Layout of multi-type firebreak network for transboundary fires—the Greater Khingan Mountains of China case

doi:10.21203/rs.3.rs-4970912/v1

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Layout of multi-type firebreak network for transboundary fires—the Greater Khingan Mountains of China case

https://doi.org/10.21203/rs.3.rs-4970912/v1

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The Yimuhe Forest Farm, located in the northern Greater Khingan Mountains of China, has been plagued by Russian transboundary fires. However, this area lacked an effective forest firebreak network, and the road network density is only 0.84m/ha. This paper aims to improve the density of forest firebreaks and the ability to resist fires in Yimuhe Forest Farm, and provide a reference for other forest areas to build multi-type firebreaks. By summarizing historical data on transboundary fires in this area, we used FARSITE to determine the planning scope of firebreaks and Geographic Information System (GIS)-Analytic Hierarchy Process (AHP) to obtain the layout of the regional emergency firebreak network of transboundary fires. The planned firebreak network had a length density of 13.11m/ha, with the engineered firebreaks contributing a length density of 4.31 m/ha. Under fire simulation, the firebreak network significantly reduced the spread of transboundary fire, confirming its fire-blocking effectiveness. The planned firebreak network met standard requirements for length density, making it a valuable tool for slowing the spread of transboundary fires. The firebreak network effectively curtails the spread of transboundary fires along the Greater Khingan Mountains, offering guidance for forestry departments to implement measures for emergency firebreak planning and layout.

the northern Greater Khingan Mountains

transboundary fires

emergency firebreak network

FARSITE

forest conservation

In recent years, there has been a surge in the frequency of wildfires, a common natural disaster in countries blessed with abundant forests (Bowman etal., 2009). A pivotal aspect of wildfire propagation is transboundary fire (Fig. 1), which entails the spread of firebrands beyond the burning area, leading to new independent ignitions. For instance, data from the Portuguese Civil Protection and Emergency Authority (ANEPC) reveals that between 2012 and 2015, there were 134 international fire incidents along the Portugal-Spain border (99 transboundary fires in Portugal and 35 transboundary fires in Spain) (Martins, 2015). From 1984 to 2015, transboundary fires in the southwestern United States burned 2.5 million hectares (MTBS, 2021). Similar trends have surfaced in other neighboring countries, including the United States-Mexico (Villarreal etal., 2019; Villarreal etal., 2020), Russia-Mongolia (Kazato and Soyollham, 2022), especially China-Russia (Stephens etal., 2014; Quan etal., 2022) and China-Mongolia (Zhang etal., 2021a; Chao etal., 2023). However, due to geopolitical reasons, border regions lack fire prevention measures against transboundary fires (Quan etal., 2022). These transboundary fires are frequently exacerbated by strong winds, resulting in substantial losses to forest resources and human lives.

Presently, most countries rely on biological firebreaks as a primary strategy to suppress wildfires. This approach involves removing combustible materials and planting tree species with high moisture content (Cui etal., 2019). The concept of biological firebreaks originated in the United States in the late 1920s and was formally established in the 1930s (Platt and Jackman, 1946). Since then, many states in the USA have planted fire-resistant trees for forest fire suppression (Detweiler and Fitzgerald, 2006; Behm, 2007). Various countries including Russia (Kukavskaya etal., 2016), Australia (Potts etal., 2022), Brazil (Kovalsyki etal., 2019), Spain (Santamarta-Cerezal etal., 2012), and China (Cui etal., 2019; Wang etal., 2021), have widely adopted biological firebreaks. However, it should be noted that solely relying on biological firebreaks may not be sufficient to effectively halt wildfires, as fires can sometimes burn through or cross them, particularly without active firefighting efforts (Oliveira etal., 2016).

Forest roads play a pivotal role in wildfire prevention as they act as physical barriers (fuel discontinuities) and provide access for firefighting activities (Hann etal., 1997; USFS, 2001). Recent research in Australia has demonstrated that roads in certain types of forests have a significant protective effect against wildfires (Price and Bradstock, 2010). A higher road density can enhance convenience for firefighting personnel and equipment (Hann etal., 1997). Additionally, larger streams can serve as natural barriers that can slow down or halt the spread of wildfires (Salazar and González-Cabán, 1987). Combining roads and streams, along with active firefighting efforts and road blockage, can contribute to effective wildfire prevention by reducing temperatures and increasing fuel moisture in streamside areas.

In 2003, Portugal established a comprehensive ‘regional fire protection network’ that included firebreak networks, strategic fuel management, forest roads, and fire protection reservoir networks (Agee etal., 2000). However, limited funding and inadequate land management resulted in only 18% of the planned firebreak networks being implemented by 2013 (ALMEIDA, 2012). Research has indicated that if the planned firebreak networks had been constructed, they could have played a significant role in reducing the size of wildfires and minimizing losses (Viegas etal., 2012). At the same time, research in Portugal’s flammable areas showed that a firebreak network could reduce the area burned by fires by 17% (Oliveira etal., 2016). But his study only considered prescribed burning in engineered firebreaks, and the study area was focused on the wildland-urban boundary. Potential Operational Delineations (PODs) in the United States divide areas where fires may develop into potential areas and conduct fire management according to potential risk levels (Greiner etal., 2020). However, Prioritizing fire management in areas with a high fire risk can significantly improve our ability to contain the wildfire spread (Johnson etal., 2011; Syphard and Keeley, 2015).

