Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(10), P. 1709 - 1709
Published: May 11, 2024
Over
the
past
decades,
cryosphere
has
changed
significantly
in
High
Mountain
Asia
(HMA),
leading
to
multiple
natural
hazards
such
as
rock–ice
avalanches,
glacier
collapse,
debris
flows,
landslides,
and
glacial
lake
outburst
floods
(GLOFs).
Monitoring
change
evaluating
its
hydrological
effects
are
essential
for
studying
climate
change,
cycle,
water
resource
management,
disaster
mitigation
prevention.
However,
knowledge
gaps,
data
uncertainties,
other
substantial
challenges
limit
comprehensive
research
climate–cryosphere–hydrology–hazard
systems.
To
address
this,
we
provide
an
up-to-date,
comprehensive,
multidisciplinary
review
of
remote
sensing
techniques
studies,
demonstrating
primary
methodologies
delineating
glaciers
measuring
geodetic
mass
balance
thickness,
motion
or
ice
velocity,
snow
extent
equivalent,
frozen
ground
soil,
ice,
glacier-related
hazards.
The
principal
results
achievements
summarized,
including
URL
links
available
products
related
platforms.
We
then
describe
main
monitoring
using
satellite-based
datasets.
Among
these
challenges,
most
significant
limitations
accurate
inversion
from
remotely
sensed
attributed
high
uncertainties
inconsistent
estimations
due
rough
terrain,
various
employed,
variability
across
same
regions
(e.g.,
depth
retrieval,
active
layer
thickness
ground),
poor-quality
optical
images
cloudy
weather.
paucity
observations
validations
with
few
long-term,
continuous
datasets
also
limits
utilization
studies
large-scale
models.
Lastly,
potential
breakthroughs
future
i.e.,
(1)
outlining
debris-covered
margins
explicitly
involving
areas
mountain
shadows,
(2)
developing
highly
retrieval
methods
by
establishing
a
microwave
emission
model
snowpack
mountainous
regions,
(3)
advancing
subsurface
complex
freeze–thaw
process
space,
(4)
filling
gaps
on
scattering
mechanisms
varying
surface
features
ice),
(5)
improving
cross-verifying
accuracy
combining
different
physical
models
machine
learning
assimilation
high-temporal-resolution
This
highlights
cryospheric
incorporating
spaceborne
diversified
techniques/methodologies
multi-spectral
thermal
bands,
SAR,
InSAR,
passive
microwave,
altimetry),
providing
valuable
reference
what
scientists
have
achieved
Third
Pole.
Remote Sensing,
Journal Year:
2023,
Volume and Issue:
15(2), P. 416 - 416
Published: Jan. 10, 2023
As
the
Third
Pole
of
Earth
and
Water
Tower
Asia,
Tibetan
Plateau
(TP)
nurtures
large
numbers
glacial
lakes,
which
are
sensitive
to
global
climate
change.
These
lakes
modulate
freshwater
ecosystem
in
region
but
concurrently
pose
severe
threats
valley
population
by
means
sudden
lake
outbursts
consequent
floods
(GLOFs).
The
lack
high-resolution
multi-temporal
inventory
TP
hampers
a
better
understanding
prediction
future
trend
risk
lakes.
Here,
we
created
using
30-year
record
42,833
satellite
images
(1990–2019),
discussed
their
characteristics
spatio-temporal
evolution
over
years.
Results
showed
that
number
area
had
increased
3285
258.82
km2
last
3
decades,
respectively.
We
noticed
different
regions
exhibited
varying
change
rates
size;
most
show
expansion
increase
while
some
decreasing
such
as
western
Pamir
eastern
Hindu
Kush.
mapping
uncertainty
is
about
17.5%,
lower
than
other
available
datasets,
thus
making
our
reliable
for
analysis
TP.
Our
data
publicly
published,
it
can
help
study
change–glacier–glacial
lake–GLOF
interactions
serve
input
various
hydro-climatic
studies.
Water Resources Research,
Journal Year:
2024,
Volume and Issue:
60(7)
Published: July 1, 2024
Abstract
GRACE
(Gravity
Recovery
and
Climate
Experiment)
has
been
widely
used
to
evaluate
terrestrial
water
storage
(TWS)
groundwater
(GWS).
However,
the
coarse‐resolution
of
data
limited
ability
identify
local
vulnerabilities
in
changes
associated
with
climatic
anthropogenic
stressors.
This
study
employs
high‐resolution
(1
km
2
)
generated
through
machine
learning
(ML)
based
statistical
downscaling
illuminate
TWS
GWS
dynamics
across
twenty
sub‐regions
Indus
Basin.
Monthly
anomalies
obtained
from
a
geographically
weighted
random
forest
(RF
gw
model
maintained
good
consistency
original
at
25
grid
scale.
The
downscaled
1
resolution
illustrate
spatial
heterogeneity
depletion
within
each
sub‐region.
Comparison
in‐situ
2,200
monitoring
wells
shows
that
significantly
improves
agreement
data,
evidenced
by
higher
Kling‐Gupta
Efficiency
(0.50–0.85)
correlation
coefficients
(0.60–0.95).
Hotspots
highest
decline
rate
between
2002
2023
were
Dehli
Doab
(−442,
−585
mm/year),
BIST
(−367,
−556
Rajasthan
(−242,
−381
BARI
(−188,
−333
mm/year).
