Articles | Volume 25, issue 1
https://doi.org/10.5194/nhess-25-383-2025
© Author(s) 2025. 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-25-383-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
27 Jan 2025
Research article | Highlight paper |
| 27 Jan 2025
| 27 Jan 2025
Modelling current and future forest fire susceptibility in north-eastern Germany
Geoinformation in Environmental Planning Lab, Technische Universität Berlin, Straße des 17. Juni 145, 10623 Berlin, Germany
Artificial Intelligence and Land Use Change, Technische Universität Berlin, Straße des 17. Juni 145, 10623 Berlin, Germany
Stenka Vulova
Geoinformation in Environmental Planning Lab, Technische Universität Berlin, Straße des 17. Juni 145, 10623 Berlin, Germany
Chair of Smart Water Networks, Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany
Hanyu Li
Geoinformation in Environmental Planning Lab, Technische Universität Berlin, Straße des 17. Juni 145, 10623 Berlin, Germany
Birgit Kleinschmit
Geoinformation in Environmental Planning Lab, Technische Universität Berlin, Straße des 17. Juni 145, 10623 Berlin, Germany
Related authors
No articles found.
Steve Ahlswede, Christian Schulz, Christiano Gava, Patrick Helber, Benjamin Bischke, Michael Förster, Florencia Arias, Jörn Hees, Begüm Demir, and Birgit Kleinschmit
Earth Syst. Sci. Data, 15, 681–695, https://doi.org/10.5194/essd-15-681-2023, https://doi.org/10.5194/essd-15-681-2023, 2023
Short summary
Short summary
Imagery from air and space is the primary source of large-scale forest mapping. Our study introduces a new dataset with over 50000 image patches prepared for deep learning tasks. We show how the information for 20 European tree species can be extracted from different remote sensing sensors. Our algorithms can detect single species with precision scores up to 88 %. With a pixel size of 20×20 cm, forestry administration can now derive large-scale tree species maps at a very high resolution.
Alby Duarte Rocha, Stenka Vulova, Christiaan van der Tol, Michael Förster, and Birgit Kleinschmit
Hydrol. Earth Syst. Sci., 26, 1111–1129, https://doi.org/10.5194/hess-26-1111-2022, https://doi.org/10.5194/hess-26-1111-2022, 2022
Short summary
Short summary
Evapotranspiration (ET) is a sum of soil evaporation and plant transpiration. ET produces a cooling effect to mitigate heat waves in urban areas. Our method uses a physical model with remote sensing and meteorological data to predict hourly ET. Designed for uniform vegetation, it overestimated urban ET. To correct it, we create a factor using vegetation fraction that proved efficient for reducing bias and improving accuracy. This approach was tested on two Berlin sites and can be used to map ET.
Lena-Marie Kuhlemann, Doerthe Tetzlaff, Aaron Smith, Birgit Kleinschmit, and Chris Soulsby
Hydrol. Earth Syst. Sci., 25, 927–943, https://doi.org/10.5194/hess-25-927-2021, https://doi.org/10.5194/hess-25-927-2021, 2021
Short summary
Short summary
We studied water partitioning under urban grassland, shrub and trees during a warm and dry growing season in Berlin, Germany. Soil evaporation was highest under grass, but total green water fluxes and turnover time of soil water were greater under trees. Lowest evapotranspiration losses under shrub indicate potential higher drought resilience. Knowledge of water partitioning and requirements of urban green will be essential for better adaptive management of urban water and irrigation strategies.
