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Article

Influence of Climatic Factors on Lightning Fires in the Primeval Forest Region of the Northern Daxing’an Mountains, China

by
Yang Shu
1,2,
Chunming Shi
3,
Bole Yi
1,
Pengwu Zhao
1,2,
Lijuan Guan
1 and
Mei Zhou
1,2,*
1
Forestry College, Inner Mongolia Agricultural University, Hohhot 010018, China
2
National Orientation Observation and Research Station of Saihanwula Forest Ecosystem in Inner Mongolia, Chifeng 024000, China
3
College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(9), 5462; https://doi.org/10.3390/su14095462
Submission received: 19 March 2022 / Revised: 26 April 2022 / Accepted: 28 April 2022 / Published: 1 May 2022
(This article belongs to the Topic Climate Change and Environmental Sustainability)
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Abstract

:
Forest fires lead to permafrost degradation and localized drought, and regional droughts increase the probability of forest fires, leading to a positive feedback loop between climate change and fires. However, the relationship between fire occurrence and climatic factors change is unclear for boreal forests, which represent the largest land-based biome and stock of carbon. Here, we analyzed the relationship between lightning fire occurrence and meteorological and topographic factors based on the fire frequency, burned area, and meteorological data from the primeval forest region of the northern Daxing’an Mountains in China. We found that lightning fires occurred most frequently at an altitude of 600 to 700 m. From 1999 to 2019, the frequency of lightning fires showed an overall upward trend, whereas the affected area had no obvious change. It can be attributed to fire suppression efforts and greatly increased investment in fire prevention in China. Snow cover had a strong regulatory effect on the start and end dates of lightning fires for seasonal cycle. The frequency of lightning fires was positively correlated with the average temperature, maximum temperature, and surface evaporation and negatively correlated with precipitation and surface soil moisture (0–10 cm). The result will be useful in the spatially assessment of fire risk, the planning and coordination of regional efforts to identify areas at greatest risk, and in designing long-term lightning fires management strategies.

1. Introduction

Climate warming is faster at higher latitudes and altitudes. Boreal forests, which are the largest biome and largest living stock of carbon on land, show climate warming faster than the global mean level and have experienced unprecedented interference from large fires during the past 10,000 years [1,2]. The increase in fire disturbances is mainly due to the increased lighting ignition associated with warming [3,4,5,6], extended fire seasons [7,8], and higher fuel aridity with better fuel composition [9,10]. Forest fires are important for controlling the structure, succession, and functions of boreal forests [11,12]. At the same time, forest fires may cause boreal forests to change to a net carbon source from a net carbon sink by emiting large amounts of organic carbon to the atmosphere [13,14]. Forest fires emit carbon dioxide, black carbon, and aerosols, which aggravate global warming [15] and thus increase fire risk [16,17]. Currently, despite extensive anthropogenic suppression efforts, the observed forest fire intensity is still increasing significantly [18,19,20,21].
Lightning fires are characterized by randomness, concurrency, and strong concealment. Lightning fires occur all over the world. For example, in Ontario, Canada, lightning fires accounted for 43% of forest fires in the past 26 years but caused 81% of the burned area [22]. In the past 30 years, 52% of all forest fires in Alaska in the United States were caused by lightning. In Spain, lightning fires only account for 3.9% of all fires but 10.7% of the total burned area [23]. Lightning fires are common in China’s forested areas, and fires caused by lightning strikes in China account for approximately 1% to 2% of the country’s forest fires, which are mainly distributed in Heilongjiang Daxing’an Mountains, Inner Mongolia Daxing’an Mountains, and Xinjiang Altai Mountains [24]. However, until 2020, in the Daxing’an Mountains forested area of Inner Mongolia, lightning fires accounted for 21% of all forest fires in the region, with a maximum of 64%. Since 2010, the Daxing’an Mountains have been affected by El Niño and La Niña phenomena, resulting in abnormal weather events such as high temperatures, strong winds, and uneven precipitation (rainy period lags, obvious rainfall periods, and drought periods) [25]. Frequent dry thunderstorms have also occurred, increasing the likelihood of forest fires [26].
Meteorological factors such as rainfall, wind speed, temperature, and humidity are critical factors for studying the formation of lightning fires [27,28]. Nash and Johnson found that under low-rainfall conditions, higher air pressure is associated with greater ignition probability of cloud-to-ground flashes [29]. Since precipitation data are easy to obtain from conventional weather stations, researchers often use daily or annual precipitation as the reference threshold for lightning fires. It has been found that in Arizona and New Mexico, 2 mm is the threshold of daily precipitation for ignition, while due to the influence of terrain, rainfall, and other factors in Spain, the fluctuation range of the daily precipitation for ignition is 9~22.5 mm [30]. In addition, Shu et al. found that the number of lightning fires in a region is directly related to the amount of precipitation in China. The longer the drought days, the more lightning fires. When the annual precipitation is more than 600 mm, the occurrence of lightning fire is lower, and when the annual precipitation is 350–580 mm, the occurrence of lightning fire is greater than in previous years [31].
The terrain factors that influence lightning strikes mainly includes slope, slope aspect, and altitude. Shu et al. found that lightning strikes mostly occur in the midslope of mountains at elevations above 800 m above sea level in the Pinus pumila forest and the Pinus sylvestris–Pinus pumila forest [32]. Some studies have suggested that lightning strikes are more likely to occur in areas with low altitude and steep slopes because these areas experience stronger thunderstorms compared to areas with higher altitude and less steep slopes [33,34,35]. It also has been found that in high-altitude areas, low temperatures and heavy rainfall significantly reduce the probability of lightning strikes igniting combustibles [36].
At present, lightning fires are complex and uncontrollable natural phenomena, which cause huge losses to human beings and the ecological environment. We can probably capture an early warning signal of lightning fires based on the geographic information and the high accuracy of meteorological monitoring data of the detection area Lightning fires are the primary origin of forest fires in the Daxing’an Mountains region of China. In the present study, we established a dataset that includes the fire frequency and burned area for boreal forest in the primeval forest region of the northern Daxing’an Mountains of China. By analyzing the occurrence law of existing lightning fires and the relationship with climatic factors, the main reasons for the occurrence of lightning fires are explored, which provides a basis for the reconstruction of the early warning system of lightning fires in the future.

