Sediment and nutrient loadings to Lake Ontario: methodological arguments

1977 ◽  
Vol 14 (7) ◽  
pp. 1555-1565 ◽  
Author(s):  
Edwin D. Ongley ◽  
John G. Ralston ◽  
Richard L. Thomas
Keyword(s):  
Suspended Sediment ◽  
Sediment Load ◽  
Lake Ontario ◽  
Environmental Issues ◽  
Surveillance Data ◽  
Nutrient Loads ◽  
Suspended Sediment Load ◽  
Sampling Strategies ◽  
Sediment Loads ◽  
Sediment Data

Environmental issues frequently require values for sediment and nutrient loads in river systems. Except in rare instances, such values must be drawn from existing surveillance data which may not reflect sampling strategies pertinent to the issue at hand. In particular, the time variant and nonlinear relationships between discharge and both suspended sediment and sediment-related parameters such as phosphorus cannot be captured in detail by surveillance data, Nevertheless, such data collected routinely by the Ontario Ministry of the Environment accurately reflect the relative loadings both in terms of unit and total yields in five basins where detailed suspended sediment data are available. Although surveillance data underestimate the absolute values of suspended sediment loads calculated from detailed sediment information, these data do capture the relative monthly and seasonal contributions to annual suspended sediment load. A comparison of three methods for calculating annual unit and total loadings of suspended and solute components from surveillance information indicates that annual mean data convey information which is not significantly less adequate than monthly aggregations of data.

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

2021 ◽  
Vol 11 (18) ◽  
pp. 8290
Author(s):  
Muhammad Adnan Khan ◽  
Jürgen Stamm ◽  
Sajjad Haider
Keyword(s):  
Suspended Sediment ◽  
Soft Computing ◽  
Sediment Load ◽  
Local Linear Regression ◽  
Data Series ◽  
Suspended Sediment Load ◽  
Dynamic Features ◽  
Sediment Loads ◽  
Soft Computing Techniques ◽  
Non Linear

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.

scholarly journals Copula-Based Infilling Methods for Daily Suspended Sediment Loads

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1701
Author(s):  
Jenq-Tzong Shiau ◽  
Yu-Cheng Lien
Keyword(s):  
Suspended Sediment ◽  
Conditional Distribution ◽  
Sediment Load ◽  
Distribution Model ◽  
Rating Curve ◽  
Similar Frequency ◽  
Sediment Rating Curve ◽  
Sediment Loads ◽  
Recorded Data ◽  
Sediment Data

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.

scholarly journals Derivation of suspended sediment data for Al-Adhiam watershed-Iraq using artificial neural network model

2018 ◽  
Vol 162 ◽  
pp. 03014
Author(s):  
Mahmoud Saleh Al-Khafaji ◽  
Mustafa Al-Mukhtar ◽  
Ahmed Saud Mohena
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Suspended Sediment Load ◽  
Data Sampling ◽  
Ann Model ◽  
Measured Flow ◽  
Sediment Data ◽  
Target Data

The mean part of river sediments is suspended sediment load, its prediction and simulation has important significance to manage the water resources and environments. In Iraq, most researchers avoid to fighting in sediment researches when related with hydrological models spatially with that need enough observed sediment data for calibration and validation because the sediment data very limitation or scars. The aim of this study is employing the Artificial Neural Network (ANN) model to estimate the suspended sediment load of Al-Adhaim watershed in Iraq from available measured sediment data, identify the suitable pattern of input and target data sampling and obtaining the best nonlinear equation between the river discharge and suspended sediment load. To this end, the ANN model was training and tested with the available sediment data, which was for water year (1983-1984). Two modes were applied for input and target data sampling each mode has two cases, where in the first mode the time series data sampling was used with flow as an input for case one while flow and average precipitation in case two with used suspended sediment as a target variable. For second mode the supervise data sampling was used with the same input and target division in first mode. The performance of the model was evaluated by using Coefficient of determination (R2) and the Nash- Sutcliffe efficiency (NS) and standardization of root mean square error (RSR), the statistical analysis model testing for Al-Adhiam watershed showed satisfactory agreement between observed and estimated daily values for Mode2- Case2. R2, NS and RSR of the testing period were 0.99 and 0.8and 0.2 respectively. The result shows that the conducted ANN model can be used with the best net as a predictor for sediment yield in this watershed. The model was used to predict daily sediment load data for period from 1Oct. 1984 to 31Spt 1985. The predicted daily sediment data was plotted against daily measured flow. The correlation between predicted sediment and measured flow was in good agreement with R2 =0.89 and the best relation was polynomial equation from second degree.

scholarly journals Suspended sediment load in the tidal zone of an Indonesian river

2012 ◽  
Vol 16 (11) ◽  
pp. 4191-4204 ◽  
Author(s):  
F. A. Buschman ◽  
A. J. F. Hoitink ◽  
S. M. de Jong ◽  
P. Hoekstra ◽  
H. Hidayat ◽  
...  
Keyword(s):  
Suspended Sediment ◽  
Soil Loss ◽  
River Flow ◽  
Sediment Load ◽  
Open Pit ◽  
Open Pit Mining ◽  
Suspended Sediment Load ◽  
Survey Period ◽  
Tidal Zone ◽  
Sediment Loads

