Forests are important natural resources and play a significant role in regulating climate and the carbon cycle. Boreal forests, also known as Taiga in high northern latitudes across North America and Eurasia, account for 29% of the world’s forests, and store 37% of global terrestrial carbon [1
]. Forest fire is primarily a natural process in boreal ecosystems [3
]. With a low decomposition rate, the post-fire productivity of boreal forests could decline for up to 80 years before the organic leaf litter layer is reestablished [4
]. Under the pressure of climate warming and accelerated human activities, fire behavior in boreal forests has been found to be undergoing dramatic changes [5
]. It is crucial to understand these changes of fire characteristics and to identify the driving factors for sustainable forest management.
Fire regime defines the combined characteristics of fire in terms of its frequency of occurrences, size, intensity, seasonality, cause, and severity. Instead of considering a forest fire as a singular random event, fire regime treats it as a landscape-level spatial process, which helps us understand the forest fire and its causal factors at a larger spatial extent in a climate change context [6
]. The interaction of top-down and bottom-up factors governs forest fire regimes over a range of spatial and temporal scales. The bottom-up controls usually act at fine scales by regulating fire physics and behavior [8
]. For instance, fire propagation is mainly controlled by weather, local terrain plus fuel load, moisture content, and fuel continuity. Topographic factors (i.e., elevation, slope, and aspect) also strongly influence the forest environment in aspects of potential incident radiation and temperature. On the other hand, climate acts as a top-down control, which impacts fire occurrence through intra- and inter-annual climatic variations. Studies have shown that the impact of intra-annual precipitation variability on fire frequency is greater than the total annual precipitation in forests of the eastern United States [9
]. It is not clear whether the top-down or bottom-up factors are leading factors. In years of extreme drought, climate would create weather and fuel conditions to overtake the bottom-up controls, allowing fires to cross natural barriers like streams or roads. Controlling factors vary in different biophysical scenarios and, sometimes, are a combination of multiple factors [10
]. Anthropogenic forces also play a significant role in influencing forest fire regime. It is reported that more human-induced fires in Russian boreal forests have occurred due to the lack of control and ineffectual fire management policies since the creation of the Russian Federation [11
]. In Northeast China, extensive logging increases the forest vulnerability to future burning and the half-century fire suppression policy has greatly altered its fire patterns [12
]. It is challenging to understand how these factors interact to regulate the fire regime.
Boreal forests of China are mainly distributed in the Greater Hinggan Mountains that are located at the southern end of Siberian boreal forest. Fire regimes vary spatially across the region due to different species compositions, physiographic conditions, climate characteristics, and characteristics of the local economies. Intensive studies have been conducted to examine the controlling factors on fires in this region. For instance, Wu et al. found that climate was the primary factor influencing fire occurrence, while human activities were the secondary control [13
]. Another study from Hu et al. reported that climatic factors were dominant drivers for lightning-caused fires, but not for human-caused ones [14
]. Three fire environment zones were identified in this area through spatial clustering of environmental variables [15
]. Chang et al. utilized a binary logistic regression to predict the fire occurrence patterns and to assess fire risks in Heilongjiang Province, China [16
]. Forest fire regime and the surrounding environments usually exhibit dramatic seasonal variations; however, few studies have examined it from this perspective.
Forest fires in the Greater Hinggan Mountains have been analyzed in a seasonal manner, with spring season from March to June, summer season from July to August, and fall season starting in September and generally lasting to October when it begins to snow [17
]. Moreover, extremely large fires, sometimes named mega fires, are catastrophic and their impacts to the landscape are complex and far reaching [18
]. Usually a small number of large fires constitute the majority of burned areas [19
]. Studies have also shown that fire burning sizes varied with environmental conditions such as vegetation, topography, and weather [20
]. It is necessary to examine how these environmental factors regulate the fires in terms of fire sizes in different seasons, which could be of great help for effective fire control in this remote, boreal forest.
However, there exist some challenges to carry out such quantitative fire studies at the landscape scale. One is the data availability. Taking fuel conditions as an example, it is difficult to obtain actual in-field fuel conditions when a fire occurs. Remote sensing imagery becomes a promising data source for its frequent updating and synoptic coverage. Studies have shown that vegetation index is correlated with fuel moisture content. For example, the Normalized Difference Vegetation Index (NDVI) products from the Advanced Very High Resolution Radiometer (AVHRR) [22
] and Moderate Resolution Imaging Spectroradiometer (MODIS) [23
] imagery have been successfully used to estimate fuel moisture content. Therefore, vegetation index could serve as a good proxy for fuel moisture at the landscape scale. Another challenge is the difficulty of quantifying human impacts on fires. In limited studies, distance to the most nearby road was used to approximate the accessibility to a fire location [24
]. Road network density could be an indicator of the intensity of human activities.
