Abstract. The seasonal and longer-term dynamics of fuel accumulation affect fire seasonality and the occurrence of extreme wildfires. Failure to account for their influence may help to explain why state-of-the-art fire models do not simulate the length and timing of the fire season or interannual variability in burnt area well. We investigated the impact of accounting for different timescales of fuel production and accumulation on burnt area using a suite of random forest regression models that included the immediate impact of climate, vegetation, and human influences in a given month and tested the impact of various combinations of antecedent conditions in four productivity-related vegetation indices and in antecedent moisture conditions. Analyses were conducted for the period from 2010 to 2015 inclusive. Inclusion of antecedent vegetation conditions representing fuel build-up led to an improvement of the global, climatological out-of-sample R2 from 0.579 to 0.701, but the inclusion of antecedent vegetation conditions on timescales ≥ 1 year had no impact on simulated burnt area. Current moisture levels were the dominant influence on fuel drying. Additionally, antecedent moisture levels were important for fuel build-up. The models also enabled the visualisation of interactions between variables, such as the importance of antecedent productivity coupled with instantaneous drying. The length of the period which needs to be considered varies across biomes; fuel-limited regions are sensitive to antecedent conditions that determine fuel build-up over longer time periods (∼ 4 months), while moisture-limited regions are more sensitive to current conditions that regulate fuel drying.

Locomotion characteristics are often recorded within bounded spaces, a constraint which introduces geometry-specific biases and potentially complicates the inference of behavioural features from empirical observations. We describe how statistical properties of an uncorrelated random walk, namely the steady-state stopping location probability density and the empirical step probability density, are affected by enclosure in a bounded space. The random walk here is considered as a null model for an organism moving intermittently in such a space, that is, the points represent stopping locations and the step is the displacement between them. Closed-form expressions are derived for motion in one dimension and simple two-dimensional geometries, in addition to an implicit expression for arbitrary (convex) geometries. For the particular choice of no-go boundary conditions, we demonstrate that the empirical step distribution is related to the intrinsic step distribution, i.e. the one we would observe in unbounded space, via a multiplicative transformation dependent solely on the boundary geometry. This conclusion allows in practice for the compensation of boundary effects and the reconstruction of the intrinsic step distribution from empirical observations.

Abstract. The seasonal and longer-term dynamics of fuel accumulation affect fire seasonality and the occurrence of extreme wildfires. Failure to account for their influence may help to explain why state-of-the-art fire models do not simulate the length and timing of the fire season or interannual variability in burnt area well. We investigated the impact of accounting for different timescales of fuel production and accumulation on burnt area using a suite of random forest regression models that included the immediate impact of climate, vegetation, and human influences in a given month, and tested the impact of various combinations of antecedent conditions in four productivity-related vegetation indices and in antecedent moisture conditions. Analyses were conducted for the period from 2010 to 2015 inclusive. We showed that the inclusion of antecedent vegetation conditions on timescales > 1 yr had no impact on burnt area, but inclusion of antecedent vegetation conditions representing fuel build-up led to an improvement of the global, climatological out-of-sample R2 from 0.567 to 0.686. The inclusion of antecedent moisture conditions also improved the simulation of burnt area through its influence on fuel build-up, which is additional to the influence of current moisture levels on fuel drying. The length of the period which needs to be considered to account for fuel build-up varies across biomes; fuel-limited regions are sensitive to antecedent conditions over longer time periods (~4 months) and moisture-limited regions are more sensitive to current conditions.

<p>Vegetation build up is a major controlling factor for wildfires globally. The exact nature of the dependency of wildfire activity on past vegetation productivity is still under debate, however. Given the potential future rise in conditions conducive to extremely damaging fires in many regions of the world, controlling factors like this need to be investigated urgently to better understand and manage especially extreme wildfire events.<br>To improve our understanding of wildfires and the advice given to policy makers, a comprehensive understanding of all contributing factors is required. Changes to land management can be controversial and thus concrete evidence is required to assess and modify longstanding management practices and regulations if needed.<br>We therefore used global satellite datasets extending from 2005 to 2011 to assess the relationship between burnt area and various biophysical variables. Vegetation proxy data included vegetation optical depth and the fraction of absorbed photosynthetically activate radiation. Different regions and time periods were analysed separately to isolate regional and temporal effects respectively. The relationship between pre-season vegetation productivity and burnt area was modelled as a regionally and temporally varying weighted sum of past monthly productivity proxies.<br>As expected, significant differences in fire regimes were found across biomes, signified for example by significant shifts in the seasonality of burnt area. Understanding these shifts in the seasonality of both burnt area and the accompanying temporal dependence on past vegetation growth is key to reproducing observed wildfire regimes in fire models. As these relationships were found to vary both temporally and regionally, judicious inclusion of biophysical variables in fire models coupled with algorithms able to capture these relationships is necessary. <br>However, remotely sensed observations were of different quality in different areas due to inhomogeneous cloud cover patterns, making assessments for much-affected regions like South America and South East Asia especially difficult. Likewise, the found correlation between decreasing cloud cover and increasing burnt area biased our results. Due also to the short time span of the data available in this investigation, these factors warrant further investigation to more fully quantify the temporal and regional relationships at work.</p>