Articles | Volume 23, issue 9
https://doi.org/10.5194/nhess-23-2937-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-23-2937-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Sep 2023
Research article |
| 06 Sep 2023
| 06 Sep 2023
Fire risk modeling: an integrated and data-driven approach applied to Sicily
Alba Marquez Torres
CORRESPONDING AUTHOR
Basque Centre for Climate Change (BC3), 48940 Leioa, Spain
Department of Agricultural and Forest Sciences and Engineering, University of
Lleida, 25003 Lleida, Spain
Giovanni Signorello
Department of Agriculture, Food and Environment, University of Catania, 95131 Catania, Italy
Centre for the Conservation and Management of Nature and
Agroecosystems (CUTGANA), 95123 Catania, Italy
Sudeshna Kumar
Basque Centre for Climate Change (BC3), 48940 Leioa, Spain
Greta Adamo
Basque Centre for Climate Change (BC3), 48940 Leioa, Spain
Ferdinando Villa
Basque Centre for Climate Change (BC3), 48940 Leioa, Spain
Ikerbasque – Basque Foundation for Science, 48009 Bilbao, Spain
Stefano Balbi
Basque Centre for Climate Change (BC3), 48940 Leioa, Spain
Ikerbasque – Basque Foundation for Science, 48009 Bilbao, Spain
Related authors
No articles found.
Angel Giménez-García, Alfonso Allen-Perkins, Ignasi Bartomeus, Stefano Balbi, Jessica L. Knapp, Violeta Hevia, Ben Alex Woodcock, Guy Smagghe, Marcos Miñarro, Maxime Eeraerts, Jonathan F. Colville, Juliana Hipólito, Pablo Cavigliasso, Guiomar Nates-Parra, José M. Herrera, Sarah Cusser, Benno I. Simmons, Volkmar Wolters, Shalene Jha, Breno M. Freitas, Finbarr G. Horgan, Derek R. Artz, C. Sheena Sidhu, Mark Otieno, Virginie Boreux, David J. Biddinger, Alexandra-Maria Klein, Neelendra K. Joshi, Rebecca I. A. Stewart, Matthias Albrecht, Charlie C. Nicholson, Alison D. O'Reilly, David William Crowder, Katherine L. W. Burns, Diego Nicolás Nabaes Jodar, Lucas Alejandro Garibaldi, Louis Sutter, Yoko L. Dupont, Bo Dalsgaard, Jeferson Gabriel da Encarnação Coutinho, Amparo Lázaro, Georg K. S. Andersson, Nigel E. Raine, Smitha Krishnan, Matteo Dainese, Wopke van der Werf, Henrik G. Smith, and Ainhoa Magrach
Web Ecol., 23, 99–129, https://doi.org/10.5194/we-23-99-2023, https://doi.org/10.5194/we-23-99-2023, 2023
Short summary
Short summary
Modelling tools may provide a method of measuring pollination supply and promote the use of ecological intensification techniques among farmers and decision-makers. This study benchmarks different modelling approaches to provide clear guidance on which pollination supply models perform best at different spatial scales. These findings are an important step in bridging the gap between academia and stakeholders in modelling ecosystem service delivery under ecological intensification.
Stefano Balbi, Ferdinando Villa, Vahid Mojtahed, Karin Tessa Hegetschweiler, and Carlo Giupponi
Nat. Hazards Earth Syst. Sci., 16, 1323–1337, https://doi.org/10.5194/nhess-16-1323-2016, https://doi.org/10.5194/nhess-16-1323-2016, 2016
Short summary
Short summary
This study develops a novel methodology to assess flood risk to people by integrating people’s vulnerability and ability to cushion hazards through coping and adapting. The model is used to estimate the effect of improving an existing early warning system. The proposed approach extends traditional risk assessments beyond material damages, complements quantitative and semi-quantitative data with subjective and local knowledge, and improves the use of commonly available information.
Related subject area
Other Hazards (e.g., Glacial and Snow Hazards, Karst, Wildfires Hazards, and Medical Geo-Hazards)
Glide-snow avalanches: a mechanical, threshold-based release area model
Improving fire severity prediction in south-eastern Australia using vegetation-specific information
Review article: A scoping review of human factors in avalanche decision-making
How hard do avalanche practitioners tap during snow stability tests?
A large-scale validation of snowpack simulations in support of avalanche forecasting focusing on critical layers
A glacial lake outburst flood risk assessment for the Phochhu river basin, Bhutan
Statistical calibration of probabilistic medium-range fire weather index forecasts in Europe
AutoATES v2.0: Automated Avalanche Terrain Exposure Scale mapping
Modelling the vulnerability of urban settings to wildland–urban interface fires in Chile
Modeling of indoor 222Rn in data-scarce regions: an interactive dashboard approach for Bogotá, Colombia
The effect of propagation saw test geometries on critical cut length
A regional early warning for slushflow hazard
A new approach for drought index adjustment to clay-shrinkage-induced subsidence over France: advantages of the interactive leaf area index
Automated Avalanche Terrain Exposure Scale (ATES) mapping – local validation and optimization in western Canada
An Efficient Method to Simulate Wildfire Propagation Using Irregular Grids
Improving the fire weather index system for peatlands using peat-specific hydrological input data
Brief communication: The Lahaina Fire disaster – how models can be used to understand and predict wildfires
Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations
Early warning system for ice collapses and river blockages in the Sedongpu Valley, southeastern Tibetan Plateau
Avalanche size estimation and avalanche outline determination by experts: reliability and implications for practice
Fluid conduits and shallow-reservoir structure defined by geoelectrical tomography at the Nirano Salse (Italy)
Estimating the effects of meteorology and land cover on fire growth in Peru using a novel difference equation model
Review article: Snow and ice avalanches in high mountain Asia – scientific, local and indigenous knowledge
Reduced-order digital twin and latent data assimilation for global wildfire prediction
A user perspective on the avalanche danger scale – insights from North America
Characterizing the rate of spread of large wildfires in emerging fire environments of northwestern Europe using Visible Infrared Imaging Radiometer Suite active fire data
Evaluation of low-cost Raspberry Pi sensors for structure-from-motion reconstructions of glacier calving fronts
Temporal evolution of crack propagation characteristics in a weak snowpack layer: conditions of crack arrest and sustained propagation
A data-driven model for Fennoscandian wildfire danger
Equivalent hazard magnitude scale
Statistical modelling of air quality impacts from individual forest fires in New South Wales, Australia
Drivers of extreme burnt area in Portugal: fire weather and vegetation
Coupling wildfire spread simulations and connectivity analysis for hazard assessment: a case study in Serra da Cabreira, Portugal
Glacial lake outburst flood hazard under current and future conditions: worst-case scenarios in a transboundary Himalayan basin
What weather variables are important for wet and slab avalanches under a changing climate in a low-altitude mountain range in Czechia?
Modelling ignition probability for human- and lightning-caused wildfires in Victoria, Australia
Automated snow avalanche release area delineation in data-sparse, remote, and forested regions
The 2017 Split wildfire in Croatia: evolution and the role of meteorological conditions
Progress and challenges in glacial lake outburst flood research (2017–2021): a research community perspective
Global assessment and mapping of ecological vulnerability to wildfires
The impact of terrain model source and resolution on snow avalanche modeling
Travel and terrain advice statements in public avalanche bulletins: a quantitative analysis of who uses this information, what makes it useful, and how it can be improved for users
Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland
On the correlation between a sub-level qualifier refining the danger level with observations and models relating to the contributing factors of avalanche danger
Automated avalanche hazard indication mapping on a statewide scale
Forecasting the regional fire radiative power for regularly ignited vegetation fires
Environmental factors affecting wildfire-burned areas in southeastern France, 1970–2019
Detrainment and braking of snow avalanches interacting with forests
Past and future trends in fire weather for the UK
Methodological and conceptual challenges in rare and severe event forecast verification
Amelie Fees, Alec van Herwijnen, Michael Lombardo, Jürg Schweizer, and Peter Lehmann
Nat. Hazards Earth Syst. Sci., 24, 3387–3400, https://doi.org/10.5194/nhess-24-3387-2024, https://doi.org/10.5194/nhess-24-3387-2024, 2024
Short summary
Short summary
Glide-snow avalanches release at the ground–snow interface, and their release process is poorly understood. To investigate the influence of spatial variability (snowpack and basal friction) on avalanche release, we developed a 3D, mechanical, threshold-based model that reproduces an observed release area distribution. A sensitivity analysis showed that the distribution was mostly influenced by the basal friction uniformity, while the variations in snowpack properties had little influence.
Kang He, Xinyi Shen, Cory Merow, Efthymios Nikolopoulos, Rachael V. Gallagher, Feifei Yang, and Emmanouil N. Anagnostou
Nat. Hazards Earth Syst. Sci., 24, 3337–3355, https://doi.org/10.5194/nhess-24-3337-2024, https://doi.org/10.5194/nhess-24-3337-2024, 2024
Short summary
Short summary
A framework combining a fire severity classification with a regression model to predict an indicator of fire severity derived from Landsat imagery (difference normalized burning ratio, dNBR) is proposed. The results show that the proposed predictive technique is capable of providing robust fire severity prediction information, which can be used for forecasting seasonal fire severity and, subsequently, impacts on biodiversity and ecosystems under projected future climate conditions.
Audun Hetland, Rebecca Anne Hetland, Tarjei Tveito Skille, and Andrea Mannberg
EGUsphere, https://doi.org/10.5194/egusphere-2024-1628, https://doi.org/10.5194/egusphere-2024-1628, 2024
Short summary
Short summary
Research on human factor in avalanche decision making has become increasingly popular the past two decades. The studies span across a wide range of disciplines and is published in a variety of journals. To provide an overview of the literature this study provide a systematic scooping review of human factor in avalanche decision making. 70 papers fulfilled the search criteria. We extracted data and sorted the papers according to their main theme.
