padua cathy meeting

R. Rigon, M. Dall’Amico, G. Formetta

GEOtop and BeyondCATHY’s days 2011

Jack

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Tuesday, January 18, 2011

Rigon et al., CATHY’s days, Padova 2011

2

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

The structure of GEOtop



3

snow, ice, permafrost

water cycle in complex terrain

landslidingevapo-transpiration, energy fluxes

Bertoldi et al., 2006Bertoldi et al 2010

Endrizzi 2007Dall’Amico 2010Endrizzi et al, 2010a,b in preparation

Simoni et al 2008Lanni et al, 2010

Rigon et al., 2006

Why this complexity ?




4

Meteo

Rainfall/Snow

Snow/Energy budget

Atm. TurbulenceRadiation

For each time step

Flows




5

Richards ++

Surface flows

Channel flow

Next time step

GEOtop




6

Richards ++

First, I would say, it means that it would be better to call it, for

instance: Richards-Mualem-vanGenuchten equation, since it is:

Se = [1 + (−αψ)m)]−n

Se :=θw − θr

φs − θr

C(ψ)∂ψ

∂t= ∇ ·

�K(θw) �∇ (z + ψ)

�

K(θw) = Ks

�Se

��1− (1− Se)1/m

�m�2

C(ψ) :=∂θw()∂ψ




6

Richards ++



Se = [1 + (−αψ)m)]−n

Se :=θw − θr

φs − θr

C(ψ)∂ψ

∂t= ∇ ·

�K(θw) �∇ (z + ψ)

�

K(θw) = Ks

�Se

��1− (1− Se)1/m

�m�2

Water balance

C(ψ) :=∂θw()∂ψ




6

Richards ++



Se = [1 + (−αψ)m)]−n

Se :=θw − θr

φs − θr

C(ψ)∂ψ

∂t= ∇ ·

�K(θw) �∇ (z + ψ)

�

K(θw) = Ks

�Se

��1− (1− Se)1/m

�m�2

Water balance

ParametricMualem

C(ψ) :=∂θw()∂ψ




6

Richards ++



Se = [1 + (−αψ)m)]−n

Se :=θw − θr

φs − θr

C(ψ)∂ψ

∂t= ∇ ·

�K(θw) �∇ (z + ψ)

�

K(θw) = Ks

�Se

��1− (1− Se)1/m

�m�2

Water balance

ParametricMualem

Parametricvan Genuchten

C(ψ) :=∂θw()∂ψ




7

Extending the soil-water relation curve

Extending Richards to treat the transition saturated to unsaturated zone. Which means:

Richards ++



8

Freezing Soils in unsaturated Conditions

Extending Richards to treat the phase transition. Which means essentially to extend the soil water retention curves to become dependent on temperature.

Unsaturatedunfrozen

Freezingstarts

Freezingprocedes

UnsaturatedFrozen

Richards ++



9

Unfrozen water content

Soil water retention curve

thermodynamic equilibrium (Clausius Clapeyron)

+

ψw =pw

ρw gpressure head:

θw(T ) = θw [ψw(T )]

+

Freezing = drying hypothesis


Richards ++



10

T ∗ := T0 +g T0

Lfψw0

Θ = θr + (θs − θr) · {1 + [−α · ψw0]n}−m

ice content: θi =ρw

ρi

�Θ− θw

�

θw = θr + (θs − θr) ·�

1 +�−αψw0 − α

Lf

g T0(T − T

∗) · H(T − T∗)

�n�−mliquid water

content:

Total water content:

depressed melting

point

M. Dall’Amico, S. Gruber and R. Rigon, 2010 in preparation


Richards ++



11

Unsaturatedunfrozen

UnsaturatedFrozen

Freezingstarts

Freezingprocedes

Freezing = Drying

Richards ++



12M. Dall’Amico, S. Gruber and R. Rigon, 2010 in preparation

Freezing = Drying

Richards ++



13M. Dall’Amico, S. Gruber and R. Rigon, 2010 in preparation

Freezing = Drying

Richards ++



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Tot Water profile: comparison with Hansson et al

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Freezing = Drying

Richards ++


http://www.the-cryosphere-discuss.net/4/1243/2010/tcd-4-1243-2010.html

http://www.the-cryosphere-discuss.net/4/1243/2010/tcd-4-1243-2010.html


15

Obviously this makes it possible to simulate a lot of new phenomenologies

Sisik, river in the artic tundra

Applications

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44

thaw depth: T(z,t)=0 water table depth: ψm(z,t)=0

Stefano Endrizzi, William Quinton, Philip Marsh, 2011 submitted to TC

Runoff on Frozen Soil



17

The model allows to show that the runoff

properties of a basin dramatically change when

soil freeze.

