Skip to main content


Log in

Statistical downscaling rainfall using artificial neural network: significantly wetter Bangkok?

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


Artificial neural network (ANN) is an established technique with a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output data. The present study utilizes ANN as a method of statistically downscaling global climate models (GCMs) during the rainy season at meteorological site locations in Bangkok, Thailand. The study illustrates the applications of the feed forward back propagation using large-scale predictor variables derived from both the ERA-Interim reanalyses data and present day/future GCM data. The predictors are first selected over different grid boxes surrounding Bangkok region and then screened by using principal component analysis (PCA) to filter the best correlated predictors for ANN training. The reanalyses downscaled results of the present day climate show good agreement against station precipitation with a correlation coefficient of 0.8 and a Nash-Sutcliffe efficiency of 0.65. The final downscaled results for four GCMs show an increasing trend of precipitation for rainy season over Bangkok by the end of the twenty-first century. The extreme values of precipitation determined using statistical indices show strong increases of wetness. These findings will be useful for policy makers in pondering adaptation measures due to flooding such as whether the current drainage network system is sufficient to meet the changing climate and to plan for a range of related adaptation/mitigation measures.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 8
Fig. 9

Similar content being viewed by others


  • ADB, 2009: The economics of climate change in Southeast Asia: a regional review, Manila

  • Anandhi A, Srinivas VV, Kumara DN, Nanjundiahb RS (2009) Role of predictors in downscaling surface temperature to river basin in India for IPCC SRES scenarios using support vector machine. Int J Climatol 29:583–603. doi:10.1002/joc.1719

    Article  Google Scholar 

  • Bachelet D, Brown D, Bohm M, Russell P (1992) Climate change in Thailand and its potential impact on rice yield. Clim Chang 21:347–366

    Article  Google Scholar 

  • Bigio A (2003) Cities and climate change. In: Kreimer A, Arnold M, Carlin A (eds) Building Safer Cities: The Future of Disaster Risk. World Bank, Washington, pp. 91–100

    Google Scholar 

  • Bishop CM (1995) Neural networks for pattern recognition. Clarendon Press, Oxford. ISBN-13: 978–0198538646.

  • Björck A (1996) Numerical methods for least squares problems. SIAM, Philadelphia. ISBN 0-89871-360-9.

  • Boochabun K, Tych W, Chappell NA, Carling PA, Lorsirirat K, Pa-Obsaeng S (2004) Statistical modelling of rainfall and river flow in Thailand. J Geo Soc India 64:503–515

    Google Scholar 

  • Brands S, Herrera S, San-Martin D, Gutierrez JM (2011) Validation of the ENSEMBLES global climate models over southwestern Europe using probability density function, from a downscaling perspective. Clim Res 48:145–161

    Article  Google Scholar 

  • Charles SP, Bates BC, Hughes JP (1999) A spatio-temporal model for downscaling precipitation occurrence and amounts. J Geophys Res 104:31657–31669. doi:10.1029/1999JD900119

    Article  Google Scholar 

  • Chen H, Xu CY, Guo S (2012a) Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff. J Hydrol 434-435:36–45

    Article  Google Scholar 

  • Chen J, Brissette F, Leconte R (2012b) Coupling statistical and dynamical methods for spatial downscaling of precipitation. Clim Chang 114:509–526. doi:10.1007/s10584-012-0452-2

    Article  Google Scholar 

  • Coulibaly P, Anctil F, Bobée B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230:244–257. doi:10.1016/S0022-1694(00)00214-6

    Article  Google Scholar 

  • Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette JJ, Park BK, Peubey C, de Rosnay P, Tavolato C, Thépaut JN, Vitart F (2011) The ERA-interim reanalysis: configuration and performance of the data assimilation system. Quar J Royal Met Soc 137:553–597. doi:10.1002/qj.828

    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 

  • Duhan D, Pandey A (2014) Statistical downscaling of temperature using three techniques in the Tons River basin in central India. Theor Appl Climatol. doi:10.1007/s00704-014-1253-5

    Google Scholar 

  • FAO (1982) A study of the agroclimatology of the humid tropics of Southeast Asia: technical report, FAO/UNESCO/WMO Interagency project on Agroclimatology, pp221

  • Fistikoglu O, Okkan U (2011) Statistical downscaling of monthly precipitation using NCEP/NCAR reanalysis data for Thatali River basin in Turkey. J Hydrol Eng 16(2):157–164

  • Ghosh S (2010) SVM-PGSL coupled approach for statistical downscaling to predict rainfall from GCM output. J of Geophys Res 115, D22102. doi:10.1029/2009JD013548.