The primary goal of this study is to identify the forest fire regimes in the Greater Hinggan Mountains, and to characterize the controlling environmental factors in spring, summer, and fall seasons. Integrating multiple sources of data sets, this study analyzes how these factors regulate the fire severity through a statistical analysis approach in this boreal region.
3.1. Characteristics of Fire Regimes in the Study Area
The 26-year variations of fire occurrences are plotted against three causal factors: lightning, human-induced, and unknown (Figure 3
). Total occurrences showed relatively stable counts in years before 2000 and an obvious increase after then, especially in 2000, 2002–2003, and 2005.
Lightning fires were dominant in the study area, accounting for ~63% of total occurrences. For the two different causal factors, an apparent increase of lightning fires was observed (r
= 0.53, p
= 0.009). As shown in the inset of Figure 3
, the counts of human-induced fires did not display a statistically significant change in 26 years (p
Fire season length in each year was calculated as the duration between the start date of the first fire and the end date of the last fire in this year. In Figure 4
, the first fire date did not show a statistically significant trend (p
= 0.58), with most outbreaks having occurred in late April. On the contrary, the last fire date showed a significantly increasing trend, revealing a prolonged fire season length in past decades (r
= 0.60, p
= 0.003). An apparent change to fire season length was caused by fall fires (Day of Years (DOY) > 240). In the 1980s to early 1990s, there were no fall fires except in 1989. After 2000, however, fall fires occurred every year except 2003.
Fire seasonality (i.e., the season when a fire broke out) in the study area was analyzed with all fire records in 1980–2005. Figure 5
a fairly reflects the seasonal categorization of this study which groups all fire records into spring, summer, and fall fires. Spring fires (March–June) accounted for the largest proportion (64%) of all fire counts, followed by summer fires (July–August) at 29% and fall fires (September–October) at 7%. The causal factors of fire ignition showed apparent seasonal variations. For fire counts, spring fires were fairly split between lightning-caused (54%) and human-caused (35%). Oppositely, almost all summer fires (96%) were lightning-caused, and most fall fires (74%) were human-caused. More specifically, lightning-caused fires mainly occurred from spring through summer (May to August), while human-caused fires were split between early spring (April–May) and fall (September–October). In Figure 5
b, spring fires had the largest burned areas, followed by fall fires (October). The extremely high burned areas in May came from the catastrophic “Black Dragon” fire in 1987. The burned areas of summer fires were limited, probably because of ground wetness in peak growing season.
In addition to the increased fire occurrences and extended fire season length, the annual total burned area also showed an upward trend (r
= 0.55, p
= 0.007). In Figure 6
, the area (ha) on the y
-axis is transformed to logarithmic form for better visualization of the plot. Before 1994, burned areas were predominantly from spring fires. In later years, areas burned from fall fires dramatically increased. Summer fires were rare in the 1980s to 1990s, but burned large areas in 1999, 2002, and 2004–2005. While areas burned from spring fires remained relatively stable, more areas were burned from summer and fall fires in recent years, contributing to a significant increase in total burned areas. It is therefore reasonable to assume that the fire regime in the study area has changed in comparison to past decades.
The total burned area in 1980–2005 was about 1.97 million ha, and a small number of severe fires disproportionately burned excessive areas. Among the 404 fire records, 27 severe fires with high FSS (>1000 ha) composed 98.8% of the total burned area. The majority of these high FSS fires took place in spring (for example, the most catastrophic fire in 1987). From Figure 5
, spring fires accounted for the largest number of fires as well as the most burned areas. No high FSS fire (>1000 ha) broke out in summer during our studied period.
The kernel densities of fire occurrences in three seasons are extracted in Figure 7
. The density maps highlight the fire hotspots in spring (Figure 7
a), summer (Figure 7
b), and fall (Figure 7
c). Both spring and fall fires were common in Huma County, which had the most agricultural lands in the study area. Summer fires were mostly located in Huzhong County at higher elevations. While fall fires mostly occurred in the agriculture-based Huma County, spring and summer fires spread across the forested mountains in other counties.
