Sediment Rating Curves Research Articles

The use of continuous sediment‐transport measurements to improve sand‐load estimates in a large sand‐bedded river: The lower Chippewa River, Wisconsin

AbstractAccurately determining sediment loads is necessary for managing river environments but is difficult because multiple processes can lead to large discharge‐independent changes in sediment transport. Thus, estimations of sediment load using discharge–sediment rating curves fit to sparse or historical sediment‐transport measurements can be inaccurate, necessitating alternative approaches to reduce uncertainty. Continuous sediment‐transport measurements reduce uncertainty because they can be used to detect discharge‐independent changes in transport and are therefore unaffected by hysteresis.We used largely continuous approaches to measure sand transport in the lower Chippewa River, a large sand‐supplying tributary to the Mississippi River. We used side‐looking acoustic‐Doppler profilers to continuously measure suspended‐sand concentration, and bedform‐tracking techniques to episodically measure bedload transport. Bedload transport was then continuously estimated using a discharge‐dependent ratio of bedload to suspended‐sand transport. This approach allowed determination of sand loads that were not estimated based only on water discharge. Our continuous suspended‐sand measurements show that hysteresis between discharge and suspended‐sand concentration occurs during most floods. Quasi‐continuous bed‐elevation measurements using a scour monitor show that lags between discharge and dune geometric adjustment is also common, causing hysteresis between discharge and bedload transport during floods. Furthermore, comparisons of our measurements with historical sediment‐transport measurements indicate large discharge‐independent declines in both suspended‐sand and bedload transport since the 1980s. These findings indicate that sand transport is a non‐stationary function of water discharge over timescales ranging from within individual floods to decades. Consequently, although our continuous‐measurement approach yields only a ~20–30% improvement over rating‐curve estimates of sand load over multi‐year periods, our approach yields up to a factor‐of‐five improvement in sand‐load estimates over the shorter, within a flood timescales, over which the largest discharge‐independent changes in sand transport occur.

Potential predictability of suspended sediment concentration in the data constrained regions of the Mahanadi River basin, Eastern India

ABSTRACT The study proposes an efficient method to evaluate the suspended sediment concentration (SSC) relative to the traditional sediment rating curves (SRC) for gauged stations and subsequently to predict the SSC in ungauged stations of a major river basin. Multiple environmental control parameters were collected from 16 stations along the Mahanadi River basin (MRB) during the monsoon season. The hysteresis behaviour of SSC is assessed and therefore considered for modelling SSC using linear mixed-effects modelling (LMM). A basin-scale rating model is proposed using principal component analysis and stepwise multiple linear regression for estimating the unmeasured SSC. The findings show that the MRB acts differently in terms of hysteresis, with distinct dilution and flushing regimes in SRC. LMM outscored SRC by nearly doubling the mean covariance and notably reducing the percent bias between observed and predicted data across stations. However, unlike LMM, SRC could not correctly estimate low and high SSCs of ≤ 0.05 g/l and ≥ 1.5 g/l, respectively. The error metrics of the proposed rating model are within acceptable limits for all ungauged stations. Nevertheless, its efficiency varies due to smaller catchment areas, non-linearity in sediment transport with respect to catchment area, and other sampling issues. As a result, compared to other known models applied on the MRB, this model has the lowest error and seems to be the best in predicting monthly SSC.

SEASONAL VARIATION OF THE RELATIONSHIP BETWEEN SUSPENDED SEDIMENT AND RIVER DISCHARGE

Information on measured sediment load is important in calibrating hydrologic models used in sediment yield prediction. The purpose of this study was to relate sediment load to discharge at a river section. An appropriate section was selected at the Lotanyanda river, Limpopo, South Africa. Depth integrated sediment samples were collected on various dates at an appropriate interval once a day and analyzed to determine the sediment concentration and turbidity. The water level and temperature readings were also taken at the time of sediment sampling. Information on sediment concentration and discharge was used to determine the sediment load on the dates when sampling commenced autumn through winter in 2016. Based on the values of measures of performance that included R2, NSE, RMSE, PBIAS and RSR, performance of predicted sediment concentration based on sediment rating curves was very good and could beused for predicting sediment load. The measures of performance, however varied from season to season. The relationship between sediment load and discharge varied from one season to another and the quality of fit (R2) based on regression analysis differed from one season to another. Knowledge of sediment load discharge relationship can be used in sediment load prediction based on discharge information.

