Seminar Given by Giacomo Bertoldi at the 2015 Workshop on Coupled Hydrological Modelling, held in Padua September 25

1. Eco-hydrological modeling
in a mountain laboratory:
the LTSER site Matsch/Mazia
Bertoldi
G.,
Cordano
E.,
Brenner
J.,
Notarnicola.
C.,
Niedrist
G.,
Tappeiner
U.
Workshop
on
coupled
hydrological
modeling,
23-­‐24
September
2015,
University
of
Padova,
Italy.

2. Outline
Overview
of
the
research
area
and
of
the
collected
data
Modelling
approach:
the
GEOtop
2
-­‐
DV
model.
Applica=ons
:
1.
Plot
scale
experiment
Modelling
snow,
soil
moisture,
ET,
biomass
along
an
elevaOon
gradient.
2.
Catchment
scale
applica=on
Modelling
impacts
of
climate
change
on
snow,
evapotranspiraOon
and
soil
moisture
spaOal
paPerns.
3.
Comparison
with
remote
sensing
data
EsOmaOon
of
soil
moisture
paPerns
by
means
of
SAR
images.
Discussion:
Limita=ons
and
uncertain=es
of
the
results.
Opportuni=es
hydrological
modelling
in
mountain
areas.

3. Matsch/Mazia,
Vinschgau,
South
Tyrol,
Italy
3
Area:
ca.
100
km2.
AlOtudinal
range:
920–
3738
m
a.s.l.
Mean
annual
precipitaOon
(Mazia,
1580
m
a.s.l.):
525
mm

4. Matsch
|
Mazia
A
dry
inneralpine
valley
4
low
precipitaOon
human
land-­‐use
closed
catchment
alOtudinal
transect
Eco
hydrological
monitoring
since
2009,
LTSER
since
2015

5. Research
topics
5
climate change & elevation
evapotranspiration
soil moisture dynamics
water and runoff
agriculture productivity
land use change ecosystem services
biodiversity
snow and ice
grasslands and forest ecosystems

6. Alps
Ecosystem
Plot
Global
Future
History
Present
Region
5
research
sites
4.
Saldur/Saldura
river
3.
Saldur/Saldura
catchment
network
5.
Glacierforefield
of
Weisskugel/Palla
Bianca
1.
Muntatschinig/
Monteschino
In
collabora=on
with:
University of Bolzano
Hydrographic
Office
(Province
BZ)
Biological
Laboratory
(Province
BZ)
Chemical
Laboratory
(Province
BZ)
In
collabora=on
with:
Hydrographic
Office
(Province
BZ)
University
of
Bolzano
University
of
Padova
University of Innsbruck (AT)
In
collabora=on
with:
University
of
Innsbruck
(AT)
BoKu
Vienna
(AT)
Duke
University
(USA)
In
collabora=on
with:
University of Innsbruck (AT)
Forest
department
(Province
BZ)
LTER
Matsch/Mazia:
Major
research
sites
2.
Al=tudinal
transect
of
Matsch/Mazia
In
collabora=on
with:
University
of
Innsbruck
(AT)
IRSTEA
Grenoble
(FR)
2000m
1500m
1000m
ΔT~
3.5K
ΔT~
3.5K
T
P

7. Matsch
|
Mazia
7
-20 permanent micro-climate. stations, soil
moisture.
-2 eddy covariance stations.
-3 gauge level/temp. measure points.
-5 SAP flow measurements points.
-5 weighting lysimeters
Site
infrastructure
Micro-­‐climate
staOons

8. Data
recorded
intervalic
8
Soil determinations
and analyses
Water quality
analyses
Vegetation
transplantation
experiments
Vegetation
surveys and
biomass
estimation
Diversity
analyses

9. Mapping
and
spaOal
data
Mapping of soil moisture: ground
spatial campaigns, remote
sensing (SAR, thermal, UAV).
Mapping of vegetation/landuse:
current and hystorical changes.
Mapping of soil type / properties.

