How did the suspended sediment load change in the North Caucasus during the Anthropocene?

2021 ◽  
Author(s):  
Anatoly Tsyplenkov ◽  
Valentin Golosov ◽  
Pelagiya Belyakova
Keyword(s):  
Suspended Sediment ◽  
Sediment Load ◽  
Suspended Sediment Load ◽  
North Caucasus ◽  
The North ◽  
Load Change

scholarly journals Contemporary Long-Term Trends in Water Discharge, Suspended Sediment Load, and Erosion Intensity in River Basins of the North Caucasus Region, SW Russia

Hydrology ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 28
Author(s):  
Artyom V. Gusarov ◽  
Aidar G. Sharifullin ◽  
Mikhail A. Komissarov
Keyword(s):  
Suspended Sediment ◽  
Sediment Load ◽  
Water Discharge ◽  
River Basins ◽  
Suspended Sediment Load ◽  
North Caucasus ◽  
Load Pressure ◽  
Study Region ◽  
Caucasus Region ◽  
The North

For the first time, contemporary trends in water discharge, suspended sediment load, and the intensity of overall erosion in the river basins of the North Caucasus region, as one of Russia’s most agriculturally developed geographic areas, were identified. The study was carried out using monitoring data of the Federal Service for Hydrometeorology and Environmental Monitoring of the country for 21 rivers by comparing two periods: 1963–1980 and 2008–2017. According to the study’s results, trends of an increase in the mean annual water discharge (by 2–97%) and the essential reduction in its intra-annual variability have been found in most of the studied rivers. On the contrary, the trends of reduction in annual suspended sediment load and the intensity of erosion in the river basins were identified in most of the study region. Their most essential and statistically significant decreases (by 47–94%) were recorded within the Stavropol Upland, which several decades ago was considered one of the most erosion-dangerous territories of the entire country, as well as in some river basins of the central part of the Greater Caucasus’s northern slope (by 17–94%). The changes in climate (reducing the depth of soil freezing and meltwater runoff on the soil) and land use/cover (reduction of acreage and load (pressure) of agricultural machinery on the soil, reducing livestock on pastures, and the transfer of water from the neighboring, more full-flowing rivers) are considered the leading causes of the aforementioned trends. The findings will contribute to solving some economic and environmental problems of both the region and adjacent territories and water areas.

scholarly journals How does the suspended sediment yield change in the North Caucasus during the Anthropocene?

2021 ◽  
Author(s):  
Anatoly Tsyplenkov ◽  
Valentin Golosov
Keyword(s):  
Suspended Sediment ◽  
Sediment Yield ◽  
Sediment Load ◽  
Kendall Test ◽  
River Basins ◽  
Anthropogenic Pressure ◽  
Suspended Sediment Load ◽  
North Caucasus ◽  
The North ◽  
Mountain Part

<p>Processes linked to climate change and intensified anthropogenic pressure influence the environment, the hydrology and by extent the denudation processes in the Caucasus mountain belt. Quantitative assessments of sediment fluxes and their temporal evolution in this mountain region required for various environmental and engineering purposes, including the planning and maintenance of water reservoirs and other structures. This paper presents an analysis of the suspended sediment load data from almost 40 gauging stations located in the mountain part of the Terek river basin (North Caucasus, Russia). The collected dataset include river basins with various glacier cover (0%-20%) and human impact. All river basins show consistent decreases in mean annual suspended sediment load (SSL, kg/s) up to 1–2% per year during 1925–2018 (according to Mann-Kendall test). The cumulative deviation curve of the mean annual SSL for the last 60 years indicates that SSL has increased significantly from ca. 1970-1980 to 1990-2000 for the most North Caucasus rivers. However, after the 2000s mean annual values of the SSL show a stable decrease in all observed rivers. Possible mechanisms of observed changes are discussed. This study provides the data on climate-related changes in the sediment yield for a previously not investigated region.</p>

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.

Enzyme activities in the Delaware Estuary affected by elevated suspended sediment load

2009 ◽  
Vol 84 (2) ◽  
pp. 253-258 ◽  
Author(s):  
K. Ziervogel ◽  
C. Arnosti
Keyword(s):  
Suspended Sediment ◽  
Enzyme Activities ◽  
Sediment Load ◽  
Suspended Sediment Load ◽  
Delaware Estuary

A comparison of various machine learning approaches performance for prediction suspended sediment load of river systems: a case study in Malaysia

2021 ◽  
Author(s):  
Marwah Sattar Hanoon ◽  
Alharazi Abdulhadi Abdullatif B ◽  
Ali Najah Ahmed ◽  
Arif Razzaq ◽  
Ahmed H. Birima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Suspended Sediment ◽  
Sediment Load ◽  
Learning Approaches ◽  
Suspended Sediment Load ◽  
River Systems

Modeling daily suspended sediment load using improved support vector machine model and genetic algorithm

2018 ◽  
Vol 25 (35) ◽  
pp. 35693-35706 ◽  
Author(s):  
Mitra Rahgoshay ◽  
Sadat Feiznia ◽  
Mehran Arian ◽  
Seyed Ali Asghar Hashemi
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Suspended Sediment ◽  
Support Vector Machine Model ◽  
Sediment Load ◽  
Support Vector ◽  
Suspended Sediment Load ◽  
Machine Model ◽  
Improved Support Vector Machine
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