Mon Feb 16, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Mo, Shaoxing, Schumacher, Maike, Dijk, Albert I. J. M., Shi, Xiaoqing, Wu, Jichun, and Forootan, Ehsan, 2025. Near–Real–Time Monitoring of Global Terrestrial Water Storage Anomalies and Hydrological Droughts. Geophysical Research Letters, 52(7):2024GL112677, doi:10.1029/2024GL112677.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025GeoRL..5212677M,
author = {{Mo}, Shaoxing and {Schumacher}, Maike and {Dijk}, Albert I.~J.~M. and {Shi}, Xiaoqing and {Wu}, Jichun and {Forootan}, Ehsan},
title = "{Near-Real-Time Monitoring of Global Terrestrial Water Storage Anomalies and Hydrological Droughts}",
journal = {\grl},
keywords = {GRACE, Bayesian convolutional neural network, hydrological drought, terrestrial water storage anomaly, data latency, deep learning},
year = 2025,
month = apr,
volume = {52},
number = {7},
pages = {2024GL112677},
abstract = "{Global terrestrial water storage anomaly (TWSA) products from the
Gravity Recovery and Climate Experiment (GRACE) and its Follow-
On mission (GRACE/FO) have an approximately three-month latency,
significantly limiting their operational use in water management
and drought monitoring. To address this challenge, we develop a
Bayesian convolutional neural network (BCNN) to predict TWSA
fields with uncertainty estimates during the latency period. The
results demonstrate that BCNN provides near-real-time TWSA
estimates that closely match GRACE/FO observations, with median
correlation coefficients of 0.92{\textendash}0.95, Nash-
Sutcliffe efficiencies of 0.81{\textendash}0.89, and root mean
squared errors of 1.79{\textendash}2.26 cm for one- to three-
month ahead predictions. More importantly, the model advances
global hydrological drought monitoring by enabling detection up
to three months before GRACE/FO data availability, with median
characterization mismatches below 16.4\%. This breakthrough in
early warning capability addresses a fundamental constraint in
satellite-based hydrological monitoring and offers water
resource managers critical lead time to implement drought
mitigation strategies.}",
doi = {10.1029/2024GL112677},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025GeoRL..5212677M},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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