Resilience

Report
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PhD Research Title:
A resilience approach to urban flood risk management
under future conditions in a developing country city
Presentation to Safe & SURE project team
PhD student: Seith Mugume
First supervisor: Professor David Butler
Second supervisor: Dr. Diego Gomez
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Presentation outline
 Background
 Scope of PhD research
 Traditional decision making approaches
 Resilience approach to decision making under uncertainty
 Concepts, frameworks and definitions of resilience
 Quantitative assessment of resilience
 Proposed research methodology
 Next steps
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Global potential risk of urban flooding
1970
2025
2011
Source: UN 2012
Multiple & uncertain drivers of future change





Extreme rainfall events
Nutrient and pollutant loading
Urbanisation effects
Land use change
Socio-economic trends
Transient shocks vs. Chronic stresses
Broad research areas
Threat
ImpactLevel of service
UWS
Water scarcity
Climate
Population
Consequence
Mitigation
Safety
Adaptation
Urban flooding
River pollution
Regulation
Sustainability
Resilience
Refined Safe & SuRe concept Butler (2013)
Society
Vulnerability
Economy
Environment
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Scope of PhD Thesis
• Investigate the use of resilience approach to study the impacts of
future change on urban drainage system performance
• To evaluate appropriate response strategies to reduce pluvial flood
risk in a developing country city
2010 flooding in Dhaka, Bangladesh, Source:
http://www.ipsnews.net/2013/02/killer-heat-waves-and-floodslinked-to-climate-change/
Pluvial flooding in the UK, Source: RAPIDS Project
http://emps.exeter.ac.uk/engineering/research/cws/research/floodrisk/rapids.html
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Traditional decision making approaches in urban flood management
Risk Assessment
R = f(failure probability, consequence)
Increasing
envelope of
uncertainty
Top-down
Bottom-up
(Cause-Effect)
(Vulnerability-Led)
Emission scenarios
Assess vulnerability
(local scale)
Global and Regional
Climate models
Identify coping
factors
Impact models (e.g.
urban flood models)
Develop adaptation
response options
Response options
Response options
Based on Wilby & Dessai (2010)
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Synthesis of global climate risk management
Response policies
Carter et al., 2007
Risk quantification
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A resilience approach to decision making under uncertainty
Impact of
disturbance
Evaluate
response
strategies
System
Resilience
Evaluate
impact on
level of
service
Assess
proximity to
critical
performance
thresholds
Investigate
response
& recovery
 How much disturbance can a system cope with ? versus
 What if future change occurs according to scenario x?
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Key conceptual definitions
Reliability, α: Probability of a system being in a non-failure state
α = Prob(Xt ∈ S),
1.
Where:
S
Xt
• t
set of all satisfactory states,
the random system output state and
time
• A measure of the design capacity that is available in a given system to enable
it operate under a specified range of conditions
 Vulnerability, ϑ: Measure of a system’s susceptibility to damage or
perturbation
=
 

Where: xj
sj
ej
F
discrete system failure state,
numerical indicator of the severity of a failure state,
probability xj, corresponding to sj, is the most severe outcome in a sojourn in F
system failure state.
Key shortcoming: Difficult to develop accurate analytical representations of
performance under uncertain and non-stationary conditions
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Characterisation and definitions of resilience
Resilience
Socioecological
resilience
Engineering
resilience
Sociotechnical
resilience
Infrastructure
system
resilience
Institutional or
organisational
resilience
Ecological
resilience
 Stability within an
attractor basin
 Remain within
critical ecological
thresholds
 Maintain system structure
and function
 Transitions management
 Adaptive capacity
 Anticipation
 Coping capacity
 Recovery capacity
 System response
 Recovery
Holling, 1973; Cumming et al., 2005; Wang and Blackmore, 2009; Blockley et al., 2012 and Cabinet Office, 2011
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Resilience against crossing critical performance thresholds
 A measure of the capacity of a system to absorb disturbances and still
persist with the same basic structure (Holling 1973, Walker et al 2004, Cumming et al 2005)
 Tendency to remain stable around an attractor basin
 Maintenance of system identity
Key resilience properties
•
•
•
•
Attractor basins & thresholds (Walker et al 2004)
Resistance
Persistence
Stability
Multiple static steady states
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Resilience for response and recovery
 A measure of how quickly a system is likely to recover from failure once a
failure has occurred (Hashimoto 1982, Kjeldsen & Rosbjerg 2004)
 Inverse of the mean time the system spends in a failure state
1 =
1


