Modeling the Ignition Risk: Analysis before and after Megafire on Maule Region, Chile

Abstract

Wildland fires are a phenomenon of broad interest due to their relationship with climate change. The impacts of climate change are related to a greater frequency and intensity of wildland fires. In this context, megafires have become a phenomenon of particular concern. In this study, we develop a model of ignition risk. We use factors such as human activity, geographic, topographic, and land cover variables to develop a bagged decision tree model. The study area corresponds to the Maule region in Chile, a large zone with a Mediterranean climate. This area was affected by a megafire in 2017. After generating the model, we compared three interface zones, analyzing the scar and the occurrences of ignition during and after the megafire. For the construction of georeferenced data, we used the geographic information system QGIS. The results show a model with high fit goodness that can be replicated in other areas. Fewer ignitions are observed after the megafire, a high recovery of urban infrastructure, and a slow recovery of forest plantations. It is feasible to interpret that the lower number of ignitions observed in the 2019–2020 season is a consequence of the megafire scar. It is crucial to remember that the risk of ignition will increase as forest crops recover. Wildland fire management requires integrating this information into decision-making processes if we consider that the impacts of climate change persist in the area.

Stochastic Approaches Systems to Predictive and Modeling Chilean Wildfires

“…Deterministic models are tested for the series of number of fires and affected surface (ha). It is important to note that deterministic models do not make assumptions about the processes, so the series applied over deterministic models are those shown in Figures 2b and 3b that considers only log-transformation, as shown in (21). Results of models on the number of fires are shown in Table 4; it can be seen how MAPE values are slightly similar to those shown by stochastic models in Table 2.…”

“…These fires have even entered urban centers, such as Valparaíso, in 2014 [20]. Meanwhile, communities in southern regions of the country, such as the town of Santa Olga (Maule Region), disappeared due to an uncontrolled fire in 2017 [20,21]. In turn, an important international tourist attraction, such as Torres del Paine National Park, in Chilea's Patagonia region, suffered a loss of more than 17 thousand hectares in a fire declared between December 2010 and March 2011, one of the largest recorded in Chile [22,23].…”

“…The operator shown in (20) helps to stabilize a time series removing changes in levels, eliminating trends, and making the mean constant over time T, depending on the levels and characteristics of the process sometimes differencing operators are required to be applied more than one time or with different levels of parameter d, such parameter is called local differentiation when d relates to immediate previous values, while if d takes seasons of the series is called seasonal differentiation denoted by D. Moreover, another way for stabilizing a time-series process is applying a transformation to the data, altogether with differentiation, a common practice is to apply a log-transformation, as shown in (21):…”