This paper addresses the growing need to shift wildfire management strategies from suppression to greater preparedness and adaptation in response to increasingly frequent and intense wildfire events. Traditional approaches prioritize suppression actions, but this study emphasizes the combined role of adaptation measures and suppression efforts in enhancing resilience to wildfires. While suppression tackles immediate threats, adaptation aims to reduce long-term vulnerabilities and enhance resilience to future wildfire risks. The European Union has made significant efforts to promote fire-resistant territories, but gaps persist in adaptation knowledge and preparedness. To address this, the study demonstrates the effectiveness of a resilient-preparedness framework to analyze the systemic impact of adaptation measures. Subsequently, the framework is extended to identify the most cost-effective combination of measures to enhance system resilience. The methodology employs a genetic multi-objective algorithm to identify the most effective set of adaptation measures across various wildfire intensities and dimensions of resilience, including physical, operational, and social aspects. By integrating grey, green, and soft adaptation measures, the methodology contributes to understanding how to enhance the wildfire resilience of road networks. Overall, it serves as a decision-support tool to guide initiatives under the EU Green Deal and improve wildfire management strategies.

Understanding and enhancing the resilience of transport networks against climate-induced extreme events, such as wildfires, is critical to minimizing disruptions and their societal impacts. In this context, resilience is essential for effectively coping with these hazards, as road disruptions can hinder evacuation efforts, reduce accessibility, and lead to significant economic losses. Despite scientific progress, existing resilience assessment frameworks have limitations, including scenario-specific results and limited consideration of the underlying resilience concepts. To address these limitations, this paper introduces a resilience framework based on dynamic thresholds and characteristic curves to evaluate system recovery capacity. The framework incorporates a temporal dimension, allowing for the analysis of recovery time and recovery rate, which depend on the resources available for recovery activities. The characteristic curves illustrate system resilience by capturing key information on the preparedness, response, and recovery capacities inherent in each network. Consequently, the framework offers a more comprehensive view of system behavior during the recovery stage, as demonstrated through its application to a Portuguese case study. The insights gained can assist stakeholders in determining the feasibility of strengthening system resilience through enhanced response and recovery efforts, as well as in identifying when it is critical to reinforce resilience at earlier stages through adaptation measures.