Therefore, to effectively control the spread of wildfires, it is crucial to integrate different types of firebreaks, including biological (such as fire-resistant tree species), engineered (such as roads), and natural (such as streams), into a coherent and comprehensive firebreak network for controlling wildfire spread, especially in the case of transboundary fires. To achieve this, our objectives are to (1) determine the planning scope of transboundary fires in the northern Greater Khingan Mountains, (2) identify suitable locations for biological and engineered firebreaks, (3) establish an emergency firebreak network, and (4) verify the effectiveness of the designed emergency firebreak network. FARSITE was used to simulate transboundary fires in the northern Greater Khingan Mountains in 2006, 2014, and 2017 to determine the planning scope. A suitable location for constructing biological and engineered firebreaks was obtained through the analysis of topography, geomorphology, hydrology, and other conditions within the planning range using ArcGIS. The final emergency firebreak network, consisting of biological, engineered, and natural firebreaks, was established for transboundary fires in the northern Greater Khingan Mountains. A comparison was made between the simulated FARSITE fires and the actual fires spread to ensure the reliability of FARSITE simulations. Furthermore, the effectiveness of the designed firebreak network in blocking fire spread was verified by simulating historical fires and random fires with and without firebreaks.

Study Area

The Yimuhe Forest Farm is in Ergun City, Inner Mongolia Autonomous Region (Fig. 2). It is an unexplored key state-owned forest farm situated along the Ergun River in the Russian Far East. The geographical coordinates are 120°1′~120°25′E, 50°34′~50°49′N, with an altitude range is 299 ~ 876 meters. The average temperature in the study area is -5.5°C(Fick and Hijmans, 2017), and annual precipitation is 360 ~ 460mm (Fan etal., 2017). The dominant wind direction is northeast-southwest, with a yearly average wind speed exceeding 4.2 m/s and the average annual count of strong wind days is as high as 17–40 days (Liu etal., 2018; Zhao etal., 2021). Additionally, there are more occurrences of strong wind days during spring and autumn, with an average wind speed of 5.1 m/s (Feng, 2012). Consequently, severe forest fires primarily occur in spring and autumn (Cai etal., 2021). The forest coverage rate of the forest farm is exceptionally high, reaching 95.4% (Fig. 3c), and all of it consists of state-owned public welfare natural forest land (Chen etal., 2015; Guo etal., 2020). The primary tree species are Larix gmelinii, supplemented by Betula platyphylla and Populus davidiana Dode (Tian etal., 2011; Zhang etal., 2021c) (Fig. 3b). Most groups of trees fall within age are near-mature and mature age; the landform is the Western Mountainous area of the Greater Khingan Mountains; the soil type is brown coniferous forest soil and ordinary chernozem (Fang etal., 2015).

Since 2006, there have been three forest fires caused by Russian transboundary fires at the border of Yimuhe Forest Farm (Table 1). The number of fires was small, but the burned area was as high as 9478.5ha, all major forest fires and above, resulting in a massive loss of forest resources. According to the State Forestry Administration’s Decision on Further Strengthening the Construction of Firebreaks ([2000] No.222) (National Forestry Administration, 2000), there should be 10m/ha ~ 15m/ha forest firebreaks in the general fire danger zone. The density construction standard of engineered firebreaks shall refer to the planning requirements of the Construction standard for forest fire break system in Northeast and Inner Mongolia border areas (National Forestry and Grassland Administration, 2016): the length density should be 3.8 m/ha when the control area is 11000 ha. However, the existing forest road mileage of the original forest area in the Northern Khingan Mountains is 796 km, and the density of the road network is only 0.84 m/ha (Gao, 2014). The backward forest road network conditions make rescue work more difficult (Li etal., 2012).

Table 1

Details of Sino-Russian border fires in the original forest area of the Northern Greater Khingan Range
Year	Location	Coordinate	Elevation (m)	Burned area(ha)
2006	Yimuhe Forest Farm, Wuma Forestry Bureau, Forest class No.108	52 °35 ′25 ″ N 120 °09 ′20 ″ E	730	6006
2014	Yimuhe Forest Farm, Wuma Forestry Bureau, Forest class No.115	52 °36 ′00 ″ N 120 °05 ′20 ″ E	468	2703
2017	Yimuhe Forest Farm, Wuma Forestry Bureau, Forest class No.124	52 °36 ′30 ″ N 120 °14 ′00 ″ E	652	769.5

Data Acquisition

The line selection of forest firebreak is based on GIS system, using ArcGIS to analyze and process required data. In this study, the geographic and projection coordinate systems are Xian_1980 and Xian_1980_GK_CM_105E, respectively. Our data sources are: (1) primary data such as the boundary of the study area, vector data of road and river system, and ground features provided by forest staff, (2) meteorological data from Genhe City Meteorological Forecast Bureau, with district station number 50431 and coordinates of 50°28′12″N, 121°18′36″E. We obtained observational data from May 30 to June 4, 2006, April 29 to May 3, 2014, and April 29 to May 2, 2017, (3) terrain data was obtained from Geospatial Data Cloud (http://www.gscloud.cn/), which was digital elevation data with a resolution of 30m, (4) fuel type data was available from the USGS (https://www.usgs.gov/).