2. Materials and Methods

2.1. Study Region

The study region was the virgin forest area in the northern part of the Daxing’an Mountains in Inner Mongolia, China. The forest area is 9 × 105 km2, the forest coverage rate is 95.0%, and the geographic coordinates are 120°01′20″–121°48′37″ E and 52°01′42″–53°20′00″ N. As of 2019, a total of 245 fires were caused by lightning strikes over the preceding 20 years. In this study, the lightning fire was investigated by the local forestry department on the fire scene, and it was confirmed as lightning fire by finding lightning strike traces and lightning strike wood. These lightning fires have occurred throughout the study region (Figure 1). The forest in the study region mainly includes typical cold-temperate forest species such as Larix olgensis, Pinus sylvestris, Populus davidiana, Betula platyphylla, and their mixed forest. The annual average temperature is −5.5 °C, the extreme minimum temperature is −53 °C, and the extreme maximum temperature is 35.4 °C. The annual average precipitation is 450–550 mm and occurs over approximately 88 days.

2.2. Fire and Climate Data

We collected lightning fire data (1999 to 2019) from the forest management and protection bureau of the primeval forest region of the northern Daxing’an Mountains, Inner Mongolia. The collected data included the time, location, and altitude of each lightning fire along with the number of fires per year. We extracted monthly climate data (resolution = 0.5°) including precipitation, temperature, and the self-calibrating Palmer drought severity index from the Climate Research Unit TS 4.03 dataset (http://climexp.knmi.nl) (accessed on 1 February 2021). The mean climatic conditions of the study region were estimated by averaging the climate data over the area from 50–56° N, 118–124° E. The soil moisture (0~10 cm) data were downloaded from ERA5 (https://cds.climate.copernicus.eu) (accessed on 1 February 2021). The snow cover data were downloaded from Rutgers University Global Snow Lab (https://climate.rutgers.edu/snowcover) (accessed on 1 February 2021). We downloaded monthly relatively humidity data (red triangles in Figure 1) from meteorological stations in the study region, while one-month standardized precipitation–evapotranspiration index (SPEI) values were obtained from the Spanish National Research Council (https://www.csic.es/en/csic) (accessed on 1 February 2021).
The years with fire occurrence and SPEI above and below the 95% distribution limits were defined as the years of most frequent fire and drought stress, respectively. The relationship between fire occurrence and drought severity was evaluated by calculating Pearson’s correlation coefficients. The level of significance of each correlation was determined using the two-tailed null hypothesis.
Digital elevation map data were used as the base map in ArcGIS 10.8 software to analyze the location of lightning fires along with the area of lightning fires in the study region and comprehensively evaluate the spatial characteristics of lightning fires in the study area.