10. ApplicaOon
1:
modelling
along
an
elevaOon
gradient
Mo=va=on
• Mountains
Region
are
considered
parOcularly
vulnerable
to
CC
1,
esp.
considering
the
alteraOons
of
the
water
cycle
2
• In
dry
inner-­‐alpine
regions,
managed
grasslands
are
irrigated.
Climate
change
raises
issues
about
future
water
availability.
Which
are
the
effects
of
the
eleva=on
gradient
on
water
budget?
(SWE,
SWC,
ET)
and
grassland
produc=vity
?
Della
Chiesa
et
al.,
Modeling
changes
in
grassland
hydrological
cycling
along
an
eleva6onal
gradient
in
the
Alps,
Ecohydrology,
2014
.
1
Bruneb
et
al.
(2006).
Temperature
and
precipitaOon
variability
in
Italy
in
the
last
two
centuries
from
homogenised
instrumental
Ome
series.
InternaOonal
Journal
of
Climatology,
26(3),
345–381.
2
Bates
et
al.
(2008).
Climate
Change
and
water.
IPCC
Technical
Paper
VI
(p.
214).
Geneva,
Switzerland:
IPCC
Secretariat.
Retrieved
from
hPp://www.ipcc.ch

11. An
experimental
elevaOon
transect
Eleva=on
as
a
proxy
of
climate
change
Sta=on
B2000
m
Hs,
SWC,
Biomass,
GAI
Sta=on
B1500
m
Hs,
SWC,
Biomass,
GAI,ET
Sta=on
B1000
m
Hs,
SWC,
Biomass,
GAI
ΔT~
3.5K
ΔT~
3.5K

12. The
GEOtop
2.0
–
DV
model
€
LWa tm
↓
V
€
D0V
€
I
€
LWs ur r
↓
1−V( )
€
SWs ur r
↓
1−V( )
€
εsσTs
4
Shortwave
radiatio n(yell ow)
Lo ngwave radiatio n
(red )
€
SW r ef l
Complex
topography
Bertoldi
et
al.,
J
of
Hydromet,
2006.
s
Snow
module
Endrizzi
et
al.,
GMDD,
2014
Zanob
et
al.,
Hydrol
Proc,
2004
Water
budget
Rigon
et
al.,
J
of
Hydromet,
2006.
Figures
adapted
from
VIC
model
(Liang
et
al.,
1994)
Energy
budget
Bertoldi
al.,
Ecohydrol,
2010.
Vegeta=on
dynamics
Della
Chiesa
et
al.,
Ecohydrol.,
2014
From
SHE
model
(Abbot
et
al.,
1986)
TRIBS-­‐VEGGIE
FaOchi
et
al.,
2012
Montaldo
et
al.,
2005
Eagleson,
2002
Alpine3D,
Lenhing
et
al.,
2006
CROCUS,
Brun
et
al.,
1992
SNTHERM,
Jordan,
1991
CLM,
Dai
et
al.,
2003
SEWAB,
Megelkamp
et
al.,
1999
Noah
LSM,
Chen
et
al.,
1996,
LSM,
Bonan,
1996
BATS,
Dickinson
et
al.,
1986,
Corripio,
2010.
Erbs
et
al.,
1983.
Iqbal,
1981.
tRIBS,
Ivanov
et
al,
2004
Cailow,
Zehe
et
al.,
2001
InHM,
VanderKwaak,
and
Loague,
2001
WaSim-­‐ETH,
Shulla
1997
Hydrogeosphere,
Therrien
and
Sudicki,
1996
Parflow,
Asby
an
Falgout,
1996
Cathy,
Paniconi
and
Pub,
1994
DHSVM,
Wigmosta
et
al.,
1994
SHE,
Abbot
et
al.
1986
Freeze
and
Harlan,
1969

13. Coupling
GEOtop
2.0
with
a
DV
model
Rigon
et
al.,
JHM,
2006;
Endrizzi
et
al.
GMDD,
2014.
Processes
Dynamic vegetation
model (for grasslands)
From
Montaldo
et
al.,
2005;
Della
Chiesa
et
al.,
2014

14. ElevaOon
gradient:
validaOon
MulOple
variables
validaOon:
SWE,
SWC,
above
ground
biomass
(Bag),
ET
Two
years
of
data:
calibra=on
in
B1500,
valida=on
in
B1000,
B2000
B2000
m
B1500
m
B1000
m
Snow
Height
[cm]
SWC
5cm
[]
ET
[mm]
Not
Measured
Not
Measured
r2=0.66
RMSE=7.1
r2=0.57
RMSE=5.9
r2=0.55
RMSE=2.9
r2=0.80
r2=0.78
r2=0.82
Bag
[gDMm-­‐2]
RMSE=0.04
RMSE=0.05
RMSE=0.04
r2=0.93
RMSE=58.39