Based
on
general
additive
model,
47%–83%
was
stressors
mainly
due
increasing
trends
crop
sown
area,
consumption,
human
settlements.
lower
(i.e.,
−25
−75
mm/year)
upstream
(e.g.,
Yogo,
Gilgit,
Khurmong,
Kabul)
where
factors
(downward
shortwave
radiations,
air
temperature,
sea
surface
temperature)
explained
72%–91%
TWS/GWS
changes.
relative
influences
varied
sub‐regions,
underscoring
complex
interplay
natural‐human
activities
basin.
These
findings
inform
place‐based
resource
management
Basin
advancing
understanding
vulnerabilities.
Journal of Glaciology,
Journal Year:
2022,
Volume and Issue:
69(275), P. 500 - 512
Published: Sept. 16, 2022
Abstract
High
Mountain
Asia
(HMA)
glaciers
are
critical
water
reserves
for
montane
regions,
which
readily
influenced
by
climate
change.
The
glacier
mass
balance
during
2000–2021
over
HMA
was
estimated
comparing
the
elevations
from
ICESat-2
and
NASADEM.
Radar
penetration
depth
could
be
one
of
intrinsic
error
sources
in
estimating
using
Therefore,
we
doubled
elevation
differences
between
X-band
Shuttle
Topography
Missions
(SRTMs)
NASADEM
to
estimate
potential
error.
spatial
characteristics
altitude-dependent
can
detected
most
sub-regions
HMA.
Relatively
deep
penetrations
Himalaya
(2.3–3.7
m)
Hissar
Alay
(4.3
regions
small
south-eastern
(1.0
were
observed.
region
experienced
a
significant
loss
at
rate
−0.18
±
0.12
m
w.e.
−1
,
Hengduan
Shan
exhibited
highest
−0.62
0.10
West
Kun
Lun
substantial
gain
0.23
0.13
Karakoram
showed
more
or
less
balance.
Our
results
agreement
with
previous
studies
that
assessed
different
methods.
Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(6), P. 956 - 956
Published: March 8, 2024
Accurately
simulating
glacier
mass
balance
(GMB)
data
is
crucial
for
assessing
the
impacts
of
climate
change
on
dynamics.
Since
physical
models
often
face
challenges
in
comprehensively
accounting
factors
influencing
glacial
melt
and
uncertainties
inputs,
machine
learning
(ML)
offers
a
viable
alternative
due
to
its
robust
flexibility
nonlinear
fitting
capability.
However,
effectiveness
ML
modeling
GMB
across
diverse
types
within
High
Mountain
Asia
has
not
yet
been
thoroughly
explored.
This
study
addresses
this
research
gap
by
evaluating
used
simulation
annual
glacier-wide
data,
with
specific
focus
comparing
maritime
glaciers
Niyang
River
basin
continental
Manas
basin.
For
purpose,
meteorological
predictive
derived
from
monthly
ERA5-Land
datasets,
topographical
obtained
Randolph
Glacier
Inventory,
along
target
rooted
geodetic
observations,
were
employed
drive
four
selective
models:
random
forest
model,
gradient
boosting
decision
tree
(GBDT)
deep
neural
network
ordinary
least-square
linear
regression
model.
The
results
highlighted
that
generally
exhibit
superior
performance
compared
ones.
Moreover,
among
models,
GBDT
model
was
found
consistently
coefficient
determination
(R2)
values
0.72
0.67
root
mean
squared
error
(RMSE)
0.21
m
w.e.
0.30
river
basins,
respectively.
Furthermore,
reveals
climatic
differentially
influence
simulations
glaciers,
providing
key
insights
into
dynamics
response
change.
In
summary,
ML,
particularly
demonstrates
significant
potential
simulation.
application
can
enhance
accuracy
modeling,
promising
approach
assess
Earth s Future,
Journal Year:
2024,
Volume and Issue:
12(4)
Published: April 1, 2024
Abstract
The
water
resources
of
the
Third
Pole
(TP),
highly
sensitive
to
climate
change
and
glacier
melting,
significantly
impact
food
security
millions
in
Asia.
However,
projecting
future
spatial‐temporal
runoff
changes
for
TP's
mountainous
basins
remains
a
formidable
challenge.
Here,
we've
leveraged
long
short‐term
memory
model
(LSTM)
craft
grid‐scale
artificial
intelligence
(AI)
named
LSTM‐grid.
This
has
enabled
production
hydrological
projections
seven
major
river
TP.
LSTM‐grid
integrates
monthly
precipitation,
air
temperature,
total
mass
(total_GMC)
data
at
0.25‐degree
grid.
Training
employed
gridded
historical
evapotranspiration
sets
generated
by
an
observation‐constrained
cryosphere‐hydrology
headwaters
TP
during
2000–2017.
Our
results
demonstrate
LSTM
grid's
effectiveness
usefulness,
exhibiting
Nash‐Sutcliffe
Efficiency
coefficient
exceeding
0.92
verification
periods
(2013–2017).
Moreover,
monsoon
region
exhibited
higher
rate
increase
compared
those
westerlies
region.
Intra‐annual
indicated
notable
increases
spring
runoff,
especially
where
meltwater
contributes
runoff.
Additionally,
aptly
captures
before
after
turning
points
highlighting
growing
influence
precipitation
on
reaching
maximum
total_GMC.
Therefore,
offers
fresh
perspective
understanding
spatiotemporal
distribution
high‐mountain
glacial
regions
tapping
into
AI's
potential
drive
scientific
discovery
provide
reliable
data.