Runoff on frozen soil

Stefano Endrizzi, William Quinton, Philip Marsh, Matteo Dall’Amico, 2010 in preparation

Runoff on Frozen Soil: main result



18

Arabba

Pordoi

Caprile

Malga Ciapela

Pescul

Ornella

Saviner

Snow generated runoff

Frozen soil can be combine with the snow module



19

Frozen soil can be combine with the snow module




20

02

46

810

1214

Date (dd/mm)

Dis

char

ge [m

3/s]

01/10 01/12 01/02 01/04 01/06 01/08 01/10

measuredGEOtop

Discharge at Saviner year 2006−2007

We have to work more here!




21

Is Richards’ True ?

Conclusions



21


Well: as representing the water budget it must be true.

Conclusions



21


Well: as representing the water budget it must be true.

However:

• The soil water retention curves need probably to be further extended or

changes beyond v.Genucthen schemes

• Hydraulic conductivity should also be, probably, re-parametrized

•Saturation-Unsaturation must be better characterized

•The theory of freezing soil revisited (I know where it is approximate)

•Therefore, also the scheme that keep the soil rigid has to be revisited.

•Clays where adsorption plays a fundamental role represents a challenge.

•.........

Conclusions



22

Is this complexity manageable ?

Going Beyond



23


Going Beyond



24

Certainly this overwhelms the capability of any single

researcher, even the most gifted and dedicated:

•too many processes to deal with at the state-of-art of:

•physics

•numerics

•informatics

•too many datasets to exploit to have reasonable validation/

falsification

•too many ancillary programs neeeded to initialize, bound,

and force the models

Managing Models’ complexity




25

This requires many gifted people to interact. One models of

development:

•The CERN/Fundamental physics way

• The Meteorological field

•.....

There could be another model ? (Which should be however as

well successful in getting finance support, and maybe more

successful in discoveries ;-)


The Big Science Model



26

It is possible that many people interact on a different basis,

with a more “bottom-up” approach, and still doing science at

the higher level (with more democracy and responsability) ?

The Open Source World style:

•GNU/Linux

•Eclipse

•GRASS

•GEOtools (OGC)

•.....


The Open Science Model



27

Tools

We need tools that helps collaboration

You can find more at:

http://www.slideshare.net/GEOFRAMEcafe/geoframe-a-system-for-doing-hydrology-by-computer







CUAHSI BIANNUAL MEETING - BOULDER (CO) - JULY 14-16 2008

Object-oriented software development . O-O programming is nothing new, but it has proven to be a successful key to the design and implementation of modelling frameworks. Models and data can be seen as objects and therefore they can exploit properties such as encapsulation, polymorphism, data abstraction and inheritance.

Component-oriented software development. Objects (models and data) should be packaged in components, exposing for re-use only their most important functions. Libraries of components can then be re-used and efficiently integrated across modelling frameworks. Yet, a certain degree of dependency of the model component from the framework can actually hinder reuse.

NEW (well relatively) MODELING PARADIGMS

Mod

ified

from

Riz

zoli

et a

l., 20

05

MODELLING BY COMPONENTSTuesday, January 18, 2011

Rigon, Antonello, Franceschi

29

JGrass 3 - OMS3 in the next future

Modeling by components

JGrass 3/ OMS 3

After David et al., 2009



Discrete units of software which are re-usable even outside the framework, both for model components and for tools components.

Seamless and transparent access to data, which are made independent of the database layer.

A number of tools (simulation, calibration, etc.) that the modeller will be free to use (including a visual modelling environment).

A model repository to store your model (and simulations) and to share it with others.

