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Impact of Spatial Aggregation Level of Climate Indicators on a National-Level Selection for Representative Climate Change Scenarios

Author

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  • Seung Beom Seo

    (Institute of Engineering Research, Seoul National University, Seoul 08826, Korea)

  • Young-Oh Kim

    (Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Korea)

Abstract

For sustainable management of water resources, adaptive decisions should be determined considering future climate change. Since decision makers have difficulty in formulating a decision when they should consider a large number of climate change scenarios, selecting a subset of Global Circulation Models (GCM) outputs for climate change impact studies is required. In this study, the Katsavounidis-Kuo-Zhang (KKZ) algorithm was used for representative climate change scenarios selection and a comprehensive analysis has been done through a national-level case study of South Korea. The KKZ algorithm was applied to select a subset of GCMs for each subbasin in South Korea. To evaluate impacts of spatial aggregation level of climate data sets on preserving inter-model variability of hydrologic variables, three different scales (national level, river region level, subbasin level) were tested. It was found that only five GCMs selected by KKZ algorithm can explain almost of whole inter-model variability driven by all the 27 GCMs under Representative Concentration Pathways (RCP) 4.5 and 8.5. Furthermore, a single set of representative GCMs selected for national level was able to explain inter-model variability on almost the whole subbasins. In case of low flow variable, however, use of finer scale of climate data sets was recommended.

Suggested Citation

  • Seung Beom Seo & Young-Oh Kim, 2018. "Impact of Spatial Aggregation Level of Climate Indicators on a National-Level Selection for Representative Climate Change Scenarios," Sustainability, MDPI, vol. 10(7), pages 1-18, July.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:7:p:2409-:d:157251
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    References listed on IDEAS

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    1. Thomas Mendlik & Andreas Gobiet, 2016. "Selecting climate simulations for impact studies based on multivariate patterns of climate change," Climatic Change, Springer, vol. 135(3), pages 381-393, April.
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    Cited by:

    1. Jang Hyun Sung & Minsung Kwon & Jong-June Jeon & Seung Beom Seo, 2019. "A Projection of Extreme Precipitation Based on a Selection of CMIP5 GCMs over North Korea," Sustainability, MDPI, vol. 11(7), pages 1-17, April.
    2. Seol A. Kwon, 2022. "Where Does an Individual’s Willingness to Act on Alleviating the Climate Crisis in Korea Arise from?," Sustainability, MDPI, vol. 14(11), pages 1-17, May.
    3. Jang Hyun Sung & Young Ryu & Seung Beom Seo, 2020. "Utilizing Bivariate Climate Forecasts to Update the Probabilities of Ensemble Streamflow Prediction," Sustainability, MDPI, vol. 12(7), pages 1-24, April.

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