Planning Scope of Border Fires

This paper mainly used FARSITE to determine the planning scope of border fires. FARSITE simulation requires topographic data, fire behavior fuel models, and meteorological data (Price and Germino, 2022).. Topographic data includes elevation, slope, aspect, and coverage (Thew etal., 2011). Fire behavior models are composed of several parameters, such as the distribution of fuel types and combustion models, and each parameter affects the results of forest fire spread (Zhang etal., 2021b). Meteorological data consists of wind direction, wind speed, temperature, humidity, and other data (Osorio etal., 2019).

Firstly, we obtained digital elevation model data of the study area through Geospatial Data Cloud and used ArcGIS to extract the raster data of the slope and aspect. Secondly, we considered factors such as climate conditions, fuel types, load, canopy coverage, and topography to conduct a comprehensive analysis combined with historical border fires. Lastly, we used FARSITE to simulate fire spread and determined the planning scope of border fires.

Locations of Biological Firebreaks

Biological firebreaks’ positions are determined based on the topography and main wind direction. Changes in topography will cause changes in the fire environment, impacting the fire intensity and severity. The main topographic factors are slope, aspect, valley, and ridge. Extreme weather and interactions of climate and topography exacerbate fire behavior. For example, slope and wind direction alignment can result in higher fire intensity on windward slopes than on leeward slopes when fuel types are similar (Prichard etal., 2020).

We used ArcGIS to conduct Hydrological Analysis on the determined planning area and extract valley lines, ridge lines, slope, and aspects. By obtaining observation data from Genhe Meteorological Forecast Bureau, the main wind direction in the study area was organized and analyzed to create a Wind Rose map. According to the locations of valley lines and ridge lines extracted from the study area, combined with the slop, aspect and main wind direction, the locations of biological firebreak was finally determined.

Locations of Engineered Firebreaks

Frame a Comprehensive Cost Model for Line Selection

When selecting locations for engineered firebreaks, calculating comprehensive cost weight can reflect whether each grid in the planning area is suitable for constructing firebreaks (Tan etal., 2019). The comprehensive cost model is the key to calculating the optimal path of engineered firebreaks (Jha and Schonfeld, 2004). We established a single-factor cost raster dataset using ArcGIS to consider the factors affecting engineered firebreaks, then generated a comprehensive cost model by weighted superposition of each single-factor cost raster dataset. The steps of establishing the comprehensive cost model of line selection in engineered firebreaks: (1) determine influencing factors; (2) determine the weight of each factor through AHP; (3) based on the elevation of the study area, obtain single-factor cost raster dataset; (4) reclassify and assign the influencing factors; (5) the weight of each factor is superimposed with the single-factor cost to obtain comprehensive cost.

The comprehensive cost calculation method is shown in Eq. (1):

$$\:S=\sum\:_{i=1}^{n}{F}_{i}{W}_{i}\left(i=\text{1,2},...,n\right)$$

where S is the final cost; F is the cost of each factor; W_i is the weight of each factor; n is the number of factors.

Determine Influencing Factors

The factors involved in the line selection of engineered firebreaks are very complex, and the results of line selection directly affect the fire resistance of the firebreak network (Fang etal., 2015). Therefore, we needed to consider many factors, such as route, direction, surrounding environment, construction difficulty, and association with natural firebreaks. Before planning the location of engineered firebreaks, we considered four aspects: (1) economic status (Li etal., 1995): the construction process and later maintenance of the firebreaks require lots of capital investment in land acquisition, machinery and materials, forest conservation and tending, environmental damage and restoration; (2) environmental impact (Song etal., 2017): understanding natural factors in the study area can ensure that adverse impact on the environment is reduced during the construction process; (3) social conditions (Puri etal., 2020): the construction of firebreaks against border fires involves the stability of foreign relations and border area, and proper planning can reduce social investment; (4) technical indicators (Aricak, 2015): considering natural conditions such as geology and topography, choose a suitable location to construct.

Following the ‘comprehensive consideration, highlighting the critical point’ principle, we selected six influencing factors: elevation, slope, aspect, slope position, fuel crown density, and ground features, which were used to establish a single-factor cost raster dataset.

