Abstract
Fires can pose a threat to forest ecosystems when those ecosystems are not fire-adapted or when forest community conditions make them vulnerable to wildfires. Thus, investigating fire-prone environmental conditions is urgently needed to create action plans that preserve these ecosystems. In this sense, climate variables can determine the environmental conditions favorable for forest fires. Our study confirms that vapor pressure deficit (VPD) is an essential climate indicator for forest fires, as it is related to maximum temperatures and low humidity, representing the stress conditions for vegetation prone to fires. This study explores the extent to which ENSO phases can modulate climatic conditions that lead to high VPD over Guanajuato, a semi-arid region in central Mexico, during the dry season (March–April-May). Using fire occurrence data from MODIS (2000–2019) and Landsat 5 (1998–1999), we developed a climatic probability model for the occurrence of forest fires using VPD estimated from ERA5 reanalysis for each ENSO phase. We found that VPD and the occurrence of forest fires were higher during El Niño than under Neutral and La Niña years, with a higher risk of forest fire occurrence in Guanajuato’s southern region. This study concludes that it is necessary to implement regional and local fire management plans, especially where the largest number of natural protected areas is located.
Similar content being viewed by others
Availability of data and material
MODIS fire locations are available from NASA Earthdata Cloud at https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms/active-fire-data. Landsat 5 TM images are available through the Earth Explorer platform at https://earthexplorer.usgs.gov/. ERA5 data are available from the Copernicus Climate Change Service at https://cds.climate.copernicus.eu/. Finally, the ONI is available from the NOAA Climate Prediction Center at https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php.
References
Abatzoglou, J. T., & Williams, A. P. (2016). Impact of anthropogenic climate change on wildfire across western US forests. Proceedings of the National Academy of Sciences, 113(42), 11770–11775. https://doi.org/10.1073/pnas.1607171113
Alencar, A. A. C., Solórzano, L. A., & Nepstad, D. C. (2004). Modeling forest understory fires in an eastern Amazonian landscape. Ecological Applications, 14(sp4), 139–149. https://doi.org/10.1890/01-6029
Anderson, D. B. (1936). Relative humidity or vapor pressure deficit. Ecology, 17(2), 277–282. https://doi.org/10.2307/1931468
Anderson, L. O., Marchezini, V., Morello, T. F., & Cunningham, C. A. (2019). Modelo conceitual de sistema de alerta e de gestão de riscos e desastres associados a incêndios florestais e desafios para políticas públicas no Brasil. Territorium, 26(I), 43–61. https://doi.org/10.14195/1647-7723_26-1_4
Barkhordarian, A., Saatchi, S. S., Behrangi, A., Loikith, P. C., & Mechoso, C. R. (2019). A recent systematic increase in vapor pressure deficit over tropical South America. Scientific Reports, 9(1), 15331. https://doi.org/10.1038/s41598-019-51857-8
Bastarrika, A., Chuvieco, E., & Martín, M. P. (2011). Mapping burned areas from Landsat TM/ETM+ data with a two-phase algorithm: Balancing omission and commission errors. Remote Sensing of Environment, 115(4), 1003–1012. https://doi.org/10.1016/j.rse.2010.12.005
Bolton, D. (1980). The computation of equivalent potential temperature. Monthly Weather Review, 108(7), 1046–1053. https://doi.org/10.1175/1520-0493(1980)108%3c1046:TCOEPT%3e2.0.CO;2
Brando, P. M., Soares-Filho, B., Rodrigues, L., Assunção, A., Morton, D., Tuchschneider, D., Fernandes, E. C. M., Macedo, M. N., Oliveira, U., & Coe, M. T. (2020). The gathering firestorm in southern Amazonia. Science Advances, 6(2), eaay1632. https://doi.org/10.1126/sciadv.aay1632
Brewer, C. K., Winne, J. C., Redmond, R. L., Opitz, D. W., & Mangrich, M. V. (2005). Classifying and mapping wildfire severity. Photogrammetric Engineering & Remote Sensing, 71(11), 1311–1320. https://doi.org/10.14358/PERS.71.11.1311
Card, D. (1982). Using known map category marginal frequencies to improve estimates of thematic map accuracy. Photogrammetric Engineering and Remote Sensing, 48(3), 431–439.
