Abstract.
In
agricultural
areas,
the
downstream
flow
can
be
highly
influenced
by
human
activities
during
low
periods,
especially
dam
releases
and
irrigation
withdrawals.
Irrigation
is
indeed
major
use
of
freshwater
in
world.
This
study
aims
at
precisely
taking
these
factors
into
account
a
watershed
model.
The
Soil
Water
Assessment
Tool
(SWAT+)
agro-hydrological
model
was
chosen
for
its
capacity
to
crop
dynamics
management.
Two
different
models
were
compared
their
ability
estimate
water
needs
actual
irrigation.
first
based
on
air
temperature
as
main
determining
factor
growth,
whereas
second
relies
high
resolution
data
from
Sentinel-2
satellite
monitor
plant
growth.
Both
are
applied
plot
scale
800
km2
characterized
Results
show
that
including
remote
sensing
leads
more
realistic
modeled
emergence
dates
summer
crops.
However
both
approaches
have
proven
able
reproduce
evolution
daily
withdrawals
throughout
year.
As
result,
allowed
simulate
with
good
accuracy,
periods.
Hydrology and earth system sciences,
Journal Year:
2024,
Volume and Issue:
28(1), P. 49 - 64
Published: Jan. 3, 2024
Abstract.
In
agricultural
areas,
the
downstream
flow
can
be
highly
influenced
by
human
activities
during
low-flow
periods,
especially
dam
releases
and
irrigation
withdrawals.
Irrigation
is
indeed
major
use
of
freshwater
in
world.
This
study
aims
at
precisely
taking
these
factors
into
account
a
watershed
model.
The
Soil
Water
Assessment
Tool
(SWAT+)
agro-hydrological
model
was
chosen
for
its
capacity
to
crop
dynamics
management.
Two
different
models
were
compared
terms
their
ability
estimate
water
needs
actual
irrigation.
first
based
on
air
temperature
as
main
determining
factor
growth,
whereas
second
relies
high-resolution
data
from
Sentinel-2
satellite
monitor
plant
growth.
Both
are
applied
plot
scale
800
km2
that
characterized
Results
show
including
remote
sensing
leads
more
realistic
modeled
emergence
dates
summer
crops.
However,
both
approaches
have
proven
able
reproduce
evolution
daily
withdrawals
throughout
year.
As
result,
allowed
us
simulate
with
good
accuracy,
periods.
Land,
Journal Year:
2024,
Volume and Issue:
13(9), P. 1374 - 1374
Published: Aug. 28, 2024
Urbanization
in
the
Haihe
River
Basin
northern
China,
particularly
upstream
mountainous
basin
of
Baiyangdian,
has
significantly
altered
land
use
and
runoff
processes.
The
is
a
key
water
source
for
downstream
areas
like
Baiyangdian
Xiong’an
New
Area,
making
it
essential
to
understand
these
changes’
implications
security.
However,
exact
processes
remain
unclear.
To
address
this
gap,
simulation
framework
combining
SWAT+
CLUE-S
was
used
analyze
responses
under
different
scenarios:
natural
development
(ND),
farmland
protection
(FP),
ecological
(EP).
model
results
were
good,
with
NSE
above
0.7
SWAT+.
Kappa
coefficient
validation
0.83.
further
study
found
that
from
2005
2015,
urban
construction
increased
by
11.50
km2
per
year,
leading
0.5–1.3
mm
rise
annual
runoff.
Although
expansion
continued,
other
scenarios,
which
emphasized
forest
preservation,
slowed
growth.
Monthly
changes
most
significant
during
rainy
season,
ND,
FP,
EP
varying
8.9%,
10.9%,
7.7%,
respectively.
While
differences
between
scenarios
not
dramatic,
findings
provide
theoretical
foundation
future
resource
planning
management
area
offer
valuable
insights
sustainable
Area.
Additionally,
contribute
broader
field
hydrology
highlighting
importance
considering
multiple
change
analysis.