Benjamin Fersch, Till Francke, Maik Heistermann, Martin Schrön, Veronika Döpper, Jannis Jakobi, Gabriele Baroni, Theresa Blume, Heye Bogena, Christian Budach, Tobias Gränzig, Michael Förster, Andreas Güntner, Harrie-Jan Hendricks Franssen, Mandy Kasner, Markus Köhli, Birgit Kleinschmit, Harald Kunstmann, Amol Patil, Daniel Rasche, Lena Scheiffele, Ulrich Schmidt, Sandra Szulc-Seyfried, Jannis Weimar, Steffen Zacharias, Marek Zreda, Bernd Heber, Ralf Kiese, Vladimir Mares, Hannes Mollenhauer, Ingo Völksch, and Sascha Oswald
Earth Syst. Sci. Data, 12, 2289–2309, https://doi.org/10.5194/essd-12-2289-2020, https://doi.org/10.5194/essd-12-2289-2020, 2020
C. Rumbaur, N. Thevs, M. Disse, M. Ahlheim, A. Brieden, B. Cyffka, D. Duethmann, T. Feike, O. Frör, P. Gärtner, Ü. Halik, J. Hill, M. Hinnenthal, P. Keilholz, B. Kleinschmit, V. Krysanova, M. Kuba, S. Mader, C. Menz, H. Othmanli, S. Pelz, M. Schroeder, T. F. Siew, V. Stender, K. Stahr, F. M. Thomas, M. Welp, M. Wortmann, X. Zhao, X. Chen, T. Jiang, J. Luo, H. Yimit, R. Yu, X. Zhang, and C. Zhao
Earth Syst. Dynam., 6, 83–107, https://doi.org/10.5194/esd-6-83-2015, https://doi.org/10.5194/esd-6-83-2015, 2015
Related subject area
Other Hazards (e.g., Glacial and Snow Hazards, Karst, Wildfires Hazards, and Medical Geo-Hazards)
The effect of propagation saw test geometries on critical cut length
Statistical calibration of probabilistic medium-range Fire Weather Index forecasts in Europe
Glide-snow avalanches: a mechanical, threshold-based release area model
Improving fire severity prediction in south-eastern Australia using vegetation-specific information
Causes, consequences and implications of the 2023 landslide-induced Lake Rasac GLOF, Cordillera Huayhuash, Peru
Review article: A scoping review of human factors in avalanche decision-making
Development of operational decision support tools for mechanized ski guiding using avalanche terrain modelling, GPS tracking, and machine learning
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
Assessing the performance and explainability of an avalanche danger forecast model
A glacial lake outburst flood risk assessment for the Phochhu river basin, Bhutan
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
A quantitative module of avalanche hazard—comparing forecaster assessments of storm and persistent slab avalanche problems with information derived from distributed snowpack simulations
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
Fire risk modeling: an integrated and data-driven approach applied to Sicily
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
Bastian Bergfeld, Karl W. Birkeland, Valentin Adam, Philipp L. Rosendahl, and Alec van Herwijnen
Nat. Hazards Earth Syst. Sci., 25, 321–334, https://doi.org/10.5194/nhess-25-321-2025, https://doi.org/10.5194/nhess-25-321-2025, 2025
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.
Stephanie Bohlmann and Marko Laine
Nat. Hazards Earth Syst. Sci., 24, 4225–4235, https://doi.org/10.5194/nhess-24-4225-2024, https://doi.org/10.5194/nhess-24-4225-2024, 2024
Short summary
Short summary
Probabilistic ensemble forecasts of the Canadian Forest Fire Weather Index (FWI) can be used to estimate the possible wildfire risk but require post-processing to provide accurate and reliable predictions. This article presents a calibration method using non-homogeneous Gaussian regression to statistically post-process FWI forecasts up to 15 d. Calibration improves the forecast especially at short lead times and in regions with high fire risk.
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.
Adam Emmer, Oscar Vilca, Cesar Salazar Checa, Sihan Li, Simon Cook, Elena Pummer, Jan Hrebrina, and Wilfried Haeberli
EGUsphere, https://doi.org/10.5194/egusphere-2024-2316, https://doi.org/10.5194/egusphere-2024-2316, 2024
Short summary
Short summary
We report in detail the most recent large landslide-triggered glacial lake outburst flood (GLOF) in the Peruvian Andes (the 2023 Rasac GLOF), analyze its preconditions, consequences, and the role of changing climate. Our study contibutes to understanding GLOF occurrence patterns in space and time and corroborates increasing frequency of such events in changing mountains.
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.
John Sykes, Pascal Haegeli, Roger Atkins, Patrick Mair, and Yves Bühler
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-147, https://doi.org/10.5194/nhess-2024-147, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
We develop decision support tools to assist professional ski guides in determining safe terrain each day based on current conditions. To understand the decision-making process we collaborate with professional guides and build three unique models to predict their decisions. The models accurately capture the real world decision-making outcomes in 85–93 % of cases. Our conclusions focus on strengths and weaknesses of each model and discuss ramifications for practical applications in ski guiding.
