Articles | Volume 17, issue 6
https://doi.org/10.5194/essd-17-2575-2025
https://doi.org/10.5194/essd-17-2575-2025
Data description paper
 | 
13 Jun 2025
Data description paper |  | 13 Jun 2025

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

Nehar Mandal, Prabal Das, and Kironmala Chanda

Data sets

ML based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data N. Mandal et al. https://doi.org/10.6084/m9.figshare.25376695

JPL GRACE and GRACE-FO Mascon Ocean, Ice, and Hydrology Equivalent Water Height Coastal Resolution Improvement (CRI) Filtered Release 06.1 Version 03 NASA/JPL https://doi.org/10.5067/TEMSC-3JC63

Improved methods for observing Earth's time variable mass distribution with GRACE using spherical cap mascons (https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06.1_V3) M. M. Watkins et al. https://doi.org/10.1002/2014JB011547

NASA/GSFC/HSL, GLDAS Catchment Land Surface Model L4 daily 0.25 x 0.25 degree V2.0 B. Li et al. https://doi.org/10.5067/LYHA9088MFWQ

NASA/GSFC/HSL, GLDAS Catchment Land Surface Model L4 daily 0.25 x 0.25 degree GRACE-DA1 V2.2 B. Li et al. https://doi.org/10.5067/TXBMLX370XX8

NASA/GSFC/HSL, GLDAS Noah Land Surface Model L4 monthly 0.25 x 0.25 degree V2.0 H. Beaudoing and M. Rodell https://doi.org/10.5067/9SQ1B3ZXP2C5

NASA/GSFC/HSL, GLDAS Noah Land Surface Model L4 monthly 0.25 x 0.25 degree V2.1 H. Beaudoing and M. Rodell https://doi.org/10.5067/SXAVCZFAQLNO

A dipole mode in the tropical Indian Ocean (https://psl.noaa.gov/gcos_wgsp/Timeseries/Data/dmi.had.long.data) N. Saji et al. https://doi.org/10.1038/43854

Possible impacts of Indian Ocean dipole mode events on global climate (https://psl.noaa.gov/gcos_wgsp/Timeseries/Data/dmi.had.long.data) N. Saji and T. Yamagata https://doi.org/10.3354/cr025151

Teleconnections in the geopotential height field during the Northern Hemisphere winter (https://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/norm.nao.monthly.b5001.current.ascii) J. M. Wallace and D. S. Gutzler https://doi.org/10.1175/1520-0493(1981)109<0784:TITGHF>2.0.CO;2

Classification, Seasonality and Persistence of Low-Frequency Atmospheric Circulation Patterns (https://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/norm.nao.monthly.b5001.current.ascii) A. G. Barnston and R. E. Livezey https://doi.org/10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2

Documentation of a highly ENSO‐related sst region in the equatorial pacific: Research note (https://www.cpc.ncep.noaa.gov/data/indices/oni.ascii.txt) A. G. Bamston et al. https://doi.org/10.1080/07055900.1997.9649597

Model code and software

ML based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data N. Mandal et al. https://doi.org/10.6084/m9.figshare.25376695

Video abstract

ML based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data N. Mandal et al. https://doi.org/10.5446/69988

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Short summary
Optimal features among hydroclimatic variables and land surface model (LSM) outputs are selected using a novel Bayesian network (BN) approach for simulating terrestrial water storage anomalies (TWSAs). TWSAs are reconstructed (BNML_TWSA) with grid-specific leader models (among four machine learning models) from January 1960 to December 2022 to generate a continuous global gridded dataset. The uncertainty in the reconstructed BNML_TWSA product is also assessed in terms of standard error.
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