15. Simula=on
extension
to
20
year
Coupling
snow
–
veg
–
ET
-­‐
SWC
Water
limitaOon
below
1500
m
SWC
along
the
year
SWC
[]
2000
m
1500
m
1000
m
SWC
along
the
year
Water
source
Water
sink
CriOcal
elevaOon
ElevaOon
gradient:
soil
moisture
and
ET

16. ElevaOon
gradient:
implicaOons
at
catchment
scale
It
exists
a
cri=cal
eleva=on
below
which
most
of
the
precipitaOon
is
used
for
ET.
Will
climate
change
move
this
cri=cal
eleva=on
upward?
2000
m
1500
m
1000
m
SWC
along
the
year

17. ApplicaOon
2:
modelling
impacts
of
CC
in
Venosta
Downscaling
of
RCMs
to
Venosta
Valley
Mapping
cri=cal
varia=ons
in
water
budget
(ET,
SMC,
snow)
Hydrological
experiment
along
an
elevaOon
gradient
as
proxy
of
CC
(Mazia,
Venosta)

18. ApplicaOon
2:
impacts
of
CC
on
sinw
ET
and
SWC
Research
ques=ons
Which
are
the
major
impacts
of
CC
on
snow,
evapotranspira=on,
soil
moisture
in
a
dry
alpine
valley?
How
to
iden=fy
the
most
vulnerable
areas
in
terms
of
topography/land
cover?
Which
are
the
major
uncertain=es?
Main
issues
Complex
topography
à
scale
vs.
computa=onal
effort
Model
parameteriza=on,
boundary
condi=ons
Brenner.,
Modeling
impacts
of
climate
change
on
evapotranspira6on
and
soil
moisture
spa6al
paTerns
in
an
alpine
catchment,
Thesis,
2014.
.

19.
ApplicaOon
2:
Study
Area
Venosta
Valley,
Upper
Adige
River
1000
km2

20.
• RCM
ensemble
based
on
SRES
A1B
(ESEMBLES
project)1
• Ctrl:
1990-­‐2010,
Scen2100:
2080-­‐2100
• ∆
approach
(30
day
moving
average)
• ∆
change
signals
at
daily
scale
for
air
temperature
and
precipitaOon
Downscaling
Technique
TopoSUB
Tool
GEOtop
Model
Simula=on
set-­‐up
1
Van
der
Linden,
P.,
&
Mitchell,
J.
(2009).
ENSEMBLES:
Climate
change
and
its
impacts
at
seasonal,
decadal
and
centennial
6mescales
(p.
160).
Exeter,
UK.
Retrieved
from
hPp://ensembles-­‐eu.metoffice.com/docs/Ensembles_final_report_Nov09.pdf
ApplicaOon
2:
Methods

21.
Downscale
Technique
TopoSUB
Tool
GEOtop
Model
Simula=on
set-­‐up
1
Fiddes,
J.,
&
Gruber,
S.
(2012).
TopoSUB:
a
tool
for
efficient
large
area
numerical
modelling
in
complex
topography
at
sub-­‐grid
scales.
Geoscien6fic
Model
Development
Discussions,
5(5),
1245–1257.
2
HarOgan,
J.
A.,
&
Wong,
M.
A.
(1979).
A
K-­‐Means
Clustering
Algorithm.
Journal
of
the
Royal
Sta6s6cal
Society.
Series
C
(Applied
Sta6s6cs),
28(1),
100–108.
Clustering
• sampling
of
most
important
aspects
of
land
surface
heterogeneiOes
and
land
cover
• K-­‐Means
clustering
algorithm
2
• based
on
20m
grids
GEOtop
• 1-­‐dimensional
simulaOons
for
cluster
centroids
Mapping
• Crisp
memberships
ApplicaOon
2:
Methods

22.
Downscale
Technique
TopoSUB
Tool
GEOtop
Model
Simula=on
set-­‐up
• GEOtop
model
• DistribuOng
meteorological
input
• Energy
and
mass
conservaOon
• Soil
volumetric
water
content
• Actual
evapotranspiraOon
• Snow
accumulaOon
&
melt
• ApplicaOon
in
mountain
areas
1
Rigon
et
al.
(2006).
GEOtop:
A
Distributed
Hydrological
Model
with
Coupled
Water
and
Energy
Budgets.
Journal
of
Hydrometeorology,
7(3),
371–388.
2
Endrizzi
et
al.
(2014).
GEOtop
2.0:
simulaOng
the
combined
energy
and
water
balance
at
and
below
the
land
surface
accounOng
for
soil
freezing,
snow
cover
and
terrain
effects.
Geoscien6fic
Model
Development
6(4),
6279–6341.
ApplicaOon
2:
Methods