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.
Cristina Pérez-Guillén, Frank Techel, Michele Volpi, and Alec van Herwijnen
EGUsphere, https://doi.org/10.5194/egusphere-2024-2374, https://doi.org/10.5194/egusphere-2024-2374, 2024
Short summary
Short summary
This study assesses the performance and explainability of a random forest classifier for predicting dry-snow avalanche danger levels during initial live-testing. The model achieved ∼70 % agreement with human forecasts, performing equally well in nowcast and forecast modes, while capturing the temporal dynamics of avalanche forecasting. The explainability approach enhances the transparency of the model's decision-making process, providing a valuable tool for operational avalanche forecasting.
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.
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.
Florian Herla, Pascal Haegeli, Simon Horton, and Patrick Mair
EGUsphere, https://doi.org/10.5194/egusphere-2024-871, https://doi.org/10.5194/egusphere-2024-871, 2024
Short summary
Short summary
We present a spatial framework for extracting information about avalanche problems from detailed snowpack simulations and compare the numerical results against operational assessments from avalanche forecasters. Despite good aggreement in seasonal summary statistics, a comparison of daily assessments revealed considerable differences while it remained unclear which data source represented reality best. We discuss how snowpack simulations can add value to the forecasting process.
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.
Alba Marquez Torres, Giovanni Signorello, Sudeshna Kumar, Greta Adamo, Ferdinando Villa, and Stefano Balbi
Nat. Hazards Earth Syst. Sci., 23, 2937–2959, https://doi.org/10.5194/nhess-23-2937-2023, https://doi.org/10.5194/nhess-23-2937-2023, 2023
Short summary
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.
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.
Cited articles
Acharya, T. D. and Yang, I.: Exploring Landsat 8, International Journal of IT, Engineering and Applied Sciences Research (IJIEASR), 4, 4–10, 2015. a
Achour, H., Toujani, A., Trabelsi, H., and Jaouadi, W.: Evaluation and comparison of Sentinel-2 MSI, Landsat 8 OLI, and EFFIS data for forest fires mapping. Illustrations from the summer 2017 fires in Tunisia, Geocarto Int., 37, 7021–7040, https://doi.org/10.1080/10106049.2021.1980118, 2022. a
Afreen, S., Sharma, N., Chaturvedi, R. K., Gopalakrishnan, R., and Ravindranath, N. H.: Forest policies and programs affecting vulnerability and adaptation to climate change, Mitig. Adapt. Strat. Gl., 16, 177–197, https://doi.org/10.1007/s11027-010-9259-5, 2011. a, b, c
Ambadan, J. T., Oja, M., Gedalof, Z., and Berg, A. A.: Satellite-Observed Soil Moisture as an Indicator of Wildfire Risk, Remote Sens.-Basel, 12, 1543, https://doi.org/10.3390/rs12101543, 2020. a, b
Amelung, W., Blume, H.-P., Fleige, H., Horn, R., Kandeler, E., Kögel-Knabner, I., Kretzschmar, R., Stahr, K., and Wilke, B.-M.: Scheffer/Schachtschabel Lehrbuch der Bodenkunde, 17th edn., Springer eBook Collection, Springer Spektrum, Berlin, Heidelberg, https://doi.org/10.1007/978-3-662-55871-3, 2018. a
Badía-Villas, D., González-Pérez, J. A., Aznar, J. M., Arjona-Gracia, B., and Martí-Dalmau, C.: Changes in water repellency, aggregation and organic matter of a mollic horizon burned in laboratory: Soil depth affected by fire, Geoderma, 213, 400–407, https://doi.org/10.1016/j.geoderma.2013.08.038, 2014. a
Barmpoutis, P., Papaioannou, P., Dimitropoulos, K., and Grammalidis, N.: A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing, Sensors, 20, 6442, https://doi.org/10.3390/s20226442, 2020. a
