Space weather describes the changing properties of near-Earth space, which influences the flow of electrical currents in this region, particularly within the ionosphere and magnetosphere. Space weather results from solar magnetic activity, which waxes and wanes over the Sunspot cycle of 11 years, due to eruptions of electrically charged material from the Sun's outer atmosphere. Particularly severe space weather can affect ground-based, electrically conducting infrastructures such as power transmission systems (National Grid), pipelines and railways. Ground based networks are at risk because rapidly changing electrical currents in space, driven by space weather, cause rapid geomagnetic field changes on the ground. These magnetic changes give rise to electric fields in the Earth that act as a 'battery' across conducting infrastructures. This 'battery' causes geomagnetically induced currents (GIC) to flow to or from the Earth, through conducting networks, instead of in the more resistive ground. These GIC upset the safe operation of transformers, risking damage and blackouts. GIC also cause enhanced corrosion in long metal pipeline networks and interfere with railway signalling systems. Severe space weather in March 1989 damaged power transformers in the UK and caused a long blackout across Quebec, Canada. The most extreme space weather event known - the 'Carrington Event' of 1859 - caused widespread failures and instabilities in telegraph networks, fires in telegraph offices and auroral displays to low latitudes. The likelihood of another such extreme event is estimated to be around 10% per decade. Severe space weather is therefore recognised in the UK government's National Risk Register as a one-in-two to one-in-twenty year event, for which industry and government needs to plan to mitigate the risk. Some studies have estimated the economic consequence of space weather and GIC to run to billions of dollars per day in the major advanced economies, through the prolonged loss of electrical power. There are mathematical models of how GIC are caused by space weather and where in the UK National Grid they may appear (there are no models of GIC flow in UK pipelines or railway networks). However these models are quite limited in what they can do and may therefore not provide a true picture of GIC risk in grounded systems, for example highlighting some locations as being at risk, when in fact any problems lie elsewhere. The electrical model that has been developed to represent GIC at transformer substations in the National Grid misses key features, such as a model of the 132kV transmission system of England and Wales, or any model for Northern Ireland. The conductivity of the subsurface of the UK is known only partly and in some areas not at all well. (We need to know the conductivity in order to compute the electric field that acts as the 'battery' for GIC.) The UK GIC models only 'now-cast', at best, and they have no forecast capability, even though this is a stated need of industry and government. We do not have tried and tested now-cast models, or even forecast models, of magnetic variations on the ground. This is because of our under-developed understanding of how currents flow in the ionosphere and magnetosphere, how these interconnect and how they relate to conditions in the solar wind. In this project we will therefore upgrade existing or create new models that relate GIC in power, pipe and railway networks to ionospheric, magnetospheric and solar wind conditions. These models will address the issues we have identified with the current generation of models and their capabilities and provide accurate data for industry and governments to assess our risk from space weather. In making progress on these issues we will also radically improve on our physical understanding of the way electrical currents and electromagnetic fields interact near and in the Earth and how they affect the important technologies we rely on.

Fire is the most important disturbance agent worldwide in terms of area and variety of biomes affected, a major mechanism by which carbon is transferred from from the land to the atmosphere, and a globally significant source of aerosols and many trace gas species. Forecasting of fire risk is undertaken in many fire-prone environments to aid dry season pre-planning, and appropriate consideration of fire is also required within dynamic vegetation models that aim to examine vegetation-climate interactions in the past, present and future. Current methods of mapping fire 'risk', 'susceptabilty' or 'danger' use empirical fire danger indexes calibrated against past weather conditions and fire events. As such, they provide little information on process, are appropriate to deal only with current climate, land use and land cover change (LULCC), and are limited in their ability to be tested and constrained by EO products or other observational data (e.g. ignition 'hotspots', burned area, pyrogenic C release etc). The objective of FireMAFS is to resolve these limitations by developing a robust method to forecast fire activity (fire danger indices, ignition probabilities, burnt area, fire intensity etc) via a process-based model of fire-vegetation interactions, tested, improved, and constrained using state-of-the-art EO data products and driven by seasonal weather forecasts issued with many months lead-time. Specific aims are to: (i) develop the methodology for using EO and other observational data on vegetation (fuel) condition, fire activity and fire effects to test, improve and constrain sub-components and end-to-end predictions of a forward model of fire-vegetation interactions and to inform, test and restrict the model when used in forecast mode to ensure it is nudged along the optimum trajectory, and is furthermore reset when the observation period catches up with the prior period of prediction; (ii) drive the improved forward model by seasonal weather forecast ensembles, predicting spatio-temporal variability in fire 'danger' indices, fire occurrence and a range of subsequent fire behaviour and fire effects (intensity, rate of spread, burned area, above/below ground C stock change, and trace gas/aerosol emissions) and evaluate their usefulness for seasonal fire prediction at 1 / 6 months lead time and for prognostic studies run under future projected climate and LULCC scenarios.

