Figure 3 . Boxplots of (a) air temperature, (b) RH, (c) FFMC, and (d) DC for the “Fire” and “No fire” events across the three tundra regions on fire-occurrence days.
4 Discussions
This study identifies the CG lightning probability as the key driver of tundra fire occurrence. Though lightning is normally assumed to be the primary ignition source in the tundra due to the remoteness of the region and the limited human activities, we provide the first quantitative piece of evidence that supports this assumption, as the results from all models in this study point to CG lightning probability as the most influential factor that predicts fire occurrence. This finding is consistent with previous research conducted in the boreal forests of North America (Veraverbeke et al., 2017). Yet, the role of lightning is not always emphasized in other ecosystems (Díaz-avalos et al., 2001; Liu et al., 2012; Vecín-Arias et al., 2016). Previous studies have also established relationships between fires and lightning characteristics observed from ground-based detection networks, such as the count, polarity, and peak current of lightning strikes (Peterson et al., 2010). This study, whereas, suggest that the probability of CG lightning modeled purely with atmospheric variables is a powerful indicator of tundra fire potential.
In addition to lightning, warmer and drier near-surface fire weather conditions support burnings in the tundra. With generally low temperatures and high water table, Arctic tundra is an unusual environment that is rarely moisture-limited and are not highly flammable, largely due to widespread underlying permafrost (Bliss et al., 1973; Wielgolaski and Goodall, 1997). Evidences from both modeling and statistical analyses in this study highlight the importance of warm and dry weather conditions in driving fire occurrence in Alaskan tundra, with near-surface air temperature and RH significantly related to fires. Higher temperature and lower moisture conditions have the potential to increase the flammability of the environment in general. In addition to the impacts of air temperature and RH on fuel flammability, they might also reflect the high likelihood of convective potential, which in turn leads to atmospheric instability and ultimately lightning occurrence. Moreover, despite the minimal elevation variations in the tundra, topographic features such as elevation could indirectly affect fire activity through their impacts on lightning potential, temperature and moisture availability (Dissing and Verbyla, 2003; Podur et al., 2003).
Our results also demonstrate the suitability of fuel moisture codes from the CFFWIS for monitoring tundra fire potential. Primarily composed of herbaceous and dwarf shrub species, the dominant fuels in the tundra are considered fine surface fuels as defined in the CFFWIS (Innes, 2013). As the most influential indicator among all fire weather indices, DC is a slow-reacting code that tracks deeper drying of fuels that responds to changes in deep moisture levels in the tundra (Lawson and Armitage, 2008). Its significance in the logistic regression highlights that long-term dry conditions of tundra fuels that accumulate for days contribute more to burnings than the short-term changes. It is also worth mentioning that that FFMC is a highly predictive variable, since it is originally designed to describe the fine surface fuels in boreal forests (Lawson and Armitage, 2008). With larger FFMC indicating higher fuel flammability, FFMC of the “Fire” events can generally reach higher than 70 for the tundra, representing dry fuels for fire occurrence. Although the CFFWIS was originally developed for boreal forests and its ability to forecast tundra conditions was most generally assumed rather than tested, our study shows that both FFMC and DC provide a reasonable approximation of fuel moisture changes that can more readily support burning. Given the impacts of fire weather on fire potential, the future increase of FWI in the tundra (French et al., 2015) will absobutely contribute to higher fire risks in this region.
More importantly, our empirical-dynamic framework involving NWPs like WRF and statistical models has demonstrated its strong capability and effectiveness for contemporary fire modeling in data-scarce regions like the tundra. The modeling experiments with both the “Current-day model” and the “Previous-day model” further indicate that using data simulated from one day earlier can achieve reasonable accuracy in forecasting fire occurrence. The critical role of CG lightning probability also suggests that current fire management efforts are inadequate without incorporating CG lightning probability for fire danger monitoring and modeling in the tundra, where fires are primarily ignited by lightning. With the future increases of lightning in the HNL (Chen et al., 2021), Arctic tundra will experience higher fire occurrence in the future under the rapid climate warming. By monitoring lightning potential and fire weather, it is promising that fire occurrence can be predicted with high accuracy in remote regions at 5km resolution.
