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Significant contribution of bias correction methods to uncertainty in future runoff projections under CMIP6 climate change
Study region: Mokgam River watershed, South Korea Study focus: In this study, the uncertainty contribution of three sources and their interaction effects on future climate and runoff projections were quantified. General circulation models (GCMs), shared socioeconomic pathways (SSPs), and bias correction (BC) methods were considered as the three sources. 20 GCMs under four SSPs (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) were used to project the future climate of the study area. Seven BC methods were used to adjust the GCMs’ daily climate data. The storm water management model (SWMM) was used as a hydrological model to simulate runoff, incorporating both natural and conduit flows according to GCMs’ climate projection. The normalized Nash-Sutcliffe efficiency (NNSE), normalized root mean square error (NRMSE), Kling-Gupta efficiency (KGE), and modified index of agreement (MD) were used to evaluate the performance of the GCMs’ climate simulations and the SWMM runoff simulations, which were based on the GCMs’ climate data. The analysis of variance (ANOVA) method was used to quantify the uncertainty. New hydrological insights for the study region: The results showed that the assumptions of the BC method had a significant impact on the variation in climate and runoff projections. In the uncertainty of future climate and runoff projection results, BC methods exhibited the predominant contribution, while SSPs showed the least contribution. However, the uncertainty contribution from SSPs and GCMs was predominant in temperature projections, and these results could vary depending on the assumptions and the number of BC methods used. Overall, this study emphasizes not only the influence of GCMs but also the impact of BC methods on future climate and runoff projections.
Significant contribution of bias correction methods to uncertainty in future runoff projections under CMIP6 climate change
Study region: Mokgam River watershed, South Korea Study focus: In this study, the uncertainty contribution of three sources and their interaction effects on future climate and runoff projections were quantified. General circulation models (GCMs), shared socioeconomic pathways (SSPs), and bias correction (BC) methods were considered as the three sources. 20 GCMs under four SSPs (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) were used to project the future climate of the study area. Seven BC methods were used to adjust the GCMs’ daily climate data. The storm water management model (SWMM) was used as a hydrological model to simulate runoff, incorporating both natural and conduit flows according to GCMs’ climate projection. The normalized Nash-Sutcliffe efficiency (NNSE), normalized root mean square error (NRMSE), Kling-Gupta efficiency (KGE), and modified index of agreement (MD) were used to evaluate the performance of the GCMs’ climate simulations and the SWMM runoff simulations, which were based on the GCMs’ climate data. The analysis of variance (ANOVA) method was used to quantify the uncertainty. New hydrological insights for the study region: The results showed that the assumptions of the BC method had a significant impact on the variation in climate and runoff projections. In the uncertainty of future climate and runoff projection results, BC methods exhibited the predominant contribution, while SSPs showed the least contribution. However, the uncertainty contribution from SSPs and GCMs was predominant in temperature projections, and these results could vary depending on the assumptions and the number of BC methods used. Overall, this study emphasizes not only the influence of GCMs but also the impact of BC methods on future climate and runoff projections.
Significant contribution of bias correction methods to uncertainty in future runoff projections under CMIP6 climate change
Seung Taek Chae (author) / Eun-Sung Chung (author)
2024
Article (Journal)
Electronic Resource
Unknown
ANOVA , Bias correction , Uncertainty analysis , GCMs , SSPs , Physical geography , GB3-5030 , Geology , QE1-996.5
Metadata by DOAJ is licensed under CC BY-SA 1.0
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