Fire is the most important disturbance agent worldwide in terms of area and variety of biomes affected, a major mechanism by which carbon is transferred from from the land to the atmosphere, and a globally significant source of aerosols and many trace gas species. Forecasting of fire risk is undertaken in many fire-prone environments to aid dry season pre-planning, and appropriate consideration of fire is also required within dynamic vegetation models that aim to examine vegetation-climate interactions in the past, present and future. Current methods of mapping fire 'risk', 'susceptabilty' or 'danger' use empirical fire danger indexes calibrated against past weather conditions and fire events. As such, they provide little information on process, are appropriate to deal only with current climate, land use and land cover change (LULCC), and are limited in their ability to be tested and constrained by EO products or other observational data (e.g. ignition 'hotspots', burned area, pyrogenic C release etc). The objective of FireMAFS is to resolve these limitations by developing a robust method to forecast fire activity (fire danger indices, ignition probabilities, burnt area, fire intensity etc) via a process-based model of fire-vegetation interactions, tested, improved, and constrained using state-of-the-art EO data products and driven by seasonal weather forecasts issued with many months lead-time. Specific aims are to: (i) develop the methodology for using EO and other observational data on vegetation (fuel) condition, fire activity and fire effects to test, improve and constrain sub-components and end-to-end predictions of a forward model of fire-vegetation interactions and to inform, test and restrict the model when used in forecast mode to ensure it is nudged along the optimum trajectory, and is furthermore reset when the observation period catches up with the prior period of prediction; (ii) drive the improved forward model by seasonal weather forecast ensembles, predicting spatio-temporal variability in fire 'danger' indices, fire occurrence and a range of subsequent fire behaviour and fire effects (intensity, rate of spread, burned area, above/below ground C stock change, and trace gas/aerosol emissions) and evaluate their usefulness for seasonal fire prediction at 1 / 6 months lead time and for prognostic studies run under future projected climate and LULCC scenarios.

The Peru-Chile subduction zone hosts many large earthquakes. A M8.8 earthquake occurred in northern Chile in 1877, and since then, no major event had re-ruptured the area prior to April 2014. The 500 km-long zone has therefore become known as the "North Chile seismic gap". In late March 2014, many small to moderate earthquakes occurred within this gap. Activity generally migrated slightly northwards. On 2 April 2014, a M8.2 earthquake occurred in the northern part of the preceding cluster, followed by many aftershocks, including a M7.6 event. Aftershock activity continues and, since the rest of the area has not experienced a major earthquake for well over a century, another large event in the area in the near future or medium term cannot be ruled out. In order to measure aftershock activity in the area of the seismic gap that ruptured recently, in addition to any other events that may occur nearby, we propose to install seismometers in the Peruvian coastal region and also offshore Chile. There are two main reasons for doing this. Firstly, the extra networks will dramatically improve station coverage around the seismic gap area, enabling us to generate detailed models of the subduction zone. This will be of great benefit for future analyses of seismic activity in this earthquake-prone area. Secondly, our records of the ongoing seismic activity will enable us to locate aftershocks accurately and infer what type of faulting occurred. This will enable us to build up a very detailed picture of how post-earthquake processes relate to preceding large seismic events. We will also use satellite radar images to construct maps of how the surface of the Earth has moved as a result of the recent seismic activity. These deformation maps can be used in computer models to estimate the location and magnitude of slip that occurred on faults beneath the surface - for instance, on the subduction zone interface, where the mainshock occurred. Essentially we are using surface measurements to infer sub-surface processes. Results from the seismological and satellite components of our project will be integrated to give us an in-depth understanding of the properties and processes occurring in the North Chile seismic gap. For instance, we will look at the spatial relationship between the area that ruptures in major earthquakes and the location of foreshock/aftershock sequences. Another important issue is to identify areas on the subduction zone interface that have not yet slipped, and that could therefore rupture in major earthquakes in the future.

Insects are the little things that run the world (E.O. Wilson). With increasing recognition of the importance of insects as the dominant component of almost all ecosystems, there are growing concerns that insect biodiversity has declined globally, with serious consequences for the ecosystem services on which we all depend. Major gaps in knowledge limit progress in understanding the magnitude and direction of change, and hamper the design of solutions. Information about insects trends is highly fragmented, and time-series data is restricted and unrepresentative, both between different groups of insects (e.g. lepidoptera vs beetles vs flies) and between different regions. Critically, we lack primary data from the most biodiverse parts of the world. For example, insects help sustain tropical ecosystems that play a major role in regulating the global climate system and the hydrological cycle that delivers drinking water to millions of people. To date, progress in insect monitoring has been hampered by many technical challenges. Insects are estimated to comprise around 80% of all described species, making it impossible to sample their populations in a consistent way across regions and ecosystems. Automated sensors, deep learning and computer vision offer the best practical and cost-effective solution for more standardised monitoring of insects across the globe. Inter-disciplinary research teams are needed to meet this challenge. Our project is timely to help UK researchers to develop new international partnerships and networks to underpin the development of long-term and sustainable collaborations for this exciting, yet nascent, research field that spans engineering, computing and biology. There is a pressing need for new research networks and partnerships to maximize potential to revolutionise the scope and capacity for insect monitoring worldwide. We will open up this research field through four main activities: (a) interactive, online and face-to-face engagement between academic and practitioner stakeholders, including key policy-makers, via online webinars and at focused knowledge exchange and grant-writing workshops in Canada and Europe; (b) a knowledge exchange mission between the UK and North America, to share practical experience of building and deploying sensors, develop deep learning and computer vision for insects, and to build data analysis pipelines to support research applications; (c) a proof-of-concept field trial spanning the UK, Denmark, The Netherlands, Canada, USA and Panama. Testing automated sensors against traditional approaches in a range of situation; (d) dissemination of shared learning throughout this project and wider initiatives, building a new community of practice with a shared vision for automated insect monitoring technology to meet its worldwide transformational potential. Together, these activities will make a significant contribution to the broader, long-term goal of delivering the urgent need for a practical solution to monitor insects anywhere in the world, to ultimately support a more comprehensive assessment of the patterns and consequences of insect declines, and impact of interventions. By building international partnerships and research networks we will develop sustainable collaborations to address how to quantify the complexities of insect dynamics and trends in response to multiple drivers, and evaluate the ecological and human-linked causes and consequences of the changes. Crucially, this project is a vital stepping-stone to help identify solutions for addressing the global biodiversity crisis as well as research to understand the biological impacts of climate change and to design solutions for sustainable agriculture. Effective insect monitoring underpins the evaluation of future socio-economic, land-use and climate mitigation policies.