3.2. Seasonal Variations of the Explanatory Variables
Descriptive statistics of the environmental parameters in Table 1
are summarized (Table 2
). The Welch’s ANOVA tests were performed to examine if a parameter showed significant differences in the three different seasons. A significant difference indicated that this parameter played an active role on the seasonality of fire occurrences. For topographic parameters, elevation and slope showed significant impacts on fire seasonality (p
< 0.0001), while aspect was irrelevant. As shown in Figure 7
a, spring fires were distributed across the whole study area, from Mohe at higher elevations to Huma at lower elevations. In Figure 7
b, summer fires exhibited a higher density in Huzhong at higher elevations (average = 641 m) and steeper slopes (average = 8.15°) than spring and fall fires. There was a higher tendency for lightning strikes at higher elevations, which explained how lightning mainly caused summer fires (as revealed in Figure 5
). Around two-thirds of fall fires occurred in Huma County, which had more cultivated lands and higher populations in plain areas. Therefore, topography (elevation and slope) had different impacts on fire occurrences in different seasons in the study area.
For meteorological parameters, the monthly temperature percentage (MTP%) was slightly higher than 100.0% in all three seasons. This indicated that temperature in each season had been slightly increasing from 1980 to 2005. However, this inter-annual increase of temperature was not seasonally different in the ANOVA test. Oppositely, the MPP% was much lower than 100% (in a range of 59–80%), indicating that there was dramatically decreased precipitation in this period. The ANOVA test confirmed that the inter-annual decrease of precipitation was seasonally different (p
= 0.0005). In other words, the decreased precipitation casted a significant impact on fire seasonality. There also existed significant seasonal variations for the mean annual temperature (MAT) and total annual precipitation (TAPcurrent
) (both with p
< 0.0001). Considering both MTP% and MPP%, it was reasonable to assume that fire seasonality could be related to seasonal temperature and precipitation in current years as well as precipitation reduction from the preceding year. For example, fall fires were often accompanied by higher mean annual temperature while summer fires were associated with much lower precipitation. While some studies indicated the effects of precipitation in the preceding year [29
], this study found that precipitation in the preceding year did not cast a significant effect on fire seasonality (p
= 0.3283). Wind speed was a categorical variable. The Kruskal Wallis test was performed to examine its seasonal differences in Table 2
< 0.001). Statistics also showed that spring and fall fires suffered more severe wind conditions than summer. The strong to stormy (scale 3 to 5) winds composed 53.7% of all wind scales for spring fires and 46.7% for fall fires, while all wind speeds were within the category of strong wind (scale 3) for summer fires.
For vegetation, no significant variations of ΣNDVIpreceding were found (p = 0.4682), indicating that fuel moisture conditions were not significantly different among the three seasons.
Regarding human impacts, the road density had a strong significant impact on fire seasonality (p
< 0.048). The distance to roads had a weaker impact (p
< 0.075). Summer fires exhibited the longest distance to roads and lowest road density in comparison to spring and fall fires. Oppositely, fall fires held the highest road density and shortest distance, while spring fires were in middle. These results were consistent with the distributions of ignition causes in Figure 5
a and Figure 7
, revealing that summer fires occurred in more remote areas at higher elevations. Fall fires were in more populated areas at lower elevations. Spring fires featured both aspects.
In short, forest fires exhibited distinct seasonal variability in terms of topographical, meteorological, and human-related conditions in this area. Specifically, summer fires mainly occurred in drier conditions by lightning and were located at high elevations and remote areas. Fall fires were in relatively flat areas and were mostly human-caused fires. Spring fires took place across the whole region. In the following analysis, we simulated how these environmental parameters regulate the fire size severity in each season.
3.3. Environmental Influences with the Ordinal Logistic Regression
The collinearity analysis showed that two topographic factors, elevation and slope, were highly correlated in both spring and summer fire datasets (
> 0.8, p
< 0.001). For forest fires in mountainous areas, the critical slope threshold is 25 degrees beyond which the burning behavior changes [33
]. The maximum slope for all fires was <20 degrees in the study area. Therefore, slope was not used in our model. There was no significant correlation among meteorological variables (
< 0.45, p
> 0.05) in spring fires. However, in summer fires the monthly precipitation was highly correlated with annual precipitation (
= 0.75, p
< 0.001), probably because summer was the primary rain season in this region. The Spearman’s correlation showed that monthly precipitation had higher correlation with FSS than annual precipitation, hence annual precipitation was not used. Variables of human influence proxy (road density and distance) did not show significant correlation (
< 0.2, p
≈ 0.05). The environmental parameters used in the model are listed in Table 3
Both final modes in Table 3
are converged and the Wald Chi-square tests for the proportional odds assumption are significant (p
< 0.1). For spring fires, the three significant environmental parameters were elevation, monthly temperature, and total annual precipitation of the preceding year. The interpretation of the results was as follows. Given that all other parameters in the model were held stable, with 1 mm more precipitation in the preceding year, the odds of a larger fire were calculated as
of a smaller fire. Similarly, a 1 m increase of elevation lowered the odds of larger fires by
. It was particularly noteworthy that when monthly temperature increases by one percent over long-term monthly mean temperature the odds for a larger fire were
of a smaller fire. The coefficient value of each parameter revealed its relative importance. For spring fires, the most influencing parameters were monthly temperature, followed by the precipitation of the preceding year, and lastly elevation. Other environmental parameters examined in this study did not significantly affect the spring fires.
