Modern Flood Forecasting: A Contemporary and Smart Approach

The modern flood forecasting
Riccardo Rigon, Francesco Serafin, Marialaura Bancheri, Michele Bottazzi, and
Giuseppe Formetta
Cosenza 27 Luglio 2017
MichelangeloBuonarroti,CappellaSistema-IldiluvioUniversale,1508ca

“Prediction is very difficult,
especially about the future!”
Niels Bohr (1885-1962)

!3
Modern ?
Much better: contemporary and smart
Modern, on a closer inspection, is what is before contemporary.
Modern:
HEC-HMS
SWAT
SWMM
*** I cannot forget the simple good old model of the instantaneous unit hydrography! Please see: http://
abouthydrology.blogspot.it/2015/03/the-geomorphic-unit-hydrograph-from.html
Rigon et al.
A few digressions at the beginning

!4
The three tools above let you estimate, after an accurate estimation,
flood wave.
They are open source, supported by a wide community of developers and
users.
They work !
What else should we wish ?
There are tens of good models of the hydrologic response*. Why are we
here talking again about them ?
One issue is the forecasting word
*not all models are good. Some are better.
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!5
Forecasting means organisation
of data, for instance
To run, a good hydrological model needs data. The minimal
set:
• digital elevation models
• precipitations (at sub hourly time scale fro small basins)
• Some discharge mesurements (for models calibration)
Many more data for accurate works:
• temperature
• radiation
• snow measurements
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!6
DATA IN
Robbed from Maurizio Napolitano
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No data, no party

!7
Forecasting implies organization and interpretation
of models
of results
DATA INFORMATION
INFORMATION KNOWLEDGE
DATA INFORMATION
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Data are just the beginning

!8
Sviluppare un adeguato sistema di modellistica per la
pianificazione e la gestione;
Sviluppare un adeguato sistema di previsione nelle
applicazioni in tempo reale;
Sostenere l’organizzazione in anticipo dei servizi di
piena e di tutte le azioni di difesa del suolo, incluse
le misure di protezione civile per la gestione delle
emergenze.
Gli obiettivi del progetto
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A couple of examples
@ARPAE
CourtesybySilvanoPecoraeCinziaAlessandrini

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@ARPAE Rete osservativa
Giornalmente il sistema acquisisce una serie di dati dalla rete osservativa
Rete di monitoraggio :
919 Idrometri (blu)
1315 Pluviometri (verde)
167 Nivometri (verde)
928Termometri (verde)
193 Dighe RID (violetto)
DATA INFORMATION
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CourtesybySilvanoPecoraeCinziaAlessandrini
A couple of examples

!10
Forcasting means to choose the right models
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Which is the best model ?
Not the perfect ones, but those needed by the goal behind us, and also
just in flood forecasting, objective can be different.
1. On the best model: http://abouthydrology.blogspot.it/2012/02/which-hydrological-
model-is-better-q.html
2. Essentials for hydrologists: http://abouthydrology.blogspot.it/2013/07/essential-for-
hydrologists.html
3. What can I estimate with this or the other ? : http://abouthydrology.blogspot.it/
2013/03/can-i-simulate-effects-of-changing.html
4. I am sick and tired to answer about those “standard” models: http://
abouthydrology.blogspot.it/2013/07/almost-perfect-answer.html
5. Again on the best model: http://abouthydrology.blogspot.it/2014/08/which-
hydrological-model-is-better.html

!11
Le catene modellistiche
HEC-HMS DHI-NAM TOPKAPI
HEC-RAS DHI – M11 SOBEK
HMS/NAM/TOPKAPI
RAS/MIKE11/SOBEK
Prima catena Seconda
catena
Terza
catena
Catena configurabile
dall’utente
PRECIPITAZIONI
TEMPERATURE
LIVELLI/PORTATE
MODELLI
METEOROLOGICI
Osservati/Telemisura
LM/Ensemble
VALIDAZIONE, INTERPOLAZIONE
E TRANSFORMAZIONE DATI
@ARPAE
DATA INFORMATION
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Meteo countsCourtesybySilvanoPecoraeCinziaAlessandrini

