Climate extremes, such as droughts, floods, and heatwaves, often trigger compound and cascading impacts due to interdependencies between coupled natural and social systems. Yet, our knowledge of these interactions remains limited mainly due to the lack of comprehensive impact data. Research typically considers only one isolated impact, system, socioeconomic sector, and/or hazard at a time, often ignoring dependencies between impacts as well as how they interact with response and adaptation measures.

Against this backdrop, the unprecedented abundance of digital texts and cutting-edge machine-learning tools has opened new research avenues for impact assessment research. In this talk, I will demonstrate how we can leverage natural language processing (NLP) and large language models on different text types to infer how climate extremes impact society. I will discuss the potential of unconventional data sources, such as meeting minutes, newspaper articles, and reports, to monitor the consequences of extreme events in near real-time.

How to cite: Madruga de Brito, M.: Climate impacts and where to find them: insights from text mining, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1930, https://doi.org/10.5194/egusphere-egu25-1930, 2025.

Integrated flood risk management requires an extension from hazard to risk analysis and an involvement of various stakeholders including the general public. Since no standard protocols for collecting data about flood-affected societies are in place, post-disaster surveys have been initiated to gain information from affected residents and companies. Using the most damaging flood events that have occurred in Germany since 2000 as examples, the lecture will address how data collected from flood-affected people have been used a) to develop and improve loss models, b) to better understand how and why people adapt to flood risk, c) to evaluate how people respond to warnings, d) to provide insights into flood-related health impacts and e) to comprehend how people recover from flood impacts. Since flood processes in Germany between 2002 to 2024 differed considerably, it will be addressed how much the flood type – in particular slow-onset river flooding, flash floods and pluvial floods – influence impacts and coping mechanisms. Research outcomes have informed flood early warning systems, risk communication and recovery programs in Germany and beyond. However, surveying or interviewing flood-affected people might also put an additional burden on them. Hence, the lecture will discuss some ethical considerations about collecting data in (highly) affected areas as well as some pros and cons of cross-sectional versus longitudinal survey designs. Finally, transfer to other regions and hazards will be highlighted.

How to cite: Thieken, A.: More than two decades of post-disaster household surveys to improve flood risk management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6429, https://doi.org/10.5194/egusphere-egu25-6429, 2025.

This lecture provides a critical analysis of drought indices, emphasizing their role in evaluating drought severity while addressing the challenges associated with their application. It highlights the inherent complexity of drought assessment, given the multifaceted nature of drought phenomena, the various types of drought, and the intricate mechanisms underlying their development. A central focus is the distinction between drought and aridity, as well as between drought metrics and indices—concepts that are frequently misunderstood or conflated.

Particular attention is given to atmospheric drought indices, especially those incorporating atmospheric evaporative demand (AED). These indices are crucial for assessing water stress but have faced criticism for certain limitations. One notable issue is the "index-impact gap," where atmospheric drought indices often indicate more severe droughts than those reflected in hydrological and ecological metrics derived from Earth System Models (ESMs), particularly in future climate scenarios. Atmospheric indices do not directly account for soil moisture or vegetation dynamics. Nonetheless, AED reflects atmospheric conditions rather than direct water reservoirs and fluxes, making AED-based indices valuable for understanding atmospheric drivers of drought. This value is reinforced by AED's critical role in intensifying drought through increased evaporation, heightened plant water stress, and reduced photosynthesis.

The lecture further focuses into the uncertainties inherent in ESM projections of ecological and hydrological variables, such as soil moisture and runoff. These uncertainties arise because ESMs often underestimate drought severity due to challenges in simulating complex hydrological and physiological processes. The difficulties stem from limitations in modelling plant physiology, water cycles, and ecosystem responses, compounded by biases in key variables such as evapotranspiration. While ESM outputs are valuable for drought assessments, relying exclusively on them risks producing misleading conclusions.

This issue connects with the role of rising atmospheric CO₂ concentrations, a factor commonly incorporated into ESM simulations, which adds another layer of complexity. Elevated CO₂ levels can enhance plant water-use efficiency and photosynthesis but also introduce uncertainties regarding their impacts on evapotranspiration and soil moisture. These dynamics generate complex feedbacks with AED and other variables, further complicating drought severity assessments, particularly in future ESM simulations.

To address these challenges, the lecture advocates for an integrated approach that combines atmospheric drought indices with hydrological and ecological metrics. Such an approach ensures that the intensifying role of AED under global warming is neither overlooked nor overstated, thereby improving the accuracy of drought assessments, especially in the context of future climate scenarios.

How to cite: Vicente Serrano, S. M.: On the Use of Drought Indices for Drought Severity Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1572, https://doi.org/10.5194/egusphere-egu25-1572, 2025.

