Since the dawn of era, the Earth’s ecosystems consider fire as an important parameter of their functioning (Pausas & Keeley 2019; Fernadez Anez et al. 2021) although nowadays they tend to be characterized more as one of the most threatening natural disasters, mostly to human communities (Pausas et al. 2017; McLauchlan et al. 2020).
In recent years, we have witnessed a dramatic increase of large wildfires (Keeley et al. 2009; Tedim et al. 2018) events worldwide along with their adverse socio-economic and ecological effects (Bowman et al. 2017). These include loss of lives, infrastructures, and ecosystem services (Gomez-Gonzales 2019; Wotton et al. 2017; Bowman et al. 2020; Turco et al. 2018; McWethy et al. 2019; Abatzoglou et al. 2021).
In August 2021 in Greece, approximately 368,263 hectares, both forests and agricultural lands, were affected in less than half month during the most intense wildfire wave since 2007 (NKUA 2021). Furthermore, during the summer of 2018, in Eastern Attica (Mati and Rafina), Greece experienced the most catastrophic wildfire during the past 100 years that burned more than 1,431 ha of Wildland-Urban Interface (WUI) zones including approximately 2,500 homes, but the most tragic aftermath was the death of 103 people died (Hellenic Forest Service 2018). In Europe the situation is also challenging. For instance, in Portugal, during the summer of 2018, the Pedrógão Grande fire killed 66 people and caused considerable household (i.e., 485 houses) and ecosystem disruption i.e., 2,018 farmers were destroyed including 53,000 hectares of land that were burned (BBC News 2021). In the summer of 2021, in the U.S., the Dixie Fire became the largest wildfire in the history of California, having destroyed more than 389,837 ha(Wikipedia Dixie Fire 2021). In Australia, in March 2020, fires burnt an estimated area of 19 million hectares, over 5,900 buildings (including 2,779 homes) and killed at least 445 people and millions of animals (Wikipedia 2020, Australian Bushfire season).
The Wildland-Urban Interface (WUI) constitutes the common space where dynamic interactions that intertwined between ontologies such flora, fauna, and human activity exist (Bar-Massada et al. 2014). Across the globe, the most challenging issue for the WUI is wildfire activity (Radeloff et al. 2018) since it is the common space where fatalities and structure losses occur. Hence, it is the WUI that requires the largest fire prevention and suppression expenses (Kramer et al. 2018, Miranda et al. 2020). Wildfires in the WUI comprise an increasing and catastrophic socio-environmental issue. Defining what the WUI is becomes a complex global problem, since climatic, demographic, and land-cover changes create interlinked and complex relations between settlements and fire-prone ecosystems, whose impacts differ from one region to another (Argañaraz et al. 2017; Radeloff et al. 2018; Tonini et al. 2018).
The increasement of evaluation methodologies contain extensive variety of effects, that differ in space and time. This becomes a turning point to design a robust decision-making framework that addresses effective social-ecological resilience in the aftermath of wildfires aligned with ecological and socioeconomic realities. To increase resilience of communities against the wildfires, we need to understand holistically the local social-ecological systems. An extensive comprehending of the interplay among the many wildfire-related components and their contribution to fire risk is essential (Aldersley et al. 2011; Rodrigues et al. 2016; De Rigo et al. 2017).
The ‘Social-ecological systems’ (SES) is a novel concept that can aid to the better understanding of the coupled realities of the interconnected and interdependent human and natural systems (Ostrom, 1990; Costanza 1992; Berkes et al.2000; Folke et al. 2011). The interconnection between societies and their ecosystems are complex and are named social-ecological systems (SES) (Ostrom 2009). Environmental risks, arising from the interaction of human societies with their living environment, are thus generated in and by the SES (Kaikkonen et al. 2020). To understand and mitigate environmental risks, it is important to acknowledge local systems by considering all those interdependent factors that contribute to increased likelihood of occurrence and affect the magnitude of their undesired impacts. Besides, it is important to gain improved knowledge that consider and capture the complex interlinkage between the social and ecological systems, and the emergent and often unexpected processes, features, problems and opportunities to which they give rise (Preiser et al. 2018). These systemic analyses of SES rely on the characterization of a comprehensive structure (Glaser et al. 2012). During the last years, resiliency studies led to an intense increase of SES research. Previous resiliency works attempted to summarize and compare the existing theoretical frameworks for SES analysis (Cote and Nightingale 2012).
