Temporal Variations of Water Discharge and Sediment Load of Huanghe River,China

Based on the data from gauging stations, the changes in water discharge and sediment load of the Huanghe （Yellow） River were analyzed by using the empirical mode decomposition （EMD） method. The results show that the periodic oscillation of water discharge and sediment load of the Huanghe River occurs at the interannual, decadal, and multi-decadal scales, caused by the periodic oscillations of precipitation, and E1 Nifio/Southern Oscillation （ENSO） af- fects water discharge by influencing precipitation distribution and contributes to periodic varations in precipitation and water discharge at interannual timescale. The water discharge and sediment load of the Huanghe River have decreased since the 1960s under the influence of precipitation and huamn activities, and human activities attribute more than precipitation to the reduction in the water discharge and sediment load, furthermore, water abstraction and water-soil conservation practices are the main causes of the decrease in water discharge and sediment load, respectively. The reduction in sediment load has directly impacted on the lower reaches of the Huanghe River and the river delta, causing considerable erosion of the river channel in the lower reaches since the 1970s along with River Delta changing siltation into erosion around 2000.

Daily and Monthly Suspended Sediment Load Predictions Using Wavelet Based Artificial Intelligence Approaches

In the current study, the efficiency of Wavelet-based Least Square Support Vector Machine （WLSSVM） model was examined for prediction of daily and monthly Suspended Sediment Load （SSL） of the Mississippi River. For this purpose, in the first step, SSL was predicted via ad hoc LSSVM and Artificial Neural Network （ANN） models; then, streamflow and SSL data were decomposed into sub- signals via wavelet, and these decomposed sub-time series were imposed to LSSVM and ANN to simulate discharge-SSL relationship. Finally, the ability of WLSSVM was compared with other models in multi- step-ahead SSL predictions. The results showed that in daily SSL prediction, LSSVM has better outcomes with Determination Coefficient （DC）=o.92 than ad hoc ANN with DC=o.88. However unlike daily SSL, in monthly modeling, ANN has a bit accurate upshot. WLSSVM and wavelet-based ANN （WANN） models showed same consequences in daily and different in monthly SSL predictions, and adding wavelet led to more accuracy of LSSVM and ANN. Furthermore, conjunction of wavelet to LSSVM and ANN evaluated via multi-step-ahead SSL predictions and, e.g., DCLssVM=0.4 was increased to the DCwLsSVM=0.71 in 7- day ahead SSL prediction. In addition, WLSSVM outperformed WANN by increment of time horizon prediction.

Sediment Load in Runoff Under Laboratory and Field Simulated Rainfall

Soil erosion is one of the most important problems in the Loess Plateau of China affectingsustainable agriculture. Near Luoyang (Henan Province, China), field plots were constructed tomeasure soil erosion rates under conventional tillage practices using field-simulated rainfall.Field rainfall experiments were carried out to compare previous results from laboratoryrainfall simulations on the same soil for interrill conditions. Although in the laboratoryexperiments, a strong correlation was found between the stream power of the runoff water andthe unit sediment load, this sediment transport equation overestimated the field rainfallsimulation results. Another sediment transport equation derived by Nearing et al. for rillerosion was in better agreement with the results of the field experiments, although it alsooverestimated these values. The measured sediment load values during the field rainfallsimulations were also lower than those found during field experiments on the same soil but witha loosened surface layer. This difference indicates the importance of soil physical conditionof surfce like soil structure and aggregate size, which may contribute to the discrepancybetween the field and laboratory experiment results.

