Rr reflections

GEOtop, NewAge and Beyond
Montpellier, October 21, 2011
Cezanne, Pine Tree near Aix

R. Rigon, G. Formetta

Monday, October 24, 11

Introduction

The good old Hydrological cycle

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2

Rigon et al., Montpellier, October 21, 2011

Introduction

Every Hydrologist would like to have
THE MODEL of IT

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

3


Introduction

So we use different models

4


Introduction


GEOtop
Fully distributed
Grid based

4


Introduction


GEOtop NewAge

Anthropic Infrastructures
Fully distributed

Large scale modelling
Hillslope - Stream
Grid based

4


Introduction

Fully distributed
Grid based GEOtop

Hillslope - Stream

NewAge

Fully Coupled
Subsurface- Surface
Grid Based
Boussinesq

4

Introduction

Fully distributed
Grid based GEOtop

Hillslope - Stream

NewAge

Fully Coupled
Subsurface- Surface
Grid Based
Boussinesq

GIUH
Peak floods
PeakFlow

4

Introduction


GEOtop NewAge Boussinesq PeakFlow


Subsurface- Surface
Fully distributed

Peak floods

Fully Coupled
Hillslope - Stream
Grid based

GIUH
Grid Based

The complexity arrow 4


Introduction

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)

5


Questions

A first question:

• How can we manage the set of activities behind all of this modeling ?

(-;
doing the models using sound science,
modern informatics,
validating them against data,
assessing their uncertainty
;-)

•Without reinventing the wheel any time

6


GEOtop

GEOtop
(Rigon et al., Jour. Hydromet., 2006)

This model focuses on the water and energy budgets at few
square meters scale with the goal of describing catchment
hydrology including (a reasonable parameterization) all
known processes. (Whatever this means)

7


Remarks

We are aware that:

“ You cannot deny that our universe is not a
chaos; we discern in it beings, things, stuff that we
name with words. These beings or things are
forms, structures endowed with a certain
stability; they fill a certain portion of space and
perdure for a certain time ...”

R. Thom, Structural stabity and morphogenesys,1975

And therefore a fully reductionist approach is stupid. However
facing with the fundamental law teaches us many thing about the
reduction of complexity with scales that naive intuition or
pedestrian simplification does not allow.

8


GEOtop structure

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

2. Water balance 6. vegetation
interaction
- effective rainfall
- surface flow (runoff and channel - multi-layer vegetation
routing) scheme
- evapotranspiration

3. Snow-glaciers
- multilayer snow
scheme 5. soil energy balance

- soil
4. surface energy balance temperature
- freezing soil
- radiation
- boundary-layer interaction
9


GEOtop structure

Why this complexity ?

snow, ice, permafrost

water cycle in
complex terrain Endrizzi 2007
Dall’Amico 2010
Rigon et al., 2006 Endrizzi et al,
2010a,b in
preparation

evapo-transpiration,
landsliding
energy fluxes

Bertoldi et al., 2006 Simoni et al 2008
Bertoldi et al 2010 Lanni et al, 2010

10


GEOtop structure

GEOtop, NewAge For each time step
Boussinesq

Al the models the
same strategy but
with different Meteo
amount of
information flowing

Rainfall/Snow Radiation Atm. Turbulence

Snow/Energy budget

11


GEOtop structure

GEOtop

Richards ++

Surface flows

Channel flow

Next time step
12


GEOtop structure

What I mean with Richards ++
First, I would say, it means that it would be better to call it, for
instance: Richards-Mualem-vanGenuchten equation, since it is:

⇤⇥ ⇥
C(⇥) = ⇥ · K( w ) ⇥ (z + ⇥)
⇤t
⇧ ⇤ ⇥ m ⌅2
K( w ) = Ks Se 1 (1 Se ) 1/m

n
Se = [1 + ( ⇥) )]
m

⇤ w () w r
C(⇥) := Se :=
⇤⇥ ⇥s r
13


GEOtop structure


⇤⇥ ⇥
C(⇥) = ⇥ · K( w ) ⇥ (z + ⇥) Water balance
⇤t
⇧ ⇤ ⇥ m ⌅2
K( w ) = Ks Se 1 (1 Se ) 1/m

