-DOCSTART- -X- O O f87ba000ebbffd8dfc076b693bce3397

Particularly O
, O
air B-climate-nature
– I-climate-nature
surface I-climate-nature
fluxes I-climate-nature
of O
methane B-climate-greenhouse-gases
and O
carbon B-climate-greenhouse-gases
dioxide I-climate-greenhouse-gases
are O
of O
interest O
as O
recent O
observations O
suggest O
that O
the O
vast O
stores O
of O
soil B-climate-nature
carbon I-climate-nature
found O
in O
the O
Arctic O
tundra B-climate-nature
are O
becoming O
more O
available O
to O
release O
to O
the O
atmosphere B-climate-nature
in O
the O
form O
of O
these O
greenhouse O
gases O
. O

We O
present O
here O
a O
two O
- O
year O
micrometeorological O
data O
set O
of O
methane B-climate-greenhouse-gases
and O
carbon B-climate-greenhouse-gases
dioxide I-climate-greenhouse-gases
fluxes O
, O
along O
with O
supporting O
soil B-climate-nature
pore I-climate-nature
gas I-climate-nature
profiles O
, O
that O
provide O
near O
- O
continuous O
data O
throughout O
the O
active O
summer O
and O
cold O
winter O
seasons O
. O

Net O
emission O
of O
methane B-climate-greenhouse-gases
and O
carbon B-climate-greenhouse-gases
dioxide I-climate-greenhouse-gases
in O
one O
of O
the O
study O
years O
totalled O
3.7 O
and O
89 O
g O
C O
m O
−2 O
a O
−1 O
respectively O
, O
with O
cold O
- O
season O
methane B-climate-greenhouse-gases
emission O
representing O
54 O
% O
of O
the O
annual O
total O
. O

In O
the O
other O
year O
, O
net O
emission O
totals O
of O
methane B-climate-greenhouse-gases
and O
carbon B-climate-greenhouse-gases
dioxide I-climate-greenhouse-gases
were O
4.9 O
and O
485 O
g O
C O
m O
−2 O
a O
−1 O
respectively O
, O
with O
cold O
- O
season O
methane B-climate-greenhouse-gases
emission O
here O
representing O
82 O
% O
of O
the O
annual O
total O
– O
a O
larger O
proportion O
than O
has O
been O
previously O
reported O
in O
the O
Arctic O
tundra B-climate-nature
. O

-DOCSTART- -X- O O 2760046c24e54b018c304556bbdb7057

Within O
the O
literature O
, O
concerns O
have O
been O
raised O
that O
centralised O
urban B-climate-assets
water I-climate-assets
systems I-climate-assets
are O
maladapted O
to O
challenges O
associated O
with O
climate O
change O
, O
population B-climate-problem-origins
growth I-climate-problem-origins
and O
other O
socio O
- O
economic O
and O
environmental O
strains O
. O

This O
paper O
provides O
a O
critical O
assessment O
of O
the O
discourse O
that O
surrounds O
emerging O
approaches O
to O
urban B-climate-mitigations
water I-climate-mitigations
management I-climate-mitigations
and O
infrastructure B-climate-assets
provision O
. O

-DOCSTART- -X- O O 41d1e197a02e34c72fa789e401bb03ef

AbstractThis O
paper O
compares O
three O
existing O
Palmer B-climate-properties
Drought I-climate-properties
Severity I-climate-properties
Index I-climate-properties
( O
PDSI B-climate-properties
) O
formulations O
for O
simulating O
summer O
moisture B-climate-properties
variability I-climate-properties
in O
western O
Canada O
and O
a O
preliminary O
analysis O
of O
climate O
change O
impacts O
on O
summer O
moisture B-climate-properties
anomalies O
. O

In O
all O
formulations O
, O
potential B-climate-properties
evapotranspiration I-climate-properties
was O
parameterized O
by O
the O
Penman B-climate-models
– I-climate-models
Monteith I-climate-models
method I-climate-models
instead O
of O
the O
traditional O
Thornthwaite B-climate-models
method I-climate-models
. O

-DOCSTART- -X- O O 5265c1e7188eed477bb316eca09e5f3f

An O
important O
result O
is O
that O
the O
possibility O
of O
a O
climate B-climate-impacts
catastrophe I-climate-impacts
is O
a O
major O
argument O
for O
greenhouse B-climate-mitigations
gas I-climate-mitigations
abatement I-climate-mitigations
even O
in O
absence O
of O
continuous B-climate-impacts
damage I-climate-impacts
. O

-DOCSTART- -X- O O cd2b3ae0c9c4c7ca08d5c899dda238f7

We O
focus O
on O
the O
impacts O
of O
irrigation B-climate-mitigations
on O
the O
urban B-climate-nature
water I-climate-nature
cycle I-climate-nature
and O
atmospheric B-climate-nature
feedback I-climate-nature
in O
arid B-climate-nature
and O
semi B-climate-nature
- I-climate-nature
arid I-climate-nature
cities O
. O

Our O
objective O
is O
to O
build O
upon O
previous O
work O
, O
focusing O
on O
improving O
the O
representation O
of O
irrigated B-climate-mitigations
urban B-climate-nature
vegetation I-climate-nature
in O
the O
numerical O
weather O
prediction O
models O
which O
are O
now O
standard O
tools O
to O
study O
urban B-climate-nature
- O
atmosphere B-climate-nature
interactions O
. O

Our O
results O
demonstrate O
a O
significant O
sensitivity O
of O
WRF B-climate-models
- I-climate-models
UCM I-climate-models
simulated O
surface B-climate-nature
turbulent I-climate-nature
fluxes I-climate-nature
to O
the O
incorporation O
of O
irrigation B-climate-mitigations
. O

The O
evaluation O
of O
the O
model O
performance O
via O
comparison O
against O
CIMIS B-climate-datasets
based O
reference O
ET B-climate-nature
indicates O
that O
WRF B-climate-models
- I-climate-models
UCM I-climate-models
, O
after O
adding O
irrigation B-climate-mitigations
, O
performs O
reasonably O
during O
the O
course O
of O
the O
month O
, O
tracking O
day O
to O
day O
variability O
of O
ET B-climate-nature
with O
notable O
fidelity O
. O

soil B-climate-hazards
moisture I-climate-hazards
depletion I-climate-hazards
leads O
to O
reduced O
latent O
heating O
and O
cooling O
effects O
of O
urban B-climate-nature
vegetation I-climate-nature
. O

Analysis O
of O
these O
results O
indicates O
the O
importance O
of O
accurate O
representation O
of O
urban B-climate-mitigations
irrigation I-climate-mitigations
in O
water O
scarce O
regions O
such O
as O
Los O
Angeles O
metropolitan O
area O
. O

-DOCSTART- -X- O O 39353585aa410c09ecb710940462cbef

Nutrient B-climate-assets
management O
planning O
is O
necessary O
for O
many O
livestock B-climate-assets
producers O
. O

Manure B-climate-problem-origins
nutrients B-climate-assets
( O
e.g. O
, O
N O
, O
P O
, O
and O
K O
) O
equal O
the O
amounts O
in O
feed B-climate-assets
consumed O
minus O
the O
amounts O
in O
products O
produced O
( O
e.g. O
, O
milk B-climate-assets
, O
eggs B-climate-assets
, O
meat B-climate-assets
, O
or O
offspring B-climate-assets
) O
whereas O
, O
the O
amount O
of O
manure B-climate-problem-origins
dry O
matter O
is O
an O
inverse O
function O
of O
the O
ration O
digestibility O
. O

The O
percentage O
compositions O
of O
nutrients B-climate-assets
in O
manure B-climate-problem-origins
recovered O
( O
accounting O
for O
nutrient B-climate-assets
losses O
as O
well O
as O
uncollected O
portions O
) O
are O
much O
more O
difficult O
to O
predict O
than O
total O
amounts O
that O
should O
be O
collected O
because O
anaerobic B-climate-mitigations
digestion I-climate-mitigations
of O
carbon O
- O
containing O
compounds O
that O
was O
initiated O
in O
the O
large O
intestines B-climate-problem-origins
of I-climate-problem-origins
animals I-climate-problem-origins
continues O
after O
excretion O
or O
the O
fermentation O
shifts O
to O
aerobic O
. O

-DOCSTART- -X- O O cc560f5c553e1b60e054abe1578227ff

Non O
- O
identifiable O
RHH O
emergency O
department O
data O
and O
climate O
data O
from O
the O
Australian B-climate-organizations
Bureau I-climate-organizations
of I-climate-organizations
Meteorology I-climate-organizations
were O
obtained O
for O
the O
period O
2003 O
- O
2010 O
. O

-DOCSTART- -X- O O c4296764da322a0b2b3dbbbbb4cf0c5f

The O
analysis O
is O
carried O
out O
using O
the O
E3ME B-climate-models
macro O
- O
econometric O
model O
, O
which O
provides O
information O
on O
sectoral O
impacts O
, O
together O
with O
the O
Warwick B-climate-models
Labour I-climate-models
Market I-climate-models
Extension I-climate-models
model O
for O
occupational O
analysis O
. O

-DOCSTART- -X- O O c6dcfd09a4d1dd9d6f1026b75a0b8ecf

multi O
- O
centennial O
variability O
of O
open O
ocean B-climate-nature
deep B-climate-nature
convection I-climate-nature
in O
the O
Atlantic O
sector O
of O
the O
Southern O
Ocean O
impacts O
the O
strength O
of O
the O
Atlantic B-climate-nature
Meridional I-climate-nature
Overturning I-climate-nature
Circulation I-climate-nature
( O
AMOC B-climate-nature
) O
in O
the O
Kiel B-climate-models
Climate I-climate-models
Model I-climate-models
. O

The O
northward O
extent O
of O
Antarctic B-climate-nature
Bottom I-climate-nature
Water I-climate-nature
( O
AABW B-climate-nature
) O
strongly O
depends O
on O
the O
state O
of O
Weddell O
Sea O
deep B-climate-nature
convection I-climate-nature
. O

