Downscaling GRACE total water storage data using random forest: a three-round validation approach under drought conditions
The application of GRACE satellite-derived Total Water Storage (TWS) data for local water management is constrained by its coarse spatial resolution (100-300 km). To address this limitation, a Random Forest-based model was employed to downscale GRACE TWS data from 100 km to 1 km resolution over Moro...
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Frontiers Media S.A.
2025-05-01
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| author | Youssef Hamou-Ali Nourlhouda Karmouda Ismail Mohsine Tarik Bouramtane Ilias Kacimi Sarah Tweed Sarah Tweed Mounia Tahiri Nadia Kassou Ali El Bilali Omar Chafki Mohamed Abdellah Ezzaouini Siham Laraichi Abdelaaziz Zerouali Marc Leblanc Marc Leblanc Marc Leblanc |
| author_facet | Youssef Hamou-Ali Nourlhouda Karmouda Ismail Mohsine Tarik Bouramtane Ilias Kacimi Sarah Tweed Sarah Tweed Mounia Tahiri Nadia Kassou Ali El Bilali Omar Chafki Mohamed Abdellah Ezzaouini Siham Laraichi Abdelaaziz Zerouali Marc Leblanc Marc Leblanc Marc Leblanc |
| author_sort | Youssef Hamou-Ali |
| collection | DOAJ |
| description | The application of GRACE satellite-derived Total Water Storage (TWS) data for local water management is constrained by its coarse spatial resolution (100-300 km). To address this limitation, a Random Forest-based model was employed to downscale GRACE TWS data from 100 km to 1 km resolution over Morocco, a drought-prone region, covering the period from 2002 to 2022. The input datasets included precipitation (GPM, 10 km), NDVI (MODIS, 1 km), land surface temperature (LST, MODIS, 1 km), evapotranspiration (MODIS, 500 m), elevation (SRTM, 30 m), and the Normalised Difference Snow Index (NDSI, MODIS, 500 m). While downscaling improves the spatial resolution of GRACE data, validating these higher-resolution outputs presents challenges. In this study, the downscaled data were validated using three complementary approaches: statistical validation, groundwater level in-situ data validation, and validation against known aquifer dynamics. Statistical validation demonstrated strong model performance, with a Nash-Sutcliffe Efficiency (NSE) of 0.80, a low RMSE of 0.82 cm, and MAE of 0.57 cm, along with an R² of 0.80 between original and downscaled data. Cross-validation confirmed the model’s consistency, yielding mean, median, and maximum R² values of 0.56, 0.64, and 0.89 respectively. Error metrics remained consistently low throughout the study period, with MAE values ranging from 0.36 cm to 0.6 cm and RMSE values between 0.5 cm and 0.8 cm. Comparison with in-situ groundwater levels showed significant improvements, with correlation coefficients increasing for 63% of the 139 analysed wells. The 1 km TWS data revealed localised variations and clearer trends across different aquifers, with aquifer systems within the same structural domain exhibiting similar TWS patterns. These findings highlight the potential of the downscaling model to enhance local water management by capturing finer hydrological variations. The proposed approach effectively overcomes GRACE’s spatial resolution limitations, as demonstrated through comprehensive validation. This methodology shows particular promise for water resource monitoring in drought-vulnerable regions such as Morocco, providing decision-makers with higher-resolution data for improved water management strategies. |
| format | Article |
| id | doaj-art-d590168f33ef45f3bf7a204331b853a1 |
| institution | DOAJ |
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| publishDate | 2025-05-01 |
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| spelling | doaj-art-d590168f33ef45f3bf7a204331b853a12025-08-20T03:12:38ZengFrontiers Media S.A.Frontiers in Water2624-93752025-05-01710.3389/frwa.2025.15458211545821Downscaling GRACE total water storage data using random forest: a three-round validation approach under drought conditionsYoussef Hamou-Ali0Nourlhouda Karmouda1Ismail Mohsine2Tarik Bouramtane3Ilias Kacimi4Sarah Tweed5Sarah Tweed6Mounia Tahiri7Nadia Kassou8Ali El Bilali9Omar Chafki10Mohamed Abdellah Ezzaouini11Siham Laraichi12Abdelaaziz Zerouali13Marc Leblanc14Marc Leblanc15Marc Leblanc16Geosciences, Water and Environment Laboratory, Faculty of Sciences, Mohammed V University of Rabat, Rabat, MoroccoGeosciences, Water and Environment Laboratory, Faculty of Sciences, Mohammed V University of Rabat, Rabat, MoroccoGeosciences, Water and Environment Laboratory, Faculty of Sciences, Mohammed V University of Rabat, Rabat, MoroccoGeosciences, Water and Environment Laboratory, Faculty of Sciences, Mohammed V University of Rabat, Rabat, MoroccoGeosciences, Water and Environment Laboratory, Faculty of Sciences, Mohammed V University of Rabat, Rabat, MoroccoGeosciences, Water and Environment Laboratory, Faculty of Sciences, Mohammed