Establishing Propagation Nodes as a Basis for Preventing Large Wildfires: The Proposed Methodology

By Pau Costa Foundation on

Establishing Propagation Nodes as a Basis for Preventing Large Wildfires: The Proposed Methodology

Raúl Quílez1*, Luz Valbuena2, Jordi Vendrell3, Kathleen Uytewaal 3 and Joaquín Ramirez4

  • 1Firefighting Consortium of the Valencia Region, Valencia, Spain
  • 2Ecology University of Leon, Leon, Spain
  • 3Pau Costa Foundation, Taradell, Spain
  • 4Technosylva, La Jolla, CA, United States

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In Spain, traditional forest fire management practices have been conducted for many decades, for both prevention- and extinction-oriented purposes. This management model has been forced to shift as a result of changes in fire behavior and has also been adapted to the use of new technologies. The challenge presented by wildfires is amplified due to socioeconomic changes in the last 40 years and inadequate land management in the context of climate change. The principal objective of this work is to establish the most adequate methodology to define the “propagation nodes” in a territory. To do that, the new simulation modes offered by the WildFire AnalystTM simulator (WFA) have been explored to obtain fire behavior data. Likewise, the behavior of large fires in the area has been extrapolated to future scenarios, according to forecasts of different climate change, analyzing extreme weather conditions that can occur in such scenarios (ONU, 2019). The WFA simulator (Tecnosylva, 2014) works efficiently in simulating fire, proving greatly useful in both real suppression operations and fire prevention analysis. It can very accurately generate large wildfires' main pathways without making any kind of adjustments; this is quite useful when planning operations at the head of a fire. It also allows evacuation time evaluation for a given Wildland Urban Interface zone. The area selected for this study is called Sot de Chera, in the Valencia region (Spain). The methodology employed here uses the simulation with WFA setting extreme meteorological and phenological windows associated with wind-driven fires or convection fires dominated with wind, from different starting points looking for the areas where they are grouped. In other words, it is a matter of identifying on the territory the areas where the heads of these higher-intensity fires will arrive, in order to offer realistic control possibilities to the firefighting teams. The results of the simulation identify the heads of the fires with the greatest rate of spread and intensity, exceeding suppression capabilities and efforts, allowing thus to plan for appropriate fuel management strategies to effectively manage emergency responses to fires in these areas.



Fire preventative infrastructures emerged in Spain in response to the low-intensity fires that occurred in the 19th century, originated from burning prairies and other activities in mountain areas (Real Orden de 12 de julio de, 1858). As early as 1869, the first forest camps were created to combat summer fires (García Martino, 1869). The vision of fire prevention, in areas referred to as firebreaks, has continued to this day. Save for a few exceptions, such as the Preventive Silviculture Plan for Valencia Region (Generalitat Valenciana, 1996), fire prevention infrastructures are still being designed in a traditional manner: that is, firebreaks dimensioned for low-intensity fires, which are easily controllable with current suppression resources, but are not in line with the parameters of current high-intensity fires (Quílez Moraga, 2017). This classic perspective of the prevention means that fuel management is limited to clearing firebreaks between municipalities or maintaining the pre-existing ones. This practice does not evaluate their functionality in current forest mass, or consider meteorological or climatic conditions in which wildfires presently develop.

According to climate change models (Canadian Global Coupled Model CGCM2), when looking at the maximum temperatures and the drought coefficient (precipitation/2 times the temperature, ECHAM4 model), years like 2012 lie in the lower part of the table of expected anomalies in the 2011–2040 period. Given the situation, fires beyond the suppression capacity and mega-fires are predicted to increase greatly.

In this global change context, it is necessary to combine preventative and suppression actions in the face of the changing fire regimes, where month-to-month and year-to-year, elevated temperatures continue to break records (Fernández-Miguelañez, 2013), taking into account as the concept of fire regime the description by Keeley et al. (2009), which includes the types of fuel consumed, the frequency, the period of the fire, the intensity of the fire, and the spatial distribution of the individual fire events. The only way to reduce the impact of the expected fire regime is to modify the landscape (Castellnou et al., 2019).

Spring drought conditions, temperatures above 35°C and winds above 30–35 km/h make fire control very complicated or impossible. It is in these conditions that uncontrollable fires (Costa et al., 2011) emerged in Spain, such as the fires in Valencia in 1994 and 2012 among others, comparable in magnitude to other fires in Greece in 2007, Portugal in 2017, the Camp and Carr Fires (US) in 2018, among others.

Nowadays, when a big forest fire starts, the political and technical strategies encourage the suppression methods that deploy large amounts of means (e.g., fire trucks and aerial resources) and the usage of all kinds of technologies. These devices usually favor a reactive response to emergencies, which can, on occasion, impose risk due to organizational complexity and excessive resource use.

However, a more profound analysis of the extreme behavior of these fires is necessary at all levels, to obtain a better effectiveness of these technologies or the options that suppression tools offer.

