Statistical downscaling rainfall using artificial neural network: significantly wetter Bangkok?
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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.
KeywordsArtificial Neural Network Artificial Neural Network Model Reanalysis Data Southwest Monsoon Statistical Downscaling
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.
- ADB, 2009: The economics of climate change in Southeast Asia: a regional review, ManilaGoogle 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. 91–100Google Scholar
- Bishop CM (1995) Neural networks for pattern recognition. Clarendon Press, Oxford. ISBN-13: 978–0198538646.Google Scholar
- Björck A (1996) Numerical methods for least squares problems. SIAM, Philadelphia. ISBN 0-89871-360-9.Google Scholar
- 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 64:503–515Google 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 CrossRefGoogle Scholar
- FAO (1982) A study of the agroclimatology of the humid tropics of Southeast Asia: technical report, FAO/UNESCO/WMO Interagency project on Agroclimatology, pp221Google Scholar
- 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):157–164Google Scholar
- 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.
- Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan College. ISBN 13: 9780023527616Google 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.Google Scholar
- Jolliffe IT (1986) Principal component analysis. Springer New York. ISBN 978–0–387–22440–4Google Scholar
- 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.), p44Google Scholar
- 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
- Martin TH, Howard BD, Mark B, Orlando DJ (1996) Neural networks design. PWS Publishing Company. ISBN-13: 978–0–9717321–1–7.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”.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 CrossRefGoogle Scholar
- von Storch H (1995) Inconsistencies at the interface of climate impact studies and global climate research. Meteorol Z 4:72–80Google 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.Google Scholar
- 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
- Zorita E, von Storch H (1997) A survey of statistical downscaling techniques 45p. ISSN 0344-9629Google Scholar