School of Geography and the Environment

There is growing recognition of the risk from wildfires (Jones et al., 2020), which is a threatening dimension of climate-related risks (Zscheischler et al., 2018). A number of wildfire risk assessment frameworks have been developed, though these tend to have been focussed at national and regional scales (Fiorucci, 2008, Scott, 2013, Thompson, 2011, 2016). Global wildfire models have been established and wildfire has been incorporated in the land surface schemes of Earth system models (Krause et al., 2014). There are also growing spatial datasets of wildfire observations, with accompanying statistical analysis (Parisien and Moritz, 2009). However, so far there has been limited analysis of the potential systemic impacts of wildfires and they ways in which they may compound with other climate-related hazards, including heat waves and droughts.

This DPhil project will take a spatial systems approach to fully appraising wildfire risks and the ways in which they may propagate through society and the economy. We are not aiming to generate a new wildfire model - we will use model simulations and projections from other sources, including the Inter-Sectoral Impact Model Intercomparison datasets and other sources. We will combine these with wildfire observations, to build up a statistical picture of wildfire hazard and supplement that with future projections. That will provide a wildfire hazard layer and associated uncertainties. The main focus of the research will be to properly characterise the impact of wildfires on communities, infrastructure and the economy. The direct damages from wildfires are documented, at least in countries that have high levels of insurance coverage. However, there is very little knowledge of the wider effects of wildfire on economic activities and how quickly communities recover. Wildfire damage to infrastructure can have widespread impacts on people and economic activities. Repairing damaged infrastructure can be a considerable burden on public finances. This study will explore these and other dimensions of wildfire impacts and develop datasets and models for damage assessment, including both direct and indirect damages. We aim to do this in a way which is scalable to global scales, so that we can generate much improved global estimates of the risks from wildfires and estimates of how these risks may change in the context of climate change, land use change, economic development and different wildfire management strategies.

The project will involve a combination of statistical analysis and geospatial modelling. It will suit students with a strong background in environmental sciences, engineering or another quantified subject. Students should be able to demonstrate aptitude for quantified geospatial analysis, and enthusiasm to address real-world problems of great policy significance.

Candidates for this project from a natural sciences background would be eligible to apply for funding from Oxford University's NERC Doctoral Training Partnership. Successful UK applicants will be eligible for full or part funding. Overseas applicants in need of financial support are encouraged to apply for one of Oxford's several doctoral scholarship schemes for UK or overseas students. Closing dates apply on these schemes and students are encouraged to apply early. Applications are made through the School of Geography and the Environment.

References:

• Fiorucci, P., Gaetani, F. and Minciardi, R. (2008) Development and application of a system for dynamic wildfire risk assessment in Italy. Environ. Modell. Softw., 23: 690-702.
• Jones, M.W., Smith, A., Betts, R., Canadell, J.G., Prentice, I.C. and Le Quéré, C. (2020) Climate change increases the risk of wildfires. ScienceBrief Review, 116: 117.
• Krause, Andreas, et al. (2014) The sensitivity of global wildfires to simulated past, present, and future lightning frequency. Journal of Geophysical Research: Biogeosciences, 119(3): 312-322.
• Parisien, Marc-André, and Max A. Moritz. 2009. Environmental controls on the distribution of wildfire at multiple spatial scales. Ecological Monographs, 79.1 (2009): 127-154.
• Scott, J. H., Thompson, M. P. and Calkin, D. E. 2013. A wildfire risk assessment framework for land and resource management. Gen. Tech. Rep. RMRS-GTR-315. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 83 p. https://doi.org/10.2737/rmrs-gtr-315
• Thompson, M. P., Bowden, P., Brough, A., Scott, J. H., Gilbertson-Day, J., Taylor, A., Anderson, J. and Haas, J. R. 2016. Application of wildfire risk assessment results to wildfire response planning in the southern Sierra Nevada, California, USA. Forests 7, 64.
• Thompson, M. P., Calkin, D. E., Finney, M. A., Ager, A. A. and Gilbertson-Day, J. W. 2011 Integrated national-scale assessment of wildfire risk to human and ecological values. Stochastic Environmental Research and Risk Assessment, 25, 761-780.
• Zscheischler, J., Westra, S., Van Den Hurk, B.J., Seneviratne, S.I., Ward, P.J., Pitman, A., AghaKouchak, A., Bresch, D.N., Leonard, M., Wahl, T. and Zhang, X. 2018. Future climate risk from compound events. Nature Climate Change, 8(6), pp.469-477.

Complex and systemic-scale risks will increase over the 21st century and threaten the well-being of millions of people, as well as national prosperity, security and competitiveness. Intensifying climate extremes, conflict, financial crises and the COVID-19 pandemic have illustrated the fragility of global systems to systemic risks. As evidenced by the COVID-19 pandemic, and the recent House of Lords Select Committee report (2021), current approaches to risk management at national and global scales are inadequate for responding to these risks.

The Oxford Martin Programme on Systemic Resilience aims to identify, appraise and advance solutions to managing systemic shocks in the 21st Century and thus improve the welfare of people and the resilience of the systems and institutions upon which they depend. The programme will examine where do complex crises fall through the cracks in our national and international systems, and how can crises be anticipated and targeted by interventions in order to enhance preparedness and system resilience. The focus is on shocks with a strong climate, social or environmental dimension, including climate, poverty and pandemics.