Håvard B. Toft, Samuel V. Verplanck, and Markus Landrø
Nat. Hazards Earth Syst. Sci., 24, 2757–2772, https://doi.org/10.5194/nhess-24-2757-2024, https://doi.org/10.5194/nhess-24-2757-2024, 2024
Short summary
Short summary
This study investigates inconsistencies in impact force as part of extended column tests (ECTs). We measured force-time curves from 286 practitioners in Scandinavia, Central Europe, and North America. The results show a large variability in peak forces and loading rates across wrist, elbow, and shoulder taps, challenging the ECT's reliability.
Florian Herla, Pascal Haegeli, Simon Horton, and Patrick Mair
Nat. Hazards Earth Syst. Sci., 24, 2727–2756, https://doi.org/10.5194/nhess-24-2727-2024, https://doi.org/10.5194/nhess-24-2727-2024, 2024
Short summary
Short summary
Snowpack simulations are increasingly employed by avalanche warning services to inform about critical avalanche layers buried in the snowpack. However, validity concerns limit their operational value. We present methods that enable meaningful comparisons between snowpack simulations and regional assessments of avalanche forecasters to quantify the performance of the Canadian weather and snowpack model chain to represent thin critical avalanche layers on a large scale and in real time.
Tandin Wangchuk and Ryota Tsubaki
Nat. Hazards Earth Syst. Sci., 24, 2523–2540, https://doi.org/10.5194/nhess-24-2523-2024, https://doi.org/10.5194/nhess-24-2523-2024, 2024
Short summary
Short summary
A glacial lake outburst flood (GLOF) is a natural hazard in which water from a glacier-fed lake is swiftly discharged, causing serious harm to life, infrastructure, and communities. We used numerical models to predict the potential consequences of a GLOF originating from the Thorthomi glacial lake in Bhutan. We found that if a GLOF occurs, the lake could release massive flood water within 4 h, posing a considerable risk. Study findings help to mitigate the impacts of future GLOFs.
Stephanie Bohlmann and Marko Laine
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-57, https://doi.org/10.5194/nhess-2024-57, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
Probabilistic ensemble forecasts of the Canadian Forest Fire Weather Index (FWI) can be used to estimate the possible risk for wildfires but requires post-processing to provide accurate and reliable predictions. We present a calibration method using non-homogeneous Gaussian regression to statistical post-process FWI forecasts up to 15 days. Calibration improves the forecast especially at short lead times and in regions with elevated FWI values.
Håvard B. Toft, John Sykes, Andrew Schauer, Jordy Hendrikx, and Audun Hetland
Nat. Hazards Earth Syst. Sci., 24, 1779–1793, https://doi.org/10.5194/nhess-24-1779-2024, https://doi.org/10.5194/nhess-24-1779-2024, 2024
Short summary
Short summary
Manual Avalanche Terrain Exposure Scale (ATES) mapping is time-consuming and inefficient for large-scale applications. The updated algorithm for automated ATES mapping overcomes previous limitations by including forest density data, improving the avalanche runout estimations in low-angle runout zones, accounting for overhead exposure and open-source software. Results show that the latest version has significantly improved its performance.
Paula Aguirre, Jorge León, Constanza González-Mathiesen, Randy Román, Manuela Penas, and Alonso Ogueda
Nat. Hazards Earth Syst. Sci., 24, 1521–1537, https://doi.org/10.5194/nhess-24-1521-2024, https://doi.org/10.5194/nhess-24-1521-2024, 2024
Short summary
Short summary
Wildfires pose a significant risk to property located in the wildland–urban interface (WUI). To assess and mitigate this risk, we need to understand which characteristics of buildings and building arrangements make them more prone to damage. We used a combination of data collection and analysis methods to study the vulnerability of dwellings in the WUI for case studies in Chile and concluded that the spatial arrangement of houses has a substantial impact on their vulnerability to wildfires.
Martín Domínguez Durán, María Angélica Sandoval Garzón, and Carme Huguet
Nat. Hazards Earth Syst. Sci., 24, 1319–1339, https://doi.org/10.5194/nhess-24-1319-2024, https://doi.org/10.5194/nhess-24-1319-2024, 2024
Short summary
Short summary
In this study we created a cost-effective alternative to bridge the baseline information gap on indoor radon (a highly carcinogenic gas) in regions where measurements are scarce. We model indoor radon concentrations to understand its spatial distribution and the potential influential factors. We evaluated the performance of this alternative using a small number of measurements taken in Bogotá, Colombia. Our results show that this alternative could help in the making of future studies and policy.
Bastian Bergfeld, Karl W. Birkeland, Valentin Adam, Philipp L. Rosendahl, and Alec van Herwijnen
EGUsphere, https://doi.org/10.5194/egusphere-2024-690, https://doi.org/10.5194/egusphere-2024-690, 2024
Short summary
Short summary
To release a slab avalanche, a crack in a weak snow layer beneath a cohesive slab has to propagate. Information on that is essential for assessing avalanche risk. In the field, information can be gathered with the Propagation Saw Test (PST). However, there are different standards on how to cut the PST. In this study, we experimentally investigate the effect of these different column geometries and provide models to correct for imprecise field test geometry effects on the critical cut length.
Monica Sund, Heidi A. Grønsten, and Siv Å. Seljesæter
Nat. Hazards Earth Syst. Sci., 24, 1185–1201, https://doi.org/10.5194/nhess-24-1185-2024, https://doi.org/10.5194/nhess-24-1185-2024, 2024
Short summary
Short summary
Slushflows are rapid mass movements of water-saturated snow released in gently sloping terrain (< 30°), often unexpectedly. Early warning is crucial to prevent casualties and damage to infrastructure. A regional early warning for slushflow hazard was established in Norway in 2013–2014 and has been operational since. We present a methodology using the ratio between water supply and snow depth by snow type to assess slushflow hazard. This approach is useful for other areas with slushflow hazard.
Sophie Barthelemy, Bertrand Bonan, Jean-Christophe Calvet, Gilles Grandjean, David Moncoulon, Dorothée Kapsambelis, and Séverine Bernardie
Nat. Hazards Earth Syst. Sci., 24, 999–1016, https://doi.org/10.5194/nhess-24-999-2024, https://doi.org/10.5194/nhess-24-999-2024, 2024
Short summary
Short summary
This work presents a drought index specifically adapted to subsidence, a seasonal phenomenon of soil shrinkage that occurs frequently in France and damages buildings. The index is computed from land surface model simulations and evaluated by a rank correlation test with insurance data. With its optimal configuration, the index is able to identify years of both zero and significant loss.
John Sykes, Håvard Toft, Pascal Haegeli, and Grant Statham
Nat. Hazards Earth Syst. Sci., 24, 947–971, https://doi.org/10.5194/nhess-24-947-2024, https://doi.org/10.5194/nhess-24-947-2024, 2024
Short summary
Short summary
The research validates and optimizes an automated approach for creating classified snow avalanche terrain maps using open-source geospatial modeling tools. Validation is based on avalanche-expert-based maps for two study areas. Our results show that automated maps have an overall accuracy equivalent to the average accuracy of three human maps. Automated mapping requires a fraction of the time and cost of traditional methods and opens the door for large-scale mapping of mountainous terrain.
Conor Hackett, Rafael de Andrade Moral, Gourav Mishra, Tim McCarthy, and Charles Markham
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-27, https://doi.org/10.5194/nhess-2024-27, 2024
Revised manuscript under review for NHESS
Short summary
Short summary
This paper reviews existing wildfire propagation models and a comparison of different grid types including random grids to simulate wildfires. This paper finds that irregular grids simulate wildfires more efficiently than continuous models while still retaining a reasonable level of similarity. It also shows that irregular grids tend to retain greater similarity to continuous models than regular grids at the cost of slightly longer computational times.
Jonas Mortelmans, Anne Felsberg, Gabriëlle J. M. De Lannoy, Sander Veraverbeke, Robert D. Field, Niels Andela, and Michel Bechtold
Nat. Hazards Earth Syst. Sci., 24, 445–464, https://doi.org/10.5194/nhess-24-445-2024, https://doi.org/10.5194/nhess-24-445-2024, 2024
Short summary
Short summary
With global warming increasing the frequency and intensity of wildfires in the boreal region, accurate risk assessments are becoming more crucial than ever before. The Canadian Fire Weather Index (FWI) is a renowned system, yet its effectiveness in peatlands, where hydrology plays a key role, is limited. By incorporating groundwater data from numerical models and satellite observations, our modified FWI improves the accuracy of fire danger predictions, especially over summer.
Timothy W. Juliano, Fernando Szasdi-Bardales, Neil P. Lareau, Kasra Shamsaei, Branko Kosović, Negar Elhami-Khorasani, Eric P. James, and Hamed Ebrahimian
Nat. Hazards Earth Syst. Sci., 24, 47–52, https://doi.org/10.5194/nhess-24-47-2024, https://doi.org/10.5194/nhess-24-47-2024, 2024
Short summary
Short summary
Following the destructive Lahaina Fire in Hawaii, our team has modeled the wind and fire spread processes to understand the drivers of this devastating event. The simulation results show that extreme winds with high variability, a fire ignition close to the community, and construction characteristics led to continued fire spread in multiple directions. Our results suggest that available modeling capabilities can provide vital information to guide decision-making during wildfire events.
Stephanie Mayer, Frank Techel, Jürg Schweizer, and Alec van Herwijnen
Nat. Hazards Earth Syst. Sci., 23, 3445–3465, https://doi.org/10.5194/nhess-23-3445-2023, https://doi.org/10.5194/nhess-23-3445-2023, 2023
Short summary
Short summary
We present statistical models to estimate the probability for natural dry-snow avalanche release and avalanche size based on the simulated layering of the snowpack. The benefit of these models is demonstrated in comparison with benchmark models based on the amount of new snow. From the validation with data sets of quality-controlled avalanche observations and danger levels, we conclude that these models may be valuable tools to support forecasting natural dry-snow avalanche activity.