  • Gyalistras D, Hv S, Fischlin A, Beniston M (1994) Linking GCM-simulated climatic changes to ecosystem models: case studies of statistical downscaling in the Alps. Clim Res 4:167–189. doi:10.3354/cr004167

    Article  Google Scholar 

  • Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan College. ISBN 13: 9780023527616

  • Hewitson BC, Crane RG (1996) Climate downscaling: techniques and application. Clim Res 7:85–96

    Article  Google Scholar 

  • Hsu KL, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of the rainfall-runoff process. Water Resour Res 31:2517–2530. doi:10.1029/95WR01955

    Article  Google Scholar 

  • Hu Y, Maskey S, Uhlenbrook S (2013) Downscaling daily precipitation over the Yellow River source region in China: a comparison of three statistical downscaling methods. Theor Appl Climatol 112(3–4):447–460. doi:10.1007/s00704-012-0745-4

    Article  Google Scholar 

  • Hu Y, Maskey S, Uhlenbrook S (2012) Trends in temperature and precipitation extremes in the yellow river source region, China. Clim Chang 110:403–429. doi:10.1007/s10584-011-0056-2

    Article  Google Scholar 

  • Hughes JP, Guttorp P (1994) A class of stochastic models for relating synoptic atmospheric patterns to regional hydrologic phenomena. Water Resour Res 30(5):1535–1546

    Article  Google Scholar 

  • Hung NQ, Babel MS, Weesakul S, Tripathi NK (2009) An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrol Earth Syst Sci 13:1413–1425

    Article  Google Scholar 

  • IPCC (2013) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.

  • Jolliffe IT (1986) Principal component analysis. Springer New York. ISBN 978–0–387–22440–4

  • Jones RG, Noguer M, Hassell DC, Hudson D, Wilson SS, Jenkins GJ, Mitchell J (2004) Generating high resolution climate change scenarios using PRECIS. Taylor R (ed.), p44

  • Kang H, An KH, Park CK, Solis ALS, Stitthichivapak K (2007) Multimodel output statistical downscaling prediction of precipitation in the Philippines and Thailand. Geophys Res Lett 34: L15710. doi: 10.1029/2007GL030730

  • Kilsby CG, Jones PD, Burton A, Ford AC, Fowler HJ, Harpham C, James P, Smith A, Wilby RL (2007) A daily weather generator for use in climate change studies. Environ Modelling Software 22:1705–1719. doi:10.1016/j.envsoft.2007.02.005

    Article  Google Scholar 

  • Kuligowski RJ, Barros AP (1998) Experiments in short-term precipitation forecasting using artificial neural networks. Mon Wea Rev 126:470–482

    Article  Google Scholar 

  • Liu W, Fu G, Liu C, Charles SP (2013) A comparison of three multi-site statistical downscaling models or daily rainfall in the North China Plain. Theor Appl Climatol 111:585–600. doi:10.1007/S00704-012-0692-0

    Article  Google Scholar 

  • Liu Z, Xu Z, Charles SP, Fu G, Liu L (2011) Evaluation of two statistical downscaling models for daily precipitation over an arid basin in China. Int J Climatol 31(13):2006–2020. doi:10.1002/joc.2211

    Article  Google Scholar 

  • Mahmood R, Babel MS (2013) Evaluation of SDSM developed by annual and monthly sub-models for downscaling temperature and precipitation in the Jhelum basin, Pakistan and India. Theor Appl Climatol 113:27–44. doi:10.1007/s00704-012-0765-0

    Article  Google Scholar 

  • Martin TH, Howard BD, Mark B, Orlando DJ (1996) Neural networks design. PWS Publishing Company. ISBN-13: 978–0–9717321–1–7.

