Journal of The Korean Society of Agricultural Engineers (한국농공학회논문집)

The Korean Society of Agricultural Engineers (한국농공학회)
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Assessing Hydrologic Impacts of Climate Change in the Mankyung Watershed with Different GCM Spatial Downscaling Methods

GCM 공간상세화 방법별 기후변화에 따른 수문영향 평가 - 만경강 유역을 중심으로 -

  • Kim, Dong-Hyeon (Department of Rural Construction Engineering, Jeonbuk National University) ;
  • Jang, Taeil (Department of Rural Construction Engineering, Jeonbuk National University) ;
  • Hwang, Syewoon (Department of Agricultural Engineering, Institute of Agriculture and Life Science, Gyeongsang National University) ;
  • Cho, Jaepil (Convergence Center for Watershed Management, Integrated Watershed Management Institute (IWMI))
  • Received : 2019.09.30
  • Accepted : 2019.10.30
  • Published : 2019.11.30
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Abstract

The objective of this study is to evaluate hydrologic impacts of climate change according to downscaling methods using the Soil and Water Assessment Tool (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 Quantile Mapping (SQM), 3) SDQDM (Spatial Disaggregation and Quantile Delta Mapping). To assess future hydrologic responses of climate change, we adopted three GCMs: CESM1-BGC for flood, MIROC-ESM for drought, and HadGEM2-AO for Korea Meteorological Administration (KMA) national standard scenario. Combined nine climate change scenarios were assessed by Expert Team on Climate Change Detection and Indices (ETCCDI). SWAT model was established at the Mankyung watershed and the applicability assessment was completed by performing calibration and validation from 2008 to 2017. Historical reproducibility results from BCSA, SQM, SDQDM of three GCMs show different patterns on annual precipitation, maximum temperature, and four selected ETCCDI. BCSA and SQM showed high historical reproducibility compared with the observed data, however SDQDM was underestimated, possibly due to the uncertainty of future climate data. Future hydrologic responses presented greater variability in SQM and relatively less variability in BCSA and SDQDM. This study implies that reasonable selection of GCMs and downscaling methods considering research objective is important and necessary to minimize uncertainty of climate change scenarios.

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References

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