GEOtop 2008

The rational behind GEOtop:
through its historical
development and applications
R. Rigon
Dipartimento di Ingegneria Civile ed Ambientale -
CUDAM
Università di Trento

Four or Five problems we wanted to
investigate

Rainfall–Runoff spatial patterns

Problem : We cannot currently predict the spatial pattern
of watershed response to precipitation and cannot
quantitatively describe the surface and subsurface
contributions to streamflow with enough accuracy and
consistency to be operationally useful.

Critical issues: Watershed runoff and streamflow are
affected by heterogeneity in soil hydraulic properties,
landscape structural properties, soil moisture profile,
surface–subsurface interaction, interception by plants,
snowpack, and storm properties.

(Committee of hydrological Sciences NRC, 2003)

Snow mantle evolution and ablation
Problem: We would like to predict the spatial pattern of
snow cover, its volumes and its effects on runoff with
enough accuracy and consistency to be operationally
useful.

Critical Issue: Also in this case we know enough of the
snow physics “in a point” but we do not have many tools
to understand the snow cover effects on larger,
catchment scales.

Related problem: snow avalanches

Soil freezing and permafrost
Problem: We would like to predict the spatial pattern of
soil temperatures even in complex terrain and in presence
of phase transitions

Critical Issue: Soil freezing introduces high non linearities
at low temperatures.

Related problem: snow avalanches

Landslide and debris flow initiation
Problem: We cannot currently predict, the triggering of
shallow landslides which eventually turns into a debris or a
mudflow.

Critical Issue: Initial and boundary conditions. Landslide
initiation is affected by heterogeneity in soil hydraulic and
geotechnical properties, landscape structural and
geological properties, soil moisture profile, surface–
subsurface interactions.

We did not forget Ecohydrology ...
but we will not discuss it here
Problem: We wouldlike to better understand the interactions
between soils, hydrology and plants.

Critical Issues: the biological system are highly non linear.
The basic physiological laws are not really known (or
hydrologist ignore many of them)

Committee of hydrological Sciences NRC, 2003:

“Although our understanding of individual processes is
improving, the integration of that body of knowledge in
spatially distributed predictive models has not been
approached systematically”.

This talk is not about new equations or new paradigms: is
mostly abot consistency of the modeling approach. It
show a tool we use to lear about the hydrological
processes at the small scales.

A small tribute to Stephan Gruber

We present here the evolution of the GEOTOP models and
discuss their limitation

GEOtop 0.5 was the ancestor (1997)

Paolo Verardo and Marco Pegoretti coded it


GIUH +
Kinematic wave+
Bucket model



Radiation Physics
Penman-Monteith

GIUH +
Kinematic wave+
+

Bucket model


GEOtop 0.5 (1997)
P
Qsup

∂Qsup ∂Qsup
+ c(x) = c(x) qL
I ∂t ∂s
c ∝ y(x)m
Qsub
Qsub = T ∇z b

Z Z
x W (x, τ) x − u(t − τ)2
t L
Qc(t) = exp −
dτ dx
4DL(t − τ)
4πDL(t − τ)
0 o

Eagleson, 1971; Beven and Kirkby, 1979; Rodriguez-Iturbe and Valdes, 1979;
Rinaldo et al., 1991

GEOtop 0.5 (1997)

ET Rn

Δ/λ(Rn − G) + ρ/ra δqa
ET =
1 + Δ/γ + rg /ra

Rn = [sh R ↓SW + V R ↓SW D ] (1 −V α) +
P
V εs R ↓LW −V εs σTs4

G

Brutsaert, 1975; Iqbal, 1983; Garrat, 1992, Enthekabi, 1997 and many others

Calculating ET in highly complex terrain needs a proper
treatment of radiation physics (including the effect of the
vie angle and the shadowing). Below you see how much
this is a limit for radiation to arrive to the surface.

