Mon Oct 13, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Gyawali, Bimal, Murgulet, Dorina, and Ahmed, Mohamed, 2022. Quantifying Changes in Groundwater Storage and Response to Hydroclimatic Extremes in a Coastal Aquifer Using Remote Sensing and Ground-Based Measurements: The Texas Gulf Coast Aquifer. Remote Sensing, 14(3):612, doi:10.3390/rs14030612.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2022RemS...14..612G,
author = {{Gyawali}, Bimal and {Murgulet}, Dorina and {Ahmed}, Mohamed},
title = "{Quantifying Changes in Groundwater Storage and Response to Hydroclimatic Extremes in a Coastal Aquifer Using Remote Sensing and Ground-Based Measurements: The Texas Gulf Coast Aquifer}",
journal = {Remote Sensing},
keywords = {groundwater storage, GRACE, Texas, coastal terrestrial water storage, hydroclimatic extreme events},
year = 2022,
month = jan,
volume = {14},
number = {3},
eid = {612},
pages = {612},
abstract = "{With the increasing vulnerability of groundwater resources, especially
in coastal regions, there is a growing need to monitor changes
in groundwater storage (GWS). Estimations of GWS have been
conducted extensively at regional to global scales using GRACE
and GRACE-FO observations. The major goal of this study was to
evaluate the applicability of uninterrupted monthly GRACE-
derived terrestrial water storage (TWS$_{GRACE}$) records in
facilitating detection of long- and short-term hydroclimatic
events affecting the GWS in a coastal area. The TWS$_{GRACE}$
data gap was filled with reconstructed values from multi-linear
regression (MLR) and artificial neural network (ANN) models and
used to estimate changes in GWS in the Texas coastal region
(Gulf Coast and Carrizo-Wilcox Aquifers) between 2002 and 2019.
The reconstructed TWS$_{GRACE}$, along with soil moisture
storage (SMS) from land surface models (LSMs), and surface water
storage (SWS) were used to estimate the GRACE-derived GWS
(GWS$_{GRACE}$), validated against the GWS estimated from
groundwater level observations (GWS$_{well}$) and extreme
hydroclimatic event records. The results of this study show: (1)
Good agreement between the predicted TWS$_{GRACE}$ data gaps
from the MLR and ANN models with high accuracy of predictions;
(2) good agreement between the GWS$_{GRACE}$ and GWS$_{well}$
records (CC = 0.56, p-value < 0.01) for the 2011-2019 period for
which continuous GWL$_{well}$ data exists, thus validating the
approach and increasing confidence in using the reconstructed
TWS$_{GRACE}$ data to monitor coastal GWS; (3) a significant
decline in the coastal GWS$_{GRACE}$, at a rate of 0.35
{\ensuremath{\pm}} 0.078 km$^{3}${\textperiodcentered}yr$^{-1}$
(p-value < 0.01), for the 2002-2019 period; and (4) the reliable
applicability of GWS$_{GRACE}$ records in detecting multi-year
drought and wet periods with good accuracy: Two drought periods
were identified between 2005-2006 and 2010-2015, with
significant respective depletion rates of -8.9
{\ensuremath{\pm}} 0.95 km$^{3}${\textperiodcentered}yr$^{-1}$
and -2.67 {\ensuremath{\pm}} 0.44
km$^{3}${\textperiodcentered}yr$^{-1}$ and one wet period
between 2007 and 2010 with a significant increasing rate of 2.6
{\ensuremath{\pm}} 0.63 km$^{3}${\textperiodcentered}yr$^{-1}$.
Thus, this study provides a reliable approach to examine the
long- and short-term trends in GWS in response to changing
climate conditions with significant implications for water
management practices and improved decision-making capabilities.}",
doi = {10.3390/rs14030612},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022RemS...14..612G},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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