This DPhil project aims to complement, and become part of, the Oxford Martin Programme on Systemic Resilience. The question we wish to address is whether it is possible to develop better early warning indicators of systemic risks. What are the preconditions for systemic failures we can observe, for example based on the structure of networks that interconnect people, businesses and institutions? We will answer this question based on insights from general systems theory and network theory. Though there has been extensive research on systemic risks and early warning in the financial system (e.g. Billio et al., 2016, Tedeschi et al., 2020) this has seldom extended to examine the wide range of factors that might impact global systems and the real economy. We wish to understand whether there are indicators that can be monitored in real time that provide some early warning of systemic failures. These warnings may exist in trade flows and supply chains (e.g. based on our analysis of automatic ship tracking data (Verschuur et al. 2020)), may be based on monitoring of correlated climatic conditions (Gaupp et al. 2017), or on monitoring migration.

The research will involve in-depth literature review into theoretical understandings of systems failure and review of the empirical literature on the precursors and determinants of systemic failures, from a range of different disciplines. It will then involve collecting many different datasets and applying statistical methods to assess the extent to which these datasets are predictors of system failure and its consequences. Our aim is to develop a composite metric that can be used to inform anticipatory action. This will need to be tailored to a range of different contexts, e.g. humanitarian assistance, food security or financial system stability. It will build upon our extensive links with international organisations, central banks and humanitarian agencies.

The research will suit students with an interdisciplinary outlook. It will involve data analysis and advanced statistical methodologies, so will require a strong quantified background, but applicants will also need to demonstrate a capacity for deep conceptual thinking, along with a problem-solving attitude.

Applicants in need of financial support are encouraged to apply for one of Oxford's several doctoral scholarship schemes for UK or overseas students. Closing dates apply on these schemes and students are encouraged to apply early. Applications are made through the School of Geography and the Environment.

References:

• Billio, M., Casarin, R., Costola, M. and Pasqualini, A., 2016. An entropy-based early warning indicator for systemic risk. Journal of International Financial Markets, Institutions and Money, 45, pp.42-59.
• Gaupp, F. Pflug, G. Hochrainer-Stigler, S. Dadson, S.J. and Hall, J.W. Dependency of crop production between global breadbaskets: A copula approach for the assessment of global and inter-regional risk pools, Risk Analysis, 37(11) (2017): 2212-2228. DOI: 10.1111/risa.12761.
• House of Lords, Preparing for Extreme Risks: Building a Resilient Society. House of Lords Select Committee on Risk Assessment and Risk Planning. December 2021.
• Tedeschi, G., Caccioli, F. and Recchioni, M.C., 2020. Taming financial systemic risk: models, instruments and early warning indicators. Journal of Economic Interaction and Coordination, 15(1), pp.1-7.
• Verschuur, J., Koks, E. and Hall, J.W. Port disruptions due to natural disasters: insights into port and logistics resilience. Transportation Research Part D: Transport and Environment, 85(2020): 102393. DOI: 10.1016/j.trd.2020.102393.

Planning infrastructure, in all parts of the world, involves difficult choices about where and what to build to provide reliable, resilient services whilst minimising costs and negative impacts on people and the environment. Versions of this spatial allocation problem exist in many situations, for example in planning power grids, water supply systems, communications or transport networks to meet economic or development objectives, including many of those framed by the sustainable development goals (SDGs). New spatial datasets derived from satellites, sensors and crowdsourcing are providing information that can enable better mapping and navigation of the trade-offs associated with spatial allocation.

This project will explore the potential for using new data sources to inform the construction of large-scale models of infrastructure systems and the planning of new infrastructure. For example, we have carried out original research using AIS ship tracking data (Verschuur et al., 2020) to improve our understanding of transport networks and trade; we are now keen to advance methods for tracking road vehicles and trains. This information could be supplemented with crowdsourcing to provide new information on the condition of infrastructure assets and services. By combining these data sources, it should be possible to make a significant next step in the modelling and monitoring of infrastructure networks anywhere on Earth.

Given better data about infrastructure asset location, service provision and user needs, it will be possible to better target interventions to improve these systems. We have made significant progress in methodologies for this spatial allocation problem.

For example, in Bangladesh we have used new geospatial datasets to optimize the location of drinking water infrastructure (Garcia et al., 2021) - a version of this method could be up-scaled to much larger areas. In rural Bangladesh there has been a proliferation of private tube wells, demonstrating some of the potential for market forces in infrastructure service provision. One framing of the problem, for water or other utilities, would consider how market forces can be combined with targeted development assistance, public investment, and/or regulation for minimum universal service provision to provide infrastructure systems that leave no-one behind.

The electrification of transport is another possible application, widely regarded as an opportunity for developing countries to 'leapfrog' fossil-fuel dependent transport and associated infrastructure networks, by co-developing renewable energy supplies, vehicle charging, and transport fleet. There are however many different versions of how such systems might develop (e.g. with centralised electricity grids, or with micro-grids). What system is viable depends, in part, on local context (population density, building density, wealth, existing infrastructure), but is also subject to other uncertainties, such as the relative price of technologies and the business models that are adopted for service provision.

