Parallel implementation of a simplified semi-physical wildland re spread model using OpenMP

[EN]We present a parallel 2D version of a simplified semi-physical wildland fire spread model based on conservation equations, with convection and radiation as the main heat transfer mechanisms. This version includes some 3D effects. The OpenMP framework allows distributing the prediction operations among the available threads in a multicore architecture, thereby reducing the computational time and obtaining the prediction results much more quickly. The results from the experiments using data from a real fire in Galicia (Spain) confirm the benefits of using the parallel version.Junta of Castilla y Leó

A PHYSICS-BASED APPROACH TO MODELING WILDLAND FIRE SPREAD THROUGH POROUS FUEL BEDS

Wildfires are becoming increasingly erratic nowadays at least in part because of climate change. CFD (computational fluid dynamics)-based models with the potential of simulating extreme behaviors are gaining increasing attention as a means to predict such behavior in order to aid firefighting efforts. This dissertation describes a wildfire model based on the current understanding of wildfire physics. The model includes physics of turbulence, inhomogeneous porous fuel beds, heat release, ignition, and firebrands. A discrete dynamical system for flow in porous media is derived and incorporated into the subgrid-scale model for synthetic-velocity large-eddy simulation (LES), and a general porosity-permeability model is derived and implemented to investigate transport properties of flow through porous fuel beds. Note that these two developed models can also be applied to other situations for flow through porous media. Simulations of both grassland and forest fire spread are performed via an implicit LES code parallelized with OpenMP; the parallel performance of the algorithms are presented and discussed. The current model and numerical scheme produce reasonably correct wildfire results compared with previous wildfire experiments and simulations, but using coarser grids, and presenting complicated subgrid-scale behaviors. It is concluded that this physics-based wildfire model can be a good learning tool to examine some of the more complex wildfire behaviors, and may be predictive in the near future

A GIS-based fire spread simulator integrating a simplified physical wildland fire model and a wind field model

[EN]This article discusses the integration of two models, namely, the Physical Forest Fire Spread (PhFFS) and the High Definition Wind Model (HDWM), into a Geographical Information System-based interface. The resulting tool automates data acquisition, preprocesses spatial data, launches the aforementioned models and displays the corresponding results in a unique environment. Our implementation uses the Python language and Esri’s ArcPy library to extend the functionality of ArcMap 10.4. The PhFFS is a simplified 2D physical wildland fire spread model based on conservation equations, with convection and radiation as heat transfer mechanisms. It also includes some 3D effects. The HDWM arises from an asymptotic approximation of the Navier–Stokes equations, and provides a 3D wind velocity field in an air layer above the terrain surface. Both models can be run in standalone or coupled mode. Finally, the simulation of a real fire in Galicia (Spain) confirms that the tool developed is efficient and fully operational.Junta de Castilla y León; Fundación General de la Universidad de Salamanc

Asimilación de datos, validación e integración en GIS de un modelo de simulación de incendios forestales

[ES]Esta tesis doctoral se ha desarrollado dentro del contexto de la investigación de la simulación numérica de incendios forestales llevada a cabo dentro del grupo de investigación reconocido SINUMCC (Simulación Numérica y Cálculo Científico) de la Universidad de Salamanca. En términos generales, el trabajo aquí recogido tiene por objeto continuar el desarrollo del modelo de simulación de incendios forestales PhyFire (Physical Forest Fire Spread) elaborado por el grupo de investigación mediante la integración de nuevas herramientas que mejoren su eficiencia, aplicabilidad y utilidad, a través de los siguientes objetivos: 1. Incorporación de técnicas de asimilación de datos basadas en el empleo del Filtro de Kalman. La asimilación de datos permite mejorar las predicciones obtenidas por el modelo mediante la incorporación de datos observados durante la evolución real del incendio, proporcionando de este modo predicciones más probables en los instantes siguientes. 2. Validación del modelo PhyFire mediante la simulación de fuegos experimentales llevados a cabo bajo condiciones controladas y el uso de técnicas de análisis de sensibilidad global. Estas técnicas permiten determinar los parámetros y variables de entrada del modelo que más influencia tienen en las variables de salida, validando el modelo y facilitando el diseño del procedimiento de ajuste de sus parámetros. 3. Ajuste de parámetros del modelo, mediante el uso de algoritmos de optimización iterativos en los que la función de coste compara la salida del modelo con medidas realizadas sobre fuegos experimentales. 4. Integración en SIG (Sistemas de Información Geográfica) de los modelos PhyFire y HDWind para mejorar su usabilidad y eficiencia al disminuir el tiempo necesario para llevar a cabo la simulación de un incendio real. Se ha creado una herramienta apta para la utilización por los potenciales usuarios, que incorpora toda la información espacial necesaria para llevar a cabo las simulaciones. 5. Simulación de incendios forestales reales, con el objetivo de validar el trabajo realizado

Front propagation in random media.