Wei Yang, Zhongyan Wang, Baosheng An, Yingying Chen, Chuanxi Zhao, Chenhui Li, Yongjie Wang, Weicai Wang, Jiule Li, Guangjian Wu, Lin Bai, Fan Zhang, and Tandong Yao
Nat. Hazards Earth Syst. Sci., 23, 3015–3029, https://doi.org/10.5194/nhess-23-3015-2023, https://doi.org/10.5194/nhess-23-3015-2023, 2023
Short summary
Short summary
We present the structure and performance of the early warning system (EWS) for glacier collapse and river blockages in the southeastern Tibetan Plateau. The EWS warned of three collapse–river blockage chain events and seven small-scale events. The volume and location of the collapses and the percentage of ice content influenced the velocities of debris flows. Such a study is helpful for understanding the mechanism of glacier hazards and for establishing similar EWSs in other high-risk regions.
Elisabeth D. Hafner, Frank Techel, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 23, 2895–2914, https://doi.org/10.5194/nhess-23-2895-2023, https://doi.org/10.5194/nhess-23-2895-2023, 2023
Short summary
Short summary
Oftentimes when objective measurements are not possible, human estimates are used instead. In our study, we investigate the reproducibility of human judgement for size estimates, the mappings of avalanches from oblique photographs and remotely sensed imagery. The variability that we found in those estimates is worth considering as it may influence results and should be kept in mind for several applications.
Gerardo Romano, Marco Antonellini, Domenico Patella, Agata Siniscalchi, Andrea Tallarico, Simona Tripaldi, and Antonello Piombo
Nat. Hazards Earth Syst. Sci., 23, 2719–2735, https://doi.org/10.5194/nhess-23-2719-2023, https://doi.org/10.5194/nhess-23-2719-2023, 2023
Short summary
Short summary
The Nirano Salse (northern Apennines, Italy) is characterized by several active mud vents and hosts thousands of visitors every year. New resistivity models describe the area down to 250 m, improving our geostructural knowledge of the area and giving useful indications for a better understanding of mud volcano dynamics and for the better planning of safer tourist access to the area.
Harry Podschwit, William Jolly, Ernesto Alvarado, Andrea Markos, Satyam Verma, Sebastian Barreto-Rivera, Catherine Tobón-Cruz, and Blanca Ponce-Vigo
Nat. Hazards Earth Syst. Sci., 23, 2607–2624, https://doi.org/10.5194/nhess-23-2607-2023, https://doi.org/10.5194/nhess-23-2607-2023, 2023
Short summary
Short summary
We developed a model of fire spread that assumes that fire spreads in all directions at a constant speed and is extinguished at a constant rate. The model was fitted to 1003 fires in Peru between 2001 and 2020 using satellite burned area data from the GlobFire project. We fitted statistical models that predicted the spread and extinguish rates based on weather and land cover variables and found that these variables were good predictors of the spread and extinguish rates.
Anushilan Acharya, Jakob F. Steiner, Khwaja Momin Walizada, Salar Ali, Zakir Hussain Zakir, Arnaud Caiserman, and Teiji Watanabe
Nat. Hazards Earth Syst. Sci., 23, 2569–2592, https://doi.org/10.5194/nhess-23-2569-2023, https://doi.org/10.5194/nhess-23-2569-2023, 2023
Short summary
Short summary
All accessible snow and ice avalanches together with previous scientific research, local knowledge, and existing or previously active adaptation and mitigation solutions were investigated in the high mountain Asia (HMA) region to have a detailed overview of the state of knowledge and identify gaps. A comprehensive avalanche database from 1972–2022 is generated, including 681 individual events. The database provides a basis for the forecasting of avalanche hazards in different parts of HMA.
Caili Zhong, Sibo Cheng, Matthew Kasoar, and Rossella Arcucci
Nat. Hazards Earth Syst. Sci., 23, 1755–1768, https://doi.org/10.5194/nhess-23-1755-2023, https://doi.org/10.5194/nhess-23-1755-2023, 2023
Short summary
Short summary
This paper introduces a digital twin fire model using machine learning techniques to improve the efficiency of global wildfire predictions. The proposed model also manages to efficiently adjust the prediction results thanks to data assimilation techniques. The proposed digital twin runs 500 times faster than the current state-of-the-art physics-based model.
Abby Morgan, Pascal Haegeli, Henry Finn, and Patrick Mair
Nat. Hazards Earth Syst. Sci., 23, 1719–1742, https://doi.org/10.5194/nhess-23-1719-2023, https://doi.org/10.5194/nhess-23-1719-2023, 2023
Short summary
Short summary
The avalanche danger scale is a critical component for communicating the severity of avalanche hazard conditions to the public. We examine how backcountry recreationists in North America understand and use the danger scale for planning trips into the backcountry. Our results provide an important user perspective on the strengths and weaknesses of the existing scale and highlight opportunities for future improvements.
Adrián Cardíl, Victor M. Tapia, Santiago Monedero, Tomás Quiñones, Kerryn Little, Cathelijne R. Stoof, Joaquín Ramirez, and Sergio de-Miguel
Nat. Hazards Earth Syst. Sci., 23, 361–373, https://doi.org/10.5194/nhess-23-361-2023, https://doi.org/10.5194/nhess-23-361-2023, 2023
Short summary
Short summary
This study aims to unravel large-fire behavior in northwest Europe, a temperate region with a projected increase in wildfire risk. We propose a new method to identify wildfire rate of spread from satellites because it is important to know periods of elevated fire risk for suppression methods and land management. Results indicate that there is a peak in the area burned and rate of spread in the months of March and April, and there are significant differences for forest-type land covers.
Liam S. Taylor, Duncan J. Quincey, and Mark W. Smith
Nat. Hazards Earth Syst. Sci., 23, 329–341, https://doi.org/10.5194/nhess-23-329-2023, https://doi.org/10.5194/nhess-23-329-2023, 2023
Short summary
Short summary
Hazards from glaciers are becoming more likely as the climate warms, which poses a threat to communities living beneath them. We have developed a new camera system which can capture regular, high-quality 3D models to monitor small changes in glaciers which could be indicative of a future hazard. This system is far cheaper than more typical camera sensors yet produces very similar quality data. We suggest that deploying these cameras near glaciers could assist in warning communities of hazards.
Bastian Bergfeld, Alec van Herwijnen, Grégoire Bobillier, Philipp L. Rosendahl, Philipp Weißgraeber, Valentin Adam, Jürg Dual, and Jürg Schweizer
Nat. Hazards Earth Syst. Sci., 23, 293–315, https://doi.org/10.5194/nhess-23-293-2023, https://doi.org/10.5194/nhess-23-293-2023, 2023
Short summary
Short summary
For a slab avalanche to release, the snowpack must facilitate crack propagation over large distances. Field measurements on crack propagation at this scale are very scarce. We performed a series of experiments, up to 10 m long, over a period of 10 weeks. Beside the temporal evolution of the mechanical properties of the snowpack, we found that crack speeds were highest for tests resulting in full propagation. Based on these findings, an index for self-sustained crack propagation is proposed.
Sigrid Jørgensen Bakke, Niko Wanders, Karin van der Wiel, and Lena Merete Tallaksen
Nat. Hazards Earth Syst. Sci., 23, 65–89, https://doi.org/10.5194/nhess-23-65-2023, https://doi.org/10.5194/nhess-23-65-2023, 2023
Short summary
Short summary
In this study, we developed a machine learning model to identify dominant controls of wildfire in Fennoscandia and produce monthly fire danger probability maps. The dominant control was shallow-soil water anomaly, followed by air temperature and deep soil water. The model proved skilful with a similar performance as the existing Canadian Forest Fire Weather Index (FWI). We highlight the benefit of using data-driven models jointly with other fire models to improve fire monitoring and prediction.
Yi Victor Wang and Antonia Sebastian
Nat. Hazards Earth Syst. Sci., 22, 4103–4118, https://doi.org/10.5194/nhess-22-4103-2022, https://doi.org/10.5194/nhess-22-4103-2022, 2022
Short summary
Short summary
In this article, we propose an equivalent hazard magnitude scale and a method to evaluate and compare the strengths of natural hazard events across different hazard types, including earthquakes, tsunamis, floods, droughts, forest fires, tornadoes, cold waves, heat waves, and tropical cyclones. With our method, we determine that both the February 2021 North American cold wave event and Hurricane Harvey in 2017 were equivalent to a magnitude 7.5 earthquake in hazard strength.
Michael A. Storey and Owen F. Price
Nat. Hazards Earth Syst. Sci., 22, 4039–4062, https://doi.org/10.5194/nhess-22-4039-2022, https://doi.org/10.5194/nhess-22-4039-2022, 2022
Short summary
Short summary
Models are needed to understand and predict pollutant output from forest fires so fire agencies can reduce smoke-related risks to human health. We modelled air quality (PM2.5) based on fire area and weather variables. We found fire area and boundary layer height were influential on predictions, with distance, temperature, wind speed and relative humidity also important. The models predicted reasonably accurately in comparison to other existing methods but would benefit from further development.
Tomás Calheiros, Akli Benali, Mário Pereira, João Silva, and João Nunes
Nat. Hazards Earth Syst. Sci., 22, 4019–4037, https://doi.org/10.5194/nhess-22-4019-2022, https://doi.org/10.5194/nhess-22-4019-2022, 2022
Short summary
Short summary
Fire weather indices are used to assess the effect of weather on wildfires. Fire weather risk was computed and combined with large wildfires in Portugal. Results revealed the influence of vegetation cover: municipalities with a prevalence of shrublands, located in eastern parts, burnt under less extreme conditions than those with higher forested areas, situated in coastal regions. These findings are a novelty for fire science in Portugal and should be considered for fire management.
