114 research outputs found

    Simulación paramétrica paralela. Aplicación a modelos de predicción de inundaciones.

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    El modelado y la simulación de inundaciones provocadas por el desborde de ríos brinda sistemas computacionales para el estudio y la predicción de estos fenómenos naturales, con el objetivo de pronosticar su comportamiento. Estos sistemas necesitan tomar gran cantidad de datos de entrada para aumentar su precisión, como también deben generar múltiples escenarios para cubrir todas las situaciones de riesgo. Por esto, son de cómputo intensivo y pueden tomar días de procesamiento hasta lograr resultados. A este problema se le suma la falta de certeza en los valores de los datos de entrada del proceso. Mediante la programación paralela y los avances en cómputo de alto rendimiento en clusters de computadoras, se pretende atenuar el problema de la incertidumbre de los datos de entrada y optimizar el proceso de predicción mediante la simulación de múltiples escenarios. Con este trabajo se pretende desarrollar una metodología para optimizar la predicción de inundaciones provocadas por el desborde de ríos, en principio de llanuras o planicies, y en particular en la Cuenca del Río Salado o en el Paraná Medio.Eje: Procesamiento Concurrente, Paralelo y DistribuidoRed de Universidades con Carreras en Informática (RedUNCI

    Simulación paramétrica paralela. Aplicación a modelos de predicción de inundaciones.

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    El modelado y la simulación de inundaciones provocadas por el desborde de ríos brinda sistemas computacionales para el estudio y la predicción de estos fenómenos naturales, con el objetivo de pronosticar su comportamiento. Estos sistemas necesitan tomar gran cantidad de datos de entrada para aumentar su precisión, como también deben generar múltiples escenarios para cubrir todas las situaciones de riesgo. Por esto, son de cómputo intensivo y pueden tomar días de procesamiento hasta lograr resultados. A este problema se le suma la falta de certeza en los valores de los datos de entrada del proceso. Mediante la programación paralela y los avances en cómputo de alto rendimiento en clusters de computadoras, se pretende atenuar el problema de la incertidumbre de los datos de entrada y optimizar el proceso de predicción mediante la simulación de múltiples escenarios. Con este trabajo se pretende desarrollar una metodología para optimizar la predicción de inundaciones provocadas por el desborde de ríos, en principio de llanuras o planicies, y en particular en la Cuenca del Río Salado o en el Paraná Medio.Eje: Procesamiento Concurrente, Paralelo y DistribuidoRed de Universidades con Carreras en Informática (RedUNCI

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

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    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

    Causes, consequences, and management of tree spatial patterns in fire-frequent forests

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    2022 Summer.Includes bibliographical references.Increasingly, restoration treatments are being implemented to dually meet wildland fire hazard reduction alongside ecological objectives. Restoration treatments however deviate from conventional fuels treatments by emphasizing the re-creation of forest structure present prior to EuroAmerican settlement, notably the retention of single and grouped trees interspersed between canopy openings. As these historical forests persisted over cycles of fire returns, it is assumed that restoring these historical complex tree spatial patterns will, in turn, restore historical ecological processes. This includes more benign fire behavior that results in only partial tree mortality, allowing persistent and partial retention of forest cover over cycles of fire return. The qualitative description of historical forest structure, lacks, however, a clear process-based explanation detailing the interactions of heterogeneous forest structures and fire. While fires were historically frequent, it is unclear what role fire played in the genesis and maintenance of tree spatial patterns. If models of tree spatial dynamics can be improved and the interactions between tree spatial patterns and fire can be elucidated, forest managers will have an improved understanding of the implications of restoration-based fuels hazard reduction treatments both during fire-free periods and during fire events. The aims of this dissertation were to: 1) explore the causes of tree spatial patterns in dry fire-frequent forests; 2) investigate the consequences of tree spatial patterns on potential fire behavior and effects; 3) determine how alternate silvicultural strategies targeted at manipulation of tree spatial patterns can influence fire behavior and effects. In Chapter 2, I explored spatial patterns of tree regeneration over 44 years in absence of fire. In cooler periods, regeneration preferred clustering in openings, including openings following overstory mortality and away from overstory trees. Mortality risk of regeneration was heightened nearer overstory trees. In warmer periods, these trends reversed, likely because of a 'nurse effect' from the overstory. In anticipation of climate change, these results suggest silviculturists may benefit by capturing regeneration mortality in within openings while keeping regeneration near the overstory. In Chapter 3, I found that regenerating trees also form heterogeneous patterns following stand-replacing fires. In these sparse, early seral forests, all species were spatially aggregated, partly attributable to the influence of topography and beneficial interspecific attractions between ponderosa pine and other species. Results from this study suggest that scale-dependent, and often facilitatory, rather than competitive, processes act on regenerating trees. In Chapter 4, I studied the interaction between fire and tree spatial patterns, both historically and in modern forests. Tree mortality in the historical period was clustered and density-dependent because tree mortality was greater among small trees, which tended to be assembled in tightly spaced clusters. Tree mortality in the contemporary period was widespread, except for dispersed large trees, because most trees were a part of large, interconnected tree groups. Postfire tree patterns in the historical period, unlike the contemporary period, were within the historical range of variability found for the western United States. This divergence suggests that decades of forest dynamics without significant disturbances have altered the historical means of pyric pattern maintenance. In Chapter 5, I examined how fuels treatment designs with different manipulations of tree spatial patterns may influence treatment effectiveness. I simulated fires on hypothetical cuttings which manipulated the arrangement of crown fuels horizontally and vertically, either increasing the distance between tree crowns or not, and either removing small trees or not. All cutting methods reduced fire behavior and severity, but the results confirm possible tradeoffs between ecological restoration and hazard reduction; treatments that separated tree crowns reduced severity the most because these treatments reduced crown fire spread. But these can easily be overcome where restoration treatments incorporate small tree removal, because this action limits crown fire initiation. Managers could also incorporate managed fires to reduce surface fuel loads and use more aggressive cuttings to further gains in hazard reduction, regardless of cutting method used

