Sediment Rating Curves Research Articles

Applicability of sediment rating curves: analysis in the state of Rio Grande do Sul

The transport of sediments is present in all watercourses, occurring naturally, however, in different ways and characteristics. Its quantification in watersheds becomes extremely important for the planning and management of water resources. The sediment rating curve, which empirically describes the relationship between stream flow and suspended sediment concentration (Css), is an alternative tool to the lack of continuous monitoring of sediment transport. The aim of this research was to evaluate the use of rating curves in sedimentometric stations in Rio Grande do Sul, Brazil. Three Css data handling scenarios were tested for the analytical fitting of sediment rating curves considering a power function as follows: complete data sets, data sets subdivided into 10-year periods and subdivided into stream flow ranges. The approaches adopted in the present study were evaluated taking as reference 58 sedimentometric stations in the state. The goodness-of-fit tests used in this study - coefficient of determination, Relative Average Percentage Error and Nash Sutcliffe coefficient, indicated that the best results of the estimation of sediment transport were observed when the sediment rating curve was fitted to the 10-year period data set.

Estimating stream sediment loads to assess management options for a Southern Appalachian mountain lake

Enka Lake is a 25-ha reservoir in the Southern Appalachian mountains surrounded by the Biltmore Lake residential neighborhood. The aesthetic and recreational value of Enka Lake can be negatively affected by turbid water and sediment buildup, resulting from stormwater originating upstream in its 15.38 km2 watershed. In this study, we monitored streamflow and sediment loads for nearly 2 years to better understand sedimentation dynamics and evaluate management options. We collected nearly 500 water samples throughout the watershed, focusing on two tributaries that provide the majority of streamflow into Enka Lake. During baseflow conditions, those tributaries are very clear (turbidity ranging from 1 to 20 NTU), but sediment-laden stormflow (turbidity > 1000 NTU) from dirt roads, gravel driveways, and poorly vegetated yards is common and, during extreme storm events, causes lake discoloration. Discharge-sediment data pairs were used to construct sediment rating curves and estimate sediment fluxes through these tributaries. The predicted sediment fluxes were tested with high-frequency sampling during and after three storms in different seasons; predicted values underestimated the peak sediment fluxes (0–400%) but generally matched total measured sediment loads. Estimates of annual sediment load from the two tributaries (540–900 tonnes/year) suggest that a dredging effort commissioned by the neighborhood association in 2016 may have removed only about 1–3 years’ worth of sediment. Installing sediment forebays or other retention structures is likely the preferred sediment management strategy moving forward (over a regular dredging schedule) since they can be maintained more easily without affecting lake levels. Designing these retention structures will benefit from the estimates of stormflow and sediment fluxes, and the study design presented here—including the help of community-based volunteer samplers—provides a model that could be used at other sites in the Southern Appalachians and elsewhere.

Development of Suspended Sediment Rating Curve Model by Statistical Classification of River Discharge Data (Case Study: Ghareh-Sou Coastal Watershed)

Development of Suspended Sediment Rating Curve Model by Statistical Classification of River Discharge Data (Case Study: Ghareh-Sou Coastal Watershed)

Estimation of suspended sediment balance of a small catchment in Southwestern Nigeria.

Human use index caused by population pressure in Opa catchment Ile Ife, Southwestern Nigeria, has resulted in catchment denudation, accelerated erosion, and sediment delivery into a man-made (1978) reservoir (Opa) in the catchment. This study is aimed at evaluating the suspended sediment balance of Opa reservoir with a view to ascertain the annual siltation rate. River discharges, water levels, suspended sediment concentrations/yield, and flow velocities were monitored, and data collected from all the tributaries of the Opa reservoir and the spillway (outlet) in the period 2017-2018. Water samples collected were subjected to gravimetric analysis, and the results utilized to obtain sediment rating curves and to compute the suspended sediment balance. Suspended sediment inflow from the six sub-catchments ranged from 8.49 to 29.05tons/ha, with a total inflow of 1146.50tons/ha into the reservoir during the hydrological year. The corresponding outflow through the spillway was 615.70tons/ha. 530.80tons/ha was sequestered in the reservoir, equivalent to an estimate of 46% suspended sediment deposited in the reservoir. The high sediment yield in Opa reservoir is due to the dredging and clearing of the river channels in the catchment of any impediment thereby enhancing sediment delivery into the reservoir. Sediment loads were higher in the rainy season suggesting catchment erosion as the main factor responsible for the sediment yield into the reservoir. The study concluded that the positive suspended sediment balance suggests sediment sequestration with an increasing tendency for accelerated elimination of the wetland in the face of poor environmental management and enhanced human activities.