GEOtop 0.5 (1997)

ET Rn

Δ/λ(Rn − G) + ρ/ra δqa
ET =
1 + Δ/γ + rg /ra

Rn = [sh R ↓SW + V R ↓SW D ] (1 −V α) +
P
V εs R ↓LW −V εs σTs4

G

Albedo is a key factor too. It can be easily detected from
Earth Observation (EO) products and simple modelling of
the canopy evolution during the seasons (actually still not
included in GEOTOP)

GEOtop 0.5 Real ET

After Feddes et al, 1988

Real ET is obtained cutting the potential ET in dependence
of water availability. In complex terrain water tend to
accumulate in lowland concave - convergent sites.

Many large scale hydrological models pretends to give ET
estimation by neglecting this fact ;-)

GEOtop 0.5 worked well for ﬂood predictions and weekly
ET (after a proper parameter calibration). It also showed
some dynamics on the soil moisture storage (dS/dt = 0 in
some models!) and redistribution at catchment scale,
HOWEVER ....

It could not describe properly the vertical distribution of
soil moisture in soils (essential to landslide forecasting and
emissivity estimations). Moreover, using air T for soil T
(Ts) was a huge limitation.

GEOtop 0.75 (2000)

Radiation Physics
Energy budget

GIUH +
integration

Kinematic wave+
Bucket model

Code integrations by Giacomo Bertoldi

GEOtop 0.75 is conceived to integrate the full energy
balance. As a consequence Ts becomes a variable of the
model (this obviuosly complicates GEOtop) but increases
at the same the possibility to check its behavior (Ts or its
radiative effect is a measurable quantity): we add
complexity but at the same time we add observables. Ts is
strongly affected by water content.

GEOtop 0.75 Energy Balance (2000)

Rn Qp
ET
H

dE/dt

Qm
G

dE dTs
= Cp = Rn − H − ET + Q p − G − Qm
dt dt
H = ρ c pCH u (T s − Ta)
ˆ

ET = λρ Ce u (q∗(Ta) − q∗(Ta) Ua)
ˆ

GEOtop 0.75 (2000): Turbulent fluxes
appear!

Ts>Ta

CH, CE ↑

CH, CE ↓
Ts<Ta

Aerodynamic roughness length

Similarity theory by Louis (1979)

Pointwise calibration of fluxes
Little Washita (OK) SGP 97 data set

Key parameters: roughness length, initial soil moisture

We did also simulation of the soil moisture content in the
Washita basin. However the soil moisture content given by
the model has no vertical distribution and any comparison
with the SGP97 dataset CANNOT be very reliable. Below
you see results of the model for other cases studies that
show the opportunities that a model like GEOtop offers.

Mean seasonal ET at Serraia (TN)

Spring
Winter

84 96 108 120 W/m2
0 12 24 36 48 60 72

Fall
Summer

Hydrological Balance 1998-2000
Serraia (TN)
Grafico bilancio del bacino del Lago della Serraia (1998 - 2000)
1.200 363.4
1.100 333.1
1.000 302.9
Portata media mensile (mc/s)

0.900 272.6

Intensita (mm/mese)
0.800 242.3
0.700 212.0
0.600 181.7
0.500 151.4
0.400 121.1
0.300 90.9
0.200 60.6
0.100 30.3
0.000 0.0
-0.100 -30.3
-0.200 -60.6
ma 98

ma 99

ma 00
no 98

no 99

no 00
lug 8
ag 98

ott 8

lug 9
ag 99

ott 9

lug 0
ag 00

ott 0
ap 98

ap 99

ap 00
ma -98

ma -99

ma -00
giu 8

giu 9

giu 0
ge 98

ge 99

-0 0
feb 8

se 8

feb 9

se 9

feb 00

se 0
dic 8

dic 9

dic 0
-9

t-9

-9

t-9

-0

t-0
g-9

g-9

g-0
n-9

o-9

n-9

o-9

o-0
v-9

v-9

v-0
-

-

-
-

-

-
-

-

-
r-

r-

r-
-

-
n-
r

r

r
ge

P - precipitazione ET - evapotraspirazione
Tempo (mese-anno) Inv - volume invasato (accumulo) R - rilascio

One interesting thing to check would be the sensitivity
of the hydrological balance partition to the parameter
set.

There is a strong spatial variability of vertical surface
ﬂuxes: do they induce feedback effects ?
Are those processes negligible at larger scales ?