BENEFITS

MODELLING BY COMPONENTSTuesday, January 18, 2011


Tools for studying feedbacks among different processes.

BENEFITS FOR SCIENTISTS

Encapsulation of single processes or submodels

MUCH MORE in the field of possibilities

MODELLING BY COMPONENTS

New educational tools and a “storage” of hydrological knowledge using appropriate onthologies



32

There exist such a modelling infrastructures ?

YES, there exist many

As a matter of fact just a few have those characteristics that I believe are important for the success of the whole ideas:




PREREQUISITES

Programming LANGUAGE NEUTRAL: Fortran, C/C++, Java ....

PLATFORM NEUTRAL: Windows, Linus and Mac

OPEN SOURCE

TARGETED AT PERSONAL PRODUCTIVITY OF DIFFERENT USERS People come before program efficiency.

BUSINNES NEUTRAL: GPL would be fine, LGPL better

DEPLOYEMENTTuesday, January 18, 2011


PREREQUISITES

ALLOWS WRAPPING OF EXISTING CODES BUT PROMOTES BETTER PROGRAMMING STRATEGIES

BUILT BY OPEN SOURCE TOOLS

DATA BASE PROVIDED

OGC COMPLIANT

CUAHSI SPECIFICATIONS AWARE

DEPLOYABLE THROUGH THE WEB

DEPLOYEMENT

CAN BE ENDOWED WITH ONTOLOGIES



35

What is next ?

The Object Modeling System OMS is a modular modeling framework that uses an open source software approach to enable all members of the scientific community to address collaboratively the many complex issues associated with the design, development, and application of distributed hydrological and environmental models.

OMS3 can be found at: http://www.javaforge.com/project/omslib

Resources

KnowledgeBase

DevelopmentTools

Products

OMS

We chose recently OMS3


http://www.javaforge.com/project/omslib

http://www.javaforge.com/project/omslib

ydroloGIS nvironmental ngineering

HydroloGIS s.r.l. - Via Siemens, 19 – 39100 Bolzano www.hydrologis.com

JGrass and OMS: what will happen to OpenMI?

A big effort has been done in the last years to bring all the models contained to OpenMI compliancy. There are several main issues that pushed the decision to migrate towards OMS:

OpenMI forces modelers to use a quite restrictive API OpenMI is currently proposing its version 2, which from 1.4 introduces several

changes. Migrate to that would require an enormous effort




JGrass and OMS: what will happen to OpenMI?

OMS already contains a set of components that are free and open sourced, and also already well tested at the USDA, which would come as a present to JGrass. OpenMI still doesn't have any open source components and seems to be focused on few proprietary applications

OMS is an annotation based modern modeling framework that really focuses on adding few overhead to the modeler

the OMS team is working on a wrapper to generate OpenMI code from OMS models




OMS: an annotations based frameworkOMS minimizes the burden on a component/model developer to buildcode into the framework by not imposing an API. (I know everyone claims it, but believe me, this time it is true)

package helloworld;import oms3.annotations.*;

public class Component { @Role(Role.PARAMETER) @In public String message; @Execute public void run() { System.out.println(message); }}




OMS: other advantagesWith OMS a bunch of important features come into JGrass's modeling

system:

Components always execute multi-threaded. If the data flow alows it, the models are executed in parallel.

Integration with JNA (same as JGrass) for native code access. Java Native Access (JNA) integration that now supports all versions of FORTRAN, C, and C++ on all major architectures in 32 and 64 bit. FORTRAN and C/C++ programmers can continue to use their respective tools to create components




OMS: other advantages

With OMS a bunch of important features come into JGrass's modeling system:

Runtime flexibility for simulation execution. Models can be executed in different environments that scale from a notebook to a computing cluster or even a cloud such as Amazon’s Elastic Computing Cloud (EC2).