For summer fires, five influencing parameters were identified: wind speed, elevation, NDVI, mean annual temperature, and distance to road. The distance to road, mean annual temperature, and wind speed positively affected the fire size severity. Overall, wind speed was a significant variable that affected the fire size severity. A change of wind speed from scale 1 to 2 (breeze to moderate wind) did not significantly affect the fire size severity (p = 0.6863). However, when wind speed increased from scale 1 to 3 (breeze to strong wind), the odds of larger fires were of smaller fires, thus greatly increasing the chance of large fires. For the mean annual temperature, a 1 °C increase would increase the odds for larger fires by 1.538 times. The impacts of distance to road, NDVI, and elevation were limited.
4.1. Fire Regime Changes
This study carried out a comprehensive analysis of fire regimes in the study area. Increasing trends for fire occurrences and burned areas were found in 1980–2005, which were greatly attributed to there being more summer and fall fires after the mid-1990s, thus resulting in a prolonged fire season length. Fire prevention in the study area has been one of the most important management activities of the National Forest Bureau of China. In the past decades, spring and fall have received the primary attention because it was not easy to form large fires in summer due to the high wetness from precipitation and leaf moisture of green canopies. The significant upward trend of summer fire occurrences since the mid-1990s indicates that more attention should be given to the summer season. While high FSS fires (burned area > 1000 ha) were not recorded in summers of 1980–2005, an increased number of moderate fires (100–1000 ha) have been observed in recent years. Under certain circumstances—for example, a stronger wind speed or insufficient fire fighting forces—these moderate-sized fires could possibly develop into larger conflagrations. Therefore, our study raised a sound alarm that summer fires cannot be ignored for effective fire prevention.
Causes of these fire regime changes could be twofold. Firstly, great efforts of fire prevention and management have been enforced in the Greater Hinggan Mountains since the catastrophic “Black Dragon” Fire in 1987 [34
]. Through a rich set of evenly distributed lookout towers, an expanded fire monitoring network was established for field staff to detect and report wildfires in a timely manner (Mr. Huadong Wu, Vice Director, Fire Prevention Office, Tuqiang Forest Bureau, personal communication on 5 June 2015). Improved community learning programs and strict fire management policies led to reduced human-caused fires. On the other hand, the increased lightning fires, especially in summer, could be strongly impacted by climate change. For all fires in 1980–2005 in the study area, apparent weather anomalies were observed when fires broke out, with a trend that the mean annual temperature increased by 0.672 °C and the total annual precipitation decreased by 32.38 mm. The prolonged fire season length observed in this study was also inter-related with climate change. Studies have projected that the fire season will be prolonged by 20 to 30 days under the Intergovernmental Panel on Climate Change (IPCC) A2 and B2 scenarios of climate change in Northeast China [35
4.2. Environmental Impacts on Fire Seaonality
Spatial patterns of fire points in different seasons varied with topography and road accessibility. Both spring and fall fires prevail in Huma County, a more populated and agriculture-heavy area at lower elevations that connects to the Northeast Plain of China. Therefore, fire prevention management should be always vigilant from spring to fall, especially in regard to human-induced fires. On the other hand, the dominancy of summer lightning fires at higher elevations in Huzhong County indicated that this area should be given higher attention during summer fire watch.
Fires in all three seasons were affected by similar meteorological changes, such as significantly higher temperature and lesser precipitation. However, they still varied in some aspects; for instance, summer fires usually occurred in dry years with dramatically reduced precipitation. Fall fires erupted when there was much higher annual temperature.
