A presentation made at the ADB to introduce the DREAMS project

1. !r"gh
Advanced'Modeling'&'Simula/on
Designing'Resilient'Environments'
D R E A M S
Alexis Drogoul
alexis.drogoul@ird.fr
vendredi 14 juin 13

2. Climate change impacts are a current issue, rather than a future
one, for most Vietnamese low-land and coastal cities.
2
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3. They are encouraged to adapt quickly through the design of
urban and social solutions resilient to climate change
3
Principles, Tools and Practice
MANAGING THE RISKS OF DISASTERS IN EAST ASIA AND THE PACIFIC
Building Urban Resilience3rd Global Forum on Urban
Resilience and Adaptation
Congress Report
Bonn, Germany, 12-15 May 2012
How to engage stakeholders so they have a say in the design
of urban forms and adaptation strategies ?
How to define and measure the resilience of the solutions
proposed, given the infinite number of possible futures ?
vendredi 14 juin 13

4. 4
An example in Can Tho city, Vietnam, where a major climate
change issue is water management and availability
Assessing the resilience of the adaptation strategies carried
out by households, communities or authorities, would ideally
require a quasi-experimental approach over a long period of
time.
However, for practical and ethical reasons, socio-
environmental systems cannot be the subject of
experiments
vendredi 14 juin 13

5. We need models that offer experimental facilities to support the
resilient design of cities, and these models should be...
5
Conurbation/catchment scale
Neighbourhood scale
Building scale
Source control, for example,
upland land management
Diversion or dualling of flood
flows away from affected areas
lignment
ways to
all events
erhangs
aterials Removable household products
Raising floor levels
Green roofs One-way valves
Widening drains to increase capacity
Sustainable drainage systems
od defences and, as a last resort,
efences and hard barriers
Flood attenuation and temporary water
storage, including use of greenspace
tegies for
ood risks
25
ises the range of actions
able to increase adaptive
en in the text on the
Dr. Tom Mitchell and Katie Harris
R
esilience, a concept concerned funda-
mentally with how a system, community
or individual can deal with disturbance,
surprise and change, is framing current
thinking about sustainable futures in an environ-
ment of growing risk and uncertainty.
Resilience has emerged as a fusion of ideas
from multiple disciplinary traditions including
ecosystem stability (Holling, 1973; Gunderson,
2009), engineering infrastructure (Tierney and
Bruneau, 2007), psychology (Lee et al., 2009),
the behavioural sciences (Norris, 2011) and dis-
aster risk reduction (Cutter et al., 2008). Its recent
appropriation by bilateral and multilateral donor
organisations is one example of how resilience is
evolvingfromtheoryintopolicyandpractice(HERR,
2011; Ramalingam, 2011; Bahadur et al., 2010;
Brown, 2011; Harris, 2011).
This appropriation has been driven by the need to
identify a broad-based discourse and set of guiding
principles to protect development advances from
multiple shocks and stresses. Consequently, ‘resil-
ience’ is an agenda shared by those concerned with
financial, political, disaster, conflict and climate
threats to development. The aim of resilience pro-
gramming is, therefore, to ensure that shocks and
stresses, whether individually or in combination, do
not lead to a long-term downturn in development
progress as measured by the Human Development
Index (HDI), economic growth or other means.
Figure 1 shows how the build-up of longer term
stress (upper diagram) and short term shocks
(lower diagram) require countermeasures at pivotal
moments to ensure that development pathways
continue on an upward trend. In reality, some coun-
termeasures are likely to be in place prior to the
impact and many different shocks and stresses may
combine or occur close together, each impacting
the level of resilience at different scales and each
requiring separate or integrated measures to reduce
the abruptness of downward development trends.
Resilience: A risk
management approach
The Overseas Development Institute is the UK’s leading independent think tank on international development and humanitarian issues.
ODI Background Notes provide a summary or snapshot of an issue or of an area of ODI work in progress. This and other ODI Background
Notes are available from www.odi.org.uk
Figure 1: The effect of shocks and stresses
on development pathways depending on
different levels of resilience
Source: (modified from Conway et al., 2010)
Resilience
Development
STRESS
Countermeasures
Time
Development
SHOCK
Countermeasures
Time
Resilience
... descriptive and versatile, to allow
designing creative solutions to the
disruptions forecasted
... generative, to allow exploring their
evolution in various scenarios and under
different hypotheses
... observable and transparent, so
that data analysis tools can compute
resilience properties at any scale
vendredi 14 juin 13