Coupling Effects of Precipitation and Vegetation on Sediment Yield from the Perspective of Spatiotemporal Heterogeneity across the Qingshui River Basin of the Upper Yellow River, China

Interactions between precipitation, vegetation, and erosion are crucial and not fully solved issues in the area of earth surface processes. The Qingshui River Basin (QRB), as the main sediment source tributary of the upper reaches of the Yellow River, is characterized by spatial heterogeneity of rainfall, vegetation, and soil erosion. In this study, we investigated the spatiotemporal variations of sediment yields within the QRB and further identified the coupling effects of precipitation and vegetation on soil erosion. We collected annual (1955 to 2016) and daily (2006 to 2016) hydrological and sediment series from six hydrological stations, which subdivided the whole basin into six different sub-basins with heterogeneity in climate and landscape. Variations in parameter a of the sediment rating curves among the six sub-basins continuously declined, showing the continuously increasing effect of vegetation coverage on reducing soil erosion. The unique combination of relationships between precipitation characteristics and vegetation patterns in six sub-basins and these coupling effects resulted in different precipitation–vegetation–erosion patterns in six sub-basins. Sediment yield followed bell-shaped relationships with vegetation and precipitation, with a clear critical threshold at normalized difference vegetation index (NDVI) = 0.36/precipitation = 100 mm at a monthly scale. Based on these thresholds, the non-linear relationships between precipitation, vegetation, and erosion were also explained. We also found that reducing the time lags in which vegetation follows precipitation may be effective in suppressing sediment yield. These findings could provide a quantitative approach to estimating the potential changes in sediment yield associated with proposed ecological rehabilitation schemes in this region.

Improving the estimation of sedimentation in multi-purpose dam reservoirs, considering hydrography and time scale classification of sediment rating curve (case study: Dez Dam)

Improving the estimation of sedimentation in multi-purpose dam reservoirs, considering hydrography and time scale classification of sediment rating curve (case study: Dez Dam)

Event scale analysis of sediment Concentration-River discharge relationship

Computation of river suspended sediment loads is highly significant now-a-days for several reasons, such as predicting reservoir sedimentation, studying land erosion rates and understanding sediment transport to oceans. For achieving this, sediment rating curves which is a plot of suspended sediment concentration (C) vs. river discharge (Q), can be used instead of sampling programs which are sometimes insufficient to explain the continuous record..Many studies showing dynamics of suspended sediment have been introduced in the past literature, but mostly they have focused on C-Q analysis by plotting together all the data points from a catchment. This generally leads to poor correlation as C-Q relationship is often non-unique due to effects like seasonality and mixing of surface with subsurface water. In this study we therefore perform event-wise C-Q analysis, particularly considering recession flow curves and more emphasis has been given to three important recession characteristics: (1) power law exponent b, (2) dynamic C-Q relationship (explained by coefficient a) and (3) recession length (duration of a recession event). Long term data from fifty US basins that include at least ten numbers of recession events for each basin, are considered for the analysis. The results reveal that C-Q relationships for individual recession events are much stronger compared to those observed when all the data points are plotted together for a basin. We thus provide a new framework for characterizing C-Q relationships, which in turn will help us in addressing several practical issues related to soil loss and sediment movement and also to develop a Rating Curve with minimum error.