-DOCSTART- -X- O O 5f205022e0de68ef4ac90326c705a0ff

Simulations O
were O
conducted O
using O
the O
Weather B-climate-models
Research I-climate-models
and I-climate-models
Forecasting I-climate-models
model O
using O
the O
pseudo O
global O
warming O
method O
. O

-DOCSTART- -X- O O d1b647e644b72e73320e0c53e1613c17

spatial O
resolutions O
( O
CMIP5 B-climate-models
GCM O
and O
EURO B-climate-models
- I-climate-models
CORDEX I-climate-models
regional O
climate O
models O

-DOCSTART- -X- O O 9b83a413f9801d5a3beea9245e0ff2b7

The O
Multivariate O
Probit O
model O
was O
preferred O
as O
it O
takes O
into O
account O
the O
inter O
- O
relationships O
of O
the O
technologies O
as O
well O
as O
heterogeneity O
of O
the O
smallholder O
farmers B-climate-assets
for O
more O
robust O
estimates O
. O

-DOCSTART- -X- O O 796915b6f1ded244f22d40e48746dc8f

Fires B-climate-hazards
are O
an O
important O
component O
in O
Earth O
system O
models O
( O
ESMs O
) O
, O
they O
impact O
vegetation B-climate-properties
carbon I-climate-properties
storage I-climate-properties
, O
vegetation B-climate-nature
distribution O
, O
atmospheric O
composition O
and O
cloud B-climate-nature
formation O
. O

The O
representation O
of O
fires B-climate-hazards
in O
ESMs O
contributing O
to O
CMIP B-climate-models
phase I-climate-models
5 I-climate-models
was O
still O
very O
simplified O
. O

-DOCSTART- -X- O O 2520bf6bd55457f37edfc2e0234aff97

A O
major O
part O
of O
the O
analysis O
is O
based O
on O
aircraft O
measurements O
from O
the O
campaign B-climate-observations
STABLE I-climate-observations
, O
which O
was O
carried O
out O
over O
the O
pack B-climate-nature
ice I-climate-nature
in O
the O
northern O
Fram O
Strait O
in O
March O
2013 O
. O

-DOCSTART- -X- O O 62607400b7b50dec7253657cbd8419cf

DryFlux B-climate-models
explicitly O
accounts O
for O
intra O
- O
annual O
variation O
in O
water B-climate-assets
availability I-climate-assets
, O
and O
accurately O
predicts O
interannual O
and O
seasonal O
variability O
in O
carbon O
uptake O
. O

-DOCSTART- -X- O O 40371c0f311f8cafba444400245525b7

Global O
emission O
estimates O
based O
on O
new O
atmospheric O
observations O
are O
presented O
for O
the O
acylic B-climate-properties
high B-climate-properties
molecular I-climate-properties
weight I-climate-properties
perfluorocarbons B-climate-greenhouse-gases
( O
PFCs B-climate-greenhouse-gases
): O
decafluorobutane B-climate-greenhouse-gases
( O
C4F10 B-climate-greenhouse-gases
) O
, O
dodecafluoropentane B-climate-greenhouse-gases
( O
C B-climate-greenhouse-gases
5F12 I-climate-greenhouse-gases
) O
, O
tetradecafluorohexane B-climate-greenhouse-gases
( O
C6F14 B-climate-greenhouse-gases
) O
, O
hexadecafluoroheptane B-climate-greenhouse-gases
( O
C B-climate-greenhouse-gases
7F16 I-climate-greenhouse-gases
) O
and O
octadecafluorooctane B-climate-greenhouse-gases
( O
C B-climate-greenhouse-gases
8F18 I-climate-greenhouse-gases
) O
. O

We O
used O
random O
forests O
( O
RF O
) O
and O
logistic O
regression O
( O
GLM O
) O
to O
model O
current O
and O
potential O
future O
distributions O
for O
2050 O
. O

-DOCSTART- -X- O O 618857223ba9983c21debc305967b9af

Recent O
climate B-climate-mitigations
regulations I-climate-mitigations
include O
increased O
utilization O
of O
natural B-climate-mitigations
gas I-climate-mitigations
- I-climate-mitigations
fired I-climate-mitigations
combined I-climate-mitigations
cycle I-climate-mitigations
( O
NGCC B-climate-mitigations
) O
generators O
as O
a O
means O
for O
offsetting O
coal B-climate-problem-origins
generation O
to O
reduce O
carbon O
emissions O
. O

Both O
low O
natural O
gas O
prices O
and O
the O
Clean B-climate-mitigations
Air I-climate-mitigations
Interstate I-climate-mitigations
Rule I-climate-mitigations
( O
CAIR B-climate-mitigations
) O
drive O
increases O
in O
utilization O
. O

-DOCSTART- -X- O O 81b3b8707268d2acfbce324fc937d437

Reynolds O
- O
Averaged O
Navier O
- O
Stokes O
and O
energy O
equations O
have O
been O
considered O
, O
where O
ANSYS B-climate-models
FLUENT I-climate-models
code O
has O
been O
employed O
to O
solve O
the O
resulting O
mathematical O
model O
. O

-DOCSTART- -X- O O bfe31a2677c6bd4527ca9fa248ba3bff

It O
uses O
the O
GLobal B-climate-models
BIOsphere I-climate-models
Management I-climate-models
Model I-climate-models
, O
a O
partial O
equilibrium O
model O
of O
the O
global O
agricultural B-climate-assets
and O
forestry B-climate-assets
sectors O
. O

-DOCSTART- -X- O O dbba14715dd4bce458b65b2142c584af

model O
algorithms O
for O
explanation O
of O
the O
relationship O
between O
the O
emergence O
of O
biological B-climate-organisms
species I-climate-organisms
and O
habitat B-climate-organisms
environments O
were O
reviewed O
to O
construct O
the O
environmental O
data O
suitable O
for O
the O
six O
models(GLM O
, O
GAM O
, O
RF O
, O
MaxEnt O
, O
ANN O
, O
and O
SVM O
) O
. O

-DOCSTART- -X- O O 10b50e3df8fe5848881f09b55f3333a7

This O
study O
developed O
a O
dynamic B-climate-models
copula I-climate-models
- I-climate-models
based I-climate-models
simulation I-climate-models
model I-climate-models
( O
DCSM B-climate-models
) O
for O
single O
- O
site O
seasonal O
rainfall B-climate-nature
generation O
, O
observed O
at O
Beijing B-climate-observations
Station I-climate-observations
in O
Haihe O
River O
basin O
, O
China O
. O

The O
model O
entailed O
four O
phases O
: O
( O
1 O
) O
nonstationary O
modelling O
of O
the O
margins O
by O
the O
Generalized B-climate-models
Additive I-climate-models
Models I-climate-models
for I-climate-models
Location I-climate-models
, I-climate-models
Scale I-climate-models
and I-climate-models
Shape I-climate-models
( O
GAMLSS B-climate-models
) O
; O
( O
2 O
) O
dynamical O
copula O
method O
to O
describe O
the O
nonstationary O
temporal O
dependence O
structure O
of O
rainfall B-climate-nature
observed O
at O
adjacent O
two O
seasons O
; O
( O
3 O
) O
a O
dynamic O
copula O
- O
based O
conditional O
quantile O
function O
to O
generate O
simulated O
series O
; O
( O
4 O
) O
performance O
assessment O
of O
the O
simulated O
series O
by O
the O
proposed O
DCSM B-climate-models
model O
with O
the O
preservation O
of O
basic O
statistics O
of O
each O
sequence O
and O
simulation O
accuracy O
at O
each O
data O
point O
. O

-DOCSTART- -X- O O d0a4f7389c82fd46522218fcc5175d37

Reference B-climate-properties
evapotranspiration I-climate-properties
( O
ETref B-climate-properties
) O
is O
an O
important O
study O
object O
for O
hydrological B-climate-nature
cycle I-climate-nature
processes O
in O
the O
context O
of O
drought B-climate-hazards
- O
flood B-climate-hazards
risks O
of O
the O
Huai O
River O
Basin O
( O
HRB O
) O
. O

In O
this O
study O
, O
the O
FAO-56 B-climate-models
Penman I-climate-models
– I-climate-models
Monteith I-climate-models
( O
PM B-climate-models
) O
model O
was O
employed O
to O
calculate O
seasonal O
and O
annual O
ETref B-climate-properties
based O
on O
137 O
meteorological O
station O
data O
points O
in O
HRB O
from O
1961 O
to O
2014 O
. O

-DOCSTART- -X- O O 720219ed4db5f7322fc759eb93a312df

The O
effective B-climate-properties
density I-climate-properties
( O
ρeff B-climate-properties
) O
of O
refractory O
black B-climate-greenhouse-gases
carbon I-climate-greenhouse-gases
( O
rBC B-climate-greenhouse-gases
) O
is O
a O
key O
parameter O
relevant O
to O
their O
mixing O
state O
that O
imposes O
great O
uncertainty O
when O
evaluating O
the O
direct O
radiation B-climate-properties
forcing I-climate-properties
effect I-climate-properties
. O

In O
this O
study O
, O
a O
novel O
tandem O
DMA B-climate-observations
- I-climate-observations
CPMA I-climate-observations
- I-climate-observations
SP2 I-climate-observations
system O
was O
used O
to O
investigate O
the O
relationship O
between O
the O
effective B-climate-properties
density I-climate-properties
( O
ρeff B-climate-properties
) O
and O
the O
mixing O
state O
of O
rBC B-climate-greenhouse-gases
particles O
during O
the O
winter O
of O
2018 O
in O
the O
Beijing O
mega O
- O
city O
. O

During O
the O
experiment O
, O
aerosols B-climate-nature
with O
a O
known O
mobility B-climate-properties
diameter I-climate-properties
( O
Dmob B-climate-properties
) O
and O
known O
ρeff B-climate-properties
values O
( O
0.8 O
, O
1.0 O
, O
1.2 O
, O
1.4 O
, O
1.6 O
, O
and O
1.8 O
g O
/ O
cm3 O
) O
were O
selected O
and O
measured O
by O
the O
SP2 B-climate-observations
to O
obtain O
their O
corresponding O
mixing O
states O
. O