Water,
Journal Year:
2024,
Volume and Issue:
16(21), P. 3030 - 3030
Published: Oct. 22, 2024
In
recent
years,
remote
sensing
data
have
revealed
considerable
potential
in
unraveling
crucial
information
regarding
water
balance
dynamics
due
to
their
unique
spatiotemporal
distribution
characteristics,
thereby
advancing
multi-objective
optimization
algorithms
hydrological
model
parameter
calibration.
However,
existing
frameworks
based
on
the
Soil
and
Water
Assessment
Tool
(SWAT)
primarily
focus
single-objective
or
multiple-objective
(i.e.,
two
three
objective
functions),
lacking
an
open,
efficient,
flexible
framework
integrate
many-objective
four
more
functions)
satisfy
growing
demands
of
complex
systems.
This
study
addresses
this
gap
by
designing
implementing
a
framework,
Py-SWAT-U-NSGA-III,
which
integrates
Unified
Non-dominated
Sorting
Genetic
Algorithm
III
(U-NSGA-III).
Built
SWAT
model,
supports
broad
range
problems,
from
single-
many-objective.
Developed
within
Python
environment,
modules
are
integrated
with
Pymoo
library
construct
U-NSGA-III
algorithm-based
framework.
accommodates
various
calibration
schemes,
including
multi-site,
multi-variable,
functions.
Additionally,
it
incorporates
sensitivity
analysis
post-processing
shed
insights
into
behavior
evaluate
results.
The
multi-core
parallel
processing
enhance
efficiency.
was
tested
Meijiang
River
Basin
southern
China,
using
daily
streamflow
Penman–Monteith–Leuning
Version
2
(PML-V2(China))
evapotranspiration
(ET)
for
efficiency
evaluation.
Three
case
studies
demonstrated
its
effectiveness
optimizing
models,
achieving
speedup
up
8.95
despite
I/O
bottlenecks.
Py-SWAT-U-NSGA-III
provides
tool
community
that
strives
facilitate
application
advancement
modeling.
Journal of Hydrologic Engineering,
Journal Year:
2023,
Volume and Issue:
28(11)
Published: Aug. 26, 2023
A
correct
estimation
of
evapotranspiration
(ET)
is
required
to
determine
the
amount
available
water
in
any
watershed.
Potential
(PET)
used
estimate
actual
(AET).
The
Soil
and
Water
Assessment
Tool
(SWAT)
has
different
methods
compute
PET;
data-scarce
watersheds
hydrological
modeling
typically
challenging.
Although
there
are
a
lot
remotely
sensed
PET
(RS-PET)
data,
validation
models
using
these
data
rarely
been
studied
such
watersheds.
Thus,
purpose
this
paper
assess
impacts
direct
assimilation
RS-PET
on
balance
components
SWAT
for
basin
Iran
with
limited
data.
To
end,
we
changed
source
code
automatically
integrate
performance
model
was
then
evaluated
streamflow
AET
period
2001–2005.
results
reveal
that
Default
overestimates
soil
moisture,
underestimates
AET,
ultimately
fails
appropriately
capture
at
watershed’s
outlet.
However,
by
incorporating
RS-PET,
improves
accuracy
PET,
streamflow.
For
example,
scenario,
Nash–Sutcliffe
efficiency
0.66,
whereas
it
15%
higher
scheme.
findings
also
demonstrate
parameters’
sensitivity
values
enhancing
hydrologic
regions
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(7), P. 1204 - 1204
Published: March 29, 2024
Scientists
widely
agree
that
anthropogenically
driven
climate
change
significantly
impacts
vegetation
growth,
particularly
in
floodplain
areas,
by
altering
river
flow
and
flood
regimes.
This
impact
will
accelerate
the
future,
according
to
projections.