n
Se = [1 + ( ⇥) )]
m

⇤ w () w r
C(⇥) := Se :=
⇤⇥ ⇥s r
13


GEOtop structure


⇤⇥ ⇥
⇤t
⇧ ⇤ ⇥ m ⌅2
Parametric
K( w ) = Ks Se 1 (1 Se ) 1/m
Mualem

n
Se = [1 + ( ⇥) )]
m

⇤ w () w r
C(⇥) := Se :=
⇤⇥ ⇥s r
13


GEOtop structure


⇤⇥ ⇥
⇤t
⇧ ⇤ ⇥ m ⌅2
Parametric
K( w ) = Ks Se 1 (1 Se ) 1/m
Mualem

n Parametric
Se = [1 + ( ⇥) )]
m
van Genuchten

⇤ w () w r
C(⇥) := Se :=
⇤⇥ ⇥s r
13


GEOtop structure


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

14


Landsliding

Landsliding

After Lanni et al, 2010 submitted 15


Landsliding

Landsliding
dry case - low intensity precipitation



Landsliding

Landsliding
wet case - high intensity precipitation



Landsliding

Landsliding

The experiments also show that triggering happens
when approximately the same critical weight of
water has been stored in the hillslope, and that the
antecedent soil moisture condition and rainfall
intensity determine the rainfall duration needed to
achieve this critical volume of water.

18


GEOtop structure


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

Freezing
Unsaturated starts
unfrozen

Unsaturated Freezing
Frozen procedes

19


GEOtop structure

Soil water retention curve
+

thermodynamic equilibrium (Clausius Clapeyron)
+
Freezing = drying hypothesis

pw
pressure head: ⇥w =
w g

Unfrozen water content

w (T ) = w [⇥w (T )]

M. Dall’Amico et al., The Cryosphere, 2011 20


GEOtop structure


m
Total water = ⇥r + (⇥s ⇥r ) · {1 + [
n
· ⇤w0 ] }
content:
⇤ ⇥n ⌅ m
liquid water ⇥w = ⇥r + (⇥s ⇥r ) · 1 + ⇤w0
Lf
(T T ⇥ ) · H(T T ⇥)
content: g T0

⇥w ⇥
ice content: i = w
⇥i

depressed g T0
T := T0 +
melting Lf
w0

point



Freezing = Drying


unfrozen starts

Frozen procedes

22


Freezing = Drying




Freezing = Drying

Tot Water profile: comparison with Hansson et al
0

after 50 hours
●

Sim
−20

●

●
● Meas
−40

●

●
−60

●

●
−80

●
soil depth [mm]

●

●

●
−120

●

●

●

●
−160

●

●

●

●
−200

●

0.25 0.30 0.35 0.40 0.45 0.50 0.55

water content [−]



Runoff on Frozen Soil

Obviously this makes it possible to simulate
a lot of new phenomenologies
Endrizzi et Al., JHR, 2010

Sisik, river in the artic tundra 26


Runoff on Frozen Soil

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

44
Stefano Endrizzi, William Quinton, Philip Marsh, Matteo Dall’Amico, 2010 in preparation
27


Runoff on Frozen Soil: main result

Runoff on frozen soil

The model allows to show that the runoff
properties of a basin dramatically change when
soil freeze.

28


Snow generated runoff

Frozen soil can be combine with the snow module

Arabba

Pordoi

Ornella

Saviner
Caprile Pescul

Malga Ciapela

29



Frozen soil can be combine with the snow module

30



We have to work more here!
Discharge at Saviner year 2006−2007

14
GEOtop measured

12
10
Discharge [m3/s]

8
6
4
2
0

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

Date (dd/mm)

31


Questions

A second set of questions:

•Is Richards equation true ?
•Is the van Genuchten-Mualem theory true ?

•What actually means “true” ?

•Where is “structure” (beside texture) in soil parametrization ?

•Are there methods for accounting the spatial and temporal
variability of soil hydraulic characteristics ?

•Soil thermodynamics .... what is it ?

32


Well,

The perfect model does not exist !

Picasso, Dora Maar

Deconstructing models 33


JGrass-NewAGE

JGrass-NewAGE
(Formetta et al., GTD, 2011)

This model focuses on the hydrological budgets of medium
scale to large scale basins as the product of the processes
averaged at the hillslope scale with the interplay of the river
network.

