Critical infrastructures such as water, power, telecommunications, and transportation systems are an important part of human life in an urban environment, which are vulnerable to disasters, failures, asset forfeiture, and sabotage. The focus of this work is on water distribution networks (WDNs) including different hydraulic elements such as pipes, tanks, valves, and reservoirs to transport drinking water from central treatment plants or sources to consumers. Thereby, efficient identification of system parts in a WDN that are vulnerable to failures is important for efficient management and to provide high reliability. Therefore, often hydraulic simulations are applied, which are computationally intensive and impractical for large networks and multiple pipe failures.

In this study, a graph theory-based analysis is used to identify the critical elements (pipes) in a WDN based on "demand edge betweenness centrality (EBC^Q)". Therefore, the connectivity of the network based on spatial layout is modelled using a mathematical graph-based approach that represents the topology of the WDN and incorporates hydraulic factors to imitate hydraulic behavior. The mathematical graph consists of #N (vertices) connected by a set of #E (edges) corresponding to nodes and pipes respectively. The ratio of length and diameter is used as edge weight to determine the shortest path, and adds the demands of the nodes to obtain EBC^Q values for all edges in that path. In case of pipe failures, the corresponding edge is removed, and the EBC^Q of the new graph is calculated and compared with the maximum possible flow to find overloaded and affected edges. Thereby, single and multiple pipe failures are investigated with this method.

The method is applied to a benchmark case study and to a real network of an Alpine municipality in Austria. Furthermore, the results of the graph-based method are compared with the results from hydraulic modelling in terms of accuracy and computational time. The first results show only slight differences in the results of the graph-based method compared to those from hydraulic modeling.  Further, the graph-based method is able to identify the same order of critical pipes with low deviation compared to those from the hydraulic model. Additionally, the computational time and data requirements, in the calculation of pipe criticality by graph-based approach is significantly less compared to the hydraulic modelling method. This method is useful in disaster or contamination scenarios where many scenarios or combinations are required.

Funding: The project “RESIST” is funded by the Austrian security research programme KIRAS of the Federal Ministry of Finance (BMF).

How to cite: Satish, R., Lindenthale, T., Dastgir, A., Hajibabaei, M., Oberascher, M., and Sitzenfrei, R.: Graph-based method for analysis of multiple pipe failures in water distribution networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-376, https://doi.org/10.5194/egusphere-egu23-376, 2023.

Electricity infrastructure is one of the most essential infrastructure systems for the functioning of our society. It forms the “lifeline” for a prosperous modern economy by supporting the delivery of health, education, and many other services in its day-to-day functioning (Rentschler et al. 2019; Arderne et al. 2020). Weather-related hazards are the leading cause of major power outages, resulting in significant damage (Alemazkoor et al. 2020; Shield et al. 2021).

The power grid is a highly intricate system with varying degrees of (inter)connectivity and redundancy over a wide geographic extent. The complexity of the power grid topology may create system-wide failures, more specifically, the power outages may escalate from local problems to broad interruptions, thereby resulting in widespread, catastrophic impacts that may seriously disrupt socioeconomic activities (Pescaroli and Alexander 2018; Suppasri et al. 2021). Transmission and distribution networks are most vulnerable to storm events and are responsible for most power outages (Nicolas et al. 2019). Another main factor behind the increasing damage from power outages is of socio-economic origin — more and more people and physical assets are located in harm's way due to the rapid development of the economy — climate change is also expected to exacerbate impacts from weather-related outages and then alter the landscape of natural hazard risk to power systems (Forzieri et al. 2018).

Understanding the potential damage caused by natural hazards requires information on their intensity and frequency, as well as how these natural hazards interact with the exposure and vulnerability of assets. In recent years, a great number of studies highlight that ongoing sea-level rise will expose the coastal area to greater risk (Hinkel et al. 2014); while more frequent extreme weather events will enhance the impact of sea-level rise on the coast. However, the risk modelling of natural hazards to power grid assets is mainly studied on a local scale, with little attention has been paid to the exposure of electricity infrastructure at the detailed asset level (Dawson et al. 2018; Arrighi et al. 2021); while many existing studies make generalized assumptions on infrastructure density when modelling the infrastructure risk (Koks et al. 2019).

