Predicting fire spread and behaviour on the fireline. Wildfire analyst pocket: A mobile app for wildland fire prediction

A novel proxy for energy flux in multi-era wildfire reconstruction

Escalations in wildfire activity are of significant global concern, particularly within vulnerable wetland ecosystems integral to natural carbon sequestration and climate change mitigation. Our understanding and management of future wildfire activity may be better contextualised through the study of historic and ancient fire records, independent of human influence. Methods of study include 'geothermometry' - approximating ancient fire intensity from temperature-dependent changes in the chemistry of fossil charcoal. Though well established in their relation to experimental charcoalification, these methods still fail to quantify the true intensity of ancient fires, as a measure of energy release. As a result, their applicability, and contributions to the characterisation of modern fire activity, remain uncertain. Here, we present a novel measure of wildfire energy release, as a proxy for true intensity, through the co-application of cone calorimetry and Raman spectroscopy of charcoal. By applying a range of wildfire heat fluxes to variable peatland fuel mixes, this research demonstrates the complexity in correlating fire behaviour and charcoal microstructure. Further statistical analyses suggest a correlation between spectroscopic results, measures of CO and CO2 release, and fire severity. This offers a principal measure of ancient wildfire intensity, consistent with modern practices in wildfire modelling, monitoring, and management.

Smoke emissions from biomass burning in Central Mexico and their impact on air quality in Mexico City: May 2019 case study

Smoke emissions from biomass burning considerably influence regional and local air quality. Many natural wildfires and agricultural burns occur annually in Central Mexico during the hot, dry season (March to May), potentially leading to air quality problems. Nevertheless, the impact of these biomass burning emissions on Mexico City's air quality has not been investigated in depth. This study examines a severely deteriorated air quality case from 11 to 16 May 2019, during which fine particle concentrations (PM2.5) exceeded the 99th percentile of the available official dataset (2005-2019). Specifically, this work aims to highlight the role of fires and regional pollution in the severe episode observed in Mexico City, identifying the fires that were the sources of regional pollution, the type of fuel burned in those fires, and the dominant atmospheric transport pattern. Biomass burning emissions were calculated for different land cover types using satellite data from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate-Resolution Imaging Spectroradiometer (MODIS). PM2.5 increased by a factor of 2 at some monitoring sites, and ozone concentration increased to 40 % in Mexico City during the poor air quality episode. Our results indicate that over 50 % of the fire activity observed during the 2019 fire season was concentrated in May in Central Mexico. The burning activity was mainly seen over shrubland and forest between 10 and 15 May. Moreover, the fire radiative power analysis indicates that most energy was associated with burning shrubland and forests. Organic carbon emissions were estimated highest on 14 and 15 May, coinciding with the largest number of fires. Back trajectory analysis indicates that enhanced concentration of air pollutants in Mexico City originated from biomass burning detected in neighboring states: Guerrero, Michoacán, and the State of Mexico. Smoke from fires on the specific vegetation cover was advected into Mexico City and contributed to the bad air quality episode. Further meteorological analysis evidenced that the fire intensity and emissions were worsened by low humidity and the late onset of the rainy season in Central Mexico.

Effect of fire spread, flame characteristic, fire intensity on particulate matter 2.5 released from surface fuel combustion of Pinus koraiensis plantation- A laboratory simulation study