=1 

−1
 Inverse of the maximum consecutive duration the system spends in a failure
state
2 =  ()
Where:
d(j)
M
duration of jth failure event
total number of failure events
Key resilience properties
 Time of failure
 System recovery (rapidity)
−1
System response curve
Failure
consequence
Exceedance
 Response vs
disturbance
 What of response
vs. time?
Flood damage
0
Flood depth
Mens et al 2011, Butler 2013
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System performance curve
Adapted from: Wang & Blackmore (2009) & Butler (2013)
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Categorising sub-properties of resilience
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Quantitative assessment of resilience
• Working definition of resilience:
 The ability of an urban drainage system to maintain an acceptable level of
functioning and to quickly recover from a shock or disturbance
• Resilience indicators
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Resilience indicators
Examples of resilience indicators (de Bruijn, 2004)
 Amplitude: Measure of the impact on flood waves on system performance
 Graduality: A measure of a change in system response with respect to a
change in the magnitude of flood waves
 Recovery rate: a measure of the rate at which the system returns to a
normal or stable state after the flood event
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Proposed resilience indicators for urban pluvial flooding
#
Resilience
property
Resilience indicators
1
Resistance
threshold
Duration of sewer surcharging
2
Response time
Duration of manhole flooding
Duration of surface/property flooding
3
System
response
Flood depth
Flooded area
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Amplitude
Graduality, G
Expected annual damage (EAD)
4
Recovery rate
Recovery time
Qualitative measures of adaptive capacity
Urban drainage
model simulations
Qualitative study
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Resilience based evaluation methods
 Robust adaptation framework
 Real ‘In’ Options
 Adaptation Mainstreaming
 Adaptive Pathways
 Adaptive Policy Making
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Quantifying resilience indicators
Urban flood modelling
o Rainfall run-off estimation
o Part-full flow in sewers
o Sewer surcharging
o Surface flooding
• MIKEURBAN (Coupled 1D-2D model)
 1D sewer flow modelling
 SWMM 5.0
 MOUSE
 2D surface flow modelling
Example of network typology, land use and above and
below ground networks (Barreto 2012)
Qualitative study of acceptability thresholds
 Delphi technique
 Interview of key stakeholders
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6. Next steps
• Identify set of resilience indicators to be used in case study
• Obtain data for a ‘test’ case study
 Urban drainage network
 Rainfall data
 Land use
 DEM
• Urban drainage model simulations using a ‘test’ case study
• Preliminary analysis of urban drainage network resilience
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References
Baños, R., Reca, J., Martínez, J., Gil, C., and Márquez, A. L. (2011) Resilience indexes for water distribution network design: A
performance analysis under demand uncertainty. Water Resources Management, 25, 2351–2366.
De Bruijn, K. M. (2004) Resilience indicators for flood risk management systems of lowland rivers. International Journal of
River Basin Management, 2(3), 199–210.
Butler, D. (2013) Resilience framework, Safe and SURE Project.
Farmani, R., Walters, G. A., and Savic, D. A. (2005) Trade-off between total cost and reliability for Anytown water distribution
network. Water Resources Planning and Management, (131), 161–171.
Hashimoto, T., Loucks, D. P., and Stedinger, J. (1982) Reliability, resilience and vulnerability criteria for water resource system
performance evaluation. Water Resources Research, 18(1), 14–20.
Holling, C. S. (1973) Resilience and stability of ecological systems. Annual Review of Ecology and Systematics, 4, 1–23.
Jones, R. N. and Preston, B. L. (2011) Adaptation and risk management. Wiley Interdisciplinary Reviews: Climate Change,
2(2), 296–308. http://doi.wiley.com/10.1002/wcc.97
Kjeldsen, T. R. and Rosbjerg, D. (2004) Choice of reliability, resilience and vulnerability estimators for risk assessments of
water resources systems. Hydrological Sciences, 49(5), 755 – 767.
McDaniels, T., Chang, S., Cole, D., Mikawoz, J., and Longstaff, H. (2008) Fostering resilience to extreme events within
infrastructure systems: Characterizing decision contexts for mitigation and adaptation. Global Environmental Change, 18,
310–318.
Mens, M. J. P., Klijn, F., de Bruijn, K. M., and van Beek, E. (2011) The meaning of system robustness for flood risk
management. Environmental Science & Policy, 14, 1121–1131.
Todini, E. (2000) Looped water distribution networks design using a resilience index based heuristic approach. Urban Water,
2(2), 115–122.
United Nations (2012) World Urbanization Prospects,The 2011 Revision - Highlights, New York.
Walker, B., Holling, C. S., Carpenter, S. R., and Kinzig, A. (2004) Resilience, adaptability and transformability in socioecological systems. Ecology and Society, 9(2).
Wang, C. and Blackmore, J. M. (2009) Resilience concepts for water resource systems. Water Resources Planning and
Management, 135(6), 528 – 536.
Wilby, R. L. and Dessai, S. (2010) Robust adaptation to climate change. Weather, 65(7), 176–180.
http://doi.wiley.com/10.1002/wea.504

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