Calculate Weights

In the AHP analysis process, each element at a different level is quantified and plays a vital role in the analysis results (Saaty, 1988). Therefore, we used the AHP to calculate the weight of the influencing factors. The AHP method has four steps.

(1) Establish a hierarchical structure model, which is divided into the highest, middle, and lowest levels according to the interrelation among issues to be addressed, factors to be considered, and specific measures of the scheme.

(2) Construct judgement matrix A=(a_ij)_n×n to compare relative importance of factors between two layers. The element a_ij is formed by assigning importance according to scales 1 to 9. Mark the scale and meaning of the judgment matrix in Table 2.

(3) Consistency check. Consistency Ratio (Eq. (2)) can be calculated with the help of a random index scale (Saaty, 1990) (Table 3) and Consistency index (Eq. (3)) obtained from the comparison matrix. If CR value is less than 1.0, then it can be concluded that the matrix’s consistency is correct and acceptable for weight analysis (Das etal., 2022).

$$\:Consistency\:Ratio\:\left(CR\right)=\frac{Consistency\:index\:\left(CI\right)}{Random\:Consistency\:Index\:\left(RI\right)}$$

$$\:Consistency\:index\:\left(CI\right)=({\lambda\:}_{max}-n)/n-1$$

Where, λ_max is the principal Eigen value; n stands for number of considering factors.

Table 2

Description of scales for pair comparison with AHP
Scales	Degree of preferences	Descriptions
1	Equally important	The contribution of the two factors is equally important.
3	Slightly important	Experiences and judgment slightly tend to certain factor.
5	Quite important	Experiences and judgment strongly tend to certain factor.
7	Extremely important	Experiences and judgment extremely strongly tend to certain factor.
9	Absolutely important	There is sufficient evidence for absolutely tending to certain factor.
2,4,6,8	Intermediate values	In between two judgments.

Table 3

Random index value
n	1	2	3	4	5	6	7	8
RI	0	0	0.52	0.89	1.12	1.26	1.36	1.41

Reclassification and Assignment of Influencing Factors of Line Selection

According to the characteristics of the six influencing factors, they were divided into five grades, and each grade was assigned a different value. The larger the value, the higher the cost; try to avoid building engineered firebreaks in this area. We used ArcGIS ‘reclassification’ tool to classify the influencing factors and assign values.

Cost Path Analysis

Cost Path Analysis (Fig. 4) is a method that uses Dijkstra algorithm to find the path with the lowest cost from ‘starting place’ to ‘destination’ based on determining comprehensive cost weight (Yildirim and Bediroglu, 2019). According to the principle from right to left and from top to bottom, search and calculate the cumulative cost of the eight adjacent cells at the starting point, and finally, find the cell with the lowest cost. In the model, for different path moving directions, the calculation formulas of path cost are as follows: (1) adjacent node cost (Eq. (4)): cost of moving from Cell 1 to adjacent position Cell 2 (horizontal or vertical directions of Grid 1); (2) diagonal node cost (Eq. (5)): cost of moving from Cell 1 to Cell 2 along the diagonal; (3) cumulative cost (Eq. (6)): cost of moving two units of Cell 1 horizontally or vertically continuously to Cell 3; (4) cumulative cost (Eq. (7)): cost of moving two units of Cell 1 to Cell 3 along the diagonal.

$$\:{a}_{1}=({cost}_{1}+{cost}_{2})/2$$

$$\:{a}_{2}=\sqrt{2}\ast\:({cost}_{1}+{cost}_{2})/2$$

$$\:{accum\_cost}_{1}={a}_{1}+({cost}_{2}+{cost}_{3})/2$$

$$\:{accum\_cost}_{2}={a}_{2}+\sqrt{2}\ast\:({cost}_{2}+{cost}_{3})/2$$

Where, cost₁ is the cost of Cell 1; cost₂ is the cost of Cell 2; cost₃ is the cost of Cell 3; a₁ is the total cost of moving from Cell 1 to Cell 2 in horizontal or vertical direction; a₂ is the total cost of moving from Cell 1 to Cell 2 along the diagonal; accum_cost₁ and accum_cost₂ are the total cost of moving from Cell 1 to Cell 3 in two directions respectively.

The Spatial Analysis of ArcGIS uses the weighting function of cost distance and shortest path function to analyze the optimal path on raster data (Tan etal., 2019). After analyzing the cost-weighted distance, two cost files can be output: girds of cost distance and backtracking link, representing cost-based and direction data, respectively (Chang et al. 2015). Using the method, it is possible to analyze the reasonableness of locations of firebreaks according to the weight values of different influencing factors to determine the optimal locations of engineered firebreaks.

