FireMAFS was led by Prof Martin Wooster (Kings College, London) as part of QUEST Theme 3 (Quantifying and Understanding the Earth System) project. This dataset collection contains the MODIS Land Cover Type product multiple classification schemes, which describe land cover properties derived from observations spanning a year’s input of Terra and Aqua data. The data are stored in a 10 arc minute grid. Fire was the most important disturbance agent worldwide in terms of area and variety of biomass affected, a major mechanism by which carbon is transferred from the land to the atmosphere, and a globally significant source of aerosols and many trace gas species. Despite such clear coupling between fire, climate, and vegetation, fire was not modelled as an interactive component of the climate/earth systems models of full complexity or intermediate complexity, that are used to model terrestrial ecosystem processes principally for simulating CO2 exchanges. The objective of FireMAFS was 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. This used a state-of-the-art EO data products and driven by seasonal weather forecasts issued with many months lead-time. Much of the activity of FireMAFS was shaped by the research and technical priorities of QUESTESM (earth system model). Key activities included the progressive development of the JULES-ED and SPITFIRE submodels. Fire is now very well represented in QESM (Quest Earth System Model), making progress towards a modelling capability for fire risk forecasting in the context of global change.

FireMAFS was led by Prof Martin Wooster (Kings College, London) as part of QUEST Theme 3 (Quantifying and Understanding the Earth System) project. The objective of FireMAFS was to resolve limitations of fire modelling 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. This used a state-of-the-art EO data products and driven by seasonal weather forecasts issued with many months lead-time. This dataset contains the MODIS Land Cover Type product multiple classification schemes, which describe land cover properties derived from observations spanning a year’s input of Terra and Aqua data. The data are stored in a 10 arc minute grid.

The data consist of daily maps of volumetric soil moisture predicted by a model based on a network of cosmic-ray neutron sensors (COSMOS-UK), the National River Flow Archive (NRFA) and remotely-sensed data. Maps cover the UK and Ireland at 2 km resolution in the Ordnance Survey National Grid (OSGB) projection. Maps are produced in near-real time, lagging by about one week. Data are available from early 2016 to 2023, on a daily basis. The model was calibrated on a network of cosmic-ray neutron sensors (COSMOS-UK) and remotely-sensed soil moisture data. A key parameter was estimated from the national-scale spatial pattern in the catchment response to rainfall seen in the National River Flow Archive (NRFA) data. Precipitation and humidity data to drive the model came from the Met Office High Resolution Numerical Weather Prediction model (NWP-UKV) which incorporates the C-band rainfall radar network. The maps have a variety of uses in hydrology and elsewhere, for example as inputs to ecosystem models of greenhouse gas exchange, where soil moisture affects numerous processes. The modelling was carried out as part of UK-SCAPE Virtual Survey Lab, and the NERC project "Detection and Attribution of Regional Emissions (DARE-UK)". There are some gaps in the time series of meteorological and remote sensing inputs, and data are unavailable for these days. The NRFA data are only available for Great Britain, so estimates in Ireland and continental Europe will be less accurate. Full details about this dataset can be found at https://doi.org/10.5285/5aa8c5b4-4485-4954-b5c3-18d937a418f7