Climatic Change

, Volume 145, Issue 1–2, pp 159–175 | Cite as

Extreme flows and water availability of the Brahmaputra River under 1.5 and 2 °C global warming scenarios

  • Khaled Mohammed
  • Akm Saiful IslamEmail author
  • GM Tarekul Islam
  • Lorenzo Alfieri
  • Sujit Kumar Bala
  • Md. Jamal Uddin Khan


The recently reached Paris Agreement at the 21st Conference of the Parties (COP21) of the United Nations Framework Convention on Climate Change (UNFCCC) in 2015 includes a goal of pursuing efforts to limit the global warming at 1.5 °C. Following this, the Intergovernmental Panel on Climate Change (IPCC) has accepted an invitation by the UNFCCC to create a special report in 2018 which will include the impacts of 1.5 °C global warming on various Earth systems. It is therefore a priority now for the scientific community to quantify these impacts at regional scales. As a contribution to this effort, this study assesses the impacts of 1.5 and 2 °C global warming on the extreme flows and water availability of the Brahmaputra River, which is both an essential source of freshwater for its lowermost-riparian Bangladesh and also an unavoidable source of disastrous floods. Future flows are simulated with the Soil and Water Assessment Tool (SWAT) and bias-corrected weather data of an ensemble of 11 climate projections from the Coordinated Regional Climate Downscaling Experiment (CORDEX). Results indicate that floods will be more frequent and flood magnitudes greater at 2 °C specific warming level (SWL) than at 1.5 °C SWL. On the contrary, low flows are expected to be less frequent and low flow values to be higher at 2 °C SWL than at 1.5 °C SWL. Water availability will likely be greater at 2 °C SWL than at 1.5 °C SWL from January to August. For the remaining months, water availability will likely be greater at 1.5 °C SWL rather than at 2 °C SWL.


Brahmaputra River Bangladesh Climate change 1.5 °C Extreme flows SWAT model 



The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement no. 603864 (HELIX: High-End cLimate Impacts and eXtremes;

Supplementary material

10584_2017_2073_MOESM1_ESM.docx (602 kb)
Supplementary Figure 1 (DOCX 602 kb)