Improved adaptive neuro-fuzzy inference system with bacterial foraging optimization algorithm for suspended sediment concentration estimation

The accuracy of predicting river-suspended sediment concentration (SSC) is crucial for evaluating the functional lifespan of reservoirs, analyzing river geomorphological evolution, and assessing riverbed stability. In this study, we aim to develop new models for SSC prediction at two hydrological stations near Puerto Rico, USA, by integrating the bacterial foraging optimization algorithm and adaptive neural fuzzy inference network (ANFIS). The models comprise ANFIS with grid partition (ANFIS-GP), ANFIS with subtractive clustering (ANFIS-SC), and ANFIS with fuzzy c-means clustering (ANFIS-FCM). Additionally, we employ an artificial neural network (ANN) and the sediment rating curve (SRC) for predicting daily series data of flow discharge-suspended sediment concentration (SSC). Different scenarios are considered based on varying input and output variables, leading to predictions for four distinct scenarios. At the Rio Valenciano Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-GP, ANFIS-SC, ANN, and SRC are 2.2172, 2.5389, 2.6627, 2.7549, 2.7994, and 3.7882, respectively. For the Quebrada Blanca Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-SC, ANFIS-GP, ANN, and SRC are 0.8295, 0.8664, 0.8964, 0.9110, 0.9684, and 1.6742, respectively. It can be inferred that ANFIS-BFO exhibits superior prediction results compared to all other models. Furthermore, ANFIS-SC and ANFIS-FCM demonstrate slightly better prediction performance than ANFIS-GP. In comparison to ANN, ANFIS-GP, ANFIS-SC, and ANFIS-FCM exhibit slightly superior prediction performance.

Applying a transfer function model to improve the sediment rating curve

ABSTRACT Information relating to suspended sediment load is of particular importance in terms of water management and environmental protection schemes. The sediment rating curve (SRC) is used to express the common relation between suspended sediment load and flow discharge in a basin. SRC, however, does not consider the effect of time lag in flow discharge; instead, it makes a kind of bias in its estimation, toward overestimation or underestimation of a sediment load. In this direction, the current research aims to improve and extend the SRC model by using a transfer function method. The proposed method was applied to a specific basin to better capture the effect of time lag on suspended sediment load estimation. This function estimated the current sediment load in current time. Three case studies were conducted in different climate conditions of Iran to evaluate the application of transfer function with SRC in assessing the relation between flow and suspended sediment discharge. The results revealed that the transfer function method demonstrated high precision in forecasting suspended sediment load, as evidenced by its mean absolute difference (MADIF) and bias criterion calculations. The findings indicated that the proposed method could enhance the accuracy of sediment load estimations in basins with similar characteristics. This approach was based on an unbiased future forecast and provided a high level of confidence. The results suggested that this method could be successfully applied to other basins to improve the accuracy of sediment load estimations.

