Assessment of Agricultural Water Management in Punjab, India, Using Bayesian Methods

  • Tess A. Russo
  • Naresh Devineni
  • Upmanu Lall


The success of the Green Revolution in Punjab, India, is threatened by a significant decline in water resources. Punjab, a major agricultural supplier for the rest of India, supports irrigation with a canal system and groundwater, which is vastly overexploited. The detailed data required to estimate future impacts on water supplies or develop sustainable water management practices is not readily available for this region. Therefore, we use Bayesian methods to estimate hydrologic properties and irrigation requirements for an under-constrained mass balance model. Using the known values of precipitation, total canal water delivery, crop yield, and water table elevation, we present a method using a Markov chain Monte Carlo (MCMC) algorithm to solve for a distribution of values for each unknown parameter in a conceptual mass balance model. Model results are used to test three water management strategies, which show that replacement of rice with pulses may be sufficient to stop water table decline. This computational method can be applied in data-scarce regions across the world, where integrated water resource management is required to resolve competition between food security and available resources.


Agricultural water management Punjab India Markov chain Monte Carlo Groundwater overdraft Mass balance model 



We thank colleagues at the Punjab Agricultural University (Ludhiana, India), the Central Groundwater Board, and the Bhakra Beas Management Board (both from Chandigarh, India) for sharing valuable data. This work was completed with funding from the Columbia University Earth Institute Postdoctoral Fellowship program and the International Development Research Centre (Canada).


