Wildfire Spread Simulation Research Articles

Facing the Wildfire Spread Risk Challenge: Where Are We Now and Where Are We Going?

Wildfire is a sudden and highly destructive natural disaster that poses significant challenges in terms of response and rescue efforts. Influenced by factors such as climate, combustible materials, and ignition sources, wildfires have been increasingly occurring worldwide on an annual basis. In recent years, researchers have shown growing interest in studying wildfires, leading to a substantial body of related research. These studies encompass various topics, including wildfire prediction and forecasting, the analysis of spatial and temporal patterns, the assessment of ecological impacts, the simulation of wildfire behavior, the identification of influencing factors, the development of risk assessment models, techniques for managing combustible materials, decision-making technologies for firefighting, and fire-retardant methods. Understanding the factors that affect wildfire spread behavior, employing simulation methods, and conducting risk assessments are vital for effective wildfire prevention, disaster mitigation, and emergency response. Consequently, it is imperative to comprehensively review and explore further research in this field. This article primarily focuses on elucidating and discussing wildfire spread behavior as a key aspect. It summarizes the driving factors of wildfire spread behavior and introduces a wildfire spread behavior simulation software and its main applications based on these factors. Furthermore, it presents the research progress in wildfire risk assessment based on wildfire spread behavior factors and simulation, and provides an overview of various methods used for wildfire risk assessment. Finally, the article proposes several prospects for future research on wildfire spread: strengthening the dynamic monitoring of wildfires and utilizing comprehensive data from multiple sources, further exploring the differential effects of key factors on wildfire spread, investigating differences in driving factors, improving wildfire models in China, developing applicable software, and conducting accurate and scientific assessments of wildfire risks to protect ecological resources.

Thermal decomposition of vegetative fuels and the impact of measured variations on simulations of wildfire spread

This manuscript presents new measurement data from milligram-scale thermal decomposition experiments – thermogravimetric analysis (TGA) and microscale combustion calorimetry (MCC) – conducted on stems and leaves (i.e., needles) of six plant species commonly found across the United States. For each fuel, measurement data from TGA experiments was analyzed to determine effective thermal decomposition mechanisms and the associated kinetics of their constituent reactions. Results of this model calibration were compared to standard (i.e., ‘generalized’) approaches to obtain kinetic parameters available in the literature. MCC experiments were repeated under identical experimental conditions to determine the heats of complete combustion of all gaseous volatiles produced by these vegetative fuels and to validate the decomposition mechanisms and species char yields determined from TGA data. Through a coupled analysis of TGA and MCC measurement data, an estimate of the heats of combustion of the gaseous volatiles produced by individual reaction steps in the fuel’s decomposition was also made. Between different fuels, distinct differences were measured in the onset temperature of decomposition, the temperature range of decomposition, the number of apparent reactions, and the peak measured mass loss and heat release rates (as well as the temperatures at which they occur). To analyze the impact of these variations on predictions of wildfire behavior, a modeling study was then conducted in which simulations of wildland fire experiments were repeated using the thermal decomposition mechanisms and heats of combustion determined for each of the fuel species tested in this work. Model-predicted fire spread rate in these simulations varied between 0.34 m s−1 and 0.80 m s−1 demonstrating a notable dependence (up to a factor of 2x) on measured variations in thermal decomposition mechanism and a second-order dependence on the use of either reaction-step-specific or single (global) heats of combustion.

Software-Based Simulations of Wildfire Spread and Wind-Fire Interaction

Wildfires are complex phenomena, both in time and space, in ecosystems. The ability to understand wildfire dynamics and to predict the behaviour of the propagating fire is essential and at the same time a challenging practice. A common approach to investigate and predict such phenomena is making the most of power of numerical models and simulators. Improved and more accurate methods for simulating fire dynamics are indispensable to managing suppression plans and controlled burns, decreasing the fuel load and having a better assessment of wildfire risk mitigation methodologies. This paper is focused on the investigation of existing simulator models applicable in predicting wildfire spread and wind fire interaction. The available software packages are outlined with their broad range of applications in fire dynamic modeling. Significance of each work and associated shortcomings are critically reviewed. Finally, advanced simulations and designs, accurate assumptions, and considerations for improving the numerical simulations, existing knowledge gaps in scientific research and suggestions to achieve more efficient developments in this area are revisited.