Uncertainties of Annual Suspended Sediment Transport Estimates Driven by Temporal Variability

AbstractThe majority of sediment transported from rivers to the global oceans is moved in suspension as fine particles. Thereby, the transported sediment shapes the physical environment regarding erosive and accumulative processes. Temporal variations in sediment supply and transport lead to unquantified uncertainties in annual load estimates, requiring high‐resolution data sets and a sound understanding of site‐specific catchment characteristics. We investigate the temporal variability of suspended sediment transport in four catchments in Germany with highly different discharge regimes and catchment sizes (<1,000 km2 to >100,000 km2). The data set consists of high‐resolution 15‐min turbidity measurements with daily discharge and frequent manual sampling. Utilizing a bootstrap approach based on the 15‐min time series, we assessed the impact of the sampling interval on annual load estimates for less frequent data sets. We use the sediment load exceedance time (Ts80%) as a measure of variability and relate it to uncertainties in annual load estimates. Since low‐frequency data sets rely on sediment rating curves, we performed a sensitivity analysis of the rating parameters a, b, and ε. Our results indicate a negative exponential relationship between Ts80% and uncertainties in annual load estimates. Based on the Ts80%, we can derive the shortest sampling frequency necessary to obtain annual load estimates with an error of <20% over varying discharge regimes. Additionally, Ts80% is linked to rating exponent b, with low b‐values indicating high Ts80%‐values and lower variability, and high b‐values indicating higher variability.

Assistance in Designing Citarum River Cliff Protection to Control River Erosion in Atirompe Village, Majalaya District, Bandung Regency

Rivers are often used as a water source for agriculture, transportation, and water supply needs. One of the causes of riverbanks' damage is scouring the cliff's foot, which cannot withstand scouring currents. If not addressed immediately, it will cause more severe damage. Citarum River is one river that passes through Atirompe Village, Majalaya District. The riverbanks in the area have a risk of cliff failure. The study objective was to analyze the problem, design the protection, and calculate the budget. The hydrology and riverbank analysis of the Majalaya River has been performed. The steps are important to analyze the depth and width of the scour caused by the flow of the Citarum River water. Sediment data were collected at three locations, namely the upstream, middle, and downstream of the river segment that will be reviewed. In data processing, to obtain a sediment rating curve, it is necessary to measure the flow velocity, which will later be calculated and used as the flow rate that flows during TSS data collection. Using the HEC – RAS 6.2 software, the calculations with the Ripley Equation were compared to obtain the scour depth. For the design alternative, constructing a steel sheet pile-type cliff protection structure is one form of planning to protect riverbanks. This study also analyzes the recapitulation of the budgeting for protecting the Citarum riverbanks in Atirompe Village, Majalaya District. Using standardized unit price, the total price for the protection is 1,8 billion rupiahs.

Estimation of daily suspended sediment concentration in the Ca River Basin using a sediment rating curve, multiple regression, and long short-term memory model

Abstract This study presents a sediment rating curve (SRC), multiple regression (MR), and long short-term memory (LSTM) model for estimating daily suspended sediment concentration (SSC). The data of daily SSC at Yen Thuong and daily flow at five locations in the Ca River Basin, Vietnam are used to demonstrate multiple approaches. Using the daily flow and SSC data in the period from 2009 to 2019, appropriate coefficients in each method are identified carefully using five popular criteria. The results showed that SRC and MR approaches reproduced acceptably the observed values, with the values of RMSE, MAE, and ME of daily SSC being less than 5% of daily SSC magnitude observed at the station, while NSE ranges from 0.47 to 0.63 and r coefficient varies between 0.69 and 0.80. The LSTM model represented the observed values of daily SSC very well. The values of two dimensionless criteria are greater than 0.94 and its values of three-dimensional criteria are smaller than 2.0% of the observed magnitude of daily SSC in both training and validation steps. The LSTM model is found to be the best among the three investigated approaches. Then, the model is applied to estimate daily SSC values for the period from 1969 to 2008 and the year 2020.