Local vs. cross station simulation of suspended sediment load in successive hydrometric stations: heuristic modeling approach

The present paper aims at modeling suspended sediment load(SSL) using heuristic data driven methodologies, e.g. Gene Expression Programming(GEP) and Support Vector Machine(SVM) in three successive hydrometric stations of Housatonic River in U.S. The simulations were carried out through local and cross-station data management scenarios to investigate the interrelations between the SSL values of upstream/downstream stations. The available scenarios were applied to predict SSL values using GEP to obtain the best models. Then, the best models were predicted by SVM approach and the obtained results were compared with those of GEP. The comparison of the results revealed that the SVM technique is more capable than the GEP for modeling the SSL through the both local and cross-station data management strategies. Besides, local application seems to be better than cross-station application for modeling SSL. Nevertheless, the cross-station application demonstrated to be a valid methodology for simulating SSL, which would be of interest for the stations with lack of observational data. Also, the prediction capability of conventional Sediment Rating Curve(SRC) method was compared with those of GEPand SVM techniques. The obtained results revealed the superiority of GEP and SVM-based models over the traditional SRC technique in the studied stations.

Change in Sediment Load of the Yangtze River after Wenchuan Earthquake

On May 12, 2008, an earthquake of 8.0 magnitude on the Richter scale and its numerous aftershocks devastatingly hit Wenchuan County and its nearby region along the Longman Mountains in Sichuan Province, China. The heavy ruined area was up to 30,000km2 and 13% of its land surface was denuded by the extremely terrible quakes. The mountain collapses, landslides and debris flows induced by the earthquake not only scared the landscape at the immense scale, but also poured L66-billion-m3 sediment combined with offscourings and rubble into the Yangtze River and its breaches. This amount of sediments is 3 times more than the normal amount discharged into the Yangtze River, and will significantly increase sediment transportation of rivers and decrease storage capacities of reservoirs downstream. The dramatic increase in sediment load will imperil the engineering safety and impact the operation of the giant Three-Gorge Hydro-power Station if no proper prevention measures are taken.

Does reduced sediment load contribute to increased outbreaks of harmful algal blooms off the Changjiang Estuary?

Harmful algal blooms（HABs） have been increasingly frequent in coastal waters around the world over the last several decades. Accelerated coastal eutrophication, resulting from the increased anthropogenic loadings of nutrients, is commonly assumed to be the primary cause of this increase. However, although important,accelerated coastal eutrophication may not be the only explanation for the increasing blooms or toxic outbreaks in estuarine waters. Changes in riverine material fluxes other than nutrients, such as sediment load, may significantly affect biological activities and HAB incidence in estuarine and coastal waters. A case study off the Changjiang（Yangtze River） Estuary indicated that with the increasing riverine loadings of nutrients, the sediment load from the Changjiang River has been reduced by 70% over the past four decades. A comparison of long-term data revealed that the phytoplankton biomass maximum has expanded to a region of much lower salinity due to the drastic reduction in riverine sediment load and the subsequent improvement in light penetration in the Changjiang River plume. Furthermore, there was an apparent mirror-image relationship between the sediment load from the Changjiang River and the HAB incidence off the Changjiang Estuary over the past four decades, and the number of HAB incidents was significantly negatively correlated with the sediment load. Therefore, it is argued that the drastic decline in sediment load from the Changjiang River reduced turbidity in the Changjiang Estuary and thus contributed to the increased frequency of HABs in the buoyant discharge plumes.

Runoff and Sediment Load from the Right Bank Valleys of Mosul Dam Reservoir

Mosul Dam is a Multipurpose Project on the River Tigris in Iraq with 11.11 billion m3 storage capacity. It is used to store the water for irrigation, hydropower generation, and flood control. As in other dams in the world, this dam also have sedimentation problem. Sediment accumulation in its reservoir can effect the dam operation （pumping station, hydropower plants, and bottom outlets） and it will definitely shorten the life span of the dam. In this study, the SWAT （soil and water assessment tool） under （]IS （Geographical Information System） was applied to simulate the yearly surface rtmoff and sediment load for the main three valleys on the right bank of Mosul Dam Reservoir. The simulation considered for the twenty one years begin from the dam operation in 1988 to 2008. The resultant values of the average annual sediment load are 35.6~ 103, 4.9 ~ 103, and 2.2~ 103 ton, while the average values of sediment concentration are 1.73, 1.65, and 2.73 kg/m3 for the considered valleys one, two and three respectively. This implies that significant sediment load enters the reservoir from these valleys. To minimize the sediment load entering the reservoir, a check dam is to be constructed in suitable sites especially for valley one. The check dam can store the runoff water and trap the sediment load, and then the flow can be released to the reservoir.