6. The core of DREAMS is constituted by a dynamic and multiscale
coupling of several sub-models to create virtual cities
6
Ecosystems
Climatology
Foundation data
Built environment
Energy & services
Population
Urbanization
Traffic
Economy
Social networks
«Systems» «Society»
Comodeling software infrastructure to
organize the interactions of models
InstitutionsHydrology
Agent-based modeling approach,
componential and versatile
vendredi 14 juin 13

7. Operationalising a resilience approach to adapting an urban delta to
uncertain climate changesJ. Arjan Wardekker a,
⁎, Arie de Jong a
, Joost M. Knoop b
, Jeroen P. van der Sluijs a,c
a
Department of Science, Technology and Society, Copernicus Institute for Sustainable Development and Innovation, Utrecht University,
Heidelberglaan 2, 3584 CS Utrecht, The Netherlands
b
Netherlands Environmental Assessment Agency (PBL), P.O. Box 303, 3720 AH Bilthoven, The Netherlands
c
Centre for Economics and Ethics of the Environment and Development, University of Versailles Saint-Quentin-en-Yvelines, 47 Boulevard Vauban,
Guyancourt 78047 cedex, France
a r t i c l e i n f o
a b s t r a c tArticle history:
Received 15 April 2009Received in revised form 2 November 2009
Accepted 10 November 2009
Climate change may pose considerable challenges to coastal cities, particularly in low-lying
urban deltas. Impacts are, however, associated with substantial uncertainties. This paper studies
an uncertainty-robust adaptation strategy: strengthening the resilience of the impacted system.
This approach is operationalised for the city of Rotterdam, using literature study, interviews, and
a workshop. Potential impacts have been explored using national climate statistics and
scenarios and a set of ‘wildcards’ (imaginable surprises). Sea level rise, particularly in
combination with storm surge, and enduring heat and drought are the most relevant potential
stresses in the area. These can lead to damage, loss of image, and societal disruption. Unclear
responsibilities enhance disruption. ‘Resilience principles’ made the concept of resilience
sufficiently operational for local actors to explore policy options. Useful principles for urban
resilience include: homeostasis, omnivory, high flux, flatness, buffering, redundancy, foresight
and preparedness/planning, compartmentalisation, and flexible planning/design. A resilience
approach makes the system less prone to disturbances, enables quick and flexible responses,
and is better capable of dealing with surprises than traditional predictive approaches. Local
actors frame resilience as a flexible approach to adaptation that would be more suitable and
tailored to local situations than rigid top–down regulations. In addition to a change in policy, it
would require a more pro-active mentality among the population.
© 2009 Elsevier Inc. All rights reserved.
Keywords:
Resilience
Resilience principlesClimate change adaptationUncertainty
Urban planning
1. Introduction
Technological Forecasting & Social Change 77 (2010) 987–998
Contents lists available at ScienceDirect
Technological Forecasting & Social Change
Each virtual city can be simulated in hundreds of experiments in
which resilience indicators are continuously computed
7
Massive simulation infrastructure
Resilience indicatorsOnline data-mining and analysis
Example
of
«objec/ve»
indicators:
homeostasis,
omnivory,
high
flux,
flatness,
buffering,
redundancy,
foresight
and
preparedness,
compartmentalisa/on,
and
flexibility.
(from
Wardekker
2010)
«What-if» experiments
«What-for» experiments
Cost-based analysis
vendredi 14 juin 13

