The value of aggregated measurements of state variables in hydrological modeling

1. 1/50
Microgravity
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The value of aggregated measurements of
state variables in hydrological modeling
Alberto Bellin
Department of Civil, Environmental and
Mechanical Engineering

2. 2/50
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observa3ons
and
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e-­‐mail:
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RESEARCH ARTICLE
10.1002/2015WR016994
On the use of spatially distributed, time-lapse microgravity
surveys to inform hydrological modeling
Sebastiano Piccolroaz1, Bruno Majone1, Francesco Palmieri2, Giorgio Cassiani3, and Alberto Bellin1
1
Department of Civil, Environmental, and Mechanical Engineering, University of Trento, Trento, Italy, 2
Istituto Nazionale di
Oceanografia e di Geofisica Sperimentale, Trieste, Italy, 3
Department of Geosciences, University of Padova, Padova, Italy
Abstract In the last decades significant technological advances together with improved modeling capa-
bilities fostered a rapid development of geophysical monitoring techniques in support of hydrological mod-
eling. Geophysical monitoring offers the attractive possibility to acquire spatially distributed information on
state variables. These provide complementary information about the functioning of the hydrological system
to that provided by standard hydrological measurements, which are either intrinsically local or the result of
a complex spatial averaging process. Soil water content is an example of state variable, which is relatively
simple to measure pointwise (locally) but with a vanishing constraining effect on catchment-scale modeling,
while streamflow data, the typical hydrological measurement, offer limited possibility to disentangle the
controlling processes. The objective of this work is to analyze the advantages offered by coupling traditional
hydrological data with unconventional geophysical information in inverse modeling of hydrological sys-
tems. In particular, we explored how the use of time-lapse, spatially distributed microgravity measurements
may improve the conceptual model identification of a topographically complex Alpine catchment (the Ver-
migliana catchment, South-Eastern Alps, Italy). The inclusion of microgravity data resulted in a better con-
straint of the inversion procedure and an improved capability to identify limitations of concurring
conceptual models to a level that would be impossible relying only on streamflow data. This allowed for a
better identification of model parameters and a more reliable description of the controlling hydrological
processes, with a significant reduction of uncertainty in water storage dynamics with respect to the case
when only streamflow data are used.
Key Points:
Microgravity data constrain
subsurface water storage in
hydrological models
Combining streamflow and
microgravity data rules out improper
conceptual models
Indirect measures of state variables
constrain inversion of hydrological
data
Supporting Information:
Supporting Information S1
Correspondence to:
S. Piccolroaz,
s.piccolroaz@unitn.it
Citation:
Piccolroaz, S., B. Majone, F. Palmieri,
G. Cassiani, and A. Bellin (2015), On the
use of spatially distributed, time-lapse
microgravity surveys to inform
hydrological modeling, Water Resour.
Res., 51, doi:10.1002/2015WR016994.
Received 30 JAN 2015
Accepted 6 AUG 2015
Accepted article online 11 AUG 2015
Water Resources Research
PUBLICATIONS

3. 3/50
Microgravity
observa3ons
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e-­‐mail:
alberto.bellin@unitn.it
Two
concurring
hydrological
models
(M1
and
M2)
having
the
same
high
efficiency
when
calibrated
using
only
streamflow
data
(single
objecve)…
Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul
0
5
10
15
20
25
30
Time
Discharge[m3
/s]
Observed
Sim. M1 (KGE
hydro
=0.92
Sim. M2 (KGE
hydro
=0.93

4. 4/50
Microgravity
observa3ons
and
hydrological
modelling
e-­‐mail:
alberto.bellin@unitn.it
…
show
remarkable
differences
when
adding
geophysical
informaon
(mul
objecve):
the
shape
of
the
Pareto
fronts
provide
useful
hints
to
idenfy
model
limitaons,
and
indicate
the
value
of
including
geophysical
data
to
beGer
constraint
of
the
inversion
procedure.

