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

Nassir El-Jabi, Noyan Turkkan, Daniel Caissie

Department of Civil Engineering, Université de Moncton, Moncton, Canada.
Department of Fisheries and Oceans Canada, Moncton, Canada.

DOI: 10.4236/ajcc.2013.22011                

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.

Keywords

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

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Parks Canada, “Air Quality, Climate Change and Canada’s National Parks,” Parks Canada, Natural Resources Branch, Air Issues Bulletin No. 100, Ottawa, 1999.
[2]	H. G. Hengeveld, “Global Climate Change: Implication for Air Temperature and Water Supply in Canada,” Transactions of the American Fisheries Society, Vol. 119, No. 2, 1990, pp. 176-182. doi:10.1577/1548-8659(1990)119<0176:GCCIFA>2.3.CO;2
[3]	Natural Resources Canada, “Climate Change Impacts and Adaptation: A Canadian Perspective,” Water Resources, Climate Change Impacts and Adaptation Directorate, Ottawa, 2002.
[4]	C. K. Minns, R. G. Randall, E. M. P. Chadwick, J. E. Moore and R. Green, “Potential Impact of Climate Change on the Habitat and Population Dynamics of Juvenile Atlantic Salmon (Salmo salar) in Eastern Canada. Climate Change and Northern Fish Population,” Canadian Special Publications of Fisheries and Aquatic Sciences, 1995, pp. 699-708.
[5]	D. Caissie,“Hydrological Conditions for Atlantic Salmon Rivers in the Maritime Provinces in 1997,” Canadian Stock Assessment SecretariatResearch Document, 1999.
[6]	D. Caissie, “Hydrological Conditions for Atlantic Salmon Rivers in the Maritime Provinces in 1998,” Canadian Stock Assessment Secretariat Research Document, 1999.
[7]	D. Caissie, “Hydrological Conditions for Atlantic salmon Rivers in 1999,” Canadian Stock Assessment Secretariat Research Document, 2000.
[8]	Environment Canada and New Brunswick Department of Municipal Affairs and Environment, “Flood Frequency Analyses, New Brunswick, a Guide to the Estimation of Flood Flows for New Brunswick Rivers and Streams,” April 1987, p. 49.
[9]	F. Aucoin, D. Caissie, N. El-Jabi and N. Turkkan, “Flood Frequency Analyses for New Brunswick Rivers,” Canadian Technical Report of Fisheries and Aquatic Sciences, 2011.
[10]	Environment Canada and New Brunswick Department of the Environment, “Low Flow Estimation Guidelines for New Brunswick,” Inland Waters Directorate, Environment Canada, Dartmouth, NS and Water Resources Planning Branch, New Brunswick Department of the Environment, Fredericton, 1990.
[11]	D. Caissie, L. LeBlanc, J. Bourgeois, N. El-Jabi and N. Turkkan, “Low Flow Estimation for New Brunswick Rivers,” Canadian Technical Report of Fisheries and Aquatic Sciences, 2011.
[12]	J. Alcamo, A. Bouwman, J. Edmonds, A. Grübler, T. Morita and A. Sugandhy, “An Evaluation of the IPCC IS92 Emission Scenarios,” In J. T. Houghton, L. G. Meira Filho, J. Bruce, H. Lee, B. A. Callander, E. Haites, N. Harris and K. Maskell, Eds., Radiative Forcing of Climate Change and an Evaluation of the IPCC IS92 Emission Scenarios, Cambridge University Press, Cambridge, 1995, pp. 233-304.
[13]	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.
[14]	D. Caissie and S. Robichaud, “Towards a Better Understanding of the Natural Flow Regimes and Streamflow Characteristics of Rivers of the Maritime Provinces,” Canadian Technical Report of Fisheries and Aquatic Sciences, 2009.
[15]	C. Prudhomme, N. Reynard and S. Crooks, “Downscaling from Global Climate Models for Flood Frequency Analysis: Where Are We Now?” Hydrological Processes, Vol. 16, No. 6, 2002, pp. 1137-1150. doi:10.1002/hyp.1054
[16]	N. Turkkan, N. El-Jabi and D. Caissie,“Floods and Droughts under Different Climate Change Scenarios in New Brunswick,” Canadian Technical Report of Fisheries and Aquatic Sciences, 2011.
[17]	H. Fowler and C. Kilsby, “Using Regional Climate Model Data to Simulate Historical and Future River Flows in Northwest England,” Climatic Change, Vol. 80, No. 3, 2007, pp. 337-367. doi:10.1007/s10584-006-9117-3
[18]	R. S. Govindaraju, “Artificial Neural Networks in Hydrology. I: Preliminary Concepts,” Journal of Hydrologic Engineering, Vol. 5, No. 2, 2000, pp.115-123. doi:10.1061/(ASCE)1084-0699(2000)5:2(115)
[19]	R. S. Govindaraju, “Artificial Neural Networks in Hydrology. II: Hydrologic Application,” Journal of Hydrologic Engineering, Vol. 5, No. 2, 2000, pp. 124-137. doi:10.1061/(ASCE)1084-0699(2000)5:2(124)
[20]	D. Bastarache, N. El-Jabi, N. Turkkan and T. A. Clair. “Predicting Conductivity and Acidity for Two Small Streams Using Learning Networks,” Canadian Journal of Civil Engineering, Vol. 24, 1997, pp. 1030-1039. doi:10.1139/cjce-24-6-1030
[21]	J. T. Houghton, D. Ding, J. Griggs, M. Noguer, P. J. Z. van der Linden and D. Xiaosu, “Climate Change 2001: The Scientific Basis,” Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press, Cambridge, 2001.