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http://dx.doi.org/10.3741/JKWRA.2013.46.3.229

GCMs-Driven Snow Depth and Hydrological Simulation for 2018 Pyeongchang Winter Olympics  

Kim, Jung Jin (Department of Biological & Agricultural Eng., Univ. of Idaho)
Ryu, Jae Hyeon (Department of Biological & Agricultural Eng., Univ. of Idaho)
Publication Information
Journal of Korea Water Resources Association / v.46, no.3, 2013 , pp. 229-243 More about this Journal
Abstract
Hydrological simulation Program-Fortran (HSPF) model was used to simulate streamflow and snow depth at Pyengchang watershed. The selected Global Climate Models (GCMs) provided by the Coupled Model Intercomparision Project Phase 3 (CMIP3) were utilized to evaluate streamflow and snow depth driven by future climate scenarios, including A1, A1B, and B1. Bias-correlation and temporal downscaling processes have been performed to minimize systematic errors between GCMs and HSPF. Based on simulated monthly streamflow and snow depth after calibration, the results indicate that HSPF performs well. The correlation coefficient between the observed and simulated monthly streamflow is 0.94. Snow depth simulations also show high correlation coefficient, which is 0.91. The results indicate that snow depth in 2018 at Pyongchang winter olympic venues will decrease by 17.62%, 9.38%, and 7.25% in January, February, and March respectively, based on streamflow realizations induced by all GCMs ensembles.
Keywords
GCMs; Climate Change; HSPF; Bias correlation; donwscaling; snow depth; 2018 Pyeongchang winter olympics;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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