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N. Nakicenovic, A. Grubler, H. Ishitani, T. Johansson, G. Marland, J. R. Moreira and H.-H. Rogner, “Energy Primer in Climate Change 1995,” In: R Watson, M. C. Zinyowera and R. Moss, Eds., Impacts, Adaptations and Mitigation of Climate Change: Scientific Analysis, Cambridge University Press, Cambridge, UK, 1996.

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• TITLE: Regional Climate Index for Floods and Droughts Using Canadian Climate Model (CGCM3.1)

  AUTHORS: Nassir El-Jabi, Noyan Turkkan, Daniel Caissie

  KEYWORDS: Canadian Climate Model; Artificial Neural Networks; Floods; Droughts; Regional Climate Index

  JOURNAL NAME: American Journal of Climate Change, Vol.2 No.2, June 24, 2013

  ABSTRACT: The impacts of climate change on the discharge regimes in New Brunswick (Canada) were analyzed, using artificial neural network models. Future climate data were extracted from the Canadian Coupled General Climate Model (CGCM3.1) under the greenhouse gas emission scenarios B1 and A2 defined by the Intergovernmental Panel on Climate Change (IPCC). The climate change fields (temperatures and precipitation) were downscaled using the delta change approach. Using the artificial neural network, future river discharge was predicted for selected hydrometric stations. Then, a frequency analysis was carried out using the Generalized Extreme Value (GEV) distribution function, where the parameters of the distribution were estimated using L-moments method. Depending on the scenario and the time slice used, the increase in low return floods was about 30% and about 15% for higher return floods. Low flows showed increases of about 10% for low return droughts and about 20% for higher return droughts. An important part of the design process using frequency analysis is the estimation of future change in floods or droughts under climate scenarios at a given site and for specific return periods. This was carried out through the development of Regional Climate Index (RCI), linking future floods and droughts to their frequencies under climate scenarios B1 and A2.