the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The Portuguese Large Wildfire Spread Database (PT-FireSprd)
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
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: final response (author comments only)
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RC1: 'Comment on essd-2022-475', Miguel Cruz, 30 Jan 2023
Review of essd-2022-475 by Miguel Cruz
I commend the authors for their work on developing a significant wildfire behaviour dataset. I found the work of great interest to the fire research community, and can see their methods and data being used by many in the I have few major comments (below) and a number of small comments that are in the attached pdf.
- The manuscript reads well, but there are a number of sentences that seem incomplete or are somewhat incoherent. I note those in the attached pdf. Authors should also be more direct and concise in their writing. The manuscript is quite long, and I made note of content that could be left out as it is not necessary.
- This is a wildfire spread database which is quite relevant. The authors should nonetheless mention clearly that no collation of data on weather, fuels, topography, etc was conducted within the context of this study. This is never mentioned. Although the authors mentioned later in the manuscript that the data can be used for better understanding of wildfire drivers, model evaluation, etc. this cannot be conducted unless other data is present. As it is, the spread data in isolation does not allow for much of an analysis.
- There are important limitations of the satellite data. Some of them are mentioned, others are not. An important point that should be made is that just because the satellite or someone says the fire is at a certain location at a given time, it does not mean that the fire just arrived there at such time. The fire might have arrived hours before, and hence the average rate of spread for a burning period is a value that is diluted, combining periods of rapid spread and no spread. This is quite relevant as fire behaviour is highly nonlinear, and averaged values over larger time periods can be misleading. These aspects should be noted in the manuscript.
- It is not clear how combining different methods to map a fire location are integrated. Do some methods have prevalence over others? Photographic evidence vs satellite information? Is there a process that is followed? If yes, this should be described.
- I have two main technical comments.
- The proposal of a fire behaviour classes based on your data is fraught with error. It is ok to explore the distribution of your data, but to propose such distribution (which you said, was biased to large fires) to derive a fire behaviour classification is wrong. The classification class threshold have no physical meaning, and you can realise that if you check into a number of fire behaviour and danger classifications developed from fire behaviour – operational implications. A proof that your proposed classification is meaningless, is the fact that if in the next two fire seasons you add 40 new wildfires all burning under moderate to high fire danger (lets say it is a mild fire season), your new fire behaviour classification classes will change drastically. What is the point then? I strongly suggest this is removed from the manuscript.
- The authors use their dataset and make a ‘finding’ that area burned is mostly a function of fire growth rate rather than rate of fire spread. This result is obvious by several reasons, the simplest one being that the rate of spread is only related to the area burned for the initial stages of a fire growing from a point source. From the moment a fire is affected by topography, fuels, and burn over several burn periods and days, it is the area growth rate that is linked with the fire area, not the rate of fire spread. I do not see this a finding, whatsoever. Of course, a 2 dimensional area growth metric is going to be more related to the final burned area than a one dimensional metric of fire propagation (ROS). As with the previous point, I strongly suggest that this is removed from the manuscript.
It is not clear why the authors depart from their main focus of the study, describing how the database was assemble, to do a spurious analysis of the data and come up with these findings, that, in my view, are not really findings. If the authors want to explore those aspects of fire behaviour, then they should do so in a different piece of work, with proper basis and analysis.
Other minor comments:
What is the certainty in the intermediate perimeters (isochrones) in figure 3? I cannot imagine you were able to collect data across all the perimeter to make such a nice polygon. Were they interpolated? If so, they might provide a false sense of certainty.
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RC2: 'Comment on essd-2022-475', Anonymous Referee #2, 25 Apr 2023
Benali et al. have developed a valuable wildfire behaviour dataset that includes the spread of 80 large wildfires in Portugal between 2015 and 2021. The authors have combined data from multiple sources, which helps to reduce gaps and uncertainties in the wildfire data collection. This open data has the potential to improve the simulation of wildfire in the context of changing environments and to better manage wildfires. The manuscript is well-written and the authors have done a commendable job of presenting the data. I think it is publishable if several minor issues can be addressed.
- The Input Data section could benefit from being more concise. Consider presenting the information in tables, especially when discussing data sources, to improve clarity for readers.
- In section 3.1 Overview of the PT-FireSprd database, the authors provide a comparison of ROS and FGR and give some preliminary results. However, this appears to detract from the main focus of the paper, and as a result, the section feels disjointed. Consider revising the section to more clearly tie to the central argument of the paper or move the comparison to an appendix to maintain focus on the main topic.
Other minor comments:
Line 508: It should be 3.2 not 2.2.
Citation: https://doi.org/10.5194/essd-2022-475-RC2 -
RC3: 'Comment on essd-2022-475', Anonymous Referee #3, 09 May 2023
Review: The Portuguese Large Wildfire Spread Database
This manuscript details the creation of the first truly multi-proxy high spatial and temporal resolution fire progression archive of its kind. Combining data from multiple remote sensing sources leveraging the full range of available temporal and spatial resolution, combined with empirical observations, written records, and photographic evidence, this is the most complete crosswalk of multi-proxy data sources I have seen. The dataset has significant potential to improve the understanding of fire activity, resource use effectiveness, fire climatology, and other fields in Portugal.
The data are catalogued in three phases, the first in my option being the largest contribution and most important- a structured approach to reconstructing fire progression at the finest resolution possible. The second phase develops three derived descriptor variables that include some straightforward calculations under the stated assumptions (rate of spread and rate of fire growth) and some more questionable calculations (average fire radiative power) given the nature of the data sources. The third phase leverages the derived characteristics to ascribe a fire behavior class to each fire growth polygon. I’m not clear on how thresholds were determined to differentiate fire behavior classes or how they would be used operationally but if determined in consultation with fire managers I can see this as a useful way to present the data.
Major concerns:
The treatment of Fire Radiative Energy as an additive measurement or something that can divided by an area is problematic. By definition, fire radiative energy is an instantaneous and constantly varying measure of energy release for a given area of measurement (Zhang et al 2018). Dividing FRE by an additive area metric to divvy the instantaneous measure by the area burned assumes that the area burned occurred in that instant. The area burned is a function of the free burning rate per 30-minute period. This is mixing average and instantaneous data sources. Unless the MSG_SEVIRI sensor does this different from MODIS, I don't think it can be applied to an area polygon.
Minor concerns:
The manual methods are quite labor intensive and leave room for standardization and automation that would alleviate some concerns I have about repeatability with other data sources and in other geographies.
Other comments:
I agree that and change detection algorithm applied to the 30-minute FRE, ROS, or FRG could be an interesting way to determine the important phases of fire growth and then relate these phases to other environmental data (e.g. weather, fuel matrix, suppression resources use, etc.). I hope the research group is able to continue updating the dataset and is able to adapt systems for standardizing quality control of records and automating generation of polygons and derived metrics. As it is the dataset serves as a valuable series of inputs for future analysis and keeping it up to date will ensure continued use and applications.
Citation: https://doi.org/10.5194/essd-2022-475-RC3
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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