Application of MGGP, ANN, MHBMO, GRG, and Linear Regression for Developing Daily Sediment Rating Curves

A data-driven relationship between sediment and discharge of a river is among the most erratic relationships in river engineering due to the existence of an inevitable scatter in sediment rating curves. Recently, Multigene Genetic Programming (MGGP), as a machine learning (ML) method, has been proposed to develop data-driven models for various phenomena in the field of hydrology and water resource engineering. The present study explores the capability of MGGP-based models to develop daily sediment ratings of two gauging sites with 30-year sediment-discharge data, which was utilized previously in the literature. The results obtained by MGGP were compared with those achieved by an empirical model and Artificial Neural Network (ANN). The coefficients of the empirical model were calibrated using linear and nonlinear regression models (Generalized Reduced Gradient (GRG) and the Modified Honey Bee Mating Optimization (MHBMO) algorithm). According to the comparative analysis, the mean absolute error (MAE) at the two gauging stations reduced from 516.54 to 519.23 obtained by nonlinear regression to 447.26 and 504.23 achieved by MGGP, respectively. Similarly, all other performance indices indicated the suitability and accuracy of MGGP in developing sediment ratings. Therefore, it was demonstrated that ML-based models, particularly MGGP-based models, outperformed the empirical models for estimating sediment loads.

Improving Flow Discharge-Suspended Sediment Relations: Intelligent Algorithms versus Data Separation

Information on the transport of fluvial suspended sediment loads (SSL) is crucial due to its effects on water quality, pollutant transport and transformation, dam operations, and reservoir capacity. As such, adopting a reliable method to accurately estimate SSL is a key topic for watershed managers, hydrologists, river engineers, and hydraulic engineers. One of the most common methods for estimating SSL or suspended sediment concentrations (SSC) is sediment rating curve (SRC), which has several weaknesses. Here, we optimize the SRC equation using two main approaches. Firstly, three well recognized metaheuristic algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and imperialist competitive algorithm (ICA)) were used together with two classical approaches (food and agriculture organization (FAO) and non-parametric smearing estimator (CF2)) to optimize the coefficients of the SRC regression model. The second approach uses separation of data based on season and flow discharge (Qw) characteristics. A support vector regression (SVR) model using only Qw as an input was employed for SSC estimation and the results were compared with the SRC and its optimized versions. Metaheuristic algorithms improved the performance of the SRC model and the PSO model outperformed the other algorithms. These results also indicate that the model performance was directly related to the temporal separation of data. Based on these findings, if data are more homogenous and related to the limited climatic conditions used in the estimation of SSC, the estimations are improved. Moreover, it was observed that optimizing SRC through metaheuristic models was much more effective than separating data in the SCR model. The results also indicated that with the same input data, SVR was superior to the SRC model and its optimized version.

Streamflow and sediment yield estimation, and area prioritization for better conservation planning in the Dawe River watershed of the Wabi Shebelle River Basin, Ethiopia

Estimating sediment yield and streamflow, as well as identifying soil erosion processes, are necessary for properly designing superior management techniques, monitoring and evaluating various management scenarios, and prioritizing better conservation planning. Therefore, this study estimates streamflow and sediment yield and prioritizes the catchment sub-watersheds for conservation planning in the Dawe River watershed. In the continuous-time step, sediment load data is generated by developing a sediment rating curve. Statistical measures were used to weigh the SWAT's performance in estimating streamflow and sediment output over the calibration (2000–2008) and validation (2009–2013) periods. The model's performance in estimating the monthly streamflow was 0.73, 0.55, and 0.79 for NSE, R-factor, and R2, respectively, according to the calibration results. For sediment yield, the Nash Sutcliffe efficiency (NSE), R-factor, and R2 were 0.77, 0.47, and 0.82, respectively. Estimated sediment output rates were higher in agricultural lands with eutric and rendzic leptosols soil types and gradients in the study watershed. For the indicated sub-watersheds in the catchment, the three best sediment management scenarios (BMPs) were considered: S1 (filter strip), S2 (terrace/bund), and S3 (reforestation). With the implementation of S1, S2, and S3, the sediment yield was reduced by 15.7 percent, 21.3 percent, and 24.9 percent, respectively, based on the chosen BMPs. As a result, adopting S3 for the Dawe River watershed for productive sediment reductions should be encouraged. As a result, our findings point to the best strategy for negotiating a stronger conservation plan.

