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Advances in Meteorology
Volume 2014 (2014), Article ID 473167, 14 pages
Research Article

A Regionalization of Downscaled GCM Data Considering Geographical Features in a Mountainous Area

1Columbia Water Center, Columbia University, New York City, NY 10027, USA
2Centre Eau Terre Environment, INRS490, rue de la Couronne, QC, Canada G1K 9A9
3Department of Civil Engineering, Inha University, Nam-Gu, Incheon 402-751, Republic of Korea

Received 19 April 2014; Revised 8 August 2014; Accepted 10 August 2014; Published 8 September 2014

Academic Editor: Richard Anyah

Copyright © 2014 Soojun Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This study establishes a methodology for the application of downscaled GCM data in a mountainous area having large spatial variations of rainfall and attempts to estimate the change of rainfall characteristics in the future under climate change. The Namhan river basin, which is in the mountainous area of the Korean peninsula, has been chosen as the study area. neural network-simple kriging with varying local means (ANN-SKlm) has been built by combining the artificial neural network, which is one of the general downscaling techniques, with the SKlm regionalization technique, which can reflect the geomorphologic characteristics. The ANN-SKlm technique was compared with the Thiessen technique and the ordinary kriging (OK) technique in the study area and the SKlm technique showed the best results. Future rainfall levels have been predicted by downscaling the data from CNRM-CM3 climate model, which was simulated under the A1B scenario. According to the results of future annual average rainfall by each regionalization technique, the Thiessen and OK techniques underestimated the future rainfall when compared to the ANN-SKlm technique. Therefore this methodology will be very useful for the prediction of future rainfall levels under climate change, most notably in a mountainous area.