Dam safety is a primary concern for countries worldwide, as dam failure can have catastrophic consequences, including fatalities and losses to the economy, ecology, and environment. In recent decades, there has been growth in consensus that climate change has enhanced the risk to dams due to floods triggered by more frequent and intense extreme precipitation events. It necessitates reviewing the Probable Maximum Floods (PMFs) considered for planning and designing large dams and updating them for different speculated climate change scenarios to determine the projected future changes in dam break risk. Global initiatives, such as the Paris Agreement, are focused on developing strategies to limit the increase in global temperatures well below 2°C (preferably 1.5°C) above pre-industrial levels by 2050. However, significant discrepancies have been identified between the current global greenhouse gas emissions trajectory and the reductions needed in emissions to achieve the Paris Agreement's target. To bridge this gap, geoengineering climate intervention methods such as Stratospheric Aerosol Injection (SAI) and Solar Dimming (SD) have been proposed as potential solar radiation management (SRM) options to offset climate change effects. The latest Geoengineering Model Intercomparison Project (GeoMIP6) provides simulations from a suite of climate model experiments designed to assess the effect of potential SRM methods, including SAI and SD. To shed light on the effectiveness of geoengineering, this study assesses the impact of the current generation climate models (from Coupled Model Intercomparison Project Phase 6, CMIP6) and geoengineering models (from GeoMIP6) on Probable Maximum Precipitation (PMP) and the corresponding Probable Maximum Flood (PMF) at a typical large dam (Hemavathi) located in the Cauvery River basin in India. The current PMF of the dam is compared with future projections of the same derived corresponding to a CMIP6 high forcing scenario (SSP585) and two GeoMIP (G6sulphur and G6solar) scenarios. For both near and far future periods, the PMF hydrograph’s peak for the SSP585 scenario (GeoMIP6 scenarios) is significantly (marginally) greater than that of the current PMF of the dam. It indicates that geoengineering methods can offset climate change's impact on PMP and the corresponding PMF (depicting hydrological risk) at dams, which is of significance as worldwide many large dams have completed their design life.

How to cite: Goel, A. and Srinivas, V. V.: Impact of Geoengineering in Offsetting Climate Change-Induced Dam Break Risk, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-395, https://doi.org/10.5194/egusphere-egu25-395, 2025.

Detecting and monitoring ongoing surface deformation with satellite geodesy is fundamental for the analysis of geophysical processes and geohazards. Here, we focused on the area of Western Greece, due to its complex geophysical setting, characterized by numerous faults and high seismicity, and we quantified the spatiotemporal patterns of land displacements in the area from 2018 to 2022. We analysed Sentinel-1 Synthetic Aperture Radar (SAR) data with Multi-temporal Interferometric SAR (MT-InSAR) techniques and calibrated the derived estimates using velocity time series from multiple permanent Global Navigation Satellite System (GNSS) stations available in the area. The derived displacement time series were also compared with openly available data from the European Ground Motion Service (EGMS) and, jointly, were used to map possible active fault areas. In addition, trajectory modelling was performed in both MT-InSAR and GNSS velocity time series through the Greedy Automatic Signal Decomposition (GrAtSiD) algorithm, in order to identify seasonal loading and therefore improve detection of accelerations in tectonic or anthropogenic motion. Overall, the study explores recent geodetic observations with state-of-the-art data analysis techniques, and, building upon previous literature, offers a comprehensive spatiotemporal assessment of land displacements in Western Greece, with implications for scientific and engineering applications.

How to cite: Fasoulis, K., Bedford, J., Garcia, C., Hadjidoukas, P., and Pappas, C.: Spatiotemporal quantification and trajectory modelling of land displacements in Western Greece using recent InSAR and GNSS observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-654, https://doi.org/10.5194/egusphere-egu25-654, 2025.

Dynamic Flood and Erosion Modeling for the Sabarmati River Using RUSLE and GEE

Nensi A. Sachapara ^{a(0009-0000-9510-6198)}, Manan Patel ^{a(0009-0004-4712-3531)} , Hasti Dhameliya ^{b(0009-0003-8908-7906)}
Keval H Jodhani ^{c (0000-0002-3800-2402)}, Nitesh Gupta ^{c(0000-0003-0471-0133)} , Dhruvesh P. Patel ^{d (0000-0002-2074-7158)} Sudhir Kumar Singh ^{e  (0000-0001-8465-0649)}

^aUnder Graduate Student, Civil Engineering Department, Nirma University, Ahmedabad, 382481, Gujarat, India.  (nensisachapara16@gmail.com; mananrp07@gmail.com )

^bUnder Graduate Student, Biomedical Engineering Department, LD College of Engineering, Ahmedabad, 382481, Gujarat, India. (dhameliyahasti8@gmail.com)

^cAssistant Professor, Department of Civil Engineering, Institute of Technology, Nirma University, Ahmedabad, 382481, Gujarat, India. (jodhanikeval@gmail.com, niteshraz@gmail.com)

^dDepartment of Civil Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, 382007, Gujarat, India (dhruvesh1301@gmail.com)

⁶ K. Banerjee Centre of Atmospheric and Ocean Studies, IIDS, Nehru Science Centre, University of Allahabad, Prayagraj-211002, Uttar Pradesh, India (sudhirinjnu@gmail.com)

Abstract: Flooding and soil erosion are major environmental challenges impacting the Sabarmati River Basin (SRB), adversely affecting its ecology, agriculture, and infrastructure. This study employs the Google Earth Engine (GEE) platform to comprehensively analyze flood-prone areas and soil erosion using the Revised Universal Soil Loss Equation (RUSLE) model. High-resolution datasets from USGS Earth Explorer and GEE are integrated with remote sensing and geospatial technologies to assess the basin's vulnerabilities. Flood-prone regions were identified by analyzing historical rainfall (maximum annual rainfall of 1,667.15 mm in 2017), hydrological patterns, and topographic features. The RUSLE model quantified soil erosion, incorporating factors such as rainfall erosivity (R factor: 11,202.65–29,243.64 MJ mm ha⁻¹ h⁻¹ yr⁻¹), soil erodibility (K factor: 0.20–0.20004 t ha h ha⁻¹ MJ⁻¹ mm⁻¹), slope length and steepness (LS factor: 0–0.499), land cover (C factor: 0.327–1.078), and conservation practices (P factor: 1). Results indicate critical hotspots of soil erosion, with losses peaking at 1,232.33 t/ha/year in the northern SRB. Flood hazard mapping revealed that low-lying areas with recurrent flood events align with regions experiencing high rainfall and sediment transport. The overlap between high soil erosion and flood-prone zones highlights the need for integrated management strategies. These risks have significant socio-economic implications, including diminished agricultural productivity, infrastructure damage, and community displacement. This dual analysis underscores the efficacy of GEE for rapid environmental assessments, providing actionable insights for policymakers to prioritize interventions. The findings align with Sustainable Development Goals 13 (Climate Action) and 15 (Life on Land), suggesting for adaptive strategies to mitigate flood and erosion risks and promoting sustainable resource management in vulnerable regions.

