A Global Bottom‐Up Approach to Estimate Fuel Consumed by Fires Using Above Ground Biomass Observations. Issue 21 (6th November 2021) - Di Giuseppe, F.; Benedetti, A.; Coughlan, R.; Vitolo, C.; Vuckovic, M.
Abstract: Real‐time estimates of the fuel consumed during a fire (dry‐matter) relies on indirect estimate from remotely sensed released energy combined with biome dependent conversion coefficients. The uncertainties in the conversions lead to the use of inflation factors to avoid large underestimations in the prediction of aerosol load during fires. In this study, adopting two different fire inventories for burned areas, we apply above ground biomass (AGB) observations [from Soil Moisture and Ocean Salinity (SMOS) L‐band vegetation optical depth] as proxy for fuel load in deriving estimates of dry‐matter. These new estimates are then converted into biomass burning aerosols and validated against independent aerosol optical depth observations from the AERONET in situ global network. Results showed that use of AGB as a proxy observation of fuel load improves fire emission estimates and substituting the need for indirect dry‐matter estimates from remotely sensed fire activity or for the use of inflation factors. Plain Language Summary: Fuel availability plays a major role in fire sustainability, intensity, and spread yet real time monitoring of fuel is not currently used in systems for fire early warning or to model atmospheric emissions. Presently, real‐time estimates of the fuel consumed during a fire is an estimation, derived from satellite observations which are factored up to avoid rejection by some atmospheric emission systems. In this study, we create a new way of estimating fuel consumed by combining burned areas (from two different fire inventories) with Above Ground Biomass [derived from Soil Moisture and Ocean Salinity (SMOS) L‐band vegetation optical depth]. Six new datasets are created and converted into atmospheric emissions and compared against independent aerosol observations from the AERONET in‐situ global network. Results show that using this new way of estimating fuel consumed during a fire improves fire aerosol estimates and could replace the current methods, substitute the need for factoring up. Key Points: Estimation of fuel load is the step introducing the greatest uncertainty in fire emissions inventories In current systems fuel load is either taken from literature values or simulated through vegetation growth model The use of above ground biomass retrieved from L‐band sensors as a proxy for fuel load improves fire emissions estimates