PM2.5 is one of major pollutants emitted from forest fires. High PM2.5 concentration not only affects short-term human respiration health, but also poses a long-term threat to human cardiopulmonary functionality. Therefore, it is of great importance to quantitatively assess the PM2.5 released by forest combustion in forest fire studies. In this study we examine relationships between the PM2.5 concentration and environment and fuel characteristics laboratory experiments. In the experiments, fuel beds with controlled moisture contents and loads were first built; then 144 ignition experiments were conducted for various combinations of wind speeds using a wind tunnel device. Fire behavior characteristics and PM2.5 concentrations released from fuel combustion were measured and analyzed. The experimental results show that the relationship between fire characteristics, fire intensity and the influencing factors of wind speed, fuel moisture content, and fuel load can be explained by the fundamental theory of forest combustion. Although PM2.5 concentration rises with the increase of wind speed, the decrease of fuel moisture content, and the increase of fuel load, there appears to be a fuel load threshold for a given combination of wind speed and fuel moisture content that the increase of PM2.5 concentration decelerates quickly after the load passes the threshold value. After screening fire behavior characteristics that affect PM2.5 concentration, we found that fire line intensity and flame width are the ones with the strongest association with the concentration. With flame width as independent variable, we have built two regression models to predict PM2.5 and fire line intensity which are treated as dependent variable; the models have high accuracy with R2 = 0.92 for predicting PM2.5 and R2 = 0.97 for predicting fire line intensity. Study results can be used as reference to protect the health of forest fire fighters, and can be helpful for forest fire smoke management.

The December 2021 Marshall Fire: Predictability and Gust Forecasts from Operational Models

We analyzed meteorological conditions that occurred during the December 2021 Boulder, Colorado, downslope windstorm. This event is of particular interest due to the ignition and spread of the Marshall Fire, which quickly became the most destructive wildfire in Colorado history. Observations indicated a rapid onset of fast winds with gusts as high as 51 m/s that generally remained confined to the east-facing slopes and foothills of the Rockies, similar to previous Boulder windstorms. After about 12 h, the windstorm shifted into a second, less intense phase. Midtropospheric winds above northwestern Colorado weakened prior to the onset of strong surface winds and the event strength started waning as stronger winds moved back into the area. Forecasts from NOAA high-resolution operational models initialized more than a few hours prior to windstorm onset did not simulate the start time, development rate and/or maximum strength of the windstorm correctly, and day-ahead runs even failed to develop strong downslope windstorms at all. Idealized modeling confirmed that predictability was limited by errors on the synoptic scale affecting the midtropospheric wind conditions representing the Boulder windstorm’s inflow environment. Gust forecasts for this event were critically evaluated.

Strategic Wildfire Response Decision Support and the Risk Management Assistance Program

In 2016, the USDA Forest Service, the largest wildfire management organization in the world, initiated the risk management assistance (RMA) program to improve the quality of strategic decision-making on its largest and most complex wildfire events. RMA was designed to facilitate a more formal risk management process, including the use of the best available science and emerging research tools, evaluation of alternative strategies, consideration of the likelihood of achieving objectives, and analysis of tradeoffs across a diverse range of incident objectives. RMA engaged personnel from a range of disciplines within the wildfire management system to co-produce actionable science that met the needs of the highly complex incident decision-making environment while aiming to align with best practices in risk assessment, structured decision-making, and technology transfer. Over the four years that RMA has been in practice, the content, structure, and method of information delivery have evolved. Furthermore, the RMA program’s application domain has expanded from merely large incident support to incorporate pre-event assessment and training, post-fire review, organizational change, and system improvement. In this article, we describe the history of the RMA program to date, provide some details and references to the tools delivered, and provide several illustrative examples of RMA in action. We conclude with a discussion of past and ongoing program adaptations and of how this can inform ongoing change efforts and offer thoughts on future directions.

Coupled effects of climate teleconnections on drought, Santa Ana winds and wildfires in southern California

Projections of future climate change impacts suggest an increase of wildfire activity in Mediterranean ecosystems, such as southern California. This region is a wildfire hotspot and fire managers are under increasingly high pressures to minimize socio-economic impacts. In this context, predictions of high-risk fire seasons are essential to achieve adequate preventive planning. Regional-scale weather patterns and climatic teleconnections play a key role in modulating fire-conducive conditions across the globe, yet an analysis of the coupled effects of these systems onto the spread of large wildfires is lacking for the region. We analyzed seven decades (1953-2018) of documentary wildfire records from southern California to assess the linkages between weather patterns and large-scale climate modes using various statistical techniques, including Redundancy Analysis, Superposed Epoch Analysis and Wavelet Coherence. We found that high area burned is significantly associated with the occurrence of adverse weather patterns, such as severe droughts and Santa Ana winds. Further, we document how these fire-promoting events are mediated by climate teleconnections, particularly by the coupled effects of El Niño Southern Oscillation and Atlantic Multidecadal Oscillation.