3. Results and Discussion

3.1. Spatial Distribution of Lightning Fires

The 245 lightning fires that occurred in the study region between 1999 and 2019 were mainly concentrated from 121°00′11″–121°10′48″ east longitude and 52°39′00″–52°45′49″ north latitude (Figure 2). The frequency of lightning fires was directly related to altitude. However, there was no lightning fire in the study area in 2009. As shown in Figure 2, all lightning fires occurred in the elevation range of 303–1445 m above sea level and were irregularly distributed. Lightning fires were mainly concentrated in areas with elevations of 600–700 m above sea level; 79 fires or 32% of all fires occurred within this elevation range. A previous study has shown that the active area of lightning strikes is affected by the local sources of humidity and the topography [37]. Du et al. reported that lightning fires occur in the elevation range of 200–1300 m above sea level, with fires being particularly concentrated in areas in the elevation range of 300–800 m, which is consistent with our results [38]. The main reasons for the easy occurrence of lightning fires in the middle-altitude area are the high temperature, the easy evaporation of precipitation, and the low water content of combustibles. In contrast, fewer lightning fires were observed at altitudes above 800 m due to lower temperatures, weaker precipitation evaporation, and higher water content of combustibles. In addition, the spatial discontinuity of combustibles in high-altitude areas has a strong inhibitory effect on the occurrence of lightning fires [36]. All together, there is a certain correlation between the occurrence of lightning fires and the altitude. Lightning fires mainly occur in the middle-altitude areas in the primeval forest region of the northern Daxing’an Mountains, Inner Mongolia.

3.2. Frequency of Lightning Fires and the Trend of Burned Area

As shown in Figure 3, the frequency of lightning fires in the study region from 1999 to 2019 was different in the early season (May to June), late season (July to September), and the entire fire season (May to September). The lightning fire frequency showed an overall upward trend over time, with increase rates of 1.95, 1.91, and 3.86 times/decade. It has been found that with the global warming in recent years, extreme weather such as high temperatures and drought in the Daxing’anling Mountains have increased significantly, which makes the summer forest lightning fires in this area increase year by year. Simultaneously, the main tree species in this study area are coniferous forests, which have high lipid levels and are flammable. The combustibles of coniferous species and coniferous forests are more susceptible to lightning fire than other types of combustibles [38,39]. Coniferous forests are mostly located at high latitudes in cold temperate zones, where the frequency of cloud-to-ground lightning is higher than in low-latitude regions, which is also an important reason for the increase in the frequency of lightning fires [40]. However, in this study, the frequency of lightning fires did not show a significant increasing trend (p > 0.05). At the same time, there is no obvious change in the area of lightning fires over the past 20 years (p > 0.05). This result may be attributed to fire suppression efforts and greatly increased investment in fire prevention in China. Therefore, strengthening the monitoring and early warning of key periods and key areas of lightning fires can effectively avoid the occurrence of major and extra-large forest fires.

3.3. Effect of Snow Accumulation on Lighting Fires

As shown in Figure 1b, snow cover has a strong effect on the start and end dates of lightning fires. Lightning fires can only occur after the snow cover melts, and the lightning fires end before the snow cover begins to accumulate. When snow and ice cover more than 20% of the woodland surface, the wildfire caused by lightning strikes are almost impossible. Lutz et al. [41] found that the reduction in spring snow cover and the earlier ablation time increased the number of lightning fires by investigating the relationship between spring snow cover and lightning fires, which is consistent with our results. The interactions among wildfire occurrence, climate change, and snow cover also create uncertainty regarding the occurrence of regional forest fires. A previous study has shown that global climate warming leads to vegetation changes characterized by a decline in alpine species, an increase in open woodland taxa, and a substantial reduction in annual snow cover, which contribute to an increase in regional fire activity [42]. Taken together, our results suggested that snow cover monitoring should be strengthened in high-risk lightning fires areas.
As shown in Figure 4, the Julian dates of summer lightning fires during 1999–2019 were concentrated in 120~280 d. Through the Julian date analysis, the summer lightning fires in this area mainly occurred in late June. With the change in climate, the fire danger period of summer lightning fires was prolonged later. Lightning fires occurred frequently from 152 d to 213 d (around 1 June to 31 July), with a total of 170 fires, accounting for 78% in the past 20 years. However, we found that snow accumulation and snow melt had no significant effect on the lightning-fire period (p > 0.05). At the same time, the high incidence of lightning fires is from June to July each year. By strengthening the aviation patrol during the high-incidence period of lightning fires and carrying out forward garrison, the frequency and the area of lightning fires can be effectively reduced.