To fill these gaps, we present the first estimate of exposure and risk of power grids in South-eastern and Eastern Asia to tropical cyclones (wind speed only) and coastal floodings. In this paper, we introduce detailed electricity infrastructure asset maps from collaborative community map – OpenStreetMap (OSM) and broadly-collected government power grid maps, state-of-the-art global hazard maps, and various vulnerability curves of wind and flooding for different types of electricity infrastructure into risk modelling. Strengthening the electricity infrastructure to withstand natural hazards takes priority, it is also important to customers and operators of other infrastructure systems, who rely heavily on electricity. The assessment provides better risk information – the annual repairing costs of electricity infrastructure damaged by natural hazards – which will help to improve power grid design and planning against natural hazards, and further make power grids resilient and sustainable.

How to cite: Ye, M., Koks, E., Ward, P., Bloemendaal, N., and Nirandjan, S.: Risk analysis of natural hazards to power grids in Southeast and East Asia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-895, https://doi.org/10.5194/egusphere-egu23-895, 2023.

In this study we present what environment and infrastructure factors should be considered when evaluating geographical accessibility to medical services in developing countries.

Access to medical services is a versatile and complex concept and includes geographical, temporal, financial and other factors. Geographical factors are considered as the basis for access to medical services. Geographical access to medical services means deployment of medical institutions within certain radius or time required for travel to the medical institution and the number of services compared to the number of potential customers. The World Health Organization gives preference to the travel time needed to reach the service compared to the distance to evaluate geographical accessibility.

As a rule, while researching the geographical access to medical services in the world, it is generally accepted that every person is in the same average travel conditions and has appropriate transportation means. A change in the travel time, such as weather conditions, road and transport availability and other variable factors, are not taken into consideration.

However, under the global warming conditions, the frequency and intensity of extreme hydrometeorological events are significantly increased and are likely to be increased further in future. Rain, snowfall, extreme temperature, hurricanes, etc. create emergencies, affect travel time and, thus, hamper geographical access. Moreover, in high mountainous regions, there are frequent landslides, mudflows, rockfalls, avalanches, etc. Developed countries are more willing to respond to such challenges and are quick to react. However, it is a significant problem in developing countries and often the rural areas are torn apart and isolated for many hours and even whole winter.

Thus, when evaluating geographical accessibility to medical service in developing countries it is crucial to take into account the risks of extreme geological and hydrometeorological phenomena on the roads, availability of road infrastructure, transport system. This approach will enable detailed modelling of real problems and their inclusion in the health care development plan. This can bring significant social-economic benefits.

This work was supported by Shota Rustaveli National Science Foundation of Georgia (SRNSFG), Grant № FR-19-14993.

How to cite: Kvirkvelia, B., Elizbarashvili, S., Kimeridze, M., Khuntselia, T., and Chikhradze, N.: Evaluation of the Geographical Accessibility to Medical Services, Taking into Account the Environmental and Infrastructural Factors, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4238, https://doi.org/10.5194/egusphere-egu23-4238, 2023.

In the aftermath of extreme weather events, disruptions to basic services, such as access to healthcare, electricity, and mobility, may severely impact the functioning of society.  As both infrastructure investments and occurrence of extreme weather and climate events are at an all-time high, critical infrastructures are more exposed than ever to such adverse phenomena. While societal impacts of basic service disruptions can be substantial and widely felt, this aspect of risk is rarely captured in classic risk assessments. In this contribution, we are shedding light on this wider, socio-technical dimension of natural hazard-induced risks, in a globally consistent manner.

For a selection of countries differing in size, world region, population density and income group, we compute spatially explicit patterns of basic service disruptions (access to power, healthcare, education, mobility and telecommunications) caused by historically observed tropical cyclone and flood events, and repeat the assessment with events commensurate with climate chance projections. To this end, we use the open-source risk assessment platform CLIMADA [1] and a bespoke network modelling approach relying on real-world infrastructure and population data [2]. We highlight geographic risk hotspots and demonstrate the importance of considering system interdependencies and cascading failures as opposed to static damage estimates to capture infrastructure risks from a human-centric perspective. Further, we study the influence of (country-specific) infrastructure network characteristics to develop heuristics (“rules of thumb”) of determinants which either perpetuate failure cascades or contribute to resilience.

First results indicate, among others, that i) basic services which heavily rely on supporting infrastructure (such as healthcare and education access) are more likely to be disrupted, ii) floods cause different service disruption patterns than strong winds, iii) locations where service disruptions are experienced may diverge from locations with highest hazard intensities.

