Future groundwater drought analysis under data scarcity using MedCORDEX regional climatic models and machine learning: The case of the Haouz Aquifer
Study region: The Haouz aquifer, situated in central Morocco, a data-scarce region. Study focus: Groundwater resources in semi-arid regions face increasing threats from climate change, particularly due to warming and overexploitation. However, data scarcity limits the ability to monitor and predict...
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Elsevier
2025-04-01
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| Series: | Journal of Hydrology: Regional Studies |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581825000734 |
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| author | El Bouazzaoui Imane Ait Elbaz Aicha Ait Brahim Yassine Machay Hicham Bougadir Blaid |
| author_facet | El Bouazzaoui Imane Ait Elbaz Aicha Ait Brahim Yassine Machay Hicham Bougadir Blaid |
| author_sort | El Bouazzaoui Imane |
| collection | DOAJ |
| description | Study region: The Haouz aquifer, situated in central Morocco, a data-scarce region. Study focus: Groundwater resources in semi-arid regions face increasing threats from climate change, particularly due to warming and overexploitation. However, data scarcity limits the ability to monitor and predict groundwater changes accurately. This study addresses this challenge by predicting future drought conditions in the Haouz aquifer using SPI and SPEI climatic drought indices, Machine Learning models, and Med-CORDEX regional climate models under RCP 4.5 and 8.5 scenarios. New Hydrological Insights for the Region: This study is the first in the region to predict groundwater drought based on precipitation and temperature data, relying on the principle of drought propagation. The comparative analysis of the machine learning models shows that Random Forest stands out for its superior predictive performance, influenced by annual trends and long-term climatic indices, with significant contributions from geographical variables. The results indicate a combined influence of land use and natural characteristics on the drought of the Haouz aquifer, following a longitudinal variation and showing a trend towards decreasing variability from the mid- to long-term. Additionally, extreme drought conditions are expected to intensify in most study points particularly under RCP 8.5. The eastern area of the aquifer remains the least impacted by this future trend, continuing to reflect normal drought conditions even in the long term under RCP 8.5. |
| format | Article |
| id | doaj-art-5f5b70f97e5a460fa86d5b323e6b98b4 |
| institution | DOAJ |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-5f5b70f97e5a460fa86d5b323e6b98b42025-08-20T03:01:35ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-04-015810224910.1016/j.ejrh.2025.102249Future groundwater drought analysis under data scarcity using MedCORDEX regional climatic models and machine learning: The case of the Haouz AquiferEl Bouazzaoui Imane0Ait Elbaz Aicha1Ait Brahim Yassine2Machay Hicham3Bougadir Blaid4International Water Research Institute, Mohammed VI Polytechnic University, Ben Guerir, Morocco; Corresponding author.Islamic Financial Engineering Laboratory, Mohammadia School of Engineering, Mohammed V University, Rabat, MoroccoInternational Water Research Institute, Mohammed VI Polytechnic University, Ben Guerir, MoroccoDepartment of Computer Science, Faculty of Science and Technology, Cadi Ayyad University, Marrakech, MoroccoLaboratory of Sciences Applied to the Environment and Sustainable Development, Cadi Ayyad University, Essaouira, MoroccoStudy region: The Haouz aquifer, situated in central Morocco, a data-scarce region. Study focus: Groundwater resources in semi-arid regions face increasing threats from climate change, particularly due to warming and overexploitation. However, data scarcity limits the ability to monitor and predict groundwater changes accurately. This study addresses this challenge by predicting future drought conditions in the Haouz aquifer using SPI and SPEI climatic drought indices, Machine Learning models, and Med-CORDEX regional climate models under RCP 4.5 and 8.5 scenarios. New Hydrological Insights for the Region: This study is the first in the region to predict groundwater drought based on precipitation and temperature data, relying on the principle of drought propagation. The comparative analysis of the machine learning models shows that Random Forest stands out for its superior predictive performance, influenced by annual trends and long-term climatic indices, with significant contributions from geographical variables. The results indicate a combined influence of land use and natural characteristics on the drought of the Haouz aquifer, following a longitudinal variation and showing a trend towards decreasing variability from the mid- to long-term. Additionally, extreme drought conditions are expected to intensify in most study points particularly under RCP 8.5. The eastern area of the aquifer remains the least impacted by this future trend, continuing to reflect normal drought conditions even in the long term under RCP 8.5.http://www.sciencedirect.com/science/article/pii/S2214581825000734GroundwaterClimate changeDroughtMachine LearningRCP scenarioMed-CORDEX |
| spellingShingle | El Bouazzaoui Imane Ait Elbaz Aicha Ait Brahim Yassine Machay Hicham Bougadir Blaid Future groundwater drought analysis under data scarcity using MedCORDEX regional climatic models and machine learning: The case of the Haouz Aquifer Journal of Hydrology: Regional Studies Groundwater Climate change Drought Machine Learning RCP scenario Med-CORDEX |
| title | Future groundwater drought analysis under data scarcity using MedCORDEX regional climatic models and machine learning: The case of the Haouz Aquifer |
| title_full | Future groundwater drought analysis under data scarcity using MedCORDEX regional climatic models and machine learning: The case of the Haouz Aquifer |
| title_fullStr | Future groundwater drought analysis under data scarcity using MedCORDEX regional climatic models and machine learning: The case of the Haouz Aquifer |
| title_full_unstemmed | Future groundwater drought analysis under data scarcity using MedCORDEX regional climatic models and machine learning: The case of the Haouz Aquifer |
| title_short | Future groundwater drought analysis under data scarcity using MedCORDEX regional climatic models and machine learning: The case of the Haouz Aquifer |
| title_sort | future groundwater drought analysis under data scarcity using medcordex regional climatic models and machine learning the case of the haouz aquifer |
| topic | Groundwater Climate change Drought Machine Learning RCP scenario Med-CORDEX |
| url | http://www.sciencedirect.com/science/article/pii/S2214581825000734 |
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