https://nhess.copernicus.org/articles/ Combined list of the recent articles of the journal Natural Hazards and Earth System Sciences and the recent discussion forum Natural Hazards and Earth System Sciences Discussions en https://doi.org/10.5194/nhess-26-3273-2026 Benefits of the simplified MEV for analyzing hourly precipitation extremes in a changing climate Marc Lennartz, Benjamin Poschlod, and Bruno Merz Nat. Hazards Earth Syst. Sci., 26, 3273–3289, https://doi.org/10.5194/nhess-26-3273-2026, 2026 Predicting hourly rainfall extremes under climate change is crucial yet highly uncertain. Using convection-permitting climate model data over Germany, we compare stationary and non-stationary general extreme value (GEV) and simplified metastatistical extreme value (sMEV) methods. We show that the sMEV approach exhibits lower uncertainty across return periods. Moreover, the non-stationary sMEV better captures climate-change-induced changes, though care is needed when projecting future extremes. Wed, 15 Jul 2026 06:18:32 +0200 https://doi.org/10.5194/nhess-26-3253-2026 Feature selection for landslide forecasting models in Southern Andes Manuel Labbé, Millaray Curilem, Ivo Fustos-Toribio, and Mario Pooley Nat. Hazards Earth Syst. Sci., 26, 3253–3272, https://doi.org/10.5194/nhess-26-3253-2026, 2026 We investigated methods to improve the prediction of landslides triggered by heavy rainfall in southern Chile, utilising local soil and climate data. We tested different models and selected the most critical environmental factors. We improved the process for making forecasts in areas with limited monitoring. Our results help create faster and more reliable warnings and can guide safety planning in other mountain regions facing similar risks. Tue, 14 Jul 2026 06:18:32 +0200 https://doi.org/10.5194/nhess-26-3231-2026 Beyond the 100-year flood: probabilistic flood hazard assessment for King and Pierce Counties under future climate scenarios Kees Nederhoff, Kai Parker, and Eric Grossman Nat. Hazards Earth Syst. Sci., 26, 3231–3251, https://doi.org/10.5194/nhess-26-3231-2026, 2026 Flooding is a growing risk for communities around Puget Sound, where rising seas and changing rainfall patterns combine with rivers and tides. We used advanced computer models to simulate decades of water levels and river flows to better understand how often and how severely flooding may occur in the future. Our results show that climate change will increase both the frequency and extent of flooding, highlighting the urgent need for adaptation planning. Mon, 13 Jul 2026 06:18:32 +0200 https://doi.org/10.5194/nhess-26-3163-2026 Usage of normalized soil moisture for improving the performance of rainfall thresholds along transportation corridors Leila Rahimikhameneh, Abraham Alvarez Reyna, Jack Montgomery, and Frances O'Donnell Nat. Hazards Earth Syst. Sci., 26, 3163–3183, https://doi.org/10.5194/nhess-26-3163-2026, 2026 Landslides along highways pose safety risks and disrupt travel. Many warning systems use rainfall data, but this alone may miss key factors. Using satellite-derived soil moisture, precipitation records, and landslide observations from Alabama, we found that wetter-than-average soil conditions increase landslide risk. We developed a framework that combines rainfall and soil moisture to improve prediction and reduce false alarms. Thu, 09 Jul 2026 06:18:32 +0200 https://doi.org/10.5194/nhess-26-3185-2026 Representation of meteotsunamis in km-scale regional simulations coupled at high frequency Nefeli Makrygianni, Ségolène Berthou, David L. A. Flack, Cindy Lebeaupin Brossier, Jonathan Beuvier, Juan Manuel Castillo, Emiliano Renzi, Clare O'Neill, Daniel Peláez-Zapata, Frederic Dias, Huw Lewis, and Diego Bruciaferri Nat. Hazards Earth Syst. Sci., 26, 3185–3211, https://doi.org/10.5194/nhess-26-3185-2026, 2026 Meteotsunamis are rare but dangerous anomalous waves triggered by atmospheric disturbances, they are not currently forecast in Northwest Europe. We analysed the strongest recorded event on June 18, 2022, which reached 1 m amplitude. We showed high-resolution, high-frequency coupled models can represent such events up to three days ahead and help better understand their atmospheric triggers. These models, together with improved observations, can enhance early warnings and