DREAM is supporting 39 Doctoral NERC, ESRC and NPIF students and affiliates, across the four partner universities. There are a wide breadth of research interests, but all with a focus on the practical application of environmental data science and environmental risk. Below are descriptions of the researchers in DREAM and their work.
Projects running at Cranfield University. For further details contact Stephen Hallett.
The uninterrupted supply and reliable distribution of drinking water is fundamental in a modern society. Buried water infrastructure is subject to a range of operational and environmental factors which can lead to asset failure. For the privatised water-sector in the UK, utility companies are moving towards the deployment of statistical-based models for proactive asset management. For some companies, statistical-based models have facilitated the migration away from static annual burst targets, to targets which are dynamic and adjusted to observed environmental conditions. There is an increasing need for the development of accurate pipeline failure prediction models to support these asset management and regulation activities. This research evaluates several quantitative measures to improve current methods of pipeline failure prediction. The aim of this research is to establish the impact of soils, weather and trees to water infrastructure failure and to develop a series of material-specific drinking water pipeline failure models for an entire distribution network.
A quantitative assessment into the impact of data cleaning to the model error of a series of previously developed models was conducted. Material-specific variable selection and step-wise modelling approaches were used to construct a series of improved statistical models which have an increased representation of the environmental factors leading to pipeline failure. A detailed national tree inventory was investigated for its use in enhancing pipeline failure predictions and for calculating failure rates of different pipe materials under varying soil shrink swell and tree density conditions. The value in creating separate winter and summer pipeline failure models was also evaluated, to increase representation of the highly seasonal nature of pipeline failure. Finally, a novel satellite-based approach to generate soil-related land surface deformation measurements across a regional area was investigated. The result is a series of enhanced statistical-based methods of water pipeline failure prediction and a greater understanding into the environmental drivers leading to asset failure.
Keywords: Statistical modelling; pipeline failure; prediction; environmental risk; water utilities
Student: Matthew North
First supervisor: Dr Timothy Farewell
Commenced: October 2015; Completed, passed and graduated
Supervisory panel: Cranfield: Dr Timothy Farewell; Dr Stephen Hallett | Cambridge: Dr Mike Bithell | Industrial partners: Anglian Water plc.; Bluesky Ltd.
Coastal regions are some of the most exposed to environmental hazards, yet the coast is the preferred settlement site for a high percentage of the global population, and most major global cities are located on or near the coast. This research adopts a predominantly anthropocentric approach to the analysis of coastal risk and resilience. This centres on the pervasive hazards of coastal flooding and erosion. Coastal management decision-making practices are shown to be reliant on access to current and accurate information. However, constraints have been imposed on information flows between scientists, policy makers and practitioners, due to a lack of awareness and utilisation of available data sources. This research seeks to tackle this issue in evaluating how innovations in the use of data and analytics can be applied to further the application of science within decision-making processes related to coastal risk adaptation. In achieving this aim a range of research methodologies have been employed and the progression of topics covered mark a shift from themes of risk to resilience. The work focuses on a case study region of East Anglia, UK, benefiting from input from a partner organisation, responsible for the region’s coasts: Coastal Partnership East.
An initial review revealed how data can be utilised effectively within risk-based coastal decision-making practices, highlighting scope for application of advanced Big Data techniques to the analysis of coastal datasets. The process of risk evaluation has been examined in detail, and the range of possibilities afforded by open source coastal datasets were revealed. Subsequently, open source coastal terrain and bathymetric, point cloud datasets were identified for 14 sites within the case study area. These were then utilised within a practical application of a geomorphological change detection (GCD) method. This revealed how analysis of high spatial and temporal resolution point cloud data can accurately reveal and quantify physical coastal impacts. Additionally, the research reveals how data innovations can facilitate adaptation through insurance; more specifically how the use of empirical evidence in pricing of coastal flood insurance can result in both communication and distribution of risk.
The various strands of knowledge generated throughout this study reveal how an extensive range of data types, sources, and advanced forms of analysis, can together allow coastal resilience assessments to be founded on empirical evidence. This research serves to demonstrate how the application of advanced data-driven analytical processes can reduce levels of uncertainty and subjectivity inherent within current coastal environmental management practices. Adoption of methods presented within this research could further the possibilities for sustainable and resilient management of the incredibly valuable environmental resource which is the coast.
Keywords: Coastal management, resilience, risk-based decision-making, data analytics, open source data, insurance, geomorphological change detection, GIS, Big Data
Student: Alexander Rumson
First supervisor: Dr Stephen Hallett
Commenced: October 2015
Supervisory panel: Cranfield Dr Stephen Hallett; Tim Brewer | Industrial partner East Suffolk Council; Anna Harrison, British Geological Survey
The day-to-day dynamics of groundwater behaviour are driven by local weather and recharge patterns, but previous research has identified that there are also significant temporal relationships large scale ocean-atmosphere conditions. This research seeks to expand on this earlier work by combining advanced statistical methods, extensive spatiotemporal (including climatological, meteorological, hydrogeological) datasets and groundwater modelling to further analyse these relationships so as to be able to better predict and manage groundwater level response to extreme events. The research focuses on a large dataset of long term groundwater levels records from boreholes across the UK (and more broadly in Europe where available) to understand the differences in sensitivity between different aquifers or climatological areas. The student is being supported by an experienced supervisory team from Cranfield and Birmingham universities and the British Geological Survey and will be provided by extensive training within the DREAM programme’s Advanced Technical Skills and Transferable Skills and Leadership training.
Student: William Rust
First supervisor: Professor Ian Holman
Telephone: +44 (0) 1234 758277
Commenced: October 2015
Supervisory panel: Cranfield: Professor Ian Holman; Dr Ron Corstanje | Birmingham: Dr Mark Cuthbert | Industrial partner: British Geological Survey
This research will use a case study approach to develop a new generation of data-oriented informatic tools for the management of the complex and multiple data underpinning total expenditure decision-making in the water utility sector. The proposed programme of research will focus on a comparative evaluation of existing multi-stakeholder tools for asset management planning and will develop a series of analytical tools, exploring contrasting technical and software approaches. The research will lead to an understanding of the range and applicability of multivariate analytic tools, and will ultimately assess how these complex outputs may be visualised such that customer-driven outcomes are derived and management decision-making is supported. Data will be drawn from case studies drawing on the Atkins UK and EU water industry networks.
Specific case studies for this research will derive from the UK Chemical Investigations Programme (CIP) (£35m) research innovation programme (2010-2013), managed by Atkins and informing UK and EU chemical regulation under the Water Framework Directive (https://www.ukwir.org/site/web/news/news-items/ukwir-chemicals-investigation-programme). Working with Defra, UK Environment Agency, UK Water Industry and Ofwat, and in response to emergent legislation on surface water quality, CIP sought to gain better understanding of the occurrence, behaviour and management of trace contaminants in wastewater treatment process and in effluents. CIP has generated a vast and varied body of data that now paves the way in this study for the development of tools for rational prioritisation of future actions, supporting a transparent and informed discussion of the available options required to manage trace substances in the water environment. Further to this, Atkins are also now supporting the UKWIR ‘follow-on’ programme (£120m) of UK innovation, embracing environmental challenges, technological development and economic decision-making. Additional case studies can draw from this programme, providing a basis for further integrated analytical tools. The outline for multivariate analysis tools linked to visualisation of outputs in this arena will be used to inform outcomes and to explore opportunities in the water sector.
A unique aspect of this project is that it will seek to inform the UK water industry as to the options and opportunities for meeting EU and UK environmental drivers and, due to its economic implications, it will also inform the introduction of competition into the UK water market. Therefore the proposed case studies approach should prove of significant importance to the UK on both an environmental and economic level.
Flood modelling and forecasting are essential tools to inform infrastructure and emergency planning. Accurate forecasts, though, are difficult to achieve, even in developed countries with decades of experience and detailed topographical and hydrological datasets for calibration and validation, as demonstrated by the recent Cumbria floods. Forecasting is even more challenging in large tropical regions, which have limited data availability (e.g. few river gauging stations), and modest meteorological forecasting capabilities. More worryingly, their large human populations, economically-important industries (e.g. oil/gas, agriculture), and ecologically-important habitats mean that flooding is connected to multiple other significant risks.
This PhD project will attempt to overcome some of these challenges by (1) using new near real-time, high resolution satellite datasets to improve the medium- and short-term flood risk assessment generated by probabilistic ensemble flood forecasting for data-poor tropical regions, and (2) applying the flood model to the assessment of flood-induced pollution risk. The case study for the project will be the Mexican State of Tabasco, which occupies a large, low-lying, topographically-complex area that experiences flooding from several large rivers (e.g. Grijalva-Usumacinta systems, 1,911 km long network, 128,390 km2 catchment area) which are affected by weather systems from both the Pacific Ocean and Caribbean Sea. The State is home to a large, economically-important on- and off-shore oil industry, which is both impacted by the flooding and a source of significant pollution risk during floods. Consequently, accurate flood forecasts are needed to advise the population, protect or relocate sensitive oil extraction and refining infrastructure, and to assess the risk of pollutant mobilisation (i.e. oil and associated chemicals) which could significantly impact water quality, agriculture or sensitive ecological habitats.
The researcher is working closely with academics at the Universidad Juaréz Autónoma de Tabasco (UJAT), who have offered additional support for the project to allow the student to spend a significant amount of time in Mexico (2-3 months per year) and to access new high-resolution, 1-day return period satellite data. The project will have a direct and immediate impact on flood and pollution risk management in Tabasco, as the PhD outputs will feed into UJAT’s development of an operational water risk management system for the State.
First supervisor: Dr Bob Grabowski
Telephone: +44 (0) 1234 758360
Commenced: October 2016
Supervisory panel: Cranfield: Dr Bob Grabowski; Tim Brewer | Partner: University of Tabasco, Mexico
Sustainable intensification of agriculture is a vital approach to maintain the balance between providing food for a growing global population and preserving soil as a valuable resource. Currently the state of arable intensive land is poor and the use of organic amendments such as compost, farm yard manure and other agricultural residues can provide soil organic matter required to restore soil health. Organic amendments also contain nutrients needed by crops but its availability needs to be determined accurately to meet crop demands. At the moment, samples need to be sent to laboratories for nutrient analysis and farmers need to consider it before accurately applying it to land. However in practice this does not happen and farmers bulk apply the organic amendments without fully considering its nutrient content. Bulk application of organic amendments whilst can build soil organic matter, its nutrient when becomes available and in excess of crop demands can pose a risk to contaminate the environment. A solution to this challenge would be development of an in-field diagnostic tool that can be used to determine the nutrient content of organic amendments in an accurate and precise manner.
