Palazzoli Irene, Ceola Serena, Gentine Pierre
Department of Civil, Chemical, Environmental, and Materials Engineering, Alma Mater Studiorum - Università di Bologna, Bologna, Italy.
Department of Earth and Environmental Engineering, Columbia University, New York, USA.
Sci Data. 2025 Jan 25;12(1):146. doi: 10.1038/s41597-025-04403-3.
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.
重力恢复与气候实验（GRACE）及其后续任务（GRACE-FO）自2002年以来提供了陆地水储量异常（TWSA）的估计值，从而能够监测全球水文变化。然而，这些数据集中的时间间隙以及2002年之前缺乏TWSA观测数据，限制了我们对长期淡水变化的理解。在本研究中，我们开发了GRAiCE，这是一组利用长短期记忆（LSTM）和双向长短期记忆（BiLSTM）神经网络从1984年到2021年以0.5°空间分辨率进行的全球每月TWSA重建。我们的模型在全球尺度上准确再现了GRACE/GRACE-FO观测结果，并有效捕捉了极端气候的影响。总体而言，GRAiCE在预测观测到的TWSA方面优于先前的参考TWSA重建，并在流域尺度上提供了可靠的水量平衡估计。通过生成长期连续的TWSA时间序列，GRAiCE将为气候变率和变化对淡水资源的影响提供有价值的见解。