Validation of FARSITE model and firebreak network

Evaluation of FARSITE Model

The data required for FARSITE model simulation is inaccurate or missing data will cause errors between the fire simulation results and the actual fire (Benali etal., 2017). To evaluate the simulation result and prove that the simulation of FARSITE is effective, this study selected the fires that occurred in the study area in 2014 and 2017 to compare with the simulation results. Simulation accuracy is calculated by using the SC (Sørensen’s coefficient) (Sorensen, 1948) (Eq. (8))comparing the similarity of two samples. The value of SC is between 0 ~ 1, and the closer it is to 1, the higher the degree of conformity between the simulated range and the actual.

$$\:SC=\frac{2A}{2A+B+C}$$

Where, A is the intersection of the actual and simulated range; B is the simulated range that does not intersect the actual fire range; C is the actual fire range that does not intersect the simulated range.

Simulation of Random Fire

Since the designed firebreak network is based on historical transboundary fires in the study area, we conducted random fire simulations to assess its effectiveness in other fire scenarios. The fire intensity will change at any time with the combustibles, temperature, humidity, and wind speed, which will cause the fluctuation of the heat flow and then make the angle of the convective column tilt. The heat flow can transport flying fire particle, and the maximum lifting height of flying fire particle is related to the intensity of heat flow (Albini, 1976).

$$\:H=0.173{E}^{\frac{1}{2}}$$

$$\:E=If\left(u\right)$$

$$\:f\left(u\right)=A{\left(0.295u\right)}^{B}$$

$$\:S=1.3\ast\:{10}^{-3}uℎ\left[0.362+{\left(\frac{H}{ℎ}\right)}^{2}\frac{1}{2}\text{ln}\frac{H}{ℎ}\right]+5.03\ast\:{10}^{-4}u{H}^{0.643}$$

Where, h is the maximum lifting height of flying fire particle, m; E is the intensity of heat flow, kJ/m; I is the average of fire intensity, kW/m; f(u) is the function of mean wind speed; A and B are parameters related to the type of combustibles; A is parameter; B is the dimension number; u is the wind speed, m/s; S is the distance of fire, m; H is the average of tree height, m; h is the lifting height of flying fire particle, m.

Planning Scope of Border Fires in Yimuhe Forest Farm

According to the processing flows of FARSITE, a spread simulation was carried out. We obtained the final results of the fire spread caused by the past three border fires in Yimuhe Forest Farm (Fig. 5). We superimposed vector maps of the fire spread range results and combined them with the forest division and natural barrier to determine the final planning scope (Fig. 6) with a total area of 10877.3 ha.

Biological Firebreaks

Slope

According to the characteristics of the study area, it is reclassified into three levels, namely 0°~20°, 20°~40°, and above 40°. In Fig. 7a, the gray area is less than 20°, and the green and red areas are greater than 20° and unsuitable for biological firebreaks. In this study, 90.5% of the planning area had a slope between 0° to 20°, which meant that most areas in the planning scope met the planning layout requirements of firebreak network.

Aspect

In this study, the results of aspect extraction (Fig. 7b) included eight directions. Then the eight directions were classified according to the sunny slope and shady slope classification criteria. The east, south, southeast, and southwest directions were divided into sunny slopes, and the west, north, northwest, and northeast directions were divided into shady slopes. Sunny slopes receive more solar radiation, and combustibles are direr and more flammable. Therefore, we should focus on constructing biological firebreaks on sunny slopes.

Valley and Ridge Lines

Valley lines (Fig. 7c) and ridge lines (Fig. 7d) were extracted through the Hydrological Analysis of ArcGIS, which could clearly reflect that extracted valley lines and ridge lines were continuous and completed. Therefore, the result was in line with the actual situation, and these lines could be used as the primary candidate locations in this study’s planning of biological firebreaks.

Main Wind Direction

The China Meteorological Administration (https://www.cma.gov.cn/) has no weather stations in the study area, so the data from weather stations close to the study area and with similar climate conditions can be used. Genhe Meteorological Station (Site number: 50431) is the closest to Yimuhe Forest Farm and is used as a reference for wind direction data in the study area. Through Excel, the wind direction of the research area is statistically analyzed, and the rose wind direction map is produced. The main wind direction of the planning area was from northeast to southwest (Fig. 8).

Locations of Biological Firebreaks

According to the locations of valley lines and ridge lines, we comprehensively considered slope, aspect, and angle of main wind direction, valley lines and ridge lines are respectively constructed as biological firebreaks. Combined with natural conditions such as regional border rivers and inland rivers, we finally determined 54 biological firebreaks in this area (Fig. 9). The whole area was divided into 52 firebreak networks of different sizes, with an average area of 209.2 ha for each fire protection unit.

Engineered Firebreaks

Comprehensive Cost Model

Through SPSS26.0 to calculate the weight of the influencing factors, the weight of influencing factors of line selection in engineered firebreak was obtained (Table 4). At the same time, the CR values of the four aspects (economic status, environmental impact, social conditions, and technical indicators) were 0.044, 0.008, 0.032, and 0.025, which were less than 0.1. Therefore, the Consistency can be considered suitable for further analysis, and the calculated weights can be used for line selection of engineered firebreaks. The reclassification and assignment results of the six influencing factors are shown in Table 5.

