Water Resources Research,
Journal Year:
2025,
Volume and Issue:
61(3)
Published: March 1, 2025
Abstract
Continuous,
high‐resolution
data
for
characterizing
freshwater
habitat
conditions
can
support
successful
management
of
endangered
salmonids.
Uncrewed
aircraft
systems
(UAS)
make
acquiring
such
fine‐scale
along
river
channels
more
feasible,
but
workflows
quantifying
reach‐scale
salmon
habitats
are
lacking.
We
evaluated
the
potential
UAS‐based
mapping
hydraulic
using
spectrally
based
depth
retrieval
and
particle
image
velocimetry
(PIV)
by
comparing
these
methods
to
a
well‐established
flow
modeling
approach.
Our
results
indicated
that
estimates
water
depth,
depth‐averaged
velocity,
direction
derived
via
remote
sensing
techniques
were
comparable
in
good
agreement
with
field
measurements.
Predictions
spring‐run
Chinook
(
Oncorhynchus
tshawytscha
)
juvenile
rearing
produced
from
PIV
model
output
similar,
small
errors
relative
direct
observations.
Estimates
heterogeneity
on
kinetic
energy
gradients
generally
consistent
between
modeling,
measurements
larger.
sensitive
velocity
index
used
convert
surface
velocities
velocities.
Sun
glint
precluded
analysis
margins
some
images
large
degree
overlap
frames
was
thus
required
obtain
continuous
coverage
reach.
Similarly,
shadows
cast
riparian
vegetation
caused
gaps
bathymetric
maps.
Despite
limitations,
our
suggest
sites
sufficient
texture,
provide
detailed
information
at
reach
scale,
accuracies
traditional
multidimensional
modeling.
Engineering Applications of Computational Fluid Mechanics,
Journal Year:
2025,
Volume and Issue:
19(1)
Published: March 25, 2025
Efficient
and
accurate
flood
inundation
mapping
is
essential
for
risk
assessment,
emergency
response,
community
safety.
The
deep
learning-enabled
rapid
simulation
demonstrates
superior
computational
efficiency
compared
to
traditional
hydrodynamic
models.
However,
most
learning-based
models
currently
focus
on
predicting
the
maximum
water
depth
face
challenges
in
generalizing
rainfall
events
of
different
durations.
This
paper
proposes
a
fast
method
based
image
super-resolution,
utilizing
novel
DenseUNet
architecture
predict
velocity
temporal
events.
proposed
integrates
physical
catchment
characteristics
enhance
resolution
maps
generated
by
coarse-grid
model
using
deep-learning
model.
applied
rural-urban
Shenzhen
River
southern
China.
effectively
reproduces
test
against
fine-grid
model,
achieving
root
mean
square
errors
below
0.06
0.07
m/s,
respectively,
with
percentage
bias
within
±5%.
For
prediction,
exhibits
Nash-Sutcliffe
Pearson
correlation
coefficient
exceeding
0.99.
Similarly,
both
metrics
exceed
0.94.
outperforms
over
2800
times.
developed
this
study
regression
classification
performance
commonly
used
ResUNet
UNet
architectures.
robust
wide
range
super-resolution
scale
factors.
presents
an
efficient
surrogate
mapping,
providing
valuable
insights
applying
methods
simulation.
Water Resources Research,
Journal Year:
2024,
Volume and Issue:
60(7)
Published: June 26, 2024
Abstract
Catchment‐scale
hydrological
models
encountered
dichotomies
with
the
numerical
hydrodynamic
when
describing
surface
routing
process.
We
propose
a
new
modeling
framework,
so‐called
“Runoff‐On‐Grid”
approach,
for
embedding
distributed
process‐based
into
shallow
water
models,
as
an
alternative
to
traditional
Fully
Hydrodynamic
Approach
(also
known
Rain‐On‐Grid).
Antecedent
Soil
Moisture,
subsurface
dynamics,
and
other
topsoil
processes
are
implicitly
integrated
in
governing
equations
via
proposed
methodology.
The
resulting
hydrological‐hydrodynamic
coupling,
based
on
DREAM
model
Iber+
model,
enhances
capabilities
of
both
reference
models.
Through
introducing
non‐negligible
runoff
generation
sources,
Runoff‐On‐Grid
approach
extends
medium‐sized
vegetated
and/or
(semi)humid
catchments,
bypassing
limitations
widespread
losses'
empirical
formulations.
Employed
event‐based
analysis
within
High‐Performance
Computing
DREAM‐Iber
provides
efficient
reliable
reconstruction
November
2020
flood
that
occurred
Crotone
(Italy),
envisaging
consequences
similar
future
scenarios.
show
technique,
nested
emerging
environmental
technologies
robust
on‐site
data,
details
hazard
inducing
merging
physical
hydrology
advanced
hydrodynamics.
Hydrology Research,
Journal Year:
2024,
Volume and Issue:
55(5), P. 576 - 594
Published: May 1, 2024
ABSTRACT
Precise
long-term
runoff
prediction
holds
crucial
significance
in
water
resource
management.
Although
the
long
short-term
memory
(LSTM)
model
is
widely
adopted
for
prediction,
it
encounters
challenges
such
as
error
accumulation
and
low
computational
efficiency.
To
address
these
challenges,
we
utilized
a
novel
method
to
predict
based
on
Transformer
base
flow
separation
approach
(BS-Former)
Ningxia
section
of
Yellow
River
Basin.
evaluate
effectiveness
its
responsiveness
technique,
constructed
LSTM
artificial
neural
network
(ANN)
models
benchmarks
comparison.
The
results
show
that
outperforms
other
terms
predictive
performance
significantly
improves
model.
Specifically,
BS-Former
predicting
7
days
advance
comparable
BS-LSTM
BS-ANN
with
lead
times
4
2
days,
respectively.
In
general,
promising
tool
prediction.
Flooding
represents
the
most
pervasive
hydrological
disaster
globally.
This
study
conducts
a
comprehensive
analysis
of
characteristics
torrential
rain
and
flooding
within
three
major
urban
agglomerations
Yangtze
River
Basin
in
China—Chengdu-Chongqing,
Middle
Region
River,
Delta—over
period
1991
to
2020.
Utilizing
satellite-derived
microwave
SSM/I
data
CHIRPS
precipitation
datasets,
we
investigated
spatial
temporal
distribution
long-term
surface
flooding.
The
also
examines
impacts
urbanization
climate
change
on
these
patterns.
Our
findings
indicate
that
region
encountered
significant
flood
disasters
1998
2020,
each
exhibiting
distinct
dynamics
across
different
areas.
Through
monitoring,
flood-affected
area
detection,
level
assessments,
highlighted
varying
responses
among
agglomerations.
Notably,
consistently
demonstrated
higher
susceptibility
during
events,
whereas
Chengdu-Chongqing
agglomeration
faced
acute
challenges
particularly
outcomes
underscore
imperative
strategic
planning
effective
water
resource
management
mitigate
future
risks.
research
not
only
contributes
ongoing
efforts
prevention
control
but
enhances
understanding
remote
sensing
applications
analyzing
