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Increasing greenhouse gas emissions will cause global temperature to rise in the coming years. Understanding the effects of rising temperature on the hydrologic cycle at a local scale is important in order to assess a wide scope of climate change impacts on management of water resources. Coupled Atmosphere-Ocean General Circulation Models (AOGCMs) are state-of-theart in climate change research, predicting the future climate based on plausible emission scenarios. Their spatial and temporal scales are quite large, so the results of their analyses must be brought to a local-scale through a downscaling process. There are several methods for downscaling AOGCM data; however each method can produce very different results. More work is necessary to develop strategies for climate change impact assessments at a local level.
In this study, statistical downscaling using a modified K-NN weather generator with perturbation and principle component analysis (WG-PCA) is employed to investigate the potential impacts of climatic change in the Upper Thames River basin. A total of 22 stations around the basin are used as inputs, each with 27 years of observed historical data. Monthly change factors are applied to the observed data from six AOGCMs, each with two to three emission scenarios. The resulting datasets are used as inputs to the WG-PCA algorithm to produce 324 years of synthetic data for two time periods, the 2020s (2011-2040) and the 2080s (2071-2100). The performance of the weather generator is evaluated by comparing a synthetic historical dataset to the observed data.
The WG-PCA algorithm is able to satisfactorily reproduce the observed monthly total precipitation values. While there is a slight overestimation in the mean of some months and an underestimation in others, the values of the observed means are well within the inter-quartile range (25th and 75th percentile) of the simulated data, thus performance is considered very good. The outliers in the historical simulated values indicate the added variability by the WG-PCA weather generator. Total monthly wet-day box plots are made, and results show that there are underestimations of the mean observed data in some months. The statistical hypothesis tests show that the difference between the mean and variance of the observed and simulated precipitation are similar. Frequency distribution curves of wet-spell lengths for winter and summer months also show a very close agreement between the observed and simulated values. Overall, the performance of the WG-PCA weather generator in reproducing historical values is very good.
The AOGCM outputs are compared using box plots of total monthly precipitation values. The results show different predictions of future precipitation, however most models predict an increase in total monthly precipitation for winter for both the 2020s and the 2080s. Summer values are less conclusive as some models predict an increase in total precipitation while other predict a decrease. The general trends of wet-spell intensities show an increase in wet spell intensity for longer time spells for winter in both time periods. For summer wet-spells, both time periods predict that shorter spells will increase in intensity as long ones decrease. The results for AOGCM simulation are quite variable, thus it is important to include several models and emission scenarios in climate change impact assessments.
Department of Civil and Environmental Engineering, The University of Western Ontario
London, Ontario, Canada
Civil and Environmental Engineering
King, Leanna; Solaiman, Tarana A.; and Simonovic, Slobodan P., "Assessment of Climatic Vulnerability in the Upper Thames River Basin: Part 2" (2010). Water Resources Research Report. 28.