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Reflections on GEOtop, NewAge and other modeling
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R. Rigon, G. Formetta
GEOtop, NewAge and BeyondMontpellier, October 21, 2011
Cez
ann
e, P
ine
Tre
e n
ear
Aix
Monday, October 24, 11
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2
The good old Hydrological cycle
Introduction
Monday, October 24, 11
Rigon et al., Montpellier, October 21, 2011
3
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
Introduction
Monday, October 24, 11
Rigon et al., Montpellier, October 21, 2011
4
So we use different models
Introduction
Monday, October 24, 11
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4
So we use different models
GEOtopFu
lly
dis
trib
ute
dG
rid
bas
ed
Introduction
Monday, October 24, 11
Rigon et al., Montpellier, October 21, 2011
4
So we use different models
GEOtopFu
lly
dis
trib
ute
dG
rid
bas
ed
NewAge
Larg
e sc
ale
mod
elli
ng
Hil
lslo
pe
- St
ream
An
thro
pic
In
fras
tru
ctu
res
Introduction
Monday, October 24, 11
Rigon et al., Montpellier, October 21, 2011
4
Boussinesq
Full
y C
ou
ple
dSu
bsu
rfac
e- S
urf
ace
Gri
d B
ased
So we use different models
GEOtopFu
lly
dis
trib
ute
dG
rid
bas
ed
NewAge
Larg
e sc
ale
mod
elli
ng
Hil
lslo
pe
- St
ream
An
thro
pic
In
fras
tru
ctu
res
Introduction
Monday, October 24, 11
Rigon et al., Montpellier, October 21, 2011
4
Boussinesq
Full
y C
ou
ple
dSu
bsu
rfac
e- S
urf
ace
Gri
d B
ased
PeakFlow
GIU
HPea
k f
lood
s
So we use different models
GEOtopFu
lly
dis
trib
ute
dG
rid
bas
ed
NewAge
Larg
e sc
ale
mod
elli
ng
Hil
lslo
pe
- St
ream
An
thro
pic
In
fras
tru
ctu
res
Introduction
Monday, October 24, 11
Rigon et al., Montpellier, October 21, 2011
4
Boussinesq
Full
y C
ou
ple
dSu
bsu
rfac
e- S
urf
ace
Gri
d B
ased
PeakFlow
GIU
HPea
k f
lood
s
So we use different models
GEOtopFu
lly
dis
trib
ute
dG
rid
bas
ed
NewAge
Larg
e sc
ale
mod
elli
ng
Hil
lslo
pe
- St
ream
An
thro
pic
In
fras
tru
ctu
res
Introduction
The complexity arrow
Monday, October 24, 11
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5
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
Monday, October 24, 11
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6
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
;-)
Questions
•Without reinventing the wheel any time
Monday, October 24, 11
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7
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)
GEOtop
Monday, October 24, 11
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8
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.
Remarks
Monday, October 24, 11
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9
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
GEOtop structure
Monday, October 24, 11
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10
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 ?
GEOtop structure
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11
Meteo
Rainfall/Snow
Snow/Energy budget
Atm. TurbulenceRadiation
For each time stepGEOtop, NewAgeBoussinesq
Al the models the same strategy but w i t h d i f f e r e n t a m o u n t o f information flowing
GEOtop structure
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12
Richards ++
Surface flows
Channel flow
Next time step
GEOtop
GEOtop structure
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13
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:
Se = [1 + (��⇥)m)]�n
Se :=�w � �r
⇥s � �r
C(⇥)⇤⇥
⇤t= ⇥ ·
�K(�w) �⇥ (z + ⇥)
⇥
K(�w) = Ks
⇧Se
⇤�1� (1� Se)1/m
⇥m⌅2
C(⇥) :=⇤�w()⇤⇥
GEOtop structure
Monday, October 24, 11
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13
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:
Se = [1 + (��⇥)m)]�n
Se :=�w � �r
⇥s � �r
C(⇥)⇤⇥
⇤t= ⇥ ·
�K(�w) �⇥ (z + ⇥)
⇥
K(�w) = Ks
⇧Se
⇤�1� (1� Se)1/m
⇥m⌅2
Water balance
C(⇥) :=⇤�w()⇤⇥
GEOtop structure
Monday, October 24, 11
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13
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:
Se = [1 + (��⇥)m)]�n
Se :=�w � �r
⇥s � �r
C(⇥)⇤⇥
⇤t= ⇥ ·
�K(�w) �⇥ (z + ⇥)
⇥
K(�w) = Ks
⇧Se
⇤�1� (1� Se)1/m
⇥m⌅2
Water balance
ParametricMualem
C(⇥) :=⇤�w()⇤⇥
GEOtop structure
Monday, October 24, 11
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13
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:
Se = [1 + (��⇥)m)]�n
Se :=�w � �r
⇥s � �r
C(⇥)⇤⇥
⇤t= ⇥ ·
�K(�w) �⇥ (z + ⇥)
⇥
K(�w) = Ks
⇧Se
⇤�1� (1� Se)1/m
⇥m⌅2
Water balance
ParametricMualem
Parametricvan Genuchten
C(⇥) :=⇤�w()⇤⇥
GEOtop structure
Monday, October 24, 11
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14
What I mean with Richards ++
Extending Richards to treat the transition from saturated to unsaturated zone. Which means:
GEOtop structure
Monday, October 24, 11
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15
Landsliding
After Lanni et al, 2010 submitted
Landsliding
Monday, October 24, 11
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16
Landslidingdry case - low intensity precipitation
Landsliding
After Lanni et al, 2010 submitted
Monday, October 24, 11
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17
Landslidingwet case - high intensity precipitation
Landsliding
After Lanni et al, 2010 submitted
Monday, October 24, 11
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18
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.
