Water Resources Research,
Год журнала:
2024,
Номер
60(3)
Опубликована: Март 1, 2024
Abstract
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
modeling
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.
found
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:
(a)
no
performed
best
every
depth
horizon
(days
future),
(b)
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
utilizing
MMEs,
rather
than
models,
operational
forecasts.
ACM/IMS Transactions on Data Science,
Год журнала:
2021,
Номер
2(3), С. 1 - 26
Опубликована: Май 18, 2021
Physics-based
models
are
often
used
to
study
engineering
and
environmental
systems.
The
ability
model
these
systems
is
the
key
achieving
our
future
sustainability
improving
quality
of
human
life.
This
article
focuses
on
simulating
lake
water
temperature,
which
critical
for
understanding
impact
changing
climate
aquatic
ecosystems
assisting
in
resource
management
decisions.
General
Lake
Model
(GLM)
a
state-of-the-art
physics-based
addressing
such
problems.
However,
like
other
studying
scientific
systems,
it
has
several
well-known
limitations
due
simplified
representations
physical
processes
being
modeled
or
challenges
selecting
appropriate
parameters.
While
machine
learning
can
sometimes
outperform
given
ample
amount
training
data,
they
produce
results
that
physically
inconsistent.
proposes
physics-guided
recurrent
neural
network
(PGRNN)
combines
RNNs
leverage
their
complementary
strengths
improves
modeling
processes.
Specifically,
we
show
PGRNN
improve
prediction
accuracy
over
(by
20%
even
with
very
little
data),
while
generating
outputs
consistent
laws.
An
important
aspect
approach
lies
its
incorporate
knowledge
encoded
models.
allows
using
few
true
observed
data
also
ensuring
high
accuracy.
Although
present
evaluate
this
methodology
context
dynamics
temperature
lakes,
applicable
more
widely
range
disciplines
where
(also
known
as
mechanistic)
used.
Geoscientific model development,
Год журнала:
2019,
Номер
12(1), С. 473 - 523
Опубликована: Янв. 29, 2019
Abstract.
The
General
Lake
Model
(GLM)
is
a
one-dimensional
open-source
code
designed
to
simulate
the
hydrodynamics
of
lakes,
reservoirs,
and
wetlands.
GLM
was
developed
support
science
needs
Global
Ecological
Observatory
Network
(GLEON),
network
researchers
using
sensors
understand
lake
functioning
address
questions
about
how
lakes
around
world
respond
climate
land
use
change.
scale
diversity
types,
locations,
sizes,
expanding
observational
datasets
created
need
for
robust
community
model
dynamics
with
sufficient
flexibility
accommodate
range
scientific
management
relevant
GLEON
community.
This
paper
summarizes
basis
numerical
implementation
algorithms,
including
details
sub-models
that
surface
heat
exchange
ice
cover
dynamics,
vertical
mixing,
inflow–outflow
dynamics.
We
demonstrate
suitability
different
types
vary
substantially
in
their
morphology,
hydrology,
climatic
conditions.
supports
dynamic
coupling
biogeochemical
ecological
modelling
libraries
integrated
simulations
water
quality
ecosystem
health,
options
integration
other
environmental
models
are
outlined.
Finally,
we
discuss
utilities
analysis
outputs
uncertainty
assessments,
operation
within
distributed
cloud-computing
environment,
as
tool
learning
participants.
Geoscientific model development,
Год журнала:
2022,
Номер
15(11), С. 4597 - 4623
Опубликована: Июнь 16, 2022
Abstract.
Empirical
evidence
demonstrates
that
lakes
and
reservoirs
are
warming
across
the
globe.
Consequently,
there
is
an
increased
need
to
project
future
changes
in
lake
thermal
structure
resulting
biogeochemistry
order
plan
for
likely
impacts.
Previous
studies
of
impacts
climate
change
on
have
often
relied
a
single
model
forced
with
limited
scenario-driven
projections
relatively
small
number
lakes.
As
result,
our
understanding
effects
fragmentary,
based
scattered
using
different
data
sources
modelling
protocols,
mainly
focused
individual
or
regions.
This
has
precluded
identification
main
at
global
regional
scales
contributed
lack
water
quality
considerations
policy-relevant
documents,
such
as
Assessment
Reports
Intergovernmental
Panel
Climate
Change
(IPCC).
Here,
we
describe
simulation
protocol
developed
by
Lake
Sector
Inter-Sectoral
Impact
Model
Intercomparison
Project
(ISIMIP)
simulating
ensemble
models
scenarios
ISIMIP
phases
2
3.
The
prescribes
simulations
driven
forcing
from
gridded
observations
Earth
system
under
various
representative
greenhouse
gas
concentration
pathways
(RCPs),
all
consistently
bias-corrected
0.5∘
×
grid.
In
phase
2,
11
were
these
62
well-studied
where
available
calibration
historical
conditions,
uncalibrated
17
500
defined
grid
cells
containing
3,
this
approach
was
expanded
consider
more
lakes,
models,
processes.
largest
international
effort
temperature,
structure,
ice
phenology
local
paves
way
Reviews of Geophysics,
Год журнала:
2024,
Номер
62(1)
Опубликована: Фев. 11, 2024
Abstract
Lake
thermal
dynamics
have
been
considerably
impacted
by
climate
change,
with
potential
adverse
effects
on
aquatic
ecosystems.