Ana C. L. Sá, Bruno Aparicio, Akli Benali, Chiara Bruni, Michele Salis, Fábio Silva, Martinho Marta-Almeida, Susana Pereira, Alfredo Rocha, and José Pereira
Nat. Hazards Earth Syst. Sci., 22, 3917–3938, https://doi.org/10.5194/nhess-22-3917-2022, https://doi.org/10.5194/nhess-22-3917-2022, 2022
Short summary
Short summary
Assessing landscape wildfire connectivity supported by wildfire spread simulations can improve fire hazard assessment and fuel management plans. Weather severity determines the degree of fuel patch connectivity and thus the potential to spread large and intense wildfires. Mapping highly connected patches in the landscape highlights patch candidates for prior fuel treatments, which ultimately will contribute to creating fire-resilient Mediterranean landscapes.
Simon K. Allen, Ashim Sattar, Owen King, Guoqing Zhang, Atanu Bhattacharya, Tandong Yao, and Tobias Bolch
Nat. Hazards Earth Syst. Sci., 22, 3765–3785, https://doi.org/10.5194/nhess-22-3765-2022, https://doi.org/10.5194/nhess-22-3765-2022, 2022
Short summary
Short summary
This study demonstrates how the threat of a very large outburst from a future lake can be feasibly assessed alongside that from current lakes to inform disaster risk management within a transboundary basin between Tibet and Nepal. Results show that engineering measures and early warning systems would need to be coupled with effective land use zoning and programmes to strengthen local response capacities in order to effectively reduce the risk associated with current and future outburst events.
Markéta Součková, Roman Juras, Kryštof Dytrt, Vojtěch Moravec, Johanna Ruth Blöcher, and Martin Hanel
Nat. Hazards Earth Syst. Sci., 22, 3501–3525, https://doi.org/10.5194/nhess-22-3501-2022, https://doi.org/10.5194/nhess-22-3501-2022, 2022
Short summary
Short summary
Avalanches are natural hazards that threaten people and infrastructure. With climate change, avalanche activity is changing. We analysed the change in frequency and size of avalanches in the Krkonoše Mountains, Czechia, and detected important variables with machine learning tools from 1979–2020. Wet avalanches in February and March have increased, and slab avalanches have decreased and become smaller. The identified variables and their threshold levels may help in avalanche decision-making.
Annalie Dorph, Erica Marshall, Kate A. Parkins, and Trent D. Penman
Nat. Hazards Earth Syst. Sci., 22, 3487–3499, https://doi.org/10.5194/nhess-22-3487-2022, https://doi.org/10.5194/nhess-22-3487-2022, 2022
Short summary
Short summary
Wildfire spatial patterns are determined by fire ignition sources and vegetation fuel moisture. Fire ignitions can be mediated by humans (owing to proximity to human infrastructure) or caused by lightning (owing to fuel moisture, average annual rainfall and local weather). When moisture in dead vegetation is below 20 % the probability of a wildfire increases. The results of this research enable accurate spatial mapping of ignition probability to aid fire suppression efforts and future research.
John Sykes, Pascal Haegeli, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 22, 3247–3270, https://doi.org/10.5194/nhess-22-3247-2022, https://doi.org/10.5194/nhess-22-3247-2022, 2022
Short summary
Short summary
Automated snow avalanche terrain mapping provides an efficient method for large-scale assessment of avalanche hazards, which informs risk management decisions for transportation and recreation. This research reduces the cost of developing avalanche terrain maps by using satellite imagery and open-source software as well as improving performance in forested terrain. The research relies on local expertise to evaluate accuracy, so the methods are broadly applicable in mountainous regions worldwide.
Ivana Čavlina Tomašević, Kevin K. W. Cheung, Višnjica Vučetić, Paul Fox-Hughes, Kristian Horvath, Maja Telišman Prtenjak, Paul J. Beggs, Barbara Malečić, and Velimir Milić
Nat. Hazards Earth Syst. Sci., 22, 3143–3165, https://doi.org/10.5194/nhess-22-3143-2022, https://doi.org/10.5194/nhess-22-3143-2022, 2022
Short summary
Short summary
One of the most severe and impactful urban wildfire events in Croatian history has been reconstructed and analyzed. The study identified some important meteorological influences related to the event: the synoptic conditions of the Azores anticyclone, cold front, and upper-level shortwave trough all led to the highest fire weather index in 2017. A low-level jet, locally known as bura wind that can be explained by hydraulic jump theory, was the dynamic trigger of the event.
Adam Emmer, Simon K. Allen, Mark Carey, Holger Frey, Christian Huggel, Oliver Korup, Martin Mergili, Ashim Sattar, Georg Veh, Thomas Y. Chen, Simon J. Cook, Mariana Correas-Gonzalez, Soumik Das, Alejandro Diaz Moreno, Fabian Drenkhan, Melanie Fischer, Walter W. Immerzeel, Eñaut Izagirre, Ramesh Chandra Joshi, Ioannis Kougkoulos, Riamsara Kuyakanon Knapp, Dongfeng Li, Ulfat Majeed, Stephanie Matti, Holly Moulton, Faezeh Nick, Valentine Piroton, Irfan Rashid, Masoom Reza, Anderson Ribeiro de Figueiredo, Christian Riveros, Finu Shrestha, Milan Shrestha, Jakob Steiner, Noah Walker-Crawford, Joanne L. Wood, and Jacob C. Yde
Nat. Hazards Earth Syst. Sci., 22, 3041–3061, https://doi.org/10.5194/nhess-22-3041-2022, https://doi.org/10.5194/nhess-22-3041-2022, 2022
Short summary
Short summary
Glacial lake outburst floods (GLOFs) have attracted increased research attention recently. In this work, we review GLOF research papers published between 2017 and 2021 and complement the analysis with research community insights gained from the 2021 GLOF conference we organized. The transdisciplinary character of the conference together with broad geographical coverage allowed us to identify progress, trends and challenges in GLOF research and outline future research needs and directions.
Fátima Arrogante-Funes, Inmaculada Aguado, and Emilio Chuvieco
Nat. Hazards Earth Syst. Sci., 22, 2981–3003, https://doi.org/10.5194/nhess-22-2981-2022, https://doi.org/10.5194/nhess-22-2981-2022, 2022
Short summary
Short summary
We show that ecological value might be reduced by 50 % due to fire perturbation in ecosystems that have not developed in the presence of fire and/or that present changes in the fire regime. The biomes most affected are tropical and subtropical forests, tundra, and mangroves. Integration of biotic and abiotic fire regime and regeneration factors resulted in a powerful way to map ecological vulnerability to fire and develop assessments to generate adaptation plans of management in forest masses.
Aubrey Miller, Pascal Sirguey, Simon Morris, Perry Bartelt, Nicolas Cullen, Todd Redpath, Kevin Thompson, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 22, 2673–2701, https://doi.org/10.5194/nhess-22-2673-2022, https://doi.org/10.5194/nhess-22-2673-2022, 2022
Short summary
Short summary
Natural hazard modelers simulate mass movements to better anticipate the risk to people and infrastructure. These simulations require accurate digital elevation models. We test the sensitivity of a well-established snow avalanche model (RAMMS) to the source and spatial resolution of the elevation model. We find key differences in the digital representation of terrain greatly affect the simulated avalanche results, with implications for hazard planning.
Kathryn C. Fisher, Pascal Haegeli, and Patrick Mair
Nat. Hazards Earth Syst. Sci., 22, 1973–2000, https://doi.org/10.5194/nhess-22-1973-2022, https://doi.org/10.5194/nhess-22-1973-2022, 2022
Short summary
Short summary
Avalanche bulletins include travel and terrain statements to provide recreationists with tangible guidance about how to apply the hazard information. We examined which bulletin users pay attention to these statements, what determines their usefulness, and how they could be improved. Our study shows that reducing jargon and adding simple explanations can significantly improve the usefulness of the statements for users with lower levels of avalanche awareness education who depend on this advice.
Cristina Pérez-Guillén, Frank Techel, Martin Hendrick, Michele Volpi, Alec van Herwijnen, Tasko Olevski, Guillaume Obozinski, Fernando Pérez-Cruz, and Jürg Schweizer
Nat. Hazards Earth Syst. Sci., 22, 2031–2056, https://doi.org/10.5194/nhess-22-2031-2022, https://doi.org/10.5194/nhess-22-2031-2022, 2022
Short summary
Short summary
A fully data-driven approach to predicting the danger level for dry-snow avalanche conditions in Switzerland was developed. Two classifiers were trained using a large database of meteorological data, snow cover simulations, and danger levels. The models performed well throughout the Swiss Alps, reaching a performance similar to the current experience-based avalanche forecasts. This approach shows the potential to be a valuable supplementary decision support tool for assessing avalanche hazard.
Frank Techel, Stephanie Mayer, Cristina Pérez-Guillén, Günter Schmudlach, and Kurt Winkler
Nat. Hazards Earth Syst. Sci., 22, 1911–1930, https://doi.org/10.5194/nhess-22-1911-2022, https://doi.org/10.5194/nhess-22-1911-2022, 2022
Short summary
Short summary
Can the resolution of forecasts of avalanche danger be increased by using a combination of absolute and comparative judgments? Using 5 years of Swiss avalanche forecasts, we show that, on average, sub-levels assigned to a danger level reflect the expected increase in the number of locations with poor snow stability and in the number and size of avalanches with increasing forecast sub-level.