A more accurate ABL model would be necessary to try
an answer.

We cannot compare our model result with ESTAR
because we do not have vertical distribution of soil
moisture.

What happens when no topographic gradient is
present ?

GEOtop 0.875 (2003)

Radiation Physics
Energy budget

GIUH +
integration

Kinematic wave+
Richards +
Soil freezing &
Snow Cover

Code integrations by Davide Tamanini

Richards’ equation is solved
∂ψ
= ∇ · (K(ψ)∇(ψ + ηz)) + qs
σ(ψ)
∂t
1/n
1/m
θr − θs
1
ψ= −1
θs − θr
α
2
m
1/m
ν
θr − θs θr − θs
K(θ) = KS 1− 1−
θs − θr θs − θr

Horton Overland Flow Dunne
Saturation
Overland Flow

Surface Layer

Unsaturated Layer

Saturated Layer:

Modiﬁed from Abbot et al., 1986
Richards, 1931; van Genuchten, 1980; Mualem, 1976; Veerecken, 1990; Sposito, 1997,
Putti et al, 2004

We acknowledge the SHE model however GEOtop REALLY
integrate the energy balance. Still GEOtop is 1D for energy
ﬂuxes but it is a complete 3D system for mass ﬂuxes. As
one can notice we used van Genuchten and Mualem
parametrizations of Richards equation. Under this
hypothesis Sposito 1997 shows that the equation is
almost scale invariant (at the price to introduce a suitable
factor in block effective hydraulic conductivities).
Parameters are obtained by the Veerecken pedotransfer
function.

Snow cover is modeled
(single layer)

Tarboton and Luce, 1996; Zanotti et al, 2004

Snow cover and soil freezing cannot be neglected in
mountain areas and if we want to model the hydrological
cycle during the whole year. Because water viscosity
strongly depends on temperature we added it to the
model as a ﬁrst step to have a consistent thermodynamic
system. As you ﬁnd below, parametrization of subgrid
variability is still needed also at these scales. Finally we
could realistically compare GEOtop and ESTAR.

- Rilling is parametrized

- Conductivity is made dependent on Ts

ESTAR vs GEOtop soil moisture
evolution

Jackson T.J., http://hydrolab.arsusda.gov/sgp97; Jackson et al, 1995

Saturation overland flow in a headwater
catchment:
application to Solstice Basin (CA)

in collaboration with Bill Dietrich and Norman Miller (Berkeley University)

The Solstice Basin (CA)

Headwater catchment located in Marin County, CA,
area 16’000 m2;
Colluvial soil: maximum thickness from 2 to 5.5 meters
in the hollows, from 0.2 to 0.7 m on sideslopes
Monitored during years 1986-’87 (C. Wilson, W.E.
Dietrich)
Basin and bedrock topography
120 piezometers on sideslopes and hollows
Saturated source area measurement.

Experimental evidence:
February 1986 storm

0 80

10 70

20 60

30 50
Rainfall mm/6h

Streamflow l/s
40 40

50 30

60 20

70 10

80 0
11- 12- 13- 14- 15- 16- 17- 18- 19- 20- 21- 22- 23- 24-
Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb

Solstice raingage mm/6h Pan Toll Raingage mm/6h
Measured Streamflow l/s

• measured rainfall each 6 h
• measured stream flow each 6 h • measured saturated area:
“squishy soil”
• Total storm 400 mm in 10 days

Experimental evidence: hypothesis on the hydrologic
behavior (Wilson and Dietrich, 1988)


a) Cross - hollow direction
- Deep water table in the sideslopes
- Infiltration gradients in the sideslopes
- Exfiltration gradients in the hollows



b) Long - hollow direction
- Saturation overland flow
- Shallow water table
- Effects of local conductivity changes



b) Long - hollow direction
- Saturation overland flow
- Shallow water table
- Effects of local conductivity changes

c) Expansion of saturation saturated area
- Expansion beginning from the
nose of the hollows

