Significant contribution of bias correction methods to uncertainty in future runoff projections under CMIP6 climate change

IF 4.7 2区 地球科学 Q1 WATER RESOURCES Journal of Hydrology-Regional Studies Pub Date : 2024-09-16 DOI:10.1016/j.ejrh.2024.101973
Seung Taek Chae, Eun-Sung Chung
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Abstract

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.

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偏差修正方法对 CMIP6 气候变化下未来径流预测不确定性的重大影响
研究区域韩国莫甘江流域研究重点在本研究中,量化了三个来源的不确定性贡献及其对未来气候和径流预测的交互影响。一般环流模式 (GCM)、共享社会经济路径 (SSP) 和偏差校正 (BC) 方法被视为这三个来源。研究使用了四种 SSP(SSP1-2.6、SSP2-4.5、SSP3-7.0 和 SSP5-8.5)下的 20 个 GCM 来预测研究区域的未来气候。使用了七种 BC 方法来调整 GCM 的每日气候数据。暴雨管理模型 (SWMM) 被用作模拟径流的水文模型,根据 GCMs 的气候预测纳入了自然流和导流。采用归一化纳什-萨特克利夫效率 (NNSE)、归一化均方根误差 (NRMSE)、克林-古普塔效率 (KGE) 和修正一致指数 (MD) 来评估 GCMs 气候模拟和基于 GCMs 气候数据的 SWMM 径流模拟的性能。结果表明,BC 方法的假设对气候和径流预测的变化有显著影响。在未来气候和径流预测结果的不确定性方面,BC 方法的贡献最大,而 SSP 方法的贡献最小。然而,在温度预测中,SSP 和 GCM 的不确定性贡献占主导地位,而且这些结果会因使用的假设和 BC 方法的数量而不同。总之,本研究不仅强调了全球气候模式的影响,还强调了业连方法对未来气候和径流预测的影响。
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来源期刊
Journal of Hydrology-Regional Studies
Journal of Hydrology-Regional Studies Earth and Planetary Sciences-Earth and Planetary Sciences (miscellaneous)
CiteScore
6.70
自引率
8.50%
发文量
284
审稿时长
60 days
期刊介绍: Journal of Hydrology: Regional Studies publishes original research papers enhancing the science of hydrology and aiming at region-specific problems, past and future conditions, analysis, review and solutions. The journal particularly welcomes research papers that deliver new insights into region-specific hydrological processes and responses to changing conditions, as well as contributions that incorporate interdisciplinarity and translational science.
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