5. 5/50
Microgravity
observa3ons
and
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modelling
e-­‐mail:
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Introduc3on
Microgravimetry
may
provide
useful
data
on
water
storage
in
the
earth’s
subsurface
• relevant
in
the
evalua3on/quan3fica3on
of
groundwater
resources
(e.g.
GRACE
at
large
scale);
• a
promising
support
in
hydrological
modeling.
Are
microgravimetry
data
useful
to
inform
hydrological
models?
We
are
interested
in
coupling
tradi3onal
hydrological
data
(inherently
global)
and
geophysical
informa3on
(spaally
distributed).
Time-­‐lapse,
rela3ve
microgravimetry
is
a
geophysical
technique
which
allows
to
obtain
an
indirect
esmate
of
surface
and
subsurface
water
storage
varia3ons
(i.e.,
soil
water
content,
groundwater
and
snowpack)
Basic
concept:
the
temporal
variaon
of
the
gravitaonal
aGracon
is
proporonal
to
the
hydrological
storage
changes
(once
all
other
contribuons
are
corrected).

6. 6/50
Microgravity
observa3ons
and
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modelling
e-­‐mail:
alberto.bellin@unitn.it
Study
site:
the
Vermigliana
catchment,
located
in
the
South-­‐Eastern
Alps
(Italy).
Main
characteris3cs:
• contribung
area:
104
km2;
• the
main
creek
has
a
length
of
4
km
and
an
average
slope
of
1%;
• elevaon
ranging
from
950
to
3558
m
a.s.l.;
• very
steep
slopes
and
a
narrow
valley
boGom
(U-­‐shaped
profile);
• nivo-­‐glacial
regime;
• almost
prisne
condions.
Microgravity
campaign

7. 7/50
Microgravity
observa3ons
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e-­‐mail:
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Fieldwork
and
data
acquisi3on1:
• 6
field
campaigns
between
June
2009
and
May
2011;
• extensive
point
gravity
measurements
on
a
network
of
53
sta3ons;
• the
Vermigliana
catchment
has
been
monitored
through
8
sta3ons;
• streamflow
data
are
available
at
the
Vermiglio
stream
gauging
staon.
1
Equipment:
LaCoste-­‐Romberg
mod
D-­‐018
equipped
with
a
Zero
Length
Spring
feedback.
Microgravity
campaign
Reference
measurement

8. 8/50
Microgravity
observa3ons
and
hydrological
modelling
e-­‐mail:
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Modeling
approach
Hydrological
model:
a
modified
version
of
GEOTRANSF
(Majone
et
al.,
2012,
WATER
RESOUR
RES),
a
distributed
hydrological
model
characterized
by
1. subdividing
the
catchment
into
slope
and
valley
boGom
areas
(assuming
they
are
governed
by
inherently
different
processes);
2. explicitly
coupling
vadose-­‐zone
and
groundwater
dynamics.
The
valley
boGom
is
idenfied
considering
thresholds
on
slope
and
elevaon
(≈
1.8
km2,
2%
of
the
catchment
area)

9. 9/50
Microgravity
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Valley bottom

10. 10/50
Microgravity
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Valley bottom
Sub-basin entirely
confined in the
hillslope unit

11. 11/50
Microgravity
observa3ons
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Valley bottom
Sub-basin
partitioned between
hillslope and valley
bottom

12. 12/50
Microgravity
observa3ons
and
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modelling
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Modeling
approach
Calibra3on
procedure:
parcle
swarm
algorithm
to
solve
a
mul-­‐objecve
opmizaon
problem
(see
e.g.,
Robinson
and
Rahmat-­‐Samii,
2004,
IEEE
T
ANTENN
PROPAG)
Objec3ve
func3on:
the
following
expression
has
been
used
as
model
performance
criterion
KGE
=
Kling–Gupta
efficiency
index
(Gupta
et
al.,
2009,
J
HYDROL)
=
µs
µ0
=
s
0
r =
covSO
( S 0)
KGE = 1
q
( 1)
2
+ ( 1)
2
+ (r 1)
2
KGEtot = (1 ↵)KGEhydro + ↵KGEgrav, ↵ = 0 ÷ 1

13. 13/50
Microgravity
observa3ons
and
hydrological
modelling
e-­‐mail:
alberto.bellin@unitn.it
Modeling
approach
Gravity
model:
the
domain
is
divided
into
n
prisms
for
which
the
individual
contribuon
to
the
hydrology-­‐
induced
gravity
variaons
is
evaluated
according
to
The
following
approximated
soluon
has
been
applied
for
distances
larger
than
a
given
threshold
from
the
gravimeter