This PhD opportunity offers an exciting and challenging offer to a suitable candidate to develop an in-field diagnostic tool. At Cranfield University, there has been some initial proof of concept work being developed which will under pin this project. In this project there will be contribution from two industrial sponsors (AKVO www.akvo.org, and the World Vegetable Centre www.avrdc.org) who have access to several thousands of field sites in Vietnam and Cambodia where data from soil and organic amendment samples will be collected and the use of in-field diagnostic tool will be developed and validated. The principles behind development of this tool is to produce a mobile phone App that can be used to determine the nutrient content of organic amendments in developing countries where access to laboratories are limited. The aspiration is to develop, use and validate this tool from a large dataset to minimise risk from over applying organic amendments.Please contact Dr Ruben Sakrabani (Cranfield):
Biological air pollution (bioaerosols) are airborne microorganisms, particularly fungi and bacteria. Bioaerosols from composting facilities have the potential to cause health impacts and are regulated by the Environment Agency. People living near composting facilities are concerned about the impacts on their health. Current monitoring methods use spot measurements and so only provide an indication of concentrations for the particular short-term measurement period. New and novel methods for monitoring bioaerosols are being tested. These newly emerging measurement techniques have the potential to radically increase the amount and extent of data collected on bioaerosol.
This exciting project provides the opportunity to work with the Environment Agency, to explore innovative methods of collating, analysing and interpreting different sources of bioaerosol data to produce new insights and risk maps. These will provide new insights into how composting can be managed for the benefit of local citizens. This project will also work with interested parties, such as the Environment Agency, local authorities and businesses, to understand the perceptions and opinions of impacts from composting facilities, for example, whether they are actively supportive of composting, unaware and uninterested in its developments and/or significantly opposed to it? All the results will be used to create a toolkit to communicate the risks of bioaerosols, focussing on how the uncertainties are explained and managed. In addition to working with the Environment Agency, this PhD student will work within a supportive team of researchers working on waste management and bioaerosol science.Student: Martina Della Casa
The problem: Vast amounts of clean water is lost from the water supply network each year. Ageing pipes often fail as a result of soil corrosivity, or the seasonal shrink-swell cycle of clay soils. Soil spatial distribution is complex and existing soil maps, while useful, do not provide sufficient detail to identify vulnerable water network segments for upgrade.
This research will address these problem through delivering three components:
Through the research, developing and using high resolution predictive soil maps, and burst models, water companies will be able to better identify, and upgrade, vulnerable parts of their networks. The many resulting benefits from your work will include a reduction in leakage, reduction in energy use and reduction in interruptions to customer supplies.
Our climate is changing. Hotter drier summers cause issues for water supply, and the chaotic weather patterns cause havoc with traditional infrastructure modelling. The old approach of comparing infrastructure performance with previous months or years no longer is fit for purpose. THis project will be closely aligned to industry, seeking to better our understanding of the dynamic interactions between soil, weather and infrastructure. Through the research, we will be able to improve our ability to benchmark and improve network performance, developing new digital soil mapping techniques to enhance our understanding of the soil. The outputs will be integrated into predictive burst models, and used to provide guidance to Anglian Water on where it can best target it’s financial investments, so they can reduce leakage to a negligible level. LandIS – the Land Information System (www.landis.org.uk)  Bluesky National Tree Map (www.blueskymapshop.com/products/national-tree-map)  Met Office 3 hourly forecast data: (www.metoffice.gov.uk/datapoint/product/uk-3hourly-site-specific-forecast) Student: Giles Mercer
Information on illicit poppy cultivation in Afghanistan is of critical importance to the opium monitoring programme of the United Nations Office on Drugs and Crime (UNODC). The pattern of cultivation is constantly evolving because of environmental pressures, such as water availability, and social and economic drivers related to counter narcotics activity. Remote sensing already plays a key role in gathering information on the area of opium cultivation and its spatial distribution.
The shift to cloud computing opens up exciting possibilities for extracting new information from the huge amounts of satellite data from long-term earth observation programmes. You will test the hypothesis that inter-annual and intra-seasonal changes in vegetation growth cycles are predictors of poppy cultivation risk. This will involve using emerging cloud based technologies for processing image data into accurate and timely information on vegetation dynamics relating to opium cultivation. The research will be conducted in collaboration with the UNODC.Student: Alex Hamer
Landscape is the arena in which natural capital (providing ‘supporting’, ‘provisioning’, ‘regulating’ and ‘cultural’ ecosystem goods and services) interacts with elements of the other four 'capital's' to create the real places that people inhabit, derive benefits from and care about. Throughout time, landscapes change as a result of natural processes but the rate of change is now orders of magnitudes greater due to anthropogenic activity. Thus key questions facing those organisations involved in landscape management and policy include:
Natural England is one such organisation, responsible for delivering the Government Agenda in this field. Conservation 21 – Natural England’s conservation strategy for the 21st Century, places emphasis on ‘resilient landscapes’. Significantly they argue that ‘resilient’ landscapes must be both ecologically and culturally resilient (implying culturally valued/supported/voted-for etc.), highlighting that landscapes lacking cultural resilience are unlikely to be ecologically resilient in the long term.
Working in partnership with Natural England’s Strategy Team, this exciting studentship will investigate the use of big data relating to natural and social sciences, together with, for example Virtual Reality visualisation and big data techniques, to provide an holistic, integrated analysis of ecosystem service provision as experienced through society’s perception of the changing landscapes around them and in a way that secures assessment of ecological and cultural aspects of the management of the natural environment equally. This will then enable development of management and intervention aimed at enhancing natural capital, ecosystem goods and services in their cultural context, by testing different scenarios. The studentship will be based in the Cranfield Institute for Resilient Futures and make extensive use of the NERC funded Ecosystem Services Databank and Visualisation for Terrestrial Informatics Laboratory, which includes a portable Virtalis VR system and GeoVisionary software.Student: Matthew Webb
The project will involve close collaboration with Natural England.
The future is already here — it’s just not very integrated… Currently around 50% of the world’s population lives in cities, this will grow to 75% by 2050. The OECD estimate it will cost $45 trillion USD between now and 2050 to upgrade infrastructure in our towns and cities across the world whilst also to managing the impact of climate change.
Here in the UK, the Government for example is targeting a 33% cost reduction across the whole life of assets by 2025, what’s more they are demanding 50% reduction in time and 50% reduction in greenhouse gas emissions.
The infrastructures of our cities; buildings, roads, power, water, internet are increasingly interconnected and Digital Engineering will transform the way in which construction projects are conceived, planned, executed – and the way in which infrastructure assets are created, operated and used by citizens. 3D object modelling, laser scanning, drones, augmented reality and the internet of things are just some of the technological innovations which are already transforming infrastructure systems. Working with Atkins, a global cross-sector infrastructure design consultancy, this project will understand how enhanced social, economic and environmental outcomes can be achieved by the adoption of Digital Transformative Technologies and protocols. Research methods will be developed to determine where city infrastructure system integration is appropriate, how metrics and measures of benefits may be established and consequently indicate how optimal, integrated infrastructure systems management could be developed. Appropriate integrated cross-sector case studies will be developed to demonstrate the findings and provide real world context.
The project will include opportunities for secondment to work alongside Atkins staff to gain a valuable first hand experience of working in this sector. The wider DREAM CDT offers an exciting range of training and support to develop both technical and personal skills.Student: Avgousta Stanitsa
If you want to help save water, save energy and save the loss of water supply to thousands of people through the use of environmental data science, read on. The problem: Water pipes often fail as a result of weather conditions, such as cold weather events, or rapid changes in soil moisture deficit. Because water companies do not know where or when to expect burst pipes, they do not respond as fast as they would like. This means that valuable water is lost, the energy used to treat that water is wasted, and people are without water, sometimes for sustained periods. Where you come in: Through your research you will predict where and when pipes will fail, enabling water companies to be on hand to respond more quickly to burst water mains. There are three main stages to this PhD. You will:
Your work will result in a new data tool which will enable Anglian Water to predict where and when its pipes should burst given the predicted weather conditions. This will enable the water utility to more quickly respond to bursts as they will have a better understanding of where the bursts are likely to occur. You will be working in an active, and truly supportive research team. We are closely aligned to industry, and seek to better our understanding of the dynamic interactions between soil, weather and infrastructure.
Through the course of this PhD you will develop big data skills which enable you to clean, process and use vast datasets ranging from 15 minute interval pipe pressure management data, 3 hourly weather forecast data and more static datasets which describe the soil1, vegetation and infrastructure parameters. Unlike some PhD projects, this project seeks to solve a real world problem. As well as being academically stimulating, the project has industrial backing. You will discuss and integrate your research with front-line infrastructure operators, so they can improve their service through the use of your results.
By the end of the PhD programme you will have developed highly transferable skills to become a leading force in environmental data science, You will have developed a network of industry contacts to ensure your science is relevant enough to bring lasting benefit. These contacts and skills will also open the doors to rewarding and stimulating careers in industry and academia. No one wants to be stuck in a boring, irrelevant, job. This PhD offers you a chance to develop skills and relationships to help ensure you never will be.
If using environmental science to better the management of our limited natural resources excites you, we strongly encourage you to apply for this PhD. LandIS – the Land Information System (www.landis.org.uk)  Bluesky National Tree Map (www.blueskymapshop.com/products/national-tree-map)  Met Office 3 hourly forecast data: (www.metoffice.gov.uk/datapoint/product/uk-3hourly-site-specific-forecast) Please contact Dr. Timothy Farewell (Cranfield), Senior Research Fellow in Geospatial Informatics
The South East of the UK is one of the densely-populated areas of Europe. The demands on infrastructure, water needs and urban development place particular pressures on greenspace and natural environment. What is still missing are a set of tools which can assess the impact of these planned developments on the natural environment and which will allow decision makers evaluate the possible benefits as well as impacts of particular development schemes (e.g. greenspaces, eco developments, better water infrastructure). This PhD will seek to develop these tools, describing the changes in ecosystem goods and services as various development schemes or infrastructure plans are assessed. For these tools to be practical and useful to industry, they need be both reflective of the underlying natural processes but also easy and time efficient in their use. Interacting closely with our Industrial partners, the PhD will apply these tools to current development schemes in the South East, ensuring that the research is exciting, current and immediately applicable to contemporary societal needs.Please contact Professor Ron Corstanje (Cranfield), Professor of Environmental Data Science Head of Centre, Centre for Environmental and Agricultural Informatics (CEAI)
Background: Currently, food quality and safety controls relies heavily on regulatory inspection and sampling regimes. Such approaches are often based on conventional chemical and microbiological analysis, making the ultimate goal of 100% real-time inspection technically, financially and logistically impossible.
Over the past decade, rapid non-invasive techniques (e.g. vibrational spectroscopy, hyperspectral / Multispectral imagining) started gaining popularity as rapid and efficient methods for assessing food quality, safety and authentication; as a sensible alternative to the expensive and time-consuming conventional microbiological techniques.
Due to the multi-dimensional nature of the data generated from such analyses, the output needs to be coupled with a suitable statistical approach or machine learning algorithms before the results can be interpreted. Although these platform has been showing great potentials to accurately and quantitatively assess freshness profiles (Panagou, Mohareb et al. 2011) (Mohareb, Iriondo et al. 2015) and safety parameters as well as adulteration (Ropodi, Panagou et al. 2016), their dependence on advanced data mining and statistical algorithms made was the main challenge facing their practical implementation across the food production and supply chain.