8. Figure 4 shows how sketching a highway can produce a more
widespread city. The designer draws a new highway and keeps the
total population and jobs constant. The system determines that the
new highway increases accessibility from the rural area to the
downtown. Then, population moves to now accessible lower land-
value areas and new roads and buildings are adaptively generated.
5.2.1 Observations and Assumptions
Our road generation method is based on the following observations
about real-world roads. (a) Road networks are designed and built to
meet a transportation demand by the population [Montes de Oca
and Levinson 2006]. The capacity of a road, reflected by its width
and the mean distance between its consecutive intersections,
responds to such a demand. (b) Road networks exhibit a variety of
styles which are difficult to be solely inferred from behavioral and
geometrical parameters. While highways are usually designed to
minimize travel distances, arterials and streets are more affected by
historical and aesthetic factors.
We select a set of design parameters sufficiently expressive to
represent a wide range of observed patterns (e.g., Figures 5 and 8).
Our road generation algorithm uses the following key assumptions:
the predominant patterns of arterials and streets are grid style
and radial style with spurious occurrences of dead-ends,
in the grid style, up to four nearly-perpendicular segments
depart from each intersection point,
in the radial style, three or more road segments depart from
some intersection points at equally spaced angles, and
the road pattern and its tortuosity is affected by the nearby
population and jobs.
5.2.2 Seed Generation Algorithm
To obtain a set of seeds for generating arterial roads, we group
grid cells using a weighted -means clustering algorithm. The
value of is a user-specified constant set by default to
, where is a small constant. We
let , for , be the center point of a cluster to be
determined. The clustering algorithm uses
The set is augmented with seeds that are created on previously
existing roads. When generating arterials, the seeds are created on
highways (if they exist). In this manner, arterial roads are also
connected to the highway network. After the arterial roads are
generated, we create seeds along them for the street expansion. In
both cases, the distance between two consecutive seeds along the
highway/arterial is inversely proportional to the amount of
population and jobs in nearby grid cells.
5.2.3 Expansion Algorithm
Starting at the previously computed seeds , we generate road
segments using a breadth-first expansion method. All pre-
computed seeds are placed into a pool . The first seed is
removed from and an attempt is made to create road segments in
several directions around the seed. A new seed is created at the
end of a newly created piecewise linear road segment provided
no previously existing seed is nearby. The new seeds are added to
and the process repeats until the pool is empty. The set of
resulting collectively form the road network .
A seed has departing directions along
which new road segments can be generated. The value of ,
for , is given by , where is a random
variable with distribution , is a small constant,
and is a reference angle. The reference angle is equal to the
orientation of the road segment to which the seed is attached.
For an urban area, the user chooses either a grid style or a radial
style road pattern. The choice affects the number of departing
directions for the seeds: for grid style, and for radial style,
for the initial seeds and for all later seeds.
The road expansion for a seed , in direction , consists of
evaluating a piecewise linear curve integral from to a point ,
using numeric integration of a function . The function
measures the population and jobs in the grid cells located within a
small distance of . The integral is given by
. (12)
Figure 4. Example Geometrical Modeling. The designer wishes to produce a more widespread city. The population is tightly gathered
a b
The platform offers a support for users and experts to attach and
design scenarios, component models and indicators
8
Flexible and adaptable visualization
User interaction
Participatory assessment
Participatory design
Participatory modeling
Economic scenarios
Demographic scenarios
Climatic scenarios
vendredi 14 juin 13

9. DREAMS is being applied to two case studies in Vietnam, in
collaboration with local partners
9
Da Nang
Evacuation planning in
case of Tsunami
Can Tho
Water management under
climate change
Participatory workshops based on simulated
scenarios and virtual experiments
Data gathering,
Prototypes of models
Can Tho Climate
Change Coordination Office
Da Nang Military
Academy
vendredi 14 juin 13

