International Journal of Digital Earth,
Journal Year:
2025,
Volume and Issue:
18(1)
Published: March 5, 2025
Aridity
index
(AI)
is
an
effective
estimator
of
drought
status,
and
spatiotemporally
continuous
long-term
AI
dataset
critical
for
assessment
applications.
Due
to
the
spatial
heterogeneity
global
climate
topography,
there
exist
significant
uncertainties
estimates
in
areas
with
sparse
ground
observations,
high-resolution
estimation
remains
a
challenge.
In
this
study,
we
propose
LSTM-based
approach
model
nonlinear
intra-annual
relationship
between
satellite-derived
data
enhance
performance
through
ensemble
learning
by
leveraging
MODIS
at
different
observation
times.
A
annually
gridded
generated
resolution
0.05°
×
from
2003
2022.
Validation
against
Global
Surface
Summary
Day
database
yields
biases,
root
mean
squared
errors
coefficients
−0.04
0.02,
0.19
0.86,
0.62
0.83
across
continents.
Comparisons
based
on
Climatic
Research
Unit
or
ERA5-Land
datasets
further
demonstrate
high
accuracy
our
estimates.
Preliminary
analysis
reveals
wetting
trend
over
past
two
decades.
This
offers
valuable
support
research
dryland
ecosystems,
agriculture,
change,
offering
insights
address
environmental
sustainability
challenges.
Climate,
Journal Year:
2022,
Volume and Issue:
10(9), P. 125 - 125
Published: Aug. 25, 2022
Droughts
are
the
most
destructive
catastrophes
in
world.
The
persistence
of
drought
is
considered
to
cause
many
challenges
for
both
humans
and
animals
ruins
ecosystem.
These
have
encouraged
scientists
search
innovative
methods
models
that
effective
assessing
predicting
events.
use
indices
has
been
extensively
employed
regions
across
globe
their
effectiveness
demonstrated.
This
review
illustrates
assessment
droughts,
with
a
focus
on
management
mitigation
measures.
Additionally,
several
ways
managing
risk
proactive
strategies
need
be
implemented
mitigate
droughts
illustrated.
In
conclusion,
this
article
suggests
should
done
more
naturally,
strongly
protect
environment
rather
than
involve
engineering
projects
which
might
degradation
rivers
land,
damage
Atmosphere,
Journal Year:
2023,
Volume and Issue:
14(6), P. 951 - 951
Published: May 29, 2023
To
increase
the
accuracy
of
drought
prediction,
this
study
proposes
a
forecasting
method
based
on
Informer
model.
Taking
Yellow
River
Basin
as
an
example,
accuracies
Autoregressive
Integrated
Moving
Average
(ARIMA),
Long
Short-Term
Memory
(LSTM),
and
models
multiple
timescales
Standardized
Precipitation
Evapotranspiration
Index
(SPEI)
were
compared
analyzed.
The
results
indicate
that,
with
increasing
timescale,
ARIMA,
LSTM,
improved
gradually,
reaching
best
24-month
timescale.
However,
predicted
values
well
those
significantly
different
from
true
SPEI
1-month
model
was
more
accurate
than
ARIMA
LSTM
all
timescales,
indicating
that
can
widely
capture
information
input
series
over
time
is
effective
in
long-term
prediction
problems.
Furthermore,
enhance
precision
prediction.
closer
to
values,
forecasted
trends
complied
actual
trends.
adaptively
and,
therefore,
better
relevance
timecales.
NSE
for
four
meteorological
stations
SPEI24
0.968,
0.974,
0.972,
0.986.
Plants,
Journal Year:
2024,
Volume and Issue:
13(16), P. 2319 - 2319
Published: Aug. 20, 2024
In
this
study,
the
emphasis
is
on
assessing
how
satellite-derived
vegetation
indices
respond
to
drought
stress
characterized
by
meteorological
observations.
This
study
aimed
understand
dynamics
of
grassland
and
assess
impact
in
Wielkopolskie
(PL41)
Podlaskie
(PL84)
regions
Poland.
Spatial
temporal
characteristics
regarding
occurrences
from
2020
2023
were
examined.
Pearson
correlation
coefficients
with
standard
errors
used
analyze
indices,
including
NDVI,
NDII,
NDWI,
NDDI,
response
drought,
parameter
Hydrothermal
Coefficient
Selyaninov
(HTC),
along
ground-based
soil
moisture
measurements
(SM).
Among
studied,
NDDI
showed
strongest
correlations
HTC
at
r
=
-0.75,
R
Land,
Journal Year:
2024,
Volume and Issue:
13(4), P. 523 - 523
Published: April 15, 2024
Recently,
the
frequent
occurrence
of
droughts
has
caused
a
serious
impact
on
vegetation
growth
and
progression.
This
research
is
based
upon
normalized
difference
index
(NDVI)
from
2001
to
2020.
The
correlation
between
NDVI
standardized
precipitation
evapotranspiration
(SPEI)
at
disparate
time
scales
was
used
assess
response
drought
in
Yinshanbeilu
region.
levels
SPEI1,
SPEI3,
SPEI6,
SPEI12
increased
prominently
eastern
region
country,
while
decreased
significantly
east
west
spring,
summer,
autumn
but
reversed
winter.
area
with
an
upward
trend
(33.86%)
slightly
lower
than
that
downward
(66.14%).
coefficients
SPEI
over
entire
year
timescale.
elevated
values
were
concentrated
southeastern
western
regions
survey
Additionally,
best
timescales
SPEI6
SPEI12.
Grassland
most
sensitive
type
NDVI.
SPEI1–12
0.313,
0.459,
0.422,
0.406.
Both
spring
summer
more
responsive
SPEI12,
whereas
winter
SPEI3.
exhibited
complex
soil
texture
features
respect
different
seasonal
scales,
showed
strong
both
autumn.
Loam,
sandy
loam,
silty
loam
all
highest
0.509,
0.474,
0.403,
respectively.
Geomatics Natural Hazards and Risk,
Journal Year:
2024,
Volume and Issue:
15(1)
Published: Aug. 2, 2024
This
literature
review
discusses
specific
challenges
associated
with
drought
risk
assessments
(DRA)
within
agricultural
systems,
particularly
concerning
the
justification
for
indicator
selection,
aggregation
methods,
and
DRA-informed
adaptation
strategies.
Employing
a
multifaceted
approach
combining
quantitative
qualitative
we
systematically
reviewed
existing
DRA
literature.
The
PRISMA
methodology
bibliometric
analysis
were
employed
to
quantitatively
reveal
trends,
key
contributors,
publication
patterns
in
research,
offering
insights
into
research
evolution
field.
Simultaneously,
aspect
of
this
involved
an
examination
selected
papers
identify
critical
gaps.
Our
reveals
that
67%
studies
lack
validation
their
methodology,
underscoring
need
rigorous
results
enhance
credibility
among
decision-makers.
Additionally,
significant
88%
primarily
focus
on
identification,
less
emphasis
strategies
derived
from
results.
Therefore,
emphasize
imperative
bridging
gap
between
practical
applications,
advocating
linking
findings
targeted
allows
identification
sector-specific
indicators,
methodologies,
address
unique
vulnerabilities
agriculture,
ultimately
enhancing
effectiveness
management
efforts
contexts.
