and temporal climatic patterns. This complexity, therefore, leads to inaccurate prediction by the conventional sediment rating curve (SRC) and other empirical methods. Over past few decades, artificial neural networks (ANNs) have emerged as one of the advanced modelling techniques capable of addressing inherent non‐linearity in the hydrological processes. In the present study, feed‐forward back propagation (FFBP) algorithm of ANNs is used to model stage–discharge–suspended sediment relationship for ablation season (May–September) for melt runoff released from Gangotri glacier, one of the largest glaciers in Himalaya. The simulations have been carried out on primary data of suspended sediment concentration (SSC) discharge and stage for ablation season of 11‐year period (1999–2009). Combinations of different input vectors (viz. stage, discharge and SSC) for present and previous days are considered for development of the ANN models and examining the effects of input vectors. Further, based on model performance indices for training and testing phase, a suitable modelling approach with appropriate model input structure is suggested. The conventional SRC method is also used for modelling discharge–sediment relationship and performance of developed models is evaluated by statistical indices, namely; root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R²). Statistically, the performance of ANN‐based models is found to be superior as compared to SRC method in terms of the selected performance indices in simulating the daily SSC. The study reveals suitability of ANN approach for simulation and estimation of daily SSC in glacier melt runoff and, therefore, opens new avenues of research for application of hybrid soft computing models in glacier hydrology. Copyright © 2015 John Wiley & Sons, Ltd.     

A new approach for linking event‐based upland sediment sources to downstream suspended sediment transport

In this study, we proposed a new approach for linking event sediment sources to downstream sediment transport in a watershed in central New York. This approach is based on a new concept of spatial scale, sub‐watershed area (SWA), defined as a sub‐watershed within which all eroded soils are transported out without deposition during a hydrological event. Using (rainfall) event data collected between July and November, 2007 from several SWAs of the studied watershed, we developed an empirical equation that has one independent variable, mean SWA slope. This equation was then used to determine event‐averaged unit soil erosion rate, Q_S/A, (in kg/km²/hr) for all SWAs in the studied watershed and calculate event‐averaged gross erosion E_ea (in kg/hr). The event gross erosion E_t (in kilograms) was subsequently computed as the product of E_ea and the mean event duration, T (in hours) determined using event hydrographs at the outlet of the studied watershed. Next, we developed two linear sediment rating curves (SRCs) for small and big events based on the event data obtained at the watershed outlet. These SRCs, together with T, allowed us to determine event sediment yield SY_e (in kilograms) for all events during the study period. By comparing E_t with SY_e, developing empirical equations (i) between E_t and SY_e and (ii) for event sediment delivery ratio, respectively, we revealed the event dynamic processes connecting sediment sources and downstream sediment transport. During small events, sediment transport in streams was at capacity and dominated by the deposition process, whereas during big events, it was below capacity and controlled by the erosion process. The key of applying this approach to other watersheds is establishing their empirical equations for Q_S/A and appropriately determining their numbers of SWAs. Copyright © 2011 John Wiley & Sons, Ltd.     

An evaluation of sediment rating curves for estimating suspended sediment concentrations for subsequent flux calculations

In the absence of actual suspended sediment concentration (SSC) measurements, hydrologists have used sediment rating (sediment transport) curves to estimate (predict) SSCs for subsequent flux calculations. Various evaluations of the sediment rating‐curve method were made using data from long‐term, daily sediment‐measuring sites within large (>1 000 000 km²), medium (<1 000 000 to >1000 km²), and small (<1000 km²) river basins in the USA and Europe relative to the estimation of suspended sediment fluxes. The evaluations address such issues as the accuracy of flux estimations for various levels of temporal resolution as well as the impact of sampling frequency on the magnitude of flux estimation errors. The sediment rating‐curve method tends to underpredict high, and overpredict low SSCs. As such, the range of errors associated with concomitant flux estimates for relatively short time‐frames (e.g. daily, weekly) are likely to be substantially larger than those associated with longer time‐frames (e.g. quarterly, annually) because the over‐ and underpredictions do not have sufficient time to balance each other. Hence, when error limits must be kept under ±20%, temporal resolution probably should be limited to quarterly or greater. The evaluations indicate that over periods of 20 or more years, errors of <1% can be achieved using a single sediment rating curve based on data spanning the entire period. However, somewhat better estimates for the entire period, and markedly better annual estimates within the period, can be obtained if individual annual sediment rating curves are used instead. Relatively accurate (errors <±20%) annual suspended sediment fluxes can be obtained from hydrologically based monthly measurements/samples. For 5‐year periods or longer, similar results can be obtained from measurements/samples collected once every 2 months. In either case, hydrologically based sampling, as opposed to calendar‐based sampling is likely to limit the magnitude of flux estimation errors. Published in 2003 John Wiley & Sons, Ltd.     

