1 geotop-summer-school2011

Hen

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Riccardo Rigon, Stefano Endrizzi, Matteo Dall’Amico, Stephan Gruber

GEOtop: the making of

Wednesday, June 29, 2011

“Prediction is very difficult,

especially about the future”Niels Bohr


Riccardo Rigon

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Objectives

•To explain what GEOtop is;

•To enumerate the basic scheme and the basic equations

The GEOtop

•To explain why GEOtop is like it is;


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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.

Rainfall–Runoff spatial patterns

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.

Traditional lumped models cannot do it!

The GEOtop


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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. Soil freezing

substantially alter the hydraulic properties of the soils.

Related problem: snow avalanches triggering

Snowpack evolution and ablation

The GEOtop


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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.

Landslide and debris flow initiation

The GEOtop


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Problem: Well, I do not want to steal the work to John and Kelly ;-)

Critical Issue: See their lectures

Ecohydrology

The GEOtop


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However, hydrology in winter is usually different

The GEOtop


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In spring time plants have vegetative growth

The GEOtop


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In summer: land use matter

The GEOtop


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And eventually autumn comes

The GEOtop


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“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”.

Committee of hydrological Sciences NRC, 2003:

The GEOtop


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Every Hydrologist would like to have THE MODEL of IT all

But in reality everybody wants just to investigate a limited set of

phenomena: for instance the discharge in a river. Or landsliding , or

soil moisture distribution.

Any problems requires its amount of prior information to

be solved: some problems needs more detailed information of others

Introduction


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So we use different models

Introduction


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GEOtopFu

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Introduction


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GEOtopFu

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Introduction


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Boussinesq

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Introduction


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Boussinesq

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Introduction


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Every one of them:

Perform the mass budget (and preserves mass)

Make hypotheses on momentum variations

Simplify the energy conservation (and its dissipation)to a certain degree

(Implicitly delineates a way to entropy increase)

Introduction


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1. Radiation

4. surface energy balance

- radiation- boundary-layer interaction

2. Water balance

- effective rainfall- surface flow (runoff and channel routing)

- distributed model- sky view factor, self and cast shadowing, slope, aspect, drainage

3. Snow-glaciers

- multilayer snow scheme

- soil temperature- freezing soil

5. soil energy balance

- multi-layer vegetation scheme- evapotranspiration

6 . v e g e t a t i o n interaction

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Dunne Saturation Overland Flow

Unsaturated Layer

Surface Layer

Saturated Layer:

Horton Overland Flow

Modified from Abbot et al., 1986

All of it starts from a DEM

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Chapter 3

Calculation domain

3.1 Domain Geometry1. Thickness: is the thickness of the layer; for numeric reasons it is advisable to settle the top layer with a thickness of 0.05,

and the first following with a thickness of 0.15m. Further layer thickness can be defined as wanted, [mm].parameters→ parameters→ soil→ 1

name unit range of value default value# 1 Thickness mm 50

Table 3.1: Domain Geometry parameters

Figure 3.1: Soil thickness discretization

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All of it starts from a DEM

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Layers, at the moment, form a structured grid.

With variable height.

The larger the height, the more uncoupled the layers.

On top there are dynamical snow layers

Chapter 10

Snow

10.1 Introduction

Figure 10.1: Snow stratigraphy

10.2 Input

10.2.1 Parameters

Keyword Description M. U. range Default

Value

Sca /

Vec

Str / Num

/ Opt

ThresSnowSoilRough Threshold on snow depth to changeroughness to snow roughness valueswith d0 set at 0, for bare soil fraction

mm 0,1000

10 sca num

ThresSnowVegUp Threshold on snow depth abovewhich the roughness is snow rough-ness, for vegetation fraction

mm 0,20000

1000 sca num

ThresSnowVegDown Threshold on snow depth belowwhich the roughness is vegetationroughness, for vegetation fraction

mm 0,20000

1000 sca num

RoughElemXUnitArea Number of roughness elements(=vegetation) per unit area - usedonly for blowing snow subroutines

Numberm−2

0, inf 0 sca num

continued on next page

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Chapter 3

Calculation domain






17

Chapter 10

Snow

10.1 Introduction


10.2 Input

10.2.1 Parameters


Value

Sca /

Vec

Str / Num

/ Opt


mm 0,1000

10 sca num


mm 0,20000

1000 sca num


mm 0,20000

1000 sca num


Numberm−2

0, inf 0 sca num


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So, the overall grid is:

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Is that the best we can do ?

