In temperate Europe, fire is already here: The case of The Netherlands

Cathelijne R. Stoof, Edwin Kok, Adria´n Cardil Forradellas, Margreet J. E. van Marle. Ambio. February 2024.

Landscape fires are usually not associated with temperate Europe, yet not all temperate countries record statistics indicating that actual risks remain unknown. Here we introduce new wildfire statistics for The Netherlands, and summarize significant events and fatalities. The period 2017–2022 saw 611 wildfires and 405 ha burned per year, which Copernicus’ European Forest Fire Information System satellite data vastly underestimate. Fires burned more heathland than forest, were small (mean fire size 1.5 ha), were caused by people, and often burned simultaneously, in Spring and in Summer drought. Suppression, restoration and traffic delays cost 3 M€ year. Dozens of significant events illustrate fire has never been away and has major societal impact amidst grave concerns for firefighter safety. Since 1833, 31 fatalities were reported. A legal framework is needed to ensure continuity of recordkeeping, as the core foundation of integrated fire management, to create a baseline for climate change, and to fulfill international reporting requirements. 

Megafire: An ambiguous and emotive term best avoided by science

Cathelijne R. Stoof, Jasper R. de Vries, Marc Castellnou Ribau, Mariña F. Fernández, David Flores, Julissa Galarza Villamar, Nicholas Kettridge, Desmond Lartey, Peter F. Moore, Fiona Newman Thacker, Susan J. Prichard, Pepijn Tersmette, Sam Tuijtel, Ivo Verhaar, Paulo M. Fernandes. Global Ecology and Biogeography. February 2024.

Background: As fire regimes are changing and wildfire disasters are becoming more frequent, the term megafire is increasingly used to describe impactful wildfires, under multiple meanings, both in academia and popular media. This has resulted in a highly ambiguous concept.

Approach: We analysed the use of the term ‘megafire’ in popular media to determine its origin, its developments over time, and its meaning in the public sphere. We subsequently discuss how relative the term ‘mega’ is, and put this in the context of an analysis of Portuguese and global data on fire size distribution.

Results: We found that ‘megafire’ originated in the popular news media over 20 years before it appeared in science. Megafire is used in a diversity of languages, considers landscape fires as well as urban fires, and has a variety of meanings in addition to size. What constitutes ‘mega’ is relative and highly context-dependent in space and time, given variation in landscape, climate, and anthropogenic controls, and as revealed in examples from the Netherlands, Portugal and the Global Fire Atlas. Moreover, fire size does not equate to fire impact.

Conclusion: Given the diverse meanings of megafire in the popular media, we argue that redefining megafire in science potentially leads to greater disparity between science and practice. Megafire is widely used as an emotive term that is best left for popular media. For those wanting to use it in science, what constitutes a megafire should be defined by the context in which it is used, not by a metric of one-size-fits-all.

VPD-based models of dead fine fuel moisture provide best estimates in a global dataset 

Marcos Rodrigues, Víctor Resco de Dios, Angelo Sil, Angel Cunill Camprubí, Paulo M. Fernandes. Agricultural and Forest Meteorology. January 2024.

Dead fine fuel moisture content (FM) is one of the most important determinants of fire behavior. Fire scientists have attempted to effectively estimate FM for nearly a century, but we are still lacking broad scale evaluations of the different approaches for prediction. Here we tackle this problem by taking advantage or a recently compiled global fire behavior database (BONFIRE) gathering 1603 records of 1h (i.e., <6 mm diameter or thickness) dead fuel moisture content from measurements before experimental fires. We compared the results of models routinely used by different agencies worldwide, empirical models, semi-mechanistic models and also non-linear and machine learning approaches based on either temperature and relative humidity or vapor pressure deficit (VPD). A semi-mechanistic model based on VPD showed the best performance across all FM ranges and a historical model developed in Australia (MK5) was additionally recommended for low fuel moisture estimations. We also observed significant differences in FM dynamics between vegetation types with FM in grasslands more responsive to changes in atmospheric dryness than woody ecosystems. The addition of computational complexity through machine learning is not recommended since the gain in model fit is small relative to the increase in complexity. Future research efforts should concentrate on predictions at low FM (<10 %) as this is the range most significant for fire behavior and where the poorest model performance was observed. Model predictions are available from

Numerical investigation of the Pedrógão Grande pyrocumulonimbus using a fire to atmosphere coupled model 

Flavio Tiago Couto, Jean-Baptiste Filippi, Roberta Baggio, Catia Campos, Rui Salgado. Atmospheric Research. January 2024.