In the last decade, extreme wildfire events (EWE) have left severe economic, environmental, and social impacts worldwide. Notable instances in 2023 occurred in Canada, Greece, and Maui in the U.S. Canada and Greece set records for the worst fire season, with the largest burned area in both regions. Meanwhile, Maui witnessed the deadliest wildfire in U.S. history, claiming over 100 lives. The societal consequences have reached unprecedented levels, highlighting the insufficient capacity to respond to this new fire regime, even with intensified suppression efforts (Bowman et al. 2017). The evolution of the new wildfire regime stems from complex interactions of biological, meteorological, physical, and social factors. The convergence of weather patterns and fuel conditions are the factors that most influence wildfire behavior. Weather patterns influence fuel conditions such as moisture and the formation of the physical conditions necessary for wildfire spread, such as unstable atmospheres, pyro convection, or the new interaction between drought, high air temperatures, low relative humidity, and wind variations. Besides, fuel conditions such as continuity, quantity, flammability, and vegetation management directly influence wildfire behavior (Rogers et al. 2020). Consequently, climate change and human-related factors compound the challenges of EWE. Human-related factors encompass social aspects such as rural depopulation, cultural elements such as neglect and arson, and political dimensions, namely, land management, afforestation, and forest restoration. Current actions to reverse climate change may not be sufficient (IPCC, 2022). In this sense, specific measures addressing human-related factors, particularly in land management, can enhance wildfire resilience against these phenomena' increasing occurrence and intensity.
Human intervention has modified the natural environment, increasing susceptibility to wildfires. For instance, the severity of recent fires in Maui (2023) is linked to the proliferation of invasive grasses covering significant portions of the islands. In Portugal, extensive and highly flammable eucalyptus plantations have reduced the country's resilience to wildfires (Weston, 2023). In addition, Portugal has extensive areas of undermanaged forests and shrublands that facilitate the occurrence of frequent, huge, and uncontrolled wildfires (Fernandes et al. 2016). This evidence the importance of effective landscape management as a key strategy for reducing landscape flammability and fuel continuity. Preparedness and adaptation activities become imperative for promoting wildfire resilience in the medium and long term, potentially mitigating the consequences of the new wildfire regimen (Loepfe, Martinez-Vilalta, and Piñol 2012).
Therefore, one of the main challenges for wildland fire scientists and managers is to promote more resilient landscapes and consequently, there is an eminent need for tools to support decision-making in this domain. Various frameworks exist for modelling fuel connectivity and assessing the spatial influence on fire spread, e.g., (Loehman, Keane, and Holsinger 2020; Sá et al. 2022; Aparício et al. 2022). However, these models are intrinsically attached to propagation models that primarily aim to predict wildfire occurrence, specifically fire ignition points. This connection introduces high uncertainty, especially considering that a significant portion of forest fires, particularly in the European Union, result from arson. In Portugal, for instance, 98% of fires are attributed to arson. Existing models fail to capture this high level of uncertainty adequately. Moreover, current methods are increasingly specialized, focusing on specific scenarios. Nevertheless, their limited ability to extrapolate and apply to diverse situations or conditions raises concerns about the conclusiveness of decision-making based on the analysis of a restricted number of fire events (Arango et al, 2023).
To address these issues, this study proposes the use of a Geographic Information System (GIS)-based methodology for fire analysis, serving as a more effective tool for landscape fuel management. This tool evaluates exposure by considering various fuels, encompassing both built and natural environments. Unlike other models, this tool does not require the definition of the wildfire conditions and the location of the fire ignition, thereby eliminating associated uncertainties. Instead, the tool focuses on the system's ability to cope with such events, incorporates different intensities of wildfires including EWE, and conducts analyses at the system level. It has previously demonstrated its effectiveness in assessing various adaptation measures, capturing the influence of different fuels (sources or barriers) in exposure assessment. This study shows the tool's efficacy in landscape management by applying different fuel treatment strategies to reduce exposure to wildfires. For this, the exposure level of a case study in the Leiria region of Portugal is compared to the conditions that led to the devastating fire in 2017 and future conditions. Future scenarios involve two cases: one without implementing fuel treatment strategies and another using treatment strategies. This approach provides stakeholders with pertinent information to support necessary changes in forest management and the development of fire-resilient landscapes. The results suggest that the tool can significantly contribute to achieving certain goals outlined in the European Green Deal.

Climate change is causing an increase in the frequency and intensity of wildfires, demonstrating that our capacity to respond to them is insufficient. Therefore, it is necessary to reconsider wildfire management policies, practices, and decision-support tools, extending beyond emergency measures. This study presents the extension of a GIS-based methodology for fire analysis, providing decision-making support for the implementation of new fire-related policies for road transportation infrastructure. It represents a novel contribution that facilitates the transition towards proactive wildfire policies. The framework is demonstrated to support informed decision-making, addressing both reactive actions, i.e., emergency response, and the evaluation of proactive adaptation measures at a system level. The results suggest that landscape management policies can play an important role in improving the resilience of road networks to wildfires.

Europe’s historic masonry arch bridges are culturally and economically significant, but their long-term safety must be ensured. Scour effects are the most common cause of collapse, so it is necessary to carry out structural assessments to mitigate the risk and prevent potential failures. In this study, a metamodel-based method was used to determine the probability of failure of an existing stone arch bridge in Portugal due to local and contraction scour on the abutments. Non-linear finite element analysis supported the calculation of the reliability index, which took into account the soil-structure interaction and the failure mechanism. The variables with the greatest influence on the load-carrying capacity of the structure were identified and a surrogate model was implemented. Fragility curves were then derived based on the surrogate model, using scour depth as a measure of intensity and load factor as an engineering requirement parameter. The results of the study indicate that the load capacity of the numerical model is compromised when the scour depth of 1.5 m reaches the base of the foundation. As a result, stability problems and settlements are observed in the model. At a depth of 2.5 m, the soil reaches its ultimate bearing capacity.