Daily suspended sediment forecast by an integrated dynamic neural network

Suspended sediment is of importance in river and dam engineering. Due to its high nonlinearity and stochasticity, sediment prediction by conventional methods is a challenging task. Consequently, this paper establishes a new hybrid model for an improved forecast of suspended sediment concentration (SSC). It is a nonlinear autoregressive network with exogenous inputs (NARX) integrated with a data pre-processing framework (thereafter INARX). In this model, wavelet transformation (WT) is used for time series decomposition and multigene genetic programing (MGGP) for details scaling. The two incorporated modules improve time and frequency domain analysis, allowing the network to unveil the embedded characteristics and capture the non-stationarity. At a hydrological station on the upper reaches of the Yangtze River, the records of daily water stage, flow discharge and suspended sediment are collected and refer to a nine-year period during 2004–2012. The data are used to evaluate the models. Several wavelets are explored, showing that the Coif3 leads to the most accurate prediction. Compared to the sediment rating curve (SRC), the conventional MGGP, multilayer perceptron neural network (MLPNN) and NARX, the INARX demonstrates the best forecast performance. Its mean coefficient of determination (CD) increases by 7.7%–38.6% and the root mean squared error (RMSE) reduces by 15.1%–54.5%. The INARX with the Coif3 wavelet is further evaluated for flood events and multistep forecasts. Under flood conditions, the model generates satisfactory results, with CD > 0.83 and 84.7% of the simulated data falling within the ±0.1 kg/m3 error. For the multistep forecast, at a one-week lead time, the network also yields predictions with acceptable accuracy (mean CD = 0.78). The model performance deteriorates if the lead time becomes larger. The established framework is robust and reliable for real-time and multistep SSC forecasts and provides reference for time series modeling, e.g. streamflow, river temperature and salinity.

Sediment regimes in South Korea

AbstractA comprehensive review of the existing river flow and sediment measurement data of South Korea shows specific sediment yield values ranging from 5 to 1,500 tons/km2·year. The watershed characteristics decisively affect the shape of mass curves for water and sediment, as well as the flow duration and sediment rating curves. In terms of flow regime, small watersheds have flashy hydrographs and high sediment concentrations at a given flow discharge. The sediment rating curve coefficients decrease from 1 to 0.02 as the watershed area increases from 100 to 20,000 km2, while the exponents of these curves remain relatively constant between 1.5 and 2.0. The sediment transport in small watersheds depends on large floods, and the half‐yield discharge decreases with watershed area and typically ranges from 5 to 40 times the mean discharge. This study defines equations to calculate the mean annual flow discharge, flow duration curves, sediment yield, and cumulative water and sediment distribution curves validated with field measurements in South Korea. Additionally, the sediment regime is also presented in a continental perspective.

The long-term spatial and temporal variations of sediment loads and their causes of the Yellow River Basin