Carrillo, G. R., Rodríguez, D., Tchikoué, H., Monterroso, A., & Santillan, J. (2012). Análisis espacial de peligro de incendios forestales en Puebla, México. Interciencia, 37(9), 678–683.
Castañeda Rojas, M. F., Endara Agramont, A. R., Villers Ruiz, M. D. L., & Nava Bernal, E. G. (2016). Evaluación forestal y de combustibles en bosques de Pinus hartwegii en el Estado de México según densidades de cobertura y vulnerabilidad a incendios. Madera y Bosques, 21(2). https://doi.org/10.21829/myb.2015.212444
Cavazos, T., & Hastenrath, S. (1990). Convection and rainfall over Mexico and their modulation by the Southern Oscillation. International Journal of Climatology, 10(4), 377–386. https://doi.org/10.1002/joc.3370100405
Chuvieco, E., Mouillot, F., van der Werf, G. R., San Miguel, J., Tanase, M., Koutsias, N., García, M., Yebra, M., Padilla, M., Gitas, I., Heil, A., Hawbaker, T. J., & Giglio, L. (2019). Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sensing of Environment, 225, 45–64. https://doi.org/10.1016/j.rse.2019.02.013
CONABIO. (2012). La biodiversidad en Guanajuato: Estudio de Estado.
Dominguez, C., Jaramillo, A., & Cuéllar, P. (2021). Are the socioeconomic impacts associated with tropical cyclones in Mexico exacerbated by local vulnerability and ENSO conditions? International Journal of Climatology, 41(S1), E3307–E3324. https://doi.org/10.1002/joc.6927
Drury, S. A., & Veblen, T. T. (2008). Spatial and temporal variability in fire occurrence within the Las Bayas Forestry Reserve, Durango, Mexico. Plant Ecology, 197(2), 299–316. https://doi.org/10.1007/s11258-007-9379-5
Eskandari, S., Miesel, J. R., & Pourghasemi, H. R. (2020). The temporal and spatial relationships between climatic parameters and fire occurrence in northeastern Iran. Ecological Indicators, 118, 106720. https://doi.org/10.1016/j.ecolind.2020.106720
Farfán, M., Pérez-Salicrup, D. R., Flamenco-Sandoval, A., Nicasio-Arzeta, S., Mas, J.-F., & Ramírez Ramírez, I. (2018). Modeling anthropic factors as drivers of wildfire occurrence at the Monarch Butterfly Biosphere. Madera y Bosques, 24(3). https://doi.org/10.21829/myb.2018.2431591
Ferreira, B. M., Soares-Filho, B. S., & Pereira, F. M. Q. (2019). The Dinamica EGO virtual machine. Brazilian Symposium on Programming Languages (SBLP ’15+16), 173, 3–20. https://doi.org/10.1016/j.scico.2018.02.002
French, N. H. F., Kasischke, E. S., Hall, R. J., Murphy, K. A., Verbyla, D. L., Hoy, E. E., & Allen, J. L. (2008). Using Landsat data to assess fire and burn severity in the North American boreal forest region: An overview and summary of results. International Journal of Wildland Fire, 17(4), 443. https://doi.org/10.1071/WF08007
Galvan-Ortiz, L. M. (2011). Impacto de la sequia meteorologica en la vegetacion en distintas regiones climaticas de Mexico (1982–2006). [Masters thesis, Universidad Nacional Autónoma de México (UNAM),]. http://132.248.9.195/ptb2011/octubre/0674287/Index.html
Ganteaume, A., Camia, A., Jappiot, M., San-Miguel-Ayanz, J., Long-Fournel, M., & Lampin, C. (2013). A review of the main driving factors of forest fire ignition over Europe. Environmental Management, 51(3), 651–662. https://doi.org/10.1007/s00267-012-9961-z
Glantz, M. H., & Ramirez, I. J. (2020). Reviewing the Oceanic Niño Index (ONI) to enhance societal readiness for El Niño’s impacts. International Journal of Disaster Risk Science, 11(3), 394–403. https://doi.org/10.1007/s13753-020-00275-w
Hartmann, D. L. (2016). Global physical climatology (second edition). Elsevier.
