TY  - Data
T1  - GTWS-MLrec: Global terrestrial water storage reconstruction by machine learning from 1940 to present
A1  - None
DO  - 10.5281/zenodo.10040927
PY  - 2024
DA  - 2024-06-18
PB  - National Cryosphere Desert Data Center
AB  - This study presents a long-term (i.e. 1940-2022) and high-resolution (i.e. 0.25 °) monthly time series of global land surface TWS anomalies. Reconstruction is achieved through a set of machine learning models that contain a large number of predictive factors, including climate and hydrological variables, land use/cover data, and vegetation indicators such as leaf area index. In addition, our reconstruction has successfully reproduced the impact 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 masks, three de trending and de seasoning reconstructions, and six global average TWS sequences for land regions (including Greenland and Antarctica). GTWS-MLrec has a wide range of properties and can support a wide range of applications, such as better understanding of global water budgets, constrained and evaluated 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  -