Coupling wildfire spread simulations and connectivity analysis for hazard assessment: a case study in Serra da Cabreira, Portugal

Abstract. This study aims to assess wildfire hazard in northern Portugal by combining landscape-scale wildfire spread modeling and connectivity analysis to help fuel management planning. We used the Minimum Travel Time (MTT) algorithm to run simulations under extreme (95th percentile) fire weather conditions. We assessed wildfire hazard through burn probability, fire size, conditional flame length and fire potential index wildfire descriptors. Simulated fireline intensity (FLI) using historical fire weather conditions were used to build landscape networks and assess the impact of weather severity in landscape wildfire connectivity (Directional Index of Wildfire Connectivity, DIWC). Our results showed that 27 % of the study area is likely to experience high-intensity fires and 51 % of it is susceptible to spread fires larger than 1000 ha. Furthermore, the increase in weather severity led to the increase in the extent of high-intensity fires and highly connected fuel patches, covering about 13 % of the landscape in the most severe weather. Shrublands and pine forests are the main contributors for the spread of these fires, and highly connected patches were mapped. These are candidates for targeted fuel treatments. This study contributes to improving future fuel treatment planning by integrating wildfire connectivity in wildfire management planning of fire-prone Mediterranean landscapes.

Modeling of fire spread in sagebrush steppe using FARSITE: an approach to improving input data and simulation accuracy

BackgroundModel simulations of wildfire spread and assessments of their accuracy are needed for understanding and managing altered fire regimes in semiarid regions. The accuracy of wildfire spread simulations can be evaluated from post hoc comparisons of simulated and actual wildfire perimeters, but this requires information on pre-fire vegetation fuels that is typically not available. We assessed the accuracy of the Fire-Area Simulator (FARSITE) model parameterized with maps of fire behavior fuel models (FBFMs) obtained from the widely used LANDFIRE, as well as alternative means which utilized the classification of Rangeland Analysis Platform (RAP) satellite-derived vegetation cover maps to create FBFM maps. We focused on the 2015 Soda wildfire, which burned 113,000 ha of sagebrush steppe in the western USA, and then assessed the transferability of our RAP-to-FBFM selection process, which produced the most accurate reconstruction of the Soda wildfire, on the nearby 2016 Cherry Road wildfire.ResultsParameterizing FARSITE with maps of FBFMs from LANDFIRE resulted in low levels of agreement between simulated and observed area burned, with maximum Sorensen’s coefficient (SC) and Cohen’s kappa (K) values of 0.38 and 0.36, respectively. In contrast, maps of FBFMs derived from unsupervised classification of RAP vegetation cover maps led to much greater simulated-to-observed burned area agreement (SC = 0.70, K = 0.68). The FBFM map that generated the greatest simulated-to-observed burned area agreement for the Soda wildfire was then used to crosswalk FBFMs to another nearby wildfire (2016 Cherry Road), and this FBFM selection led to high FARSITE simulated-to-observed burned area agreement (SC = 0.80, K = 0.79).ConclusionsUsing RAP to inform pre-fire FBFM selection increased the accuracy of FARSITE simulations compared to parameterization with the standard LANDFIRE FBFM maps, in sagebrush steppe. Additionally, the crosswalk method appeared to have regional generalizability. Flanking and backfires were the primary source of disagreements between simulated and observed fire spread in FARSITE, which are sources of error that may require modeling of lateral heterogeneity in fuels and fire processes at finer scales than used here.

Combining wildfire behaviour simulations and network analysis to support wildfire management: A Mediterranean landscape case study

The recent extreme wildfire events in the Mediterranean region overwhelmed fire suppression capabilities of national authorities, evidencing the need for a paradigm shift in forest and wildfire management. Wildfire spread and behaviour simulations can provide relevant information to assess fire hazard and to guide decision-makers in implementing fuel treatment strategies. Here, we quantify the influence of spatial arrangement of fuels on fire spread connectivity by developing a new graph-based connectivity index and by applying well established graph-based metrics. These spatial connectivity metrics provide useful information for wildfire hazard and fuel management plans, since they can be used to map vegetation patches able to generate large and intense wildfires, with consequent large impacts. This approach was applied in the fire-prone landscape of Serra de Monchique, southwestern Portugal, under different fire weather scenarios and various linear fuelbreak layout options. We show that network metrics allow the location of the most important areas of wildfire connectivity whereas wildfire suppression may be challenging under specific fire weather conditions. We further show how the construction of fuelbreaks allows to modify the wildfire spread by directly reducing the overall connectivity by 20% and the amount of area with limited opportunities for ground-based suppression by up to 70%, which may, however, be insufficient under extreme weather conditions. This information can be used to prioritize the implementation of fuelbreak segments given their relevance to contain wildfire spread across the landscape and to provide defensible areas for ground-based wildfire suppression. We anticipate that these network metrics will be helpful to both land planners and wildfire researchers seeking to disrupt fuel connectivity and assess different fuel reduction strategies in fire-prone Mediterranean regions.