Hessl, A., Miller, J., Kernan, J., Keenum, D., & McKenzie, D. (2007). Mapping paleo-fire boundaries from binary point data: Comparing interpolation methods. The Professional Geographer, 59(1), 87–104. https://doi.org/10.1111/j.1467-9272.2007.00593.x
Holmgren, M., Scheffer, M., Ezcurra, E., Gutiérrez, J. R., & Mohren, G. M. (2001). El Niño effects on the dynamics of terrestrial ecosystems. Trends in Ecology & Evolution, 16(2), 89–94. https://doi.org/10.1016/s0169-5347(00)02052-8
Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression: Hosmer/applied logistic regression. John Wiley & Sons, Inc. https://doi.org/10.1002/0471722146
Huang, B., L’Heureux, M., Hu, Z.-Z., & Zhang, H.-M. (2016). Ranking the strongest ENSO events while incorporating SST uncertainty: ENSO RANKING. Geophysical Research Letters, 43(17), 9165–9172. https://doi.org/10.1002/2016GL070888
Ibarra-Montoya, J. L., & Huerta-Martínez, F. M. (2016). Modelado espacial de incendios: Una herramienta predictiva para el Bosque La Primavera, Jalisco México. Ambiente e Agua - an Interdisciplinary Journal of Applied Science, 11(1), 35–49. https://doi.org/10.4136/ambi-agua.1536
IEE. (2014). Mapa digital de uso de suelo y vegetación 2014 para el estado de Guanajuato. Coordinación de Ordenamiento Ecológico y Programas Especiales / Dirección de Recursos Naturales, Instituto de Ecología del Estado de Guanajuato. http://mapas.ecologia.guanajuato.gob.mx
INEGI. (2001). Conjunto de datos vectoriales Fisiográficos. Continuo Nacional serie I. Provincias fisiográficas [Map]. https://www.inegi.org.mx/app/biblioteca/ficha.html?upc=702825267575
Key, C., & Benson, N. (2006). Landscape assessment: Ground measure of severity, the Composite Burn Index; and remote sensing of severity, the Normalized Burn Ratio. In FIREMON: Fire effects monitoring and inventory system (p. LA-1–51).
Kolden, C. A., & Weisberg, P. J. (2007). Assessing accuracy of manually-mapped wildfire perimeters in topographically dissected areas. Fire Ecology, 3(1), 22–31. https://doi.org/10.4996/fireecology.0301022
Maeda, E. E., Arcoverde, G. F. B., Pellikka, P. K. E., & Shimabukuro, Y. E. (2011). Fire risk assessment in the Brazilian Amazon using MODIS imagery and change vector analysis. Applied Geography, 31(1), 76–84. https://doi.org/10.1016/j.apgeog.2010.02.004
Magaña, V. O., Vázquez, J. L., Pérez, J. L., & Pérez, J. B. (2003). Impact of El Niño on precipitation in Mexico. Geofísica Internacional, 42(3), 313–330.