!12
HMS/NAM/TOPKAPI
RAS/MIKE11/SOBEK
catena
Terza
catena
dall’utente
PRECIPITAZIONI
TEMPERATURE
LIVELLI/PORTATE
MODELLI
METEOROLOGICI
LM/Ensemble
DATA INFORMATION
@ARPAE
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Hydrology countsCourtesybySilvanoPecoraeCinziaAlessandrini

!13
I modelli idrologici
• HEC – HMS
(SMA model, Bennett, 1985)
• DHI – NAM
(NAM model, S.A. Nielsen and E. Hansen, 1973)
• TOPKAPI
(TOPKAPI model, Todini 1995)
DATA INFORMATION
@ARPAE
Rigon et al.
Hydrology counts. It is usually a bunch of reservoirsCourtesybySilvanoPecoraeCinziaAlessandrini

!14
HMS/NAM/TOPKAPI
RAS/MIKE11/SOBEK
catena
Terza
catena
dall’utente
PRECIPITAZIONI
TEMPERATURE
LIVELLI/PORTATE
MODELLI
METEOROLOGICI
LM/Ensemble
DATA INFORMATION
@ARPAE
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Flood wave propagation (does it matters ?)CourtesybySilvanoPecoraeCinziaAlessandrini

!15
Let me say that maybe ARPAE is not contemporary, but for sure post-
modern.
DATA INFORMATION
@ARPAE
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Post - modern
https://it.pinterest.com/source/onpostmodernism.com/

!16
Some traits:
•Real time (with real time flowing of information)
•Multimodels ( based on Delt-Fews)
•Uses meteo models and data (for real time)
DATA INFORMATION
@ARPAE
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The post modern
Let me say that maybe ARPAE is not contemporary, but for sure post-
modern.

!17
Connec9ng Rainfall to Flooding
Connec9ng Rainfall to Flooding
@IOWAINFORMATION
KNOWLEDGE
Witold F. Krajewski, Daniel Ceynar, Ibrahim Demir, Radoslaw Goska, Anton Kruger, Carmen Langel, Ricardo Mantilla, James Niemeier, Felipe
Quintero, Bong-Chul Seo, Scott J. Small, Larry J. Weber, and Nathan C. Young, Real-Time Flood Forecasting and Information System for the State of
Iowa, Real-time flood forecasting and information system for the State of Iowa, Bull. Am. Meteorol. Soc., doi:10.1175/BAMS-D-15-00243.1, 2017.
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The contemporary ?CourtesybyRicardoMantilla

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@IOWAINFORMATION
KNOWLEDGE
Hyperresolution
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CourtesybyRicardoMantilla
The contemporary ?

!19
@IOWAINFORMATION
KNOWLEDGE
However, using high resolution spatola data is not all the story.
Making hydrology at high resolution is.
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The contemporary ?

!20
Radar rainfall coverage of Iowa
Radar Information
@IOWAINFORMATION
KNOWLEDGE
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The contemporary ?

!21
Statewide implementa9on of the HLM-Async Model
Our distributed hydrological model provides streamﬂow predic9ons everywhere in Iowa
@IOWAINFORMATION
KNOWLEDGE
Further information @ AboutHydrology
Rigon et al.
The contemporary ?

!22
HPC Cluster
University of Iowa
Data Center
Virtual Servers
IFC Central
Database Server
Model
Forecasted
Discharge
River Network Topology,
Rainfall Maps

IFIS
Underlying Cyber-Infrastructure: System Overview
Rainfall Maps
HTTP Data Transfer
Reading from DB (TCP 5432)
Wri-ng into DB (TCP 5432)
Push Informa-on Pull Informa-on
Model Forecasted and
Real Stage and Discharge
IFC Real Time Flood
Forecas9ng Model
Iowa Flood
Informa9on System
IFC Rainfall System
@IOWAINFORMATION
KNOWLEDGE
Smart = Cyberinfrastructure + realtime measurements*
*Iy must be clear: ARPAE too has a complex and similar cyberinfrastructure.
Rigon et al.
The contemporary - smart