Abstract. Forest clearing for reasons of timber production, open pit mining and the establishment of oil palm plantations generally results in excessively high sediment loads in tropical rivers. The increasing sediment loads pose a threat to coastal marine ecosystems, such as coral reefs. This study presents observations of suspended sediment loads in the Berau River (Kalimantan, Indonesia), which debouches into a coastal ocean that is a preeminent center of coral diversity. The Berau River is relatively small and drains a mountainous, still relatively pristine basin that receives abundant rainfall. In the tidal zone of the Berau River, flow velocity was measured over a large part of the river width using a horizontal acoustic Doppler current profiler (HADCP). Surrogate measurements of suspended sediment concentration were taken with an optical backscatter sensor (OBS). Averaged over the 6.5 weeks covered by the benchmark survey period, the suspended sediment load was estimated at 2 Mt yr−1. Based on rainfall-runoff modeling though, the river discharge peak during the survey was supposed to be moderate and the yearly averaged suspended sediment load is most likely somewhat higher than 2 Mt yr−1. The consequences of ongoing clearing of rainforest were explored using a plot-scale erosion model. When rainforest, which still covered 50–60% of the basin in 2007, is converted to production land, soil loss is expected to increase with a factor between 10 and 100. If this soil loss is transported seaward as suspended sediment, the increase in suspended sediment load in the Berau River would impose a severe stress on this global hotspot of coral reef diversity.

scholarly journals Land-Use/-Cover Changes and Their Effect on Soil Erosion and River Suspended Sediment Load in Different Landscape Zones of European Russia during 1970–2017

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1631
Author(s):  
Artyom V. Gusarov
Keyword(s):  
Soviet Union ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Gully Erosion ◽  
European Russia ◽  
Relative Reduction ◽  
Forest Steppe ◽  
Suspended Sediment Load ◽  
Cultivated Land ◽  
Study Region

Contemporary trends in cultivated land and their influence on soil/gully erosion and river suspended sediment load were analyzed by various landscape zones within the most populated and agriculturally developed part of European Russia, covering 2,222,390 km2. Based on official statistics from the Russian Federation and the former Soviet Union, this study showed that after the collapse of the Soviet Union in 1991, there was a steady downward trend in cultivated land throughout the study region. From 1970–1987 to 2005–2017, the region lost about 39% of its croplands. Moreover, the most significant relative reduction in cultivated land was noted in the forest zone (south taiga, mixed and broadleaf forests) and the dry steppes and the semi-desert of the Caspian Lowland—about 53% and 65%, respectively. These territories are with climatically risky agriculture and less fertile soils. There was also a widespread reduction in agricultural machinery on croplands and livestock on pastures of the region. A decrease in soil/gully erosion rates over the past decades was also revealed based on state hydrological monitoring data on river suspended sediment load as one of the indicators of the temporal variability of erosion intensity in river basins and the published results of some field research in various parts of the studied landscape zones. The most significant reduction in the intensity of erosion and the load of river suspended sediment was found in European Russia’s forest-steppe zone. This was presumably due to a favorable combination of the above changes in land cover/use and climate change.

scholarly journals Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models

2021 ◽  
Author(s):  
Hamid Darabi ◽  
Sedigheh Mohamadi ◽  
Zahra Karimidastenaei ◽  
Ozgur Kisi ◽  
Mohammad Ehteram ◽  
...  
Keyword(s):  
Suspended Sediment ◽  
Soft Computing ◽  
Sediment Load ◽  
Optimization Algorithms ◽  
Training Phase ◽  
Suspended Sediment Load ◽  
Engineering Structures ◽  
Northern Iran ◽  
Taylor Diagram ◽  
Computing Models

AbstractAccurate modeling and prediction of suspended sediment load (SSL) in rivers have an important role in environmental science and design of engineering structures and are vital for watershed management. Since different parameters such as rainfall, temperature, and discharge with the different lag times have significant effects on the SSL, quantifying and understanding nonlinear interactions of the sediment dynamics has always been a challenge. In this study, three soft computing models (multilayer perceptron (MLP), adaptive neuro-fuzzy system (ANFIS), and radial basis function neural network (RBFNN)) were used to predict daily SSL. Four optimization algorithms (sine–cosine algorithm (SCA), particle swarm optimization (PSO), firefly algorithm (FFA), and bat algorithm (BA)) were used to improve the capability of SSL prediction of the models. Data from gauging stations at the mouth of the Kasilian and Talar rivers in northern Iran were used in the analysis. The selection of input combinations for the models was based on principal component analysis (PCA). Uncertainty in sequential uncertainty fitting (SUFI-2) and performance indicators were used to assess the potential of models. Taylor diagrams were used to visualize the match between model output and observed values. Assessment of daily SSL predictions for Talar station revealed that ANFIS-SCA yielded the best results (RMSE (root mean square error): 934.2 ton/day, MAE (mean absolute error): 912.2 ton/day, NSE (Nash–Sutcliffe efficiency): 0.93, PBIAS: 0.12). ANFIS-SCA also yielded the best results for Kasilian station (RMSE: 1412.10 ton/day, MAE: 1403.4 ton/day, NSE: 0.92, PBIAS: 0.14). The Taylor diagram confirmed that ANFIS-SCA achieved the best match between observed and predicted values for various hydraulic and hydrological parameters at both Talar and Kasilian stations. Further, the models were tested in Eagel Creek Basin, Indiana state, USA. The results indicated that the ANFIS-SCA model reduced RMSE by 15% and 21% compared to the MLP-SCA and RBFNN-SCA models in the training phase. Comparing models performance indicated that the ANFIS-SCA model could decrease MAE error compared to ANFIS-BA, ANFIS-PSO, ANFIS-FFA, and ANFIS models by 18%, 32%, 37%, and 49% in the training phase, respectively. The results indicated that the integration of optimization algorithms and soft computing models can improve the ability of models for predicting SSL. Additionally, the hybridization of soft computing models with optimization algorithms can decrease the uncertainty of models.

Sign in / Sign up

Export Citation Format

Share Document