Evaluating the Efficiency of Skew Correction Factors in Sediment Rating Curve and Comparison with Intelligent Models (Case Study: Jelogir Station, Khuzestan - Karkheh Catchment)

Evaluating the Efficiency of Skew Correction Factors in Sediment Rating Curve and Comparison with Intelligent Models (Case Study: Jelogir Station, Khuzestan - Karkheh Catchment)

Strategies analysis for improving SWAT model accuracy and representativeness of calibrated parameters in sediment simulation for various land use and climate conditions

Sediment yields in a watershed are usually affected by various natural disturbances and anthropogenic activities, and can be simulated by using hydrological models. However, due to the availability and type of data (i.e., continuous or discrete) of measured sediment data, model calibration and validation performances could be significantly affected and the calibrated parameters may not be representative for the area. In this study, the sediment rating curves (SRCs) were developed to increase measurements of sediments for model calibration, and five strategies of using measured and estimated sediment data on improving a hydrological model (Soil and Water Assessment Tool, SWAT) calibration and validation for multiple sediment stations were analyzed. The five strategies were: using measured sediment data (S1), using measured data and different portions of estimated sediment during typhoon events (S2 to S4), and using estimated sediment data during entire simulation period (S5). The results showed that although the S1 mostly performed better than other sediment strategies, S4 and S5 were also suitable for improving sediment simulation at the downstream stations (STN1, 2, and 3) and upstream station (STN4), respectively. Moreover, the impact of incorporating different portions of typhoon-induced sediment data (S2, S3, and S4) on the model performance showed differently at stations. In sum, the proposed analytical procedure is expected to be useful for calibrating sediment parameters of hydrological models with limited or highly unevenly distributed measured sediment data.

Suspended Sediment Transport in Mediterranean Streams: Monitoring and Load Estimation

The suspended sediment (SS) load provides valuable insights into soil loss magnitude, requiring comprehensive monitoring of streamflow (Q) and SS concentrations (SSC) across various hydrological conditions. The primary aim of this paper was to quantify SS loads in two mountainous river basins: the Carapelle (506 km2) and the Celone (72 km2) located in Apulia (Southeast Italy) where different monitoring strategies were adopted (i.e., continuous and discrete). The specific objective was to develop sediment rating curves to address gaps in the SSC time series. An optical probe was used to continuously monitor the SSC during 2007 to 2011 in a river section of the Carapelle river, while Q was measured with the ultrasonic method. A comprehensive dataset comprising continuous Q measurements and discrete SSC measurements was systematically acquired for the Celone river over the period of 2010 to 2011. Distinct sediment rating curves were formulated for three specific subsets of data delineated by discernible hydrological conditions (i.e., high, normal, and low flow) and SSCs were computed for the missing daily records. The annual specific sediment load exhibited a range of 2.4 to 6.06 t ha−1 yr−1 for the Celone river, while the Carapelle river displayed a range of 0.9 to 7.45 t ha−1 yr−1. A significant majority of the SS load was transported during high-flow conditions, accounting for over 80% of the total load. In contrast, during low-flow conditions, the SS load constituted less than 1% of the total load. The findings of this study highlight the significance of the hydrological regime as a critical factor influencing sediment transport in mountainous Mediterranean rivers. Furthermore, it demonstrates that the duration of the sampling period, along with its specific characteristics, such as dry or wet conditions, can have a substantial impact on the accurate quantification of the sediment load.

Development of a machine learning model for river bed load

Abstract. Prediction of bed load sediment transport rates in rivers is a notoriously difficult problem due to inherent variability in river hydraulics and channel morphology. Machine learning (ML) offers a compelling approach to leverage the growing wealth of bed load transport observations towards the development of a data-driven predictive model. We present an artificial neural network (ANN) model for predicting bed load transport rates informed by 8117 measurements from 134 rivers. Inputs to the model were river discharge, flow width, bed slope, and four bed surface sediment sizes. A sensitivity analysis showed that all inputs to the ANN model contributed to a reasonable estimate of bed load flux. At individual sites, the ANN model was able to reproduce observed sediment rating curves with a variety of shapes without site-specific calibration. This ANN model has the potential to be broadly applied to predict bed load fluxes based on discharge and reach properties alone.

