Reconstructed Global Total Water Storage Products (1923-2022): Insights and Challenges in Humid and Arid Regions
- Jielong Wang
, - Yunzhong Shen,
- Joseph L Awange,
- Maryam Tabatabaeiasl,
- Tengfei Feng,
- Ling Yang,
- Yongze Song
Jielong Wang
College of Surveying and Geo-informatics, Tongji University, School of Earth and Planetary Sciences, Spatial Sciences Discipline, Curtin University
Corresponding Author:[email protected]
Author ProfileYunzhong Shen
College of Surveying and Geo-informatics, Tongji University
Joseph L Awange
School of Earth and Planetary Sciences, Spatial Sciences Discipline, Curtin University
Maryam Tabatabaeiasl
School of Earth and Planetary Sciences, Spatial Sciences Discipline, Curtin University
Tengfei Feng
College of Surveying and Geo-informatics, Tongji University
Ling Yang
College of Surveying and Geo-informatics, Tongji University
Yongze Song
School of Design and the Built Environment, Curtin University
Abstract
A deep learning model for reconstructing global climate-driven total water storage changes is presented for 1923-2022. Our reconstruction exhibits superior consistency with GRACE observations compared to GRACE-REC. The reconstructed datasets reveal relative reliability and challenges in humid and arid regions.06 Mar 2024Submitted to ESS Open Archive 15 Mar 2024Published in ESS Open Archive