For
example,
Australia,
has
been
attributed
a
decrease
winter
precipitation
range
of
56%
72.9%
an
increase
summer
from
11%
27%,
different
scenarios.
research
attempts
understand
responses
variability
at
level.
Further,
this
study
is
effort
enlighten
our
understanding
temporal
under
To
achieve
these
aims,
semi-distributed
hydrological
model
was
applied
sub-catchment
level
simulate
Leaf
Area
Index
(LAI).
The
simulated
against
future
time
series
data
Global
Climate
Model
(GCM)
underwent
non-parametric
Mann–Kendall
test
detect
trends
assess
magnitude
change.
quantify
model’s
performance,
calibration
validation
were
conducted
Moderate
Resolution
Imaging
Spectroradiometer
(MODIS)
LAI.
results
show
Nash–Sutcliffe
efficiency
(NSE)
values
0.85
0.78,
respectively,
suggesting
performance
very
good.
modeling
reveal
rainfall
pattern
fluctuates
projections
within
site,
which
tends
be
more
vibrant
during
warmer
seasons.
Moreover,
highlighted
increases
average
projected
temperatures,
can
help
growth
winter.
may
employed
for
sustainable
management,
restoration,
land-use
planning,
policymaking,
communities
better
prepare
respond
changing
patterns
related
challenges
climate.
Abstract.
In
agricultural
areas,
the
downstream
flow
can
be
highly
influenced
by
human
activities
during
low
periods,
especially
dam
releases
and
irrigation
withdrawals.
Irrigation
is
indeed
major
use
of
freshwater
in
world.
This
study
aims
at
precisely
taking
these
factors
into
account
a
watershed
model.
The
Soil
Water
Assessment
Tool
(SWAT+)
agro-hydrological
model
was
chosen
for
its
capacity
to
crop
dynamics
management.
Two
different
models
were
compared
their
ability
estimate
water
needs
actual
irrigation.
first
based
on
air
temperature
as
main
determining
factor
growth,
whereas
second
relies
high
resolution
data
from
Sentinel-2
satellite
monitor
plant
growth.
Both
are
applied
plot
scale
800
km2
characterized
Results
show
that
including
remote
sensing
leads
more
realistic
modeled
emergence
dates
summer
crops.
However
both
approaches
have
proven
able
reproduce
evolution
daily
withdrawals
throughout
year.
As
result,
allowed
simulate
with
good
accuracy,
periods.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
Journal Year:
2024,
Volume and Issue:
17, P. 9226 - 9239
Published: Jan. 1, 2024
The
Community
Land
Model
version
4
with
carbon
and
nitrogen
components
(CLM4CN)
is
coupled
Data
Assimilation
Research
Testbed
(DART)
to
assimilate
remotely
sensed
leaf
area
index
(LAI),
analyze
the
improvement
in
model
performance
for
simulating
land
surface
variables
land–atmospheric
exchange
fluxes.
results
demonstrate
that
assimilation
effectively
addresses
issue
of
significant
overestimation
LAI
values,
particularly
noticeable
regions
characterized
by
low
latitudes
dense
vegetation
coverage.
On
a
global
scale,
disparities
between
simulated
assimilated
relative
observational
data,
are
measured
at
0.90
-0.07,
representing
54.1%
3.9%
observed
respectively.
root
mean
square
difference
(RMSD)
1.61
comparing
1.85.
Assimilating
globally
leads
noteworthy
1%
reduction
average
2-meter
air
temperature
(T
2m
)
concurrent
decrease
0.15℃
RMSD.
However,
level,
does
not
yield
enhancement
modeling
capability
heat
fluxes,
although
sensible
(HS)
slightly
outperforms
latent
(LE).
Improvements
after
show
variations
regional
scales
due
factors
such
as
coverage
climatic
conditions.
Overall,
periodic
changes
vegetation,
forested
areas
Western
Eurasian
Continent
(Region
5),
enhancements
T
HS
assimilating
notable,
reduced
7%
20%,
