Water Resources Research,
Год журнала:
2024,
Номер
60(7)
Опубликована: Июль 1, 2024
Abstract
GRACE
(Gravity
Recovery
and
Climate
Experiment)
has
been
widely
used
to
evaluate
terrestrial
water
storage
(TWS)
groundwater
(GWS).
However,
the
coarse‐resolution
of
data
limited
ability
identify
local
vulnerabilities
in
changes
associated
with
climatic
anthropogenic
stressors.
This
study
employs
high‐resolution
(1
km
2
)
generated
through
machine
learning
(ML)
based
statistical
downscaling
illuminate
TWS
GWS
dynamics
across
twenty
sub‐regions
Indus
Basin.
Monthly
anomalies
obtained
from
a
geographically
weighted
random
forest
(RF
gw
model
maintained
good
consistency
original
at
25
grid
scale.
The
downscaled
1
resolution
illustrate
spatial
heterogeneity
depletion
within
each
sub‐region.
Comparison
in‐situ
2,200
monitoring
wells
shows
that
significantly
improves
agreement
data,
evidenced
by
higher
Kling‐Gupta
Efficiency
(0.50–0.85)
correlation
coefficients
(0.60–0.95).
Hotspots
highest
decline
rate
between
2002
2023
were
Dehli
Doab
(−442,
−585
mm/year),
BIST
(−367,
−556
Rajasthan
(−242,
−381
BARI
(−188,
−333
mm/year).
Based
on
general
additive
model,
47%–83%
was
stressors
mainly
due
increasing
trends
crop
sown
area,
consumption,
human
settlements.
lower
(i.e.,
−25
−75
mm/year)
upstream
(e.g.,
Yogo,
Gilgit,
Khurmong,
Kabul)
where
factors
(downward
shortwave
radiations,
air
temperature,
sea
surface
temperature)
explained
72%–91%
TWS/GWS
changes.
relative
influences
varied
sub‐regions,
underscoring
complex
interplay
natural‐human
activities
basin.
These
findings
inform
place‐based
resource
management
Basin
advancing
understanding
vulnerabilities.
Hydrological Processes,
Год журнала:
2025,
Номер
39(2)
Опубликована: Фев. 1, 2025
ABSTRACT
Climate
change
significantly
impacts
natural
hydrological
systems
worldwide,
affecting
water
availability
and
sedimentation
dynamics.
The
upper
Indus
Basin
is
one
of
the
most
crucial
basins
in
South
Asia,
which
undergoing
severe
climatic
variations,
resulting
extreme
flooding.
This
study
examines
impact
climate
on
cycle,
availability,
dynamics
Shyok
River
basin
Karakoram
region.
focuses
investigating
increasing
outflows
a
seasonal
basis,
as
found
previous
studies,
by
utilising
daily
river
discharge
data
between
2003
2014
at
Yugo
hydrographic
station
operated
WAPDA
with
geographic
information
(GIS),
SWAT+
(Soil
Water
Assessment
Tool),
remote
sensing,
statistical
techniques.
To
analyse
variables,
using
only
available
ground
weather
stations
inside
basin,
we
have
utilised
ERA
5
reanalysis
dataset
to
evaluate
precipitation
temperature
trends.
significance
ERA5‐derived
variables
has
been
assessed
Mann‐Kendall
test,
Sen's
slope
analysis
Coefficient
Determination
R
2
from
2000
2020
monthly
basis.
Analysis
reveals
decline
flow
July
October
general
upward
trend
during
last
20
years,
such
significant
decrease
streamflow
simulated
trend,
was
293
m
3
/s
2005
dropped
158
2017.
In
contrast,
maximum
actual
vs.
decreased
10
2010
2019,
respectively.
increased
approximately
1.5°C.
Seasonal
an
winter,
while
other
seasons
shown
fluctuating
Where
trends
were
be
non‐significant,
p
>
0.05.
An
sediment
load
model
output
suggests
active
erosion
channel
rate
40
megatons/ha.
highlights
It
attempts
contribute
existing
literature
evaluation
climate‐induced
changes
morphology.
also
necessity
continuous
scale.
Water Resources Research,
Год журнала:
2025,
Номер
61(2)
Опубликована: Фев. 1, 2025
Abstract
Hydrological
Models
face
limitations
in
simulating
the
water
cycle
due
to
deficiencies
process
representation
and
such
problems
also
weaken
their
forecasting
skills.
Here,
we
use
Machine
Learning
(ML)
forecast
Gravity
Recovery
Climate
Experiment
(GRACE)
derived
total
storage
anomaly
(TWSA)
up
1
year
ahead
over
Europe
with
near
real‐time
meteorological
observations
as
predictors.
