Use of Quantitative Resilience in Managing Urban Infrastructure Response to Natural Hazards

Use of Quantitative Resilience in Managing Urban Infrastructure Response to Natural Hazards

Andre Schardong Slobodan P. Simonovic Howard Tong 

Department of Civil and Environmental Engineering, The University of Western Ontario, London, Canada

24 January 2019
| Citation



Damages to urban systems as a result of various natural hazards have escalated in recent years. The observed trend is expected to increase in the future as the impacts of population growth, rapid urbanization and climate change persist. To alleviate the damages associated with these impacts, it is recommended to integrate disaster management methods into planning, design and operational policies under all levels of government. This manuscript proposes the use of quantitative resilience concept (dynamic in time and space) to assess the response of an urban system to natural hazards. The implementation of the concept has been done in the form of the web-based decision support system that operates in near real-time. It is designed to assist decision makers in selecting the best options for integrating adaptive capacity into their communities to protect against the negative impacts of hazards. The tool is developed for application in Toronto, Ontario, Canada.


adaptation, decision support, disaster management, hydro-meteorological, online tool, Resilience, urban systems


[1] Irwin, S., Schardong, A., Simonovic, S.P. & Nirupama, N., ResilSIM – A decision support tool for estimating resilience of urban systems. Water, 8, p. 377, 2016.

[2] World Bank. Disaster Risk Management: Overview, 2015, available at

[3] IPCC. Summary for Policymakers. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, ed. C.B. Field, V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach & G.K. Plattner, Cambridge University Press, Cambridge, 2012.

[4] World Health Organization. Health Emergency and Disaster Risk Management: Overview, available at, 2017.

[5] Simonovic, S.P., From risk management to quantitative disaster resilience: a paradigm shift. International Journal of Safety and Security Engineering, 6, pp. 85–95, 2016.

[6] Simonovic, S.P. & Peck, A., Dynamic resilience to climate change caused natural disasters in coastal megacities quantification framework. British Journal of Environment and Climate Change, 3, pp. 378–401, 2013.

[7] Bruneau, M., Chang, S.E., Eguchi, R.T., Lee, G.C., O’Rourke, T.D., Reinhorn, A.M., Shinozuka, M., Tierney, K., Wallace, W.A. & von Winterfeldt, D., A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake Spectra, 19(4), pp. 733–752, 2003.

[8] Chang, S.E. & Shinozuka, M., Measuring improvements in disaster resilience of communities. Earthquake Spectra, 20(3), pp. 739–755, 2004.

[9] Cutter, S.L., Barnes, L., Berry, M., Burton, C., Evans, E., Tate, E. & Webb, J., A place-based model for understanding community resilience to natural disasters. Global Environmental Change, 18, pp. 598–606, 2008.

[10] Simonovic, S.P. & Arunkumar, R., Comparison of static and dynamic resilience for a multipurpose reservoir operation. Water Resources Research, 52, pp. 8630–8649, 2016.

[11] Kong, J. & Simonovic, S.P., Multi-Hazard Resilience Model of an Interdependent Infrastructure System (Water Resources Research Report no. 102), Facility for Intelligent Decision Support, Department of Civil and Environmental Engineering, London, Ontario, Canada, 2017.

[12] Peck, A. & Simonovic, S.P., Coastal Cities at Risk (CCaR): Generic System Dynamics Simulation Models for Use with City Resilience Simulator Final Report (Water Resources Research Report No. 083) Facility for Intelligent Decision Support, Department of Civil and Environmental Engineering: London, ON, Canada, 2013.

[13] OSM. The OpenStreetMap Project, available at, 2018.