An inter-disciplinary approach to explore the dynamics of risk

An inter-disciplinary approach
to explore the dynamics of risk
Giuliano Di Baldassarre
WE (HUMANS) ARE UNFAIR.
History
History
• Early 1960s, Italy
• Construction of the Vajont Dam (280m)
Vajont dam disaster
• 9 October 1963 at 22:39
• Landslide into this “brand new” hydroelectric reservoir
• Giant wave
Vajont dam disaster
• 9 October 1963 at 22:39
• Giant wave raised by a landslide into this “brand new”
hydroelectric reservoir
• The wave affected five towns, killing 1918 people
Longarone (BEFORE 9 October 1963)
Longarone (AFTER 9 October 1963)
A thought experiment
A thought experiment
• Late 1950s, Italy
• Roberto Camorani, Minister of Public Works (fictitious)
ictions
A thought experiment
• Following the advices of some concerned geologists,
Camorani did NOT authorize the Vajont dam construction
• The Vajont dam disaster did NOT happen
Longarone (BEFORE 9 October 1963)
Longarone (AFTER 9 October 1963)
A thought experiment
• Would the strictness of Roberto Camorani be appreciated?
• Would he be rewarded for avoiding the Vajont disaster?
• Would History actually remember him?
“everybody knows that you need more prevention than treatment,
but few reward acts of prevention”
N.N. Taleb (2007)
PREVENTION IS INVISIBLE.
Culture of risk prevention
(KULTURisk)
Title: Knowledge-based approach to develop a culture of risk prevention, KULTURisk
Consortium: 11 partners from 6 European countries (www.kulturisk.eu)
Project Coordinator: Giuliano Di Baldassarre
Instrument: EC FP7, Collaborative project
Start Date: January 2011
Duration: 36 months
Progresses,
among many others, but…
Recent floods and landslides
(Sources: Bbc.co.uk, 2014; Sydsvenskan.Se, 2014; Ansa.It; 2014)
Increasing losses (worldwide)
Floods affect more than 100 million people a year,
Increasing losses (see e.g. Africa)
Flood fatalities
15000
12000
9000
6000
3000
0
1950-1969
1970-1989
1990-2009
(Di Baldassarre et al., Geophysical Research Letters, 2010)
Besides societal challenges…
Uncertainty!
Aleatory
• Latin alea (die or game of dice)
• Alea iacta est ("the die is cast“, Julius Caesar, 49BC)
• Random variability of processes
Epistemic
• Greek ἐπιστήμη (knowledge)
• Lack of knowledge
(Ruhr Universität Bochum, 2011, Beven and Smith, JHE, 2014, Di Baldassarre et al., under review)
Aleatory uncertainty
Aleatory uncertainty is often treated in probabilistic terms,
e.g. floodplain mapping (deterministic vs probabilistic)
Flood inundation modeling
(simulated flood depths)
Deterministic
(either WET or DRY)
Probabilistic
(between 0 and 1)
(Di Baldassarre et al.,Hydrological Sciences Journal, 2010)
Epistemic uncertainty
Over-confidence of experts/scientists
(Cooke, 1991; Shlyakhter et al., 1994; Lin and Bier, 2008)
Hypothesis: “epistemic uncertainty” not sufficiently considered
• wrong assumptions
• known unknowns
• unknown unknowns
Wrong assumptions
things we think we know, but we actually don’t know
Let’s introduce two ladies*
Dr. Maria Smith
Graduated summa cum laude at MIT
Youngest professor at Princeton University
She is a “logical positivist”
“Smart” Angie
Graduated, somehow/somewhere
Great entrepreneur, got very rich in a few years
She is a “sceptical empiricist”
*Fictitious names, this experiment is inspired by the “ludic fallacy” of Taleb (2007)
Wrong assumptions
A coin is flipped 99 times, and each time it comes up heads.
What are the odds that the 100th flip would also come up heads?