  1. Abbaspour KC (2015) SWAT-CUP: SWAT calibration and uncertainty programs – a user manual. Swiss Federal Institute of Aquatic Science and Technology, DübendorfGoogle Scholar
  2. Abbaspour KC, Rouholahnejad E, Vaghefi S et al (2015) A continental-scale hydrology and water quality model for Europe: calibration and uncertainty of a high-resolution large-scale SWAT model. J Hydrol 524:733–752CrossRefGoogle Scholar
  3. Alam S, Ali MM, Islam ZI (2016) Future streamflow of Brahmaputra River basin under synthetic climate change scenarios. J Hydrol Eng.
  4. Arino O, Bicheron P, Achard F et al (2008) GlobCover. The most detailed portrait of Earth. ESA Bull 136:25–31Google Scholar
  5. Arnell NW, Brown S, Gosling SN et al (2016) The impacts of climate change across the globe: a multi-sectoral assessment. Clim Chang 134:457–474CrossRefGoogle Scholar
  6. Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment Part I: Model development. J Am Water Resour Assoc 34(1):73–89CrossRefGoogle Scholar
  7. FAO (1974) FAO-UNESCO soil map of the world, 1:5,000,000. UNESCO, ParisGoogle Scholar
  8. Friedlingstein P, Andrew RM, Rogelj J et al (2014) Persistent growth of CO2 emissions and implications for reaching climate targets. Nat Geosci 7:709–714CrossRefGoogle Scholar
  9. Gain AK, Immerzeel WW, Weiland FCS, Bierkens MFP (2011) Impact of climate change on the stream flow of the lower Brahmaputra: trends in high and low flows based on discharge-weighted ensemble modelling. Hydrol Earth Syst Sci 15:1537–1545CrossRefGoogle Scholar
  10. Gassman PW, Sadeghi AM, Srinivasan R (2014) Applications of the SWAT model special section: overview and insights. J Environ Qual 43:1–8CrossRefGoogle Scholar
  11. Grillakis MG, Koutroulis AG, Tsanis IK (2013) Multisegment statistical bias correction of daily GCM precipitation output. J Geophys Res Atmos 118(8):3150–3162CrossRefGoogle Scholar
  12. Hargreaves GL, Hargreaves GH, Riley JP (1985) Agricultural benefits for Senegal River basin. J Irrig and Drain Engr 111(2):113–124CrossRefGoogle Scholar
  13. Immerzeel WW (2008) Historical trends and future predictions of climate variability in the Brahmaputra basin. Int J Climatol 28:243–254CrossRefGoogle Scholar
  14. Lehner B, Verdin K, Jarvis A (2008) New global hydrography derived from spaceborne elevation data. Eos, Transactions AGU 89(10):93–94CrossRefGoogle Scholar
  15. Masood M, Yeh PJ-F, Hanasaki N, Takeuchi K (2015) Model study of the impacts of future climate change on the hydrology of Ganges–Brahmaputra–Meghna basin. Hydrol Earth Syst Sci 19(2):747–770CrossRefGoogle Scholar
  16. Nepal S, Shrestha AB (2015) Impact of climate change on the hydrological regime of the Indus, Ganges and Brahmaputra river basins: a review of the literature. Int J Water Resour Dev 31(2):201–218CrossRefGoogle Scholar
  17. Pervez MS, Henebry GM (2015) Assessing the impacts of climate and land use and land cover change on the freshwater availability in the Brahmaputra River basin. J Hydrol: Reg Stud 3:285–311Google Scholar
  18. Pilon PJ, Harvey KD (1993) Consolidated frequency analysis – reference manual. Environment Canada, OttawaGoogle Scholar
  19. Pilon PJ, Jackson RJ (1988) Low flow frequency analysis package – LFA. Environment Canada, OttawaGoogle Scholar
  20. Piontek F, Muller C, Pugh TAM et al (2014) Multisectoral climate impact hotspots in a warming world. Proc Natl Acad Sci U S A 111(9):3233–3238CrossRefGoogle Scholar
  21. Refsgaard JC, Madsen H, Andréassian V et al (2014) A framework for testing the ability of models to project climate change and its impacts. Clim Chang 122(1–2):271–282CrossRefGoogle Scholar
  22. Schleussner C-F, Lissner TK, Fischer EM et al (2016) Differential climate impacts for policy-relevant limits to global warming: the case of 1.5°C and 2°C. Earth System Dynamics 7:327–351CrossRefGoogle Scholar
  23. Shaw R, Mallick F, Islam A (2013) Disaster risk reduction approaches in Bangladesh. Springer Japan, TokyoCrossRefGoogle Scholar
  24. Singh VP, Sharma N, Ojha CSP (2004) The Brahmaputra basin water resources. Springer Netherlands, DordrechtCrossRefGoogle Scholar
  25. UNFCCC (2015a) Report on the structured expert dialogue on the 2013–2015 review. FCCC/SB/2015/INF.1Google Scholar
  26. UNFCCC (2015b) Decision 1/CP.21, The Paris Agreement. FCCC/CP/2015/10/Add.1Google Scholar
  27. van Vuuren DP et al (2011) The representative concentration pathways: an overview. Clim Chang 109:5–31CrossRefGoogle Scholar
  28. Weedon GP, Balsamo G, Bellouin N et al (2014) The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data. Water Resour Res 50(9):7505–7514CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Khaled Mohammed
    • 1
  • Akm Saiful Islam
    • 1
    Email author
  • GM Tarekul Islam
    • 1
  • Lorenzo Alfieri
    • 2
  • Sujit Kumar Bala
    • 1
  • Md. Jamal Uddin Khan
    • 1
  1. 1.Institute of Water and Flood Management (IWFM)Bangladesh University of Engineering and Technology (BUET)DhakaBangladesh
  2. 2.Directorate E—Space, Security and MigrationEuropean Commission—Joint Research CentreIspraItaly

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