Spatiotemporal analysis of monthly rainfall over Saudi Arabia and global teleconnections
This research focuses on extracting the statistical features, in space and time, of the monthly rainfall in Saudi Arabia (SA) and the relation to the large-scale atmospheric variability through teleconnection for strategic water resources planning. These features are useful for future predictions. 2...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Geomatics, Natural Hazards & Risk |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19475705.2022.2127379 |
| _version_ | 1818025636425367552 |
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| author | Amro Elfeki Jarbou Bahrawi Muhammad Latif Abdelwaheb Hannachi |
| author_facet | Amro Elfeki Jarbou Bahrawi Muhammad Latif Abdelwaheb Hannachi |
| author_sort | Amro Elfeki |
| collection | DOAJ |
| description | This research focuses on extracting the statistical features, in space and time, of the monthly rainfall in Saudi Arabia (SA) and the relation to the large-scale atmospheric variability through teleconnection for strategic water resources planning. These features are useful for future predictions. 28 stations distributed over SA for a period between 1970 and 2012 are utilized. According to the Kolmogorov–Smirnov (K-S) test, the Log-normal and Gamma distributions are dominant, while for the Chi-squared (Chi2) test, the Beta distribution is dominant. The K-S is preferable since it works with the original data rather than the Chi2 that uses binning, and therefore, some information is lost. The L-moment analysis showed that Person type III is dominant for the wet season while there is no obvious distribution for the dry season. Empirical Orthogonal Function (EOF) analysis is applied to seasonal rainfall data for studying the dominant modes of climate variability and associated large-scale circulation patterns. Our results demonstrate a robust relationship between the wet season (November – April) with El Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO), whereas the dry season (June – September) is associated with the Indian Ocean Dipole (IOD). Moreover, the warm (cold) phase of PDO is associated with excess (deficit) rainfall, indicating some predictability of the seasonal rainfall over SA. |
| first_indexed | 2024-12-10T04:19:16Z |
| format | Article |
| id | doaj.art-3c71a7219bfe48718b243f328eaa47fd |
| institution | Directory Open Access Journal |
| issn | 1947-5705 1947-5713 |
| language | English |
| last_indexed | 2024-12-10T04:19:16Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj.art-3c71a7219bfe48718b243f328eaa47fd2022-12-22T02:02:29ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132022-12-011312618264810.1080/19475705.2022.2127379Spatiotemporal analysis of monthly rainfall over Saudi Arabia and global teleconnectionsAmro Elfeki0Jarbou Bahrawi1Muhammad Latif2Abdelwaheb Hannachi3Department of Hydrology and Water Resources Management, Faculty of Meteorology, Environment & Arid Land Agriculture, King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Hydrology and Water Resources Management, Faculty of Meteorology, Environment & Arid Land Agriculture, King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Meteorology, COMSATS University Islamabad (CUI), Islamabad, PakistanDepartment of Meteorology, Stockholm University, Stockholm, SwedenThis research focuses on extracting the statistical features, in space and time, of the monthly rainfall in Saudi Arabia (SA) and the relation to the large-scale atmospheric variability through teleconnection for strategic water resources planning. These features are useful for future predictions. 28 stations distributed over SA for a period between 1970 and 2012 are utilized. According to the Kolmogorov–Smirnov (K-S) test, the Log-normal and Gamma distributions are dominant, while for the Chi-squared (Chi2) test, the Beta distribution is dominant. The K-S is preferable since it works with the original data rather than the Chi2 that uses binning, and therefore, some information is lost. The L-moment analysis showed that Person type III is dominant for the wet season while there is no obvious distribution for the dry season. Empirical Orthogonal Function (EOF) analysis is applied to seasonal rainfall data for studying the dominant modes of climate variability and associated large-scale circulation patterns. Our results demonstrate a robust relationship between the wet season (November – April) with El Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO), whereas the dry season (June – September) is associated with the Indian Ocean Dipole (IOD). Moreover, the warm (cold) phase of PDO is associated with excess (deficit) rainfall, indicating some predictability of the seasonal rainfall over SA.https://www.tandfonline.com/doi/10.1080/19475705.2022.2127379Monthly rainfallstatistical analysisempirical orthogonal functionteleconnections |
| spellingShingle | Amro Elfeki Jarbou Bahrawi Muhammad Latif Abdelwaheb Hannachi Spatiotemporal analysis of monthly rainfall over Saudi Arabia and global teleconnections Geomatics, Natural Hazards & Risk Monthly rainfall statistical analysis empirical orthogonal function teleconnections |
| title | Spatiotemporal analysis of monthly rainfall over Saudi Arabia and global teleconnections |
| title_full | Spatiotemporal analysis of monthly rainfall over Saudi Arabia and global teleconnections |
| title_fullStr | Spatiotemporal analysis of monthly rainfall over Saudi Arabia and global teleconnections |
| title_full_unstemmed | Spatiotemporal analysis of monthly rainfall over Saudi Arabia and global teleconnections |
| title_short | Spatiotemporal analysis of monthly rainfall over Saudi Arabia and global teleconnections |
| title_sort | spatiotemporal analysis of monthly rainfall over saudi arabia and global teleconnections |
| topic | Monthly rainfall statistical analysis empirical orthogonal function teleconnections |
| url | https://www.tandfonline.com/doi/10.1080/19475705.2022.2127379 |
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