Table 4

Weight of influencing factors of line selection in engineered firebreak
Influencing factors	Weight
Elevation	0.1852
Slope	0.1438
Slope aspect	0.1326
Slope position	0.1201
Fuel crown density	0.2136
Surface feature	0.2047

Table 5

Reclassification and assignment of influencing factors of the line selection
Influencing factors	Value assignment
Influencing factors	1	3	5	7	9
Elevation/(m)	400 ~ 599	299 ~ 399、 600 ~ 649	650 ~ 699	700 ~ 749	750 ~ 833
Slope/(°)	0 ~ 10.00	10.01 ~ 15.00	15.01 ~ 20.00	20.01 ~ 25.00	25.01 ~ 54.00
Aspect/(°)	0 ~ 22.5、 247.5 ~ 360	−1 ~ 0	22.5 ~ 67.5	202.5 ~ 247.5	67.5 ~ 202.5
Slope position	Lower	Upper	Middle	Valley	Flat
Fuel crown density	0.10 ~ 0.39	/	0.40 ~ 0.69	0.70 ~ 0.99	0
Landscape	Wasteland suitable for planting trees	Special shrubs (Pinus pumila)	Pure forest	Mixed stand	Open forest/ River

Calculated according to the weight and assignment results, and finally got the overall comprehensive cost weight of the study area. It can be seen from Fig. 10 that the comprehensive cost of areas with large slopes, high altitudes, and rivers is relatively high.

Cost Path Analysis

The Spatial Analysis of ArcGIS obtained the raster data of cost distance and cost backtracking link. The rescue and ignition points (Table 6) of the three border fires were taken as the cost distance analysis’s starting and end points, respectively. The line selection results of engineered firebreaks (Fig. 11) were obtained by analysis and processing.

Table 6

Fire points and rescue points of the planning scope.
Point	Year	Coordinate
Point	Year	Longitude	Latitude
Fire Point	2006	120 °09 ′20 ″	52 °35 ′25 ″
	2014	120 °05 ′20 ″	52 °36 ′00 ″
	2017	120 °14 ′00 ″	52 °36 ′30 ″
Rescue Point	2006	120 °08 ′33 ″	52 °38 ′18 ″
	2006	120 °05 ′44 ″	52 °37 ′41 ″
	2014	120 °04 ′60 ″	52 °36 ′22 ″
		120 °06 ′32 ″	52 °37 ′35 ″
		120 °05 ′34 ″	52 °37 ′46 ″
		120 °04 ′30 ″	52 °35 ′00 ″
		120 °02 ′42 ″	52 °37 ′06 ″
	2017	120 °12 ′40 ″	52 °35 ′48 ″
		120 °12 ′42 ″	52 °35 ′40 ″
		120 °06 ′16 ″	52 °37 ′42 ″
		120 °10 ′30 ″	52 °34 ′52 ″
		120 °16 ′39 ″	52 °37 ′21 ″

Planning and Layout of Firebreak Network for border fires

In this study, more than 70% of the line selection results of engineered firebreaks were along valley lines and ridge lines in biological firebreaks. Therefore, we made a fitting analysis of engineered and biological firebreaks to determine the final locations of engineered firebreaks. The other lines, except the engineered firebreaks, were constructed as biological firebreaks. Then, the natural barriers, such as rivers and wetlands in the study area, were combined with engineered and biological firebreaks as natural firebreaks. Finally, we formed the overall spatial planning and layout of the transboundary fire emergency firebreak network in the northern forest region of the Greater Khingan Mountains.

From the layout results of the forest firebreaks network in the planning range (Fig. 12), the entire study area had formed a complete closed-forest firebreaks network. Firebreaks divided the study area into independent units with a relatively uniform area. The original river with a narrow channel was a natural firebreak in the study area, and there were no original engineered or biological firebreaks. In the study, the total length of engineered and biological firebreaks after planning was 142579.7m. The length of engineered firebreaks was 46891.7m, and the length of biological firebreaks was 95688.1m (the length of ridge biological firebreaks was 45812.4m, and the length of valley biological firebreaks was 49875.7m). The length density of the firebreak network was 13.11m/ha, and the length density of engineered firebreaks was 4.31m/ha.

Validation of FARSITE model and fire belt network

Evaluation of FARSITE Model

Using Eq. (8), we obtained Simulation Coefficients (SC) for the years 2014 and 2017, resulting in SC₂₀₁₄ = 0.86 and SC₂₀₁₇ = 0.80. These coefficients indicate that, in the absence of human intervention, the FARSITE model accurately replicates more than 80% of the fire’s spread area (Fig. 13). Additionally, the simulated fire direction and overall shape align reasonably well with actual fire events. Consequently, the forest fire simulations generated using the FARSITE model hold valuable reference significance.

