Assessing urban strategies for reducing the impacts of extreme weather on infrastructure networks

Abstract

Critical infrastructure networks, including transport, are crucial to the social and economic function of urban areas but are at increasing risk from natural hazards. Minimizing disruption to these networks should form part of a strategy to increase urban resilience. A framework for assessing the disruption from flood events to transport systems is presented that couples a high-resolution urban flood model with transport modelling and network analytics to assess the impacts of extreme rainfall events, and to quantify the resilience value of different adaptation options. A case study in Newcastle upon Tyne in the UK shows that both green roof infrastructure and traditional engineering interventions such as culverts or flood walls can reduce transport disruption from flooding. The magnitude of these benefits depends on the flood event and adaptation strategy, but for the scenarios considered here 3–22% improvements in city-wide travel times are achieved. The network metric of betweenness centrality, weighted by travel time, is shown to provide a rapid approach to identify and prioritize the most critical locations for flood risk management intervention. Protecting just the top ranked critical location from flooding provides an 11% reduction in person delays. A city-wide deployment of green roofs achieves a 26% reduction, and although key routes still flood, the benefits of this strategy are more evenly distributed across the transport network as flood depths are reduced across the model domain. Both options should form part of an urban flood risk management strategy, but this method can be used to optimize investment and target limited resources at critical locations, enabling green infrastructure strategies to be gradually implemented over the longer term to provide city-wide benefits. This framework provides a means of prioritizing limited financial resources to improve resilience. This is particularly important as flood management investments must typically exceed a far higher benefit–cost threshold than transport infrastructure investments. By capturing the value to the transport network from flood management interventions, it is possible to create new business models that provide benefits to, and enhance the resilience of, both transport and flood risk management infrastructures. Further work will develop the framework to consider other hazards and infrastructure networks.

Methodological Framework for Analysing Cascading Effects from Flood Events: The Case of Sukhumvit Area, Bangkok, Thailand

“…An impact from a flood event depends on a number of factors, which can be classified as impact parameters, and these may include water depth, water velocity, flood duration and the spatial extent of inundation [36], floodwater contamination, debris or sediments, rate of floodwater rise, frequency of inundation and timing and the resistance parameters, like early warning, robustness of infrastructure, etc. [37].…”

Delineation of flooding risk hotspots based on digital elevation model, calculated and historical flooding extents: the case of Ouagadougou

“…In a natural basin, the topography of the area plays a very important role in flood routing (Pregnolato et al 2016). In fact, the TWI as a proxy for delineating potentially flood-prone areas works best in a natural basin.…”

Linking critical infrastructure resilience to social vulnerability through minimum supply concepts: review of gaps and development of an integrative framework

“…Sci. Also the academic literature has a strong focus on technological perspective towards CI resilience (Pye et al 2011, Cimellaro 2014, Liu et al 2014, Pregnolato et al 2016) although some papers raise questions on shortcomings of a limited technological perspective. Sage et al (2015) call for a more socio-ecological understanding of infrastructure which is in line with Comes (2016) who claims that although communities are recognized as being at the heart of resilience, research still focuses on more 30 on responders instead of considering individuals or local communities as actors.…”