Improved understanding of the functioning of hydrological systems and dependent ecology is essential for optimal environmental management. Floodplains in particular are important due to the ecosystem services they provide. The species composition of floodplain vegetation and their ecosystem functions (e.g. leaf CO2 uptake and transpiration) are very sensitive to the soil hydrological regime, which is highly variable both spatially and temporally. The hydrological regime also affects the temperature and nutrient regime of the root environment, leading to indirect impacts on vegetation. However, the mechanisms controlling these interdependencies are not well established. The proposed project, FUSE, aims to advance this knowledge at a variety of scales. A better understanding of these vulnerable ecosystems will allow improved environmental management, under current and future conditions. A field study is proposed in the Oxford Floodplain (OFP). This study will build upon an existing hydrological monitoring network currently in place in the Oxford Meadows Special Area of Conservation (SAC). The aims will be achieved by a sophisticated combination of environmental data and computer models. This involves state-of-the-art tools: a Wireless Underground Sensor Network (WUSN) and related monitoring of environmental variables, as well as high-resolution Earth Observation (EO, i.e. satellite) data. WSNs are a relatively recent application of technology; uptake of this technology by environmental scientists enables continuous monitoring that is both scalable and less intrusive on its surroundings. It is desirable for land-based sensor networks to have few or no above-ground components, for aesthetic and security reasons, as well as to avoid interference with land management practices. Recently, this has led to the introduction of WUSNs where all or at least the majority of the sensing and transmitting components are underground. WUSNs are rare, especially in the UK, and have not been tested long-term in a challenging environment such as the OFP. Reliability and the potential distance of data transmission depend on a number of factors, including the soil type, sensor installation depth, soil moisture content and technological factors. These will be researched extensively in the FUSE project, initially using existing data on the OFP hydrological regime, soils and vegetation height/density. The precise design of the WUSN will be determined with the aid of a geostatistical procedure. FUSE will allow researchers to reliably measure underground spatial variability at hitherto unachievable resolutions of less than a metre. The project will use a mesh of simple wireless sensor nodes previously developed at Imperial College ('Beasties'). These nodes will gather environmental data, and route these to a base-station that transmits to a remote database via GPRS. The low-cost, low-power Beasties have been used extensively in similar, but less challenging environments. The enhanced sensor technology will be entirely transferable. Theoretical tools in FUSE comprise of a simulation model (SCOPE_SUB), that can be used to describe and predict the interaction between the soil (soil moisture content, soil temperature and nutrient status), the vegetation (root water/nutrient uptake, CO2 uptake and transpiration), and the hydrometeorological regime. Furthermore we will use geospatial models to spatially interpolate between measured, modelled and EO data, thereby increasing data-density. EO data will serve to guide the continuous (in time) simulation model predictions. In that way high resolution maps of key soil and vegetation variables can be constructed. Computer Science tools, e.g. a so-called Integrated Development Environment to help environmental scientist to set up and test the WUSN, and a Web portal for quality control, sensor calibration, time series- and geospatial-analysis, parameter estimation and real-time model output, will be developed.