Multifidelity prediction in wildfire spread simulation: Modeling, uncertainty quantification and sensitivity analysis

Wildfire behavior predictions typically suffer from significant uncertainty. However, wildfire modeling uncertainties remain largely unquantified in the literature, mainly due to computing constraints. New multifidelity techniques provide a promising opportunity to overcome these limitations. Therefore, this paper explores the applicability of multifidelity approaches to wildland fire spread prediction problems. Using a canonical simulation scenario, we assessed the performance of control variates Monte-Carlo (MC) and multilevel MC strategies, achieving speedups of up to 100x in comparison to a standard MC method. This improvement was leveraged to quantify aleatoric uncertainties and analyze the sensitivity of the fire rate of spread (RoS) to weather and fuel parameters using a full-physics fire model, namely the Wildland-Urban Interface Fire Dynamics Simulator (WFDS), at an affordable computation cost. The proposed methodology may also be used to analyze uncertainty in other relevant fire behavior metrics such as heat transfer, fuel consumption and smoke production indicators.

Uncertainty quantification of forecast error in coupled fire–atmosphere wildfire spread simulations: sensitivity to the spatial resolution

A methodology to quantify uncertainty in wildfire forecast using coupled fire-atmosphere computational models is presented. In these models, an atmospheric solver is coupled with a fire-spread module. In order to maintain a low computational cost, the atmospheric simulation is limited to a coarse numerical resolution, which increases the uncertainty in the wildfire spread prediction. Generalised polynomial chaos is proposed to quantify this uncertainty and obtain a response function for the forecast error in terms of the atmospheric resolution and the forecast horizon. The response is obtained from a set of simulations of a grassland fire with an in-house coupled fire-atmosphere model at varying degrees of resolution. Global sensitivity analysis of the response shows that the resolution is the primary parameter affecting the error. However, due to the strongly coupled fire-atmosphere dynamics in the initial fire development, the forecast time horizon locally becomes the dominant variable. A parametric study on the effect of the fire spreading regime suggests that the forecast uncertainty is large in plume-dominated spreading conditions: coarse resolution forecasts accumulate a large error because they cannot capture the intense small-scale vortical motion at the fire front.

Generation and evaluation of an ensemble of wildland fire simulations

Numerical simulations of wildfire spread can provide support in deciding firefighting actions but their predictive performance is challenged by the uncertainty of model inputs stemming from weather forecasts, fuel parameterisation and other fire characteristics. In this study, we assign probability distributions to the inputs and propagate the uncertainty by running hundreds of Monte Carlo simulations. The ensemble of simulations is summarised via a burn probability map whose evaluation based on the corresponding observed burned surface is not obvious. We define several properties and introduce probabilistic scores that are common in meteorological applications. Based on these elements, we evaluate the predictive performance of our ensembles for seven fires that occurred in Corsica from mid-2017 to early 2018. We obtain fair performance in some of the cases but accuracy and reliability of the forecasts can be improved. The ensemble generation can be accomplished in a reasonable amount of time and could be used in an operational context provided that sufficient computational resources are available. The proposed probabilistic scores are also appropriate in a calibration process to improve the ensembles.