Chapter 3

Calculation domain






17

Chapter 10

Snow

10.1 Introduction


10.2 Input

10.2.1 Parameters


Value

Sca /

Vec

Str / Num

/ Opt


mm 0,1000

10 sca num


mm 0,20000

1000 sca num


mm 0,20000

1000 sca num


Numberm−2

0, inf 0 sca num


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Chapter 3

Calculation domain






17

Chapter 10

Snow

10.1 Introduction


10.2 Input

10.2.1 Parameters


Value

Sca /

Vec

Str / Num

/ Opt


mm 0,1000

10 sca num


mm 0,20000

1000 sca num


mm 0,20000

1000 sca num


Numberm−2

0, inf 0 sca num


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Put vegetation on top !!!


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Arabba

Pordoi

Caprile

Malga Ciapela

Pescul

Ornella

Saviner

Places where John goes skiing!

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What do we put above the grid ?

Chapter 11

Vegetation

11.1 Vegetation

Figure 11.1: Precipitation

11.2 Input

11.2.1 Parameters


Value

Sca /

Vec

Str / Num

/ Opt

VegHeight vegetation height mm 0,

20000

1000 sca num


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12. Surface Fluxes 12.2. Values of reference

Surface description z 0(cm) ReferenceMud flats, ice 0.001 Sutton (1953)Smooth tarmac 0.002 Bradley (1968)Large water surfaces 0.01 - 0.06 Numerous referencesGrass (lawn up to 1 cm) 0.1 Sutton (1953)Grass (artificial, 7.5 cm high) 1.0 Chamberlain (1966)Grass (thick up to 10 cm high) 2.3 Sutton (1953)Grass (thin up to 50 cm) 5 Sutton (1953)Trees (10-15 m high) 40-70 Fichtl and McVehil (1970)Large city 165 Yamamoto and Shimanuki (1964)

Table 12.9: Example of roughness parameters for various surfaces (Evaporation into the Atmosphere, Wilfried Brutsaert, 1984)

Figure 12.1: Water fluxes

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GEOtop structure

What do we put above the grid ?


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12.2. Values of reference 12. Surface Fluxes

Figure 12.2: Radiation

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11. Vegetation 11.3. Output

Figure 11.2: Vegetation parameters

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reflectivity

reflectivity

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12. Surface Fluxes 12.2. Values of reference

Figure 12.3: Energy Budget

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Chapter 3

Calculation domain






17

Chapter 10

Snow

10.1 Introduction


10.2 Input

10.2.1 Parameters


Value

Sca /

Vec

Str / Num

/ Opt


mm 0,1000

10 sca num


mm 0,20000

1000 sca num


mm 0,20000

1000 sca num


Numberm−2

0, inf 0 sca num


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What does the model do actually ?

Dynamic runoff

Dynamic energy and

mass budget

Dynamic snow or

Parametrizations of

radiation and turbulence

Boundary conditions

Boundary conditions

Blue are parametrizations Black are equations

Differentianl and other equations


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Chapter 3

Calculation domain






17

Chapter 10

Snow

10.1 Introduction


10.2 Input

10.2.1 Parameters


Value

Sca /

Vec

Str / Num

/ Opt


mm 0,1000

10 sca num


mm 0,20000

1000 sca num


mm 0,20000

1000 sca num


Numberm−2

0, inf 0 sca num


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Parametrizations of


Dynamic runoff

Dynamic energy and

mass budget

Dynamic snow or

Boundary conditions

Dynamic Boundary conditions

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Chapter 3

Calculation domain






17

Chapter 10

Snow

10.1 Introduction


10.2 Input

10.2.1 Parameters


Value

Sca /

Vec

Str / Num

/ Opt


mm 0,1000

10 sca num


mm 0,20000

1000 sca num


mm 0,20000

1000 sca num


Numberm−2

0, inf 0 sca num


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Parametrizations of


Dynamic Boundary conditions Dynamic runoff

Dynamic energy and

mass budget

Boundary conditions

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Dynamic vegetation

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NOT YET BUT UPCOMING !


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Downloading

Chapter 1

Compiling Instructions

GEOtop runs properly under:

• Linux platform;

• Mac platform;

• Windows platform.