Keyword: RUSLE, GEE, Flood Hazard, SDG 13 & 15, Sabarmati Basin

How to cite: Sachapara, N., Patel, M., Dhameliya, H., Jodhani, K., Gupta, N., Patel, D., and Singh, S. K.: Dynamic Flood and Erosion Modeling for the Sabarmati River Using RUSLE and GEE, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-664, https://doi.org/10.5194/egusphere-egu25-664, 2025.

Improved and affordable prediction techniques are required because the growing frequency of shallow landslides caused by shifting weather patterns poses severe dangers to ecosystems, infrastructure, and communities. Although comprehensive monitoring systems are available, their high costs and complexity often make them impractical in resource-constrained regions. This study aims to evaluate the predictive potential of volumetric water content (VWC) measurements for shallow landslides and leverage machine learning techniques to develop cost-effective prediction models. The study employed one-dimensional modified column tests to simulate various scenarios (e.g., soil densities, drainage conditions) using a one-meter-high acrylic column to measure VWC, pore water, and air pressure. Key findings include the identification of VWC-related parameters (e.g., steady-state VWC and its gradient) as effective predictors of slope failure. When integrated with ML models, these parameters demonstrate the potential for enhancing prediction accuracy. This study provides a pathway to developing cost-effective early warning systems for slope instability, offering a practical solution for improving safety, using volumetric water content measurements to protect infrastructure, and enhancing resilience in landslide-prone regions, mainly where comprehensive monitoring systems are infeasible.

How to cite: Avzalshoev, Z., Ahmad, W., and Ahmad, T.: Using volumetric water content measurements with the implementation of machine learning for monitoring shallow landslides induced by rainfall, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-851, https://doi.org/10.5194/egusphere-egu25-851, 2025.

In recent years, the variety of satellite data that can be used for analysis in the event of a disaster has increased. At the same time, there is a need to process different satellite data using a unified analysis method, especially when extracting mudslide scars that have been newly exposed after a sediment disaster. Nonetheless, comparative studies focusing on spatial resolution, a potential factor affecting applicability and accuracy, have been lagging. Therefore, this study targeted the area surrounding Murakami City, Niigata Prefecture, which was the site of extensive sediment outflows due to heavy rainfall in August 2022. Specifically, the mudslide scar was estimated by calculating NDVI difference values (ΔNDVI) for four types of optical satellite data with different spatial resolutions. The data was extracted over a wide area and the effects of differences in spatial resolution on the applicability of the extraction method and the extraction rate were clarified. The relationship between precision and recall can be approximated by the quadratic equation y=ax²+bx+c, and there was a trade-off relationship between the two metrics; as the threshold value rose, precision increased while recall decreased. The optimal NDVI threshold for maximizing the F-measure ranged from 0.20 to 0.25. The medium-resolution satellite platforms Planet and Sentinel-2 had higher F-measure values, and the efficacy of NDVI extraction was not proportional to the fineness of the spatial resolution. The reason for this was that the area distribution of the mudslide scar in the target area was dominated by relatively small areas with a mode of 42 m² and a median of 253 m², which were considered to increase precision and recall. Consequently, selecting a spatial resolution that matches the area of the mudslide scar in the target area is considered to be effective.

How to cite: Akita, H.: Differences in applicability of mudslide scars estimation methods due to different spatial resolutions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1357, https://doi.org/10.5194/egusphere-egu25-1357, 2025.

Nature-based solutions (NBS) employ natural processes to mitigate climatic risks and evolving environmental challenges, offering sustainable, cost-effective alternatives to traditional grey infrastructure. Traditional stone weirs are considered multifunctional and environmental friendly structures contributing to sustain ecosystems and protect communities from water-related hazards. This type of NBS has shown potential to mitigate flood impacts through controlled water flow and sedimentation retention by reducing both water velocity and erosion during peak flows, with main objective to enhance community resilience to climate change. During CARDIMED project a network of 120 traditional stone weirs will be developed and applied in Sifnos island (Greece) strategically placed across two main streams aimed at mitigating flood risks, recharge aquifers, enhancing biodiversity, and supporting small-scale agricultural water use, tailored to the unique arid ecosystems of the Greek islands.