IRIS – Rapid response fire spread forecasting system: Development, calibration and evaluation

This work presents the development of a rapid response forecasting system for the prediction of wildfire spread. Named IRIS, the forecasting system was primarily designed and developed to support the operational fire suppression activities of the Greek Fire Service. It employs a coupled atmosphere-fire modeling system, comprised of a state-of-the-art numerical weather prediction model and an advanced fire spread model, accounting for the two-way interactions between fire and weather. For the implementation of IRIS, a prototype fuel models’ geospatial dataset was constructed for Greece, exploiting high-resolution, open-source pan-European vegetation and land use datasets of the Copernicus Land Monitoring Service. Calibration of the fire spread component of IRIS was carried out by retrospective forecasting of 8 wildfires that took place in Greece during the 2016–2017 fire seasons, while the evaluation of the calibrated forecasting system was conducted by forecasting 4 major wildfires of the 2018 fire season. Our overarching aim was to assess the capacity of IRIS for providing accurate real-time wildfire spread simulations. Results clearly highlight that reasonably accurate fire spread predictions can be obtained by proper calibration of the fire spread component. IRIS will be operationally deployed in Greece during the 2019 fire season, providing 6 h and 24 h wildfire spread forecasts, when necessary, in about 15 min and 60 min, respectively. To our best knowledge, IRIS is one of the few operational fire spread forecasting systems based on coupled atmosphere-fire modeling.

Ensemble transform Kalman filter (ETKF) for large-scale wildland fire spread simulation using FARSITE tool and state estimation method

Ensemble transform Kalman filter (ETKF) is an extension of ensemble Kalman filter (EnKF), which avoids using “perturbed observations” to eliminate additional sampling errors. This paper demonstrates the capability of ETKF algorithm for sequentially correcting dynamically evolving fire perimeter positions at regular time intervals to enhance the prediction accuracy of wildfire spread. Forecast error covariance inflation scheme is adopted in the ETKF to address the underestimation problem of forecast error covariance of EnKF. Coupled to a widely-used fire spread simulator, FARSITE, the proposed approach is employed to a landscape with complex topography, where a fire barrier is also considered. The merits of ETKF algorithm for wildfire spread prediction are highlighted by simulation experiments using synthetically-generated observations. In order to quantitatively evaluate the prediction performance of ETKF, this paper has adopted a conservative index, Hausdorff distance, which is widely used in image processing area. This work is the first attempt of applying ETKF to wildfire spread simulation. The ETKF algorithm has been demonstrated to be more accurate than EnKF for a given ensemble size for wildfire spread simulation. The findings show that the ETKF-based data assimilation strategy is a promising tool for large-scale wildfire spread simulation.

Crowd-Sourced Wildfire Spread Prediction With Remote Georeferencing Using Smartphones

Wildfires are natural hazards with severe consequences worryingly worsening for many climate-change affected regions of our planet. Unfortunately, technologies that can provide real-time fire-line information, such as satellites, in-field sensors, and social media texts, exhibit low spatial/temporal resolution or cannot be deployed cost-effectively in widespread geographical areas. We present the design, development, and implementation of a novel software service, called CITISENS, which by exploiting commodity smartphone sensors allows ordinary citizens to easily georeference a fire-line in real-time and report its coordinates as they are photographing a wildfire. The location/orientation sensors and the camera are used to compute the view-ray of the smartphone, and a digital elevation model is employed to estimate the ray's intersection with the topography. We have tested the georeferencing accuracy obtained and it is to be on par with, or even better, than that of existing satellite wildfire hotspot services. When combined with FLogA, a flexible wildfire spread simulator we have also developed, CITISENS offers the following unique advantages: real-time prediction of burn probabilities, dynamic assimilation of citizen-reported hotspots into ongoing simulations for improved predictive accuracy, and decision support to issue citizen alarms based on the estimated time-dependent risk at their location due to an approaching wildfire.

On-demand data assimilation of large-scale spatial temporal systems using sequential Monte Carlo methods

The proliferation of sensors is generating rapidly increasing quantities of data like never before. These extensive amounts of data can provide useful information for more accurate state inference of large-scale spatial temporal systems. Sequential Monte Carlo methods are used to assimilate the observed data from sensors to improve the state estimation of large-scale spatial temporal systems, which highly rely on the available real time observation data. In many scenarios, the real time data are limited in space and time. Therefore, it is important to effectively obtain critical sensor data in real time and then dynamically feed them into the running model. In this paper, we propose the on-demand data assimilation method for large-scale spatial temporal systems, in which we quantify the spatial states using run-time state quantification methods and decide if we need to trigger data assimilation on demand and obtain more relevant real time data when the state uncertainty is high. The effectiveness of the developed framework is evaluated based on large-scale wildfire spread simulations.

