Assessment of Climate Change Impacts on Precipitation and Temperature in the Ghataprabha Sub-basin Using CMIP5 Models


The study on historical and future variation in precipitation and temperature is needed to develop effective adaptation strategies for changing climate. The present study investigated the potential impacts of climate change on precipitation and temperature and the performance of individual downscaled global circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 dataset over the Ghataprabha sub-basin, India. The simulations were carried out using observed, historical and future climate datasets. With the help of statistical indicators, the study also aims at the selection of reliable GCMs for the multi-model ensembling to reduce the uncertainty in the projected results over the sub-basin. The percentage change in precipitation and temperature relative to the historical period was presented based on the multi-model ensemble average under 4 representative concentration pathways (RCPs), viz. RCP 2.6, 4.5, 6.0 and 8.5 scenarios, for the three time slices: the beginning of the century (2010–2039), middle century (2040–2069) and end century (2070–2099). Results revealed that the percentage change in annual mean precipitation over the study area for three time slices beginning century, mid-century and end century may likely to increase by 1.68% to 3.57%, 9.35% to 15.07% and 19.51% to 32.28% and the daily average mean temperature may likely to increase by 4.15% to 4.38%, 5.76% to 9.72% and 6.11% to 16.64%, respectively.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5


  1. [1]

    A. Anandhi, V.V. Srinivas, R.S. Nanjundiah and D. Nagesh Kumar, Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine. Int. J. Climatol. J. R. Meteorol. Soc., 28 (2008) 401–420

    Article  Google Scholar 

  2. [2]

    D. Singh, S.K. Jain and R.D. Gupta, Statistical downscaling and projection of future temperature and precipitation change in middle catchment of Sutlej River Basin, India. J. Earth Syst. Sci., 124 (2015) 843–860

    ADS  Article  Google Scholar 

  3. [3]

    T. Tahir, A.M. Hashim and K.W. Yusof, Statistical downscaling of rainfall under transitional climate in Limbang River Basin by using SDSM. In IOP conference series: earth and environmental science (Vol. 140, No. 1, p. 012037). IOP Publishing.

  4. [4]

    F. Kaspar, M. Meinshausen and M. Schulz, IPPC Introduction Climate Change 2013 PhysSci Basis Contrib Work Gr 1 to Fifth Assess Rep Intergov Panel Clim Chang, (2013) pp 1–90.

  5. [5]

    S. Kannan and S. Ghosh, Prediction of daily rainfall state in a river basin using statistical downscaling from GCM output. Stoch. Environ. Res. Risk Assess., 25 (2011) 457–474

    Article  Google Scholar 

  6. [6]

    C.F. Keller, Global warming: a review of this mostly settled issue. Stoch. Environ. Res. Risk Assess., 23 (2009) 643–676

    ADS  MathSciNet  Article  Google Scholar 

  7. [7]

    J. Liu, D. Yuan, L. Zhang, X. Zou and X. Song, Comparison of three statistical downscaling methods and ensemble downscaling method based on Bayesian model averaging in upper Hanjiang River Basin, China. Adv. Meteorol., 2016 (2016) 1–13.

    Article  Google Scholar 

  8. [8]

    K. Shashikanth, C.G. Madhusoodhanan, S. Ghosh, T.I. Eldho, K. Rajendran and R. Murtugudde, Comparing statistically downscaled simulations of Indian monsoon at different spatial resolutions. J. Hydrol., 519 D (2014) 3163–3177.

