3 **Authors:**

4 Aydogan Avcioglu¹*, ², Abdullah Akbaş³, Tolga Görüm¹, Omer Yetemen¹

5 ¹ Eurasia Institute of Earth Sciences, Istanbul Technical University, Istanbul, Türkiye

6 ² BRGM, 3 avenue Claude Guillemin, 45060 Orléans, France

7 ³ Department of Geography, Faculty of Arts and Science, Bursa Uludağ University, Bursa,

8 Türkiye

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10 **Keywords:**

11 Wildfire spread and severity; Topographic Impacts; LULC; Türkiye

12 * Corresponding author, Email: avcioglua19@edu.itu.tr

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**Abstract**

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The large wildfire sequence took place in July and August 2023 in Çanakkale, recorded as the largest wildfire incident in NW, Türkiye. The total affected area in two successive wildfires is 79.1 km². This study presents an observation-based instance and statistical model findings of how topography as a major determinant controls wildfire propagation direction and burn severity with the contribution of weather conditions (particularly wind and temperature) and specific land use and land cover (LULC) types. The findings reveal that Çanakkale Strait and Biga Mountains as regional geomorphic units that extend from northeast to southwest portray the main direction of wildfire incidents guided by prevailing wind patterns and specific LULC types. The marginal section of the Biga Mountains, where the topographic relief and slope largely increase, constrain wildfire propagation, while vegetation density is higher in the steeper areas. Notably, the specific LULC which is a harvested wheat field plays an important role in determining the major direction of wildfire, primarily influenced by the prevailing northeast-to-southwest wind direction observed in the July case. Furthermore, the ordinary least square model results showed that rougher topography tends to exhibit higher burn severity which is the case for the August wildfire. The local topographic conditions (i.e., valley shape morphology) offer an appropriate observational insight for general mathematical models with the increased burn severity, and the main direction of fire and impeding areas in the August case. This study also sheds light on the significance of wind direction that can surpass the slope orientation for burn severity which is expected higher potential in equator-faced compared to the polar-faced.

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**Introduction**

Wildfires play a crucial role on a global scale due to their potential, causing economic and environmental destruction. This global phenomenon is considered among the primary natural disturbances on Earth, exerting a substantial impact on ecological balance and contributing significantly to climate change (Liu et al., 2021) with 448 million hectares of land burn
51 (Boschettiet al., 2021). Given that one of the primary disturbances affecting the Mediterranean
52 Basin which is one the important hot-spot in terms of climate change (Giorgi, 2006), wildfires are
53 anticipated to increase due to the increase of extreme events (Keeley et al., 2011; Russo et al.,
54 2017; Cramer et al., 2018). Fires also have an impact on a variety of earth system's components
55 and processes, including wildlife habitat, nitrogen cycling, hydrology, and carbon dynamics
56 (Collins and Stephens, 2007; Falk et al., 2011; Holsinger et al., 2016), soil erosion (Moody et al.,
57 2013), mass wasting (Yıldız et al., 2023), and biodiversity (Tavsanoglu & Pausas, 2022), forming
58 badland landforms through forest loss and accelerated sediment flux (Torri et al., 2018; Avcioğlu
59 et al., 2022; 2024).

60 The literature has shown that a wide range of parameters have a major impact on the intensity and
61 possible spread of fires in a given region. These include the local terrain, past disturbances, the
62 distribution of surface and canopy fuels, synoptic weather patterns linked to fire, and the prevailing
63 climate. (Barton, 1994; Agee 1996; Mermoz et al., 2005; Iniguez et al., 2008; Krawchuk et al.,
64 2009; Carmo et al., 2011; Flatley et al., 2011; Holsinger et al., 2016; Parks et al., 2016).
65 Understanding the effects of topography on wildfire spread has been recognized as a highly
66 complicated mechanism (Heyerdahl et al., 2001), mainly due to its intricate interplay with weather
67 conditions and fuel characteristics. Povak et al. (2018), for instance, showed that the contribution
68 of topography on wildfire spread remarkably differs among the ecoregions and across topographic
69 features, which are the most marked mountainous ecoregions and are least important in arid
70 regions. Supportingly, the rugged topography exerts a greater influence on fire propagation and
71 intensity compared to subtle landscapes (McKenzie et al. 2006; Parks et al., 2012) which has been
72 considered as a direct impact of topography by promoting the radiant energy towards upslopes in
73 the rugged terrains (Rothermel, 1983; Carmo et al., 2011). On the other hand, in an indirect way,
74 topography plays a role in influencing fires through the creation of diverse microenvironments that 75 regulate the moisture content in fuels, atmospheric temperature, and the spatial distribution of plant 76 species (Heyerdahl et al., 2001; Mermoz et al., 2005). For example, the influence of contrasting 77 slope orientations, north vs. south-faced slopes, in the Rock Mountains (Rollins et al., 2002) – has 78 been shown as a potential driver that may act as a natural barrier to wildfire spread due to their 79 differential fuel moisture and fuel quantity capacity (Iniguez et al., 2008; Holsinger et al., 2016; 80 Flatley et al., 2011). Furthermore, ridgetops or divides can be assessed as a natural barrier to the 81 spread of fires, particularly where the topographic slope decreases, and topography forms 82 shoulders, resulting in abrupt shifts in aspect. These variations in terrain can lead to abrupt changes 83 in vegetation and fuel types, potentially influencing the heterogeneity of vegetation patterns due 84 to alterations in soil characteristics (Povak et al., 2018). Contrary to the influence of topography 85 acting as natural barriers, canyons, and narrow-deep valleys can indeed play a significant role in 86 facilitating rapid fire spread due to terrain-wind-fire interactions (Viegas et al., 2009; Liu et al., 87 2021). These geomorphic units concentrate and channel the airflow, acting as a kind of natural 88 wind tunnel.