Yves Bühler, Peter Bebi, Marc Christen, Stefan Margreth, Lukas Stoffel, Andreas Stoffel, Christoph Marty, Gregor Schmucki, Andrin Caviezel, Roderick Kühne, Stephan Wohlwend, and Perry Bartelt
Nat. Hazards Earth Syst. Sci., 22, 1825–1843, https://doi.org/10.5194/nhess-22-1825-2022, https://doi.org/10.5194/nhess-22-1825-2022, 2022
Short summary
Short summary
To calculate and visualize the potential avalanche hazard, we develop a method that automatically and efficiently pinpoints avalanche starting zones and simulate their runout for the entire canton of Grisons. The maps produced in this way highlight areas that could be endangered by avalanches and are extremely useful in multiple applications for the cantonal authorities, including the planning of new infrastructure, making alpine regions more safe.
Tero M. Partanen and Mikhail Sofiev
Nat. Hazards Earth Syst. Sci., 22, 1335–1346, https://doi.org/10.5194/nhess-22-1335-2022, https://doi.org/10.5194/nhess-22-1335-2022, 2022
Short summary
Short summary
The presented method aims to forecast regional wildfire-emitted radiative power in a time-dependent manner several days in advance. The temporal fire radiative power can be converted to an emission production rate, which can be implemented in air quality forecasting simulations. It is shown that in areas with a high incidence of wildfires, the fire radiative power is quite predictable, but otherwise it is not.
Christos Bountzouklis, Dennis M. Fox, and Elena Di Bernardino
Nat. Hazards Earth Syst. Sci., 22, 1181–1200, https://doi.org/10.5194/nhess-22-1181-2022, https://doi.org/10.5194/nhess-22-1181-2022, 2022
Short summary
Short summary
The study addresses the evolution of burned areas in southeastern France from 1970 to 2019 through the scope of a firefighting policy shift in 1994 that resulted in a significant decrease in the burned area. Regions with large fires were particularly impacted, whereas, in other areas, the fires remained frequent and occurred closer to built-up zones. Environmental characteristics such as south-facing slopes and low vegetation (bushes) are increasingly associated with burned areas.
Louis Védrine, Xingyue Li, and Johan Gaume
Nat. Hazards Earth Syst. Sci., 22, 1015–1028, https://doi.org/10.5194/nhess-22-1015-2022, https://doi.org/10.5194/nhess-22-1015-2022, 2022
Short summary
Short summary
This study investigates how forests affect the behaviour of snow avalanches through the evaluation of the amount of snow stopped by the trees and the analysis of energy dissipation mechanisms. Different avalanche features and tree configurations have been examined, leading to the proposal of a unified law for the detrained snow mass. Outcomes from this study can be directly implemented in operational models for avalanche risk assessment and contribute to improved forest management strategy.
Matthew C. Perry, Emilie Vanvyve, Richard A. Betts, and Erika J. Palin
Nat. Hazards Earth Syst. Sci., 22, 559–575, https://doi.org/10.5194/nhess-22-559-2022, https://doi.org/10.5194/nhess-22-559-2022, 2022
Short summary
Short summary
In the past, wildfires in the UK have occurred mainly in spring, with occasional events during hot, dry summers. Climate models predict a large future increase in hazardous fire weather conditions in summer. Wildfire can be considered an
emergent riskfor the UK, as past events have not had widespread major impacts, but this could change. The large increase in risk between the 2 °C and 4 °C levels of global warming highlights the importance of global efforts to keep warming below 2 °C.
Philip A. Ebert and Peter Milne
Nat. Hazards Earth Syst. Sci., 22, 539–557, https://doi.org/10.5194/nhess-22-539-2022, https://doi.org/10.5194/nhess-22-539-2022, 2022
Short summary
Short summary
There is no consensus about how to assess the quality of binary (yes or no) rare and severe event forecasts, i.e. forecasts involving natural hazards like tornadoes or avalanches. We offer a comprehensive overview of the challenges we face when making such an assessment and provide a critical review of existing solutions. We argue against all but one existing solution to assess the quality of such forecasts and present practical consequences to improve forecasting services.
Cited articles
Ager, A. and Finney, M. A.: Climate change impact on fire probability and
severity in Mediterranean areas, in: Proceedings of the VI International Conference on Forest Fire Research, 15–18 November 2010, University of Coimbra Viegas, Coimbra, Portugal, p. 9, https://www.fs.usda.gov/research/treesearch/39345, last access: 30 August 2023), U.S. Department of Agriculture, 2010.
Aguilera, P. A., Fernández, A., Fernández, R., Rumí, R., and
Salmerón, A.: Bayesian networks in environmental modelling, Environ. Model. Softw., 26, 1376–1388, https://doi.org/10.1016/j.envsoft.2011.06.004, 2011.
Aldersley, A., Murray, S. J., and Cornell, S. E.: Global and regional analysis of climate and human drivers of wildfire, Sci. Total Environ., 409, 3472–3481, https://doi.org/10.1016/j.scitotenv.2011.05.032, 2011.
Angelini, P., Bianco, P., Cardillo, A., Francescato, C., and Oriolo, G.: Gli
habitat in Carta della Natura – schede descrittive degli habitat per la
cartografia alla scala 1:50.000, ISPRA – Istituto Superiore per la Protezione e la Ricerca Ambientale, ISBN 978-88-448-0382-7, 2009.
Antrop, M.: Why landscapes of the past are important for the future, Landsc. Urban Plan., 70, 21–34, https://doi.org/10.1016/j.landurbplan.2003.10.002, 2005.
Arno, S. F. and Brown, J. K.: Overcoming the paradox in managing wildland
fire, Western Wildlands, 17, 40–46, 1991.
Artés, T., Oom, D., de Rigo, D., Durrant, T. H., Maianti, P., Libertà, G., and San-Miguel-Ayanz, J.: A global wildfire dataset for the
analysis of fire regimes and fire behaviour, Sci. Data, 6, 296,
https://doi.org/10.1038/s41597-019-0312-2, 2019.
Baas, S., Conforti, P., Ahmed, S., and Markova, G.: The impact of disasters
and crises on agriculture and food security, FAO, https://doi.org/10.4060/cb3673en (last access: 30 August 2023), 2018.
Bacciu, V., Hatzaki, M., Karali, A., Cauchy, A., Giannakopoulos, C., Spano,
D., and Briche, E.: Investigating the Climate-Related Risk of Forest Fires for Mediterranean Islands' Blue Economy, Sustainability, 13, 10004,
https://doi.org/10.3390/su131810004, 2021.
Baiamonte, G., Domina, G., Raimondo, F. M., and Bazan, G.: Agricultural
landscapes and biodiversity conservation: a case study in Sicily (Italy),
Biodivers. Conserv., 24, 3201–3216, https://doi.org/10.1007/s10531-015-0950-4, 2015.
Balbi, S., Selomane, O., Sitas, N., Blanchard, R., Kotzee, I., O'Farrell, P., and Villa, F.: Human dependence on natural resources in rapidly urbanising South African regions, Environ. Res. Lett., 14, 044008, https://doi.org/10.1088/1748-9326/aafe43, 2019.
Balbi, S., Bagstad, K. J., Magrach, A., Sanz, M. J., Aguilar-Amuchastegui, N., Giupponi, C., and Villa, F.: The global environmental agenda urgently
needs a semantic web of knowledge, Environ. Evidence, 11, 1–6, https://doi.org/10.1186/s13750-022-00258-y, 2022.
Bao, J., Kendall, E. F., McGuinness, D. L., and Patel-Schneider, P. F.: OWL 2 Web Ontology Language Quick Reference Guide, in: 2nd Edn., W3C (World Wide Web Consortium), https://doi.org/10.1007/978-3-642-15970-1_5, 2012.
Bazan, G., Marino, P., Guarino, R., Domina, G., and Schicchi, R.: Bioclimatology and Vegetation Series in Sicily: A Geostatistical Approach,
Ann. Bot. Fenn., 52, 1–18, https://doi.org/10.5735/085.052.0202, 2015.
Bazan, G., Castrorao Barba, A., Rotolo, A., and Marino, P.: Geobotanical
approach to detect land-use change of a Mediterranean landscape: a case study in Central-Western Sicily, Geo J., 84, 795–811, https://doi.org/10.1007/s10708-018-9892-1, 2019.
Beuzen, T., Marshall, L., and Splinter, K. D.: A comparison of methods for
discretizing continuous variables in Bayesian Networks, Environ. Model. Softw., 108, 61–66, https://doi.org/10.1016/j.envsoft.2018.07.007, 2018.
Bielza, C. and Larrañaga, P.: Discrete Bayesian Network Classifiers: A
Survey, ACM Comput. Surv., 47, 5-1–5-43, https://doi.org/10.1145/2576868, 2014.
Birkmann, J., Buckle, P., Jaeger, J., Pelling, M., Setiadi, N., Garschagen, M., Fernando, N., and Kropp, J.: Extreme events and disasters: a window of
opportunity for change? Analysis of organizational, institutional and political changes, formal and informal responses after mega-disasters, Nat.
Hazards, 55, 637–655, https://doi.org/10.1007/s11069-008-9319-2, 2010.
Bisson, M., Favalli, M., Fornaciai, A., Mazzarini, F., Isola, I., Zanchetta,
G., and Pareschi, M. T.: A rapid method to assess fire-related debris flow
hazard in the Mediterranean region: An example from Sicily (southern Italy),
Int. J. Appl. Earth Obs. Geoinf., 7, 217–231, https://doi.org/10.1016/j.jag.2005.04.003, 2005.
Bonanno, G.: Adaptive management as a tool to improve the conservation of
endemic floras: the case of Sicily, Malta and their satellite islands, Biodivers. Conserv., 22, 1317–1354, https://doi.org/10.1007/s10531-013-0473-9, 2013.
Bond, W. J. and Keeley, J. E.: Fire as a global `herbivore': the ecology and
evolution of flammable ecosystems, Trends Ecol. Evol., 20, 387–394, https://doi.org/10.1016/j.tree.2005.04.025, 2005.
