A number of indicative projects are listed below (click on the link for further information), but you may propose your own variant that better suits your interests, or perhaps propose something that fits our research programme but not listed here.
This PhD will explore the management of city-scale disasters and develop simulation methods to test evacuation measures and other incident management measures.
Events such as the ‘Toon Monsoon’ – the large scale flooding in Newcastle on the 28th June 2012 (and subsequent events that year), Hurricane Katrina in New Orleans and elsewhere in the world have highlighted the fragility of key infrastructure systems to wide-area events. Other disruptive events might include industrial accident or terrorist events.
Simulation approaches, such as the agent-based model produced at Newcastle University (Dawson et al. 2011) have shown promise for planning city-scale evacuation and identifying how best to deploy emergency services to minimise risk to civilians.
This PhD will couple our CityCAT flood model with this agent-based model and apply it to Newcastle to reproduce the disruptive event on the 28th June by using social media data (e.g. Twitter), crowd-sourced images (http://ceg-morpethflood.ncl.ac.uk/toonflood/) and other information on traffic flow that was captured on the day. This model will then be used to explore alternative approaches to managing flooding and other disruptive events in Newcastle. Other case studies may be developed.
Dawson, R. J., Peppe, R. and Wang, M. (2011) An agent based model for risk-based flood incident management, Natural Hazards, 59(1):167-189.
This project will analyse the relationship between urbanization and critical infrastructure and develop methods to reduce the vulnerability of existing and rapidly developing urban areas.
Recent floods, earthquakes and terrorist attacks, have highlighted interdependencies between critical infrastructure systems and led to the impacts of a disaster being felt far beyond the original disaster footprint (e.g. as failure of transport or energy infrastructure disrupts distribution of water or other supplies). Meanwhile, increased reliance on ICT; ‘just in time’ supply chains; policies aimed at reducing greenhouse gases (e.g. decentralisation of supplies, electrification of sectors such as transport, construction of new networks such as heat distribution) and rapid urbanization in many countries are altering these interdependencies in urban areas in non-intuitive ways.
This project will bridge two well established initiatives within our research group – our urban land use modelling that integrates socio-economic, land use and climate change in cities over extended timescales, with our interdependent infrastructure network model to provide a model that simulates the co-evolution of cities, their infrastructure and its interdependencies. The model will be applied to current and future scenarios to identify strategies to manage urban growth, or the adaptation of the existing urban environment, that do not increase its vulnerability.
This PhD will develop methods to design transformation of urban infrastructure to improve urban resilience to extreme events and their sustainability more generally.
Transforming cities to reduce their vulnerability to climate change and their greenhouse gas emissions will remain an abstract concept unless it can be incorporated into long term planning strategies. The aim of the project is to develop methodologies for analysis of options for planning, engineering and managing urban areas in the face of climate change, and to use those methodologies to identify well justified strategies for transition to a sustainable configuration over the 21st century.
The research team, as part of the Tyndall Centre for Climate Change Research Cities Programme recently completed the development of an Urban Integrated Assessment Facility (UIAF). This pioneering integrated assessment facility is intended to support policy decision making and has the capacity to test a host of policy, planning and engineering adaptation strategies, from land use planning to construction of flood defences. The scope of potential policy options is huge and the nature of the decision problem complex.
This PhD project will make use of the UIAF to demonstrate its use in support of policy analysis. It will aim to develop strategies for adaptation to climate change and mitigation of greenhouse gas emissions, by considering what alternatives should be implemented when and in the context of which future scenarios. In other words, the aim will be to use the Tyndall Centre model to design transitions to more sustainable cities by:
(i) developing methods for searching large numbers of policy, planning and engineering options;
(ii) identifying options that perform well under a wide range of future conditions or, in other words, are robust to future uncertainties;
(iii) develop ingplanning pathways to help London make a transition towards a more sustainable configuration.
The research will make use of established theoretical frameworks for analysis of robustness to uncertainty, including work by the Lempert et al. (2003) on long term policy analysis. The PhD research will therefore be seeking to address policy questions of practical relevance to stakeholders in London with whom we have built a successful working relationship over the last five years.
