Geoscientific model development,
Год журнала:
2024,
Номер
17(22), С. 8353 - 8372
Опубликована: Ноя. 25, 2024
Abstract.
Efficient
methods
for
predicting
weather-related
hazards
are
crucial
the
effective
management
of
environmental
risk.
Many
depend
on
evolution
meteorological
conditions
over
protracted
periods,
requiring
assessments
that
account
evolving
conditions.
The
TAMSAT-ALERT
approach
addresses
this
challenge
by
combining
observational
monitoring
with
a
weighted
multi-year
ensemble.
In
way,
it
enhances
utility
existing
systems
enabling
users
to
combine
multiple
streams
and
forecasting
data
into
holistic
hazard
assessments.
forecasts
now
used
in
number
regions
Global
South
soil
moisture
forecasting,
drought
early
warning
agricultural
decision
support.
model
presented
here,
General
TAMSAT-ALERT,
represents
significant
scientific
functional
advance
previous
implementations.
Notably,
is
applicable
any
variable
which
time
series
available.
addition,
functionality
has
been
introduced
climatological
non-stationarity
(for
example
due
climate
change),
large-scale
modes
variability
El
Niño)
persistence
land-surface
conditions).
paper,
we
present
full
description
model,
along
case
studies
its
application
prediction
central
England
temperature,
Pakistan
vegetation
African
precipitation.
Wiley Interdisciplinary Reviews Water,
Год журнала:
2023,
Номер
11(2)
Опубликована: Окт. 25, 2023
Abstract
Advances
in
impact
modeling
and
numerical
weather
forecasting
have
allowed
accurate
drought
monitoring
skilful
forecasts
that
can
drive
decisions
at
the
regional
scale.
State‐of‐the‐art
early‐warning
systems
are
currently
based
on
statistical
indicators,
which
do
not
account
for
dynamic
vulnerabilities,
hence
neglect
socio‐economic
initiating
actions.
The
transition
from
conventional
physical
of
droughts
toward
impact‐based
(IbF)
is
a
recent
paradigm
shift
early
warning
services,
to
ultimately
bridge
gap
between
science
action.
demand
generate
predictions
“what
will
do”
underpins
rising
interest
IbF
across
all
weather‐sensitive
sectors.
Despite
large
expected
benefits,
migrating
this
new
presents
myriad
challenges.
In
article,
we
provide
comprehensive
overview
IbF,
outlining
progress
made
field.
Additionally,
present
road
map
highlighting
current
challenges
limitations
practice
possible
ways
forward.
We
identify
seven
scientific
practical
challenges/limitations:
contextual
challenge
(inadequate
accounting
spatio‐sectoral
dynamics
vulnerability
exposure),
human‐water
feedbacks
(neglecting
how
human
activities
influence
propagation
drought),
typology
(oversimplifying
meteorological),
model
(reliance
mainstream
machine
learning
models),
data
(mainly
textual)
with
linked
sectoral
geographical
limitations.
Our
vision
facilitate
its
use
making
informed
timely
mitigation
measures,
thus
minimizing
impacts
globally.
This
article
categorized
under:
Science
Water
>
Extremes
Methods
Environmental
Change
Environmental Research Letters,
Год журнала:
2023,
Номер
18(9), С. 094060 - 094060
Опубликована: Сен. 1, 2023
Abstract
Advances
in
hydrological
modeling
and
numerical
weather
forecasting
have
allowed
hydro-climate
services
to
provide
accurate
impact
simulations
skillful
forecasts
that
can
drive
decisions
at
the
local
scale.
To
enhance
early
warnings
long-term
risk
reduction
actions,
it
is
imperative
better
understand
extremes
explore
drivers
for
their
predictability.
Here,
we
investigate
seasonal
forecast
skill
of
streamflow
over
pan-European
domain,
further
attribute
discrepancy
predictability
river
system
memory
as
described
by
regimes.
Streamflow
about
35
400
basins,
generated
from
E-HYPE
model
driven
with
bias-adjusted
ECMWF
SEAS5
meteorological
forcing
input,
are
explored.
Overall
results
show
adequate
both
Europe,
despite
spatial
variability
skill.
The
high
extreme
deteriorates
faster
a
function
lead
time
than
low
extreme,
positive
persisting
up
12
20
weeks
ahead
extremes,
respectively.
A
strong
link
between
underlying
regime
identified
through
comparative
analysis,
indicating
systems
analogous
memory,
e.g.
fast
or
slow
response
rainfall,
similarly
predict
extremes.
improve
our
understanding
geographical
areas
periods,
where
timely
information
on
very
conditions,
including
controlling
This
consequently
benefits
regional
national
organizations
embrace
prediction
capacity
act
order
reduce
disaster
support
climate
adaptation.
In
the
context
of
repeated
droughts
that
have
affected
central
Europe
over
last
years
(2018–2020,
2022),
climate-resilient
management
water
resources,
based
on
timely
information
about
current
state
terrestrial
cycle
and
forecasts
its
evolution,
has
gained
an
increasing
importance.
To
achieve
this,
we
propose
a
new
setup
for
simulations
using
integrated
hydrological
model
ParFlow/CLM
at
high
spatial
temporal
resolution
(i.e.,
0.611
km,
hourly
time
step)
Germany
neighboring
regions.
We
show
this
can
be
used
as
basis
monitoring
forecasting
system
aims
to
provide
stakeholders
from
many
sectors,
but
especially
agriculture,
with
diagnostics
indicators
highlighting
different
aspects
subsurface
states
fluxes,
such
storage,
seepage
water,
capillary
rise,
or
fraction
plant
available
(root-)depths.
The
validation
simulation
observation-based
data
monthly
period
2011–2020
yields
good
results
all
major
components
analyzed
here,
i.e.,
volumetric
soil
moisture,
evapotranspiration,
table
depth,
river
discharge.
As
relies
standardized
grid
definition
recent
globally
static
fields
parameters
(e.g.,
topography,
hydraulic
properties,
land
cover),
workflow
could
easily
transferred
regions
Earth,
including
sparsely
gauged
regions,
since
does
not
require
calibration.