However, access to silicon prototyping facilities remains a challenge in the UK due to the high cost of both equipment and the cleanroom facilities that are required to house the equipment. Furthermore, there is often a disconnect in communication between industry and academia, resulting in some industrial challenges remaining unsolved, and support, training, and networking opportunities for academics to engage with commercialisation activities isn't widespread. The C-PIC host institutions comprising University of Southampton, University of Glasgow and the Science and Technologies Facilities Council (STFC), together with 105 partners at proposal stage, will overcome these challenges by uniting leading UK entrepreneurs and researchers, together with a network of support to streamline the route to commercialisation, translating a wide range of technologies from research labs into industry, underpinned by the C-PIC silicon photonics prototyping foundry. Applications will cover data centre communications; sensing for healthcare, the environment & defence; quantum technologies; artificial intelligence; LiDAR; and more. We will deliver our vision by fulfilling these objectives: Translate a wide range of silicon photonics technologies from research labs into industry, supporting the creation of new companies & jobs, and subsequently social & economic impact. Interconnect the UK silicon photonics ecosystem, acting as the front door to UK expertise, including by launching an online Knowledge Hub. Fund a broad range of Innovation projects supporting industrial-academic collaborations aimed at solving real world industry problems, with the overarching goal of demonstrating high potential solutions in a variety of application areas. Embed equality, diversity, and inclusion best practice into everything we do. Deliver the world's only open source, fully flexible silicon photonics prototyping foundry based on industry-like technology, facilitating straightforward scale-up to commercial viability. Support entrepreneurs in their journey to commercialisation by facilitating networks with venture capitalists, mentors, training, and recruitment. Represent the interests of the community at large with policy makers and the public, becoming an internationally renowned Centre able to secure overseas investment and international partners. Act as a convening body for the field in the UK, becoming a hub of skills, knowledge, and networking opportunities, with regular events aimed at ensuring possibilities for advancing the field and delivering impact are fully exploited. Increase the number of skilled staff working in impact generating roles in the field of silicon photonics via a range of training events and company growth, whilst routinely seeking additional funding to expand training offerings.

The community that investigates light propagation, localization and its nano-scale interaction with quantum emitters in complex photonic and biological media is standing up with multidisciplinary background and a joint interest in complex nanophotonics. A new generation of scientists engaging with the topics of complex nanophotonics is emerging from different fields, from single-molecule nano-optics to biomedical imaging and sensing, from quantum networks to light management for solar cells, from Anderson localization of light to high sensitive biosensing. The Complex Nanophotonics Science Camp will discuss latest progress, future developments and facilitate the formation of a community driven by the next generation of junior scientists linked by the common passion for complexity and nano- and bio-photonics, by giving them visibility and building a contact network around them. We have chosen the format of a "science camp" to break from the traditional conference format, which are often showcases of career-long investigations, to try to engage the creativity of early-stage scientists (strictly <10 years from their Ph.D.) and create new scientific connections, fostering critical thinking. For this reasons we have left one last afternoon free to self-organize during the conference, with an open program, taking inspiration from other science camp events, like Google SciFoo, and we have chosen a long poster session to prepare for an evening debate on science-related topics like science communication, data visualization or open access and social media for science.

On a daily basis huge amounts of geospatial data and information that record location is created across a wide range of environmental, engineered and social systems. Globally approximately 2 quintillion bytes of data is generated daily which is location based. The economic benefits of geospatial data and information have been widely recognised, with the global geospatial industry predicted to be worth $500bn by 2020. In the UK the potential benefits of 'opening' up geospatial data is estimated by the government to be worth an additional £11bn annually to the economy and led to the announcement of a £80m Geospatial Commission. However, if the full economic benefits of the geospatial data revolution are to be realised, a new generation of geospatial engineers, scientists and practitioners are required who have the knowledge, technical skills and innovation to transform our understanding of the ever increasingly complex world we inhabit, to deliver highly paid jobs and economic prosperity, coupled with benefits to society. To seize this opportunity, the Centre for Doctoral Training in Geospatial Systems will deliver technically skilled doctoral graduates equipped with an industry focus, to work across a diverse range of applications including infrastructure systems, smart cities, urban-infrastructure resilience, energy systems, spatial mobility, structural monitoring, spatial planning, public health and social inclusion. Doctoral graduates will be trained in five core integrated geospatial themes: Spatial data capture and interpretation: modern spatial data capture and monitoring approaches, including Earth observation satellite image data, UAVs and drone data, and spatial sensor networks; spatial data informs us on the current status and changes taking place in different environments (e.g., river catchments and cities). Statistical and mathematical methods: innovative mathematical approaches and statistical techniques, such as predictive analytics, required to analyse and interpret huge volumes of geospatial data; these allow us to recognise and quantify within large volumes of data important locations and relationships. Big Data spatial analytics: cutting edge computational skills required for geospatial data analysis and modelling, including databases, cloud computing, pattern recognition and machine learning; modern computing approaches are the only way that vast volumes of location data can be analysed. Spatial modelling and simulation: to design and implement geospatial simulation models for predictive purposes; predictive spatial models allow us to understand where and when investment, interventions and actions are required in the future. Visualisation and decision support: will train students in modern methods of spatial data visualisation such as virtual and augmented reality, and develop the skills on how to deliver and present the outputs of geospatial data analysis and modelling; skills required to ensure that objective decisions and choices are made using geospatial data and information. The advanced training received by students will be employed within interdisciplinary PhD research projects co-designed with 40 partners ranging from government agencies, international engineering consultants, infrastructure operators and utility companies, and geospatial technology companies; organisations that are ideally positioned to leverage of the Big Data, Cloud Computing, Artificial Intelligence and Internet of Things (IoT) technologies that are predicted to be the key to "accelerating geospatial industry growth" into the future. Throughout their training and research, students will benefit from cohort-based activities focused on group-working and industry interaction around innovation and entrepreneurship to ensure that our outstanding researchers are able to deliver innovation for economic prosperity across the spectrum of the geospatial industry and applied user sectors.