We have developed unique datasets of road infrastructure globally (Koks et al., 2019) and methodology for simulating electricity transmission and distribution networks all over the world. This is coupled with population datasets for analysing energy and transport demand and global datasets of potential for renewable energy supply. We propose to combine these datasets with different scenarios for costs and business models to explore and optimise scenarios for roll-out of these technologies.

These are just two examples of the sorts of problems that could be addressed with methodologies for spatial allocation and optimisation (Faiz and Krichen, 2012). We expect that other opportunities will materialise during the research, so the thesis will combine investigation of novel datasets with development of methods and case studies. Overall, the objective of the research would be to develop a broad framework to characterise different infrastructures and their relationship with the space and people around them.

The project will involve application of methods for spatial optimisation. The derived solutions need to take account of local economic, societal and governance conditions, so the student should also study these important contextual issues. It will suit students with a strong quantified background (e.g. engineering, economics, physics, geostatistics) but also a good appreciation of the wider societal context of infrastructure service provision.

Candidates for this project from an engineering, mathematics or physical sciences background would be eligible to apply for funding from Oxford University's EPSRC Doctoral Training Partnership. Successful UK applicants will be eligible for full or part funding. Overseas applicants in need of financial support are encouraged to apply for one of Oxford's several doctoral scholarship schemes for UK or overseas students. Closing dates apply on these schemes and students are encouraged to apply early. Applications are made through the School of Geography and the Environment.

References

• Faiz, S. and Krichen, S., 2012. Geographical information systems and spatial optimization. CRC Press.
• Flanagan, S. V., Johnston, R. B. and Zheng, Y. (2012). Arsenic in tube well water in Bangladesh: health and economic impacts and implications for arsenic mitigation. Bulletin of the World Health Organization, 90, 839-846.
• Garcia, O.R., Hoque, S.F., Ford, L., Salehin, M., Alam, M.M., Hope, R. and Hall, J.W. Optimizing rural drinking water supply infrastructure to account for spatial variations in groundwater quality and household welfare in coastal Bangladesh. Water Resources Research, 57(8) (2021): e2021WR029621. DOI: 10.1029/2021WR029621
• Koks, E.E., Rozenberg, J., Zorn, C., Tariverdi, M., Vousdoukas, M., Fraser, S.A., Hall, J.W., Hallegatte, S. A global multi-hazard risk analysis of road and railway infrastructure assets. Nature Communications, 10(1) (2019): 2677. DOI: 10.1038/s41467-019-10442-3.
• Verschuur, J., Koks, E. and Hall, J.W. Port disruptions due to natural disasters: insights into port and logistics resilience, Transportation Research Part D: Transport and Environment, 85(2020): 102393. DOI: 10.1016/j.trd.2020.102393.

Infrastructure systems that deliver essential services to society (e.g. energy, water, transport and telecommunications) are increasingly regarded as being cyber-physical systems, as they are controlled by digital networks and depend upon software and digital communication systems. The risks to these systems have been widely studied, but from rather different perspectives. There has been extensive research, much of it by our group, on physical risks to infrastructure networks, with a focus on weather-related extremes (Koks et al., 2019, Lamb et al., 2019) but also including terrorist threats (Oughton et al., 2019). Meanwhile, there has been extensive research on questions of cyber security for infrastructure networks, for example relating to the security of the Internet of Things (IoT). Our aim in this project is to bring these perspectives together.

In the first instance the focus will be on modelling the networks of interdependent electricity and telecommunications systems. We have a fairly complete model of electricity transmission and distribution networks in Britain, and recently as part of research with the National Infrastructure Commission we coupled this with a representation of telecommunications networks in Britain.

The DPhil project will involve modelling of electricity and digital communications networks (including SCADA systems), which we will seek to validate with data on faults in the electricity and telecommunications networks. This will be used to model possible interdependent and cascading failures. The analysis will be used to identify how these interdependent networks can be made more resilient. For example, what is the potential benefit of increased connectivity or backup capacity within the network? We also wish to examine how technological trends (like electrification of transport and the proliferation of renewable energy supply technologies) could impact the resilience of infrastructure networks.

The project will therefore involve using and adapting existing simulation models and creating new models of infrastructure systems and development of methods for vulnerability analysis and optimisation. It will suit students from any quantified background, including engineering, mathematics and the physical sciences. Students should be able to demonstrate aptitude for computer modelling and enthusiasm to address real-world problems of great policy significance.

Candidates for this project from an engineering of physical sciences background would be eligible to apply for funding from Oxford University's EPSRC Doctoral Training Partnership. Successful UK applicants will be eligible for full or part funding. Overseas applicants in need of financial support are encouraged to apply for one of Oxford's several doctoral scholarship schemes for UK or overseas students. Closing dates apply on these schemes and students are encouraged to apply early. Applications are made through the School of Geography and the Environment.