23.
Downscale
Technique
TopoSUB
Tool
GEOtop
Model
Simula=on
set-­‐up
• SimulaOon
calibraOon/performance
•
2010/2011
(AlOtudinal
Transect)
• MulOple
Point
SimulaOon
(300
cluster
centroids)
• baseline
simulaOon
1990-­‐2010
• 7
scenario
simulaOon
2080-­‐2100
ApplicaOon
2:
Methods

24.
scen2100
DJF
MAM
JJA
SON
∆P
(%)
+14
+1.7
-­‐13
+16
∆T
(°C)
+3.1
+3.3
+4.2
+3.2
ApplicaOon
2:
Results
Climate
Change
Projec=ons
for
the
Venosta
Valley

25.
Baseline
SimulaOon
∆%
(scen2100-­‐ctrl)
ApplicaOon
2:
Results
Climate
Change
Impact
–
Snow
Cover
Dura=on

26.
Baseline
SimulaOon
∆abs
(scen2100-­‐ctrl)
ApplicaOon
2:
Results
Climate
Change
Impact
–
Actual
Evapotranspira=on

27.
∆abs
(scen2100-­‐ctrl)
Change
in
Mean
Annual
ETA
(mm)
Aspect
Forest:
South-­‐east
Major
impact
Pasture:
East
Bare
Soil:
South-­‐east
Grassland
&
Agriculture:
No
effect
of
aspect
ApplicaOon
2:
Results
Climate
Change
Impact
–
Actual
Evapotranspira=on

28.
ApplicaOon
2:
Results
Climate
Change
Impact
–
Actual
Evapotranspira=on
4
14
+
250%
48
69
+
43%
131
149
+
12%
53
62
+
17%

29.
ApplicaOon
2:
Results
Climate
Change
Impact
–
Soil
Mositure–
Severe
Water
Stress
CriOcal
soil
moisture
level
is
refered
to
plant
available
water
1
1
Jasper
et
al.
(2006).
Changes
in
summerOme
soil
water
paPerns
in
complex
terrain
due
to
climaOc
change.
Journal
of
Hydrology,
327(3-­‐4),
550–563.

30.
ApplicaOon
2:
Results
Climate
Change
Impact
–
Soil
Water
Content
–
Severe
Water
Stress

31.
ApplicaOon
2:
Conclusions
Conclusions
• General
decrease
in
snow
cover
duraOon
(max
9
weeks),
which
drives
major
increase
in
evapotranspira=on
in
winter
and
spring
(+25%).
• LiPle
decrease
of
catchment-­‐averaged
soil
moisture
(except
for
some
rainfall
scenarios).
• Specific
sites,
which
are
already
characterized
by
water
stress,
show
an
increase
in
drought
days
(esp.
pastures
and
forests
~
1500
m
a.s.l.).

32.
Major
uncertainOes
and
perspecOves
Clima=c
scenarios
• Temperature
-­‐>
Depends
on
concentraOon
scenarios
(IPPC,
2013)*
• PrecipitaOon
-­‐>
No
clear
trend.
RCMs
do
not
reproduce
local
climatology.
• No
info
on
trends
of
air
humidity,
wind,
radiaOon
(clouds).
Hydrological
model
(GEOtop
2.0)
• ComputaOonal
limitaOons
(full
3D
vs.
1D)
for
soil
water
distribu=on
and
runoff
simulaOon.
• Full
dynamic
vegetaOon
and
glaciers.
• Land
cover
scenarios.
Data
availability
• PrecipitaOon
in
high
elevaOon
regions
(>
2000
m)
(Mair
et
al.,
2013)**
• InformaOon
on
soil
properOes
(IRKIS).
*IPPC
(2013).
Climate
Change
2013:
The
Physical
Science
Basis.
IPCC
Working
Group
I
ContribuOon
to
AR5.
**
Mair,
et
al..
(2013).
ESOLIP;
esOmate
of
solid
and
liquid
precipitaOon
at
sub-­‐daily
Ome
resoluOon
by
combining
snow
height
and
rain
gauge
measurements.
Hydrology
and
Earth
System
Sciences
Discussions,
10(7),
8683–8714.