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.     

Daily suspended sediment modelling using a fuzzy differential evolution approach / Modélisation journalière des matières en suspension par une approche d’évolution différentielle floue

Abstract

Abstract Correct estimation of the sediment volume carried by a river is important with respect to pollution, channel navigability, reservoir filling, hydroelectric equipment longevity, fish habitat, river aesthetics and scientific interests. However, conventional sediment rating curves are not able to provide sufficiently accurate results. In this study, models incorporating fuzzy logic are developed as a superior alternative to the sediment rating curve technique for determining the daily suspended sediment concentration for a given river cross-section. This study provides forecasting benchmarks for sediment concentration prediction in the form of a numerical and graphical comparison between fuzzy and rating curve models. Benchmarking was based on a five-year period of continuous streamflow and sediment concentration data from the Quebrada Blanca Station operated by the United States Geological Survey (USGS). Nine different fuzzy models were developed to estimate sediment concentration from streamflow. Each fuzzy model has a different number of membership functions. The parameters of the membership functions were found using a differential evolution algorithm. The benchmark results showed that the fuzzy models were able to produce much better results than rating curve models for the same data inputs.     

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 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.     

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.     

Trend analyses with river sediment rating curves

Sediment rating curves, which are fitted relationships between river discharge (Q) and suspended‐sediment concentration (C), are commonly used to assess patterns and trends in river water quality. In many of these studies, it is assumed that rating curves have a power‐law form (i.e. C = aQ^b, where a and b are fitted parameters). Two fundamental questions about the utility of these techniques are assessed in this paper: (i) how well to the parameters, a and b, characterize trends in the data, and (ii) are trends in rating curves diagnostic of changes to river water or sediment discharge? As noted in previous research, the offset parameter, a, is not an independent variable for most rivers but rather strongly dependent on b and Q. Here, it is shown that a is a poor metric for trends in the vertical offset of a rating curve, and a new parameter, â, as determined by the discharge‐normalized power function [C = â (Q/Q_GM)^b], where Q_GM is the geometric mean of the Q‐values sampled, provides a better characterization of trends. However, these techniques must be applied carefully, because curvature in the relationship between log(Q) and log(C), which exists for many rivers, can produce false trends in â and b. Also, it is shown that trends in â and b are not uniquely diagnostic of river water or sediment supply conditions. For example, an increase in â can be caused by an increase in sediment supply, a decrease in water supply or a combination of these conditions. Large changes in water and sediment supplies can occur without any change in the parameters, â and b. Thus, trend analyses using sediment rating curves must include additional assessments of the time‐dependent rates and trends of river water, sediment concentrations and sediment discharge. Published 2014. This article is a U.S. Government work and is in the public domain in the USA. Hydrological Processes published by John Wiley & Sons Ltd.     

Evaluating spatial variation of suspended sediment rating curves in the middle Yellow River basin,China