4.3. Environmental Influences on Fire Size Severity
Elevation had a negative impact on both spring and summer fires in the Greater Hinggan Mountains. At lower elevations, local temperature tended to be higher, which promoted dry fuel accumulation and led to an enlarged combustion area. Good accessibility to road network at lower elevations could facilitate the occurrences of human-caused fires in spring.
It is counterintuitive that wind speed was an influencing factor in summer but not in spring. Spring is prone to large forest fires due to the rising temperature and accumulated dry leaves on the ground, accompanied with prevailing strong wind speeds. Wind speed is expected to be one of the most influential factors for fire spread and consequently larger burned area [19
]. This study found that about half of spring fires were accompanied with strong to stormy winds (scale 3 to 5, respectively). In contrast, wind condition was much milder in summer, all within the scale of 1 to 3 which represents breezes to strong winds, respectively. The logistic regression results revealed that wind speed was a significantly positive factor facilitating the formation of larger fires in summer. In other words, although wind speed was still the key factor affecting large fire formation, summer fires were more sensitive to wind speed than spring fires at the landscape scale. Wind not only directly propagated the fire spread, but affected the moisture content of surface fuels more rapidly.
Fuel moisture significantly influenced summer fires. As shown in Table 3
, NDVI has a negative coefficient in the logistic model. Summer fires were often caused by lightning—in most cases by dry thunderstorms, occurring in dense forests at higher elevations, where the high leaf moisture in peak growing season restricted the occurrences of large fires. Moreover, distance to nearest roads acted as a constraint to developing large fires in summer, since the distant location of fire incidences hindered firefighters to extinguish the fires in time. Even worse, if accompanied with strong wind, the situation became more difficult to control. In contrast, distance to nearest roads may act as a driving factor of fire occurrences in other seasons. In spring and fall, for example, high accessibility to road network could facilitate the occurrences of human-caused fires in Huma County.
Two other significant factors for spring fires were the monthly temperature (MTP against long-term mean) and the precipitation in the preceding year. Usually, fine fuels like fallen leaves and forest litter accumulate in spring. High temperature would expedite the evaporation and drying of fire fuel, resulting in a high flammability and propagation speed. A dry preceding meteorological condition created a vulnerable condition for fires in the coming spring, consequently posing an elevated risk for larger fires. Taking the preceding meteorological factors into consideration helps us develop more appropriate measures for fire prevention in the coming spring.
4.4. Limitations and Future Work
Some limitations remain in this study. The fire records that we could access are only available for a relatively short period (26 years), and fall fire is not examined in this study, given its low number of fire records. This study indicates that more fall fires would occur with a prolonged fire season. When longer data series are available in the future, fall fires could be better studied, thus allowing for a more comprehensive understanding of the fire regime and its evolution in the long run. This study also implies the promising application of integrating remote-sensed data, such as NDVI, into fire studies. In the future, more remote sensing products, for example, land surface temperature and active fire data from coarse-resolution satellite imagery, could be applied to substitute the limited in-field observations in fire studies.
Human impacts on fires in boreal forests were not deeply examined in this study. The distance to the nearest road and road density surrounding a fire point were simply extracted to approximate the anthropogenic disturbances. In the past decades, human activities in the study area have been accelerating (e.g., logging, planting, sawmills, and wood product transportation). In the early 2000s, the “Tian Bao” Project was enforced to permanently prohibit logging activities in the Greater Hinggan Mountains for natural forest protection [34
]. It is therefore necessary to integrate these human activities and consequences of policies into fire studies in order to establish a better understanding of human impacts in this unique boreal forest region.
This study explored the statistical characteristics of fire records in boreal forests of the Greater Hinggan Mountains in the period of 1985–2006, analyzed the impacts of different environmental parameters on fire seasonality, and performed an ordinary logistic regression in order to identify the influencing environmental parameters on the fire regime in this region. It was found that spring fires accounted for the largest proportion of fire occurrences as well as the most burned areas, and fall fires were more related to anthropogenic activities in harvesting season. Summer fires were mostly lightning-caused and were rare before the mid-1990s. However, the increased summer fires in recent years, together with prolonged fire season length, deserve higher attention as a result of the possibly changing fire regime of the region. Different sets of significant environmental factors were identified: elevation, temperature, and precipitation in the preceding year for spring fires; wind speed, elevation, temperature, NDVI, and distance to road for summer fires. Spatial distributions and densities of fires in different seasons varied across the study area. The spatially and seasonally specific fire patterns extracted from this study could help to develop more localized fire prevention strategies for sustainable forest management.