Natural Hazards

, Volume 93, Issue 1, pp 109–124 | Cite as

Assessment of uncertainty in estimating future flood return levels under climate change

  • Jew Das
  • N. V. Umamahesh
Original Paper


In the context of climate change, it is essential to quantify the uncertainty for effective design and risk management practices. In the present study, we have accessed the climate model and flood return level uncertainties over a river basin. Six high-resolution global climate models (GCMs) with two Representative Concentration Pathways (RCPs) are used to project the future climate change impact on streamflow of Wainganga River basin. Uncertainty associated with the use of high-resolution multiple GCM is treated with reliability ensemble average (REA) followed by bias correction. The bias-corrected weighted outputs are used as input to variable infiltration capacity (VIC) model, a physically based hydrological model. Calibration and validation are carried out for the hydrological model, and the parameters of VIC are fixed through trial-and-error method. The uncertainty in flood return level associated with the future projected flows is dealt with the Bayesian analysis and modelled through Markov Chain Monte Carlo (MCMC) simulation technique using Metropolis–Hastings algorithm with the non-informative prior distribution. The study provides a robust framework, which will help in effective decision-making and adaptation strategies over the river basin.


Bayesian analysis Climate change Reliability ensemble average Uncertainty Wainganga River 


  1. Ashfaq M, Bowling LC, Cherkauer K et al (2010) Influence of climate model biases and daily-scale temperature and precipitation events on hydrological impacts assessment: a case study of the United States. J Geophys Res Atmos 115:1–15. CrossRefGoogle Scholar
  2. Bennett KE, Werner AT, Schnorbus M (2012) Uncertainties in hydrologic and climate change impact analyses in headwater basins of British Columbia. J Clim 25:5711–5730. CrossRefGoogle Scholar
  3. Chandra R, Saha U, Mujumdar PP (2015) Model and parameter uncertainty in IDF relationships under climate change. Adv Water Resour 79:127–139. CrossRefGoogle Scholar
  4. Christensen JH, Boberg F, Christensen OB, Lucas-Picher P (2008) On the need for bias correction of regional climate change projections of temperature and precipitation. Geophys Res Lett. Google Scholar
  5. Clark MP, Wilby RL, Gutmann ED et al (2016) Characterizing uncertainty of the hydrologic impacts of climate change. Curr Clim Change Reports 2:55–64. CrossRefGoogle Scholar
  6. Coles S (2001) An introduction to statistical modeling of extreme values. Springer, LondonCrossRefGoogle Scholar
  7. Coles S, Pericchi LR, Sisson S (2003) A fully probabilistic approach to extreme rainfall modeling. J Hydrol 273:35–50. CrossRefGoogle Scholar
  8. Feyen L, Vázquez R, Christiaens K et al (2000) Application of a distributed physically-based hydrological model to a medium size catchment. Hydrol Earth Syst Sci 4:47–63. CrossRefGoogle Scholar
  9. Gain AK, Immerzeel WW, Sperna Weiland FC, 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 15:1537–1545. CrossRefGoogle Scholar
  10. Gao H, Tang Q, Shi X, et al (2010) Water budget record from variable infiltration capacity (VIC) model. Algorithm Theor Basis Doc Terr Water Cycle Data Rec 120–173Google Scholar
  11. Ghosh S, Mujumdar PP (2009) Climate change impact assessment: uncertainty modeling with imprecise probability. J Geophys Res Atmos 114:D18113. CrossRefGoogle Scholar
  12. Ghosh S, Raje D, Mujumdar PP (2010) Mahanadi streamflow_climate change impacts assessment and adaptive strategies.pdf. Curr Sci 98:1084–1091Google Scholar
  13. Giorgi F, Mearns LO (2002) Calculation of average, uncertainty range, and reliability of regional climate changes from AOGCM simulations via the “Reliability Ensemble Averaging” (REA) method. J Clim 15:1141–1158CrossRefGoogle Scholar
  14. Giorgi F, Mearns LO (2003) Probability of regional climate change based on the Reliability Ensemble Averaging (REA) method. Geophys Res Lett. Google Scholar
  15. Gosain AK, Rao S, Arora A (2011) Climate change impact assessment of water resources of India. Curr Sci 101:356–371Google Scholar
  16. Gudmundsson L, Bremnes JB, Haugen JE, Engen-Skaugen T (2012) Technical note: downscaling RCM precipitation to the station scale using statistical transformations–a comparison of methods. Hydrol Earth Syst Sci 16:3383–3390. CrossRefGoogle Scholar
  17. Hoang LP, Lauri H, Kummu M et al (2016) Mekong River flow and hydrological extremes under climate change. Hydrol Earth Syst Sci 20:3027–3041. CrossRefGoogle Scholar
  18. Huard D, Mailhot A, Duchesne S (2010) Bayesian estimation of intensity-duration-frequency curves and of the return period associated to a given rainfall event. Stoch Environ Res Risk Assess 24:337–347. CrossRefGoogle Scholar