34


The structure of NewAge

JGrass-NewAge
(Formetta et al., GTD, 2011

Hillslope Storage
Dynamics

Surface flows
Aggregation

Channel flow

Next time step
35



JGrass-NewAge
(Formetta et al., GTD, 2011 Calibration tools

Input Data treatment

Goodness of fit

Next time step
36



JGrass-NewAge
(Formetta et al., GTD, 2011 Data assimilation

Input Data treatment

Goodness of fit

Next time step
37



JGrass-NewAge
(Formetta et al., GTD, 2011

Evapotranspiration Hillslope Storage
Dynamics

Radiation Surface flows
Aggregation

Channel flow

Next time step
38



Someone call them Hydrologic Runoff Units
Rinaldo, Geomorphic Flood Research, 2006

we call them hillslope-link partition of the basin 39



For each of the variable of the hydrological cycle

a statistics is made for each hillslope and a single value is returned

so, we have 5 values of the prognostics quantities here, that are space
time-averages of what happens inside each hillslope 40



They are estimated
for each hillslope

•mean rainfall
•mean radiation (we exploit some old idea by Ian Moore)
•mean evapotranspiration
•mean snow cover
•mean runoff production

41



When runoff is collected

then is routed, for small basins, with a modification of the Muskingum-Cunge
algorithm, or directly with a semi-implict solver of the de Saint-Venant 1D 42



Thus we have discharges


Here, Here ... and here again
43


And the complexity of Richards equations ?

Remind that, in general,
you cannot

assume constant flow velocity through the network
in all conditions of flow. So the simplifications that
brings to the W-GIUH (Rinaldo et al., 1991,1995; Saco
and Kumar, 2002; D’Odorico and Rigon, 2003)
cannot be made.

44



Observe,
that I did not mention the complexity implies by the
Richards equation.

WHERE IS IT NOW ?

45



IT WAS ASSUMED more than DERIVED*

- that something averages out*

- that the same averages modify the structure of the
equations and the parameters (which could possibly
vary seasonally)

Can we built a statistical theory that rigorously
derives the simplified equations ?

for a derivation of part of it see Cordano and Rigon, 2008
46


The need for a statistical theory

A rigorous statistical theory would be needed that
allows for

•doing rigorously such simplifications*, not just on the basis of the personal Art
of modelling^;

•quantify the uncertainty remained after the simplifications**

*for a derivation of part of it see Cordano and Rigon, 2008 and BTW compare it with the abstract view
Reggiani et al., 1999

^This will be remain, however ...

** The distribution around the mean quantities could not be sharp. Variances can be important ...

47


The need for a statistical theory

However, the more “reductionist” GEOtop

could be used to test the solutions implemented in the simplified NewAGE and
evaluate the non-acceptable behaviors.

Obviously, this is not as simple as
it can be, because GEOtop itself
comes with its simplifications and
errors

48



Assume that now a reservoir


is made here

49



Well, you can have the discharges also there


once you embeds the characteristics of the reservoirs in the model 50



However

for doing it seamlessly you need to made a topological description of the network
and capture it in a suitable object-oriented-geographic infrastructure.

NewAge DOES it!

details in the upcoming papers and manual

51


Modeling by component

The modeling by component paradigm was adopted

This interface was automatically created from OMS v3 annotations

automagically inside the udig GIS 52


Modeling by component

The modeling by component paradigm was adopted
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.

Products

Development
Tools

OMS

Knowledge
Base

Resources

OMS3 can be found at: http://www.javaforge.com/project/
http://www.javaforge.com/project/oms
53


Questions

Is the mean value for a hillslope enough ?

from the point of view of the prognostic variable it could be. It Depends on what
the observer is looking for and for what.

from the point of view of the input data, inferring the space-time mean could not
be enogh. In fact:

•for evaluating evapotranspiration properly we need for accountng of the
subgrid variability of soil moisture distribution, vegetation and radiation.

•for evaluating the snow pack evolution we need to account, at least, for the
variability of radiation

54



So

Any hillslope is subdivided in

- zone of about the same elevation (elevation classes)

- areas that receives the same amount of radiation (radiation classes)

- soil cover classes

An this subgrid variability is used to estimated the
mean values for each hillslope.

55


Questions

A third set of questions:

• Is it possible (and how) to identify sets of spatial points that behave
hydrologically in a similar way ? (a question that pervades Hydrology since many years:
google hydrological symilarity)

•What is explained by the form, topology, and geometry of catchments ?

•What we can do to characterize uncertainties in hydrological modeling ?
And which is the acceptable degree of confidence to say that a model is a
good model ?