The OMS modeler environment bases on Groovy scripting language, exactly as JGrass's console does



41

GEOFRAME 2011 blueprint

GEOFRAME

GEOtop NewAge Boussinesq PeakFlowModels

SHALSTAB GEOtop-FS The Horton Machine

JGrass-udig- OMS3Out R NWW

JGrass-udig- OMS3

Environmental Data Center (Postgres/Postgis/Ramadda/H2)Data

In

METEO/IO



EPILOGUE

OUR AIM IS NOT TO MODEL EVERYTHING*OR DO A MODEL OF EVERYTHING BUT GIVE A S P A C E W E R E D I F F E R E N T , E V E N CONTRADICTORY, IDEAS, AND DATA CAN BE EXPLOITED IN A WAY WHICH PROPELS COLLABORATIVE EFFORTS BY SCIENTISTS AND USERS.

*“Correctly interpreted, you know, pi contains the entire history of the human race.”-Dr. Irving Joshua Matrix, from M. Gardner, “The magic numbers of dr. Matrix”



43

Find this presentation at

http://www.slideshare.net/posterVienna

Ulr

ici, 2

00

0 ?

Other material at

http://www.slideshare.net/GEOFRAMEcafe/rr-reflections







44

From the work "the thousand rivers” (i mille fiumi) by Arrigo Boetti and Anna-marie Sauzeau-Boetti

classification by order of magnitude is the most common method for classifying information relative to a certain category, in the case of rivers, size can be understood to the power of one, two, or three, that is, it can be expressed in km, km2, or km3 (length, catchment area, or discharge), the length criterion is the most arbitrary and naive but still the most widespread, and yet it is impossible to measure the length of a river for the thousand and more perplexities that its fluid nature brings up (because of its meanders and its passage through lakes, because of its ramifications around islands or its movements in the delta areas, because of manʼs intervention along its course, because of the elusive boundaries between fresh water and salt water...) many rivers have never been measured because their banks and waters are inaccessible, even the water spirits sympathize at times with the flora and the fauna in order to keep men away, as a consequence some rivers flow without name, unnamed because of their untouched nature, or unnamable because of human aversion (some months ago a pilot flying low over the brazilian forest discovered a “new” tributary of the amazon river). other rivers cannot be measured, instead, because they have a name, a casual name given to them by men (a single name along its entire course when the river, navigable, becomes means of human communication; different names when the river, formidable, visits isolated human groups); now the entity of a river can be established either with reference to its name (trail of the human adventure), or with reference to its hydrographic integrity (the adventure of the water from the remotest source point to the sea, independently of the names assigned to the various stretches), the problem is that the two adventures rarely coincide, usually the adventure of the explorer is against the current, starting from the sea; the adventure of the water, on the other hand, finishes there, the explorer going upstream must play heads or tails at every fork, because upstream of every confluence everything rarefies: the water, sometimes the air, but always oneʼs certainty, while the river that descends towards the sea gradually condenses its waters and the certainty of its inevitable path, who can say whether it is better to follow man or the water? the water, say the modern geographers, objective and humble, and so the begin to recompose the identity of the rivers, an example: the mississippi of new orleans is not the extension of the mississippi that rises from lake itasca in minnesota, as they teach at school, but of a stream that rises in western montana with the name jefferson red rock and then becomes the mississippi-missouri in st louis, the number of kilometres upstream is greater on the missouri side, but in fact this “scientific” method is applied only to the large and prestigious rivers, those likely to compete for records of length, the methodological rethinking is not wasted on minor rivers (less than 800km) which continue to be called, and measured, only according to their given name, even if, where there are two source course (with two other given names), the longer of the two could be rightly included in the main course, the current classification reflects this double standard, this follows the laws of water and the laws of men, because that is how the relevant information is given, in short, it reflects the biased game of information rather than the fluid life of water, this classification was began in 1970 and ended in 1973, some data were transcribed from famous publications, numerous data were elaborated from material supplied non-european geographic institution, governments, universities, private research centres, and individual accademics from all over the world, this convergence of documentation constitutes the the substance and the meaning of the work, the innumerable asterisks contained in these thousand record cards pose innumerable doubts and contrast with the rigid classification method, the partialness of the existing information, the linguistic problems associated with their identity, and the irremediably elusive nature of water all mean that this classification, like all those that proceeded it or that will follow, will always be provisional and illusionary

Anne-marie Sauzeau-Boetti

(TN the text is published without capital letters)

Thank you for your attention


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