3.4. Correlation between Lightning Fires and Meteorological Indicators

Weather conditions influence the likelihood of ignition, and the likelihood of lightning fires can be reconstructed based on existing meteorological data [43,44]. In the present study, the frequency of lightning fires in the early season (May to June), late season (July to September), and the entire season (May to September) from 1999 to 2019 was positively correlated with the average temperature, maximum temperature, and surface evaporation (Figure 5), whereas it was negatively correlated with surface soil moisture (0–10 cm) and precipitation. Among these factors, the correlations with soil moisture and evaporation rate were the most significant, with correlation coefficients of −0.6 and 0.5, respectively. We can infer that the surface evaporation rate increases as the temperature increases. Drought conditions (lack of precipitation) decrease the water contents of surface soil and surface combustibles, thereby increasing the likelihood of lightning fires. It has also been shown that lightning fires in Inner Mongolia were mainly concentrated in spring (March to May) and summer (June to August) [45]. The likelihood of lighting fires and fire severity are probably increased by a long dry season without any change in seasonal thunderstorms [46]. Taken together, the frequency of lightning fires was positively correlated with average temperature, maximum temperature, and surface evaporation and negatively correlated with precipitation and surface soil moisture. We should strengthen the monitoring of meteorological data in this area to provide support for lightning fires early warning.

3.5. Trends in Key Climate Indices over the Past 20 Years

From 1999 to 2019, the overall climate conditions in the study region during the early lightning fire season (May to June), late season (July to September), and the entire season (May to September) became warmer and more humid. Specifically, the average temperature, SPEI index (higher SPEI indicates a wetter climate, whereas lower SPEI indicates a drier climate), and precipitation all increased from 1999 to 2019 (Figure 6). Fuel aridity is enhanced as drought severity increases, leading to more frequent forest fires and larger burned areas [47]. The warming itself along with the corresponding enhancement in drought stress, increased frequency of extreme hot events, and longer fire season also likely increase the risk for fire [48,49]. Ni et al. found that rainfall and summer temperature in the Greater Khingan Mountains are the main meteorological factors that affect the occurrence of lightning fires. Lightning fires were negatively correlated with rainfall and positively correlated with summer temperature, among which rainfall and lightning fires had the strong correlation, which is consistent with our results [50]. In addition, Fill et al. found that less rainfall over a longer dry season likely increases both the potential for lightning-ignited wildfires and fire severity [46].
Meanwhile, we found that the soil moisture and SPEI index increased most significantly from May to June, whereas the soil moisture decreased significantly from July to September. The reduction in soil surface moisture has a certain promotion effect on the formation and spread of fire after lightning strikes ignited combustibles. If the opposite trend in soil moisture continues, it may lead to lightning fires in the early days in the future. Altogether, we concluded that the changes in soil moisture, SPEI index, and precipitation should be paid more attention during the lightning-fire periods.

4. Conclusions

We found that lightning fires occurred most frequently at an altitude of 600–700 m in the primeval forest region of the northern Daxing’an Mountains. Snow cover had a strong regulatory effect on the start and end dates of lightning fires, and snow cover monitoring should be strengthened in high-risk lightning fires areas. The frequency of lightning fires was positively correlated with average temperature, maximum temperature, and surface evaporation and negatively correlated with surface soil moisture (0–10 cm) and precipitation (Figure 7). Acquiring more accurate advance and real-time meteorological monitor data in the future will be useful in the spatially assessment of fire risk, in identifying areas at greatest risk, and in designing long-term lightning fires management strategies.

Author Contributions

Conceptualization, Y.S. and M.Z.; methodology, Y.S., L.G. and P.Z.; validation, Y.S. and C.S.; formal analysis, Y.S. and C.S.; investigation, Y.S. and B.Y.; writing—original draft preparation, Y.S.; writing—review and editing, M.Z.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Inner Mongolia Autonomous Region Science and Technology Achievement Transformation Project (2021CG0002), the National Natural Science Foundation of China (32001325), the Inner Mongolia Natural Science Foundation, China (2020MS03049), and the Inner Mongolia Autonomous Region High-end Foreign Expert Introduction Project.

