Fire Modelling & Forecasting System (FireMAFS)

Lead Research Organisation: University of Reading
Department Name: Meteorology


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.


10 25 50
Description Impact of el nino on rainfall and fire in indonesia
Exploitation Route inclusion of fire model into JULES land surface model
Sectors Environment