!23
Predic9ons from The Model @IOWAINFORMATION
KNOWLEDGE
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Great resultsCourtesybyRicardoMantilla

!24
Hyperresolution* (~1 km2)
Multiobjective -Multipurpose (at least for discharges)
-Multiprocesses (describes multiple processes)
Open (I will be back on this in a couple of slides)
Coming with confidence of estimates
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The post-contemporary ;-)
The Manifest of Contemporary flood models, IMO*:
* In My Opinion
* Wood, E. F., Roundy, J. K., Troy, T. J., van Beek, L. P. H., Bierkens, M. F. P., Blyth, E., et al. (2011).
Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's
terrestrial water. Water Resources Research, 47(5). http://doi.org/10.1029/2010WR010090

!25
So now, the poor hydrologists go home:
all has been already done
all is in the hands of (big) institutions
all is too big, too complex, out of singles capacity
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Sigh!

!26
Is not exactly like this
The forecasting quality is not everywhere accurate. On the contrary,
usually, is usually poor at the small scale. There is a problem of
verification that the big guys cannot pursue alone.
Environmental engineers does not necessario need that real-time
knowledge. Planning requires statistical analysis.
Water management, for the common good, implies the verification
and balancing of many factors, to highlighten alternative answers,
which corresponds to different need s of local and global societies.
Laws and norms count.
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Gulp!

!27
Fran Orford www.francartoons.com
Supermodel
Having a supermodel infrastructure does not solve, alone, the problems
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Cartoons !

!28
Open
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IMO, a contemporary and smart approach should allow
collaborative work in which the capacity of single actors
to go deeper are harmonised and contribute to Institution
work. Institutions efforts, in turn, looks towards to share
and avoid asymmetry in information.

!29
Studying floods is not only calculate a flood
and, not even, many floods
hazards
defense works
landslides
forest
agricolture
glacier
melting
permafrost
degradation
agricolture
ecology
ﬁshing
drinking
water
hunting
water withdraw
A. Zisch, 2013
Besides
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!30
Flood forecasting
in the contest of engineers work require more complex thinking that just estimatin
flood wave. As European norms remark
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Norms and laws

!31
It is needed to move from estimating floods in a catchment “as is” to
forecasting “in which certain characteristics are modified (by
climate change or human intervention)” while other are kept fixed.
To do this it is necessary that the varying characteristics have been
described explicitly in models.
A tipica question: does increase of forest or
changing agricolture management change
floods (water yeld)?
Rigon et al.
Flood forecasting in a more wider context

!32
P
Q
S = 0 t ⇠ 10 anni
Per una discussione più dettagliata sul tema:
http://abouthydrology.blogspot.it/search?q=Acqua%2C+suolo%2C+foreste
Rigon et al.
Quantitative studies

!33
that could account explicitly for atrophic actions and a full set of feedbacks at
multiple scales
Entropy 2014, 16 3484
Figure 1. Quantiﬁcation of the entropy or exergy budgets in the Critical Zone at different
spatial scales.
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They require a new modeling approach
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!34
We actually do not know how to put this stuff all together in models
H067:Hydrologic Dynamics, Complexity and Predictability: Physical and
Analytical Approaches for Improving System Understanding and Prediction
Session ID#: 23200
Session URL and Abstract Submission (deadline 3 August):
https://agu.confex.com/agu/fm17/preliminaryview.cgi/Session23200
Session Description:
Hydrology is a rich multidisciplinary ﬁeld encompassing a complex process network involving interactions of diverse nature and scales. Still, it abides to core dynamical
principles regulating individual and cooperative processes and interactions, ultimately relating to the overall Earth System dynamics.
This session focuses on advances in theoretical and applied studies in hydrologic dynamics, regimes, transitions and extremes along with their physical understanding,
predictability and uncertainty. Moreover, it welcomes research on dynamical co-evolution, feedbacks and synergies among hydrologic and other earth system processes at
multiple spatiotemporal scales.
The session further encourages discussion on physical and analytical approaches to hydrologic dynamics ranging from traditional stochastic, information-theoretical and
dynamical analysis to general frameworks addressing non-ergodic and thermodynamically unstable processes and interactions.
This session is further complemented by an ongoing special issue at the EGU journal Earth System Dynamics:
http://www.earth-syst-dynam.net/special_issue892.html
Grateful for your attention and consideration, we look forward to your contributions.
With warm regards,
The Conveners:
Rui A. P. Perdigão and Julia Hall
TU Wien, Austria
Rigon et al.
It is a topic for research