Determination of influential parameters for prediction of total sediment loads in mountain rivers using kernel-based approaches

It is important to have a reasonable estimation of sediment transport rate with respect to its significant role in the planning and management of water resources projects. The complicate nature of sediment transport in gravel-bed rivers causes inaccuracies of empirical formulas in the prediction of this phenomenon. Artificial intelligences as alternative approaches can provide solutions to such complex problems. The present study aimed at investigating the capability of kernel-based approaches in predicting total sediment loads and identification of influential parameters of total sediment transport. For this purpose, Gaussian process regression(GPR), Support vector machine(SVM) and kernel extreme learning machine(KELM) are applied to enhance the prediction level of total sediment loads in 19 mountain gravel-bed streams and rivers located in the United States. Several parameters based on two scenarios are investigated and consecutive predicted results are compared with some well-known formulas. Scenario 1 considers only hydraulic characteristics and on the other side, the second scenario was formed using hydraulic and sediment properties. The obtained results reveal that using the parameters of hydraulic conditions asinputs gives a good estimation of total sediment loads. Furthermore, it was revealed that KELM method with input parameters of Froude number(Fr), ratio of average velocity(V) to shear velocity(U*) and shields number(θ) yields a correlation coefficient(R) of 0.951, a Nash-Sutcliffe efficiency(NSE) of 0.903 and root mean squared error(RMSE) of 0.021 and indicates superior results compared with other methods. Performing sensitivity analysis showed that the ratio of average velocity to shear flow velocity and the Froude number are the most effective parameters in predicting total sediment loads of gravel-bed rivers.

Sediment Load of Asian Rivers flowing into the Oceans and their Regional Variation

Study of the major Asian rivers discharge to the ocean reveals variations of their water discharges and sediment loads, and local characteristics of river sediment concentrations. On the basis of this, the Asian rivers fall into three regions, including Eurasia Arctic, East Asia, Southeast and South Asia Regions. The Eurasia Arctic Region is characterized by the lowest sediment concentration and load, while the East Asia Region is of the highest sediment concentration and higher sediment load, and the South-East and South Asia Region yields higher sediment concentration and highest sediment load.The sediment loads of these regions are mainly controlled by climate, geomorphology and tectonic activity. The Eurasia Arctic rivers with large basin areas and water discharge, drain low relief which consists of tundra sediment, thus causing the lowest sediment load. The East Asia rivers with small basin areas and lowest water discharges, drain extensive loess plateau, and transport most erodible loess material, which results in highest sediment concentration. The SE and South Asia rivers originating from the Tibet Plateau have large basin areas and the largest water discharges because of the Summer Monsoon and high rainfall influence, causing the highest sediment load.In Asia, tectonic motion of the Tibet Plateau plays an important role. Those large rivers originating from the Tibet Plateau transport about 50% of the world river sediment load to ocean annually, forming large estuaries and deltas, and consequently exerting a great influence on sedimentation in the coastal zone and shelves.