244 p.This PhD thesis deals with the problem of the propagation of fronts under random circumstances. Astatistical model to represent the motion of fronts when are evolving in a media characterized bymicroscopical randomness is discussed and expanded, in order to cope with three distinctapplications: wild-land fire simulation, turbulent premixed combustion, biofilm modeling. In thestudied formalism, the position of the average front is computed by making use of a sharp-frontevolution method, such as the level set method. The microscopical spread of particles which takesplace around the average front is given by the probability density function linked to the underlyingdiffusive process, that is supposedly known in advance. The adopted statistical front propagationframework allowed a deeper understanding of any studied field of application. The application ofthis model introduced eventually parameters whose impact on the physical observables of the frontspread have been studied with Uncertainty Quantification and Sensitivity Analysis tools. Inparticular, metamodels for the front propagation system have been constructed in a non intrusiveway, by making use of generalized Polynomial Chaos expansions and Gaussian Processes.bcam:basque center for applied mathematic

Front Propagation in Random Media

This PhD thesis deals with the problem of the propagation of fronts under random circumstances. A statistical model to represent the motion of fronts when are evolving in a media characterized by microscopical randomness is discussed and expanded, in order to cope with three distinct applications: wild-land fire simulation, turbulent premixed combustion, biofilm modeling. In the studied formalism, the position of the average front is computed by making use of a sharp-front evolution method, such as the level set method. The microscopical spread of particles which takes place around the average front is given by the probability density function linked to the underlying diffusive process, that is supposedly known in advance. The adopted statistical front propagation framework allowed a deeper understanding of any studied field of application. The application of this model introduced eventually parameters whose impact on the physical observables of the front spread have been studied with Uncertainty Quantification and Sensitivity Analysis tools. In particular, metamodels for the front propagation system have been constructed in a non intrusive way, by making use of generalized Polynomial Chaos expansions and Gaussian Processes.The Thesis received funding from Basque Government through the BERC 2014-2017 program. It was also funded by the Spanish Ministry of Economy and Competitiveness MINECO via the BCAM Severo Ochoa SEV-2013-0323 accreditation. The PhD is fundend by La Caixa Foundation through the PhD grant “La Caixa 2014”. Funding from “Programma Operativo Nazionale Ricerca e Innovazione” (PONRI 2014-2020) , “Innotavive PhDs with Industrial Characterization” is kindly acknowledged for a research visit at the department of Mathematics and Applications “Renato Caccioppoli” of University “Federico II” of Naples