Borsuk, M. E.: Bayesian Networks, in: Encyclopedia of Ecology, edited by:
Jørgensen, S. E. and Fath, B. D., Academic Press, Oxford, 307–317,
https://doi.org/10.1016/B978-008045405-4.00144-0, 2008.
Bouckaert, R. R.: Bayesian network classifiers in Weka, Department of Computer Science, Waikato University, Hamilton, https://hdl.handle.net/10289/85 (last access: 29 August 2023), 2004.
Bradley, A. P.: The use of the area under the ROC curve in the evaluation of
machine learning algorithms, Pattern Recog., 30, 1145–1159,
https://doi.org/10.1016/S0031-3203(96)00142-2, 1997.
Castellnou, M., Prat-Guitart, N., Arilla, E., Larrañaga, A., Nebot, E.,
Castellarnau, X., Vendrell, J., Pallàs, J., Herrera, J., Monturiol, M.,
Cespedes, J., Pagès, J., Gallardo, C., and Miralles, M.: Empowering
strategic decision-making for wildfire management: avoiding the fear trap
and creating a resilient landscape, Fire Ecol., 15, 1–17, https://doi.org/10.1186/s42408-019-0048-6, 2019.
Catalano, R., Di Stefano, P., Sulli, A., and Vitale, F. P.: Paleogeography
and structure of the central Mediterranean: Sicily and its offshore area,
Tectonophysics, 260, 291–323, https://doi.org/10.1016/0040-1951(95)00196-4, 1996.
Certini, G.: Effects of fire on properties of forest soils: a review, Oecologia, 143, 1–10, https://doi.org/10.1007/s00442-004-1788-8, 2005.
Chappaz, F. and Ganteaume, A.: Role of land-cover and WUI types on spatio-temporal dynamics of fires in the French Mediterranean area, Risk Analysis, 43, 1032–1057, https://doi.org/10.1111/risa.13979, 2022.
Chen, S. H. and Pollino, C. A.: Good practice in Bayesian network modelling,
Environ. Model. Softw., 37, 134–145, https://doi.org/10.1016/j.envsoft.2012.03.012, 2012.
Chen, X.-W., Anantha, G., and Lin, X.: Improving Bayesian Network Structure
Learning with Mutual Information-Based Node Ordering in the K2 Algorithm,
IEEE T. Knowl. Data Eng., 20, 628–640, https://doi.org/10.1109/TKDE.2007.190732, 2008.
Chuvieco, E., Allgöwer, B., and Salas, J.: Integration of Physical and
Human Factors in Fire Danger Assessment, in: Wildland Fire Danger Estimation
and Mapping, Vol. 4, World Scientific, 197–218, https://doi.org/10.1142/9789812791177_0007, 2003.
Cohen, J.: The wildland-urban interface fire problem: A consequence of the
fire exclusion paradigm, Forest History Today, https://www.fs.usda.gov/research/treesearch/33787
(last access: 30 August 2023), 20–26, 2008.
Cooper, G. F. and Herskovits, E.: A Bayesian method for the induction of
probabilistic networks from data, Mach. Learn., 9, 309–347, 1992.
Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M., and Jones,
P. D.: An Ensemble Version of the E-OBS Temperature and Precipitation Data
Sets, J. Geophys. Res.-Atmos., 123, 9391–9409, https://doi.org/10.1029/2017JD028200, 2018.
Corrao, C.: Forest fires in Sicily, in: The Management of Mass Burn Casualties and Fire Disasters: Proceedings of the First International
Conference on Burns and Fire Disasters, edited by: Masellis, M. and Gunn, S.
W. A., Springer Netherlands, Dordrecht, 60–63, https://doi.org/10.1007/978-0-585-33973-3_9, 1992.
Cullotta, S. and Marchetti, M.: Forest types for biodiversity assessment at
regional level: the case study of Sicily (Italy), Eur. J. Forest Res., 126,
431–447, https://doi.org/10.1007/s10342-006-0166-y, 2007.
Daily, G. C.: Nature's Services: Societal Dependence on Natural Ecosystems, in: The Future of Nature: Documents of Global Change, Yale University Press, 454–464, https://doi.org/10.12987/9780300188479-039, 1997.
Di Maida, G.: The earliest human occupation of Sicily: A review, The Journal of Island and Coastal Archaeology, 17, 402–419, https://doi.org/10.1080/15564894.2020.1803460, 2020.
Drago, A.: Desertification processes and possible mitigation actions
[Sicily], Tecnica Agricola (Italy), http://www.sias.regione.sicilia.it/pdf/Desertificazione_mitigazione_SIAS.pdf
(last access: 31 August 2023), 2005.
Duane, A., Castellnou, M., and Brotons, L.: Towards a comprehensive look at
global drivers of novel extreme wildfire events, Climatic Change, 165, 1–21, https://doi.org/10.1007/s10584-021-03066-4, 2021.
Faivre, N., Jin, Y., Goulden, M. L., Randerson, J. T., Faivre, N., Jin, Y.,
Goulden, M. L., and Randerson, J. T.: Controls on the spatial pattern of
wildfire ignitions in Southern California, Int. J. Wildland Fire, 23, 799–811, https://doi.org/10.1071/WF13136, 2014.
Falcucci, A., Maiorano, L., and Boitani, L.: Changes in land-use/land-cover
patterns in Italy and their implications for biodiversity conservation,
Landscape Ecol., 22, 617–631, https://doi.org/10.1007/s10980-006-9056-4, 2007.
Fawcett, T.: An introduction to ROC analysis, Pattern Recog. Lett., 27, 861–874, https://doi.org/10.1016/j.patrec.2005.10.010, 2006.
Ferrara, C., Salvati, L., Corona, P., Romano, R., and Marchi, M.: The background context matters: Local-scale socioeconomic conditions and the
spatial distribution of wildfires in Italy, Sci. Total Environ., 654, 43–52, https://doi.org/10.1016/j.scitotenv.2018.11.049, 2019.
Frank, E., Hall, M. A., and Witten, I. H.: The WEKA Workbench, in: Online
Appendix for “Data Mining: Practical Machine Learning Tools and Techniques”, 4th Edn., Morgan Kaufmann, https://www.cs.waikato.ac.nz/ml/weka/Witten_et_al_2016_appendix.pdf
(last access: 31 August 2023), 2016.
Friedman, N. and Goldszmidt, M.: Discretizing Continuous Attributes While
Learning Bayesian Networks, in: Proceedings of the 13th International Conference on Machine Learning, 1996, San Francico, Ca, USA, 157–165,
ISBN 1558604197, 1996.
Fu, Y., Li, R., Wang, X., Bergeron, Y., Valeria, O., Chavardès, R. D.,
Wang, Y., and Hu, J.: Fire Detection and Fire Radiative Power in Forests and
Low-Biomass Lands in Northeast Asia: MODIS versus VIIRS Fire Products, Remote Sens., 12, 2870, https://doi.org/10.3390/rs12182870, 2020.
Ganteaume, A., Camia, A., Jappiot, M., San-Miguel-Ayanz, J., Long-Fournel, M., and Lampin, C.: A review of the main driving factors of forest fire
ignition over Europe, Environ. Manage., 51, 651–662, https://doi.org/10.1007/s00267-012-9961-z, 2013.
Ganteaume, A., Barbero, R., Jappiot, M., and Maillé, E.: Understanding
future changes to fires in southern Europe and their impacts on the wildland-urban interface, J. Safe. Sci. Resil., 2, 20–29, https://doi.org/10.1016/j.jnlssr.2021.01.001, 2021.
Gibbs, H. K. and Ruesch, A.: New IPCC Tier-1 Global Biomass Carbon Map for
the Year 2000, Environmental System Science Data Infrastructure for a
Virtual Ecosystem (ESS-DIVE) (United States), https://doi.org/10.15485/1463800, 2008.
Giglio, L., Schroeder, W., and Justice, C. O.: The collection 6 MODIS active
fire detection algorithm and fire products, Remote Sens. Environ., 178, 31–41, https://doi.org/10.1016/j.rse.2016.02.054, 2016.
Giorgi, F., Jones, C., and Asrar, G.: Addressing Climate Information Needs
at the Regional Level: the CORDEX Framework, World Meteorological Organization, https://public.wmo.int/en/bulletin/addressing-climate-information-needs-regional-level-cordex-framework
(last access: 31 August 2023), 175–183, 2009.
Grothmann, T. and Patt, A.: Adaptive capacity and human cognition: The
process of individual adaptation to climate change, Global Environ. Change, 15, 199–213, https://doi.org/10.1016/j.gloenvcha.2005.01.002, 2005.
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., and
Pedreschi, D.: A Survey of Methods for Explaining Black Box Models, ACM
Comput. Surv., 51, 93-1–93-42, https://doi.org/10.1145/3236009, 2018.
Guptill, S. C. and Morrison, J. L.: Elements of Spatial Data Quality,
Elsevier, 219 pp., ISBN 9781483287942, 2013.
Halofsky, J. E., Peterson, D. L., and Harvey, B. J.: Changing wildfire, changing forests: the effects of climate change on fire regimes and
vegetation in the Pacific Northwest, USA, Fire Ecol., 16, 1–26, https://doi.org/10.1186/s42408-019-0062-8, 2020.
Hantson, S., Padilla, M., Corti, D., and Chuvieco, E.: Strengths and weaknesses of MODIS hotspots to characterize global fire occurrence, Remote
Sens. Environ., 131, 152–159, https://doi.org/10.1016/j.rse.2012.12.004, 2013.
Hantson, S., Pueyo, S., and Chuvieco, E.: Global fire size distribution is
driven by human impact and climate, Global Ecol. Biogeogr., 24, 77–86, https://doi.org/10.1111/geb.12246, 2015.