89 Three primary types of mathematical models—physical, empirical, and semi-empirical—are 90 developed to enhance our understanding of wildfire propagation and direct firefighting practices 91 (Wu et al., 2022). Physical models, like the Rothermel model (Stocks et al., 1989), evaluate 92 wildfire propagation based on fluid dynamics and heat transmission by utilizing factors including 93 wind speed, fuel moisture content, and topography. Historical wildfire data is used for forecasts in 94 empirical models such as the Canadian Forest Fire Danger Rating System (CFFDRS) (Stocks et 95 al., 1989). Empirical and physical concepts are combined with observational data in semi- 96 empirical models, like the Fire Spread Probability (FSP) model (US Forest Service, 2009). Due to
the highly nonlinear nature of wildfire propagation, these models have limitations that make using fixed expressions for forecasts challenging (Jimenez et al., 2008; San Jose et al., 2014; Cruz and Alexander, 2017). Furthermore, extreme or exceptional conditions are frequently not sufficiently taken into consideration, with regional variations in flora, topography, and climate impacting projections. Small-scale fire theories may not be able to effectively anticipate the behavior of large-scale fires (Liu et al., 2021). Essential to understanding wildfire behavior in a variety of situations and validating and refining modeling approaches are observation-based case studies (Hessburg et al., 2015; Schwartz et al., 2015; Marjani et al., 2023). These studies provide relevant information, improving our comprehension of how environmental factors interact with wildfires and facilitating more precise forecasts for wildfire control.

Based on the aforementioned factors, this study mainly aims to show how the largest wildfires that occurred in the NW region of Turkey in 2023 were significantly controlled by the intricate interplay between topography, weather conditions, and fuel type. More specifically, this study answers the following questions: 1) To what extent does topography, at both regional and local scales, directly influence wildfire propagation dynamics in the NW region of Turkey? 2) Does wind direction, surpassing aspect controls, notably contribute to increased fire propagation and burn severity on southern slopes in this region? 3) What is the contribution of LULC type in the affected areas for the direction of fire spread and burn severity during the 2023 wildfires in NW Turkey?
118 **Study area**

119

120 The study area is experienced of two successive wildfires, that occurred in July and August 2023,
121 in upper-most of the Mediterranean basin, on the administrative border of Çanakkale, NW Türkiye
122 (Figure 1). Because of the type of forest, climatic conditions, and land use characteristic in the
123 area, it is also recognized as a wildfire-prone area (Akbulak et al., 2018; Tatli & Türkeş, 2014).

124 The first wildfire started on July 16, 2023, near Kızılkeçili at 4.30 pm and lasted 3 days. In this
125 wildfire, almost 3340ha (33.4 km²) area has been affected. The second wildfire started on August
126 22, 2023, at Kayadere village around 1 pm and the affected area is almost 4570ha (45.7 km²).
<figure><img src="image_1.png" /><figcaption>**Figure 1:** The location map of the study area. A) The spatial distribution of historical wildfires within the regional context of the study area, as documented by (Tyukavina et al., 2022) (The wildfire inventory can be obtained through the link: https://glad.umd.edu/dataset/Fire_GFL/). B)</figcaption></figure>
131 The spatial extent of two major wildfires that occurred successively, together with an inset graph
132 showing the average yearly wind direction and speed in the study area.

133 The spatial spread of these wildfires is located on the margins of the mountainous terrain which is
134 parallel to the Çanakkale strait. While topographic relief reaches up to 177m in a 50 x 50m
135 sampling window, the elevation ranges between 25m to 400m in these wildfires. The July wildfire
136 stretched between Umurbey (northern margin) and Kayadere (southern margin) measured
137 approximately 4 kilometers in width and 11.5 kilometers in length, during the August case, the fire
138 expanded to cover an area approximately 5 kilometers wide and 15.5 kilometers long. The area is
139 characterized by the Subtropical Mediterranean Climate Zone, which means that summers are hot
140 and dry in this region. While the average temperature is around 15°C, the maximum temperature
141 has been recorded in July (25°C on average). Due to the general precipitation characteristics of the
142 Mediterranean climate, vegetation and crops suffer from water deficits, particularly in the summer
143 months in this region. The successive heat waves starting from July 10, 2023, till the end of the
144 month caused high temperatures (~38°C) and accelerated vegetation drought in the entire
145 Mediterranean coast. The wind speed has reached up to 48 km/h and 72 km/h on the first days of
146 June and August wildfires with the north-northeast (NNE) direction. The general characteristics
147 of land use and land cover (LULC) are cropland (mainly, wheat and sunflower fields), trees, and
148 shrubland.
As a result of these two successive wildfires, a total of 41 people were injured, 10 villages were evacuated, and 9 houses were damaged.