Inverse modelling in wildfire spread forecasting: towards a data-driven system

Wildfires are an ecological phenomenon inherent to earth dynamics and widely spread over the globe. In addition to the environmental impact, when wildfires grow beyond controllable magnitudes, they pose a principal threat to human lives and properties. On many countries, the rural abandonment of last decades, the forest continuity growth and the Wildland Urban Interface increase are exposing entire communities and rendering them vulnerable to a major fire event. Coupled together with a global warming that seems to be enlarging and worsening wildfire-prone weather conditions, the wildfire problem is becoming a recurrent and repetitive natural hazard that is in urgent needs of research development, planning and organizational changes to minimize its impact. In this context, the thesis at hand focuses on the development, implementation and initial validation of a wildfire perimeter spread prediction model that might help emergency responders on taking sound decisions to efficiently employ resources and protect valuable assets. This forecasting model is a particular implementation of a data-driven system. That is, available data are used to improve and calibrate the spread model results with the aim of delivering a more accurate and timely forecast of the fire spread for the upcoming hours. This thesis builds up the mentioned system by increasing its complexity and tackling required improvements and adaptations on fuel characterization and wind projection on topography. Initially, a simplified proof of concept that uses front perimeter (isochrones) evolution extracted from infrared imagery of the fire is challenged with data from real-scale burning experiments conducted in Australia. Despite the positive outcome of this initial investigation, some advancements are identified to further upgrade the system. Thus, the following chapters focus on the fuel and wind sub-models together with the spread model topographic upgrade and the different mathematical algorithms and strategies necessary to conduct the data-driven process. Regarding fuels, the thesis presents an in-depth analysis of fuel characterization to be used by fire spread models. This is done by a thorough sensitivity analysis of the most commonly used fuel characterization systems. In the light of these results, a simplified model that integrates all different fuel properties is proposed to be used by the data-driven framework at hand. To properly resolve the wind interaction with the terrain and to couple it into the data-driven system, the WindNinja diagnose software is employed. However, long computational times do not allow for its integration into any data-assimilation strategy. Thus, a full interpolating framework is developed and validated to allow fast and computationally inexpensive wind field updates. This key element becomes then a cornerstone of the full data-driven approach. For the optimization process (embedded into any data-driven systems) six different mathematical algorithms were compared and evaluated. Three of them being line-search strategies and the other three being global search. It was found that the algorithm selection has an impact on the final results in terms of forecast accuracy and computing time. Finally, the overall system is verified and validated using two source of available data: (1) well characterized, homogeneous slope, medium-scale experimental fires conducted in Portugal and (2) with synthetically generated fronts reproducing a real large-scale fire. These validations were aimed at studying the overall performance, checking the system functionality and highlighting possible flaws and necessary improvements if the tool is to be deployed in a real emergency situation. Whereas the results show the potential of the approach by delivering an acceptable forecast usable for emergency responders, further validations are required to check the robustness and reliability of the tool before using it in operational situations.Els incendis forestals són, al cap i a la, un fenomen ecològic inherent a la dinàmica de la terra i àmpliament escampats per tot el món. A més de l'impacte ambiental, quan els incendis forestals excedeixen, en magnitud i intensitat, la capacitat d’extinció, representen una amenaça per a propietats i vides humanes. En molts països, l’abandó rural de les últimes dècades, el creixement de la continuïtat forestal i l'augment de la interfície urbana-forestal (Wildand-Urban Interface) està comportant l'augment de comunitats exposades al foc forestal alhora que les fan més vulnerables a un gran incendi. A més a més, l'escalfament global sembla que està afavorint i facilitant la recurrència de les condicions climàtiques propícies pels incendis forestals. El problema de l'incendi forestal s’està convertint en un perill natural recurrent i repetitiu que clama avanços urgents en recerca, planificació i gestió per tal de minimitzar-ne el seu impacte. En aquest context, la present tesi se centra en el desenvolupament, la implementació i la validació inicial d'un model de predicció de la propagació del perímetre d'incendis forestals que podria ajudar als responsables de l’emergència a prendre decisions més oportunes per a emprar els recursos de forma eficient tot protegint els actius valuosos. Aquest model predictiu és una implementació particular d'un sistema basat en dades. És a dir, les dades disponibles s'utilitzen per a millorar i calibrar els resultats del model de propagació del front amb l'objectiu de proporcionar un pronòstic més precís i oportú de l’evolució del perímetre del foc en les pròximes hores. Aquesta tesi construeix el sistema esmentat pas a pas, tot incrementant-ne la seva complexitat i abordant les millores i adaptacions necessàries en cada etapa, per exemple, en la caracterització del combustible i la projecció i interacció del vent amb la topografia. Inicialment, s'utilitzen imatges infraroges de l’evolució del perímetre (isòcrones) de dos cremes experimentals dutes a terme a Austràlia per a realitzar una prova de concepte del sistema. Malgrat el resultat favorable d'aquesta primera investigació, s'identifiquen alguns avenços per millorar-ne l'efectivitat i permetre'n l’aplicació en incendis reals. Així, els capítols següents se centren en els submodels de combustible i vent juntament amb l’actualització topogràfica del model de propagació i els diferents algorismes i estratègies matemàtiques necessàries per dur a terme el procés d’assimilació basat en dades. Pel que fa als combustibles, la tesi presenta una anàlisi en profunditat de la caracterització dels combustibles que han d'utilitzar els models de propagació de foc. Això es fa mitjançant una anàlisi minuciosa de la sensibilitat dels paràmetres de caracterització del sistema més utilitzats. A la llum d'aquests resultats, es proposa un model simplificat que integri totes les propietats de combustible diferents per ser utilitzat pel model predictiu desenvolupat en la present tesi. Per resoldre adequadament la interacció del vent amb el terreny i acoblar-la al model de propagació bastat en dades, s'utilitza el programa de diagnòstic WindNinja. Els temps necessaris de computació, però, no permeten la seva directa integració en una estratègia d’assimilació de dades. Així doncs, en aquesta tesi, es desenvolupa i valida un marc interpolador que permeti actualitzacions ràpides i computacionalment assequibles del camp de vents a nivell topogràfic. Aquest element clau es converteix en una peça clau per aconseguir el model de propagació basat en dades que es cerca en aquesta tesi. Per al procés d’optimització (present en qualsevol model conduit per dades) es comparen i s'avaluen sis algorismes matemàtics diferents. Tres d'ells són estratègies de cerca basades en programació lineal i les altres tres són estratègies de recerca global. L’exploració conclou que la selecció d'algorismes té un gran impacte en els resultats finals en termes de la precisió de pronòstic i temps de computació. Finalment, tot el sistema es verifica i valida utilitzant les dades de dues fonts disponibles: (1) incendis experimentals de mitjana escala realitzats a Portugal en un pendent homogeni ben caracteritzat i (2) amb fronts generats sintèticament que reprodueixen un incendi real a gran escala. Aquestes validacions estan orientades a estudiar el rendiment general, comprovar la funcionalitat del sistema això com ressaltar possibles defectes i millores necessàries per tal de poder utilitzar l'eina en una situació d’emergència real. Malgrat els resultats mostren els potencials del sistema tot proporcionant un pronòstic acceptable, utilitzable com a eina de suport per a la gestió d'una emergència, queda també palès que es requereixen més validacions per comprovar la robustesa i fiabilitat de l'eina abans d'utilitzar-la en situacions operatives

