The aim of the project is to quantify uncertainties in the vulnerability of bridges and culverts to blockage or scour, in order to support better guidance and risk models for infrastructure managers and their partners, such as Network Rail and the Environment Agency. The approach will be a formal 'expert elicitation' to quantify the fragility of bridges and culverts at risk of scour or blockage. Erosion and blockage are two significant hazards for major infrastructure networks where they cross rivers or smaller watercourses. In the UK there are estimated to have been 15 fatalities due to flood/scour failure of a structure since the 1840s (RSSB, 2004). In recent years notable incidents include the Glanrhyd railway bridge in Wales, which collapsed in 1989 due to scour of a pier, resulting in four fatalities when a train attempted to cross the collapsed bridge and fell into the river. The Lower Ashenbottom viaduct in Lancashire failed in June 2002 as its central pier collapsed, partially due to scour during a flood event but exacerbated by the presence of debris. In the 2009 Cumbria floods, seven road and foot bridges failed due to the combination of scour and hydrodynamic loading. The collapse of the Northside road bridge in Workington caused one fatality and massive disruption. Whilst catastrophic bridge failures are rare, blockages of culverts and bridges, even over relatively small rivers, can cause flooding and erosion of safety-critical earthworks. Even minor incidents can lead to additional operational costs and risks for infrastructure operators, including those associated with debris clearance and emergency structural inspections. For the wider public, these incidents can cause disruption because of operational measures such as speed restrictions, delays, time-table changes or diversions. With nearly 10,000 bridges over watercourses on the rail network alone, the scale of the asset stock is significant. Despite industry efforts over the years, there remains much uncertainty about the individual resilience of these assets, and there is limited quantitative knowledge of this uncertainty. The uncertain and disparate nature of information about scour and blockage probabilities indicates that a formal elicitation of expert judgements will be useful in seeking to draw out a synthesis of current knowledge. Inevitably, uncertainty has a major influence on a risk assessment and on any associated decisions in circumstances such as this; a structured procedure for eliciting expert judgements from a range of opinions is needed to obtain a rational consensus on the appropriate level of uncertainty quantification to use in the appraisal of contributory factors. Soliciting expert advice for decision support is not new. Generally, however, it has been pursued on an informal basis. However, an unstructured approach is rarely, if ever, entirely satisfactory to all parties. Neither is it likely to be immune to legitimate criticism or auditing from one side or another. To address these shortcomings, structured expert judgement makes it possible to tie the whole process to stated and transparent methodological rules, with the goal of treating expert judgements in the same way as 'normal' scientific data, in a formal decision process. Various methods for assessing and combining expert uncertainty are described in the literature. Until recently, the most familiar approach has been one that advocates a group decision-conferencing framework for eliciting opinions, but other approaches now exist for carrying out this process more objectively. Prominent amongst these is the expert weighting procedure known as the Classical Model, formulated by Cooke (1991). Drawing on the knowledge and expertise of UK and international experts, this project will use Cooke's Classical Model to quantify uncertainties in the vulnerability of bridges and culverts to blockage or scour - to support better guidance and risk models for infrastructure managers.

Soils are a life support system for global society and our planet. Soils directly provide the vast majority of our food; they are the largest store of carbon in the earth system; and they regulate water quality and quantity reducing the risk of floods, droughts and pollution. In this way, soils provide a natural form of infrastructure that is critical to supporting both rural and urban communities and economies. Despite the criticality of this infrastructure, we do not understand: - the current delivery of services in terms of food production, water flow and quality regulation and carbon storage - from which soils do these services derive and what value do they have for rural/urban communities? - how the decisions we make regarding land drainage, tillage, crop choice, livestocking, tree planting, deforestation, and urban development influence the capability of the soil to provide its' multiple services, or how these decisions may interact. - how resilient our soil infrastructure will be to a changing climate and the increasing pressures to produce more food from less land that our global society faces in trying to feed a population of 9 billion by 2050, and ongoing urbanisation. This lack of understanding stems from a lack of integration across traditionally separate scientific fields that relate to soil infrastructure. Soil functioning is the product of hydrological, physical (soil erosion and weathering), biological and chemical processes, and as such it requires knowledge to be combined across these fields. This fellowship will draw together these disciplines to create a new computer model that will improve our understanding of soil infrastructures, their value to society and their resilience. This model will be used to explore how future scenarios will influence the provision of food-water-carbon services to our societies. Uncertainty and risk analyses will be performed to provide a coherent robust evidence base for decision-making. This will allow us to find ways to enhance our soils to provide more benefits for our societies, improving sustainability and well-being. This fellowship aims to: a. Assess the value of soils as a natural infrastructure that protects and enhances both rural and urban areas through food production, water regulation and carbon storage. b. Estimate the resilience of soil infrastructure to climate change and changing land-use pressures and explore the potential for managing soil infrastructures to mitigate risks and enhance their value and resilience. c. Transform the perceived value of soil infrastructure in communities and businesses, and enhance decision-making capabilities across sectors to help create sustainable resilient societies. The outputs of this fellowship will include: - Scientific insights into soil functioning, sustainability and resilience. - The first valuations of soil as an infrastructure, it's capacity for enhancement, and it's vulnerability to a changing climate and increasing land use pressures. - Estimates of the uncertainties surrounding these estimations, and how this influences to the risk to delivery of food, water and carbon services. - Quantitative predictive modelling frameworks that can support sustainable, resilient decision making across food, water and environment sectors. - Deepened engagement between scientists, businesses, policy makers, and NGOs.