Bauhus, J., Forrester, D. I., Gardiner, B., Jactel, H., Vallejo, R., and Pretzsch, H.: Ecological Stability of Mixed-Species Forests, in: MIXED-species forests: Ecology and management, edited by: Pretzsch, H., Forrester, D. I., and Bauhus, J., Springer-Verlag, Berlin, 337–382, https://doi.org/10.1007/978-3-662-54553-9_7, 2017. a, b, c, d
Berčák, R., Holuša, J., Kaczmarowski, J., Tyburski, L., Szczygieł, R., Held, A., Vacik, H., Slivinský, J., and Chromek, I.: Fire Protection Principles and Recommendations in Disturbed Forest Areas in Central Europe: A Review, Fire, 6, 310, https://doi.org/10.3390/fire6080310, 2023. a
Boháč, A. and Drápela, E.: Present Climate Change as a Threat to Geoheritage: The Wildfire in Bohemian Switzerland National Park and Its Use in Place-Based Learning, Geosciences, 13, 383, https://doi.org/10.3390/geosciences13120383, 2023. a
Bradley, A. P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms, Pattern Recogn., 30, 1145–1159, https://doi.org/10.1016/S0031-3203(96)00142-2, 1997. a
Breiman, L.: Using adaptive bagging to debias regressions, Tech. rep., University of California at Berkeley, https://www.stat.berkeley.edu/users/breiman/adaptbag99.pdf (last access: 10 August 2023), 1999. a
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
Buras, A., Schunk, C., Zeiträg, C., Herrmann, C., Kaiser, L., Lemme, H., Straub, C., Taeger, S., Gößwein, S., Klemmt, H.-J., and Menzel, A.: Are Scots pine forest edges particularly prone to drought-induced mortality?, Environ. Res. Lett., 13, 025001, https://doi.org/10.1088/1748-9326/aaa0b4, 2018. a, b, c
Burge, J., Bonanni, M., Ihme, M., and Hu, L.: Convolutional LSTM Neural Networks for Modeling Wildland Fire Dynamics, arXiv [preprint], https://doi.org/10.48550/arXiv.2012.06679, 2021. a
Chanthiya, P. and Kalaivani, V.: Forest fire detection on LANDSAT images using support vector machine, Concurr. Comp.-Pract. E., 33, e6280, https://doi.org/10.1002/cpe.6280, 2021. a
Chicas, S. D. and Østergaard Nielsen, J.: Who are the actors and what are the factors that are used in models to map forest fire susceptibility? A systematic review, Nat. Hazards, 114, 2417–2434, https://doi.org/10.1007/s11069-022-05495-5, 2022. a, b, c
Cilli, R., Elia, M., D'Este, M., Giannico, V., Amoroso, N., Lombardi, A., Pantaleo, E., Monaco, A., Sanesi, G., Tangaro, S., Bellotti, R., and Lafortezza, R.: Explainable artificial intelligence (XAI) detects wildfire occurrence in the Mediterranean countries of Southern Europe, Sci. Rep.-UK, 12, 16349, https://doi.org/10.1038/s41598-022-20347-9, 2022. a, b, c
Crecente-Campo, F., Pommerening, A., and Rodríguez-Soalleiro, R.: Impacts of thinning on structure, growth and risk of crown fire in a Pinus sylvestris L. plantation in northern Spain, Forest Ecol. Manag., 257, 1945–1954, https://doi.org/10.1016/j.foreco.2009.02.009, 2009. a
DWD: Klimareport Brandenburg. Fakten bis zur Gegenwart – Erwartungen für die Zukunft, Report 1, Deutscher Wetterdienst, Offenbach am Main, Deutschland, https://www.dwd.de/DE/leistungen/klimareport_bb/klimareport_bb_2019_download.pdf;jsessionid=5BB70292853A51CB031B8D1E338E7F56.live21071?__blob=publicationFile&v=5 (last access: 20 December 2023), 2019. a
DWD Climate Data Center (CDC): Grids of monthly averaged daily air temperature (2 m) over Germany, version v1.0., Tech. rep., Deutscher Wetterdienst, https://opendata.dwd.de/climate_environment/CDC/grids_germany/monthl/air_temperature_mean/ (last access: 14 March 2024), 2023a. a
DWD Climate Data Center (CDC): Grids of monthly total precipitation over Germany, version v1.0., Tech. rep., Deutscher Wetterdienst, https://opendata.dwd.de/climate_environment/CDC/grids_germany/monthly/precipitation/ (last access: 14 March 2024), 2023b. a
EEA – European Environment Agency: Forest Type 2018 (raster 10 m), Europe, 3 yearly, October 2020, Tech. rep., European Environment Agency, https://land.copernicus.eu/en/products/high-resolution-layer-dominant-leaf-type (last access: 30 May 2023), 2020a.