V University of Rabat, Rabat, MoroccoInstitut de Recherche Pour le Développement (IRD), UMR Geau, Montpellier, FranceGeosciences, Water and Environment Laboratory, Faculty of Sciences, Mohammed V University of Rabat, Rabat, MoroccoGeosciences, Water and Environment Laboratory, Faculty of Sciences, Mohammed V University of Rabat, Rabat, MoroccoRiver Basin Agency of Bouregreg and Chaouia, Chaouia, MoroccoRiver Basin Agency of Bouregreg and Chaouia, Chaouia, MoroccoRiver Basin Agency of Loukkos, Tétouan, MoroccoResearch and Water Planning Direction, Rabat, MoroccoResearch and Water Planning Direction, Rabat, MoroccoGeosciences, Water and Environment Laboratory, Faculty of Sciences, Mohammed V University of Rabat, Rabat, MoroccoInstitut de Recherche Pour le Développement (IRD), UMR Geau, Montpellier, FranceHydrogeology Laboratory, UMR EMMAH, University of Avignon, Avignon, FranceThe application of GRACE satellite-derived Total Water Storage (TWS) data for local water management is constrained by its coarse spatial resolution (100-300 km). To address this limitation, a Random Forest-based model was employed to downscale GRACE TWS data from 100 km to 1 km resolution over Morocco, a drought-prone region, covering the period from 2002 to 2022. The input datasets included precipitation (GPM, 10 km), NDVI (MODIS, 1 km), land surface temperature (LST, MODIS, 1 km), evapotranspiration (MODIS, 500 m), elevation (SRTM, 30 m), and the Normalised Difference Snow Index (NDSI, MODIS, 500 m). While downscaling improves the spatial resolution of GRACE data, validating these higher-resolution outputs presents challenges. In this study, the downscaled data were validated using three complementary approaches: statistical validation, groundwater level in-situ data validation, and validation against known aquifer dynamics. Statistical validation demonstrated strong model performance, with a Nash-Sutcliffe Efficiency (NSE) of 0.80, a low RMSE of 0.82 cm, and MAE of 0.57 cm, along with an R² of 0.80 between original and downscaled data. Cross-validation confirmed the model’s consistency, yielding mean, median, and maximum R² values of 0.56, 0.64, and 0.89 respectively. Error metrics remained consistently low throughout the study period, with MAE values ranging from 0.36 cm to 0.6 cm and RMSE values between 0.5 cm and 0.8 cm. Comparison with in-situ groundwater levels showed significant improvements, with correlation coefficients increasing for 63% of the 139 analysed wells. The 1 km TWS data revealed localised variations and clearer trends across different aquifers, with aquifer systems within the same structural domain exhibiting similar TWS patterns. These findings highlight the potential of the downscaling model to enhance local water management by capturing finer hydrological variations. The proposed approach effectively overcomes GRACE’s spatial resolution limitations, as demonstrated through comprehensive validation. This methodology shows particular promise for water resource monitoring in drought-vulnerable regions such as Morocco, providing decision-makers with higher-resolution data for improved water management strategies.https://www.frontiersin.org/articles/10.3389/frwa.2025.1545821/fullGRACE datatotal water storagedownscalinghydrological validationdroughtMorocco |
| spellingShingle | Youssef Hamou-Ali Nourlhouda Karmouda Ismail Mohsine Tarik Bouramtane Ilias Kacimi Sarah Tweed Sarah Tweed Mounia Tahiri Nadia Kassou Ali El Bilali Omar Chafki Mohamed Abdellah Ezzaouini Siham Laraichi Abdelaaziz Zerouali Marc Leblanc Marc Leblanc Marc Leblanc Downscaling GRACE total water storage data using random forest: a three-round validation approach under drought conditions Frontiers in Water GRACE data total water storage downscaling hydrological validation drought Morocco |
| title | Downscaling GRACE total water storage data using random forest: a three-round validation approach under drought conditions |
| title_full | Downscaling GRACE total water storage data using random forest: a three-round validation approach under drought conditions |
| title_fullStr | Downscaling GRACE total water storage data using random forest: a three-round validation approach under drought conditions |
| title_full_unstemmed | Downscaling GRACE total water storage data using random forest: a three-round validation approach under drought conditions |
| title_short | Downscaling GRACE total water storage data using random forest: a three-round validation approach under drought conditions |
| title_sort | downscaling grace total water storage data using random forest a three round validation approach under drought conditions |
| topic | GRACE data total water storage downscaling hydrological validation drought Morocco |
| url | https://www.frontiersin.org/articles/10.3389/frwa.2025.1545821/full |
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