Using cellular automata to simulate wildfire propagation and to assist in fire management

Cellular automata have been successfully applied to simulate the propagation of wildfires with the aim of assisting fire managers in defining fire suppression tactics and in planning fire risk management policies. We present a cellular automaton designed to simulate a severe wildfire episode that took place in Algarve (southern Portugal) in July 2012. During the episode almost 25&thinsp;000&thinsp;ha burned and there was an explosive stage between 25 and 33&thinsp;h after the onset. Results obtained show that the explosive stage is adequately modeled when introducing a wind propagation rule in which fire is allowed to spread to nonadjacent cells depending on wind speed. When this rule is introduced, deviations in modeled time of burning (from estimated time based on hot spots detected from satellite) have a root-mean-square difference of 7.1 for a simulation period of 46&thinsp;h (i.e., less than 20&thinsp;%). The simulated pattern of probabilities of burning as estimated from an ensemble of 100 simulations shows a marked decrease out of the limits of the observed scar, indicating that the model represents an added value to help decide locations of where to allocate resources for fire fighting.</p

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

Scale and abstraction : the sensitivity of fire regime simulation to nuisance parameters

Fire plays a key role in ecosystem dynamics and its impact on environmental, social and economic assets is increasingly a critical area of research. Fire regime simulation models are one of many approaches that provide insights into the relative importance of factors driving the dynamics of fire-vegetation systems. Fire propagates as a contagious process and simulation is an approach that captures this behaviour explicitly, integrating spatial and temporal data to produce auto-correlated patterns of fire regimes. However, when formulating these models, time and many aspects of space must be made discrete. These parameters are 'nuisance parameters': parameters necessary for the model formulation but not otherwise of interest. Fire growth simulations are therefore discrete approximations of continuous non-linear systems, and it might be expected that the values chosen for these nuisance parameters will be important. While it is well known that discrete geometries have consequences for the shape and area of simulated fires, no research has investigated the consequence this may have for estimates of the relative importance of the various drivers of fire regimes. I argue that nuisance parameters can be demonstrated to be unimportant for this class of model. I use the idea of 'importance' to underline the need for context with such an assertion. With sufficient replication, any parameter can be found statistically significant. A parameter is important, on the other hand, if different values produce qualitatively different outcomes. Models are commonly either re-parameterised to account for changes in resolution or scaling-up methods applied if such exist. I will further argue that such differences as there are in model outputs due to spatial resolution, cannot be accounted for by either re-parameterising or using a common approach that allows resolution to vary over the spatial extent. A set of experiments were devised using a published fire regime simulation model, modified, verified and validated, to isolate just those aspects of the model's sensitivity to resolution and discrete geometries that are unavoidable or intrinsic to these choices. This new model was used to test the above hypotheses, using peer-reviewed treatments that stand as yardsticks by which formal estimates of the importance of nuisance parameters can be made. As estimated by the model, neither spatio-temporal resolution nor any of the various choices available for discrete geometries, altered the model predictions. As expected, it is spatial resolution that has the greatest impact on running times for the model but this study finds that neither calibration, nor taking an approach that allows resolution to vary over the spatial extent, can account for differences in model outputs that arise from running simulations at coarser resolutions. All models are abstractions and a good model should ideally hold over levels of abstraction. This is rarely the case, but this study shows that results obtained through simulation in estimating the drivers of fire frequency in large landscapes, are robust with regard to these aspects of abstraction. This adds considerable confidence to a significant body of work that has used this approach over the last two decades