EEA – European Environment Agency: Tree Cover Density 2018 (raster 10 m), Europe, 3 yearly, September 2020, Tech. rep., European Environment Agency, https://land.copernicus.eu/en/products/high-resolution-layer-tree-cover-density (last access: 24 May 2023), 2020c.
El Garroussi, S., Di Giuseppe, F., Barnard, C., and Wetterhall, F.: Europe faces up to tenfold increase in extreme fires in a warming climate, npj Climate and Atmospheric Science, 7, 30, https://doi.org/10.1038/s41612-024-00575-8, 2024. a
Eslami, R., Azarnoush, M., Kialashki, A., and Kazemzadeh, F.: GIS-Based Forest Fire Susceptbility Assessment By Random Forest, Artificial Neural Network And Logistic Regression Methods, J. Trop. For. Sci., 33, 173–184, 2021. a
Federal Office for Agriculture and Food: Waldbrandstatistik der Bundesrepublik Deutschland für das Jahr 2022, Tech. rep., Federal Office for Agriculture and Food, Bonn, https://www.bmel-statistik.de/fileadmin/daten/0302250-2022.pdf (last access: 13 June 2023), 2023. a
Fekete, A. and Nehren, U.: Assessment of social vulnerability to forest fire and hazardous facilities in Germany, Int. J. Disaster Risk Re., 87, 103562, https://doi.org/10.1016/j.ijdrr.2023.103562, 2023. a
Feng, L., Lysakowski, B., Eisenschmidt, J., and Birkhofer, K.: The impact of wildfire and mammal carcasses on beetle emergence from heathland soils, Ecol. Entomol., 47, 883–894, https://doi.org/10.1111/een.13179, 2022. a
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.-Basel, 12, 2870, https://doi.org/10.3390/rs12182870, 2020. a
Gislason, P. O., Benediktsson, J. A., and Sveinsson, J. R.: Random Forests for land cover classification, Pattern Recogn. Lett., 27, 294–300, https://doi.org/10.1016/j.patrec.2005.08.011, 2006. a
Guo, H., Bao, A., Liu, T., Jiapaer, G., Ndayisaba, F., Jiang, L., Kurban, A., and De Maeyer, P.: Spatial and temporal characteristics of droughts in Central Asia during 1966–2015, Sci. Total Environ., 624, 1523–1538, https://doi.org/10.1016/j.scitotenv.2017.12.120, 2018. a
Hilker, J. M., Busse, M., Müller, K., and Zscheischler, J.: Photovoltaics in agricultural landscapes: “Industrial land use” or a “real compromise” between renewable energy and biodiversity? Perspectives of German nature conservation associations, Energy, Sustainability and Society, 14, 6, https://doi.org/10.1186/s13705-023-00431-2, 2024. a
Holsten, A., Vetter, T., Vohland, K., and Krysanova, V.: Impact of climate change on soil moisture dynamics in Brandenburg with a focus on nature conservation areas, Ecol. Model., 220, 2076–2087, https://doi.org/10.1016/j.ecolmodel.2009.04.038, 2009. a
Horn, K. H.: Forest Fire Susceptibility Modelling in north-east Germany, Zenodo [code], https://doi.org/10.5281/zenodo.14214917, 2024. a
Horn, K. H.: Forest fire susceptibility modelling in north-eastern Germany, Zenodo [code and data set], https://doi.org/10.5281/zenodo.14710876, 2025. a
Kemter, M., Fischer, M., Luna, L. V., Schönfeldt, E., Vogel, J., Banerjee, A., Korup, O., and Thonicke, K.: Cascading Hazards in the Aftermath of Australia's 2019/2020 Black Summer Wildfires, Earths Future, 9, e2020EF001884, https://doi.org/10.1029/2020EF001884, 2021. a
Kroll, F. and Haase, D.: Does demographic change affect land use patterns?: A case study from Germany, Land Use Policy, 27, 726–737, https://doi.org/10.1016/j.landusepol.2009.10.001, 2010. a, b
Kühn, M.: Planungskonflikte und Partizipation: die Gigafactory Tesla, Raumforschung und Raumordnung | Spatial Research and Planning, 81, 538–556, https://doi.org/10.14512/rur.1698, 2023. a