  • Mendes D, Marengo JA (2010) Temporal downscaling: a comparison between artificial neural network and autocorrelation techniques over the Amazon Basin in present and future climate change scenarios. Theor Appl Climatol 100:413–421. doi:10.1007/s00704-009-0193-y

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Murphy J (2000) Predictions of climate change over Europe using statistical and dynamical downscaling techniques. Int J Climatol 20:489–501

    Article  Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10(3):282–290

    Article  Google Scholar 

  • Okkan U, Fistikoglu O (2013) Evaluating climate change effects on runoff by statistical downscaling and hydrological model GR2M. Theor Appl Climatol. doi:10.1007/s00704-013-1005-y

    Google Scholar 

  • Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of the Koppen-Geiger climate classification. Hydrol Earth Syst Sci 11:1633–1644

    Article  Google Scholar 

  • Priddy KL, Keller PE (2005) Artificial neural networks: an introduction. SPIE – The International Society for Optical Engineering, Bellingham

    Book  Google Scholar 

  • Senga R (2009) Natural or unnatural disasters: the relative vulnerabilities of Southeast Asian megacities to climate change. WWF Report on “Mega-Stress for Mega-Cities”.

  • Sharma D, Babel M (2013) Application of downscaled precipitation for hydrological climate-change impact assessment in the upper Ping River Basin of Thailand. Clim Dyn 41:2589–2602. doi:10.1007/s00382-013-1788-7

    Article  Google Scholar 

  • Tangang FT, Tang B, Monahan AH, Hsieh WW (1998) Forecasting ENSO events: a neural network–extended EOF approach. J Clim 11:29–41

    Article  Google Scholar 

  • Tripathi S, Srinivas VV, Nanjundiah RS (2006) Downscaling of precipitation for climate change scenarios: a support vector machine approach. J Hydrol 330:621–640

    Article  Google Scholar 

  • van Vuuren DP, Edmonds JA, Kainuma M, Riahi K, Thomson AM, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque J-F, Masui T, Meinshausen M, Nakicenovic N, Smith SJ, Rose S (2011) The representative concentration pathways: an overview. Clim Chang 109:5–31. doi:10.1007/s10584-011-0148-z

    Article  Google Scholar 

  • von Storch H (1995) Inconsistencies at the interface of climate impact studies and global climate research. Meteorol Z 4:72–80

    Google Scholar 

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

    Article  Google Scholar 

  • Widmann M, Bretherton CS (2000) Validation of mesoscale precipitation in the NCEP reanalysis using a new gridcell dataset for the northwestern United States. J Clim 13:1936–1950

    Article  Google Scholar 

  • Wilby RL, Dawson CW (2004) Using SDSM version 3.1—a decision support tool for the assessment of regional climate change impacts, user manual.

  • Wilby RL, Dawson CW, Barrow EM (2002) SDSM—a decision support tool for the assessment of regional climate change impacts. Environ Modelling Software 17: 145–157. doi: 10.1016/S1364-8152(01)00060-3

  • Wilby RL, Hassan H, Hanaki K (1998) Statistical downscaling of hydrometeorological variables using general circulation model output. J Hydrol 205: 1–19. doi: 10.1016/S0022-1694(97)00130–3

  • Wilby RL, Tomlinson OJ, Dawson CW (2003) Multi-site simulation of precipitation by conditional resampling. Clim Res 23:183–194

    Article  Google Scholar 

  • Wilby RL, Wigley TML (1997) Downscaling general circulation model output: a review of methods and limitations. Prog Phys Geogr 21:530–548. doi:10.1177/030913339702100403 21

    Article  Google Scholar 

  • Wilby RL, Wigley TML (2000) Precipitation predictors for downscaling: observed and general circulation model relationships. Int J Climatol 20:641–661

    Article  Google Scholar 

  • Zorita E, von Storch H (1997) A survey of statistical downscaling techniques 45p. ISSN 0344-9629

Download references


The authors thank the Center for Hazards Research, Department of Civil and Environmental Engineering and CENSAM, SMART, both at the National University of Singapore. The second co-author wishes to thank the Thailand Research Fund through the Royal Golden Jubilee Ph.D. Programme (PHD/0221/2550) funded to Rangsit University for the doctoral programme and the Tropical Marine Science Institute, National University of Singapore, where he spent his internship.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Minh Tue Vu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vu, M.T., Aribarg, T., Supratid, S. et al. Statistical downscaling rainfall using artificial neural network: significantly wetter Bangkok?. Theor Appl Climatol 126, 453–467 (2016).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: