The rational behind GEOtop:through its historical

development and applicationsR. Rigon

Dipartimento di Ingegneria Civile ed Ambientale - CUDAM

Università di Trento

Four or Five problems we wanted toinvestigate

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 initiationProblem: 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

The GEOtop Project

GEOtop 0.5 was the ancestor (1997)

Paolo Verardo and Marco Pegoretti coded it

GEOtop 0.5 was the ancestor (1997)

GIUH +Kinematic wave+

Bucket model

Paolo Verardo and Marco Pegoretti coded it

GEOtop 0.5 was the ancestor (1997)

GIUH +Kinematic wave+

Bucket model

Penm

an-M

onte

ith

+Ra

diat

ion

Phys

ics

Paolo Verardo and Marco Pegoretti coded it

GEOtop 0.5 (1997)P

Qsup

Qsub

I∂Qsup

∂t+ c(x)

∂Qsup

∂s= c(x) qLc ∝ y(x)m

Qsub = T∇z b

Qc(t) =Z t

0dτ

Z L

odx

x W (x,τ)!4πDL(t! τ)

exp"!x!u(t! τ)

2

4DL(t! τ)

#

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

GEOtop 0.5 (1997)

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

ET =Δ/λ(Rn!G) +ρ/ra δqa

1+Δ/γ+ rg/ra

Rn = [sh R "SW P + V R "SW D] (1!V α)+VεsR "LW !VεsσT 4s

ET Rn

G

GEOtop 0.5 (1997)

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

ET =Δ/λ(Rn!G) +ρ/ra δqa

1+Δ/γ+ rg/ra

Rn = [sh R "SW P + V R "SW D] (1!V α)+VεsR "LW !VεsσT 4s

ET Rn

G

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

G

ET =Δ/λ(Rn!G) +ρ/ra δqa

1+Δ/γ+ rg/ra

Rn = [sh R "SW P + V R "SW D] (1!V α)+VεsR "LW !VεsσT 4s

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)

GIUH +Kinematic wave+

Bucket model

Radi

atio

n Ph

ysic

s

Ener

gy b

udge

t in

tegr

atio

n

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)

dEdt

=CpdTsdt

= Rn!H!ET +Qp!G!Qm

H = ρ cpCH u(Ts!Ta)

ET = λρCe u(q"(Ta)!q"(Ta)Ua)

G

HRn ET Qp

Qm

dE/dt

CH, CE ↑

CH, CE ↓

Aerodynamic roughness length

Ts>Ta

Ts<Ta

Similarity theory by Louis (1979)

GEOtop 0.75 (2000): Turbulent fluxes appear!

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.

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

Winter

Summer

Spring

Fall

Mean seasonal ET at Serraia (TN)

Grafico bilancio del bacino del Lago della Serraia (1998 - 2000)

-0.200-0.1000.0000.1000.2000.3000.4000.5000.6000.7000.8000.9001.0001.1001.200

gen-98

feb-98

mar-98

apr-9

8

mag

-98

giu-98

lug-98

ago-98

set-9

8

ott-9

8

nov-98

dic-98

gen-99

feb-99

mar-99

apr-9

9

mag

-99

giu-99

lug-99

ago-99

set-9

9

ott-9

9

nov-99

dic-99

gen-00

feb-00

mar-00

apr-0

0

mag

-00

giu-00

lug-00

ago-00

set-0

0

ott-0

0

nov-00

dic-00

Tempo (mese-anno)

Po

rta

ta m

ed

ia m

en

sil

e (

mc

/s)

-60.6-30.30.030.360.690.9121.1151.4181.7212.0242.3272.6302.9333.1363.4

Inte

ns

ita

(m

m/m

es

e)

P - precipitazione ET - evapotraspirazioneInv - volume invasato (accumulo) R - rilascio

Hydrological Balance 1998-2000Serraia (TN)

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

set.

Serraia

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)

GIUH +Kinematic wave+

Richards +Soil freezing &

Snow Cover

Radi

atio

n Ph

ysic

s

Ener

gy b

udge

t in

tegr

atio

n

Code integrations by Davide Tamanini

Dunne Saturation Overland Flow

Unsaturated Layer

Surface Layer

Saturated Layer:

Horton Overland Flow

Modified from Abbot et al., 1986

Richards’ equation is solved

σ(ψ)∂ψ∂t

= ∇ · (K(ψ)∇(ψ+ηz))+qs

ψ=1α

!"θr!θsθs!θr

#1/m

!1$1/n

K(θ) = KS"θr!θsθs!θr

#ν!1!

!1!

"θr!θsθs!θr

#1/m$m$2

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

Rigon et al., JHM, 2006

Rigon et al., JHM, 2006

Some other case studies

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)

120 piezometers on sideslopes and hollows

Saturated source area measurement.

Basin and bedrock topography

Experimental evidence:February 1986 storm

• measured rainfall each 6 h• measured stream flow each 6 h

0

10

20

30

40

50

60

70

8011-Feb

12-Feb

13-Feb

14-Feb

15-Feb

16-Feb

17-Feb

18-Feb

19-Feb

20-Feb

21-Feb

22-Feb

23-Feb

24-Feb

Rai

nfal

l mm

/6h

0

10

20

30

40

50

60

70

80

Stre

amflo

w l/

s

Solstice raingage mm/6h Pan Toll Raingage mm/6h

Measured Streamflow l/s

• Total storm 400 mm in 10 days• measured saturated area: “squishy soil”

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

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

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

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

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

Conductivities against depht: model with 8 layers

0

200

400

600

800

1000

1200

1400

1.00E-071.00E-061.00E-051.00E-041.00E-031.00E-021.00E-01Kv cm/s

dep

ht

cm

Solstice1 Solstice2 Sideslope Model

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

Bedrock shape variation:• With uniform soil profile• With measured bedrock shape • Bedrock with different permeability

Soil and bedrock properties:

Sideslopes: shallow conductive bedrock

K decreasing with depth

Hollows: loamy-sand thick colluvium, deep impermeable bedrock, some conductive points (Lehre et al. , 1986)

GEOtop model settings

• 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?

GEOtop model results

GEOtop model results Saturated area - water content first layer 5 cm

Hollows partially saturated at the beginning of the storm

0

10

20

30

40

50

60

70

8011-Feb

12-Feb

13-Feb

14-Feb

15-Feb

16-Feb

17-Feb

18-Feb

19-Feb

20-Feb

21-Feb

22-Feb

23-Feb

24-Feb

Rai

nfal

l mm

/6h

0

10

20

30

40

50

60

70

80

Stre

amflo

w l/

s

Solstice raingage mm/6h Pan Toll Raingage mm/6h

Measured Streamflow l/s

GEOtop model results Saturated area - water content first layer 5 cm

0

10

20

30

40

50

60

70

8011-Feb

12-Feb

13-Feb

14-Feb

15-Feb

16-Feb

17-Feb

18-Feb

19-Feb

20-Feb

21-Feb

22-Feb

23-Feb

24-Feb

Rai

nfal

l mm

/6h

0

10

20

30

40

50

60

70

80

Stre

amflo

w l/

s

Solstice raingage mm/6h Pan Toll Raingage mm/6h

Measured Streamflow l/s

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

Measured discontinuous patterns at the end of the event

0

10

20

30

40

50

60

70

8011-Feb

12-Feb

13-Feb

14-Feb

15-Feb

16-Feb

17-Feb

18-Feb

19-Feb

20-Feb

21-Feb

22-Feb

23-Feb

24-Feb

Rai

nfal

l mm

/6h

0

10

20

30

40

50

60

70

80

Stre

amflo

w l/

s

Solstice raingage mm/6h Pan Toll Raingage mm/6h

Measured Streamflow l/s

GEOtop model results Saturated area - water content first layer 5 cm

GEOtop model results total head gradient- first layer 5 cm

Mostly topographically driven

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

GEOtop model results total head gradient- first layer 5 cm

Mass movementsat Sauris (UD)

basin

Mostly worked out by S. Simoni and F. Zanotti

GEOtop 0.875

•DTM•Meteo data•Rain data

•Soil characterization (geotechnical parameters)

3D Mass and Energy budgets at catchment

scale

GEOtop-FS

•dynamic probability of landslide triggering •sediment availability

Run-out module

•liquid and solid discharge•detailed topography•closure relations (concentration & shear stress)•sediment and transport

•run-out distance•erosion-deposit height•flow velocity•hazard mapstr

en

t-2

d

1

Geotop and trent-2d

Geo

top p

ro

ject

•Soil characterization (hydraulic parameters)•land use•vegetation

Sim

oni e

t al

., H

ydro

l. Pr

oc.,

2008

•Soil characterization (hydraulic parameters)•land use•vegetation

GEOtop 0.875

Geo

top p

ro

ject

•DTM•Meteo data•Rain data

•Soil characterization (geotechnical parameters)

3D Mass and Energy budgets at catchment

scale

GEOtop-FS

•dynamic probability of landslide triggering •sediment availability

Run-out module •liquid and solid discharge•detailed topography•closure relations (concentration & shear stress)•sediment and transport

•run-out distance•erosion-deposit height•flow velocity•hazard mapstr

en

t-2

d

Geomorphologicalanalysis

Geological info

Geophysics

stratigraphyquaternary covers

Soil DepthWater content

traditional photointerpretation

rock presenceerosion signatureshuman activities

soil Covertype and weight of soil

cover

model structure data exploited

Climate and Weathermodels

Rainfall, wind...

Earth Observations

A conceptual experiment

Effects of topography on hydrological balance

Elevations 25% Elevations 50%Elevations 40%

Elevations 60% Elevations 125%Elevations 100%

Serraia Basin, 15 km2, 1 year simulationSecurity Exit

Effects of topography on hydrological balance

Bertoldi et al.,JHM, 2006

Security Exit

Effects of topography on hydrological balance

Bertoldi et al.,JHM, 2006

An application to the Valsugana valleyM

ostl

y wo

rked

out

by

S. E

ndri

zzi

xBorgo Valsugana

● Caldonazzo

● Pergine

● Levico

An application to the Valsugana valleyM

ostl

y wo

rked

out

by

S. E

ndri

zzi

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

An application to the Valsugana valleyM

ostl

y wo

rked

out

by

L. F

orlin

Monthly mean discharge (m3/s) from HAD_P model, for historic (1961-1988) and simulated control and future scenario.

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.

An application to the Valsugana valley

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

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

Mos

tly

work

ed o

ut b

y L

. For

lin

An application to the Valsugana valleyM

ostl

y wo

rked

out

by

L. F

orlin

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 +Kinematic wave+

Richards +Soil freezing &

Snow Cover

Radi

atio

n Ph

ysic

s En

ergy

bud

get

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

Mos

tly

work

ed o

ut b

y S.

End

rizz

i

GEOtop 0.9375 (2006-2008)

Multilayer parameterizationFor each layer a system of 5 equations is solved

€

θw =1ρw

∂W∂t

+∂Qw

∂z

€

θ i =1ρi

∂W∂t

+∂Qi

∂z

€

C∂T∂t

+ Lf∂W∂T

=∂∂z

k∂T∂z

+∂ QwUw( )

∂z

€

k∂T∂z

= −Rn +H + L

€

θw +θ i +θ v = 1

€

W ≠ 0 if T = 273.15K ; W = 0 if T ≠ 273.15K

Liquid and solid water budget equations

Energy budget

equation

Continuity equation

Link phase change - temperature

Mos

tly

work

ed o

ut b

y S.

End

rizz

i

GEOtop 0.9375 (2006-2008)

Mos

tly

work

ed o

ut b

y S.

End

rizz

i

GEOtop model mm SWEMODIS

24 OTTOBRE 2003

GEOtop 0.9375 (2006-2008)

Mos

tly

work

ed o

ut b

y S.

End

rizz

i

GEOtop model mm SWEMODIS

17 January 2004

Application of GEOtop to the Adamello-Mandrone Glacier

(Trentino, Italy)

Mos

tly

work

ed o

ut b

y S.

End

rizz

i

73

Distributed results

Mass balance 1 Oct 2004 - 30 Sep 2005

mm w.eq.

Mos

tly

work

ed o

ut b

y S.

End

rizz

i

74

Comparison model - measurements

• 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

Ice melting after snow disappearance

The Stubaital case

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

The Stubaital case

by G. Bertoldi, P. Rastner, C. Notarnicola, and U. TappeinerData from G. Wolfhart, Institute of Echology, Innsbruck

• 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.

Model Obs Model-ObsH [W/m2] 26 20 6LE [W/m2] 88 85 3G [W/m2] 4 4 0H [W/m2] 80 76 4

Ts [K] 11 12 -1SWC [ ] 0.48 0.37 0.11

Model - Observations comparisonSnow - free season 2005

Initial assumption: time constant vegetation density

The Stubaital case

by G. Bertoldi, P. Rastner, C. Notarnicola, and U. TappeinerData from G. Wolfhart, Institute of Echology, Innsbruck

• 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.

LANDSAT LST GEOTOP LSTΔT=LSTGeoTop-LSTLandsat

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

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

The Stubaital case

by G. Bertoldi, P. Rastner, C. Notarnicola, and U. TappeinerData from G. Wolfhart, Institute of Echology, Innsbruck

Some of the next steps

GEOtop Development

Automatic Calibration

Data Assimilation

GEOTOP-SF0.75

GEOTOP-SF0.875

GEOTOP-SF0.9375

0.5

0.75

0.875

0.9375

0.9375- EO

GEOFRAME

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

=

www.jgrass.org

GEOFRAME: JGrass 3.0

Tools di analisi(UNITN/R)

Database (PostgresSQL/PostGIS/CUAHSI)

Modelli(UNITN/OpenMI)

Interfacce(Java/JGRASS)

users webexternal database

GEOFRAME

WEBservices

WMSWFS-TWPS

PostGISPostgres

JGrass

GIS engine

UIBuilder

HSQLDB

GRASS GIS

Eclipse RCPuDig

JGrassJ-Console engine

OpenMI

Hor

ton

Mac

hine

Hyd

rolo

g. m

odel

Sta

tistic

al a

nal.

GEOFRAME: JGrass 3.0 structure

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.

=

88

Serially linked models by file transfer. Feedback loops not represented.

Interface

Model

Data

Interface

Model

Data

Interface

Model

Data

Interface

Model

Data

FileFile

File

GEOFRAME: OpenMI

88

Serially linked models by file transfer. Feedback loops not represented.

Interface

Model

Data

Interface

Model

Data

Interface

Model

Data

Interface

Model

Data

FileFile

File

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

GEOFRAME: OpenMI

88

Serially linked models by file transfer. Feedback loops not represented.

Interface

Model

Data

Interface

Model

Data

Interface

Model

Data

Interface

Model

Data

FileFile

File

GEOFRAME: OpenMI

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

=

9018

3-D3-D

3-DSea

2-DEstuary

1-DRiver

Graph tool

Connection toolGEOFRAME: OpenMI

9018

3-D3-D

3-DSea

2-DEstuary

1-DRiver

Graph tool

Connection tool

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

GEOFRAME: OpenMI

9018

3-D3-D

3-DSea

2-DEstuary

1-DRiver

Graph tool

Connection toolGEOFRAME: OpenMI

OpenMI provides methods to change at run-time the model

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

alternative seamlessly.

=

9218

WeatherGenerator

Monitoreddata

WeatherForecast

SurfacesInterception

SurfacesRunoff

Subsurface Flow

Evapotranspiration

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

GEOFRAME: OpenMI

9318

SurfacesRunoff

Subsurface Flow

Evapotranspiration

Channel RoutingII

Channel Routing III

Channel Routing 1

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

GEOFRAME: OpenMI

GEOFRAME : J-Hydro

JGrass provides also the database to store and retrieve simulations

=

after Dietrich et al., 2001

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 ;-)

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

DICA Dipartimento di Ingegneria Civile ed AmbientaleCUDAM 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.

Core contributors

Analytics

Dan

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Numerics

Physics

Geography

Hydrology

Thank you for your attention

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 "idra2006 : XXX Convegno di Idraulica e Costruzioni Idrauliche", 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.

A few relevant presentations

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 Università di Trento

Geography Dept. Eastern Illinois University

CAHM

DA I

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Prince

ton,

Oct

ober

25-

27, 20

04

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’AmicoDICA & CUDAM Università di Trento - ITALY

Beyond and side by side with Numerics

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

Dan

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The snow and glacier description in the GEOtop

model

Stefano Endrizzi

Department of Civil and Environmental EngineeringUniversity of Trento, Italy

Zürich, 18th March 2008

Application of a physically-based hydrological model to the

Adamello-Mandrone Glacier

Stefano Endrizzi, Riccardo RigonDepartment of Civil and Environmental Engineering

Università di TrentoItaly

Obergurgl (Austria), 28 August 2007

The water and energy balance in mountain catchments:

a distributed modelling approach

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

Institute for Alpine EnvironmentEURAC, Bozen, Italy

Innsbruck, 31th March 2008