Estimating the effects of  meteorology and land cover on fire growth in Peru using a novel difference equation model

Podschwit, Harry; Jolly, William; Alvarado, Ernesto; Markos, Andrea; Verma, Satyam; Barreto-Rivera, Sebastian; Tobón-Cruz, Catherine; Ponce-Vigo, Blanca

doi:https://doi.org/10.5194/nhess-23-2607-2023

Articles | Volume 23, issue 7

https://doi.org/10.5194/nhess-23-2607-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

The role of fire in the Earth system: understanding interactions...

https://doi.org/10.5194/nhess-23-2607-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 7

Research article

|

24 Jul 2023

Research article |

| 24 Jul 2023

Estimating the effects of meteorology and land cover on fire growth in Peru using a novel difference equation model

Harry Podschwit, William Jolly, Ernesto Alvarado, Andrea Markos, Satyam Verma, Sebastian Barreto-Rivera, Catherine Tobón-Cruz, and Blanca Ponce-Vigo

Data sets

A global wildfire dataset for the analysis of fire regimes and fire behaviour T. Artes, D. Oom, D. De Rigo, T. H. Durrant, P. Maianti, G. Liberta and J. San-Miguel-Ayanz https://doi.org/10.1038/s41597-019-0312-2

Terrestrial Ecoregions The Nature Conservancy https://geospatial.tnc.org/datasets/b1636d640ede4d6ca8f5e369f2dc368b/

GlobCover ESA http://due.esrin.esa.int/page_globcover.php

Model code and software

CURATE_REGIONAL_GLOBFIRE_DATASET.R H. Podschwit https://doi.org/10.6084/m9.figshare.23674185.v1

NHESS_PAPER_CODE.R H. Podschwit https://doi.org/10.6084/m9.figshare.23673306.v1

WILDFIRE_GROWTH_FUNCTIONS.R H. Podschwit https://doi.org/10.6084/m9.figshare.23673393.v1

DERIVE_BATS_DATASET_FROM_GLOBFIRE.R H. Podschwit https://doi.org/10.6084/m9.figshare.23673465.v1

Short summary

We developed a model of fire spread that assumes that fire spreads in all directions at a constant speed and is extinguished at a constant rate. The model was fitted to 1003 fires in Peru between 2001 and 2020 using satellite burned area data from the GlobFire project. We fitted statistical models that predicted the spread and extinguish rates based on weather and land cover variables and found that these variables were good predictors of the spread and extinguish rates.