14. 14/50
Microgravity
observa3ons
and
hydrological
modelling
e-­‐mail:
alberto.bellin@unitn.it
Domain
discre3za3on:
carried
out
at
two
levels
• Horizontal
plane
based
on
the
10x10
m
resoluon
DEM
available
for
the
catchment
• Ver3cal
direc3on
considering
the
same
layers
of
the
hydrological
model,
including
snowpack
Modeling
approach
Main
water
storage
components:
soil
moisture
in
the
rizosphere
and
in
the
vadose
zone,
groundwater
and
snowpack.

15. 15/50
Microgravity
observa3ons
and
hydrological
modelling
e-­‐mail:
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Model
M1 Model
M2

16. 16/50
Microgravity
observa3ons
and
hydrological
modelling
e-­‐mail:
alberto.bellin@unitn.it
Two
concurring
hydrological
models
(M1
and
M2)
having
the
same
high
efficiency
when
calibrated
using
only
streamflow
data
(single
objecve)…
Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul
0
5
10
15
20
25
30
Time
Discharge[m3
/s]
Observed
Sim. M1 (KGE
hydro
=0.92
Sim. M2 (KGE
hydro
=0.93

17. 17/50
Microgravity
observa3ons
and
hydrological
modelling
e-­‐mail:
alberto.bellin@unitn.it
…
show
remarkable
differences
when
adding
geophysical
informaon
(mul
objecve):
the
shape
of
the
Pareto
fronts
provide
useful
hints
to
idenfy
model
limitaons,
and
indicate
the
value
of
including
geophysical
data
to
beGer
constraint
of
the
inversion
procedure.

18. 18/50
Microgravity
observa3ons
and
hydrological
modelling
e-­‐mail:
alberto.bellin@unitn.it
Uncertainty
assessment
Results
Comparison
among
parameters
and
model
output
uncertainty
(model
M1)
for
two
significant
cases:
• only
streamflow
data
are
available
for
model
calibraon
(α=0)
• both
streamflow
and
microgravity
data
are
used
for
parameters
inference
with
a
balanced
aggregated
objecve
funcon
(α=0.5)
Among
the
soluons
obtained
during
the
exploraon
of
the
parameters
space,
only
those
with
KGEtot
differing
less
the
1%
from
the
maximum
value
(colored
squares
in
the
previous
slide)
have
been
retained
for
the
uncertainty
analysis.
The
analysis
has
been
focused
on
the
100%
confidence
bands
resulng
from
the
retained
soluons.
To
address
the
c l u s t e r i n g
effect
of
PSO.

19. 19/50
Microgravity
observa3ons
and
hydrological
modelling
e-­‐mail:
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Model
parameters
Results
The
values
of
the
parameters
are
normalized
with
respect
to
their
range
Confidence
bands
are:
• 45%
smaller
for
α=0.5
than
for
α=0
• well
distributed
between
0
and
1
(proper
choice
of
their
prior
range
of
variaon)

20. 20/50
Microgravity
observa3ons
and
hydrological
modelling
e-­‐mail:
alberto.bellin@unitn.it
Results
Water
storage
When
including
microgravity
data,
confidence
bands
reduce
by
• 26%
in
the
hillslope
• 95%
in
the
valley
boGom
The
accumulaon
of
water
in
the
valley
boGom
(due
to
a
parcularly
wet
summer
in
2010)
is
observed
only
for
α=0.5

21. 21/50
Microgravity
observa3ons
and
hydrological
modelling
e-­‐mail:
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Results
Water
table
fluctua3on
When
including
microgravity
data,
confidence
bands
reduce
by
• 39%
in
the
hillslope
• 93%
in
the
valley
boGom

22. 22/50
Microgravity
observa3ons
and
hydrological
modelling
e-­‐mail:
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Results
Streamflow
The
averaged
confidence
band
reduces
from
0.95
m3/
s
to
0.78
m3/s
when
microgravity
data
are
included
microgravity
data
not
only
allow
for
a
beGer
representaon
of
subsurface
storage,
but
they
also
reduce
uncertainty
of
streamflow.

