Preprints
https://doi.org/10.5194/essd-2022-475
https://doi.org/10.5194/essd-2022-475
 
23 Jan 2023
23 Jan 2023
Status: this preprint is currently under review for the journal ESSD.

The Portuguese Large Wildfire Spread Database (PT-FireSprd)

Akli Benali1, Nuno Guiomar2, Hugo Gonçalves3,4, Bernardo Mota5, Fábio Silva3,4, Paulo M. Fernandes6, Carlos Mota3,4, Alexandre Penha4, João Santos3,4, José M. C. Pereira1,7, and Ana C. L. Sá1 Akli Benali et al.
  • 1Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portugal
  • 2MED - Mediterranean Institute for Agriculture, Environment and Development; CHANGE - Global Change and Sustainability; EaRSLab - Earth Remote Sensing Laboratory Institute; IIFA - Institute for Advanced Studies and Research; University of Évora, 7006-554 Évora, Portugal
  • 3Força Especial de Proteção Civil, 2080-221 Almeirim, Portugal
  • 4Autoridade Nacional de Emergência e Proteção Civil, 2799-51 Carnaxide, Portugal
  • 5National Physical Laboratory (NPL), Climate Earth Observation (CEO), Hampton Rd. Teddington, TW11 0LW, UK
  • 6CITAB - Centro de Investigação e de Tecnologias Agro-Ambientais e Biológicas, Universidade de Trás-os-Montes e Alto Douro, 5001-801 Vila Real, Portugal
  • 7Laboratório Associado TERRA, Tapada da Ajuda, 1349-017 Lisboa, Portugal

Abstract. Wildfire behaviour depends on complex interactions between fuels, topography and weather, over a wide range of scales, being important for fire research and management applications. To allow for a significant progress towards better fire management, the operational and research communities require detailed open data on observed wildfire behaviour. Here, we present the Portuguese Large Wildfire Spread Database (PT-FireSprd) that includes the reconstruction of the spread of 80 large wildfires that occurred in Portugal between 2015 and 2021. It includes a detailed set of fire behaviour descriptors, such as rate-of-spread (ROS), fire growth rate (FGR), and fire radiative energy (FRE). The wildfires were reconstructed by converging evidence from complementary data sources, such as satellite imagery/products, airborne and ground data collected by fire personnel, official fire data and information in external reports. We then implemented a digraph-based algorithm to estimate the fire behaviour descriptors and combined it with MSG-SEVIRI fire radiative power estimates. A total of 1197 observations of ROS and FGR were estimated along with 609 FRE estimates. The extreme fires of 2017 were responsible for the maximum observed values of ROS (8956 m/h) and FGR (4436 ha/h). Combining both descriptors, we defined 6 fire behaviour classes that can be easily communicated to both research and management communities and support a wide number of applications. Analysis also showed that the area burned by a wildfire is mostly determined by its FGR rather than by its forward speed. Finally, we explored a practical example to show the PT-FireSprd database can be used to study the dynamics of individual wildfires and build robust case studies for training and capacity building.

The PT-FireSprd is the first open access fire progression and behaviour database in Mediterranean Europe, dramatically expanding the extant information. Updating the PT-FireSprd database will require a continuous joint effort by researchers and fire personnel. Updating the PT-FireSprd database will require a continuous joint effort by researchers and fire personnel. PT-FireSprd data are publicly available through https://doi.org/10.5281/zenodo.7495506 (last access: 30th December 2022) and have a large potential to improve current knowledge on wildfire behaviour and support better decision-making (Benali et al. 2022).

Akli Benali et al.

Status: open (until 20 Mar 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-475', Miguel Cruz, 30 Jan 2023 reply

Akli Benali et al.

Data sets

The Portuguese Large Wildfire Spread Database (PT-FireSprd) (v0.08) Akli Benali, Nuno Guiomar, Hugo Gonçalves, Bernardo Mota, Fábio Silva, Paulo M. Fernandes, Carlos Mota, Alexandre Penha, João Santos, José M. C. Pereira, Ana C. L. Sá https://doi.org/10.5281/zenodo.7495506

Akli Benali et al.

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Short summary
To allow for a significant progress towards better fire management, detailed good quality open data on observed wildfire behavior are crucial. We reconstructed the spread of 80 large wildfires that burned recently in Portugal, and calculated metrics that describe “how” the wildfires behaved (example: fire front speed). We used this data to build 6 easy-to-communicate fire behavior classes. The database will improve our current knowledge on wildfires and contribute to better fire management.