Climate
change
occurs
due
to
long-term
changes
in
temperature
and
atmosphere,
one
of
the
obvious
examples
climate
is
global
warming
that
increasing
every
region
world
this
reaches
dangerous
levels.
The
business
people
has
always
attached
importance
a
profit
from
being
run,
but
they
do
not
think
about
how
environmental
can
have
big
impact
later
on
people.
This
study
uses
qualitative
research
with
literature
method
use
articles
provide
relevance.
results
an
explanation
role
communication
handling
order
be
able
implement
SDGs
through
transparency,
awareness
education,
green
marketing
also
partnership
collaboration.
Business
main
foundation
because
it
effective
flow,
inform
mobilize
as
stakeholder
taking
action
fight
change.
Remote Sensing,
Journal Year:
2025,
Volume and Issue:
17(3), P. 493 - 493
Published: Jan. 31, 2025
The
Gravity
Recovery
and
Climate
Experiment
(GRACE)
introduces
a
new
approach
to
accurately
monitor,
in
real
time,
regional
groundwater
resources,
which
compensates
for
the
limitations
of
traditional
hydrological
observations
terms
spatiotemporal
resolution.
Currently,
storage
changes
Jiangsu
Province
face
issues
such
as
low
spatial
resolution,
limited
applicability
downscaling
models,
insufficient
water
resource
observation
data.
This
study
based
on
GRACE
employs
Random
Forest
Regression
(RFR)
Geographically
Weighted
(GWR)
methods
order
obtain
high-resolution
information
change.
results
indicate
that
among
established
66
×
158
local
GWR
coefficient
determination
(R2)
ranges
from
0.39
0.88,
with
root
mean
squared
error
(RMSE)
approximately
2.60
cm.
proportion
models
an
R2
below
0.5
was
18.52%.
Similarly,
RFR
trained
above
time
series
grid
data
achieved
0.50,
RMSE
fluctuating
around
1.59
In
validation,
monthly
correlation
coefficients
between
measured
stations
ranged
0.37
0.66,
53.33%
having
greater
than
0.5.
seasonal
0.41
0.62,
60%
exceeding
0.44
ranging
0.49
0.84.
Only
one
station
had
both
results.
validation
accuracy
levels,
model
demonstrated
better
predictive
performance,
offers
distinct
advantages
improving
resolution
Province.
Remote Sensing,
Journal Year:
2025,
Volume and Issue:
17(6), P. 988 - 988
Published: March 12, 2025
Groundwater
systems
are
important
for
maintaining
ecological
balance
and
ensuring
water
supplies.
However,
under
the
combined
pressures
of
shifting
climate
patterns
human
activities,
their
responses
to
extreme
events
have
become
increasingly
complex.
As
China’s
largest
freshwater
lake,
Poyang
Lake
supports
critical
resources,
health,
adaptation
efforts.
Yet,
relationship
between
groundwater
storage
(GWS)
hydrological
in
this
region
remains
insufficiently
studied,
hindering
effective
management.
This
study
investigates
GWS
response
by
downscaling
Gravity
Recovery
Climate
Experiment
(GRACE)
data
validating
it
with
five
years
observed
daily
levels.
Using
GRACE,
Global
Land
Data
Assimilation
System
(GLDAS),
ERA5
data,
a
convolutional
neural
network
(CNN)–attention
mechanism
(A)–long
short-term
memory
(LSTM)
model
was
selected
downscale
high
resolution
(0.1°
×
0.1°)
estimate
recovery
times
return
baseline.
Our
analysis
revealed
seasonal
fluctuations
that
phase
precipitation,
evapotranspiration,
runoff.
durations
flood
(2020)
drought
(2022)
ranged
from
0.8
3.1
months
0.2
4.8
months,
respectively.
A
strong
correlation
meteorological
droughts,
while
agricultural
significantly
weaker.
These
results
indicate
precipitation
runoff
more
sensitive
than
evapotranspiration
influencing
changes.
findings
highlight
significant
sensitivity
GWS,
despite
improved
management
Remote Sensing,
Journal Year:
2025,
Volume and Issue:
17(8), P. 1333 - 1333
Published: April 8, 2025
The
Qinghai–Tibet
Plateau
(QTP),
a
critical
hydrological
regulator
for
Asia
through
its
extensive
glacier
systems,
high-altitude
lakes,
and
intricate
network
of
rivers,
exhibits
amplified
sensitivity
to
climate-driven
alterations
in
precipitation
regimes
ice
mass
balance.
While
the
Gravity
Recovery
Climate
Experiment
(GRACE)
Follow-On
(GRACE-FO)
missions
have
revolutionized
monitoring
terrestrial
water
storage
anomalies
(TWSAs)
across
this
hydrologically
sensitive
region,
spatial
resolution
limitations
(3°,
equivalent
~300
km)
constrain
process-scale
analysis,
compounded
by
mission
temporal
discontinuity
(data
gaps).
In
study,
we
present
novel
downscaling
framework
integrating
gap
compensation
refinement
0.25°
Gated
Recurrent
Unit
(GRU)
neural
networks,
an
architecture
optimized
univariate
time
series
modeling.
Through
assimilation
multi-source
parameters
(glacier
flux,
cryosphere–precipitation
interactions,
land
surface
processes),
GRU-based
result
resolves
nonlinear
dynamics
while
bridging
inter-mission
observational
gaps.
Grid-level
implementation
preserves
conservation
principles
heterogeneous
topographies,
successfully
reconstructing
seasonal-to-interannual
TWSA
variability
also
long-term
trends.
Comparative
validation
against
GRACE
mascon
solutions
process-based
models
demonstrates
enhanced
capacity
resolving
sub-basin
heterogeneity.
This
GRU-derived
high-resolution
is
especially
valuable
dissecting
local
areas
such
as
Brahmaputra
Basin,
where
complex
cycling
can
affect
downstream
security.
Our
study
provides
transferable
methodologies
mountainous
hydrogeodesy
analysis
under
evolving
climate
regimes.
Future
enhancements
physics-informed
deep
learning
next-generation
climatology–hydrology–gravimetry
synergy
(e.g.,
observations
models)
could
further
uncertainties
extreme
elevation
zones,
advancing
predictive
understanding
Asia’s
tower
sustainability.
