Mesoscale-to-microscale coupling is an important tool to conduct turbulence-resolving multiscale simulations of realistic atmospheric flows, which are crucial for applications ranging from wind energy to wildfire spread studies. Different techniques are used to facilitate the development of realistic turbulence in the large-eddy simulation (LES) domain while minimizing computational cost. Here, we explore the impact of a simple and computationally efficient Stochastic Cell Perturbation method using momentum perturbation (SCPM-M) to accelerate turbulence generation in boundary-coupled LES simulations using the Weather Research and Forecasting (WRF) model. We simulate a convective boundary layer (CBL) to characterize the production and dissipation of turbulent kinetic energy (TKE) and the variation of TKE budget terms. Furthermore, we evaluate the impact of applying momentum perturbations of three magnitudes below, up to, and above the CBL on the TKE budget terms. Momentum perturbations greatly reduce the fetch associated with turbulence generation. When applied to half the vertical extent of the boundary layer, momentum 1 perturbations produce an adequate amount of turbulence. However, when applied above the CBL, additional structures are generated at the top of the CBL, near the inversion layer. The magnitudes of the TKE budgets produced by SCPM-M when applied at varying heights and with different perturbation amplitudes are always higher near the surface and inversion layer than those produced by No-SCPM, as are their contributions to the TKE. This study provides a better understanding of how SCPM-M reduces computational costs and how different budget terms contribute to TKE in a boundary-coupled LES simulation.

Due to the mixed distribution of buildings and vegetation, the wildland-urban interface (WUI) areas are characterized by complex fuel distributions and geographical environments. The behaviors of wildfires occurring in WUIs are significantly different from those occurring only in wildland vegetation or building fires, often leading to more severe hazards. Therefore, WUI areas warrant more attention during the wildfire season. Currently, most of the widely used WUI maps were calculated and drawn based on the housing data in the decennial Census and the vegetation data from the National Land Cover Database (NLCD) updated every three to five years. In the context of the current increase in California’s population and housing, this update frequency and map resolution can no longer meet the firefighting frontline’s needs. The developments of remote sensing technology and data analysis algorithms have brought opportunities for improvement in WUI mapping. In this study, WUI was directly mapped with the building footprints in California extracted from satellite data by Microsoft along with the fuel vegetation cover from the LANDFIRE dataset based on maximum spot fire distance. This method did not require the calculation of housing density but designated the adjacent area of each building with large and dense areas of vegetation as WUI, which avoided the modifiable areal unit problem (MAUP). This method can not only refine the scope of the WUI area to each building, but also have the capability of updating the WUI map in real-time according to the update frequency of satellite data and operational needs. Therefore, this method is suitable for local governments to map exhaustive local WUI areas, formulating detailed wildfire emergency plans, evacuation routes, and management measures.

The severity and frequency of wildfires have risen dramatically in recent years, drawing attention to the term ‘wildland-urban interface’ (WUI), the region where man-made constructions meet flammable vegetation. Herein, we mapped a finer-scale, novel linear WUI for California (CA) based on the intersection of boundaries of wildland vegetation and building footprint. The direct intersection is referred to as a direct WUI, whereas the intersection at 100-m is known as an indirect WUI. More fires were ignited closer to direct WUI than indirect WUI due to their proximity to communities. However, the overlap of past fire perimeters with indirect WUI is greater than that with direct WUI which shows that more areas were burned in the indirect WUI due to embers transported by strong wind gusts during large wildfires. The study’s findings will help land managers and policymakers in controlling fire dangers, land-use planning, and reducing threats to fire-prone communities.

Wildland fires are becoming more destructive and costly in the United States, posing increased environmental, social, and economic threats to fire-prone regions. Quantifying current wildfire risk by considering a wide range of multi-scale, and multi-disciplinary variables such as socio-economic and biophysical indicators for resiliency and mitigation measures, deems inherently challenging. To systematically examine wildfire threats amongst humans and their physical and social environment on multiple scales, a livelihood vulnerability index (LVI) analysis can be employed. Therefore, we produce a framework needed to compute the LVI for the top 14 American States that are most exposed to wildfires, based on the 2019 Wildfire Risk report of the acreage size burnt in 2018 and 2019: Arizona, California, Florida, Idaho, Montana, Nevada, New Mexico, Oklahoma, Oregon, Utah, Washington, and Wyoming. The LVI is computed for each State by first considering the State’s exposure, sensitivity, and adaptive capacity to wildfire events (known as the three contributing factors). These contributing factors are determined by a set of indicator variables (vulnerability metrics) that are categorized into corresponding major component groups. The framework structure is then justified by performing a principal component analysis (PCA) to ensure that each selected indicator variable corresponds to the correct contributing factor. The LVI for each State is then calculated based on a set of algorithms relating to our framework. LVI values rank between 0 (low LVI) to 1 (high LVI). Our results indicate that Arizona and New Mexico experience the greatest livelihood vulnerability, with an LVI of 0.57 and 0.55, respectively. In contrast, California, Florida, and Texas experience the least livelihood vulnerability to wildfires (0.44, 0.35, 0.33 respectively). LVI is strongly weighted on its contributing factors and is exemplified by the fact that even though California has one of the highest exposures and sensitivity to wildfires, it has very high adaptive capacity measures in place to withstand its livelihood vulnerability. Thus, States with relatively high wildfire exposure can exhibit relatively lower livelihood vulnerability because of adaptive capacity measures in place. On the other hand, States can exhibit a high LVI (such as Arizona) despite having a low exposure, due to lower adaptive capacities in place. The results from this study are critical to wildfire managers, government, policymakers, and research scientists for identifying and providing better resiliency and adaptation measures to support the American States that are most vulnerable to wildfires.

Past studies reported a drastic growth in the wildland-urban interfaces (WUI), the locations where man-made structures meet or overlap wildland vegetation. There is a perception that damages due to wildfires are mainly located at the WUI. However, there is no clear evidence that wildfire intensity and frequency are highest in these regions. In this work, we have reported the actual occurrences of wildfires with respect to WUI and how much of the WUI are on complex topography in California (CA), the state with the highest burned area and risk of wildfires. We calculated the overlap of the burned area from previous wildfire events in California in the last ten years with the WUI perimeters. Two currently existing WUI definitions are used for this purpose. Furthermore, we also calculated the number of fire ignition points that lie within the WUI perimeters. We found that a very small percentage of wildfire ignitions actually occurred in the WUI areas. Moreover, the overlap between the wildfire burned area and WUI areas was also found to be small. To find out if the wildfires burned in the vicinity of WUI areas, we created buffers around both the WUI areas and the wildfire perimeters separately and computed the impact of buffer distance on the overlap. This behavior has been connected to the importance of firebrand ignition from spot fires in the WUI. Moreover, a majority of WUI areas in CA was found to be situated on complex topography. Therefore, we conclude that in CA, wildfires are not limited to WUI regions only, but their main fire fronts burn farther away from the WUI and are mostly located on complex topography, where controlling large wildfires is more difficult and fire behavior is more complex. Results from this study will give direction for remapping the existing WUI definitions, will be helpful for wildfire management and will benefit policymakers and land managers at the state and local level to focus on the factors that determine the high-risk prone areas for future wildfires.