10. Feedback
Requirem
ents
Com
ponents
Scenarios
Indicators
Sim
ula8ons
Documenta8on
Ontology and library of urban
models
High-level Visualization
Prototypes
Training
Access to high performance computing
simulation resources
Generic urban modeling & simulation
platform
Workshops Design
Coupling of heterogeneous
models
Indicator-based Analysis
and Exploration of Models
Empowerement of stakeholders
through model-based SLD
Capacity Building in Modeling and
Simulation
Socio-environmental models
Improved assessment
of adaptation options
Improved understanding
of climate change impacts
Scenarios & recommandations
for adaptation planning
DREAMS is based on a spiral methodology that is expected to
produce outcomes in both real cases and virtual cities
Computer
science
R&D
Can
Tho
&
Da
Nang
case
studies
vendredi 14 juin 13

11. DREAMS has been submitted by an international consortium to
the Belmont-Forum IOF 2012 call
11
Stakeholders AcademicIndustrialN.G.O
Da Nang case collectionDa Nang data
Da Tho data collectionCan
Comodeling infrastructure
Simulation infrastructure
Online data analysis
IRD/UMMISCO
Can Tho University/
DREAM team
Kyoto University/Dept of
Social Informatics
VAST/Institute of
Geophysics
Can Tho Climate Change
Coord. Office
Da Nang Military
Academy
AIST/Center for Service
Research
CSIRO/Sustainable
EcoSystems
Université de Rouen/
IDEES
Université de Toulouse/
IRIT
Université de Paris-Sud/
LRI
EDF R&D/SINETICS
CEA/LIST
ISET
Université de Grenoble/
LIG
Can Tho City Institute
for Socio-Economic
Agent-based modeling (GAMA)
vendredi 14 juin 13

12. What we propose in DREAMS was not possible to do 5 years ago.
12
Source control, for example,
upland land management
Diversion or dualling of flood
flows away from affected areas
realignment
athways to
ainfall events
overhangs
t materials Removable household products
Raising floor levels
Green roofs One-way valves
Widening drains to increase capacity
Sustainable drainage systems
flood defences and, as a last resort,
t defences and hard barriers
Flood attenuation and temporary water
storage, including use of greenspace
25
R
esilience, a concept concerned funda-
mentally with how a system, community
or individual can deal with disturbance,
surprise and change, is framing current
thinking about sustainable futures in an environ-
ment of growing risk and uncertainty.
Resilience has emerged as a fusion of ideas
from multiple disciplinary traditions including
ecosystem stability (Holling, 1973; Gunderson,
2009), engineering infrastructure (Tierney and
Bruneau, 2007), psychology (Lee et al., 2009),
the behavioural sciences (Norris, 2011) and dis-
aster risk reduction (Cutter et al., 2008). Its recent
appropriation by bilateral and multilateral donor
organisations is one example of how resilience is
evolvingfromtheoryintopolicyandpractice(HERR,
2011; Ramalingam, 2011; Bahadur et al., 2010;
Brown, 2011; Harris, 2011).
This appropriation has been driven by the need to
identify a broad-based discourse and set of guiding
principles to protect development advances from
multiple shocks and stresses. Consequently, ‘resil-
ience’ is an agenda shared by those concerned with
financial, political, disaster, conflict and climate
threats to development. The aim of resilience pro-
gramming is, therefore, to ensure that shocks and
stresses, whether individually or in combination, do
not lead to a long-term downturn in development
progress as measured by the Human Development
Index (HDI), economic growth or other means.
Figure 1 shows how the build-up of longer term
stress (upper diagram) and short term shocks
(lower diagram) require countermeasures at pivotal
moments to ensure that development pathways
continue on an upward trend. In reality, some coun-
termeasures are likely to be in place prior to the
impact and many different shocks and stresses may
combine or occur close together, each impacting
the level of resilience at different scales and each
requiring separate or integrated measures to reduce
the abruptness of downward development trends.
advancing knowledge, shaping policy, inspiring practice
The Overseas Development Institute is the UK’s leading independent think tank on international development and humanitarian issues.
ODI Background Notes provide a summary or snapshot of an issue or of an area of ODI work in progress. This and other ODI Background
Notes are available from www.odi.org.uk
Figure 1: The effect of shocks and stresses
on development pathways depending on
different levels of resilience
Source: (modified from Conway et al., 2010)
Resilience
Development
STRESS
Countermeasures
Time
Development
SHOCK
Countermeasures
Time
Resilience
Such
modeling
and
simula=on
technologies
can
change
the
way
stakeholders
interact
and
design
their
shared
future
together
Comodeling software infrastructure
Agent-based modeling platform
Massive simulation infrastructure
Online data-mining and analysis
vendredi 14 juin 13