London case study:
Lempert RJ, Popper SW, Bankes SC. (2003) Shaping the Next 100 Years: New Methods for Quantitative, Long-Term Policy Analysis. RAND Corporation, Santa Monica, CA, USA.
Delivering sustainable policy is placing new and complex demands on decision-makers because it necessitates delivery of integrated responses that encompass a whole range of urban functions, responsibility for which is usually fragmented across a number of citizens and organizations. For example, no single organisation is able to deliver measures across, for example, energy, transport, flood management and the many other issues such as demographic change.
The aim of the project is to develop an ‘Urban Decision Theatre’ that helps policy makers to grapple with the complexity of addressing so many different (and often conflicting) engineering and planning issues.
This requires tools to communicate and visualise changes in cities in time and space. This PhD will apply the urban simulation tools that integrate land use change, analysis of climate impacts such as flooding, heatwaves and water scarcity that has been developed at Newcastle University through the Tyndall Cities project. Broadly, this may involve:
1. Development of a 3D database of a city (probably London or Newcastle) from OS Mastermap, GoogleEarth, LiDAR and other datasets to provide a platform for visualisation.
2. Integration of the Tyndall Urban Integrated Assessment Facility model and results with this database to provide visualisations of the greenhouse gas emissions and climate change impacts in London.
3. Development of a visualisation topology to visualise urban areas at different spatial scales – ranging from whole city overviews down to street level.
4. Development of methods to visualise change (e.g. from different scenarios of land use change, building standards, planning policy, climate change adaptation, mitigation of greenhouse gases etc. ) in the urban environment that are given by the Tyndall Cities model. This may involve exploring a range of visualisation options by applying innovative techniques such as www.viewsoftheworld.net.
5. Development of a user-friendly interface, that can present different information to multiple stakeholders (e.g. with a flooding view; heat risk view; greenhouse gas emissions view) to enable stakeholders, policy leaders, and community members to interact with the 3D environment and test different policies.
This project will provide a comprehensive and integrated flood model and risk analysis tool, at the catchment scale to support the design of long term flood risk management strategy. London (and the River Thames) or Newcastle (and the RiverTyne) are likely case studies.
The supervisory team have worked for a number of years on flood risk management (including contributing to the Thames Estuary 2100 project). This PhD will substantially enhance the existing flood risk modelling capacity by integrating state of the art climate, catchment hydrology, inundation modelling, flood defence failure and impacts analysis for a comprehensive cloud-household flood risk analysis. This involves integration over a wide range of hydraulic loadings, flood defence responses and flood inundation scenarios. Accurate risk estimates in complex simulations may require thousands of flood simulations. However, cloud computing technologies now make this feasible.
This project will develop and test new business models, based upon the analysis of interdependencies between sub-systems to target infrastructure investment.
Our national infrastructure – the system of systems that underpins our social, economic and environmental wellbeing – is rapidly approaching a critical condition which, if untreated, will be irreversible. This arises from a combination of physical decay, increasing interdependence, changing demands for infrastructure services and an evolving operational context. The UK Government developed a National Infrastructure Plan (NIP) that recognised that the root cause of the criticality is “timid, uncoordinated, incremental [and] wasteful” underinvestment; a symptom of undervaluing infrastructure’s contribution to the nation’s social, political, environmental and macroeconomic wellbeing.
This PhD will consider new business models that reduce the costs of infrastructure delivery by understanding and exploiting interdependencies between different infrastructure types and alternative finance models for their design, build and operation. To explore some of these aspects network models of infrastructure systems and their underlying economic support systems will be developed to identify and assess risks and threats (natural and manmade). Alternative business and infrastructure service models will be tested to identify the most resilient infrastructure and economic configurations. A possible case study is the Newcastle City Centre Accelerated Development Zone.
This project will develop methods to improve the design of protection and recovery strategies for critical infrastructure systems by taking into account interconnectivity between systems.
