Reduction of the uncertainties in the hydrological projections in Korean river basins using dynamically downscaled climate projections
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Author(s)
Qiu, Liying
Kim, Jeong-Bae
Kim, Seon-Ho
Choi, Yeon-Woo
Im, Eun-Soon
Bae, Deg-Hyo
Date Issued
March 2, 2022
Publisher
Springer Berlin Heidelberg
Citation
Qiu, Liying, Kim, Jeong-Bae, Kim, Seon-Ho, Choi, Yeon-Woo, Im, Eun-Soon et al. 2022. "Reduction of the uncertainties in the hydrological projections in Korean river basins using dynamically downscaled climate projections."
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Author's final manuscript
Abstract
Abstract
How the value of higher-resolution climate variables dynamically downscaled can affect the hydrological impact assessment has been a long standing issue. This study investigates the potential benefit of high-resolution climate data locally tailored over South Korea in terms of the reduction of uncertainties in hydrological projections. For this purpose, a large ensemble consisting of three Global Climate Model (GCM) projections and their dynamical downscaling products in different resolutions (i.e., 20 and 5 km), and four bias correction (BC) methods is fed into a semi-distributed hydrological model (HM) customized over Korean river basins. The in-depth comparison among the 45-members hydrological simulations proves the benefit in using high-resolution Regional Climate Model (RCM) for the runoff projections. While this study acknowledges the necessity of BC to remove the systematic bias in climate simulations, it is found that the high-resolution dynamical downscaling can significantly narrow the spread brought with different BC methods, thus reducing the uncertainty in the projected hydrological change. The projected runoff changes for both the mean of wet season and the high flows indicate that there will be an intensified runoff, especially for the extremes, over South Korea under the warming. Altogether, this study provides a valuable exploration of uncertainty reduction in hydrological projections from the perspective of resolution effect of dynamical downscaling, which is meaningful for hydroclimate studies and climate change impact assessment.
MIT Department
Parsons Laboratory for Environmental Science and Engineering (Massachusetts Institute of Technology)
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DOI of Published Version
https://doi.org/10.1007/s00382-022-06201-8
