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NH10.3

EDI
Advancing critical infrastructure modelling in a complex world

This session aims to share the latest developments in critical infrastructure risk modelling with a focus on multi-hazard, multi-risk, cascading events, and compound risks.

Critical infrastructure, such as the energy, water and waste systems, transportation networks, telecommunication systems, education, and health infrastructures - play an essential role in societies’ day-to-day functioning. At the same time, occurrences of natural hazards highlight the importance of improving our understanding on how these infrastructures respond under stress: a disruption of a single critical infrastructure service can quickly result in a cascading effect to households, companies, or other infrastructure systems, thereby causing wide-spread impacts to the economy and society.

Compound events and connected extremes put pressure on infrastructure systems beyond their design specifications, making it crucial to understand and incorporate such effects into infrastructure planning and risk assessments. In this session, we therefore encourage abstracts aimed at:

1) Improving our understanding of exposure and vulnerability of critical infrastructure systems to (multiple) natural hazards.
2) Collecting and analyzing empirical data of past events/disruptions to inform, validate and improve risk modelling.
3) Impact (modelling) that is sensitive to the specificities of different hazards / sub-hazards / concurring multi-hazards (e.g. TC sub-hazards- flash floods bring very different impacts than strong winds, occur at different geographies, etc.).
4) Impact modelling that captures network character and interdependencies of critical infrastructures, and modelling that doesn’t end at infrastructure asset damages: e.g. differentiated social impacts, business & supply chain disruptions.
5) Dealing with the inherent uncertainty within infrastructure risk modelling and the applicability of these risk models for decision making and adaptation planning. More specifically, we welcome studies applying DMDU (Decision-Making under Deep Uncertainty) approaches to infrastructure risk modelling.
6) Progressing the achievement of global goals (e.g. SDGs) in the context of resilient infrastructure and the advancement of accessible infrastructure to the global population.

Convener: Elco Koks | Co-conveners: Evelyn MühlhoferECSECS, Jasper VerschuurECSECS, Sadhana NirandjanECSECS, Kees van GinkelECSECS
Presentations
| Tue, 24 May, 10:20–11:50 (CEST)
 
Room 1.34

Tue, 24 May, 10:20–11:50

Chairperson: Elco Koks

10:20–10:25
Introduction

10:25–10:35
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EGU22-1484
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solicited
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Highlight
Jim Hall et al.

There has been rapid progress in the development of capabilities to analyse infrastructure networks on very large scales, up to global scales. This is enabled by the growing availability of geospatial data products with global coverage and computational capabilities, which enable processing of these datasets and analytics on large-scale. Global analyses of the risks from climatic hazards to infrastructure networks serve several important purposes:

  • Quantified risk estimates in future climate scenarios contribute to the overall picture of the scale of climate risks worldwide, which helps to motivate climate mitigation and adaptation.
  • Geospatial analysis of hotspots of infrastructure vulnerability helps to target adaptation actions.
  • Cost-benefit analysis of adaptation enables the prioritization of scarce adaptation resources.
  • Quantified climate risk analysis is increasingly required for financial disclosure of physical climate risks by infrastructure investors.

There are inevitable limitations to global-scale analyses, but they enable cross-country comparisons, and the monitoring of changing risks and national infrastructure resilience. Global analyses also provide a convenient starting point for national analyses and a motivation to collect better data to inform national-scale decisions.

 

Here we present recent developments in capability for global-scale climate risk analysis to infrastructure networks. The analysis combines (i) global-scale probabilistic hazard layers (including floods, hurricanes and coastal storm surges); (ii) infrastructure asset and network exposure, for energy, transport and telecommunications networks (iii) analysis of the people and economic activities that are dependent upon these networks. This quantified risk analysis framework has been efficiently implemented for global-scale computations, yielding new results on the scale of climate-related risks. Analysis of resource flows on networks and their connection to infrastructure users is enabling calculation of the numbers of people and economic activity that may be disrupted in catastrophic events. A recent development has been in the introduction of probabilistic event sets for hurricanes and flooding, which enables accurate estimation of the impacts from spatially extensive extreme events. The research is being made available as part of the Global Resilience Index Initiative https://www.cgfi.ac.uk/global-resilience-index-initiative/ and as an open source toolset and interface for geospatial visualisation.