The erosion, deposition and transport processes of sediment alter the basin surface environment, and affect the function and structure of ecosystems. The sediment loads showed a significant decreased trend under the impacts of climate changes and human activities in the Yellow River Basin (YRB). Understanding the sediment transport patterns and causes is a prerequisite of sediment management and ecological restoration of the river basins. This study explored the sediment long-term fluctuation characteristics and the influences of various driving factors on the sediment variations based on the sediment loads data from the source region to the delta of the YRB. We found that the sediment fluxes showed a significant decreasing trend in the mainstream and tributaries of the YRB. The average sediment loads of the upper basin decreased to 42.88 ± 26.14 Mt/yr in 2000–2019, which is less than 30% of the average value recorded before 1968. Compared with 1950–1968, the sediment loads in the middle and lower reaches decreased by approximately 88% and 89% from 2000 to 2019, respectively. In addition, the middle reaches of the Yellow River (YR) is in a persistent degradation state prior to 2000, the source-to-sink relationships of sediment downstream the Huayuankou station are changing frequently due to the operation of Sanmenxia and Xiaolangdi reservoirs. According to the sediment rating curves (SRCs), the sediment transport regimes have changed at time scale along the mainstream of the YR in recent three decades. Moreover, the human activities (reservoir constructions, water-soil conservation, etc.) are the dominant factors for explaining the progressively reduction trend of sediment loads, the contributions of human activities to sediment reduction was approximately 93% in recent two decades in the YRB. Precipitation and temperature are the predominant meteorological factors affecting sediment variations, and ENSO is the dominant teleconnection pattern for explaining the multiscale characteristics of sediment loads in the lower reaches of the YRB. The findings of this study could provide effective reference for the ecological restoration and sediment management of the YRB.

Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy system model

Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm (ANFIS-FCM-PSOGSA) in improving the estimation accuracy of river suspended sediment loads (SSLs) is investigated in the current study. The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization (ANFIS-FCM-PSO), ANFIS-FCM, and sediment rating curve (SRC) models. Various input combinations involving lagged river flow (Q) and suspended sediment (S) values were used for model development. The effect of Q and S on the model's accuracy also was assessed by including the difference between lagged Q and S values as inputs. The model performance was assessed using the root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe Efficiency (NSE), and coefficient of determination (R2) and several graphical comparison methods. The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO (or ANFIS-FCM) models by 8.14% (1.72%), 14.7% (5.71%), 12.5% (2.27%), and 25.6% (1.86%), in terms of the RMSE, MAE, NSE and R2, respectively. The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load. The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification.

Constraining Dynamic Sediment‐Discharge Relationships in Cold Environments: The Sediment‐Availability‐Transport (SAT) Model

AbstractAccelerated glacier‐snow‐permafrost erosion due to global warming amplifies the sediment availability in cold environments and affects the time‐varying suspended sediment concentration (SSC) and discharge (Q) relationship. Here, the sediment‐availability‐transport (SAT) model is proposed to simulate dynamic SSC‐Q relationships by integrating the sediment availability coupled by thermal processes, fluvial processes and long‐term storage exhaustion into a sediment rating curve (SSC = a × Qb with a and b as fitting parameters). In the SAT‐model, increased sediment sources from glacier‐snow‐permafrost erosion are captured by changes in basin temperature, showing an exponential amplification of SSC when basin temperature increases. Enhanced fluvial erosion by the elevated water supply from rainfall and meltwater is captured by the factor of runoff surge, which results in a linear amplification of SSC. The SAT‐model is validated for the permafrost‐dominated Tuotuohe basin on Tibetan Plateau utilizing multi‐decadal daily SSC/Q in‐situ observations (1985–2017). Results show that sediment rating curves for Tuotuohe display significant inter‐annual variations. The higher parameter‐b in a warmer and wetter climate confirms the increased sediment availability due to the expanded erodible landscapes and gullying‐enhanced connectivity between channels and slopes. Through capturing such time‐varying sediment availability, the SAT‐model can robustly reproduce the long‐term evolution, seasonality, and various event‐scale hysteresis of SSC, including clockwise, counter‐clockwise, figure‐eight, counter‐figure‐eight, and more complex hysteresis loops. Overall, the SAT‐model can explain over 75% of long‐term SSC variance with stable performance under hydroclimate abrupt changes, outperforming the conventional and static sediment rating curve approach by 20%. The SAT‐model not only advances understanding of sediment transport mechanisms by integrating thermal‐ and fluvial‐erosion processes, but also provides a model framework to simulate and project future sediment loads in other cold basins.

Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers

A key goal of sediment management is the quantification of suspended sediment load (SSL) in rivers. This research focused on a comparison of different means of suspended sediment estimation in rivers. This includes sediment rating curves (SRC) and soft computing techniques, i.e., local linear regression (LLR), artificial neural networks (ANN) and the wavelet-cum-ANN (WANN) method. Then, different techniques were applied to predict daily SSL at the Pirna and Magdeburg Stations of the Elbe River in Germany. By comparing the results of all the best models, it can be concluded that the soft computing techniques (LLR, ANN and WANN) better predicted the SSL than the SRC method. This is due to the fact that the former employed non-linear techniques for the data series reconstruction. The WANN models were the overall best performer. The WANN models in the testing phase showed a mean R2 of 0.92 and a PBIAS of −0.59%. Additionally, they were able to capture the suspended sediment peaks with greater accuracy. They were more successful as they captured the dynamic features of the non-linear and time-variant suspended sediment load, while other methods used simple raw data. Thus, WANN models could be an efficient technique to simulate the SSL time series because they extract key features embedded in the SSL signal.

Deciphering the role of meteorological parameters controlling the sediment load and water discharge in the Sutlej basin, Western Himalaya

The Sutlej River basin of the western Himalaya (study area), owing to its unique geographical disposition, receives precipitation from both the Indian summer monsoon (ISM) and the Westerlies. The characteristic timing and intensity of the ISM and Westerlies, leaves a distinct footprint on the sediment load of the River. Analysis with the last forty years data, shows an increasing trend for temperature. While for precipitation during the same period, the Spiti watershed on the west has highest monthly accumulated precipitation with long term declining trend, in contrast to the other areas where an increasing trend has been observed. Thus, to probe the hydrological variability and the seasonal attributes, governed by the Westerlies and ISM in the study area, we analyzed precipitation, temperature, snow cover area (in %), discharge, suspended sediment concentration (SSC) and suspended sediment load (SSL) for the period 2004 - 2008. To accomplish the task, we used the available data of five hydrological stations located in the study area. Inter-annual shift in peak discharge during the monsoon period is controlled by the variation in precipitation, snow melt, glacier melt and temperature. Besides seasonal variability has been observed in generation of the sediments and its delivery to the river. Our analysis indicates, dominance of the Westerlies footprints in the hydrological parameters of the Spiti region, towards western part of the study area. While, it is observed that the hydrology of the Khab towards eastern part of the study area shows dominance of ISM. Further downstream, the hydrology of Nathpa station also shows dominance of ISM. It also emerged out that the snowmelt contribution to the River flow is mostly during the initial part, at the onset of the monsoon, while for rest and major part of the summer monsoon season, the River flow is augmented by the precipitation, glacial melt and some snow melt. We observed, that the SSC increases exponentially in response to increase in temperature and correlates positively with River discharge. The average daily SSL in the summer monsoon is many times more than that in the winter monsoon. The downstream decrease in steepness of the sediment rating curve is attributed to either a change in the River-sediment dynamics or on account of the anthropogenic forcing. The top 1% of the extreme summer monsoon events (only 4 events) in our study area contribute up to 45% of SSL to the total sediment load budget. It has also been observed that the River-sediment dynamics in the upstream catchments are more vulnerable and sensitive to the extreme events in comparison to the downstream catchments. The present study for the first time gives a holistic insight in to the complex dynamics of the hydrological processes operational in the study area. The research findings would be crucial for managing the water resources of the region and the linked water and food security.