This study aims to monitor the efficiency of stone weir NBS in order to quantify climate adaptation benefits, particularly in relation to stormwater regulation, with application area Sifnos island (Aegean sea, Greece). The analysis utilises an integrated monitoring approach which couples remote sensing observations with in-situ data collected through monitoring stations, off-the-shelf sensors, and crowdsourcing participatory campaigns. Earth Observation techniques based on Sentinel-2 are employed to derive relevant vegetation and water indices (i.e. NDVI, NDWI), enabling to assess of vegetation health, soil water availability, and land surface dynamics. These are expected to be complemented by high-resolution datasets from Copernicus Contributing Missions, such as WorldView and Pleiades imagery, to enhance spatial and temporal resolution at locations of interest. EO techniques are validated by in-situ data derived from monitoring systems installed at strategic locations which provide localized, real-time measurements of hydrological, meteorological, and ecological parameters under different climatic conditions. The proposed methodology has the potential to provide key information about the quantified impacts from the application of stone weirs but also an understanding about their scalability as sustainable solutions for enhancing climate resilience at regional scale.

Acknowledgement:

This research has been funded by European Union’s Horizon Europe research and innovation programme under CARDIMED project (Grant Agreement No. 101112731) (Climate Adaptation and Resilience Demonstrated in the MEDiterranean region).

How to cite: Michalis, P., Kossieris, S., Papachristos, E., Petrakos, K., Sakellarakis, F.-N., Tsimiklis, G., and Amditis, A.: Assessing the Potential of Traditional Stone Weirs in Stormwater Management Through Integrated EO, In-situ and Crowdsourcing Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1523, https://doi.org/10.5194/egusphere-egu25-1523, 2025.

Predicting the dynamics of flood processes is paramount for effective disaster prevention and mitigation. Recently, Physics-Informed Neural Networks (PINNs) have been employed for flood dynamic prediction, demonstrating commendable performance in wave propagation forecasting. However, PINNs, which rely on traditional fully connected neural networks, exhibit certain limitations. Notably, their capacity for learning long-term wave propagation processes remains insufficient, and they struggle to generalize across diverse, previously untrained scenarios.In this study, we propose an innovative model that integrates a Convolutional Autoencoder (CAE) with a Long Short-Term Memory network (LSTM) to overcome these challenges. Drawing inspiration from the finite-difference method employed to solve the Shallow Water Equations (SWE), the CAE-LSTM model adeptly captures and predicts flow characteristics from both spatial and temporal dimensions. The CAE harnesses the power of convolutional neural networks to extract spatial features and generate compact latent representations, thereby reducing the complexity inherent in the physical system. Meanwhile, the LSTM captures the temporal dependencies within the latent feature space, enabling the prediction of the dynamic process based on time-series data.The efficacy of this model was validated through three classical two-dimensional dam-break scenarios. In the 60-second rolling prediction case, the accuracy of CAE-LSTM surpassed that of PINNs by approximately 60%, while its computational efficiency was enhanced by a factor of approximately 100. These results underscore the potential of CAE-LSTM to effectively capture the intricate dynamic behaviors of fluids, thereby offering a robust tool for predicting flood dynamics.

How to cite: Han, Z., Long, G., Li, C., Li, Y., Su, B., Xu, L., Wang, W., and Chen, G.: A Deep Learning-Based CAE-LSTM Model for Enhanced Long-Term Prediction of Flood Wave Propagation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3920, https://doi.org/10.5194/egusphere-egu25-3920, 2025.

In this study, the hydrodynamic behavior of a section of the Santa Catarina River in Nuevo León, Mexico, during Tropical Storm Alberto was investigated. A three-dimensional numerical simulation of river flow was performed using unsteady Reynolds-Averaged Navier-Stokes (RANS) equations coupled with the Volume of Fluid (VOF) method to model the water-air interface. The computational domain was constructed based on the specific area Digital Elevation Model (DEM), accurately capturing the river's morphology, with a structured mesh refined near the riverbed to resolve localized velocity gradients. The simulations focused on high-density water flows induced by extreme precipitation, analyzing key parameters, including velocity distribution, turbulence intensity, and effective viscosity, to evaluate the performance of turbulence models in replicating fluvial dynamics. Validation was achieved using velocity data derived from video footage of the storm, tracked via motion analysis techniques and compared against simulation outputs to ensure accuracy.

The comparative study included the Spalart-Allmaras, standard k-ε, realizable k-ε, and standard k-ω turbulence models. A sensitivity analysis and mesh independence verification ensured robust numerical predictions validated against field data obtained from video-derived velocity measurements.

Findings reveal distinct model performance under varying turbulence conditions. The realizable k-ε model captured peak effective viscosity (μ_eff) values of up to 820 kg/m·s at low turbulence intensities, demonstrating its suitability for flows with strong energy gradients and lower dissipation rates. Conversely, the standard k-ω model excelled under high turbulence intensity, effectively resolving dissipation dynamics and exhibiting μ_eff ​ values between 150–500 kg/m·s. These results highlight the capacity of these models to represent different aspects of riverine hydrodynamics, although neither achieved full optimization across all conditions.

Velocity profiles showed significant gradients near the riverbed, where high shear stress and energy dissipation dominated, reinforcing the importance of mesh refinement in capturing localized effects. Turbulence intensity exhibited a sharp decrease in shallow areas and near structural boundaries, directly influencing μ_eff ​ distributions.

While the evaluated turbulence models provided reliable frameworks for simulating complex fluvial flows, further refinements are needed. Incorporating advanced turbulence models, such as Reynolds Stress Models (RSM) or Large Eddy Simulations (LES), could enhance predictions, particularly for cases involving sediment transport and fluid-structure interactions.

This study contributes to the development of robust methodologies for river modeling under extreme conditions, with practical implications for flood management, hydraulic structure design, and sediment transport assessments. Future research should explore the performance of these models in simulating freshwater flows, assess their application under varying sediment concentrations, and investigate their capability to account for fluid-structure interactions related to bridge columns and other critical infrastructure.