Landsliding
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What I mean with Richards ++
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
GEOtop structure
Monday, October 24, 11
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20
Unfrozen water content
Soil water retention curve
thermodynamic equilibrium (Clausius Clapeyron)
+
⇥w =pw
�w gpressure head:
�w(T ) = �w [⇥w(T )]
What I mean with Richards ++
+
Freezing = drying hypothesis
GEOtop structure
M. Dall’Amico et al., The Cryosphere, 2011
Monday, October 24, 11
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21
T � := T0 +g T0
Lf�w0
� = ⇥r + (⇥s � ⇥r) · {1 + [�� · ⇤w0]n}�m
ice content: �i =⇥w
⇥i
��� �w
⇥
⇥w = ⇥r + (⇥s � ⇥r) ·⇤
1 +���⇤w0 � �
Lf
g T0(T � T ⇥) · H(T � T ⇥)
⇥n⌅�mliquid water
content:
Total water content:
depressed melting
point
What I mean with Richards ++
GEOtop structure
M. Dall’Amico et al., The Cryosphere, 2011
Monday, October 24, 11
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22
Unsaturatedunfrozen
UnsaturatedFrozen
Freezingstarts
Freezingprocedes
What I mean with Richards ++
Freezing = Drying
Monday, October 24, 11
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23
What I mean with Richards ++Freezing = Drying
M. Dall’Amico et al., The Cryosphere, 2011
Monday, October 24, 11
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24
What I mean with Richards ++Freezing = Drying
M. Dall’Amico et al., The Cryosphere, 2011
Monday, October 24, 11
Rigon et al., Montpellier, October 21, 2011
25water content [−]
so
il d
ep
th [
mm
]
Tot Water profile: comparison with Hansson et al−
20
0−
16
0−
12
0−
80
−6
0−
40
−2
00
0.25 0.30 0.35 0.40 0.45 0.50 0.55
after 50 hours●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Sim
● Meas
M. Dall’Amico et al., The Cryosphere, 2011
Freezing = Drying
Monday, October 24, 11
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26
Obviously this makes it possible to simulate a lot of new phenomenologies
Sisik, river in the artic tundra
Runoff on Frozen SoilEn
dri
zzi
et A
l., JH
R, 2
01
0
Monday, October 24, 11
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27
44
thaw depth: T(z,t)=0 water table depth: ψm(z,t)=0
Stefano Endrizzi, William Quinton, Philip Marsh, Matteo Dall’Amico, 2010 in preparation
Runoff on Frozen Soil
Monday, October 24, 11
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The model allows to show that the runoff
properties of a basin dramatically change when
soil freeze.
Runoff on frozen soil
Runoff on Frozen Soil: main result
Monday, October 24, 11
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Arabba
Pordoi
Caprile
Malga Ciapela
Pescul
Ornella
Saviner
Snow generated runoff
Frozen soil can be combine with the snow module
Monday, October 24, 11
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Frozen soil can be combine with the snow module
Snow generated runoff
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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!
Snow generated runoff
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A second set of questions:
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 ?
Monday, October 24, 11
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33
The perfect model does not exist !
Well,
Pic
asso
, Dora
Maa
r
Deconstructing models
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34
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.
