Water,
Journal Year:
2023,
Volume and Issue:
15(19), P. 3408 - 3408
Published: Sept. 28, 2023
Social
vulnerability
indices
are
often
used
to
quantify
differential
the
impacts
of
climate
change
within
coastal
communities.
In
this
review,
we
examine
how
“tried
and
tested”
methodologies
for
analysing
social
hazards
at
coast
being
challenged
by
a
new
wave
that
offer
more
nuanced
conclusions
about
who
is
vulnerable,
how,
why.
Instead
producing
high-level,
generalised,
static
vulnerability,
engages
deeply
with
interlinked
socioeconomic,
cultural,
political,
economic
specificities
place,
as
well
multi-scalar
temporal
dynamics,
incongruities,
inconsistencies
inherent
peoples’
lived,
felt
experiences
vulnerability.
By
integrating
these
complex
observations
into
an
output
still
readily
accessible
decision-
policy-makers,
supports
pursuit
tailored,
context-appropriate,
equitable
adaptation.
We
suggest
one
way
forms
analyses
might
be
operationalised,
reflecting
on
experimental
research
project
uses
personas
or
fictional
characters
in
Aotearoa
New
Zealand.
Water,
Journal Year:
2025,
Volume and Issue:
17(2), P. 252 - 252
Published: Jan. 17, 2025
This
paper
presents
a
holistic
assessment
framework
for
the
impacts
of
water
distribution
pipe
breaks
to
promote
environmentally
sustainable
and
socially
resilient
cities.
considers
social,
environmental,
economic
vulnerabilities
as
well
probabilities
associated
with
failure.
The
integration
these
features
provides
comprehensive
approach
understanding
infrastructure
risks.
Taking
city
Vancouver
case
study,
social
vulnerability
index
(SVI)
is
obtained
following
application
cross-correlation
matrix
principal
component
analysis
(PCA)
identify
most
influential
among
33
selected
variables
from
2021
census
Canadian
population.
Environmental
Vulnerability
Index
(EVI)
evaluated
by
considering
park
floodplain
areas.
Economic
(ECI)
derived
replacement
cost
pipes.
These
indices
offer
valuable
insights
into
spatial
(consequences)
across
urban
Subsequently,
Consequence
Failure
(COF)
computed
aggregating
three
equal
weights.
Pipe
probability
failure
(POF)
Weibull
model
calibrated
on
real
break
data
function
age.
enables
dynamic
evaluation
deterioration
over
time.
Risk
finally
assessed
combining
COF
POF
prioritizing
rehabilitation,
final
objective
mitigating
adverse
findings
show
significant
impact
ethnicity,
socioeconomic
indices,
education
index.
Moreover,
areas
close
English
Bay
Fraser
River
are
more
vulnerable.
pipes
high
primarily
concrete
pipes,
due
their
expensive
costs.
Finally,
risk
resulting
used
rank
City
network
ranking
system
highlights
critical
requiring
different
levels
attention
improvements.
All
corresponding
risks
illustrated
in
maps,
highlighting
that
very
level
mostly
south
north
Vancouver.
International Journal of Disaster Risk Reduction,
Journal Year:
2023,
Volume and Issue:
101, P. 104221 - 104221
Published: Dec. 24, 2023
Large
and
intense
wildfires
are
an
integral
part
of
many
Canadian
landscapes,
playing
a
critical
role
in
ecosystem
dynamics.
However,
the
recent
catastrophic
fire
seasons
have
highlighted
threat
that
can
pose
to
human
communities.
Identifying
areas
at
higher
risk
is
therefore
crucial
order
mitigate
impacts
on
society.
This
study
presents
standardized
method
for
nationwide
wildfire
assessment,
focusing
buildings
populations.
Using
Burn-P3
simulation
model,
along
with
building
footprint
census
data,
we
generated
hazard,
vulnerability,
maps
Canada's
forested
regions.
Our
findings
demonstrate
nuanced
understanding
when
considering
interaction
between
hazard
physical
vulnerability.
Approximately
32.3%
6.3%
land
classified
as
High
Very
high
risk,
respectively.
We
estimate
111,519
units
(5.8%)
directly
exposed
10,622
(0.6%)
risk.
Moreover,
found
approximately
283,200
people
reside
while
30,500
live
Indigenous
on-reserve
communities
particularly
vulnerable
impact.
18.9%
living
reserves
fire,
compared
only
2.4%
non-reserve
population.
The
present
offers
information
development
national
management
policy
provides
new
insights
support
implementation
effective
measures
reduction.
Nature Communications,
Journal Year:
2023,
Volume and Issue:
14(1)
Published: Dec. 4, 2023
Abstract
Climate
change
is
leading
to
more
extreme
weather
hazards,
forcing
human
populations
be
displaced.
We
employ
explainable
machine
learning
techniques
model
and
understand
internal
displacement
flows
patterns
from
observational
data
alone.