How to cite: Bonasia, R., De la Cruz-Ávila, M., Barrios Piña, H. A., and Castillo Guerrero, F. J.: Three-Dimensional Numerical Modeling of a River Section under Extreme Discharge Conditions from a Tropical Storm: The Santa Catarina River Case Study, Mexico, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4693, https://doi.org/10.5194/egusphere-egu25-4693, 2025.

On 1 November 2000, an intense rainfall event triggered a catastrophic debris flow in the Dacukeng Creek region of Ruifang Township in Taiwan, resulting in seven fatalities, one missing person, and extensive damage to residential structures and farmland. This disaster underscored the critical need for integrated debris flow mitigation strategies and rigorous engineering interventions within a comprehensive regional disaster prevention framework. In response, the present study developed a multifaceted approach combining high-resolution UAV-based terrain mapping, advanced numerical modeling, and immersive virtual reality (VR) simulations to quantitatively characterize debris flow dynamics and facilitate stakeholder engagement in risk assessment and mitigation planning. First, unmanned aerial vehicles (UAVs) were utilized to capture high-precision topographic data, which were processed with ContextCapture to generate a detailed 3D photogrammetric model. Next, FLO-2D simulations were employed to approximate debris flow rheology, analyzing flow depth, velocity, and inundation extents under various rainfall intensities. The resulting data were subsequently imported into Blender to create dynamic 3D visualizations illustrating potential flow pathways and associated hazards. Finally, a VR-based debris flow mitigation platform was constructed in Unity, featuring six degrees of freedom for user movement and interactivity. This platform enables engineers, policymakers, and community stakeholders to virtually navigate realistic hazard scenarios and evaluate the efficacy and cost-effectiveness of different structural and non-structural mitigation measures. By merging cutting-edge computational modeling with immersive visualization, the proposed framework allows for enhanced comprehension of debris flow mechanisms, fosters more productive communication among diverse stakeholders, and supports evidence-based policymaking. The real-time and interactive nature of the VR environment promotes deeper public engagement, improves collaborative planning, and ultimately strengthens regional resilience against debris flow hazards.

How to cite: Tsai, Y.-F., Gao, C., Wei, H.-Y., and Yang, M.-C.: Application of Virtual Reality in Debris Flow Control Engineering Planning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4951, https://doi.org/10.5194/egusphere-egu25-4951, 2025.

The fatal rock avalanche type landslide occurred in the northern part of the village Nergeeti (Imereti region) on February 7, 2024, which destroyed private houses, damaged a road, water supply, gas pipelines and different infrastructure objects, moreover, 9 persons lost their lives. The study area is located in the Khanistskali river valley and tectonically represents a frontal part of the Adjara-Trialeti fold-and-thrust Belt. Here, it is represented the data based on a detailed field investigation conducted to characterize the landslide body and identify its parameters (using a UAV). Slope is represented by the Middle Eocene (Zekari suite) volcanic and sedimentary rocks such as - tuffs, volcanic sandstones, volcanic breccias, and clays. These sediments are overlaid by the Quaternary diluvium-colluvium deposits. According to the local meteorological station, the total amount of precipitation during February 5-7 was 81 mm, which represents 46% of the entire month’s precipitation, generally. The AMSL of a main scarp and a base of the landslide body varies from 378 to 215 meters. Based on a DTM and field investigations, the total area of the landslide mass is 4.45 ha, while the height of a main scarp reaches up to 30 meters. The width in the upper part is 45-50 meters, while in the lower parts, it widens up to 140-160 meters. Moreover, nearby living 7 families were recommended to be moved to a low-risk area by the specialists of the Department of Geology of the National Environmental Agency. Event once again clearly shows the importance of integrating and advancing interdisciplinary methods in studying geohazards in a rapidly changing environment.

How to cite: Gaprindashvili, G., Gaprindashvili, M., Giorgadze, A., and Kurtsikidze, O.: Geologic and morphologic characteristics of Nergeeti landslide, Imereti, Georgia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5029, https://doi.org/10.5194/egusphere-egu25-5029, 2025.

Landslide risk is the product of landslide hazards, exposure, and vulnerability. Spatial and temporal variations in risk and its three components of rainfall-triggered landslides were examined on multiple scales in Japan. Landslide fatalities in Japan decreased between the 1940s and the 1990s. The factors affecting the decrease changed the decrease in household members, increase in people evacuated, and change in the structure of houses to the increase in forest maturity and implementation of structural measures. Similar trends were also found in Kure City with three destructive landslide events in 1945, 1967, and 2018. However, the timing of the main contributions was different from that in Japan overall. In Japan, landslide frequency (i.e., landslide hazards) also decreased with time. Based on a model estimating landslide frequency from the forest age components and rainfall, a larger contribution of the increase in forest maturity to landslide frequency than rainfall was demonstrated on the national scale. Factors determining the number of landslide disasters were examined using generalized linear models on prefectural scales. The factor differed among the three landslide types (i.e., steep-slope failure, deep-seated landslide, and debris flow). For all types, rainfall and the number of landslide-prone areas were selected with positive coefficients: the accretionary complexes geological type with negative coefficients. In addition, forests and land for buildings were selected for steep-slope failures with negative and positive coefficients, respectively, which were not selected for deep-seated landslides and debris flows. The historical and future populations in landslide-affected areas (i.e., landslide exposure) were examined in all municipalities of Japan. The population in the landslide-affected areas continuously decreased during the analysis period. The decrease was gentler than those in landslide risk, hazards, and vulnerability, suggesting that the effects of landslide exposure on temporal changes in landslide risk were less than those of landslide hazards and vulnerability, on the national scale. Finally, the mortality rate in collapsed-houses by landslides was examined from 2014 to 2027. The database for victims and survivors in collapsed houses was developed mainly based on newspapers. The floor number, gender, and type of trigger affected the mortality of landslides. These evaluations can be used to develop strategies for the mitigation of landslide disasters.