## Data and methods

We employed various satellite images, including Landsat-8 (30 m), Sentinel-2 (10 m), and Planetscope Scene (3 m), to assess pre- and post-fire conditions (Table 1). To investigate the spread of the fire, the severity of the burns, and changes in NDVI, we gathered these photos taken before and after the fire. We selected the images taken on July 9, 2023, for pre-fire analysis, and July 21, 2023, for post-fire evaluation, for the wildfire that occurred between July 16 and July 19, 2023, for these goals. Also, the images were captured on August 5, 2023, and August 25, 2023, for pre- and post-fire assessments. Since reflectance rates of the surface dropped quickly, we were immediately able to obtain satellite images after the wildfire. We used the ArcGIS Pro 3.0 for extraction of wildfire areas by manually tracing the boundary of the burnt area.

<table><caption>**Table 1:** The specifications of satellite/sensors which are used in this study.</caption><thead><tr><td>Satellite</td><td>Resolution</td><td>Number of Bands</td><td>Wavelength/Spectrum</td></tr></thead><tbody><tr><td>Landsat-8</td><td>30 meters</td><td>11</td><td>Visible, Near Infrared, Thermal</td></tr><tr><td>Sentinel-2</td><td>10 meters</td><td>13</td><td>Visible, Near Infrared, Shortwave Infrared</td></tr><tr><td>PlanetScope</td><td>3 meters</td><td>4</td><td>Blue, Green, Red, Near Infrared</td></tr></tbody></table>

We used Sentinel-2 data, which comprises 13 different spectral bands taken by two identical satellites in the same orbit in order to calculate the burn severity map, which is generated as the difference normalized burn ratio (dNBR, Key and Benson, 2006) as following:
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$$NBR = \frac{NIR-SWIR}{NIR+SWIR} \qquad (1)$$

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$$dNBR = (NBR_{pre} - NBR_{post}) * 1000 \qquad (2)$$

170 where, NIR stands for near-infrared, SWIR for short-wave infrared, and $NBR_{pre}$ and $NBR_{post}$ stand for pre- and post-fire conditions, respectively. The “burn severity” has been evaluated according to Keeley, (2009), and the United States Geological Survey-Fire Effects Monitoring and Inventory Protocol (Lutes et al., 2006) has been employed in the classification of burn severity (Table 2) in the study areas. We used ArcGIS Pro 3.0 for the calculation of NDVI, NBR, and dNBR.

<table><caption>Table 2: Ordinal severity scales and example classes of dNBR (scaled by 10³) (Keeley, 2009).</caption><thead><tr><td>Burnt Severity</td><td>dNBR classes</td></tr></thead><tbody><tr><td>Unburnt</td><td>-100 to +99</td></tr><tr><td>Low</td><td>100 to 269</td></tr><tr><td>Low to medium</td><td>270 to 439</td></tr><tr><td>Medium to high</td><td>440 to 659</td></tr><tr><td>High</td><td>600 to 1300</td></tr></tbody></table>

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177 The normalized difference vegetation index (NDVI, Rouse et al., 1974) has been also produced
178 near-infrared (NIR) and red bands of the Sentinel-2 satellite images in order to evaluate pre- and
179 post-fire vegetation conditions:

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$$NDVI = \frac{NIR - RED}{NIR + RED} \qquad (3)$$
181 where the NIR indicates the near-infrared band, and the RED refers to the red band.

182 Dynamic World, near real-time global 10 m land use land cover (Brown et al., 2022) data has been
183 used to determine affected areas. It is a new approach for globally consistent, high resolution, near
184 real-time (NRT) land use land cover classification leveraging deep learning on 10 m Sentinel-2
185 imagery.

186 We used swath profiles produced in SAGA GIS using "Swath Profile [interactive]" tool (Conrad
187 et al, 2015), which includes the mean, maximum, and minimum values of elevation, topographic
188 relief, and slope and encompasses the entire extent of wildfires. The swaths investigate the spatial
189 changes in elevation, topographic relief, and slope as well as the interactions of these parameters
190 with the spatial distribution of wildfires in the terrain. In particular, over two wildfire zones in the
191 W-E direction, we derived three combined swath profiles for elevation, topographic relief, and
192 slope in varied lengths and 2 km width for each profile.

193 Melton ruggedness number (MNR) was used to understand the extent of the instability of the land
194 surface which influences the severity and spread of wildfire. This parameter combines the slope
195 steepness and length in the topography (Strahler, 1957; Kabite & Gessesse, 2018) and is calculated
196 as the difference between maximum and minimum elevation in the catchment area divided by the
197 square root of catchment area size (Melton, 1965; Olaya & Conrad, 2009). We used SAGA GIS
198 v. 7.8.2 to calculate the MNR as a part of the hydrology module which takes an input elevation
199 grid (Conrad et al, 2015).

200 We employed an ordinary least squares (OLS) regression model to test the contribution of the
201 ruggedness of topography to burn severity in the study areas. Furthermore, to ensure that our OLS
202 assumptions were satisfied, we conducted data transformations, where necessary, to enhance
linearity, normalize distributions, promote equal variances, and achieve homoscedasticity (Harrel Jr., 2001.; Acheampong et al., 2019)

By minimizing the sum of squared differences between the observed values (Y) and the predicted values (Xᵢ), the main goal of OLS is to determine the best-fitting linear model that explains the connection between the dependent variable (Ȳ) and one or more independent variables (Xᵢ):

$$Y = \beta_0 + \beta_1 * X_1 + \varepsilon \quad (4)$$

where Y is the dependent variable (i.e., burn severity), and X₁ is the independent or explanatory variable (i.e., ruggedness). β₀ (intercept) and β₁ (slope) are the parameters to be estimated. ε refers to the error term (i.e., residuals) representing the difference between observed and predicted values.