coastal safety. Thu, 09 Jul 2026 06:18:32 +0200 https://doi.org/10.5194/nhess-26-3129-2026 Evaluation of AI-based seasonal weather ensembles as input for fluvial flood risk estimation: a case study over the Elbe basin John Ashcroft, Alison Poulston, Marius Koch, Georg Ertl, Kirsty Brown, James Butler, Anthony Hammond, Owen Jordan, Sarah Warren, Rob Lamb, Paul J. Young, and David Wood Nat. Hazards Earth Syst. Sci., 26, 3129–3161, https://doi.org/10.5194/nhess-26-3129-2026, 2026 Floods cause major social and economic losses, but estimating risk is difficult because extreme events are rare. We used artificial intelligence to generate over a thousand realistic winter weather seasons and river flows for the Elbe basin. The approach reproduced observed patterns and produced a wider range of extreme storms, showing that artificial intelligence can expand plausible flood scenarios for improved risk assessment. Tue, 07 Jul 2026 06:18:32 +0200 https://doi.org/10.5194/nhess-26-3107-2026 Automated dock-based UAV systems for geohazard monitoring in mountainous terrain Alexander Maschler, Sarah Langes, Lukas Schild, Thomas Scheiber, Paula Snook, Jacob Clement Yde, Harald Zandler, and Ueli Sager Nat. Hazards Earth Syst. Sci., 26, 3107–3127, https://doi.org/10.5194/nhess-26-3107-2026, 2026 Geohazards occur more often and at larger scale due to climate change. In this study, we used for the first time automated drone docks to monitor geohazards at 3 different sites in Norway and Switzerland. We present a monitoring workflow for automated Uncrewed Aerial Vehicle operations which can produce frequent, detailed maps showing surface change and displacement. Our results show how this approach can improve future hazard monitoring and early warning and help to protect communities at risk. Mon, 06 Jul 2026 06:18:32 +0200 https://doi.org/10.5194/nhess-26-3085-2026 Enhancing long-term reservoir inflow forecasting: an integrated approach combining switch prediction method, ensemble rainfall forecasts, and machine learning techniques Hsuan-Yu Lin, Jhih-Huang Wang, and Ming-Jui Chang Nat. Hazards Earth Syst. Sci., 26, 3085–3106, https://doi.org/10.5194/nhess-26-3085-2026, 2026 Reservoir operators need reliable forecasts during typhoons to reduce flood risks and secure water supplies. In this study, we developed a method that combines multiple weather forecasts with computer models trained on past rainfall and reservoir inflow records. The approach improved inflow forecasts up to three days ahead, helping reservoir managers make earlier decisions, improve flood control, and better prepare for future extreme weather. Fri, 03 Jul 2026 06:18:32 +0200 https://doi.org/10.5194/nhess-26-3077-2026 Brief communication: A century of landslide records in Calabria, southern Italy, looking for changes and trends through a dynamic analysis Stefano Luigi Gariano and Olga Petrucci Nat. Hazards Earth Syst. Sci., 26, 3077–3084, https://doi.org/10.5194/nhess-26-3077-2026, 2026 Long-term changes in rainfall conditions triggering landslides in Calabria (Southern Italy) over 1921–2020 are analysed through a dynamic approach in rainfall threshold definition. Shifts in seasonality and a decreasing trend in triggering rainfall are observed over the years, suggesting an increased propensity of the territory to generate landslides, even with less severe triggering rainfall in recent decades. The work highlights the essential role of landslide data collection and archiving. Thu, 02 Jul 2026 06:18:32 +0200 https://doi.org/10.5194/nhess-26-3045-2026 The long-term hazard cascade of an unprecedented wildfire in a tropical mountain ecosystem William Veness, Martha Day, Anthony C. Ross, Yazidhi Bamutaze, Jiayuan Han, Douglas Mulangwa, Andrew Mwesigwa, Emmanuel Ntale, Callist Tindimugaya, Brian Guma, Elisabeth Stephens, and Wouter Buytaert Nat. Hazards Earth Syst. Sci., 26, 3045–3075, https://doi.org/10.5194/nhess-26-3045-2026, 2026 Climate change is driving wildfires to higher elevations in tropical mountains, exposing ecosystems with little or no fire history. We analyse a 2012 fire in Uganda’s Rwenzori Mountains that burned pristine forest and wetland above 3800 m, where no major fire had occurred for 12,000 years. The fire triggered a long-term cascade of floods, debris flows, landslides, erosion and mine-waste