Long-term seasonal trends of nitrogen, phosphorus, and suspended sediment load from the non-tidal Susquehanna River Basin to Chesapeake Bay

2013 ◽  
Vol 452-453 ◽  
pp. 208-221 ◽  
Author(s):  
Q. Zhang ◽  
D.C. Brady ◽  
W.P. Ball
Keyword(s):  
Chesapeake Bay ◽  
Suspended Sediment ◽  
River Basin ◽  
Sediment Load ◽  
Suspended Sediment Load ◽  
Seasonal Trends ◽  
Susquehanna River ◽  
Susquehanna River Basin ◽  
Nitrogen Phosphorus

scholarly journals Dynamics of suspended sediment load in the upper part of the Rasina River Basin in 2010

2013 ◽  
Vol 93 (4) ◽  
pp. 23-40
Author(s):  
Sanja Mustafic ◽  
Predrag Manojlovic ◽  
Predrag Kostic
Keyword(s):  
Suspended Sediment ◽  
River Basin ◽  
Sediment Load ◽  
Suspended Sediment Load ◽  
Spring Season ◽  
Autumn Season ◽  
Specific Transport ◽  
Suspended Sediment Concentrations ◽  
Load Transport

The paper treats the issue of the suspended sediment load transport in the upper part of the Rasina River Basin, upstream from the "Celije" reservoir during the year of 2010. Measurements of the suspended sediment concentrations were being done at two hydrological profiles Brus and Ravni. Total quantity of the suspended sediment load that was transported at the profile of Brus in 2010 amounted to 3,437.3 t, which gave the specific transport of 16.4 t/km2/year. At the downstream profile of Ravni, 43,165 t of the suspended sediment load was transported, that is, 95.7 t/km2/year. The basin on the whole is characterized by the existence of two seasons, which by their characteristics in the load transport represent the extreme variants. During the winter-spring season, 74-85.8 % of the total annual load was transported, ?nd during the summer-autumn season between 14.2 and 26 %.

scholarly journals Artificial Neural Network Model for Estimation of Suspended Sediment Load in Krishna River Basin, India

2020 ◽  
Vol 9 (3) ◽  
pp. 3701-3705
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Suspended Sediment ◽  
River Basin ◽  
Flood Control ◽  
Sediment Load ◽  
Water Discharge ◽  
Suspended Sediment Load ◽  
Artificial Neural

The correct assessment of amount of sediment during design, management and operation of water resources projects is very important. Efficiency of dam has been reduced due to sedimentation which is built for flood control, irrigation, power generation etc. There are traditional methods for the estimation of sediment are available but these cannot provide the accurate results because of involvement of very complex variables and processes. One of the best suitable artificial intelligence technique for modeling this phenomenon is artificial neural network (ANN). In the current study ANN techniques used for simulation monthly suspended sediment load at Vijayawada gauging station in Krishna river basin, Andhra Pradesh, India. Trial & error method were used during the optimization of parameters that are involved in this model. Estimation of suspended sediment load (SSL) is done using water discharge and water level data as inputs. The water discharge, water level and sediment load is collected from January 1966 to December 2005. This approach is used for modelled the SSL. By considering the results, ANN has the satisfactory performance and more accurate results in the simulation of monthly SSL for the study location.

scholarly journals Contemporary Trends in River Flow, Suspended Sediment Load, and Soil/Gully Erosion in the South of the Boreal Forest Zone of European Russia: The Vyatka River Basin

Water ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 2567
Author(s):  
Artyom V. Gusarov ◽  
Aidar G. Sharifullin ◽  
Achim A. Beylich
Keyword(s):  
Land Use ◽  
Suspended Sediment ◽  
River Basin ◽  
Sediment Load ◽  
Water Discharge ◽  
East European Plain ◽  
Suspended Sediment Load ◽  
Forest Zone ◽  
East European ◽  
Erosion Intensity

Recent decades in the north of the East European Plain have been characterized by significant changes in climate and land use/cover, especially after the collapse of the USSR in 1991. At the same time, the hydrological consequences of these changes, especially changes in erosion processes and river sediment load, have been studied insufficiently. This paper partially covers this existing knowledge gap using the example of the Vyatka River basin. Draining an area of 129,000 km2, the Vyatka River is among the largest rivers in the boreal forest zone of European Russia. Cultivated land occupies about one-fifth of the river basin area; about three-fourths is covered by taiga forest vegetation. The results of state long-term hydrometeorological monitoring and information on land use/cover made it possible to reveal contemporary (since the 1960s) hydrological and erosion-intensity trends and their drivers within the greater (96%) part of the river basin. There has been a statistically insignificant increase in water discharge in the Vyatka River basin during recent decades. This is due to a statistically insignificant increase (for the entire basin studied) in the spring snowmelt-induced floodwater flow and a statistically significant rise in the discharge in the year’s warm and cold seasons. The main reason for the detected trends is increased precipitation, including heavy rainfall during the warm season. In contrast to this, the total annual suspended sediment load of the river (especially that which was snowmelt-induced) and, consequently, soil/gully erosion intensity have experienced a significant decrease in recent decades (up to 58% between 1960–1980 and 2010–2018). Land-use/-cover changes (a reduction of cultivated land area and agricultural machinery, a decline of livestock in pastures) following the collapse of the Soviet Union are considered the main reasons for this decrease. The most noticeable changes in water discharge, suspended sediment load, and erosion intensity were observed in the most agriculturally developed southwest and south parts of the Vyatka River basin. All the above trends may be considered with a high probability to be representative for the south sector of the taiga zone of the East European Plain.

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