Recent floods, earthquakes and terrorist attacks, have highlighted interdependencies between critical infrastructure systems and led to the impacts of a disaster being felt far beyond the original disaster footprint (e.g. as failure of transport or energy infrastructure disrupts distribution of water or other supplies). Strategies to protect and operate specific infrastructure networks or components have typically been developed in isolation and do not therefore consider the threats, and opportunities, these interdependencies provide.
Our infrastructure programme to date has focused on analysing the performance of interconnected infrastructure networks when subjected to natural hazards and terrorist attack. This PhD will take this work in a new and exciting direction to understand how best to adapt these networks so they are more generally resistant to extreme events. Because of the sheer number of potential vulnerabilities within networked infrastructure systems, particularly as they are often spread out over very large areas, it is impractical and costly to make each component completely resilient. Therefore, it is important to identify and prioritise the various critical points where resilience is mostly needed. Furthermore, , risks can never be fully designed out and so a crucial new development will be to design recovery strategies so that they can return to full operation as efficiently as possible.
The work will involve development and adaptation of our existing network models to incorporate infrastructure resilience protection and recovery strategies. Optimal strategies will be identified by applying techniques such as genetic algorithms.
This project seeks to develop a model of infrastructure and urban resource use, tracking the main fluxes through cities and their infrastructure, from sources of resource (e.g. construction materials, wastewater), to sinks (e.g. buildings, in effluent discharges and sludge disposal).
Infrastructure systems such as water, energy, transportation and waste are the array of physical assets (and associated processes) responsible for moving the goods and services that ensure the safety, health and wealth of cities and their inhabitants. Thus, design and management of infrastructure has implications in terms of vulnerability and resource consumption (e.g. denser cities use less energy per capita on private transport, but can aggravate flooding and heat stress). However, effective management of infrastructure systems is challenging because they (a) vary in space, (b) are highly interconnected, (c) interact strongly with an everchanging environment and population, and, (d)deteriorate with age. Nowhere is this more evident than cities, where over half the global population live and more than three quarters of global resources (e.g. materials, energy, water) are consumed. As cities adapt in response to global pressures such as climate change, it is crucial to understand the resource implications of these adaptations to avoid undermining parallel sustainability initiatives (e.g. desalination reduces vulnerability to droughts, but consumes substantial amounts of energy).
This PhD will develop a comprehensive database containing estimates of the whole life resource needs of civil infrastructure in the urban environment. This will be used to constructed a resource model of the fluxes within cities and the infrastructure that mediates these flows, before testing the influences of different adaptation options and the extent to which they are able to increase the resource efficiency of cities.
The aim of the project is to develop case studies for climate impacts, adaptation and mitigation analysis in international cities, typically where data is scarce (in comparison to UK cities).
Cities are considered responsible for as much as 80% of global greenhouse gas emissions. They currently house over half the world’s population and yet they continue to expand. This concentration of people and assets makes them concentrations of vulnerability to climate impacts. Examples of impacts of climate change in cities include excessive heat, water scarcity and flooding.
The Tyndall Centre for Climate Change Research Cities Programme was launched from Newcastle University in 2005 to develop a practical approach to whole systems analysis of cities has been implemented in a quantified integrated model of long term (21st Century) change in cities, in order to inform planning and engineering design decisions. This integrated systems model couples analysis of long term change to the economy, land use, transport and other infrastructures (e.g. water supply and flood defences) in order to understand the potential impacts of climate change and the potential effectiveness of adaptation options. The research has also incorporated analysis of scenarios of greenhouse gas emissions and mitigation strategies. The research is drawn together in an Urban Integrated Assessment Facility (UIAF) that enables exploration of a wide range of scenarios and their implications. The components of the UIAF were designed to take advantage of the outstanding datasets that are available, with national coverage, in the UK – with a flagship case study application in London.
This PhD will develop an internationalised version of our Urban Integrated Assessment Facility. This ‘light’ version of our UIAF will provide a core modelling capability, consistent with those developed in the London study, but with simplified capabilities to enable application elsewhere in the world – often where climate pressures are greatest. At the coarsest level this will necessarily be limited to widely available information (e.g. from IPCC climate model archives, UN Urbanization World Prospects), but will be flexible enough to incorporate information from higher resolution studies if available. Likely case studies are Durban, South Africa where we already work closely with the local authority and Shanghai, China where we collaborate with Fudan University who are partners of the Tyndall Centre.