Henriksen, H. J., Rasmussen, P., Brandt, G., von Bülow, D., and Jensen,
F. V.: Public participation modelling using Bayesian networks in management
of groundwater contamination, Environ. Model. Softw., 22, 1101–1113, https://doi.org/10.1016/j.envsoft.2006.01.008, 2007.
Intini, P., Ronchi, E., Gwynne, S., and Bénichou, N.: Guidance on Design
and Construction of the Built Environment Against Wildland Urban Interface
Fire Hazard: A Review, Fire Technol., 56, 1853–1883, https://doi.org/10.1007/s10694-019-00902-z, 2020.
IPCC: 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared
by the National Greenhouse Gas Inventories Programm, IGES, Japan, https://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html
(last access: 2 August 2023), 2006.
IPCC: Managing the Risks of Extreme Events and Disasters to Advance Climate
Change Adaptation – IPCC, IPCC, Cambridge University Press, Cambridge, UK and New York, NY, USA, ISBN 9781107607804, 2012.
Jain, P., Coogan, S. C. P., Subramanian, S. G., Crowley, M., Taylor, S., and
Flannigan, M. D.: A review of machine learning applications in wildfire science and management, Environ. Rev., 28, 478–505, https://doi.org/10.1139/er-2020-0019, 2020.
Kangas, A. S. and Kangas, J.: Probability, possibility and evidence: approaches to consider risk and uncertainty in forestry decision analysis,
Forest Policy Econ., 6, 169–188, https://doi.org/10.1016/S1389-9341(02)00083-7, 2004.
Kelly, L. T. and Brotons, L.: Using fire to promote biodiversity, Science, 355, 1264–1265, https://doi.org/10.1126/science.aam7672, 2017.
Kocher, S. D. and Butsic, V.: Governance of Land Use Planning to Reduce Fire
Risk to Homes Mediterranean France and California, Land, 6, 24,
https://doi.org/10.3390/land6020024, 2017.
Kraak, M.-J. and Ormeling, F.: Cartography: Visualization of Geospatial Data, in: 4th Edn., CRC Press, Boca Raton, 261 pp., https://doi.org/10.1201/9780429464195, 2020.
Kumar, S. and Banerji, H.: Bayesian network modeling for economic-socio-cultural sustainability of neighborhood-level urban
communities: Reflections from Kolkata, an Indian megacity, Sustain. Cities Soc., 78, 103632, https://doi.org/10.1016/j.scs.2021.103632, 2022.
Lasaponara, R., Lanorte, A., and Pignatti, S.: Characterization and Mapping
of Fuel Types for the Mediterranean Ecosystems of Pollino National Park in
Southern Italy by Using Hyperspectral MIVIS Data, Earth Interact., 10, 1–11, https://doi.org/10.1175/EI165.1, 2006.
Leone, V., Lovreglio, R., Martín, M. P., Martínez, J., and Vilar, L.: Human Factors of Fire Occurrence in the Mediterranean, Earth Observation of Wildland Fires in Mediterranean Ecosystems, Springer, 149–170, https://doi.org/10.1007/978-3-642-01754-4_11, 2009.
Li, Z., Kamnitsas, K., and Glocker, B.: Analyzing Overfitting Under Class
Imbalance in Neural Networks for Image Segmentation, IEEE T. Medic. Imag., 40, 1065–1077, https://doi.org/10.1109/TMI.2020.3046692, 2021.
Lozano, O. M., Salis, M., Ager, A. A., Arca, B., Alcasena, F. J., Monteiro,
A. T., Finney, M. A., Del Giudice, L., Scoccimarro, E., and Spano, D.:
Assessing Climate Change Impacts on Wildfire Exposure in Mediterranean Areas, Risk Anal., 37, 1898–1916, https://doi.org/10.1111/risa.12739, 2017.
Marcot, B. G., Steventon, J. D., Sutherland, G. D., and McCann, R. K.: Guidelines for developing and updating Bayesian belief networks applied to
ecological modeling and conservation, Can. J. Forest Res., 36, 3063–3074,
https://doi.org/10.1139/x06-135, 2006.
Marquez Torres, A.: amarqueztorres/fire_risk_sicily: Fire Sicily, Zenodo [code and data set], https://doi.org/10.5281/zenodo.7616451, 2023.
Martínez-López, J., Bagstad, K. J., Balbi, S., Magrach, A., Voigt,
B., Athanasiadis, I., Pascual, M., Willcock, S., and Villa, F.: Towards globally customizable ecosystem service models, Sci. Total Environ., 650, 2325–2336, https://doi.org/10.1016/j.scitotenv.2018.09.371, 2019.
Merrill, D. F. and Alexander, M. E.: Glossary of forest fire management terms, National Research Council of Canada, Canadian Committee on Forest
Fire Management, 91 pp., https://cfs.nrcan.gc.ca/publications?id=35337
(last access: 31 August 2023), 1987.
Michael, Y., Helman, D., Glickman, O., Gabay, D., Brenner, S., and Lensky, I. M.: Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series, Sci. Total Environ., 764, 142844, https://doi.org/10.1016/j.scitotenv.2020.142844, 2021.
Mohajane, M., Costache, R., Karimi, F., Bao Pham, Q., Essahlaoui, A., Nguyen, H., Laneve, G., and Oudija, F.: Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area, Ecol. Indicat., 129, 107869, https://doi.org/10.1016/j.ecolind.2021.107869, 2021.
Molina, M. O., Sánchez, E., and Gutiérrez, C.: Future heat waves over the Mediterranean from an Euro-CORDEX regional climate model ensemble, Sci. Rep., 10, 8801, https://doi.org/10.1038/s41598-020-65663-0, 2020.
Moreira, F. and Russo, D.: Modelling the impact of agricultural abandonment
and wildfires on vertebrate diversity in Mediterranean Europe, Landscape Ecol., 22, 1461–1476, https://doi.org/10.1007/s10980-007-9125-3, 2007.
Mouillot, F., Ratte, J.-P., Joffre, R., Mouillot, D., and Rambal, S.: Long-term forest dynamic after land abandonment in a fire prone Mediterranean landscape (central Corsica, France), Landscape Ecol., 20, 101–112, https://doi.org/10.1007/s10980-004-1297-5, 2005.
Narcizo, L., Santos, F. L., Peres, L. F., Trigo, R., and Libonati, R.: The
impact of heat waves in forest fires over the Amazon rainforest, in: EGU General Assembly 2022, EGU22-10772, https://doi.org/10.5194/egusphere-egu22-10772, 2022.
Nikolakis, W. and Roberts, E.: Wildfire governance in a changing world: Insights for policy learning and policy transfer, Risk Hazards Crisis Publ. Policy, 13, 144–164, https://doi.org/10.1002/rhc3.12235, 2022.
Nojarov, P. and Nikolova, M.: Heat waves and forest fires in Bulgaria, Nat.
Hazards, https://doi.org/10.1007/s11069-022-05451-3, in press, 2022.
Oliveira, S., Oehler, F., San-Miguel-Ayanz, J., Camia, A., and Pereira, J.
M. C.: Modeling spatial patterns of fire occurrence in Mediterranean Europe
using Multiple Regression and Random Forest, Forest Ecol. Manage., 275, 117–129, https://doi.org/10.1016/j.foreco.2012.03.003, 2012.
OpenStreetMap contributors: Planet dump, https://planet.osm.org (last access: 1 August 2023), 2020.
Ortega, M., Saura, S., González-Avila, S., Gómez-Sanz, V., and
Elena-Rosselló, R.: Landscape vulnerability to wildfires at the forest-agriculture interface: half-century patterns in Spain assessed
through the SISPARES monitoring framework, Agroforest Syst., 85, 331–349,
https://doi.org/10.1007/s10457-011-9423-2, 2012.
Otero, I., Castellnou, M., González, I., Arilla, E., Castell, L., Castellví, J., Sánchez, F., and Nielsen, J. Ø.: Democratizing
wildfire strategies. Do you realize what it means? Insights from a participatory process in the Montseny region (Catalonia, Spain), PLOS ONE,
13, e0204806, https://doi.org/10.1371/journal.pone.0204806, 2018.
Paracchini, M. L., Zulian, G., Kopperoinen, L., Maes, J., Schägner, J. P., Termansen, M., Zandersen, M., Perez-Soba, M., Scholefield, P. A., and
Bidoglio, G.: Mapping cultural ecosystem services: A framework to assess the
potential for outdoor recreation across the EU, Ecol. Indicat., 45, 371–385, https://doi.org/10.1016/j.ecolind.2014.04.018, 2014.
Parente, J., Pereira, M. G., Amraoui, M., and Fischer, E. M.: Heat waves in
Portugal: Current regime, changes in future climate and impacts on extreme
wildfires, Sci. Total Environ., 631–632, 534–549,
https://doi.org/10.1016/j.scitotenv.2018.03.044, 2018.
Pausas, J. G. and Fernández-Muñoz, S.: Fire regime changes in the
Western Mediterranean Basin: from fuel-limited to drought-driven fire regime, Climatic Change, 110, 215–226, https://doi.org/10.1007/s10584-011-0060-6, 2012.
Pausas, J. G., Bradstock, R. A., Keith, D. A., and Keeley, J. E.: Plant
Functional Traits in Relation to Fire in Crown-Fire Ecosystems, Ecology, 85,
1085–1100, https://doi.org/10.1890/02-4094, 2004.
Pausas, J. G., Llovet, J., Rodrigo, A., Vallejo, R., Pausas, J. G., Llovet, J., Rodrigo, A., and Vallejo, R.: Are wildfires a disaster in the Mediterranean basin? – A review, Int. J. Wildland Fire, 17, 713–723,
https://doi.org/10.1071/WF07151, 2008.
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of
Plausible Inference, Morgan Kaufmann, 576 pp., https://doi.org/10.1016/C2009-0-27609-4, 1988.