Manel, S., Dias, J. M., Buckton, S. T., & Ormerod, S. J. (1999). Alternative methods for predicting species distribution: An illustration with Himalayan river birds. Journal of Applied Ecology, 36(5), 734–747. https://doi.org/10.1046/j.1365-2664.1999.00440.x
Manzo-Delgado, L., Aguirre-Gómez, R., & Álvarez, R. (2004). Multitemporal analysis of land surface temperature using NOAA-AVHRR: Preliminary relationships between climatic anomalies and forest fires. International Journal of Remote Sensing, 25(20), 4417–4424. https://doi.org/10.1080/01431160412331269643
Manzo-Delgado, L., Sánchez-Colón, S., & Álvarez, R. (2009). Assessment of seasonal forest fire risk using NOAA-AVHRR: A case study in central Mexico. International Journal of Remote Sensing, 30(19), 4991–5013. https://doi.org/10.1080/01431160902852796
Martínez, J., Vega-Garcia, C., & Chuvieco, E. (2009). Human-caused wildfire risk rating for prevention planning in Spain. Journal of Environmental Management, 90(2), 1241–1252. https://doi.org/10.1016/j.jenvman.2008.07.005
Martínez Orea, Y., Argüero Castillo, S., Chávez Guadarrama, M. P., & Sánchez, I. (2010). Post-fire seed bank in a xerophytic shrubland. Botanical Sciences, 86(0). https://doi.org/10.17129/botsci.2316
Mas, J.-F., Soares Filho, B., Pontius, R., Farfán Gutiérrez, M., & Rodrigues, H. (2013). A suite of tools for ROC analysis of spatial models. ISPRS International Journal of Geo-Information, 2(3), 869–887. https://doi.org/10.3390/ijgi2030869
Mateo Rodrigues, J. M., Silva, E. V., & Figueiró, A. S. (2019). La geoecología de los paisajes como base teórico-metodológica para incorporar la dimensión tecnológica a la temática ambiental. Desenvolvimento e Meio Ambiente, 51. https://doi.org/10.5380/dma.v51i0.65410
Mueller, S. E., Thode, A. E., Margolis, E. Q., Yocom, L. L., Young, J. D., & Iniguez, J. M. (2020). Climate relationships with increasing wildfire in the southwestern US from 1984 to 2015. Forest Ecology and Management, 460, 117861. https://doi.org/10.1016/j.foreco.2019.117861
Nathan, R. J., McMahon, T. A., Peel, M. C., & Horne, A. (2019). Assessing the degree of hydrologic stress due to climate change. Climatic Change, 156(1), 87–104. https://doi.org/10.1007/s10584-019-02497-4
Návar, J., & Lizárraga-Mendiola, L. (2013). Hydro-climatic variability and forest fires in Mexico’s northern temperate forests. Geofísica Internacional, 52(1), 5–20. https://doi.org/10.1016/S0016-7169(13)71458-2
Naveh, Z. (1994). The role of fire and its management in the conservation of Mediterranean ecosystems and landscapes. In J. M. Moreno & W. C. Oechel (Eds.), The role of fire in Mediterranean-type ecosystems (pp. 163–185). Springer New York. https://doi.org/10.1007/978-1-4613-8395-6_9
Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42–57. https://doi.org/10.1016/j.rse.2014.02.015
Olofsson, P., Foody, G. M., Stehman, S. V., & Woodcock, C. E. (2013). Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sensing of Environment, 129, 122–131. https://doi.org/10.1016/j.rse.2012.10.031
Pavón, N. P. (2011). El Niño y lo incendios en matorrales semiáridos de México (G. Sánchez-Rojas, C. Ballesteros-Barrera, & N. P. Pavón, Eds.; pp. 69–80).
