An improved statistical bias correction method for Global Climate Model (GCM) precipitation projection: A case study on the CMCC-CM2-SR5 model projection in China’s Huaihe River Basin
Study region: We selected the China’s Huaihe River Basin as the study region.Study focus: Bias correction is crucial for improving the accuracy of Global Climate Model (GCM) predictions. We proposed an improved statistical bias correction method called the EquiDistant Cumulative Distribution Functio...
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Elsevier
2025-02-01
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| Series: | Journal of Hydrology: Regional Studies |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581824004956 |
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| author | Yuning Luo Ke Zhang Wen Wang Xinyu Chen Jin Feng Haijun Wang Wei Liu Cheng Guo Cuiying Chen Xiaozhong Wang |
| author_facet | Yuning Luo Ke Zhang Wen Wang Xinyu Chen Jin Feng Haijun Wang Wei Liu Cheng Guo Cuiying Chen Xiaozhong Wang |
| author_sort | Yuning Luo |
| collection | DOAJ |
| description | Study region: We selected the China’s Huaihe River Basin as the study region.Study focus: Bias correction is crucial for improving the accuracy of Global Climate Model (GCM) predictions. We proposed an improved statistical bias correction method called the EquiDistant Cumulative Distribution Function (CDF) matching method with least square fitting of CDF’s deviation (LS-EDCDF) to correct biases in GCM precipitation. The LS-EDCDF method incorporates recurrence periods and least squares with the determination of transfer functions relating observation to model simulation. We applied this and three other methods—EquiDistant Cumulative Distribution Function matching method (EDCDF), combined Linear Scaling and Cumulative Distribution Function matching method (LS-CDF) and Quantile-quantile mapping method (QUANT)—to correct biases in the CMCC-CM2-SR5 model. Performance was evaluated in four aspects. Taylor diagram statistical metrics and correlation coefficients are used to measure agreements between the basin-average observations and bias-corrected model precipitation and their CDFs during the validation period (2015–2020), respectively. The Expert Team on Climate Change Detection and Indices (ETCCDI) extreme precipitation indices and spatial patterns of extreme precipitation in July 2020 in the observations and bias-corrected data were then compared to evaluate the effectiveness of these methods on capturing precipitation extremes.New hydrological insights for the region: Results show that the LS-EDCDF method outperforms the other three methods in correcting monthly CDFs, reducing biases, and preserving extreme precipitation. All results of these methods indicate that the CMCC-CM2-SR5 model tends to overestimate precipitation but performs well during the main flood seasons, especially for extreme precipitation. Future projections suggest decreasing interannual variability of precipitation, with a lower change rate during the flood season and a higher rate during the non-flood season. The bias-corrected data from this method can serve as a valuable input for projecting future floods in this important area of China. |
| format | Article |
| id | doaj-art-702a53dd555e47b9a9d51d685e4ea493 |
| institution | Kabale University |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-702a53dd555e47b9a9d51d685e4ea4932025-01-22T05:42:12ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-02-0157102146An improved statistical bias correction method for Global Climate Model (GCM) precipitation projection: A case study on the CMCC-CM2-SR5 model projection in China’s Huaihe River BasinYuning Luo0Ke Zhang1Wen Wang2Xinyu Chen3Jin Feng4Haijun Wang5Wei Liu6Cheng Guo7Cuiying Chen8Xiaozhong Wang9National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu 210024, China; Yangtze Institute for Conservation and Development, Hohai University, Nanjing, Jiangsu 210024, China; College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu 210024, ChinaNational Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu 210024, China; Yangtze Institute for Conservation and Development, Hohai University, Nanjing, Jiangsu 210024, China; College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu 210024, China; China Meteorological Administration Hydro-Meteorology Key Laboratory, Hohai University, Nanjing, Jiangsu 210024, China; Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, Jiangsu 210024, China; Key Laboratory of Hydrologic-Cycle and Hydrodynamic-System of Ministry of Water Resources, Hohai University, Nanjing, Jiangsu 210024, China; Corresponding author at: National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu 210024, China.National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu 210024, China; College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu 210024, ChinaSchool of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ, USANational Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu 210024, China; Yangtze Institute for Conservation and Development, Hohai University, Nanjing, Jiangsu 210024, China; College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu 210024, ChinaShandong Hydrological Center, Jinan, Shandong 250002, China; Corresponding author.Shandong Hydrological Center, Jinan, Shandong 250002, ChinaShandong Hydrological Center, Jinan, Shandong 250002, ChinaShandong Hydrological Center, Jinan, Shandong 250002, ChinaShandong Hydrological Center, Jinan, Shandong 250002, ChinaStudy region: We selected the China’s Huaihe River Basin as the study region.Study focus: Bias correction is crucial for improving the accuracy of Global Climate Model (GCM) predictions. We proposed an improved statistical bias correction method called the EquiDistant Cumulative Distribution Function (CDF) matching method with least square fitting of CDF’s deviation (LS-EDCDF) to correct biases in GCM precipitation. The LS-EDCDF method incorporates recurrence periods and least squares with the determination of transfer functions relating observation to model simulation. We applied this and three other methods—EquiDistant Cumulative Distribution Function matching method (EDCDF), combined Linear Scaling and Cumulative Distribution Function matching method (LS-CDF) and Quantile-quantile mapping method (QUANT)—to correct biases in the CMCC-CM2-SR5 model. Performance was evaluated in four aspects. Taylor diagram statistical metrics and correlation coefficients are used to measure agreements between the basin-average observations and bias-corrected model precipitation and their CDFs during the validation period (2015–2020), respectively. The Expert Team on Climate Change Detection and Indices (ETCCDI) extreme precipitation indices and spatial patterns of extreme precipitation in July 2020 in the observations and bias-corrected data were then compared to evaluate the effectiveness of these methods on capturing precipitation extremes.New hydrological insights for the region: Results show that the LS-EDCDF method outperforms the other three methods in correcting monthly CDFs, reducing biases, and preserving extreme precipitation. All results of these methods indicate that the CMCC-CM2-SR5 model tends to overestimate precipitation but performs well during the main flood seasons, especially for extreme precipitation. Future projections suggest decreasing interannual variability of precipitation, with a lower change rate during the flood season and a higher rate during the non-flood season. The bias-corrected data from this method can serve as a valuable input for projecting future floods in this important area of China.http://www.sciencedirect.com/science/article/pii/S2214581824004956Global Climate ModelsBias correctionLeast square fittingRecurrence periodPrecipitation projectionHuaihe River Basin |
| spellingShingle | Yuning Luo Ke Zhang Wen Wang Xinyu Chen Jin Feng Haijun Wang Wei Liu Cheng Guo Cuiying Chen Xiaozhong Wang An improved statistical bias correction method for Global Climate Model (GCM) precipitation projection: A case study on the CMCC-CM2-SR5 model projection in China’s Huaihe River Basin Journal of Hydrology: Regional Studies Global Climate Models Bias correction Least square fitting Recurrence period Precipitation projection Huaihe River Basin |
| title | An improved statistical bias correction method for Global Climate Model (GCM) precipitation projection: A case study on the CMCC-CM2-SR5 model projection in China’s Huaihe River Basin |
| title_full | An improved statistical bias correction method for Global Climate Model (GCM) precipitation projection: A case study on the CMCC-CM2-SR5 model projection in China’s Huaihe River Basin |
| title_fullStr | An improved statistical bias correction method for Global Climate Model (GCM) precipitation projection: A case study on the CMCC-CM2-SR5 model projection in China’s Huaihe River Basin |
| title_full_unstemmed | An improved statistical bias correction method for Global Climate Model (GCM) precipitation projection: A case study on the CMCC-CM2-SR5 model projection in China’s Huaihe River Basin |
| title_short | An improved statistical bias correction method for Global Climate Model (GCM) precipitation projection: A case study on the CMCC-CM2-SR5 model projection in China’s Huaihe River Basin |
| title_sort | improved statistical bias correction method for global climate model gcm precipitation projection a case study on the cmcc cm2 sr5 model projection in china s huaihe river basin |
| topic | Global Climate Models Bias correction Least square fitting Recurrence period Precipitation projection Huaihe River Basin |
| url | http://www.sciencedirect.com/science/article/pii/S2214581824004956 |
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