pollution, showing the need for rapid post-fire risk assessment and restoration. Wed, 01 Jul 2026 06:18:32 +0200 https://doi.org/10.5194/nhess-26-3025-2026 The catastrophic floods in 2008, 2010 and 2020 in western Ukraine: Hydrometeorological processes and the role of upper-level dynamics Ellina Agayar, Moshe Armon, Michael Sprenger, and Heini Wernli Nat. Hazards Earth Syst. Sci., 26, 3025–3043, https://doi.org/10.5194/nhess-26-3025-2026, 2026 This study investigates the hydrometeorological features of the major floods of 2008, 2010, and 2020 in western Ukraine. All cases were linked to upper-level potential vorticity (PV) anomalies. We also conducted a climatological analysis of PV structure associated with 22 summer heavy precipitation events (2000–2022), highlighting their key role in determining the location and intensity of flood-inducing rainfall events in the Carpathians. Tue, 30 Jun 2026 06:18:32 +0200 https://doi.org/10.5194/nhess-26-2975-2026 Review article: Harnessing data-driven methods for climate multi-hazard and multi-risk assessment Davide Mauro Ferrario, Marcello Sanò, Margherita Maraschini, Andrea Critto, and Silvia Torresan Nat. Hazards Earth Syst. Sci., 26, 2975–3024, https://doi.org/10.5194/nhess-26-2975-2026, 2026 This review examines how machine learning methods can improve the assessment of interacting climate hazards and their risks, covering data processing, hazard prediction, risk assessment, and future scenarios. Machine learning is widely used, from analysing satellite data to predicting extreme events and estimating impacts. Key research gaps include better modelling of hazard interactions and changing vulnerability, transparent model outputs, and frameworks including uncertainty quantification. Fri, 26 Jun 2026 06:18:32 +0200 https://doi.org/10.5194/nhess-26-2921-2026 Exploring seismic mass-movement data with anomaly detection and dynamic time warping Francois Kamper, Fabian Walter, Patrick Paitz, Matthias Meyer, Michele Volpi, and Mathieu Salzmann Nat. Hazards Earth Syst. Sci., 26, 2921–2953, https://doi.org/10.5194/nhess-26-2921-2026, 2026 We use anomaly detection to automatically find patterns in seismic data that may signal dangerous mass-movement events such as landslides, glacier collapses, or debris flows. Because such movements are rare, our approach reduces the amount of data that must be analyzed to find them, whether by experts, semi-supervised methods or clustering procedures. We demonstrate the usefulness of our approach by mining for mass movements in Switzerland and Greenland. Thu, 25 Jun 2026 06:18:32 +0200 https://doi.org/10.5194/nhess-26-2955-2026 Managing glacial and periglacial hazards in the Alps: a geohistorical approach Juliette Bazin, Ludovic Ravanel, and Sandrine Caroly Nat. Hazards Earth Syst. Sci., 26, 2955–2973, https://doi.org/10.5194/nhess-26-2955-2026, 2026 Through a geo-historical study, we compile an inventory of glacial and periglacial events that have prompted risk management actions in the Alps. Analysed through eight guiding principles, this research examines how methods have evolved across Alpine countries. Certain events mark turning points in risk perception. Today, management benefits from improved understanding of both hazards and vulnerabilities. Thu, 25 Jun 2026 06:18:32 +0200 https://doi.org/10.5194/nhess-26-2903-2026 Buried and displaced: moving characteristics of building fragments in debris flows Lei Feng and Dongri Song Nat. Hazards Earth Syst. Sci., 26, 2903–2919, https://doi.org/10.5194/nhess-26-2903-2026, 2026 Accurate prediction of the relocated position of buildings (and the trapped victims) within debris-flow and landslide deposits is challenging. We elucidate the complicated physical mechanisms associated with the movement of building fragments within debris flows. A set of analytical models considering the physical mechanisms is further established. These findings provide reference for post-disaster emergency rescue by enabling positioning of buried structures. Wed, 24 Jun 2026 06:18:32 +0200 https://doi.org/10.5194/nhess-26-2897-2026 Brief communication: “Multi-hazard-to-health-outcome” (MH2O) pathways: the known, the unknown, and ten most urgent priorities Harriet Moore, Qiuhua Liang, Lee Bosher, John Atanbori, Mark Gussy, Amogh Mudbhatkal, Joe Swift, Jaspreet