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

Risk Minimization for Spreading Processes over Networks via Surveillance Scheduling and Sparse Control

Spreading processes, such as epidemics and wildfires, have an initial localized outbreak that spreads rapidly throughout a network. The real-world risks associated with such events have stressed the importance and current limitations of methods to quickly map and monitor outbreaks and to reduce their impact by planning appropriate intervention strategies. This thesis is, therefore, concerned with risk minimization of spreading processes over networks via surveillance scheduling and sparse control. This is achieved by providing a flexible optimization framework that combines surveillance and intervention to minimize the risk. Here, risk is defined as the product of the probability of an outbreak occurring and the future impact of that outbreak. The aim is now to bound or minimize the risk by allocation of resources and use of persistent monitoring schedules. When setting up an optimization framework, four other aspects have been found to be of importance. First of all, being able to provide targeted risk estimation and minimization for more vulnerable or high cost areas. Second and third, scalability of algorithms and sparsity of resource allocation are essential due to the large network structures. Finally, for wildfires specifically, there is a gap between the information embedded in fire propagation models and utilizing it for path planning algorithms for efficient remote sensing. The presented framework utilizes the properties of positive systems and convex optimization, in particular exponential cone programming, to provide flexible and scalable algorithms for both surveillance and intervention purposes. We demonstrate with different spreading process examples and scenarios, focusing on epidemics and wildfires, that the presented framework gives convincing and scalable results. In particular, we demonstrate how our method can include persistent monitoring scenarios and provide more targeted and sparse resource allocation compared to previous approaches

Decreasing average wildfire size through random fuel treatments: A boreal forest case study.

Area burned in boreal forests is increasing due to climate change effects and regional increases in fuels due to a history of successful fire suppression. An increase in area burned threatens valuable resources and infrastructure in timber resources areas and communities. The ecological integrity of protected areas may also be threatened if fires increase in frequency and size beyond what would have occurred prior to effective fire suppression and the effects of climate change. Fuel management is one strategy being tested by fire management agencies and researchers to address these concerns. However the pattern of fuel management that best regulates area burned has yet to be determined. This thesis investigates random fragmentation of highly flammable fuels in the boreal forests of North-western Ontario. A case study of Quetico Provincial Park is used. Using the fire growth simulation model, Prometheus, I tested whether, under extreme fire behaviour conditions, fuel isolation (FI) and fuel conversion (FC) were effective at reducing average area burned in the park. Through the simulation of over 21,000 large fires, I determined that FI and FC are effective in significantly reducing area burned for this case study. Based on these findings, random FI and FC should be studied further on a regional basis and as a prescriptive, proactive method of reducing area burned in boreal forests

Optimum sensors allocation for a forest fires monitoring system

Every year forest fires destroy millions of hectares of land worldwide. Detecting forest fire ignition in the early stages is fundamental to avoid forest fires catastrophes. In this approach, Wireless Sensor Network is explored to develop a monitoring system to send alert to authorities when a fire ignition is detected. The study of sensors allocation is essential in this type of monitoring system since its performance is directly related to the position of the sensors, which also defines the coverage region. In this paper, a mathematical model is proposed to solve the sensor allocation problem. This model considers the sensor coverage limitation, the distance, and the forest density interference in the sensor reach. A Genetic Algorithm is implemented to solve the optimisation model and minimise the forest fire hazard. The results obtained are promising since the algorithm could allocate the sensor avoiding overlaps and minimising the total fire hazard value for both regions considered.This research received no external funding.info:eu-repo/semantics/publishedVersio

The Acceptance of Using Information Technology for Disaster Risk Management: A Systematic Review

The numbers of natural disaster events are continuously affecting human and the world economics. For coping with disaster, several sectors try to develop the frameworks, systems, technologies and so on. However, there are little researches focusing on the usage behavior of Information Technology (IT) for disaster risk management (DRM). Therefore, this study investigates the affecting factors on the intention to use IT for mitigating disaster’s impacts. This study conducted a systematic review with the academic researches during 2011-2018. Two important factors from the Technology Acceptance Model (TAM) and others are used in describing individual behavior. In order to investigate the potential factors, the technology platforms are divided into nine types. According to the findings, computer software such as GIS applications are frequently used for simulation and spatial data analysis. Social media is preferred among the first choices during disaster events in order to communicate about situations and damages. Finally, we found five major potential factors which are Perceived Usefulness (PU), Perceived Ease of Use (PEOU), information accessibility, social influence, and disaster knowledge. Among them, the most essential one of using IT for disaster management is PU, while PEOU and information accessibility are more important in the web platforms

Cellular automata for population growth prediction: Tripoli-Libya case

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDue to obstruction in the national plan of urbanization in Tripoli (Libya) and population growth, serious problems have emerged in the form of random settlements, overcrowding and poor infrastructure. After more than two decades of inertia, the government has created a national plan in order to resolve the problems, hence it has enforced the demolition of some zones and modified other (irregularly built) ones, however the process is extremely costly. This research introduces a solution through cellular automata (CA) model to predict growth trends; size of residential, industrial and utilities areas; and to project future population. The model is implemented using digitized land use maps of Tripoli to indicate each areas as group of cells to predict their growth. The model incorporates two types of fuzzy rules bases, the first of which is based on the inputs population and area, and the second of which is based on the three inputs of population, area and density. The population prediction is performed using three scenarios, namely decreasing, fixed and increasing growth rates, such that all possibilities of growth are covered. In addition, the residential area prediction is performed based on two cases: normal density and low density. The former is introduced since new areas tend to have more open spaces and bigger houses. Furthermore, the model considers the growth of the industrial areas to be slower than that of residential areas. The model is developed and validated for the period of 1980 to 2010. The prediction is performed for thirty years from 2010 to 2040. In addition to the CA model, a regression model is developed and tested on the three growth scenarios for the same period (30 years). The prediction results are very close for 2040 in terms of population. The model incorporates the introduction of public services areas that are distributed equally on the growth areas, which occupy about 15-20% of the total area. This model can help the government to develop areas in a proper way and controls the expansion to have well layout and planned of the city, improving people's standard of living sustainably, while protecting the environment with better planning

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