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

This is the final version of the article. Available from MDPI via the DOI in this record.Impacts from floods in urban areas can be diverse and wide ranging. These can include the loss of human life, infrastructure and property damages, as well as other kinds of nuisance and inconvenience to urban life. Hence, the ability to identify and quantify wider ranging effects from floods is of the utmost importance to urban flood managers and infrastructure operators. The present work provides a contribution in this direction and describes a methodological framework for analysing cascading effects from floods that has been applied for the Sukhumvit area in Bangkok (Thailand). It demonstrates that the effects from floods can be much broader in their reach and magnitude than the sole impacts incurred from direct and immediate losses. In Sukhumvit, these include loss of critical services, assets and goods, traffic congestion and delays in transportation, loss of business and income, disturbances and discomfort to the residents, and all these can be traced with the careful analysis of cascading effects. The present work explored the use of different visualization options to present the findings. These include a casual loop diagram, a HAZUR resilience map, a tree diagram and GIS maps.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under Grant Agreement No. 603663 for the research project PEARL (Preparing for Extreme and Rare events in coastaL regions). The authors are grateful to Opticits for providing the HAZUR software licence, within the collaboration of the EU H2020 research project RESCCUE (RESilience to cope with Climate Change in Urban arEas—a multisectorial approach focusing on water) Grant Agreement 700174

Resilience assessment for interdependent urban infrastructure systems using dynamic network flow models

Critical infrastructure systems are becoming increasingly interdependent, which can exacerbate the impacts of disruptive events through cascading failures, hindered asset repairs and network congestion. Current resilience assessment methods fall short of fully capturing such interdependency effects as they tend to model asset reliability and network flows separately and often rely on static flow assignment methods. In this paper, we develop an integrated, dynamic modelling and simulation framework that combines network and asset representations of infrastructure systems and models the optimal response to disruptions using a rolling planning horizon. The framework considers dependencies pertaining to failure propagation, system-of-systems architecture and resources required for operating and repairing assets. Stochastic asset failure is captured by a scenario tree generation algorithm whereas the redistribution of network flows and the optimal deployment of repair resources are modelled using a minimum cost flow approach. A case study on London’s metro and electric power networks shows how the proposed methodology can be used to assess the resilience of city-scale infrastructure systems to a local flooding incident and estimate the value of the resilience loss triangle for different levels of hazard exposure and repair capabilities

Network Interdependency Modeling for Risk Assessment on Built Infrastructure Systems

As modern infrastructures become more interconnected, the decision-making process becomes more difficult because of the increased complexity resulting from infrastructure interdependencies. Simulation and network modeling provide a way to understand system behavior as a result of interdependencies. One area within the asset management literature that is not well covered is infrastructure system decay and risks associated with that decay. This research presents an enhanced version of Haimes\u27 input-output inoperability model (IIM) in the analysis of built infrastructure systems. Previous applications of the IIM characterized infrastructure at the national level utilizing large economic databases. This study develops a three-phased approach that takes component level data stored within geographic information systems (GIS) to provide a metric for network interdependency across a municipal level infrastructure. A multi-layered approach is proposed which leverages the layered data structure of GIS. Furthermore, Monte Carlo simulation using stochastic decay estimates shows how infrastructure risk as a result of interdependency effects changes over time. Such an analysis provides insight to infrastructure asset managers on the impact of policy and strategy decision-making regarding the maintenance and management of their infrastructure systems

Critical Infrastructure Interdependency Modeling: A Survey of U.S. and International Research