Prototyping a Geospatial Atlas for Wildfire Planning and Management

Wildland fire managers are increasingly embracing risk management principles by being more anticipatory, proactive, and “engaging the fire before it starts”. This entails investing in pre-season, cross-boundary, strategic fire response planning with partners and stakeholders to build a shared understanding of wildfire risks and management opportunities. A key innovation in planning is the development of potential operational delineations (PODs), i.e., spatial management units whose boundaries are relevant to fire containment operations (e.g., roads, ridgetops, and fuel transitions), and within which potential fire consequences, suppression opportunities/challenges, and strategic response objectives can be analyzed to inform fire management decision making. As of the summer of 2020, PODs have been developed on more than forty landscapes encompassing National Forest System lands across the western USA, providing utility for planning, communication, mitigation prioritization, and incident response strategy development. Here, we review development of a decision support tool—a POD Atlas—intended to facilitate cross-boundary, collaborative strategic wildfire planning and management by providing high-resolution information on landscape conditions, values at risk, and fire management resource needs for individual PODs. With the atlas, users can rapidly access and assimilate multiple forms of pre-loaded data and analytics in a customizable manner. We prototyped and operationalized this tool in concert with, and for use by, fire managers on several National Forests in the Southern Rocky Mountains of the USA. We present examples, discuss real-world use cases, and highlight opportunities for continued decision support improvement.

Stochastic decision trigger modelling to assess the probability of wildland fire impact

The ability to estimate both the time and probability of a wildfire reaching an area to be protected is critically important to preventing loss of human life and property, and damage to ecological and economic assets. Wildfire decision trigger modelling has been used to assess fire exposure and create evacuation trigger buffers around the communities providing a specific amount of warning time. This approach has been applied in multiple scenarios including household-level and community-level evacuation planning and during suppression operations. However, little attention has been paid to input data uncertainty using this modelling approach. This study presents an innovative stochastic fire simulation decision trigger modelling method that produces a probability map of the fire arrival to areas to be protected by simulating (n) wildfire decision trigger buffers with varied input data according to a potential range of deviations. The Tubbs fire (USA) was used as case study to show the applicability of this approach to estimate the probability of wildland fire impact. Our results highlighted the importance of considering input data uncertainty in operational environments to estimate fire progression and decision trigger buffers to better develop suppression tactic and strategy. The method presented may be solved in real-time and used with any empirical fire propagation model as a core engine. Practical real-time implications of this fire simulation mode are discussed.

Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics

Wildfires are one of the most common natural hazards worldwide. Here, we compared the capability of bivariate and multivariate models for the prediction of spatially explicit wildfire probability across a fire-prone landscape in the Zagros ecoregion, Iran. Dempster–Shafer-based evidential belief function (EBF) and the multivariate logistic regression (LR) were applied to a spatial dataset that represents 132 fire events from the period of 2007–2014 and twelve explanatory variables (altitude, aspect, slope degree, topographic wetness index (TWI), annual temperature, and rainfall, wind effect, land use, normalized difference vegetation index (NDVI), and distance to roads, rivers, and residential areas). While the EBF model successfully characterized each variable class by four probability mass functions in terms of wildfire probabilities, the LR model identified the variables that have a major impact on the probability of fire occurrence. Two distribution maps of wildfire probability were developed based upon the results of each model. In an ensemble modeling perspective, we combined the two probability maps. The results were verified and compared by the receiver operating characteristic (ROC) and the Wilcoxon Signed-Rank Test. The results showed that although an improved predictive accuracy (AUC = 0.864) can be achieved via an ensemble modeling of bivariate and multivariate statistics, the models fail to individually provide a satisfactory prediction of wildfire probability (EBFAUC = 0.701; LRAUC = 0.728). From these results, we recommend the employment of ensemble modeling approaches for different wildfire-prone landscapes.