In order to overcome these challenges, we have developed sorfML (http://elvis.misc.cranfield.ac.uk/SORF), a Web platform prototype compatible with outputs from 5 instrumental platforms (See Figure) which provides means for interactive data visualisation, multivariate analysis (Principal component analysis and hierarchical clustering), as well as the ability to use stored datasets to develop predictive models to estimate food quality. Currently, the platform provides users with means to upload their experimental datasets to the server, thanks to the truly generic MongoDB NoSQL database backend, and to develop classification and regression models to estimate quality parameters. Objectives: The aim of this PhD is to expand the existing sorfML platform into a cloud-enabled framework that supports real-time monitoring of food products throughout the production chain. In order to achieve this, a series of advanced portable sensory devices will be deployed to examine their suitability as “Connected devices” in predicting quality and safety indices for various food perishable food products. A series of machine learning and pattern recognition models will be developed and integrated within the cloud system. This includes Ordinary Least Squares, Stepwise Linear classification and regression, Principal Component regression, Partial Least Squares discriminant analysis, support vector machine, Random forests and k-Nearest Neighbours.
References: Mohareb, F., M. Iriondo, A. I. Doulgeraki, A. Van Hoek, H. Aarts, M. Cauchi and G.-J. E. Nychas (2015). "Identification of meat spoilage gene biomarkers in Pseudomonas putida using gene profiling." Food Control 57: 152-160.
Panagou, E. Z., F. R. Mohareb, A. A. Argyri, C. M. Bessant and G. J. Nychas (2011). "A comparison of artificial neural networks and partial least squares modelling for the rapid detection of the microbial spoilage of beef fillets based on Fourier transform infrared spectral fingerprints." Food Microbiol 28(4): 782-790.
Ropodi, A. I., E. Z. Panagou and G. J. E. Nychas (2016). "Data mining derived from food analyses using non-invasive/non-destructive analytical techniques; determination of food authenticity, quality & safety in tandem with computer science disciplines." Trends in Food Science & Technology 50: 11-25.Student: Emma Sims
Background: The falling costs and the extremely high yield of genomic DNA sequencing data from next generation sequencing (NGS) technologies means that it is now routine to produce more than one billion sequencing reads within a few days. This allows us to describe nearly all the sequence differences between hundreds of different plant lines[2,3]. But, to maximise the benefit from these rapid advances in NGS, we also need “next-generation phenotyping” to link genotypes and phenotypes—this allows us to understand which DNA differences cause a plant to look or respond differently. These differences can then be used by plant breeders to select genetic combinations that perform better in particular environments.
Cranfield University and AgriEPI Centre have recently acquired a state-of-the-art phenotypic platform installed within a purpose-built 300 m2 glasshouse facility as part of a £5.5m investment. This unique LemnaTec® multi-sensor platform moves in three dimensions within the partially environmentally controlled glasshouse, while the plants remain static in containers of up to 1 m3. It is designed to precisely monitor the growth and physiology of crops under a range of soil conditions and rootzone stresses such as salinity, drought and compaction using RGB, hyperspectral, fluorescence and thermal cameras and a 3D laser scanner. Objectives: In ongoing projects at Cranfield, the response of a population of tomato genotypes to various rootzone stresses (e.g. drought, compaction, salinity, pH) will be assessed using the Lemnatec platform, and the genotypes of these plants will be defined NGS.
The aim of this PhD is to develop a cloud-based platform, integrating NGS and phenotypic measures acquired via the LemnaTec platform. Due to huge size and heterogeneous nature of phenotypic and genotypic data being integrated, the developed platform will be coupled with a Big Data-compatible database backend (e.g. Hadoop, NoSQL). The database will host a variety of short and long read sequencing genomic (and possibly transcriptomic) data. The platform will be coupled with an interactive Web-based UI that allow the integration of phenotypic and genomic datasets by providing several data analysis pipelines for automating the variant calling and SNP identification as well crawling genomic and proteomic annotation from public databases. A series of mathematical models, including machine learning and pattern recognition models, will be developed to allow prediction of genotypic impacts on plant phenotypes.
References: Metzker, M.L., Sequencing technologies - the next generation. Nat Rev Genet, 2010. 11(1): p. 31-46. Koboldt, D.C., et al., The next-generation sequencing revolution and its impact on genomics. Cell, 2013. 155(1): p. 27-38. Morozova, O. and M.A. Marra, Applications of next-generation sequencing technologies in functional genomics. Genomics, 2008. 92(5): p. 255-64. Hennekam, R.C. and L.G. Biesecker, Next-generation sequencing demands next-generation phenotyping. Hum Mutat, 2012. 33(5): p. 884-6.Student: Ewelina Sowka
Libya faces a serious problem from desertification, manifested through vegetation deterioration, settlement expansion, and an increase in saline lands. These effects are obvious to see in many parts of the country. It is therefore both important and timely to develop a tool which can assess the desertification threat, so as to allow policy makers to undertake the appropriate measures required to protect and reverse the consequences of desertification. This research concerns the development of an Early Warning System to combat desertification in Libya. A key theme to this research is to establish an understanding of the impact of desertification in the country, by developing an integrated model of early warning for the causes of desertification, used as a method to identify the affected areas, where efforts can be best directed to combat desertification.
The ultimate research aim is to develop an Early Warning System (EWS) accounting for the drivers of desertification in Libya, by employing an integrated monitoring model approach. In concrete terms, the study seeks to develop a suitable methodology to represent Libyan conditions, to help combat desertification and promote the sustainable use of natural resources. Key tasks include: the appraisal of existing thematic information available including soil condition, land cover, erosion hazards and climatic information for the region; a review and selection of data themes; appropriate identification of risk; provision to decision-makers of necessary and timely information concerning challenges associated with desertification, and; the development of a prototype framework for desertification assessment in Libya supporting a risk assessment approach, adopting standardised observation methods for long-term monitoring of desertification indicators.
The research develops a novel implementation of a methodology and prototype framework, and is applied successfully in an area of the world where such approaches have never before been undertaken. To achieve this, a MEDALUS-derived approach has been selected to provide an initial basis for a methodology suitable for monitoring desertification, in turn providing the foundation for an Early Warning System for desertification in Libya.
The MEDALUS methodology adopted, has been used to map Environmentally Sensitive Areas of desertification (ESAs), based on MEDALUS classes and parameter weightings, but in each case adapted to suit the conditions of the selected study area, the Jeffara Plain, in Libya. The final Environmentally Sensitive Areas (ESAs) used to assess desertification in the Jeffara Plain were created by combining five qualities, namely: climate, soil, vegetation, management and ground water quality.
A sensitivity analysis and map output comparison was conducted in order to analyse whether the influence of using different scores for parameter classes had an impact on the outputs. This approach permitted testing and evaluation of the modified scores developed within this research, being used to determine the degree to which model outputs were changed with respect to the standard MEDALUS scores. In particular this approach was applied to the vegetation quality parameters.
Production of the Environmentally Sensitivity Areas (ESAs) maps of desertification for the periods between 1986–2016 identified the trend of desertification that was used to model future change to the year 2030, revealing that the study area has a high sensitivity to desertification in the future.
The quantitative approach of ESAs used in assessing desertification provides an important basis for planning sustainable development programmes such as in the production of early warning systems for desertification. ESAs of desertification enable and demonstrate a clear vision of the risk state of desertification permitting quick actions to be planned.
Keywords: Desertification, early warning system, Environmentally Sensitive Areas, land degradation, mitigation
Student: Azalarib Ali
First supervisor: Dr Stephen Hallett
Supervisory panel: Dr Stephen Hallett; Tim Brewer
Natural rangelands are one of the significant pillars of support for the Libyan national economy. The total area of rangelands in Libya is c.13.3 million hectares. This resource plays an important role providing part of the food needs of the large numbers of grazing animals, in turn providing food for human consumption. In the eastern Libyan rangelands, vegetation cover has changed both qualitatively and quantitatively due to natural factors and human activity. This raises concerns about the sustainability of these resources. Observation methods at ground-based sites are widely used in studies assessing rangeland degradation in Libya. However, observations across the periods of time between the studies are often not integrated nor repeatable, making it difficult for rangeland managers to detect degradation consistently. The cost of such studies can be high in comparison to their accuracy and reliability, in terms of the time and resources required. These costs are not expected to encourage the local administrators of rangelands to make repeated or continuous observations in order to determine possible changes in managed areas. This has led to a lack of time-series data, and a lack of regularly updated information. The sustainability of rangelands requires effective management, which in turn is dependent upon accurate and timely monitoring data to support the assessment of rangeland deterioration.
The aim of this research has been to develop a framework for monitoring and evaluating rangeland condition in the east of Libya with a prediction of the future condition based on a historical assessment. This approach was achieved through the utilisation of medium resolution satellite imagery to classify vegetation cover using vegetation indices. A number of vegetation indices applied in arid and semi-arid rangelands similar to the study area were assessed using ground-based colour vertical photography (GBVP) methods to identify the most appropriate index for classifying percentage vegetation cover. The vegetation cover data were integrated with climate data, topography and soil erosion assessment using the RUSLE system to form a Rangeland Assessment Management Information System (RAMIS). These data were used to assess the historical and predicted future rangeland condition.
The MSAVI2 vegetation index was identified as the most appropriate index to map vegetation cover as this had good correlation with the ground data (R2 = 0.874). The RUSLE prediction identified that over 1,300,000 hectares were affected by soil loss over the time period from 1986 to 2010 representing nearly 97% of the study area. The RAMIS output indicated that most of the study area in 1986 was affected by a high risk of rangeland degradation, with less than 10 % of the area having a moderate and low-risk of degradation. The rangeland condition up to 2010 indicated a slight improvement in degradation distribution, with a slight decrease from 90% to 85% in the high risk of degradation area and the area having a low risk of degradation increasing from 2% to 8% in 2010. The result of the predictions made showed that the area of low cover class, which in 2017 reached about 1,280,000 hectares continues to increase through 2030 to 2050, to some 1,400,000 hectares, with a consequent increase in areas of high risk of rangeland degradation.
The result of implementing the RAMIS framework over the historical period illustrates changes in the rangeland condition, reflecting the fluctuation in the effectiveness of rangeland management development projects linked to the financial resources available in the 1980s, with increasing numbers of grazing animals exceeding the rangeland capacity and the expansion of rangeland cultivation. Libyan rangeland managers need to focus more on expanding the fenced area, conducting soil survey, and implementing soil erosion studies that can be used in erosion model calibration at a large scale to better inform rangeland management planning. Otherwise, the future projections of change up to 2050 indicate a continuance of the deterioration of rangeland condition, increasing the areas of low vegetation. However, this projection is based only on the vegetation data as the lack of available climate data did not permit its incorporation into the prediction.
Keywords: Libyan rangeland, land degradation indicators, RUSLE, rangeland comdition, rangeland management
Student: Abdul Al-Bukaris
First supervisor: Tim Brewer
Supervisory panel: Tim Brewer; Dr Stephen Hallett
The vegetation cover in Al Jabal Al Akhdar has been subjected to human and natural pressures that have contributed to the deterioration and shrinking of the vegetated area. Therefore, the principle goal of this dissertation was to establish and evaluate the changes in the natural vegetation of the Al Jabal Al Akhdar region in the period following the 2011 Libyan uprising. The thesis is comprised of three main objectives; the first is to provide a quantitative assessment of changes in natural vegetation cover over a period from 2004-2016, and identify the consequent impact of human activity; the second is to investigate the impact of climate on the natural vegetation cover; and the third objective is to evaluate the ability of machine learning techniques to predict the natural vegetation cover types.