23. 23/50
Microgravity
observa3ons
and
hydrological
modelling
e-­‐mail:
alberto.bellin@unitn.it
Results
Gravity
change
(for
α=0.5)
Valley
boGom
staons
(green)
show
a
good
agreement
between
simulated
and
observed
data,
while
hillslope
staons
(blue)
show
larger
deviaons.
In
general,
seasonal
variaons
are
well
reproduced.
Errors
are
likely
due
to
the
simplificaon,
dictated
by
the
lack
of
detailed
stragraphic
data,
of
assuming
the
water
table
moving
parallel
to
the
ground
surface.

24. 24/50
Microgravity
observa3ons
and
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e-­‐mail:
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Conclusions
Conclusions
Coupled
approach
tradi3onal
streamflow
data
+
me-­‐lapse,
spaally
distributed
microgravity
measurements
Main
results
• significant
reduc3on
of
the
uncertainty
associated
with
the
esmaon
of
the
me
variaons
of
the
main
hydrological
state
variables
(e.g.,
total
water
volume,
water
table
elevaon)
• the
major
effects
can
be
observed
in
the
valley
boaom
area
→ higher
density
of
gravimetric
staons
• uncertainty
on
simulated
streamflow
decreases
as
well
• gravimetric
data
allowed
for
a
beaer
idenficaon
of
the
hydrological
conceptual
model
including
the
definion
of
the
valley
boGom
region
Thank
you

25. 25/50
Microgravity
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26. 26/50
Microgravity
observa3ons
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Pareto
front

27. 27/50
Microgravity
observa3ons
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HydroSCAPE: a multi-scale
framework for streamflow routing in
large-scale hydrological models
Leno River at Rovereto

28. 28/50
Microgravity
observa3ons
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Open
un3l
30
October
2015

29. 29/50
Microgravity
observa3ons
and
hydrological
modelling
e-­‐mail:
alberto.bellin@unitn.it
Large
scale
hydrological
model
• Rainfall-­‐runoff
model
aimed
at
simulang
the
occurrence
of
floods
at
the
large
scale
(i.e.,
catchments
with
an
extension
ranging
from
1000
km2
up
to
the
connental
scale).
Parcular
emphasis
is
given
to
extreme
events.
• Possible
extensions
include
the
applicaon
to
several
water
resources
issues:
aquifer
recharge,
storage
dynamics,
water
resources
availability,
interacons
with
river
ecosystems
etc.
• Specific
aGenon
is
given
to
the
flow
rou3ng
algorithm,
which
has
been
indicated
as
an
aspect
of
LSM
and
global
hydrological
models
needing
some
improvement
(Clark
et
al,
WRR
2015)
REVIEW ARTICLE
10.1002/2015WR017096
Improving the representation of hydrologic processes in Earth
System Models
Martyn P. Clark1, Ying Fan2, David M. Lawrence1, Jennifer C. Adam3, Diogo Bolster4, David J. Gochis1,
Richard P. Hooper5, Mukesh Kumar6, L. Ruby Leung7, D. Scott Mackay8, Reed M. Maxwell9,
Chaopeng Shen10, Sean C. Swenson1, and Xubin Zeng11
1
National Center for Atmospheric Research, Boulder, Colorado, USA, 2
Department of Earth and Planetary Sciences,
Rutgers University, New Brunswick, New Jersey, USA, 3
Department of Civil and Environmental Engineering, Washington
State University, Pullman, Washington, USA, 4
Department of Civil Environmental Engineering and Earth Sciences,
University of Notre Dame, South Bend, Indiana, USA, 5
The Consortium of Universities for the Advancement of Hydrologic
Science, Inc., 6
Nichols Schools of Environment, Duke University, Durham, North Carolina, USA, 7
Pacific Northwest National
Laboratory, Richland, Washington, USA, 8
Department of Geography, University at Buffalo, State University of New York,
Special Section:
The 50th Anniversary of Water
Resources Research
Key Points:
Land model development can
benefit from recent advances in
hydrology
Accelerating modeling advances
requires comprehensive
benchmarking activities
Water Resources Research
PUBLICATIONS