Estimation of Sediment Concentration Using Sediment Rating Curve Approach in Isser Watershed (North-West of Algeria)

Estimation of Sediment Concentration Using Sediment Rating Curve Approach in Isser Watershed (North-West of Algeria)

Daily suspended sediment concentration forecast in the upper reach of Yellow River using a comprehensive integrated deep learning model

The precise prediction of suspended sediment concentration (SSC) is of great importance for river reservoir construction planning, water resource management, and ecological environment restoration. This research aims to improve SSC prediction accuracy by constructing a comprehensive and integrated deep learning model Wavelet-MGGP-CNN-LSTM (ICNN-LSTM), combining wavelet transformation (WT), multi-gene genetic programming (MGGP), convolutional neural network (CNN), and long short-term memory (LSTM) simultaneously. In ICNN-LSTM, the WT decomposes the signal and extracts time and frequency domain information, while the MGGP filters out redundant information. Then, the CNN and LSTM are integrated in a parallel and loosely coupled manner to form an initial combined model CNN-LSTM (CNN combined with LSTM) to process filtered information by WT and MGGP. Furthermore, this study compares the performance of ICNN-LSTM with CNN, LSTM, CNN-LSTM, ICNN (CNN embedded with WT and MGGP), ILSTM (LSTM embedded with WT and MGGP), artificial neural network (ANN), and the traditional sediment rating curve (SRC). The evaluation of prediction accuracy for all models was conducted using root mean square error (RMSE), Nash-Sutcliffe coefficient (NSC), coefficient of determination (R2), and mean absolute error (MAE) as performance indicators. The daily discharge and suspended sediment concentration series data from Tangnaihai Hydrological Station in the upper reaches of the Yellow River spanning from 1977 to 1987 were selected to train and test the models. Results show that, first, deep learning networks such as CNN and LSTM outperform the shallow neural network ANN, with LSTM providing higher accuracy than CNN. Second, the CNN-LSTM hybrid outperforms both CNN and LSTM models, exhibiting a nearly 89% improvement in NSC value compared to SRC in the test phase. Third, deep learning models such as ICNN, ILSTM, and ICNN-LSTM show significantly higher NSC values than CNN, LSTM, and CNN-LSTM models in the test phase, with improvements of 13.8%, 5.7%, and 12.1%, respectively. Moreover, compared to SRC, the proposed ICNN-LSTM model improves NSC value by nearly 140% in the test phase. The proposed ICNN-LSTM model, integrating the advantages of WT, deep learning, and ensemble learning, provides accurate and reliable predictions and serves as a reference for time series prediction modeling.

Reconstructing five decades of sediment export from two glacierized high-alpine catchments in Tyrol, Austria, using nonparametric regression

Abstract. Knowledge on the response of sediment export to recent climate change in glacierized areas in the European Alps is limited, primarily because long-term records of suspended sediment concentrations (SSCs) are scarce. Here we tested the estimation of sediment export of the past five decades using quantile regression forest (QRF), a nonparametric, multivariate regression based on random forest. The regression builds on short-term records of SSCs and long records of the most important hydroclimatic drivers (discharge, precipitation and air temperature – QPT). We trained independent models for two nested and partially glacier-covered catchments, Vent (98 km2) and Vernagt (11.4 km2), in the upper Ötztal in Tyrol, Austria (1891 to 3772 m a.s.l.), where available QPT records start in 1967 and 1975. To assess temporal extrapolation ability, we used two 2-year SSC datasets at gauge Vernagt, which are almost 20 years apart, for a validation. For Vent, we performed a five-fold cross-validation on the 15 years of SSC measurements. Further, we quantified the number of days where predictors exceeded the range represented in the training dataset, as the inability to extrapolate beyond this range is a known limitation of QRF. Finally, we compared QRF performance to sediment rating curves (SRCs). We analyzed the modeled sediment export time series, the predictors and glacier mass balance data for trends (Mann–Kendall test and Sen's slope estimator) and step-like changes (using the widely applied Pettitt test and a complementary Bayesian approach). Our validation at gauge Vernagt demonstrated that QRF performs well in estimating past daily sediment export (Nash–Sutcliffe efficiency (NSE) of 0.73) and satisfactorily for SSCs (NSE of 0.51), despite the small training dataset. The temporal extrapolation ability of QRF was superior to SRCs, especially in periods with high-SSC events, which demonstrated the ability of QRF to model threshold effects. Days with high SSCs tended to be underestimated, but the effect on annual yields was small. Days with predictor exceedances were rare, indicating a good representativity of the training dataset. Finally, the QRF reconstruction models outperformed SRCs by about 20 percent points of the explained variance. Significant positive trends in the reconstructed annual suspended sediment yields were found at both gauges, with distinct step-like increases around 1981. This was linked to increased glacier melt, which became apparent through step-like increases in discharge at both gauges as well as change points in mass balances of the two largest glaciers in the Vent catchment. We identified exceptionally high July temperatures in 1982 and 1983 as a likely cause. In contrast, we did not find coinciding change points in precipitation. Opposing trends at the two gauges after 1981 suggest different timings of “peak sediment”. We conclude that, given large-enough training datasets, the presented QRF approach is a promising tool with the ability to deepen our understanding of the response of high-alpine areas to decadal climate change.

