Mon Feb 16, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Zhu, Yonghua, Zhou, Longfei, Zhang, Qi, Han, Zhiming, Li, Jiamin, Chao, Yan, Wang, Xiaohan, Yuan, Hui, Zhang, Jie, and Xia, Bisheng, 2025. Assessing the Use of the Standardized GRACE Satellite Groundwater Storage Change Index for Quantifying Groundwater Drought in the Mu Us Sandy Land. Remote Sensing, 17(24):4015, doi:10.3390/rs17244015.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025RemS...17.4015Z,
author = {{Zhu}, Yonghua and {Zhou}, Longfei and {Zhang}, Qi and {Han}, Zhiming and {Li}, Jiamin and {Chao}, Yan and {Wang}, Xiaohan and {Yuan}, Hui and {Zhang}, Jie and {Xia}, Bisheng},
title = "{Assessing the Use of the Standardized GRACE Satellite Groundwater Storage Change Index for Quantifying Groundwater Drought in the Mu Us Sandy Land}",
journal = {Remote Sensing},
keywords = {standardized groundwater index, hysteresis time, probability density function, drought, Mu Us Sandy Land},
year = 2025,
month = dec,
volume = {17},
number = {24},
eid = {4015},
pages = {4015},
abstract = "{What are the main findings? Based on GRACE-derived groundwater storage
anomaly data, the Anderson─Darling test revealed that the
Pearson III distribution function provides the best fit for
calculating the standardized groundwater index (GRACE\_SGI)
across different time scales, significantly improving accuracy.
Cross-correlation analysis between the GRACE\_SGI and the
standardized precipitation index (SPI) demonstrated a notable
time lag effect, with lag times of up to 12 months being
observed at longer time scales, indicating a delayed response of
groundwater levels to precipitation changes. Based on GRACE-
derived groundwater storage anomaly data, the Anderson─Darling
test revealed that the Pearson III distribution function
provides the best fit for calculating the standardized
groundwater index (GRACE\_SGI) across different time scales,
significantly improving accuracy. Cross-correlation analysis
between the GRACE\_SGI and the standardized precipitation index
(SPI) demonstrated a notable time lag effect, with lag times of
up to 12 months being observed at longer time scales, indicating
a delayed response of groundwater levels to precipitation
changes. What are the implications of the main findings? The
identification of the optimal probability density function for
GRACE\_SGI calculation enhances the reliability of groundwater
drought monitoring, particularly in data-scarce regions,
providing a robust scientific foundation for quantitative
assessments. Understanding the time lag effect between
precipitation and groundwater recharge aids in more accurately
predicting groundwater drought events, facilitating proactive
water resource management and drought preparedness strategies.
The identification of the optimal probability density function
for GRACE\_SGI calculation enhances the reliability of
groundwater drought monitoring, particularly in data-scarce
regions, providing a robust scientific foundation for
quantitative assessments. Understanding the time lag effect
between precipitation and groundwater recharge aids in more
accurately predicting groundwater drought events, facilitating
proactive water resource management and drought preparedness
strategies. The increasingly severe phenomenon of groundwater
drought poses a dual threat to the development and construction
of a region, as well as its ecological environment. Traditional
groundwater drought monitoring methods rely on observation
wells, which makes it difficult to obtain dynamic drought
information in areas with limited measurement data. Based on
Gravity Recovery and Climate Experiment (GRACE) satellite
technology and data, the suitability of the standardized
groundwater index (GRACE\_SGI) was explored for drought
characterization in the Mu Us Sandy Land. Multiscale and
seasonal trend changes in groundwater drought in the study area
from 2002 to 2021 were comprehensively identified. Subsequently,
the characteristics of hysteresis time between the GRACE\_SGI
and the standardized precipitation index (SPI) were clarified.
The results show that (1) different fitting functions impact the
parameterized GRACE\_SGI fitting results. The Anderson─Darling
method was used to find the best-fitting function for
groundwater data in the study area: the Pearson III
distribution. (2) The gain and loss characteristics of the
GRACE\_SGI are similar, showing downward trends at different
time scales, including seasonal scales. (3) The absolute values
based on the maximum correlation coefficients between the SPI
and the GRACE\_SGI at different time scales were 0.1296, 0.2483,
0.2427, and 0.5224, with time lags of 0, 0, 12, and 11 months,
respectively. The vulnerability of semiarid ecosystems to
hydroclimatic changes is highlighted by these findings, and a
satellite-based framework for monitoring groundwater drought in
data-scarce regions is provided.}",
doi = {10.3390/rs17244015},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025RemS...17.4015Z},
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
Mon Feb 16, 2026 23:51:58
GRACE-FO
Mon Feb 16, F. Flechtner