GEOtop model
settings
Soil and bedrock properties:
Sideslopes: shallow conductive bedrock Conductivities against depht: model with 8 layers
Solstice1 Solstice2 Sideslope Model
K decreasing with depth
0
Hollows: loamy-sand thick
colluvium, deep impermeable bedrock, 200

some conductive points (Lehre et al. ,
400
1986)

depht cm
600

Soil parameters settings:
800
• 8 soil layers with 20 m thickness
• Impermeable boundary condition 1000
• Spin-up of 3 years
1200

Bedrock shape variation: 1400
• With uniform soil profile 1.00E-01 1.00E-02 1.00E-03 1.00E-04 1.00E-05 1.00E-06 1.00E-07
Kv cm/s
• With measured bedrock shape
• Bedrock with different permeability

GEOtop model results

• Surface conductivity 0.01 m/s
• Subsurface conductivity in first layer 0.001 m/s

Either slow turbulent surface flow or quick shallow subsurface strom flow:
equifinallity or preferential flow evidence?

Saturated area - water content first layer 5 cm

0 80

10 70

20 60

30 50

Rainfall mm/6h

Streamflow l/s
40 40

50 30

60 20

70 10

80 0
11- 12- 13- 14- 15- 16- 17- 18- 19- 20- 21- 22- 23- 24-


Hollows partially saturated at the beginning of the storm


0 80

10 70

20 60

30 50

Rainfall mm/6h

Streamflow l/s
40 40

50 30

60 20

70 10

80 0
11- 12- 13- 14- 15- 16- 17- 18- 19- 20- 21- 22- 23- 24-


Different behavior of the hollows and the side slopes at the
peak


0 80

10 70

20 60

30 50

Rainfall mm/6h

Streamflow l/s
40 40

50 30

60 20

70 10

80 0
11- 12- 13- 14- 15- 16- 17- 18- 19- 20- 21- 22- 23- 24-


Measured discontinuous patterns at the end of the event

total head gradient- first layer 5 cm

Mostly topographically driven

total head gradient- first layer 5 cm

Not only topography drives down-slope water flow but also suction potential drives
the up-slope expansion of the saturated area. Delay in basins response, increase of
saturated area

Mass movements
at Sauris (UD)
basin

Mostly worked out by S. Simoni and F. Zanotti

Geotop and trent-2d
•DTM •Soil characterization
•Meteo data (hydraulic parameters)
•Rain data •land use
•vegetation

GEOtop 0.875
Geotop project

3D Mass and Energy
budgets at catchment
scale

•Soil characterization
GEOtop-FS (geotechnical parameters)

•dynamic probability of
landslide triggering
•sediment availability

•liquid and solid
discharge 1
•detailed topography
Run-out module
trent-2d

•closure relations
(concentration & shear
•run-out distance stress)
•sediment and transport
•erosion-deposit height
•ﬂow velocity
•hazard maps

Simoni et al., Hydrol. Proc., 2008

model structure data exploited
soil Cover
GEOtop 0.875 type and weight of soil
(hydraulic parameters)
Geotop project

cover
•land use
3D Mass and Energy
•vegetation
budgets at catchment
Geomorphological
scale
analysis
•DTM
•Meteo data
Geophysics
•Rain data
GEOtop-FS Soil Depth
Water content
•dynamic probability of
landslide triggering Geological info
(geotechnical parameters)
•sediment availability
stratigraphy
quaternary covers

traditional photo
•liquid and solid
Run-out module
trent-2d

interpretation
discharge
•detailed topography rock presence
•run-out distance •closure relations erosion signatures
•erosion-deposit height human activities
(concentration & shear
•ﬂow velocity stress)
•hazard maps •sediment and transport Climate and Weather
models
Rainfall, wind...

Earth Observations

Effects of topography on hydrological balance

Elevations 25% Elevations 40% Elevations 50%

Elevations 100%
Elevations 60% Elevations 125%

Security Exit
2, 1 year simulation
Serraia Basin, 15 km


Security Exit
Bertoldi et al.,JHM, 2006


Bertoldi et al.,JHM, 2006

An application to the Valsugana valley
Mostly worked out by S. Endrizzi

x
● Pergine Borgo Valsugana

● Levico

● Caldonazzo

GCMs

Step1: Dynamical downscaling

RCMs

Step2: Bias-correction and data
disaggregation

From daily to
hourly data

Step3: Rainfall-runoff model
calibrated

Impacts of
climate change

Monthly mean discharge (m3/s) from HAD_P model, for historic
(1961-1988) and simulated control and future scenario.
Mostly worked out by L. Forlin

Results indicate how the approximation is excellent from May to
July, with overestimation during autumn and underestimation in
winter.The ﬂow is predicted substantially to increase from
October to April and decrease during summer months.

Monthly mean ﬂuxes (mm/month) from HAD_P model,
for control and future scenario.

Results indicate a future seasonal variability with drier summers and
wettest winters.

Monthly mean snow cover (mm/month) and surface temperature
(°C) from HAD_P model, for control and future scenario.

Results indicate a substantial decrease in snow cover and increase
in surface temperature.

GEOtop 0.9375
(2006-2008)

GIUH +
Radiation Physics
Energy budget

Kinematic wave+
Richards +
Soil freezing &
Snow Cover

Mostly worked out by S. Endrizzi, E. Cordano, s, Simoni e M. Dall’Amico

GEOtop 0.9375
(2006-2008)

Radiation Physics

improved by
accepting several
parameterizations

GEOtop 0.9375
(2006-2008)
Multilayer parameterization
For each layer a system of 5 equations is solved

1  ∂W ∂Qw 
θw = +
 
Liquid and solid water budget
ρ w  ∂t ∂z 
equations
1  ∂W ∂Qi 
θi =  + 
ρ i  ∂t ∂z 
Energy
€
budget
 ∂T 
∂W ∂  ∂T  ∂ (QwUw)
∂T  k  = − Rn + H + L
C + Lf = k +
equation
 ∂z 
∂T ∂z  ∂z 
∂t ∂z
€

Continuity equation
θ w +θ i +θ v = 1
€
€
W ≠ 0 if T = 273.15K ; W = 0 if T ≠ 273.15K
Link phase change - temperature
€

€

GEOtop 0.9375
(2006-2008)
MODIS GEOtop model mm SWE

24 OTTOBRE 2003

GEOtop 0.9375
(2006-2008)
MODIS GEOtop model mm SWE

17 January 2004

Application of GEOtop to the
Adamello-Mandrone Glacier

(Trentino, Italy)

Distributed results
mm w.eq.

Mass balance 1 Oct 2004 - 30 Sep 2005 73

Comparison model - measurements
Ice melting after snow disappearance

• Problems in estimating the snow disapperance date (underestimation
of snow precipitation measured with the classical rain gauge)
• Good agreement, in particular for stakes 2 and 7 74

The Stubaital case

by G. Bertoldi, P. Rastner, C. Notarnicola, and U. Tappeiner
Data from G. Wolfhart, Institute of echology, Innsbruck

The Stubaital case

Data from G. Wolfhart, Institute of Echology, Innsbruck
Initial assumption: time constant vegetation density

Model - Observations comparison
Snow - free season 2005
Model Obs Model-Obs
2
H [W/m ] 26 20 6
2
LE [W/m ] 88 85 3
G [W/m2] 4 4 0
2
H [W/m ] 80 76 4
Ts [K] 11 12 -1
SWC [ ] 0.48 0.37 0.11

• Can we perform a process based calibration ?
• Can we avoid parameter equi-ﬁnality ?
Yes if are considered …
• overall consistency (different components of the water and energy balance)
• different time and spatial scales.

The Stubaital case

Preliminary simulation with UNIFORM LAND COVER; meadow valley model calibration.

LANDSAT LST GEOTOP LST
ΔT=LSTGeoTop-LSTLandsat

• The illuminations alone (sun incidence angle, shadows) explains 71% of the variability.
• Best agreement for valley meadows and alpine pasture.
• Major differences for high elevations regions (glaciers and south facing slopes) and for forests.

What are the dominant processes ?
What is the optimal model complexity level ?

The Stubaital case


GEOtop Development
0.5

0.75
GEOTOP-SF
0.875 0.75

0.9375 GEOTOP-SF
0.875
0.9375- EO
Automatic
Calibration
GEOTOP-SF
0.9375
Data GEOFRAME
Assimilation

GEOtop will be splitted in
components and the components
managed by JGrass. The
=

components will be based on the
OpenMI standard.

