Browsing by Author "Farkhondehmaal, Farshad"

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    A cyclical wildfire pattern as the outcome of a coupled human natural system
    Farkhondehmaal, Farshad; Ghaffarzadegan, Navid (Nature Portfolio, 2022-03-28)
    Over the past decades, wildfire has imposed a considerable cost on natural resources and human lives. In many regions, annual wildfire trends show puzzling oscillatory patterns with increasing amplitudes for burned areas over time. This paper aims to examine the potential causes of such patterns by developing and examining a dynamic simulation model that represents interconnected social and natural dynamics in a coupled system. We develop a generic dynamic model and, based on simulation results, postulate that the interconnection between human and natural subsystems is a source of the observed cyclical patterns in wildfires in which risk perception regulates activities that can result in more fire and development of vulnerable properties. Our simulation-based policy analysis points to a non-linear characteristic of the system, which rises due to the interconnections between the human side and the natural side of the system. This has a major policy implication: in contrast to studies that look for the most effective policy to contain wildfires, we show that a long-term solution is not a single action but is a combination of multiple actions that simultaneously target both human and natural sides of the system.
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    Wildfire as Coupled Human Natural System
    Farkhondehmaal, Farshad (Virginia Tech, 2022-02-01)
    Wildfire activity has increased in recent years in the United States, endangering both environment and society. Appropriate management of this phenomenon is only achievable with a thorough understanding of the critical factors influencing wildfire activity in each region. In three essays, I use statistical and mathematical models to examine wildfires and propose solutions to mitigate their impact on society. In the first essay, I focused on building a systematic framework for modeling wildfire as a coupled human-natural system. I employ system dynamics modeling, which was previously applied in various fields, including healthcare, sustainability, and disaster mitigation. I show how, in the absence of exogenous factors such as temperature or lightning, the human perception of fire danger may establish a feedback loop that can yield significant trends such as fluctuation or even fluctuation with rising amplitude when linked with the natural system. This conclusion is counter-intuitive, given that the human contribution to wildfire is typically described in the literature using constant or semi-constant variables. Additionally, I analyzed the impact of three important fire protection measures on reducing burning rates (prescribed burning, enhancing immediate suppression accomplishment, and regulating the rate of WUI growth). The research concludes that appropriately integrating several policies can result in a synergistic effect that is greater than the sum of the effects of the individual policies. The second essay calibrates the model built in the first essay and examines wildfire trends across the contiguous United States. The simulation results closely match the real data, and the model serves as a foundation for data-driven policy research. To be more precise, I fit the model to each state separately and then compare the model's goodness of fit. Following that, I examine the influence of various policies and scenarios on wildfire behavior. In the scenario, I examine the effect of maintaining constant temperatures and precipitation levels relative to the average values for these variables over the last century. For the policy analysis, I examine the influence of three policies on each state (prescribed burning, increasing immediate suppression achievement, and regulating the rate of WUI development). Here, I provide state-specific suggestions about the primary factors that contribute to wildfires and the most effective policies for each state. In the third essay, I have implemented the Oregon wildfire history dataset and integrated it with two other aerial datasets, including meteorological data gathered by weather stations located around the state and counties. Then, using hierarchical modeling on over 10,000 wildfire ignitions, I developed a classification system for determining if a given fire has the potential to grow major or not. However, utilizing a huge dataset and a variety of resources presents several obstacles, such as the presence of missing data. I imputed the missing numbers using a sophisticated mathematical approach called "Predictive Mean Matching".