 

 

 

 

How to cite: Hall, J., Russell, T., Verschuur, J., Pant, R., Robertson, M., Lestang, T., Tkachenko, N., Lamb, R., and Oughton, E.: Recent advances in global analysis of critical infrastructure networks in a changing climate, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1484, https://doi.org/10.5194/egusphere-egu22-1484, 2022.

10:35–10:40
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EGU22-4030
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ECS
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Highlight
Maria Pregnolato and Chiara Arrighi

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.

10:40–10:45
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EGU22-5389
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ECS
Panagiotis Asaridis et al.

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.

10:45–10:50
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EGU22-841
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ECS
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Roman Schotten and Daniel Bachmann

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.

10:50–10:55
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EGU22-1784
Olivia Becher et al.

Water utility assets are particularly vulnerable to the impacts of climate change, from multiple climate-related hazards including droughts, floods and hurricanes. There are increasingly calls for disclosure and reporting of physical climate risks to companies, but dependable probabilistic risk estimates are challenging for companies whose asset networks extend over large areas and are subject to multiple hazards. Here we examine the financial impact of present and future (mid-century projections under RCP2.6, 4.5 and 8.5 warming scenarios) climate extremes on the national water supply utility in Jamaica. The potable water supply system is stress tested with a large set of spatially coherent hurricane, drought and pluvial and fluvial flood events, combining observed events with synthetic statistical and model-based events. The water utility’s assets (reservoirs, pumping stations, treatment works, etc.) are embedded in a system model, which also represents water usage for municipal use, loss through leakage and major storage dynamics in the supply network. For each disruptive event, the number of water users impacted is computed. The financial loss incurred by the utility is estimated as the sum of cost of disruption (cost of tankering water and lost tariffs during disruptions/periods of asset reconstruction post event) and the expected cost of asset reconstruction. An expected Value at Risk (VAR), both at present and in future scenarios, is estimated by integrating over the probabilistic event set. The calculation is an extension of the established framework for catastrophe loss modelling used by insurers. We show how climate-induced, widespread water supply disruptions translate into the VAR of a utility’s balance sheet. As water utilities are largely state-owned enterprises, these impacts impose a major burden on the fiscal budget. Therefore, the framework presented provides a basis for identifying interventions that promote both water infrastructure and fiscal resilience.

How to cite: Becher, O., Pant, R., and Hall, J.: Multi-hazard stress testing framework for quantifying climate-related Value at Risk for water utilities, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1784, https://doi.org/10.5194/egusphere-egu22-1784, 2022.

10:55–11:00
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EGU22-911
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ECS
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Evelyn Mühlhofer et al.

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.

11:00–11:05
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EGU22-1427
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ECS
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Highlight
Jasper Verschuur et al.

Reliable port infrastructure is essential to facilitate maritime trade across global supply-chains. Physical climate risks can disrupt port operations, which, apart from infrastructure damages (i.e. direct impacts), can have domestic and cross-border economic losses through transport dependencies on ports (i.e. systemic impacts). For instance, Hurricane Katrina (2005) disrupted operations in multiple ports in New Orleans, resulting in more than USD800 million export losses and price spikes of food products, affecting supply-chains globally (Trepte and Rice, 2014). Both climate change and changes in global trade flows (in absolute terms and trade patterns) can increase systemic risks to ports and economies in the future. In order to improve the resilience of the transport and supply-chain networks, present-day and future climate-induced systemic risks to ports need to be quantified on a global scale.

Here we present a global analysis of present-day and future systemic risk to ports due to physical climate impacts (cyclones, fluvial flooding, coastal flooding, pluvial flooding). To do this, we combine multi-hazard risk estimates of global port infrastructure (Verschuur et al. 2021, under review), covering ~1400 ports, with the output of a newly developed global maritime freight model that quantifies the dependencies of sectors and nations on ports. We show how climate-induced port disruptions can initiate economic ripple effects across geographies, although the vulnerability to these impacts differ across countries and sectors. Moreover, we project how systemic risk would increase by 2050 under various climate and trade scenarios, supporting the business case for adaptation.  

These results can help inform resilience strategies at the port-level (e.g. port elevation) , as well as the supply-chain level (e.g. diversification of transport and import). Moreover, it can support national port infrastructure planning to reduce the systemic risk.  

