CCI4SOFIE - CCI data for assessing soil moisture controls on fire emissions

Fig. ESA CCI data for controls on fire

Overview

Biomass burning is a globally significant source of aerosols, greenhouse gases and other trace gas species, influencing both regional and global climate. Although fire occurrence is for a large part driven by humans for land management purposes, fire extent and emission types are greatly controlled by biotic factors (species composition, biomass) and climate (soil moisture, temperature, precipitation). Thus, climate change may lead to more frequent and intense fires if drought conditions in areas with abundant fuel loads become more severe. Still, the impact of soil moisture on fire goes well beyond the paradigm “it only burns when it’s dry”: Soil moisture does not only impact the occurrence and spatial extent of wildfires, but as a driver of biomass production also controls fuel load, ecosystem composition, and combustion efficiency and, hence, the quantity and type of fire emissions. Yet, the precise control of soil moisture on fire emissions is only poorly understood and parameterized in state-of-the-art fire emission models. Earth observation data provided by ESA programmes (e.g. CCI soil moisture, CCI fire, CCI land cover, CCI aerosol, CCI greenhouse gases, DUE GlobEmission, STSE BIOMASAR, SMOS soil moisture and VOD) and other (space) agencies provide a wealth of information to improve our knowledge on the role of soil moisture in driving biomass burning emissions.


Aims

CCI4SOFIE aims to explore multiple Earth Observation datasets to

  • infer relationships between soil moisture, fuel load production, and fire extent and emissions by using observations and machine learning techniques, and to
  • evaluate and improve state-of-the-art coupled dynamic global vegetation/fire models.


Funding

CCI4SOFIE is a Living Planet Fellowship granted to Matthias Forkel by the European Space Agency.

 

CLIMERS members involved


Results and publications

Forkel, M., Dorigo, W., Lasslop, G., Teubner, I., Chuvieco, E., Thonicke, K., 2016. Identifying required model structures to predict global fire activity from satellite and climate data. Geosci. Model Dev. Discuss. 2016, 1–35. doi:10.5194/gmd-2016-301