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GCM 공간상세화 방법별 기후변화에 따른 수문영향 평가- 만경강 유역을 중심으로 -Assessing Hydrologic Impacts of Climate Change in the Mankyung Watershed with different GCM Spatial Downscaling Methods

Other Titles
Assessing Hydrologic Impacts of Climate Change in the Mankyung Watershed with different GCM Spatial Downscaling Methods
Authors
김동현장태일황세운조재필
Issue Date
Nov-2019
Publisher
한국농공학회
Keywords
SWAT; climate change; GCM; downscaling; hydrologic response
Citation
한국농공학회논문집, v.61, no.6, pp 81 - 92
Pages
12
Indexed
KCI
Journal Title
한국농공학회논문집
Volume
61
Number
6
Start Page
81
End Page
92
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/10078
DOI
10.5389/KSAE.2019.61.6.081
ISSN
1738-3692
2093-7709
Abstract
The objective of this study is to evaluate hydrologic impacts of climate change according to downscaling methods using the Soil and Water AssessmentTool (SWAT) model at watershed scale. We used the APCC Integrated Modeling Solution (AIMS) for assessing various General Circulation Models(GCMs) and downscaling methods. AIMS provides three downscaling methods: 1) BCSA (Bias-Correction & Stochastic Analogue), 2) Simple QuantileMapping (SQM), 3) SDQDM (Spatial Disaggregation and Quantile Delta Mapping). To assess future hydrologic responses of climate change, weadopted three GCMs: CESM1-BGC for flood, MIROC-ESM for drought, and HadGEM2-AO for Korea Meteorological Administration (KMA) nationalstandard scenario. Combined nine climate change scenarios were assessed by Expert Team on Climate Change Detection and Indices (ETCCDI). SWATmodel was established at the Mankyung watershed and the applicability assessment was completed by performing calibration and validation from 2008to 2017. Historical reproducibility results from BCSA, SQM, SDQDM of three GCMs show different patterns on annual precipitation, maximumtemperature, and four selected ETCCDI. BCSA and SQM showed high historical reproducibility compared with the observed data, however SDQDMwas underestimated, possibly due to the uncertainty of future climate data. Future hydrologic responses presented greater variability in SQM andrelatively less variability in BCSA and SDQDM. This study implies that reasonable selection of GCMs and downscaling methods considering researchobjective is important and necessary to minimize uncertainty of climate change scenarios.
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