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Sensitivity of hydrological systems to GCM-based climate scenarios in northern Manitoba (Canada) boreal forest region

IWRA World Water Congress 2008 Montpellier France
1. Water availability, use and management
Author(s): Woonsup Choi
Adam Moore
Kristina Koenig
Peter Rasmussen
Contact information for Woonsup Choi Department of Civil Engineering University of Manitoba Winnipeg, Manitoba R3T 5V6 Canada Phone: +1 204 297 4042 Fax: + 204 474 7513 Email: supchoi@yahoo.com

Keyword(s): climate change, hydrological modeling, statistical downscaling, runoff
Article: PDF

AbstractThis study investigated how river basins respond to climate scenarios generated from different GCMs (HadCM3 and CGCM) in the northern Manitoba boreal forest region focusing on low flow statistics. The SLURP hydrological model was applied to the Taylor and Burntwood River basins using climate scenarios generated with the output from the GCMs under A2 and B2 SRES scenarios. The GCM output was downscaled using the delta method and the Statistical DownScaling Model (SDSM). The GCMs consistently forecast wetter and warmer climate with the emission scenarios. Warming is the most prominent during winter, especially with CGCM3 under A2 scenario. Precipitation projection reveals more variability between models and scenarios than temperature projection, but a general trend is that large increases are expected during winter and spring and only small fluctuations during summer. Such changes in climate are projected to lead to an overall increase in runoff in the second half of the 21st century. The increase is the most remarkable in April and May and the least so in winter and summer months. It is also projected that there will be much fewer days with extremely low flows than there are currently. In spite of different magnitudes, changing trends are consistent throughout the models and scenarios. The results indicate that the communities in the region need to be better prepared for spring floods while the increased runoff may contribute to more hydroelectric power generation.
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