Complex network statistics to the design of fire breaks for the control of fire spreading

A computational approach for identifying efficient fuel breaks partitions for the containment of fire incidents in forests is proposed. The approach is based on the complex networks statistics, namely the centrality measures and cellular automata modeling. The efficiency of various centrality statistics, such as betweenness, closeness, Bonacich and eigenvalue centrality to select fuel breaks partitions vs. the random-based distribution is demonstrated. Two examples of increasing complexity are considered: (a) an artificial forest of randomly distributed density of vegetation, and (b) a patch from the area of Vesuvio, National Park of Campania, Italy. Both cases assume flat terrain and single type of vegetation. Simulation results over an ensemble of lattice realizations and runs show that the proposed approach appears very promising as it produces statistically significant better outcomes when compared to the random distribution approach

A review of wildland fire spread modelling, 1990-present, 1: Physical and quasi-physical models

In recent years, advances in computational power and spatial data analysis (GIS, remote sensing, etc) have led to an increase in attempts to model the spread and behaviour of wildland fires across the landscape. This series of review papers endeavours to critically and comprehensively review all types of surface fire spread models developed since 1990. This paper reviews models of a physical or quasi-physical nature. These models are based on the fundamental chemistry and/or physics of combustion and fire spread. Other papers in the series review models of an empirical or quasi-empirical nature, and mathematical analogues and simulation models. Many models are extensions or refinements of models developed before 1990. Where this is the case, these models are also discussed but much less comprehensively.Comment: 31 pages + 8 pages references + 2 figures + 5 tables. Submitted to International Journal of Wildland Fir

Speeding Up Network Simulations Using Discrete Time

We develop a way of simulating disease spread in networks faster at the cost of some accuracy. Instead of a discrete event simulation (DES) we use a discrete time simulation. This aggregates events into time periods. We prove a bound on the accuracy attained. We also discuss the choice of step size and do an analytical comparison of the computational costs. Our error bound concept comes from the theory of numerical methods for SDEs and the basic proof structure comes from the theory of numerical methods for ODEs

The Fire and Smoke Model Evaluation Experiment—A Plan for Integrated, Large Fire–Atmosphere Field Campaigns

The Fire and Smoke Model Evaluation Experiment (FASMEE) is designed to collect integrated observations from large wildland fires and provide evaluation datasets for new models and operational systems. Wildland fire, smoke dispersion, and atmospheric chemistry models have become more sophisticated, and next-generation operational models will require evaluation datasets that are coordinated and comprehensive for their evaluation and advancement. Integrated measurements are required, including ground-based observations of fuels and fire behavior, estimates of fire-emitted heat and emissions fluxes, and observations of near-source micrometeorology, plume properties, smoke dispersion, and atmospheric chemistry. To address these requirements the FASMEE campaign design includes a study plan to guide the suite of required measurements in forested sites representative of many prescribed burning programs in the southeastern United States and increasingly common high-intensity fires in the western United States. Here we provide an overview of the proposed experiment and recommendations for key measurements. The FASMEE study provides a template for additional large-scale experimental campaigns to advance fire science and operational fire and smoke models

Data Assimilation for Wildland Fires: Ensemble Kalman filters in coupled atmosphere-surface models

Two wildland fire models are described, one based on reaction-diffusion-convection partial differential equations, and one based on semi-empirical fire spread by the level let method. The level set method model is coupled with the Weather Research and Forecasting (WRF) atmospheric model. The regularized and the morphing ensemble Kalman filter are used for data assimilation.Comment: Minor revision, except description of the model expanded. 29 pages, 9 figures, 53 reference

The Brazilian Developments on the Regional Atmospheric Modeling System (BRAMS 5.2): An Integrated Environmental Model Tuned for Tropical Areas

We present a new version of the Brazilian developments on the Regional Atmospheric Modeling System where different previous versions for weather, chemistry and carbon cycle were unified in a single integrated software system. The new version also has a new set of state-of-the-art physical parameterizations and greater computational parallel and memory usage efficiency. Together with the description of the main features are examples of the quality of the transport scheme for scalars, radiative fluxes on surface and model simulation of rainfall systems over South America in different spatial resolutions using a scale-aware convective parameterization. Besides, the simulation of the diurnal cycle of the convection and carbon dioxide concentration over the Amazon Basin, as well as carbon dioxide fluxes from biogenic processes over a large portion of South America are shown. Atmospheric chemistry examples present model performance in simulating near-surface carbon monoxide and ozone in Amazon Basin and Rio de Janeiro megacity. For tracer transport and dispersion, it is demonstrated the model capabilities to simulate the volcanic ash 3-d redistribution associated with the eruption of a Chilean volcano. Then, the gain of computational efficiency is described with some details. BRAMS has been applied for research and operational forecasting mainly in South America. Model results from the operational weather forecast of BRAMS on 5 km grid spacing in the Center for Weather Forecasting and Climate Studies, INPE/Brazil, since 2013 are used to quantify the model skill of near surface variables and rainfall. The scores show the reliability of BRAMS for the tropical and subtropical areas of South America. Requirements for keeping this modeling system competitive regarding on its functionalities and skills are discussed. At last, we highlight the relevant contribution of this work on the building up of a South American community of model developers

An interpretable wildfire spreading model for real-time predictions

Forest fires pose a natural threat with devastating social, environmental, and economic implications. The rapid and highly uncertain rate of spread of wildfires necessitates a trustworthy digital tool capable of providing real-time estimates of fire evolution and human interventions, while receiving continuous input from remote sensing. The current work aims at developing an interpretable, physics-based model that will serve as the core of such a tool. This model is constructed using easily understandable equations, incorporating a limited set of parameters that capture essential quantities and heat transport mechanisms. The simplicity of the model allows for effective utilization of data from sensory input, enabling optimal estimation of these parameters. In particular, simplified versions of combustion kinetics and mass/energy balances lead to a computationally inexpensive system of differential equations that provide the spatio-temporal evolution of temperature and flammables over a two-dimensional region. The model is validated by comparing its predictions and the effect of parameters such as flammable bulk density, moisture content, and wind speed, with benchmark results. Additionally, the model successfully captures the evolution of the firefront shape and its rate of spread in multiple directions

Assessing the Impact of Parallel Burnout Fires on Flank Rate of Spread

The effects of flank-parallel suppression fires on the local rate of spread (ROS) of freely burning headfires through fully cured homogeneous grass fuels are assessed. Data sets include: one thermal image stack of a prescribed burn recorded by drone, and a suite of simulation experiments carried out in Wildland Urban Interface Fire Dynamics Simulator (WFDS). A new approach to computing ROS, curvature proxy driven normals to convex polylines, was developed to carry out this analysis. ROS time series depicting flank acceleration of the prescribed burn and simulation experiments, observable under coarse and fine directional classification schemes respectively, are the primary results. Pixelwise ROS magnitude and direction sensitivites to combined temperature threshold and curvature proxy localization parameter selection are also included