GEOFRAME: JGrass 3.0

http://www.jgrass.org

JGrass (www.jgrass.org) is a full
featured GIS system based on udig
(www.refractions.net ). It allows
=

communication to databases,
internet and provides an interfaces
to components’ Input-Outputs

GEOFRAME: JGrass 3.0

www.jgrass.org

GEOFRAME

external
users web database

Interfacce
(Java/JGRASS)

Tools di analisi Modelli
(UNITN/R) (UNITN/OpenMI)

Database
(PostgresSQL/PostGIS/CUAHSI)

GEOFRAME: JGrass 3.0 structure
JGrass
Eclipse RCP
uDig
JGrass
PostGIS
J-Console engine
Postgres GIS engine
OpenMI
Hydrolog. model
Horton Machine

Statistical anal.
WEB UIBuilder
services

WMS HSQLDB
WFS-T
WPS
GRASS GIS

udig itself lives upon the Rich
Client Platform given by Eclipse
=

(www.eclipse.or). JGrass uses also
HSQL as internal database, and a
custom interfaces builder to give a
GUI to any command.

GEOFRAME: OpenMI
Serially linked models by ﬁle transfer. Feedback loops not
represented.

Interface

Interface
Model Interface
Interface
Model
Data
File Model
Model
File
Data
Data File
Data

88

GEOFRAME: OpenMI
Serially linked models by ﬁle transfer. Feedback loops not
represented.

Interface

Interface
Model Interface
Interface
Model
Data
File Model
Model
File
Data
Data File
Data

from HarmonIT Roger Moore’s, CEH, Wallingford, UK presentation
88

OpenMI gives a set of standard
interfaces to make model
=

components to communicate, even
having feedbacks between
components, and can link
components programmed in C,
FORTRAN or PASCAL

GEOFRAME: OpenMI tool
Connection
1-D
River

3-D
Sea
3-D

2-D
Estuary

Graph tool
18
90

GEOFRAME: OpenMI tool
Connection
1-D
River

3-D
Sea
3-D

2-D
Estuary

Graph tool
18
from HarmonIT Roger Moore’s, CEH, Wallingford, UK presentation
90

OpenMI provides methods to
change at run-time the model
=

configuration. Different components
doing the same job can be used in
alternative seamlessly.

GEOFRAME: OpenMI

Monitored Evapotranspiration
data

Weather Surfaces Surfaces
Generator Interception Runoff

Weather
Subsurface Flow
Forecast

18
http://www.openmi.org, http://www.openmi-life.org/, http://public.wldelft.nl/display/OPENMI/Home
92

GEOFRAME: OpenMI

Channel Routing
Evapotranspiration
1

Surfaces Channel Routing
Runoff II

Channel Routing
Subsurface Flow
III

18
http://www.openmi.org, http://www.openmi-life.org/, http://public.wldelft.nl/display/OPENMI/Home
93

JGrass provides also the database
to store and retrieve simulations
=

Still, as the painting by Rosseau, shows GEOtop is a
mosaic of “realistic pieces” inside an improbable
ecosystem (not to speak of other model). Things are
however getting better ;-)

Core contributors

DICA Dipartimento di Ingegneria Civile ed Ambientale
CUDAM Centro Universitario per la Difesa dell’Ambiente Montano
riccardo rigon

GEOtop Developers Team (GDT)
stefano endrizzi, emanuele cordano, christian tiso, giacomo
bertoldi

Mountain-eering: matteo dall’amico, silvia simoni, fabrizio
zanotti.

HYDROLOGIS: andrea antonello, silvia franceschi,
www.hydrologis.com

Thank you for your attention
Numerics

Physics
Dance, Henry Matisse, Hotel Biron early 1909

Analytics
Hydrology

Geography

Comprehensive GEOtop Bibliography

•Simoni, S., F. Zanotti, G. Bertoldi and R. Rigon, Modelling
the probability of occurrence of shallow landslides and
channelized debris ﬂows using GEOtop-FS, accepted
for Hydrol. Proc., published on-line, Dec 2007

•Rigon R., Bertoldi G e T. M. Over, GEOtop: A distributed
hydrological model with coupled water and energy
budgets, Jour. of Hydromet., Vol. 7, No. 3, pages 371-
388., Vol. 7, No. 3, pages 371-388.