30. 30/50
Microgravity
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• Some
key
scien3fic
ques3ons:
• How
climate
change
may
modify
the
stascs
of
floods
occurring
IN
large
river
basins?
• What
are
the
consequences
on
the
management
of
the
hydraulic
structures?
• Can
we
determine
the
uncertainty
associated
with
the
esmate
of
extreme
hydrological
events?
Large
scale
hydrological
model
LETTERS
PUBLISHED ONLINE: 21 OCTOBER 2012 | DOI: 10.1038/NCLIMATE1719
Hydroclimatic shifts driven by human water use
for food and energy production
Georgia Destouni*, Fernando Jaramillo and Carmen Prieto
Hydrological change is a central part of global change1–3
. Its
drivers in the past need to be understood and quantified for ac-
curate projection of disruptive future changes4
. Here we anal-
yse past hydro-climatic, agricultural and hydropower changes
from twentieth century data for nine major Swedish drainage
basins, and synthesize and compare these results with other
regional5–7
and global2
assessments of hydrological change by
irrigation and deforestation. Cross-regional comparison shows
similar increases of evapotranspiration by non-irrigated agri-
culture and hydropower as for irrigated agriculture. In the
Swedish basins, non-irrigated agriculture has also increased,
whereas hydropower has decreased temporal runoff variability.
A global indication of the regional results is a net total increase
of evapotranspiration that is larger than a proposed associated
planetary boundary8
. This emphasizes the need for climate
and Earth system models to account for different human uses
of water as anthropogenic drivers of hydro-climatic change.
The present study shows how these drivers and their effects
can be distinguished and quantified for hydrological basins on
different scales and in different world regions. This should en-
courage further exploration of greater basin variety for better
understanding of anthropogenic hydro-climatic change.
Drivers of freshwater changes are multiple and difficult to
distinguish and quantify9–12
. Global increase of evapotranspiration
while instrumental hydro-climatic data have become available for
direct analysis of their hydrological effects.
In this study, we investigate twentieth century land–water use
and hydro-climatic data, and interpret them by the fundamental
water balance quantification P ET R DS = 0 (where P is
precipitation, R is runoff and DS is water storage change) for
nine major Swedish drainage basins (Fig. 1a). The investigation
has three goals. First, a basin-scale distinction of hydrological
changes and their drivers in the Swedish basins, which among
them represent different land–water uses (Fig. 1a, green: dominant
non-irrigated agriculture, red: dominant hydropower, blue: with
essentially unregulated rivers and little agriculture) and hydro-
climatic conditions in terms of surface temperature (T; Fig. 1b)
and P (Fig. 1c). Second, cross-regional result comparison across
the complementary Swedish, Aral and Mahanadi basins (Fig. 1a),
representing an even wider range of different hydro-climatic
conditions (Fig. 1b,c) and land–water uses (irrigated and non-
irrigated agriculture, hydropower, unregulated). Third, cross-
scale/method comparison between the extrapolation implications
of the regional basin results and previous spatial estimates of
global hydrological changes and drivers2
. Hydrological, and other
physical and ecological science communities11,17,18
have identified
fundamental needs for such synthesis and comparisons along
climatic and other gradients.
Resilience of river flow regimes
Gianluca Bottera
, Stefano Bassoa,b
, Ignacio Rodriguez-Iturbec
, and Andrea Rinaldoa,d,1
a
Department of Civil, Architectural, and Environmental Engineering, University of Padua, I-35100 Padua, Italy; b
Department of Water Resources and Drinking
Water, Swiss Federal Institute of Aquatic Science and Technology, CH-8600 Dübendorf, Switzerland; c
Department of Civil and Environmental Engineering,
Princeton University, Princeton 08540, NJ; and d
Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique
Fédérale de Lausanne, Lausanne CH-1015, Switzerland
Contributed by Andrea Rinaldo, June 25, 2013 (sent for review March 3, 2013)
Landscape and climate alterations foreshadow global-scale shifts of
river flow regimes. However, a theory that identifies the range of
foreseen impacts on streamflows resulting from inhomogeneous
forcings and sensitivity gradients across diverse regimes is lacking.
Here, we derive a measurable index embedding climate and
landscape attributes (the ratio of the mean interarrival of stream-
flow-producing rainfall events and the mean catchment response
time) that discriminates erratic regimes with enhanced intraseaso-
nal streamflow variability from persistent regimes endowed with
regular flow patterns. Theoretical and empirical data show that
from the censoring operated by catchment soils on daily rainfall,
and they are modeled as a spatially uniform marked Poisson
process with mean depth α [L] and mean frequency λ ½T−1
Š. Al-
though α quantifies the average daily intensity of rainfall events, λ
is smaller than the underlying precipitation frequency because the
soil–water deficit created by plant transpiration in the root zone
may hinder the routing of some inputs to streams (Eq. S3).
Therefore, λ crucially embeds rainfall attributes; soil/vegetation
properties; and other climate variables, such as temperature, hu-
midity, and wind speed.

