!r"gh Advanced'Modeling'&'Simula/onDesigning'Resilient'Environments'D R E A M S

Alexis Drogoulalexis.drogoul@ird.frvendredi 14 juin 13

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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vendredi 14 juin 13

They are encouraged to adapt quickly through the design of urban and social solutions resilient to climate change

3

Principles, Tools and Practice

MA NAGING TH E R ISK S OF D I S AS T ER S IN E AST A S I A AND T HE PAC I F IC

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

Vietnam - CanTho City - Land Use/ Land Cover 2006

Scale:

Landcover/landuse classification digitized from a High Resolution QuickBird Image

Map created April 2010 by WISDOMImage processing and map creation by DLR

Geographic (DMS)WGS 84WGS 84

Geographic coord. system:Reference coordinate system:Projection:Spheroid:Datum:

UTM Zone 48 NWGS 84WGS 84

WISDOM ProjectCoordinated by DLRwww.wisdom.caf.dlr.deFor questions contact: claudia.kuenzer@dlr.dephone: 0049-8153-28-3280

Map Info

Data Sources

Interpretation

Legend

Scale / Reference System

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!!!!

Vietnam

CambodiaSouth China

Sea

Gulf of Thailand

Ha Noi

Phnom PenhHo Chi Minh City

Thailand

Myanmar

Laos

China

Andaman Sea

Bay ofBengal

Bangkok

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±0 0,5 1 1,5 2km

!! !!!!

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Vietnam

Long Xuyen

Can Tho

Cao Lanh

Mekong River

Mekong River

Cambodia Ho Chi Minh City

My Tho

Bac Lieu

Ca Mau

Rach Gia

Soc Trang

Tra Vinh

Vinh Long

South China Sea

Gulf of Thailand

Landcover/ Landuse classification

Canal

Road

Flooded Agriculture Land

Agriculture Land

Fruit Tree

Other Area

Natural Vegetation

Sparse Vegetation

Open Area

Sealed Surface

House

Large Building

Pond

River

Quickbird data from 2006/12/22© DigitalGlobe Corporate

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

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

Managed realignment

Managing flood pathways to cope with heavy rainfall events

Rain proofing and overhangs

Flood resilient materials Removable household products

Raising floor levels

Green roofs One-way valves

Widening drains to increase capacity

Sustainable drainage systems

‘Set-back’ flood defences and, as a last resort, permanent defences and hard barriers

Flood attenuation and temporary water storage, including use of greenspace

menu of strategies for managing fl ood risks

25

The diagram summarises the range of actions and techniques available to increase adaptive capacity. Detail is given in the text on the proceeding pages.

Overseas Development Institute

Dr. Tom Mitchell and Katie Harris

Resilience, 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 evolving from theory into policy and practice (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

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

January 2012

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

Dev

elop

men

t

STRESS

Countermeasures

Time

Dev

elop

men

t

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

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

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 Netherlandsb 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 oa b s t r a c tArticle history:

Received 15 April 2009Received in revised form 2 November 2009Accepted 10 November 2009

Climate change may pose considerable challenges to coastal cities, particularly in low-lying

urban deltas. Impacts are, however, associatedwith 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:ResilienceResilience principlesClimate change adaptationUncertaintyUrban planning

1. Introduction

Technological Forecasting & Social Change 77 (2010) 987–998

Contents lists available at ScienceDirectTechnological 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

arterial roads to individual parcels. First, in order to connect the main population clusters, a set of seeds are generated considering the population and jobs distribution and the location of designer-sketched highways. Each seed is converted to an intersection of the arterial roads network and is used to generate arterial road segments. Second, street seeds are generated along arterial road segments and used to create streets. The expansion of both streets and arterials is guided by the terrain, the spatial distribution of population and jobs, and user-specified style parameters. 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: x the predominant patterns of arterials and streets are grid style

and radial style with spurious occurrences of dead-ends, x in the grid style, up to four nearly-perpendicular segments

depart from each intersection point, x in the radial style, three or more road segments depart from

some intersection points at equally spaced angles, and x 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 ߢ seeds for generating arterial roads, we group grid cells using a weighted -means clustering algorithm. The value of ߢ is a user-specified constant set by default to ඥσߛ ሺሺǡ ሻ ሺǡ ሻሻ , where ߛ ͳ is a small constant. We let ݏ௨, for ݑ א ሾͳǡ ௨ to beܭ ሿ, be the center point of a clusterߢdetermined. The clustering algorithm uses ݓ௨ ൌ ሺ௨ǡ ௨ሻ