33.
Summer
2015
Courtesy
od
Andrea
Debiasi,
27
Luglio
2015

34. ApplicaOon
3:
remote
sensing
of
soil
moisture
Mo=va=on
Limited
availability
of
reliable
soil
moisture
high
resoluOon
products
on
mountain
areas.
Heterogeneity
in
soil
type,
land
cover,
topography
limits
distributed
models
parameteriza=on.
How
far
can
SAR
remote
sensing
help
for
improving
modelling
surface
soil
moisture
in
mountain
grassland
areas?
Bertoldi,
G.,
et
al.
Es6ma6on
of
soil
moisture
paTerns
in
mountain
grasslands
by
means
of
SAR
RADARSAT2
images
and
hydrological
modeling.
J.
Hydrol.
(2014)
RADASAT2
SAR
Distributed
models
are
“hungry”
of
spa=ally
distributed
informa=on1
1Grayson
et
al.,
1998

35. Soil
moisture:
observaOons
Fixed
Sta=ons
Field
surveys
Mazia,
South
Tyrol,
Italy
~
100
km2
RADASAT2
SAR
images
20m
res
Surface
SWC
retrieval
(SVR
Pasolli
et
el.,
2011)

36. Ground
observaOons:
mobile
surveys
• Monitoring
SMC
spa=al
paserns
at
hillslope
scale;
• Survey
planned
to
map
land
cover/topographic
features;
• Good
correspondence
with
staOon
values.
• More
than
10
surveys
between
2010
and
2014;
• More
than
1000
points
with
mobile
Delta-­‐T
wet
sensor
(TDR)
0
–
5
cm
depth;
10
%
50
%
SWC

37. Remote
sensing:
SAR
datasets
RADARSAT2
images:
• Fully
polarimetric
images
(HH,
HV,
VH,
VV)
and
dual
pol
(HH-­‐HV)
• 5.5
cm
wavelength
(C-­‐band
radar)
• Almost
all
images
with
45°nominal
incidence
angle
• Final
spaOal
resoluOon
20x20
m2
(RADARSAT-­‐2
Data
and
Products©
MacDonald,
DeTwiler
and
Associates
Ltd.
(2010)
–
All
Rights
Reserved)
Data
Period
Number
RADARSAT2
2010-­‐2011,
2013-­‐2015
Un=l
now
20
ASAR
WS
2005-­‐2012
Un=l
now
analyzed
around
200
images
ASAR
WS:
• Mainly
VV
pol.
• 5.5
cm
wavelength
(C-­‐band
radar)
• Ascending
nd
descending
• Final
spaOal
resoluOon
150x150
m2

38. Methodological approach
GEOtop
Model
(Rigon
et
al.,
2006)
Support
Vector
Regression
(Pasolli
et
al.,
2011)
gsr QQQETP
t
SMC
−−−−=
∂
∂
ET
Qr
QrQs
Qs
Qg
P
Mass
and
enegy
budget
3D
Richard
3D
equa=ons
SMC
es=ma=on
@
5cm
HH HV
NDVI
Modis
Elev.DEM
Land
use
Radarsat polarizations
Features
SMC
observations
Target
SVR
Param.
SVR
Regression
Analysis
SVR
Map Estimation
Estimated
SMC
Estimation
Training

39. GEOtop
validaOon
in
staOons
locaOons
Model
validated
for
SMC
for
staOons
located
both
in
pastures
and
irrigated
meadows
Bias -0.047 m3/m3
RMSE 0.054 m3/m3
Bias -0.016 m3/m3
RMSE 0.041 m3/m3

40. SAR
SMC
validaOon
Outcome:
1. The
proposed
es:ma:on
system
is
effec:ve
in
handling
the
challenging
soil
moisture
retrieval
problem
in
Alpine
areas.
2. Mul:ple
polariza:ons
and
ancillary
data
are
needed
to
disentangle
the
effects
of
local
scale
vegeta:on
and
roughness.
RADARSAT 2 ASAR WS
R2=0.89
R2=0.88
ValidaOon
on
a
different
ground
observaOons
subset

41. Soil
moisture:
Radarsat
2
maps
Wettest locations are along the valley bottom and in irrigated areas.
Driest locations are south-facing low elevation pastures.