Assessment of Sediment Load of Langtang River in Rasuwa District, Nepal

This paper assesses the sediment load of the glacier fed Langtang River, Nepal from April 2014 to March 2015. Water samples were collected from the centre of the river with a frequency of two samples per each sampling day using the Depth Integration Technique (DIT) on daily basis in the monsoon season, weekly in the pre- and post-monsoon seasons and bi-monthly in the winter season. Suspended sediment concentration (SSC) is calculated from the water samples using filtration followed by oven-drying, and a rating curve is used to calculate daily discharge of the Langtang River. The annual sediment yield is 109,276.75 tons and 37.69, 11.52 and 5.54 tons of sediment is transported per day in the pre-monsoon, post-monsoon and winter seasons, respectively. There is a very high value of 872.86 tons per day in the monsoon season, which contributes the highest sediment load among all of the seasons comprising 83% of the total sediment transport. Diurnal cycle of sediment discharge is clearly seen with higher sediment discharge during the evening than the morning and reaching maximum values of 41.1 kg·s-1 and 61.5 kg·s-1, respectively. A clock-wise hysteresis loop has been obtained for discharge and sediment discharge where sediment flux is higher in the early monsoon than in the late monsoon for a corresponding discharge.

Influence of Tea Plantations, Forest and Mixed Farming on Stream Flow and Sediment Loads, Case of Sondu Miriu River Basin, Kenya

The changing patterns of land cover and land use in the tropical river basin over time are critical. The hydrological phenomena at basin and sub basin scale are affected positively or negatively by dynamics of the land cover and land use patterns. Hence identifying causes and driving factors aid in taking appropriate measures to avert the impacts. This study determined the influences of sub basins dominated by tea plantations, forests and agricultural land uses in terms of streamflow and sediment flux variability in Sondu Miriu River Basin in Kenya, East Africa. Field-based investigations were conducted through sampling of flow velocities, turbidity and TSSC obtained from existing River Gauging Stations established within the three sub basins. The sub basin dominated by mixed farming land cover exhibits high turbidity approximately 620 NTU and high levels of total suspended sediment concentration (TSSC) of the order of 630 mg/l in wet seasons. The turbidity levels and TSSC were low in sub basins dominated by forest and tea plantations with approximately mean value of 17 - 29 NTU and 0.019 g/l. The sediment loads in sub basin dominated by mixed farming in the pre planting season in January to February were about 900 tonnes/day higher than that in crop growing season. In sub basins dominated by forest cover and tea plantations, sediment loads were low ranging between 2 - 7 tonnes/day. The relationship between stream flows and area under tea plantations, forests and mixed farming ranged between R2 of 0.025 and 0.16. Tea plantations and forests influence the stream flows and sediment yields in long term duration while in mixed farming variations were observed seasonally. The strong relationships between rainfall and stream flows at the sub basins ranging between R2 of 0.84 and 0.97 revealed the significance of rainfall in hydrologic response of the Sondu Miriu River Basin.

Can the Grain-for-Green Program Really Ensure a Low Sediment Load on the Chinese Loess Plateau?

The Chinese Loess Plateau is the most seriously eroded area in the world and contributes the vast majority of the sediment that goes into the Yellow River.Since the 1950s,progressive soil and water conservation measures have been implemented—in particular,large-scale ecological restoration has been ongoing since 1999—resulting in a significant reduction of the sediment load.However,the mechanism of the sediment transport dynamics is not fully understood due to multiple and complicated influencing factors including climate change and human activities(e.g.,ecological restoration).A challenging question,then,arises:Is the current low sediment level a“new normal”in this era and in the future?To address this question,we selected a typical loess hilly region where considerable ecological restoration has been implemented,and which is regarded as the site of the first and most representative Grainfor-Green program in the Loess Plateau.We investigated the evolution of discharge–sediment relationships in the past decades(1960–2010)and their association with the soil and water conservation measures in this area.The results showed that there was a distinct change in the regression parameters of the commonly used annual discharge–sediment regression equation—a continuously increasing trend of parameter b and a decreasing trend of parameter a,accompanying the ecological restoration.The increase in exponent b(i.e.,a steeper slope)implies a potential lower sediment load resulting from low discharge and a potential higher sediment load resulting from large discharge.This finding may question the new normal of a low sediment level and implies the potential risk of a large sediment load during extremely wet years.

