Natural and anthropogenic long-term variations of water runoff and suspended sediment load in the Huanghe river

Numerical hydro-morphodynamic models can simulate the impact of future changes in climate and land cover on river channel dynamics. Accurate predictions of the hydro-morphological changes within river channels require a realistic representation of controlling factors and boundary conditions (BC), such as the sediment load. This is, in particular, true where simulations are run over longer timescales and when sparse data on sediment load is available. Using sediment rating curves to reconstruct the missing sediment load data can lead to poor estimates of temporal variations in sediment load, and hence, erroneous predictions of channel morphodynamics. Furthermore, when simulating channel morphological changes at longer timescales, this comes at a high computational cost making it impossible to run various scenarios of changing boundary conditions to long river reaches with sufficient spatial detail. &#160;Here, we apply different methods (morphological factors (MFs) and wavelet transform (WT)) to overcome these problems and to arrive at faster and more accurate predictions of long-term morphodynamic simulations.&#160;We modelled river channel bed level changes of the River Dijle (central Belgium) from 1969 to 1999. Detailed cross-sectional surveys every 20 to 25 m along the river axis were collected in 1969, 1999 and 2018. Since 1969, the river has been incised by about 2 m most probably as a response to land-use/land-cover changes and subsequent changes in discharge and sediment load. &#160;Daily discharge and water level measurements are available for the entire period; however, daily suspended sediment load was only collected between 1998 and 2000. Therefore, WTs were coupled with artificial neural networks (WT-ANN) to calculate long-term sediment load BCs (1969-1999) from the short-term collected suspended sediment concentration samples. Sediment load predictions with sediment rating curves only obtain an R2 of 0.115, whereas WT-ANN predictions of suspended sediment load data show an R2 of 0.902.&#160;Using MFs the reference hydrograph was condensed with a factor of 10 and 20. WT is a mathematical tool that can convert time-domain signals into time-frequency domain signals by passing through low and high-level filters. Passing sediment load time series through these filters create another synthetic BCs containing the frequential and spatial information with half the original signal's temporal length. Thus we also compare the modelling performance using WT generated synthetic BCs with MFs. Similarly, 36x1 to 36x10 processors of an HPC was used to simulate 16 km river reach containing 3,33,305 mesh nodes (with 1.5 m mesh resolution). &#160;Interestingly, with a significant reduction in computational cost, there was a mild difference (R2=0.802 using MFs 10 and R2=0.763 using MFs 20) in model performance without using MFs during initial trials. Surprisingly, generating a synthetic time series using WT did not perform well. Therefore, hydrograph compression using MFs is found the best option to reduce the computational cost, significantly. Although the computational time reduced from 30 days to only 3 days using MFs and more precise BCs calibrated model with R2=0.70, WT poor performance needs to be still investigated.

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Sub-Bankfull Flow Frequency versus Magnitude of Flood Events in Outlining Effective Discharges. Case Study: Trotuș River (Romania)

10.20944/preprints201809.0053.v1 ◽

2018 ◽

Author(s):

Dan Dumitriu

Keyword(s):

Suspended Sediment ◽

Sediment Load ◽

Temporal Variations ◽

Data Series ◽

Suspended Sediment Transport ◽

Suspended Sediment Load ◽

Flood Events ◽

Effective Discharge ◽

Bankfull Discharge

Effective discharge, which represents the flow, or range of flows, that transport the most sediment over long term, was determined based on the mean daily flow discharge and mean daily suspended sediment discharge recorded between 1994 and 2014 at four gauging stations along the Trotuș River. This study proposes an efficient method for the estimation of effective discharge based on observed values of the suspended sediment load. By employing this method the suspended sediment load is no longer either under- or overestimated as in the cases when the assessment is based on sediment rating curves. The assessment on effective discharge was performed at two distinct levels: for the entire data series during the investigated time spans and, subsequently, for flows less than the bankfull discharge. The effectiveness curves of the suspended sediment transport characteristics revealed highly multimodal characteristics with many peaks, indicating ample ranges for the effective discharges. The main effective discharge corresponded to large flood events, which are typical for the upper end of the discharge range, whereas the secondary effective discharges corresponded to sub-bankfull flows, which are more frequent. The changes that occurred in the channel bed are reflected by the temporal variations in the effective discharge.

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SIMULATION OF LONG-TERM AVERAGE STREAMFLOW AND NITROGEN, PHOSPHORUS AND SUSPENDED SEDIMENT LOAD IN THE CONTERMINOUS UNITED STATES USING REGIONAL SPARROW MODELS

10.1130/abs/2020nc-347661 ◽

2020 ◽

Author(s):

David A. Saad ◽

Keyword(s):

United States ◽

Suspended Sediment ◽

Sediment Load ◽

Suspended Sediment Load ◽

Nitrogen Phosphorus

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Sub-Bankfull Flow Frequency versus Magnitude of Flood Events in Outlining Effective Discharges. Case Study: Trotuș River (Romania)

Water ◽

10.3390/w10101292 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1292 ◽

Cited By ~ 2

Author(s):

Dan Dumitriu

Keyword(s):

Suspended Sediment ◽

Sediment Load ◽

Temporal Variations ◽

Data Series ◽

Suspended Sediment Transport ◽

Suspended Sediment Load ◽

Flood Events ◽

Effective Discharge ◽

Bankfull Discharge

Effective discharge, which represents the flow, or range of flows, that transport the most sediment over the long-term, was determined based on the mean daily flow discharge and mean daily suspended sediment discharge recorded between 1994 and 2014 at four gauging stations along the Trotuș River. This study proposes an efficient method for the estimation of effective discharge based on observed values of the suspended sediment load. By employing this method the suspended sediment load is no longer either under- or overestimated as in the cases when the assessment is based on sediment rating curves. The assessment on effective discharge was performed at two distinct levels: for the entire data series during the investigated time spans and, subsequently, for flows less than the bankfull discharge. The effectiveness curves of the suspended sediment transport characteristics revealed highly multimodal characteristics with many peaks, indicating ample ranges for the effective discharges. The main effective discharge corresponded to large flood events, which are typical for the upper end of the discharge range, whereas the secondary effective discharges corresponded to sub-bankfull flows, which are more frequent. The changes that occurred in the channel bed are reflected by the temporal variations in the effective discharge.

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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 ◽

10.3390/w13121631 ◽

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.

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

Soft Computing ◽

10.1007/s00500-021-05721-5 ◽

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.

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