-DOCSTART- -X- O O 4516817256e64da95dd92cf0fe0be66f

play O
important O
roles O
in O
ecosystem O
due O
to O
their O
extensive O
geographical O
coverage O
on O
the O
Qinghai O
- O
Tibetan O
Plateau O
( O
QTP O
) O
. O

The O
key O
environmental O
factors O
which O
determined O
Bryophytes B-climate-organisms
’s O
habitats B-climate-organisms
and O
range O
shifts O
were O
also O
examined O
. O

-DOCSTART- -X- O O e286f3e2a40021cf3d440580a95573af

Agreeing O
with O
geo O
- O
sciences O
I O
do O
not O
understand O
natural O
disasters O
( O
such O
as O
hurricanes B-climate-hazards
, O
earthquakes B-climate-hazards
or O
storm B-climate-hazards
surges I-climate-hazards
) O
as O
single O
events O
, O
like O
journalism O
mainly O
covers O
them O
. O

-DOCSTART- -X- O O 4f06b511e3b4a73b5ac2ba66171df7c7

Abstract O
Study O
region O
The O
Chancay O
- O
Huaral O
( O
CH O
) O
coastal B-climate-nature
river I-climate-nature
basin I-climate-nature
in O
the O
Lima O
Region O
, O
Peru O
, O
between O
the O
Pacific O
Ocean O
and O
the O
Andean O
Cordillera O
. O

Therefore O
, O
bias O
- O
corrected O
time O
series O
of O
temperature B-climate-properties
and O
precipitation B-climate-nature
from O
31 O
General O
Circulation O
Models O
( O
GCMs O
) O
with O
the O
emission B-climate-problem-origins
scenarios O
RCP4.5 B-climate-datasets
and O
RCP8.5 B-climate-datasets
( O
Representative O
Concentration O
Pathways O
) O
were O
used O
as O
inputs O
for O
the O
Water B-climate-models
Evaluation I-climate-models
and I-climate-models
Planning I-climate-models
System I-climate-models
model O
( O
WEAP B-climate-models
) O
. O

Bias O
correction O
and O
downscaling O
of O
the O
GCMs O
were O
implemented O
using O
a O
quantile O
mapping O
method O
. O

New O
hydrological O
insights O
for O
the O
region O
On O
average O
, O
GCMs O
indicate O
increased O
annual B-climate-properties
mean I-climate-properties
temperatures I-climate-properties
by O
3.1 O
° O
C O
( O
RCP4.5 B-climate-datasets
) O
and O
by O
4.3 O
° O
C O
( O
RCP8.5 B-climate-datasets
) O
and O
precipitation B-climate-properties
sum I-climate-properties
by O
20 O
% O
( O
RCP4.5 B-climate-datasets
) O
and O
by O
28 O
% O
( O
RCP8.5 B-climate-datasets
) O
. O

-DOCSTART- -X- O O 28767921addeb63241b498b7080d1807

The O
kinetic O
formalism O
developed O
includes O
the O
coupling O
of O
the O
rate O
equations O
of O
each O
of O
the O
different O
species O
considered O
( O
electrons O
, O
ions O
, O
atoms O
and O
molecules O
) O
with O
the O
Boltzmann O
transport O
equation O
so O
that O
, O
in O
this O
way O
, O
all O
the O
kinetics O
is O
self O
- O
consistent O
, O
although O
, O
in O
the O
present O
approach O
, O
the O
electrodynamics O
( O
no O
Poisson O
equation O
is O
considered O
) O
is O
not O
coupled O
. O

The O
chemical O
model O
set O
up O
for O
air O
plasmas B-climate-nature
includes O
more O
than O
75 O
species O
and O
almost O
500 O
reactions O
. O

This O
study O
also O
considers O
the O
vibrational O
kinetics O
of O
N2 O
and O
CO2 B-climate-greenhouse-gases
and O
explicitly O
evaluates O
the O
optical B-climate-properties
emissions I-climate-properties
associated O
with O
a O
number O
of O
excited O
states O
of O
N2 O
, O
O O
2 O
, O
O O
in O
the O
visible O
, O
CO2 B-climate-greenhouse-gases
in O
the O
infrared O
( O
IR O
) O
and O
ultraviolet O
( O
UV O
) O
emissions O
of O
sprite O
streamers O
due O
to O
the O
N2 O
Lyman B-climate-observations
– I-climate-observations
Birge I-climate-observations
– I-climate-observations
Hopfield I-climate-observations
( O
LBH B-climate-observations
) O
and O
the O
NO O
- O
γ O
band B-climate-observations
systems I-climate-observations
. O

All O
the O
calculations O
are O
conducted O
for O
midnight O
conditions O
in O
mid O
- O
latitude B-climate-properties
regions O
( O
+38 O
◦ O
N O
) O
and O
0 O
◦ O
longitude B-climate-properties
, O
using O
as O
initial O
values O
for O
the O
neutral O
species O
those O
provided O
by O
the O
latest O
version O
of O
the O
Whole B-climate-models
Atmosphere I-climate-models
Community I-climate-models
Climate I-climate-models
Model I-climate-models
( O
WACCM B-climate-models
) O
. O

-DOCSTART- -X- O O 7fb6a223b2a349c2300a6e3e959b4b67

The O
Biosphere B-climate-models
Energy I-climate-models
- I-climate-models
Transfer I-climate-models
and I-climate-models
Hydrology I-climate-models
model O
( O
BETHY B-climate-models
) O
is O
used O
to O
simulate O
global O
photosynthesis B-climate-nature
, O
and O
plant B-climate-organisms
and O
soil B-climate-nature
respiration I-climate-nature
embedded O
within O
the O
full O
energy O
and O
water B-climate-nature
balance I-climate-nature
, O
based O
on O
13 O
years O
of O
meteorological O
data O
. O

-DOCSTART- -X- O O b8e14d567da2c96c1d9126b6418075ef

Fires B-climate-hazards
in O
the O
boreal B-climate-nature
forests I-climate-nature
of O
North O
America O
are O
generally O
severe O
, O
killing B-climate-impacts
the O
majority O
of O
trees B-climate-organisms
and O
initiating O
succession O
that O
may O
last O
over O
a O
century O
. O

Burn B-climate-impacts
area I-climate-impacts
across O
Alaska O
and O
Canada O
has O
increased O
in O
the O
last O
few O
decades O
, O
and O
is O
projected O
to O
be O
substantially O
higher O
by O
the O
end O
of O
the O
21st O
century O
due O
to O
a O
warmer O
climate O
with O
longer O
growing O
seasons O
. O

Further O
work O
is O
needed O
to O
integrate O
all O
the O
climate O
drivers O
from O
boreal O
forest B-climate-hazards
fires I-climate-hazards
, O
including O
aerosols B-climate-nature
and O
greenhouse O
gasses O
, O
and O
to O
incorporate O
Eurasia O
. O

-DOCSTART- -X- O O d5ed4d64bd716ca943bfbee19177a4ef

Here O
we O
assess O
the O
analytic O
utility O
of O
the O
well O
- O
known O
IPAT B-climate-models
identity I-climate-models
, O
the O
newly O
developed O
ImPACT B-climate-models
identity I-climate-models
, O
and O
their O
stochastic O
cousin O
, O
the O
STIRPAT B-climate-models
model O
. O

We O
then O
refine O
the O
STIRPAT B-climate-models
model O
by O
developing O
the O
concept O
of O
ecological B-climate-properties
elasticity I-climate-properties
( O
EE B-climate-properties
) O
. O

To O
illustrate O
the O
application O
of O
STIRPAT B-climate-models
and O
EE B-climate-properties
, O
we O
compute O
the O
ecological B-climate-properties
elasticities I-climate-properties
of O
population B-climate-properties
, O
affluence B-climate-properties
and O
other O
factors O
for O
cross B-climate-properties
- I-climate-properties
national I-climate-properties
emissions I-climate-properties
of O
carbon B-climate-greenhouse-gases
dioxide I-climate-greenhouse-gases
( O
CO2 B-climate-greenhouse-gases
) O
from O
fossil B-climate-problem-origins
fuel I-climate-problem-origins
combustion I-climate-problem-origins
and O
for O
the O
energy O
footprint O
, O
a O
composite O
measure O
comprising O
impacts O
from O
fossil B-climate-problem-origins
fuel I-climate-problem-origins
combustion I-climate-problem-origins
, O
fuel B-climate-problem-origins
wood I-climate-problem-origins
, O
hydropower B-climate-mitigations
and O
nuclear B-climate-mitigations
power I-climate-mitigations
. O

-DOCSTART- -X- O Ofc04919547675a121cb0acf75f63c2b5

Measured O
data O
on O
space B-climate-problem-origins
heating I-climate-problem-origins
is O
not O
available O
on O
country O
scale O
, O
nor O
are O
highly O
granular O
estimates O
. O

The O
method O
is O
presented O
in O
the O
paper O
" O
Estimating O
country O
- O
specific O
space O
heating B-climate-problem-origins
threshold B-climate-properties
temperatures I-climate-properties
from O
national O
gas B-climate-problem-origins
and I-climate-problem-origins
electricity I-climate-problem-origins
consumption I-climate-problem-origins
data O
" O
, O
and O
shows O
that O
current O
results O
can O
improve O
significantly O
by O
including O
weather O
and O
primary O
energy O
use O
. O

With O
a O
careful O
modelling O
of O
the O
coefficient B-climate-properties
of I-climate-properties
performance I-climate-properties
, O
I O
show O
that O
heat B-climate-mitigations
pumps I-climate-mitigations
become O
more O
economically O
feasible O
with O
rising O
ambient B-climate-properties
temperatures I-climate-properties
. O

