Climatic Teleconnections and Fire Hotspot Seasonality in Bolivia (2000–2025): Association with Niño 3.4, PDO, and SAM Indices
DOI:
https://doi.org/10.14738/ejas.1403.1266Keywords:
Fire hotspots, climatic teleconnections, seasonality, time series, Seasonal Variation Index (SVI)Abstract
The occurrence of fire hotspots in Bolivia results from a complex interaction between regional climatic seasonality and large-scale ocean–atmosphere variability. The objective of this study was to analyze the relationship between global climate indices and the monthly dynamics of fire hotspots across four Bolivian biomes during the period 2000–2025. This was achieved through time-series analysis, the calculation of the Seasonal Variation Index (SVI), and the evaluation of monthly variability in local meteorological variables. A quantitative, observational, longitudinal, and retrospective approach was adopted, based on monthly series of fire hotspots, maximum temperature, minimum relative humidity, and maximum wind speed from the meteorological stations of El Alto, Cochabamba, Viru Viru, and San José de Chiquitos. Temporal characterization included descriptive statistics, Sen’s slope estimator, autocorrelation functions, and the Ljung–Box test, while intra-annual seasonality was examined using the SVI. Furthermore, associations between the Niño 3.4 index, the Pacific Decadal Oscillation (PDO), and the Southern Annular Mode (SAM) with local meteorological variables were estimated using Pearson correlations with lags ranging from 0 to 5 months. The results revealed a non-random temporal structure dominated by a persistent annual periodicity and, in several cases, a secondary biennial signal. The SVI indicated that fire hotspot activity was seasonally concentrated between August and October, with a principal peak in September, coinciding with positive anomalies in maximum temperature and negative anomalies in minimum relative humidity. In the association analysis, the Niño 3.4 index exhibited the strongest positive correlations with maximum temperature, particularly in Cochabamba, whereas the SAM displayed the clearest inverse signal with minimum relative humidity in El Alto. The PDO showed weaker and more heterogeneous associations. It is concluded that the monthly dynamics of fire hotspots in Bolivia are strongly structured by climatic seasonality, and that local meteorological variability exhibits differential associations with global climate forcings. This provides an analytical foundation useful for seasonal monitoring and wildfire risk management.
References
Aragão, L. E. O. C., Malhi, Y., Roman-Cuesta, R. M., Saatchi, S., Anderson, L. O., & Shimabukuro, Y. E. (2007). Spatial patterns and fire response of recent Amazonian droughts. Geophysical Research Letters, 34(7), L07701. https://doi.org/10.1029/2006GL028946
Arguez, A., Bissolli, P., Ganter, C., Martinez, R., Mekonnen, A., Stevens, L., & Zhu, Z. (Eds.). (2025). State of the Climate in 2024: Regional climates [Special supplement]. Bulletin of the American Meteorological Society, 106(8). https://doi.org/10.1175/2025BAMSStateoftheClimate_Chapter7.1
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2016). Time series analysis: Forecasting and control (5th ed.). Wiley.
Burton, C., Kelley, D. I., Jones, C. D., Betts, R. A., Cardoso, M., & Anderson, L. (2021). South American fires and their impacts on ecosystems increase with continued emissions. Climate Resilience and Sustainability, 1, e8. https://doi.org/10.1002/cli2.8
Cardil, A., Rodrigues, M., Tapia, M., Barbero, R., Ramírez, J., Stoof, C. R., Silva, C. A., Mohan, M., & de-Miguel, S. (2023). Climate teleconnections modulate global burned area. Nature Communications, 14(1), Article 427. https://doi.org/10.1038/s41467-023-36052-8
Chatfield, C. (2004). The analysis of time series: An introduction (6th ed.). Chapman & Hall/CRC. https://doi.org/10.4324/9780203491683
Scholar Publishing Devisscher, T., Anderson, L. O., Aragão, L. E. O. C., Galván, L., & Malhi, Y. (2016). Increased wildfire risk driven by climate and development interactions in the Bolivian Chiquitania, southern Amazonia. PLOS ONE, 11(9), e0161323. https://doi.org/10.1371/journal.pone.0161323
Gomes, M. S., Cavalcanti, I. F. de A., & Müller, G. V. (2021). 2019/2020 drought impacts on South America and atmospheric and oceanic influences. Weather and Climate Extremes, 34, 100404. https://doi.org/10.1016/j.wace.2021.100404
Hendon, H. H., Lim, E.-P., & Nguyen, H. (2014). Seasonal variations of subtropical precipitation associated with the Southern Annular Mode. Journal of Climate, 27(9), 3446–3460. https://doi.org/10.1175/JCLI-D-13-00550.1
Ibisch, P. L., & Mérida, G. (Eds.). (2003). Biodiversidad: La riqueza de Bolivia. Estado de conocimiento y conservación. Editorial FAN.
Kendall, M. G. (1975). Rank correlation methods (4th ed.). Charles Griffin. https://archive.org/details/rankcorrelationm0000kend
Maillard, O., Vides-Almonacid, R., Flores-Valencia, M., Coronado, R., Vogt, P., Vicente-Serrano, S. M., Azurduy, H., Añivarro, R., & Cuéllar, R. L. (2020). Relationship of forest cover fragmentation and drought with the occurrence of forest fires in the Department of Santa Cruz, Bolivia. Forests, 11(9), 910. https://doi.org/10.3390/f11090910
Seiler, C., Hutjes, R. W. A., & Kabat, P. (2013). Climate variability and trends in Bolivia. Journal of Applied Meteorology and Climatology, 52(1), 130–146. https://doi.org/10.1175/JAMC-D-12-0105.1
Singh, M., Sood, S., & Collins, C. M. (2022). Fire dynamics of the Bolivian Amazon. Land, 11(9), 1436. https://doi.org/10.3390/land11091436
von Storch, H., & Zwiers, F. W. (1999). Statistical analysis in climate research. Cambridge University Press. http://hvonstorch.de/klima/books/block/sz.pdf
Wilks, D. S. (2019). Statistical methods in the atmospheric sciences (3rd ed.). Academic Press.
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