Contrasting Impacts of a Hotter and Drier Future on Streamflow and Catchment Scale Sediment Flux in the High Andes

AbstractFluvial sediment transport in high mountain regions is uniquely sensitive to changes in hydroclimate that alter the timing and magnitude of streamflow contributions from snowmelt, glaciers, and other cryoforms. In the high Andes of Argentina and Chile, mountain river systems serve millions of people in foothill communities and large metropolitan areas alike, yet these same rivers can also carry high sediment loads that compromise water quality and infrastructure. Regional climate predictions are also increasingly forecasting a warmer and drier future that is likely to alter hydrologic regimes and sediment transport dynamics. In this study, we use hydrologic modeling techniques and sediment rating curves compiled from historical data to propagate a suite of endmember climate projections to estimate changes in streamflow and consequent impacts on catchment‐scale sediment transport on both the Argentine and Chilean sides of the high Andes. Our results show reductions in streamflow of up to 57% under scenarios with reduced precipitation and even larger decreases in sediment flux, while outcomes under a warming scenario with no changes to precipitation were highly variable. Our results further show that high winter streamflow events may increase in magnitude in Chile while mountain communities in Argentina may increasingly face water shortages. These results indicate that different adaptation and mitigation strategies may be needed on either side of the Andes in response to a changing hydroclimate.

Copula-Based Infilling Methods for Daily Suspended Sediment Loads

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

Natural and anthropogenic controls on sediment rating curves in northern California coastal watersheds

AbstractThe watersheds along the north coast of California span a wide range of geologic settings, tectonic uplift rates, and historic timber harvest activity. Known trends in how each of these factors influence erosion rates provides an opportunity to examine their relative importance. We analyzed 71 watersheds within nine larger river basins, investigated the factors influencing suspended sediment rating curves (SRCs), investigated how SRCs varied among our study watersheds, and used Random Forest modeling (RFM) to determine which environmental characteristics and land management metrics influence SRC shapes, vertical offsets, and slopes. While SRCs typically take the form of a power function, they also can exhibit threshold or peak relationships. First, we found both power and threshold relationships for the SRCs within our study watersheds. Second, the SRC offsets and slopes systematically varied with regional tectonic uplift. Third, SRC offsets increased in several watersheds following intensive timber harvest events and SRC slopes decreased due to a greater relative increase in suspended sediment concentration at lower flows than higher flows. Our RFM correctly classified 96% of the SRC shapes using two near‐channel metrics; near‐channel precipitation‐sensitive deep‐seated landslide susceptibility and near‐channel soil erodibility. Our RFM models also showed that timber harvest activity and near‐channel local relief can explain 40% of the variability in SRC offsets, whereas tectonic uplift rates, millennial‐scale erosion rates, and precipitation patterns explain 40% of the variability in SRC slopes.

Quantifying Climatic and Anthropogenic Influences on Water Discharge and Sediment Load in Xiangxi River Basin of the Three Gorges Reservoir Area

Quantifying the relative contributions of climate change and human activities to water and sediment variations is of great importance for regional resources and river basin management, which has become a hot topic in hydrology research. In this study, water discharge and sediment load in Xiangxi River basin of Three Gorges Reservoir area were investigated based on hydrometeorological time series since 1960s. The non-parametric Mann-Kendall (M-K) test was used to detect both the trends and abrupt changes in the time series. Double mass curve and sediment rating curve method were used to quantify and evaluate the climatic and anthropogenic effects on water discharge and sediment load. Results indicated that both water discharge and sediment load showed significant decreasing trends with annual decrement rate of –0.08 × 108 and ‒1.011 × 104 t/yr, respectively. The abrupt change points were observed in 1991 for water discharge and 1999 for sediment load by sequential M-K test and cumulative anomaly curve analysis. This study demonstrated that water discharge was mainly influenced by precipitation in 1990s while by human activities ever since 2000, and sediment load was predominantly influenced by human activities (relative contribution 86–98%, regardless of whether the effect is negative or positive). In this study, soil and water conservation as well as land use change were primarily responsible for water and sediment variations in Xiangxi river basin especially since 2000. Further studies need to be done to quantify the importance of a specific driving factor at multi-timescales.