Though existing efforts have been made to incorporate lightning characteristics for fire modeling by matching lightning strikes detected by ground-based networks and fires (Peterson et al., 2010; Wotton and Martell, 2005), we recommend using simulated CG lightning probability for fire management efforts for several reasons. The ground-based lightning detection networks typically have a location accuracy of 1 ~ 5km and a detection efficiency of about 70% ~ 90% (Biagi et al., 2007; Dissing and Verbyla, 2003; Koshak et al., 2015; Nag et al., 2014). This suggests the potential missing of lightning strikes by the detection systems and the inaccuracy of the triangulated lightning locations. Therefore, the commonly used method of matching lightning and fire locations can largely miss the actual lightning strikes that ignite the fires, further introducing errors and uncertainties in the modeling and analysis efforts. The modeling results could be affected by the choices of matching methods as well (Moris et al., 2020). Finally, since no simulation of lightning characteristics has been developed based on existing NWPs so far, this limits the potential of integrating NWPs for fire ignition modeling and forecasting.
5 Conclusions
This study explores the key drivers of wildfire occurrences in Arctic tundra of Alaska by modeling the impacts of environmental factors on fire probability from 2001 to 2019. Among all factors, CG lightning probability is found to be the most important driver of tundra fire occurrences in Alaska, with a significant positive relationship between lightning and fire probabilities. Warmer and drier weather conditions also support burnings in the tundra. Air temperature, fuel moisture codes show significant positive relationships with fire occurrences, while RH is negatively related. Moreover, the empirical-dynamical modeling method in this study has demonstrated a strong capability in predicting fire occurrence probability, using the WRF-simulated fire weather variables on both fire ocurrence day and one day before. Our findings highlight the necessity of incorporating CG lightning modeling and the benefits of WRF simulation for wildfire monitoring efforts in data-scarce regions like tundra.
Availability Statement
Data and software to support this manuscript are publicly and freely available online from their websites. CAVM was obtained from Alaska Geobotany Center, University of Alaska, Fairbanks (https://www.geobotany.uaf.edu/cavm/). MODIS fire product MCD14ML was obtained from NASA’s Fire Information for Resource Management System (https://firms.modaps.eosdis.nasa.gov/). Fuel component maps were accessed from the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC;https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1761). MODIS surface reflectance data MOD09A1 was downloaded from NASA’s Land Processes Distributed Active Archive Center (LP DAAC;https://e4ftl01.cr.usgs.gov/MOLT/MOD09A1.006/). IfSAR DEM product was downloaded from the Alaska Elevation Portal (https://elevation.alaska.gov) hosted by Alaska Division of Geological and Geophysical Surveys. NCEP FNL data were obtained from the Research Data Archive (https://rda.ucar.edu/datasets/ds083.2/) managemend by the National Center for Atmospheric Research (NCAR).
The Advanced Research WRF Model Version 4.0 used for simulation lightning and near-surface weather is available via the Mesoscale and Microscale Meteorology Laboratory of NCAR (https://www2.mmm.ucar.edu/wrf/users/).
Acknowledgments
This study was supported by the NASA Terrestrial Ecology program grant NNX15AT79A.
References
Adámek, M., Jankovská, Z., Hadincová, V., Kula, E., Wild, J., 2018. Drivers of forest fire occurrence in the cultural landscape of Central Europe. Landsc. Ecol. 33, 2031–2045. https://doi.org/10.1007/s10980-018-0712-2
Biagi, C.J., Cummins, K.L., Kehoe, K.E., Krider, E.P., 2007. National Lightning Detection Network (NLDN) performance in southern Arizona, Texas, and Oklahoma in 2003-2004. J. Geophys. Res. Atmos. 112, 1–17. https://doi.org/10.1029/2006JD007341
Bliss, L.C., Courtin, G.M., Pattie, D.L., Riewe, R.R., Whitfield, D.W.A., Widden, P., 1973. Arctic Tundra Ecosystems, in: Annual Review of Ecology and Systematics. pp. 359–399.