Interpolation framework to speed up near-surface wind simulations for data-driven wildfire applications

Local wind fields that account for topographic interaction are a key element for any wildfire spread simulator. Currently available tools to generate near-surface winds with acceptable accuracy do not meet the tight time constraints required for data-driven applications. This article presents the specific problem of data-driven wildfire spread simulation (with a strategy based on using observed data to improve results), for which wind diagnostic models must be run iteratively during an optimisation loop. An interpolation framework is proposed as a feasible alternative to keep a positive lead time while minimising the loss of accuracy. The proposed methodology was compared with the WindNinja solver in eight different topographic scenarios with multiple resolutions and reference – pre-run– wind map sets. Results showed a major reduction in computation time (~100 times once the reference fields are available) with average deviations of 3% in wind speed and 3° in direction. This indicates that high-resolution wind fields can be interpolated from a finite set of base maps previously computed. Finally, wildfire spread simulations using original and interpolated maps were compared showing minimal deviations in the fire shape evolution. This methodology may have an important effect on data assimilation frameworks and probabilistic risk assessment where high-resolution wind fields must be computed for multiple weather scenarios.

Spatial Partition-Based Particle Filtering for Data Assimilation in Wildfire Spread Simulation

This article develops a spatial partition-based particle filtering framework to support data assimilation for large-scale wildfire spread simulation effectively. The developed spatial partition-based particle filtering framework breaks the system state and observation data into smaller spatial regions and then carries out localized particle filtering based on these spatial regions. Particle Filters (PFs) hold great promise to support data assimilation for spatial temporal simulations, such as wildfire spread simulation, to achieve more accurate simulation or prediction results. However, PFs face major challenges to work effectively for complex spatial temporal simulations due to the high-dimensional state space of the simulation models, which typically cover large areas and have a large number of spatially dependent state variables. The developed framework exploits the spatial locality property of system state and observation data and employs the divide-and-conquer principle to reduce state dimension and data complexity. This framework is especially developed for a discrete event cellular space model (the wildfire simulation model), which significantly differs from prior works that use numerical models specified by partial differential equations (PDEs) with continuous variables. Within this framework, a two-level automated spatial partitioning method is presented to provide automated and balanced spatial partitions with fewer boundary sensors. The developed framework is applied to a wildfire spread simulation and achieved improved results compared to using standard PF-based data assimilation methods.

Localized recursive spatial-temporal state quantification method for data assimilation of wildfire spread simulation

Data assimilation is a procedure to improve the state inference by assimilating the real-time observation data into dynamic systems, such as wildfire spread simulation. Various techniques are used for data assimilation, such as sequential Monte Carlo methods, also called particle filters. In the standard sequential Monte Carlo methods, the used number of particles is the same during the entire process and their convergence is not measured or characterized for runtime state estimation. Therefore, to guarantee the convergence, an abundance of particles is required so that they can be widely distributed in the state space to converge to the true posterior of the system state. In the application of wildfire spread simulation, the spatial states are large and the system behaves in a heterogeneous manner in different fire areas. The heterogeneous feature and the spatially and temporally dynamic behavior of the wildfire spread simulation cause the state inference uncertainty to be dynamically changed. In this paper, we propose the localized recursive spatial-temporal state quantification method to measure the convergence of particles at runtime and apply various approaches to improve the state inference. To show its effectiveness, we apply two different algorithms – the adaptively perturbing the localized state space algorithm and the adaptive particle filtering algorithm – to the localized recursive spatial-temporal state quantification method to improve the state estimation and enhance the performance respectively. The designed experiments are used to show the effectiveness of the adaptively perturbing the localized state space algorithm and the adaptive particle filtering algorithm in the improvement of the state estimation and the performance of data assimilation in wildfire spread simulation.

Indices for the evaluation of wildfire spread simulations using contemporaneous predictions and observations of burnt area

Methods to objectively evaluate performance are critical for model development. In contrast to recent advances in wildfire simulation, there has been limited attention to evaluating fire model performance. Information to validate fire models is typically limited, commonly to a few perimeter observations at a small number of points in time. We review metrics for comparing two burnt areas at a point in time: observed and predicted. These are compared in an idealised landscape and with a case study evaluating the performance of simulations of an Australian wildfire. We assessed: Shape Deviation Index (SDI), Jaccard's coefficient, F1, Sørensen's Similarity and Area Difference Index (ADI). For decomposing fit into error components (overprediction and underprediction) we assessed the partial indices of SDI and ADI, Precision and Recall. The various metrics were evaluated for their ability to represent error and their suitability for use in model improvement frameworks.