•Bertoldi, G., R. Rigon & T. M. Over, Impact of watershed
geomorphic characteristics on the energy and water
budgets, Jour. of Hydromet., Vol. 7, No. 3, p. 371-
388. Vol. 7, No. 3, pages 389 - 394, 2006.
B-

•Simoni S., Zanotti F., Rigon R., Squarzoni C., Approccio
probabilistico alla determinazione dell'innesco di frane
superﬁciali con in modello accoppiato idro-geotecnico:
GEOTOP-SF, Atti del convegno quot;idra2006 : XXX
Convegno di Idraulica e Costruzioni Idraulichequot;, Roma,
10-15 Settembre, 2006.

•Bertoldi G., Dietrich W.E., Miller N. L., Rigon R.. Bedrock
and soil contribution to the formation of sub-surface
runoff by saturation in headwater catchments:
observations and simulation using a distributed
hydrological model, Atti del XXIX Convegno di Idraulica
e Costruzioni Idrauliche, Trento, Settembre 2004.

• Zanotti F, Endrizzi S, Bertoldi G, Rigon R. 2004. The
GEOTOP snow module. Hydrological Processes 18:
3667–3679. DOI:10/1002/hyp.5794.
B-

•Zanotti, F., Endrizzi S., Rigon R. Il modulo di accumulo e
scioglimento della neve in Geotop. Atti del XXIX
Convegno di Idraulica e Costruzioni Idrauliche, Trento,
Settembre 2004.

•Tiso, C., Bertoldi G. and R. Rigon. Il modello Geotop-SF
per la determinazione dell'innesco di fenomeni di
franamento e di colata. Atti del Convegno
Interpraevent 2004, Riva del Garda, 24-28 Maggio
2004.

•Bertoldi G., Rigon R. and Over T.M., Un'indagine sugli
effetti della topograﬁa sul ciclo idrologico con il
modello GEOTOP, Atti del XXVIII Convegno di Idraulica
e Costruzioni Idrauliche, Potenza, Italy, 2002.

CAHMDA II - Princeton, October 25-27, 2004

GEOTOP:
a distributed modeling of the
hydrological cycle in the
remote sensing era
R. Rigon , G. Bertoldi, T.M. Over., D. Tamanini,

Dipartimento di Ingegneria Civile ed Ambientale - CUDAM
Geography Dept. Eastern Illinois University

San Francisco AGU Fall meeting - Dec 15 2006

The triggering of shallow landslides and
channelized debris ﬂows
analyzed with the distributed model
GEOtop - FS

R. Rigon, S. Simoni, F. Zanotti & M. Dall’Amico
DICA & CUDAM Università di Trento - ITALY

Beyond and side by side
with Numerics
Dance, Henry Matisse, Hotel Biron early 1909

A reﬂection on making applicable hydrology today
Riccardo Rigon - Group of Hydrology - Trento University

The snow and glacier
description in the GEOtop
model
Stefano Endrizzi

Department of Civil and Environmental Engineering
University of Trento, Italy

Zürich, 18th March 2008

Application of a physically-based
hydrological model to the
Adamello-Mandrone Glacier

Stefano Endrizzi, Riccardo Rigon
Department of Civil and Environmental Engineering
Italy

Obergurgl (Austria), 28 August 2007

The water and energy balance
in mountain catchments:
a distributed modelling approach

G.Bertoldi
S. Endrizzi, F. Zanotti, T.M. Over, S. Simoni, R.Rigon, U. Tappeiner

Institute for Alpine Environment
EURAC, Bozen, Italy

Innsbruck, 31th March 2008

GEOtop 2008

Recomendados

Recomendados

Más contenido relacionado

Similar a GEOtop 2008

Similar a GEOtop 2008 (20)

Más de Riccardo Rigon

Más de Riccardo Rigon (20)

Último

Último (20)

GEOtop 2008