GIS and remote sensing techniques and data have been used to achieve these objectives, along with the ancillary data, across 53 sites in the area of interest. Six classified Landsat image scenes have been used for undertaking a post-classification comparison approach to detect the changes and the types of changes, with ENVI, ArcGIS, and MS-Excel software and programme scripts used to detect LULC changes and determine human activities impact, and statistical analyses between MODIS NDVI and climate satellite-based data. Statistical analyses have been undertaken using SPPS and MS-Excel software. Lastly the machine learning ‘J48’ algorithm, within the WEKA tool, was applied on ANDVI, climate data, and spatial characteristics for 53 sites and analysed statistically to test its ability to predict the natural vegetation type.
The main research findings have confirmed that from 2004-2016, natural forest and rangelands decreased by 71,543 ha or 7.10% of the total area of interest as a result of urbanisation and agricultural expansion. Human activities have had more impact than climate impact on LULC changes. The machine learning classifier decision tree ‘J48’ algorithm was also found to have the ability to classify and predict the natural vegetation cover type.
Finally, an evaluation was undertaken of the current distribution of natural vegetation cover, and a forecast of future changes, utilising high-resolution imagery. A conclusion considers how developing action plans using tools such as those described to manage and protect the natural vegetation cover are highly recommended.
Keywords: Post-classification comparison, Land use cover change, J48 algorithm, MODIS NDVI, Urbanisation, Al Jabal Al Akhdar
Student: Nagat Al Mesmari
First supervisor: Dr Stephen Hallett
Supervisory panel: Dr Stephen Hallett; Dr Rob Simmons
Projects running at Newcastle University. For further details contact Professor Stuart Barr.
The project will address a current national issue of evaluating flood risk from multiple sources (fluvial, pluvial, and groundwater), something that has become evident in the extensive flooding over the past 10 years (e.g. summer flooding in 2007, and winter flooding in 2013/14). Existing computer modelling techniques are designed to handle each type of flooding separately. This study will integrate existing state-of-the-art models to provide a novel modelling capability, which will be used with new data on high-intensity rainfall patterns. Following model integration and testing for a range of case study sites across the UK, the integrated model will be used with rainfall data representing current and possible future climates to assess the enhanced flood risk arising from multiple sources, providing an improved basis for flood management in the UK and a methodology that can be applied internationally.
Depending on the background of the student, training will be provided through formal courses in hydrogeology, hydrology, hydraulics, modelling, climate change, statistics, and programming. The student would expect to gain skills in fundamental modelling techniques, as well as experience in current issues in flood risk assessment.Student: Ben Smith
Infrastructure systems (energy, transport, water, waste and telecoms) globally face serious challenges. Analysis in the UK and elsewhere identifies significant vulnerabilities, capacity limitations and assets nearing the end of their useful life. Against this backdrop, policy makers internationally have recognised the urgent need to de-carbonise infrastructure, to respond to changes in demographic, social and life style preferences, and to build resilience to intensifying impacts of climate change.
This PhD will draw on advances in (i) methods for broad scale infrastructure risk analysis, (ii) readily available datasets describing global climate and associated hazards, global exposure, and increasingly information on the location of key infrastructure networks, and, (iii) 'big data' processing and cloud computing techniques, to enable the first global infrastructure risk analysis.
The research will develop an integrated model that uses data from global mapping sources such as Google, Open Streetmap; i-COOL global marine networks (port flooding); CAA (airport flooding); global flood hazard maps (WRI: floods.wri.org) and climate model outputs (climateprediction.net); population location (Global Rural Urban Mapping of Project) to look at future risks.
The project will develop an integrated assessment model of global transport networks, where the importance of major infrastructure network components are assessed based upon population served, information on route type (e.g. main, secondary road etc.), other published information (e.g. route frequency for airlines) and so on. This information, integrated with hazard extents, will provide a unique global risk assessment.
The size of the spatial datasets necessitates a cloud or distributed computing approach to handle and process the data. Web-enabled tools will be developed and the integrated framework for managing the workflow of these web-based tools will be designed with extensibility in mind to enable other researchers to augment the model as new data and capabilities become available.Student: Feargus McClean
Remote sensing provides the mechanism for forewarning of risks of potential loss of life due to water failure and flooding through monitoring of reservoir and lake levels and river discharge. Further, many river catchments are managed with dams constructed for hydroelectric power, fisheries and water resources and these dams often have a detrimental effect on livelihoods, particularly downstream while river discharge across major catchments suffer from either lack of gauge data or data unavailability. With precipitation data lacking in many parts of the world, information concerning water failure or flood events is often not communicated downstream with potentially catastrophic consequences.
Near real-time quantification of lake/reservoir levels and volumes and river stage heights and discharge can be recovered from satellite altimetry, an estimation of the lake area or river width from near real-time satellite imagery and some mechanism to develop a stage-discharge relationship perhaps based on a single gauge data or hydrological modelling. This project will develop a near-real time capability for the latest delay-doppler type of altimeter (carried onboard Cryosat-2 and the soon to be launched Sentinel3 satellites) and optical and/or Synthetic Aperture Radar imagery for river and reservoir/lack extent. The river mask will be used to both constrain the satellite altimetry to the inland water target but also supply river width ad lake extent for inferences of variations in discharge and lake volume.
Such a capability will reduce the risk associated with water failure and floods providing an early warning with time lapse of less than 24 hours limited by the time that the quick-look satellite altimetric waveforms are made available to the user.Student: Miles Clement
Spatial risk assessment cannot be considered in isolation from other factors such as the development of long term sustainable plans for land use development and the implementation of planning decisions that mitigate adverse climate change impacts such as increased heat. To date however, the ability to develop spatial, multi-objective risk and sustainability planning tools has been limited by intractable computational run-times. Cloud computing now offers the potential to overcome this limitation, facilitating the development of spatial optimisation risk and sustainability planning tools that allow a large number of risk and sustainability objectives to be considered in the production of optimised spatial development plans.
In this PhD, cloud computing will be employed to provide the next generation of multi-objective, spatial risk and sustainability development plans for the UK. Using nationally available data-sets on climate related hazards, such as probabilistic predictions of heat, pluvial, fluvial and storm surge related flooding, along with future predictions of population demographics, the PhD will investigate how temporal spatial plans can be developed that minimise exposure to future spatial risk whilst maximising key local, regional and national sustainability objectives (e.g., minimising overcrowding, reducing urban sprawl, maximising access to low emission public transport etc.).
During the PhD, cloud computing training will be provided via a number of the Newcastle EPSRC CDT Big Data & Cloud Computing modules.Student: Grant Tregonning
The Gravity Recovery and Circulation Explorer (GRACE) mission, a tandem satellite pair, was launched in 2002 and has provided unprecedented insight into mass change including polar ice mass, glaciation, hydrology and earthquakes. The novel aspect is the inter-satellite microwave ranging device that is accurate to a few microns and is highly sensitive to the differential pull from gravity on the two satellites. Solutions for gravity field snapshots are made every 10 days to a month with the differences revealing mass change associated with surface processes once longer term trends such as Glacial Isostatic Adjustment (GIA) are allowed for. Of the solution approaches the mass concentration (mascon) method has been established as superior to spherical harmonics. At Newcastle 2 degree mascons over successive 10 day periods form the basis of time series of mass change in our current studies. The mascons are constrained geographically over Antarctica, Greenland, land and the oceans to enable a solution to be obtained for over 10,000 masons globally. The usage of 2 degree masons is a limiting factor and needs to be refined for basin-scale change detection, particularly in delineating the basin boundaries.
The GRACE mission is near the end of its operational lifetime but is still providing data. The uniqueness and scientific value of the mission has led to a GRACE Follow-on (FO) mission scheduled for launch in December 2017/January 2018. The GRACE FO mission has the same inter-satellite K band microwave device for providing range-rate measurements but will also carry a laser interferometer on each satellite to provide range and directional data. Although experimental and not continuously operational, when available the laser data will be an enhancement over the microwave data.Student: Jerome Richmond
Data collection systems for the Internet of Things (IoT) derived from pervasive sensors are being deployed in many cities across the UK including Newcastle. These systems produce significant data volumes for real-time computation and analysis of historical data such as data mining. Cloud computing is the cutting-edge computation platform that offers nearly unlimited computation resources through cloud datacentres (CDC). In addition to cloud, a new type of computing resource, an Edge datacentre (EDC), offering computing and storage on a smaller scale than a CDC, and positioned closer to data sources or sinks. It can provide immediate analysis of streaming data, and better support for real-time decision making in latency-sensitive workflows, leaving CDCs to deal with intensive computing and large-scale storage. EDCs has the following benefits: (i) energy saving for the resource constrained edge devices which, currently, primarily upload data to the CDC; (ii) reduced network congestion by filtering non-relevant events at the edge; and (iii) reduction in latency for event detection as sensors no longer need to send data to far off CDCs.
Data analytics requires data analysis while it is shared across multiple parties. For example, in a smart hospital, data IoT gathered through sensors stream will be shared via all participants including doctors, patients, data analysts, emergency departments and government auditors / law enforcers. These data need to be shared with the participants to a certain degree in real-time along with the patient record database, where privacy-preserving techniques such as anonymization and permutation need to be applied before sharing. Current privacy-preserving data publishing technologies enable data anonymization via generalisation/specialisation on data records. However, current research for PPDP offers limited support for cloud and IoT, whereas the performance and scalability of the data anonymization/permutation algorithms are not satisfactory under a hybrid cloud environment with CDC/EDC/private cloud and distributed programming models such as mapReduce.
This project will focus on designing advanced data privacy preserving tools and methodologies to efficiently protect the key information included in dynamic and large-scale IoT sensor data, based on the existing data anonymization algorithms. The main outcomes of this project will include a framework for securely processing and sharing of heterogeneous datasets under the specification of law and user privacy requirements.
This project aligns mainly with the call themes of “Security and legal issues for handling data and information as it relates to risk management”. This proposal combines the disciplines of Engineering and Computer Science/Informatics to understand and morel the requirements from users and laws, and develop privacy-preserving tools for the shared data analysis and risk evaluation in urban data.
Many cities in the UK and abroad are attempting to move forward the smart cities agenda through urban sensing programmes. These programmes typically involve the deployment of low cost environmental sensors measuring climate air quality and people movement, along with other metrics. Whilst current data volumes are relatively small, over the next few years these pervasive sensors (and other platforms such as smartphone enabled sensors) have the potential to provide enormous quantities of streaming data. The cloud provides a means to process these large volumes using an on-demand model that potentially reduces the overall cost of investment in data storage and processing requirements within a city. In Newcastle as part of the Science Central programme is an investment is £500k for sensor hardware deployed around the city and a further investment in a fully instrumented building due to open in 2017. These data streams will provide a platform for developing an integrated framework for developing workflows integrating urban observational and static data for urban risk analysis across spatial and temporal scales, where a new layer of information security and data protection is essential.
Understanding and forecasting rainfall, especially from thunderstorms, and the consequent risk of flooding in cities is a long standing and important problem in both practical and research terms. |Rainfall radar is our most powerful observational tool giving real time spatial images of rainfall, but unfortunately has major issues with accuracy and reliability. There are many sources of error in the calibration and interpretation of radar data, as the return signal can be the product of reflection from multiple rainfall targets, buildings and atmospheric phenomena.