Abstract The suspended sediment load in the middle Yellow River basin (YRB) cannot be well predicted by capacity‐based transport formulas because a large fraction of suspended sediment load is composed of wash load. This study evaluated the spatial variations of sediment rating curves (SRCs) in the middle YRB. Both power and linear SRCs were used to fit daily flow and suspended sediment concentration (SSC) historical data at 49 gauging stations throughout the middle YRB. The spatial variation in regression coefficients was investigated, and the relationship between regression coefficients and the physical characteristics of watersheds was discussed. The results indicate that SRC regression coefficients vary with drainage area and basin slope, but their responses to these parameters are remarkably different in watersheds with different underlying surfaces, which indicates the significance of sediment availability, erodibility, and grain size distribution. For power SRCs representing sediment transport in unsaturated flows, the regression coefficients are more closely correlated with the drainage area in loess regions and with the basin slope in rock mountain regions. For linear SRCs representing sediment transport in saturated flows, saturated SSCs vary with coarse (particle size > 0.05 mm) and fine (particle size < 0.01 mm) fractions in suspended sediment. The maximum saturated SSC among the different gauging stations is associated with the optimal grain size composition of suspended sediment, which has been proposed for loess regions in previous studies. This study provides theoretical support for estimating the regression parameters for sediment transport modelling, especially in ungauged basins.     

A modelling approach for estimating suspended sediment concentrations for multiple rivers influenced by agriculture

A data-driven model based on an adaptive neuro-fuzzy inference system (ANFIS) was tested for the estimation of suspended sediment concentrations within watersheds influenced by agriculture. ANFIS models were developed using different combinations of inputs such as precipitation, streamflow, surface runoff and the watershed vulnerability index. A multi-watershed ANFIS model was also developed combining the datasets from all studied watersheds. The best results were obtained from a combination of precipitation, streamflow and watershed vulnerability index as input variables. Nash-Sutcliffe coefficients were improved for the multi-watershed ANFIS compared to watershed-specific ANFIS models. The introduction of the erosion vulnerability index significantly improved the ability of the ANFIS model to estimate suspended sediment concentrations within the watersheds. Furthermore, the inclusion of this index opens the possibility of using the ANFIS model to investigate the impact of land-use changes on sediment delivery.     

Post‐wildfire changes in suspended sediment rating curves: Sabino Canyon,Arizona

Wildfire has been shown to increase erosion by several orders of magnitude, but knowledge regarding short‐term variations in post‐fire sediment transport processes has been lacking. We present a detailed analysis of the immediate post‐fire sediment dynamics in a semi‐arid basin in the southwestern USA based on suspended sediment rating curves. During June and July 2003, the Aspen Fire in the Coronado National Forest of southern Arizona burned an area of 343 km². Surface water samples were collected in an affected watershed using an event‐based sampling strategy. Sediment rating parameters were determined for individual storm events during the first 18 months after the fire. The highest sediment concentrations were observed immediately after the fire. Through the two subsequent monsoon seasons there was a progressive change in rating parameters related to the preferential removal of fine to coarse sediment. During the corresponding winter seasons, there was a lower supply of sediment from the hillslopes, resulting in a time‐invariant set of sediment rating parameters. A sediment mass‐balance model corroborated the physical interpretations. The temporal variability in the sediment rating parameters demonstrates the importance of storm‐based sampling in areas with intense monsoon activity to characterize post‐fire sediment transport accurately. In particular, recovery of rating parameters depends on the number of high‐intensity rainstorms. These findings can be used to constrain rapid assessment fire‐response models for planning mitigation activities. Copyright © 2006 John Wiley & Sons, Ltd.     

Evaluating suspended sediment and turbidity reduction projects in a glacially conditioned catchment through dynamic regression and fluvial process-based modelling

Elevated turbidity (T_n) and suspended sediment concentrations (SSC) during and following flood events can degrade water supply quality and aquatic ecosystem integrity. Streams draining glacially conditioned mountainous terrain, such as those in the Catskill Mountains of New York State, are particularly susceptible to high levels of T_n and SSC sourced from erosional contact with glacial-related sediment. This study forwards a novel approach to evaluate the effectiveness of stream restoration best management practices (BMPs) meant to reduce stream T_n and SSC, and demonstrates the approach within the Stony Clove sub-basin of the Catskills, a water supply source for New York City. The proposed approach is designed to isolate BMP effects from natural trends in T_n and SSC caused by trends in discharge and shifts in average T_n or SSC per unit discharge (Q) following large flood events. We develop Dynamic Linear Models (DLMs) to quantify how T_n-Q and SSC-Q relationships change over time at monitoring stations upstream and downstream of BMPs within the Stony Clove and in three other sub-basins without BMPs, providing observational evidence of BMP effectiveness. A process-based model, the River Erosion Model, is then developed to simulate natural, hydrology-driven SSC-Q dynamics in the Stony Clove sub-basin (absent of BMP effects). We use DLMs to compare the modelled and observed SSC-Q dynamics and isolate the influence of the BMPs. Results suggest that observed reductions in SSC and T_n in the Stony Clove sub-basin have been driven by a combination of declining streamflow and the installed BMPs, confirming the utility of the BMPs for the monitored hydrologic conditions.     