As such, the habitual responses normally begin with an initial dispatch of means that increase according to the growth of the fire. In other circumstances, these strategies are applied regardless of the nature of the territory through which the fire spreads, along with the meteorological and climatic conditions under which it develops, often leaving the final suppression results to chance.

In addition, it is necessary to develop a new vision of wildfires that moves away from the loss of visible values (e.g., production of timber, biomass, and hunting) and focuses on the loss of ecosystem services (e.g., air quality and water quality), considering especially future scenarios of global change with impacts at a short, medium, and long term (IPCC, 2018ONU, 2019). The new vision is directed toward land planning, integrating the current problem that fires pose.

Technical tools, as land planning models and fire modeling software, evolved very rapidly over the past few years. Concepts such as fire pathways (Minimum Travel Time, hereinafter MTT) (Finney, 2002) can prove very useful for their design. In short, this work aims to greatly reduce the uncertainty of most firefighting operations (Simons, 2013) in relation to large forest fires' spread directions.

To decrease the incertitude and focus a wildfire management in a way in which the decision making is more adequate, it is necessary to consult the knowledge gained from historical fires in a territory, and, through the support of new simulation technologies and GIS analysis, determine the areas with high potential of large wildfires propagation.

This knowledge will form the basis to develop pre-established fire suppression plans, which will provide emergency officers with a series of predetermined operational decisions under different weather windows, overall aiding to change the current reactive response to a more proactive one. The emergency officers will have technical data to support their actions, reducing the pressure on decision-making, increasing the safety of the crews involved and populations concerned, while reducing fire damage in a given region.

This is the reason why the idea of establishing, knowing, and developing the idea of the propagation nodes is posed. Propagation nodes can be defined as areas where the MTT from different fire simulations in an area accumulate under distinct meteorological conditions, with the potential to generate large wildfires (Finney, 2002). Propagation nodes differ from “critical points” defined by Costa et al. (2011) as the latter are used to locate inflection points in the behavior of specific fire patterns; they tend to be located in riverbeds and are closely linked with changes in slope, and they also differ from the Nodes Grid concept (Alcasena et al., 2018), since this attempts to quantify the cells that are burned from a given cell, while the propagation nodes attempt to identify areas where the main fire paths intersect. The former, meanwhile, are determined at a broader scale and are found in areas where large-scale surface fires propagate.


The definition of the propagation nodes then facilitates the identification of the most appropriate areas for preventive treatments. Thus, the research proposes drafting proactive operation plans in order to anticipate the spread of fire and reduce its broad effects on the territory, while providing a safer scenario for the crews involved in wildfire control.

In this sense, the following steps aim to achieve this goal:

(1) explore the new simulation modes offered by the WildFire AnalystTM simulator (WFA) to obtain fire behavior data;

(2) take advantage of the information of the past scenarios of large fires in the area, to carry out the analysis of future forest fire scenarios, according to forecasts of different climate change, analyzing the extreme meteorological conditions that can appear in these scenarios;

(3) analyze fire behavior in these scenarios using the WFA to determine how these fires will spread.

On the basis of the information obtained on the previous points,

(4) define and establish propagation nodes, where most fires will spread under the worst weather conditions, and (5) set the basis to design fire prevention actions to limit the spread of large fires using the propagation nodes and areas of greatest interest for confinement. These will abide by the parameters of fire behavior and aim to provide safe areas to the units involved in their control.

Materials and Methods

Study Area

The used area for the development of this study located in the northeast of the province of Valencia (Figure 1) was selected: “Sot de Chera” (the central municipality in the area of study). This area constitutes a dense forest mass, limited to the south by the Utiel Requena plain and the A3 freeway, to the west by the N330 from Utiel to Sinarcas, and from there to the Benagéber swamp, to the north by the Turia riverbed, and to the east by cropland along the axis of Pedralba, Chiva. This area constitutes the southernmost tip of the Iberian System. This territory is sparsely populated and few activities center on managing the forest structure, so past fires have contributed largely to creating a continuous and homogenous landscape, as far as forest vegetation is concerned, with a surface of 103.238,45 hectares.


Calculation Location of Propagation Nodes With Wildfire Analyst

In order to carry out the MTT calculations with WFA, one must consider that the simulator works in the following way (a) to calculate the MTT of a normal simulation in the propagation module, the WFA, and (b) to calculate the propagation nodes:

a1. Calculate the number of burned cells caused by one single cell; in Figures 3A,B, an example is shown using for that the zone under study.

a2. Calculate the logarithm of the value of the previous layer (a.1) to homogenize the data.

a3. The obtained logarithm is divided by the maximum value of the layer so that the WFA legend is always defined between 0 and 1 (this is what makes the calculation not absolute, as said before, but only relative to the simulation in question). It is actually divided by the starting point value, corresponding to the logarithm of the total number of cells burned in the fire. For a singular simulation, B is the number of total cells burned in a simulation and x(i, j) is the number of cells burned due to cell with coordinates (i, j), and then the value of the fire pathway (Firepath) over every WFA cell is:

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