In the wildfire risk module of EFFIS, a prototype is presented contributing to a harmonised Wildfire Risk Assessment (WRA) over wide spatial areas, by presenting a first consistent methodology and set of data for the pan-European region, acting as a starting point for a more comprehensive risk assessment.
Wildfires are here understood in terms of coupled human and natural systems. Given the inescapable complexity and the non-linear interplay between the components of wildfire risk, a mere aggregated risk ranking – isolated from basic contextual information – may be difficult to understand alone. This is why the proposed approach is explicitly designed to provide not only the final risk ranking, but also to complement it with a rich set of spatial quantities, either directly used to estimate the risk, or useful as basic layers to ease a contextual comparison with selected background data.
The development of this first pan-European approach to assess wildfire risk follows from a series of EU regulations that require the EC to have a wide overview of the risk by wildfires in the European region, to support the actions of its Member States and to ensure compliance in the implementation of EU regulations related to wildfires. The process is closely linked with the Expert Group on Forest Fires (EGFF), composed of fire management representatives from 43 countries in the region and part of the EFFIS - European Forest Fire Information System, which was established jointly by the EC services (DG ENV and JRC) and the relevant fire services from the EU Member States, other non-EU European countries and Middle East and North African countries. The collaboration with the EGFF countries experts was essential for the design and first prototype implementation of the pan-European wildfire risk assessment.
The spatial extent of this first prototype version of the risk ranking was carefully selected to ensure the broadest possible spatial coverage, given the limits and gaps of the currently available data, and at the price of some sub-optimal approximations. In some cases, the available data only covered most of the region of interest (so that some included areas have partial data gaps and cannot yet rely on an optimal data coverage) and may be complemented in future by national datasets, provided that these more accurate local data can be assimilated to the European datasets in terms of format and information content.
Two main groups of components are defined by considering the fire danger (or hazard: in the wildfire research domain, the two terms are often used as synonyms) and the vulnerability (including the exposure of people and assets) on three categories: people, ecological, and economic values exposed in vulnerable areas.
The WRA was assessed through a semi-quantitative approach by using the available quantitative data as proxy information for the wildfire danger and vulnerability components, along with a robust quantitative aggregation of them in classes of importance (from low to high importance). The integration of multiple dimensions is the very basis for a sound risk assessment, which requires us to consider together the possibility (wildfire hazard/danger, with its components) of negative outcomes (vulnerability for people, ecosystems, and assets, with their components). Several components themselves are described by using multiple dimensions (e.g. multiple proxy indicators). Their proposed integration (based on Pareto ranking) is stable for any possible transformation of the components where the ranking of values is preserved, including change of units and percentile ranking. Moreover, lower priority is given to the areas where all the dimensions of a risk component are consistently lower than in other areas. This mathematically ensures that, irrespective of any special preference for a given dimension over the other ones, these lower-concern areas would be de-prioritised in the same consistent, unambiguous way – allowing the assessment to focus on the higher-concern areas.
Virtually all the WRA components are associated with an intrinsic uncertainty, so that integrating them in order to estimate the final risk magnifies the cumulative aggregated uncertainty. Therefore, this structural uncertainty is the key element of a robust method for identifying which areas to prioritise, where the estimated risk is consistently higher than in other areas. This robust risk assessment is computed by considering multiple simulations of the uncertainty, as explored in a corresponding set of multiple model instances (or model runs). In each model instance, the risk components and their dimensions are aggregated with the aforementioned integration up to a corresponding risk ranking for each instance. The degree of agreement between model instances is estimated, thereby identifying the high-priority areas where most instances agree on these areas being at high risk. Analogously, areas with relatively low risk (lower priority areas) can also be identified, where most instances agree on the same low-risk classification.
The information in this first prototype of pan-European wildfire risk could be informative to identify the areas where the risk is higher, providing a baseline for understanding the risk and helping with the development of efficient fire management strategies across the pan-European scale. However, given the limitations of the available continental-scale data (a full comprehensive risk including all the fire risk components is not yet technically feasible at this spatial scale), the information is meant to be indicative at the continental scale, while it should be carefully used when applying it to local, specific real-world situations.
Oom, D., de Rigo, D., Pfeiffer, H., Branco, A., Ferrari, D., Grecchi, R., Artés-Vivancos, T., Houston Durrant, T., Boca, R., Maianti, P., Libertá, G., San-Miguel-Ayanz, J., et al., 2022. Pan-European wildfire risk assessment. Publications Office of the European Union, Luxembourg. ISBN 978-92-76-55137-9 https://doi.org/10.2760/9429
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