Prediction of Sediment Yields Using a Data-Driven Radial M5 Tree Model

Reliable estimations of sediment yields are very important for investigations of river morphology and water resources management. Nowadays, soft computing methods are very helpful and famous regarding the accurate estimation of sediment loads. The present study checked the applicability of the radial M5 tree (RM5Tree) model to accurately estimate sediment yields using daily inputs of the snow cover fraction, air temperature, evapotranspiration and effective rainfall, in addition to the flow, in the Gilgit River, Upper Indus Basin (UIB) tributary, Pakistan. The results of the RM5Tree model were compared with support vector regression (SVR), artificial neural network (ANN), multivariate adaptive regression spline (MARS), M5Tree, sediment rating curve (SRC) and response surface method (RSM) models. The resulting accuracy of the models was assessed using Pearson’s correlation coefficient (R2), the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE). The prediction accuracy of the RM5Tree model during the testing period was superior to the ANN, MARS, SVR, M5Tree, RSM and SRC models with the R2, RMSE and MAPE being 0.72, 0.51 tons/day and 11.99%, respectively. The RM5Tree model predicted suspended sediment peaks better, with 84.10% relative accuracy, in comparison to the MARS, ANN, SVR, M5Tree, RSM and SRC models, with 80.62, 77.86, 81.90, 80.20, 74.58 and 62.49% relative accuracies, respectively.

Remote‐Sensing Method for Monitoring Suspended‐Sediment Concentration on the Middle‐Mississippi and Lower‐Missouri Rivers

AbstractSediment transport, erosion, and deposition are primary drivers of river geomorphic processes and ecological services. Suspended‐sediment concentration (SSC) is an important parameter for evaluating these processes and is accordingly of significant interest to engineers, scientists, and water resource managers. The United States Geological Survey (USGS) previously operated nine daily SSC gauging stations along the Mississippi River, with operating dates ranging from 1974 to 2018. Currently, there are no USGS gauging stations reporting daily SSC values along the Mississippi River. For this study, regression models were developed to compute the SSC along the Middle‐Mississippi River (MMR) and Lower‐Missouri River (LMOR) using publicly and freely available Landsat imagery. Surface reflectance data from Landsat satellites were used with USGS‐measured SSC to develop regression models for three different Landsat sensors (Landsat 8 OLI/TIRS, Landsat 7 ETM+, and Landsat 4‐5 TM). Previous models published for predicting SSC in the MMR and LMOR from Landsat images have a linear‐regression form and have provided invalid negative values when extrapolated outside of the dataset used for development. The objectives of this study were to develop reflectance‐SSC regression models using a power‐function form and demonstrate their extrapolation performance using multiple novel applications in the MMR basin. The reflectance‐SSC regression models were applied to the following conditions: 1) mixing at the Mississippi and Missouri River confluence, 2) point‐source pollution, and 3) SSC changes along the entire MMR reach for a range of discharges. The regression models were also used to develop sediment rating curves for the four largest tributaries of the MMR.

