Global trends in wildfire and its impacts: perceptions versus realities in a changing world

Wildfire has been an important process affecting the Earth's surface and atmosphere for over 350 million years and human societies have coexisted with fire since their emergence. Yet many consider wildfire as an accelerating problem, with widely held perceptions both in the media and scientific papers of increasing fire occurrence, severity and resulting losses. However, important exceptions aside, the quantitative evidence available does not support these perceived overall trends. Instead, global area burned appears to have overall declined over past decades, and there is increasing evidence that there is less fire in the global landscape today than centuries ago. Regarding fire severity, limited data are available. For the western USA, they indicate little change overall, and also that area burned at high severity has overall declined compared to pre-European settlement. Direct fatalities from fire and economic losses also show no clear trends over the past three decades. Trends in indirect impacts, such as health problems from smoke or disruption to social functioning, remain insufficiently quantified to be examined. Global predictions for increased fire under a warming climate highlight the already urgent need for a more sustainable coexistence with fire. The data evaluation presented here aims to contribute to this by reducing misconceptions and facilitating a more informed understanding of the realities of global fire. This article is part of themed issue ‘The interaction of fire and mankind’

Data-Driven Power System Optimal Decision Making Strategy under Wildfire Events

Wildfire activities are increasing in the western United States in recent years, causing escalating threats to power systems. This paper developed an optimal and data-driven decision-making framework that improves power system resilience under wildfire risks. An optimal load shedding plan is formulated based on optimal power flow analysis. To avoid power system cascading failure caused by wildfire, we added additional transmission line flow constraints based on the identification of power lines with high ignition risk. Finally, a data-driven method is developed, leveraging multiple machine learning techniques, to model the complex correlations between input wildfire scenarios and the output power management strategy with significantly reduced computational complexities. The proposed data-driven decision-making framework can reduce the safety impacts on the electricity consumers, improve power system resilience under wildfire events

The contributing factors of large wildfires : exploring the main structural factors driving large wildfire ignition and spread in central Portugal (2005-2015)

Dissertation presented as partial requirement for obtaining the Master’s degree in Statistics and Information Management, with a specialization in Information Analysis and ManagementLarge wildfires have devastating human, environmental and economic consequences and are responsible for the majority of total burned area in Mediterranean Europe, even though they account for only a marginal portion of all fire occurrences. Most predictions suggest a global intensification of fire danger, and among all European Mediterranean countries Portugal displays the highest fire incidence. The purpose of this work is to examine the main factors driving large wildfire ignition and spread in central Portugal between 2005 and 2015, contributing with empiric knowledge on their importance and variability throughout the study area. This research was successful at listing a comprehensive set of elements contributing to fire occurrence and at gathering data on these phenomena. Spatial cluster analysis was used to find homogeneous regions within the study area concerning the main factors influencing both fire ignition and burned area. Probit and two-part regression techniques were used to model the contribution of the different elements driving large fire occurrence and propagation. The main findings of this analysis confirm the presence of spatial variability in the contribution exerted by most structural factors driving large wildfire ignition and spread in central Portugal. Additionally, while vegetation characteristics appear much more relevant for fire propagation, socioeconomic elements seem to be connected to fire incidence. All in all, this research provides relevant input with implementation in different fields, from large fire awareness and prevention to the development of wildfire policies, as well as appropriate contributions to methodological concerns in fire danger and fire risk analyses.Os grandes incêndios rurais têm como consequência impactos socioeconómicos e ambientais devastadores e são responsáveis pela maior parte do total de área ardida na Europa mediterrânica, ainda que representem apenas uma fração pouco expressiva do total de ocorrências. A maioria dos estudos prevê uma intensificação do perigo de incêndio, sendo que, entre todos os países europeus da bacia mediterrânica, é Portugal quem apresenta a mais alta incidência deste fenómeno. O objetivo deste trabalho é estudar os fatores que mais contribuíram para a ignição e propagação de grandes incêndios rurais no centro de Portugal entre 2005 e 2015, concorrendo assim com conhecimento empírico relativamente à sua importância e variabilidade na área de estudo. Esta investigação conseguiu listar um conjunto abrangente de elementos que contribuem para a ocorrência de incêndios rurais, assim como reunir os dados necessários. Uma análise de clusters espacial foi aplicada para identificar regiões homogéneas dentro da área de estudo no que respeita aos principais fatores influenciando a ignição e o alastrar dos grandes incêndios. Modelos probit e em duas partes foram utilizados para analisar a contribuição dos diferentes elementos para a ocorrência e propagação dos fogos. Os resultados deste estudo confirmam a presença de variação espacial no impacto exercido pela maioria dos fatores estruturais que contribuem para a ocorrência e propagação dos grandes incêndios rurais. Por outro lado, enquanto as características da vegetação se revelam mais relevantes na perspetiva do alastrar dos incêndios, os fatores socioeconómicos parecem estar relacionados com a ignição destes fenómenos. Em suma, este estudo contribui com informação relevante, a implementar em diferentes âmbitos, desde a consciencialização das populações à prevenção e ao desenvolvimento de políticas na área dos fogos rurais. Este apresenta ainda contributos apropriados na área de metodologias de análise do perigo e risco de incêndio

Quantifying the Risk of Wildfire Ignition by Power Lines under Extreme Weather Conditions

