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Failure of critical national infrastructures can cause disruptions with widespread economic impacts. To analyze these economic impacts, we present an integrated modeling framework that combines: (1) geospatial information on infrastructure assets/networks and the natural hazards to which they are exposed; (2) geospatial modeling of the reliance of businesses upon infrastructure services, in order to quantify disruption to businesses locations and economic activities in the event of infrastructure failures; and (3) multiregional supply-use economic modeling to analyze wider economic impacts of disruptions to businesses. The methodology is exemplified through a case study for the United Kingdom. The study uses geospatial information on the location of electricity infrastructure assets and local industrial areas, and employs a multiregional supply-use model of the UK economy that traces the impacts of floods of different return intervals across 37 subnational regions of the UK. The results show up to a 300% increase in total economic losses when power outages are included in the risk assessment, compared to analysis that just includes the economic impacts of business interruption due to flooded business premises. This increase indicates that risk studies that do not include failure of critical infrastructures may be underestimating the total losses.

Many drinking water utilities face immense challenges in supplying sustainable, drought-resilient services to households. Here we propose a quantified framework to perform drought risk analysis on ~5600 potable water supply utilities and evaluate the benefit of adaptation actions. We identify global hotspots of present-day and mid-century drought risk under future scenarios of climate change and demand growth (namely, SSP1-2.6, SSP3-7.0, SSP5-8.5). We estimate the mean rate of unsustainable or disrupted utility supply at 15% (interquartile range, 0–26%) and project a global increase in risk of between 30–45% under future scenarios. Implementing the most cost-effective adaptation action identified per utility would mitigate additional future risk by 75–80%. However, implementing the subset of cost-effective options that generate sufficient tariff revenue to provide a benefit-cost ratio that is greater than 1 would only achieve 5–20% of this benefit. The results underline the challenge of attracting the financing required to close the climate adaptation gap for water supply utilities.

Water utilities' supply systems are vulnerable to several climate‐related hazards, including droughts, floods and cyclones. Here we propose a generally applicable framework for conducting multi‐hazard risk assessments of water supply systems and use it to quantify the impact of present and future climate extremes on the national water supply network in Jamaica. The proposed framework involves stress‐testing a model of the system with a large set of spatially coherent drought, cyclone and pluvial and fluvial flood events to calculate the number of water users whose supplies would be disrupted during an event, that is, the Customer disruption days (CDD). We estimate the total multi‐hazard annual expected disruption to be approximately 5 days per year per utility customer under present conditions. This is increased by a factor of between 2 and 2.5 when end‐of‐century climate scenarios are propagated through the model. Our analysis shows that more high probability drought events lead to greater CDD compared with asset damage events. However, extreme asset damage events, despite manifesting over shorter timescales (days) compared to drought events (months), can lead to more widespread CDD. This quantified risk framework would allow utility managers to compare the risk of both asset damage‐ and water shortage‐induced disruptions via a common, decision‐relevant metric. However, applications to other utilities would require tailored hazard modeling approaches. The proposed risk assessment is intended to inform prioritization of infrastructure investments, ranging from asset protection to drought mitigation projects, with the goal of enhancing water supply resilience in the face of a changing climate. Key Points We propose a spatial multi‐hazard risk framework for analyzing present‐day and future climate risks to water users We propose the use of customer disruption days as a common metric for comparing different hazards impacts The framework can be used by decision makers to prioritize investments across asset protection against flooding and cyclones, and drought mitigation options

Ports are embedded in different networks, including the local critical infrastructure network, the regional hinterland transport network and the global maritime transport network. These networks are exposed to a variety of natural hazards, which cause disruptions that can propagate to other network components, resulting in wider supply chain losses. However, the risks of such indirect network disruptions, or systemic risks, are often not considered in risk analyses of ports. We propose a systemic risk framework for different networks interconnected through ports, and describe the state-of-the-art risk modelling approaches to quantify systemic risks. In addition, we present a port risk layering framework that can help identify how resilience against systemic risks can be improved. As climate change will likely increase the occurrence of natural hazards to ports and transport networks, efforts to enhance system-wide resilience should be considered, alongside port adaptation, to prevent exacerbation of supply chain losses in the future.

Localised erosion (scour) during flood flow conditions can lead to costly damage or catastrophic failure of bridges, and in some cases loss of life or significant disruption to transport networks. Here, we take a broad scale view to assess risk associated with bridge scour during flood events over an entire infrastructure network, illustrating the analysis with data from the British railways. There have been 54 recorded events since 1846 in which scour led to the failure of railway bridges in Britain. These events tended to occur during periods of extremely high river flow, although there is uncertainty about the precise conditions under which failures occur, which motivates a probabilistic analysis of the failure events. We show how data from the historical bridge failures, combined with hydrological analysis, have been used to construct fragility curves that quantify the conditional probability of bridge failure as a function of river flow, accompanied by estimates of the associated uncertainty. The new fragility analysis is tested using flood events simulated from a national, spatial joint probability model for extremes in river flows. The combined models appear robust in comparison with historical observations of the expected number of bridge failures in a flood event, and provide an empirical basis for further broad-scale network risk analysis.