!35
H023 "Balancing the Water Budget: A Physical Basis for Quantifying Water Fluxes
Using Data and Models"
at the 2017 Fall Meeting of the American Geophysical Union. We are excited to explore the emerging challenges of balancing the water budget through a fusion of
mechanistic and statistical approaches to hydrology.
Session Description:
Mass balance is the governing principle in the characterization of hydrologic processes. The water budget, ΔStorage = Input – Output, is a “first principles lens” that can
be used to guide exploration, observations, and process representation of hydrologic fluxes. Furthermore, it constitutes the framework in which we integrate and
evaluate hydrologic models at multiple spatio-temporal scales (from single events to multi-decadal cycles, and from watersheds to continents). Given its fundamental
importance, an important question is to what degree and with how much confidence can the water budget be balanced. This session solicits studies exploring this
question at multiple spatial and temporal scales using both data-driven and model-based approaches. We especially welcome efforts to incorporate a broad
understanding of conservation of mass into the evaluation or integration of hydrologic models. Improved system-wide understanding is paramount as the globalization of
water and trade of “virtual” water stocks become the status quo.
Confirmed Invited Speakers
Jim Kirchner, ETH Zürich, Zurich, Switzerland
Richard Hooper, Tufts University, Medford, MA
Best regards,
William Farmer
Jessica Driscoll
Christopher Tennant
Dino Bellugi
Rigon et al.
It is a topic for research

!36
(4.1)
k
@t
= Jk(t)+
i
Qki(t)° ETk(t)°Qk(t)
for an appropriate set of elementary control volumes connected together. In Eq.(5.1),
S [L3
] represents the total water storage of the basin, J [L3
T°1
], ET [L3
T°1
], and Q
[L3
T°1
] are precipitation, evapotranspiration, and runoff (surface and groundwater)
respectively. The Qis represent input fluxes, of the same nature of Q, coming from
adjacent control volumes.
a
b
Figure 4.1: The location of the Posina basin in the Northeast of Italy (a) and DEM elava-
tion, location of rain gauges and hydrometer stations, subbasin-channel link partitions
used for this modelling (b).
It is clear that Eq.(5.1) is governed by two types of terms, which can be easily identi-
fied as “inputs" and “outputs". The outputs are certainly evapotranspiration, ET, and
discharges, Q, including the Qis, because they come from the assembly of control volumes.
The inputs are J(t), but this term has to be split into rainfall and snowfall. Moreover,
other inputs are ancillary to the estimation of outputs, in particular temperature, T and
radiation Rn. Another input of the equation is the definition of the domain of integration
and its“granularity", i.e. its partition into elements for which a singe value of the state
variables is produced.
In this paper we discuss the estimation of all of these input quantities, with the
A starting point
Abera et al., HESS, 2017
Posina
A small basin of 114 km2 in Vicenza province, that flows into the As
Bacchiglione-Brenta system. The model is JGrass-NewAGE
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!37
The scheme, for each subbasin could be represented as*:
receive precipitation (the flux can
be separated in rainfall and
snowfall )
gives evapotranspiration and
discharge
this “reservoir, can be further
“ e x p l o d e d ” i n m u l t i p l e
reservoirs, according to
various solutions
a flux
a quantity that varies
The mathematical skeleton
*http://abouthydrology.blogspot.it/2016/11/reservoirology-3.html
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!38
All us multiplied by the number of Hydrologic Response Units
Here just 5
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!39
The IOWA model covers thousands of basins like this
Mass Conservation Equations
Fluxes between control volumes
dsp
dt
= P t( )− qpl − qpc − ep
dsl
dt
= qpl − qls − el
dss
dt
= qls − qsc − es
qpc = k2sp
1.67
and qpl = kl sp
qls = kisl and qsc = k3ss
kl = kDRY 1−
sl
Sl
⎛
⎝
⎜
⎞
⎠
⎟
N
Channel Routing
dqc
dt
=
voqc
λ1
Aλ1
1− λ1( )l
qpc + psc
− qc + qu
u∈c
∑
⎛
⎝
⎜
⎞
⎠
⎟
Modeling the Land-Surface (Under the hood)
CourtesyofRicardoMantilla
Rigon et al.
Which is also what the IOWA model does