    Fire effects on soil and hydrology

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    Fire can significantly increase a landscape’s vulnerability to flooding and erosion events. By removing vegetation, changing soil properties and inducing soil water repellency, fire can increase the risk and erosivity of overland flow. Mitigation of land degradation and flooding events after fire can help safeguard natural resources and prevent further economical and ecological havoc, but can benefit from an improved understanding of its drivers. The aim of this thesis is to improve the understanding of the effects of fire on soil and hydrology. Laboratory and field studies focus on the relation between fire, soil, vegetation and hydrology as well as the effects of scale, in order to find the drivers of post-fire flooding and erosion events. The effect of soil heating on soil physical properties is evaluated, and the above- and belowground drivers of soil heating are investigated. Furthermore, the results of a unique field experiment are presented in which the Portuguese Valtorto catchment was burned by experimental fire. The effects of fire on soil and surface properties is assessed, as well as the changes in the temporal evolution of soil water repellency, Finally, the hydrological implications are discussed. The thesis concludes with recommendations for mitigation of fire-induced land degradation; focusing on guidelines for prescribed burns, that are used to prevent fire, and on reducing runoff and erosion in burned lands where fire prevention was unsuccessful. </p

    Multi-scale Fire Modelling of Combustible Building Materials

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    The utilisation of lightweight polymers in building materials has come under tremendous scrutiny, driven by the numerous high-profile fire incidents (e.g., Grenfell Tower UK, 2017) and heightened public awareness of highly combustible materials in the past decade. Consequently, this creates significant interest in developing robust numerical tools to effectively assess the fire behaviours and toxicity of these combustible materials and establish safe use guidelines. In this dissertation, a modelling framework has been developed incorporating multi-scale computational techniques that capture and couple the thermal degradation and combustion characteristics of building materials. This includes (i) characterisation of essential pyrolysis kinetics from thermogravimetric analysis (TGA) via machine learning aided algorithm; (ii) in-depth pyrolysis breakdown from molecular dynamics (MD) simulations coupled with reactive force fields (ReaxFF); and (iii) Computational Fluid Dynamics (CFD) pyrolysis model involving char formation, moving boundary surface tracking and gas-phase combustion considering detailed chemical reaction mechanisms and soot particle formation. The framework was adopted to assess the fire performance of a selection of FR/non-FR building materials. For the first time, the composition of char formations for the selective polymers was predicted by the MD simulation by analysing the accumulation of pure carbon chain compounds. The extracted pyrolysis kinetics achieved accurate fits with the experimental data. Furthermore, the application of MD allowed the characterisation of the full distribution of volatile and toxic gas species without substantial prior knowledge or experimental testing. The realised pyrolysis inputs were applied in the CFD model for cone calorimeter simulations, which yielded good agreement with experiments in terms of heat release, ignition time and burning duration. With the incorporation of solid interface tracking and char formation, the model was able to predict the thermal degrading solid surface and capture the prolonged burn duration. The char formation acts as a thermal layer to protect the unburnt virgin material from heat penetration during the pyrolysis process. Furthermore, with the application of detailed chemical kinetics for combustion and soot formation reaction mechanisms, the fire model was able to aptly predict the generation of asphyxiant gas such as CO and CO2 during the burning process
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