There is increasing interest in solving partial differential equations (PDEs) by casting them as machine learning problems. Recently, there has been a spike in exploring Kolmogorov-Arnold Networks (KANs) as an alternative to traditional neural networks represented by Multi-Layer Perceptrons (MLPs). While showing promise, their performance advantages in physics-based problems remain largely unexplored. Several critical questions persist: Can KANs capture complex physical dynamics and under what conditions might they outperform traditional architectures? In this work, we present a comparative study of KANs and MLPs for learning physical systems governed by PDEs. We assess their performance when applied in deep operator networks (DeepONet) and graph network-based simulators (GNS), and test them on physical problems that vary significantly in scale and complexity. Drawing inspiration from the Kolmogorov Representation Theorem, we examine the behavior of KANs and MLPs across shallow and deep network architectures. Our results reveal that although KANs do not consistently outperform MLPs when configured as deep neural networks, they demonstrate superior expressiveness in shallow network settings, significantly outpacing MLPs in accuracy over our test cases. This suggests that KANs are a promising choice, offering a balance of efficiency and accuracy in applications involving physical systems.

We present the first comprehensive geolocated multi-modal transport database for the whole continent of Africa, the African Transport Systems Database (AfTS-Db), including road, rail, aviation, maritime and inland waterway networks. To do so, we created and standardized asset and network data across all transport modes, including inter-modal connections, attributes of road and rail corridors and estimated annual statistics for airports and ports. The African Transport Systems Database includes 234 airports including their airline routes, 179 maritime ports and their connections with each other, 132 inland ports and docking sites with river and lake connections, 4,412 railway stations connected across 99,373 kilometers of rail lines, and 1,004,512 kilometers of roads mainly comprised of all motorways, trunk roads, primary and secondary routes across Africa and some local roads that connect to other transport modes. The AfTS-Db provides key information for transport planning, resilience assessments, asset management and development of transport models and applications. Furthermore, we expect the data will also be of relevance for environmental, health, social and economic studies.

Critical infrastructure (CI) is exposed to natural hazards that may lead to the devastation of these infrastructures and burden society with the indirect consequences that stem from this. Fragility and vulnerability curves, which quantify the likelihood of a certain damage state and the level of damage of an element under varying hazard intensities, play a crucial role in comprehending, evaluating, and mitigating the damage posed by natural hazards to these infrastructures. To date, however, these curves for CI have been distributed across the literature instead of being accessible through a centralized database. This study, through a systematic literature review, synthesizes the state of the art of fragility and vulnerability curves for the CI assets of energy, transport, water, waste, telecommunication, health, and education in context of natural hazards and offers a unique physical vulnerability database. The publicly available centralized database that contains over 1510 curves can directly be used as input for risk assessment studies that evaluate the potential physical damage to assets due to flooding, earthquakes, windstorms, and landslides. The literature review highlights that vulnerability development has mainly focused on earthquake curves for a wide range of infrastructure types. The curves for windstorms have the second largest share in the database, but they are especially limited to energy curves. While all CI systems require more vulnerability research, additional efforts are needed for telecommunication, which is largely underrepresented in our database.

The economy and well-being of modern societies relies on complex and interdependent infrastructure systems to enable delivery of utilities and movement of goods, people and services. This complexity has resulted in an increased potential for cascading failures, whereby small scale initial failures in one system can result in events of catastrophic proportions across the wider network. Resilience and the emerging concept of resilience engineering within infrastructure are among the main concerns of those managing such complex systems. However, the disparate nature of resilience engineering development in various academic and industrial regimes has resulted in a diversity of definitions and characterisations. These are discussed in this paper, as are the commonalities between sectors and between different engineering disciplines. The paper also highlights the various methodologies used as part of resilience engineering implementation and monitoring, current practices including existing approaches and metrics, and an insight into the opportunities and potential barriers associated with these methodologies and practices. This research was undertaken for the Resilience Shift initiative to shift the approach to resilience in practice for critical infrastructure sectors. The programme aims to help practitioners involved in critical infrastructure to make decisions differently, contributing to a safer and better world.

We present an empirical study to systemically estimate flooding impacts, linking across scales from individual firms through to the macro levels in China. To this end, we combine a detailed firm-level econometric analysis of 399,356 firms with a macroeconomic input-output model to estimate flood impacts on China’s manufacturing sector over the period 2003–2010. We find that large flooding events on average reduce firm outputs (measured by labor productivity) by about 28.3% per year. Using an input-output analysis, we estimate the potential macroeconomic impact to be a 12.3% annual loss in total output, which amounts to 15,416 RMB billion. Impacts can propagate from manufacturing firms, which are the focus of our empirical analysis, through to other economic sectors that may not actually be located in floodplains but can still be affected by economic disruptions. Lagged flood effects over the following two years are estimated to be a further 5.4% at the firm level and their associated potential effects are at a 2.3% loss in total output or 2,486 RMB billion at the macro-level. These results indicate that the scale of economic impacts from flooding is much larger than microanalyses of direct damage indicate, thus justifying greater action, at a policy level and by individual firms, to manage flood risk.