Journal
SUSTAINABILITY
Volume 10, Issue 7, Pages -Publisher
MDPI
DOI: 10.3390/su10072409
Keywords
climate change; scenario selection; global circulation model; Katsavounidis-Kuo-Zhang algorithm; uncertainty; water resources
Funding
- National Research Foundation of Korea [NRF-2017R1A6A3A11031800]
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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.
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