Water,
Journal Year:
2024,
Volume and Issue:
16(21), P. 3108 - 3108
Published: Oct. 30, 2024
Dams
are
vital
for
irrigation,
power
generation,
and
domestic
water
needs,
but
siltation
poses
a
significant
challenge,
especially
in
areas
prone
to
erosion,
potentially
shortening
dam’s
lifespan.
The
Ahmed
El
Hansali
Dam
Morocco
faces
heightened
due
its
upstream
region
being
susceptible
erosion-prone
rocks
high
runoff.
This
study
estimates
the
at
dam
from
construction
up
2014
using
bathymetric
data
Brown
model,
which
is
widely-used
empirical
model
that
calculates
reservoir
trap
efficiency.
Additionally,
evaluates
impact
of
Land
Use
Cover
(LULC)
changes
projected
future
rainfall
until
around
2076
based
on
rates.
results
indicate
LULC,
particularly
temporal
variations
precipitation,
have
dam.
Notably,
strongly
correlated
with
rate,
an
R2
0.92.
efficiency
sediment
trapping
(TE)
97.64%,
meaning
97.64%
catchment
area
trapped
or
deposited
bottom
estimated
annual
specific
yield
about
32,345.79
tons/km2/yr,
accumulation
rate
approximately
4.75
Mm3/yr.
half-life
be
2076,
precipitation
projections
may
extend
this
timeframe
strong
correlation
between
precipitation.
soil
erosion
driven
by
land
management
practices
plays
crucial
role
dynamics.
Hence,
offers
comprehensive
assessment
dynamics
dam,
providing
essential
information
long-term
effects
use
changes,
climate
projections.
These
findings
assist
decision
makers
managing
sedimentation
more
effectively,
ensuring
durability
extending
life.
Hydrological Sciences Journal,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 7, 2025
Severe
droughts
have
historically
plagued
Brazil's
semi-arid
region,
leading
to
mass
migrations
and
significant
loss
of
life.
Although
these
events
sparked
a
unique
coevolutionary
relationship
between
humans
water
systems
in
this
historical
narrative
remains
largely
unexplored
from
hydrological
point
view.
This
study
delves
into
the
multifaceted
history
development
infrastructure
region
by
compiling
analyzing
records,
official
documents,
drought
literature
novels,
newspaper
reports.
Thus,
points
that
societal
perception
regional
memory
emerge
as
feasible
pathways
heritage
toward
community
resilience
droughts.
Through
analyse,
valuable
insights
are
provided
for
understanding
human-water
system
interactions
offering
potential
lessons
other
regions
grappling
with
similar
challenges.
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(11), P. 1870 - 1870
Published: May 24, 2024
It
is
crucial
to
monitor
algal
blooms
in
freshwater
reservoirs
through
an
examination
of
chlorophyll-a
(Chla)
concentrations,
as
they
indicate
the
trophic
condition
these
waterbodies.
Traditional
monitoring
methods,
however,
are
expensive
and
time-consuming.
Addressing
this
hindrance,
we
conducted
a
comprehensive
investigation
using
several
machine
learning
models
for
Chla
modeling.
To
end,
used
situ
collected
water
sample
data
remote
sensing
from
Sentinel-2
satellite,
including
spectral
bands
indices,
large-scale
coverage.
This
approach
allowed
us
conduct
analysis
characterization
concentrations
across
149
Ceará,
semi-arid
region
Brazil.
The
implemented
included
k-nearest
neighbors,
random
forest,
extreme
gradient
boosting,
least
absolute
shrinkage,
group
method
handling
(GMDH);
particular,
GMDH
has
not
been
previously
explored
context.
forward
stepwise
was
determine
best
subset
input
parameters.
Using
70/30
split
training
testing
datasets,
best-performing
model
model,
achieving
R2
0.91,
MAPE
102.34%,
RMSE
20.4
μg/L,
which
were
values
consistent
with
ones
found
literature.
Nevertheless,
predicted
concentration
most
sensitive
red,
green,
near-infrared
bands.
Eutrophication,
a
global
concern,
impacts
water
quality,
ecosystems,
and
human
health.
It’s
crucial
to
monitor
algal
blooms
in
freshwater
reservoirs,
as
they
indicate
the
trophic
condition
of
waterbody
through
Chlorophyll-a
(Chla)
concentration.
Traditional
monitoring
methods,
however,
are
expen-sive
time-consuming.
Addressing
this
hindrance,
we
developed
models
using
remotely
sensed
data
from
Sentinel-2
satellite
for
large-scale
coverage,
including
its
bands
spectral
indexes,
estimate
Chla
concentration
on
149
reservoirs
Ceará,
Brazil.
Several
machine
learning
were
trained
tested,
k-nearest
neighbours,
random
forests,
extreme
gradient
boosting,
least
absolute
shrinkage,
group
method
handling
(GMDH),
sup-port
vector
models.
A
stepwise
approach
determined
best
subset
input
parameters.
Using
70/30
split
training
testing
datasets,
best-performing
model
was
GMDH,
achieving
an
R2
0.91,
MAPE
102.34%,
RMSE
20.38
g/L,
which
values
consistent
with
ones
found
literature.
Nevertheless,
predicted
most
sensitive
red,
green,
near
infra-red
bands.