Many model types have been applied to explain the human-nature relationships, varying from graphic mind maps to mathematical models (Zingraff-Hamed, et al. 2018). Graphical presentations are an effective way to structure and explain how the environmental risks are generated in SESs (Kaikkonen et al. 2020). Such “visuals” often function as natural boundary objects, being part of various social worlds and supporting the communication among actors (Van der Hoorn, 2020). Causal diagrams can help to enhance the inclusivity of multiple viewpoints, improving inference and common understanding about the conditional aspects of complex systems and management problems, as well as the impact mechanisms of potential interventions (Carriger et al. 2018; Luoma et al. 2021). Such causal argumentation by drawing can be called e.g. cognitive mapping or mental modelling (LaMere et al. 2020). The qualitative causal presentations provide a basis for constructing quantitative models to assess risks, make predictions and analyze counterfactual scenarios. Potential semi-quantitative causal approaches are e.g. Bayesian networks (REF), fuzzy cognitive maps (REF) and system dynamics models (REF) (Dlamini 2011; Penman et al. 2020; Carriger et al. 2021).
The wildfire-driven losses result from the complicated intertwined relationships between social and ecological strength and institutions (Abatzoglou and Williams 2016; Balch et al. 2017; Syphard et al. 2017; Oliveira et al. 2021). As the deficits from wildfires are globally increasing, a climbing research and management goal is to understand how to preserve ecologically operational levels of wildfire on the biosphere while at the same time properties are bringing down. The ability to live with wildfire, termed as “fire-adapted”, has become the most important principle of wildfire policy in many regions of the world (Tedim et al. 2016, Brenkert-Smith et al. 2017, Schoennagel et al. 2017).
Most previous research focused on experiences preliminary, during, and immediately following wildfires (Steelman 2016). At the same time, some researchers examined the intertwined social-ecological dynamics that concentrate on pre-fire vulnerability [56]. There are not studies that have targeted explicitly at the post-wildfire recovery through linked interactions between social ecological figures (Palaiologou et al 2020).
Resilience strategies focus on strengthening a system’s ability to withstand and recover from disturbances, while adapting to post-disturbance conditions (Linkov et al. 2016; IRGC, 2018; Linkov and Trump, 2019). Resilience can thereby be seen as the buffering capacity of a system to absorb a disturbance while retaining its most critical function (OECD, 2019). Resilient systems are characterized by their ability to adapt in changing conditions and/or stresses; in plain words, they are robust to disturbance (Levin 1998, Turner 2010, Falk et al. 2022). Systems without the ability to adapt are vulnerable. Vulnerability is defined as the possibility of a system to continue its functioning, at the same level, after its exposure to damage due to the introduction of a disturbance (Turner et al. 2003). By definition, considering resilience, robustness and vulnerability are key elements to enter the context of understanding and analyzing complex system (the internet, trade networks, financial systems, ecosystems, etc.), specifically in terms of sustainable development (Walker and Salt 2012; Reggiani 2013; Wu 2013; Staal et al. 2015; Suweis et al. 2015).
Generally, Performance-Based Design (PBD) was initially evolved for the field of nuclear engineering, but soon afterwards it was applied to performance-based earthquake engineering (PBEE) (Porter, 2003). Recently, there has been an increasing knowledge in this design framework in the following engineering sectors: i) blast (Hamburger and Whittaker, 2003), ii) fire (Lamont and Rini, 2008), iii) tsunami (Riggs et al. 2008), and iv) wind (Petrini et al. 2009; Griffis et al. 2013). Performance-Based Engineering (PBE) is a design approach that have been applied to improve earthquake and hurricane risk decision-making through assessment and design methods that have a strong scientific basis, providing options that enable stakeholders to make informed decisions (Moehle and Deirlein 2004; Pinelli and Barbato 2019). Therefore, PBE is a design framework that: (1) explicitly describes the performance requirements for any complexity of infrastructure during their design life, (2) specifies criteria and methods for validating the accomplishment of the performance objectives, and (3) suggests convenient methodologies to enhance the design of any building complexity (Cornell 2000; Cornell and Krawinkler 2000). In contrast, PBE examines dissimilar design, modify, and/or preservation solutions based on the probabilistic assessment of a series of decision variables (DV), where each DV symbolize dissimilar performance and safety purposes (Barbato et al. 2013; Pinelli and Barbato 2019).
This proposal describes a holistic integrative framework and robust methodology for performance-based wildfire engineering to improve resilience of social-ecological systems in wildfire-prone areas. To achieve this goal, the performance evaluation and design procedure has been disaggregated into logical components that can be examined and resolved separately in an accurate and logical way, but on the other hand also linked to a causal inference chain, providing a holistic picture, and enabling decision analysis to identify the best management strategies. Key elements of the process include the description, definition, and quantification of (1) hazard analysis, (2) social-ecological characterization, (3) social-ecological interaction analysis, (4) social-ecological analysis, (5) damage analysis, and (6) loss analysis.