Temporal Change of Runoff and Sediment Load and their Differential Response to Human Activities:A Case Study for a Well-vegetated Mountain Watershed of Southern China

This study examined the temporal trends of runoff and sediment load and their differential response to human activities in the Lishui river,a tributary of the Yangtze river in southern China.The long-term observation data at four gauging stations,generally involving two periods from 1954 to 1985 and from 2007 to 2011,were used.We detected no significant temporal trend for both the annual runoff volume(Q) and the annual suspended Sediment Load(SL) over more than 30 years before 1985.The flow duration curves and the Suspended Sediment Concentration(SSC) also hold constant before 1985.Compared with the period before 1985,SL has decreased by about 80% though Q remains unchanged for the period after 2007.Detailed examination shows that the flow duration curves after 2007 have changed with a significant decrease in the high-flow component,which acts as a major cause for the decreasing SL.In addition,SSC has decreased by several times,which also contributes to the decrease in SL after 2007.Both decreases in high-flow discharges and in SSC can be linked with recent human activities,mainly including vegetation establishment and dam constructions.The constant Q and the decreasing SL are also reported for the main stream of the Yangtze River and other major rivers in southern China,although they are orders of magnitude larger than our study area in drainage area size.The present study highlights the importance of high-flow discharges on SL and suggests that the use of SL is more appropriate to reflect environmental change than Q.

Effects of sediment load on the abrasion of soil aggregate and hydraulic parameters in experimental overland flow

The breakdown of soil aggregates under rainfall and their abrasion in overland flow are important processes in water erosion due to the production of more fine and transportable particles and,the subsequent significant effect on the erosion intensity.Currently,little is known about the effects of sediment load on the soil aggregate abrasion and the relationship of this abrasion with some related hydraulic parameters.Here,the potential effects of sediment load on soil aggregate abrasion and hydraulic parameters in overland flow were investigated through a series of experiments in a 3.8-m-long hydraulic flume at the slope gradients of 8.7 and 26.8%,unit flow discharges from 2×10^-3 to 6×10^-3 m^2 s^-1,and the sediment concentration from 0 to 110 kg m-3.All the aggregates from Ultisols developed Quaternary red clay,Central China.The results indicated that discharge had the most significant(P<0.01)effect on the aggregates abrasion with the contributions of 58.76 and 60.34%,followed by sediment feed rate,with contributions of 39.66 and 34.12%at the slope gradients of 8.7 and 26.8%,respectively.The abrasion degree of aggregates was found to increase as a power function of the sediment concentration.Meanwhile,the flow depth,friction factor,and shear stress increased as a power function along with the increase of sediment concentration at different slope gradients and discharges.Reynolds number was obviously affected by sediment concentration and it decreased as sediment concentration increased.The ratio of the residual weight to the initial weight of soil aggregates(Wr/Wi)was found to increase as the linear function with an increasing flow depth(P=0.008)or Reynolds number(P=0.002)in the sediment-laden flow.The Wr/Wi values followed a power function decrease with increasing friction factor or shear stress in the sediment-laden flow,indicating that friction factor is the best hydraulic parameter for prediction of soil aggregate abrasion under different sediment load conditions.The information regarding the soil aggregate abrasion under various sediment load conditions can facilitate soil process-based erosion modeling.

Spatio-temporal trends and causes of variations in runoff and sediment load of the Jinsha River in China