To
better
understand
the
impacts
of
future
change
lake
and
related
processes,
use
mathematical
models
is
essential.
In
this
study,
we
provide
a
comprehensive
review
water
temperature
modeling.
We
begin
discussing
physical
concepts
that
regulate
in
lakes,
which
serve
as
primer
for
description
process‐based
models.
then
an
overview
different
sources
observational
data,
including
situ
monitoring
satellite
Earth
observations,
used
field
classify
various
available,
discuss
model
performance,
commonly
performance
metrics
optimization
methods.
Finally,
analyze
emerging
modeling
approaches,
forecasting,
digital
twins,
combining
deep
learning,
evaluating
structural
differences
through
ensemble
modeling,
adapted
management,
coupling
This
aimed
at
diverse
group
professionals
working
fields
limnology
hydrology,
ecologists,
biologists,
physicists,
engineers,
remote
sensing
researchers
from
private
public
sectors
who
are
interested
understanding
its
applications.
Environmental Modelling & Software,
Год журнала:
2020,
Номер
128, С. 104697 - 104697
Опубликована: Март 13, 2020
In
this
paper,
we
introduce
the
CSPS
framework
for
hierarchical
assessment
of
aquatic
ecosystem
models
built
on
a
range
metrics
and
characteristic
signatures
relevant
to
condition.
The
is
comprised
four
levels:
0)
conceptual
validation;
1)
comparison
simulated
state
variables
with
observations
('state
validation');
2)
fluxes
measured
process
rates
('process
3)
system-level
emergent
properties,
patterns
relationships
('system
validation').
Of
these,
only
levels
0
1
are
routinely
undertaken
at
present.
To
highlight
diverse
contexts
modelling
community,
present
several
case
studies
improved
validation
approaches
using
level
0–3
hierarchy.
We
envision
that
community–driven
adoption
these
will
lead
more
rigorously
assessed
models,
ultimately
accelerating
advances
in
model
structure
function,
confidence
predictions.
Current Opinion in Environmental Sustainability,
Год журнала:
2018,
Номер
36, С. 1 - 10
Опубликована: Сен. 25, 2018
Algal
blooms
increasingly
threaten
lake
and
reservoir
water
quality
at
the
global
scale,
caused
by
ongoing
climate
change
nutrient
loading.
To
anticipate
these
algal
blooms,
models
to
project
future
worldwide
are
required.
Here
we
present
state-of-the-art
in
projection
modelling
explore
requirements
of
an
ideal
model.
Based
on
this,
identify
current
challenges
opportunities
for
such
model
development.
Since
most
building
blocks
present,
foresee
that
any
earth
can
be
developed
near
future.
Finally,
think
bloom
a
scale
will
provide
valuable
contribution
policymaking,
particular
with
respect
SDG
6
(clean
sanitation).
Hydrology and earth system sciences,
Год журнала:
2021,
Номер
25(2), С. 1009 - 1032
Опубликована: Фев. 25, 2021
Abstract.
The
concentration
of
oxygen
is
fundamental
to
lake
water
quality
and
ecosystem
functioning
through
its
control
over
habitat
availability
for
organisms,
redox
reactions,
recycling
organic
material.
In
many
eutrophic
lakes,
depletion
in
the
bottom
layer
(hypolimnion)
occurs
annually
during
summer
stratification.
temporal
spatial
extent
hypolimnetic
anoxia
determined
by
interactions
between
external
drivers
(e.g.,
catchment
characteristics,
nutrient
loads,
meteorology)
as
well
internal
feedback
mechanisms
matter
recycling,
phytoplankton
blooms).
How
these
interact
evolution
decadal
timescales
will
determine,
part,
future
quality.
this
study,
we
used
a
vertical
one-dimensional
hydrodynamic–ecological
model
(GLM-AED2)
coupled
with
calibrated
hydrological
(PIHM-Lake)
simulate
thermal
dynamics
Lake
Mendota
(USA)
37
year
period.
calibration
validation
consisted
global
sensitivity
evaluation
application
an
optimization
algorithm
improve
fit
observed
simulated
data.
We
calculated
stability
indices
(Schmidt
stability,
Birgean
work,
stored
heat),
identified
spring
mixing
stratification
periods,
quantified
energy
required
mixing.
To
qualify
which
factors
were
most
important
driving
interannual
variation
anoxia,
applied
random-forest
classifier
multiple
linear
regressions
modeled
variables
onset
offset,
ice
duration,
gross
primary
production).
exhibited
prolonged
each
summer,
lasting
50–60
d.
heat
budget,
timing
stratification,
production
epilimnion
prior
predictors
periods
Mendota.
Interannual
variability
was
largely
driven
physical
factors:
earlier
combination
higher
strongly
affected
duration
anoxia.
A
measured
step
change
upward
2010
unexplained
GLM-AED2
model.
Although
cause
remains
unknown,
possible
include
invasion
predacious
zooplankton
Bythotrephes
longimanus.
As
budget
depended
primarily
on
meteorological
conditions,
likely
increase
near
result
projected
climate
region.