## 213 Results

We determined the total burnt areas via PlanetScope Scene and Sentinel-2 by analyzing pre- and post-fire images and results showed that 25.2 km² and 36.4 km² area burned during the July and August 2023 wildfires, respectively. The total affected areas by wildfires in July and August were 33.4 km² and 45.7 km², respectively. These two wildfires considering their total absolute burnt areas of 61.6 km² (6160 hectares) have been recorded as the largest wildfire events, which correspond to occurred in a summer season in the NW of Türkiye.

July wildfire spanned 3 days between 16 to July 18, 2023, and the fire started near Kızılkeçili village in a cropland (i.e., harvested wheat field) area. The Landsat-8 images show that in the first two days of the fire (16 and 17 July 2023), the 14.6 km² aerial coverage has burnt in line with the main wind direction towards south south-west (Kemalköy) and the wildfire total has reached 33.4 km² spatial coverage in three days (Figure 2).
<figure><img src="image_2.png" /><figcaption>**Figure 2:** The spatiotemporal spread and spatial coverage of the July 2023 wildfire. The black dots show the buildings nearby or within the wildfire-affected areas.</figcaption></figure>

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229 We identified the burn severity by examining the pre- and post-fire Sentinel-2 satellite images

230 (Figure 3A). Results showed that most of the areal coverage of this wildfire was identified as low

231 severity with 80% followed by 19% and %1 for medium and high severity classes, respectively

232 (Figure 3A and C).

<figure><img src="image_3.png" /></figure>

233
**Figure 3:** A) The burn severity, B) land use and land cover classes (the red polygons show built up areas), and C) their fraction within the corresponding wildfire areas.

Overall, the crop areas which are composed of mainly wheat fields are the major LULC class, which was burnt in the July wildfire, with 51% followed by trees with 40% and minor areas are grass and shrub with 6% and %4, respectively (Figure 3B). The July wildfire has affected the terrain with a range of elevation between 29 – 389 m. in the transition from subtle topography to mountainous areas (Kocakir Hill, 920m.). This terrain demonstrates slightly higher median relief, 86m. than the August wildfire areas (57), even though it shows a lower median slope degree (10.7°) than the other area (Table 3).

**Table 3:** Descriptive statistics of topographic parameters in the wildfire areas. Note that Std refers to Standard Deviation and Pct90 indicates the 90th percentile.

<table><caption>July 2023 Wildfire</caption><thead><tr><th></th><th>Minimum</th><th>Maximum</th><th>Range</th><th>Mean</th><th>Std</th><th>Median</th><th>Pct90</th></tr></thead><tbody><tr><th>Elevation (m.)</th><td>29</td><td>389</td><td>360</td><td>184.9</td><td>80.3</td><td>176</td><td>302</td></tr><tr><th>Relief (m.)</th><td>23</td><td>184</td><td>161</td><td>88.6</td><td>23.9</td><td>86</td><td>121</td></tr><tr><th>Slope (°)</th><td>0.0</td><td>66.7</td><td>66.7</td><td>12</td><td>10.2</td><td>10.7</td><td>29.0</td></tr></tbody></table>

<table><caption>August 2023 Wildfire</caption><thead><tr><th></th><th>Minimum</th><th>Maximum</th><th>Range</th><th>Mean</th><th>Std</th><th>Median</th><th>Pct90</th></tr></thead><tbody><tr><th>Elevation (m.)</th><td>25</td><td>460</td><td>435</td><td>218.9</td><td>103.3</td><td>212</td><td>369</td></tr><tr><th>Relief (m.)</th><td>0</td><td>177</td><td>177</td><td>60.4</td><td>24.3</td><td>57</td><td>95</td></tr><tr><th>Slope (°)</th><td>0</td><td>75.2</td><td>75.2</td><td>15.5</td><td>10.6</td><td>12.8</td><td>30.2</td></tr></tbody></table>
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246 August wildfire, like the July wildfire, lasted 3 days from 22 to 24 August 2023, and started near Kayadere village, on the cropland (i.e., harvested wheat field). In this fire, we obtained Planetscope Scene images, higher resolution than Landsat-8, and determined the spread of wildfire through 3-days. Results show that the August wildfire spread faster than the July wildfire, and it reached 36.4 km² of spatial coverage on the first day together with a high wind speed reaching up to 72 km/h of fire, and this wildfire affected 45.7 km² total area which largest individual wildfire in NW Türkiye (Figure 4).

253 The burn severity of the August wildfire highly differs from that of the July wildfire. The notable difference is evident in the low burn severity level between August and July, which stands at around 42% in August, proportionally almost two magnitudes lower than in July. The medium and high severity classes also demonstrate significant proportional increase with 50% and 8%, respectively, when compared to July wildfire.