The Nation’s health, wealth, and security rely on the production and distribution of certain goods and services. The array of physical assets, processes, and organizations across which these goods and services move are called "critical infrastructures".1 This statement is as true in the U.S. as in any country in the world. Recent world events such as the 9-11 terrorist attacks, London bombings, and gulf coast hurricanes have highlighted the importance of stable electric, gas and oil, water, transportation, banking and finance, and control and communication infrastructure systems. Be it through direct connectivity, policies and procedures, or geospatial proximity, most critical infrastructure systems interact. These interactions often create complex relationships, dependencies, and interdependencies that cross infrastructure boundaries. The modeling and analysis of interdependencies between critical infrastructure elements is a relatively new and very important field of study. The U.S. Technical Support Working Group (TSWG) has sponsored this survey to identify and describe this current area of research including the current activities in this field being conducted both in the U.S. and internationally. The main objective of this study is to develop a single source reference of critical infrastructure interdependency modeling tools (CIIMT) that could be applied to allow users to objectively assess the capabilities of CIIMT. This information will provide guidance for directing research and development to address the gaps in development. The results will inform researchers of the TSWG Infrastructure Protection Subgroup of research and development efforts and allow a more focused approach to addressing the needs of CIIMT end-user needs. This report first presents the field of infrastructure interdependency analysis, describes the survey methodology, and presents the leading research efforts in both a cumulative table and through individual datasheets. Data was collected from open source material and when possible through direct contact with the individuals leading the research

Systemic criticality

With high certainty, extreme weather events will intensify in their impact within the next 10 years due to climate change-induced increases in hazard probability of occurrence and simultaneous increases in socio-economic vulnerability. Data from previous mega-disasters show that losses from disruptions of critical services surpass the value of direct damages in the exposed areas because critical infrastructures [CI] are increasingly (inter-) dependent. Local events may have global impacts. Systemic criticality, which describes the relevance of a critical infrastructure due to its positioning within the system, needs to be addressed to reduce the likelihood of cascading effects. This paper presents novel approaches to operationalise and assess systemic criticality. Firstly, the paper introduces systemic cascade potential as a measurement of systemic criticality. It takes the relevance of a sector and the relevance of its interdependencies into account to generate a relative value of systemic importance for a CI sector. Secondly, an exemplary sectoral assessment of the road network allows reflecting the spatial manifestation of the first level of cascading effects. It analyses the impact of traffic interruptions on the accessibility of critical facilities to point out the systemically most critical segments of the municipal road network. To further operationalise the spatial dimension of criticality, a normative assertion determining the worthiness of protection of system components is required. A nationwide spatial flood protection plan incorporates this aspect in Germany for the first time. Its formal approval process was initiated in February 2020

Functionally Fractal Urban Networks: Geospatial Co-location and Homogeneity of Infrastructure

Just as natural river networks are known to be globally self-similar, recent research has shown that human-built urban networks, such as road networks, are also functionally self-similar, and have fractal topology with power-law node-degree distributions (p(k) = a k). Here we show, for the first time, that other urban infrastructure networks (sanitary and storm-water sewers), which sustain flows of critical services for urban citizens, also show scale-free functional topologies. For roads and drainage networks, we compared functional topological metrics, derived from high-resolution data (70,000 nodes) for a large US city providing services to about 900,000 citizens over an area of about 1,000 km2. For the whole city and for different sized subnets, we also examined these networks in terms of geospatial co-location (roads and sewers). Our analyses reveal functional topological homogeneity among all the subnets within the city, in spite of differences in several urban attributes. The functional topologies of all subnets of both infrastructure types resemble power-law distributions, with tails becoming increasingly power-law as the subnet area increases. Our findings hold implications for assessing the vulnerability of these critical infrastructure networks to cascading shocks based on spatial interdependency, and for improved design and maintenance of urban infrastructure networks

Anticipating and Adapting to Increases in Water Distribution Infrastructure Failure Caused by Interdependencies and Heat Exposure from Climate Change

abstract: This dissertation advances the capability of water infrastructure utilities to anticipate and adapt to vulnerabilities in their systems from temperature increase and interdependencies with other infrastructure systems. Impact assessment models of increased heat and interdependencies were developed which incorporate probability, spatial, temporal, and operational information. Key findings from the models are that with increased heat the increased likelihood of water quality non-compliances is particularly concerning, the anticipated increases in different hardware components generate different levels of concern starting with iron pipes, then pumps, and then PVC pipes, the effects of temperature increase on hardware components and on service losses are non-linear due to spatial criticality of components, and that modeling spatial and operational complexity helps to identify potential pathways of failure propagation between infrastructure systems. Exploring different parameters of the models allowed for comparison of institutional strategies. Key findings are that either preventative maintenance or repair strategies can completely offset additional outages from increased temperatures though-- improved repair times reduce overall duration of outages more than preventative maintenance, and that coordinated strategies across utilities could be effective for mitigating vulnerability.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201