Data Availability Statement

All data generated or analysed during this study are included in this published article.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kelly, R.; Chipman, M.L.; Higuera, P.E.; Stefanova, I.; Brubaker, L.B.; Hu, F.S. Recent burning of boreal forests exceeds fire regime limits of the past 10,000 years. Proc. Natl. Acad. Sci. USA 2013, 110, 13055–13060. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Feurdean, A.; Florescu, G.; Tantau, I.; Vanniere, B.; Kirpotin, S. Recent fire regime in the southern boreal forests of western Siberia is unprecedented in the last five millennia. Quat. Sci. Rev. 2020, 244, 106495. [Google Scholar] [CrossRef]
  3. Price, C.; Rind, D. Possible implications of global climate change on global lightning distributions and frequencies. J. Geophys. Res. Atmos. 1994, 99, 10823–10831. [Google Scholar] [CrossRef]
  4. Romps, D.M.; Seeley, J.T.; Vollaro, D.; Molinari, J. Projected increase in lightning strikes in the United States due to global warming. Science 2014, 346, 851–854. [Google Scholar] [CrossRef] [PubMed]
  5. Veraverbeke, S.; Rogers, B.M.; Goulden, M.L.; Jandt, R.R.; Miller, C.E.; Wiggins, E.B.; Randerson, J.T. Lightning as a major driver of recent large fire years in North American boreal forests. Nat. Clim. Chang. 2017, 7, 529. [Google Scholar] [CrossRef]
  6. Hanes, C.C.; Wang, X.; Jain, P.; Parisien, M.; Little, J.M.; Flannigan, M.D. Fire-regime changes in Canada over the last half century. Can. J. For. Res. 2019, 49, 256–269. [Google Scholar] [CrossRef]
  7. Westerling, A.L. Increasing western US forest wildfire activity: Sensitivity to changes in the timing of spring. Philos. Trans. R. Soc. B 2016, 371, 20150175. [Google Scholar] [CrossRef]
  8. Westerling, A.L.; Hidalgo, H.G.; Cayan, D.R.; Swetnam, T.W. Warming and earlier spring increase western US forest wildfire activity. Science 2006, 313, 940–943. [Google Scholar] [CrossRef] [Green Version]
  9. Portier, J.; Gauthier, S.; Leduc, A.; Arseneault, D.; Bergeron, Y. Fire regime along latitudinal gradients of continuous to discontinuous coniferous boreal forests in eastern Canada. Forests 2016, 7, 211. [Google Scholar] [CrossRef] [Green Version]
  10. Gonzalez, M.E.; Gomez-Gonzalez, S.; Lara, A.; Garreaud, R.; Diaz-Hormazabal, I. The 2010–2015 Megadrought and its influence on the fire regime in central and south-central Chile. Ecosphere 2018, 9, e02300. [Google Scholar] [CrossRef] [Green Version]
  11. Overpeck, J.T.; Rind, D.; Goldberg, R. Climate-induced changes in forest disturbance and vegetation. Nature 1990, 343, 51–53. [Google Scholar] [CrossRef]
  12. Johnstone, J.F.; Hollingsworth, T.N.; Chapin, F.S.I.; Mack, M.C. Changes in fire regime break the legacy lock on successional trajectories in Alaskan boreal forest. Glob. Chang. Biol. 2010, 16, 1281–1295. [Google Scholar] [CrossRef]
  13. Bond-Lamberty, B.; Peckham, S.D.; Ahl, D.E.; Gower, S.T. Fire as the dominant driver of central Canadian boreal forest carbon balance. Nature 2007, 450, 89. [Google Scholar] [CrossRef] [Green Version]
  14. Walker, X.J.; Baltzer, J.L.; Cumming, S.G.; Day, N.J.; Ebert, C.; Goetz, S.; Johnstone, J.F.; Potter, S.; Rogers, B.M.; Schuur, E.A.G.; et al. Increasing wildfires threaten historic carbon sink of boreal forest soils. Nature 2019, 572, 520. [Google Scholar] [CrossRef] [PubMed]
  15. Li, F.; Lawrence, D.M.; Bond-Lamberty, B. Impact of fire on global land surface air temperature and energy budget for the 20th century due to changes within ecosystems. Environ. Res. Lett. 2017, 12, 044014. [Google Scholar] [CrossRef]
  16. Oris, F.; Asselin, H.; Ali, A.A.; Finsinger, W.; Bergeron, Y. Effect of increased fire activity on global warming in the boreal forest. Environ. Rev. 2014, 22, 206–219. [Google Scholar] [CrossRef] [Green Version]
  17. Van der Werf, G.R.; Randerson, J.T.; Giglio, L.; van Leeuwen, T.T.; Chen, Y.; Rogers, B.M.; Mu, M.; van Marle, M.J.E.; Morton, D.C.; Collatz, G.J.; et al. Global fire emissions estimates during 1997–2016. Earth. Syst. Sci. Data 2017, 9, 697–720. [Google Scholar] [CrossRef] [Green Version]