Kuhn, M. and Johnson, K.: Applied Predictive Modeling, Springer New York, New York, NY, https://doi.org/10.1007/978-1-4614-6849-3, 2013. a
Kussul, N., Fedorov, O., Yailymov, B., Pidgorodetska, L., Kolos, L., Yailymova, H., and Shelestov, A.: Fire Danger Assessment Using Moderate-Spatial Resolution Satellite Data, Fire, 6, 72, https://doi.org/10.3390/fire6020072, 2023. a
Lang, N., Jetz, W., Schindler, K., and Wegner, J. D.: A high-resolution canopy height model of the Earth, Nature Ecology & Evolution, 7, 1778–1789, https://doi.org/10.1038/s41559-023-02206-6, 2023. a
Leibert, T., Wolff, M., and Haase, A.: Shifting spatial patterns in German population trends: local-level hot and cold spots, 1990–2019, Geogr. Helv., 77, 369–387, https://doi.org/10.5194/gh-77-369-2022, 2022. a
Li, H., Vulova, S., Rocha, A. D., and Kleinschmit, B.: Spatio-temporal feature attribution of European summer wildfires with Explainable Artificial Intelligence (XAI), Sci. Total Environ., 916, 170330, https://doi.org/10.1016/j.scitotenv.2024.170330, 2024. a, b
Littell, J. S., Peterson, D. L., Riley, K. L., Liu, Y., and Luce, C. H.: Effects of drought on forests and rangelands in the United States: A comprehensive science synthesis, Fire and Drought, 135–154, https://www.fs.usda.gov/research/treesearch/download/50971.pdf (last access: 23 February 2024), 2016. a
Lizundia-Loiola, J., Otón, G., Ramo, R., and Chuvieco, E.: A spatio-temporal active-fire clustering approach for global burned area mapping at 250 m from MODIS data, Remote Sens. Environ., 236, 111493, https://doi.org/10.1016/j.rse.2019.111493, 2020. a
Maingi, J. K. and Henry, M. C.: Factors influencing wildfire occurrence and distribution in eastern Kentucky, USA, Int. J. Wildland Fire, 16, 23, https://doi.org/10.1071/WF06007, 2007. a, b
Mallik, A. U., Gimingham, C. H., and Rahman, A. A.: Ecological Effects of Heather Burning: I. Water Infiltration, Moisture Retention and Porosity of Surface Soil, J. Ecol., 72, 767, https://doi.org/10.2307/2259530, 1984. a
Martell, D. L.: Forest Fire Management, in: Handbook of Operations Research in Natural Resources, edited by: Weintraub, A., International Series in Operations Research & Management Science, Springer Science+Business Media, LLC, Boston, MA, 489–509, https://doi.org/10.1007/978-0-387-71815-6_26, 2007. a, b, c
Matos, E. S., Freese, D., Śla̧zak, A., Bachmann, U., Veste, M., and Hüttl, R. F.: Organic-carbon and nitrogen stocks and organic-carbon fractions in soil under mixed pine and oak forest stands of different ages in NE Germany, J. Plant Nutr. Soil Sc., 173, 654–661, https://doi.org/10.1002/jpln.200900046, 2010. a, b
Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., Canadell, J. G., Daniel, J. S., John, A., Krummel, P. B., Luderer, G., Meinshausen, N., Montzka, S. A., Rayner, P. J., Reimann, S., Smith, S. J., van den Berg, M., Velders, G. J. M., Vollmer, M. K., and Wang, R. H. J.: The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500, Geosci. Model Dev., 13, 3571–3605, https://doi.org/10.5194/gmd-13-3571-2020, 2020. a
Milanović, S., Marković, N., Pamučar, D., Gigović, L., Kostić, P., and Milanović, S. D.: Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method, Forests, 12, 5, https://doi.org/10.3390/f12010005, 2021. a, b
Ministry for Rural Development, Environment and Agriculture in Brandenburg: Waldzustandsbericht 2023 des Landes Brandenburg, Tech. rep., Ministry for Rural Development, Environment and Agriculture in Brandenburg, Potsdam, https://forst.brandenburg.de/sixcms/media.php/9/wzb23.pdf (last access: 13 March 2024), 2023. a
