Machine learning downscaling of GRACE/GRACE-FO data to capture spatial-temporal drought effects on groundwater storage at a local scale under data-scarcity
Abstract The continued threat from climate change and human impacts on water resources demands high-resolution and continuous hydrological data accessibility for predicting trends and availability. This study proposes a novel threefold downscaling method based on machine learning (ML) which integrat...
Gardado en:
| Principais autores: | , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
SpringerOpen
2024-09-01
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| Series: | Environmental Systems Research |
| Assuntos: | |
| Acceso en liña: | https://doi.org/10.1186/s40068-024-00368-1 |
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