Bogdanova, E., Andronov, S., Soromotin, A., Detter, G., Sizov, O., Hossain, K., Raheem, D., Lobanov, A., 2021. The Impact of Climate Change on the Food (In)security of the Siberian Indigenous Peoples in the Arctic: Environmental and Health Risks. Sustain. . https://doi.org/10.3390/su13052561
Bret-Harte, M.S., Mack, M.C., Shaver, G.R., Huebner, D.C., Johnston, M., Mojica, C. a, Pizano, C., Reiskind, J. a, 2013. The response of Arctic vegetation and soils following an unusually severe tundra fire. Philos. Trans. R. Soc. B 368, 20120490. https://doi.org/10.1098/rstb.2012.0490
Ceccato, P., Flasse, S., Grégoire, J.M., 2002. Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1: Theoretical approach. Remote Sens. Environ. 82, 188–197. https://doi.org/10.1016/S0034-4257(02)00037-8
Chambers, S.D., Beringer, J., Randerson, J.T., Chapin III, F.S., 2005. Fire effects on net radiation and energy partitioning: Contrasting responses of tundra and boreal forest ecosystems. J. Geophys. Res. Atmos. 110. https://doi.org/https://doi.org/10.1029/2004JD005299
Chen, Y., Romps, D.M., Seeley, J.T., Veraverbeke, S., Riley, W.J., Mekonnen, Z.A., Randerson, J.T., 2021. Future increases in Arctic lightning and fire risk for permafrost carbon. Nat. Clim. Chang. 11, 404–410. https://doi.org/10.1038/s41558-021-01011-y
Díaz-avalos, C., Peterson, D.L., Alvarado, E., Ferguson, S.A., Besag, J.E., 2001. Space-time modelling of lightning-caused ignitions in the Blue Mountains , Oregon. Can. J. For. Res. 31, 1579–1593. https://doi.org/10.1139/cjfr-31-9-1579
Dissing, D., Verbyla, D.L., 2003. Spatial patterns of lightning strikes in interior Alaska and their relations to elevation and vegetation. Can. J. For. Res. 782, 770–782. https://doi.org/10.1139/X02-214
Ester, M., Kriegel, H.-P., Sander, J., Xu, X., 1996. A density-based algorithm for discovering clusters in large spatial databases with noise., in: Kdd. pp. 226–231.
Forbes, B.C., 2013. Cultural Resilience of Social-ecological Systems in the Nenets and Yamal-Nenets Autonomous Okrugs, Russia. Ecol. Soc. 18.
French, N.H.F., Jenkins, L.K., Loboda, T. V., Flannigan, M., Jandt, R., Bourgeau-Chavez, L.L., Whitley, M., 2015. Fire in arctic tundra of Alaska: Past fire activity, future fire potential, and significance for land management and ecology. Int. J. Wildl. Fire 24, 1045–1061. https://doi.org/10.1071/WF14167
Frost, G. V, Loehman, R.A., Saperstein, L.B., Macander, M.J., Nelson, P.R., Paradis, D.P., Natali, S.M., 2020. Multi-decadal patterns of vegetation succession after tundra fire on the Yukon-Kuskokwim Delta, Alaska. Environ. Res. Lett. 15, 25003. https://doi.org/10.1088/1748-9326/ab5f49
Gao, B., 1996. NDWI – A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space. Remote Sens. Environ. 58, 257. https://doi.org/10.1016/S0034-4257(96)00067-3
Giglio, L., Descloitres, J., Justice, C.O., Kaufman, Y.J., 2003. An Enhanced Contextual Fire Detection Algorithm for MODIS. Remote Sens. Environ. 87, 273–282. https://doi.org/https://doi.org/10.1016/S0034-4257(03)00184-6
Gustine, D.D., Brinkman, T.J., Lindgren, M.A., Schmidt, J.I., Rupp, T.S., Adams, L.G., 2014. Climate-driven effects of fire on winter habitat for Caribou in the Alaskan-Yukon Arctic. PLoS One 9. https://doi.org/10.1371/journal.pone.0100588
Hardisky, M.A., Klemas, V., Smart, R.M., 1983. The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogramm. Eng. Remote Sens. 49, 77–83.
