Can we estimate, and even predict, terrestrial water storage months in advance during severe large-scale Amazon droughts

Bibliographic Data

ID15548487
AuthorsShuang Liu (0000-0002-8749-3607, University of Technology Sydney, corresponding author), Tim R McVicar (0000-0002-0877-8285, Commonwealth Scientific and Industrial Research Organisation), Xue Wu (0000-0002-9461-3558, UNSW Sydney), Xin Cao (0000-0002-9516-8222, UNSW Sydney), Yi Liu (0000-0001-7733-3290, UNSW Sydney)
Year2025
Volume20
Issue11
Pages114023-114023
Publication date2025-09-30
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ae0da7
OpenAlexW4414637245
LanguageEN
References cited82

Over the past two decades, the Amazon has experienced four severe large-scale droughts (i.e. 2005, 2010, 2015/16 and 2023), leading to drastically reduced water availability, slowed vegetation growth and higher forest mortality. As future droughts are expected to become more frequent and severe, accurately predicting the unprecedentedly low water storage levels and water shortages in advance is crucial. Herein, we developed a new approach to predict terrestrial water storage (TWS) during droughts, based on monthly changes in TWS (ΔTWS) and meteorological variables from 2003 to 2023. The model was trained during non-drought months and assessed during the four droughts when TWS values are well below the range of training data. The ΔTWS-based model excels in predicting drought-month TWS even only using precipitation and incoming solar radiation, with average correlation ( R ) over 0.9 and RMSE below 50 mm. The model also showed superior skills for predicting drought TWS months lead-time, with the 3-month prediction achieved high performance ( R > 0.8, RMSE < 80 mm). We further examined TWS predictions during the large-scale 2023 drought and found that the predicted TWS showed high spatial agreement with observed TWS, with all 1-, 2-, and 3-month lead-times reaching average R values over 0.9. Then we evaluated water deficits in the driest months (September—December) in 2023. The model predicted the affected regions with reasonable accuracy, achieving an average of 72% even at 3-month lead-time. We also analyzed how uncertainty in meteorological inputs affects model performance, revealing higher input uncertainty reduced the model performance. This study presents a reliable approach for estimating and predicting low water storage during severe large-scale droughts, enabling early warnings of water deficits across the Amazon. This study could be generalized to other regions, supporting proactive water resource management, water security policies, ecosystem protection and climate adaptation strategies

Amazon rainforest · Economic shortage · Mean squared error · Precipitation · Range (aeronautics · Vegetation (pathology · Water cycle · Water storage · Geophysics and Gravity Measurements

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