Thu Aug 14, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Mandal, Nehar, Das, Prabal, and Chanda, Kironmala, 2025. Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data. Earth System Science Data, 17(6):2575–2604, doi:10.5194/essd-17-2575-2025.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025ESSD...17.2575M,
author = {{Mandal}, Nehar and {Das}, Prabal and {Chanda}, Kironmala},
title = "{Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data}",
journal = {Earth System Science Data},
year = 2025,
month = jun,
volume = {17},
number = {6},
pages = {2575-2604},
abstract = "{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 follow-on mission (GRACE-FO) poses
challenges for comprehensive long-term analysis. In this study,
we reconstruct TWS anomalies (TWSAs) for the period from January
1960 to December 2022, thereby filling data gaps between the
GRACE and GRACE-FO missions and generating a complete dataset
for the pre-GRACE era. The workflow involves identifying optimal
predictors from land surface model (LSM) outputs, meteorological
variables and climatic indices using a novel Bayesian network
(BN) technique for raster-based TWSA simulations. Climate
indices, like the Oceanic Ni{\~n}o Index and Dipole Mode Index,
are selected as optimal predictors for a large number of grid
cells globally, along with TWSAs from LSM outputs. The most
effective machine learning (ML) algorithms among convolutional
neural network (CNN), support vector regression (SVR), extra
trees regressor (ETR) and stacking ensemble regression (SER)
models are evaluated at each grid cell to achieve optimal
reproducibility. Globally, ETR performs best for most of the
grid cells; this is also noticed at the river basin scale,
particularly for the Ganga-Brahmaputra-Meghna, Godavari,
Krishna, Limpopo and Nile river basins. The simulated TWSAs
(BNML\_TWSA) outperformed the TWSAs from LSM outputs when
evaluated against GRACE datasets. Improvements are particularly
noted in river basins such as the Godavari, Krishna, Danube and
Amazon, with median correlation coefficient, Nash-Sutcliffe
efficiency, and RMSE values for all grid cells in the Godavari
Basin, India, being 0.927, 0.839 and 63.7 mm, respectively. A
comparison with TWSAs reconstructed in recent studies indicates
that the proposed BNML\_TWSA outperforms them globally as well
as for all of the 11 major river basins examined. Furthermore,
the uncertainty of BNML\_TWSA is assessed for each grid cell in
terms of the standard error. Results show smaller standard error
magnitudes in grid cells in arid regions compared to other
regions. The presented gridded dataset is published at
https://doi.org/10.6084/m9.figshare.25376695 , featuring a
spatial resolution of 0.50{\textdegree} {\texttimes}
0.50{\textdegree} and offering global coverage.}",
doi = {10.5194/essd-17-2575-2025},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025ESSD...17.2575M},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
Generated by
bib2html_grace.pl
(written by Patrick Riley
modified for this page by Volker Klemann) on
Thu Aug 14, 2025 17:55:12
GRACE-FO
Thu Aug 14, F. Flechtner