How to cite: Shinohara, Y.: Risk evaluation of rainfall-triggered landslides on multiple scales of Japan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7494, https://doi.org/10.5194/egusphere-egu25-7494, 2025.

Glacial debris flows are prevalent across the southeastern Tibetan Plateau, driven by climate change-induced glacier retreat in this region. This retreat has facilitated an increased frequency of debris flow events, underscoring the need for a comprehensive understanding of their susceptibility to enhance hazard mitigation strategies. However, significant gaps remain in integrating climate change projections and glacier retreat dynamics into susceptibility assessments. This study presents a novel method for predicting the susceptibility of glacial debris flows under future climate change scenarios on the southeastern Tibetan Plateau. The proposed approach incorporates dynamic variables into susceptibility modeling, including annual precipitation, average annual temperature, projected glacier extents, and anticipated land cover changes. The analysis utilizes combined scenarios from Representative Concentration Pathways (RCPs) and Shared Socioeconomic Pathways (SSPs), specifically SSP1-2.6, SSP2-4.5, and SSP5-8.5, to evaluate the impacts of future climate conditions. Results indicate a notable increase in the number of glacier catchments with very high annual average temperatures from SSP1-2.6 to SSP5-8.5, particularly in the eastern portion of the study area, while annual precipitation exhibits minimal change. Land cover projections for 2030 suggest a shift from shrubland to bare land, signaling land degradation. Additionally, glacier retreat is evident, with a growing number of catchments projected to have a glacier area percentage below 0.05% by 2030. The susceptibility analysis reveals an increase in glacier catchments with high and very high susceptibility from SSP1-2.6 to SSP5-8.5. Notably, the number of catchments with very high susceptibility under SSP5-8.5 exceeds that of 2010 and closely resembles 2020 levels. These findings emphasize the escalating risks posed by climate change and glacier retreat, providing critical insights for developing adaptive hazard mitigation strategies in the region. 

How to cite: Zhao, F., Gong, W., Biachini, S., and Tang, Y.: Dynamic susceptibility assessment of glacial debris flows on the southeastern Tibetan Plateau under future climate change scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7529, https://doi.org/10.5194/egusphere-egu25-7529, 2025.

Collection of data from landslides monitoring is crucial for a sustainable risk management. With this aim, the integrated monitoring systems combining in situ and remote sensing techniques provide a comprehensive understanding of landslide activity. One of the tasks of the Innovation Ecosystem "Tech4You - Technologies for Climate Change Adaptation and Quality of Life Improvement" focuses on analysing case studies to compare different landslide types, their associated monitoring networks and the displacements entity.

A key objective is to create a catalogue of displacements for typifying landslides. To achieve this goal, a comprehensive literature review was conducted. Only landslides with displacement data over time were considered. The catalogue records the landslide type, location, monitoring system, sensor type, installation year, monitoring period, and main dimensions.

A notable challenge in this research was the limited availability of raw displacement data. Many studies present monitoring results in graphical form, often as images, making numerical data extraction difficult. To overcome this, software tools and artificial intelligence (AI) methods have been employed to analyse graph images and extract numerical values. However, AI often encounters limitations in accurately interpreting and extracting numerical values from diverse graph formats. While AI offers rapid initial analyses, the use of dedicated software guarantees precision in data extraction. The combined workflow of inspection, validation, and software application ensures reliable outcomes, making the process more efficient than manual or traditional methods.

The catalogue now includes more than 60 classified landslides, and research on new case studies is always ongoing. For this reason, and to overcome the limitation of the reduce number of studies with associated data, this work serves as encouragement to increase the number of cases registered in the database.

A specialized digital tool will be developed to integrate in a general platform and utilize collected landslide displacement data. This platform aims to: i) support local and national public institutions, ii) facilitate widespread access to and utilization of the data for monitoring and mitigating landslide risk, and iii) assist in the identification and classification of landslides with characteristics similar to those catalogued in the database.

ACKNOWLEDGEMENTS

This work was funded by the Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of Innovation Ecosystems', building 'Territorial R&D Leaders' (Directorial Decree n. 2021/3277) - project Tech4You – Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.

How to cite: Vennari, C., Coscarelli, R., and Gullà, G.:  Populating a catalogue with displacement vs. time data: a tool for typifing landslides kinematic and a support for sustainable risk management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8988, https://doi.org/10.5194/egusphere-egu25-8988, 2025.

Characterized by sudden occurrence, high velocity and long runout distance, flow-like landslides pose great threats to human communities. In essence, flow-like landslides can be regarded as the flow of granular materials under different topographic conditions, driven by external triggers or internal state changes. During the movement of landslides, the motion behavior transitions from a solid-like state to a fluid-like state, finally resulting in its extreme mobility. Based on the granular flow physics, we investigate the dynamic process of flow-like landslides from a rheological perspective, thereby exploring the motion transition from a solid-like state to a fluid-like state and its hypermobility feature. We utilize an elastic viscoplastic constitutive model to capture the changes in the motion behavior of landslides during their movement. This model accounts for both the elastic response of the material under low-strain conditions and the viscoplastic behavior under large strains, and incorporates both stress and strain rate dependencies, which help in describing the progressive transition from a solid-like deformation to a fluid- like flow. For practice, numerical analyses of column collapse are conducted using the Material Point Method (MPM), a numerical technique well-suited for simulating large deformations. Moreover, a typical flow-like landslide in China, the Luanshibao landslide, is well studied to investigate its long runout mechanism.

