Hydrology and earth system sciences,
Год журнала:
2022,
Номер
26(24), С. 6339 - 6359
Опубликована: Дек. 16, 2022
Abstract.
Climate
change
may
systematically
impact
hydrometeorological
processes
and
their
interactions,
resulting
in
changes
flooding
mechanisms.
Identifying
such
is
important
for
flood
forecasting
projection.
Currently,
there
a
lack
of
observational
evidence
regarding
trends
mechanisms
Europe,
which
requires
reliable
methods
to
disentangle
emerging
patterns
from
the
complex
interactions
between
drivers.
Recently,
numerous
studies
have
demonstrated
skill
machine
learning
(ML)
predictions
hydrology,
e.g.,
predicting
river
discharge
based
on
its
relationship
with
meteorological
The
relationship,
if
explained
properly,
provide
us
new
insights
into
hydrological
processes.
Here,
by
using
novel
explainable
ML
framework,
combined
cluster
analysis,
we
identify
three
primary
that
drive
53
968
annual
maximum
events
around
thousand
European
catchments.
can
be
associated
catchment-wide
mechanisms:
recent
precipitation,
antecedent
precipitation
(i.e.,
excessive
soil
moisture),
snowmelt.
results
indicate
over
half
studied
catchments
are
controlled
combination
above
mechanisms,
especially
moisture,
dominant
mechanism
one-third
Over
past
70
years,
significant
been
detected
within
number
Generally,
snowmelt-induced
floods
has
decreased
significantly,
whereas
driven
increased.
consistent
expected
climate
responses,
highlight
risks
seasonality
magnitude.
Overall,
study
offers
perspective
understanding
weather
extreme
demonstrates
prospect
future
scientific
discoveries
supported
artificial
intelligence.
Abstract
Precipitation
extremes
are
increasing
globally
due
to
anthropogenic
climate
change.
However,
there
remains
uncertainty
regarding
impacts
upon
flood
occurrence
and
subsequent
population
exposure.
Here,
we
quantify
changes
in
exposure
hazard
across
the
contiguous
United
States.
We
combine
simulations
from
a
model
large
ensemble
high‐resolution
hydrodynamic
model—allowing
us
directly
assess
wide
range
of
extreme
precipitation
magnitudes
accumulation
timescales.
report
mean
increase
100‐year
event
~20%
(magnitude)
>200%
(frequency)
high
warming
scenario,
yielding
~30–127%
further
find
nonlinear
for
most
intense
events—suggesting
accelerating
societal
historically
rare
or
unprecedented
events
21st
century.
Communications Earth & Environment,
Год журнала:
2021,
Номер
2(1)
Опубликована: Авг. 26, 2021
Precipitation
extremes
will
increase
in
a
warming
climate,
but
the
response
of
flood
magnitudes
to
heavier
precipitation
events
is
less
clear.
Historically,
there
little
evidence
for
systematic
increases
magnitude
despite
observed
extremes.
Here
we
investigate
how
change
warming,
using
large
initial-condition
ensemble
simulations
with
single
climate
model,
coupled
hydrological
model.
The
model
chain
was
applied
historical
(1961–2000)
and
warmer
future
(2060–2099)
conditions
78
watersheds
Bavaria,
region
comprising
headwater
catchments
Inn,
Danube
Main
River,
thus
representing
an
area
expressed
heterogeneity.
For
majority
catchments,
identify
‘return
interval
threshold’
relationship
between
increases:
at
return
intervals
above
this
threshold,
further
extreme
frequency
clearly
yield
increased
magnitudes;
below
modulated
by
land
surface
processes.
We
suggest
that
threshold
behaviour
can
reconcile
climatological
perspectives
on
changing
risk
climate.
Germany
rainfall
processes
not
above,
Water Resources Research,
Год журнала:
2021,
Номер
57(4)
Опубликована: Фев. 10, 2021
Abstract
Hydrometeorological
flood
generating
processes
(excess
rain,
short
long
snowmelt,
and
rain‐on‐snow)
underpin
our
understanding
of
behavior.
Knowledge
about
improves
hydrological
models,
frequency
analysis,
estimation
climate
change
impact
on
floods,
etc.
Yet,
not
much
is
known
how
catchment
attributes
influence
the
spatial
distribution
processes.
This
study
aims
to
offer
a
comprehensive
structured
approach
close
this
knowledge
gap.
We
employ
large
sample
(671
catchments
across
contiguous
United
States)
evaluate
use
two
complementary
approaches:
A
statistics‐based
which
compares
attribute
distributions
different
processes;
random
forest
model
in
combination
with
an
interpretable
machine
learning
(accumulated
local
effects
[ALE]).