Deciphering the impact of uncertainty on the accuracy of large wildfire spread simulations

Predicting wildfire spread is a challenging task fraught with uncertainties. ‘Perfect’ predictions are unfeasible since uncertainties will always be present. Improving fire spread predictions is important to reduce its negative environmental impacts. Here, we propose to understand, characterize, and quantify the impact of uncertainty in the accuracy of fire spread predictions for very large wildfires. We frame this work from the perspective of the major problems commonly faced by fire model users, namely the necessity of accounting for uncertainty in input data to produce reliable and useful fire spread predictions. Uncertainty in input variables was propagated throughout the modeling framework and its impact was evaluated by estimating the spatial discrepancy between simulated and satellite-observed fire progression data, for eight very large wildfires in Portugal. Results showed that uncertainties in wind speed and direction, fuel model assignment and typology, location and timing of ignitions, had a major impact on prediction accuracy. We argue that uncertainties in these variables should be integrated in future fire spread simulation approaches, and provide the necessary data for any fire model user to do so.

Particle Routing in Distributed Particle Filters for Large-Scale Spatial Temporal Systems

Particle filters are important techniques to support data assimilation for large-scale spatial temporal simulation systems. Distributed particle filters improve the performance of particle filtering by distributing particles to multiple processing units (PUs). While different resampling algorithms have been developed for distributed particle filters, less research has been conducted to investigate how to route particles among the PUs after resampling in effective and efficient manners. This paper develops particle routing policies in distributed particle filters with both the centralized resampling and the distributed resampling. The developed routing policies are evaluated from the aspects of the communication cost and the data assimilation accuracy based on an application of data assimilation for large-scale wildfire spread simulations.

Towards predictive data-driven simulations of wildfire spread – Part II: Ensemble Kalman Filter for the state estimation of a front-tracking simulator of wildfire spread

Abstract. This paper is the second part in a series of two articles, which aims at presenting a data-driven modeling strategy for forecasting wildfire spread scenarios based on the assimilation of the observed fire front location and on the sequential correction of model parameters or model state. This model relies on an estimation of the local rate of fire spread (ROS) as a function of environmental conditions based on Rothermel's semi-empirical formulation, in order to propagate the fire front with an Eulerian front-tracking simulator. In Part I, a data assimilation (DA) system based on an ensemble Kalman filter (EnKF) was implemented to provide a spatially uniform correction of biomass fuel and wind parameters and thereby, produce an improved forecast of the wildfire behavior (addressing uncertainties in the input parameters of the ROS model only). In Part II, the objective of the EnKF algorithm is to sequentially update the two-dimensional coordinates of the markers along the discretized fire front, in order to provide a spatially distributed correction of the fire front location and thereby, a more reliable initial condition for further model time-integration (addressing all sources of uncertainties in the ROS model). The resulting prototype data-driven wildfire spread simulator is first evaluated in a series of verification tests using synthetically generated observations; tests include representative cases with spatially varying biomass properties and temporally varying wind conditions. In order to properly account for uncertainties during the EnKF update step and to accurately represent error correlations along the fireline, it is shown that members of the EnKF ensemble must be generated through variations in estimates of the fire's initial location as well as through variations in the parameters of the ROS model. The performance of the prototype simulator based on state estimation (SE) or parameter estimation (PE) is then evaluated by comparison with data taken from a reduced-scale controlled grassland fire experiment. Results indicate that data-driven simulations are capable of correcting inaccurate predictions of the fire front location and of subsequently providing an optimized forecast of the wildfire behavior at future lead times. The complementary benefits of both PE and SE approaches, in terms of analysis and forecast performance, are also emphasized. In particular, it is found that the size of the assimilation window must be specified adequately with the persistence of the model initial condition and/or with the temporal and spatial variability of the environmental conditions in order to track sudden changes in wildfire behavior. The present prototype data-driven forecast system is still at an early stage of development. In this regard, this preliminary investigation provides valuable information on how to combine observations with a fire spread model in an efficient way, as well as guidelines to design the future system evolution in order to meet the operational requirements of wildfire spread monitoring.