He, J., Chen, D., Jenkins, L., Loboda, T. V, 2021. Impacts of wildfire and landscape factors on organic soil properties in Arctic tussock tundra. Environ. Res. Lett. 16, 85004. https://doi.org/10.1088/1748-9326/ac1192
He, J., Loboda, T. V, 2020. Modeling cloud-to-ground lightning probability in Alaskan tundra through the integration of Weather Research and Forecast (WRF) model and machine learning method. Environ. Res. Lett. 15, 115009. https://doi.org/10.1088/1748-9326/abbc3b
He, J., Loboda, T. V, Jenkins, L., Chen, D., 2019. Mapping fractional cover of major fuel type components across Alaskan tundra. Remote Sens. Environ. 232, 111324. https://doi.org/https://doi.org/10.1016/j.rse.2019.111324
Higuera, P.E., Chipman, M.L., Barnes, J.L., Urban, M.A., Hu, F.S., 2011. Variability of tundra fire regimes in Arctic Alaska: Millennial-scale patterns and ecological implications. Ecol. Appl. 21, 3211–3226. https://doi.org/10.1890/11-0387.1
Hu, F.S., Higuera, P.E., Duffy, P., Chipman, M.L., Rocha, A. V., Young, A.M., Kelly, R., Dietze, M.C., 2015. Arctic tundra fires: Natural variability and responses to climate change. Front. Ecol. Environ. https://doi.org/10.1890/150063
Innes, R.J., 2013. Fire regimes of Alaskan tundra communities, Fire Effects Information System, [Online].
Jiang, Y., Rocha, A. V, O’Donnell, J. a, Drysdale, J. a, Rastetter, E.B., Shaver, G.R., Zhuang, Q., 2015. Contrasting soil thermal responses to fire in Alaska tundra and boreal forest. J. Geophys. Res. Earth Surf. 1–16. https://doi.org/10.1002/2014JF003180.Received
Joly, K., Duffy, P. a., Rupp, T.S., 2012. Simulating the effects of climate change on fire regimes in Arctic biomes: implications for caribou and moose habitat. Ecosphere 3, art36. https://doi.org/10.1890/ES12-00012.1
Jones, B.M., Grosse, G., Arp, C.D., Miller, E., Liu, L., Hayes, D.J., Larsen, C.F., 2015. Recent Arctic tundra fire initiates widespread thermokarst development. Sci. Rep. 5, 15865. https://doi.org/10.1038/srep15865
Koshak, W.J., Cummins, K.L., Buechler, D.E., Vant-Hull, B., Blakeslee, R.J., Williams, E.R., Peterson, H.S., 2015. Variability of CONUS lightning in 2003-12 and associated impacts. J. Appl. Meteorol. Climatol. 54, 15–41. https://doi.org/10.1175/JAMC-D-14-0072.1
Lawson, B.D., Armitage, O.B., 2008. Weather Guide Canadian Forest Fire Danger Rating System, Canadian Forest Service Northern Forestry Centre.