JGrass-NewAGE
Monday, October 24, 11
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Hillslope Storage Dynamics
Surface flows Aggregation
Channel flow
Next time step
JGrass-NewAge
The structure of NewAge
(Formetta et al., GTD, 2011
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Input Data treatment
Goodness of fit
Next time step
JGrass-NewAge
The structure of NewAge
Calibration tools(Formetta et al., GTD, 2011
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37
Input Data treatment
Goodness of fit
Next time step
JGrass-NewAge
The structure of NewAge
Data assimilation(Formetta et al., GTD, 2011
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Hillslope Storage Dynamics
Surface flows Aggregation
Channel flow
Next time step
JGrass-NewAge
The structure of NewAge
(Formetta et al., GTD, 2011
Evapotranspiration
Radiation
Monday, October 24, 11
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39
Rin
ald
o, G
eom
orp
hic
Flo
od
Res
earc
h, 2
00
6
Someone call them Hydrologic Runoff Units
we call them hillslope-link partition of the basin
The structure of NewAge
Monday, October 24, 11
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40
Rin
ald
o, G
eom
orp
hic
Flo
od
Res
earc
h, 2
00
6
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
The structure of NewAge
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They are estimatedfor each hillslope
•mean rainfall
•mean radiation (we exploit some old idea by Ian Moore)
•mean evapotranspiration
•mean snow cover
•mean runoff production
The structure of NewAge
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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
The structure of NewAge
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43
Thus we have discharges
Rinaldo, Geomorphic Flood Research, 2006
Here, Here ... and here again
The structure of NewAge
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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.
And the complexity of Richards equations ?
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Observe,that I did not mention the complexity implies by the
Richards equation.
WHERE IS IT NOW ?
And the complexity of Richards equations ?
Monday, October 24, 11
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46
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)
for a derivation of part of it see Cordano and Rigon, 2008
And the complexity of Richards equations ?
Can we built a statistical theory that rigorously derives the simplified equations ?
Monday, October 24, 11
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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
The need for a statistical theory
^This will be remain, however ...
** The distribution around the mean quantities could not be sharp. Variances can be important ...
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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
The need for a statistical theory
Monday, October 24, 11
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49
Assume that now a reservoir
is made here
Rinaldo, Geomorphic Flood Research, 2006
The structure of NewAge
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Well, you can have the discharges also there
once you embeds the characteristics of the reservoirs in the model
Rinaldo, Geomorphic Flood Research, 2006
The structure of NewAge
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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
The structure of NewAge
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The modeling by component paradigm was adopted
Modeling by component
automagically inside the udig GISThis interface was automatically created from OMS v3 annotations
Monday, October 24, 11
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The modeling by component paradigm was adopted
Modeling by component
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/
Resources
KnowledgeBase
DevelopmentTools
Products
OMS
http://www.javaforge.com/project/oms
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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
Questions
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55
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.
The structure of NewAge
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A third set of questions:
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 ?
•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)
•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 ?
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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.
Peakflow
Hoku
sai, 1
82
9-3
2
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Effective rainfall
Surface flows Aggregation (Width function)
Diffusive wave
Peakflow (a W-GIUH) model
The structure of Peakflow
(Rigon et al., HESS, 2011)
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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
The structure of Peakflow
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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
The structure of Peakflow
• well, I did not talk about the runoff coefficient
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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.
The structure of Peakflow
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Flood wave celerity can be kept constant (as a first approx.)
Leopold and Maddock, 1953
The structure of Peakflow
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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
The structure of Peakflow
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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
The structure of Peakflow
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Good resultsFort Cobb, OK USA
05/26/2008
Aft
er P
erat
hon
er, 2
01
1Results with Peakflow
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65
Aft
er P
erat
hon
er, 2
01
1
Less good result*Little Washita, OK
19/06/2007
* On Little Washita we had also good results
Results with Peakflow
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Aft
er P
erat
hon
er, 2
01
1
Less good resultPassirio, Italy23/07/2008
Results with Peakflow
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67
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
Results with Peakflow
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68
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.
Results with Peakflow
Monday, October 24, 11
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69
Questions
•How, the hell, can you estimated that damned runoff coefficient ?
A fourth set of questions
•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 ?
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70
GEOFRAME 201* Vision
GEOFRAME
GEOtop NewAge Boussinesq PeakFlowModels
SHALSTAB GEOtop-FS The Horton Machine
J G r a s s - u d i g - OMS3- NetCDF
Out R NWW
JGrass-udig- OMS3- NetCDF
Environmental Data Center (Postgres/Postgis/Ramadda/H2)Data
In
METEO/IO
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Find this presentation at
http://abouthydrology.blogspot.com
Ulr
ici, 2
00
0 ?
Other material at
http://www.slideshare.net/GEOFRAMEcafe/rr-reflections
Monday, October 24, 11
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72
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)
Thank you for your attention
Monday, October 24, 11
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