Presentation to Safe & SURE project team PhD student: Seith Mugume

1. 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: SeithMugume First supervisor: Professor David Butler Second supervisor: Dr. Diego Gomez

2. 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

3. Global potential risk of urban flooding 1970 2011 2025 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

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

5. Scope of PhD Thesis • Investigate the use of resilience approachto study the impacts of future changeon urban drainage system performance • To evaluate appropriate response strategiesto 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/

6. Traditional decision making approaches in urban flood management Risk Assessment R = f(failure probability, consequence) Top-down (Cause-Effect) Bottom-up (Vulnerability-Led) Assess vulnerability (local scale) Emission scenarios Increasing envelope of uncertainty Identify coping factors Global and Regional Climate models Develop adaptation response options Impact models (e.g. urban flood models) Response options Response options Based on Wilby & Dessai (2010)

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

8. A resilience approach to decision making under uncertainty • How much disturbance can a system cope with ? versus • What if future change occurs according to scenario x?

9. 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 Where: xjdiscrete system failure state, sjnumerical indicator of the severity of a failure state, ej probability xj, corresponding to sj, is the most severe outcome in a sojourn in F Fsystem failure state. Key shortcoming: Difficult to develop accurate analytical representations of performance under uncertain and non-stationary conditions

10. Characterisation and definitions of resilience Resilience Socio-ecological resilience Engineering resilience Institutional or organisational resilience Ecological resilience Infrastructure system resilience Socio-technical 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

11. 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 Attractor basins & thresholds (Walker et al 2004)

12. Resilience for response and recovery • A measure of how quickly a system is likely to recoverfrom 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 Mtotal number of failure events Key resilience properties • Time of failure • System recovery (rapidity)

13. 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

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

15. Categorising sub-properties of resilience

16. 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

17. 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

18. Proposed resilience indicators for urban pluvial flooding Urban drainage model simulations Qualitative study

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

20. 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)

21. 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

22. 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