TY  - Data
T1  - Reconstruction of Global Land Water Storage Dataset Based on Machine Learning (1940 present)
A1  - Yin Jiabo
DO  - 10.5281/zenodo.10040927
PY  - 2024
DA  - 2024-06-18
PB  - National Cryosphere Desert Data Center
AB  - This study presents a long-term (1940-2022) and high-resolution (0.25 °) monthly time series of global land surface TWS anomalies. Reconstruction is achieved through a set of machine learning models that include a large number of predictive factors, including climate and hydrological variables, land use/land cover data, and vegetation indicators such as leaf area index. In addition, our reconstruction successfully reproduced the effects of climate variability, such as the strong El Ni ñ o phenomenon.   The GTWS MLrec dataset includes three reconstructions based on JPL, CSR, and GSFC mascons, three de trending and de seasonal reconstructions, and global average TWS sequences for six land regions (including Greenland and Antarctica). GTWS.MRec has a wide range of attributes that can support a wide range of applications, such as better understanding global water budgets, constraining and evaluating hydrological models, climate carbon coupling, and water resource management.
DB  - NCDC
UR  - http://www.ncdc.ac.cn/portal/metadata/6b23bed9-627c-4653-8157-2ddd5b323888
ER  -