References:

• Koks, E., Pant, R., Thacker, S., Hall, J.W. Understanding business disruption and economic losses due to electricity failures and flooding, International Journal of Disaster Risk Science, 10(2019): 421-438. DOI:10.1007/s13753-019-00236-y
• Lamb, R., Garside, P., Pant, R. and Hall, J.W. A network-scale analysis of the risk of railway bridge failure from scour during flood events in Britain. Risk Analysis, 39(11) (2019): 2457-2478. DOI: 10.1111/risa.13370.
• Oughton, E., Ralph, D., Leverett, E., Pant, R., Thacker, S., Hall, J.W., Copic, J., Ruffle, S. and Tuveson, M. Stochastic counterfactual analysis for the vulnerability assessment of cyber-physical attacks on electricity distribution infrastructure networks, Risk Analysis, 39(9) (2019): 2012-2031. DOI: 10.1111/risa.13291.
• Thacker, S., Barr, S., Pant, R., Hall, J.W., and Alderson, D. Geographic hotspots of critical national infrastructure. Risk Analysis, 11(1) (2018): 22-33. DOI: 10.1111/risa.12840.
• Thacker, S., Kelly, S., Pant, R. and Hall, J.W. Evaluating the benefits of adaptation of critical infrastructures to hydrometeorological risks. Risk Analysis, 38(1) (2018): 134-150. DOI: 10.1111/risa.12839.
• Thacker, S., Hall, J.W. and Pant, R. Preserving key topological and structural features in the synthesis of multi-level electricity networks for modeling of resilience and risk. Journal of Infrastructure Systems, ASCE, 24(1) (2018): 04017043. DOI: 10.1061/(ASCE)IS.1943-555X.0000404
• Thacker, S., Pant, R. and Hall, J.W. System-of-systems formulation and disruption analysis for multi-scale critical national infrastructures, Reliability Engineering and Systems Safety, 167(2017): 30-41. DOI: 10.1016/j.ress.2017.04.023.

There is growing recognition of the urgency and importance of adapting to climate change, especially in infrastructure networks which provide essential services and last for a long time (Hallegatte et al., 2019). In recent years, our OPSIS research group has made rapid progress in the development of methods for risk analysis of infrastructure systems at a global scale (Hall et al., 2019, Koks et al., 2019). This analysis combines climate hazard layers (e.g. floods, hurricanes, droughts) with high resolution data on infrastructure exposure and vulnerability and economic modelling of the impacts of infrastructure failure on supply chains and the economy. Analysis of this type provides estimates of the overall scale of climate risks to infrastructure and helps to pinpoint locations where climate risks are greatest, which should be a priority for adaptation. The next step is to use this information to inform the type, scale and location of adaptation planning. Should infrastructure networks be strengthened, relocated or made less vulnerable with the help of nature-based solutions (NbS)? Is it more efficient to wait for infrastructure to be rebuilt before factoring in adaptation? Answering these questions involves matching adaptation solutions to particular locations and then estimating what might be a reasonable level of investment in adaptation. In principle this process is well understood, but applying the methods of cost-benefit analysis and spatial optimization on a very large scale is a vast challenge. Designing adaptation pathways through time involves understanding the lifecycle of infrastructure assets and predicting future infrastructure needs. This DPhil project aims to address that challenge, by developing methodology to systematically explore adaptation options for different types of infrastructure networks across different geographies, and then match adaptation options to particular locations. By combining with information on the costs of individual adaptation options, it will be possible to come up with a much better estimate of the costs of adaptation than has previously been achieved (Global Commission on Adaptation, 2020, Neuman et al., 2021).

A critical aspect of this research will be dealing with the many inevitable uncertainties in the analysis. The project will therefore have a particular focus upon uncertainty and sensitivity analysis (Saltelli et al., 2004). Sensitivity analysis will help to identify the factors that are most influential in the model predictions. Is uncertainty in climate model projections most important, or are adaptation performance and cost estimates more significant? Conducting uncertainty and sensitivity analysis on a model of this scale will be a considerable challenge because of the very high dimensionality and computational expense of each model run. It will also be necessary to analysis the spatial statistics of the various factors that influence model outputs. We also wish to examine the robustness of solutions to deep uncertainty to establish whether there are categories of adaptation option that are more robust. This will involve methodologies for decision making under deep uncertainty (Marchau et al., 2019).

The project will involve a combination of geospatial analysis, decision analysis and multi-objective optimisation. It will suit students from any quantified background, including environmental sciences, engineering or economics. Students should be able to demonstrate aptitude for computer modelling and geospatial analysis, and enthusiasm to address real-world problems of great policy significance.

Candidates for this project from an engineering of physical sciences background would be eligible to apply for funding from Oxford University's EPSRC Doctoral Training Partnership. Successful UK applicants will be eligible for full or part funding. Overseas applicants in need of financial support are encouraged to apply for one of Oxford's several doctoral scholarship schemes for UK or overseas students. Closing dates apply on these schemes and students are encouraged to apply early. Applications are made through the School of Geography and the Environment.