Liu, Z., Yang, J., Change, Y., Weisberg, P.J., He, H.S., 2012. Spatial patterns and drivers of fire occurrence and its future trend under climate change in a boreal forest of Northeast China. Glob. Chang. Biol. 18, 2041–2056. https://doi.org/10.1111/j.1365-2486.2012.02649.x
Loboda, T.V., Hall, J.V., Hall, A.H., Shevade, V.S., 2017. ABoVE: Cumulative Annual Burned Area, Circumpolar High Northern Latitudes, 2001-2015. https://doi.org/10.3334/ornldaac/1526
Loboda, T. V, Csiszar, I.A., 2007. Reconstruction of fire spread within wildland fire events in Northern Eurasia from the MODIS active fire product. Glob. Planet. Change 56, 258–273. https://doi.org/10.1016/j.gloplacha.2006.07.015
Mack, M.C., Bret-Harte, M.S., Hollingsworth, T.N., Jandt, R.R., Schuur, E.A.G., Shaver, G.R., Verbyla, D.L., 2011. Carbon loss from an unprecedented Arctic tundra wildfire. Nature 475, 489–92. https://doi.org/10.1038/nature10283
Masrur, A., Petrov, A.N., DeGroote, J., 2018. Circumpolar spatio-temporal patterns and contributing climatic factors of wildfire activity in the Arctic tundra from 2001-2015. Environ. Res. Lett. 13. https://doi.org/10.1088/1748-9326/aa9a76
McCarty, J.L., Aalto, J., Paunu, V.-V., Arnold, S.R., Eckhardt, S., Klimont, Z., Fain, J.J., Evangeliou, N., Venäläinen, A., Tchebakova, N.M., Parfenova, E.I., Kupiainen, K., Soja, A.J., Huang, L., Wilson, S., 2021. Reviews and syntheses: Arctic fire regimes and emissions in the 21st century. Biogeosciences 18, 5053–5083. https://doi.org/10.5194/bg-18-5053-2021
Mölders, N., 2010. Comparison of Canadian Forest Fire Danger Rating System and National Fire Danger Rating System fire indices derived from Weather Research and Forecasting (WRF) model data for the June 2005 Interior Alaska wildfires. Atmos. Res. 95, 290–306. https://doi.org/10.1016/j.atmosres.2009.03.010
Moris, J. V, Conedera, M., Nisi, L., Bernardi, M., Cesti, G., Pezzatti, G.B., 2020. Lightning-caused fires in the Alps: Identifying the igniting strokes. Agric. For. Meteorol. 290, 107990. https://doi.org/https://doi.org/10.1016/j.agrformet.2020.107990
Müller, M.M., Vacik, H., 2017. Characteristics of lightnings igniting forest fires in Austria. Agric. For. Meteorol. 240–241, 26–34. https://doi.org/10.1016/j.agrformet.2017.03.020
Nag, A., Murphy, M.J., Schulz, W., Cummins, K.L., 2014. Lightning locating systems: Characteristics and validation techniques. 2014 Int. Conf. Light. Prot. ICLP 2014 1070–1082. https://doi.org/10.1109/ICLP.2014.6973283
National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce, 2000. NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999. https://doi.org/10.5065/D6M043C6
Peterson, D., Wang, J., Ichoku, C., Remer, L.A., 2010. Effects of lightning and other meteorological factors on fire activity in the North American boreal forest: implications for fire weather forecasting. Atmos. Chem. Phys. 10, 6873–6888. https://doi.org/10.5194/acp-10-6873-2010
Pineda, N., Montanyà, J., van der Velde, O.A., 2014. Characteristics of lightning related to wildfire ignitions in Catalonia. Atmos. Res. 135–136, 380–387. https://doi.org/10.1016/j.atmosres.2012.07.011
Podur, J., Martell, D.L., Csillag, F., 2003. Spatial patterns of lightning-caused forest fires in Ontario, 1976-1998. Ecol. Modell. 164, 1–20. https://doi.org/10.1016/S0304-3800(02)00386-1
Pyne, S.J., Andrews, P.L., Laven, R.D., 1996. Introduction to Wildland Fire, Second Edi. ed. John Wiley & Sons, Inc.
