Anthromes displaying evidence of weekly cycles in active fire data cover 70% of the global land surface.

Across the globe, human activities have been gaining importance relatively to climate and ecology as the main controls on fire regimes and consequently human activity became an important driver of the frequency, extent and intensity of vegetation burning worldwide. Our objective in the present study is to look for weekly cycles in vegetation fire activity at global scale as evidence of human agency, relying on the original MODIS active fire detections at 1 km spatial resolution (MCD14ML) and using novel statistical methodologies to detect significant periodicities in time series data. We tested the hypotheses that global fire activity displays weekly cycles and that the weekday with the fewest fires is Sunday. We also assessed the effect of land use and land cover on weekly fire cycle significance by testing those hypotheses separately for the Villages, Settlements, Croplands, Rangelands, Seminatural, and Wildlands anthromes. Based on a preliminary data analysis of the daily global active fire counts periodogram, we developed an harmonic regression model for the mean function of daily fire activity and assumed a linear model for the de-seasonalized time series. For inference purposes, we used a Bayesian methodology and constructed a simultaneous 95% credible band for the mean function. The hypothesis of a Sunday weekly minimum was directly investigated by computing the probabilities that the mean functions of every weekday (Monday to Saturday) are inside the credible band corresponding to mean Sunday fire activity. Since these probabilities are small, there is statistical evidence of significantly fewer fires on Sunday than on the other days of the week. Cropland, rangeland, and seminatural anthromes, which cover 70% of the global land area and account for 94% of the active fires analysed, display weekly cycles in fire activity. Due to lower land management intensity and less strict control over fire size and duration, weekly cycles in Rangelands and Seminatural anthromes, which jointly account for 53.46% of all fires, although statistically significant are weaker than those detected in Croplands.

In search of spatial extremes.

In environmental studies attention increasingly focuses on identification of spatial extremes: locations with observations that are apparently higher than either a preset background threshold or neighbouring observations. We consider various procedures for identifying values and locations of these extremes: extreme value theory, conditionally simulated fields and disjunctive kriging. In a recent research project we studied the distribution of nine environmental pollutants (heavy metals, polyaromatic hydrocarbons and mineral oil) in a large industrial estate in the southern Netherlands. This area is interesting as it is influenced by three contaminating processes: inundation by the river Meuse, anthropogenic heightening with contaminated materials and local industrial activities. Concentrations are measured. In this paper the peaks over threshold method was useful for finding extremes for spatially uncorrelated variables, whereas conditional simulations applied in particular to areas with spatial dependence.

The Measurement of Averages and Extremes of Environmental Variables.

The relationship between the statistics of environmental measurements averaged over different time scales is related to extreme levels of the variables. Results on the asymptotic joint distributions of extreme averages over different time periods are treated.