[1] Fire plays a crucial role in many ecosystems, and a better understanding of different controls on fire activity is needed. Here we analyze spatial variation in fire danger during episodic wind events in coastal southern California, a densely populated Mediterranean-climate region. By reconstructing almost a decade of fire weather patterns through detailed simulations of Santa Ana winds, we produced the first high-resolution map of where these hot, dry winds are consistently most severe and which areas are relatively sheltered. We also analyzed over half a century of mapped fire history in chaparral ecosystems of the region, finding that our models successfully predict where the largest wildfires are most likely to occur. There is a surprising lack of information about extreme wind patterns worldwide, and more quantitative analyses of their spatial variation will be important for effective fire management and sustainable long-term urban development on fire-prone landscapes.

Statistical characterization of past fire regimes is important for both the ecology and management of fire-prone ecosystems. Survival analysis-or fire frequency analysis as it is often called in the fire literature-has increasingly been used over the last few decades to examine fire interval distributions. These distributions can be generated from a variety of sources (e.g., tree rings and stand age patterns), and analysis typically involves fitting the Weibull model. Given the widespread use of fire frequency analysis and the increasing availability of mapped fire history data, our goal has been to review and to examine some of the issues faced in applying these methods in a spatially explicit context. In particular, through a case study on the massive Cedar Fire in 2003 in southern California, we examine sensitivities of parameter estimates to the spatial resolution of sampling, point-and area-based methods for assigning sample values, current age surfaces versus historical intervals in generating distributions, and the inclusion of censored (i.e., incomplete) observations. Weibull parameter estimates were found to be roughly consistent with previous fire frequency analyses for shrublands (i.e., median age at burning of ∼30-50 years and relatively low age dependency). Results indicate, however, that the inclusion or omission of censored observations can have a substantial effect on parameter estimates, far more than other decisions about specifics of sampling.

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Studies of historical fire and vegetation conditions in dry conifer forests have demonstrated a high degree of heterogeneity across landscapes. However, there is a limit to the amount of inference that can be drawn from historical fire reconstructions. Contemporary-reference‖ landscapes may be able to provide information that is not available from historical reconstructions. In this study, we characterized variability in vegetation structure and composition across two Sierra Nevada landscapes with long-established fire restoration programs. We used tree, shrub, and surface fuel data from 117 initial plots, 86 of which were remeasured 8-12 years later, to identify the mechanisms driving variability in vegetation and fuel conditions. Our analyses identified nine distinct vegetation groups, with mean live tree basal area and density ranging from 0.3 to 72.7 m 2 ha-1 and 2.5 to 620 trees ha-1 for individual groups. For all plots combined, mean live tree basal area and density was 28.4 m 2 ha-1 and 215 trees ha-1 , but standard deviations (SD) were 29.1 m 2 ha-1 and 182 trees ha-1 , respectively. These ranges and SDs demonstrate considerable variability in vegetation structure, which was partially related to site productivity and previous fire severity. Fine surface fuel loads were generally low (overall mean, 16.1 Mg ha-1), but also exhibited high variability (SD, 12.6 Mg ha-1). Surprisingly, surface fuel loads based on initial measurement and change between measurements were not related to fire characteristics. The only statistical relationship found was that surface fuel loads were associated with forest structure and composition. These results capture a contemporary ‗natural' range of variability and can be used to guide landscape-level restoration efforts. More specifically, these results can help identify distinct targets for variable forest structures across landscapes.