Browse > Article
http://dx.doi.org/10.5389/KSAE.2010.52.4.083

Projection and Analysis of Future Temperature and Precipitation using LARS-WG Downscaling Technique - For 8 Meteorological Stations of South Korea -  

Shin, Hyung-Jin (건국대학교 대학원 사회환경시스템공학과)
Park, Min-Ji (건국대학교 대학원 사회환경시스템공학과)
Joh, Hyung-Kyung (건국대학교 대학원 사회환경시스템공학과)
Park, Geun-Ae (건국대학교 사회환경시스템공학과)
Kim, Seong-Joon (건국대학교 사회환경시스템공학과)
Publication Information
Journal of The Korean Society of Agricultural Engineers / v.52, no.4, 2010 , pp. 83-91 More about this Journal
Abstract
Generally, the GCM (General Circulation Model) data by IPCC climate change scenarios are used for future weather prediction. IPCC GCM models predict well for the continental scale, but is not good for the regional scale. This paper tried to generate future temperature and precipitation of 8 scattered meteorological stations in South Korea by using the MIROC3.2 hires GCM data and applying LARS-WG downscaling method. The MIROC3.2 A1B scenario data were adopted because it has the similar pattern comparing with the observed data (1977-2006) among the scenarios. The results showed that both the future precipitation and temperature increased. The 2080s annual temperature increased $3.8{\sim}5.0^{\circ}C$. Especially the future temperature increased up to $4.5{\sim}7.8^{\circ}C$ in winter period (December-February). The future annual precipitation of 2020s, 2050s, and 2080s increased 17.5 %, 27.5 %, and 39.0 % respectively. From the trend analysis for the future projected results, the above middle region of South Korea showed a statistical significance for winter precipitation and south region for summer rainfall.
Keywords
GCM; MIROC3.2; LARS-WG; downscaling; temperature; precipitation;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Wood, A. W., L. R. Leung, V. Sridhar, and D. P. Lettenmaier, 2004. Hydrologic implications of dynamic and statistical approaches to downscaling climate model outputs. Climate Change 62: 189-216.   DOI
2 Yevjevich, V., 1972. Probability and Statistics in Hydrology. Highlands Ranch, Colo.: Water Resources Publications
3 Jung, I. W., D. H. Bae, and E. S. Im, 2007. Generation of high resolution scenarios for climate change impacts on water resources (II): Runoff scenarios on each subbasins. Journal of Korea Water Resources Association 40(3): 205-214 (in Korean).   과학기술학회마을   DOI
4 Korea Rural Infrastructure and Rural Corporation (KARICO), 2009. A study on the impact assessment of climate change on agricultural water, Project paper (in Korean).
5 Oh, J, H., T. Kim, M. K. Kim, S. H. Lee, S. K. Min, and W. T. Kwon, 2004. Regional climate simulation for Korea using dynamic downscaling and statistical for adjustment. Journal of the Meteorological Society of Japan 82(6): 1629-1643.
6 Park G. A., S. R. Ahn, Y. J. Lee, H. J. Shin, M. J. Park, and S. J. Kim, 2009. Assessment of Climate Change Impact on the Inflow and Outflow of Two Agricultural Reservoirs in Korea, American Society of Agricultural and Biological Engineers 52(6): 1869-1883.
7 Racsko, P., L. Szeidla and M. Semenovb, 1991. A serial approach to local stochastic weather models, Ecological Modelling 57: 27-41   DOI   ScienceOn
8 Richardson C. W., 1981. Stochastic Simulation of Daily Precipitation, Temperature, and Solar Radiation, Water resources research 17(1): 182-190   DOI
9 Sailor, D. J., and X. Li, 1999. A Semi-Empirical Downscaling Approach for Predicting Regional Temperature Impacts Associated with Climatic Change. Journal of Climate 12(1): 103-114.   DOI   ScienceOn
10 Semenov, M. A., R. J. Brooks, E. N. Barrow, and C. W. Richardson, 1998. Comparison of the WGEN and LARSWG stochastic weather generators for diverse climates. Climate Research 10: 95-107.   DOI
11 Bae, D. H., I. W. Jung, and W. T. Kwon, 2007. Generation of high resolution scenarios for climate change impacts on water resources (I): Climate scenarios on each sub-basins. Journal of Korea Water Resources Association 40(3): 191-204 (in Korean).   과학기술학회마을   DOI
12 Wilks, D. S., and R. L. Wilby, 1999. The weather generation game: a review of stochastic weather models. Progress in Physical Geography 23: 329-357.   DOI
13 Alcamo, J., P. Doll, F. Kaspar, and S. Siebert, 1997. Global change and global scenarios of water use and availability: an application of WaterGAP 1.0. Report A9701, Center for Environmental Systems Research, University of Kassel, Germany.
14 Gibbons, J. D., 1990. Handbook of statistical methods for engineers and scientists. McGrawHill, ed. Harroson M. W.,: 11-26.
15 Bardossy, A., and H. J. Caspary, 1991. Conceptual model for the calculation of the regional hydrologic effects of climate change. Hydrology for the Water Management of Large River Basins. IAHS Publ. 201: 73-82.
16 Conover, W. J,. 1971. Practical Nonparametric Statistics. New York: J. Wiley and Sons Inc..
17 Hirsch, R. M., and J. R. Slack, 1984. A nonparametric trend test for seasonal data with serial dependence, Water Resources Research 20: 727-732.   DOI
18 Hong, E. M., J. Y. Choi, S. H. Lee, S. H. Yoo, and M. S. Kang, 2009. Estimation of Paddy Rice Evapotranspiration Considering Climate Change Using LARS-WG, Journal of the Korean Society of Agricultural Engineers 51(3): 25-35 (in Korean).   과학기술학회마을   DOI
19 Mason S. J., 2004. Simulating Climate over Western North America Using Stochastic Weather Generators. Climate Change 62: 155-187.   DOI
20 IPCC. 2001. Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, U.K: Cambridge University Press.