Research Square (Research Square),
Journal Year:
2023,
Volume and Issue:
unknown
Published: Nov. 7, 2023
Abstract
The
effects
of
global
warming
and
climate
change
are
being
felt
through
more
extreme
prolonged
periods
drought.
Multiple
meteorological
indices
used
to
measure
drought,
but
they
require
hydrometeorological
data;
however,
other
measured
by
remote
sensing
quantify
vegetation
vigor
can
be
correlated
with
the
former.
This,
this
study
investigated
correlation
between
both
index
types
type
season.
correlations
were
also
spatially
modeled
in
a
drought
event
southwestern
Spain.
In
addition,
three
maps
different
levels
detail
terms
categorization
compared.
results
generally
showed
that
grassland
was
most
well
category
SPEI
FAPAR,
LAI
NDVI.
This
pronounced
autumn
spring,
which
is
when
changes
senescence
occur.
spatiotemporal
analysis
indicated
very
similar
behavior
for
grasslands
grouped
an
area
adaptation
as
having
high
evapotranspiration
forecast.
Finally,
forest-based
forecast
analysis,
best
explained
performance
again
NDVI,
lag
up
20
days.
Therefore,
remotely
sensed
good
indicators
status
variably
explanatory
traditional
indicators.
Moreover,
complementing
made
it
possible
detect
areas
particularly
vulnerable
change.
Abstract.
Adaptation
to
an
increasingly
dry
regional
climate
requires
spatially
explicit
information
about
current
and
future
risks.
Existing
drought
risk
studies
often
rely
on
expert-weighted
composite
indicators,
while
empirical
evidence
impact-relevant
factors
is
still
scarce.
The
aim
of
this
study
investigate
what
extent
hazard
vulnerability
indicators
can
explain
observed
agricultural
impacts
via
data-driven
methods.
We
focus
the
German
federal
state
Brandenburg,
2013–2022,
including
several
consecutive
years.
As
impact
we
use
thermal-spectral
anomalies
(LST/NDVI)
field
level,
yield
gaps
from
reported
statistics
county
level.
Empirical
associations
both
spatial
levels
are
compared.
Non-linear
models
up
60
%
variance
in
gap
data,
with
lumped
for
all
crops
being
more
stable
than
individual
crops,
years
performing
better
pre-drought
Meteorological
June
soil
quality
selected
as
strongest
factors.
Rye
found
less
vulnerable
wheat,
despite
growing
poorer
soils.
LST/NDVI
only
weakly
relates
our
gaps.
recommend
comparing
different
multiple
scales
proceed
development
empirically
grounded
maps.
Theoretical and Applied Climatology,
Journal Year:
2024,
Volume and Issue:
155(5), P. 3757 - 3770
Published: Jan. 31, 2024
Abstract
The
effects
of
global
warming
and
climate
change
are
being
felt
through
more
extreme
prolonged
periods
drought.
Multiple
meteorological
indices
used
to
measure
drought,
but
they
require
hydrometeorological
data;
however,
other
measured
by
remote
sensing
quantify
vegetation
vigor
can
be
correlated
with
the
former.
This
study
investigated
correlation
between
both
index
types
type
season.
correlations
were
also
spatially
modeled
in
a
drought
event
southwestern
Spain.
In
addition,
three
maps
different
levels
detail
terms
categorization
compared.
results
generally
showed
that
grassland
was
most
well
category
SPEI
FAPAR,
LAI,
NDVI.
pronounced
autumn
spring,
which
is
when
changes
senescence
growth
occur.
spatiotemporal
analysis
indicated
very
similar
behavior
for
grasslands
grouped
an
area
adaptation
as
having
high
evapotranspiration
forecast.
Finally,
forest-based
forecast
analysis,
best
explained
performance
again
NDVI,
lag
up
20
days.
Therefore,
remotely
sensed
good
indicators
status
variably
explanatory
traditional
indicators.
Moreover,
complementing
made
it
possible
detect
areas
particularly
vulnerable
change.
Natural hazards and earth system sciences,
Journal Year:
2024,
Volume and Issue:
24(12), P. 4237 - 4265
Published: Nov. 29, 2024
Abstract.
Adaptation
to
an
increasingly
dry
regional
climate
requires
spatially
explicit
information
about
current
and
future
risks.
Existing
drought
risk
studies
often
rely
on
expert-weighted
composite
indicators,
while
empirical
evidence
impact-relevant
factors
is
still
scarce.
The
aim
of
this
study
investigate
what
extent
hazard
vulnerability
indicators
can
explain
observed
agricultural
impacts
via
data-driven
methods.
We
focus
the
German
federal
state
Brandenburg,
2013–2022,
including
several
consecutive
years.
As
impact
we
use
thermal–spectral
anomalies
(land
surface
temperature
(LST)
normalized
difference
vegetation
index
(NDVI))
field
level,
yield
gaps
from
reported
statistics
county
level.
Empirical
associations
both
spatial
levels
are
compared.
Extreme
gradient
boosting
(XGBoost)
models
up
60
%
variance
in
gap
data
(best
R2
=
0.62).
Model
performance
more
stable
for
years
when
using
all
crops
training
rather
than
individual
crops.
Meteorological
June
soil
quality
selected
as
strongest
factors.
Rye
empirically
found
be
less
vulnerable
wheat,
even
poorer
soils.
LST
/
NDVI
only
weakly
relates
our
gaps.
recommend
comparing
different
multiple
scales
proceed
with
development
grounded
maps.