London case study:
This project will develop a new risk-based approach to decision making and planning of water resource infrastructure.
Sustainable management of water resources requires a long-term perspective with respect to pressures from climate change, demographic change, land-use changes and other socio-economic drivers. The emergence of new, probabilistic UK Climate Projections provides a stimulus for moving towards a risk-based approach. However, risk is a function of probabilities of hazards and their consequences. A crucial step is to therefore integrate the social, environmental and economic impacts of drought before risk-based water resource management is possible. The PhD will involve:
The Asian ‘Water Towers’ of the Hindu-Kush-Himalaya (HKH) region, which provide a critical source of water for India, China and Pakistan, may be one of the most sensitive areas to global warming. Over 750 million people depend on water from the main Indian rivers: the Indus, Ganges, and Brahmaputra. In general, it is thought that global warming will cause an intensification of the hydrological cycle and thus, increased water availability. However, in snow- and glacier-ice-dominated regions, where annual water availability is closely related to winter snowfall and summer melt, changes in temperature may have more complex impacts. Additionally, projected changes to the frequency and intensity of precipitation may also affect the magnitude and timing of runoff – with the potential for major floods and/or, droughts.
In such regions, observational data is scarce and located predominantly in valley stations and it has therefore been difficult to determine both trends in recent climate and how these may affect the high-elevation glaciers which are the main sources of water in much of the region. Remotely-sensed satellite datasets like MODIS have helped to fill the gap but they are short in length (from 2000), although providing full spatial coverage. Developing further remotely-sensed tools with longer datasets such as AVHRR will help to corroborate recent trends from valley stations and to determine how higher-elevations are responding to warming; giving an indication of how glaciers and other processes might respond to future warming.
This studentship will develop a method of retrieving meteorological (e.g. precipitation, temperature, cloud, etc.) and other variables (e.g. snow/ice covered area) from AVHRR (1km & 4km) satellite data across the HKH. A pilot study in the Upper Indus Basin has developed some methods of converting AVHRR data into Land Surface Temperature (LST), Cloud Cover, and Snow Covered Area (SCA) and validated this against MODIS datasets (available for 2000-2010) for these climate variables. This study shows promising results but needs improvement and extension to the larger Himalayan region.
The use of AVHRR data potentially allows analyses to extend back for over 30 years to the mid-1980s. These datasets can then be analysed. In particular, attempts will be made to characterise cryospheric changes by differentiating between changes to SCA and glacier extent, i.e. snow- and glacier-melt, by comparing annual SCA minima estimates to GLIMS data. Trends can also be assessed against those from valley stations to establish the validity of this product.
 Viviroli D et al. 2007. Water Resour. Res. 43, W07447.
Recent extreme weather events across the world have focussed attention on the possible impacts of climate change on our society. However, we do not even understand how current climate variability contributes to extreme events such as floods and droughts
This project will take advantage of the rich archive of data available for the UK to explore the process-links between large-scale atmospheric circulation and extreme spatial-temporal rainfall events that cause flooding. An event based analysis will first identify the largest flood/rainfall events over the past 100 years in the UK. The student will then explore the linkages between large-scale atmospheric circulation (e.g. synoptic scale patterns, Lamb Weather Types, continuous circulation indices, teleconnections such as the NAO, and patterns established using ERA-interim reanalysis data) and the space-time characteristics of rainfall and flood events at the daily time scale. It is anticipated that the student would explore whether there have been temporal changes in atmospheric circulation characteristics and the pattern and sequencing of associated rainfall and also the same analysis for spatial changes. In particular, is there increased clustering of such extreme events over time?
The second stage of the project would try to identify UK flood types using descriptive methods, such as time-integration of flow over a percentile threshold, to identify different types of flood event; then linking these flood types to atmospheric circulation and rainfall event characteristics.