258 The LULC categories also exhibit varying proportions relative to the July wildfire. The tree category dominates the LULC classification in the August wildfire zone, accounting for 75% of the total. Also, a notable decrease has been recorded in the cropland areas with a 21% ratio relative to the July wildfire, which is 51%. The grassland and shrubland constitute a relatively small portion of the burned areas in a manner analogous to their behavior during the July wildfire event. The August wildfire has primarily occurred around 218.9m, which is a mean elevation while its elevation range is 435m. The elevation values showed that the August wildfire took place at a higher elevation than the July wildfire. Additionally, the August wildfire occurred in steeper areas than the July event with 15.5 and 12 mean slope degrees, respectively.
Before wildifre

<figure><img src="image_4.png" /></figure>

23rd August

<figure><img src="image_5.png" /></figure>

25th August

<figure><img src="image_6.png" /></figure>
268 **Figure 4:** The spatiotemporal spread and spatial coverage of the August 2023 wildfire.

269 The notable fluctuations in NDVI have been discerned in both pre- and post-fire NDVI
270 assessments across the two wildfire-affected regions (Figure 5). Within the context of the July
271 wildfire (Figure 5A and C), the pre-fire NDVI values exhibited a prevailing range of 0.14 to 0.4,
272 spanning from the 5th to the 95th percentile (Figure 5B). However, after the wildfire, these values
273 substantially declined, narrowing to a range of 0.07 to 0.19, representing an average reduction of
274 51.2%. The most pronounced alteration, amounting to a 97.5% decrease, was observed in the
275 extensively burned area, where the pre-fire NDVI value of 0.35 declined to 0.01 in the post-fire
276 assessment.
<figure><img src="image_7.png" /><figcaption>**Figure 5:** The assessment of NDVI changes in pre- and post-wildfires. A) Pre-fire, B) histograms and kernel density estimation plots (KDE plots with line-bordered indicates post-fire values), and C) post-fire NDVI values for the July 2023 wildfire. D) Pre-fire, E) histograms and kernel density estimation plots (KDE plots with line-bordered indicates post-fire values), and F) post-fire NDVI values for the August 2023 wildfire. Note that the colors of hexagons indicate the number of points in the corresponding hexagon.</figcaption></figure>

Furthermore, noteworthy variations have been ascertained in the context of the August wildfire (Figure 5D and F). In the pre-fire assessment, the NDVI values exhibited a dominant range

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between 0.11 and 0.32, encompassing the 5th to the 95th percentile (Figure 5E). However, subsequent to the wildfire, these values diminished to a range of 0.08 to 0.18, coinciding with the same NDVI value distribution range, signifying a 35% decrease on average. Notably, the most striking alteration, amounting to a 96% decline, took place within the highly burned areas, where the pre-fire NDVI value of 0.3 dropped to a mere 0.01 in the post-fire assessment.

Melton Ruggedness Number (MRN) and dNBR metrics were integrated within the study regions to create the quantitative bivariate cartographic representations (Figure 6A and B), and the results show distinctive characteristics between the August and July wildfires. Particularly, in contrast to the July wildfire, the August wildfire displays a spatially coherent pattern of higher dNBR and MRN values, marked by the recognizable purple hexagonal patches. Applying Ordinary Least Squares (OLS) models to both wildfire cases further supports this association. Our results show that MRN and dNBR have a statistically significant relationship ($p < 0.001$) in the case of August, which is characterized by a strong and positive coefficient of determination ($r^2 = 0.83$, Supplementary material Appendix 1, Fig. 1), as opposed to the July wildfire, where this relationship is found to be statistically insignificant ($p > 0.001$) and has a noticeably lower $r^2$ value of 0.05 (Supplementary material Appendix 1, Fig. 2). The standardized residual maps (Figure 6C and D), along with an inset plot displaying the distribution of residuals, further validate our model's predictions. It is remarkable that, in contrast to the residuals from the July wildfire, those from the August wildfire follow a normal distribution and have a near-zero mean residual value of -2.43e⁻⁸.
<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>
**Figure 6:** The bivariate maps of Melton Ruggedness Number and difference normalized burn ratio in July (A) and August (B) wildfires and the standardized residuals of ordinary least square models for July (C) and August (D) wildfires. Note that the area of hexagons (0.44km²) is the same in both study areas.

## Discussion

The two subsequent wildfires show a similar spread process in a regional context, which is influenced by topography, weather, and fuel type, in the transitional areas from gentle topography to the edge of mountainous terrain (Figure 7). It is essential to explain the intriguing relationship between wildfire spread patterns and the Çanakkale Strait, a valley-like geomorphic unit carved by regional uplift-induced river incision of Biga Peninsula since Pliocene (Gökaşan et al., 2008, 2012; Yaltırak et al. 2000) that remarkably affects local weather dynamics, particularly the speed and direction of wind. Our observations and results show a considerable association between wildfire spread and the main northeasterly (NE) wind direction in the area. The Çanakkale Strait, serving as a natural wind corridor where a wind speeds up to 48 km/h and 72 km/h were observed in the first days of June and August wildfires, exerts a pronounced influence on local atmospheric circulation patterns. This empirical observation highlights the profound interplay between geomorphic features and wildfire behavior, shedding new light on the complex dynamics governing wildfire occurrences in the region.
<figure><img src="image_12.png" /><figcaption>**Figure 7:** The variations and comparisons of the parameters evaluated to understand wildfire behavior. A) Burn severity map of study areas showing also the locations of swath profiles. B) The rose diagram shows mean dominant wind direction and speed in July. C) The swath profiles of elevation and topographic relief with mean slopes in those areas.</figcaption></figure>

The outcomes of our OLS models and bivariate maps (Figure 6) show a clear association between wildfire spread and terrain roughness, with higher burn severity reported in regions with greater terrain ruggedness, as shown by the August fire case. But it's important to remember that correlation does not indicate causation, despite the evidence presented in our OLS models and