42. Soil
moisture:
spaOal
comparison

43. Results:
Radarsat
–
GEOtop
differences
• Major
differences
in
in
irrigated
meadows;
• Too
coarse
scale
model
soil
and
land
cover
parameterizaOon.
• Radarsat
captures
the
small
scale
variability
related
to
land
cover/irrigaOon

44. What
controls
the
observed
SMC
paPerns?
Coupling
between
(surface)
soil
type
and
land
management.
Model
helps
to
understand
physical
reasons
of
observed
paserns.
Topography, soil type or land use?

45. SAR
soil
moisture
esOmaOon:
conclusions
Modelling:
GEOtop
+ conOnuous
spaOal
and
temporal
coverage;
+ good
capability
to
capture
temporal
paPerns;
- limitaOons
due
land
cover
/
soil
/
irrigaOon
parameterizaOon.
SAR:
RADARSAT
2
+ good
capability
to
capture
fine
scale
spaOal
paPerns;
+ strong
signature
of
land
cover
/
vegetaOon
/
irrigaOon
paPerns;
+ High
spaOal
resoluOon,
limited
temporal
coverage;
- Possible
ambiguity
due
soil/land
cover
coupling;
- limited
to
surface
layer
(~5
cm)
and
grassland
areas.

46. PerspecOves:
toward
an
integraOon
strategy
…
temporal
Possible
integra=on
strategy:
temporal
driving
from
the
model,
spaOal
paPerns
SAR
imaging
Average
and
std
ASAR
and
GEOtop
SMC

47. Toward
an
integraOon
strategy
…
spaOal
ASAR
GEOtop
Use
model-­‐derived
data
as
addiOonal
input
feature
for
a
SVR
approach
in
areas
where
limited
ground
truth
is
available.

48. Overall Conclusions
Experimental
observa=ons
Experimental
design
Models
valida=on
parameteriza=on
Process
understanding
Eco-­‐hydrological
modelling
ET
2 W/m2
286 W/
m2

49. Come
and
visit
us,
we
are
waiOng
for
you
J
Matsch
|
Mazia
49
Our data need modellers !

50. Acknowledgments
This
study
is
supported
by
the
projects
“and
“HydroAlp”
and
“HiResAlp”
financed
by
Provincia
Autonoma
di
Bolzano,
Alto
Adige,
Ripar=zione
Diriso
allo
sudio,
Università
e
ricerca
scien=fica.
We
hereby
would
like
to
thank:
M.
Dall´Amico,
Mountaneering
s.r.l.
S.
Endrizzi,
University
of
Zurich.
R.
Rigon,
University
of
Trento.
G.
Wohlxart,
University
of
Innsbruck
Thank
you
for
your
aGen:on!

52. Opportunites
and
challenges
Ø Using
physically
models
in
real
contexts
is
someOmes
more
Ome-­‐consuming
than
doing
real
experiments.
Ø A
deep
knowledge
of
the
system
is
needed
for
set-­‐up
proper
assumpOons
in
model
parameterizaOon
(a
lot
on
unknown
informaOon).
Ø
Great
tools
for
tesOng
hypotheses
and
generalize
results.

53. Opportunites
and
challenges
Ø The
parOcularly
dry
area
represents
a
unique
chance
to
study
climate
change
allowing
predicOons
of
future
climate
on
mountain
ecosystems.
Ø The
eleva=on
transect
allows
for
experimental
and
numerical
invesOgaOon
on
effects
of
elevaOon
on
eco-­‐hydrological
processes.
Ø The
site
allows
interdisciplinary
observaOons
of
relevant
eco-­‐hydrological
processes
in
a
human-­‐
influenced
mountain
region.
Ø The
climaOc
condiOons
of
Val
Mazia
may
allow
interesOng
comparisons
among
different
mountain
sites
of
the
MRI
/
LTER
network.
Ø Chance
to
be
part
of
a
well
organized
and
good
structured
scien=fic
network.

