Transport infrastructure is exposed to natural hazards all around the world. Here we present the first global estimates of multi-hazard exposure and risk to road and rail infrastructure. Results reveal that ~27% of all global road and railway assets are exposed to at least one hazard and ~7.5% of all assets are exposed to a 1/100 year flood event. Global Expected Annual Damages (EAD) due to direct damage to road and railway assets range from 3.1 to 22 billion US dollars, of which ~73% is caused by surface and river flooding. Global EAD are small relative to global GDP (~0.02%). However, in some countries EAD reach 0.5 to 1% of GDP annually, which is the same order of magnitude as national transport infrastructure budgets. A cost-benefit analysis suggests that increasing flood protection would have positive returns on ~60% of roads exposed to a 1/100 year flood event.

Limited data on global power infrastructure makes it difficult to respond to challenges in electricity access and climate change. although high-voltage data on transmission networks are often available, medium-and low-voltage data are often non-existent or unavailable. this presents a challenge for practitioners working on the electricity access agenda, power sector resilience or climate change adaptation. Using state-of-the-art algorithms in geospatial data analysis, we create a first composite map of the global power system with an open license. We find that 97% of the global population lives within 10 km of a MV line, but with large variations between regions and income levels. We show an accuracy of 75% across our validation set of 14 countries, and we demonstrate the value of these data at both a national and regional level. The results from this study pave the way for improved efforts in electricity modelling and planning and are an important step in tackling the Sustainable Development Goals.

Critical infrastructure networks are geographically distributed systems spanning multiple scales. These networks are increasingly interdependent for normal operations, which causes localized asset failures from natural hazards or man-made interference to propagate across multiple networks, affecting those far removed from an initiating failure event. This paper provides methodology to identify such failure propagation effects by quantifying the spatial variability in magnitude, frequency, and disruptive reach of failures across national infrastructure networks. To achieve this, we present methodology to combine functionally interdependent infrastructure networks with geographic interdependencies by simulating complete asset failures across a national scale grid of spatially localized hazards. A range of metrics are introduced to compare the systemic vulnerabilities of infrastructure systems and the resulting spatial variability in both the potential for initiating widespread failures and the risk of being impacted by distant hazards. We demonstrate the approach through an application in New Zealand of infrastructures across the energy (electricity, petroleum supply), water and waste (water supply, wastewater, solid waste), telecommunications (mobile networks), and transportation sectors (passenger rail, ferry, air, and state highways). In addition to identifying nationally significant systemic vulnerabilities, we observe that nearly half (46%) of the total disruptions across the simulation set can be attributed to network propagation initiated asset failures. This highlights the importance in considering interdependencies when assessing infrastructure risks and prioritizing investment decisions for enhancing resilience of national networks.

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Flood damage assessments provide critical insights on processes controlling building damage and loss. Here, we present a novel damage assessment approach to develop an empirical residential building damage database from five flood events in New Zealand. Object-level damage data was collected for flood hazard and building characteristics, along with relative building component and sub-components damage ratios. A Random Forest Model and Spearman's Rank correlation test were applied to analyse damage data variable importance and monotonic relationships. Model and test results reveal flood inundation depth above first finished floor level is highly important and strongly correlated with total building damage ratios while flow velocity is important for structure component damage. Internal finishes components contribute highly to total building damage ratios as higher value sub-component materials are susceptible to direct damage from water contact and indirect damage during repair. The empirical damage data has several implications for damage model development due to the limited heterogeneity of flood hazard intensities and building attributes observed. Extending empirical damage data with synthetic damage data in future would support development of more representative object-specific damage models to evaluate direct tangible damages for local contexts.

Lifeline utilities and critical infrastructures are becoming increasingly interactive and dependent on one another for normal operation. With a natural disaster or disruptive event, these dependencies can be studied under stressed conditions. To replicate events and inform future simulations, such dependencies can be quantified in both magnitude and direction. This paper builds on recent efforts by proposing a new dependency index methodology that gives importance to the direction of dependency between coupled infrastructures and equally weighting the multiple dependencies that may be realized across a variety of lag times. The effectiveness of this methodology is presented as a case study for the 22 February 2011 earthquake experienced in Christchurch, New Zealand. Dependencies are quantified for a range of critical infrastructure couplings, which provide insight into the future application of these results and the requirement for integration with qualitative studies to accurately inform interdependency models.

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The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. This paper is a product of the Global Facility for Disaster Reduction and Recovery, Climate Change Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world.