Spatio-Temporal Configurations of Human-Caused Fires in Spain through Point Patterns

Abstract

Human-caused wildfires are often regarded as unpredictable, but usually occur in patterns aggregated over space and time. We analysed the spatio-temporal configuration of 7790 anthropogenic wildfires (2007)(2008)(2009)(2010)(2011)(2012)(2013) in nine study areas distributed throughout Peninsular Spain by using the Ripley's K-function. We also related these aggregation patterns to weather, population density, and landscape structure descriptors of each study area. Our results provide statistical evidence for spatio-temporal structures around a maximum of 4 km and six months. These aggregations lose strength when the spatial and temporal distances increase. At short time lags after a wildfire (<1 month), the probability of another fire occurrence is high at any distance in the range of 0-16 km. When considering larger time lags (up to two years), the probability of fire occurrence is high only at short distances (>3 km). These aggregated patterns vary depending on location in Spain. Wildfires seem to aggregate within fewer days (heat waves) in warm and dry Mediterranean regions than in milder Atlantic areas (bimodal fire season). Wildfires aggregate spatially over shorter distances in diverse, fragmented landscapes with many small and complex patches. Urban interfaces seem to spatially concentrate fire occurrence, while wildland-agriculture interfaces correlate with larger aggregates.

Human-caused fire occurrence modelling in perspective: a review

“…Only a few recent studies have been able to analyse the spatial-specific location of each event as a point pattern in a certain location and date (Yang et al 2007;Juan et al 2012;Liu et al 2012;Miranda et al 2012;Fuentes-Santos et al 2013;Serra et al , 2014Costafreda-Aumedes et al 2016b). Spatially explicit point process models have so far been considered as statistical tools to analyse space (-time) structures of wildfires, but not to model such spatial structures.…”

Spatiotemporal clustering of flash floods in a changing climate (China, 1950–2015)

“…This increase in probability can be captured through clustering analyses and various examples already exist in literature where this has been done at different spatial and temporal scales and via different analytical approaches. Notably, this type of application spans in many areas of natural hazards and have become mainstream in case of seismicity (e.g., Fischer and Horálek, 2003;Georgoulas et al, 2013;Varga et al, 2012;Woodward et al, 2018;Yang et al, 2019), joint sets and their orientation in rock outcrops (e.g., Tokhmechi et al, 2011;Zhan et al, 2017), groundwater monitoring (Chambers et al, 2015), wildfires (e.g., Orozco et al, 2012;Costafreda-Aumedes et al, 2016;Fuentes-Santos et al, 2013;Tonini et al, 2017), and landslides (e.g., Lombardo et al, 2018Lombardo et al, , 2019aTonini and Cama, 2019). In the specific case of flooding, Zhao et al (2014) used the projection pursuit theory to cluster spatial data and to build a dynamic risk assessment model for flood disasters.…”

Have Historical Land Use/Land Cover Changes Triggered a Fire Regime Shift in Central Spain?

“…In the Iberian Peninsula, Pausas & Fernández-Muñoz [58] and Silva et al [30] have considered the influence of landscape dynamics in fire regime changes at the national and regional scale, both long term (130-year time series) and medium term . Costafreda-Aumedes et al [59] have also analyzed the relationship between landscape patterns and human-caused fire occurrence in Spain at a national scale between 1989 and 1993. Moreover, several studies have explored the influence of socioeconomic landscape drivers, namely LULC and demography, on fire trends in Sardinia [60,61], Greece [46] and Turkey [62].…”