The Jinsha River Basin is an important basin for hydropower in China and it is also the main runoff and sediment source area for the Yangtze River,which greatly influence the runoff and sediment in the Three Gorges Reservoir.This study aims to characterize the spatial distribution,inter-annual variation of runoff and sediment load in the Jinsha River Basin,and to analyze the contribution of rainfall and human activities to the runoff and sediment load changes.The monitoring data on runoff,sediment load and precipitation were collected from 11hydrological stations in the Jinsha River Basin from1966 to 2016.The data observed at the outlet of the basin showed that 71.4%of the runoff is from the upper reaches of the Jinsha River Basin and the Yalong River,while 63.3%of the sediment is from the lower reaches(excluding the Yalong River).There is no significant increase in runoff on temporal scale in the Jinsha River Basin,while it has an abrupt change in runoff in both upstream and midstream in 1985,and an abrupt change in downstream in 1980 and2013.The sediment load demonstrated a significantincreasing trend in the upstream,no significant reducing trend in the midstream,but significant reducing trend in the downstream.The sediment load in upstream showed abrupt change in 1987,in midstream in 1978 and 2014,in downstream in 2012.Rainfall dominated runoff variation,contributing more than 59.0%of the total variation,while human activity,including reservoirs construction,the implementation of soil and water conservation projects,is the major factor to sediment load variation,contributing more than 87.0%of the total variation.

Using Artificial Neural Network to Estimate Sediment Load in Ungauged Catchments of the Tonle Sap River Basin, Cambodia

Concern on alteration of sediment natural flow caused by developments of water resources system, has been addressed in many river basins around the world especially in developing and remote regions where sediment data are poorly gauged or ungauged. Since suspended sediment load (SSL) is predominant, the objectives of this research are to: 1) simulate monthly average SSL (SSLm) of four catchments using artificial neural network (ANN);2) assess the application of the calibrated ANN (Cal-ANN) models in three ungauged catchment representatives (UCR) before using them to predict SSLm of three actual ungauged catchments (AUC) in the Tonle Sap River Basin;and 3) estimate annual SSL (SSLA) of each AUC for the case of with and without dam-reservoirs. The model performance for total load (SSLT) prediction was also investigated because it is important for dam-reservoir management. For model simulation, ANN yielded very satisfactory results with determination coefficient (R2) ranging from 0.81 to 0.94 in calibration stage and 0.63 to 0.87 in validation stage. The Cal-ANN models also performed well in UCRs with R2 ranging from 0.59 to 0.64. From the result of this study, one can estimate SSLm and SSLT of ungauged catchments with an accuracy of 0.61 in term of R2 and 34.06% in term of absolute percentage bias, respectively. SSLA of the AUCs was found between 159,281 and 723,580 t/year. In combination with Brune’s method, the impact of dam-reservoirs could reduce SSLA between 47% and 68%. This result is key information for sustainable development of such infrastructures.

Comparison between Multi-Layer Perceptron and Radial Basis Function Networks for Sediment Load Estimation in a Tropical Watershed

Prediction of highly non-linear behavior of suspended sediment flow in rivers has prime importance in environmental studies and watershed management. In this study, the predictive performance of two Artificial Neural Networks (ANNs), namely Radial Basis Function (RBF) and Multi-Layer Perceptron (MLP) were compared. Time series data of daily suspended sediment discharge and water discharge at the Langat River, Malaysia were used for training and testing the networks. Mean Square Error (MSE), Normalized Mean Square Error (NMSE) and correlation coefficient (r) were used for performance evaluation of the models. Using the testing data set, both models produced a similar level of robustness in sediment load simulation. The MLP network model showed a slightly better output than the RBF network model in predicting suspended sediment discharge, especially in the training process. However, both ANNs showed a weak robustness in estimating large magnitudes of sediment load.