How to cite: Verschuur, J., Koks, E., and Hall, J.: Present-day and future global economic losses associated with physical climate risks to ports, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1427, https://doi.org/10.5194/egusphere-egu22-1427, 2022.

11:05–11:10
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EGU22-12094
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ECS
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Mengqi Ye et al.

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.

11:10–11:15
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EGU22-12600
Suci Dewi Anugrah et al.

Due to the existence of the megathrust, south of Sunda Strait potentially generate the powerful earthquake and tsunami. It may impact to South of Java Island and Sunda strait coastal zone. One of the city that may impacted by the earthquake and tsunami is the Cilegon city located in the north-east of Sunda Strait. The city is the strategic area which has industrial estate, critical infrastructures, as well as a tourist destination. The earthquake and tsunami hazard may followed by the collateral hazard. 78 petrochemicals factory as well as steel industry, and other national vital object such as electric stream power plant could give a contribution to the industrial hazard.

Based on a seismological study, the maximum magnitude estimated in megathrust zone Sunda Strait is M 8.7. The existing of active faults and active volcano of Krakatau in Sunda Strait add a complexity of earthquake and tsunami potential in the area. According to historical documentation, there are destructive earthquake associated to south of Sunda Strait megathrust such as West Java earthquake (January 5, 1699), Batavia earthquake (January 22, 1780), Jakarta Earthquake (February 23, 1903), and destructive tsunami associated to the Krakatau eruption (August 27, 1883).

This study aims to assess the multi hazard potential generated by the megathrust earthquake in the south of Sunda Strait. We simulate the worst case earthquake scenario on the south of Sunda Strait megathrust zone, located at 7.53 S;104.04 E, with 10 km fixed depth. Both simulation of earthquake shakemap and tsunami inundation were carried out in this study.

The modeling of earthquake indicates ground shaking possibly generates VI-VII MMI in Cilegon. Moreover, the inundation tsunami modeling estimated there are 4 sub-regencies of industrial estate (Ciwandan, Citangkil, Gerogol, and Pulomerak) will be impacted. The highest tsunami inundation may approximately reach 9 m hit a critical infrastructure of the Merak harbor. The maximum distance of tsunami penetration is estimated to be 1.5 km from the coastal line.

Keywords: earthquake and tsunami potential, multi collateral hazard, industrial estate, cilegon city, critical infrastructure

How to cite: Anugrah, S. D., Yatimantoro, T., Julius, A. M., Daryono, D., Hidayanti, H., Yogaswara, D. S., Anggraini, S., Simangunsong, G., Kriswinarso, T., Harvan, M., Karnawati, D., Prayitno, B. S., and Adi, S. P.: A complex hazard assessment due to earthquake and tsunami potential in the industrial strategic estate of the Cilegon city , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12600, https://doi.org/10.5194/egusphere-egu22-12600, 2022.

11:15–11:20
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EGU22-5054
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ECS
Dorothee Fehling et al.

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.

11:20–11:25
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EGU22-7632
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ECS
Margreet van Marle et al.

Quantifying natural hazard impacts on critical infrastructure networks inherently involves uncertainties which makes decision-making complex. Here, we present an approach on how to account for uncertainties in the resilience assessment and in adaptation planning. These uncertainties stem from the hazard, exposure, vulnerability, and end-user data, as well as economic valuation. The consequences of natural hazards on critical infrastructure networks such as road transport networks has been proven to be evident, illustrated by recent flooding events in Western Europe. Due to climate change, many of these hazards may intensify and occur more frequently. Over the past years this has invoked progress in research that has led to an increased understanding of the effects of natural hazards on infrastructure networks. Currently, most analyses focus on the estimation of exposure, vulnerability, and the estimation of (annual expected) damages to the infrastructure assets and socio-economic losses for the users. This is subsequently used to identify hotspots for potential measures. The next step is to include adaptation in maintenance and construction planning. However, this step is often not linked to the assessment preceding the hotspot selection and because uncertainties in the assessment are not quantified, this results in decision making under (very deep) uncertainty. Here, we show the results for the Dutch highway network where we used the RA2CE - Resilience Assessment and Adaptation for Critical infrastructurE - platform, which makes use of hazard maps, user defined vulnerability curves and traffic information to produce resilience and risk maps for the infrastructure networks (resulting annual expected damages for the road operator and socio-economic losses for the road user), but also offers the possibility to perform cost-benefit analyses for proposed adaptation measures. Based on the cost-effectiveness analysis of potential measures, economically viable intervention strategies can be defined, including spatially explicit cost-benefit ratios to demonstrate economic performance of the different strategies. However, cost-benefit assessments should acknowledge the uncertain future related to climate change and socio-economic developments. Therefore, we progress the current state of the art by adding an uncertainty analysis, which takes into account all identified uncertainties in the model chain. This is based on Monte Carlo analyses providing insight in the sensitivity to all uncertainties in the process stemming from hazard, exposure, vulnerability and traffic data, as well as from the changes to the future related to climate change and socio-economic developments. The results provide an increased insight in the robustness of the strategies, instead of only one (best guess) prediction. It further allows the user and decision-maker not only to look at the expected change, but also at the high-impact, low-likelihood events. Based on validation with decision-makers future research has been identified to include black swans (unknown-unknown events) in decision-making, but also progressing on the user level, by for example including equity.

