Large fires account for a disproportionally high percentage of area burned with potentially severe environmental and socioeconomic impacts. This study characterizes extremely large fires (ELF, 2500–24,843 ha) in Portugal (1998–2013) and the concomitant fuel and weather conditions, analyzing the response of ELF size to their variation. ELF burned less shrubland-grassland (33% of the total ELF area) than forest (59% of total), the latter primarily composed by pine and pine-eucalypt. High fuel hazard was the norm, as indicated by median values of 0.98 for fuel load as a fraction of potential (maximum) load, and time since fire >14 years over 91% of the burned area. ELF occurred under anticyclonic circulation patterns, especially ridging, and 78% of them coincided with extreme fire danger days (corresponding to infrequent conditions) in conjunction with unstable atmosphere. Containment time, fire growth rate and energy release metrics varied by one more order of magnitude than ELF size, hence indicating that size alone is insufficient to describe extreme fires. Distinct combinations between ambient weather conditions, atmospheric instability and drought defined three categories of ELF as defined by size. Quantile regression indicated that increasingly larger fires showed gradually stronger responses to fire weather severity, highlighting the difficulty in restraining fire spread in flammable landscapes in the absence of extensive fuel treatments. Data limitations inherent to the methods used are discussed, and improvements to further advance the understanding of extreme fires are suggested.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2016JG003389

Characteristics and controls of extremely large wildfires in the western Mediterranean Basin

Prescribed fires can lessen wildfire severity and control invasive species, but they can also be risky and costly. Unmanned aerial systems can reduce those drawbacks by, for example, dropping ignition spheres to ignite the most hazardous areas. Existing systems, however, lack awareness of the fire vectors to operate autonomously, safely, and efficiently. In this work we address that limitation, introducing an approach that integrates a lightweight fire simulator and a planner for trajectories and ignition sphere drop waypoints. Both components are unique in that they are amenable to input from the system's sensors and the fire crew to increase fire awareness. We conducted a preliminary study that confirms that such inputs improve the accuracy of the fire simulation to counter the unpredictability of the target environment. The field study of the system showed that the fire-aware planner generated safe trajectories with effective ignitions leveraging the fire simulator predictions.

Fire-Aware Planning of Aerial Trajectories and Ignitions

We present a shape-oriented data assimilation strategy suitable for front-tracking problems through the example of wildfire. The concept of " front " is used to model, at regional scales, the burning area delimitation that moves, undergoes shape and topological changes under heterogeneous orography, biomass fuel and micrometeorology. The simulation-observation discrepancies are represented using a front shape similarity measure deriving from image processing and based on the Chan-Vese contour fitting functional. We show that consistent corrections of the front location and uncertain physical parameters can be obtained using this measure applied on a level-set fire growth model solving for an eikonal equation. This study involves a Luenberger observer for state estimation, including a topological gradient term to track multiple fronts, and of a reduced-order Kalman filter for joint parameter estimation. We also highlight the need – prior to parameter estimation – for sensitivity analysis based on the same discrepancy measure, and for instance using polynomial chaos metamodels, to ensure a meaningful inverse solution is achieved. The performance of the shape-oriented data assimilation strategy is assessed on a synthetic configuration subject to uncertainties in front initial position, near-surface wind magnitude and direction. The use of a robust front shape similarity measure paves the way toward the direct assimilation of infrared images and is a valuable asset in the perspective of data-driven wildfire modeling.