References:

• Global Commission on Adaptation (2020) Adapt Now: A global call for leadership on climate resilience.
• Hall, J.W., et al. (2019) Adaptation of Infrastructure Systems: Background Paper for the Global Commission on Adaptation. Oxford: Environmental Change Institute, University of Oxford. December 2019, 64pp.
• Hallegatte, S., Rentschler, J. and Rozenberg, J. (2019) Lifelines: The Resilient Infrastructure Opportunity. Washington, DC: World Bank.
• Koks, E.E., Rozenberg, J., Zorn, C., Tariverdi, M., Vousdoukas, M., Fraser, S.A., Hall, J.W., Hallegatte, S. (2019) A global multi-hazard risk analysis of road and railway infrastructure assets. Nature Communications, 10(1): 2677. DOI: 10.1038/s41467-019-10442-3.
• Marchau, et al. (2019) Decision Making under Deep Uncertainty: from theory to practice. Springer.
• Neumann, J.E., P. Chinowsky, J. Helman, M. Black, C. Fant, K. Strzepek and J. Martinich (2021) Climate effects on US infrastructure: the economics of adaptation for rail, roads, and coastal development. Climatic Change, 166(44).
• Saltelli, et al. (2004) Sensitivity Analysis in Practice: a guide to assessing scientific models. Wiley.

Global energy networks are undergoing rapid transformations to meet growing needs for energy services and to rapidly transition away from fossil fuels. However, electric power networks are also vulnerable to the impacts of climate change. Transmission pylons and cables can be destroyed by hurricanes whilst substations can be flooded and thermo-electric power plants can be impacted by cooling water shortages (Hall et al., 2019). Climate change is also impacting demand for electricity supplies.

There have been several studies of climate impacts for power networks at local and national scales (e.g. Fant et al. 2020, Koks et al. 2019). Meanwhile, there is growing capability for energy systems modelling and network analysis at global scales, assisted by the existence of global power plant databases and Gridfinder (Arderne et al., 2020) a global model of transmission networks. There are also more country and continental specific datasets of detailed spatial representation of electricity transmission systems (HIFLD 2022, Hörsch et al., 2018). By combining these datasets with statistical and model-based data on climate-related hazards, it will be possible to generate new estimates of the climate risks to power networks on a global scale and the economic implications of their failure. This will also help to identify hotspots of vulnerability and prioritize locations for network adaptation. Satellite night-time lights datasets and documented records of power failures provide the possibility to calibrate and validate large-scale models of power network failure.

Looking into the future, the type and location of power generation, transmission and distribution networks will change significantly. The next step in the research will be to develop methods for simulating how the energy systems may evolve into the future, given changing patterns of demand, investment and energy technologies. The research will combine socio-economic and technology scenarios to develop a range for scenarios for future energy networks. Future climate scenarios will be imposed upon these networks to analysis their possible exposure to increasing climatic extremes.

The project will involve a combination of geospatial analysis and energy systems modelling. It will suit students with a strong background in engineer, physics or another quantified subject. Students should be able to demonstrate aptitude for computer modelling and geospatial analysis, and enthusiasm to address real-world problems of great policy significance.

Candidates for this project from an engineering of physical sciences background would be eligible to apply for funding from Oxford University's EPSRC Doctoral Training Partnership. Successful UK applicants will be eligible for full or part funding. Overseas applicants in need of financial support are encouraged to apply for one of Oxford's several doctoral scholarship schemes for UK or overseas students. Closing dates apply on these schemes and students are encouraged to apply early. Applications are made through the School of Geography and the Environment.

References:

• Arderne, C., Zorn, C., Nicolas, C. et al. Predictive mapping of the global power system using open data. Sci Data, 7, 19 (2020). https://doi.org/10.1038/s41597-019-0347-4
• Fant, C., B. Boehlert, K. Strzepek, P. Larsen, A. White, S. Gulati, Y. Li and J. Martinich (2020) Climate change impacts and costs to U.S. electricity transmission and distribution infrastructure. Energy, 195, 116899 (doi: 10.1016/j.energy.2020.116899)
• Hall, J.W., et al. (2019) Adaptation of Infrastructure Systems: Background Paper for the Global Commission on Adaptation. Oxford: Environmental Change Institute, University of Oxford. December 2019, 64pp.
• Homeland Infrastructure Foundation Level Data (2022). https://hifld-geoplatform.opendata.arcgis.com/datasets/geoplatform::transmission-lines/about
• Hörsch, J., Hofmann, F., Schlachtberger, D., & Brown, T. (2018). PyPSA-Eur: An open optimisation model of the European transmission system. Energy Strategy Reviews, 22, 207-215.
• Koks, E., Pant, R., Thacker, S., Hall, J.W. (2019) Understanding business disruption and economic losses due to electricity failures and flooding, International Journal of Disaster Risk Science, 10(2019): 421-438. DOI:10.1007/s13753-019-00236-y

Transport networks, which include road, rail, ports and airports, are exposed to the impacts of natural hazards and climate change (Koks et al., 2019, World Bank, 2019). These networks carry goods and people, whose mobility is essential for the smooth functioning of global supply chains. The lack of resilience of global supply chains has been illustrated by the impacts of the covid-19 pandemic (Verschuur et al., 2021) and by the war in Ukraine. Whilst some points of vulnerability within global supply chains (e.g. ports at risk from climatic extremes: Verschuur et al., 2022) can be identified, researchers have not yet achieved a credibly complete point-to-point mapping of global supply chain networks, which links land, maritime and airport transport modes with locations of supply and demand. That will be one of the objectives of this DPhil. It will be achieved by using a combination of data sources (building upon our previous research with AIS shipping data: Verschuur et al., 2020), integrating other novel datasets on the flow of goods and services and the locations of production sites.