-DOCSTART- -X- O O93e8e89fbf2e0a69cfad0f8acbd47dbc

The O
design O
of O
pavement B-climate-assets
structure O
is O
as O
a O
set O
of O
several O
activities O
related O
to O
the O
design O
of O
road B-climate-assets
construction O
, O
dimension O
and O
model O
calculations O
. O

An O
increase O
in O
air B-climate-properties
temperature I-climate-properties
is O
assumed O
, O
with O
an O
increase O
in O
average B-climate-properties
monthly I-climate-properties
temperatures I-climate-properties
of O
2.0 O
to O
4.8 O
° O
C O
. O

-DOCSTART- -X- O O5dc497441bfb97397499483e461f3c0c

Oklahoma O
watersheds B-climate-nature
and O
urban O
areas O
are O
subject O
to O
increased O
water B-climate-hazards
shortages I-climate-hazards
, O
woody B-climate-hazards
plant I-climate-hazards
encroachment I-climate-hazards
, O
and O
other O
socio O
- O
environmental O
issues O
. O

Our O
three O
study O
areas O
represent O
the O
diversity O
within O
Oklahoma O
: O
Oklahoma O
City O
( O
urban O
) O
, O
Kiamichi O
watershed O
( O
timber B-climate-assets
, O
reservoir B-climate-mitigations
) O
, O
and O
Cimarron O
watershed O
( O
agriculture B-climate-assets
, O
grassland B-climate-nature
) O
. O

-DOCSTART- -X- O Of1be8507c3bbef488835a821280d421d

Besides O
the O
factors O
of O
climate O
change O
, O
fire B-climate-hazards
, O
plant B-climate-impacts
diseases I-climate-impacts
and O
insect B-climate-hazards
pests I-climate-hazards
, O
human B-climate-problem-origins
damage I-climate-problem-origins
activities I-climate-problem-origins
, O
the O
influences O
of O
adaptive B-climate-mitigations
management I-climate-mitigations
methods I-climate-mitigations
to O
the O
factors O
of O
productivity O
were O
firstly O
considered O
in O
the O
model O
. O

-DOCSTART- -X- O O3814a47de6c59d5cf7edd05ab94af571

Life O
cycle O
assessment O
( O
LCA O
) O
framework O
will O
be O
utilised O
as O
a O
method O
for O
both O
economic O
feasibility O
and O
climate O
impact O
evaluation O
. O

-DOCSTART- -X- O O9d0ddf556b8ae379b631da671d7d6800

Renewable B-climate-mitigations
energy I-climate-mitigations
( O
RE B-climate-mitigations
) O
generation O
including O
wind B-climate-mitigations
and I-climate-mitigations
solar I-climate-mitigations
farms I-climate-mitigations
has O
experienced O
significant O
growth O
due O
to O
the O
challenge O
of O
climate O
and O
energy O
crisis O
. O

In O
this O
paper O
, O
voltage B-climate-properties
stability I-climate-properties
L I-climate-properties
- I-climate-properties
index I-climate-properties
is O
proposed O
as O
the O
constraint O
of O
the O
primal O
- O
dual O
interior O
point O
method O
for O
optimal O
power O
flow O
by O
considering O
the O
integration O
of O
wind B-climate-nature
and O
photovoltaic B-climate-mitigations
cell I-climate-mitigations
power I-climate-mitigations
on O
system O
. O

-DOCSTART- -X- O O495412548716e741c1758bcffe9fe929

The O
paper O
( O
i O
) O
describes O
the O
conceptual O
design O
of O
the O
Elbe B-climate-models
- I-climate-models
DSS I-climate-models
, O
( O
ii O
) O
demonstrates O
the O
applicability O
of O
the O
integrated O
catchment O
model O
by O
running O
three O
different O
management O
options O
for O
phosphate B-climate-mitigations
discharge I-climate-mitigations
reduction I-climate-mitigations
( O
reforestation B-climate-mitigations
, O
erosion B-climate-mitigations
control I-climate-mitigations
and O
ecological B-climate-mitigations
- I-climate-mitigations
farming I-climate-mitigations
) O
under O
the O
assumption O
of O
regional O
climate O
change O
based O
on O
IPCC O
scenarios O
, O
( O
iii O
) O
evaluates O
the O
effectiveness O
of O
the O
management O
options O
, O
and O
( O
iv O
) O
provides O
some O
lessons O
for O
the O
DSS O
- O
development O
in O
similar O
settings O
. O

-DOCSTART- -X- O Oc5cfe2ae2494e461d69e06ee6bd260f8

CIERA B-climate-organizations
is O
building B-climate-assets
an O
international O
community O
to O
catalyze O
emissions O
research O
by O
facilitating O
: O
the O
consistent O
, O
timely O
, O
and O
transparent O
development O
of O
emission B-climate-problem-origins
inventories O
at O
all O
scales O
; O
evaluations O
and O
analyses O
of O
emissions B-climate-problem-origins
datasets O
; O
and O
the O
exchange O
and O
communication O
of O
emissions B-climate-problem-origins
information O
. O

-DOCSTART- -X- O Oa30e7ccfd008ac09618c2fa028f6675c

Abstract O
Understanding O
the O
impact O
of O
climate O
change O
on O
borehole B-climate-assets
yields I-climate-assets
from O
fractured O
aquifers B-climate-nature
is O
essential O
for O
future O
water B-climate-mitigations
resources I-climate-mitigations
planning I-climate-mitigations
and I-climate-mitigations
management I-climate-mitigations
. O

We O
developed O
a O
simple O
two O
- O
layered O
radial B-climate-nature
groundwater I-climate-nature
flow I-climate-nature
model O
of O
an O
idealised O
pumping B-climate-assets
borehole I-climate-assets
in O
the O
fractured O
Chalk B-climate-nature
aquifer I-climate-nature
of O
south O
- O
east O
England O
, O
and O
applied O
11 O
VKD B-climate-properties
profiles O
based O
on O
a O
simple O
conceptual O
representation O
of O
variation B-climate-properties
in I-climate-properties
hydraulic I-climate-properties
conductivity I-climate-properties
with I-climate-properties
depth I-climate-properties
in O
the O
Chalk B-climate-nature
. O

-DOCSTART- -X- O O095139ddcef03ac156752c6cb1bfe90a

For O
thousands O
of O
years O
, O
the O
Huang O
- O
Hai O
Plain O
in O
northeast O
China O
has O
been O
one O
of O
the O
most O
productive O
agricultural B-climate-assets
regions O
of O
the O
country O
. O

The O
IPCC B-climate-organizations
estimates O
that O
40 O
Pg O
of O
C O
could O
be O
sequestered O
in O
cropland B-climate-assets
soils B-climate-nature
worldwide O
over O
the O
next O
several O
decades O
; O
however O
, O
changes O
in O
global O
climate O
may O
impact O
this O
potential O
. O

To O
assess O
the O
influence O
of O
these O
management O
practices O
under O
a O
changing O
climate O
, O
we O
use O
two O
climate O
change O
scenarios O
( O
A2 B-climate-datasets
and O
B2 B-climate-datasets
) O
at O
two O
time O
periods O
in O
the O
EPIC B-climate-models
agro O
- O
ecosystem O
simulation O
model O
. O

The O
EPIC B-climate-models
model O
indicates O
that O
winter B-climate-assets
wheat I-climate-assets
yields O
would O
increase O
on O
average O
by O
0.2 O
Mg O
ha O
� O
1 O
in O
the O
earlier O
period O
and O
by O
0.8 O
Mg O
ha O
� O
1 O
in O
the O
later O
period O
due O
to O
warmer O
nighttime B-climate-properties
temperatures I-climate-properties
and O
higher O
precipitation B-climate-nature
. O

-DOCSTART- -X- O Od4ce990cf7cf6d1b9b33810c03c9e558

In O
this O
paper O
, O
we O
evaluate O
the O
impact O
of O
mineral B-climate-nature
dust I-climate-nature
( O
MD B-climate-nature
) O
on O
snow B-climate-nature
radiative O
properties O
in O
the O
European O
Alps O
at O
ground O
, O
aerial O
, O
and O
satellite O
scale O
. O

A O
field O
survey O
was O
conducted O
to O
acquire O
snow B-climate-nature
spectral B-climate-properties
reflectance I-climate-properties
measurements O
with O
an O
Analytical B-climate-observations
Spectral I-climate-observations
Device I-climate-observations
( I-climate-observations
ASD I-climate-observations
) I-climate-observations
Field I-climate-observations
Spec I-climate-observations
Pro I-climate-observations
spectroradiometer O
. O

An O
overflight O
of O
a O
four O
- O
rotor O
Unmanned O
Aerial O
Vehicle O
( O
UAV O
) O
equipped O
with O
an O
RGB O
digital O
camera O
sensor O
was O
carried O
out O
during O
the O
field O
operations O
. O

Finally O
, O
Landsat B-climate-observations
8 I-climate-observations
Operational I-climate-observations
Land I-climate-observations
Imager I-climate-observations
( O
OLI B-climate-observations
) O
data O
covering O
the O
central O
European O
Alps O
were O
analyzed O
. O

We O
also O
estimated O
a O
positive O
instantaneous O
radiative B-climate-properties
forcing I-climate-properties
that O
reaches O
values O
up O
to O
153 O
W O
/ O
m2 O
for O
the O
most O
concentrated O
sampling O
area O
. O

These O
maps O
show O
the O
spatial O
distribution O
of O
MD O
in O
snow B-climate-nature
after O
a O
natural O
deposition O
from O
the O
Saharan O
desert O
. O

-DOCSTART- -X- O O8188cbe873246fbee7885c7009446024

Previous O
studies O
have O
confirmed O
the O
positive O
influence O
of O
increasing O
CO2 B-climate-greenhouse-gases
on O
photosynthesis O
and O
survival O
of O
the O
temperate O
eelgrass B-climate-organisms
Zostera B-climate-organisms
marina I-climate-organisms
L. I-climate-organisms
, O
but O
the O
acclimation O
of O
photoprotective O
mechanisms O
in O
this O
context O
has O
not O
been O
characterized O
. O