1.1 Compile GEOtop through a makefile

The GEOtop source code can be downloaded through a terminal (or command prompt if you are using Win-

dows) by typing, as shown in Figure 1.1:

”svn co https://dev.fsc.bz.it/repos/geotop/trunk/0.9375KMacKenzie”

Figure 1.1: Download GEOtop source code through a terminal

The downloaded folder contains the folders:

• Debug: which contains the object file created during the compilation and the makefile

• geotop: which contains the code

• Libraries: which contains the support libraries

Open a terminal, go into the folder Debug by typing:

$ cd Debug

3

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Running

1. Compiling Instructions 1.2. How to Run GEOtop

To compile GEOtop, type:

$ make all

The executable file GEOtop1.2 is now created in the Debug folder.

1.2 How to Run GEOtop1.2.1 From TerminalOpen a terminal, go into the folder Debug by typing:

$ cd Debug

Write:

$ ./GEOtop1.2

Leave one space and type now the path to the folder where the simulation files are:

$./GEOtop_1.2 /Users/matteo/Duron/

Remember to put a“/” (slash) at the end and the type Return. The simulation should start.

Figure 1.2: SVN

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6. Simulation flow-chart

Figure 6.1

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6. Simulation flow-chart

Figure 6.1

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Chapter 10

Surface Fluxes

10.1 Input

10.1.1 Parameters


Value

Sca /

Vec

Str / Num

/ Opt

SoilRoughness Roughness length of soil surface mm 0,1000

10 sca num

PointLandCoverType Land Cover type of the simulationpoint

- NA vec num

Table 10.1: Keywords of surface characteristics affecting surface energy fluxes


Value

Sca /

Vec

Str / Num

/ Opt

NumLandCoverTypes Number of Classes of land cover.Each land cover type corresponds to aparticular land-cover state, describedby a specific set of values of the pa-rameters listed below. Each set ofland cover parameters will be dis-tributively assigned according to theland cover map, which relates eachpixel with a land cover type num-ber. This number corresponds to thenumber of component in the numeri-cal vector that is assigned to any landcover parameters listed below.

- 1, inf 1 sca num

SoilAlbVisDry Ground surface albedo without snowin the visible - dry

- 0, 1 0.2 sca num

SoilAlbNIRDry Ground surface albedo without snowin the near infrared - dry

- 0, 1 0.2 sca num

SoilAlbVisWet Ground surface albedo without snowin the visible - saturated

- 0, 1 0.2 sca num

SoilAlbNIRWet Ground surface albedo without snowin the near infrared - saturated

- 0, 1 0.2 sca num

SoilEmissiv Ground surface emissivity - 0, 1 0.96 sca num

Table 10.2: Keywords of land cover characteristics affecting surface energy fluxes

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Parameters: an excerpt from the dry manual

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14. Templates 14.2. 3D distributed simulation

- raster maps

- time series (discharge, air temperature, evaporation, latent heat fluxes, etc.....) at specific points (Figure 14.10).

The output raster maps (Figure 14.9) have to be specified by the user through appropriate keywords in the parameter file (see Table

14.9), in addition, their output frequency has to be assigned through the OutputXXXMaps parameter.

Figure 14.9: One of the many distributed output, the mean air temperature

page 74 of 78

Forcings where made spatial

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14.2. 3D distributed simulation 14. Templates

0.0 0.5 1.0 1.5 2.0

05

1015

2025

3035

Days

T [°C

]

Surface TemperatureAir Temperature

Figure 14.10: Two day-time series of mean air temperature output for a specified point

page 75 of 78

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Simulating is NOT the same as understanding

Simulating


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But understanding without modeling is difficult

Simulating


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In general before doing a simulation.Plan:

•Space and Time Resolutions

•Address subgrid variability

•Computational Burden

•Non calibrated parameters

•Calibration Strategy

•Model initialization•To carefully analyze the spatial characters of soil properties

•To carefully analyze the spatial time series of meteorological

data

Simulating


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In general before doing a simulation.

•Plan a validation strategy

•Make some null hypothesis

•Check the statistical structure of forcings and their correlation

In general after simulation.

•Always check mass and energy conservation

•Assess physical realism with quantitative objective tools in selected

points or transects.

•Compare spatial distributions of quantities, correlations, and patterns

(numbers of cluster of points above a threshold, size of above thresholds

islands, etc. )

http://abouthydrology.blogspot.com/search/label/Initial%20Conditions

Simulating




Hen

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The DreamAn example of fantastic realism (Dietrich et al. 200). Components

are realistic. The ecosystem is not. This is a methaphor of

inaccurate modeling.


Riccardo Rigon

Thank you for your attention.

G.U

lric

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20

00

?

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Thanks, Thanks, Thanks


Education

1 geotop-summer-school2011