Phull, Kirsten Guy, Lynsey Collinson, Andy Penny, Maria Athanassiadou, Kaja Milczewska, Ebenezer Amankwaa, Lucy Kennedy, Edward Hanna, Gregory Sutton, Bartholomew Hill, and Colin Hopkirk Nat. Hazards Earth Syst. Sci., 26, 2897–2902, https://doi.org/10.5194/nhess-26-2897-2026, 2026 Climate change will increase the frequency of extreme weather events like floods, droughts and storms. These hazards have impacts on human health. Some hazards, like storms, cause additional hazards like flooding. Little is known about the compound effects of multiple hazards on health, or how impacts vary between communities and areas. We present ten urgent research priorities related to the impact of multiple hazards on health to guide future decision-making towards climate resilience. Tue, 23 Jun 2026 06:18:32 +0200 https://doi.org/10.5194/nhess-26-2871-2026 Predicting spatio-temporal wildfire propagation with dynamic firebreaks Jiahe Zheng, Zhengsen Xu, Rossella Arcucci, Sandy P. Harrison, Lincoln Linlin Xu, and Sibo Cheng Nat. Hazards Earth Syst. Sci., 26, 2871–2895, https://doi.org/10.5194/nhess-26-2871-2026, 2026 We introduce the first AI model that predicts wildfire spread with the placement of both permanent and temporary firebreaks. Our spatiotemporal model learns from simulation data to capture how fire interacts with changing suppression efforts over time. Our model runs fast enough for near real-time use and performs well across different wildfire events. This approach could lead to better tools for helping decision-makers understand where and when firebreaks are most effective. Wed, 17 Jun 2026 06:18:32 +0200 https://doi.org/10.5194/nhess-26-2841-2026 Psychometric and cultural characterization of relative volcanic risk perception levels of individuals highly exposed to proximal activity from Villarrica volcano, Chile Rodrigo A. Calderon, Luis E. Lara, Marcelo A. Lagos, and Alfredo A. Vives Nat. Hazards Earth Syst. Sci., 26, 2841–2863, https://doi.org/10.5194/nhess-26-2841-2026, 2026 This study explores how people perceive volcanic risk around Villarrica volcano in Chile. We surveyed about 400 residents and visitors in the high-risk area. Residents showed stronger trust within their communities but less knowledge of volcanic hazards, while visitors showed greater knowledge but lower trust. Both groups expressed confidence in authorities. Findings highlight the need for risk communication adapted to different groups to improve preparedness and strengthen community resilience. Tue, 16 Jun 2026 06:18:32 +0200 https://doi.org/10.5194/nhess-26-2865-2026 Brief communication: Drought economic assessments must include human health impacts Alexandre Cunha Costa, Francisco Gildemir Ferreira da Silva, Rafaella Pessoa Moreira, Eduardo Sávio Passos Rodrigues Martins, Luewton Lemos Felício Agostinho, and Pieter Richard van Oel Nat. Hazards Earth Syst. Sci., 26, 2865–2869, https://doi.org/10.5194/nhess-26-2865-2026, 2026 Many studies highlight drought impacts on productive sectors, such as agriculture and navigation, but economic consequences for human health remain understudied. We conducted an economic valuation of drought-related health interventions in Brazil, showing that ensuring groundwater access during severe droughts can avert substantial losses. Estimated benefits from reduced diarrhea hospitalizations total 9.92 % of local GDP (Gross Domestic Product), with avoidable losses reaching USD 1.15 billion at the state level. Tue, 16 Jun 2026 06:18:32 +0200 https://doi.org/10.5194/nhess-26-2817-2026 Unravelling wind-driven impact of storm clusters, a case study for the insurer Generali France Laura Hasbini, Pascal Yiou, Quentin Hénaff, Laurent Boissier, and Arthur Perringaux Nat. Hazards Earth Syst. Sci., 26, 2817–2839, https://doi.org/10.5194/nhess-26-2817-2026, 2026 Winter windstorms are the main natural hazard for Generali France. We present a method linking storm events to insurance claims, with a focus on clustered events (multiple storms hitting the same region within 96 h). These account for 85 % of losses over the period 1998-2024 and include major events like Lothar and Klaus. Damaging storms are twice as likely to occur in clusters, underlining the need to account for their impact in risk, loss, and reinsurance modelling. Fri, 12 Jun 2026 06:18:32 +0200