This O
study O
aimed O
to O
quantify O
the O
long O
- O
term O
impacts O
of O
ocean B-climate-hazards
acidification I-climate-hazards
on O
photochemical O
control O
mechanisms O
that O
promote O
photosynthesis O
while O
simultaneously O
protecting O
eelgrass B-climate-organisms
from O
photodamage O
. O

-DOCSTART- -X- O O40cf3e88ecb323889a7e33322c6de2c0

RegCM4 B-climate-models
is O
driven O
with O
European B-climate-organizations
Centre I-climate-organizations
for I-climate-organizations
Medium I-climate-organizations
- I-climate-organizations
Range I-climate-organizations
Weather I-climate-organizations
Forecasts I-climate-organizations
( O
ECMWF B-climate-organizations
) O
ERA B-climate-datasets
- I-climate-datasets
Interim I-climate-datasets
6 I-climate-datasets
- O
hourly O
boundary O
condition O
fields O
for O
the O
CORDEX B-climate-models
- I-climate-models
MENA I-climate-models
/ O
Arab O
domain O
. O

Besides O
ERA B-climate-datasets
- I-climate-datasets
Interim I-climate-datasets
lateral O
boundary O
conditions O
data O
, O
the O
Climatic B-climate-organizations
Research I-climate-organizations
Unit I-climate-organizations
( O
CRU B-climate-organizations
) O
data O
is O
also O
used O
to O
assess O
the O
performance O
of O
RegCM4 B-climate-models
. O

Overall O
, O
RegCM4 B-climate-models
simulates O
large O
pressure B-climate-properties
and O
water B-climate-nature
vapor I-climate-nature
values O
along O
with O
lower O
wind B-climate-properties
speeds I-climate-properties
compared O
to O
the O
driving O
fields O
, O
which O
are O
the O
key O
sources O
of O
bias O
in O
simulating O
rainfall B-climate-nature
and O
temperature B-climate-properties
. O

The O
most O
suitable O
option O
Grell B-climate-models
over I-climate-models
Land I-climate-models
and I-climate-models
Emanuel I-climate-models
over I-climate-models
Ocean I-climate-models
in O
wet O
( O
GLEO B-climate-models
wet O
) O
delivers O
a O
rainfall B-climate-nature
wet B-climate-properties
bias I-climate-properties
of O
2.96 O
% O
and O
a O
temperature B-climate-properties
cold B-climate-properties
bias I-climate-properties
of O
0.26 O
° O
C O
, O
compared O
to O
CRU B-climate-datasets
data O
. O

-DOCSTART- -X- O Oa274dcc6a7364222319b75f364dd5655

The O
rich O
mountain B-climate-nature
- O
top O
natural O
forests B-climate-nature
base O
was O
a O
potential O
livelihood B-climate-assets
asset O
( O
93.2 O
% O
) O
. O

Food B-climate-impacts
insecurity I-climate-impacts
and O
income B-climate-assets
poverty B-climate-impacts
contributes O
to O
unsustainable B-climate-impacts
livelihoods I-climate-impacts
( O
94.7 O
% O
) O
. O

-DOCSTART- -X- O Od1e811320192266eb659bbb9ecd31b8d

Thus O
, O
this O
study O
tries O
to O
identify O
the O
in O
- O
built O
crop B-climate-assets
production O
technology O
related O
adaptation O
and/or O
risk O
management O
strategies O
with O
smallholder B-climate-assets
farmers O
’ O
perception O
in O
moisture B-climate-properties
- O
stressed O
areas O
of O
the O
central O
rift O
valley O
of O
Ethiopia O
. O

Results O
of O
descriptive O
analysis O
showed O
that O
high O
temperature B-climate-properties
, O
short O
rain B-climate-nature
and O
pests B-climate-hazards
( O
indirectly O
) O
have O
been O
the O
most O
important O
phenomena O
of O
climate O
change O
causing O
loss B-climate-impacts
of I-climate-impacts
crop I-climate-impacts
production O
, O
exhaustion B-climate-impacts
and O
illness B-climate-impacts
. O

-DOCSTART- -X- O O708121b86d8c0d1450b80e52981a0ce5

The O
Providing B-climate-models
Regional I-climate-models
Climates I-climate-models
for I-climate-models
Impacts I-climate-models
Studies I-climate-models
( O
PRECIS B-climate-models
) O
regional O
modeling O
system O
is O
adopted O
to O
conduct O
ensemble O
simulations O
in O
a O
continuous O
run O
from O
1950 O
to O
2099 O
, O
driven O
by O
the O
boundary O
conditions O
from O
a O
HadCM3 B-climate-models
- O
based O
perturbed O
physics O
ensemble O
. O

Simulations O
of O
temperature B-climate-properties
and O
precipitation B-climate-nature
for O
the O
baseline O
period O
are O
first O
compared O
to O
the O
observed O
values O
to O
validate O
the O
performance O
of O
the O
ensemble O
in O
capturing O
the O
current O
climatology O
over O
Ontario O
. O

-DOCSTART- -X- O O0be4db59e5d250a53f4c444057e5989b

Inland O
- O
penetrating O
atmospheric B-climate-nature
rivers I-climate-nature
( O
ARs B-climate-nature
) O
affect O
the O
United O
States O
Southwest O
and O
significantly O
contribute O
to O
cool O
season O
precipitation B-climate-nature
. O

In O
this O
study O
, O
we O
examine O
the O
results O
from O
an O
ensemble O
of O
dynamically O
downscaled O
simulations O
from O
the O
North B-climate-organizations
American I-climate-organizations
Regional I-climate-organizations
Climate I-climate-organizations
Change I-climate-organizations
Assessment I-climate-organizations
Program I-climate-organizations
( O
NARCCAP B-climate-organizations
) O
and O
their O
driving O
general O
circulation O
models O
( O
GCMs O
) O
in O
order O
to O
determine O
statistically O
significant O
changes O
in O
the O
intensity O
of O
the O
cool O
season O
ARs O
impacting O
Arizona O
and O
the O
associated O
precipitation B-climate-nature
. O

Future O
greenhouse O
gas O
emissions O
follow O
the O
A2 B-climate-datasets
emission B-climate-problem-origins
scenario O
from O
the O
Intergovernmental B-climate-organizations
Panel I-climate-organizations
on I-climate-organizations
Climate I-climate-organizations
Change I-climate-organizations
Fourth B-climate-datasets
Assessment I-climate-datasets
Report I-climate-datasets
simulations O
. O

We O
find O
that O
there O
is O
a O
consistent O
and O
clear O
intensification O
of O
the O
AR O
- O
related O
water B-climate-nature
vapor I-climate-nature
transport O
in O
both O
the O
global O
and O
regional O
simulations O
which O
reflects O
the O
increase O
in O
water B-climate-nature
vapor I-climate-nature
content O
due O
to O
warmer O
atmospheric B-climate-properties
temperatures I-climate-properties
, O
according O
to O
the O
Clausius O
- O
Clapeyron O
relationship O
. O

-DOCSTART- -X- O O30ee93b474fc6b121913a273233158b4

Visual O
performance O
related O
simulations O
are O
executed O
in O
Grasshopper B-climate-models
, O
a O
graphical O
algorithm O
editor O
which O
uses O
plug O
- O
ins O
to O
apply O
performance O
simulations O
. O

These O
simulations O
showed O
that O
vertical O
blinds B-climate-mitigations
perform O
well O
with O
low O
angled O
sun O
( O
winter O
conditions O
) O
and O
horizontal O
blinds O
with O
high O
angled O
sun O
( O
summer O
conditions O
) O
. O

Polycarbonate O
has O
been O
selected O
in O
this O
case O
based O
on O
its O
transparent O
, O
tough O
and O
non O
- O
flammable O
character O
. O

-DOCSTART- -X- O O47466fc6d8cd80070ea97f11efc3a2a2

The O
main O
objective O
of O
this O
study O
was O
to O
develop O
past O
and O
present O
vulnerability O
index O
using O
climate O
and O
socioeconomic O
data O
at O
district O
level O
in O
Nepal O
based O
on O
IPCC B-climate-organizations
2007 O
framework O
of O
vulnerability O
. O

The O
assessment O
was O
carried O
out O
from O
1975 O
to O
2012 O
at O
a O
decadal O
scale O
. O

NASA B-climate-organizations
climate O
and O
land B-climate-properties
cover I-climate-properties
datasets O
were O
leveraged O
to O
advance O
the O
climate O
change O
portion O
of O
the O
vulnerability O
assessment O
. O

The O
resultant O
climate O
change O
and O
geographic O
vulnerability O
index O
were O
combined O
in O
ArcMap B-climate-models
to O
derive O
the O
overall O
vulnerability O
index O
. O

Monitoring O
potential O
climate O
change O
using O
NASA B-climate-organizations
satellite O
data O
and O
ESRI B-climate-models
ArcGIS I-climate-models
software O
could O
save O
innumerable O
in O
situ O
man O
hours O
and O
expedite O
the O
environmental O
monitoring O
required O
for O
natural O
hazard O
early B-climate-mitigations
warning I-climate-mitigations
systems I-climate-mitigations
. O

-DOCSTART- -X- O O565d8b22ade8a2a4b4c6813844447ee5

The O
impact O
of O
climate O
change O
on O
the O
stability O
of O
soil B-climate-nature
organic I-climate-nature
carbon I-climate-nature
( O
SOC B-climate-nature
) O
remains O
a O
major O
source O
of O
uncertainty O
in O
predicting O
future O
changes O
in O
atmospheric B-climate-nature
CO2 B-climate-greenhouse-gases
levels O
. O

-DOCSTART- -X- O Oe8f5743d6eb66fb5f32501efbcc541f0

The O
challenge O
of O
recent O
years O
is O
represented O
by O
the O
possibility O
to O
confer O
a O
sustainable O
approach O
to O
disaster B-climate-mitigations
relief I-climate-mitigations
strategies I-climate-mitigations
. O

