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Quantifying Uncertainties in Extreme Flood Predictions under Climate Change for a Medium-Sized Basin in Northeastern China

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  • 1 School of Hydraulic Engineering, Dalian University of Technology, Dalian, China, and Centre for Water Systems, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom, and School of Environmental Science and Engineering, South University of Science and Technology of China, Shenzhen, China
  • | 2 School of Hydraulic Engineering, Dalian University of Technology, Dalian, China
  • | 3 Centre for Water Systems, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
  • | 4 School of Hydraulic Engineering, Dalian University of Technology, Dalian, China
  • | 5 School of Environmental Science and Engineering, South University of Science and Technology of China, Shenzhen, China
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Abstract

This study develops a new variance-based uncertainty assessment framework to investigate the individual and combined impacts of various uncertainty sources on future extreme floods. The Long Ashton Research Station Weather Generator (LARS-WG) approach is used to downscale multiple general circulation models (GCMs), and the dynamically dimensioned search approximation of uncertainty approach is used to quantify hydrological model uncertainty. Extreme floods in a region in northeastern China are studied for two future periods: near term (2046–65) and far term (2080–99). Six GCMs and three emission scenarios (A1B, A2, and B1) are used. Results obtained from this case study show that the 500-yr flood magnitude could increase by 4.5% in 2046–65 and by 6.4% in 2080–99 in terms of median values; in worst-case scenarios, it could increase by 63.0% and 111.8% in 2046–65 and 2080–99, respectively. It is found that the combined effect of GCMs, emission scenarios, and hydrological models has a larger influence on the discharge uncertainties than the individual impacts from emission scenarios and hydrological models. Further, results show GCMs are the dominant contributor to extreme flood uncertainty in both 2046–65 and 2080–99 periods. This study demonstrates that the developed framework can be used to effectively investigate changes in the occurrence of extreme floods in the future and to quantify individual and combined contributions of various uncertainty sources to extreme flood uncertainty, which can guide future efforts to reduce uncertainty.

Corresponding author e-mail: Chi Zhang, czhang@dlut.edu.cn

Abstract

This study develops a new variance-based uncertainty assessment framework to investigate the individual and combined impacts of various uncertainty sources on future extreme floods. The Long Ashton Research Station Weather Generator (LARS-WG) approach is used to downscale multiple general circulation models (GCMs), and the dynamically dimensioned search approximation of uncertainty approach is used to quantify hydrological model uncertainty. Extreme floods in a region in northeastern China are studied for two future periods: near term (2046–65) and far term (2080–99). Six GCMs and three emission scenarios (A1B, A2, and B1) are used. Results obtained from this case study show that the 500-yr flood magnitude could increase by 4.5% in 2046–65 and by 6.4% in 2080–99 in terms of median values; in worst-case scenarios, it could increase by 63.0% and 111.8% in 2046–65 and 2080–99, respectively. It is found that the combined effect of GCMs, emission scenarios, and hydrological models has a larger influence on the discharge uncertainties than the individual impacts from emission scenarios and hydrological models. Further, results show GCMs are the dominant contributor to extreme flood uncertainty in both 2046–65 and 2080–99 periods. This study demonstrates that the developed framework can be used to effectively investigate changes in the occurrence of extreme floods in the future and to quantify individual and combined contributions of various uncertainty sources to extreme flood uncertainty, which can guide future efforts to reduce uncertainty.

Corresponding author e-mail: Chi Zhang, czhang@dlut.edu.cn
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