!40
Going this way means to assume* that our catchment could be
modeled (reduced to) a set of ordinary differential equations**
Mathematical muscles
tuple of water quantity in reservoirs
tuple of inputs tuple of outputstuple of parameters
tuple of initial conditions
From a mathematical point of view, this is a dynamical system
** To which corresponds a view for travel time
Rigon et al.
* Here a trial to get ODEs from PDEs systems

!41
This mathematics is common to many other disciplines
Rigon et al.
I say that because there is plenty of literature treating with
dynamical systems
which can be easily borrowed to analyse catchments then. In particular,
they are open (have inputs and outputs), non-linear (equations are
often non linear), parametric (the “quality of equations depends on
their parameters) dynamical systems.
Since our system is open (but it could be potentially closed, speaking
about mass flow) methods used to study Dynamical Systems and the
related nomenclature were never used though.

!42
0
50
100
1994 1995 1996 1997 1998 1999
Time [h]
Q[m3
/s]
Measured
Hymod
Model
Discharge
Here a result
with many comments to do* ….
*Both simulations preserve the mass budget
All this work … just for this ?
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!43
0
10
20
30
Jan 2005 Apr 2005 Jul 2005 Oct 2005 Jan 2006
Date
Discharge(m3/s)
0.0
2.5
5.0
7.5
10.0
Date
Discharge(m3
/s)
OBS
SIM
0
5
10
15
20
25
Date
Discharge(m3
/s)
0
1
2
3
Date
Discharge(m3
/s)
0.0
0.2
0.4
0.6
Date
Discharge(m3/s)
Link 14
Link 9
Link 32
Link 1
Link 3
1
Link 9
Figure 9: NewAGE model forecasting validation at internal links. Discharge is estimated for all links, here plotted for links 1,3, 9 (the outlet
links), and 14, 27, and 32 as samples. When data is available at any internal point, model performances can be evaluated by comparison (e.g. for
links 14 and 32).
12
Rigon et al.
Projections inside the basin
Abera,W.,Formetta,G.,Borga,M.,&Rigon,R.(2017).

!44
ation. It is highest in June
er to February, as expected,
nual budget. In 2012, from
ation is sustained by the wa-
recipitation, indicating that,
tion of the catchment could
in these months.
HRUs, the monthly means of
the 18 years of simulations
monthly estimates for four
October, one from each sea-
result conﬁrms the monthly
ure 13). The trend in Q fol-
arly proportional.
River basin has been an-
at hourly time-steps, using
infall and temperature) and
ude estimations of the four
recipitation, discharge, rel-
on) under the hypothesis of
ne of the years where mea-
ystem components are used
ecast the water cycle. The
and can be transposed to all
a are available. Whilst pre-
locations, part of the work
lyse when they were liquid
S). The upper graph shows the total available water, J, divided in its
snow and rainfall parts.
0
100
200
300
400
500
01-2012
02-2012
03-2012
04-2012
05-2012
06-2012
07-2012
08-2012
09-2012
10-2012
11-2012
12-2012
Date(months)
Q,ET,S(mm/month)
Q
ET
S
0
100
200
300J(mm/month)
Figure 13: The same as ﬁgure 12, but monthly variability for the year
2012.
14
What mass conservation implies, explained
precipitation
discharges
storage variation
evapotranspiration
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!45
And then, perhaps, we can
treat draughts also
Why the water budget ?
Rigon et al.