Wildfire spread simulation modeling for risk assessment and management in Mediterranean areas

Wildfires are a key problem in many terrestrial ecosystems, particularly in the Mediterranean Basin, and climate change will likely cause their increase in future years. Wildfire behavior simulator models are very useful to characterize wildfire risk, identify the valued resources more exposed to wildfires and to plan the best strategies to mitigate risk. In this work, we first carried out a review of wildfire spread and behavior modelling, and then focusing on FLAMMAP model. Then, we evaluated the effects of diverse strategies of fuel treatments on wildfire risk in an agro-pastoral area of the North-central Sardinia (Italy) that has been affected by the largest Sardinian wildfire of recent years (Bonorva wildfire, about 10,500 ha burned, on July 2009). Finally we analyzed the combined effects of fuel treatments and post-fire treatments with the aim to mitigate wildfire and erosion risk, linking the minimum travel time algorithm with the Ermit modeling approach in a study area located in Northern Sardinia (Italy), mostly classified as European Site of Community Importance. Overall, the results obtained showed that wildfire behavior simulator models can support forest fire management and planning and can provide key spatial information and data that can be helpful to policy makers and land managers

Cellular automata simulations of field scale flaming and smouldering wildfires in peatlands

In peatland wildfires, flaming vegetation can initiate a smouldering fire by igniting the peat underneath, thus, creating a positive feedback to climate change by releasing the carbon that cannot be reabsorbed by the ecosystem. Currently, there are very few models of peatland wildfires at the field-scale, hindering the development of effective mitigation strategies. This lack of models is mainly caused by the complexity of the phenomena, which involves 3-D spread and km-scale domains, and the very large computational resources required. This thesis aims to understand field-scale peatland wildfires, considering flaming and smouldering, via cellular automata, discrete models that use simple rules. Five multidimensional models were developed: two laboratory-scale models for smouldering, BARA and BARAPPY, and three field-scale models for flaming and smouldering, KAPAS, KAPAS II, and SUBALI. The models were validated against laboratory experiments and field data. BARA accurately simulates smouldering of peat with realistic moisture distributions and predicts the formation of unburned patches. BARAPPY brings physics into BARA and predicts the depth of burn profile, but needs 240 times more computational resources. KAPAS showed that the smouldering burnt area decreases exponentially with higher peat moisture content. KAPAS II integrates daily temporal variation of moisture content, and revealed that the omission of this temporal variation significantly underestimates the smouldering burnt area in the long term. SUBALI, the ultimate model of the thesis, integrates KAPAS II with BARA and considers the ground water table to predict the carbon emission of peatland wildfires. Applying SUBALI to Indonesia, it predicts that in El Niño years, 0.40 Gt-C in 2015 (literature said 0.23 to 0.51 Gt-C) and 0.16 Gt-C in 2019 were released, and 75% of the emission is from smouldering. This thesis provides knowledge and models to understand the spread of flaming and smouldering wildfires in peatlands, which can contribute to efforts to minimise the negative impacts of peatland wildfires on people and the environment, through faster-than-real-time simulations, to find the optimum firefighting strategy and to assess the vulnerability of peatland in the event of wildfires.Open Acces