This project will take a fresh view of making better use of radar data, using new mathematical and statistical approaches, empowered by Big Data computational methods. A new high resolution rainfall radar is operating in Newcastle for use in flood risk studies in combination with a dense telemetered rain gauge network across the city and surrounding area. This project will develop and apply new statistical simulation approaches to using the radar and rain gauge data effectively, recognising the usefulness and importance of both the space and time properties of the rainfall fields, together with large volumes of data from ground-based rain gauges.
The student will undertake a varied research programme, which could include: meteorology of rainfall, physics of radar signals, statistics of time series and spatial dependence of (pseudo-)random fields, mathematics of conditional simulation, and use of Cloud technology for data processing and computational simulation. Finally, the student will liaise with operational engineers and designers in the best use of their results in real time and long term assessments of flood risk and intervention in cities.Please contact Professor Chris Kilsby (Newcastle):
This project makes use of the increasing interest and technological developments in ‘citizen-science’ environmental observations, and aims to bring together national scale hydroclimatic data from formal observation networks with observations made by individuals and communities, to improve risk assessments related to flash flooding. Our recent research in Africa (Walker et al., 2016) and in the UK (Starkey et al., in review) has demonstrated both that good quality information can be provided by citizen observers, and the value of community-based observations in modelling catchment response from intense localized convective rainfall events, which were not picked up by formal raingauges or radar coverage.
The Met Office have recently upgraded a web-based platform, WOW (Weather Observations Website), designed to collect, store, and disseminate observations by the growing weather observation community in the UK. It is available for use worldwide, and currently holds over 900 million observations recorded since 2011 from over 10,000 sites globally. In the UK, there are currently 3,100 active weather observing sites reporting a range of parameters including in many cases rainfall, of which 318 are formal Met Office sites. . There are also around 1,600 formal rainfall observation sites in the UK (monitored by the Met Office, Environment Agency (EA), Natural Resources Wales (NRW) and Scottish Environmental Protection Agency (SEPA)). The citizen-based observations therefore represent a significant additional resource which at present has neither been systematically analysed nor protocols developed for its use in the assessment or mitigation of risk from flooding.
The student will address this research gap, working within a strong research team at Newcastle University, linking with current research projects on development of sub-daily precipitation datasets (INTENSE), and assessment of flood risk in catchments and urban areas (SINATRA and TENDERLY, part of the NERC Flooding From Intense Rainfall programme). The project will assess the value of citizen observations in improving spatial rainfall estimation during extreme events, and the subsequent impact on flood risk assessment.
Starkey, E, Parkin, G, Birkinshaw, SJ, Quinn, PF, Large, ARG, and Gibson, C (in review). Demonstrating the value of community-based (‘citizen science’) observations for catchment modelling and characterisation. Journal of Hydrology. Walker D, Forsythe N, Parkin G, Gowing J (2016). Filling the observational void; scientific value and quantitative validation of hydro-meteorological data from a community-based monitoring programme in highland Ethiopia. Journal of Hydrology 538, 713–725.
Blenkinsop, S, Lewis, E, Chan, SC, Fowler, HJ (2016). Quality-control of an hourly rainfall dataset and climatology of extremes for the UK. Int. J. Climatol. DOI: 10.1002/joc.4735
Approaches and tools for the identification of sources of risks, their drivers and impacts within complex systems; the project will use multiple sources of data (formal monitoring networks, citizen observations, and radar) to improve the understanding and characterisation of localised intense rainfall, and will use hydrological and hydrodynamic models to assess their impact on flood risk.
Robust methods to quantify and analyse risks and their drivers including sound mathematical and statistical approaches; approaches have been developed under the Intense project for data Quality Control, and temporal and spatial interpolation (Blenkinsop et al., 2016); these will be used as the basis for analysing the use of citizen observations particularly focussing on extreme events.
Tools for developing, managing and analysing ‘Big Data’, to understand risk better, and to apply modern cloud computing approaches; the project will require cloud computing approaches to firstly analyse large national high-temporal-resolution (hourly or sub-hourly) rainfall datasets, and secondly to run hydrological and hydrodynamic impact models to assess uncertainty reduction for flood impacts arising from the enhanced datasets.
Utilisation of multiple models and integrated modelling (for multi-hazard modelling and to combine environmental hazard models with information about vulnerabilities/exposure of a population); the study will consider flooding from intense rainfall, as fluvial flooding in rapid response catchments and as direct pluvial (surface water) flooding. This will involve a combination of catchment hydrological models and hydrodynamic flood models to assess flood risk from these sources.
Measurement, characterisation and handling of uncertainty, including within model chains; the study will assess the value of local citizen or community-based monitoring of rainfall and river levels in reducing uncertainty of flood risk assessment.
New approaches to visualise and communicate risk, including the public, to enable decision-making; a key benefit of developing improved real-time flood risk assessment using citizen observations is to help inform communities in rapid response catchments or outside main flood plains that are not part of the national EA flood warning service. The Tenderly project will work with a selection of pilot communities to assess their use of rainfall and flood risk information; availability of appropriate information suitably communicated is an important part of motivation for effective long-term monitoring. This study will build on these pilot sites, producing relevant risk information using national data.
The risk of flooding, and particularly flash flooding, appears to be rising and heavy rainfall and flooding is expected to increase with global warming. Understanding how intense rainfall patterns might change, both temporally and spatially, is important for understanding potential changes to future flood risk. However, current formal rainfall observation networks are scarce spatially, especially at sub-daily scales. The growth of technology and big data has, however, allowed individual citizens to make their own measurements and the number of these far surpass those of formal observations. There is great potential for these to be used to supplement formal observations in space and time to better understand flood risk.
Citizen science observations present an opportunity to improve the assessment of flood risk. However, we are not sure of the uncertainties associated with such observations and how they can be best used to supplement the existing formal observational networks. This project seeks to identify and quantify these uncertainties and develop new methods for using these informal observations in flood risk assessment.
The overall aim of the project is to develop and test methodologies for using citizen observations in flood risk assessments in the UK.
This project is about designing the visualisations and maps produced from complex risk based models more effective. Stakeholders can then easily understand the information and data presented to them, allowing decisions can be made.
Humans are only able to process a certain about of information. It is about understanding this quantity but also understanding the background knowledge, experience and perception of the user and how this alters what is ‘seen’, what is ‘understood’ and what is ‘knowledge’.
Projects running at University of Cambridge. For further details contact Professor Tom Spencer.
The H1N1 pandemic in 2009 has been the catalyst which has shown how important locally based human observers are for outbreak detection, verification and pinpoint geocoding. Given the limited resources of public health agencies, the paramount challenge now is to harness the power of Web-based reports. Global bio-surveillance systems such as GPHIN, MediSys, BioCaster and HealthMap trawl through high volume, high velocity news reports in real time and are now seen as adding substantial value to the efforts of national and transnational public health agencies. With concerns about the rapid spread of newly emerging diseases such as A(H5N1), re-emerging diseases such as Ebola and the threat of bioterrorism there has been increasing attention on bio-surveillance systems which can complement traditional indicator networks by detecting events on a global scale so that they can be acted on close to source. The goal of this research project (‘Panda Alert’) is to produce a robust computational model that understands unstructured natural language signals in online news media reports for early risk alerting of adverse health events. Key beneficiaries will be the public health and animal health surveillance communities. The project will build on substantial groundwork already done in the BioCaster project (2006-2012, PI: Collier) and inter-disciplinary collaborations with the international public health community.
The project will involve inter-disciplinary collaboration with experts at the Joint Research Centre (JRC) at Ispra (Institute for the Protection and Security of the Citizen) who provide the European Commission’s MediSys bio-surveillance system. The JRC group will provide (1) expert background information into the context of biomedical surveillance, (2) an introduction to the state of the art techniques used in Medisys, (3) guidance on the selection of real world agency interest for analysis, (4) feedback on system performance.
The Panda Alert project is aligned with DREAM themes in several areas:
1. Approaches and tools for the identification of sources of risks, their drivers and impacts within complex systems.
Previous research by the lead supervisor in the BioCaster project (2006-2012) pioneered the use of time series analysis for alerting epidemics at the country level (e.g. using CDC’s Early Aberration and Reporting method for syndromic surveillance) using hand crafted features mined from news media reports. Similarly JRC’s MediSys pioneered Boolean rule alerting and Harvard University’s HealthMap team has recently applied Incidence Decay and Exponential Adjustment to project growth of outbreaks based on news data. Panda Alert proposes to improve the state of the art in health risk detection by incorporating richer and more complex features into the language models than features which are known a priori to indicate risk to human health (e.g. see the International Health Regulations (IHR) 2005 Annex B decision instrument).
2. Tools for developing, managing and analyzing ‘Big Data’, to understand risk better, and to apply modern cloud computing approaches.
As noted above, our methods will develop the use of statistical machine learning on natural language texts (e.g. recursive neural networks, kernel methods) to classify news reports for public health risk. All tools and data sets from this research will be available for download from public repositories such as GitHub and Zenodo. The software where appropriate will be cloud ready and tested on our own group HPC cluster (SLURM middleware, currently 32 cores, 512 GB RAM expanding soon to 48 cores, 756 GB RAM). The techniques will be tested on real-world high volume, high veracity data in the open source GDELT data set. We estimate that real-time analysis will have to scale to handle approximately 300,000 news items per day in >40 languages with surge capacity during epidemics such as AH1N1 (2009) or SARS (2002). An integral part of the project will be to develop a fully functioning system for real-time alerting and mapping of risk based on the new techniques.
3. New tools and approaches to multi-hazard assessment and interconnected risks (cascade effect).
The use of distributed semantic representations for event reporting brings with it the powerful possibility to generate compositional representations that can be tested for alerting severity. We propose to explore the space of distributed semantic compositions, bounded within a time window, in order to find potential interconnected risks.
Epidemics such as SARS, Ebola and pandemic influenza threaten lives and livelihoods across the globe. Based on the PI’s nine years of experience in epidemic intelligence from social sensors the goal of this project is to build a high quality risk alerting system using distributed semantic representations of news about public health hazards. The PI’s previously reported experiments against human standards have shown that health news is effective at outbreak event detection (F1=0.56) measured against ProMed-mail alerts. The resulting BioCaster system was in regular use by national and international public health agencies such as the World Health Organization. This project aims to build on this legacy by investigating novel methods for event alerting using the latest advances in natural language processing technology by groups at Stanford University (PI: C. Manning) and McGill University (PI: Y. Bengio), i.e. in distributed semantic feature representations and deep learning.
Importantly we expect the distributed semantic techniques we explore to be able to learn novel health risk features using deep machine learning. This is a key step as it avoids the fragility of hand-built feature selection upon which all previous operational systems have been designed. The distributed features will be learnt in an unsupervised mode from high volumes of un-annotated news data over a 10 year period. Examples of a priori features used in existing systems include international travel, infection of healthcare workers and mention of deliberate release of agents. Examples of complex linguistic features we might see learnt could include linguistic clues related to the geography/environment, language of event reporting by officials as well as indicators of economic/social disruption and their combinations. We expect the result to be a system that automatically learns the most relevant clues for disease alerting and which is robust to a range of event types (e.g. early stages of novel asyndromic outbreaks) and reporting styles.
