water resources modeling of the ganges-brahmaputra-meghna river basins using satellite remote...

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INTERUNIVERSITY PROGRAMME INWATER 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

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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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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.



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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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Case study Area



• 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.

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Case study Area



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Case study Area(sub-catchments)



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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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Ganges basin Budyko curve



Rainfall , P (mm/month)

AET (mm/month)

PET(mm/month)

AET/P PET/P

94.4 83.2 117.92 0.88 1.25

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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.



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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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Model descriptions



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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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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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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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Calibration & Validation



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

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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.


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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.


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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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REFERENCES1. 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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THANK YOU FOR YOUR ATTENTION



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Data



water resources modeling of the ganges-brahmaputra-meghna river basins using satellite remote...

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