As part of Parks Canada’s management initiatives, St. Lawrence Islands National Park (SLINP) funded this study to learn more about the natural local fire regime, learn about the risks associated with fire in a changing climate scenario, and to aid in protection efforts of the fire-dependent species Pinus rigida (pitch pine), which is listed provincially as a species at risk. The study site selected was Little Black Lake (44º 32'45.20" N, 76 º 03'12.06” W), which is ideal because of its small size and isolated watershed. A 4.5 m Livingston-piston core and a 0.5 m Glew gravity core were extracted from the lake basin. Charcoal macrofossils >125 µm were quantified at 0.5 cm intervals to produce a high-resolution (14 years) fire record. A chronology was created for each of the two cores together using a combination of 13 14C dates and 20 210Pb dates to complete a record spanning from 2008 to >11000 Cal yrs BP. In general, the fire regime appears to be non-stationary with overall low CHAR (charcoal particles per cm2 of sediment per year) throughout the Holocene. The mean fire return interval for the entire record was on the century scale, at 244 years. The early- and mid-Holocene show low CHAR and few peaks during a period dominated by spruce and pine. Contrastingly, the late Holocene shows an increase in CHAR and peaks during hardwood dominance, which may be due to a change in fuel, as suggested by charcoal morphotypes. A detailed look at the Late Holocene through an analysis of the Glew gravity core, shows a shorter mean fire interval. Comparisons of the Little Black Lake fire record with other vegetation and charcoal records from this region indicate interactions between climate and changing fuel sources may be explanations for the non-stationarity of the fire regime. Management steps for St. Lawrence Islands National Park could include continuing small, isolated and infrequent burns and continued monitoring of local Pitch Pine populations provided spatial and temporal heterogeneity are taken into account. / Thesis (Master, Environmental Studies) -- Queen's University, 2010-01-31 23:58:09.579

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Evaluating options, making informed decisions, measuring performance, and achieving management objectives in forest fire management organizations (FFMO) requires the development and application of measures that reflect how an organization has managed challenges presented. This thesis makes use of historical fire records from 1961 – 2008 to assess the impact of weather and management interventions on fire suppression effectiveness and annual area burned (AAB) within Alberta’s Boreal Natural Region. Statistical models relating AAB to variations in the proportion of extreme fire behaviour potential days suggest a significant portion of inter-annual variation in AAB (82 %) can be explained by the proportion of days when the Build-Up Index exceeds its 95th percentile. Probability of containment and large fire occurrence models are also developed that provide the framework for a new approach to presuppression planning in Alberta that can account for factors significantly influencing fire occurrence and containment outcome.

Evaluating options, making informed decisions, measuring performance, and achieving management objectives in forest fire management organizations (FFMO) requires the development and application of measures that reflect how an organization has managed challenges presented. This thesis makes use of historical fire records from 1961 – 2008 to assess the impact of weather and management interventions on fire suppression effectiveness and annual area burned (AAB) within Alberta’s Boreal Natural Region. Statistical models relating AAB to variations in the proportion of extreme fire behaviour potential days suggest a significant portion of inter-annual variation in AAB (82 %) can be explained by the proportion of days when the Build-Up Index exceeds its 95th percentile. Probability of containment and large fire occurrence models are also developed that provide the framework for a new approach to presuppression planning in Alberta that can account for factors significantly influencing fire occurrence and containment outcome.