    ADS  Article  Google Scholar 

  9. [9]

    D.P. Lettenmaier, A.W. Wood, R.N. Palmer, E.F. Wood and E.Z. Stakhiv, Water resources implications of global warming: a US regional perspective. Clim. Change, 43 (1999) 537–579

    Article  Google Scholar 

  10. [10]

    A.W. Wood, E.P. Maurer, A. Kumar and D.P. Lettenmaier, Long-range experimental hydrologic forecasting for the eastern United States. J. Geophys. Res. Atmos., 107 (2002) ACL-6

    Article  Google Scholar 

  11. [11]

    N. Reddy, N.S. Patil, M. Nataraja and K. Shashikanth, Spatio-temporal long-term variability and trend analysis of precipitation and temperature series in the Ghataprabha sub basin of Krishna Basin. Int. J. Innov. Technol. Explor. Eng. (IJITEE), 8 (2019) 2547–2552

    Article  Google Scholar 

  12. [12]

    T. Chanapathi, S. Thatikonda and S. Raghavan, Analysis of rainfall extremes and water yield of Krishna river basin under future climate scenarios. J. Hydrol. Reg. Stud., 19 (2018) 287–306

    Article  Google Scholar 

  13. [13]

    A.K. Gosain, S. Rao and D. Basuray, Climate change impact assessment on hydrology of Indian river basins. Curr. Sci., 90 (2006) 346–353

    Google Scholar 

  14. [14]

    B.D. Kulkarni, N.R. Deshpande, S.K. Patwardhan and S.D. Bansod, Assessing hydrological response to changing climate in the Krishna basin of India. J. Earth Sci. Clim. Change, 5 (2014) 1–6

    Google Scholar 

  15. [15]

    G.E. Soro, A.B. Yao, Y.M. Kouame and T.A.G. Bi, Climate change and its impacts on water resources in the Bandama basin, Côte D’ivoire. Hydrology, 4 (2017) 18

    Article  Google Scholar 

  16. [16]

    V. Mishra and R. Lilhare, Hydrologic sensitivity of Indian sub-continental river basins to climate change. Glob. Planet. Change, 139 (2016) 78–96

    ADS  Article  Google Scholar 

  17. [17]

    M. Minville, F. Brissette and R. Leconte, Uncertainty of the impact of climate change on the hydrology of a nordic watershed. J. Hydrol., 358 (2008) 70–83

    ADS  Article  Google Scholar 

  18. [18]

    D.P. Rowell, A demonstration of the uncertainty in projections of UK climate change resulting from regional model formulation. Clim. Change, 79 (2006) 243–257

    ADS  Article  Google Scholar 

  19. [19]

    Robert L. Wilby, Uncertainty in water resource model parameters used for climate change impact assessment. Hydrol. Process. Int. J., 19 (2005) 3201–3219

    ADS  Article  Google Scholar 

  20. [20]

    R. Knutti, R. Furrer, C. Tebaldi, J. Cermak and G.A. Meehl, Challenges in combining projections for multiple models. J. Clim., 23 (2010) 2739–2758.

    ADS  Article  Google Scholar 

  21. [21]

    K.S. Raju, D.N. Kumar and I.N. Babu, Ranking of global climate models for Godavari and Krishna river basins, India, using compromise programming. In: Sustainable Water Resources Planning and Management Under Climate Change. Springer, Singapore, 2017, pp. 87–100

  22. [22]

    NWDA (National Water Development Agency), Technical Study NO.17, Water Balance Study of the Ghataprabha Sub-basin of the Krishna Basin. 1991.

  23. [23]

    A.A. Pathak and B.M. Dodamani, Trend analysis of groundwater levels and assessment of regional groundwater drought: Ghataprabha River Basin, India. Nat. Resour. Res., 28 (2019) 631–643

    Article  Google Scholar 

  24. [24]

    R.L. Wilby, C.W. Dawson and E.M. Barrow, SDSM—a decision support tool for the assessment of regional climate change impacts. Environ. Model. Softw., 17 (2002) 145–157

    Article  Google Scholar 

  25. [25]

    B.B. Alkan, C. Atakan and Y. Akdi, Visual analysis using biplot techniques of rainfall changes over Turkey. Mapan, 30 (2015) 25–30

    Article  Google Scholar 

  26. [26]

    C. Tisseuil, F. Leprieur, G. Grenouillet, M. Vrac and S. Lek, Projected impacts of climate change on spatio-temporal patterns of freshwater fish beta diversity: a deconstructing approach. Glob. Ecol. Biogeogr., 21 (2012) 1213–1222

