Research Square (Research Square),
Journal Year:
2023,
Volume and Issue:
unknown
Published: Jan. 17, 2023
Abstract
The
study
goal
was
to
determine
spatio-temporal
variations
in
chlorophyll-a
(Chl-a)
concentration
using
models
that
combine
hydroclimatic
and
nutrient
variables
150
tropical
reservoirs
Brazil.
investigation
of
seasonal
variability
indicated
Chl-a
varied
response
changes
total
nitrogen
(TN),
phosphorus
(TP),
volume
(V),
daily
precipitation
(P).
Simple
linear
regression
showed
nutrients
yielded
better
predictability
than
variables.
Fitted
relationships
between
the
above-mentioned
parameters
resulted
equations
capable
representing
algal
temporal
dynamics
blooms,
with
an
average
coefficient
determination
R²
=
0.70.
blooms
presented
interannual
variability,
being
more
frequent
periods
high
low
volume.
demonstrate
different
responses
parameters.
In
general,
positively
related
TN
and/or
TP.
However,
some
cases
(22%),
concentrations
reduced
Chl-a,
which
attributed
limited
phytoplankton
growth
driven
by
light
deficiency
due
increased
turbidity.
49%
models,
intensified
levels,
increases
from
external
sources
rural
watersheds.
Contrastingly,
51%
faced
a
decrease
precipitation,
can
be
explained
opposite
effect
dilution
at
reservoir
inlet
urban
terms
volume,
67%
reservoirs,
water
level
reduction
promoted
increase
as
higher
concentration.
other
cases,
decreased
lower
levels
wind-induced
destratification
column,
potentially
internal
release
bottom
sediment.
Finally,
application
model
two
largest
studied
greater
sensitivity
use
classes
regarding
TN,
followed
TP,
V,
P.
Water,
Journal Year:
2022,
Volume and Issue:
14(8), P. 1300 - 1300
Published: April 16, 2022
The
outbreak
of
cyanobacterial
blooms
is
a
serious
water
environmental
problem,
and
the
harm
it
brings
to
aquatic
ecosystems
supply
systems
cannot
be
underestimated.
It
very
important
establish
an
accurate
prediction
model
bloom
concentration,
which
challenging
issue.
Machine
learning
techniques
can
improve
accuracy,
but
large
amount
historical
monitoring
data
needed
train
these
models.
For
some
waters
with
inconvenient
geographical
location
or
frequent
sensor
failures,
there
are
not
enough
model.
To
deal
this
fused
based
on
transfer
method
proposed
in
paper.
In
study,
environment
taken
as
source
domain
order
learn
knowledge
growth
characteristics
small
target
load
trained
domain.
Then,
training
set
used
participate
inter-layer
fine-tuning
obtain
At
last,
convolutional
neural
network
Various
experiments
conducted
for
2
h
test
results
show
that
significantly
accuracy
low
volume.
Lake and Reservoir Management,
Journal Year:
2023,
Volume and Issue:
39(3), P. 213 - 230
Published: July 3, 2023
Julian
PJ
II,
Schafer
T,
Cohen
MJ,
Jones
P,
Osborne
TZ.
2023.
Changes
in
the
spatial
distribution
of
total
phosphorus
sediment
and
water
column
a
shallow
subtropical
lake.
Lake
Reserv
Manage.
XX:XXX–XXX.In
lakes,
interactions
between
bed
strongly
influence
availability
transport
nutrients.
Okeechobee,
South
Florida,
is
eutrophic,
shallow,
polymictic
lake
that
exhibits
frequent
mixing
resuspension
unconsolidated
sediments.
The
objective
this
study
was
to
evaluate
temporal
patterns
characteristics
linkage
(TP)
concentrations.
Spatiotemporal
generalized
additive
models
identified
key
periods
during
which
both
surface
suspended
solids
(TSS)
TP
increased,
corresponding
hurricane
tropical
storm
activity.
Our
regions
with
persistently
greater
concentrations
than
average,
indicating
potential
hot
spots
processes
and/or
internal
loading.
Sediment
bulk
density
(BD)
were
inversely
correlated,
light,
less
dense
sediments
have
concentrations,
potentially
contributing
redistribution
P.
An
integrated
evaluation
using
model
revealed
influences
explaining
area
concentration
≤500
mg/kg
increasing
while
low
decreasing,
marking
improvement
conditions.
If
trend
persists,
it
indicates
increasingly
storing
P
can
resist
entrainment,
significant
implications
for
assessing
trajectory
restoration.
Research Square (Research Square),
Journal Year:
2023,
Volume and Issue:
unknown
Published: Jan. 17, 2023
Abstract
The
study
goal
was
to
determine
spatio-temporal
variations
in
chlorophyll-a
(Chl-a)
concentration
using
models
that
combine
hydroclimatic
and
nutrient
variables
150
tropical
reservoirs
Brazil.
investigation
of
seasonal
variability
indicated
Chl-a
varied
response
changes
total
nitrogen
(TN),
phosphorus
(TP),
volume
(V),
daily
precipitation
(P).
Simple
linear
regression
showed
nutrients
yielded
better
predictability
than
variables.
Fitted
relationships
between
the
above-mentioned
parameters
resulted
equations
capable
representing
algal
temporal
dynamics
blooms,
with
an
average
coefficient
determination
R²
=
0.70.
blooms
presented
interannual
variability,
being
more
frequent
periods
high
low
volume.
demonstrate
different
responses
parameters.
In
general,
positively
related
TN
and/or
TP.
However,
some
cases
(22%),
concentrations
reduced
Chl-a,
which
attributed
limited
phytoplankton
growth
driven
by
light
deficiency
due
increased
turbidity.
49%
models,
intensified
levels,
increases
from
external
sources
rural
watersheds.
Contrastingly,
51%
faced
a
decrease
precipitation,
can
be
explained
opposite
effect
dilution
at
reservoir
inlet
urban
terms
volume,
67%
reservoirs,
water
level
reduction
promoted
increase
as
higher
concentration.
other
cases,
decreased
lower
levels
wind-induced
destratification
column,
potentially
internal
release
bottom
sediment.
Finally,
application
model
two
largest
studied
greater
sensitivity
use
classes
regarding
TN,
followed
TP,
V,
P.