Simulation of Original Fire

To assess the fire prevention impact of firebreaks, we conducted simulations based on historical ignition points and climate conditions of three significant past transboundary fires in the study area. Due to the substantial time and financial investments required for firebreak construction and the risks associated with conducting large-scale controlled burning experiments, we relied on FARSITE simulations. We considered two scenarios: (1) firebreaks are fully intact and continuous; (2) in reality, firebreak may become damaged, so we introduced random interruptions to simulated real-world conditions more accurately. Our simulations in Fig. 14 show the fire spread in 2006, 2014, and 2017 when firebreaks are intact (a, b, c) and when they are partially damaged (d, e, f). The presence of complete firebreaks yields the best fire prevention results. However, even firebreaks are damaged to some extent, they still play a crucial role in containing the fire’s spread, significantly reducing the loss of forest resources compared to situations without firebreaks.

Simulation of Random Fire

Wang (Wang et al. 2011) verified the effectiveness of the Eq. (9)-(12), whose data selection was suitable for our study area. Among them, A of mature shrubs is 301; B is -1.05; the average tree height is 20m; H is 10m/s, 15m/s and 20m/s, respectively; I is 8.7*10³ kW/m, 4.0*10⁴ kW/m and 1.8*10⁵ kW/m, respectively. In different wind speed and fire intensity conditions, they estimated the fire spotting distance, as shown in Table 7. Notably, when the fire intensity is 1.8*10⁵ kW/m and the wind speed is 20 m/s, the spotting fire distance is maximized at 3.34km. To simulate the spread of fire accurately, we generated three random transboundary fire points within a 3.34km radius from the Russian border along the Ergun River, considering different wind directions (northeast or northwest wind). The fire spread simulations on these points (Fig. 15) confirmed the firebreaks’ efficacy by comparison.

Table 7

Distance of spotting fire (10³ m)
Fire intensity (kW/m)	Wind speed (m/s)
	10	15	20
8.7*10³	0.68	0.85	0.99
4.0*10⁴	1.24	1.58	1.87
1.8*10⁵	2.19	2.81	3.34

In light of the recurrent transboundary fires along the China-Russia border, which have led to substantial losses in forest resources and human casualties, we present an integrated approach that utilizes FARSITE, Analytic Hierarchy Process (AHP), and ArcGIS to develop a firebreak network design for the border forest region in the Greater Khingan Mountains of China. To illustrate this approach, we use three significant historical transboundary fires in the northern Greater Khingan Mountains as case studies. FARSITE is employed to delineate the fire spread boundaries and define our research area. ArcGIS is used to extract essential terrain features, including slope, aspect, valleys, and ridges within the study area. By considering prevailing wind directions, we identify optimal locations for natural firebreaks. Additionally, AHP is applied to assign weights to key factors influencing engineered firebreak placement, such as elevation, slope, aspect, slope position, combustible density, and surface vegetation. These weighted factors are then integrated into ArcGIS for Cost Analysis to pinpoint the most effective locations for engineered firebreaks. Furthermore, in conjunction with the presence of natural firebreaks, such as streams, the firebreak network designed using this method is tailored to the geographical and climatic characteristics of the study area, enhancing its fire-blocking efficiency. Implementing such designs in regions with recurrent transboundary fires can lead to cost savings in firebreak construction over expansive territories.

Various factors can influence the effectiveness of firebreaks in mitigating fire spread (Syphard and Keeley, 2015). Previous research has demonstrated that ridge locations tend to be drier and more exposed, making them favorable sites for establishing biological firebreaks to halt fire progression (Narayanaraj and Wimberly, 2012; Yuan etal., 2020). Our findings align with this, we recommend placing biological firebreaks on slopes with 20° or less, oriented perpendicularly to prevailing winds during the fire season. These measures can significantly impede the spread of transboundary fires by reducing wind speeds and extinguishing fires that typically struggle to ignite less flammable vegetation (Narayanaraj and Wimberly, 2011; Weise etal., 2023). Terrain plays a crucial role in determining the characteristics of engineered firebreaks, including road width and runoff management (Calkin etal., 2015). In regions like the Cascade mountains of Oregon and Washington, engineered firebreaks often follow narrow streambeds into watersheds and then ascend adjacent steep hillsides (Forman, 2003). Additionally, flat regions make it more manageable to control fires and conducting firefighting operations (Narayanaraj and Wimberly, 2012). These consistent with our analysis results. We consider local terrain features such as slope, elevation, and local conditions when designing engineered firebreaks, 75% of them located in flat areas beneath the mountains.

Due to the lack of firebreak construction in the study area, our actions during a fire outbreak are primarily reactive, often relying heavily on helicopters for firefighting. But a firebreak network can prevent the spread of fires as much as possible and buy firefighters more time. Furthermore, the engineered firebreak can help firefighters quickly enter the fire scene and implement firefighting operations in a variety of ways (Syphard etal., 2011). Our research is consistent with international efforts to enhance forest response capabilities, similar to the United States’ Potential Operational Delineations and Europe’s Strategic Management Areas/Points (Thompson etal., 2016; Mauri etal., 2023). However, we introduced the AHP methodology as a critical component of our research. This method has not been extensively applied in the firebreak design. The use of GIS-AHP in this study allows for the consideration of multiple factors and more precise determination of the layout of the firebreak network. And it can be applied to border countries and regions that also face transboundary fire invasion Portugal has implemented comprehensive measures, but sustaining them can be challenging due to their extensive scope (Agee etal., 2000). We minimize initial construction and long-term maintenance costs by designing a firebreak network for high-incidence transboundary fire areas. Prioritizing areas with a high fire risk is unanimously agreed upon (Johnson etal., 2011; Liu etal., 2013).

