Diurnal variations in discharge and suspended sediment concentration, including runoff-delaying characteristics, of the Gangotri Glacier in the Garhwal Himalayas

2005 ◽  
Vol 19 (7) ◽  
pp. 1445-1457 ◽  
Author(s):  
Pratap Singh ◽  
Umesh K. Haritashya ◽  
K. S. Ramasastri ◽  
Naresh Kumar
2013 ◽  
Vol 45 (2) ◽  
pp. 292-306 ◽  
Author(s):  
Manohar Arora ◽  
Rakesh Kumar ◽  
Naresh Kumar ◽  
Jatin Malhotra

An assessment of suspended sediment concentration (SSC), load, yield and erosion rate has been undertaken for the Gangotri Glacier drainage basin (nearly 50% glaciated) located in the Garhwal Himalayas. Data were collected for four ablation seasons (2008–2011). Mean monthly SSCs, for May, June, July, August and September during the study period was 1,011, 1,384, 1,916, 1,675 and 567 ppm, respectively, indicating highest SSC in July, followed by August. For the entire melt season, the mean daily SSC was computed to be 1,320 ppm. Similar trends were also found for the sediment load and about 67% of the total suspended sediment load of the melt period was transported during the months of July and August. Sediment yield for the study basin was computed to be about 2,863 tonnes km–2yr–1. For the entire ablation period, the erosion from the Gangotri Glacier basin is estimated to be about 1.0 mm. There was a poor relationship between SSC and discharge and hysteresis effect was prominent in the melt stream. The average percentages of clay, silt and sand were found to be 3, 67 and 30%, respectively, which suggest maximum content of silt followed by sand.


2003 ◽  
Vol 34 (3) ◽  
pp. 221-244 ◽  
Author(s):  
Pratap Singh ◽  
K. S. Ramasatri ◽  
Naresh Kumar ◽  
N. K. Bhatnagar

Estimation of sediment load from glacierized basins is very important for planning, designing, installation and operation of hydro-power projects, including management of reservoirs. In the present study, an assessment of suspended sediment concentration, load, yield and erosion rate has been undertaken for the Dokriani Glacier drainage basin located in the Garhwal Himalayas. About 60% of the total drainage area of this basin is glacierized. Data were collected for four ablation seasons (1995-1998). The mean daily suspended sediment concentrations for June, July, August and September were 452, 933, 965 and 275 mg 1-1, respectively, indicating highest suspended sediment concentration in August, followed by July. Similar trends were also found for the sediment load and about 88% of the total suspended sediment load of the melt period was transported during the months of July and August. Sediment yield for the study basin was computed to be about 2,800 t km-2 yr-1, which is comparable with glacierized basins (10-30% glacierized) in the Pamir region. For the entire ablation period, the erosion from the Dokriani Glacier basin is estimated to be about 1.0 mm. There was a poor relationship between suspended sediment concentration and discharge. The average percentages of clay, silt and sand were found to be 1.4, 67.3 and 31.3%, respectively, which suggest maximum content of silt followed by sand. There was limited variation in the content of clay, silt and sand in the suspended sediment during the ablation period.


2013 ◽  
Vol 11 (4) ◽  
pp. 457-466

Artificial neural networks are one of the advanced technologies employed in hydrology modelling. This paper investigates the potential of two algorithm networks, the feed forward backpropagation (BP) and generalized regression neural network (GRNN) in comparison with the classical regression for modelling the event-based suspended sediment concentration at Jiasian diversion weir in Southern Taiwan. For this study, the hourly time series data comprised of water discharge, turbidity and suspended sediment concentration during the storm events in the year of 2002 are taken into account in the models. The statistical performances comparison showed that both BP and GRNN are superior to the classical regression in the weir sediment modelling. Additionally, the turbidity was found to be a dominant input variable over the water discharge for suspended sediment concentration estimation. Statistically, both neural network models can be successfully applied for the event-based suspended sediment concentration modelling in the weir studied herein when few data are available.


2021 ◽  
Vol 180 ◽  
pp. 108107
Author(s):  
Guillaume Fromant ◽  
Nicolas Le Dantec ◽  
Yannick Perrot ◽  
France Floc'h ◽  
Anne Lebourges-Dhaussy ◽  
...  

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