Wildfires and the role of their drivers are changing over time in a large rural area of west-central Spain

Abstract

During the last decades, wildfires have been changing in many areas across the world, due to changes in climate, landscapes and socioeconomic drivers. However, how the role of these drivers changed over time has been little explored. Here, we assessed, in a spatially and temporally explicit way, the changing role of biophysical and human-related factors on wildfires in a rural area in west-central Spain from 1979 to 2008. Longitudinal Negative Binomial (NB) and Zero-Inflated Negative Binomial (ZINB) mixed models, with time as interacting factor (continuous and categorical), were used to model the number of fires of increasing size (≥1-10 ha, >10-100 ha, >100 ha) per 10 × 10 km cell per year, based on fire statistics. We found that the landscape was rather dynamic, and generally became more hazardous over time. Small fires increased and spread over the landscape with time, with medium and large fires being stable or decreasing. NB models were best for modelling small fires, while ZINB for medium and large; models including time as a categorical factor performed the best. Best models were associated to topography, land-use/land cover (LULC) types and the changes they underwent, as well as agrarian characteristics. Climate variables, forest interfaces, and other socioeconomic variables played a minor role. Wildfires were initially more frequent in rugged topography, conifer forests, shrublands and cells undergoing changes in LULC types of hazardous nature, for all fire sizes. As time went by, wildfires lost the links with the initial fire-prone areas, and as they spread, became more associated to lower elevation areas, with higher solar radiation, herbaceous crops, and large size farms. Thus, the role of the fire drivers changed over time; some decreased their explaining power, while others increased. These changes with time in the total number of fires, in their spatial pattern and in the controlling drivers limit the ability to predict future fires.

Figure 1

Location of the study area in west-central Spain (A); map of elevation range (B); map of mean annual rainfall (C); map of soil types (D).

Figure 2

Flow of the strategy to model wildfires per 10 × 10 km cell per year of increasing size (≥1–10 ha,> 10–100 ha, >100 ha), by using longitudinal Negative Binomial (NB) and Zero-Inflated Negative Binomial (ZINB) mixed models in west-central Spain from 1979 to 2008.

Figure 3

Accumulated number of fires aggregated by time (1979–2008) (A); temporal trends of the number of fires aggregated spatially (all cells) applying a Mann- Kendall test (B); temporal trends of the number of fires by cell (C). Upper panels correspond to small fires (≥1–10 ha), middle panels to medium fires (>10–100 ha), and bottom panels to large fires (> 100 ha). In grey, the cells with no significant temporal trend.

Figure 4

Spatial pattern of the number of fires per decades (1979–1989, 1990–2000 and 2001–2008) and temporal dynamic of logit intercepts [Ln (Intercept * Time categorical terms)] for the null “Annual change” longitudinal Negative Binomial (NB) and Zero-Inflated Negative Binomial (ZINB) mixed models. Upper panels correspond to small fires (≥1–10 ha) (A), middle panels to medium fires (>10–100 ha) (B), and bottom panels to large fires (>100 ha) (C).

Figure 5

Number of co-variates with the highest DIC (Deviance Information Criterion) reduction in: longitudinal Negative Binomial (NB) and Zero-Inflated Negative Binomial (ZINB) mixed models (A) and in models in which time was not considered (“Average”) or considered as a continuous (“Average change”) or categorical (“Annual change”) factor (independently of whether they were NB or ZINB mixed models) (B).

Figure 6

Linear regression between observed versus predicted number of fires averaged for all significant co-variates and univariate longitudinal NB and ZINB mixed models (A). Observed and predicted number of fires aggregated over time (1979–2008) for the same models (B,C). Spatial accuracy of predicted number of fires (difference between observed and predicted fires) (D). Small fires (≥1–10 ha) in upper panels, medium fires (>10–100 ha) in middle panels, and large fires (> 100 ha) in bottom panels.