Extreme gradient and boosting algorithm for improved bias-correction and downscaling of CMIP6 GCM data across indian river basin
Study region: The Godavari River basin, situated between the geographical coordinates of 73°21′ E to 83°09′ E and 16°07′ N to 22°50′ N, India Study focus: The present study employed an extreme gradient boosting algorithm to enhance bias correction and spatial downscaling of climate model data from t...
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Elsevier
2025-06-01
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| Series: | Journal of Hydrology: Regional Studies |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S221458182500268X |
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| author | Chandni Thakur Venkatesh Budamala KS Kasiviswanathan Claudia Teutschbein Bankaru-Swamy Soundharajan |
| author_facet | Chandni Thakur Venkatesh Budamala KS Kasiviswanathan Claudia Teutschbein Bankaru-Swamy Soundharajan |
| author_sort | Chandni Thakur |
| collection | DOAJ |
| description | Study region: The Godavari River basin, situated between the geographical coordinates of 73°21′ E to 83°09′ E and 16°07′ N to 22°50′ N, India Study focus: The present study employed an extreme gradient boosting algorithm to enhance bias correction and spatial downscaling of climate model data from the Coupled Model Intercomparison Project Phase 6. The methodology utilized diverse training datasets, including five plausible climate models and topographic variables such as elevation, slope, and aspect. The effectiveness of the extreme gradient boosting framework in reproducing climate data was compared with the conventional quantile delta mapping approach across the basin. Additionally, both methods were evaluated across different seasons, including monsoon, pre-monsoon, and post-monsoon. New hydrological insights for the region: The results demonstrated that the extreme gradient boosting model outperformed the quantile delta mapping approach and significantly reduced biases in downscaled climate variables. For instance, the proposed model achieved Nash-Sutcliffe efficiency values of 0.44, 0.96, and 0.97 for precipitation, maximum temperature, and minimum temperature, respectively, whereas the conventional quantile delta mapping method yielded a comparatively less values of −0.34, 0.56, and 0.75. Additionally, uncertainty estimates using the p-factor indicated that the extreme gradient boosting model exhibited lower uncertainty in reproducing the observed spatio-temporal patterns of climate variables. Overall, the proposed framework enhances the reliability of global climate model simulations, supporting robust regional-scale hydrological modeling and climate change impact assessments. |
| format | Article |
| id | doaj-art-2edf6f28f8fe40d2ab4a10ae1fd90dd4 |
| institution | Kabale University |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-2edf6f28f8fe40d2ab4a10ae1fd90dd42025-08-20T03:47:32ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-06-015910244310.1016/j.ejrh.2025.102443Extreme gradient and boosting algorithm for improved bias-correction and downscaling of CMIP6 GCM data across indian river basinChandni Thakur0Venkatesh Budamala1KS Kasiviswanathan2Claudia Teutschbein3Bankaru-Swamy Soundharajan4Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, IndiaDepartment of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, IndiaDepartment of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India; Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, IndiaDepartment of Earth Sciences, Uppsala University, Uppsala, Sweden; Corresponding author.Department of Civil Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, Tamilnadu, IndiaStudy region: The Godavari River basin, situated between the geographical coordinates of 73°21′ E to 83°09′ E and 16°07′ N to 22°50′ N, India Study focus: The present study employed an extreme gradient boosting algorithm to enhance bias correction and spatial downscaling of climate model data from the Coupled Model Intercomparison Project Phase 6. The methodology utilized diverse training datasets, including five plausible climate models and topographic variables such as elevation, slope, and aspect. The effectiveness of the extreme gradient boosting framework in reproducing climate data was compared with the conventional quantile delta mapping approach across the basin. Additionally, both methods were evaluated across different seasons, including monsoon, pre-monsoon, and post-monsoon. New hydrological insights for the region: The results demonstrated that the extreme gradient boosting model outperformed the quantile delta mapping approach and significantly reduced biases in downscaled climate variables. For instance, the proposed model achieved Nash-Sutcliffe efficiency values of 0.44, 0.96, and 0.97 for precipitation, maximum temperature, and minimum temperature, respectively, whereas the conventional quantile delta mapping method yielded a comparatively less values of −0.34, 0.56, and 0.75. Additionally, uncertainty estimates using the p-factor indicated that the extreme gradient boosting model exhibited lower uncertainty in reproducing the observed spatio-temporal patterns of climate variables. Overall, the proposed framework enhances the reliability of global climate model simulations, supporting robust regional-scale hydrological modeling and climate change impact assessments.http://www.sciencedirect.com/science/article/pii/S221458182500268XGlobal Climate ModelsBias correction and downscalingMachine LearningUncertainty AnalysisGodavari River Basin |
| spellingShingle | Chandni Thakur Venkatesh Budamala KS Kasiviswanathan Claudia Teutschbein Bankaru-Swamy Soundharajan Extreme gradient and boosting algorithm for improved bias-correction and downscaling of CMIP6 GCM data across indian river basin Journal of Hydrology: Regional Studies Global Climate Models Bias correction and downscaling Machine Learning Uncertainty Analysis Godavari River Basin |
| title | Extreme gradient and boosting algorithm for improved bias-correction and downscaling of CMIP6 GCM data across indian river basin |
| title_full | Extreme gradient and boosting algorithm for improved bias-correction and downscaling of CMIP6 GCM data across indian river basin |
| title_fullStr | Extreme gradient and boosting algorithm for improved bias-correction and downscaling of CMIP6 GCM data across indian river basin |
| title_full_unstemmed | Extreme gradient and boosting algorithm for improved bias-correction and downscaling of CMIP6 GCM data across indian river basin |
| title_short | Extreme gradient and boosting algorithm for improved bias-correction and downscaling of CMIP6 GCM data across indian river basin |
| title_sort | extreme gradient and boosting algorithm for improved bias correction and downscaling of cmip6 gcm data across indian river basin |
| topic | Global Climate Models Bias correction and downscaling Machine Learning Uncertainty Analysis Godavari River Basin |
| url | http://www.sciencedirect.com/science/article/pii/S221458182500268X |
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