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bivariate maps, particularly for the August case clearly demonstrates a relationship between
wildfire spread and terrain roughness. While more severe burns are frequently associated with
difficult terrain, this is not always the case. The degree of vegetation and wind patterns that affect
the intensity of wildfires can be inferred from the roughness of the terrain. Therefore, even if these
correlations help understand patterns, caution should be taken when inferring causal relationships
without further empirical data or in-depth investigation. The results are corroborated by prominent
LULC patterns in addition to topographical factors. The August wildfire scenario includes forested
areas, which are often on steep slopes and greatly raise dNBR values. On the other hand, the
harvested wheat fields in the July wildfire scenario resulted in a large fall in dNBR values. These
observations and results might refer to how land cover and terrain combine to influence how severe
wildfires are.

The swath profiles (Figure 7) illustrate wildfire behavior which spread towards southwest from
the ignition spots conformably with the predominant wind direction (i.e., north-north-east) and
parallel to the mountainous terrain (Figure 7B). The most striking behavior of the wildfire spread
which is depicted from the swath profiles is that sharp topographic relief increases, particularly
swaths A and B, coincide spatially with the easterly boundary of the wildfire spread. Accordingly,
the northern border of the August wildfire also is delimited by the high topographic relief change
which also constitutes a leeward side. The swath C delimits the lower part August wildfire which
shows an appropriate example of channelized wildfire spread demonstrating the interaction of
terrain and wind. The valley-shaped morphology, acting as a natural wind corridor from Kayadere
to the southwest, serves as an acceleration zone for wildfire propagation, particularly when
experiencing maximum wind speeds of 72 km/h from the northeast to the southwest (Figure 7 and
8A).
<figure><img src="image_13.png" /><figcaption>**Figure 8:** The forested area also reflects light green in the low degree of burn severity because of the overlay function of two layers – yellow, low burn severity, and green forested area.</figcaption></figure>

A remarkable example, the valley in Figure 8B, also demonstrates how the interaction of topography, fuel availability, and wind direction influences burn severity in the study area. Figure 8B illustrates a cross-sectional profile of valley-shaped topography, where the burn severity aligns with spatially corresponding slopes. This area elucidates the susceptibility of windward slopes, specifically north-facing slopes for this study which are tend to more humid in the northern hemisphere (Kumari et al., 2020), to wildfire propagation and burn severity. This systemic pattern of wildfire spread also reveals the importance of wind direction against slope orientation, which highly impacts burn severity in mountainous terrain. This finding highlights the important influence of wind direction in potentially surpassing the significance of equator-facing slopes concerning burn severity and fire spread propensity. Because several studies (Rollins et al., 2002;
Iniguez et al., 2008; Flatley et al., 2011; Holsinger et al., 2016) showed that polar-facing slopes may serve as natural barriers to the spread of fires due to the elevated moisture retention capacity exhibited by the vegetation inhabiting these slope orientations. On the other hand, on the western section of this profile, slopes provide an illustrative example of how wildfires impede their progress at hilltops, subsequently resulting in increased burn severity on the steep windward slopes (Figure 8B).

The behavior of wildfires is greatly influenced by topography, which frequently directs the spread towards steeper slopes. But it's interesting to note that, in contrast to the August fire (mean slope of 15.5), the July case occurred in an area with subtle topography (mean slope of 12). This implies that in some circumstances, variables like fuel type may take precedence over topographic effects. For example, in the July wildfire, 51% of the burned area was made up of crops, which provided fine fuels that aided in the spread of the fire, particularly because agricultural residues have a low fuel moisture content. Additionally, it underlines the exacerbation of these conditions due to the indirect influence of weather; a recorded heatwave during July 2023 in the Mediterranean basin where the study area is located suffering from rainfall scarcity (Akbaş, 2023). During the summer, high pressure system Azores which corresponds to the negative phase of North Atlantic Oscillation may directly create hot and dry conditions in these areas and make them more susceptible to wildfires (Akbaş and Ozdemir, 2023).

It is noteworthy to highlight that observation-based results supported by statistical model limit the implications among topography, fuel characteristics, and weather that shape wildfire behavior in this study. Therefore, this limitation underscores the need for more study (i.e., mathematical spreading models) to fully comprehend these dynamics.
396 **Conclusion**

397 This study presents observation-based findings supported by statistical analyses and bivariate maps
398 on how topography, weather, and fuel properties interact and have an impact on the propagation
399 and severity of the largest wildfires that occurred in NW Turkey in 2023. This region of Turkey
400 experienced its most extensive and historically significant wildfires on record, with two
401 consecutive large wildfires in July and August, resulting in damages spanning 33.4 km² and 45.7
402 km², respectively, within the study area. The results showed that wildfire spread has been
403 predominantly controlled by the coupled influence of topography and wind direction. The regional
404 geomorphic characteristics of Çanakkale Strait and Biga Mountainous portray the wind direction
405 which resulted in the general direction of the wildfire NE to SW. Consistent with the literature,
406 local topographic conditions, such as narrow valleys and ridgetops or divides, contributed to the
407 propagation direction which confirms the general mathematical models. However, our findings
408 point the importance of the dominant wind direction which surpassed the impact of the slope aspect
409 on burn severity which is assumed to have more damage on southern slopes in mountainous terrain.
410 Our findings also demonstrated how a particular LULC type, in this case, harvested wheat fields,
411 can control the spread of wildfires due to the low moisture content of the fuel. This study not only
412 enhances our understanding of fire behavior but also has practical implications for informing
413 extinguishing practices and post-fire management strategies, particularly in mitigating soil erosion
414 and mass wasting problems in affected areas.