Utilities in California conduct Public Safety Power Shut-offs (PSPSs) to eliminate the elevated chances of wildfire ignitions caused by power lines during extreme weather conditions. We propose Wildfire Risk Aware operation planning Problem (WRAP), which enables system operators to pinpoint the segments of the network that should be de-energized. Sustained wind and wind gust can lead to conductor clashing, which could ignite surrounding vegetation. The 3D non-linear vibration equations of power lines are employed to generate a dataset that considers physical, structural, and meteorological parameters. With the help of machine learning techniques, a surrogate model is obtained which quantifies the risk of wildfire ignition by individual power lines under extreme weather conditions. The cases illustrate the superior performance of WRAP under extreme weather conditions in mitigating wildfire risk and serving customers compared to the naive PSPS approach and another method in the literature. Cases are also designated to sensitivity analysis of WRAP to critical load-serving control parameters in different weather conditions. Finally, a discussion is provided to explore our wildfire risk monetization approach and its implications for WRAP decisions

GIS-based methodology for prioritization of preparedness interventions on road transport under wildfire events

Climate change is leading to a rise in the occurrence and intensity of wildfires, exacerbated by the growing encroachment of communities into the natural environment, posing challenges to our global capacity to respond to wildfires. During wildfire events, road transport infrastructure becomes crucial for the evacuation of people and accessibility to an emergency by first responders. Nevertheless, resilience management of transportation infrastructure affected by wildfires is poorly considered, despite its relevant role and high exposure to wildfires. Therefore, this study proposes a new methodology to estimate the priority level for wildfire preparation by combining exposure and criticality of road transportation infrastructure to wildfire hazards with consideration of different wildfire categories. The analysis is conducted at the system level considering interdependencies and redundancies among infrastructure components and using a geographic information system (GIS) to automate the modelling process and visualization of results. The proposed methodology is applied to a case study in the Leiria region of Portugal, demonstrating its utility in prioritizing economic resources and decision-making for areas requiring preparation. This approach can serve as a resilience-based tool for decision-making, supporting the implementation of effective adaptation strategies to enhance wildfire resilience.This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020, and under the Associate Laboratory Advanced Production and Intelligent Systems ARISE under reference LA/P/0112/2020. This work is financed by national funds through the Foundation for Science and Technology (Fundaç˜ao para a ciˆencia e tecnologia, FCT, Portugal), under grant agreement 2020.05755.BD attributed to the first author

Machine learning techniques for fine dead fuel load estimation using multi‐source remote sensing data

Fine dead fuel load is one of the most significant components of wildfires without which ignition would fail. Several studies have previously investigated 1‐h fuel load using standard fuel parameters or site‐specific fuel parameters estimated ad hoc for the landscape. On the one hand, these methods have a large margin of error, while on the other their production times and costs are high. In response to this gap, a set of models was developed combining multi‐source remote sensing data, field data and machine learning techniques to quantitatively estimate fine dead fuel load and understand its determining factors. Therefore, the objectives of the study were to: (1) estimate 1‐h fuel loads using remote sensing predictors and machine learning techniques; (2) evaluate the performance of each machine learning technique compared to traditional linear regression models; (3) assess the importance of each remote sensing predictor; and (4) map the 1‐h fuel load in a pilot area of the Apulia region (southern Italy). In pursuit of the above, fine dead fuel load estimation was performed by the integration of field inventory data (251 plots), Synthetic Aperture Radar (SAR, Sentinel‐1), optical (Sentinel‐2), and Light Detection and Ranging (LIDAR) data applying three different algorithms: Multiple Linear regression (MLR), Random Forest (RF), and Support Vector Machine (SVM). Model performances were evaluated using Root Mean Squared Error (RMSE), Mean Squared Error (MSE), the coefficient of determination (R2) and Pearson’s correlation coefficient (r). The results showed that RF (RMSE: 0.09; MSE: 0.01; r: 0.71; R2: 0.50) had more predictive power compared to the other models, while SVM (RMSE: 0.10; MSE: 0.01; r: 0.63; R2: 0.39) and MLR (RMSE: 0.11; MSE: 0.01; r: 0.63; R2: 0.40) showed similar performances. LIDAR variables (Canopy Height Model and Canopy cover) were more important in fuel estimation than optical and radar variables. In fact, the results highlighted a positive relationship between 1‐h fuel load and the presence of the tree component. Conversely, the geomorphological variables appeared to have lower predictive power. Overall, the 1‐h fuel load map developed by the RF model can be a valuable tool to support decision making and can be used in regional wildfire risk management

Wildfires Triggering Natech Events: A structural analysis of Natech hazards in the context of the emerging wildfire threat in Europe

The wildfire risk in Europe is increasing, as is the geographic expansion of wildfires to north and southeast Europe. When wildfires intersect with hazardous industry in Wildland Industrial Interfaces, they can trigger toxic spills, fires or explosions. Climate change and the associated transformation of the surroundings of industrial installations raise concerns about future industrial plant safety in wildfire zones, as external hazards can also be carried into industrial sites. However, there is no integrated European fire management system that would meet the requirements for the prevention of wildfire triggered industrial accidents. This study focuses on Wildland Industrial Interfaces and analyses the vulnerability of European industrial sites to wildfires based on current scientific knowledge and international initiatives. It also makes recommendations for policy makers, industry, emergency responders and academia on how to close existing risk management gaps. Past data shows that there have already been incidents due to wildfires, confirming that this hazard has the potential to increasingly cause damage to technological systems in the future. If no appropriate protection measures are implemented, wildfires can harm industrial facilities via thermal radiation (heat), ember flight or direct flame impingement to industrial infrastructure or process equipment. Since the necessary level of safety can only be reached by an integrated risk management approach involving all stakeholders, concerted action of policy makers, industry, emergency responders and science is required.JRC.E.2-Technology Innovation in Securit