The influence of planting size and configuration on landscape fire risk

Integrated wildfire danger models and factors: A review

Wildfires have been systematically studied from the early 1950s, with significant progress in the applied computational methodologies during the 21st century. However, modern methods are barely adopted by administrative authorities, globally, especially those considering probabilistic models concerning human-caused fires. An exhaustive review on wildfire danger studies has not yet been performed. Therefore, the present review aims at collecting and analyzing integrated modeling approaches in estimating forest fire danger, examining the driving factors, and evaluating their influence on fire occurrence. The main objective is to propose the top performing methods and the most important risk factors for the development of an Integrated Wildfire Danger Risk System (IWDRS). Studies were classified based on the applied technique, i.e., geographic information systems, remote sensing, statistics, machine learning, simulation modeling and miscellaneous techniques. The conclusions of each study concerning the relative importance of model input variables are also reported. Online search engines such as 'Scopus', 'Google Scholar', 'WorldWideScience', 'ScienceDirect' and 'ResearchGate' were used in relevant literature searches published in scientific journals, manuals and technical documentation. A total of 230 studies were gathered with a selected subset being evaluated in a meta-analysis process. Machine learning techniques outperform average classic statistics, although their predictability relies heavily on the quantity and the quality of the input data. Geographic information systems and remote sensing are considered valuable yet supplementary tools. Modeling techniques apply best to fire behavior prediction, while other techniques referenced in the current review are potentially useful but further investigation is needed. In conclusion, wildfire danger is a function of seven thematic groups of variables: meteorology, vegetation, topography, hydrology, socio-economy, land use and climate. Ninety-five explanatory drivers are proposed.

Improved accuracy of wildfire simulations using fuel hazard estimates based on environmental data

Wildfire extent and their impacts are increasing around the world. Fire management agencies use fire behaviour simulation models operationally (during a wildfire event) or strategically for risk assessment and treatment. These models provide agencies with increased knowledge of fire potential to improve identification of the best strategies for reducing risk. One of the greatest areas of uncertainty in fire simulations is the data relating to fuel, which are usually based on simplified response trajectories with time since fire within vegetation communities. There is a clear need to better predict relevant fuel variables across landscapes to reduce uncertainties in fire simulations. In this study, we compare the performance of fuel hazard models based on environmental variables (environmental model) with those currently implemented based on a negative exponential relationship with time since fire (NEGEXP) using the state of Victoria in south-eastern Australia as an environmentally diverse case study. The models predicted similar broadscale patterns in fuel hazard but with considerable regional variation. The NEGEXP model was less accurate than the environmental model, which had 41-47% accuracy on an independent data set cf. 24-35% for NEGEXP. Model differences resulted in significant differences in the extent and spatial location of predicted fires with NEGEXP consistently predicting larger fires. Fuel is made up of the live and dead components of vegetation, both of which are influenced by a range of environmental factors. As our study highlights, ignoring environmental factors in simple fuel models based on broad vegetation types (like NEGEXP) will likely compromise the predictive accuracy of fire behaviour models. Only when environmental factors are accounted for can we more accurately predict fuels across landscapes and thereby improve the accuracy of fire behaviour predictions and the estimation of fire risks.