However, some limitations exist in the designed firebreak network in this study. Firstly, it is not always accurate to assume that simulated firebreaks have identical effects on fire control, as different types of firebreaks may have varying impacts (Oliveira etal., 2016). Secondly, the study and simulation only considered terrain, elevation and climate conditions in the study area were considered, without accounting for parameters such as firebreak width and fire-resistant tree species. Firebreak construction is a multifaceted undertaking that must consider the broader external environment (Ascoli etal., 2018). Different environments may require different fire-resistant tree species and firebreak widths to achieve optimal fire resistance. Therefore, adjustments should be made according to specific environmental conditions. Finally, this study primarily focused on optimizing the firebreak network to reduce transboundary fire spread and did not thoroughly explore its impact on ecosystem stability. While prior studies have suggested that firebreaks do not adversely affect the landscape and plant diversity that firebreak do not adversely the landscape and plant diversity of the Greater Khingan Mountains (Li etal., 2012), future firebreak network designs should consider their impact on regional ecosystems. An increased density of firebreaks may create isolated landscape patches, potentially impacting ecosystem functions (Caliskan, 2013; Hacisalihoğlu etal., 2019). Consequently, future firebreak network designs should strive to strike a balance between effective fire control and ecosystem stability.

In this study, we utilized the FARSITE simulation to model historical transboundary fires in the northern forest area of the Greater Khingan Mountains. This enabled us to define the planning scope for forest areas affected by transboundary fires. Subsequently, we employed ArcGIS Hydrological and Cost Path Analysis to identify and extract both biological and engineered firebreaks within this planning scope. We then conducted fitting and analysis of the results to determine the specific locations for biological and engineered firebreaks. These selected firebreak locations were integrated with natural firebreaks to create the overall lay out of the emergency firebreak network. Ultimately, the total length of the firebreak network was determined to be 142,579.7m, with engineered firebreaks accounting for 46891.7m and biological firebreaks covering 95688.1m. After careful calculation, the length density of the firebreak network was found to be 13.11m/ha, while the length density of engineered firebreaks was 4.31m/ha. These metrics met the requirements outlined in the Construction Standard of Forest Firebreaks System and exhibited effective fire resistance capabilities. When simulating fires, both original and randomly generated scenarios were considered. The results clearly indicate that the firebreak network effectively curbed fire spread and reduced the loss of forest resources, validating the efficacy of the firebreak network designed in this research.

Planning a firebreak network is a multifaceted task that necessitates the consideration of numerous influencing factors. In this study, we focused on some key factors. Nonetheless, we acknowledge that there are other relevant factors, such as tree height, soil type, and soil thickness, which were not included in our analysis. Furthermore, our study primarily determined the spatial locations of firebreaks, without delving into aspects such as the width and grade of engineered firebreaks or the specific tree species for biological firebreaks. In future research endeavors, we recommend exploring additional influencing factors in firebreak planning, including the width, grade, and tree species selection for firebreaks. Such an approach will contribute to a more scientific and comprehensive firebreak network planning process, ensuring its effectiveness and suitability for various forest environments.

Conflicts of Interest

The authors declare no conflicts of interest.

Declaration of Funding

This research was funded by the Project (LH2020C052) of the Natural Science Foundation of Heilongjiang Province of China.

Data Availability Statement

Data sharing is not applicable as no new data were generated or analyzed during this study.

Author Contribution

Yue Xu: Writing, Formal Analysis.

Shufa Sun: Funding Acquisition.

Miao Yu: Methodology.

Yuan Zhou: Data Curation.

Xuekai Zhang: Data acquisition.

Jiangwei Chu: Conceptualization.

Xingdong Li: Software.

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Layout of multi-type firebreak network for transboundary fires—the Greater Khingan Mountains of China case

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Abstract

Figures

Introduction

Materials and Methods

Study Area

Data Acquisition

Planning Scope of Border Fires

Locations of Biological Firebreaks

Locations of Engineered Firebreaks

Determine Influencing Factors

Calculate Weights

Reclassification and Assignment of Influencing Factors of Line Selection

Validation of FARSITE model and firebreak network

Results

Planning Scope of Border Fires in Yimuhe Forest Farm

Biological Firebreaks

Engineered Firebreaks

Planning and Layout of Firebreak Network for border fires

Validation of FARSITE model and fire belt network

Discussion

Conclusion

Declarations

Conflicts of Interest

Declaration of Funding

Data Availability Statement

Author Contribution

References

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