Agricultural Water Management,
Год журнала:
2024,
Номер
301, С. 108955 - 108955
Опубликована: Июль 16, 2024
Soon,
water
scarcity
is
expected
to
worsen
due
several
factors
including
the
population
growth
and
climate
change.
To
address
this,
European
Water
Framework
Directive
(WFD)
mandates
an
increase
in
use
efficiency
of
agrosystems.
In
this
context,
aim
study
was
provide
a
novel
methodological
approach,
based
on
satellite-based
classification
algorithms
(i.e.,
artificial
neural
networks,
ANN,
Optical
Trapezoid
Model,
OPTRAM),
agro-hydrological
modelling
ArcDualKc
model
versus
traditional
FAO-56
approach)
combined
with
different
sources
agrometeorological
data
ground-based
ERA5
Land
data),
for
mapping
irrigated
crops
determining
their
irrigation
requirements
(IWR)
at
district
level.
The
carried
out,
during
period
2019–20,
district,
named
"Quota
102,50"
(Eastern
Sicily,
Italy)
managed
by
local
reclamation
consortium.
ANN
OPTRAM
allowed
obtain
accurate
detection
crops,
overall
accuracy
82
%
88
%,
respectively
2019–20.
IWR
retrieved
standard
approach
were
generally
underestimated
comparison
volumes
supplied
farmers.
best
performance
resulted
when
implemented
data,
average
values
coefficient
determination,
residual
error
slope
0.99,
975.31
m3
0.78,
respectively,
outputs
scale
compared
declared
consortium
overestimations
terms
both
areas
IWR,
absolute
errors
about
1539
ha
1431
ha,
9
106
12
m3,
Finally,
provided
useful
framework
supporting
management
authorities
better
planning
monitoring
uses
under
current
WFD.
Agricultural Water Management,
Год журнала:
2022,
Номер
274, С. 107975 - 107975
Опубликована: Окт. 24, 2022
Under
the
current
water
scarcity
scenario,
promotion
of
saving
strategies
is
essential
for
improving
sustainability
irrigated
agriculture.
In
particular,
high
resolution
area
maps
are
required
better
understanding
uses
and
supporting
management
authorities.
The
main
purpose
this
study
was
to
provide
a
stand-alone
remote
sensing
(RS)
methodology
mapping
areas.
Specifically,
an
unsupervised
classification
approach
on
Normalized
Difference
Vegetation
Index
(NDVI)
data
coupled
with
OPtical
TRApezoid
Model
(OPTRAM)
detecting
actual
areas
without
use
any
reference
data.
proposed
firstly
applied
validated
at
Marchfeld
Cropland
region
(Austria)
during
irrigation
season
2021,
showing
good
agreement
overall
accuracy
70%.
Secondly,
it
district
Quota
102,50
(Italy)
seasons
2019–2020.
results
latter
were
instead
compared
declared
by
Reclamation
Consortium,
finding
overestimation
21%.
conclusion,
suggests
easy-to-use
approach,
eventually
independent
such
as
agricultural
statistical
surveys
or
records
replicable
under
different
settings
in
continental
Mediterranean
climates
support
stakeholders
regular
estimation
growing
years
eventual
unauthorized
uses.
However,
some
uncertainties
should
be
considered,
needing
further
analyses
approach.
Water,
Год журнала:
2023,
Номер
15(9), С. 1711 - 1711
Опубликована: Апрель 27, 2023
In
the
context
of
implementing
European
Flood
Directive
in
Greece,
a
large
set
rainfall
data
was
compiled
with
principal
aim
constructing
intensity–timescale–return
period
relationships
for
entire
country.
This
included
ground
as
well
non-conventional
from
reanalyses
and
satellites.
Given
declaration
climate
emergency,
along
establishment
ministry
crisis
this
dataset
also
investigated
climatic
perspective
using
longest
records
to
assess
whether
or
not
they
support
doctrine.
Monte
Carlo
simulations,
stationary
Hurst–Kolmogorov
(HK)
stochastic
dynamics,
were
employed
compare
theoretical
expectations.
Rainfall
extremes
are
proven
conform
statistical
expectations
under
stationarity.