Understanding the development of fire-generated thunderstorms in mega fire events is important given their high impact on the evolution of the fire fronts, where the fire spread becomes highly unpredictable and difficult to suppress. This study aims to investigate numerically the influence of strong pyro-convective activity on the local atmospheric conditions by means of a numerical simulation based on the coupled Meso-NH/ForeFire code. To our knowledge, it is the first time that the effect of wildfire spread on the local atmospheric conditions is accounted explicitly in a high-resolution NWP model to investigate pyro-convection activity. More specifically, we study numerically the Portuguese Pedrógão Grande mega fire, which was one of the most destructive and deadliest wildfire hazards affecting the Mediterranean region in the recent years. The spatio-temporal propagation of the wildfire was assigned a priori on the basis of the official investigation’s reports, while the impact of the forced fire evolution and of the ensuing heat and water vapour emissions on the local atmospheric conditions is accounted explicitly. The simulation, configured with very-high spatial and temporal resolutions, was capable of resolving the intense convective column reaching the upper troposphere and the fast development of the associated cloud system. The numerical fire produced intense updraughts with vertical velocities above 15 m/s, whereas the associated pyroCb cloud was composed by five different hydrometeor species along the main convective column and reached an altitude of 10 km. It is remarkable that the numerical experiment reproduced phenomena occurring at a fine scale related to cloud microphysics, such as very-localized outflows. This study, based on a coupled numerical simulation, was capable of illustrating in detail the development of a pyroCb cloud from strong pyro-convective activity. 

Francesco Pirotti, José R González-Olabarria, Erico Kutchartt. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. December 2023.

In this work, an ensemble of machine learning algorithms was trained using stratified sampling from an existing European-scale biomass map from 2018 to predict an updated version for 2020. The objective of stratification is to make sure that the full range of biomass values is represented. The sampled biomass values from 2018 were filtered to remove areas that did were subject to forest disturbances between 2018 and 2020. This information was available from forest cover/loss/gain maps derived from satellite imagery. We train using a total of 49 features derived from the following sources: bioclimatic data, maps of land-cover, tree cover, tree height, annual composites of vegetation indices per pixel (EVI and NDVI) obtained from Sentinel-2, radar backscatter median annual values from Sentinel-1 and ALOS-2, and the ALOS DSM (3D) elevation grid. A model was created dividing Europe into 19 tiles to limit variability due to very different bioclimatic zones. The result is a raster with 100 m × 100 m resolution and an estimated value of biomass (Mg ha−1) at each node. Overall results on validation data over Europe report a root mean square error (RMSE) of 32.4 Mg ha−1 and a mean absolute error (MAE) of 21.5 Mg ha−1; when considering single tiles, the largest RMSE was 54.7 Mg ha−1 in tile D2, which can be explained by the very high variance of climate, environment, terrain topography and biomass values as the tile enclosed the Alpine region and the western part of Eastern Europe.

Erico Kutchartt, José R González-Olabarria, Antoni Trasobares, Sergio de-Miguel, Adrian Cardil, Brigite Botequim, Vassil Vassilev, Palaiologos Palaiologou, Martino Rogai, Francesco Pirotti. iForest Biogeosciences and Forestry. October 2023.

We present a browser-based App for smartphones that is freely available to end-users for collecting geotagged and oriented photos depicting vegetation biomass and fuel characteristics. Our solution builds on advantages of smartphones, allowing their use as easy sensors to collect data by imaging forest ecosystems. The strength and innovation of the proposed solution is based on the following points: (i) using a low memory footprint App, streaming images and data with as little data-volume and memory as needed; (ii) using JavaScript APIs that can be launched from both a browser or as an installed App, as it applies features such as service workers and Progressive Web App; (iii) storing both image and survey data (geolocation and sensor orientation) internally to the device on an indexed database, and synchronizing the data to a cloud-based server when the smartphone is online and when all other safety tests have been successfully passed. The goal is to achieve properly positioned and oriented photos that can be used as training and testing data for future estimation of the surface fuel types based on automatic segmentation and classification via Machine Learning and Deep Learning.

Alejandro Miranda, Alexandra D. Syphard, Miguel Berdugo, Jaime Carrasco, Susana Gómez-González, Juan F. Ovalle, Cristian A. Delpiano, Solange Vargas, Francisco A. Squeo, Marcelo D. Miranda, Cynnamon Dobbs, Rayen Mentler, Antonio Lara & René Garreaud. Nature. October 2023.

