Quarterly Journal of the Royal Meteorological Society,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 16, 2024
Abstract
Wildfire
incidents
have
seen
an
exponential
rise
in
the
past
few
decades
India,
particularly
over
Indian
Himalayan
region,
which
has
led
to
a
huge
loss
of
life
and
property.
To
mitigate
manage
impact
wildfires,
better
understanding
key
physical
atmospheric
processes
conducive
spread
wildfires
is
required.
This
study
aims
analyze
conditions
associated
with
propagation
state
Uttarakhand
(India).
For
this,
wildfire
burned‐area
data
from
(India)
State
Forest
Department,
in‐situ
precipitation
information
India
Meteorological
variables
(temperature,
relative
humidity,
soil
moisture)
European
Centre
for
Medium‐Range
Weather
Forecasts
Reanalysis
v5
Global
Land
Data
Assimilation
System
datasets
years
2000–2022
been
critically
analyzed
infer
cause
unprecedented
Uttarakhand.
The
analysis
suggests
that
strength
El
Niño
Southern
Oscillation
Ocean
Dipole
phases
along
pattern
pre‐fire
season
due
western
disturbances
are
dominant
factors
fires.
Further,
bimodal
distribution
vapor
pressure
deficit,
having
peak
during
fire
post‐monsoon
period,
indicates
increased
dryness
fuels
susceptibility
vegetation
wildfires.
These
findings
could
be
utilized
impacts
vulnerable
state.
International Journal of Wildland Fire,
Год журнала:
2024,
Номер
33(3)
Опубликована: Март 18, 2024
Background
Development
of
the
Australian
Fire
Danger
Rating
System
began
in
2017
with
a
project
aimed
at
demonstrating
feasibility
new
fire
danger
rating
system
through
Research
Prototype
(AFDRSRP)
that
accounted
for
variability
vegetation
types,
was
nationally
applicable,
modular
and
open
to
continuous
improvement.
Aims
In
this
manuscript,
we
identify
define
transition
points
categories
AFDRSRP.
We
discuss
user
responses
categorisation
during
live
trial
evaluation
AFDRSRP
reflect
on
limitations
potential
improvements.
Methods
A
review
available
literature,
broad
consultation
stakeholders
reanalysis
impact
data
were
used
determine
suitable
thresholds
categorising
within
Key
results
transitions
behaviour
result
application
different
management
strategies
or
are
associated
variation
serious
consequences
impacts.
Conclusions
The
incorporated
best
science,
supported
by
well-defined
framework
defining
making
it
across
jurisdictions
range
fuel
types.
Implications
allows
managers
assess
accuracy
appropriateness
forecasted
danger.
International Journal of Wildland Fire,
Год журнала:
2024,
Номер
33(4)
Опубликована: Март 28, 2024
Background
The
Australian
Fire
Danger
Rating
System
program
(AFDRS)
has
built
a
new
fire
danger
rating
system
for
Australia.
A
live
trial
of
the
system’s
Research
Prototype
(AFDRSRP),
based
on
behaviour
thresholds,
was
run
and
evaluated
between
October
2017
March
2018.
Aims
Live
results
are
critically
analysed,
knowledge
gaps
recommendations
future
work
discussed.
Methods
bushfire
experts
assessed
wildfires
prescribed
burns
across
range
vegetation
types
weather
conditions.
Forecast
ratings
calculated
using:
(1)
AFDRSRP;
(2)
Forest
Index
(FFDI)
Grassland
(GFDI)
were
compared
against
derived
by
expert
opinion
each
evaluation
(n
=
336).
Key
Overall
performance
AFDRSRP
superior
to
FFDI/GFDI
(56
vs
43%
correct),
with
tendency
over-predict
rather
than
under-predict
potential.
also
demonstrated
its
value
assess
in
fuel
not
conforming
current
grassland
or
forest
models;
e.g.
fuels
that
grouped
use
mallee-heath,
spinifex
shrubland
spread
models.
Conclusions
successful,
outperforming
existing
operational
system.
Implications
Identified
improvements
would
further
enhance
performance,
ensuring
readiness
implementation.
International Journal of Wildland Fire,
Год журнала:
2024,
Номер
33(4)
Опубликована: Апрель 10, 2024
Background
The
Australian
Fire
Danger
Rating
System
(AFDRS)
was
implemented
operationally
throughout
Australia
in
September
2022,
providing
calculation
of
fire
danger
forecasts
based
on
peer-reviewed
behaviour
models.
system
is
modular
and
allows
for
ongoing
incorporation
new
scientific
research
improved
datasets.
Aims
Prior
to
operational
implementation
the
AFDRS,
a
Research
Prototype
(AFDRSRP),
described
here,
built
test
input
data
systems
evaluate
performance
potential
outputs.
Methods
spread
models
were
selected
aligned
with
fuel
types
process
that
captured
bioregional
variation
characteristics.
National
spatial
datasets
created
identify
history
alignment
existing
weather
forecast
layers.
