Evaluation of CORDEX-Africa Model Data Reliability and Bias Correction for Climate Change Impact Assessment: Upper Tekeze River Basin, Tigray (Ethiopia)
Model simulation evaluation is crucial for selecting the best regional climate models, as their performance may vary across different locations or variables. This research aims to examine and correct potential biases in the CORDEX ensemble climate dataset over the period of 1987-2005 so as to estab...
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| Format: | Article |
| Language: | English |
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Bon View Publishing Pte Ltd.
2024-01-01
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| Series: | Green and Low-Carbon Economy |
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| Online Access: | https://ojs.bonviewpress.com/index.php/GLCE/article/view/1893 |
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| author | Ghrmawit Haile Gebrehiwot Kassahun Ture Bekitie Fikru Abiko Weldemariam Seifu Haftu Brhane Gebremichael |
| author_facet | Ghrmawit Haile Gebrehiwot Kassahun Ture Bekitie Fikru Abiko Weldemariam Seifu Haftu Brhane Gebremichael |
| author_sort | Ghrmawit Haile Gebrehiwot |
| collection | DOAJ |
| description |
Model simulation evaluation is crucial for selecting the best regional climate models, as their performance may vary across different locations or variables. This research aims to examine and correct potential biases in the CORDEX ensemble climate dataset over the period of 1987-2005 so as to establish trust in utilizing the CORDEX ensemble forecasts for climate change impact assessment focusing on the UTRB. The Pearson correlation coefficient is employed to assess the degree of correlation between CORDEX and observation data, and the applicability of the CORDEX ensemble data for the UTRB. The statistical analysis reveals a significant correlation between the monthly mean rainfall and temperature in the CORDEX-Africa ensemble simulation and the corresponding observation data for most of the 18 stations. The finding suggests that the CORDEX-Africa ensemble dataset holds promise for future climate projection in the UTRB. The statistical approaches of bias, RMSE, and MAE are employed to assess the adequacy of the CORDEX ensemble model in reproducing observed data. Various bias correction approaches are employed to enhance the accuracy of rainfall and temperature datasets, addressing discrepancies from over and under simulation. The reliability evaluation results indicate that the CORDEX-Africa ensemble precipitation and temperature data set has undergone bias adjustment in order to accurately reproduce the observed gridded dataset for the same period. This adjustment was performed using various methodologies across the 18 stations. Following the bias modifications, the CORDEX ensemble's precipitation and temperature dataset exhibited a high degree of concordance with the grid observation dataset across all 18 observation stations, for the corresponding time. The approaches utilized in this work possess the potential for practical applicability in generating dependable climate data that may be employed in evaluating and forecasting the consequences of climate change using globally accessible data resources.
Received: 17 October 2023 | Revised: 25 December 2023 | Accepted: 5 January 2024
Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.