415 **Acknowledgment**

416 This study is supported by the 2232 International Fellowship for Outstanding Researchers Program
417 of the Scientific and Technological Research Council of Turkey (TUBITAK) through grant
118C329 and also funded by the NATO Science for Peace and Security Program (SPS project G6190).

## Author contribution

Aydoğan Avcioğlu: Writing – review & editing, Writing – original draft, Visualization, Investigation, Formal analysis, Data curation, Conceptualization. Abdullah Akbaş: Data curation, Conceptualization, Writing – original draft, Writing – review & editing. Tolga Görüm: Writing – review & editing, Writing – original draft, Conceptualization, Funding acquisition, Resources.
Ömer Yetemen: Writing – review & editing, Funding acquisition.

## References:

Akbas, A., & Ozdemir, H. (2023). Influence of atmospheric circulation on the variability of hydroclimatic parameters in the Marmara Sea river basins. Hydrological Sciences Journal, 1-12.

Akbas, A. (2023). Seasonality, persistency, regionalization, and control mechanism of extreme rainfall over complex terrain. Theoretical and Applied Climatology, 152(3), 981-997.

Acheampong, M., Ejiofor, C., Salinas-Miranda, A., Wall, B., & Yu, Q. (2019). Priority setting towards achieving under-five mortality target in Africa in context of sustainable development goals: an ordinary least squares (OLS) analysis. *Global Health Research and Policy*, 4(1), 17. https://doi.org/10.1186/s41256-019-0108-0

Akbulak, C., Tatlı, H., Aygün, G., & Sağlam, B. (2018). Forest fire risk analysis via integration of GIS, RS and AHP: The Case of Çanakkale, Turkey. *Journal of Human Sciences*, 15(4), 2127. https://doi.org/10.14687/jhs.v15i4.5491
439 Avcıoğlu, A., Görüm, T., Akbaş, A., Moreno-de las Heras, M., Yıldırım, C., & Yetemen, Ö.

440 (2022). Regional distribution and characteristics of major badland landscapes in Turkey.

441 *Catena*, 218(August). https://doi.org/10.1016/j.catena.2022.106562

442 Brown, C. F., Brumby, S. P., Guzder-Williams, B., Birch, T., Hyde, S. B., Mazzariello, J.,

443 Czerwinski, W., Pasquarella, V. J., Haertel, R., Ilyushchenko, S., Schwehr, K., Weisse, M.,

444 Stolle, F., Hanson, C., Guinan, O., Moore, R., & Tait, A. M. (2022). Dynamic World, Near
445 real-time global 10 m land use land cover mapping. *Scientific Data*, 9(1), 251.

446 https://doi.org/10.1038/s41597-022-01307-4

447 Carmo, M., Moreira, F., Casimiro, P., & Vaz, P. (2011). Land use and topography influences on
448 wildfire occurrence in northern Portugal. *Landscape and Urban Planning*, 100(1–2), 169–

449 176. https://doi.org/10.1016/j.landurbplan.2010.11.017

450 Cramer, W., Guiot, J., Fader, M., Garrabou, J., Gattuso, J.-P., Iglesias, A., Lange, M. A.,

451 Lionello, P., Llasat, M. C., Paz, S., Peñuelas, J., Snoussi, M., Toreti, A., Tsimplis, M. N., &
452 Xoplaki, E. (2018). Climate change and interconnected risks to sustainable development in
453 the Mediterranean. *Nature Climate Change*, 8(11), 972–980.

454 https://doi.org/10.1038/s41558-018-0299-2

455 Flatley, W. T., Lafon, C. W., & Grissino-Mayer, H. D. (2011). Climatic and topographic controls
456 on patterns of fire in the southern and central Appalachian Mountains, USA. *Landscape*

457 *Ecology*, 26(2), 195–209. https://doi.org/10.1007/s10980-010-9553-3

458 Gökaşan, E., Ergin, M., Özyalvaç, M., Sur, H. I., Tur, H., Görüm, T., Ustaömer, T., Batuk, F. G.,
459 Alp, H., Birkan, H., Türker, A., Gezgin, E., & Özturan, M. (2008). Factors controlling the

460 morphological evolution of the Çanakkale Strait (Dardanelles, Turkey). *Geo-Marine*
461 *Letters*, 28(2), 107–129. https://doi.org/10.1007/s00367-007-0094-y

462 Göktaşan, E., Görüm, T., Tur, H., & Batuk, F. (2012). Morpho-tectonic evolution of the

463 Çanakkale Basin (NW Anatolia): Evidence for a recent tectonic inversion from

464 transpression to transtension. *Geo-Marine Letters*, 32(3), 227–239.

465 https://doi.org/10.1007/s00367-011-0262-y

466 Heyerdahl, E. K., Brubaker, L. B., & Agee, J. K. (2001). Spatial controls of historical fire

467 regimes: A multiscale example from the interior west, USA. *Ecology*, 82(3), 660–678.

