Journal of Wetlands Research (한국습지학회지)

Korean Wetlands Society (한국습지학회)
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Spatial Downscaling Method for Use of GCM Data in A Mountainous Area

산악지역에 GCM 자료를 이용하기 위한 공간 축소방법 개발

  • Received : 2012.11.02
  • Accepted : 2013.02.14
  • Published : 2013.02.28
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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.

본 연구에서는 강수의 공간적 편차가 큰 산악지역에서 축소기법을 적용하기 위한 방법론을 마련하고 이를 이용하여 미래 강수특성의 변화를 추정하고자 하였다. 이를 위하여 한반도내 산악지역이라고 할 수 있는 남한강유역을 대상유역으로 선정하였고 일반적인 축소기법 중의 하나인 신경망과 고도자료를 부가자료로 활용하여 유역의 지형적 특성을 반영할 수 있는 SKlm 기법을 연계하여 신경망-SKlm 모형(ANN-SKlm : Artificial Neural Network - Simple Kriging with varying local means)을 구축하였다. 유역내 6개의 기상관측소 지점의 월강수량을 이용하여 신경망-SKlm 기법과 기존 강수량의 공간분포 방법인 Thiessen 및 Ordinary Kriging 을 적용하여 비교 평가하였다. 유역내에 보다 밀도있게 구성되어 있는 25개 강우관측소 지점을 대상으로 각 기법을 평가한 결과 고도자료를 부가자료로 사용하는 SKlm 기법이 가장 우수한 결과를 나타내었다.

Keywords

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