Thu Aug 14, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Zhu, Ziang, Zhao, Qian, and Chai, Rongzi, 2025. Response Analysis of Terrestrial Water Storage Components to Drought Based on Random Forests During 20112020 in Yunnan, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18:1537–1550, doi:10.1109/JSTARS.2024.3507853.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025IJSTA..18.1537Z,
author = {{Zhu}, Ziang and {Zhao}, Qian and {Chai}, Rongzi},
title = "{Response Analysis of Terrestrial Water Storage Components to Drought Based on Random Forests During 20112020 in Yunnan, China}",
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
keywords = {Drought index, GRACE, hydrological drought, random forests (RFs), terrestrial water storage (TWS)},
year = 2025,
month = jan,
volume = {18},
pages = {1537-1550},
abstract = "{The interaction between terrestrial water storage (TWS) and drought is a
crucial aspect of hydrological dynamics. We utilized data from
the gravity recovery and climate experiment (GRACE) to derive
the TWS of Yunnan from 2011 to 2020 and calculated the GRACE
drought severity index (GRACE-DSI) to assess hydrological
drought events based on the run theory. To understand the
underlying causes of drought, we analyzed precipitation data
alongside the spatial and temporal distribution of drought
events. Each component of TWS was obtained through hydrological
models, and we used random forest to determine the contribution
of each component to drought. Our findings revealed that Yunnan
experienced four drought events from 2011 to 2020, with the
third and fourth drought events between 2019 and 2020 being
particularly severe. The occurrence of these drought events was
primarily attributed to a relative lack of precipitation. Among
the components of TWS, water storage in groundwater, canopy,
soil, and lake were identified as having the highest
contribution rates to drought. We also applied random forest to
simulate the contribution rates of each TWS component to drought
in other regions of China. Our analysis confirmed that
groundwater in the North China Plain, lake, and soil water in
the Yangtze River middle-downstream Plain, snow, and groundwater
in Tianshan Mountain were the components with the highest
contribution rates to drought, respectively. Finally, we found
that the contribution of TWS components to drought in different
regions of Yunnan Province was significantly different. This
novel method was valuable for hydrological drought management.}",
doi = {10.1109/JSTARS.2024.3507853},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025IJSTA..18.1537Z},
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