How to cite: Tang, X., He, S., Zhu, L., Zhang, H., Jaboyedoff, M., and Huo, Z.: Runout Mechanism of Flow-like Landslides Based on Granular Flow Physics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9062, https://doi.org/10.5194/egusphere-egu25-9062, 2025.

This short paper presents preliminary results ofstudy aimed at evaluating the effects of tree cutting as a predisposing factor of debris-flow triggering (Lepri et al., 2024). The study area (Nottoria, Perugia, Italy) was affected by debris flow events in 2012 and in 2015.

The material of the debris flow source area is classified as calcareous pebbles in a marly-clayey matrix, with very angular grains.

Woody beeches and oaks’ roots, with diameters varying between 0.5 and 2 mm were found in the retrieved soil samples.

In-situ investigations on the material involved in the debris flows, consisting of corkscrew tests, water content and suction monitoring, lidar drone is in progress, jointly with geotechnical laboratory experiments.

In this abstract we present the results of corkscrew tests.

The equipment presents a rotating arm at the end of which there is a load cell and a steel screw (Figure 1).

Figure 1. Corkscrew equipment.

The screw has a height H₄ = 125 mm, a diameter d_cs = 40 mm, a helix diameter = 6 mm and an helix pitch of 28 mm.

The peak strength was recorded using a 300 kg load cell (Steinberg systems – SBK-KW-300KG).

The corkscrew was driven into the ground by manual rotation, after which the load cell is connected, and the soil sample is pulled out by using a lever system. The load cell provides the pullout force T_max.

The shear stress along the lateral surface of the soil sample is then calculated following equation (1) provided by Meijer et al. (2018):

                                                                                         (1)

Corkscrew tests were performed at increasing depths (0–125, 125–250, 250–375 mm). Once the soil sample was extracted, the roots content was assessed and the water content and suction measured.

Figure 2 shows the location where corkscrew tests were performed, while the results are plotted in Figure 3 in terms of peak shear stress against the horizontal effective stress.

Figure 2. Corkscrew tests location

• a)    b)

Figure 3. a) Extracted rooted sample; b) Results from corkscrew tests: shear stress vs vertical effective stress

References

Lepri, A., Fraccica, A., Cencetti, C., and Cecconi, M. (2024a). A preliminary study on the possible effect of deforestation in debris flows deposits, EGU24-15726, Vienna, Austria, 14–19 Apr 2024.

Lepri A., Fraccica A., Cecconi M., Pane V. (2024b). Effetti del taglio di vegetazione sull'innesco di una colata detritica a Nottoria (PG): caratterizzazione geotecnica preliminare. Incontro Annuale dei Ricercatori di Geotecnica 2024- IARG 2024 - Gaeta, 4-6 Settembre 2024.

Meijer, G.J., Bengough, A.G., Knappett, J.A., Loades, K.W., Nicoll, B.C. (2018). In situ measurement of root-reinforcement using the corkscrew extraction method. Can. Geotech. J. 55 (10), 1372–1390. (https://doi.org/10.1139/cgj-2017-0344).

How to cite: Lepri, A.: Preliminary results of in situ corkscrew tests in coarse-grained debris with vegetation roots , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9090, https://doi.org/10.5194/egusphere-egu25-9090, 2025.

Rockfall modelling in Caminito del Rey (Málaga, Spain) represents a scientific and technical challenge due to the high geomorphological complexity of the environment, characterized by vertical cliffs, numerous overhangs, and complex geometries. In this context, within one of Malaga’s most visited tourist attractions (more than 300,000 people per year), a comprehensive study was required to address challenges across all phases, from the detailed characterization of the inventory to trajectory modelling. To address these difficulties, the most advanced technology currently available for remote data adquisitation (UAV, LIDAR and satellite) and three-dimensional modelling was used, along with the development and application of ad hoc methods and techniques specifically tailored to this study.

The high-precision georeferenced digital rockfall inventory had to tackle issues such as data heterogeneity, limitations in the available documentation, and errors related to mapping accuracy of the trail layout. On the other hand, the modelling process required a multiscale approach, examining all sections of Caminito del Rey with a focus on detailed scales for individual blocks. Custom input data were obtained for this purpose: (i) elevation models accounting for overhangs and both gorge walls; (ii) source areas for rockfalls derived using probabilistic approaches; (iii) block size estimation based on lithology type; and (iv) calibration and validation of the three coefficients maps in narrow and vertical sections (i.e., dynamic rolling friction, normal energy restitution, and tangential energy restitution) that simulate energy loss by a boulder when rolling and bouncing at impact points.

Reducing uncertainty in each input dataset is essential not only for improving the reliability and accuracy of analytical models but also for effectively establishing preventive measures. Furthermore, it plays a key role in identifying critical areas that require continuous monitoring. This abstract was supported by the KINGSTONE project, the Rockfall Susceptibility Study in Caminito del Rey (a collaboration among IGME-CSIC, the University of Granada, the University of Jaén, and the Caminito del Rey UTE), and the SARAI project (PID2020-116540RB-C21), funded by MCIN/AEI/10.13039/501100011033.