13. 13
D5.2.1 D5.2.2 D5.2.3 D5.2.4 D5.2.5
D5.1.1
D6.1.2 D6.1.6D6.1.5D6.1.4D6.1.3
Task-3.1-Par0cipa0on...-D3.1.1
Task-4.1-Par0cipa0on...-D4.1.1
Task-3.2-Library-of-data-and-models---
Task-3.3-Integrated-model...-
Task-6.2-Prototypes-and-documenta0on
Task-6.3-Prepara0on-of-training-sessions
Task-6.4-Remote-and-High-Performance-Simula0on
Task-1.1-Coupled-Models-D1.1.1 D1.1.2 D1.1.3 D1.1.4 D1.1.5 D1.1.6
Task-1.2-Descrip0on-of-models-D1.2.1 D1.2.2 D1.2.3 D1.2.4 D1.2.5 D1.2.6
Task-1.3-Models-library-D1.3.1 D1.3.2 D1.3.3 D1.3.4 D.1.3.5
Task-2.1-Visualiza0on…--D2.1.1 D2.1.2 D2.1.3 D2.1.4 D2.1.5 D2.1.6
Task-2.2-Simula0on-analysis-...-D2.2.1 D2.2.2 D2.2.3 D2.2.4 D2.2.5 D2.2.6
Task-2.3-Assessment-of-experiments-D2.3.1 D2.3.2 D2.3.3 D2.3.4 D2.3.5 D2.3.6
Task-6.1-Specifica0ons-D6.1.1
D3.1.2
Task-4.2-Library-of-data-and-models
Task-4.3-Integrated-model...-
T5.1.-Guidelines
T5.2.CoOdesign-of-the-plaPorm
D4.1.2
D6.2.1
D6.3.1
D6.4.1
D3.1.3
D4.1.3
D6.2.2
D6.3.2
D6.4.2
D3.1.4
D4.1.4
D6.2.3
D6.3.3
D6.4.3
D3.1.5
D4.1.5
D6.2.4
D6.3.4
D6.4.4
D3.1.6
D4.1.6
D6.2.5
D6.3.5
D6.4.5
D1.3.6
T+0 T+3 T+6 T+9 T+12 T+15 T+18 T+21 T+24 T+27 T+30 T+33 T+36
KickOoff-mee0ng First-project-mee0ng Second-project-mee0ng-(w.-IOF-program-mee0ng) Third-project-mee0ng EndOofOproject-mee0ng-(w.-IOF-program-mee0ng)
First-prototype Second-prototype Third-prototype Fourth-prototype Final-version
SLD-workshop
SLD-workshop
Technical-workshop-&-training
Technical-workshop-&-training
SLD-workshop
SLD-workshop
SLD-workshop
SLD-workshop
Technical-workshop-&-training
Technical-workshop-&-training
WP1
WP2
WP6
WP3
WP4
WP5
Coupled-
Models
Virtual-
Experiments
Produc0on
Can-Tho-
Case-Study
Da-Nang-
Case-Study
Par0cipa0on
4.2.1
4.3.1
3.2.1
3.3.1
One-complete-cycle-of-the-spiral-methodology
vendredi 14 juin 13