Articles | Volume 26, issue 8
https://doi.org/10.5194/nhess-26-4013-2026
© Author(s) 2026. 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-26-4013-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Aug 2026
Research article |
| 26 Aug 2026
| 26 Aug 2026
Exposure of settlements to wildfires in a transboundary wildland–urban interface region in Central Europe
Evripidis Avouris
Faculty of Environmental Sciences, Environmental Remote Sensing Group, TUD Dresden University of Technology, Dresden, Germany
Christopher Marrs
Faculty of Environmental Sciences, Environmental Remote Sensing Group, TUD Dresden University of Technology, Dresden, Germany
Kristina Beetz
Faculty of Environmental Sciences, Environmental Remote Sensing Group, TUD Dresden University of Technology, Dresden, Germany
now at: ICEYE, Espoo, Finland
Lucie Kudláčková
Global Change Research Institute of the Czech Academy of Sciences, Brno, Czech Republic
Johanna Kranz
Faculty of Environmental Sciences, Environmental Remote Sensing Group, TUD Dresden University of Technology, Dresden, Germany
Markéta Poděbradská
Global Change Research Institute of the Czech Academy of Sciences, Brno, Czech Republic
Miroslav Trnka
Global Change Research Institute of the Czech Academy of Sciences, Brno, Czech Republic
Matthias Forkel
CORRESPONDING AUTHOR
Faculty of Environmental Sciences, Environmental Remote Sensing Group, TUD Dresden University of Technology, Dresden, Germany
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Siyuan Wang, Hui Yang, Sujan Koirala, Maurizio Santoro, Anna Candotti, Ulrich Weber, Ranit De, Claire Robin, Felix Cremer, Matthias Forkel, Markus Reichstein, and Nuno Carvalhais
Earth Syst. Sci. Data, 18, 5895–5913, https://doi.org/10.5194/essd-18-5895-2026, https://doi.org/10.5194/essd-18-5895-2026, 2026
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Forest disturbances are difficult to predict in models because they occur randomly. We discovered that the long-term rules of disturbance known as
regimeleave a unique footprint in a forest's spatial biomass patterns. We trained a model on millions of computer simulations to learn this link. By applying this model to detailed satellite biomass, we could read these patterns to infer the disturbance regime globally, helping make climate projections more accurate.
Monika Hlavsová, Lauro Rossi, Kathrin Szillat, Kerstin Stahl, Mirko D'Andrea, Veit Blauhut, Gabriela Ivaňáková, Livia Labudová, and Miroslav Trnka
EGUsphere, https://doi.org/10.5194/egusphere-2026-3041, https://doi.org/10.5194/egusphere-2026-3041, 2026
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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Information about drought impacts is often incomplete because collecting such data requires substantial time and resources. We tested whether online news articles available in all official EU languages could help fill gaps in the European Drought Impact Database. The approach added many new records, especially for recent droughts, but results varied strongly between countries and sectors. Online sources can improve drought monitoring, but expert observations and local reporting remain essential.
Andreia F. S. Ribeiro, Maik Biling, Kirsten Thonicke, Werner von Bloh, Jakob Wessel, Sabine Undorf, Matthias Forkel, and Jakob Zscheischler
EGUsphere, https://doi.org/10.5194/egusphere-2026-2952, https://doi.org/10.5194/egusphere-2026-2952, 2026
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Wildfires are becoming more extreme, yet our state-of-the-art tools fail to capture the full risk. We simulate a large ensemble of wildfire simulations capturing a broader range of physically plausible extreme wildfire events beyond what observations alone can reveal. Extreme fire danger alone does not explain the worst impacts: ignitions, fuel and vegetation-fire feedback need to be incorporated. This modelling framework is transferable to other climate-impact sectors beyond wildfires.
Matěj Orság, Radovan Kopp, Jan Balek, Daniela Semerádová, Adam Vizina, Milan Fischer, Petr Skalák, Petr Štěpánek, Pavel Zahradníček, Jan Mareš, and Miroslav Trnka
EGUsphere, https://doi.org/10.5281/zenodo.19493262, https://doi.org/10.5281/zenodo.19493262, 2026
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Rivers are warming. In the Czech Republic, water temperatures have risen steadily, shrinking the cold-water habitat that brown trout depend on for survival and reproduction. Using climate model projections, we showed that thermally suitable river reaches may decline from nearly the entire network today to just four percent by 2085. The greatest threat is not rare heat extremes but chronic moderate warming that erodes favourable conditions across the warm season and during winter spawning.
Olivia Hau, Matthias Forkel, Wolfgang Buermann, Johanna Kranz, Mirco Migliavacca, Ulrich Weber, and Alexander Josef Winkler
EGUsphere, https://doi.org/10.5194/egusphere-2026-910, https://doi.org/10.5194/egusphere-2026-910, 2026
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Shifts in spring and autumn growth due to climate warming change how plants reflect sunlight and release heat and moisture into the air, modulating surface warming. The strength of these effects and their regional variability remain poorly understood. Using satellite and climate data, we show that earlier spring growth increases moisture release, especially in forests, while autumn changes are smaller and less consistent. Impacts on land-atmosphere interactions vary by ecosystem and data source.