Pompa-García, M., Camarero, J. J., Rodríguez-Trejo, D. A., & Vega-Nieva, D. J. (2018). Drought and spatiotemporal variability of forest fires across Mexico. Chinese Geographical Science, 28(1), 25–37. https://doi.org/10.1007/s11769-017-0928-0
Pontius, R. G., & Schneider, L. C. (2001). Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment, 85(1–3), 239–248. https://doi.org/10.1016/S0167-8809(01)00187-6
Pourghasemi, H. R., Gayen, A., Panahi, M., Rezaie, F., & Blaschke, T. (2019). Multi-hazard probability assessment and mapping in Iran. Science of the Total Environment, 692, 556–571. https://doi.org/10.1016/j.scitotenv.2019.07.203
Poveda, G., & Mesa, Ó. J. (1996). Las fases extremas del fenómeno ENSO (El Niño y La Niña) y su influencia sobre la hidrología de Colombia. Tecnología y Ciencias Del Agua, 11(1), 21–37.
Pyne, S. J., Andrews, P. L., & Laven, R. D. (1996). Introduction to wildland fire (2nd ed.). Wiley; /z-wcorg/.
Quintero, N., Viedma, O., Urbieta, I. R., & Moreno, J. M. (2019). Assessing landscape fire hazard by multitemporal automatic classification of Landsat Time Series using the Google Earth Engine in West-Central Spain. Forests, 10(6). https://doi.org/10.3390/f10060518
Ray, D., Nepstad, D., & Moutinho, P. (2005). Micrometeorological and canopy controls of fire susceptibility in a forested Amazon landscape. Ecological Applications, 15(5), 1664–1678. https://doi.org/10.1890/05-0404
Rodríguez Trejo, D. A. (2008). Fire regimes, fire ecology, and fire management in Mexico. AMBIO: A Journal of the Human Environment, 37(7), 548–556. https://doi.org/10.1579/0044-7447-37.7.548
Rodríguez-Trejo, D. A., & Fulé, P. Z. (2003). Fire ecology of Mexican pines and a fire management proposal. International Journal of Wildland Fire, 12(1), 23–37.
Rodríguez-Trejo, D. A., Martínez-Muñoz, P., & Martínez-Lara, P. J. (2019). Efectos del fuego en el arbolado de un bosque tropical de pino y en el de una selva baja caducifolia en Villaflores, Chiapas. Ciência Florestal, 29(3), 1033. https://doi.org/10.5902/1980509833952
Rodríguez-Trejo, D. A., & Pyne, S. J. (1999). Mexican fires of 1998. International Forest Fire News, 20, 61–63.
Rojo Hernández, J. D., Mesa, Ó. J., & Lall, U. (2020). ENSO dynamics, trends, and prediction using machine learning. Weather and Forecasting, 35(5), 2061–2081. https://doi.org/10.1175/WAF-D-20-0031.1
Román-Cuesta, R. M. (2000). Forest fire situation in the state of Chiapas, Mexico. In J. Pugliese (Ed.), Global Forest Fire Assessment 1990–2000. (pp. 426–437). FRA 2000 main report. Working paper 55. Forestry Department, FAO.
Román-Cuesta, R. M., Gracia, M., & Retana, J. (2003). Environmental and human factors influencing fire trends in ENSO and non-ENSO years in tropical Mexico. Ecological Applications, 13(4), 1177–1192. JSTOR.
Roth, D., Moreno-Sanchez, R., Torres-Rojo, J. M., & Moreno-Sanchez, F. (2016). Estimation of human induced disturbance of the environment associated with 2002, 2008 and 2013 land use/cover patterns in Mexico. Applied Geography, 66, 22–34. https://doi.org/10.1016/j.apgeog.2015.11.009
Seager, R., Hooks, A., Williams, A. P., Cook, B., Nakamura, J., & Henderson, N. (2015). Climatology, variability, and trends in the U.S. vapor pressure deficit, an important fire-related meteorological quantity. Journal of Applied Meteorology and Climatology, 54(6), 1121–1141. https://doi.org/10.1175/JAMC-D-14-0321.1
Sedano, F., & Randerson, J. T. (2014). Multi-scale influence of vapor pressure deficit on fire ignition and spread in boreal forest ecosystems. Biogeosciences, 11(14), 3739–3755. https://doi.org/10.5194/bg-11-3739-2014
SEMARNAP. (1999). Informe final de la campaña de prevención y combate de incendios forestales en el estado de Chiapas. Temporada 1998–1999.