Challenges to the representation of suspended sediment transfer using a depth‐averaged flux

The sediment saturation recovery process (i.e. the adaptation of suspended sediment concentration [SSC] to local forcing) is the main feature of the non‐equilibrium suspended sediment transport (SST) frequently occurring in fluvial, estuarine and coastal waters. In order to quantitatively describe this phenomenon, a series solution is analytically derived, including the evolution of both vertical SSC profile and near‐bed sediment flux (NBSF), and is verified by net erosion and net deposition experiments, respectively. The results suggest that the sediment saturation recovery process involves vertically varying fluxes that are not represented correctly by depth‐averaging. Consequently, a vertical two‐dimensional (2D) combined scheme is established and applied respectively in to a dredged trench and to a sand wave feature to demonstrate this argument. By analyzing the variations of the calculated depth‐averaged SSC and NBSF we reveal that the equilibrium state presented by the sediment carrying capacity (SCC) form of the NBSF, which is usually applied in depth‐integrated SST models, lags behind the actual dynamic bed equilibrium state. Moreover, the key factor α, the so‐called saturation recovery coefficient within this form, is not only a function of local Rouse number but also is influenced by the local SSC profile. Finally, a three‐dimensional (3D) non‐orthogonal curvilinear body‐fitted SST model is developed and validated in the Yangtze estuary, China, combined with the in situ hourly hydrographic data from August 14–15, 2007 during spring tide in the wet season. Model results confirm that the vertically varying sediment saturation recovery process, the discrepancies between the actual and SCC form of NBSF and non‐constant value of α are significant in actual real geomorphic cases. The quantitative morphological change resulting from variations in environmental conditions may not be correctly represented by uncorrected depth‐integrated SST models if they do not treat the effects of vertical motion on the sediment saturation recovery process. Copyright © 2016 John Wiley & Sons, Ltd.     

Flood‐based analysis of high‐magnitude sediment transport using a non‐parametric method

Upland erosion and the resulting reservoir siltation is a serious issue in the Isábena catchment (445 km² Central Spanish Pyrenees). During a three‐month period, water and sediment fluxes have been monitored at the catchment outlet (Capella), two adjacent subcatchments (Villacarli, 41 km²; Cabecera, 145 km²) and the elementary badland catchment Torrelaribera (8 ha). This paper presents the results of the monitoring, a method for the calculation of a sedigraph from intermittent measurements and the derived sediment yields at the monitored locations. The observed suspended sediment concentrations (SSCs) demonstrate the role of badlands as sediment sources: SSCs of up to 280 g l^?1 were encountered for Villacarli, which includes large badland areas. SSCs at the Cabecera catchment, with great areas of woodland, barely exceeded 30 g l^?1. SSCs directly at the sediment source (Torrelaribera) were comparable to those at Villacarli, suggesting a close connection within this subcatchment. At Capella, SSCs of up to 99 g l^?1 were observed. For all sites, SSC displayed only a loose correlation with discharge, inhibiting the application of a simple sediment rating curve. Instead, ancillary variables acting as driving forces or proxies for the processes (rainfall energy, cumulative discharge, rising/falling limb data) were included in a quantile regression forest model to explain the variability in SSC. The variables with most predictive power vary between the sites, suggesting the predominance of different processes. The subsequent flood‐based calculation of sediment yields attests high specific sediment yields for Torrelaribera and Villacarli (6277 and 1971 t km^?2) and medium to high yields for Cabecera and Capella (139 and 410 t km^?2) during the observation period. In all catchments, most of the sediment was exported during intense storms of late summer. Later flood events yield successively less sediment. Relating upland sediment production to yield at the outlet suggests considerable effects of sediment storage within the river channel. Copyright © 2008 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.