Factors such as human activity and climate change are contributing to an increase in the frequency and intensity of wildfires. This problem has challenged society's knowledge, response capacity, and resilience, revealing its inadequacy to cope with the new wildfire regime characterized by extreme wildfire events (EWE). Policies on wildfire management mainly focus on suppression and managing emergencies, which may be insufficient to reduce EWE's incidence and cope with its impact. Consequently, there is a lack of tools to support decision-making in wildfire management in other important aspects, such as prevention and protection. This study examines global wildfire policies specifically in the Iberian Peninsula (Portugal and Spain), including cross-border policies. A GIS-based tool to evaluate different normal and extreme wildfire management policies is applied to a cross-border case study, paying attention to the impact on critical land-based transport systems. A relevant outcome of the tool application is that suppression must be complemented with other wildfire management strategies in the analyzed area. The gained insights can help stakeholders to improve decision-making in wildfire management to successfully address EWE.

Bridges, essential for economic and social development, face significant deterioration due to aging and environmental factors. Consequently, assessing the condition of reinforced concrete bridges is essential to anticipate their future performance and optimize maintenance, rehabilitation, and replacement needs. While traditional visual inspections are widely used, the integration of unmanned aerial vehicles (UAVs) presents a more efficient approach, particularly in inaccessible areas. This article presents a comprehensive evaluation of the condition of a bridge over a decade through a case study in Ecuador. The assessment revealed significant deterioration in several structural elements, especially the substructure and road elements. These findings underscore the urgent need to improve maintenance practices and integrate advanced inspection techniques to ensure the safety and longevity of bridges in Ecuador.

Wildfires are becoming more intense and frequent. This problem has tested the knowledge, response capacity, and resilience achieved by society throughout history, making it clear that they are insufficient to face this new wildfire regime. The effectiveness of the related policies mainly focused on fire suppression rather than prevention is increasingly insufficient and questionable. Consequently, there is a clear lack of tools to assess the impact of wildfire preventive actions. Therefore, it is imperative to review wildfire management practices, policies, and the tools used to support decision-making in this regard. This study performs an analysis of wildfire policies applied in the Iberian Peninsula case (Portugal and Spain), including cross-border policies and the role of road transport networks. A novel simplified methodology is included to evaluate different normal and extreme forest fire management policies in road transport infrastructures. The methodology includes different parameters related to wildfires, such as sources of exposure, identification of natural and artificial barriers, and traffic conditions that capture the economic characteristics of the studied area. The information provided by the tool is useful for strategic investment planning, resource prioritization, and evacuation time management. In addition, due to its simplicity of application, it is a useful tool for cross-border areas.

Wildfires have become a source of concern for society due to the increase in frequency, intensity, and unpredictability. This has caused serious impacts all over the world, even in areas where this type of problem did not occur before. Studies on the adaptation of critical infrastructure have been conducted to reduce the impacts of this type of hazard influenced by climate change. However, there are currently no tools to evaluate adaptation measures and their influence on the resilience of transport infrastructure to wildfires. Therefore, this paper proposes the application of a simplified methodology to assess the priority level in interventions on bridge networks and the effectiveness of different adaptation measures. The methodology is applied to a case study in Portugal. In that sense, the results show that adaptation measures such as changing vegetation management policy and implementing wildfire spread barriers effectively reduce the exposure of bridges. Therefore, this tool can be very useful for stakeholders and practitioners supporting wildfire management in terms of adaptation measures.

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.