  18. Turetsky, M.R.; Kane, E.S.; Harden, J.W.; Ottmar, R.D.; Manies, K.L.; Hoy, E.; Kasischke, E.S. Recent acceleration of biomass burning and carbon losses in Alaskan forests and peatlands. Nat. Geosci. 2011, 4, 27–31. [Google Scholar] [CrossRef]
  19. Westerling, A.L.; Turner, M.G.; Smithwick, E.A.H.; Romme, W.H.; Ryan, M.G. Continued warming could transform Greater Yellowstone fire regimes by mid-21st century. Proc. Natl. Acad. Sci. USA 2011, 108, 13165–13170. [Google Scholar] [CrossRef] [Green Version]
  20. Flannigan, M.; Cantin, A.S.; de Groot, W.J.; Wotton, M.; Newbery, A.; Gowman, L.M. Global wildland fire season severity in the 21st century. For. Ecol. Manag. 2013, 294, 54–61. [Google Scholar] [CrossRef]
  21. Jolly, W.M.; Cochrane, M.A.; Freeborn, P.H.; Holden, Z.A.; Brown, T.J.; Williamson, G.J.; Bowman, D.M.J.S. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 2015, 6, 7537. [Google Scholar] [CrossRef] [PubMed]
  22. Wotton, B.M.; Martell, D.L. A lightning fire occurrence model for Ontario. Can. J. For. Res. 2005, 35, 1389–1401. [Google Scholar] [CrossRef]
  23. Nieto, H.; Aguado, I.; Garcia, M.; Chuvieco, E. Lightning-caused fires in central Spain: Development of a probability model of occurrence for two Spanish regions. Agric. Forest Meteorol. 2012, 162, 35–43. [Google Scholar]
  24. Lin, Q.Z.; Zhu, Q.P.; Wang, Q.A. Research on forest fire caused by lightning strike. Fire Sci. 1998, 8, 17–23. [Google Scholar]
  25. Tian, X.R.; Shu, L.F.; Wang, M.Y.; Zhao, F.J. Review on the Researches of Forest Fireand Climate Change. World For. Res. 2006, 19, 38–42. [Google Scholar]
  26. Zhang, Q.; Hu, X.F.; Yang, C.F.; Lü, Q.L.; Geng, L. Lightning Fire Rules and Probability of Daxing’an Mountain. J. Northeast For. Univ. 2014, 42, 149–153. [Google Scholar]
  27. Zhang, J.L.; Wu, B.I.; Wang, X.H.; Wang, Z.B.; Li, D.F. Lightning-caused fire, its affecting factors and prediction: A review. Chin. J. Appl. Ecol. 2013, 24, 2674. [Google Scholar]
  28. Anderson, K.R.; Martell, D.L.; Flannigan, M.D.; Wang, D. Modeling of fire occurrence in the boreal forest region of Canada. In Fire, Climate Change, and Carbon Cycling in the Boreal Forest; Springer: New York, NY, USA, 2000; pp. 357–367. [Google Scholar]
  29. Nash, C.H.; Johnson, E.A. Synoptic climatology of lightning-caused forest fires in subalpine and boreal forest. Can. J. For. Res. 1996, 26, 1859–1874. [Google Scholar] [CrossRef]
  30. Evett, R.R.; Mohrle, C.R.; Hall, B.L.; Brown, T.J.; Stephens, S.L. The effect of monsoonal atmospheric moisture on lightning fire ignitions in southwestern North America. Agric. For. Meteorol. 2008, 148, 1478–1487. [Google Scholar] [CrossRef]
  31. Feng, J.W.; Shen, H.; Liang, D. Research on the occurrence law of forest lightning strikes. J. Sun Yatsen Univ. (Nat. Sci. Ed.) 2021, 60, 131–137. [Google Scholar]
  32. Shu, L.F.; Wang, M.Y.; Tian, X.R. The fire environment mechanism of lightning fire formed for Daxing’an Mountains. Sci. Silvae Sin. 2003, 39, 94–99. [Google Scholar]
  33. Conedera, M.; Cesti, G.; Pezzatti, G.B.; Zumbrunnen, T.; Spinedi, F. Lightning-induced fires in the alpine region: An increasing problem. Forest Ecol. Manag. 2006, 234, S68. [Google Scholar] [CrossRef]
  34. Dissing, D.; Verbyla, D.L. Spatial patterns of lightning strikes in interior Alaska and their relations to elevation and vegetation. Can. J. For. Res. 2003, 33, 770–782. [Google Scholar] [CrossRef]
  35. Díaz-Avalos, C.; Peterson, D.L.; Alvarado, E.; Ferguson, S.A.; Besag, J.E. Space time modelling of lightning-caused ignitions in the Blue Mountains, Oregon. Can. J. For. Res. 2001, 31, 1579–1593. [Google Scholar]
  36. Castedo-Dorado, F.; Rodríguez-Pérez, J.R.; Marcos-Menendez, J.L.; Taboada, M.F.Á. Modelling the probability of lightning-induced forest fire occurrence in the province of León (NW Spain). Forest Syst. 2011, 20, 95–107. [Google Scholar] [CrossRef] [Green Version]