En diferentes áreas científicas, el uso de modelos para representar sistemas físicos se ha tornado una tarea habitual. Estos modelos reciben parámetros de entradas representando condiciones particulares y proveen una salida que representa la evolución del sistema. Usualmente, dichos modelos están integrados en herramientas de simulación que pueden ser ejecutadas en una computadora.Un caso particular donde los modelos resultan muy útiles es la predicción de la propagación de Incendios Forestales. Los incendios se han vuelto un gran peligro que cada año provoca grandes pérdidas desde el punto de vista ambiental, económico, social y humano. En particular, las estaciones secas y calurosas incrementan seriamente el riesgo de incendios en el área Mediterránea. Por lo tanto, el uso de modelos es relevante para estimar el riesgo de incendios y predecir el comportamiento de los mismos.Sin embargo, en muchos casos, los modelos presentan una serie de limitaciones. Estas se relacionan con la necesidad de un gran número de parámetros de entrada. En muchos casos, tales parámetros presentan cierto grado de incertidumbre debido a la imposibilidad de medirlos en tiempo real, y deben ser estimados a partir de datos indirectas. Además, en muchos casos estos modelos no se pueden resolver analíticamente y deben ser calculados aplicando métodos numéricos que son una aproximación de la realidad.Se han desarrollado diversos métodos basados en asimilación de datos para optimizar los parámetros de entrada. Comúnmente, estos métodos operan sobre un gran número de parámetros de entrada y, a través de optimización, se enfocan en hallar un único conjunto de parámetros que describa de la mejor forma posible el comportamiento previo. Por lo tanto, es de esperar que el mismo conjunto de valores pueda ser usado para describir el futuro inmediato.Sin embargo, esta clase de predicción se basa en un solo conjunto de parámetros y, por lo que se explicó, debido a aquellos parámetros que presentan un comportamiento dinámico, los valores optimizados pueden no resultar adecuados para el siguiente paso.El presente trabajo propone un método alternativo. Nuestro sistema, llamado Sistema Estadístico para la Gestión de Incendios Forestales, se basa en conceptos estadísticos. Su objetivo es hallar un patrón del comportamiento del incendio, independientemente de los valores de los parámetros. En este método, cada parámetro es representado mediante un rango de valores y una cardinalidad. Se generan todos los posibles escenarios considerando todas las posibles combinaciones de los valores de los parámetros de entrada, y entonces se evalúa la propagación para cada caso. Los resultados son agregados estadísticamente para determinar la probabilidad de que cada área se queme. Esta agregación se utiliza para predecir el área quemada en el siguiente paso.Para validar nuestro método, usamos un conjunto de quemas reales prescritas. Además, comparamos nuestro método contra otros dos. Uno de estos dos métodos fue implementado para este trabajo: GLUE (Generalized Likelihood Uncertainty Estimation). Dicho método corresponde a una adaptación de un sistema hidrológico. El otro caso (Método Evolutivo) es un algoritmo genético previamente desarrollado e implementado también por nuestro equipo de investigación.Los sistemas propuestos requieren un gran número de simulaciones, razón por la cual decidimos usar un esquema paralelo para implementarlos. Esta forma de trabajo difiere del esquema tradicional de teoría y experimentación, lo cual es la forma común de la ciencia y la ingeniería. El cómputo científico está en continua expansión, principalmente a través del análisis de modelos matemáticos implementados en computadores. Los científicos e ingenieros desarrollan programas de computador que modelan los sistemas bajo estudio. Esta metodología está creando una nueva rama de la ciencia basada en métodos computacionales, la cual crece de forma acelerada. Esta aproximación es llamada Ciencia Computacional. / In many different scientific areas, the use of models to represent the physical system has become a common strategy. These models receive some input parameters representing the particular conditions and provide an output representing the evolution of the system. Usually, these models are integrated in simulation tools that can be executed on a computer.A particular case where models are very useful is the prediction of Forest Fire propagation. Forest fire is a very significant hazard that every year provokes huge looses from the environmental, economical, social and human point of view. Particularly dry and hot seasons seriously increase the risk of forest fires in the Mediterranean area. Therefore, the use of models is very relevant to estimate fire risk, and predict fire behavior.However, in many cases models present a series of limitations. Usually, such limitations are due to the need of a large number of input parameters. In many cases such parameters present some uncertainty due to the impossibility to measure all of them in real time and must be estimated from indirect measurements. Moreover, in most cases these models cannot be solved analytically and must be solved applying numerical methods that are only an approach to reality (still without considering the limitations that present the translations of these solutions when they are carried out by means of computers).Several methods based on data assimilation have been developed to optimize the input parameters. In general, these methods operate over a large number of input parameters, and, by mean of some kind of optimization, they focus on finding a unique parameter set that would describe the previous behavior in the best form. Therefore, it is hoped that the same set of values could be used to describe the immediate future.However, this kind of prediction is based on a single value of parameters and, as it has been said above, for those parameters that present a dynamic behavior the new optimized values cannot be adequate for the next step.The objective of this work is to propose an alternative method. Our method, called Statistical System for Forest Fire Management, is based on statistical concepts. Its goal is to find a pattern of the forest fire behavior, independently of the parameters values. In this method, each parameter is represented by a range of values with a particular cardinality for each one of them. All possible scenarios considering all possible combinations of input parameters values are generated and the propagation for each scenario is evaluated. All results are statically aggregated to determine the burning probability of each area. This aggregation is used to predict the burned area in the next step.To validate our method, we use a set of real prescribed burnings. Furthermore, we compare our method against two other methods. One of these methods was implemented by us for this work: GLUE (Generalized Likelihood Uncertainty Estimation). It corresponds to an adaptation of a hydrological method. The other method (Evolutionary method) is a genetic algorithm previously developed and implemented by our research team.The proposed system requires a large number of simulations, a reason why we decide to use a parallel-scheme to implement them. This way of working is different from traditional scheme of theory and experiment, which is the common form of science and engineering. The scientific computing approach is in continuous expansion, mainly through the analysis of mathematical models implemented on computers. Scientists and engineers develop computer programs that model the systems under study. This methodology is creating a new branch of science based on computational methods that is growing very fast. This approach is called Computational Science.