!46
So, perhaps, we avoid embarrassing
situations
(where water inputs are less than outputs)
Rigon et al.
StefanoTasin,2017
Why the water budget ?

!47
Multipurpose-Multiprocesses
root zone
ground
canopy
surface waters
Rigon et al.
neve
A more reasonable representation of the processes in a HRU requires these
storages
sediment
(and
watered
sediment)

!48
1
2
3
4
5
6
7
0
When we add them all together with ﬂuxes
snow
root
ground
canopy
surface waters
Rigon et al.
A

!49
This whole system must be added together with repeating this
scheme for any of the HRU, but potentially it could be varied for each
HRU.
Scaling up
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!50
Introducing this complexity causes, obviously, issues about
parameters identification, computational burden (to be performed
in paralalle), issues about data acquisition and storage,
representation and analysis of the data.
Issues
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!51
All of this does not exists yet in a system.
but is what my group is working on
Composite Pattern - A very ﬁrst idea
COMPONENT
. . .
Leaf
Local Node
Ghost Node
Node
Abstra
ctclass
TRAVERSER
37 / 68
Answers
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!52
The representation of catchements and human
infrastructures (and/or actions) with graphs*, allows not
only the representation of the ordinary differenziali
equazioni systems that are thought to rule the systems,
but also a split of those systems in parts that can be
implemented separately and assembled at the end, and
eventually modeled in parallel, by means by smart graphs
“traverser” that analyses and understand graphs
interdipendencies.
*with theirs mathematics
The prototype “deployment” of this informatics is already
used by the authors and will be soon openly released to
all open source.
Answers
Rigon et al.

!53
Details of recent papers
Abera, W., Formetta, G., Brocca, L., & Rigon, R. (2017). Modeling the water budget of the Upper
Blue Nile basin using the JGrass-NewAge model system and satellite data. Hydrology and
Earth System Sciences, 21(6), 3145–3165. http://doi.org/10.5194/hess-21-3145-2017
Abera, W., Formetta, G., Borga, M., & Rigon, R. (2017). Estimating the water budget
components and their variability in a pre-alpine basin with JGrass-NewAGE, 1–18. http://
doi.org/10.1016/j.advwatres.2017.03.010
Rigon, R., Bancheri, M., & Green, T. R. (2016). Age-ranked hydrological budgets and a travel
time description of catchment hydrology. Hydrology and Earth System Sciences, 20(12), 4929–
4947. http://doi.org/10.5194/hess-20-4929-2016
Formetta, G., Antonello, A., Franceschi, S., David, O., & Rigon, R. (2014). Hydrological modelling
with components: A GIS-based open source framework, 55(C), 190–200. http://doi.org/
10.1016/j.envsoft.2014.01.019
Rigon, R., Bancheri, M., Formetta, G., & de Lavenne, A. (2015). The geomorphological unit
hydrograph from a historical-critical perspective. Earth Surface Processes and
Landforms, n/a–n/a. http://doi.org/10.1002/esp.3855
The path so far
Rigon et al.

!54
The great, important, and ofted debated question is: Hoe the
events that happens in catchements, in soils, plants, forests,
can be described by physics and chemistry (in a word by
hydrology)?
The preliminary answer that this talk would rise is the following:
There is a partial ability of present hydrology to account for
these events, and there is no reason to doubt that they can be
even more in the next future development of this science.*
* Paraphrasing What is life ? by E. Schroedinger
R. Rigon
The scope of hydrologists

!55
This presentation can be found at
http://abouthydrology.blogspot.com
Ulrici,2000?
Other material at
Questions ? Stay tuned for future evolutions and releases of our open source softwares
R. Rigon
http://abouthydrology.blogspot.it/2017/07/the-post-contemporary-flood-forecasting.html

Modern Flood Forecasting: A Contemporary and Smart Approach

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Modern Flood Forecasting: A Contemporary and Smart Approach