SEMARNAT. (2014). Resultados del inventario estatal de Guanajuato.Secretaría de Medio Ambiente y Recursos Naturales. Available at. https://snigf.cnf.gob.mx/producto/resultados-del-inventario-estatal-de-guanajuato
Silvestrini, R. A., Soares-Filho, B. S., Nepstad, D., Coe, M., Rodrigues, H., & Assunção, R. (2011). Simulating fire regimes in the Amazon in response to climate change and deforestation. Ecological Applications, 21(5), 1573–1590. https://doi.org/10.1890/10-0827.1
Stehman, S. V., & Foody, G. M. (2019). Key issues in rigorous accuracy assessment of land cover products. Remote Sensing of Environment, 231, 111199. https://doi.org/10.1016/j.rse.2019.05.018
Sunderman, S. O., & Weisberg, P. J. (2011). Remote sensing approaches for reconstructing fire perimeters and burn severity mosaics in desert spring ecosystems. Remote Sensing of Environment, 115(9), 2384–2389. https://doi.org/10.1016/j.rse.2011.05.001
Trenberth, K. E. (1991). General characteristics of El Nino-Southern Oscillation. Teleconnections Linking Worldwide Climate Anomalies: Scientific Basis and Societal Impact, 13–42.
Vadrevu, K. P., Eaturu, A., & Badarinath, K. V. S. (2010). Fire risk evaluation using multicriteria analysis—A case study. Environmental Monitoring and Assessment, 166(1–4), 223–239. https://doi.org/10.1007/s10661-009-0997-3
Vázquez, A., & Moreno, JoséM. (1993). Sensitivity of fire occurrence to meteorological variables in Mediterranean and Atlantic areas of Spain. Landscape and Urban Planning, 24(1–4), 129–142. https://doi.org/10.1016/0169-2046(93)90091-Q
Westerling, A. L., Gershunov, A., Brown, T. J., Cayan, D. R., & Dettinger, M. D. (2003). Climate and wildfire in the western United States. Bulletin of the American Meteorological Society, 84(5), 595–604. https://doi.org/10.1175/BAMS-84-5-595
Williams, A. P., Seager, R., Macalady, A. K., Berkelhammer, M., Crimmins, M. A., Swetnam, T. W., Trugman, A. T., Buenning, N., Noone, D., McDowell, N. G., Hryniw, N., Mora, C. I., & Rahn, T. (2015). Correlations between components of the water balance and burned area reveal new insights for predicting forest fire area in the southwest United States. International Journal of Wildland Fire, 24(1), 14. https://doi.org/10.1071/WF14023
Zamudio, S. (2012). Diversidad de ecosistemas del estado de Guanajuato. In La biodiversidad de Guanajuato: Estudio de Estado (pp. 19–55). Comisión Nacional para el Conocimiento y Uso de la Biodiversidad / Instituto de Ecología del estado de Guanajuato.
Acknowledgements
We highly appreciate the helpful comments and suggestions provided by the editor and three anonymous reviewers. We acknowledge the support of the Dirección de Apoyo a la Investigación y al Posgrado of the Universidad de Guanajuato. The authors also thank the National Oceanic and Atmospheric Administration (NOAA) for the ONI data, the National Aeronautics and Space Administration (NASA) for the MODIS fire location data, and the US Geological Survey (USGS) for the Landsat 5 TM images used in this work.
Funding
Funding was provided by Universidad de Guanajuato.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Farfán, M., Dominguez, C., Espinoza, A. et al. Forest fire probability under ENSO conditions in a semi-arid region: a case study in Guanajuato. Environ Monit Assess 193, 684 (2021). https://doi.org/10.1007/s10661-021-09494-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10661-021-09494-0