The
ALE
method
has
been
used
often
hydrology,
it
overcomes
significant
obstacle
many
statistical
methods,
confounding
effect
correlated
attributes.
As
expected,
we
find
(fraction
snow,
aridity,
precipitation
seasonality,
mean
precipitation)
be
most
influential
process
distribution.
However,
varies
both
type.
also
can
predicted
for
ungauged
relatively
high
accuracy
(
R
2
between
0.45
0.9).
implication
these
findings
should
considered
future
studies,
as
changes
characteristics
Despite
the
recent
prevalence
of
severe
drought,
California
faces
a
broadly
underappreciated
risk
floods.
Here,
we
investigate
physical
characteristics
"plausible
worst
case
scenario"
extreme
storm
sequences
capable
giving
rise
to
"megaflood"
conditions
using
combination
climate
model
data
and
high-resolution
weather
modeling.
Using
from
Community
Earth
System
Model
Large
Ensemble,
find
that
change
has
already
doubled
likelihood
an
event
producing
catastrophic
flooding,
but
larger
future
increases
are
likely
due
continued
warming.
We
further
runoff
in
scenario
is
200
400%
greater
than
historical
values
Sierra
Nevada
because
increased
precipitation
rates
decreased
snow
fraction.
These
findings
have
direct
implications
for
flood
emergency
management,
as
well
broader
hazard
mitigation
adaptation
activities.
Journal of Hydrologic Engineering,
Год журнала:
2022,
Номер
27(6)
Опубликована: Март 24, 2022
This
review
provides
a
broad
overview
of
the
current
state
flood
research,
challenges,
and
future
directions.
Beginning
with
discussion
flood-generating
mechanisms,
synthesizes
literature
on
forecasting,
multivariate
nonstationary
frequency
analysis,
urban
flooding,
remote
sensing
floods.
Challenges
research
directions
are
outlined
highlight
emerging
topics
where
more
work
is
needed
to
help
mitigate
risks.
It
anticipated
that
systems
will
likely
have
significant
risk
due
compounding
effects
continued
climate
change
land-use
intensification.
The
timely
prediction
floods,
quantification
socioeconomic
impacts
developing
mitigation
strategies
continue
be
challenging.
There
need
bridge
scales
between
model
capabilities
end-user
needs
by
integrating
multiscale
models,
stakeholder
input,
social
citizen
science
input
for
monitoring,
mapping,
dissemination.
Although
much
progress
has
been
made
in
using
applications,
recent
upcoming
Earth
Observations
provide
excellent
potential
unlock
additional
benefits
applications.
community
can
benefit
from
downscaled,
as
well
ensemble
scenarios
consider
changes.
Efforts
also
data
assimilation
approaches,
especially
ingest
local,
citizen,
media
data.
Also
enhanced
compound
hazards
assess
reduce
vulnerability
impacts.
dynamic
complex
interactions
climate,
societal
change,
watershed
processes,
human
factors
often
confronted
deep
uncertainty
highlights
transdisciplinary
science,
policymakers,
stakeholders
vulnerability.
Reviews of Geophysics,
Год журнала:
2020,
Номер
58(4)
Опубликована: Окт. 24, 2020
Abstract
Hydroclimatic
changes
associated
with
global
warming
over
the
past
50
years
have
been
documented
widely,
but
physical
landscape
responses
are
poorly
understood
thus
far.
Detecting
sedimentary
and
geomorphic
signals
of
modern
climate
change
presents
challenges
owing
to
short
record
lengths,
difficulty
resolving
in
stochastic
natural
systems,
influences
land
use
tectonic
activity,
long‐lasting
effects
individual
extreme
events,
variable
connectivity
sediment‐routing
systems.
We
review
existing
literature
investigate
nature
extent
change,
focusing
on
western
United
States,
a
region
generally
high
relief
sediment
yield
likely
be
sensitive
climatic
forcing.
Based
fundamental
theory
empirical
evidence
from
other
regions,
we
anticipate
climate‐driven
slope
stability,
watershed
yields,
fluvial
morphology,
aeolian
mobilization
States.
find
for
recent
stability
increased
dune
dust
whereas
yields
morphology
linked
more
commonly
nonclimatic
drivers
will
require
better
understanding
how
response
scales
disturbance,
lag
times
hysteresis
operate
within
distinguish
relative
influence
feedbacks
superimposed
disturbances.
The
ability
constrain
rapidly
progressing
has
widespread
implications
human
health
safety,
infrastructure,
water
security,
economics,
ecosystem
resilience.