Pere Joan Gelabert, Adrián Jiménez-Ruano, Clara Ochoa, Fermín Alcasena, Johan Sjöström, Christopher Marrs, Luís Mário Ribeiro, Palaiologos Palaiologou, Carmen Bentué Martínez, Emilio Chuvieco, Cristina Vega-García, and Marcos Rodrigues
Nat. Hazards Earth Syst. Sci., 25, 4713–4729, https://doi.org/10.5194/nhess-25-4713-2025, https://doi.org/10.5194/nhess-25-4713-2025, 2025
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Wildfires threaten ecosystems and communities across Europe. Our study developed models to predict where and why these ignitions occur in different European environments. We found that weather anomalies and human factors, like proximity to urban areas and roads, are key drivers. Using Machine Learning our models achieved strong predictive accuracy. These insights help design better wildfire prevention strategies, ensuring safer landscapes and communities as fire risks grow with climate change.
Jan Řehoř, Rudolf Brázdil, Oldřich Rakovec, Martin Hanel, Milan Fischer, Rohini Kumar, Jan Balek, Markéta Poděbradská, Vojtěch Moravec, Luis Samaniego, Yannis Markonis, and Miroslav Trnka
Hydrol. Earth Syst. Sci., 29, 3341–3358, https://doi.org/10.5194/hess-29-3341-2025, https://doi.org/10.5194/hess-29-3341-2025, 2025
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We present a robust method for identification and classification of global land drought events (GLDEs) based on soil moisture. Two models were used to calculate soil moisture and delimit soil drought over global land from 1980–2022, with clusters of 775 and 630 GLDEs. Using four spatiotemporal and three motion-related characteristics, we categorized GLDEs into seven severity and seven dynamic categories. The frequency of GLDEs has generally increased in recent decades.
Adrianus de Laat, Vincent Huijnen, Niels Andela, and Matthias Forkel
EGUsphere, https://doi.org/10.5194/egusphere-2024-732, https://doi.org/10.5194/egusphere-2024-732, 2024
Preprint archived
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This study assesses state-of-the art and more advanced and innovative satellite-observation-based (bottom-up) wildfire emission estimates. They are evaluated by comparison with satellite observation of single fire emission plumes. Results indicate that more advanced fire emission estimates – more information – are more realistic but that especially for a limited number of very large fires certain differences remain – for unknown reasons.
Hoontaek Lee, Martin Jung, Nuno Carvalhais, Tina Trautmann, Basil Kraft, Markus Reichstein, Matthias Forkel, and Sujan Koirala
Hydrol. Earth Syst. Sci., 27, 1531–1563, https://doi.org/10.5194/hess-27-1531-2023, https://doi.org/10.5194/hess-27-1531-2023, 2023
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We spatially attribute the variance in global terrestrial water storage (TWS) interannual variability (IAV) and its modeling error with two data-driven hydrological models. We find error hotspot regions that show a disproportionately large significance in the global mismatch and the association of the error regions with a smaller-scale lateral convergence of water. Our findings imply that TWS IAV modeling can be efficiently improved by focusing on model representations for the error hotspots.
Luisa Schmidt, Matthias Forkel, Ruxandra-Maria Zotta, Samuel Scherrer, Wouter A. Dorigo, Alexander Kuhn-Régnier, Robin van der Schalie, and Marta Yebra
Biogeosciences, 20, 1027–1046, https://doi.org/10.5194/bg-20-1027-2023, https://doi.org/10.5194/bg-20-1027-2023, 2023
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Vegetation attenuates natural microwave emissions from the land surface. The strength of this attenuation is quantified as the vegetation optical depth (VOD) parameter and is influenced by the vegetation mass, structure, water content, and observation wavelength. Here we model the VOD signal as a multi-variate function of several descriptive vegetation variables. The results help in understanding the effects of ecosystem properties on VOD.
Matthias Forkel, Luisa Schmidt, Ruxandra-Maria Zotta, Wouter Dorigo, and Marta Yebra
Hydrol. Earth Syst. Sci., 27, 39–68, https://doi.org/10.5194/hess-27-39-2023, https://doi.org/10.5194/hess-27-39-2023, 2023
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The live fuel moisture content (LFMC) of vegetation canopies is a driver of wildfires. We investigate the relation between LFMC and passive microwave satellite observations of vegetation optical depth (VOD) and develop a method to estimate LFMC from VOD globally. Our global VOD-based estimates of LFMC can be used to investigate drought effects on vegetation and fire risks.
Benjamin Wild, Irene Teubner, Leander Moesinger, Ruxandra-Maria Zotta, Matthias Forkel, Robin van der Schalie, Stephen Sitch, and Wouter Dorigo
Earth Syst. Sci. Data, 14, 1063–1085, https://doi.org/10.5194/essd-14-1063-2022, https://doi.org/10.5194/essd-14-1063-2022, 2022
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Gross primary production (GPP) describes the conversion of CO2 to carbohydrates and can be seen as a filter for our atmosphere of the primary greenhouse gas CO2. We developed VODCA2GPP, a GPP dataset that is based on vegetation optical depth from microwave remote sensing and temperature. Thus, it is mostly independent from existing GPP datasets and also available in regions with frequent cloud coverage. Analysis showed that VODCA2GPP is able to complement existing state-of-the-art GPP datasets.
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Short summary
Wildfires are increasing in Central Europe. We studied how they could threaten settlements in the Saxon–Czech border region. Using satellite information, local data, and computer simulations, we mapped where fires are most likely and how intense they could be. We tested our results against a destructive fire that occurred in 2022. An interactive web map presents these results with the aim of helping residents and agencies improve preparedness and coordinate cross-border disaster response.
Wildfires are increasing in Central Europe. We studied how they could threaten settlements in...
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