A) Less than 50%
B) 50%
C) More than 50%
Dr. Smith: “Odds are not affected by previous outcomes,
so the odds must be 50%!”
Smart Angie: “Well, if it came up heads 99 times in a row
there must be something wrong with this coin!
So, odds must be much more than 50%!”
In classical terms: odds of the coin coming up heads 99 times in a row are so low that the
assumption that the coin had a 50% chance of coming up heads is most likely wrong
*Fictitious names, this experiment is inspired by the “ludic fallacy” of Taleb (2007)
Wrong assumptions
• Use direct, tangible losses as proxy for damages
• Damage (and risk) is much more complex!
direct, indirect, tangible and intangible costs
(CONHAZ, 2011; KULTURisk, 2013)
Wrong assumptions
• Dynamics matter!
• Not only risk reduction, but also wealth 
Wealth
Wealth
disaster
Time
Time
(CONHAZ, 2011; KULTURisk, 2013)
Known unknowns
things we know we don’t know
Current Approach
water system
hydrological processes
(Probability)
human system
socio-economic processes
(Consequences)
Risk = Probability X Consequences
(Di Baldassarre et al., Wires Water, 2014)
Known unknowns: learning effect
Flooding in Venice, Italy
(Campostrini, KULTURisk workshop, 2011, www.atlantic.com; 2012)
Known unknowns: levee effect
RIVER
FLOODPLAIN
Levee building/heightening
Frequent flooding to rare, but catastrophic, flooding
Risk = Probability X Consequences
(White, Human adjustments to floods, 1945)
Dynamics around the world
lower probability associated to higher consequences (levee effect)
higher probability associated to lower consequences (learning effect)
The Netherlands
Levee effect
(1954-today)
Sacramento
Levee effect
(1930’s-today)
Meuse River
Learning effect
(1993,1995)
Paraná River
Learning effect
(1983, 1992)
Prague
Learning effect
(1997,2002)
New Orleans
Levee effect
(1930’s-2005)
Po River
Levee effect
(1951-today)
Mozambique
Learning effect
(2000,2007)
Brisbane
Levee effect
(1974-2011)
Bangladesh
Learning effect
(1971,2007)
(e.g. Penning-Rowsell, GR, 1996; Wind et al., WRR, 1999; Kates et al., PNAS, 2006; Bohensky et al., 2014)
An interdisciplinary approach
Current Approach
water system
hydrological processes
(Probability)
human system
socio-economic processes
(Consequences)
Novel, Interdisciplinary Approach
coupled human-water system
physical processes
(Probability)
human interventions
(policies, structures)
human experience
(memory, learning)
socio-economic processes
(Consequences)
(Viglione et al., Journal of Hydrology, 2014; Di Baldassarre et al., Wires Water, 2014)
policies
floods
damages
Current paradigm
An interdisciplinary approach
…scenarios
societies
New paradigm
time
(decades)
floods
…dynamics
societies
(Di Baldassarre et al., under review)
Human-flood interactions
Community settling and developing in a
floodplain, gaining the associated
benefits (e.g. trading)
Occurrence of flooding causes
economic damages
Community builds memory
Human response:
(a) Non-structural measures
(b) Structural measures
(Di Baldassarre et al., Hydrology and Earth System Sciences, 2013)
Socio-hydrological model
Flood system
F = flooding
G = wealth
D = distance
H = levees
M = memory
Human system
(Di Baldassarre et al., Hydrology and Earth System Sciences, 2013)
Socio-hydrological model
Dynamic model simulating feedbacks between floods and societies
Human and water systems are interlinked and gradually co-evolve
while being abruptly altered by the occurrence of flooding events
(Di Baldassarre et al., Hydrology and Earth System Sciences, 2013)
Risk dynamics
in a changing climate
Main prototypes:
(a) Green-societies: non-structural measures
(b) Techno-societies: structural measures, e.g. levees
Green-society
Techno-society
Coupled dynamics: results
Flood levels
Levees
Memory
Damage
(Di Baldassarre et al., under review)
Collective memory
• Sociologist Maurice Halbwachs (1877-1945): “Memory is a matter of
how minds work together in society”. It fades away if not renewed
• Capacity of the community to keep the risk awareness high
Socio-hydrological model
(Viglione et al., Journal of Hydrology, 2014)
Risk-taking attitude
• General disposition towards risk, which varies between
countries/cultures, male and female, social groups, etc…
• The amount of risk the community is willing to be exposed to
Socio-hydrological model
(Viglione et al., Journal of Hydrology, 2014)
Evolution of “wealth”
Kos 18 Oct 2013
Socio-hydrology modelling
Unknown unknowns
things we don’t know we don’t know
Black swans
unexpected events with an extremely high impact on the system,
which are (essentially) impossible to predict
(Taleb, Black Swans, 2007)
Unknown unknowns
We can do little about them (in terms of scientific understanding)
But, being aware of their existence is crucial!