The O
consequences O
of O
the O
destruction B-climate-impacts
of O
Typhoon O
Yolanda O
in O
the O
Philip O
- O
pines O
have O
been O
raised O
as O
case O
study O
for O
the O
rethinking O
of O
the O
strategies O
of O
governments O
with O
respect O
to O
serious O
calamities B-climate-impacts
. O

-DOCSTART- -X- O O3c4e976a9f6a5ccc8eeda54436bde7cd

The O
paper O
examines O
innovative O
and O
promising O
trends O
in O
in O
the O
design O
of O
high B-climate-assets
- I-climate-assets
rise I-climate-assets
buildings O
that O
challenge O
traditional O
typologies O
and O
are O
adapted O
for O
specific O
climatic O
conditions O
. O

The O
purpose O
of O
the O
study O
is O
to O
investigate O
modern O
methods O
of O
designing O
building B-climate-mitigations
envelopes I-climate-mitigations
for O
bioclimatic O
skyscrapers B-climate-assets
taking O
into O
account O
heat O
impact O
of O
climate O
on O
the O
thermal O
balance O
of O
buildings B-climate-assets
. O

The O
authors O
analyze O
the O
efficiency B-climate-properties
of O
using O
double B-climate-mitigations
facades I-climate-mitigations
in O
different O
climatic O
conditions O
with O
account O
of O
their O
interaction O
with O
other O
technological O
, O
constructive O
and O
planning O
elements O
, O
such O
as O
" O
solar B-climate-mitigations
chimney I-climate-mitigations
" O
, O
passive B-climate-mitigations
and I-climate-mitigations
active I-climate-mitigations
solar I-climate-mitigations
control I-climate-mitigations
systems I-climate-mitigations
, O
landscaping O
, O
intelligence O
control O
systems O
of O
temperature B-climate-properties
and O
humidity B-climate-properties
conditions O
in O
premises O
and O
buildings B-climate-assets
, O
etc O
. O

-DOCSTART- -X- O O59c54f8589aedb493dad2fa676e25453

The O
functional O
composition O
of O
plant B-climate-organisms
communities O
is O
a O
critical O
modulator O
of O
climate O
change O
impacts O
on O
ecosystems O
, O
but O
it O
is O
not O
a O
simple O
function O
of O
regional O
climate O
. O

In O
the O
Arctic O
tundra B-climate-nature
, O
where O
climate O
change O
is O
proceeding O
the O
most O
rapidly O
, O
communities O
have O
not O
shifted O
their O
trait O
composition O
as O
predicted O
by O
spatial O
temperature B-climate-properties
- I-climate-properties
trait I-climate-properties
relationships I-climate-properties
. O

We O
consider O
the O
community O
weighted O
means O
of O
plant B-climate-organisms
vegetative B-climate-properties
height I-climate-properties
, O
as O
well O
as O
two O
traits O
related O
to O
the O
leaf O
economic O
spectrum O
. O

-DOCSTART- -X- O O6879cc830ada3560be00e542b477a2f2

resulted O
in O
humid O
bias O
, O
i.e. O
, O
an O
increase O
in O
evapotranspiration B-climate-properties
by O
+ O
0.5 O
mm O
d O
−1 O
( O
latent B-climate-properties
heat I-climate-properties
flux I-climate-properties
is O
1.3 O
MJ O
m O
−2 O
d O
−1 O
) O
, O

-DOCSTART- -X- O O7a02bd13025e5ee7c7012981b03d60a9

Bovine B-climate-impacts
tuberculosis I-climate-impacts
is O
a O
zoonosis B-climate-impacts
which O
affects O
the O
livestock B-climate-assets
industry O
, O
the O
public O
health B-climate-assets
sector O
and O
wildlife B-climate-mitigations
reservoirs I-climate-mitigations
. O

-DOCSTART- -X- O O47f520c8c659afe5603cd16b92ddb6fa

Assessment O
of O
outdoor O
thermal B-climate-properties
comfort I-climate-properties
provides O
a O
valuable O
insight O
into O
the O
performance O
of O
outdoor O
built B-climate-assets
environments I-climate-assets
. O

During O
the O
last O
four O
decades O
the O
number O
of O
the O
thermal B-climate-properties
comfort I-climate-properties
studies O
focused O
on O
the O
impact O
of O
microclimate O
parameters O
on O
human O
health B-climate-assets
and O
wellbeing B-climate-assets
has O
increased O
. O

The O
data O
used O
in O
this O
study O
was O
collected O
during O
three O
rounds O
of O
the O
field O
surveys O
consisting O
of O
measurement O
and O
questionnaire O
surveys O
. O

Using O
a O
Socio O
- O
ecological O
System O
Model O
( O
SESM O
) O
as O
the O
research O
framework O
, O
this O
study O
aimed O
to O
investigate O
the O
role O
of O
non O
- O
thermal O
factors O
that O
are O
classified O
under O
the O
individual O
environment O
( O
gender B-climate-properties
, O
age B-climate-properties
group O
, O
exposure B-climate-properties
to I-climate-properties
sun I-climate-properties
, O
level O
of O
activity O
and O
clothing B-climate-mitigations
insulation I-climate-mitigations
. O

-DOCSTART- -X- O O28a8b130cae164fdd877f50443c2675d

Sea B-climate-hazards
level I-climate-hazards
rise I-climate-hazards
( O
SLR B-climate-hazards
) O
is O
one O
of O
the O
major O
socioeconomic O
risks O
associated O
with O
global O
warming O
. O

Mass O
losses O
from O
the O
Greenland O
ice O
sheet O
( O
GrIS O
) O
will O
be O
partially O
responsible O
for O
future O
SLR B-climate-hazards
, O
although O
there O
are O
large O
uncertainties O
in O
modeled O
climate O
and O
ice B-climate-nature
sheet I-climate-nature
behavior O
. O

We O
used O
the O
ice B-climate-nature
sheet I-climate-nature
model O
SICOPOLIS B-climate-models
( O
SImulation B-climate-models
COde I-climate-models
for I-climate-models
POLythermal I-climate-models
Ice I-climate-models
Sheets I-climate-models
) O
driven O
by O
climate O
projections O
from O
20 O
models O
in O
the O
fifth O
phase O
of O
the O
Coupled B-climate-models
Model I-climate-models
Intercomparison I-climate-models
Project I-climate-models
( O
CMIP5 B-climate-models
) O
to O
estimate O
the O
GrIS O
contribution O
to O
global O
SLR B-climate-hazards
. O

-DOCSTART- -X- O Ob9c65823fe403a7497046cc548b28a40

The O
Tangxun O
Lake O
was O
used O
as O
a O
testbed O
here O
, O
and O
the O
impacts O
of O
rainfall B-climate-nature
intensity O
and O
urbanization B-climate-problem-origins
on O
lake B-climate-nature
water O
quality O
was O
discussed O
through O
the O
scenario O
analysis O
. O

-DOCSTART- -X- O Ob5bb465968307ca3532597ed20842ac5

The O
proposed O
approach O
was O
applied O
to O
the O
20 O
- O
county O
Atlanta O
metropolitan O
area O
using O
regional O
climate O
model O
( O
RCM O
) O
simulations O
from O
the O
North B-climate-organizations
American I-climate-organizations
Regional I-climate-organizations
Climate I-climate-organizations
Change I-climate-organizations
Assessment I-climate-organizations
Program I-climate-organizations
. O

-DOCSTART- -X- O O999875b15180b9673bb91a78afa8ccba

The O
ReCiPe B-climate-models
2016 I-climate-models
Midpoint O
and O
Endpoint O
characterization O
model O
was O
used O
for O
the O
data O
expression O
. O

In O
the O
assessed O
framework O
, O
pea B-climate-assets
monocrops B-climate-assets
or O
intensively O
fertilized O
oat B-climate-assets
monocrops B-climate-assets
can O
also O
be O
considered O
as O
alternatives O
with O
relatively O
low O
impact O
on O
the O
environment O
. O

-DOCSTART- -X- O O3222950539971fa07f01a3625a1020c4

The O
Antarctic O
Peninsula O
is O
one O
of O
the O
regions O
on O
the O
Earth O
with O
the O
clearest O
evidence O
of O
recent O
and O
fast O
air O
warming O
. O

The O
model O
takes O
into O
account O
sediment B-climate-nature
and O
population B-climate-properties
dynamics I-climate-properties
with O
Lotka B-climate-models
- I-climate-models
Volterra I-climate-models
competition I-climate-models
, O
a O
sediment B-climate-nature
- O
dependent O
mortality B-climate-impacts
term O
and O
a O
randomized O
ice B-climate-nature
- I-climate-nature
scouring I-climate-nature
biomass O
removal O
. O

With O
the O
developed O
algorithm O
, O
and O
using O
a O
MATLAB B-climate-models
environment O
, O
numerical O
simulations O
for O
scenarios O
with O
different O
sedimentation B-climate-properties
and O
ice B-climate-properties
- I-climate-properties
impact I-climate-properties
rates I-climate-properties
were O
undertaken O
in O
order O
to O
evaluate O
the O
effect O
of O
this O
phenomenon O
on O
biological O
dynamics O
. O

-DOCSTART- -X- O O595ea9c60e8faf5fb99fc08b809e05c0

In O
the O
current O
study O
, O
we O
present O
and O
document O
the O
development O
of O
a O
new O
simplified O
set O
- O
up O
within O
the O
ECHAM B-climate-models
/ O
MESSy B-climate-models
model O
, O
namely O
the O
dry O
dynamical O
core O
model O
set O
- O
up O
ECHAM B-climate-models
/ I-climate-models
MESSy I-climate-models
IdeaLized I-climate-models
( O
EMIL B-climate-models
) O
. O