Subsequently,
assimilate
forecasted
GRACE
TWSA
into
Community
Land
Model
(CLM)
enhance
its
performance
both
reanalysis
forecast.
As
found
five
hindcast
experiments,
ML
for
following
fits
quite
well
actual
Europe,
an
average
correlation
of
0.91,
0.92,
0.94
Iberian
peninsula,
Danube,
Volga
basins.
Validation
by
data
suggests
that
assimilating
can
improve
CLM's
capacity
not
only
hydrological
states
but
droughts.
Additionally,
is
a
viable
alternative
terms
enhancing
on
seasonal
annual
scales
through
Data
assimilation
(DA).
We
highlight
contribution
DA
generating
CLM
based
overcomes
purely
model‐based
TWSA.
This
study
drought
or
resource
services
might
consider
integrate
would
benefit
from
constraining
models
ML‐forecasted
At
shorter
timescales,
forecasts
could
be
useful
quick‐look
analysis
processing
suggested
upcoming
satellite
gravity
missions.
ENVIRONMENTAL SYSTEMS RESEARCH,
Год журнала:
2024,
Номер
13(1)
Опубликована: Сен. 3, 2024
The
continued
threat
from
climate
change
and
human
impacts
on
water
resources
demands
high-resolution
continuous
hydrological
data
accessibility
for
predicting
trends
availability.
This
study
proposes
a
novel
threefold
downscaling
method
based
machine
learning
(ML)
which
integrates:
normalization;
interaction
of
hydrometeorological
variables;
the
application
time
series
split
cross-validation
that
produces
high
spatial
resolution
groundwater
storage
anomaly
(GWSA)
dataset
Gravity
Recovery
Climate
Experiment
(GRACE)
its
successor
mission,
GRACE
Follow-On
(GRACE-FO).
In
study,
relationship
between
terrestrial
(TWSA)
other
land
surface
variables
(e.g.,
vegetation
coverage,
temperature,
precipitation,
in
situ
level
data)
is
leveraged
to
downscale
GWSA.
predicted
downscaled
GWSA
datasets
were
tested
using
monthly
observations,
results
showed
model
satisfactorily
reproduced
temporal
variations
area,
with
Nash-Sutcliffe
efficiency
(NSE)
correlation
coefficient
values
0.8674
(random
forest)
0.7909
(XGBoost),
respectively.
Evapotranspiration
was
most
influential
predictor
variable
random
forest
model,
whereas
it
rainfall
XGBoost
model.
particular,
excelled
aligning
closely
observed
patterns,
as
evidenced
by
positive
correlations
lower
error
metrics
(Mean
Absolute
Error
(MAE)
54.78
mm;
R-squared
(R²)
0.8674).
5
km
(based
decreasing
trend
associated
variability
pattern.
An
increase
drought
severity
during
El
Niño
lengthened
full
recovery
historical
trends.
Furthermore,
lag
occurrence
precipitation
recharge
likely
controlled
intensity
characteristics
aquifer.
Projected
increases
could
further
times
response
droughts
changing
climate,
resetting
new
tipping
condition.
Therefore,
adaptation
strategies
must
recognise
less
will
be
available
supplement
supply
droughts.
Hydrology,
Год журнала:
2024,
Номер
11(11), С. 186 - 186
Опубликована: Ноя. 1, 2024
In
situ
groundwater
monitoring
is
critical
for
irrigated
agroecosystems
and
informs
land
cover
changes.
Yet,
such
data
can
pose
management
challenges
confound
agroecological
relationships.
Correspondingly,
satellite-based
approaches,
including
the
GRACE-constellation,
are
increasing.
Although
in
GRACE-derived
comparisons
occur,
limited
research
considers
dependencies.
Herein,
we
examined
differences
approaches
(observed
[in
situ,
O]
vs.
predicted
[GRACE-derived,
P])
within
Yazoo–Mississippi
Delta
(YMD),
an
agroecosystem
southeastern
USA.
We
compared
variations
modeled
hydrology,
cover,
irrigation
dynamics
of
YMD
upper-quartile
(UQ)
area
interest
(AOI)
(highest
levels)
lower-quartile
(LQ)
AOI
(lowest
every
year
from
2008
to
2020.
Spatially,
OUQ
PUQ
were
northern
portions
YMD,
with
OLQ
PLQ
southern
portions.
Groundwater
levels
between
OUQ:PUQ
OLQ:PLQ
each
had
correlations
>
0.85.
Regarding
most
categories
varied
±2.50%
model
estimates
over
time.
Relatedly,
documented
14
instances
where
use
category
level
inverted
across
models
(OLQ:PLQ
(5),
OUQ:OLQ
(6),
PUQ:PLQ
(3)).
Irrigation
results
not
statistically
different
among
all
models.
Overall,
our
highlight
importance
quantifying
incongruences
management.