Modeling anthropogenic affected sediment transport in a mid-sized European river catchment–extension of the sediment rating curve equation

Land use and water resource management influence the suspended sediment concentration (SSC) in rivers. Fine sediments are an important driver for river development, even in coarse-material-rich rivers. In this study, the sediment rating curve approach is modified to predict SSC several river-km downstream of a sampling site. Further, the prediction is improved by adding sediment input, storage, and dilution effects through relevant anthropogenic measures through a model identification approach. Thus, the impact of the most severe anthropogenic measures, damming and changes in the length of a channel section for the Rur River, could be identified. Further, the impact of describing parameter changes for those measures on the SSC can be computed and considered in future water resource management. In this approach, particle swarm optimization was used to fit parameters in permutable test- and training data sets to identify linear extensions to the sediment rating curve. The input data consists of (1) SSC, which was obtained by sampling along the river section four times a year over approximately two years, (2) discharge data from river gauges supplemented by rainfall-runoff modeling between stations, (3) rainfall data from meteorological stations, and (4) sub-catchment characteristics like river section length and erosivity obtained with GIS. Via incorporating the river section length and sediment deposition in response to damming, we reduced the RMSE (root mean squared error) from 152.27 to 131.83% with a p-value of 0.073 in the Wilcoxon Signed Rank Test. Further integration of sub-catchment parameters like erosivity led to overfitting and decreased prediction accuracy. A catchment-wide prediction was achieved, but sub-catchments operate on different spatial scales with different connectivity behavior, which restricts the transferability of the equation. SSC-Q hystereses provide the first indications of characteristic sediment sources and were used to discuss connectivity behavior within the study area. They are recommended as part of a (sub-) catchment characterization for further studies.

Changes in suspended‐sediment yields under divergent land‐cover disturbance histories: A comparison of two large watersheds, Olympic Mountains, USA

AbstractImprovements in timber harvest practices and reductions in harvest volumes over the past half century are commonly presumed to have reduced sediment loads in many western US rivers. However, direct assessments in larger watersheds are relatively sparse. Here, we compare 2019–21 sediment concentrations against those of the late 1970s in the Bogachiel and Calawah River watersheds, adjacent and similarly sized (~300 km2) basins in the western Olympic Mountains of Washington State. The Calawah River watershed has experienced significant land‐cover disturbance, including a large 1951 fire, extensive post‐fire salvage logging, and relatively high rates of timber harvest through the 1990s. In contrast, the Bogachiel River watershed did not burn, and experienced only modest timber harvest that largely post‐dated 1970s sediment monitoring. Channel‐width trends suggest the Calawah River was still recovering from 1950s disturbances in the late 1970s. We found that 2019–21 suspended‐sediment loads in the Calawah River were 2.3–2.6 times lower than would have been expected based on 1970s sediment rating curves, while recent loads in the Bogachiel River were a factor of 1.4 ± 1.0 lower. We consider the plausibility and possible explanations of declining concentrations in the less‐disturbed Bogachiel River. Suspended‐sediment yields in the Bogachiel River were two times higher than yields in the Calawah River, which is attributed to a combination of modestly higher precipitation, more efficient runoff generation, and more extensive and erodible Quaternary valley fills in the Bogachiel River. Regional shifts in flood hydrology have also influenced suspended‐sediment loads in both watersheds. Our results then document a significant decline in suspended‐sediment concentrations in the Calawah River over the past half century. Reduced land‐cover disturbance provides the simplest and most likely explanation for this decline, though the wide range of possible concentration changes in the Bogachiel River leaves open possibilities that other processes (human, natural, or methodologic) could be a factor.