Large-scale, abrupt ecosystem change in direct response to climate extremes is a critical but poorly documented phenomenon1. Yet, recent increases in climate-induced tree mortality raise concern that some forest ecosystems are on the brink of collapse across wide environmental gradients2,3. Here we assessed climatic and productivity trends across the world’s five Mediterranean forest ecosystems from 2000 to 2021 and detected a large-scale, abrupt forest browning and productivity decline in Chile (>90% of the forest in <100 days), responding to a sustained, acute drought. The extreme dry and warm conditions in Chile, unprecedented in the recent history of all Mediterranean-type ecosystems, are akin to those projected to arise in the second half of the century4. Long-term recovery of this forest is uncertain given an ongoing decline in regional water balance. This dramatic plummet of forest productivity may be a spyglass to the future for other Mediterranean ecosystems.

Anna Karali, Konstantinos V. Varotsos, Christos Giannakopoulos, Panagiotis P. Nastos, and Maria Hatzaki. Natural Hazards and Earth System Sciences. February 2023.

This study focuses on predicting seasonal fire danger in the fire-prone Attica region of Greece using high-resolution forecasts. By analyzing weather conditions and utilizing the Forest Fire Weather Index (FWI) and Initial Spread Index (ISI), the study found that these indices are reliable in forecasting above-normal fire danger conditions. The forecasts were compared with historical fire data, showing the ability to identify years with high fire occurrences. This information can assist regional authorities in implementing effective fire prevention strategies and allocating resources accordingly.

Jaime Carrasco, Rodrigo Mahaluf, Fulgencio Lisón, Cristobal Pais, Alejandro Miranda, Felipe de la Barra, David Palacios, Andrés Weintraub. Journal of Environmental Management. May 2023.

This study proposes the use of a specific approach to optimize the selection of firebreak locations for better wildfire protection.
The approach considers ecological values, historical fire patterns, and fire behavior. By using a computer model, the optimal placement of firebreaks is determined by balancing the tradeoff between potential biodiversity loss from vegetation removal and the protection provided by the firebreaks. The model’s optimal solution reduces expected biodiversity losses from wildfires by 30% compared to doing nothing and performs 16% better than randomly chosen firebreak locations. This highlights the potential of firebreaks to mitigate biodiversity loss by effectively protecting against future forest fires.

Karol Bot and José G. Borges. Inventions. January 2022.

This paper aims to provide an overview of recent applications of machine learning methods for decision support in wildfire management. The focus is on providing a summary of these applications with classification according to case study type, machine learning method, case study location, and performance metrics. This publication highlights that the application of machine learning methods can help to improve support at different stages of fire management.

Míriam Piqué, José Ramón González-Olabarria and Eduard Busquets. Forests. May 2022.

This publication investigates the effectiveness of precommercial thinning, over time, implemented on Pinus halepensis (Aleppo pine) thickets, regarding fuel evolution and potential fire behavior. The study was implemented in 44 plots at different stages of fuel evolution. The results show that precommercial thinning has a positive impact on fire mitigation, but the impact that opening the tree canopy has on ground vegetation development must be considered in order to plan more efficient management strategies.

Adrián Cardil, Marcos, Rodrigues, Mario Tapia, Renaud Barbero,Joaquin Ramírez, Cathelijne R. Stoof, Carlos Alberto Silva, Midhun Mohan, and Sergio de-Miguel. Nature Communications. January 2023.

Climate teleconnections (CT) remotely influence weather conditions in many regions on Earth, entailing changes in primary drivers of fire activity such as vegetation biomass accumulation and moisture. This publication summarises the CT-fire relationships into a set of six global CT domains that are discussed by continent, considering the underlying mechanisms relating weather patterns and vegetation types with burned areas across the different world’s biomes. Additionally, it highlights the regional CT-fire relationships worldwide, aiming to further support fire management and policy-making.

Margarita Bachantourian, Kostas Kalabokidis, Palaiologos Palaiologou, Kyriakos Chaleplis. February 2023

The paper focuses on the critical issue of fuel treatment allocation in Kassandra, located in northern Greece. The objective of the study is to safeguard the wildland-urban interface from the risk of large-scale wildfires. The outcomes of this research have been successfully implemented by the Greek Forest Service to reduce the area burned in the region by future wildfires, while simultaneously moderating fire intensity and spread rates.

Jose Ramon Gonzalez-Olabarria, Jaime Carrasco, Cristobal Pais, Jordi Garcia-Gonzalo, David Palacios-Meneses, Rodrigo Mahaluf-Recasens, Olena Porkhum and Andres Weintraub. Fire. February 2023.