For
this
purpose,
a
large,
harmonized,
global
database
of
disaster-induced
movements
in
the
presence
floods,
storms,
landslides
during
2016–2021
presented.
account
for
environmental,
societal,
economic
factors
predict
number
displaced
persons
per
event
affected
regions.
Here
we
show
that
displacements
can
primarily
attributed
combination
poor
household
conditions
intense
precipitation,
as
revealed
through
interpretation
trained
models
using
both
Shapley
values
causality-based
methods.
hence
provide
empirical
evidence
differential
or
uneven
vulnerability
exists
means
its
quantification,
which
could
help
advance
evidence-based
mitigation
adaptation
planning
efforts.
Journal of Hydrology Regional Studies,
Journal Year:
2023,
Volume and Issue:
47, P. 101434 - 101434
Published: June 1, 2023
The
Han
River
Basin,
China
As
the
water
source
of
Middle
Route
Project
South
to
North
Water
Transfer
Project,
security
in
Basin
deeply
impacts
national
resource
allocation.
This
study
constructed
a
Multi-Dimensional
Flood
Risk
Assessment
(MDFRA)
framework
combining
comprehensive
index
system,
an
integrated
weight
method,
and
clustering
algorithm
assess
flood
risk
Basin.
We
first
system
including
eight
hazard
indexes,
four
exposure
vulnerability
indexes
demographic,
economic,
ecological,
infrastructural
categories
risk.
weights
were
determined
by
which
Shapley
value
Analytic
Hierarchy
Process
method
(SAHP).
Finally,
possibilistic
fuzzy
C-means
was
employed
identify
levels.
MDFRA
presents
realistically
comprehensively,
revealing
composition
providing
more
detailed
information.
Results
show
that
high-risk
regions
accounted
for
34.51%
basin
area
mainly
concentrated
mid-lower
reaches,
while
middle-risk
(19.13%)
middle-high-risk
(23.35%)
distributed
upstream.
4.52%,
5.12%,
13.37%
assigned
as
very-low,
low,
middle-low
respectively,
adjacent
Danjiangkou
reservoir.
Flood-prone
natural
conditions
dense
population
assets
causes
high
risk,
reservoir
regulation
storage
capacity
had
significantly
alleviated
Risk Analysis,
Journal Year:
2022,
Volume and Issue:
43(5), P. 1058 - 1078
Published: June 10, 2022
This
study
presents
the
first
nationwide
spatial
assessment
of
flood
risk
to
identify
social
vulnerability
and
exposure
hotspots
that
support
policies
aimed
at
protecting
high-risk
populations
geographical
regions
Canada.
The
used
a
national-scale
hazard
dataset
(pluvial,
fluvial,
coastal)
estimate
1-in-100-year
all
residential
properties
across
5721
census
tracts.
Residential
data
were
spatially
integrated
with
census-based
multidimensional
index
(SoVI)
included
demographic,
racial/ethnic,
socioeconomic
indicators
influencing
vulnerability.
Using
Bivariate
Local
Indicators
Spatial
Association
(BiLISA)
cluster
maps,
identified
geographic
concentration
where
high
coincided
exposure.
results
revealed
considerable
variations
in
tract-level
Flood
belonged
410
tracts,
21
metropolitan
areas,
eight
provinces
comprising
about
1.7
million
total
population
51%
half-a-million
Results
near
core
dense
urban
areas
predominantly
occupying
those
hotspots.
Recognizing
priority
locations
is
critically
important
for
government
interventions
mitigation
initiatives
considering
socio-physical
aspects
flooding.
Findings
reinforce
better
understanding
flood-disadvantaged
neighborhoods
Canada,
are
required
target
preparedness,
response,
recovery
resources
foster
socially
just
management
strategies.
International Journal of Disaster Risk Reduction,
Journal Year:
2023,
Volume and Issue:
97, P. 104052 - 104052
Published: Oct. 1, 2023
Social
vulnerability
assessment
to
flood
hazard
depends
upon
multiple
factors
that
can
vary
across
the
different
indicators.
However,
there
is
limited
knowledge
on
specific
indicators
suitable
for
assessing
social
Sarawak.
This
study
systematically
analyzed
important
components
of
and
mapped
them
by
weight
12
divisions.
Indices
focusing
two
dimensions
(physical
exposure
resistances)
were
identified
based
literature.
Data
these
indices
then
collected
through
relevant
government
agencies.
Components
assessed
significantly
contributing
Principal
Component
Analysis
(PCA).
An
entropy
method
was
used
Vulnerability
estimated
Iyengar
Sudarshan
methodology
data
produce
a
map
proposed
Index
(SVI).
The
results
indicated
divisions
Kuching,
Miri,
Sibu
Bintulu
more
vulnerable
(score
over
than
0.81)
those
in
other
Greater
mainly
due
high
extreme
events
less
adaptive
capacity
resistance,
which
affect
agricultural
production
negatively,
combination
with
population
density
communities.
clearly
shows
areas
are
susceptible,
indicating
government's
adaptation
measures
should
depending
available
resources,
urgency,
means
survival
needed
achieve
resilience
against
climate
change.