Supply chain networks are rapidly expanding to meet growing demands for mobility and trade, and will adapt to reflect changing economic structure and the phasing out of fossil fuels. Thus, to fully understand the scale of future risks to supply chain networks, we need to be able to able to predict where they will exist in the future, as well as using geospatial information on where they exist at the moment. This is a complex challenge, as there is a two-way relationship between the geography of economic activity and the provision of transport infrastructure. By using a combination of trade projections and big datasets of the factors that influence transport infrastructure provision, we aim to project realistic patters of future transport networks and supply chains.

The aim of this research is to develop methods for projecting where transport and supply chain networks will be located across the globe, under different scenarios of economic development. The results of this new model development will be used to understand the changing future vulnerability of transport networks to the impacts of climate change.

The proposed research will take a combination of a model-based and empirical approach to understanding the relationship between infrastructure and economic development at broad scales. The model-based analysis may start with stylised models, for instance by exploring insights from New Economic Geography (NEG) models. Meanwhile, we will seek datasets that can be used to characterise spatial changes. The analysis will be used to understand future demands for infrastructure services and how patterns of economic development may evolve in future. The work will be applied to a large geographical region, such as a national-scale or multi-national scale to see how infrastructure developments can create positive and negative effects for different regions.

The project will involve computer model development, along with parameterization and validation using empirical data. Candidates must therefore be ready to take on a highly interdisciplinary analysis and modelling task. It will require a candidate with advanced computational and mathematical skills, coming from an engineering, economics or physical sciences background. Students should be able to demonstrate aptitude for computer modelling and enthusiasm to address real-world problems of great policy significance.

Candidates for this project from an engineering of physical sciences background would be eligible to apply for funding from Oxford University's EPSRC Doctoral Training Partnership. Successful UK and EU applicants will be eligible for full or part funding. Overseas applicants in need of financial support are encouraged to apply for one of Oxford's several doctoral scholarship schemes for UK or overseas students. Closing dates apply on these schemes and students are encouraged to apply early. Applications are made through the School of Geography and the Environment.

References:

• Koks, E., Pant, R., Thacker, S., Hall, J.W. (2019) Understanding business disruption and economic losses due to electricity failures and flooding. International Journal of Disaster Risk Science, 10(2019): 421-438. DOI:10.1007/s13753-019-00236-y
• World Bank (2019) Lifelines: the resilient infrastructure opportunity.
• Venables, A., Laird, J. and Overman, H. (2014) Transport investment and economic performance: Implications for project appraisal. (Department for Transport, 2014).
• Bird, J. H. and Venables, A.J. (2019) Growing a Developing City: A Computable Spatial General Equilibrium Model Applied to Dhaka. The World Bank.
• Lall, S. V. and Mathilde S. M. L. (2019) "Who Wins, Who Loses? Understanding the Spatially Differentiated Effects of the Belt and Road Initiative."
• Hall, J.W., Tran, M., Hickford, A.J. and Nicholls, R.J. (2016) The Future of National Infrastructure: A System of Systems Approach. Cambridge University Press, 2016.
• Verschuur, J., Koks, E. and Hall, J.W. (2021) Global economic impacts of COVID-19 lockdown measures stand out in high-frequency shipping data. PLOS ONE, 16(4): e0248818. DOI: 10.1371/journal.pone.0248818.
• Verschuur, J., Koks, E. and Hall, J.W. (2022) Ports' criticality in international trade and global supply-chains. Nature Communications, 13 (4351). DOI: 10.1038/s41467-022-32070-0.
• Verschuur, J., Koks, E. and Hall, J.W. (2020) Port disruptions due to natural disasters: insights into port and logistics resilience. Transportation Research Part D: Transport and Environment, 85(2020): 102393. DOI: 10.1016/j.trd.2020.102393.

Increasing frequency and severity of heatwaves is one of the most obvious consequences of climate change. This will impact people directly and will also have effects upon the built environment and infrastructure upon which we depend. There has been quite extensive research upon the impact of extreme heat on buildings and human comfort/health (e.g. Jenkins et al., 2014a), and analysis of specific instances of the impact of extreme heat on infrastructure systems, e.g. rail buckling (Mulholland and Feyen, 2021), road surfaces (Smoyer-Tomic et al., 2003), power transmission (Cadini et al., 2017) and underground metro systems (Jenkins at al., 2014b). However, there has not been a full global analysis of the risks of extreme temperatures for infrastructure networks. This is now becoming possible thanks to the assembly of global datasets of infrastructure assets and networks, assembled by the OPSIS team (Koks et al., 2019) and others. The research will combine (i) climatic information on temperature extremes (ii) geospatial analysis of the exposure of infrastructure assets and networks (iii) assessment of the vulnerability of these networks to temperature extremes, and (iv) quantification of the impacts of temperature-related infrastructure failures.

