NH10.3

Flood is among the most frequent and damaging natural hazards worldwide. Assessment of direct losses due to flooding is well-advanced, and include multiple models for the built environment (infrastructure, buildings). On the contrary, the knowledge and the literature on the assessment of indirect losses and cascading effects is less developed. Impacts on infrastructure are not necessarily due to the physical contact with floodwater but also result from a reduced performance of the service/functionality, which usually propagate outside the flooded area and beyond the impacted infrastructure (e.g. power disruptions resulting in communications failures). This work presents the risk analysis of two linear infrastructure systems, i.e. the water distribution system (WSS) and the road network system, for flooding. The evaluation of indirect flood impacts on the two networks is carried out for four probabilistic flood scenarios, obtained by a coupled 1D-quasi 2D hydraulic model. The impacts on the water distribution system and on the road network are simulated with a Pressure-Driven Demand model and a transport network disruption model respectively. Common impact metrics, similarities and differences of the methodological aspects for the two networks and risks are identified. The method is applied to the metropolitan area of Florence (Italy). The risk assessment is first carried out considering the two systems as separately affected; in a second analysis, the risk assessment includes the cascading effect and systemic interdependency, i.e. it evaluates the consequences on WSS due to the lack of accessibility, which prevents timely repairs and replacement at the WSS lifting stations. The results show that the risk to the WSS in terms of Population Equivalent not served (PE/year) can be reduced by the 71.5% and the 41.8% respectively, if timely repairs to the WSS stations are accomplished by 60 and 120 minutes. The study highlighted that systemic risk-informed planning can support timely interventions and enhance infrastructure resilience; however, it is recommended to conduct further studies which focuses on the complex dynamics of water runoff, water supply and traffic flows to support practical action planning.

How to cite: Pregnolato, M. and Arrighi, C.: Cascading effects of floods on interdependent infrastructure systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4030, https://doi.org/10.5194/egusphere-egu22-4030, 2022.

Natural hazards are a leading driver of power outages worldwide. Although flooding has lower impact compared to other natural hazards, it may still have a significant impact on power grids functionality in terms of frequency, magnitude, and duration of power outage. Maintaining the security of power supply under emergency conditions triggered by natural hazards, such as floods, is a challenging task because of the inherent structural and dynamic complexity of the system. In such a context, this paper presents a new model for the estimation of direct, indirect, and systemic flood damage to power grids. The key objective of the model is to be an operational tool able to: (i) consider the magnitude, probability of occurrence, and spatiotemporal variability of flood hazard, (ii) identify the vulnerable components of power grids and evaluate their probability of failure in case of flood, (iii) analyze the cascading effects of individual or multiple failure states on the power transmission and distribution networks, (iv) and assess the impacts of power outages on the power-dependent economic activities and infrastructures. To achieve this goal, the model combines deterministic flood hazard scenarios, a spatially distributed power flow model, fragility curves of power grid components for different voltage levels, and a social model, describing the various users connected to the power grid. For quantitative illustration purposes, a synthetic model has been developed by referring to the IEEE 14 bus system benchmark, to which a spatial dimension has been allocated. Furthermore, to account for differentiated social impacts, the power flow model has been linked to a synthetic social model including several communities (hubs) with different social and economic characteristics.

The development of the synthetic model constitutes a preliminary step in understanding and quantifying the impacts that sustained power interruptions caused by floods can have on the customers of power grids. Next research efforts will be devoted, on the one hand, to the adoption of a probabilistic approach, by substituting deterministic hazard scenarios with spatial dependent, probabilistic ones; on the other hand, to the sensitivity analysis of the different modeling phases to identify the components of the model on which the final damage scenario depends mostly. The final aim is to provide a modeling and simulation tool for risk analysis, so as to enable stakeholders, authorities, and policy makers to formulate effective strategies to guarantee public security and ensure financial well-being.

