Work Package 2: Fuel moisture regime characterisation

Fire occurrence is strongly dependent on fuel moisture which is dependent on antecedent weather, moderated by cross scale interactions of vegetation species, seasonality, soil type, aspect, canopy cover, topographic wetness and fuel position in the canopy. Fuel moisture depends on its living or dead state where live fuels may induce strong threshold responses, regulating moisture to a point through physiological controls and it is highly likely that vegetation regulation of fuel moisture is also adapted to the localised environments.

There is limited empirical information about how fuel moisture varies in time and space in UK land cover types, which leads to the inability to fully test the validity of existing fire weather indices (e.g. MOFSI) or derive models that enable accurate prediction of changes in fuel moisture. Empirically based, deterministic fuel moisture simulations are currently driven from weather conditions (e.g. MOFSI); however, there is the capacity to build the next generation fuel moisture early warning system that targets the highly managed and densely populated regions which are also regions of high fire risk in the UK. Integrating simple mechanics simulations with advanced data assimilation and uncertainty analysis pioneered in flood forecasting, we aim to produce enhanced predictions of fuel moisture through the fire season, informed by real time distributed sensor networks and integrative remote sensing assessment.

Key research questions

RQ2.1: What are the cross-scale spatiotemporal dynamics of dead and living UK fuel moisture, from within canopy to landscape, from daily to seasonal and decadal extremes?

RQ2.2: Do current or newly developed next generation fuel moisture models represent the moisture of UK temperate dead and living fuels?

Planned deliverables

D2.1: Quantitative model of fuel moisture spatiotemporal variability and protocol for a simple network for long-term soil moisture assessment.

D2.2: A next-generation fuel moisture model, built on streamlined mechanistic framework and refined with in line real-time direct fuel moisture measurement and remote sensing data. 

Key organisations and team members: University of Birmingham (NK)