Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data

Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data

<p>Understanding long-term terrestrial water storage (TWS) variations is vital for investigating hydrological extreme events, managing water resources and assessing climate change impacts. However, the limited data duration from the Gravity Recovery and Climate Experiment (GRACE) and its follo...

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Bibliographic Details
Main Authors: N. Mandal, P. Das, K. Chanda
Format: Article
Language:English
Published: Copernicus Publications 2025-06-01
Series:Earth System Science Data
Online Access:https://essd.copernicus.org/articles/17/2575/2025/essd-17-2575-2025.pdf
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