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http://dx.doi.org/10.17663/JWR.2013.15.1.115

Spatial Downscaling Method for Use of GCM Data in A Mountainous Area  

Kim, Soojun (Columbia Water Center, Columbia University)
Kang, Na Rae (Department of Civil Engineering, Inha university)
Kim, Yon Soo (Department of Civil Engineering, Inha university)
Lee, Jong So (Department of Civil Engineering, Inha university)
Kim, Hung Soo (Department of Civil Engineering, Inha university)
Publication Information
Journal of Wetlands Research / v.15, no.1, 2013 , pp. 115-125 More about this Journal
Abstract
This study established a methodology for the application of downscaling technique in a mountainous area having large spatial variations of rainfall and tried to estimate the change of rainfall characteristics in the future under climate change using the established method. The Namhan river basin, which is in the mountainous area of the Korean peninsula, has been chosen as the study area. Artificial Neural Network - Simple Kriging with varying local means (ANN-SKlm) has been built by combining artificial neural network, which is one of the general downscaling techniques, and SKlm technique, which can reflect the geomorphologic characteristics like elevation of the study area. The evaluation of SKlm technique was done by using the monthly rainfalls at six weather stations which KMA(Korea Meteorological Administration) is managing in the basin. The ANN-SKlm technique was compared with the Thiessen technique and ordinary kriging(OK) technique. According to the evaluation result of each technique the SKlm technique showed the best result.
Keywords
Climatic Change; Downscaling Technique; Neural Network; Simple Kriging with varying local means;
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