The final stage would develop techniques for the assessment of flood risk under changing conditions; critical as climate models project large increases in the frequency of rainfall extremes worldwide. This will include assessing whether climate models are able to reproduce the identified flood-producing atmospheric circulation patterns and whether these are projected to increase or decrease in frequency and the potential impact on the magnitude of flooding.
Critical infrastructure systems are vulnerable to extreme weather events. However, although studies have been performed on how critical infrastructure responds to individual types of extreme weather, there has been little research on how these systems may respond to juxtaposed extreme weather events. For example, rainfall in combination with high winds can cause larger damage to buildings than rainfall or wind alone. Similarly, combinations of extreme rainfall, wind and temperature affect electricity demand at the same time as network capacity and resilience are affected. Other combinations of meteorological variables are also associated with significant weather events.
Existing studies of weather impacts have concentrated primarily on a single meteorological variable. This project will examine combinations of weather variables, both in current and projected future climates. Using historical observations, output from weather reanalysis and from climate models on different scales, including new high resolution climate models run by the MetOffice under the CONVEX project (http://research.ncl.ac.uk/convex/), relationships will be investigated between temperature, wind and rainfall in the UK, examining the spatial and temporal scales and variability of weather events likely to cause impacts of practical significance to critical infrastructure systems.
This PhD project will quantify uncertainty in runoff from snow and glacial melt model components in a hierarchy of increasingly process-based models; ranging from temperature-index techniques (e.g. SWAT and SNOWMOD) to a full energy balance model (JIM). New snow-ice routines for estimating mass and energy fluxes have recently been incorporated into the Joint UK Land Environment Simulator (JULES version 2.1.1: www.jchmr.org/jules), the land surface model used in the Unified Model of the UK Met Office. This new version of JULES (the JULES Investigation Model: JIM) can be operated in a stand alone manner for research and development. JIM is a full energy and mass balance model with the JULES surface exchange and soil physics and includes a new multi-layer snow module developed by Essery. JIM separates snow canopy and ground/glacier ice energy balances, the horizontal redistribution of snow, and allows for the formation of supra-glacial ice which improves near-surface ice temperatures by allowing for latent heat release. It enables spatially distributed and ensemble simulations and can easily ingest meteorological driving data through data assimilation.
The modelling framework will first be constructed, calibrated and validated using good datasets from European case studies which are dominated by glacial and snow melt (and particularly debris-covered glaciers) processes. Models will be driven and constrained by a fusion of in situ, modelled (i.e. Met Office Unified Model, reanalyses) and remotely sensed data at different scales.
Together, these data sources will allow interrelation of spatial information on Snow Covered Area (SCA), glacier extent, precipitation and Land Surface Temperature to snow and glacier accumulation and melt, and allow the estimation of distributed runoff from snow and glacier dominated catchments, at a range of scales.
Models throughout the hierarchy will first be tested and trained using available snow water equivalent (SWE) data from high mountain environments (e.g. Svalbard and European Alps) from previous work by Rutter, and then evaluated using hydrological flows from selected data-rich catchments in the Himalayan region, e.g. the Satluj basin where Singh found that ~75% of the summer runoff is generated from snowmelt. The methods can potentially then be applied in other regions where snow- and glacier-melt dominate hydrological regimes.
 Singh P, Jain SK. 2002. Hydrol. Sci. J. 47, 93-106.
The quality and detail of a vulnerability and risk assessment is contingent on the availability and quality of data to support the analysis. There are many datasets available across the EU (e.g. Urban Audit and the European Climate Assessment & Dataset programme) and globally (e.g. GRUMP: global land use data, WorldClim: climate observations and model outputs). Generally, these provide only a limited number of variables or are reported at low resolutions. Meanwhile, many countries and cities have bespoke data collection activities that provide opportunities for more detailed analysis. To take advantage of the opportunities provided by ‘the best available’ data, whilst also providing a platform for ‘comparable and transferable’ risk assessments this PhD will develop a scaled approach, to risk assessment of cities. At the highest level this is likely to be a data-driven approach, but complemented by more detailed process modelling at finer spatial scales.
Please contact one of the named PhD supervisors directly, or the Postgraduate Academic Secretary Melissa Ware.