Randerson, J.T., Liu, H., Flanner, M.G., Chambers, S.D., Jin, Y., Hess, P.G., Pfister, G., Mack, M.C., Treseder, K.K., Welp, L.R., Chapin, F.S., Harden, J.W., Goulden, M.L., Lyons, E., Neff, J.C., Schuur, E. a G., Zender, C.S., 2006. The impact of boreal forest fire on climate warming. Science 314, 1130–1132. https://doi.org/10.1126/science.1132075
Rocha, A. V., Shaver, G.R., 2011. Postfire energy exchange in arctic tundra: The importance and climatic implications of burn severity. Glob. Chang. Biol. 17, 2831–2841. https://doi.org/10.1111/j.1365-2486.2011.02441.x
Sae-Lim, J., Russell, J.M., Vachula, R.S., Holmes, R.M., Mann, P.J., Schade, J.D., Natali, S.M., 2019. Temperature-controlled tundra fire severity and frequency during the last millennium in the Yukon-Kuskokwim Delta, Alaska. The Holocene 29, 1223–1233. https://doi.org/10.1177/0959683619838036
Van Heerwaarden, C.C., Teuling, A.J., 2014. Disentangling the response of forest and grassland energy exchange to heatwaves under idealized land-atmosphere coupling. Biogeosciences 11, 6159–6171. https://doi.org/10.5194/bg-11-6159-2014
Van Wagner, C.E., 1987. Development and Structure of the Canadian Forest FireWeather Index System, in: Canadian Forestry Service. Forestry Technical Report 35.
van Wees, D., van der Werf, G.R., Randerson, J.T., Andela, N., Chen, Y., Morton, D.C., 2021. The role of fire in global forest loss dynamics. Glob. Chang. Biol. 27, 2377–2391. https://doi.org/https://doi.org/10.1111/gcb.15591
Vecín-Arias, D., Castedo-Dorado, F., Ordónez, C., Rodríguez-Pérez, J.R., 2016. Biophysical and lightning characteristics drive lightning-induced fire occurrence in the central plateau of the Iberian Peninsula. Agric. For. Meteorol. 225, 36–47. https://doi.org/10.1016/j.agrformet.2016.05.003
Veraverbeke, S., Rogers, B.M., Goulden, M.L., Jandt, R.R., Miller, C.E., Wiggins, E.B., Randerson, J.T., 2017. Lightning as a major driver of recent large fire years in North American boreal forests. Nat. Clim. Chang. 7, 529–534. https://doi.org/10.1038/nclimate3329
Vermote, E.F., Roger, J.C., Ray, J.P., 2015. MODIS Surface Reflectance User’s Guide Correspondence. Http://Modis-Sr.Ltdri.Org 35.
Walker, D.A., Raynolds, M.K., Daniëls, F.J.A., Einarsson, E., Elvebakk, A., Gould, W.A., Katenin, A.E., Kholod, S.S., Markon, C.J., Melnikov, E.S., Moskalenko, N.G., Talbot, S.S., Yurtsev, B.A., The other members of the, C.T., 2009. The Circumpolar Arctic vegetation map. J. Veg. Sci. 16, 267–282. https://doi.org/10.1111/j.1654-1103.2005.tb02365.x
Wang, J.A., Baccini, A., Farina, M., Randerson, J.T., Friedl, M.A., 2021. Disturbance suppresses the aboveground carbon sink in North American boreal forests. Nat. Clim. Chang. 11, 435–441. https://doi.org/10.1038/s41558-021-01027-4
Wielgolaski, F.E., Goodall, D.W., 1997. Polar and alpine tundra. Elsevier.
Wotton, B.M., Martell, D.L., 2005. A lightning fire occurrence model for Ontario. Can. J. For. Res. 35, 1389–1401. https://doi.org/10.1139/X05-071
Yebra, M., Chuvieco, E., Riaño, D., 2008. Estimation of live fuel moisture content from MODIS images for fire risk assessment. Agric. For. Meteorol. 148, 523–536. https://doi.org/10.1016/j.agrformet.2007.12.005
Young, A.M., Higuera, P.E., Duffy, P.A., Hu, F.S., 2017. Climatic thresholds shape northern high-latitude fire regimes and imply vulnerability to future climate change. Ecography (Cop.). 40, 606–617. https://doi.org/10.1111/ecog.02205