For the analysis of temperature extremes, particular attention will be paid to the spatial extent of heatwaves, given their potential to impact large areas at the same time. Statistical analysis of temperature extremes will use weather station observations and reanalysis data to assemble a global spatial catalogue of heatwave events. It may be possible to combine this analysis with climate attribution studies (Perkins-Kirkpatrick and Lewis, 2020) to understand the non-stationarity in the temperature record. Future spatial heatwave projections will be obtained from climate model outputs (e.g. CMIP6, CMIP7). To obtain a large ensemble of spatial heatwave events, it may be necessary to develop a statistical model of spatial extremes.

Analysis of the exposure of infrastructure assets will be based upon OPSIS's ongoing activities to assemble global infrastructure asset and network data. A review of temperature vulnerability functions will cover mechanisms including: road/runway surface softening, railway line buckling, overheating of mechanical and electrical equipment, transmission line sagging, etc. Where appropriate these functions will be modified to reflect local conditions, e.g. local temperature rating/standards of equipment.

The research will also examine the impacts of heatwaves on infrastructure demand, notably on electricity demand for cooling. This can place additional demand upon electricity supplies and power networks which combine with other heatwave effects to result in systemic failures. Given satisfactory progress, it may also be possible to examine coincident risks e.g. wildfires and droughts, and their impacts on cooling water availability, hydropower and inland waterway navigation.

The research will quantify the impacts of heatwave-related failures in terms of the numbers of infrastructure users who are impacted. We will also seek to quantify the economic impacts of heatwave-related infrastructure failures. There have already been several studies of the impacts of heat on economic production (Burke et al., 2015) but none of these have sought to disentangle the specific effects of infrastructure failures in heatwave. The output will be a global analysis of hotpots of infrastructure heatwave vulnerability, taking into account the degree of local adaptation, and probabilistic quantification of the scale of potential disruptions at present and in future scenarios.

Finally, the research will examine the potential benefits of future adaptations, examining a range of adaptation options, which may be applied to existing infrastructure or incorporated when infrastructure is replaced or upgraded.

The project will involve a combination of geospatial analysis and spatial statistics. It will suit students with a strong background in engineer, physics or another quantified subject. Students should be able to demonstrate aptitude for computer modelling and geospatial analysis, and enthusiasm to address real-world problems of great policy significance.

Candidates for this project from an engineering of physical sciences background would be eligible to apply for funding from Oxford University's EPSRC Doctoral Training Partnership. Successful UK applicants will be eligible for full or part funding. Overseas applicants in need of financial support are encouraged to apply for one of Oxford's several doctoral scholarship schemes for UK or overseas students. Closing dates apply on these schemes and students are encouraged to apply early. Applications are made through the School of Geography and the Environment.

References:

• Burke, M., Hsiang, S.M. and Miguel, E., 2015. Global non-linear effect of temperature on economic production. Nature, 527(7577), pp.235-239.
• Jenkins, K., Hall, J.W., Glenis, V., Kilsby, C.G., McCarthy, M., Goodess, C., Smith, D., Malleson, N. and Birkin, M. Probabilistic spatial risk assessment of heat impacts and adaptations for London, Climatic Change, 124(1-2) (2014a): 105-117.
• Jenkins, K., Gilby, M., Hall, J.W., Glenis, V., Kilsby, C.G. Implications of climate change for thermal discomfort on underground railways. Transportation Research Part D: Transport and Environment, 30 (2014b):1-9.
• Cadini, F., Agliardi, G.L. and Zio, E., 2017. A modeling and simulation framework for the reliability/availability assessment of a power transmission grid subject to cascading failures under extreme weather conditions. Applied energy, 185, pp.267-279.
• Koks, E.E., Rozenberg, J., Zorn, C., Tariverdi, M., Vousdoukas, M., Fraser, S.A., Hall, J.W., Hallegatte, S. A global multi-hazard risk analysis of road and railway infrastructure assets. Nature Communications, 10(1) (2019): 2677. DOI: 10.1038/s41467-019-10442-3.
• Mulholland, E. and Feyen, L. Increased risk of extreme heat to European roads and railways with global warming, Climate Risk Management, 34 (2021): 100365
• Perkins-Kirkpatrick, S.E. and Lewis, S.C., 2020. Increasing trends in regional heatwaves. Nature communications, 11(1), p.3357.
• Smoyer-Tomic, K.E., Kuhn, R. and Hudson, A., 2003. Heat wave hazards: an overview of heat wave impacts in Canada. Natural hazards, 28, pp.465-486.

Critical infrastructure networks of energy, transport, water, along with social infrastructure such as schools and hospitals are constantly under threat from extreme natural hazards which intensify with climate change (World Bank, 2019). While globally the impacts of these hazards are felt strongly, the most vulnerable regions of the world such as SIDS incur some of the largest losses from severe hazards due to lack of development and investment into climate resilience. For example, Hurricane Maria in 2017 destroyed 95% of Dominica's housing stock, 90% of the island was without electricity for about 4 months, and the economic losses were estimated to be 226% of Dominica's GDP (Government of the Commonwealth of Dominica, 2020). Integrating climate resilience with long-term sustainable development goals is therefore integral for SIDS. Given the scale of potential hazard impacts, the scale of climate adaptation investments needed for safeguarding very high infrastructure resilience might be unrealistic to finance in SIDS. Hence, the key challenge is to understand for how climate adaptation investments for infrastructures could be prioritised over space and time to guarantee varying levels of resilience, while meeting long-term SDG targets for infrastructure provision. This will be the objective of this DPhil.