31. 31/50
Microgravity
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Characteristics of the model
Macro-­‐regional/connental
hydrological
model
for
flood
assessment.
Possible
applicaon
to
water
resources
problems:
aquifer
recharge,
storage
dynamics,
water
resources
availability,
interacons
with
river
ecosystems
etc.
Discrezaon
into
large
macro-­‐cells:
computaonal
requirements,
direct
coupling
with
climate
models,
focus
on
large
scale
dynamics.
Simplicity:
a
few
parameters,
possibly
linked
to
physical
properes
for
which
distributed
informaon
is
easily
available.
Linearity
of
the
processes:
in
order
to
ensure
a
simple
and
efficient
numerical
parallelizaon.
Rigorous
upscaling
of
the
spaal
distribuon
of
water
fluxes.
Physically
based
(WFIUH
approach).

32. 32/50
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Delineaon
of
the
catchment
area.
Pre-processing

33. 33/50
Microgravity
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Pre-processing
Discre3za3on
of
the
domain
into
a
number
of
macrocells.
Delineaon
of
the
catchment
area.

34. 34/50
Microgravity
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Pre-processing
Discre3za3on
of
the
domain
into
a
number
of
macrocells.
Extracon
of
the
river
network
from
the
DEM.
Delineaon
of
the
catchment
area.

35. 35/50
Microgravity
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Discre3za3on
of
the
domain
into
a
number
of
macrocells.
Extracon
of
the
river
network
from
the
DEM.
Iden3fica3on
of
target
points
(hereater
referred
to
as
nodes):
gauged
secons,
residenal
areas,
hydraulic
structures,
reservoir
etc.
Definion
of
the
width
func3on
for
each
node-­‐macrocell
pair.
Delineaon
of
the
catchment
area.
Pre-processing

36. 36/50
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Structure of the model
Water
balance
dynamics
at
the
hillslope
scale
are
simulated
using
a
simple
rainfall-­‐
runoff
model
(e.g.,
SCS)
Hillslope
drains
into
the
river
network,
where
water
is
transferred
towards
nodes,
with
a
given
channel
velocity.
WFIUH
approach
(e.g.
Rinaldo,
Marani,
Rigon,
1991)
The
streamflow
simulated
at
each
node
is
given
by
the
sum
of
the
contribuons
of
all
macrocells
contribung
to
that
node.
The
model
is
linear
→
the
streamflow
generated
at
each
macrocell
is
evaluated
independently
from
the
others.

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Microgravity
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Characteris3cs:
• Few
reservoir
and
hydraulic
structures.
• Snow
precipitaon
and
melng
is
negligible.
• Historical
series
(a
few
decades)
of
streamflow,
rainfall
and
air
temperature
are
available.
• Surface
area
of
the
catchment
equal
to
4116
km2.
• 5
nodes,
in
correspondence
to
gauging
secons.
Example application: Upper Tiber

38. 38/50
Microgravity
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e-­‐mail:
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Example application: Upper Tiber

39. 39/50
Microgravity
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3
grid
sizes:
5,
10
and
50
km
Example application: Upper Tiber

40. 40/50
Microgravity
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Width
funcons
at
Ponte
Nuovo
obtained
aggregang
the
original
20
m
DEM
to
5,
10
and
50
km:
Models
in
which
the
geomorphological
descripon
of
the
drainage
network
has
the
same
scale
of
the
computaonal
grid,
suffer
from
a
deterioraon
of
the
geomorphological
response
of
the
watershed.
This
is
not
the
case
of
the
proposed
formulaon,
in
which
the
width
funcon
maintains
all
the
informaon
derived
from
the
original
DEM
Example application: Upper Tiber
Figure 5. Width functions of the Upper Tiber river basin at Ponte Nuovo (PN) outlet (4116 km2
) derived from
DEMs obtained aggregating the original 20 m DEM to 5, 10 and 50 km (lower panels). The width function
obtained with the original 20 m DEM is also shown with a red line.
to represent the travel time distribution at the hillslope scale. In this case, rescaling may be obtained
by using a hillslope specific velocity V` Vc.