The O
set O
- O
up O
is O
achieved O
by O
the O
implementation O
of O
a O
new O
submodel O
for O
relaxation O
of O
temperature B-climate-properties
and O
horizontal O
winds B-climate-nature
to O
given O
background O
values O
( O
the O
RELAX B-climate-models
submodel O
) O
, O
which O
replaces O
all O
other O
physics O
submodels O
in O
the O
EMIL B-climate-models
set O
- O
up O
. O

The O
RELAX B-climate-models
submodel O
incorporates O
options O
to O
set O
the O
needed O
parameters O
( O
e.g. O
equilibrium B-climate-properties
temperature I-climate-properties
, O
relaxation O
time O
and O
damping O
coefficient O
) O
to O
functions O
used O
frequently O
in O
the O
past O
( O
given O
by O
Held O
and O
Suarez O
, O
1994 O
; O
Polvani O
and O
Kushner O
, O
2002 O
) O
. O

Test O
simulations O
with O
the O
EMIL B-climate-models
model O
set O
- O
up O
show O
that O
results O
from O
earlier O
studies O
with O
other O
dry O
dynamical O
core O
models O
are O
reproduced O
under O
same O
set O
- O
ups O
. O

-DOCSTART- -X- O Oab6cb78bf43eabd2596856f21fdc4c1d

A O
majority O
of O
IPCC B-climate-organizations
scenarios O
show O
that O
often O
very O
significant O
amounts O
( O
20 O
Gt O
CO2e O
/ O
yr O
) O
of O
Greenhouse O
Gas O
Removal O
technologies O
( O
GGRs O
) O
are O
required O
to O
reach O
a O
2 O
° O
C O
target O
by O
2100 O
. O

Given O
that O
most O
models O
fail O
to O
reach O
a O
2 O
° O
C O
target O
without O
GGRs O
, O
it O
seems O
impossible O
that O
the O
aspirational O
target O
of O
1.5 O
° O
C O
of O
the O
Paris B-climate-mitigations
Agreement I-climate-mitigations
could O
be O
met O
without O
GGRs O
. O

It O
appears O
that O
sequestration O
in O
soils B-climate-nature
and O
vegetation B-climate-nature
have O
significant O
potential O
for O
GGR O
, O
and O
may O
do O
so O
with O
much O
less O
competition O
for O
land O
, O
water O
and O
nutrients O
than O
, O
for O
example O
, O
Bioenergy O
with O
Carbon O
Capture O
and O
Storage O
( O
BECCS O
) O
. O

-DOCSTART- -X- O O >>> bpf FROM HERE ON LOOKED FOR MODELS (but not everything was one)

-DOCSTART- -X- O Of76923e191ef686464ec0a15330975d4

Skills O
in O
reproducing O
monthly O
rainfall B-climate-nature
over O
Calabria O
( O
southern O
Italy O
) O
have O
been O
validated O
for O
the O
Climate B-climate-datasets
Hazards I-climate-datasets
group I-climate-datasets
InfraRed I-climate-datasets
Precipitation I-climate-datasets
with I-climate-datasets
Station I-climate-datasets
data I-climate-datasets
( O
CHIRPS B-climate-datasets
) O
satellite O
data O
, O
the O
E B-climate-datasets
- I-climate-datasets
OBS I-climate-datasets
dataset O
and O
13 O
Global O
Climate O
Model O
- O
Regional O
Climate O
Model O
( O
GCM O
- O
RCM O
) O
combinations O
, O
belonging O
to O
the O
ENSEMBLES B-climate-organizations
project I-climate-organizations
output O
set O
. O

The O
relative O
mean O
and O
standard O
deviation O
errors O
, O
and O
the O
Pearson O
correlation O
coefficient O
have O
been O
used O
as O
validation O
metrics O
. O

-DOCSTART- -X- O O851b71730174981ade32c27fda772d3d

Keeping O
this O
in O
mind O
, O
phase O
five O
of O
the O
Coupled B-climate-models
Model I-climate-models
Intercomparison I-climate-models
Project I-climate-models
( O
CMIP5 B-climate-models
) O
, O
for O
the O
first O
time O
, O
provides O
10–30 O
years O
predictions O
obtained O
from O
the O
General O
Circulation O
Models O
( O
GCMs O
) O
. O

This O
study O
aims O
to O
analyse O
the O
CMIP5 B-climate-models
decadal O
predictions O
for O
precipitation B-climate-nature
over O
five O
sub O
- O
basins O
of O
river O
Brahmaputra O
. O

Daily O
precipitation B-climate-nature
data O
of O
five O
GCMs O
, O
namely O
F B-climate-models
- I-climate-models
GOALS I-climate-models
- I-climate-models
g2 I-climate-models
, O
BCC B-climate-models
- I-climate-models
CSM1 I-climate-models
- I-climate-models
1 I-climate-models
, O
IPSL B-climate-models
- I-climate-models
CM5A I-climate-models
, O
CanCM4 B-climate-models
and O
MRI B-climate-models
- I-climate-models
CGCM3 I-climate-models
are O
used O
for O
this O
assessment O
. O

-DOCSTART- -X- O Of79aef571d4f68304c875988c3a99763

Reliability O
and O
accuracy O
of O
soil B-climate-properties
moisture I-climate-properties
datasets O
are O
essential O
for O
understanding O
changes O
in O
regional O
climate O
such O
as O
precipitation B-climate-nature
and O
temperature B-climate-properties
. O

Soil B-climate-properties
moisture I-climate-properties
datasets O
from O
the O
Essential O
Climate O
Variable O
( O
ECV O
) O
, O
the O
Coupled B-climate-models
Model I-climate-models
Intercomparison I-climate-models
Project I-climate-models
Phase I-climate-models
5 I-climate-models
( O
CMIP5 B-climate-models
) O
, O
the O
Inter B-climate-models
- I-climate-models
Sectoral I-climate-models
Impact I-climate-models
Model I-climate-models
Intercomparison I-climate-models
Project I-climate-models
( O
ISIMIP B-climate-models
) O
, O
the O
Global B-climate-models
Land I-climate-models
Data I-climate-models
Assimilation I-climate-models
System I-climate-models
( O
GLDAS B-climate-models
) O
, O
and O
reanalysis O
products O
are O
widely O
used O
. O

In O
terms O
of O
unbiased O
Root O
- O
Mean O
- O
Square O
Difference O
( O
unRMSE O
, O
i.e. O
, O
removing O
the O
differences O
in O
absolute O
values O
) O
, O
all O
modeled O
datasets O
obtain O
performances O
comparable O
with O
ECV O
observations O
. O

In O
winter O
, O
GLDAS B-climate-models
performs O
the O
best O
in O
the O
east O
of O
south O
China O
, O
followed O
by O
the O
Reanalysis O
dataset O
. O

-DOCSTART- -X- O Oe61cef21ed616e4f9aa537388eef6b49

We O
study O
the O
Sarychev O
Peak O
eruption O
, O
using O
the O
Community B-climate-models
Earth I-climate-models
System I-climate-models
Model I-climate-models
version I-climate-models
1.0 I-climate-models
( O
CESM1 B-climate-models
) O
Whole B-climate-models
Atmosphere I-climate-models
Community I-climate-models
Climate I-climate-models
Model I-climate-models
( O
WACCM B-climate-models
) O
– O
Community B-climate-models
Aerosol I-climate-models
and I-climate-models
Radiation I-climate-models
Model I-climate-models
for I-climate-models
Atmospheres I-climate-models
( O
CARMA B-climate-models
) O
sectional O
aerosol B-climate-nature
microphysics O
model O
( O
with O
no O
a O
priori O
assumption O
on O
particle B-climate-properties
size I-climate-properties
) O
. O

-DOCSTART- -X- O O40515d17c1cb65c678e559ed76118d08

Simulations O
were O
performed O
for O
maize B-climate-assets
and O
soybean B-climate-assets
using O
the O
pSIMS B-climate-models
platform O
across O
the O
U.S O
Midwest O
by O
incrementally O
accounting O
for O
five O
sources O
of O
uncertainty O
with O
a O
7 O
km×7 O
km O
resolution O
using O
the O
APSIM B-climate-models
and O
DSSAT B-climate-models
crop B-climate-assets
growth O
models O
. O

Then O
, O
a O
series O
of O
nitrate B-climate-hazards
leaching I-climate-hazards
hotpots O
were O
identified O
and O
a O
regional O
maize B-climate-assets
yield O
productivity O
index O
was O
estimated O
by O
decomposing O
the O
uncertainty O
in O
the O
same O
scenario O
using O
a O
hierarchical O
Bayesian O
random O
- O
effect O
model O
. O

-DOCSTART- -X- O O540e95b9dd97f43e20971ddd6d625132

For O
this O
, O
the O
INteractive B-climate-models
Fires I-climate-models
and I-climate-models
Emissions I-climate-models
algoRithm I-climate-models
for I-climate-models
Natural I-climate-models
environments I-climate-models
( O
INFERNO B-climate-models
) O
fire B-climate-hazards
model O
is O
coupled O
to O
the O
atmosphere B-climate-nature
- O
only O
configuration O
of O
the O
UK B-climate-models
’s I-climate-models
Earth I-climate-models
System I-climate-models
Model I-climate-models
( O
UKESM1 B-climate-models
) O
. O

This O
fire B-climate-hazards
- O
atmosphere B-climate-nature
interaction O
through O
atmospheric O
chemistry O
and O
aerosols B-climate-nature
allows O
for O
fire B-climate-hazards
emissions I-climate-hazards
to O
influence O
radiation O
, O
clouds B-climate-nature
, O
and O
generally O
weather O
, O
which O
can O
consequently O
influence O
the O
meteorological O
drivers O
of O
fire B-climate-hazards
. O

Additionally O
, O
INFERNO B-climate-models
is O
updated O
based O
on O
recent O
developments O
in O
the O
literature O
to O
improve O
the O
representation O
of O
human O
/ O
economic O
factors O
in O
the O
anthropogenic B-climate-problem-origins
ignition I-climate-problem-origins
and O
suppression B-climate-mitigations
of I-climate-mitigations
fire I-climate-mitigations
. O