There is presently no wildfire model developed for Swedish conditions, only a fire danger rating system (FWI) has been developed for Swedish conditions. The demand for a wildfire model has not been great in the past in Sweden but the climate changes now taking place increases the risk of large and intensive wildfires in Sweden. The need for additional and better tools for sizing-up wildfires will be in great demand in the future. This pre-study is aimed at: - Presenting what has been done in the wildfire modeling field during the years and mainly the last twenty years. - Giving recommendations on the continued work with developing a Swedish wildfire model. The method that was used was literature and article survey. The study also looks into the required input data for a wildfire model and the input data available at the moment. This issue is highly crucial as the quality of the output of a wildfire model is depending upon the quality of the input data. During the study, a primitive wildfire model was constructed and refined in order to get an insight in the complexities and problems with developing an operational model. The following characterization of wildfire models was used during the study: - Statistical models: based primarily on statistics from earlier or experimental fires. They do not explicitly consider the controlling physical processes. - Semi-empirical models: based on physical laws, but enhanced with some empirical factors, often by lumping all physical mechanisms for heat transfer together. - Physical models: based on physical principles and distinguishing between physical mechanisms for heat transfer. The statistical models make no attempt to involve physical processes, as they are merely a statistical description of test fires. Thus the lack of a physical basis means that statistical models must be used carefully outside the test conditions. Semi-empirical models are often based on conservation of energy principles but do not make any difference between conduction, convection and radiation heat transfer. The semi-empirical model has low computational requirements and includes variables that are generally easy to measure in the field. So despite the issue with limited accuracy, the speed and simplicity of these models make them useful for operational use. Physical models have the advantage that they are based on known relationships and thus facilitating their scaling. Thus we can expect that physical models would provide the most accurate predictions and have the widest applicability. But the work on physical models is suffering of for example the lack of understanding of several processes, such as the characterization of the chemical processes taking place during combustion, the resulting flame characteristics and the isolation and quantification of physical processes governing heat transfer. The input data available today are generally not detailed enough for physical models. As a result, a very detailed physical model will still only give imprecise predictions. As better and more detailed input will be available, the use of physical models will be more justified. A semi-empirical model is recommended being developed in Sweden. This conclusion is based upon the following factors: - The accuracy of a semi-empirical model is generally much better than for a statistical model, also the use of a semi-empirical model is much wider than the use of a statistical model. - The amount of work required for developing a semi-empirical model will not differ much from the amount of work required for a statistical model. In both cases a number of test fires will have to be conducted to define and calibrate a number of fuel models representative of Sweden. - Presently the performance and application of physical models is not at an acceptable level (due to for example the complexity which they are to model and the computational capabilities of the PC’s of today) for operational use. The semi-empirical model for Sweden is recommended to be built upon Swedish conditions (i.e. built upon the type of vegetation found in Sweden) instead of trying to retrofit the local Swedish conditions into an existing model. This would most likely give the best output for Swedish conditions. A system for better input data - weather and fuel data – should be worked on as well. This could for example take advantage of the results of the very promising “Alarm”-project that is being conducted in western part of Sweden. Regarding the issue on better fuel data, new technology for satellite images or aerial photos and image classification techniques must be monitored as one major problem to be solved is distinguishing between the canopy fuel and the ground fuel. For more specific conclusions and reflections, please see the analysis and discussion, and conclusions sections of this report.

The Ontario Ministry of Natural Resources uses airtankers for forest fire suppression that now have onboard GPS units that track their real-time location, velocity and altitude. However, the GPS data does not indicate which fire is being fought, the time each airtanker spends travelling to and from each fire or the time each airtanker spends flying between each fire and the lake from which it scoops water to drop on the fire. A pattern recognition algorithm was developed and used to determine what was happening at each point along the airtanker’s track, including the time and location of every water pickup. This pre-processed data was used to develop detailed models of the airtanker service process. A discrete-event simulation model of the initial attack airtanker system was also developed and used to show how service process models can be incorporated in other models to help solve complex airtanker management decision-making problems.

The Ontario Ministry of Natural Resources uses airtankers for forest fire suppression that now have onboard GPS units that track their real-time location, velocity and altitude. However, the GPS data does not indicate which fire is being fought, the time each airtanker spends travelling to and from each fire or the time each airtanker spends flying between each fire and the lake from which it scoops water to drop on the fire. A pattern recognition algorithm was developed and used to determine what was happening at each point along the airtanker’s track, including the time and location of every water pickup. This pre-processed data was used to develop detailed models of the airtanker service process. A discrete-event simulation model of the initial attack airtanker system was also developed and used to show how service process models can be incorporated in other models to help solve complex airtanker management decision-making problems.

Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographic references (leaves [111]-127).

Thesis (M.S.)--Florida State University, 2004. / Advisor: Dr. James J. O'Brien, Florida State University, College of Arts and Sciences, Dept. of Meteorology. Title and description from dissertation home page (viewed Jan. 12, 2005). Includes bibliographical references.