How to cite: Asaridis, P., Molinari, D., Di Maio, F., Ballio, F., and Zio, E.: Impact assessment of flood damage on power grid customers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5389, https://doi.org/10.5194/egusphere-egu22-5389, 2022.

In flood risk analysis it is a key element to determine consequences of flooding to assets, people and infrastructures. However, damages to critical infrastructure networks (CIN) are not always restricted to inundated areas. The effects of directly impacted objects cascade to other infrastructures, which are indirectly affected by a flood. Modelling critical infrastructure networks is one possible answer to the question ‘how to include indirect and direct impacts to critical infrastructures in a flood risk analysis?’.

The modelling of complex CIN is utilized for different purposes: For modelling transportation routing, for damage assessments due to cyber attacks or infrastructure and interdependency analysis of water and waste water flow. For the purpose of flood risk assessments and, finally, in flood risk management application cases are scarce. The presented work introduced a method to overcome this gap. Major challenge is to balance the simplicity of a modeling approach with the resemblance of real interdependencies in a CIN and their task to supply services to end users. The more complex and realistic the network model is desired to be, the harder it is to gather the necessary data and the more expertise is necessary for potential users of this method. Additionally, users are required to switch from a raster or cell-based calculation philosophy to a network-based philosophy including points, connectors (edges) and areas (surfaces).

In this work, a network-based and topology-based method for a catchment-wide analysis is presented. The basic model elements (points, connectors and polygons) are utilized to model the complex CIN interdependencies. The CIN-module of the freely available software package ProMaIDes¹, a state-of-the-art flood risk analysis tool, is used. The module is suited for an analysis of critical infrastructure damages, disruption of infrastructures and quantifies those damages by the number of disrupted users and the disruption duration. In a case study in Accra, Ghana, the method capabilities are showcased in a multisectoral model. Sectors included are electricity supply, fresh water supply, telecommunication services, health sector, emergency services and transportation. The model consists of 419 point elements, 472 polygon elements and 1124 connector elements. A synthetic precipitation event is used to visualize the reactions of the model as well as display first results. The case study has shown the flexibility and scalability of the introduced method to differentiate CI sector specifics. Consequently, the potential of the method to support flood risk management is discussed.

¹ https://promaides.h2.de

How to cite: Schotten, R. and Bachmann, D.: Concept of a Critical Infrastructure Network Modelling Approach for Flood Risk Management, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-841, https://doi.org/10.5194/egusphere-egu22-841, 2022.

Critical infrastructures (CIs) such as powerlines, roads, telecommunication and healthcare systems across the globe are more exposed than ever to the risks of extreme weather events in a changing climate. Damages to CIs often lead to failure cascades with catastrophic impacts in terms of people being cut off from basic service access. Yet, there is a gap between traditional CI failure models, operating often at local scales, with detailed proprietary, non-transferrable data, and the large scales and global occurrences of natural disasters.

We demonstrate a way to bridge those incompatibilities by linking a globally consistent and spatially explicit natural hazard risk modelling platform (CLIMADA) and a CI failure cascade model. The latter is built on publicly available infrastructure, end-user and supply data, and makes use of consistent and transferrable dependency heuristics between CIs to represent infrastructure systems at national scales for any place interest. Impacts are then spatially mapped in terms of people experiencing disruptions to basic service access.

With this approach, we aim to showcase how the interplay of available data and well-informed heuristics can allow large-scale impact models to produce consistent hot-spot analyses and rapid emergency assessments, which may then provide a starting point for more detailed, local studies.

How to cite: Mühlhofer, E., Koks, E., Sansavini, G., and Bresch, D. N.: A globally consistent approach for basic service disruptions after natural disasters, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-911, https://doi.org/10.5194/egusphere-egu22-911, 2022.

