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Presentation to Safe & SURE project team

PhD student: Seith Mugume

First supervisor: Professor David ButlerSecond supervisor: Dr. Diego Gomez

PhD Research Title:A resilience approach to urban flood risk management under future conditions in a developing country city

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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

Source: UN 2012

197020112025

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

Sustainability

Resilience

Threat

Mitigation Safety

Impact- Level of service

ConsequenceUWSSociety

Economy

Environment

Climate

Population

Regulation

Water scarcity

Urban flooding

River pollution

VulnerabilityAdaptation

Refined Safe & SuRe concept Butler (2013)

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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

Pluvial flooding in the UK, Source: RAPIDS Project http://emps.exeter.ac.uk/engineering/research/cws/research/flood-risk/rapids.html

2010 flooding in Dhaka, Bangladesh, Source: http://www.ipsnews.net/2013/02/killer-heat-waves-and-floods-linked-to-climate-change/

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Traditional decision making approaches in urban flood management

Increasing envelope of uncertainty

Top-down (Cause-Effect)

Impact models (e.g. urban flood models)

Global and Regional Climate models

Response options

Emission scenarios

Risk AssessmentR = f(failure probability, consequence)

Bottom-up (Vulnerability-Led)

Develop adaptation response options

Identify coping factors

Response options

Assess vulnerability (local scale)

Based on Wilby & Dessai (2010)

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Synthesis of global climate risk management

Carter et al., 2007

Response policies Risk quantification

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A resilience approach to decision making under uncertainty

System Resilience

Impact of disturbance

Assess proximity to

critical performance thresholds

Investigate response

& recovery

Evaluate impact on

level of service

Evaluate response strategies

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

1. Reliability, α: Probability of a system being in a non-failure stateα = Prob(Xt S), ∈Where: S set of all satisfactory states,

Xt the random system output state and• t 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 discrete system failure state, sj numerical indicator of the severity of a failure state, ej probability xj, corresponding to sj, is the most severe outcome in a sojourn in F 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

Stability within an attractor basin

Remain within critical ecological thresholds System response

Recovery

Adaptive capacity Anticipation

Coping capacity Recovery capacity

Resilience

Holling, 1973; Cumming et al., 2005; Wang and Blackmore, 2009; Blockley et al., 2012 and Cabinet Office, 2011

Ecological resilience

Socio-ecological resilience

Socio-technical resilience

Infrastructure system

resilience

Institutional or organisational

resilience

Engineering resilience

Maintain system structure and function

Transitions management

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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

Attractor basins & thresholds (Walker et al 2004)

Key resilience properties• 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

Inverse of the maximum consecutive duration the system spends in a failure state

Where: d(j) duration of jth failure event M total number of failure events

Key resilience properties Time of failureSystem recovery (rapidity)

System response curve

Flood damage

Flood depth0

Failure consequence

Exceedance

Mens et al 2011, Butler 2013

Response vs disturbance

What of response vs. time?

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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 floodingDuration of surface/property flooding

3 System response

Flood depthFlooded area

5 Amplitude Graduality, GExpected annual damage (EAD)

4 Recovery rate Recovery timeQualitative 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 indicatorsUrban 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 modellingSWMM 5.0MOUSE

2D surface flow modelling

Qualitative study of acceptability thresholds Delphi technique Interview of key stakeholders

Example of network typology, land use and above and below ground networks (Barreto 2012)

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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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http://doi.wiley.com/10.1002/wea.504