This article highlights the importance of fire simulation tools in landscape and forest management. It emphasizes the need to consider fire risk and mitigation goals and the benefits of combining fire simulation with growth and yield simulation for maximizing ecosystem services. The article focuses on the requirements for a fire simulator for tactical forest planning and introduces Cell2Fire_SB, a simulator capable of simulating crown fires. Its goal is to be integrated into a decision support system for solving temporal dynamic tactical forest problems.

Modelling pyro-convection phenomenon during a mega-fire event in Portugal

Cátia Campos, Flavio Tiago Couto, Jean-Baptiste Filippi, Roberta Baggio, Rui Salgado. Atmospheric Research. July 2023.

This study enhances our understanding of pyro-convection using a coupled fire-atmosphere simulation. It focuses on the large-scale meteorological conditions in Portugal during the multiple mega-fire events on October 15, 2017. Two simulations were conducted using the MesoNH model. The first, a large single-domain simulation, examined general conditions. The second, coupled with the ForeFire model, delved into the Quiaios fire in detail, using nested domains for high resolution. The simulations highlighted the impact of south/southwest winds from Hurricane Ophelia on fire spread and revealed pyro-convective activity influenced by subtropical moisture transport. The coupled simulation successfully replicated the formation of a PyroCu cloud within the smoke plume, showcasing convective updrafts’ role in transporting water vapor to higher levels and creating a high-based cloud over a dry atmospheric layer.

Combining optimization and fire simulation modeling to protect biodiversity values at a landscape scale

Rodrigo Mahaluf Recasens, Jaime Carrasco, Fulgencio Lisón, Cristobal Pais, Alejandro Miranda, Felipe de la Barra, Andrés Weintraub. 2022 IEEE Latin American Conference on Computational Intelligence (LA-CCI). December 2022.

One way to mitigate the uncontrolled effect of fires and, at the same time, protect our communities and ecological values, is through forest fuel management. Theses activities constitute a means of fire prevention, involving planned changes to living or dead wildland fuels (prescribed burning, pruning, firebreaks, etc.) in order to lessen fire behaviour potential. In this study, we propose an integrated fire optimization and simulation approach to locate firebreaks on the landscape, so that the ecological damage resulting from the removal of vegetation in areas allocated to firebreaks is offset by the preservation of ecological values as a result of the treatment protective action. We use a prioritization metric —called Downstream Protection Value— that identifies crucial cells that have a significant influence on the spread of fires in the landscape and their potential for damaging ecological values. Our solution approach was tested on a real landscape located in Araucania Region, Chile, whose wildland fuels were classified according to the chilean KITRAL fire behavior system, and with real species observations taken from Global Biodiversity Information Facility occurrence dataset.

Incorporating fire-smartness into agricultural policies reduces suppression costs and ecosystem services damages from wildfires

Judit Lecina-Diaz, María-Luisa Chas-Amil, Núria Aquilué, Ângelo Sil, Lluís Brotons, Adrián Regos, Julia Touza. Journal of Environmental Management. July 2023.

In Southern Europe, increasing wildfire risk due to land abandonment and a focus on fire suppression prompts a need for policy change. Using scenario analysis, landscape modeling, and economic tools, we assessed land-use policies in the Gerês-Xurés Biosphere Reserve. Four scenarios were considered: Business as Usual (BAU), fire-smart, High Nature Value farmlands (HNVf), and a combination of HNVf and fire-smart. The HNVf + fire-smart scenario emerged as the most efficient, reducing wildfire hazard through recultivation and promoting fire-resistant tree species. This approach, rewarding farmers for wildfire prevention, can enhance local support for Payments of Ecosystem Services policies.

Determination of subpicogram levels of airborne polycyclic aromatic hydrocarbons for personal exposure monitoring assessment

Barend L. van Drooge, Raimon M. Prats, Clara Jaén & Joan O. Grimalt. Environmental Monitoring and Assessment. February 2023.

A highly sensitive method using GC-Orbitrap-MS was developed for analyzing PAHs at sub-picogram levels in outdoor air PM2.5 and SRM2260a. The approach demonstrated low instrumental uncertainties (1–22%) and high linearity (0.5 pg to 500 pg/μL). Compared to conventional GC–MS, it showed good reproducibility in PM samples, with a quantification limit of 0.5 pg/μL. This method successfully analyzed PAHs in low-volume samples, such as filter strips from eBC analyzers and personal exposure monitors of firefighters. Strong correlations (r2 ≥ 0.93) between PAHs and eBC revealed distinct emission sources like traffic or wood burning. The method enables high precision analysis of PAHs in atmospheric PM samples, facilitating frequent sampling for personal exposure monitoring assessments.