Pearl, J.: Causality, Cambridge University Press, 486 pp., https://doi.org/10.1017/CBO9780511803161, 2009.
Penman, T. D., Price, O., Bradstock, R. A., Penman, T. D., Price, O., and
Bradstock, R. A.: Bayes Nets as a method for analysing the influence of
management actions in fire planning, Int. J. Wildland Fire, 20, 909–920,
https://doi.org/10.1071/WF10076, 2011.
Peruzzi, L., Conti, F., and Bartolucci, F.: An inventory of vascular plants
endemic to Italy, Phytotaxa, 168, 1–75, 2014.
Planistat Europe and Bradley Dunbar Association: Analysis of the island
regions and outermost regions of the European Union: Part I The island
regions and territories, European Commission, https://ec.europa.eu/regional_policy/sources/docgener/studies/pdf/ilesrup/islands_part1_summary_en.pdf
(last access: 6 August 2020), 2003.
Prestia, G. and Scavone, V.: Enhancing the Endogenous Potential of
Agricultural Landscapes: Strategies and Projects for a Inland Rural Region
of Sicily, in: Smart and Sustainable Planning for Cities and Regions, Sspcr 2017, April 2018, New York, 635–648, https://doi.org/10.1007/978-3-319-75774-2_43, 2018.
Ragusa, M. A. and Rapicavoli, V.: Levels of desertification risk in the
Sicilian forests according to MEDALUS-ESPI protocol, AIP Conf. Proc., 1863, 530004, https://doi.org/10.1063/1.4992674, 2017.
Regos, A., Aquilué, N., Retana, J., Cáceres, M. D., and Brotons, L.:
Using Unplanned Fires to Help Suppressing Future Large Fires in Mediterranean Forests, PLOS ONE, 9, e94906, https://doi.org/10.1371/journal.pone.0094906, 2014.
Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K., and Yoder: Predicting soil erosion by water: a guide to conservation planning with the revised universal soil loss equation (RUSLE), US Department of Agriculture, Agricultural Research Service, Tucson, 404 pp., 1997.
Rennie, J. D. M., Shih, L., Teevan, J., and Karger, D. R.: Tackling the poor
assumptions of naive bayes text classifiers, in: Proceedings of the Twentieth International Conference on International Conference on Machine Learning, August 2003, Washington, DC, USA, 616–623, https://doi.org/10.5555/3041838.3041916, 2003.
Ricotta, C. and Di Vito, S.: Modeling the Landscape Drivers of Fire Recurrence in Sardinia (Italy), Environ. Manage., 53, 1077–1084,
https://doi.org/10.1007/s00267-014-0269-z, 2014.
Robinne, F.-N., Parisien, M.-A., Flannigan, M., Robinne, F.-N., Parisien, M.-A., and Flannigan, M.: Anthropogenic influence on wildfire activity in
Alberta, Canada, Int. J. Wildland Fire, 25, 1131–1143, https://doi.org/10.1071/WF16058, 2016.
Roces-Díaz, J. V., Santín, C., Martínez-Vilalta, J., and Doerr, S. H.: A global synthesis of fire effects on ecosystem services of forests and woodlands, Front. Ecol. Environ., 20, 170–178, https://doi.org/10.1002/fee.2349, 2022.
Rodrigues, M. and de la Riva, J.: An insight into machine-learning algorithms to model human-caused wildfire occurrence, Environ. Model. Softw., 57, 192–201, https://doi.org/10.1016/j.envsoft.2014.03.003, 2014.
Roy, D. P., Jin, Y., Lewis, P. E., and Justice, C. O.: Prototyping a global
algorithm for systematic fire-affected area mapping using MODIS time series
data, Remote Sens. Environ., 97, 137–162, https://doi.org/10.1016/j.rse.2005.04.007, 2005.
Ruffault, J., Curt, T., Moron, V., Trigo, R. M., Mouillot, F., Koutsias, N.,
Pimont, F., Martin-StPaul, N., Barbero, R., Dupuy, J.-L., Russo, A., and
Belhadj-Khedher, C.: Increased likelihood of heat-induced large wildfires in
the Mediterranean Basin, Sci. Rep., 10, 13790, https://doi.org/10.1038/s41598-020-70069-z, 2020.
Satir, O., Berberoglu, S., and Donmez, C.: Mapping regional forest fire
probability using artificial neural network model in a Mediterranean forest
ecosystem, Geomat. Nat. Hazards Risk, 7, 1645–1658,
https://doi.org/10.1080/19475705.2015.1084541, 2016.
Schroeder, W., Csiszar, I., and Morisette, J.: Quantifying the impact of cloud obscuration on remote sensing of active fires in the Brazilian Amazon,
Remote Sens. Environ., 112, 456–470, https://doi.org/10.1016/j.rse.2007.05.004, 2008.
Schroeder, W., Oliva, P., Giglio, L., and Csiszar, I. A.: The New VIIRS 375 m active fire detection data product: Algorithm description and initial
assessment, Remote Sens. Environ., 143, 85–96, https://doi.org/10.1016/j.rse.2013.12.008, 2014.
Sereni, E.: Storia del paesaggio agrario italiano, 20th edn., Laterza, 499 pp., ISBN 978-88-420-2094-3, 1961.
Sicilia Assessorato beni culturali ed ambientali e pubblica istruzione:
Linee guida del piano territoriale paesistico regionale/Regione siciliana,
Assessorato dei beni culturali ambientali e della pubblica istruzione,
Regione siciliana, Assessorato regionale dei beni culturali ed ambientali e
della pubblica istruzione, \Palermo!, vii + 472 pp., https://www2.regione.sicilia.it/beniculturali/dirbenicult/bca/ptpr/LineeGuida.pdf
(last access: 31 August 2023), 1996.
Tedim, F., McCaffrey, S., Leone, V., Delogu, G. M., Castelnou, M., McGee, T.
K., and Aranha, J.: 13 – What can we do differently about the extreme wildfire problem: An overview, in: Extreme Wildfire Events and Disasters,
edited by: Tedim, F., Leone, V., and McGee, T. K., Elsevier, 233–263,
https://doi.org/10.1016/B978-0-12-815721-3.00013-8, 2020.
Thomaidis, K., Troll, V. R., Deegan, F. M., Freda, C., Corsaro, R. A., Behncke, B., and Rafailidis, S.: A message from the `underground forge of
the gods': history and current eruptions at Mt Etna, Geol. Today, 37, 141–149, https://doi.org/10.1111/gto.12362, 2021.
Trumbore, S., Brando, P., and Hartmann, H.: Forest health and global change,
Science, 349, 814–818, https://doi.org/10.1126/science.aac6759, 2015.
Tymstra, C., Stocks, B. J., Cai, X., and Flannigan, M. D.: Wildfire management in Canada: Review, challenges and opportunities, Progr. Disast. Sci., 5, 100045, https://doi.org/10.1016/j.pdisas.2019.100045, 2020.
UNEP-WCMC and IUCN: Protected Planet: The World Database on Protected Areas
(WDPA) [Online], UNEP-WCMC and IUCN, Cambridge, UK, https://www.protectedplanet.net/en/search-areas?filters%5Bdb_type%5D%5B%5D=wdpa&geo_type=region
(last access: 31 August 2023), 2022.
Villa, F., Balbi, S., Athanasiadis, I. N., and Caracciolo, C.: Semantics for
interoperability of distributed data and models: Foundations for better-connected information, F1000 Res., 6, 686, https://doi.org/10.12688/f1000research.11638.1, 2017.
Weichselgartner, J. and Pigeon, P.: The Role of Knowledge in Disaster Risk
Reduction, Int. J. Disast. Risk Sci., 6, 107–116, https://doi.org/10.1007/s13753-015-0052-7, 2015.
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Santos, L. B. S., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, A. J. G., Groth, P., Goble, C., Grethe, J. S., Heringa, J., t'Hoen, P. A. C., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A., Packer, A. L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone, S.-A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz,
M. A., Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., and Mons, B.:
The FAIR Guiding Principles for scientific data management and stewardship,
Sci. Data, 3, 1–9, https://doi.org/10.1038/sdata.2016.18, 2016.
Willcock, S., Martínez-López, J., Hooftman, D. A. P., Bagstad, K.
J., Balbi, S., Marzo, A., Prato, C., Sciandrello, S., Signorello, G., Voigt,
B., Villa, F., Bullock, J. M., and Athanasiadis, I. N.: Machine learning for
ecosystem services, Ecosyst. Serv., 33, 165–174, https://doi.org/10.1016/j.ecoser.2018.04.004, 2018.
Wittenberg, L. and Malkinson, D.: Spatio-temporal perspectives of forest
fires regimes in a maturing Mediterranean mixed pine landscape, Eur. J. Forest Res., 128, 297–304, https://doi.org/10.1007/s10342-009-0265-7, 2009.
Wunder, S., Calkin, D. E., Charlton, V., Feder, S., Martínez de Arano,
I., Moore, P., Rodríguez y Silva, F., Tacconi, L., and Vega-García, C.: Resilient landscapes to prevent catastrophic forest fires: Socioeconomic insights towards a new paradigm, Forest Policy Econ., 128, 102458, https://doi.org/10.1016/j.forpol.2021.102458, 2021.
Short summary
Only by mapping fire risks can we manage forest and prevent fires under current and future climate conditions. We present a fire risk map based on k.LAB, artificial-intelligence-powered and open-source software integrating multidisciplinary knowledge in near real time. Through an easy-to-use web application, we model the hazard with 84 % accuracy for Sicily, a representative Mediterranean region. Fire risk analysis reveals 45 % of vulnerable areas face a high probability of danger in 2050.
Only by mapping fire risks can we manage forest and prevent fires under current and future...
Altmetrics
Final-revised paper
Preprint