Abstract.
In
the
eastern
region
of
North
American
Continental
Divide
in
upper
Colorado
Rockies,
this
study
demonstrates
that
enhancing
streamflow
predictability
from
May
to
July
Yellowstone
River
Basin
is
enabled.
This
improvement
achieved
by
employing
a
land
surface
hydrology
model
watershed,
coupled
with
an
updated
winter
precipitation
weather
forcing
dataset.
Utilizing
13
snowpack
telemetry
stations
US
Department
Agriculture
Basin,
paper
calculates
ratios
between
baseline
simulated
initial
application
and
observed
snowpack.
The
average
ratio
serves
as
constant
multiplier
for
existing
snowfall
applied
second
simulation.
As
result
simulation,
reaches
Nash-Sutcliffe
Efficiency
(NSE)
0.91,
contrast
simulation's
0.73
NSE
during
peak
periods.
also
explores
cold
hydrological
processes,
particularly
those
related
snowmelt-driven
streamflow.
addition
streamflow,
two
variables
such
soil
moisture
are
assessed
against
in-situ
satellite-based
observations
Basin.
comparisons
reveal
mainly
driven
springtime
snowmelt
diminishes
summer.
findings
confirmed
both
simulations
satellite-borne
observations.
Another
noteworthy
discovery
infiltration
properties
wetter
than
western
America,
resulting
amplified
side
despite
similar
levels
runoff
on
either
Rockies
United
States.
Environmental Research Communications,
Journal Year:
2024,
Volume and Issue:
6(10), P. 102003 - 102003
Published: Oct. 1, 2024
Abstract
Hydrometeorological
disasters,
including
floods
and
droughts,
have
intensified
in
both
frequency
severity
recent
years.
This
trend
underscores
the
critical
role
of
timely
monitoring,
accurate
forecasting,
effective
warning
systems
facilitating
proactive
responses.
Today’s
information
offer
a
vast
intricate
mesh
data,
encompassing
satellite
imagery,
meteorological
metrics,
predictive
modeling.
Easily
accessible
to
general
public,
these
cyberinfrastructures
simulate
potential
disaster
scenarios,
serving
as
invaluable
aids
decision-making
processes.
review
collates
key
literature
on
water-related
systems,
underscoring
transformative
impact
emerging
Internet
technologies.
These
advancements
promise
enhanced
flood
drought
timeliness
greater
preparedness
through
improved
management,
analysis,
visualization,
data
sharing.
Moreover,
aid
hydrometeorological
predictions,
foster
development
web-based
educational
platforms,
support
frameworks,
digital
twins,
metaverse
applications
contexts.
They
further
bolster
scientific
research
development,
enrich
climate
change
vulnerability
strengthen
associated
cyberinfrastructures.
article
delves
into
prospective
developments
realm
natural
pinpointing
primary
challenges
gaps
current
highlighting
intersections
with
future
artificial
intelligence
solutions.
npj Climate and Atmospheric Science,
Journal Year:
2025,
Volume and Issue:
8(1)
Published: May 3, 2025
Abstract
Sea
ice
and
snow
are
crucial
components
of
the
cryosphere
climate
system.
Both
sea
spring
in
Northern
Hemisphere
(NH)
have
been
decreasing
at
an
alarming
rate
a
changing
climate.
Changes
NH
linked
with
variety
weather
extremes
including
cold
spells,
heatwaves,
droughts
wildfires.
Understanding
these
linkages
will
benefit
predictions
extremes.
However,
existing
work
on
this
has
largely
fragmented
is
subject
to
large
uncertainties
physical
pathways
methodologies.
This
prevented
further
substantial
progress
attributing
change,
potentially
risk
loss
critical
window
for
effective
change
mitigation.
In
review,
we
synthesize
current
by
evaluating
observed
linkages,
their
pathways,
suggesting
ways
forward
future
research
efforts.
By
adopting
same
framework
both
snow,
highlight
combined
influence
cryospheric
feedback
We
suggest
that
from
improving
observational
networks,
addressing
causality
complexity
using
multiple
lines
evidence,
large-ensemble
approaches
artificial
intelligence,
achieving
synergy
between
different
methodologies/disciplines,
widening
context,
coordinated
international
collaboration.
EarthArXiv (California Digital Library),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Aug. 29, 2024
Drought
indicators,
which
are
quantitative
measurements
of
drought
severity
and
duration,
used
to
monitor
predict
the
risk
effects
drought,
particularly
in
relation
sustainability
agriculture
water
supplies.
This
research
uses
causal
inference
information
theory
discover
index,
is
most
efficient
indicator
for
agricultural
productivity
a
valuable
metric
estimating
predicting
crop
yield.