This project will conduct bioinformatics analyses of the genomes of Antarctic fish to assess the risk of the potential for climate change induced extinction of high latitude fish species, and the impact on high latitude Southern Ocean fisheries.
Antarctic fish have evolved in isolation for millions of years in a very stable, near-freezing environment and have developed a number of well-documented adaptions to cope with life in the cold (e.g. antifreeze glycoproteins, large muscle fibres and lack of haemoglobin). Unlike their temperate counterparts, these fish are remarkably sensitive to even small changes in temperature: they are highly stressed by just a 3oC rise in their environment and die at temperatures 5oC above normal physiological ranges. There is strong evidence that this may be caused by the instability of the proteins at around 0oC. It is known that protein folding is more problematic at low temperatures. In the few examples studied to date, amino acid substitutions have been characterized that produce a more flexible protein structure which works more efficiently in the cold. Additionally, in all Antarctic fish species studied to date, the 70-kDa heat-shock protein chaperones (which aid protein folding) are constitutively expressed at much higher levels than their temperate relatives. These organisms also have higher RNA:protein ratios, dedicate more of their energetic budget to protein synthesis, and retain only 30% of the protein they produce, (compared with over 70% for similar tropical species). Taken together, this indicates that protein folding is only marginally successful and that the Antarctic marine fauna are ‘clinging on’ to life in their environment (http://www.sciencemag.org/content/347/6226/1259038.full.pdf).
There has, however, been no comprehensive analysis of the proteome of these species to identify if this protein instability is restricted to certain key proteins or is more widespread. Given that the waters around the Antarctic Peninsula and related island archipelagos (a key fishing ground) is warming at one of the most rapid rates on the planet and that the Antarctic fisheries are the only large-scale exploitation in the region, there is a real risk of extinction for key Antarctic fish species and collapse of the Southern Ocean fisheries industry. mRNA-seq has already been performed using Illumina Hi-seq on 2-12 tissues in different individuals from 8 Antarctic fish species (either in-house or via US collaborators). The aim here is to mine this extensive dataset to identify how extensive cold-adapted changes are in Antarctic fish, perform in silico analyses to determine how these changes affect protein folding and functioning and therefore predict future vulnerabilities.
The transcriptomes of 8 Antarctic fish species (multiple tissues for each species, several individuals for each species and sequenced via Illumina 100bp paired end reads) are already available. These represent a considerable dataset (Tb of sequence data: Big Data). Detailed analysis across species will require the development of bespoke tools to comparatively map both the genes identified in each species with each other, but also to the publicly available genomes and transcriptomes of temperate fish species (e.g. the Japanese pufferfish, zebrafish, tilapia etc). Tools will need to be developed to be able to discriminate between species-specific amino acid changes with more generic cold-adapted changes, thus enabling the evaluation of the level of vulnerability for each species.
The need to get a better understanding of species vulnerability to climate change; to identify how species have adapted to low temperatures and provide background information to allow assessments to be made of sensitivities/resilience to climate change. We aim in future or related projects, to expand this approach beyond fish through to a range of polar invertebrate species to develop more ecosystem level analyses of climate change effects. We want to train high quality mathematically skilled students in the field of bioinformatics, specifically as it applies to extreme environments and non-model organisms. The lack of scientists with such skills currently limits progress in understanding in this field.
The research seeks to identify the extent of cold-adapted amino acid changes in the genomes of Antarctic fish species to enable estimation of their sensitivity/vulnerability in the face of climate change. Additionally identify whether there are particular biochemical pathways affected which could specifically impact on life history traits such as reproductive abilities which could further compromise recruitment and population sustainability.
It is estimated that the world’s food demand will increase by 70 % by 2050. Terrestrial, airborne and high resolution satellite spectral imaging systems can enable effective and timely crop monitoring, helping agriculturalists manage resources more effectively to meet this requirement: monitor crop yields and optimise decisions relating to irrigation, disease control, harvesting, fertiliser and agro-chemical applications. The project aims to i) develop an extensible, framework for analysing data incorporating machine learning and human expertise, ii) using i), build a database of spectral signatures of different plant species, accounting for plant life cycles and health status, and iii) use i) and ii) to provide near real-time summary information about crop status to support on-the-ground decision-making.
The proposed project fits within the remit of big data, since not only will large quantities of data be analysed, but also a sparse model of the data will be developed to aid deployment in the field. The project will develop the technical expertise in data analysis and machine learning as applied in remote sensing and plant sciences. This project aims to be able to alert to risks to crop so that these can be mitigated.
This project is a continuation of two projects undertaken within the MPhil in Scientific Computing at Cambridge University supervised by Faul. One project was entitled “Deep Learning to Exploit Multi-Spectral Satellite Data for Landcover Mapping and Crop Classification”. The other project is ongoing and explores deep neural networks and parallelization, since it is envisaged that large quantities of data need to be processed.
The project also builds on the ongoing collaboration between Coomes and Schönlieb, who have co-supervised one doctoral student (to completion), two postdoctoral researchers and a summer intern student within the broad area of forest assessment from airborne imaging data.
J. Lee, X. Cai, J. Lellmann, M. Dalponte, Y. Malhi, N. Butt, M. Morecroft , C.-B. Schönlieb, and D. A. Coomes, Individual tree species classification from airborne multi-sensor imagery using robust PCA, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, PP(99), 2016.
J. Lee, X. Cai, C.-B. Schönlieb, and D. Coomes, Non-parametric Image Registration of Airborne LiDAR, Hyperspectral and Photographic Imagery of Wooded Landscapes, Geoscience and Remote Sensing, IEEE Transactions on, 53(11), 6073-6084, 2015.
ESA’s sentinel satellite constellations will eventually gather 4 terabytes of data per day. This presents an opportunity to extend the automation of landcover mapping and vegetation classification.
The aim is to not just have a database of the multi-(hyper-)spectral signatures of different plant species, but also to incorporate phenology and health status of the plants. With regards to crops this will help mitigate the risks of droughts and diseases. Irrigation can be directed where needed and fertilizer used more effectively. Early intervention can stop diseases spreading and there will be less use of pesticides and fungicides. Being able to choose an optimal time to harvest will lead to less food wastage.
Using sophisticated machine learning techniques, sparse models of the landcover can be created. These models will help where there is limited up- and downlink bandwidth as there are with airborne technologies as well as with satellites. The device gathering the data can carry a sparse model and only where new data is significantly different to the model action is necessary. In the first instance this action will be an alert to an anomaly. Further analysis is then necessary whether the anomaly is expected due to e.g. change of the season, or the anomaly needs intervention or the model needs updating (e.g. a change of crop).
The cilium is present in most mammalian cells and ciliates. Ciliates are an important group of protists (» 3,500 species), common almost everywhere there is water. The cilium acts as an antenna and central processing unit that receives diverse signals from the extracellular environment, such as light, proteins, and mechanical stimuli.
The project will use multi omic data from Antarctic and temperate region living ciliates and available mammalian data on cilia to 1) build the first multi omic metabolic model of a ciliate, 2) integrate cilia mechanics with metabolic models; 3) calibrate ciliate models as environmental change responders on a number of scenarios; 4) identify analogous sensor functions dynamics of cilia in mammalian cells; understand linearity and non-linearity of physiological responses to network variations.Student: Catalina Cangea
The H1N1 pandemic in 2009 has been the catalyst which has shown how important human observers are for outbreak detection, verification and pinpoint geocoding. Given the limited resources of public health agencies, the paramount challenge now is to harness the power of citizen reports. Global bio-surveillance systems such as GPHIN, MediSys, BioCaster and HealthMap trawl through high volume, high velocity news reports in real time and are now seen as adding substantial value to the efforts of national and transnational public health agencies. With concerns about the rapid spread of newly emerging diseases such as A(H5N1), re-emerging diseases such as Ebola and the threat of bioterrorism there has been increasing attention on bio-surveillance systems which can complement traditional indicator networks by detecting events on a global scale so that they can be acted on close to source. Whilst volume and velocity in Big news Data have to some extent been addressed in the previous projects the problem of veracity remains a challenging issue. The goal of this research project (‘Rumour Mill) is to produce a robust computational model that can measure the likelihood of citizen’s assertions made about breaking news events. Key beneficiaries will be the public health and animal health surveillance communities. The project will build on substantial groundwork already done in the BioCaster project (2006-2012, PI: Collier), inter-disciplinary collaborations with the Joint Research Centre (JRC) at Ispra and a new shared task data set (RumourEval at SemEval 2017).
The JRC group will provide (1) expert background information into the context of biomedical surveillance, (2) an introduction to the state of the art techniques used in Medisys, (3) guidance on the selection of real world agency interest for analysis, (4) feedback on system performance.
The Rumour Mill project is aligned with DREAM themes in several areas:
1. Tools for developing, managing and analyzing ‘Big Data’, to understand risk better, and to apply modern cloud computing approaches.
Our methods will develop the use of statistical machine learning on social media texts (e.g. Twitter) to identify natural hazard rumours and classify the veracity of their assertions. Machine learning methods will include deep learning to provide an integrated representation of words and concepts using distributed vector spaces using samples of language from large scale generic corpora (e.g. Google News), social media authors and their social networks. All tools and data sets (including Twitter messages IDs) from this research will be available for download from public repositories such as GitHub and Zenodo. The software will be tested on our own group HPC cluster (SLURM middleware, currently 64 cores). The techniques will be tested on the 2017 SemEval RumourEval data set as well as a new gold standard set of natural hazard rumours to be constructed within the project. An integral part of the project will be to develop a fully functioning cloud-ready prototype for real-time analysis of rumour veracity based on the new techniques.
2. Measurement, characterisation and handling of uncertainty, including within model chains.
Previous research by the lead supervisor in the BioCaster project (2006-2012) pioneered the use of time series analysis for alerting epidemics at the country level (e.g. using CDC’s Early Aberration and Reporting method for syndromic surveillance) using hand crafted features mined from news media reports. Similarly JRC’s MediSys pioneered Boolean rule alerting and Harvard University’s HealthMap team has applied Incidence Decay and Exponential Adjustment to project growth of outbreaks based on news data. Rumour Mill proposes to improve the state of the art in natural hazard alerting by providing a measure of veracity. Within a deep learning framework, features will include (a) linguistic content from the rumour, (b) the graph structure of the social network involved in spreading the rumour, and (c) real world knowledge graphs, e.g. in the form of BabelNet. Since each new rumour event is unique, the project also expects to look at recent advances in one shot learning using e.g. memory augmented neural networks to try and leverage small amounts of new data for improved rumour veracity classification/quantification performance.
3. Risk perception, communication, decision making and management
After extracting messages that contain rumours about a natural hazard event, risk perception will be done in two stages following the RumourEval share task approach. The first stage will be to determine the stance of the assertion, i.e. whether a rumour about an unsubstantiated event either supports, denies, questions or comments on the rumour. The second stage will be to determine to what degree a rumour about an unsubstantiated event is true.