    Article  Google Scholar 

  27. [27]

    A. Busuioc and H.V. Storch, Changes in the winter precipitation in Romania and its relation to the large-scale circulation. Tellus A, 48 (1996) 538–552

    ADS  Article  Google Scholar 

  28. [28]

    H. Von Storch, E. Zorita and U. Cubasch, Downscaling of global climate change estimates to regional scales: an application to Iberian rainfall in winter time. J. Clim., 6 (1993) 1161–1171

    ADS  Article  Google Scholar 

  29. [29]

    R.S. Laddimath and N.S. Patil, Artificial neural network technique for statistical downscaling of global climate model. MAPAN, 34 (2019) 121–127

    Article  Google Scholar 

  30. [30]

    J. Chen, F.P. Brissette and R. Leconte, Uncertainty of downscaling method in quantifying the impact of climate change on hydrology. J. Hydrol., 401 (2011) 190–202

    ADS  Article  Google Scholar 

  31. [31]

    J. Diaz-Nieto and R.L. Wilby, A comparison statistical downscaling and climate change factor methods: impacts on low flows in the river Thanes, United Kingdom. Clim. Change, 69 (2005) 245–268

    ADS  Article  Google Scholar 

  32. [32]

    L.E. Hay, R.L. Wilby and H.H. Leavesly, Comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States. J. Am. Water Resour. Assoc., 36 (2000) 387–397

    ADS  Article  Google Scholar 

  33. [33]

    R.L. Wilby, S.P. Charles, E. Zorita, B. Timbal, P. Whetton and L.O. Mearns, Guidelines for use of climate scenarios developed from statistical downscaling methods. Supporting material of the Intergovernmental Panel on Climate Change. Available from the DDC of IPCC TGCIA, 27 (2004).

  34. [34]

    M. Akhtar, N. Ahmad and M.J. Booij, The impact of climate change on the water resources of Hindukush–Karakorum–Himalaya region under different glacier coverage scenarios. J. Hydrol., 355 (2008) 148–163

    ADS  Article  Google Scholar 

  35. [35]

    C.G. Kilsby, P.D. Jones, A. Burton, A.C. Ford, H.J. Fowler, C. Harpham and R.L. Wilby, A daily weather generator for use in climate change studies. Environ. Model. Softw., 22 (2007) 1705–1719

    Article  Google Scholar 

  36. [36]

    K.E. Taylor, Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. Atmos., 106 (2001) 7183–7192

    ADS  Article  Google Scholar 

  37. [37]

    D.N. Moriasi, J.G. Arnold, M.W. Van Liew, R.L. Bingner, R.D. Harmel and T.L. Veith, Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE, 50 (2007) 885–900

    Article  Google Scholar 

  38. [38]

    F.A.M. Nazri, J.S. Mandeep and H. Husain, Statistical modelling of 1-min rain rate derived from 1-h integration time in Malaysia. MAPAN, 33 (2018) 179–184

    Article  Google Scholar 

  39. [39]

    K.S. Raju and D.N. Kumar, Selection of global climate models. In Impact of climate change on water resources. Springer, Singapore (2018) pp. 27–75.

  40. [40]

    K. Shashikanth, S. Ghosh, H. Vittal and S. Karmakar, Future projections of Indian summer monsoon rainfall extremes over India with statistical downscaling and its consistency with observed characteristics. Clim. Dyn., 51 (2018) 1–15

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Nagendra Reddy.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Reddy, N., Patil, N.S. & Nataraja, M. Assessment of Climate Change Impacts on Precipitation and Temperature in the Ghataprabha Sub-basin Using CMIP5 Models. MAPAN (2021).

Download citation


  • Precipitation
  • Temperature
  • Ghataprabha sub-basin
  • Mann
  • Kendall test
  • Global circulation model (GCM)
  • Change factor (CF) method