55. ElevaOon
gradient:
results
B2000
m
B1500
m
B1000
m
Coupling
snow
–
veg
–
ET
-­‐
SWC
SWC
along
the
year
IrrigaOon
below
1500
m

56. GEOtop validation in stations locations
Model
validated
for
SMC
for
staOons
located
both
in
pastures
and
irrigated
meadows

57. Study
Area:
meadows
57
Mazia
Valley,
South
Tyrol,
Italy
Meadows
Up
to
~
1700m
a.s.l.
Intensively
managed:
-­‐
cubng
-­‐
manuring
-­‐
irrigaOon
Homogenous
soil
surface
VegetaOon
dominated
by
grasses

58. Study
Area:
pastures
58
Mazia
Valley,
South
Tyrol,
Italy
Pastures
Located
at
1700
to
2400m
a.s.l.
Steep
terrain
Heterogeneous
soil
surface:
-­‐
bare
soil
-­‐
stones
-­‐
large
rocks
VegetaOon
dominated
by
grasses

59. Study
area:
soil
properOes
Kolmann and Tasser, 2012
• Two
main
soil
types:
1. Haplic
Leptosol
(ranker)
mainly
in
pastures;
2. Dystric
Cambisol
(braunerde)
mainly
in
meadows
(Kollman,
M.
Th.,
2013).
• Observed
soil
parameters
are
in
the
typical
range
of
loamy
sand
(Leptosoil)
and
sandy
loam
(Cambisoil).
Kollmann,
K..
Klima-­‐
und
landnutzungsbedingte
Bodenverteilung
im
Matschertal,
SüdOrol.
Ms.
Thesis,
Universität
Innsbruck.(2012).

60. Ground
observaOons:
fixed
staOons
Network
of
14
staOons
with:
•
Meteorological
data
•
SMC
5
and
20
cm
depth
(Decagon
capaciOve
sensors
10Hs)
Transect
sta=ons
Catchment
sta=ons
Run-­‐off
measurements
Area
~100
km2
• Monitoring
SMC
temporal
dynamic
at
catchment
scale.

61. Ground
observaOons:
mobile
surveys
• Monitoring
SMC
spa=al
paserns
at
hillslope
scale;
• Survey
planned
to
map
land
cover/topographic
features;
• Good
correspondence
with
staOon
values.
• More
than
10
surveys
between
2010
and
2014;
• More
than
1000
points
with
mobile
Delta-­‐T
wet
sensor
(TDR)
0
–
5
cm
depth;
10
%
50
%
SWC

62. Hydrological
modeling:
GEOtop
SMC
simulaOon
GEOtop
model
Rigon
et
al.,
JHM,
2006.
Endrizzi
et
al.,
GMDD,
2013.
∂SMC
∂t
= P − ET −Qr −Qs −Qg
ET
Qr
Qr
Qs
Qs
Qg
P
Plot
scale
water
budget
Catchment
scale
SMC
@
5cm
3D
Richard’s
eq.
Endrizzi,
S.,
et
al.
GEOtop
2.0:
simulaOng
the
combined
energy
and
water
balance
at
and
below
the
land
surface
accounOng
for
soil
freezing,
snow
cover
and
terrain
effects.
Geosci.
Model
Dev.
Disc.
6,
6279–6341
(2013).
Rigon,
R,
et
al.
GEOtop:
a
distributed
hydrological
model
with
coupled
water
and
energy
budgets.
J.
Hydrometeorol.
7
(3),
371–388
(2006).

63. GEOtop
–
DVM
coupling
GEOtop
VDM
-­‐
Rad,Rh,PAR,T,
Wind
-­‐ Ini=al
Condi=ons
-­‐ Meteo
input
-­‐ Soil
and
topography
Montaldo
et
al.,
2005
Endrizzi
et
al.,
2013
Canopy
Frac=on
Canopy
Height
Leaf
Area
Index
Senescence
Respira=on
Trasloca=on
Biomass
Budget
Photosynthesis
Evapotranspira=on
Intercep=on
Energy
Balance
Throughfall
Infiltra=on
Soil
Water
Balance
Runoff
Drainage
Rain/Snowfall
Rigon
et
al.,
2006
Della
Chiesa
et
al.,
2014

64. Data
recorded
with
high
frequency
(15´since
2009)
Matsch
|
Mazia
64
precipitation (mm)
global radiation (W/m²)
soil temperature (°C) and soil moisture (Vol%)
logger
air temperature (°C)
and humidity (%)
Snow/Vegetation height (cm)
Photosynthetic active radiation (µmol s−1W*−1)
Radiation balance (W/m²)
Soil surface temperature (°C)
Soil heat flux (W/m²)
Latent and sensible fluxes (W/m²)
Soil water potential (hPa)
wind speed and direction (m/sec, °)

65. Coupled ecohydrological modelling
How
to
use
experimental
observa=ons
to
validate
a
distributed
ecohydrological
models?
How
to
use
model
results
to
improve
our
knowledge
of
the
ecohydrological
behavior
of
mountain
catchments?