Power systems provide vital services to modern society and are characterized by being highly interconnected in a variety of ways. The loss of power systems during weather extremes can potentially result in widespread, catastrophic impacts that may seriously disrupt socioeconomic activities. In practical terms, there are many interdependencies through which the indirect impacts of a major power outage ripple through social interactions and economic activities. However, both the scarcity of power infrastructure data and the complexity of power systems make it challenging to model these exact socioeconomic impacts of power outages in the aftermath of weather extremes. Unfortunately, power system datasets remain incomplete regarding most geographic areas, and the access to power infrastructure data in an open and standardized way is one of the main bottlenecks in risk modelling, especially for the medium and lower voltage distribution networks.

Limited spatial information on power infrastructure makes it difficult to respond to challenges in natural disasters and electricity reliability. Therefore, data collection of power systems should be the main priority in power infrastructure risk assessments. One of the possibilities to fill these data gaps is through the use of satellite imagery. However, automating the process of satellite imagery data classification and translating the extracted information into semantic classes, specifically for power infrastructure, has three main challenges: 1) images from different sources have complete different spatial resolutions, which makes it difficult to consistently identify power infrastructure; 2) most existing satellite imagery datasets are prepared for training classification models but do not include annotations for training detection models; 3) and there are few training datasets available for training power infrastructure detection models.

To fill this research gap, we will develop a state-of-the-art deep learning method to identify power infrastructure. Our methodology consists of two parts: 1) image segmentation for power lines by StackNetMTL, which helps to learn the interconnectivity within the system; 2) object detection for other power infrastructure (i.e., power plants, substations, and towers) by Mask R-CNN. This will provide us with a geospatial power infrastructure map of the Southeast and East Asia to support power systems risk assessment, for both the system itself and the potential societal impacts. This research will provide a consistent and reproducible way for machine-driven mapping of power infrastructure, paving the way for improved efforts in power system modelling and risk management in Southeast and East Asia.

How to cite: Ye, M., Koks, E., and Ward, P.: Developing a composite map of the Southeast and East Asia power systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12094, https://doi.org/10.5194/egusphere-egu22-12094, 2022.

We assess the exposure of Critical Infrastructure (CI) to storm surge flooding in the city of Rostock, Germany. The city endured two severe storm surges in 2006 and 2017 that caused the flooding of a main road in the city centre and proved the existing coastal protection measures to be insufficient regarding future sea-level rise (SLR). Using the hydrodynamic model Delft3D-FLOW, we simulate severe storm surge flooding under SLR scenarios of + 30 cm, + 50 cm, + 80 cm and + 100 cm and assess the extent to which CI in the city is affected. Our results show that Rostock’s city harbour (german: Stadthafen) and the adjacent primary road are highly exposed to coastal flooding in all scenarios. Furthermore, transport infrastructure, such as road and railway networks, as well as fire stations are potentially at risk of getting flooded. Besides direct monetary damage, flooding can cause so-called “cascading effects” which are damages that are directly linked to the flooding but occur outside of the directly affected areas. Hence, the cut-off of the primary road can lead to sensitive time loss during emergency situations. The results also indicate that the train connection between Hamburg and large parts of the federal state of Mecklenburg-West Pomerania could fail due to flooding, already in the + 30 cm scenario.
Our study does not account for impacts on the electricity grid as relevant data are not openly available because of data sensitivity. However, electricity data would lead to an improved assessment of the magnitude of the cascading effects more accurately.
We conclude that the critical infrastructure of the city of Rostock is not sufficiently protected against storm surges in the future and emphasise the importance of the plans of the federal state of Mecklenburg-West Pomerania to build new coastal protection measures until the year 2030.

How to cite: Fehling, D., Arns, A., and Vafeidis, A.: Current and future Exposure of Critical Infrastructure to Coastal Flooding in Rostock, Germany, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5054, https://doi.org/10.5194/egusphere-egu22-5054, 2022.