[1] Aznar-Siguan, G. and D.N. Bresch (2019) CLIMADA v1: A Global Weather and Climate Risk Assessment Platform. Geoscientific Model Development 2 (7): 3085–9. https://doi.org/10.5194/gmd- 12-3085-2019 

[2] Mühlhofer, E., E. E. Koks, C. M. Kropf, G.  Sansavini and D. N. Bresch. (in review). “A Generalized Natural Hazard Risk Modelling Framework for Infrastructure Failure Cascades.” https://doi.org/10.31223/X54M17 

How to cite: Mühlhofer, E., Bresch, D. N., and Koks, E. E.: Flood and Wind-Induced Basic Service Disruptions across the Globe - A Modelling Approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5573, https://doi.org/10.5194/egusphere-egu23-5573, 2023.

Current effects of emergencies on human be amplified as extreme weather and outbreaks of epidemic disease increasing. The transport system is crucial for daily life and threatened heavily by these emergencies. Despite advances in emergency management for transportation, we still lack an integrated framework to examine the impact of transport system under different types of extreme event.

First, we develop a failure model to study the effect of floods on road networks; the result covers 90.6% of road closures and 94.1% of flooded streets resulting from Hurricane Harvey. We study the effects of floods on road networks in China and the United States, showing a discontinuous phase transition, indicating that a small local disturbance may lead to a large-scale systematic malfunction of the entire road network at a critical point.

Second, we propose an integrated approach to quantitatively assess how floods impact the functioning of a highway system. This approach is illustrated with a case study of the Chinese highway network. The results show that or different global climate models, the associated flood damage to a highway system is not linearly correlated with the forcing levels of RCPs, or with future years.

Finally, we propose an analysis framework combining with Simulation of Urban Mobility (SUMO) simulator to evaluate the impact of the road transport system within an urban agglomeration from the views of structure and function under four types of emergencies: natural disaster, traffic accident, public health event and social security incident. Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is one of the regions with the highest degree of openness and the strongest economic vitality in China, with convenient transportation conditions. This method is applied to a case study of the GBA urban agglomeration in China. These results have critical implications for transport sector policies and can be used to guide highway design and infrastructure protection. These approaches can be extended to analyze other networks with spatial vulnerability, and it is an effective quantitative tool for reducing systemic disaster risk.

How to cite: Wang, W.: Modelling of emergencies on transportation system in the context of climate change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7083, https://doi.org/10.5194/egusphere-egu23-7083, 2023.

Natural hazards impact the closely webbed infrastructure networks that keep a modern society functional in its current form. A variety of critical infrastructure network (CIN) modelling methods is available to represent functions and purposes of CI networks on different levels of boundaries. A recurring challenge for all modelling approaches is the availability and accessibility of input and validation data. Those gaps constrain modellers to make assumptions for specific technical parameters. In other cases insular expert knowledge from one sector is extrapolated to other sectoral structures or even cross-sectorally applied to fill data gaps. Those assumptions lead to uncertainty and can potentially devalue a per se valuable CIN modelling method.

In the presented work, a schematized workflow for a CIN model generation is defined and the potentially needed input datasets are highlighted and categorised. This categorization features obvious CI data like infrastructure component locations, quantitative measures of the services they are supporting and the relation between natural hazard impacts, functionality thresholds and the degree of disruption to a CI structure. It also tackles less straight-forward relations such as  recovery times after disruptions, interdependencies among CIN and the redundancy of those interdependencies. Invariably, the availability of those datasets is tied to a CIN model’s performance, aptness for answering specific problems, and quality. A range of performance indicators are hence compiled including granularity, fidelity, accuracy, sensitivity and the ability to resemble cascading effects. The relation of these performance indicators and data availability are outlined. Finally, it is suggested to overcome the challenges of data scarcity with participatory methods, anonymized data sharing platforms for CI operators and event based datasets.

With this contribution, we aim to provide systematised orientation to fellow critical infrastructure network modellers on the diverse data needs throughout the modelling chain, from setting up a model to results validation, explore implications of data scarcity, and suggest mitigation strategies.

How to cite: Schotten, R., Mühlhofer, E., and Chatzistefanou, G. A.: Data Scarcity in Critical Infrastructure Network Modelling: Impacts on Model Performances and Mitigation Strategies, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15868, https://doi.org/10.5194/egusphere-egu23-15868, 2023.