Distribution of suspended sediment in the coastal sea off the Ganges–Brahmaputra River mouth: observation from TM data

2002 ◽  
Vol 32 (4) ◽  
pp. 307-321 ◽  
Author(s):  
Mohammad Rezwanul Islam ◽  
Syeda Fahliza Begum ◽  
Yasushi Yamaguchi ◽  
Katsuro Ogawa
1994 ◽  
Vol 120 (1-2) ◽  
pp. 41-61 ◽  
Author(s):  
Dilip K. Barua ◽  
Steven A. Kuehl ◽  
Richard L. Miller ◽  
Williard S. Moore

Author(s):  
Nguyen Ngoc Tien ◽  
Dinh Van Uu ◽  
Nguyen Tho Sao ◽  
Do Huy Cuong ◽  
Nguyen Trung Thanh ◽  
...  

2017 ◽  
Vol 18 (8) ◽  
pp. 3003-3015 ◽  
Author(s):  
Takuya Manaka ◽  
Daisuke Araoka ◽  
Toshihiro Yoshimura ◽  
H. M. Zakir Hossain ◽  
Yoshiro Nishio ◽  
...  

2015 ◽  
Vol 17 (1) ◽  
pp. 195-210 ◽  
Author(s):  
Safat Sikder ◽  
Xiaodong Chen ◽  
Faisal Hossain ◽  
Jason B. Roberts ◽  
Franklin Robertson ◽  
...  

Abstract This study asks the question of whether GCMs are ready to be operationalized for streamflow forecasting in South Asian river basins, and if so, at what temporal scales and for which water management decisions are they likely to be relevant? The authors focused on the Ganges, Brahmaputra, and Meghna basins for which there is a gridded hydrologic model calibrated for the 2002–10 period. The North American Multimodel Ensemble (NMME) suite of eight GCM hindcasts was applied to generate precipitation forecasts for each month of the 1982–2012 (30 year) period at up to 6 months of lead time, which were then downscaled according to the bias-corrected statistical downscaling (BCSD) procedure to daily time steps. A global retrospective forcing dataset was used for this downscaling procedure. The study clearly revealed that a regionally consistent forcing for BCSD, which is currently unavailable for the region, is one of the primary conditions to realize reasonable skill in streamflow forecasting. In terms of relative RMSE (normalized by reference flow obtained from the global retrospective forcings used in downscaling), streamflow forecast uncertainty (RMSE) was found to be 38%–50% at monthly scale and 22%–35% at seasonal (3 monthly) scale. The Ganges River (regulated) experienced higher uncertainty than the Brahmaputra River (unregulated). In terms of anomaly correlation coefficient (ACC), the streamflow forecasting at seasonal (3 monthly) scale was found to have less uncertainty (>0.3) than at monthly scale (<0.25). The forecast skill in the Brahmaputra basin showed more improvement when the time horizon was aggregated from monthly to seasonal than the Ganges basin. Finally, the skill assessment for the individual seasons revealed that the flow forecasting using NMME data had less uncertainty during monsoon season (July–September) in the Brahmaputra basin and in postmonsoon season (October–December) in the Ganges basin. Overall, the study indicated that GCMs can have value for management decisions only at seasonal or annual water balance applications at best if appropriate historical forcings are used in downscaling. The take-home message of this study is that GCMs are not yet ready for prime-time operationalization for a wide variety of multiscale water management decisions for the Ganges and Brahmaputra River basins.


2017 ◽  
Vol 122 (12) ◽  
pp. 9591-9604 ◽  
Author(s):  
S. Fournier ◽  
J. Vialard ◽  
M. Lengaigne ◽  
T. Lee ◽  
M. M. Gierach ◽  
...  

2007 ◽  
Vol 27 (6) ◽  
pp. 854-871 ◽  
Author(s):  
Houjie Wang ◽  
Zuosheng Yang ◽  
Yunhai Li ◽  
Zhigang Guo ◽  
Xiaoxia Sun ◽  
...  

Eos ◽  
2015 ◽  
Vol 96 ◽  
Author(s):  
JoAnna Wendel

Researchers know that the Ganges-Brahmaputra river delta is sinking. Now they have an accurate reading on the rate of subsidence.


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