Supplementary Information
Supplementary file
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| first_indexed | 2024-04-25T01:38:30Z |
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| id | doaj.art-02e975ccec3a4cbbb56093b10854f3e8 |
| institution | Directory Open Access Journal |
| issn | 2972-3787 |
| language | English |
| last_indexed | 2024-04-25T01:38:30Z |
| publishDate | 2024-01-01 |
| publisher | Bon View Publishing Pte Ltd. |
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| spelling | doaj.art-02e975ccec3a4cbbb56093b10854f3e82024-03-08T08:15:07ZengBon View Publishing Pte Ltd.Green and Low-Carbon Economy2972-37872024-01-0110.47852/bonviewGLCE42021893Evaluation of CORDEX-Africa Model Data Reliability and Bias Correction for Climate Change Impact Assessment: Upper Tekeze River Basin, Tigray (Ethiopia)Ghrmawit Haile Gebrehiwot0Kassahun Ture Bekitie1Fikru Abiko2Weldemariam Seifu3Haftu Brhane Gebremichael4Center for Environmental Sciences, Addis Ababa University, EthiopiaCenter for Environmental Sciences, Addis Ababa University, EthiopiaCollege of Natural and Computational Sciences, Wolkite University, EthiopiaCollege of Agriculture and Natural Resources, Salale University, EthiopiaCollege of Natural and Computational Sciences, Haramaya University, Ethiopia Model simulation evaluation is crucial for selecting the best regional climate models, as their performance may vary across different locations or variables. This research aims to examine and correct potential biases in the CORDEX ensemble climate dataset over the period of 1987-2005 so as to establish trust in utilizing the CORDEX ensemble forecasts for climate change impact assessment focusing on the UTRB. The Pearson correlation coefficient is employed to assess the degree of correlation between CORDEX and observation data, and the applicability of the CORDEX ensemble data for the UTRB. The statistical analysis reveals a significant correlation between the monthly mean rainfall and temperature in the CORDEX-Africa ensemble simulation and the corresponding observation data for most of the 18 stations. The finding suggests that the CORDEX-Africa ensemble dataset holds promise for future climate projection in the UTRB. The statistical approaches of bias, RMSE, and MAE are employed to assess the adequacy of the CORDEX ensemble model in reproducing observed data. Various bias correction approaches are employed to enhance the accuracy of rainfall and temperature datasets, addressing discrepancies from over and under simulation. The reliability evaluation results indicate that the CORDEX-Africa ensemble precipitation and temperature data set has undergone bias adjustment in order to accurately reproduce the observed gridded dataset for the same period. This adjustment was performed using various methodologies across the 18 stations. Following the bias modifications, the CORDEX ensemble's precipitation and temperature dataset exhibited a high degree of concordance with the grid observation dataset across all 18 observation stations, for the corresponding time. The approaches utilized in this work possess the potential for practical applicability in generating dependable climate data that may be employed in evaluating and forecasting the consequences of climate change using globally accessible data resources. Received: 17 October 2023 | Revised: 25 December 2023 | Accepted: 5 January 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Supplementary Information Supplementary file https://ojs.bonviewpress.com/index.php/GLCE/article/view/1893CORDEX-Africaclimate dataclimate projectionperformance evaluationbias refinement |
| spellingShingle | Ghrmawit Haile Gebrehiwot Kassahun Ture Bekitie Fikru Abiko Weldemariam Seifu Haftu Brhane Gebremichael Evaluation of CORDEX-Africa Model Data Reliability and Bias Correction for Climate Change Impact Assessment: Upper Tekeze River Basin, Tigray (Ethiopia) Green and Low-Carbon Economy CORDEX-Africa climate data climate projection performance evaluation bias refinement |
| title | Evaluation of CORDEX-Africa Model Data Reliability and Bias Correction for Climate Change Impact Assessment: Upper Tekeze River Basin, Tigray (Ethiopia) |
| title_full | Evaluation of CORDEX-Africa Model Data Reliability and Bias Correction for Climate Change Impact Assessment: Upper Tekeze River Basin, Tigray (Ethiopia) |
| title_fullStr | Evaluation of CORDEX-Africa Model Data Reliability and Bias Correction for Climate Change Impact Assessment: Upper Tekeze River Basin, Tigray (Ethiopia) |
| title_full_unstemmed | Evaluation of CORDEX-Africa Model Data Reliability and Bias Correction for Climate Change Impact Assessment: Upper Tekeze River Basin, Tigray (Ethiopia) |
| title_short | Evaluation of CORDEX-Africa Model Data Reliability and Bias Correction for Climate Change Impact Assessment: Upper Tekeze River Basin, Tigray (Ethiopia) |
| title_sort | evaluation of cordex africa model data reliability and bias correction for climate change impact assessment upper tekeze river basin tigray ethiopia |
| topic | CORDEX-Africa climate data climate projection performance evaluation bias refinement |
| url | https://ojs.bonviewpress.com/index.php/GLCE/article/view/1893 |
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