-DOCSTART- -X- O O60825be4f200cb922765c1141fd1eaf8

DAMOCLES B-climate-organizations
( O
Developing B-climate-organizations
Arctic I-climate-organizations
Modeling I-climate-organizations
and I-climate-organizations
Observing I-climate-organizations
Capabilities I-climate-organizations
for I-climate-organizations
Long I-climate-organizations
- I-climate-organizations
term I-climate-organizations
Environmental I-climate-organizations
Studies I-climate-organizations
) O
aimed O
at O
reducing O
the O
uncertainties O
in O
our O
understanding O
, O
modeling O
and O
forecasting O
of O
climate O
changes O
in O
the O
Arctic O
. O

DAMOCLES B-climate-organizations
was O
especially O
concerned O
with O
a O
significantly O
reduced O
sea B-climate-properties
- I-climate-properties
ice I-climate-properties
cover I-climate-properties
and O
the O
impact O
this O
drastic O
sea B-climate-nature
- I-climate-nature
ice I-climate-nature
retreat I-climate-nature
might O
have O
on O
the O
environment O
and O
on O
human O
activities O
both O
regionally O
and O
globally O
. O

-DOCSTART- -X- O O3caf6c2b344e0cab6ab0d65ac0b304ba

Highlights O
during O
SALTRACE B-climate-observations
included O
the O
Lagrangian O
sampling O
of O
a O
dust B-climate-nature
plume I-climate-nature
in O
the O
Cape O
Verde O
area O
on O
17 O
June O
which O
was O
again O
measured O
with O
the O
same O
instrumentation O
on O
21 O
and O
22 O
June O
2013 O
near O
Barbados O
. O

The O
event O
was O
also O
captured O
by O
the O
ground O
- O
based O
lidar B-climate-observations
and O
in O
- O
situ O
instrumentation O
. O

-DOCSTART- -X- O O https://semanticscholar.org/paper/fa8d288a6b10e694b1243326af60f683d8ef3331

An O
Assessment O
of O
Spatial O
Distribution O
of O
Four O
Different O
Satellite O
- O
Derived O
Rainfall B-climate-nature
Estimations O
and O
Observed O
Precipitation B-climate-properties
over O
Bangladesh O
. O

Given O
that O
precipitation B-climate-properties
is O
a O
major O
component O
of O
the O
earth O
’s O
water O
and O
energy O
cycles O
, O
reliable O
information O
on O
the O
monthly O
spatial O
distribution O
of O
precipitation B-climate-properties
is O
also O
crucial O
for O
climate O
science O
, O
climatological O
water B-climate-nature
- I-climate-nature
resource I-climate-nature
research O
studies O
, O
and O
for O
the O
evaluation O
of O
regional O
model O
simulations O
. O

In O
this O
paper O
, O
four O
satellite O
derived O
precipitation B-climate-properties
datasets O
: O
Climate B-climate-datasets
Prediction I-climate-datasets
Center I-climate-datasets
MORPHING I-climate-datasets
( O
CMORPH B-climate-datasets
) O
, O
Tropical B-climate-datasets
Rainfall I-climate-datasets
Measuring I-climate-datasets
Mission I-climate-datasets
( O
TRMM B-climate-datasets
) O
, O
the O
Precipitation B-climate-datasets
Estimation I-climate-datasets
Algorithm I-climate-datasets
from I-climate-datasets
Remotely I-climate-datasets
- I-climate-datasets
Sensed I-climate-datasets
Information I-climate-datasets
using I-climate-datasets
an I-climate-datasets
Artificial I-climate-datasets
Neural I-climate-datasets
Network I-climate-datasets
( O
PERSIANN B-climate-datasets
) O
, O
and O
the O
global B-climate-datasets
Satellite I-climate-datasets
Mapping I-climate-datasets
of I-climate-datasets
Precipitation I-climate-datasets
( O
GSMaP B-climate-datasets
) O
are O
spatially O
analyzed O
and O
compared O
with O
the O
observed O
precipitation B-climate-properties
data O
provided O
by O
Bangladesh B-climate-organizations
Meteorological I-climate-organizations
Department I-climate-organizations
( O
BMD B-climate-organizations
) O
. O

For O
this O
study O
, O
the O
different O
precipitations B-climate-properties
data O
sets O
are O
spatially O
analyzed O
from O
2nd O
May O
2019 O
to O
4th O
May O
2019 O
at O
the O
time O
of O
Cyclone O
“ O
FANI O
” O
. O

-DOCSTART- -X- O O https://semanticscholar.org/paper/233313e25b6d9f4760ad29252fb44df3980c7f9f

Centennial O
- O
scale O
variability O
of O
soil B-climate-properties
moisture I-climate-properties
in O
eastern O
Australia O
. O

Soil B-climate-properties
moisture I-climate-properties
is O
of O
critical O
importance O
to O
maintaining O
agricultural B-climate-assets
productivity I-climate-assets
and O
is O
used O
as O
an O
indicator O
of O
agricultural B-climate-assets
drought B-climate-hazards
. O

In O
- O
situ O
measurements O
of O
soil B-climate-properties
moisture I-climate-properties
, O
however O
, O
are O
exceedingly O
sparse O
at O
a O
global O
scale O
compared O
to O
most O
other O
hydroclimatic B-climate-nature
variables O
, O
and O
the O
temporal O
coverage O
of O
most O
records O
is O
limited O
to O
15–20 O
years O
at O
best O
. O

To O
overcome O
this O
, O
water B-climate-nature
balance I-climate-nature
models O
have O
been O
developed O
and O
applied O
to O
evaluate O
soil B-climate-nature
water B-climate-assets
availability I-climate-assets
at O
centennial O
- O
scales O
. O

These O
include O
the O
Australian B-climate-organizations
Water I-climate-organizations
Availability I-climate-organizations
Project I-climate-organizations
( O
AWAP B-climate-organizations
) O
Waterdyn B-climate-models
model O
, O
and O
the O
Australian B-climate-models
Water I-climate-models
Resource I-climate-models
Assessment I-climate-models
( O
AWRA B-climate-models
- I-climate-models
L I-climate-models
) O
models O
; O
two O
of O
the O
major O
water B-climate-nature
balance I-climate-nature
models O
used O
in O
Australia O
. O

This O
study O
looks O
to O
extend O
on O
their O
validation O
and O
application O
using O
a O
unique O
in O
- O
situ O
soil B-climate-properties
moisture I-climate-properties
data O
set O
from O
the O
Scaling B-climate-organizations
and I-climate-organizations
Assimilation I-climate-organizations
of I-climate-organizations
Soil I-climate-organizations
Moisture I-climate-organizations
and I-climate-organizations
Streamflow I-climate-organizations
( O
SASMAS B-climate-organizations
) O
project O
for O
the O
Krui O
and O
Merriwa O
River O
catchments O
in O
eastern O
NSW O
, O
Australia O
. O

Modelled O
outputs O
were O
compared O
against O
catchment B-climate-nature
average O
in O
- O
situ O
data O
and O
validated O
using O
correlation O
analyses O
. O

Many O
studies O
exist O
that O
look O
at O
this O
issue O
in O
response O
to O
the O
recent O
Millennium O
Drought B-climate-hazards
across O
the O
MurrayDarling O
Basin O
, O
however O
, O
the O
East O
Coast O
of O
Australia O
is O
identified O
as O
its O
own O
separate O
climate O
entity O
. O

-DOCSTART- -X- O O https://semanticscholar.org/paper/9bbcf4d55d8e781724c0c4e8fda3f69886a3715b

Inundation B-climate-hazards
mapping O
using O
C- B-climate-observations
and I-climate-observations
X I-climate-observations
- I-climate-observations
band I-climate-observations
SAR B-climate-observations
data O
: O
From O
algorithms O
to O
fully O
- O
automated O
flood B-climate-hazards
services O
. O

Since O
the O
establishment O
of O
the O
ZKI B-climate-organizations
( O
Center B-climate-organizations
for I-climate-organizations
Satellite I-climate-organizations
- I-climate-organizations
Based I-climate-organizations
Crisis I-climate-organizations
Information I-climate-organizations
) O
at O
the O
German B-climate-organizations
Aerospace I-climate-organizations
Center I-climate-organizations
( O
DLR B-climate-organizations
) O
, O
the O
development O
of O
EO O
- O
based O
methodologies O
for O
the O
rapid O
mapping O
of O
flood B-climate-hazards
situations O
has O
been O
of O
major O
concern O
. O

These O
requirements O
have O
led O
to O
the O
development O
of O
dedicated O
SAR B-climate-observations
- O
based O
flood B-climate-hazards
mapping O
tools O
which O
have O
been O
utilized O
during O
numerous O
rapid O
mapping O
activities O
of O
flood B-climate-hazards
situations O
. O

With O
respect O
to O
accuracy O
and O
computational O
effort O
, O
experiments O
performed O
on O
a O
data O
set O
of O
> O
200 O
different O
TerraSAR B-climate-observations
- I-climate-observations
X I-climate-observations
scenes O
acquired O
during O
flooding B-climate-hazards
all O
over O
the O
world O
with O
different O
sensor O
configurations O
confirmed O
the O
robustness O
and O
effectiveness O
of O
the O
flood B-climate-hazards
mapping O
service O
. O

processing O
chain O
has O
recently O
been O
adapted O
to O
the O
new O
European B-climate-organizations
Space I-climate-organizations
Agency I-climate-organizations
’s O
C B-climate-observations
- I-climate-observations
band I-climate-observations
SAR B-climate-observations
mission O
Sentinel-1 B-climate-observations
. O

In O
contrast O
to O
the O
current O
TerraSAR B-climate-observations
- I-climate-observations
X I-climate-observations
based O
thematic O
service O
, O
Sentinel-1 B-climate-observations
enables O
a O
systematic O
disaster B-climate-impacts
monitoring O
with O
high O
spatial O
and O
temporal O
resolutions O
. O