  1. Aggarwal R, Kaushal M, Kaur S, Farmaha B (2009) Water resource management for sustainable agriculture in Punjab, India. Water Sci TechnolA J Int Assoc Water Pollut Res 60(11):2905–2911. doi: 10.2166/wst.2009.348 CrossRefGoogle Scholar
  2. Ambast SK, Tyagi NK, Raul SK (2006) Management of declining groundwater in the Trans Indo-Gangetic Plain (India): some options. Agric Water Manag 82(3):279–296. doi: 10.1016/j.agwat.2005.06.005 CrossRefGoogle Scholar
  3. Brahmanand PS, Kumar A, Ghosh S, Chowdhury SR, Singandhupe RB, Singh R, Nanda P, Chakraborthy H, Srivastava SK, Behera MS (2013) Challenges to food security in India. Curr Sci 104(7):841–846Google Scholar
  4. CGWB (2012) Dynamic ground water resources of Punjab state. Water Resources and Environment Directorate, and Central Ground Water Board, ChandigarhGoogle Scholar
  5. Chatterjee R, Purohit RR (2009) Estimation of replenishable groundwater resources of India and their status of utilization. Curr Sci 96(12):1581–1591Google Scholar
  6. Devineni N, Perveen S (2012) Securing the future of India’s “Water, energy and food.” GWF discussion paper 1240 (p. GWF discussion paper 1240). GWF discussion paper 1240, Global Water Forum, CanberraGoogle Scholar
  7. Devineni N, Perveen S, Lall U (2013) Assessing chronic and climate-induced water risk through spatially distributed cumulative deficit measures: a new picture of water sustainability in India. Water Resour Res 49(4):2135–2145. doi: 10.1002/wrcr.20184 CrossRefGoogle Scholar
  8. Engeland K, Gottschalk L (2002) Bayesian estimation of parameters in a regional hydrological model. Hydrol Earth Syst Sci 6(5):883–898. doi: 10.5194/hess-6-883-2002 CrossRefGoogle Scholar
  9. Gelman A, Rubin D (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7(4):457–511CrossRefGoogle Scholar
  10. Gilks W, Richardson S, Spiegelhalter D (1996) Markov chain Monte Carlo in practice. Chapman and Hall, London, p 486Google Scholar
  11. Goswami BN, Venugopal V, Sengupta D, Madhusoodanan MS, Xavier PK (2006) Increasing trend of extreme rain events over India in a warming environment. Science 314(5804):1442–1445. doi: 10.1126/science.1132027 CrossRefGoogle Scholar
  12. Hira GS (2004) Status of water resources in Punjab and management strategies. In: Abrol I, Sharma B, Sekhon G (eds) Groundwater use in North-West India – workshop papers. Centre for Advancement of Sustainable Agriculture, New Delhi, pp 65–71Google Scholar
  13. Humphreys E, Kukal SS, Christen EW, Hira GS, Sharma RK (2010) Halting the groundwater decline in North-West India — which crop technologies will be winners? Adv Agron 109: 155–217. Elsevier Ltd. doi: 10.1016/B978-0-12-385040-9.00005-0
  14. Jalota S, Arora V (2002) Model-based assessment of water balance components under different cropping systems in north-west India. Agric Water Manag 57(1):75–87. doi: 10.1016/S0378-3774(02)00049-5 CrossRefGoogle Scholar
  15. Kahn S, Kumar G, Kumar S, Marwaha S, Pandey S, Rani V, Saigal SK, Sharma A, Singh AK, Singh GP, Singh S, Singh T (2007) In: Gupta S (ed) Punjab district reports. Central Ground Water Board, Government of India, ChandigarhGoogle Scholar
  16. Kaur S (2009) On-farm water management practices in Punjab. Curr Sci 97(3):307–309Google Scholar
  17. Kaur S, Aggarwal R, Soni A (2011) Study of water-table behaviour for the Indian Punjab using GIS. Water Sci Technol 63(8):1574. doi: 10.2166/wst.2011.212 CrossRefGoogle Scholar
  18. Krishnamurthy CKB, Lall U, Kwon H-H (2009) Changing frequency and intensity of rainfall extremes over India from 1951 to 2003. J Clim 22(18):4737–4746. doi: 10.1175/2009JCLI2896.1 CrossRefGoogle Scholar
  19. Lunn DJ, Thomas A, Best N, Spiegelhalter D (2000) WinBUGS – a Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput 10:325–337CrossRefGoogle Scholar
  20. MWR (1997) Ground water estimation methodology. Ministry of Water Resources, Government of India, New DelhiGoogle Scholar
  21. Ngo-Duc T, Polcher J, Laval K (2005) A 53-year forcing data set for land surface models. J Geophys Res 110:D06116. doi: 10.1029/2004JD005434 Google Scholar
  22. Perveen S, Krishnamurthy CKB, Sindu RS, Vatta B, Kaur B, Modi V, Fishman RM, Polycarpou L, Lall U (2012) Restoring groundwater in Punjab, India’s breadbasket: finding agricultural solutions for water sustainability. Columbia Water Center – White PaperGoogle Scholar
  23. Rajeevan M, Bhate J (2008) A high resolution daily gridded rainfall data set (1971–2005) for Mesoscale meteorological studies. National Climate Center, IMD, Pune, p 14Google Scholar
  24. Ranade A, Singh N (2013) Large-scale and spatio-temporal extreme rain events over India: a hydrometeorological study. Theor Appl Climatol (in press). doi: 10.1007/s00704-013-0905-1
  25. Russo T, Fisher A, Winslow D (2013) Regional and local increases in storm intensity in the San Francisco Bay Area, USA, between 1890 and 2010. J Geophys Res Atmos 118(8):3392–3401. doi: 10.1002/jgrd.50225 CrossRefGoogle Scholar
  26. Shah T (2009) Climate change and groundwater: India’s opportunities for mitigation and adaptation. Environ Res Lett 4(3):035005. doi: 10.1088/1748-9326/4/3/035005 CrossRefGoogle Scholar
  27. Shah N, Nachabe M, Ross M (2007) Extinction depth and evapotranspiration from ground water under selected land covers. Groundwater 45(3):329–338. doi: 10.1111/j.1745-6584.2007.00302.x CrossRefGoogle Scholar
  28. Sidhu R, Vatta K, Lall U (2011) Climate change impact and management strategies for sustainable water-energy-agriculture outcomes in Punjab. Indian J Agric Econ 66(3):328–339Google Scholar
  29. Spiegelhalter D, Thomas A, Best N, Gilks W (1996) BUGS 0.5: Bayesian inference using gibbs sampling (version ii). Medical Research Council Biostatistics Unit, CambridgeGoogle Scholar
  30. Takshi K, Chopra R (2004) Monitoring and assessment of groundwater resources in Punjab state. In: Abrol I, Sharma B, Sekhon G (eds) Groundwater use in North-West India – workshop papers. Centre for Advancement of Sustainable Agriculture, New Delhi, pp 8–15Google Scholar
  31. Tyagi NK, Agrawal A, Sakthivadivel R, Ambast SK (2005) Water management decisions on small farms under scarce canal water supply: a case study from NW India. Agric Water Manag 77(1–3):180–195. doi: 10.1016/j.agwat.2004.09.031 CrossRefGoogle Scholar
  32. Winslow DM, Fisher AT, Becker K (2013) Characterizing borehole fluid flow and formation permeability in the ocean crust using linked analytic models and Markov Chain Monte Carlo analysis. Geochem Geophys Geosyst 14(9):3857–3874CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Tess A. Russo
    • 1
    • 2
  • Naresh Devineni
    • 2
    • 3
  • Upmanu Lall
    • 2
  1. 1.Department of GeosciencesThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Columbia Water CenterColumbia UniversityNew YorkUSA
  3. 3.Department of Civil EngineeringThe City College of New YorkNew YorkUSA

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