GRAiCE: reconstructing terrestrial water storage anomalies with recurrent neural networks
Abstract The Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) missions have provided estimates of Terrestrial Water Storage Anomalies (TWSA) since 2002, enabling the monitoring of global hydrological changes. However, temporal gaps within these datasets and the lack of TW...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04403-3 |
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| author | Irene Palazzoli Serena Ceola Pierre Gentine |
| author_facet | Irene Palazzoli Serena Ceola Pierre Gentine |
| author_sort | Irene Palazzoli |
| collection | DOAJ |
| description | Abstract The Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) missions have provided estimates of Terrestrial Water Storage Anomalies (TWSA) since 2002, enabling the monitoring of global hydrological changes. However, temporal gaps within these datasets and the lack of TWSA observations prior to 2002 limit our understanding of long-term freshwater variability. In this study, we develop GRAiCE, a set of four global monthly TWSA reconstructions from 1984 to 2021 at 0.5° spatial resolution, using Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) neural networks. Our models accurately reproduce GRACE/GRACE-FO observations at the global scale and effectively capture the impacts of climate extremes. Overall, GRAiCE outperforms a previous reference TWSA reconstruction in predicting observed TWSA and provides reliable water budget estimates at the river basin scale. By generating long-term continuous TWSA time series, GRAiCE will offer valuable insights into the impacts of climate variability and change on freshwater resources. |
| format | Article |
| id | doaj-art-19baaf08518a4d95aa61e4be30d88fb6 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-19baaf08518a4d95aa61e4be30d88fb62025-01-26T12:14:55ZengNature PortfolioScientific Data2052-44632025-01-0112111310.1038/s41597-025-04403-3GRAiCE: reconstructing terrestrial water storage anomalies with recurrent neural networksIrene Palazzoli0Serena Ceola1Pierre Gentine2Department of Civil, Chemical, Environmental, and Materials Engineering, Alma Mater Studiorum – Università di BolognaDepartment of Civil, Chemical, Environmental, and Materials Engineering, Alma Mater Studiorum – Università di BolognaDepartment of Earth and Environmental Engineering, Columbia UniversityAbstract The Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) missions have provided estimates of Terrestrial Water Storage Anomalies (TWSA) since 2002, enabling the monitoring of global hydrological changes. However, temporal gaps within these datasets and the lack of TWSA observations prior to 2002 limit our understanding of long-term freshwater variability. In this study, we develop GRAiCE, a set of four global monthly TWSA reconstructions from 1984 to 2021 at 0.5° spatial resolution, using Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) neural networks. Our models accurately reproduce GRACE/GRACE-FO observations at the global scale and effectively capture the impacts of climate extremes. Overall, GRAiCE outperforms a previous reference TWSA reconstruction in predicting observed TWSA and provides reliable water budget estimates at the river basin scale. By generating long-term continuous TWSA time series, GRAiCE will offer valuable insights into the impacts of climate variability and change on freshwater resources.https://doi.org/10.1038/s41597-025-04403-3 |
| spellingShingle | Irene Palazzoli Serena Ceola Pierre Gentine GRAiCE: reconstructing terrestrial water storage anomalies with recurrent neural networks Scientific Data |
| title | GRAiCE: reconstructing terrestrial water storage anomalies with recurrent neural networks |
| title_full | GRAiCE: reconstructing terrestrial water storage anomalies with recurrent neural networks |
| title_fullStr | GRAiCE: reconstructing terrestrial water storage anomalies with recurrent neural networks |
| title_full_unstemmed | GRAiCE: reconstructing terrestrial water storage anomalies with recurrent neural networks |
| title_short | GRAiCE: reconstructing terrestrial water storage anomalies with recurrent neural networks |
| title_sort | graice reconstructing terrestrial water storage anomalies with recurrent neural networks |
| url | https://doi.org/10.1038/s41597-025-04403-3 |
| work_keys_str_mv | AT irenepalazzoli graicereconstructingterrestrialwaterstorageanomalieswithrecurrentneuralnetworks AT serenaceola graicereconstructingterrestrialwaterstorageanomalieswithrecurrentneuralnetworks AT pierregentine graicereconstructingterrestrialwaterstorageanomalieswithrecurrentneuralnetworks |