  37. Kochtubajda, B.; Flannigan, M.D.; Gyakum, J.R.; Stewart, R.E.; Logan, K.A.; Nguyen, T.V. Lightning and fires in the Northwest Territories and responses to future climate change. Arctic 2006, 59, 211–221. [Google Scholar] [CrossRef] [Green Version]
  38. Du, C.Y.; Yu, C.L.; Liu, D. Analysis on the Environment of Lightning Fire in Daxinganling Area. Chin. Agric. Meteorol. 2010, 31, 596–599. [Google Scholar]
  39. Reineking, B.; Weibel, P.; Conedera, M.; Bugmann, H. Environmental determinants of lightning.V. Human-induced forest fire ignitions differ in a temperate mountain region of Switzerland. Int. J. Wildland Fire 2010, 19, 541–557. [Google Scholar] [CrossRef]
  40. Müller, M.M.; Vacik, H.; Diendorfer, G.; Arpaci, A. Analysis of lightning-induced forest fires in Austria. Theor. Appl. Climatol. 2013, 111, 183–193. [Google Scholar] [CrossRef] [Green Version]
  41. Lutz, J.A.; Van Wagtendonk, J.W.; Thode, A.E.; Miller, J.D.; Franklin, J.F. Climate, lightning ignitions, and fire severity in Yosemite National Park, California, USA. Int. J. Wildland Fire 2009, 18, 765–774. [Google Scholar] [CrossRef]
  42. Thomas, Z.A.; Mooney, S.; Cadd, H.; Baker, A.; Turney, C.; Schneider, L.; Khan, S.J. Late Holocene climate anomaly concurrent with fire activity and ecosystem shifts in the eastern Australian Highlands. Sci. Total. Environ. 2022, 802, 149542. [Google Scholar] [CrossRef]
  43. Clarke, H.; Gibson, R.; Cirulis, B.; Bradstock, R.A.; Penman, T.D. Developing and testing models of the drivers of anthropogenic and lightning-caused wildfire ignitions in south-eastern Australia. J. Environ. Manag. 2019, 235, 34–41. [Google Scholar] [CrossRef] [PubMed]
  44. Ganteaume, A.; Camia, A.; Jappiot, M.; San-Miguel-Ayanz, J.; Long-Fournel, M.; Lampin, C. A review of the main driving factors of forest fire ignition over Europe. Environ. Manag. 2013, 51, 651–662. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Zhang, H.; Zhang, X.; Zhao, P.W.; Zhou, M.; Yin, J.Y. Temporal and Spatial Distribution Characteristics of Lightning Fires in Forests and Grasslands in Inner Mongolia. J. Northeast For. Univ. 2020, 48, 46–51. [Google Scholar]
  46. Fill, J.M.; Davis, C.N.; Crandall, R.M. Climate change lengthens southeastern USA lightning-ignited fire seasons. Glob. Chang. Biol. 2019, 25, 3562–3569. [Google Scholar] [CrossRef] [PubMed]
  47. Kodandapani, N.; Parks, S.A. Effects of drought on wildfires in forest landscapes of the Western Ghats, India. Int. J. Wildland Fire 2019, 28, 431–444. [Google Scholar] [CrossRef]
  48. Crockett, J.L.; Westerling, A.L. Greater temperature and precipitation extremes intensify western US droughts, Wildfire Severity; and Sierra Nevada Tree Mortality. J. Clim. 2018, 31, 341–354. [Google Scholar] [CrossRef]
  49. Ruffault, J.; Curt, T.; Martin-StPaul, N.K.; Moron, V.; Trigo, R.M. Extreme wildfire events are linked to global-change-type droughts in the northern Mediterranean. Nat. Hazard. Earth. Sys. 2018, 18, 847–856. [Google Scholar] [CrossRef] [Green Version]
  50. Ni, C.H.; Di, X.Y. Occurrence Regularity of Lightning Fire in Daxing’anling, Hei longjiang. J. Northeast For. Univ. 2009, 37, 55–57. [Google Scholar]
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Figure 1. The location of lightning strikes (blue dots) in the study region (the primeval forest region of the northern Daxing’an Mountains, Inner Mongolia, China; (120–122° E and 52–54° N) from 1999 to 2019. The black box in the upper-right inset image represents the boundary of the study region on the map. The red triangles are the location of the weather stations in the study area. The upper-left inset plot (a) shows the monthly average temperature and precipitation in the study area. The lower-right inset plot (b) shows the monthly average snow cover and fire frequency.
