EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

An early warning system of fire danger for the Brazilian Pantanal

Sílvia A. Nunes1, Liz B. C. Belém2, Renata Libonati1,2, and Carlos C. DaCamara1
Sílvia A. Nunes et al.
  • 1Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, Portugal
  • 2Departamento de Meteorologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

The devastating fires that in 2020 burned more than 3.9 million hectares in the Brazilian Pantanal, the largest tropical wetland in the world, were the result of a complex interplay among human activity, landscape characteristics, and meteorological conditions, and have deepened the concerns about the future of that unique region of the world.

The meteorological component has played a prominent role in 2020, Pantanal having been affected by the most extreme drought since 1950 and by long periods of extremely high temperature. Both factors, combined with fire ignitions, mostly related to human activities, have contributed to the onset of large fire events that spread over water-stressed vegetation.

The aim of the present work is to set up a statistical model that is able to provide reliable forecast of probability of occurrence of a wildfire, taking into consideration both the longer and shorter effects of atmospheric conditions on vegetation stress and, provided an ignition has occurred, on the building up and spreading of a wildfire.

Fire data cover the period 2001-2020 and consist of Fire Radiative Power (FRP) as acquired by the MODIS instrument on-board Aqua and Terra Satellites. Meteorological fire danger was characterized by the Fire Weather (FWI) data covering the same period from the Copernicus Emergency Management Service.

Statistical models used in this study combine a lognormal distribution central body with a lower and an upper tail, both consisting of Generalized Pareto (GP) distributions, and daily FWI is used as a covariate of the parameters of the lognormal and the two GP distributions. First a base model (with fixed parameters) is fitted to the decimal logarithm of FRP, and the quality of fit is assessed using an Anderson-Darling test. Then the model is improved using FWI as a covariate, and performances of models without and with covariate are compared by computing the Bayes Factor as well as by applying the Vuong’s closeness test.

Statistical models were developed for the nine hydrological subregions of Pantanal using data for the period 2001-2019. Five classes of meteorological fire danger were then defined based on probabilities of exceedance of predefined values of FRP. The procedure was then separately applied to the extreme year of 2020.

The developed procedure is on the basis of an operational early warning system of fire danger in Pantanal that is currently being set up.


This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under project FIRECAST (PCIF/GRF/0204/2017), and by the State Public Prosecutor's Office of Mato Grosso do Sul.

How to cite: Nunes, S. A., Belém, L. B. C., Libonati, R., and DaCamara, C. C.: An early warning system of fire danger for the Brazilian Pantanal, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12293,, 2022.


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