ሺ௨ǡ ௨ሻ as grid cell weights in order to pull the cluster centers towards areas of larger population and jobs, though they will still be connected with sparsely populated clusters. Further, we denote the center of a grid cell ሺǡ ሻ by ݔ. Thus, the algorithm searches for a set ൌ ሼݏଵǡ ଶǡݏ ǥ ǡ ሽ that minimizes the expressionݏ

ݔ௨൫ฮݓ െ ௨ฮ൯ݏଶ

ሺǡሻאೠ

௨ୀଵ (11)

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 ݏ௨ is removed from and an attempt is made to create road segments in several directions around the seed. A new seed ݏ௩ is created at the end of a newly created piecewise linear road segment ܥ௨௩ 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 ܥ௨௩ collectively form the road network . A seed ݏ௨ has departing directions ȣ௨ ൌ ሼߠǡ ଵǡߠ ǥ ǡ ଵሽ alongߠwhich new road segments can be generated. The value of ߠ, for Ͳ, is given by ߠ ൌ ߠ ଶగ

, where is a random variable with distribution ሺͲǡ ,is a small constant ߪ ,ଶሻߪand ߠ 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, ൌ Ͷ and for radial style, ͵ for the initial seeds and ൌ Ͷ for all later seeds. The road expansion for a seed ݏ௨, in direction ߠ, consists of evaluating a piecewise linear curve integral from ݏ௨ to a point ݏ௩, using numeric integration of a function ሺݔሻ. The function ሺݔሻ measures the population and jobs in the grid cells located within a small distance of ݔ. The integral is given by ݔሻݔሺ ൌ ೠೡߩ . (12)

Figure 4. Example Geometrical Modeling. The designer wishes to produce a more widespread city. The population is tightly gathered around a downtown (a). The user draws a new highway and, as a result, the population redistributes along the highway, the downtown density decreases, and new roads, parcels, and buildings are automatically created (b).

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 interactionParticipatory assessment

Participatory design

Participatory modeling

Economic scenarios

Demographic scenarios

Climatic scenarios

vendredi 14 juin 13

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 ThoWater 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

Feedback

Requirements

Components

Scenarios

 Indicators

Simula8ons

Documenta8on

Ontology and library of urban models

High-level Visualization

PrototypesTraining

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  Nangcase  studies

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DREAMS has been submitted by an international consortium to the Belmont-Forum IOF 2012 call

11Stakeholders 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)

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What we propose in DREAMS was not possible to do 5 years ago.

12

Conurbation /catchment scale

Neighbourhood scale

Building scale

Source control, for example, upland land management

Diversion or dualling of flood flows away from affected areas

Managed realignment

Managing flood pathways to cope with heavy rainfall events

Rain proofing and overhangs

Flood resilient materials Removable household products

Raising floor levels

Green roofs One-way valves

Widening drains to increase capacity

Sustainable drainage systems

‘Set-back’ flood defences and, as a last resort, permanent defences and hard barriers

Flood attenuation and temporary water storage, including use of greenspace

menu of strategies for managing fl ood risks

25

The diagram summarises the range of actions and techniques available to increase adaptive capacity. Detail is given in the text on the proceeding pages.

Overseas Development Institute

Dr. Tom Mitchell and Katie Harris

Resilience, 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 evolving from theory into policy and practice (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

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

January 2012

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

Dev

elop

men

t

STRESS

Countermeasures

Time

Dev

elop

men

t

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

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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

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