The Intra-Annual Variability of Discharge, Sediment Load and Chemical Flux from the Monitoring: The Yukon River, Alaska

The covered-ice breakup in subarctic to arctic rivers in the early snowmelt season often gives any damage to instruments monitoring physical and chemical factors of water. The serious condition has brought few time series data during the snowmelt runoff except the river stage or discharge. In this study, the contribution of snowmelt runoff to the discharge and sediment load is quantified by monitoring water turbidity and temperature at the lowest gauging station of U. S. Geological Survey in the Yukon River, Alaska, for more than 3 years (June 2006 to September 2009). The turbidity was recorded by a self-recording turbidimeter with a sensor of infrared-ray back-scattering type, of which the window is cleaned by a wiper just before a measurement. The turbidity time series, coupled with frequent river water sampling at mid-channel, produce time series of suspended sediment (SS) concentration, particulate organic carbon (POC) concentration and particulate organic nitrogen (PON) concentration (mg?L–1) by using the high correlation (R2 = 0.747 to 0.790;P 11 to 2.01 × 1011 m3), 8.7% - 22.5% of the annual sediment load (3.94 × 107 to 5.08 × 107 ton), 11.6% - 23.7% of the annual POC flux (4.05 × 105 to 4.77 × 105 ton), and 10.3% - 24.5% of the annual PON flux (2.80 × 104 to 3.44 × 104 ton). In the snowmelt season, the peak suspended sediment concentration preceded the peak discharge by a few days. This probably results from the fluvial sediment erosion in the river channels.

Coupling Singular Spectrum Analysis with Artificial Neural Network to Improve Accuracy of Sediment Load Prediction

Sediment load estimation is generally required for study and development of water resources system. In this regard, artificial neural network (ANN) is the most widely used modeling tool especially in data-constraint regions. This research attempts to combine SSA (singular spectrum analysis) with ANN, hereafter called SSA-ANN model, with expectation to improve the accuracy of sediment load predicted by the existing ANN approach. Two different catchments located in the Lower Mekong Basin (LMB) were selected for the study and the model performance was measured by several statistical indices. In comparing with ANN, the proposed SSA-ANN model shows its better performance repeatedly in both catchments. In validation stage, SSA-ANN is superior for larger Nash-Sutcliffe Efficiency about 24% in Ban Nong Kiang catchment and 7% in Nam Mae Pun Luang catchment. Other statistical measures of SSA-ANN are better than those of ANN as well. This improvement reveals the importance of SSA which filters noise containing in the raw time series and transforms the original input data to be near normal distribution which is favorable to model simulation. This coupled model is also recommended for the prediction of other water resources variables because extra input data are not required. Only additional computation, time series decomposition, is needed. The proposed technique could be potentially used to minimize the costly operation of sediment measurement in the LMB which is relatively rich in hydrometeorological records.

Dramatic sediment load changes and sedimentation characteristics upstream of the Three Gorges Dam due to the large reservoirs construction

After the construction of cascade reservoirs in the upper reaches of the Three Gorges Reservoir(TGR),the sediment load outflow of the upper Yangtze River Basin(YRB)has been significantly altered,decreasing from 491.8 Mt/yr(1956–2002)to 36.1 Mt/yr(2003–2017)at Yichang station.This has widely affected river hydrology,suspended sediment grain size distribution,and channel morphology.This study analyzed hydrological variations in water discharge and sediment load of the upper YRB over the past 62 years(1956–2017)by employing a double mass curve.The variations in the source areas of sediment yielding for the upper YRB were quantified,and field measurement data of the cross-channel profile were collected to investigate the sedimentation process in the TGR from 2003 to 2017.More than 90%of the sediment load reduction in the upper YRB may be explained by human activities.The Jinshajiang River was no longer the largest sediment source area for the Zhutuo station(accounting for 5.23%)in the 2013–2017 time span,and the sediment rating rates for the inflow and outflow of the TGR shifted to negatively correlated.A longitudinal fining trend was revealed in the suspended sediment size.Still,the mean median grain size of suspended sediment in the TGR had an increasing trend in the 2013–2017 period.This result may be closely related to sediment regulation in reservoirs and incoming sediment load reduction.Sedimentation in the TGR decreased sharply from 299.8 Mt/yr in 2003–2012 to 47.2 Mt/yr in 2013–2017,but the sedimentation rate of the TGR remained at>80%annually.Moreover,some cross sections in the fluctuating backwater zone experienced scouring.