The severe effects of extreme wildfire events in recent years have shown that the fire suppression approach is not enough to solve the problem. An alternative to dealing with this issue is to accept the impossibility of eliminating wildfire hazards and focus on preparing systems to be more resilient. However, existing decision-making tools based on resilience present important drawbacks that make them inadequate for this task. This paper proposes a new approach and methodology for the resilience assessment of road traffic networks to wildfires that overcomes the main drawbacks, paying attention to the different functions of the system and the acceptance of a specific loss of performance. The latter is done through the introduction of dynamic thresholds that reflect the different requirements of the system under different wildfire conditions, including normal and extreme fires. The methodology is exemplified for five traffic networks. The results support the relevance of appropriate wildfire management through the adaptation of the natural and built environment to increase the capacity of the traffic networks to cope with wildfires.

Structural reliability has become a widely accepted performance indicator for infrastructures over the past decade, providing valuable information about their structural condition. As a result, it has been assessed in combinationwith deterioration prediction, aiming at defining optimal maintenance, and rehabilitation strategies for bridge networks. In that case, reliability values need to be updated based on collected data. To this purpose, there has been a rapid development of advanced bridge condition assessment techniques, both in the fields of structural health monitoring as well as on non-destructive assessment techniques. Most of the sophisticated non-destructive methods are the preferred option but sometimes are not possible. Thus, visual inspection is still the predominant bridge condition assessment technique being adopted within the majority of Bridge Management Systems (BMS). However, there is a procedural gap when incorporating information obtained from visual inspections into a reliability assessment. Therefore, this paper describes a methodology for a time-dependent reliability-based condition evaluation of existing bridges. The procedure is applied to a pre-stressed reinforced concrete railway bridge located in Portugal, in which prediction of reliability levels are calculated for different periods assuming corrosion initiation, causing a reduction in the cross-section area of the steel reinforcement and residual strength reduction, based on onsite inspection evidence. Finally, the updating is made through a Bayesian approach to compute the posterior bridge reliability based on inspection results. This approach may apply to other types of structures considering information obtained from visual inspection concerning the actual deterioration state in a quantitative way.

Assessing flood risks on road infrastructures is critical for the definition of mitigation strategies and adaptation processes. Some efforts have been made to conduct a regional flood risk assessment to support the decision-making process of exposed areas. However, these approaches focus on the physical damage of civil infrastructures without considering indirect impacts resulting from social aspects or traffic delays due to the functionality loss of transportation infrastructures. Moreover, existing methodologies do not include a proper assessment of the uncertainties involved in the risk quantification. This work aims to provide a consistent quantitative flood risk estimation and influence factor modelling for road infrastructures. To this end, a Flood Risk Factor (FRF) is computed as a function of hazard, vulnerability, and infrastructure importance factors. A Bayesian Network (BN) is constructed for considering the interdependencies among the selected input factors, as well as accounting for the uncertainties involved in the modelling process. The proposed approach allows weighting the relevant factors differently to compute the FRF and improves the understanding of the causal relations between them. The suggested method is applied to a case study located in the region of Santarem Portugal, allowing the identification of the sub-basins where the road network has the highest risks and illustrating the potential of Bayesian inference techniques for updating the model when new information becomes available.

Road infrastructures are crucial for societies daily life due to the dependency of other critical infrastructures upon it. Therefore, society expects an uninterrupted availability of the road network. However, maintain this constant availability is often a difficult task as, in the last decades, climate change has significantly affected transport networks, especially due to the occurrence of extreme natural events leading to their disruption. Those events include floods, wild fires, landslides and others, and all of are varying both in frequency and intensity presently and in the coming years. Therefore, there is a clear need for timely adaptation. Regarding these adaptability measures, an important step is needed to quantify how the transport network is directly and indirectly affected by extreme weather events, which can be obtained within a risk assessment. Nonetheless, there are many questions and variability about this topic such as uncertainties in projections of future climate, cause-effects assessment, and how it can be an integration of all these aspects into a single decision-making process. In that scope, this work describes a risk assessment methodology having account the cause, effect, and consequences of extreme events in road networks to identify the major risks and therefore the assets that may be suitable to be analyzed within a selection of adaptation measures aiming at a holistic decision-making support tool.