  19. Liang X, Lettenmaier DP, Wood EF, Burges SJ (1994) A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J Geophys Res 99:14415. CrossRefGoogle Scholar
  20. Liang X, Wood EF, Lettenmaier DP (1996) Surface soil moisture parameterization of the VIC-2L model: evaluation and modification. Glob Planet Change 13:195–206. CrossRefGoogle Scholar
  21. Lohmann D, Nolte-Holube R, Raschke E (1996) A large-scale horizontal routing model to be coupled to land surface parametrization schemes. Tellus Ser A Dyn Meteorol Oceanogr 48:708–721CrossRefGoogle Scholar
  22. Lu G, Xiao H, Wu Z et al (2014) Assessing the impacts of future climate change on hydrology in Huang-Huai-Hai Region in China using the PRECIS and VIC models. J Hydrol Eng 18:1077–1087. CrossRefGoogle Scholar
  23. Martins ES, Stedinger JR (2000) Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data. Water Resour Res 36:737–744. CrossRefGoogle Scholar
  24. Mondal A, Mujumdar PP (2015) Detection of change in flood return levels under global warming. J Hydrol Eng. Google Scholar
  25. Mujumdar PP, Ghosh S (2008) Modeling GCM and scenario uncertainty using a possibilistic approach: application to the Mahanadi River, India. Water Resour Res 44:1–15. CrossRefGoogle Scholar
  26. Najafi MR, Moradkhani H, Jung IW (2011) Assessing the uncertainties of hydrologic model selection in climate change impact studies. Hydrol Process 25:2814–2826. CrossRefGoogle Scholar
  27. Raju KS, Sonali P, Kumar DN (2017) Ranking of CMIP5-based global climate models for India using compromise programming. Theor Appl Climatol. Google Scholar
  28. Rauscher SA, Coppola E, Piani C, Giorgi F (2010) Resolution effects on regional climate model simulations of seasonal precipitation over Europe. Clim Dyn 35:685–711. CrossRefGoogle Scholar
  29. Regan HM, Colyvan M, Burgman MA (2002) A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecol Appl 12:618–628. CrossRefGoogle Scholar
  30. Riahi K, Rao S, Krey V et al (2011) RCP 8.5—a scenario of comparatively high greenhouse gas emissions. Clim Change 109:33–57. CrossRefGoogle Scholar
  31. Rojas R, Feyen L, Dosio A, Bavera D (2011) Improving pan-European hydrological simulation of extreme events through statistical bias correction of RCM-driven climate simulations. Hydrol Earth Syst Sci 15:2599–2620. CrossRefGoogle Scholar
  32. Rusli N, Majid MR, Yusop Z, et al (2016) Integrating manual calibration and auto-calibration of SWAT model in Muar Watershed, Johor. In: 2016 7th IEEE control and system graduate research colloquium (ICSGRC). IEEE, pp 197–202Google Scholar
  33. Sheffield J, Goteti G, Wood EF (2006) Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J Clim 19:3088–3111CrossRefGoogle Scholar
  34. Suh M-S, Oh S-G, Lee D-K et al (2012) Development of new ensemble methods based on the performance skills of regional climate models over South Korea. J Clim 25:7067–7082. CrossRefGoogle Scholar
  35. Sunyer MA, Madsen H, Ang PH (2012) A comparison of different regional climate models and statistical downscaling methods for extreme rainfall estimation under climate change. Atmos Res 103:119–128. CrossRefGoogle Scholar
  36. Tebaldi C, Smith RL, Nychka D, Mearns LO (2005) Quantifying uncertainty in projections of regional climate change: a Bayesian approach to the analysis of multimodel ensembles. J Clim 18:1524–1540. CrossRefGoogle Scholar
  37. Teng J, Vaze J, Chiew FHS et al (2012) Estimating the relative uncertainties sourced from GCMs and hydrological models in modeling climate change impact on runoff. J Hydrometeorol 13:122–139. CrossRefGoogle Scholar
  38. Teutschbein C, Seibert J (2010) Regional climate models for hydrological impact studies at the catchment scale: a review of recent modeling strategies: regional climate models for hydrological impact studies. Geogr Compass 7:834–860. CrossRefGoogle Scholar
  39. Thomson AM, Calvin KV, Smith SJ et al (2011) RCP4.5: a pathway for stabilization of radiative forcing by 2100. Clim Change 109:77–94. CrossRefGoogle Scholar
  40. Uusitalo L, Lehikoinen A, Helle I, Myrberg K (2015) An overview of methods to evaluate uncertainty of deterministic models in decision support. Environ Model Softw 63:24–31. CrossRefGoogle Scholar
  41. Wilby RL (2010) Evaluating climate model outputs for hydrological applications. Hydrol Sci J 55:1090–1093. CrossRefGoogle Scholar
  42. Wilby RL, Hay LE, Leavesley GH (1999) A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River basin, Colorado. J Hydrol 225:67–91. CrossRefGoogle Scholar
  43. Wilby R, Dawson C, Murphy C et al (2014) The statistical downscaling model—decision centric (SDSM-DC): conceptual basis and applications. Clim Res 61:259–276. CrossRefGoogle Scholar

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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Civil EngineeringNational Institute of TechnologyWarangalIndia

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