Mon Oct 13, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Ali, Shoaib, Ran, Jiangjun, Luan, Yi, Khorrami, Behnam, Xiao, Yun, and Tangdamrongsub, Natthachet, 2024. The GWR model-based regional downscaling of GRACE/GRACE-FO derived groundwater storage to investigate local-scale variations in the North China Plain. Science of the Total Environment, 908:168239, doi:10.1016/j.scitotenv.2023.168239.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2024ScTEn.90868239A,
author = {{Ali}, Shoaib and {Ran}, Jiangjun and {Luan}, Yi and {Khorrami}, Behnam and {Xiao}, Yun and {Tangdamrongsub}, Natthachet},
title = "{The GWR model-based regional downscaling of GRACE/GRACE-FO derived groundwater storage to investigate local-scale variations in the North China Plain}",
journal = {Science of the Total Environment},
keywords = {NCP, North China Plain, GRACE, Gravity Recovery and Climate Experiment, GRACE-FO, Gravity Recovery and Climate Experiment Follow-On, TWSA, Terrestrial Water Storage Anomaly, GWSA, Groundwater Storage Anomaly, STL, Seasonal Trend Decomposition LOESS, GWR, Geographically Weighted Regression, SHCs, spherical harmonic coefficients, CSR, Center for Space Research at the University of Texas at Austin, JPL, Jet Propulsion Laboratory, North China Plain, GRACE, GWSA, STL, GWR, Downscaling},
year = 2024,
month = jan,
volume = {908},
eid = {168239},
pages = {168239},
abstract = "{Groundwater storage and depletion fluctuations in response to
groundwater availability for irrigation require understanding on
a local scale to ensure a reliable groundwater supply. However,
the coarser spatial resolution and intermittent data gaps to
estimate the regional groundwater storage anomalies (GWSA)
prevent the Gravity Recovery and Climate Experiment (GRACE) and
GRACE Follow-On (GARCE-FO) mission from being applied at the
local scale. To enhance the resolution of GWSA measurements
using machine learning approaches, numerous recent efforts have
been made. With a focus on the development of a new algorithm,
this study enhanced the GWSA resolution estimates to
0.05{\textdegree} by extensively investigating the continuous
spatiotemporal variations of GWSA based on the regional
downscaling approach using a regression algorithm known as the
geographically weighted regression model (GWR). First, the
modified seasonal decomposition LOESS method (STL) was used to
estimate the continuous terrestrial water storage anomaly
(TWSA). Secondly, to separate GWSA from TWSA, a water balance
equation was used. Third, the continuous GWSA was downscaled to
0.05{\textdegree} based on the GWR model. Finally, spatio-
temporal properties of downscaled GWSA were investigated in the
North China Plain (NCP), China's fastest-urbanizing area, from
2003 to 2022. The results of the downscaled GWSA were spatially
compatible with GRACE-derived GWSA. The downscaled GWSA results
are validated (R = 0.83) using in-situ groundwater level data.
The total loss of GWSA in cities of the NCP fluctuated between
2003 and 2022, with the largest loss seen in Handan
(<mml:math><mml:mo>‑</mml:mo></mml:math>15.21 {\ensuremath{\pm}}
7.25 mm/yr), Xingtai
(<mml:math><mml:mo>‑</mml:mo></mml:math>14.98 {\ensuremath{\pm}}
7.25 mm/yr), and Shijiazhuang
(<mml:math><mml:mo>‑</mml:mo></mml:math>14.58 {\ensuremath{\pm}}
7.25 mm/yr). The irrigated winter-wheat farming strategy is
linked to greater groundwater depletion in several cities of NCP
(e.g., Xingtai, Handan, Anyang, Hebi, Puyang, and Xinxiang). The
study's high-resolution findings can help with understanding
local groundwater depletion that takes agricultural water
utilization and provide quantitative data for water management.}",
doi = {10.1016/j.scitotenv.2023.168239},
adsurl = {https://ui.adsabs.harvard.edu/abs/2024ScTEn.90868239A},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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