Figure 1. The location of lightning strikes (blue dots) in the study region (the primeval forest region of the northern Daxing’an Mountains, Inner Mongolia, China; (120–122° E and 52–54° N) from 1999 to 2019. The black box in the upper-right inset image represents the boundary of the study region on the map. The red triangles are the location of the weather stations in the study area. The upper-left inset plot (a) shows the monthly average temperature and precipitation in the study area. The lower-right inset plot (b) shows the monthly average snow cover and fire frequency.
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Figure 2. Locations of lightning fires in the study region from 1999 to 2019.
Figure 2. Locations of lightning fires in the study region from 1999 to 2019.
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Figure 3. Fire frequency (blue line plot) and burned area (brown bars) from May to June (a), July to September (b), and May to September (c). The straight blue line is the linear trend in the frequency of lightning fires.
Figure 3. Fire frequency (blue line plot) and burned area (brown bars) from May to June (a), July to September (b), and May to September (c). The straight blue line is the linear trend in the frequency of lightning fires.
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Figure 4. The time of lightning fire occurrence (brown bars), the end date of snow melt (red line), and the beginning date of snow accumulation (blue line) in the study region from 1999 to 2019. Snow cover data are from Rutgers GSL 1°.
Figure 4. The time of lightning fire occurrence (brown bars), the end date of snow melt (red line), and the beginning date of snow accumulation (blue line) in the study region from 1999 to 2019. Snow cover data are from Rutgers GSL 1°.
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Figure 5. Correlation coefficients between meteorological factors and lightning fire frequency in May to June, July to September, and May to September. The black and red asterisks represent significant levels of over 95% and 99%, respectively. The seven meteorological elements from left to right are: average temperature, maximum temperature, minimum temperature, precipitation, drought index, soil moisture, and transpiration rate.
Figure 5. Correlation coefficients between meteorological factors and lightning fire frequency in May to June, July to September, and May to September. The black and red asterisks represent significant levels of over 95% and 99%, respectively. The seven meteorological elements from left to right are: average temperature, maximum temperature, minimum temperature, precipitation, drought index, soil moisture, and transpiration rate.
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Figure 6. Trends in the meteorological indices in the study region from 1999 to 2019. (a) the average temperature (Temp), (b) maximum temperature (Tmax), (c) precipitation (Pre), (d) drought index (SPEI), (e) evapotranspiration (Eva), (f) soil moisture (SM).
Figure 6. Trends in the meteorological indices in the study region from 1999 to 2019. (a) the average temperature (Temp), (b) maximum temperature (Tmax), (c) precipitation (Pre), (d) drought index (SPEI), (e) evapotranspiration (Eva), (f) soil moisture (SM).
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Figure 7. Graphical Abstract.
Figure 7. Graphical Abstract.
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Shu, Y.; Shi, C.; Yi, B.; Zhao, P.; Guan, L.; Zhou, M. Influence of Climatic Factors on Lightning Fires in the Primeval Forest Region of the Northern Daxing’an Mountains, China. Sustainability 2022, 14, 5462. https://doi.org/10.3390/su14095462

AMA Style

Shu Y, Shi C, Yi B, Zhao P, Guan L, Zhou M. Influence of Climatic Factors on Lightning Fires in the Primeval Forest Region of the Northern Daxing’an Mountains, China. Sustainability. 2022; 14(9):5462. https://doi.org/10.3390/su14095462

Chicago/Turabian Style

Shu, Yang, Chunming Shi, Bole Yi, Pengwu Zhao, Lijuan Guan, and Mei Zhou. 2022. "Influence of Climatic Factors on Lightning Fires in the Primeval Forest Region of the Northern Daxing’an Mountains, China" Sustainability 14, no. 9: 5462. https://doi.org/10.3390/su14095462

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