Epidemics such as SARS, Ebola and pandemic influenza threaten lives and livelihoods across the globe. Based on the PI’s nine years of experience in epidemic intelligence from social sensors the goal of this project overall goal is to contribute to a new generation of high quality risk alerting systems that can incorporate an understanding of the veracity of the event. The PI’s previously reported experiments against human standards have shown that temporal patterns in health news are effective at outbreak event detection (F1=0.56) measured against ProMed-mail alerts. The resulting BioCaster system was in regular use by national and international public health agencies such as the World Health Organization. This project aims to build on this legacy by investigating novel methods for measuring veracity. This will build on (a) recent community challenges in rumour veracity detection in RumourEval, and (b) the latest advances in natural language processing technology by groups at Stanford University (PI: C. Manning) and McGill University (PI: Y. Bengio), i.e. in distributed semantic feature representations and deep learning.
Importantly we expect the deep learning techniques we explore to be able to learn novel veracity features. This is a key step as it avoids the fragility of hand-built feature selection upon which all previous operational systems have been designed. The distributed features will be learnt in an unsupervised mode from high volumes of un-annotated news and Twitter data. Examples of a priori features used in existing systems include international travel, infection of healthcare workers and mention of deliberate release of agents. We expect the result of our new approach to be a system that automatically learns the most relevant clues for assessing rumour veracity and which is robust to a range of event types (e.g. early stages of novel asyndromic outbreaks) and reporting styles.
This study will develop a data-driven regional scale model of the interaction between the physical dynamism of the coastal landscape and human responses as well as drivers of such dynamism. It will achieve this though an integration of state-of-the-art satellite derived earth observation products, aerial photography, and spatial datasets on socio-economic indicators and change to investigate the link between socio-economic and natural system dynamics in the coastal zone. Particular attention will be given to how the modelled dynamism of this society-nature coupled system affects the extent, value, and use of regional natural capital and ecosystem services. Validation/modification will be achieved through a comparison of model predictions with an in-depth analysis of change derived from 26 years of annual aerial photography of the UK coast (Environment Agency photography archive) and historic socio-economic data on coastal protection and other infrastructures and services. The model development will be regionally focused on the UK East coast but this is seen very much as a prototype for future application across the globe.
Some £150 billion of assets and 4 million people are currently at risk from coastal flooding in the UK. Protecting lives, livelihoods and infrastructure with ‘hard’, engineered defences is costly; these costs will rise with climate change and increasing human occupation and use of the coastal zone. Within this context, there has been considerable interest in the role of vegetated foreshores in providing natural coastal protection, both through the dissipation of storm wave energy and in increasing the stability of intertidal surfaces. The UK National Ecosystem Assessment (2011) provisionally estimated that for England this buffering role provides £3.1 – £33.2 billion worth of capital savings in sea-defence costs. However, a much more systematic approach is now required, one which looks at the value of coastal natural capital assets against artificial assets, enabling decision-makers to make a strong case for investment in coastal foreshore restoration and creation. This approach is particularly needed along the low lying, storm surge-threatened coastline of eastern England where coastal habitats are essential to the economy, but are under severe threat from erosion, flooding and climate change (sea level rise and changing storminess).
Machine learning for satellite imagery is now developing into a methodology that can be used for pattern recognition and analysis across large spatial and temporal scales. Historical satellite data allow this to be extended back more than 30 years, while newer satellites with higher resolution and temporal frequency offer the prospect of improved predictive capabilities for estimation of future coastal risk. Tools such as Google Earth Engine now make it possible to apply this kind of learning technique rapidly to petabytes of data and to test the effects of multiple different algorithms, including deep learning frameworks such as tensorflow. At the same time agent-based modelling is maturing as a technique to represent the dynamic effects of human-environment interaction and to assess the likely effects of different impacts on the built environment, using intelligent agents that are able to learn from the past and adapt to future change. Combining these techniques to look at risk in vulnerable coastal environments would allow better understanding of the economic impacts of coastal change processes.Student: Martin Rogers
Projects running at University of Birmingham. For further details contact Dr Emmanouil Tranos.
Geological Disposal Facilities (GDFs) are, at present, the preferred option for the safe disposal of high and intermediate level waste. The internationally accepted conceptual model that has been firmly established for the last 30 years is based upon the 'multi-barrier system', whereby a series of engineered and natural barriers act together to isolate the wastes and contain the associated radionuclides. One of the disposal concepts under consideration is the KBS-3V disposal concept, a multi-barrier system consisting of a metal container, copper overpack, buffer/backfill and a high strength natural rock. A key part of this concept is the use of bentonite clay as the engineered barrier, it swells in contact with water to provide a mechanical buffer to protect the metal container and to mop up any radionuclides that may might leak from the container.
Water seeping from the natural geological environment produces bentonite hydration, swelling and the formation of a clay gel that can penetrate into pores and fine fractures of the rock. Chemical or physical erosion processes on the clay surface gel may generate mobile nano-sized colloidal particles which are potential carriers for radionuclide contaminants. Recent studies have shown that the in-situ migration of actinides (i.e. Am and Pu) were strongly facilitated by bentonite colloids. For this reason, it is important to study colloid generation mechanisms, to establish their role on radionuclide transport in the environment and to evaluate the associated risks.
The safety case for the implementation of a GDF requires the consideration of processes evolving over a long timescale, these necessitate the development of sound predictive modelling tools that are based on rigorous short term experiments and sensible extrapolation over the lifetime of a GDF. The technical approach that will be adopted in this study therefore uses a variety of methods (laboratory experimentation and modelling) to build evidence for a greater understanding and use of bentonite buffers that will underpin the required safety case for a GDF. Amongst the questions we will attempt to address are: (1) How is radionuclide uptake and mobility over rock affected by clay colloid particle size, morphology and surface charge? (2) What clay colloid particles are released by bentonite materials, what is their radionuclide uptake and mobility? (3) Can colloid mobility be successfully modelled under different hydrogeochemical conditions at the level of an intact natural fracture core sample in the laboratory? (4) Can this then be represented appropriately in field-scale numerical models?Student: Lauren Dackombe
The reconstruction of the Alberta oil sands represents one of the largest restoration challenges on the planet. Such constructed landscapes must be designed to develop rapidly over time, producing expansive productive forests that maximize the economic potential of the landscape and maintain high water fluxes to flush ecologically harmful salts. At the same time, the landscape design must limit the risk of failure, forming resilient environments which follow planned trajectories within the dry sub-humid climate of the Western Boreal. This research project integrates sound mathematical and statistical analysis of big data to identify the sources of such risk, their drivers and impacts within these complex boreal systems.
A system dynamics model informed from current process based understanding will be developed representing hydrological stores and transfers within the Western Boreal Plain. These landscapes have formed the focus of close to two decades of research by the project partners; legacy data which is being compiled into a readily available data rich achieve (measurements from ~15000 wells in addition to a range of supplementary hydrological, meteorological and ecological data). The models will be optimised and evaluated against this hydrological data. The modelling approach will be developed within STELLA, an intuitive icon-based graphical interface that enables detailed exploration and development of system dynamics. It will offer the opportunity to develop multi-level, hierarchical model structures that can serve as building blocks for large complex systems. The top down approach will enable key leverage points to be identified and the analysis of different system organisations and structures. The modelling framework will be applied to explore how the climate cycle, a superposition of varying climate signals of different intensities and phases, provides the overarching driver of the ecohydrology behaviour of these landscape. Further, how this climate cycle cascades through landscape storage units that vary in configuration, extent and scale of connectivity. It will determine how such complex interactions trigger periods of water scarcity over varying spatial and temporal scales that place individual ecosystem at risk of failure. The project will identify how such collapses and the resultant shift in hydrological function result in a loss of system resilience that may cascade through the landscape leading to its potential large scale failure.
The research will also involve integration within the Water Science group and Birmingham Institute of Forest Research (BIFoR), joining the core team of researchers within the Hydrology Ecology and Disturbance (HEAD3) project funded in partnership by industrial partners Syncrude and Canadian Natural Resource Ltd. This consortium composes of the University of Birmingham, University of Alberta, McMaster University and University of Waterloo.Student: Samantha Probert
This project aims to explore the relationship between (tele)commuting and weather. Researchers have spent significant effort in modelling the effects of weather conditions and also extreme weather events on commuting and transport infrastructure. Also, prior research has tried to understand the role that Information and Communication Technologies (ICTs) can perform as an enabling platform for working remotely and avoiding or decreasing physical commuting. This PhD project will build upon these two streams of research and also incorporate a risk dimension which is related to extreme weather and climate change. For instance, changes to the daily commute can be made during extreme weather (e.g. floods, heatwaves, snow), allowing commuters to select the mode of transport which is most resilient for the conditions. At the extreme, telecommuting can be seen as a powerful tool to increase resilience.
Cities are organised in space as complex urban networks, which are connected together through various diverse layers of infrastructure (from transport to digital infrastructure). These infrastructural layers vary from city to city and will affect the capacity of individuals to commute. With respect to telecommuting, the complexity of the above argumentation increases if we consider labour and housing markets. For instance, not every industry can support and take advantage of telecommuting opportunities. Similarly, people whose occupation enables telecommuting may reside in close proximity or in areas of similar socio-economic profile. For instance, problems with Internet broadband connectivity in rural areas might still be a deteriorating factor for working from home and avoiding physical commuting. This PhD project will build upon the above narrative and answer research questions related with the capacity of places and individuals to telecommute, the relation of telecommuting with with weather and extreme weather events, and the link between infrastructure - both digital and transport infrastructure - with telecommuting.Student:Hannah Budnitz
Ice shelves comprise floating extensions of the inland ice of the East, West and Antarctic Peninsula ice sheets. They provide crucial buttressing forces holding back the flow of the ice sheets towards the sea, thus regulating rates of global sea-level rise. In recent years, ice shelves in the Antarctic Peninsula have been observed to substantially retreat and even catastrophically collapse. These major global change episodes have been linked to a variety of causal mechanisms, yet no single clear explanation has emerged, making physically-based forecasts of future change problematic.
This project will provide expertise and training in satellite remote sensing, expert elicitation, Bayesian methods and risk assessment to address the problem of ice shelf collapse. New satellite data (microwave and optical imagery) will be analysed to assess ice shelf retreat and collapse since 2010, placing these new observations in the context of the last half decade of observational ice shelf history. An expert elicitation exercise will quantitatively assess expert opinion of ice shelf collapse risk in the next 100 years.
These datasets will then be combined with existing environmental, geophysical and glaciological ‘Big Data’-sets in a Bayesian nonparametric statistical model framework to calculate the probabilities of ice shelf collapse risk during the next 100 years. The candidate will gain expertise and experience in glaciology, satellite remote sensing analysis, expert elicitation, and Bayesian numerical methods. This combination of skills is unique and in high demand and is expected to result in a number of high-impact outputs. The collapse timing estimates generated by this project may then be used by ice sheet modellers to more accurately forecast the future contributions of the Antarctic ice sheets to global sea-level rise.Please contact Dr Nicholas Barrand (Birmingham):
The project aims to develop a holistic strategy for UK flood defence assets in order to limit the impacts of fluvial flooding under future climate and land use scenarios. Assessment of the success of international flood defences and their suitability in application to the UK system will be key to the project. The PhD will focus particularly on examples from The Netherlands. Here, the success of Dutch techniques such as the Polder system, Dykes and Dams, and “Room for the River” will be assessed. Application of Dutch water management techniques to the UK system will be considered. Through the use of computer modelling, map analysis, private/public/government and community engagement, adaptations to Dutch techniques will be developed so as to fit with UK hydrological processes and requirements.
