Every year vegetation fires (wildfires and management burns) affect ~4% of the global vegetated land surface. This includes forests, grasslands or peatlands, which provide 60% of the water supply for the world's largest 100 cities and for 70% of for the UK's population. In England 114 km2 of uplands are affected by management burns alone and the UK Fire and Rescue Services attend to over 70,000 vegetation fires per year. Vegetation fires can have serious impacts on water quality, which, combined with the current and projected future further decline in fresh water availability and increase in fire risk in many regions around the world, has given rise to increased attention to water contamination risks from fire. The primary threat is ash left behind by fire, which can be transported very easily into water bodies by water erosion. Ash is typically rich in contaminants and its transfer into water supply catchments has led to numerous drinking-water restrictions and substantial treatments costs in recent years (e.g. for Belfast, Canberra, Denver, Fort McMurray, Sydney). In the UK, losses to the water industry from vegetation fires are estimated at £16 Mill. per year. Models are widely used by scientists and land managers to predict soil erosion or flood risks after disturbance events such as harvesting or wildfire, however, no models currently exist that allow predicting of ash transport and associated water contamination risk following fire. This gap in knowledge and resource seriously compromises the ability of land managers to anticipate water contamination risks from fire and to implement effective mitigation treatments to reduce fire risk, prevent erosion after fire and, adjust water treatment capabilities. This timely project brings together an interdisciplinary team of international experts from the UK, USA and Australia with the aim to address this critical knowledge and tools gap. Building on recent advances and proof-of-concept work in this field, we are now able to (i) obtain critical fundamental knowledge on wildfire ash transport processes and its contamination potential and, using this knowledge, to (b) develop the first end-user probabilistic model that allows predicting ash delivery and associated water contamination risk to the hydrological network. The model will be validated for key fire-prone and fire-managed land cover types that have suffered critical ash-induced water pollution events in the past (including UK uplands, US conifer forest and Australian eucalyptus forest) using the first field dataset on ash transport parameters by water erosion and an extensive dataset on potential contamination by ash obtained through this project for these key regions. To maximize the impact of the project, the web-based model will developed in collaboration with, and be made available to, users from land and catchment management sectors to support effective protection of aquatic ecosystems and drinking water supply from contamination by ash.

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).

Human activities are causing atmospheric carbon dioxide to rise and as a consequence our planet's climate is changing. Forests exert huge influence over the amount of carbon dioxide in the atmosphere, and northern hemisphere forests currently store nearly half of the CO2 released by anthropogenic emissions each year. Whether forests will continue to act as a net sink for some of the CO2 released by anthropogenic emissions is uncertain and depends upon numerous factors such as future land use change, climate regimes and forest disturbance rates. Over the last ten years, an outbreak of bark beetle has covered approximately 47 million hectares of forest in North America, resulting in widespread tree mortality. The huge loss of green leaf area is clearly visible from satellite imagery and has a direct impact on carbon dioxide uptake. It is likely that forests in the region will become a net source of carbon dioxide to the atmosphere as respiration becomes dominant over photosynthesis. Carbon from plants is the source for microbial respiration in soil; some of this carbon is older decaying material but some is from recent photosynthesis. The relative contribution of each carbon source in healthy forests is unclear, and less clear is whether these contributions will change after a significant mortality event accompanied by large quantities of decaying dead plant material. This proposed research will improve our understanding of the capacity of forests to continue to absorb anthropogenic emissions after large scale disturbances though an intensive study of an outbreak of mountain pine beetle in Colorado, USA. Mountain bark beetle has progressively infected Pinus ponderosa trees at Fraser Experiment Forest (FEF) over the past five years. In 2004, 48 forest survey plots were established in FEF where detailed measurements of the stand structure and carbon pools and fluxes were carried out from 2004 through 2006. Since the establishment of these plots approximately 30% have been infected by mountain pine beetle (MPB), and in 2010, these plots will represent a 5 year chronosequence of MPB infection. I propose to measure soil efflux, microbial biomass and labile and older carbon. This will allow me to determine the magnitude and dyanmics of any decline in soil efflux caused by the MPB infection while controlling for variation in stand properties which were estimated previously. As part of different research efforts at Niwot Ridge in 2002, 2003 and 2008, selected trees were killed by removing phloem from the trunk at 1.3m above the ground. This process (girdling) cuts off the transport of carbohydrates below ground and over a period of a year kills the tree, mimicking the effects of bark beetle. Before, and for 1-2 years immediately after girdling, soil efflux, microbial biomass and labile and older carbon pools were measured. I propose to repeat these measurements over a two week period in July 2010 in the girdled plots and the associated (non-girdled) reference plots. These measurements be used to parameterize a simple ecosystem model which has been modified to make use of soil efflux, labile carbon pool measurements and estimates of microbial biomass. Predictions of carbon exchange at FEF for the period 2005 through 2010 will be compared to direct observations. By comparing different representations of the model I will test different ways of representing the below ground component of carbon cycling. This research will directly quantify the effect of disease outbreak and tree mortality on belowground carbon cycling in high elevation forests; provide insight into the poorly understood process of belowground carbon cycling; thus improving projections of carbon sequestration by these forest ecosystems under changing climate scenarios. More broadly, this research fits centrally into the emerging needs for understanding carbon-climate relationships and the potential effects of future climate on ecosystem health and function.