Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Окт. 17, 2024
Analysing
non-stationarity
in
runoff
and
sediment
load
is
crucial
for
effective
water
resource
management
the
Dongting
Lake
basin
amid
climate
change
human
impacts.
Using
Mann-Kendall
test,
Generalized
Additive
Models
Location,
Scale,
Shape
framework,
Random
Forest
models,
we
evaluated
its
drivers
annual
series
at
eight
hydrological
stations
from
1961
to
2021.
These
include
three
inflow
sites
Jingjiang
Three
Outlets
(Ouchi,
Songzi,
Hudu
Rivers),
four
Four
Rivers
(Xiang,
Zi,
Yuan,
Li
one
outflow
site
Chenglingji.
Results
revealed
a
significant
decrease
Chenglingji,
while
showed
no
trend.
The
non-stationary
models
with
multiple
physically-based
covariates
better
captured
compared
single
covariate
models.
Annual
rainfall
was
key
contributor
basin,
reservoir
storage
capacity
played
more
dominant
role
Outlets.
At
Chenglingji
station,
both
factors
significantly
influenced
runoff.
For
load,
emerged
as
most
critical
factor
across
all
regions.
findings
provide
basis
improving
regulation
basin.
Journal of Water and Climate Change,
Год журнала:
2024,
Номер
15(9), С. 4418 - 4433
Опубликована: Авг. 21, 2024
ABSTRACT
All
river
basins
have
ever-evolving
land
use
and
cover
(LULC)
attributes.
The
impact
of
these
changes
may
not
be
significant
on
short
time
scales
(i.e.,
monthly,
seasonal,
yearly),
but
over
a
decadal
scale,
they
can
substantially
alter
the
hydrological
processes
basin.
This
study
comprehensively
quantifies
impacts
LULC
Cauvery
basin
in
India
using
maps
from
four
decades
spanning
1980
to
2020.
Simulations
were
performed
Soil
Water
Assessment
Tool
(SWAT)
with
various
datasets.
To
isolate
effects
changes,
two
sets
SWAT
models
developed:
A-set
for
calibration
validation
establish
parameters
B-set
examine
change
while
isolating
other
factors
such
as
terrain
climate
changes.
Key
findings
include
increase
urban
areas
(0.87%
1985
5.54%
2015),
decline
vegetation
(25.34%
21.32%
an
Curve
Number
average
annual
surface
runoff,
highlighting
processes.
achieved
R-squared
values
0.831,
0.728,
0.715,
0.757,
showcased
due
CLEAN - Soil Air Water,
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 28, 2024
Abstract
This
study
focuses
on
the
hydro‐sedimentological
characterization
and
modeling
of
Dhauliganga
River
in
Uttarakhand,
India.
Field
data
collected
from
2018–2020,
including
stage,
velocity,
suspended
sediment
concentration
(SSC),
showed
notable
variations
influenced
by
melting
snow,
glaciers,
precipitation.
Challenges
accurately
rivers
with
a
topography
sparse
gauging
stations
were
addressed
using
artificial
neural
networks
(ANN).
The
calibrated
models
precisely
predicted
stage‐discharge
sediment‐discharge
relationships,
demonstrating
effectiveness
machine
learning,
particularly
ANN‐based
modeling,
such
challenging
terrains.
model's
performance
was
assessed
coefficient
determination
(
R
2
),
root
mean
square
error
(RMSE),
(MSE).
During
calibration
phase,
model
exhibited
values
0.96
for
discharge
0.63
SSC,
accompanied
low
RMSE
5.29
cu
m
s
–1
0.61
g
SSC.
Subsequently,
prediction
maintained
its
robustness,
achieving
0.97
along
5.67
0.68
also
found
strong
agreement
between
water
flow
estimates
derived
traditional
methods,
ANN,
actual
measurements.
load,
both
varied
annually,
potentially
modifying
aquatic
habitats
through
deposition,
altering
communities.
These
findings
offer
crucial
insights
into
dynamics
studied
river,
providing
valuable
applications
sustainable
water‐resource
management
terrains
addressing
environmental
concerns
related
to
sedimentation,
quality,
ecosystem.
Frontiers of Urban and Rural Planning,
Год журнала:
2024,
Номер
2(1)
Опубликована: Июнь 11, 2024
Abstract
Floods
are
recurrent
global
catastrophes
causing
substantial
disruptions
to
human
life,
extensive
land
degradation,
and
economic
losses.
This
study
aims
identify
flood-triggering
watershed
features
employ
a
Multi-Criteria
Decision-Making
(MCDM)
approach
based
on
the
Analytical
Hierarchy
Process
(AHP)
model
delineate
flood-prone
zones.
Weights
for
various
flood-influencing
factors
(slope,
rainfall,
drainage
density,
land-use/land-cover,
geology,
elevation,
soil)
were
derived
using
7
×
AHP
decision
matrix,
reflecting
their
relative
importance.
A
Consistency
Ratio
(CR)
of
0.089
(within
acceptable
limits)
confirms
validity
assigned
weights.
The
analysis
identified
approximately
128.51
km
2
as
highly
vulnerable
flooding,
particularly
encompassing
entire
stretch
riverbanks
within
watershed.
Historically,
snow
avalanches
flash
floods
have
been
primary
water-related
disasters
in
region,
posing
significant
threats
critical
infrastructure.
In
this
context,
model-based
facilitates
proactive
identification
susceptible
areas,
thereby
promoting
improved
flood
risk
mitigation
response
strategies.
