Programme  OS3c Climate change: detecting trends, projecting future  abstract 691

Utilizing North American Regional Reanalysis for Climate Change Impact Assessment on Water Resources in Central Canada

Author(s): Sung Joon Kim, Woonsup Choi, Mark Lee, Peter F. Rasmussen
Sung Joon Kim Department of Civil Engineering, University of Manitoba, 15 Gillson St., Winnipeg, Manitoba, Canada Email: Phone: +1 (204) 474-6862 Fax : +1 (204) 474-7531

Keyword(s): North American Regional Reanalysis, Hydrological Modeling, SLURP, Statistical Downscaling, Nearest Neighbour Resampling

Article: abs691_article.pdf
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Session: OS3c Climate change: detecting trends, projecting future
AbstractThe province

of Manitoba, Canada, is blessed with abundant surface water resources but lacks in weather stations where the

water resources are. As a result, hydrological modeling and climate change impact assessments for water resources

management face difficulty mainly due to limited input data. Recent studies found that the North American Regional

Reanalysis (NARR) has high potential for use as input data for hydrological modeling and statistical downscaling of

GCM data.
The objective of this study is (1) to utilize the NARR data for hydrologic modeling and statistical

downscaling of GCM data and (2) to assess the climate change impact on Manitoba water resources. Two

catchments (the Taylor and Burntwood River basins) in northern Manitoba and two (the Sturgeon and Troutlake

River basins) in north-western Ontario were selected for this study. The SLURP model was set up with

meteorological input data from NARR and calibrated for each catchment against the observed streamflow data. K

-Nearest Neighbour (k-NN) resampling, a statistical downscaling technique, was used to downscale the output from

the recent Canadian GCM (CGCM3) simulation under the IPCC SRES A2 emission scenario (2081-2100). The

downscaled CGCM3 data were used as input to the calibrated SLURP model to assess the future climate change

impact on water resources.
The results indicate that (1) the SLURP hydrological model can be reasonably

calibrated with the meteorological input data from NARR, (2) the results from the statistical downscaling with NARR

are comparable to those with weather station data, and (3) the warmer and wetter climate in the future is likely to

increase the runoff. NARR is found to be a good alternative to weather station data for climate change impact

studies in data-scarce central Canada, where higher risk of flooding and lower risk of extended droughts are

projected due to climate change.

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