Fri Apr 10, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Zhong, Yulong, Wang, Yingying, Kusche, Jürgen, Tian, Baoming, and Wu, Yunlong, 2026. A Novel Metric for Quantifying Precipitation–Driven Variability in Global Terrestrial Water Storage. IEEE Transactions on Geoscience and Remote Sensing, 64:TGRS.2026, doi:10.1109/TGRS.2026.3650800.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2026ITGRS..64S0800Z,
author = {{Zhong}, Yulong and {Wang}, Yingying and {Kusche}, J{\"u}rgen and {Tian}, Baoming and {Wu}, Yunlong},
title = "{A Novel Metric for Quantifying Precipitation-Driven Variability in Global Terrestrial Water Storage}",
journal = {IEEE Transactions on Geoscience and Remote Sensing},
keywords = {Gravity Recovery and Climate Experiment (GRACE), nonstationarity, precipitation variability, standard deviation (STD), terrestrial water storage},
year = 2026,
month = jan,
volume = {64},
eid = {TGRS.2026},
pages = {TGRS.2026},
abstract = "{Under global climate change and human intervention, an increased
frequency of alternating drought and flood events exists, making
such extreme conditions seemingly the new normal. These frequent
events result in greater variability in terrestrial water
storage anomalies (TWSA) (i.e., nonstationary behavior). In this
study, we introduce a novel and straightforward metric, i.e.,
the standard deviation of TWSA (STD), to detect regions with
nonstationary in TWSA and gain insight into the impact of global
change on regional TWSA. TWSA estimates from the Center for
Space Research (CSR) Mascon are used to calculate STDTWSA and
estimated in a rolling window approach, and the results are
compared with STD in precipitation anomalies (PAs) and
temperature anomalies (TAs). Our results reveal that 63.8\% of
global land areas show significant prevalence of increasing TWSA
variability with nonstationary, while only 3.5\% exhibit a
decrease. Among the 40 large basins analyzed, 11 show
significant upward trends in STDTWSA, with only one basin
showing a significant decline from 2002 to 2022. In 34 of these
basins, precipitation accounts for over 50\% of TWSA
variability, suggesting that variabilities in PAs can be used to
model and explain the nonstationary behavior of TWSA in these
regions. In addition, in three of the remaining six basins, we
observed significant correlations between TWSA and PA
variability at a five-year window. Furthermore, WaterGAP v2.2e
hydrological model (WGHM)-modeled STDTWSA estimates align with
the STDTWSA from CSR and show consistent trends in most basins.
By linking precipitation-driven dynamics to TWSA variability,
this study demonstrates the utility of STDTWSA as a practical
tool for identifying regions vulnerable to hydrological
extremes. This study provides valuable insights into global TWSA
nonstationarity and enhances our understanding of the impacts of
global climate change and human intervention on water storage
changes.}",
doi = {10.1109/TGRS.2026.3650800},
adsurl = {https://ui.adsabs.harvard.edu/abs/2026ITGRS..64S0800Z},
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
Fri Apr 10, 2026 11:13:49
GRACE-FO
Fri Apr 10, F. Flechtner