Ministry for Rural Development, Environment and Agriculture in Brandenburg: Strategie des Landes Brandenburg zur Anpassung an die Folgen des Klimawandels, Tech. rep., Ministry for Rural Development, Environment and Agriculture in Brandenburg, Potsdam, https://mluk.brandenburg.de/sixcms/media.php/9/Klimaanpassungsstrategie-BB-Kurzfassung.pdf (last access: 3 July 2024), 2024. a
Natekar, S., Patil, S., Nair, A., and Roychowdhury, S.: Forest Fire Prediction using LSTM, in: 2021 2nd International Conference for Emerging Technology (INCET), 21–23 May 2021, Belagavi, India, 1–5, https://doi.org/10.1109/INCET51464.2021.9456113, 2021. a
Nguyen, Q. H., Ly, H.-B., Ho, L. S., Al-Ansari, N., van Le, H., van Tran, Q., Prakash, I., and Pham, B. T.: Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil, Math. Probl. Eng., 2021, 1–15, https://doi.org/10.1155/2021/4832864, 2021. a
Oliveira, S., Oehler, F., San-Miguel-Ayanz, J., Camia, A., and Pereira, J. M.: Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest, Forest Ecol. Manag., 275, 117–129, https://doi.org/10.1016/j.foreco.2012.03.003, 2012. a
Oliveira, T. M., Barros, A. M. G., Ager, A. A., and Fernandes, P. M.: Assessing the effect of a fuel break network to reduce burnt area and wildfire risk transmission, Int. J. Wildland Fire, 25, 619, https://doi.org/10.1071/WF15146, 2016. a, b
OroraTech: Wildfire Solution | OroraTech, https://ororatech.com/wildfire-solution/ (last access: 6 May 2024), 2021. a
Oyonarte, C., Escoriza, I., Delgado, R., Pinto, V., and Delgado, G.: Water-retention capacity in fine earth and gravel fractions of semiarid Mediterranean Montane soils, Arid Soil Res. Rehab., 12, 29–45, https://doi.org/10.1080/15324989809381495, 1998. a, b
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, 2021. a
Pourtaghi, Z. S., Pourghasemi, H. R., and Rossi, M.: Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran, Environ. Earth Sci., 73, 1515–1533, https://doi.org/10.1007/s12665-014-3502-4, 2015. a
Preston, B. L., Brooke, C., Measham, T. G., Smith, T. F., and Gorddard, R.: Igniting change in local government: lessons learned from a bushfire vulnerability assessment, Mitig. Adapt. Strat. Gl., 14, 251–283, https://doi.org/10.1007/s11027-008-9163-4, 2009. a
Rad, A. M., AghaKouchak, A., Navari, M., and Sadegh, M.: Progress, Challenges, and Opportunities in Remote Sensing of Drought, in: Global drought and flood: Observation, modeling, and prediction, edited by: Wu, H., Lettenmaier, D. P., Tang, Q., and Ward, P. J., Geophysical monograph series, vol. 265, American Geophysical Union, 1–28, https://doi.org/10.1002/9781119427339.ch1, 2021. a, b
Razavi-Termeh, S. V., Sadeghi-Niaraki, A., and Choi, S.-M.: Ubiquitous GIS-Based Forest Fire Susceptibility Mapping Using Artificial Intelligence Methods, Remote Sens.-Basel, 12, 1689, https://doi.org/10.3390/rs12101689, 2020. a
Reyer, C., Bachinger, J., Bloch, R., Hattermann, F. F., Ibisch, P. L., Kreft, S., Lasch, P., Lucht, W., Nowicki, C., Spathelf, P., Stock, M., and Welp, M.: Climate change adaptation and sustainable regional development: a case study for the Federal State of Brandenburg, Germany, Reg. Environ. Change, 12, 523–542, https://doi.org/10.1007/s10113-011-0269-y, 2012. a
Silva, C. V. J., Aragão, L. E. O. C., Barlow, J., Espirito-Santo, F., Young, P. J., Anderson, L. O., Berenguer, E., Brasil, I., Foster Brown, I., Castro, B., Farias, R., Ferreira, J., França, F., Graça, P. M. L. A., Kirsten, L., Lopes, A. P., Salimon, C., Scaranello, M. A., Seixas, M., Souza, F. C., and Xaud, H. A. M.: Drought-induced Amazonian wildfires instigate a decadal-scale disruption of forest carbon dynamics, Philos. T. Roy. Soc. B, 373, 20180043, https://doi.org/10.1098/rstb.2018.0043, 2018. a, b, c, d