41. 41/50
Microgravity
observa3ons
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Rainfall-­‐runoff
models
Model
parameters
v λ cS kH
SCSLR
H
✓ ✓ ✓ ✓
Data requirements
• SCSLRH:
SCS
+
linear
reservoir
for
the
hillslope
QUESTO
E’
IL
MODELLO
DI
PRODUZIONE
USATO
IN
HESS,
LE
ALTRE
SLIDE
LE
HO
NASCOSTE

42. 42/50
Microgravity
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Rainfall-­‐runoff
models
• none:
runoff=rainfall
Model
parameters
v λ cS k kH
none ✓ - - - -
SCS ✓ ✓ ✓ - -
SCSLR
I
✓ ✓ ✓ ✓ -
SCSLR
H
✓ ✓ ✓ - ✓
SCSLR
HI
✓ ✓ ✓ ✓ ✓
Data requirements

43. 43/50
Microgravity
observa3ons
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e-­‐mail:
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Rainfall-­‐runoff
models
• none:
runoff=rainfall
Model
parameters
v λ cS k kH
none ✓ - - - -
SCS ✓ ✓ ✓ - -
SCSLR
I
✓ ✓ ✓ ✓ -
SCSLR
H
✓ ✓ ✓ - ✓
SCSLR
HI
✓ ✓ ✓ ✓ ✓
Data requirements
• SCS:
Soil
Conservaon
Service
–
Curve
Number
Methodology

44. 44/50
Microgravity
observa3ons
and
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modelling
e-­‐mail:
alberto.bellin@unitn.it
Rainfall-­‐runoff
models
• none:
runoff=rainfall
Model
parameters
v λ cS k kH
none ✓ - - - -
SCS ✓ ✓ ✓ - -
SCSLR
I
✓ ✓ ✓ ✓ -
SCSLR
H
✓ ✓ ✓ - ✓
SCSLR
HI
✓ ✓ ✓ ✓ ✓
Data requirements
• SCS:
Soil
Conservaon
Service
–
Curve
Number
Methodology
• SCSLRI:
SCS
+
linear
reservoir
for
infiltraon

45. 45/50
Microgravity
observa3ons
and
hydrological
modelling
e-­‐mail:
alberto.bellin@unitn.it
Rainfall-­‐runoff
models
• none:
runoff=rainfall
Model
parameters
v λ cS k kH
none ✓ - - - -
SCS ✓ ✓ ✓ - -
SCSLR
I
✓ ✓ ✓ ✓ -
SCSLR
H
✓ ✓ ✓ - ✓
SCSLR
HI
✓ ✓ ✓ ✓ ✓
Data requirements
• SCS:
Soil
Conservaon
Service
–
Curve
Number
Methodology
• SCSLRH:
SCS
+
linear
reservoir
for
the
hillslope
• SCSLRHI:
SCS
+
linear
reservoir
for
the
hillslope
+
linear
reservoir
for
infiltraon
• SCSLRI:
SCS
+
linear
reservoir
for
infiltraon

46. 46/50
Microgravity
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e-­‐mail:
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Calibraon
at
Ponte
Nuovo
Example application: Upper Tiber

47. 47/50
Microgravity
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modelling
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Mul
site
validaon
Example application: Upper Tiber

48. 48/50
Microgravity
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and
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modelling
e-­‐mail:
alberto.bellin@unitn.it
Mul
event
validaonExample application: Upper Tiber

49. 49/50
Microgravity
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Conclusions
• We proposed a routing scheme independent on
the gridding of the LSM;
• “perfect” upscaling with geomorphological
dispersion preserved;
• The scheme is computational efficient (major
computational burden in the preprocessing);
• Routing dependent on 2 parameters.

50. 50/50
Microgravity
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and
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e-­‐mail:
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THANK YOU