The
only
notable
events
found
clustering
(reflecting
HK
dynamics)
water
abundance
1960s
dry
years
around
1990,
followed
by
recovery
drought
conditions
recent
years.
Environmental Data Science,
Год журнала:
2024,
Номер
3
Опубликована: Янв. 1, 2024
Abstract
Climate
trends
and
weather
indicators
are
used
in
several
research
fields
due
to
their
importance
statistical
modeling,
frequently
as
covariates.
Usually,
climate
available
grid
files
with
different
spatial
time
resolutions.
The
availability
of
a
series
compatible
administrative
boundaries
is
scattered
Brazil,
not
fully
for
years,
produced
diverse
methodologies.
In
this
paper,
we
propose
the
Brazilian
municipalities
using
zonal
statistics
derived
from
ERA5-Land
reanalysis
indicators.
As
result,
present
datasets
daily
data,
covering
period
1950
2022.
Computers and Electronics in Agriculture,
Год журнала:
2024,
Номер
222, С. 109098 - 109098
Опубликована: Май 30, 2024
The
ability
to
delineate
site-specific
management
zones
is
a
key
feature
for
precision
agriculture
applications.
In
this
study,
novel
methodological
protocol
mapping
the
water
status,
i.e.
stem
potential
(SWP),
of
citrus
orchards
was
developed.
Specifically,
observed
(SWPobs)
values
and
unmanned
aerial
vehicle
multispectral
information
(i.e.,
vegetation
indices,
VIs,
spectral
bands,
SBs)
were
integrated
implement
twofold
approach
based
on:
(i)
spatial
interpolation
(SWPint)
SWPobs,
(ii)
stepwise
regression
models
(SWPproxy)
between
SWPobs
VIs
(scenario
1)
or
SBs
2).
Then,
derived
crop
status
maps
(SWPint
SWPproxy)
customized
by
applying
an
absolute
(scientific-driven),
relative
(quantile-driven),
automated
clustering
(K-means)
classification
method.
accuracy
proposed
approach,
evaluated
comparing
SWPint
SWPproxy
with
using
linear
models,
showed
reliable
results,
average
mean
error
root
square
ranging
from
0.13
0.19
MPa
0.24
MPa,
respectively.
These
results
provide
practical
insights
identifying
spatial-temporal
variability
SWP
orchard
under
study.
Additionally,
study
highlights
importance
scientific-driven
support
adoption
irrigation
criteria
decision-making
process
non-expert
users,
as
indicated
assessment
Silhouette
index.
International Journal of Applied Earth Observation and Geoinformation,
Год журнала:
2024,
Номер
132, С. 104026 - 104026
Опубликована: Июль 10, 2024
Since
the
arid
regions
of
Central
Asia
(ACA)
are
located
in
interior
Eurasia,
water
resources
play
a
vital
role
stability
its
ecosystem
and
economic
development.
Based
on
terrestrial
storage
anomaly
(TWSA)
Gravity
Recovery
Climate
Experiment
(GRACE),
we
analyze
observed
characteristics
TWSA
over
ACA
during
2003–2014.
Results
indicate
that
(TWS)
region
showed
an
overall
declining
trend
from
2003
to
2014,
autumn
TWS
this
is
smallest
compared
other
seasons
exhibits
strong
decreasing
at
least
−4.5
cm/decade.
This
means
scarcer
more
vulnerable
autumn.
The
Distance
between
Indices
Simulation
Observation
(DISO)
method
employed
evaluate
performance
sixth
phase
Coupled
Model
Intercomparison
Project
(CMIP6)
models
simulating
ACA.
Compared
with
observational
results,
values
captured
by
CMIP6
larger
trends
weaker.
Using
optimal
models,
statistical
downscaling
constrains
projection
results
using
GRACE
datasets.
It
shows
will
continue
decrease
most
parts
future,
scarcity
be
severe
Tajikistan
southwestern
Kazakhstan.
Under
SSP126,
Tajikistan's
projected
11.0
cm
long
term.
study
reveals
current
situation
possible
future
changes
autumn,
providing
references
for
resource
management
sustainable
development
policies
area
avoid
losses
caused
scarcity.
