Authorea (Authorea),
Год журнала:
2023,
Номер
unknown
Опубликована: Июль 27, 2023
Water
temperature
forecasting
in
lakes
and
reservoirs
is
a
valuable
tool
to
manage
crucial
freshwater
resources
changing
more
variable
climate,
but
previous
efforts
have
yet
identify
an
optimal
modelling
approach.
Here,
we
demonstrate
the
first
multi-model
ensemble
(MME)
reservoir
water
forecast,
method
that
combines
individual
model
strengths
single
framework.
We
developed
two
MMEs:
three-model
process-based
MME
five-model
includes
empirical
models
forecast
profiles
at
temperate
drinking
reservoir.
Our
results
showed
improved
performance
by
8-30%
relative
MME,
as
quantified
using
aggregated
probabilistic
skill
score.
This
increase
was
due
large
improvements
bias
despite
increases
uncertainty.
High
correlation
among
resulted
little
improvement
models.
The
utility
of
MMEs
highlighted
results:
1)
no
performed
best
every
depth
horizon
(days
future),
2)
avoided
poor
performances
rarely
producing
worst
for
any
forecasted
period
(<6%
ranked
forecasts
over
time).
work
presents
example
how
existing
can
be
combined
improve
discusses
value
utilising
MMEs,
rather
than
models,
operational
forecasts.
bioRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 13, 2024
Summary
As
natural
ecosystems
experience
unprecedented
human-made
degradation,
it
is
urgent
to
deliver
quantitative
anticipatory
forecasts
of
biodiversity
change
and
identify
relevant
biotic
abiotic
predictors.
Forecasting
has
been
challenging
due
their
complexity,
chaotic
nonlinear
nature
the
availability
adequate
data.
Here,
we
use
four
years
daily
abundance
a
complex
lake
planktonic
ecosystem
its
environment
model
forecast
metrics.
Using
state-of-the-art
equation-free
modelling
technique,
community
richness
turnover
with
proficiency
greater
than
constant
predictor
several
generations
ahead
(30
days).
Short-term
improve
substantially
using
predictors
(i.e.,
autoregressive
term
or
richness).
Long-term
require
more
set
variables
interactions),
depends
strongly
on
including
such
as
water
temperature.
Depending
horizon,
can
interact
nonlinearly
synergistically,
enhancing
each
other’s
effects
Our
findings
showcase
challenges
forecasting
in
stress
importance
monitoring
focal
anticipate
undesired
changes.
Authorea (Authorea),
Год журнала:
2023,
Номер
unknown
Опубликована: Июль 27, 2023
Water
temperature
forecasting
in
lakes
and
reservoirs
is
a
valuable
tool
to
manage
crucial
freshwater
resources
changing
more
variable
climate,
but
previous
efforts
have
yet
identify
an
optimal
modelling
approach.
Here,
we
demonstrate
the
first
multi-model
ensemble
(MME)
reservoir
water
forecast,
method
that
combines
individual
model
strengths
single
framework.
We
developed
two
MMEs:
three-model
process-based
MME
five-model
includes
empirical
models
forecast
profiles
at
temperate
drinking
reservoir.
Our
results
showed
improved
performance
by
8-30%
relative
MME,
as
quantified
using
aggregated
probabilistic
skill
score.
This
increase
was
due
large
improvements
bias
despite
increases
uncertainty.
High
correlation
among
resulted
little
improvement
models.
The
utility
of
MMEs
highlighted
results:
1)
no
performed
best
every
depth
horizon
(days
future),
2)
avoided
poor
performances
rarely
producing
worst
for
any
forecasted
period
(<6%
ranked
forecasts
over
time).
work
presents
example
how
existing
can
be
combined
improve
discusses
value
utilising
MMEs,
rather
than
models,
operational
forecasts.
