WATER RESOURCES MODELING OF THE GANGES-BRAHMAPUTRA-MEGHNA RIVER BASINS USING SATELLITE REMOTE SENSING DATA

1. 1
INTERUNIVERSITY PROGRAMME IN
WATER RESOURCES ENGINEERING
Surface Water Hydrology
WATER RESOURCES MODELING OF THE GANGES-
BRAHMAPUTRA-MEGHNA
RIVER BASINS USING SATELLITE REMOTE SENSING
DATA
Presented by:
Md Moudud Hasan
&
Farida Yasmin Ruma

2. Contents
• Introduction
• Objectives
• Case Study Area
• Mass Balance Analysis Ganges Basin
• Ganges Basin Budyko Curve
• Model Descriptions
• Data For Model Set Up
• Data Sources
• Calibration & Validation
• Conclusion
• References
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3. Introduction
• The Ganges, Brahmaputra, and Meghna (GBM) rivers
originate in the Himalayan and Vindhya ranges, flow through
China, Bhutan, Nepal, India, and Bangladesh and ultimately
join the Bay of Bengal.
• About 92-93% of the surface water flowing through
Bangladesh is generated from the upstream regions of GBM
that are transboundary to the country.
• Hence, the study and understanding of cross-border hydro-
meteorological processes, interactions, and interventions, is
very important for comprehensive water resources
management, planning, impact assessment, and flood
forecasting in Bangladesh. 3
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4. Objectives
• To set up a water resources management model over the
GBM basins.
• The model is set up with the objective of providing
Bangladesh a framework for assessing proposed water
diversion and redistribution scenarios in the upstream
transboundary regions and derive quantitative impacts on
water availability based on ‘‘what-if’’ scenarios.
•
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5. Case study Area
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• Area: 1.75 million km2
• Average annual runoff : 1,200 km3
• Subdivided into 148 sub-catchments with area
ranging from 415 to 96,000 km2;
• 109 of these catchments are within the
Ganges basin, 34 in the Brahmaputra basin,
and 5 in the Meghna basin.

6. Case study Area
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7. Case study Area
(sub-catchments)
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8. Mass balance analysis Ganges basin
Rainfall
(mm/month)
Runoff
(mm/month)
AET
(mm/month)
Excess
(mm/month)
94.4 4.75 83.2 6.45
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9. Ganges basin Budyko curve
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Rainfall , P
(mm/month)
AET
(mm/month)
PET
(mm/month)
AET/P PET/P
94.4 83.2 117.92 0.88 1.25

10. Model descriptions
• MIKE BASIN software was used as the primary hydrologic model for
simulating the hydrologic processes of the GBM basins.
• It is a simulation model that can represent the hydrology of the
basin in space and time.
• Technically, it is a network model in which the rivers and their main
tributaries are represented by a network of branches and nodes.
• For addressing water allocation, conjunctive use, reservoir
operation, MIKE BASIN couples the power of a GIS tool in a
comprehensive hydrologic modeling framework to provide basin-
scale assessment.
• As a simple water resource model, MIKE BASIN does not require
detailed specification of river profiles. 10
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11. Model descriptions
• MIKE BASIN uses the Nedbør-Afstrømnings-Model (NAM
hydrological model) to simulate the rainfall-runoff processes
occurring at the catchment scale.
• In the GBM model, Muskingum and Wave Translation routing
methods for different reaches.
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12. Model descriptions
•
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13. DATA FOR MODEL SET UP
• Topography and Land Level Data
• Hydro-Meteorological Data
• Rainfall,
• Potential evapotranspiration (ET), and
• Temperature (for snowmelt considerations).
• Snowmelt and Glacier Data From Himalayas
• Discharge data
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14. DATA SOURCES
• Meteorological data inside Bangladesh territory:
– Bangladesh Meteorological Department (BMD) and
Bangladesh Water Development Board (BWDB).
• Meteorological data from upper riparian countries:
– Rainfall and climate data derived from meteorological
satellites and other secondary sources.
• GIS-based tools have been used to extract and process the
data to estimate average rainfall, ET, and temperature for
each sub-basin.
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15. Calibration & Validation
• Discharge data was used for calibration of
the GBM model.
• The final parameter was calibrated against
time series of hydrological observations at
– Hardinge Bridge on the Ganges,
– Bahadurabad on the Brahmaputra, and
– Amalshid on Barak, a tributary of the Meghna
for the period 2005- 2006.
• The basin model has been validated for 2007
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16. Calibration & Validation
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During the July to
September period,
simulated monthly
flow volume differs
from actual by
(-) 8% to (+) 20% in
the Ganges basin.
The correlation coefficient is 0.79

17. Calibration & Validation
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During the July to
September period,
simulated monthly
flow volume differs
from actual by
(-) 15% to (+) 12% in
the Brahmaputra
basin.
The correlation coefficient is 0.90

18. Calibration & Validation
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During the July to
September period,
simulated monthly
flow volume differs
from actual by
(-) 15% to (+) 19% in
the Meghna basin.
The correlation coefficient is 0.71

19. Conclusion
• It is possible to calibrate a large-scale water resources model
to a satisfactory level using an array of satellite remote
sensing data.
• The model simulations are a function of rainfall only.
• Upstream water usage or diversions have not been included
in the model due to lack of data and information.
• To develop a more consistent and accurate GBM model and
improve the representation of the physical phenomenon of
the basin, discharge data of some stations situated in
upstream areas within the GBM basins are essential.
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20. REFERENCES
1. Nishat, Bushra and S.M. Mahbubur Rahman, 2009. Water Resources Modeling of
the Ganges-BrahmaputraMeghna River Basins Using Satellite Remote Sensing
Data. Journal of the American Water Resources Association (JAWRA) 45(6):1313-
1327. DOI: 10.1111 ⁄ j.1752-1688.2009.00374.x
2. Syed, T. H., P. J. Webster, and J. S. Famiglietti (2014), Assessing variability of
evapotranspiration over the Ganga river basin using water balance computations,
Water Resour. Res., 50, 2551–2565, doi:10.1002/2013WR013518.
3. Mirza, M. Monirul Qader, R. A. Warrick, and N. J. Ericksen. 2003. “The Implications
of Climate Change on Floods of the Ganges, Brahmaputra and Meghna Rivers in
Bangladesh.” Climatic Change 57 (3): 287–318.
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22. Data
•
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