Skip to main content

Advertisement

Log in

Prediction of the likely impact of climate change on monthly mean maximum and minimum temperature in the Chaliyar river basin, India, using ANN-based models

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

Abstract

In this work, an approach based on Artificial Neural Networks (ANN) has been employed to assess the likely impact of climate change on mean monthly maximum and minimum temperature (T max and T min) in the Chaliyar river basin, Kerala, India. ANN is trained to downscale temperature from the General Circulation Model (GCM) from a coarser resolution to the required resolution of the river basin. The work aims to estimate the GCMs’ output to the scales compatible with that employed in a hydrologic model of the river basin. In order to satiate this purpose, predictor variables were obtained from the National Centre for Environmental Prediction and National Centre for Atmospheric Research (NCEP/NCAR) reanalysis data; this was utilized for training the ANN using a feed-forward network with a back-propagation algorithm. These models were validated further and used to downscale CGCM3 GCM simulations for the scenarios outlined in the IPCC Special Report on Emission Scenarios (SRES). Results showed that both T max and T min are increasing consistently in all the scenarios. T max exhibited an average increase of maximum 3 °C during the dry season (December–May) and 1 °C during the wet season (June–November) by the year 2100, while T min showed an average increase of 2.5 °C in the dry season and 0.5 °C in the wet season.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • ACIA (2005) Arctic Climate Impact Assessment. Cambridge University Press, 1042p.

  • Anandhi A, Srinivas VV, Kumar DN, Nanjundiah RS (2008) Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine. Int J Climatol 28:401–420. doi:10.1002/joc.1529

    Article  Google Scholar 

  • Berg P, Haerter JO (2013) Unexpected increase in precipitation intensity with temperature—a result of mixing of precipitation types. Atm Res 119:56–61

    Article  Google Scholar 

  • Cavazos T, Hewitson BC (2005) Performance of NCEP variables in statistical downscaling of daily precipitation. Clim Res 28:95–107

    Article  Google Scholar 

  • Cawley GC, Haylock M, Dorling SR, Goodess C, Jones PD (2003) Statistical downscaling with artificial neural networks. ESANN'2003 proceedings—European Symposium on Artificial Neural Networks Bruges (Belgium), 23–25 April 2003, d-side publi., ISBN 2-930307-03-X, pp. 167–172.

  • Chong-yu X (1999) From GCMs to river flow: a review of downscaling methods and hydrologic modeling approaches. Prog Phys Geogr 23(2):229–249

    Article  Google Scholar 

  • Dibike YB, Coulibaly P (2005) Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models. J Hydrol 307:145–163

    Article  Google Scholar 

  • Dibike YB, Coulibaly P (2006) Temporal neural networks for downscaling climate variability and extremes. Neural Netw 19(2):135–144

    Article  Google Scholar 

  • Fistikoglu O, Okkan U (2011) Statistical downscaling of monthly precipitation using NCEP/NCAR reanalysis data for Tahtali River Basin in Turkey. J Hydrol Eng 16(2):157–164. doi:10.1061/(ASCE)HE.1943-5584.0000300

    Article  Google Scholar 

  • Gardner MW, Dorling SR (1998) Artificial neural networks (the multi layer perceptron)—a review of applications in the atmospheric sciences. Atmos Environ 32:2627–2636

    Article  Google Scholar 

  • Hagan MT, Menhaj MB (1994) Training feed forward network with Marquardt algorithm. IEEE Trans Neural Networks 5(6):989–993

    Article  Google Scholar 

  • Haylock MR, Cawley GC, Harpham C, Wilby RL, Goodess C (2006) Downscaling heavy precipitation over the United Kingdom: a comparison of dynamical and statistical methods and their future scenarios. Int J Climatol 26:1397–1415

    Article  Google Scholar 

  • Hewitson BC, Crane RG (1996) Climate downscaling: techniques and application. Clim Res 7:85–95

    Article  Google Scholar 

  • Johnson MS, Coon WF, Mehta VK, Steenhuis TS, Brooks ES, Boll J (2003) Application of two hydrologic models with different runoff mechanisms to a hillslope dominated watershed in the northeastern US: a comparison of HSPF and SMR. J Hydrol 284:57–76

  • Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77(3):437–471

    Article  Google Scholar 

  • Karl TR, Wang WC, Schlesinger ME, Knight RW, Portman D (1990) A method of relating general circulation model simulated climate to the observed local climate part I: seasonal statistics. J Clim 3:1053–1079

    Article  Google Scholar 

  • Maheras P, Tolika K, Anagnostopoulou C, Vafiadis M, Patrikas I, Flocas H (2004) On the relationships between circulation types and changes in rainfall variability in Greece. Int J of Clim 24:1695–1712

    Article  Google Scholar 

  • Mondal A, Mujumdar PP (2012) On the basin-scale detection and attribution of human-induced climate change in monsoon precipitation and streamflow. Water Resour Res 48, W10520. doi:10.1029/2011WR011468

    Google Scholar 

  • Murphy JM (1999) An evaluation of statistical and dynamical techniques for downscaling local climate. J Clim 12:2256–2284

    Article  Google Scholar 

  • Pearson K (1896) Mathematical contributions to the theory of evolution III regression heredity and panmixia. Philos Trans R Soc Lond Ser 187:253–318

    Article  Google Scholar 

  • Raneesh KY, Thampi SG (2011) A study on the impact of climate change on streamflow at watershed scale in the humid tropics. Hydro Sci J 56(6):946–965

    Article  Google Scholar 

  • Rasul G, Chaudhry QZ, Mahmood A, Hyder KW (2011) Effect of temperature rise on crop growth and productivity. Pak J Met 8(15):53–62

    Google Scholar 

  • Sailor DJ, Li X (1999) A semi-empirical downscaling approach for predicting regional temperature impacts associated with climatic change. J Clim 12(1):103–114

    Article  Google Scholar 

  • Salathé EP (2005) Downscaling simulations of future global climate with application to hydrologic modeling. Int J Climatol 25:419–436

    Article  Google Scholar 

  • Schoof JT, Pryor SC (2001) Downscaling temperature and precipitation: a comparison of regression-based methods and Artificial Neural Networks. Int J Climatol 21:773–790

  • Skelly WC, Henderson-Sellers A (1996) Grid box or grid point: what type of data do GCMs deliver to climate impacts researchers? Int J Climatol 16:1079–1086

    Article  Google Scholar 

  • Srinivasa, Gail MB (2005) Artificial Neural Networks in water supply engineering. American Society of Civil Engineers

  • Stuert RG, Cynthia R, Angela YYK (2012) Adapting to climate change through urban green infrastructure. Nat Climate Chang 2:704. doi:10.1038/nclimate1685

    Article  Google Scholar 

  • Tatli H, Dalfes HN, Mentes SS (2004) A statistical downscaling method for monthly total precipitation over Turkey. Int J Climatol 24:161–180

    Article  Google Scholar 

  • Walther GR, Post E, Convey P, Menzel A, Parmesan C, Beebee T, Fromentin JM, Hoegh-Guldberg O, Bairlein F (2001) Ecological responses to recent climate change. Nature 416:389–395. doi:10.1038/416389a

    Article  Google Scholar 

  • Wetterhall F, Halldin S, Xu CY (2005) Statistical precipitation downscaling in central Sweden with the analogue method. J Hydrol 306:136–174

    Article  Google Scholar 

  • Wilby RL, Charles SP, Zorita E, Timbal B, Whetton P, Mearns LO (2004) Guidelines for use of climate scenarios developed from statistical downscaling methods. Supporting material of the Intergovernmental Panel on Climate Change (IPCC), prepared on behalf of Task Group on Data and Scenario Support for Impacts and Climate Analysis (TGICA), (http://ipccddc.cru.uea.ac.uk/guidelines/StatDownGuide.pdf).

  • Wilby RL, Dawson CW, Barrow EM (2002) SDSM—a decision support tool for the assessment of regional climate change impacts. Env Mod Soft 17:147–159

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to N. R. Chithra.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chithra, N.R., Thampi, S.G., Surapaneni, S. et al. Prediction of the likely impact of climate change on monthly mean maximum and minimum temperature in the Chaliyar river basin, India, using ANN-based models. Theor Appl Climatol 121, 581–590 (2015). https://doi.org/10.1007/s00704-014-1257-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-014-1257-1

Keywords

Navigation