• Moderate over-confidence
•
Numerous studies have showed that most experts/scientists
underestimate the uncertainty of the analytical frameworks
• Support water management and disaster risk reduction
•
Increasing resilience more robust than heavily relying on predictions
wildly
Conclusions
 Societal challenges
o Prevention is not (yet?) politically popular
o Prevention is invisible
 Scientific challenges: Uncertainty
o Aleatory (random)
Probabilistic methods, still need for better guidelines, terminology, etc…
o Epistemic
• Wrong assumptions
Challenge assumptions behind analytical frameworks
• Known unknowns
Interdisciplinary research on nature-human interactions
• Unknown unknowns
Caution on heavily relying on predictions
Some references
Di Baldassarre, G., Viglione, A., Carr, G., Kuil, L., Salinas, J. L., and G. Blöschl (2013). Socio-hydrology: conceptualising
human-flood interactions, Hydrology and Earth System Sciences, 17, 3295-3303, doi:10.5194/hess-17-3295-2013.
Di Baldassarre, G., Kemerink, J. S., Kooy, M., and L. Brandimarte (2014). Floods and societies: the spatial distribution of
water-related disaster risk and its dynamics. WIREs Water, doi: 10.1002/wat2.1015.
Di Baldassarre, G., Kooy, M., Kemerink, J. S., and L. Brandimarte (2013). Towards understanding the dynamic behaviour
of floodplains as human-water systems, Hydrology and Earth System Sciences, 17, 3235-3244, doi:10.5194/hess-173235-2013.
Kareiva, P., Watts, S., McDonald, R., T. Boucher, 2007. Domesticated Nature: Shaping Landscapes and Ecosystems for
Human Welfare. Science, 316(5833), 1866-1869.
Savenije, H. H. G., Hoekstra, A. Y., and P. van der Zaag, 2014. Evolving water science in the Anthropocene, Hydrology
and Earth System Sciences, 18, 319-332.
Sivapalan, M.; Savenije, H., G. Bloeschl 2012. Socio-hydrology: A new science of people and water. Hydrological
Processes, 26 (8), 1270–1276.
Srinivasan, V., E. F. Lambin, S. M. Gorelick, B. H. Thompson, and S. Rozelle, 2012. The nature and causes of the global
water crisis: Syndromes from a meta-analysis of coupled human-water studies, Water Resour. Res., 48, W10516.
Viglione A., G. Di Baldassarre, L. Brandimarte, L. Kuil, G. Carr, J.L. Salinas, A. Scolobig, G. Blöschl (2014). Insights from
socio-hydrology modelling on dealing with flood risk–roles of collective memory, risk-taking attitude and trust. Journal of
Hydrology, http://dx.doi.org/10.1016/j.jhydrol.2014.01.018.
*DISCLAIMER: Roberto Camorani is a fictitious name.
The picture of this presentation is of Friedrich August von Hayek, economist and philosopher (Nobel Price, 1974)