Suryabhagavan, K. V., Alemu, M., and Balakrishnan, M.: GIS-based multi-criteria decision analysis for forest fire susceptibility mapping: a case study in Harenna forest, southwestern Ethiopia, Trop. Ecol., 57, 33–43, 2016. a
Thonicke, K. and Cramer, W.: Long-term Trends in Vegetation Dynamics and Forest Fires in Brandenburg (Germany) Under a Changing Climate, Nat. Hazards, 38, 283–300, https://doi.org/10.1007/s11069-005-8639-8, 2006. a
Wang, S. S.-C., Qian, Y., Leung, L. R., and Zhang, Y.: Identifying Key Drivers of Wildfires in the Contiguous US Using Machine Learning and Game Theory Interpretation, Earths Future, 9, 2020001910, https://doi.org/10.1029/2020EF001910, 2021. a, b, c, d
Wen, X., Tu, Y.-H., Tan, Q.-F., Li, W.-Y., Fang, G.-H., Ding, Z.-Y., and Wang, Z.-N.: Construction of 3D drought structures of meteorological drought events and their spatio-temporal evolution characteristics, J. Hydrol., 590, 125539, https://doi.org/10.1016/j.jhydrol.2020.125539, 2020. a
Wu, H., Lettenmaier, D. P., Tang, Q., and Ward, P. J. (Eds.): Global drought and flood: Observation, modeling, and prediction, Geophysical monograph series, vol. 265, American Geophysical Union, print ISBN 9781119427308, online ISBN 9781119427339, https://doi.org/10.1002/9781119427339, 2021. a, b, c
Xu, F., Bento, V. A., Qu, Y., and Wang, Q.: Projections of Global Drought and Their Climate Drivers Using CMIP6 Global Climate Models, Water, 15, 2272, https://doi.org/10.3390/w15122272, 2023. a
Zhang, G., Wang, M., and Liu, K.: Forest Fire Susceptibility Modeling Using a Convolutional Neural Network for Yunnan Province of China, Int. J. Disast. Risk Sc., 10, 386–403, https://doi.org/10.1007/s13753-019-00233-1, 2019. a, b
Zhou, Z., Zhang, L., Chen, J., She, D., Wang, G., Zhang, Q., Xia, J., and Zhang, Y.: Projecting Global Drought Risk Under Various SSP-RCP Scenarios, Earths Future, 11, e2022EF003420, https://doi.org/10.1029/2022EF003420, 2023. a
Executive editor
Forest fires have become a major problem in many regions of the world, including parts of Central Europe. The modelling study addresses the different factors for Forest Fire Susceptibility (FFS), making use of high spatial resolution of input data for the state of Brandenburg, Germany. An increasing susceptibility is found under rising greenhouse gas forcing scenarios when other changes are not taken into account. Extreme weather periods are of particular relevance in this respect. However, the importance of anthropogenic and vegetation parameters for modelling FFS on a regional level can outweigh the pure climatic effects. The paper also suggests some recommendations for forest management and environmental planning for a reduction of fire risk.
Forest fires have become a major problem in many regions of the world, including parts of...
Short summary
In this study we applied a random forest machine learning algorithm to model current and future forest fire susceptibility (FFS) in north-eastern Germany using anthropogenic, climatic, topographic, soil, and vegetation variables. Model accuracy ranged between 69 % and 71 %, showing moderately high model reliability for predicting FFS. The model results underline the importance of anthropogenic and vegetation parameters. This study will support regional forest fire prevention and management.
In this study we applied a random forest machine learning algorithm to model current and future...
Altmetrics
Final-revised paper
Preprint