Our previous research has shown that infrastructure directly or indirectly influences the attainment of 72% of all SDG targets (Thacker et al., 2019). Research has also show how long-term infrastructure planning metrics aligned with meeting SDGs could be done effectively in small islands (Adshead et al., 2019). A conceptual framework has also mapped high-level linkages between climate impacts to ecosystems and socio-economic sectors and their effects on meeting SDGs (Fuldauer et al., 2022b), and demonstrated to a limited extent for some spatial climate risks and infrastructure assets (Fuldauer et al., 2022a). However, there is no research that has shown how infrastructure network failure impacts lead to losses in socio-economic service provision and how these hamper the ability to meet specific targets of the SDGs. This requires a spatial risk assessment approach integrating climate hazards datasets, infrastructure assets and network flow models, vulnerability assessment models, and impact assessment models to quantify population and economic service losses. This is followed by an assessment of asset and network level adaptation options, their costs and benefits of risk reduction, and prioritisation of options that provide the highest benefits in meeting given levels of socio-economic service. The overall climate adaptation needs at aggregated sector or regional scales can then be estimated and aligned with SDGs.

The proposed research will propose a spatial climate adaptation assessment methodology to align resilience goals with SDGs in SIDS. It will involve undertaking compilation and quantitative assessment of datasets of SIDS-specific climate hazards, creation of unique datasets on infrastructure assets (energy, transport, water, schools, hospitals, buildings) from open or remote sensed data, compilation of hazard damage cost data and adaptation options and their costs and socio-economic datasets, and creation of risk indicators that align national adaptation and resilience plans with SDGs.

The project will involve a combination of geospatial analysis, decision analysis and multi-objective optimisation. It will suit students from any quantified background, including environmental sciences, engineering or economics. Students should be able to demonstrate aptitude for computer modelling and geospatial analysis, and enthusiasm to address real-world problems of great policy significance.

Candidates for this project from an engineering of physical sciences background would be eligible to apply for funding from Oxford University's EPSRC Doctoral Training Partnership. Successful UK applicants will be eligible for full or part funding. Overseas applicants in need of financial support are encouraged to apply for one of Oxford's several doctoral scholarship schemes for UK or overseas students. Closing dates apply on these schemes and students are encouraged to apply early. Applications are made through the School of Geography and the Environment.

References:

• Adshead, D., Thacker, S., Fuldauer, L. I., & Hall, J. W. (2019). Delivering on the Sustainable Development Goals through long-term infrastructure planning. Global Environmental Change, 59, 101975.
• Fuldauer, L. I., Adshead, D., Thacker, S., Gall, S., & Hall, J. W. (2022a). Evaluating the benefits of national adaptation to reduce climate risks and contribute to the Sustainable Development Goals. Global Environmental Change, 76, 102575.
• Fuldauer, L. I., Thacker, S., Haggis, R. A., Fuso-Nerini, F., Nicholls, R. J., & Hall, J. W. (2022b). Targeting climate adaptation to safeguard and advance the Sustainable Development Goals. nature communications, 13(1), 3579.
• Government of the Commonwealth of Dominica (2020). Dominica Climate Resilience and Recovery Plan 2020-2030. https://odm.gov.dm/wp-content/uploads/2022/02/CRRP-Final-042020.pdf
• Thacker, S., Adshead, D., Fay, M., Hallegatte, S., Harvey, M., Meller, H., ... & Hall, J. W. (2019). Infrastructure for sustainable development. Nature Sustainability, 2(4), 324-331.
• World Bank (2019) https://www.worldbank.org/en/news/infographic/2019/06/17/lifelines-the-resilient-infrastructure-opportunity.

The dry valleys of the Namibian Skeleton Coast are significant emitters of atmospheric mineral dust. Preliminary studies show that Holocene valley fills are the primary source of dust (Thomas et al., in press). There are however distinct spatial variations in both dust sources, revealed by remote sensing analysis, and the accumulation of fluvial valley deposits. The latter are a potentially significant source of Holocene palaeoclimatic data, but disparate studies to date reveal complex and sometimes conflicting records of climate change (Stone and Thomas, 2013). The aim of this project is to provide a robust, chronometrically controlled, record of extensive valley fill deposits and past fluvial system behaviour. This will involve examining the spatial variability in deposit characteristics and age that will allow potential dust emission sources and controls to be identified. The initial focus will be on the Huab Valley system, but this is extendable to other valleys as results emerge. The project will include fieldwork in the Skeleton Coast region that will examine and map sedimentary exposures and drill fill deposits, allowing sampling for sedimentary analyses including mineral properties, provenance work and age control through OSL dating. This project offers a unique opportunity to develop and apply field and laboratory skills, and will make a major contribution to southern African Quaternary research and investigations of atmospheric dust emissions, both major research themes in the School.