Many current or projected future land-based renewable energy schemes are highly dependent on very localised climatic conditions, especially in regions of complex terrain. For example, mean wind speed, which is the determining factor in assessing the viability of wind farms, varies considerably over distances no greater than the size of a typical farm. Variations in the productivity of bio-energy crops also occur on similar spatial scales. This localised climatic variation will lead to significant differences in response of the landscape in hosting land-based renewables (LBR) and without better understanding could compromise our ability to deploy LBR to maximise environmental and energy gains. Currently climate prediction models operate at much coarser scales than are required for renewable energy applications. The required downscaling of climate data is achieved using a variety of empirical techniques, the reliability of which decreases as the complexity of the terrain increases. In this project, we will use newly emerging techniques of very high resolution nested numerical modelling, taken from the field of numerical weather prediction, to develop a micro-climate model, which will be able to make climate predictions locally down to scales of less than one kilometre. We will conduct validation experiments for the new model at wind farm and bio-energy crop sites. The model will be applied to the problems of (i) predicting the effect of a wind farm on soil carbon sequestration on an upland site, thus addressing the question of carbon payback time for wind farm schemes and (ii) for predicting local yield variations of bio-energy crops. Extremely high resolution numerical modelling of the effect of wind turbines on each other and on the air-land exchanges will be undertaken using a computational fluid dynamics model (CFD). The project will provide a new tool for climate impact prediction at the local scale and will provide new insight into the detailed physical, bio-physical and geochemical processes affecting the resilience and adaptation of sensitive (often upland) environments when hosting LBR.

Climate change is arguably the biggest challenge facing people this century, and changes to the intensity and frequency of climatic and hydrologic extremes will have large impacts on our communities. We use climate models to tell us about what weather in the future will be like and these computer models are based on fundamental physical laws and complicated mathematical equations which necessarily simplify real processes. One of the simplifications that really seems to matter is that of deep convection (imagine the type of processes that cause a thunderstorm). However, computers are so powerful now that we are able to produce models that work on smaller and smaller scales, and recently we have developed models which we call "convection-permitting" where we stop using these simplifications of deep convection. These "convection-permitting" models are not necessarily better at simulating mean rainfall or rainfall occurrence but they are much better at simulating heavy rainfall over short time periods (less than one day) which cause flooding, in particular flash-flood events. They are also better at simulating the increase in heavy rainfall with temperature rise that we can observe; therefore we are more confident in their projections of changes in heavy rainfall for the future. A few "convection-permitting" modelling experiments have now been run for different parts of the world but all of these have been over small regions, only the same size as the UK, or smaller. All of the experiments so far have concentrated on rainfall and none have examined how "convection-permitting" models might improve the simulation of other types of extreme weather such as hail, lightning or windstorms. In fact we know very little about how these types of extremes might change in the future. We also have no idea of the uncertainty in our experiments in terms of our predictions of future changes as we have only run one model simulation in each region - this is not useful for planning climate adaptation strategies where we really need to understand the uncertainties in our future predictions so we can plan for them. In FUTURE-STORMS we are running these "convection-permitting" models over a very large area (the whole of Europe) and we are comparing models from two different climate modelling teams at the UK Met Office and ETH Zurich in Switzerland. In addition to this we are now able to run a number of different climate models over the same region, which allows us to assess some of the uncertainties in future changes to heavy rainfall and other storm-related extreme weather. This will let us explore how heavy rainfall might change across Europe and what might be causing this. It will also allow us to look at whether these new models are able to simulate other types of extreme weather like hail, lightning and windstorms which have a huge impact on Europe, and how these might change in the future. Ultimately, we need better information on how extreme weather events might change in the future on which to make adaptation decisions and FUTURE-STORMS intends to provide this important advance, alongside translating this information into useful tools and metrics for use in climate change adaptation.

Wildfires are a natural phenomenon in many regions of the world (e.g. the boreal and temperate North America or the Mediterranean Basin) but, in others (e.g. Atlantic Europe), they are mostly human-caused. Irrespective of their origin, wildfires burn, on average, an area equivalent to about 20 times the size of the UK every year. When they burn through populated areas they can be deadly. For example, in 2018, they resulted in 100 deaths in Greece, 99 in Portugal, and 104 in California alone. In the UK, fires have to date rarely resulted in losses of life but, on average, ~£55M are spent annually in wildfire responses and they have threatened infrastructures and communities (e.g. several wildfires last summer led to evacuations). A combination of climate and land use changes is already increasing wildfire risk in many areas, both inside and outside the UK, and this trend is expected to worsen. In order to develop more effective tools for mitigating and fighting extreme wildfires, we need to advance our ability to understand, predict and, where possible, control fire behaviour. In this project we aim to improve understanding and mitigation of wildland fire by advancing wildfire behaviour model capabilities through the development of new automated methods (algorithms) to implement, for the first time, ground-breaking real 3D fuel data into physics-based wildfire behaviour models. These models are the most advanced in terms of their ability to forecast fire behaviour, but they remain constrained by the lack of detailed fuel input information to work with (i.e. the amount and structure of live and dead vegetation susceptible to burn). The advancement we aim to deliver will provide a step-change in physical fire modelling capabilities. The new algorithms will be implemented in the powerful fuel models FUEL3D and STANDFIRE, which provide fuels inputs for the physics-based fire behaviour models FIRETEC and WFDS. We will apply these to forest stands that typify some of the most common flammable conifer forests in the UK, NW Europe and North America. The algorithms produced will be made publicly available and, therefore, can be adapted and applied to many other forest types around the world. Three-dimensional fuel datasets will be acquired in field campaigns using a range of state-of-the-art laser scanning (terrestrial, wearable and aerial UAV-based laser scanners) and 'Structure from Motion' methods, with traditional fuel inventory measurements being carried out for comparison and model validation. Our case studies will focus on conifer stands in England, Scotland, Wales and the US. In the UK, conifer forests comprise half of the UK's 3.2 Mill. ha of forested land, and they have the greatest potential for crown fires, which spread along treetops and are the most dangerous and challenging to fight. In the US, the work will include real forest fires, carried out for research purposes, which will provide valuable fire behaviour and fuel consumption datasets to validate the improved fuel and fire models. Fire behaviour depends on weather, topography, and on the type and amount of vegetation fuels, with the latter being the only factor that can be meaningfully influenced through management efforts. By managing fuels, we can reduce the risk of extreme fire behaviour and its impacts. Our project provides a novel approach for designing and testing of 'virtual fuel treatments' aimed at decreasing fuel hazard and, thus, fire risk, under current and predicted future climatic and land use scenarios. The involvement of key UK end-users (Forestry Commission, Met Office, Natural Resources Wales and South Wales Fire & Rescue Service) as partners will maximise the applicability and impact of the project's outputs. The novel 3D fuel data and algorithms will also present a major advance for other forestry applications (e.g. forestry inventory, timber forecasting, forest carbon budgeting, ecosystem services assessment).

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.