468 https://doi.org/https://doi.org/10.1890/0012-9658(2001)082[0660:SCOHFR]2.0.CO;2

469 Holsinger, L., Parks, S. A., & Miller, C. (2016). Weather, fuels, and topography impede wildland

470 fire spread in western US landscapes. *Forest Ecology and Management*, 380, 59–69.

471 https://doi.org/10.1016/j.foreco.2016.08.035

472 Iniguez, J. M., Swetnam, T. W., & Yool, S. R. (2008). Topography affected landscape fire

473 history patterns in southern Arizona, USA. *Forest Ecology and Management*, 256(3), 295–

474 303. https://doi.org/10.1016/j.foreco.2008.04.023

475 Keeley, J. E. (2009). Fire intensity, fire severity and burn severity: a brief review and suggested

476 usage. *International Journal of Wildland Fire*, 18(1), 116. https://doi.org/10.1071/WF07049

477 Liu, N., Lei, J., Gao, W., Chen, H., & Xie, X. (2021). Combustion dynamics of large-scale

478 wildfires. *Proceedings of the Combustion Institute*, 38(1), 157–198.

479 https://doi.org/10.1016/j.proci.2020.11.006

480 Melton, M. A. (1965). The Geomorphic and Paleoclimatic Significance of Alluvial Deposits in

481 Southern Arizona. *The Journal of Geology*, 73(1), 1–38. https://doi.org/10.1086/627044
482 Mermoz, M., Kitzberger, T., & Veblen, T. T. (2005). LANDSCAPE INFLUENCES ON

483 OCCURRENCE AND SPREAD OF WILDFIRES IN PATAGONIAN FORESTS AND

484 SHRUBLANDS. *Ecology*, 86(10), 2705-2715. https://doi.org/10.1890/04-1850

485 Moody, J. A., Shakesby, R. A., Robichaud, P. R., Cannon, S. H., & Martin, D. A. (2013).

486 Current research issues related to post-wildfire runoff and erosion processes. *Earth-Science*

487 *Reviews*, 122, 10-37. https://doi.org/10.1016/j.earscirev.2013.03.004

488 Olaya, V., & Conrad, O. (2009). Geomorphometry in SAGA. In *Developments in Soil Science*

489 (Vol. 33, Issue C). https://doi.org/10.1016/S0166-2481(08)00012-3

490 Parks, S. A., Miller, C., Abatzoglou, J. T., Holsinger, L. M., Parisien, M. A., & Dobrowski, S. Z.

491 (2016). How will climate change affect wildland fire severity in the western US? In

492 *Environmental Research Letters* (Vol. 11, Issue 3). https://doi.org/10.1088/1748-

493 9326/11/3/035002

494 Parks, S. A., Parisien, M.-A., & Miller, C. (2012). Spatial bottom-up controls on fire likelihood

495 vary across western North America. *Ecosphere*, 3(1), art12. https://doi.org/10.1890/ES11-

496 00298.1

497 Povak, N. A., Hessburg, P. F., & Salter, R. B. (2018). Evidence for scale-dependent topographic

498 controls on wildfire spread. *Ecosphere*, 9(10). https://doi.org/10.1002/ecs2.2443

499 Rollins, M. G., Morgan, P., & Swetnam, T. (2002). Guía de biopesticidas. *Landscape Ecology*,

500 17(1990), 539-557. https://doi.org/10.1023/A:1021584519109

501 Russo, A., Gouveia, C. M., Páscoa, P., DaCamara, C. C., Sousa, P. M., & Trigo, R. M. (2017).

502 Assessing the role of drought events on wildfires in the Iberian Peninsula. *Agricultural and*
503 *Forest Meteorology*, 237–238, 50–59. https://doi.org/10.1016/j.agrformet.2017.01.021

504 Tatli, H., & Türkeş, M. (2014). Climatological evaluation of Haines forest fire weather index over the Mediterranean Basin. *Meteorological Applications*, 21(3), 545–552.

505 https://doi.org/10.1002/met.1367

506

507 Tavsanoglu, Ç., & Pausas, J. G. (2022). Turkish postfire action overlooks biodiversity. *Science*, 375(6579), 391–391. https://doi.org/10.1126/science.abn5645

508

509 Torri, D., Rossi, M., Brogi, F., Marignani, M., Bacaro, G., Santi, E., Tordoni, E., Amici, V., & Maccherini, S. (2018). Badlands and the Dynamics of Human History, Land Use, and Vegetation Through Centuries. In *Badlands Dynamics in a Context of Global Change*.

510

511

512 Elsevier Inc. https://doi.org/10.1016/b978-0-12-813054-4.00004-6

513 Tyukavina, A., Potapov, P., Hansen, M. C., Pickens, A. H., Stehman, S. V., Turubanova, S., Parker, D., Zalles, V., Lima, A., Kommareddy, I., Song, X.-P., Wang, L., & Harris, N. (2022). Global Trends of Forest Loss Due to Fire From 2001 to 2019. *Frontiers in Remote Sensing*, 3(March), 1–20. https://doi.org/10.3389/frsen.2022.825190

514

515

516

517 Yıldız, C., Çömert, R., Tanyaş, H., Yılmaz, A., Akbaş, A., Akay, S. S., Yetemen, Ö., & Görüm, T. (2023). The effect of post-wildfire management practices on vegetation recovery: Insights from the Sapadere fire, Antalya, Türkiye. *Frontiers in Earth Science*, 11(April), 1–12. https://doi.org/10.3389/feart.2023.1174155

518

519

520

521