Ensuring a reliable and safe supply of water is essential for the socioeconomic and environmental sustainability of our society. In the UK, several water companies are responsible for supplying clean water to industrial and domestic users in different parts of the country. Water companies need to estimate what the water demand and the available resource will be in the future (typically over a 25-years ahead period) so to be able to plan infrastructure development (for example, building a new reservoir) or changes in their management (for example, reducing or increasing river abstractions that feed into an existing reservoir) wherever they anticipate a gap between demand and supply. Making decisions is becoming increasingly complex in the fast-changing world we live in. On the supply side, extreme events such as floods and droughts are becoming more frequent and unpredictable under the combined effect of climate and land-use change. On the demand side, water demand is also becoming more variable due to changes in population density and distribution, changing life-style and socioeconomic conditions, and technological developments (for example, the introduction of smart water meters), which all together may affect water consumption in different ways in different places. To tackle all these complexities, the water industry needs to adopt innovative, flexible and adaptive planning and management solutions, which will increase the efficiency and resilience of water systems while avoiding raising costs. Mathematical models can provide a vital contribution to this end. By reproducing the behavior of the main components of a water resource system (such as reservoirs, pumping stations, treatment plants, etc.) and their connections among each other and with the natural environment, mathematical models enable water practitioners to predict the key system variables (for example, the future storage levels in a reservoir, the amount of energy consumed for pumping, the supply rate of clean water to a group of domestic users) and to simulate the system response under different infrastructural/management scenarios. The use of mathematical models in the water industry has increased in recent years, however their adoption is still relatively limited with respect to their potential. A key challenge water resource practitioners face is in recognising the uncertainty and errors that unavoidably affect all model predictions while still extracting useful information from them. A great opportunity that they are offered today, is to extract more and more useful information from fast growing sensing and computing technology, for example satellite data, smart sensors and high-performance computers. In this research project, I aim to tackle the uncertainty challenge and take the IT opportunity to develop the next-generation modelling tools that will support more sustainable water resource management in the UK. This project will develop mathematical methods and software tools to assist water system managers in their day-to-day decisions (for example, how much water to abstract from a river or a reservoir, how much water to pump to a treatment plant, etc.) as well as long-term decisions (for example, whether to build a new reservoir or connect existing ones) by finding "low-regret" solutions that would prove effective across a range of possible futures. All methods will be developed and tested on case study applications provided by water companies, so to ensure that they are actually valuable to address the most urgent issues they face, and they will be implemented in open-source software packages so that also other water practitioners besides those directly involved in the project will benefit from its findings and outputs.

Floods are the most common and widely distributed natural risk to life and property worldwide, causing over £4.5B worth of damage to the UK since 2000. Managing flood risk therefore presents a substantial challenge to this and future governments. Arising from the requirements of the EU Floods Directive (2007/60/EC), flood hazard maps for the UK must be delivered by December 2013. Due to limitations in current methodologies these maps take a deterministic approach to mapping catchment scale flood hazard, and do not incorporate climate change projections. Climate projections are predicted to result in the increase of UK properties at risk from flooding and coastal erosion: understanding the uncertainty these bring to flood hazard is therefore of vital economic significance to the UK. Different methods to assess or determine flood hazards have evolved through research and practice. However, these do not allow for uncertainty estimates to be explicitly included within the process. While uncertainty analysis has been an area of research over a number of years, it has not yet achieved widespread implementation in flood modelling studies and decision making for a number of reasons. With developments in the field, such as improved computational power and newly available standardised climate datasets, incorporating uncertainty into assessments is becoming increasingly possible and indeed essential. It is clear that a gap currently exists in uncertainty estimation in flood hazard prediction, particularly in relation to climate change projections, and that this area of research is critical to current policy and operational drivers. This proposal has been developed to comprehensively address this gap. The project will develop a novel probabilistic modelling framework to assess the impact of uncertainty arising from climate change on flood hazard predictions, generate exemplar probabilistic flood hazard maps for selected case study catchments and attempt to quantify the change to flood hazard as a result of climate projections.

Cloud computing is one of the most important technological developments of the last few years, with the technology having a major and transformative impact on many areas of society and the economy. For example, cloud computing underpins developments in smart cities, ecommerce and eGovernment and also provides the storage and computational capabilities that underpin data science (cf. big data). The software support offered by cloud computing is however in its infancy and is tailored towards particular styles of application. This is particularly true with respect to computation in the cloud, where the programming approach offered by the computational framework MapReduce dominates and yet MapReduce assumes a particular style of programming where potentially massive data sets are analysed by a map() operation before results are collated through an associated reduce() operation. This is powerful but very limited. In parallel, researchers are also interested in realising the benefits of cloud computing in many other areas of application. This project focuses on the support offered by cloud computing to Environmental Science and, in particular, to the execution of potentially complex environmental models in the cloud. This is an area of huge significance, with environmental modelling being the key tool to evaluate uncertainty, risk assessment, and mitigation strategies around flood/ drought, food security and the impact of climate change (with major consequences for the economy and for society). We particularly focus on the principles and techniques in the key areas of Platform as a Service (or PaaS), effectively the middleware for cloud computing. The central insight is that services at the PaaS level need to be more carefully tailored to the needs of key application domains, including but not limited to support for the execution of complex environmental models. We advocate a novel approach based on a combination of model-driven engineering coupled with software frameworks and argue that this enables a paradigm shift in terms of the flexible and tailored support offered by cloud computing for given application domains. Key beneficiaries of this work include the computer science communities working on model-driven engineering and cloud computing, researchers from environmental modelling in areas ranging from climate change modellers to flood prediction, and also key stakeholders related to environmental management and we include an exciting range of partner institutions from this area to maximise the impact of this work.