Presentation to Safe & SURE project team 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

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

Global potential risk of urban flooding 2025 2011 1970 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

Impact- Level of service Broad research areas Sustainability Resilience Threat Mitigation Safety Impact- Level of service Consequence UWS Society Economy Environment Climate Population Regulation Water scarcity Urban flooding River pollution Vulnerability Adaptation Mitigation: Refined Safe & SuRe concept Butler (2013)

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

Traditional decision making approaches in urban flood management Risk Assessment R = f(failure probability, consequence) Top-down (Cause-Effect) Bottom-up (Vulnerability-Led) Impact models (e.g. urban flood models) Global and Regional Climate models Response options Emission scenarios Develop adaptation response options Identify coping factors Response options Assess vulnerability (local scale) Increasing envelope of uncertainty Key limitations of traditional risk based approaches lack of relatively long, accurate and reliable data sets Overreliance on climate model projections need for downscaling of climate model data Limitations in out ability to anticipate highly uncertain and complex events Based on Wilby & Dessai (2010)

Synthesis of global climate risk management Response policies Risk quantification Carter et al., 2007

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 Shifts attention from accurate prediction of future risks to a prospective view Aim is to assess a system’s proximity to critical performance thresholds Focus on investigating system response and recovery behaviour Analysing impacts of disturbances on level of service Identification of pathways for adapting the system to future change How much disturbance can a system cope with ? versus What if future change occurs according to scenario x?

Key conceptual definitions Reliability, α: Probability of a system being in a non-failure state α = 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 𝜗= 𝑗𝜖𝐹 𝑠 𝑗 𝑒 𝑗 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 Reliability in water distribution networks: Used to account for uncertainties such as: nodal demand, pipe roughness and reservoir and tank roughness (Farmani et al., 2005). Definition of reliability Definition does not account for: System failure severity and likely magnitude of consequences Effects of chronic disturbances on system performance Assumes predictable and stationary conditions Vulnerability: Measures the susceptibility of an infrastructure system especially when a small damage leads to disproportionate consequences Definition of Sojourn: temporary

Characterisation and definitions of resilience Socio-ecological resilience Engineering resilience Institutional or organisational resilience Ecological resilience Socio-technical resilience Infrastructure system resilience Stability within an attractor basin Remain within critical ecological thresholds Adaptive capacity Anticipation Coping capacity Recovery capacity Maintain system structure and function Transitions management System response Recovery Holling, 1973; Cumming et al., 2005; Wang and Blackmore, 2009; Blockley et al., 2012 and Cabinet Office, 2011

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 Resistance Persistence Stability Multiple static steady states Applied mainly in ecological and socio-ecological systems Lattitude: measure of the change in position of thresholds (edges) between basins Attractor basins & thresholds (Walker et al 2004)

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 𝑅𝑒𝑠 1 = 1 𝑀 𝑗=1 𝑀 𝑑 𝑗 −1 Inverse of the maximum consecutive duration the system spends in a failure state 𝑅𝑒𝑠 2 = 𝑚𝑎𝑥 𝑗 𝑑(𝑗) −1 Where: d(j) duration of jth failure event M total number of failure events Key resilience properties Time of failure System recovery (rapidity) Hashimoto 1982: Resilience defined as the inverse of the mean time the system spends in a failure states Rapidity: Ability to recover from a reduction in system performance

System response curve Failure consequence Response vs disturbance Flood damage Flood depth Failure consequence Exceedance Response vs disturbance What of response vs. time? Shows system response as a function of the magnitude of disturbance (Mens et al., 2011) . Where is this from? – think I prefer time-based performance curve Mens et al 2011, Butler 2013

System performance curve Adapted from: Wang & Blackmore (2009) & Butler (2013)

Categorising sub-properties of resilience

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

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

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 5 Amplitude Graduality, G Expected annual damage (EAD) 4 Recovery rate Recovery time Qualitative measures of adaptive capacity Urban drainage model simulations Qualitative study

Resilience based evaluation methods Robust adaptation framework Real ‘In’ Options Adaptation Mainstreaming Adaptive Pathways Adaptive Policy Making

Quantifying resilience indicators Urban flood modelling Rainfall run-off estimation Part-full flow in sewers Sewer surcharging Surface flooding MIKEURBAN (Coupled 1D-2D model) 1D sewer flow modelling SWMM 5.0 MOUSE 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) y = flow depth (m), v = velocity (m/s), x = distance (m), t = time (s), So = bedslope (-), Sf = friction slope (-) Kinematic wave: Only wave translation; Diffusion wave: wave translation + backwater + wave attenuation; Dynamic wave: All + flow acceleration

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

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 socio-ecological 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