The environmental pollution, property losses and casualties caused by wildfires in California are getting worse by the year. To minimize the interference of wildfires on economic and social development, and formulate targeted mitigation strategies, it is imperative to understand the scale and extent of previous wildfire occurrences. In this study, we first investigated the temporal distributions of past wildfires in California divided by size and causes and analyzed the changes observed in the past two decades against the last century. The trend of wildfires in different time scales (yearly and monthly), as well as the distribution of wildfires across different spatial scales (administrative units, climate divisions in California from 2000 to 2019) were also studied. Furthermore, to extract the significant variables on the risk of wildfire occurrence, multivariate analyses of environmental and human-related variables with wildfire densities were carried out. The results show that the wildfire density distribution of the burned area in California conforms to the characteristics of the Pareto distribution. Over the past two decades, the frequency of small (< 500 acres), human-caused wildfires has increased most rapidly, and they are widely distributed in central and western California. The wildfire season has lengthened and the peak months have been advanced from August to July. In terms of the variables related to the risk of wildfire occurrence, the temperature, vapor pressure deficit, grass cover, and the distance to roads are crucial. This study reveals the relationship between environmental and social background conditions and the spatial-temporal distribution of wildfires, which can provide a reference for wildfire management, the formulation of future targeted wildfire emergency plans, and the planning of future land use in California.

Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the “firehose” of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.

A logarithmic scaling for the streamwise turbulent intensity ${\sigma }_u^2/{u_{*}}^2=B_1-A_1\ln \left(z/\delta \right)$ was reported across several high Reynolds number laboratory experiments as predicted from Townsend's attached eddy hypothesis, where u_* is the friction velocity and z is the height normalized by the boundary layer thickness δ. A phenomenological explanation for the origin of this log-law in the intermediate region is provided here based on a solution to a spectral budget where the production and energy transfer terms are modeled. The solution to this spectral budget predicts A₁ = (18/55)C_o/κ^2/3 and B₁ = (2.5)A₁, where C_o and κ are the Kolmogorov and von Kármán constants. These predictions hold when very large scale motions do not disturb the k⁻¹ scaling existing across all wavenumbers 1/δ < k < 1/z in the streamwise turbulent velocity spectrum E_u(k). Deviations from a k⁻¹ scaling along with their effects on A₁ and B₁ are discussed using published data and field experiments.

Due to the mixed distribution of buildings and vegetation, wildland-urban interface (WUI) areas are characterized by complex fuel distributions and geographical environments. The behavior of wildfires occurring in the WUI often leads to severe hazards and significant damage to man-made structures. Therefore, WUI areas warrant more attention during the wildfire season. Due to the ever-changing dynamic nature of California’s population and housing, the update frequency and resolution of WUI maps that are currently used can no longer meet the needs and challenges of wildfire management and resource allocation for suppression and mitigation efforts. Recent developments in remote sensing technology and data analysis algorithms pose new opportunities for improving WUI mapping methods. WUI areas in California were directly mapped using building footprints extracted from remote sensing data by Microsoft along with the fuel vegetation cover from the LANDFIRE dataset in this study. To accommodate the new type of datasets, we developed a threshold criteria for mapping WUI based on statistical analysis, as opposed to using more ad-hoc criteria as used in previous mapping approaches. This method removes the reliance on census data in WUI mapping, and does not require the calculation of housing density. Moreover, this approach designates the adjacent areas of each building with large and dense parcels of vegetation as WUI, which can not only refine the scope and resolution of the WUI areas to individual buildings, but also avoids zoning issues and uncertainties in housing density calculation. Besides, the new method has the capability of updating the WUI map in real-time according to the operational needs. Therefore, this method is suitable for local governments to map local WUI areas, as well as formulating detailed wildfire emergency plans, evacuation routes, and management measures.