Infrastructure systems are susceptible to hazard events, which disrupt their functionality leading to direct and indirect consequences for the users and the owners. To ensure these consequences are not excessive, infrastructure managers have to plan and execute interventions to improve the resilience of their networks. Interventions can contribute to enhancing resilience through a multitude of techniques such as reducing hazard intensity, reducing exposure to hazards, decreasing the vulnerability of exposed assets, implementing adaptive capacity, enhancing restoration programs, and the combination thereof. Moreover, interventions can be executed on a spatially and temporally variant domain, including those that can be executed prior to the occurrence of hazard events, e.g., construction of flood levees; while the hazard is taking place and evolving, e.g., deployment of temporary sandbags during a flood; and after the hazard event, e.g., repair activities. This spatiotemporal variability, along with the possibility of having a portfolio of interventions each targeting certain aspects of resilience, however, has not yet been adequately addressed in the literature. Most studies focused on standalone pre-hazard interventions, and have not addressed the collective benefit of interventions of different types. This study addresses this limitation by proposing a simulation approach to quantitatively evaluate the effects of portfolios of interventions of various types for improving the resilience of transport networks against climate-related hazards. It is achieved through a resilience assessment methodology that, under both existing and intervened conditions, exhaustively models the: 1) occurrence and evolution of hazards, 2) performance of transport network over the course of hazard evolution, and 3) restoration efforts to recover performance. Subsequently, through generating a host of simulated scenarios, interventions are evaluated based on their contribution to reducing the ensuing economic consequences, e.g., repair costs, as well as socio-economic ones, e.g., increased travel time and loss of connectivity. This approach will be showcased through an application to a transport network located in Switzerland subject to heavy rainfall, flooding, and landslides. The proposed simulation approach serves as a virtual representation of the real system that can enable decision-makers to objectively investigate and compare the effects of various interventions.

How to cite: Nasrazadani, H. and Adey, B.: Evaluating Interventions to Improve the Resilience of Transport Networks against Climate-Induced Hazards: A Simulation Approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-675, https://doi.org/10.5194/egusphere-egu22-675, 2022.

Critical infrastructure is a complex system that provide essential services to the society such as food, water, energy, transportation, health, financial services. Any potential dysfunction of Critical Infrastructure might result in severe consequences for the human life, the environment, the economy and the security of the country. The recently experienced repercussions of COVID-19 pandemic exposed major deficiencies in terms of protection of Critical Infrastructure. The implemented approaches focusing on the threat identification and prevention strategies, without efficient organisational resilience, proved to be ineffective, especially in case of unanticipated or low-probability threats. The biological threats, such as pandemics, are relatively rare and difficult to estimate and prevent. They affect the whole organisation and contrary to the most of the natural hazards, such as floods, fires or hurricanes, have constant, permanent character. The COVID-19 pandemic forced Critical Infrastructure operators to operate in crisis mode as a result of shortages of staff, disruption of supply chains and increased vulnerability to cyber-attacks. The occurrence of these consequences unveiled the underlying vulnerabilities of Critical infrastructure. Namely, the lack of capabilities to successfully detect the possible threats resulting from dependencies and interdependencies and vulnerabilities related to internal procedures, plans or capabilities to respond and recover after the adverse event. The protection of Critical Infrastructure based on identification and assessment of vulnerabilities would enable Critical Infrastructure operators to apply adequate measures tailored to address the causes of identified vulnerabilities, to prioritise actions and to concentrate resources on the most pressing issues. The understanding of vulnerability of Critical Infrastructure to biological threats, would help Critical Infrastructure operators to prepare better for future “black swan” events and cascading disruptions across sectoral boundaries. The elimination or reduction of vulnerabilities would make Critical Infrastructure more resilient to future crisis situations and would ensure the undisturbed continuity of the essential services.

How to cite: Tomalska, A.: Vulnerability of Critical Infrastructure to biological threats, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1371, https://doi.org/10.5194/egusphere-egu22-1371, 2022.