Key
results
AFDRSRP
demonstrated
improvements
over
McArthur
Forest
Grass
due
its
use
models,
as
well
more
accurately
reflecting
fuels.
Conclusions
design
robust
allowed
updates
prior
AFDRS.
Journal of Southern Hemisphere Earth System Science,
Год журнала:
2025,
Номер
75(1)
Опубликована: Март 18, 2025
The
Australian
Fire
Danger
Rating
System
(AFDRS)
is
a
nationally
consistent
approach
to
forecasting
fire
danger
for
all
major
vegetation
types
found
in
Australia.
AFDRS
climate
outlooks
(Fire
Outlooks,
FDOs)
extending
out
3
months
ahead
are
the
first
such
operational
products
of
their
kind
world.
use
Bureau’s
seasonal
model
Community
Climate
Earth
simulator
–
Seasonal
(ACCESS-S2).
FDOs
currently
available
agencies,
and
partner
agencies
involved
land
management
prevention
activities.
To
make
sound
planning
decisions,
should
be
used
with
other
sources
intelligence
understand
which
components
might
driving
risk.
It
prudent
consult
temperature
rainfall
as
both
these
contributing
factors
conditions,
but
have
differing
data
foundations
(hindcast
periods)
that
need
understood
correct
interpretation.
Previous
comparative
analysis
showed
hindcast
period
warmer
during
shoulder
seasons
some
regions;
thus,
high
chance
above
average
not
reflected
expected
outlooks.
For
this
reason,
it
has
been
important
provide
users
advice
on
how
best
interpret
alongside
In
work,
we
continued
determine
differs
over
periods
subsequent
implications
when
interpreting
strategic
context.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Март 28, 2025
Wildfires
play
a
pivotal
role
in
environmental
processes
and
the
sustainable
development
of
ecosystems.
Timely
responses
can
significantly
reduce
damages
consequences
caused
by
their
spread.
Several
critical
issues
wildfire
behavior
analysis
include
fire
occurrence
forecasting,
early
detection,
spread
prediction.
In
this
study,
we
focus
on
which
is
valuable
tool
for
facilitating
earlier
intervention.
Conventional
approaches
primarily
rely
computation
indices
based
weather
conditions.
However,
solutions
that
utilize
more
comprehensive
data,
remote
sensing
information,
artificial
intelligence
(AI)
algorithms
may
offer
substantial
advantages
rapid
decision-making
extensive
territory
monitoring.
The
wide
variety
spatial
parameters
great
diversity
geographical
regions
influence
complicate
task.
Consequently,
there
no
unified
approach
predicting
occurrences
using
data
AI
techniques.
goal
study
to
explore
potential
various
available
-
meteorological,
geo-spatial,
anthropogenic
machine
learning
(ML)
algorithms.
We
developed
pipeline
acquisition
subsequent
ML-based
algorithm
development.
includes
following
algorithms:
Random
Forest,
XGBoost,
Autoencoder,
ConvLSTM,
Attention
Multilayer
Perceptron,
RegNetX.
addition,
several
metrics
assess
quality
models
case
highly
imbalanced
spatio-temporal
data.
To
conduct
collected
unique
dataset
covering
large
central
Russia,
incorporating
than
17,000
verified
events
over
period
10
years.
findings
underscore
necessity
developing
individual
ML
tailored
each
region,
taking
into
account
specific
features
correlated
with
probability
occurrence.
achieved
models,
as
measured
F1-score,
varies
from
0.7
0.87
depending
demonstrating
integrating
such
emergency
response
systems.
Biological Conservation,
Год журнала:
2024,
Номер
296, С. 110700 - 110700
Опубликована: Июнь 29, 2024
Prescribed
burning
of
vegetation
is
a
land
management
approach
that
can
be
used
to
prevent
or
reduce
the
risk
wildfires
yet
burns
escape
and
an
ignition
source
for
wildfires.
In
Scotland,
muirburn
form
prescribed
encourage
new
growth
heather
grouse
livestock.
Future
regulation
aims
mitigate
potential
negative
impacts
on
biodiversity,
peat
carbon
stocks
wildfire
risk.
this
study,
we
assessed
spatial
co-occurrence
across
Scotland.
Evidence
was
quantified
using
high-resolution
aerial
imagery
from
2018
occurrence
composite
satellite
detection
covering
2015
2020.
Fires
were
mapped
dominant
habitat
types
(acid
grasslands,
heathlands,
peatbogs)
soil.
Overall,
96
%
total
area
occurred
outside
moorlands
managed
by
muirburn,
with
1.1
moorland
area.
Heather
peatbogs
habitats
soils
had
greater
proportional
than
other
non-peat
soils.
contrast,
heathlands
muirburn.
We
suggest
limited
wildfires,
may
due
fuel
load
reduction
following
muirburns,
but
arise
prevention
strategies
estates
and/or
lower
recreational
caused
addition
regulation,
our
findings
emphasise
need
policy,
management,
awareness
education
areas