Both soft and hard engineering techniques and community engagement will be considered and assessed equally. Through analysis, adaptation and modelling of international measures to limit fluvial flooding, the project will produce a flood defence strategy ready for application to the UK.
To allow for more detailed and accurate analysis and modelling of flood defence recommendations, the project may be developed to focus on a specific area of the UK. However, to account for multi-hazard or cascade effects, the scale should be reduced no further than River Basin level. At this scale, general geology/hydrology/ meteorological etc. trends can be accounted for but the diversity and interconnectivity of processes (e.g influence of tributaries on main channel) is not lost.
Big Data – Meteorological (precipitation, temperature, air moisture etc.), River Stage, Soil moisture and Groundwater data for the study area (River Basin/UK) will all be assessed to determine long term hydrological trends and changes to flood intensity/ frequency. Historical and contemporary map analysis will be undertaken to look at land use change. This will place modern flooding and processes in a historical context and help understand the severity of modern day flood risk.
Datasets available from the relevant UK organisations extend at least 40 years, there is expected to be a significant amount of secondary data to be used within this project. Multi-hazard modelling of future climatic, hydrological and land use scenarios will also be undertaken and will help determine and understand future flood risk. Importantly, multi-defence modelling will also be undertaken to determine the influence of flood defence assets on future flood risk.
Risk – Big Data analysis of historical flooding and modelling of future flooding and map analysis will help visualise risk and put it into historical context. Maps will be produced, indicating the extent of future flooding with and without mitigation and will be available for the study area community to access. Community/private/government organisations will be included in discussion regarding flood defence proposals, the maps and model outputs will improve understanding of future flood risk and will support positive discussion regarding mitigation. The researcher will take on community/stakeholder concerns to develop mitigation recommendations which meet both hydrological and public requirements.
Mitigation - The ultimate aim of the project is to develop a holistic plan for fluvial flood mitigation within a River Basin (RB)/ UK. Recommendations will be based on extensive international research, analysis of long term hydrological trends and future climate scenarios, and modelling of the influence of flood defence assets. Due to the extent of research used in development of recommendations it is highly likely that a successful project will effectively help mitigate against flood risk on a large, River Basin of National scale.
Flooding is the most significant natural hazard in the UK and under future climate scenarios is projected to increase in frequency and magnitude. The extensive environmental, social and economic cost of flooding have driven traditional implementation of point based, hard engineering flood defence structures. These structures are now recognised to limit natural hydrological processes and exacerbate flooding downstream of the structure. There is now a paradigm shift in flood mitigation away from traditional measures towards holistic, catchment based approaches which focus on green engineering and reinstatement of natural hydrological functions. This approach, driven by incentives such as the EU Water Framework Directive and DEFRA Making Space for Water, has multiple hydrological and ecological benefits and is widely perceived as the most sustainable approach to managing catchment water balance and flooding under future climate scenarios.
The UK has made some progress within this field and 12 River Basin Management Plans (RBMPS) are now in place within England, Wales and Scotland. First implemented in 2009 these RBMPS offer the first significant approach to holistic water management however, they are yet to achieve their full potential, localised/disconnected water management and flooding remains a significant issue across the UK.
Innovative and proactive work is needed in order to prepare the UK for more significant flooding under future climate and land use scenarios. RBMPs offer a good base but much more work is needed, flooding and RBMPs need to be highlighted and supported in the public and political domain but in order to do this, each RBMP needs to have clear and achievable objectives. RBMPS and effective flood mitigation are poorly understood, and agencies responsible for implementation of RBMPS are often curtailed by finance, time politics or lack of invention. It is therefore essential that academia supports the development of RBMPS through provision of new concepts and knowledge. Only together will we truly be able to prepare society and our environment for the hydrological challenges that lie ahead.
This research will provide new and tested techniques for sustainable fluvial flood mitigation. Working closely with, and providing recommendations to government partners such as the EA this work will drive the advancement and development of effective RBMPS supporting a holistic approach to UK flood mitigation.
This PhD project will investigate the effect of enhanced carbon dioxide (CO2) concentrations, caused by anthropogenic climate change, upon ecological networks within oak woodlands. It will characterise the response of ecological networks to CO2 enhancement by measuring species networks under both enhanced (550 ppm) and non-enhanced (ambient ca. 400 ppm) CO2 concentrations. This CO2 enhancement is highly likely to occur in the next 50-100 years.
Oak species dominate the UK’s lowland woods and, like many native trees, are under threat from emerging pests and disease, particularly oak processionary moth (OPM) and acute oak decline (AOD). These new pests and diseases act alongside more established environmental stresses that are often associated with climate change. They add to the constant pressure of established and endemic threats such as oak mildew and honey fungus.
To determine the interaction pathways between oak (and other woodland species), invertebrate pests and tree pathogens, the student will construct the most comprehensive and highly-resolved oak woodland ‘networks of ecological networks’ using state-of-the art molecular ‘community metagenomic approaches. These networks together with environmental data will then be applied in environmental modelling to predict long-term risks associated with tree pathogens.
A ground-breaking ‘Free-Air Carbon Dioxide Enrichment' (FACE) facility, currently being installed in the research forest (Mill Haft) of the University of Birmingham, will be used to investigate the effect of enhanced CO2 on these sensitive ecological networks. The Mill Haft site enables world-leading scientists to explore the forest thoroughly, taking measurements everywhere from deep within the soil to above the tree canopy. The FACE experiment, applied to oak and other woodland tree species, will provide a formidable addition to the Mill Haft site’s capability to probe the biosphere’s response to increases in atmospheric CO2.Please contact Dr Francis Pope (Birmingham):
The transport of people and goods is key to the success of the modern city. Hence good transportation is a pre-requisite for a successful city. However, transport has a darker side in its generation of atmospheric pollutants such as nitrogen oxides and particulate matter. Ambient air pollution is a major risk factor for premature deaths, years of life lost, and years of life lost to disability. The cost of air pollution through loss of life and ability to work has a very detrimental impact upon the worldwide economy; in the UK this economic impact is estimated to be £54B per year (OECD). In particular air pollution is a key risk factor for cancers, lower respiratory infections, cardiovascular and cerebrovascular disease. Worldwide air pollution kills in excess of 7 million people each year (WHO). In the UK, the major emitters of the key air pollutant (PM and nitrogen oxides) are from vehicle use. The Department of Health’s Committee on the Medical Effects of air Pollutants (COMEAP) estimate that long term exposure to air pollutants result in 29,000 deaths in the UK each year and is instrumental in triggering more than 200,000 heart attacks.
This project will investigate the relationships between traffic fleet composition and movement within the city of Birmingham, UK, and how this affects the spatial distribution and concentration of air pollutants. Telematics data will provide real time data on fine grained vehicle flow in the city allowing an understanding of precise acceleration and braking behaviour and behavioural aspects of fine grained mobility.
The objectives of the project are as follows:
As cities become increasingly congested with growing traffic, the increase in transport-related emissions has raised concerns over the risk on human health and urban environment. Street canyons are hotspots of traffic-related air pollution, because at this spatial level significant variation of traffic volumes and the pollutant transport to receptors (i.e. exposed population) are both at a time scale of minutes. Many emission pollutants are chemically reactive, and fast reactions can take place at second-to-minute timescales to generate secondary pollutants. As a result, the variation of urban air quality can only be understood by considering the combined effects of fleet composition, traffic-produced turbulence, street and building geometries, and meteorological conditions in real time (i.e., a time scale of minutes). Fundamental scientific questions remain, for example, how to model real-time dynamics of the cause-and-effect process from transport activity to distribution of air pollution within street canyons, and how to account for both the heterogeneous distribution of air pollution across an urban street network and its variation in abundance with time. Answers to these questions are of utmost importance to better assess individual or population exposure and the potential risks on human health.
To model this cause-and-effect chain, an integrated modelling approach is built upon bridging research on three complex systems: (i) dynamical changes in travel demand, (ii) vehicle emissions, and (iii) dispersion of air pollutants in street canyons. This approach is thus, not only able to provide detailed indication of air quality in street canyons, but also can identify street sections with high volumes of vulnerable travellers, such as pedestrians and cyclists. These together contribute to a better quantification of potential health risks that could impose on a population in question. Furthermore, travel demand changes respond to different policies, such as speed limit and road pricing in Clean Air Zones; the effects propagate from varying traffic to emissions and cause the change in pollution concentrations. It is through the causes and effects reflected by this model chain that transport policies aiming at risk mitigation can be evaluated in regards to their effectiveness in reducing adverse impacts on human health and environment.
This project inherently deals with issues of Big Data, risk and mitigation in the integrated modelling approach. PhD students will receive related training from three aspects: 1) urban transport and air quality modelling and applications, 2) programming, computing and data handling, and 3) uncertainty and risk related analysis.Student: Helen Pearce
Urban shrinkage is a common phenomenon throughout the world despite urbanisation being a well-established trend. With increasing globalisation, cities in both developed and developing counties experience economic downturn, population decline, de-urbanisation. Reasons and solutions of urban shrinkage have been discussed and documented extensively for developed countries (e.g. UK, US, Germany, and Japan). However, deeper understanding of urban shrinkage issues and how to resolve them in the developing world, especially in China with a large number of fast growing cities, is still lacking. Insights from developed countries could be learned in order to better address the challenges for building resilience into shrinking cities of the developing world. Northeast China provinces, including Liaoning, Jilin, and Heilongjiang, now known as the “rust belt” in China, have topped the chart in the number of shrinking cities due to resource depletion, deindustrialization, and demographic changes. Similarly, most of the top UK declining cities are in the north of England as a strong indication of the North-South divide. Core cities of North England, such as Liverpool, Manchester, Leeds, Sheffield and Newcastle, share some common characteristics with their counterpart in Northeast China in terms of industrial legacy, aging population, and loss of growth power to support surrounding areas. Insights could be gained for those cities in both countries by a comparative study of their resilience to internal and external changes.
With a focus on Northeast China cities, this project seek to 1) identify and better understand the spatial, economic and social issues of shrinking cities and the underpinning mechanisms in relation to other Chinese cities, and 2) design adaptive strategies to build resilience into these cities through a comparative study of urban shrinkage in China and UK. This project will expand the existing research by combining the spatial, economic and social dimensions of human mobility and urban interactions and considering the interplay of all three dimensions in defining a multidimensional measurement and assessment of urban resilience. Furthermore, this project will promote the collaboration between the UoB research team and the Chinese stakeholders in order to incorporate local interests and benefit decision-makers with both general and place-based strategies in policy-making.
This project will facilitate the identification and acquisition of various traditional and novel sources of data, which can be leveraged them to gain better insights by leading-edge big data analytics and AI techniques. The substantive and methodological knowledge that this PhD project will generate will directly contribute to the UK Industrial Strategy Grant Challenge of Artificial Economy and the Data Economy as well as on the Key Policies on Infrastructure and Places. Moreover, this PhD project will contribute to the research objectives of the Alan Turing Institute, which the University of Birmingham recently joined. The latter signifies the broader recognition of AI and the Data Economy as a research priority for the University of Birmingham.Please contact: Dr Zhaoya Gong (Birmingham)