Meeting the Paris climate change commitments will be extraordinarily challenging, and even if they are met, may require extensive global deployment of greenhouse gas removal (GGR) technologies resulting in net negative emissions. If certain major emitters do not meet their Paris commitments and/or wider international cooperation is reduced then the trajectory needed to reduce emissions to Paris levels after a delay will be even more severe, potentially leading to the need for even greater reliance on such net negative emissions technologies. At present, the technical feasibility, economics, implementation mechanisms and wider social and environmental implications of GGR technologies remain relatively poorly understood. It is highly uncertain that GGR technologies can be implemented at the scales likely to be required to avoid dangerous climate change and without causing significant co-disbenefits or unintended consequences. Our GGR proposal presents a unique combination of a multi-scale assessment of the technical performance of GGR technologies with an analysis of their political economy and social license to operate, with a particular focus on how these elements vary around the world and how such considerations impact region-specific GGR technology portfolios. Currently, some portray GGR technologies as a panacea and virtually the only way of meeting aggressive climate targets - an essential backstop technology or a 'bridge' to a low-carbon future. One part of our project is to work with the models of the global economy (integrated assessment models) and better reflect these technologies within those models but also to use models at different scales (global, regional, national, laboratory scales) to understand the technologies better. We also seek to better understand how deployment of these technologies interact with the climate system and the carbon cycle and what the implications are for the timings of wide-scale rollout. By contrast, sceptics have expressed concerns over moral hazard, the idea that pursuing these options may divert public and political attention from options. Some critics have even invoked terms such 'unicorns', or 'magical thinking' to describe the view that many GGR technologies may be illusory. We will seek to understand these divergent framings and explicitly capture what could emerge as important social and political constraints on wide-scale deployment. As with nuclear power, will many environmentalists come to view GGR technologies as an unacceptable option? Understanding the potential scaling up of GGR technologies requires an understanding of social and political concerns as well as technical and resource constraints and incorporating them in engineering, economic and climate models. This aspect of our proposal necessarily brings together social science, engineering and environmental sciences. What is the biggest challenge to scaling up BECCS for example? Is it the creation of the sustainable biomass supply chain, the deployment of CO2 capture technology or the transport and storage infrastructure that is rate limiting? Or is it more likely the social acceptability of this technology? Further, we will provide insight into the value of international and inter-regional cooperation in coordinating GGR efforts. For e.g., would it make more sense for the UK to import biomass, convert it to electricity and sequester the CO2, or would it be preferable pay for this to happen elsewhere? Conversely, how might the UK benefit from utilising our relatively well characterised and extensive CO2 storage infrastructure in the North Sea to store CO2 on behalf of both the UK and others? More generally, we will explore how stakeholders in key regions view the suite of GGR technologies. Finally, we will quantify the option value of GGR - what is the value in early deployment of GGR technologies? How does it provide flexibility in meeting our near term carbon targets?