The
connection
between
precipitation,
maximum
air
temperature,
indices
corn
soybean
yield
ascertained
by
cross
convergent
mapping
(CCM),
while
transfer
them
determined
through
entropy
(TE).
conducted
on
rainfed
lands
Iowa,
considering
phenological
stages
crops.
Based
nonlinearity
analysis
using
S-map,
it
that
causality
could
not
be
carried
out
CCM
due
absence
data.
results
intriguing
as
they
uncover
both
precipitation
temperature
indices.
analysis,
with
strongest
relationship
production
SPEI-9m
SPI-6m
during
silking
period,
SPI-9m
doughing
period.
Therefore,
these
may
considered
effective
predictors
prediction
models.
study
highlights
need
periods
when
production,
differs
two
periods.
EarthArXiv (California Digital Library),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Aug. 2, 2024
Harmful
Algal
Blooms
(HABs)
can
originate
from
a
variety
of
reasons,
including
water
pollution
coming
agriculture,
effluent
treatment
plants,
sewage
system
leaks,
pH
and
light
levels,
the
consequences
climate
change.
In
recent
years,
HAB
events
have
become
serious
environmental
problem,
paralleling
population
growth,
agricultural
development,
increasing
air
temperatures,
declining
precipitation.
Hence,
it
is
crucial
to
identify
mechanisms
responsible
for
formation
harmful
algal
blooms
(HABs),
accurately
assess
their
short-
long-term
impacts,
quantify
variations
based
on
projections
developing
accurate
action
plans
effectively
managing
resources.
This
present
study
utilizes
empirical
dynamic
modeling
(EDM)
predict
chlorophyll-a
(chl-a)
concentration
Lake
Erie.
method
characterized
by
its
nonlinearity
nonparametric
nature.
EDM
has
significant
benefit
in
that
surpasses
constraints
conventional
statistical
through
use
data-driven
attractor
reconstruction.
Chl-a
critical
commonly
used
parameter
prediction
events.
Erie
an
inland
body
experiences
frequent
phenomena
as
result
location.
With
MAPE
4.31%,
RMSE
6.24,
coefficient
determination
0.98,
showed
exceptional
performance.
These
findings
suggest
underlying
dynamics
chl-a
changes
be
well
captured
model.
Abstract.
In
the
eastern
region
of
North
American
Continental
Divide
in
upper
Colorado
Rockies,
this
study
demonstrates
that
enhancing
streamflow
predictability
from
May
to
July
Yellowstone
River
Basin
is
enabled.
This
improvement
achieved
by
employing
a
land
surface
hydrology
model
watershed,
coupled
with
an
updated
winter
precipitation
weather
forcing
dataset.
Utilizing
13
snowpack
telemetry
stations
US
Department
Agriculture
Basin,
paper
calculates
ratios
between
baseline
simulated
initial
application
and
observed
snowpack.
The
average
ratio
serves
as
constant
multiplier
for
existing
snowfall
applied
second
simulation.
As
result
simulation,
reaches
Nash-Sutcliffe
Efficiency
(NSE)
0.91,
contrast
simulation's
0.73
NSE
during
peak
periods.
also
explores
cold
hydrological
processes,
particularly
those
related
snowmelt-driven
streamflow.
addition
streamflow,
two
variables
such
soil
moisture
are
assessed
against
in-situ
satellite-based
observations
Basin.
comparisons
reveal
mainly
driven
springtime
snowmelt
diminishes
summer.
findings
confirmed
both
simulations
satellite-borne
observations.
Another
noteworthy
discovery
infiltration
properties
wetter
than
western
America,
resulting
amplified
side
despite
similar
levels
runoff
on
either
Rockies
United
States.
EarthArXiv (California Digital Library),
Journal Year:
2023,
Volume and Issue:
unknown
Published: Aug. 18, 2023
Floods
and
droughts
significantly
affect
agricultural
activities
pose
a
threat
to
food
security
by
subsequently
reducing
production.
The
impact
of
flood
events
is
distributed
disproportionately
among
communities
based
on
their
socio-economic
fabric.
Understanding
climate-related
hazards
critical
for
planning
mitigation
measures
secure
vulnerable
communities.
This
research
presents
comprehensive
risk
evaluation
methodology
assessing
the
combined
drought
in
United
States.
By
integrating
social
vulnerability
levels
with
exposure
data,
study
identifies
most
individually,
aiming
provide
significant
insights
into
community
continental
U.S.
addresses
scientific
gap
through
nationwide
assessment,
evaluating
expected
annual
losses
hazards,
combining
losses.
analyses
were
conducted
adapting
datasets
methodologies
that
are
developed
federal
institutions
such
as
FEMA,
USACE,
USDA.
identified
30
socially
counties
assessed
flooding,
finding
Mendocino,
Sonoma,
Humboldt,
El
Dorado,
Fresno,
Kern
California
had
highest
losses,
Humboldt
(CA)
Montgomery
(TX)
having
risk.