•Is really possible to work cooperatively building, we dwarfs, on the
shoulder of each other, and maybe of some giant ? Or is hydrology
condemned to an endemic dilettantism ? (e.g Klemes, WRR, 1986)

56


Peakflow

Peakflow
(Rigon et al., HESS, 2011)

Is a “minimalistic effort” when compared to the others. It is
an event based GIUH (width function flavor) model of
rainfall runoff which try to use the topographic information
for appropriate modeling.
Hokusai, 1829-32

57


The structure of Peakflow

Peakflow
(a W-GIUH) model
(Rigon et al., HESS, 2011)

Effective rainfall

Surface flows Aggregation
(Width function)

Diffusive wave

58



The main news is that during flood peaks

•Radiation and evapotranspiration are neglected (what is relevant is
included in the iniital conditions)

•you can assume very simplified mechanisms of runoff production

•flood wave celerity can be kept constant (as a first approx.)

•the most of the variance of flood hydrograph is explained by the
geometry and topology of the basin (and the space-time variation of
rainfall

59



The main news is that during flood peaks

•Radiation and evapotranspiration are neglected (what is relevant is
included in the iniital conditions)

•you can assume very simplified mechanisms of runoff production

•flood wave celerity can be kept constant (as a first approx.)

•the most of the variance of flood hydrograph is explained by the
geometry and topology of the basin (and the space-time variation of
rainfall

• well, I did not talk about the runoff coefficient

59



You can assume very simplified mechanisms of
runoff production

Well, more based on heuristics, since evidence shows that initial
condition for large floods (don’t want to talk of return period!) in a
basin, and rainfall space-time distribution (but mostly timing counts,
Rinaldo et al., 200X) are similar for a given basin.

60



Flood wave celerity can be kept constant
(as a first approx.)

Leopold and Maddock, 1953

61



Flood wave celerity can be kept constant
(as a first approx.)

Follows also from theory of
minimum energy dissipation:
- Rodriguez-Iturbe et al., Energy dissipation, runoff production and the
three-dimensional structure of river networks, WRR, 1992

- Rodriguez-Iturbe and Rinaldo, Fractal River Basin, CUP 1997

- Rinaldo et al., Channel Networks, Rev. Earth and Plan. Sciences, 1998

62



The most of the variance of flood
hydrograph is explained by the geometry
and topology of the basin (and the space-
time variation of rainfall)

- Rinaldo et al., Geomorphological Dispersion, WRR, 1992

- Rinaldo et al, Can you gauge the shape of a basin ? , WRR, 1995

- D’Odorico and Rigon, Hillslope and channel contributions to the
hydrologic response, WRR, 2003

63


Results with Peakflow

Good results
Fort Cobb, OK USA
05/26/2008
After Perathoner, 2011

64



Less good result*
Little Washita, OK
19/06/2007

* On Little Washita we had also good results 65



Less good result
Passirio, Italy
23/07/2008

66



Observations

There was a big trick: the runoff coefficient was estimated “a -priori”
and was:

Fort Cobb <- 0.14
Little Washita <- 0.7
Passirio <- 0.2

67



Observations

It seems that in some situations there is a delayed production of runoff which
produces large recession curves with local maxima of discharges that do not
correspond to rainfall impulses. Therefore the “tricky runoff coefficient” could
be different from surface and subsurface flows. In the case of Passirio, it could
be snow melting.

PBIAS is always negative, meaning that a systematic underestimation of flow
discharge.

68


Questions

A fourth set of questions

•How, the hell, can you estimated that damned runoff coefficient ?

•Is there really there the minimal information for forecasting floods or
can we do even better ?

•We used everywhere (with some tricks but with ) with success. Why we
did not systematize the parameters choice ?

•Can we modify the model structure to include spatial variability of
storms ?

•Which storms should be use for envisioning extreme events ?

69


GEOFRAME

GEOFRAME 201* Vision

Out R JGrass-udig- NWW
OMS3- NetCDF

GEOtop NewAge Boussinesq PeakFlow
Models

SHALSTAB GEOtop-FS The Horton Machine

In JGrass-udig- OMS3- NetCDF
METEO
/IO
Environmental Data Center
Data (Postgres/Postgis/Ramadda/H2)
70


Find this presentation at
http://www.slideshare.net/GEOFRAMEcafe/rr-reflections

Ulrici, 2000 ?

Other material at
http://abouthydrology.blogspot.com 71


Thank you for your attention
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 man’s 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 one’s
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) 72


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