A Regionalization of Downscaled GCM Data Considering Geographical Features in a Mountainous Area
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 ar...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2014/473167 |
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| author | Soojun Kim Jaewon Kwak Hung Soo Kim Yonsoo Kim Narae Kang Seung Jin Hong Jongso Lee |
| author_facet | Soojun Kim Jaewon Kwak Hung Soo Kim Yonsoo Kim Narae Kang Seung Jin Hong Jongso Lee |
| author_sort | Soojun Kim |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b8e8d0dbb8504f1eae5713340ff74653 |
| institution | DOAJ |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-b8e8d0dbb8504f1eae5713340ff746532025-08-20T03:05:01ZengWileyAdvances in Meteorology1687-93091687-93172014-01-01201410.1155/2014/473167473167A Regionalization of Downscaled GCM Data Considering Geographical Features in a Mountainous AreaSoojun Kim0Jaewon Kwak1Hung Soo Kim2Yonsoo Kim3Narae Kang4Seung Jin Hong5Jongso Lee6Columbia Water Center, Columbia University, New York City, NY 10027, USACentre Eau Terre Environment, INRS490, rue de la Couronne, QC, G1K 9A9, CanadaDepartment of Civil Engineering, Inha University, Nam-Gu, Incheon 402-751, Republic of KoreaDepartment of Civil Engineering, Inha University, Nam-Gu, Incheon 402-751, Republic of KoreaDepartment of Civil Engineering, Inha University, Nam-Gu, Incheon 402-751, Republic of KoreaDepartment of Civil Engineering, Inha University, Nam-Gu, Incheon 402-751, Republic of KoreaDepartment of Civil Engineering, Inha University, Nam-Gu, Incheon 402-751, Republic of KoreaThis 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.http://dx.doi.org/10.1155/2014/473167 |
| spellingShingle | Soojun Kim Jaewon Kwak Hung Soo Kim Yonsoo Kim Narae Kang Seung Jin Hong Jongso Lee A Regionalization of Downscaled GCM Data Considering Geographical Features in a Mountainous Area Advances in Meteorology |
| title | A Regionalization of Downscaled GCM Data Considering Geographical Features in a Mountainous Area |
| title_full | A Regionalization of Downscaled GCM Data Considering Geographical Features in a Mountainous Area |
| title_fullStr | A Regionalization of Downscaled GCM Data Considering Geographical Features in a Mountainous Area |
| title_full_unstemmed | A Regionalization of Downscaled GCM Data Considering Geographical Features in a Mountainous Area |
| title_short | A Regionalization of Downscaled GCM Data Considering Geographical Features in a Mountainous Area |
| title_sort | regionalization of downscaled gcm data considering geographical features in a mountainous area |
| url | http://dx.doi.org/10.1155/2014/473167 |
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