How to cite: van Marle, M., Bles, T., van Muiswinkel, K., de Bel, M., Kwant, M., Boonstra, H., and Brinkman, R.: Uncertainty as part of multi-hazard resilience and adaptation planning for road infrastructure, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7632, https://doi.org/10.5194/egusphere-egu22-7632, 2022.

11:25–11:30
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EGU22-675
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ECS
Hossein Nasrazadani and Bryan Adey

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.

11:30–11:35
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EGU22-1371
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ECS
Amelia Tomalska

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.

11:35–11:40
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EGU22-13262
Kai Liu et al.

Tropical cyclones pose great risks to infrastructures. We used records of road and electric power system damage from TC events in Hainan Province, China, to construct vulnerability models that quantify the relationship between road and electric power system damage level and different TC intensity measures. These measures include cumulative precipitation and maximum wind speed, as well as their joint effect. We found that the derived vulnerability model of the joint effect of precipitation and wind speed outperforms models constructed with single TC intensity measures. The derived functions show a good fit to the observed data and can provide an accurate estimate of road and electric power system damage from TCs, as validated by historical damage records.

 

How to cite: Liu, K., Zhu, J., and Wang, M.: An Empirical Approach for Developing Vulnerability Functions of critical infrastructures to Tropical Cyclones, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13262, https://doi.org/10.5194/egusphere-egu22-13262, 2022.

11:40–11:45
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EGU22-13096
Elco Koks et al.

Germany, Belgium and The Netherlands were hit by extreme precipitation and flooding in July 2021. The event not only caused major damages to residential and commercial structures, but also to critical infrastructure in particular. Not only vital functions in the first response were affected (e.g. hospitals, fire departments), but also railways, bridges and utility networks (e.g. water and electricity supply) were severely damaged, expecting to take months to years to fully rebuild. This study provides an overview of the impacts to large-scale critical infrastructure systems and how recovery has progressed during the first six months after the event. The results show that Germany and Belgium were particularly affected, with many infrastructure assets severely damaged or completely destroyed. Impacts range from completely destroyed bridges and sewage systems, to severely damaged schools and hospitals. While some of the infrastructure systems, such as electricity, were relatively quickly restored (e.g. several weeks to a month), are others still not fully rebuild six months after the event (e.g. several road and railway bridges).  

We find that large-scale risk assessments, often focused on larger (river) flood events, do not find these local, but severe, impacts. On a local and regional level, the disruptions in daily lives and to the economy were enormous. Yet, zoomed out on a national scale, the impacts were relatively small. While large-scale studies are useful to identify potential hotspots and bottlenecks in the system, local-scale studies are essential to better understand the real impacts (and are also better able to do so). This may be the result of limited availability of validation material. As such, this study not only helps to better understand how critical infrastructure can be affected by flooding, but can also be used as validation material for future flood risk assessments that include critical infrastructure failure in their risk modelling framework.

How to cite: Koks, E., Van Ginkel, K., Van Marle, M., and Lemnitzer, A.: Critical Infrastructure impacts of the 2021 mid-July western European flood event, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13096, https://doi.org/10.5194/egusphere-egu22-13096, 2022.

11:45–11:50
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