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Sustainability 2018, 10(7), 2409; https://doi.org/10.3390/su10072409

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

1
Institute of Engineering Research, Seoul National University, Seoul 08826, Korea
2
Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Korea
*
Author to whom correspondence should be addressed.
Received: 30 May 2018 / Revised: 5 July 2018 / Accepted: 5 July 2018 / Published: 10 July 2018
(This article belongs to the Section Sustainable Engineering and Science)
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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. View Full-Text
Keywords: climate change; scenario selection; global circulation model; Katsavounidis-Kuo-Zhang algorithm; uncertainty; water resources climate change; scenario selection; global circulation model; Katsavounidis-Kuo-Zhang algorithm; uncertainty; water resources
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Seo, S.B.; Kim, Y.-O. Impact of Spatial Aggregation Level of Climate Indicators on a National-Level Selection for Representative Climate Change Scenarios. Sustainability 2018, 10, 2409.

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