/pdf/front-shape-similarity-measure-for-shape-oriented-n7flgb06er.pdf

Front shape similarity measure for shape-oriented sensitivity analysis and data assimilation for Eikonal equation

This paper presents the development of an interface for small Unmanned Aerial Systems to allow the deployment of ignition spheres at a prescribed fire, real-time fire modeling, and user updates to the automated fire model. Current systems are limited to fire monitoring or modeling, generally rely on a desktop computer, and do not allow updates to the model nor parameter adjustments in the field. The novelty in the current approach is in enabling user control of all aspects of flight, including take-off, waypoint navigation, payload delivery, and landing from the interface while also allowing fire modeling and incorporating this information into the flight planning to increase safety and effectiveness of the vehicle. This system will allow fire experts to reach previously inaccessible terrain to ignite controlled burns, model fire progression through novel terrain and vegetation to improve current models, and allow team members to maintain higher levels of situation awareness through the ability to project fire spread at future times. Initial user testing at a 40 acre prescribed burn shows that the model is considerably more accurate with user corrections, and that even half the user corrections dramatically reduced the distance between the projected and actual fire lines. Future tests are planned with more users in challenging terrain to provide new information to the fire management communities.

UAS-Rx interface for mission planning, fire tracking, fire ignition, and real-time updating

Communities like Santa Barbara, California appear to have it all – beaches, mountains, sunshine, moderate temperatures, small urban population, and close proximity to the large metropolis of Los Angeles. What is not to love? Climate change, drought, flammable vegetation, and naturally prevailing weather conditions make a significant portion of the population vulnerable in many ways. Earthquakes and tsunamis might come to mind, but perhaps more of a threat is fire and/or flooding at, on or near the wildland-urban interface. The recent Thomas fire in December of 2017 and subsequent flooding, debris flow and mudslides in Montecito that followed in January of 2018 highlight what coastal vulnerability means under the new normal of extreme wildfire and flooding danger for this region. This paper discusses the unique hazards along with local weather conditions that contribute to vulnerability. We then detail spatial analytics to assess, model and predict risks. Insights are offered for the Santa Barbara region associated with extreme weather vulnerabilities.

Coastal Vulnerability under Extreme Weather

The National Science Foundation via Award #1331615 as part of the Interdisciplinary Research in Hazards and Disasters (Hazards SEES) program.

Towards Data-Driven Operational Wildfire Spread Modeling: A Report of the NSF-Funded WIFIRE Workshop

Haines Index (HI) was developed by USDA Forest Service to measure the atmosphere's contribution to the growth potential of a wildfire. The Haines Index combines two atmospheric factors that are known to have an effect on wildfires: Stability and Dryness. As operational tools, HI proved its ability to predict plume dominated high intensity wildfires. However, since HI does not take into account the fuel continuity, composition and moisture conditions and the effects of wind and topography on fire behaviour, its use as forecasting tool should be carefully considered. In this work we propose the use of HI, predicted from HR Limited Area Model forecasts, coupled with a Fire Weather model (i.e., RISICO system) fully operational in Italy since 2003. RISICO is based on dynamic models able to represent in space and in time the effects that environment and vegetal physiology have on fuels and, in turn, on the potential behaviour of wildfires. The system automatically acquires from remote databases a thorough dataset of input information both of in situ and spatial nature. Meteorological observations, radar data, Limited Area Model weather forecasts, EO data, and fuel data are managed by a Unified Interface able to process a wide set of different data. Specific semi-physical models are used in the system to simulate the dynamics of the fuels (load and moisture contents of dead and live fuel) and the potential fire behaviour (rate of spread and linear intensity). A preliminary validation of this approach has been conducted with reference to Sardinia and Corsica, two major islands of the Mediterranean See frequently affected by extreme plume dominated wildfires. A time series of large wildfires burnt in Sardinia and Corsica in 2007, 2008 and 2009 have been used to evaluate the capability of HI coupled with the outputs of the FireWeather model to forecast the actual risk in time and in space.

Using Haines Index coupled with fire weather model predicted from high resolution LAM forecasts to asses wildfire extreme behaviour in Southern Europe.

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Towards Data-Driven Operational Wildfire Spread Modeling: A Report of the NSF-Funded WIFIRE Workshop

Using Haines Index coupled with fire weather model predicted from high resolution LAM forecasts to asses wildfire extreme behaviour in Southern Europe.