How to cite: Sarro, R., Galve, J. P., Martínez-Corbella, M., Fernández-Naranjo, F. J., Miranda-García, P. V., López-Vinielles, J., Jerez-Longres, P. S., Ruiz-Fuentes, A., Béjar-Pizarro, M., Guardiola-Albert, C., Azañón, J. M., and Mateos, R. M.: Challenges in rockfall modelling in active tourism gorges: The case study of Caminito del Rey (Malaga, Spain), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9241, https://doi.org/10.5194/egusphere-egu25-9241, 2025.

The Akatani landslide, located on the Kii Peninsula of Japan, is a catastrophic deep-seated landslide triggered by intense rainfall during Typhoon Talas in 2011. The landslide mass travels a considerable distance, forming a landslide dam at the slope foot. Its instability is primarily attributed to the rapid reduction of shear strength in sandstone–mudstone (shale) materials and elevated pore water pressure. In this study, a fully three-dimensional physical model based on the Material Point Method (MPM) is applied for the first time to investigate the Akatani landslide. By employing the high-performance solver MaterialPointSolver.jl, an advanced numerical simulation is conducted, integrating geotechnical parameters from ring shear tests, pore pressure characteristics, and field-based geological and topographical data. The proposed model effectively replicates the rainfall-triggered reactivation of the landslide along pre-existing sliding surfaces identified through the Sloping Local Base Level (SLBL) ^{[1, 2]}. It captures the failure process, from initial instability to rapid downslope movement and channel blockage, under a coupled solid–fluid framework. Comparisons with field observations and previous LS-Rapid simulations demonstrate the high accuracy and applicability of this modeling approach. These findings provide essential insights for understanding the dynamic mechanisms of deep-seated rainfall-induced landslides, evaluating secondary disaster risks, and developing effective disaster mitigation strategies.

References

[1]. Chigira, M., Tsou, C. Y., Matsushi, Y., Hiraishi, N., & Matsuzawa, M. (2013). Topographic precursors and geological structures of deep-seated catastrophic landslides caused by Typhoon Talas. Geomorphology, 201, 479-493.

[2]. Jaboyedoff, M., Chigira, M., Arai, N., Derron, M. H., Rudaz, B., & Tsou, C. Y. (2019). Testing a failure surface prediction and deposit reconstruction method for a landslide cluster that occurred during Typhoon Talas (Japan). Earth Surface Dynamics, 7(2), 439-458.

How to cite: Huo, Z., Tang, X., Jaboyedoff, M., Podladchikov, Y., and Chigira, M.: High-Resolution 3D MPM Simulation of the 2011 Akatani Landslide, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9926, https://doi.org/10.5194/egusphere-egu25-9926, 2025.

This short communication presents a new low-cost capacitive Soil Water Content (SWC) sensor, originally developed, whose application in situ in natural rooted soils could be of some interest for its impact in geotechnical engineering applications. It is very well known that in recent years, significant advancement has been made in laboratory and field testing for the understanding of the hydro-mechanical coupled behaviour of unsaturated soils. The complexity in characterizing such behaviour increases when the role of vegetation and the presence of organic matter is considered. The amount of literature on water content (SWC) measurements and related sensors is huge and involves several scientific fields. Among indirect methods to evaluate the SWC, time domain reflectometer (TDR), time domain transmissometer (TDT) and impedance sensors, such as resistive and capacitive, are the most common. Capacitive sensors are usually directly dependent on soil apparent dielectric constant K_a which increases with SWC. They have a little sensitivity compared to TDR/TDT, however, they find several applications due to their lower cost. Vegetation affects the hydrology and the effects of plant evapotranspiration may induce some changes in the water content and soil suction and therefore the soil water retention properties. The mutual interaction among roots and soils is very variable, depending on roots-type and soil type; the beneficial influence due to the reduction of water content/degree of saturation, due to the capacity of the plant system to absorb water from the surrounding soil and transfer it to the atmosphere through transpiration is also acknowledged in the literature. Therefore, quantifying root-induced modification in soil hydraulic properties, including SWRC, is vital to predict correctly the hydrology and, hence, for the analysis of slope stability of shallow soil covers. In this note, a new low-cost capacitive sensor, characterized by an interdigit layout and produced following a PCB process, is introduced (Figure 1).

The performance of this device are under evaluation with laboratory activities: several tests have been performed preparing samples of different-type granular materials at different SWC keeping constant the dry density: natural sandy soils, glass beads, and ground coffee mixtures were investigated. The electrical capacitance and conductance of the sensor were measured in the 10 – 100 kHz frequency range by using the HP 4275A LCR meter. Some results are shown in Figure 2. It is shown that the sensor response is affected by the measurement frequency. Moreover, a saturation behaviour is highlighted for both the capacitance and conductance at increasing SWC. The sensor impedance is affected also by the electrical conductivity of the medium surrounding the sensor, e.g. solid grains, water and organic materials, and for this reason the SWC estimation requires a correction to minimize the impact of water salinity. The experimental activity performed in the laboratory is a preliminary investigation aimed at identifying an analytical model of the electrical behaviour of the sensor. Once the model is defined, the sensor could be integrated with a portable system to be validated for in-situ applications.

How to cite: Papini, N.: A new low-cost and low-power capacitive sensor for soil water content measurements: preliminary analysis for possible application in rooted soils, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12296, https://doi.org/10.5194/egusphere-egu25-12296, 2025.

Being a natural calamity, flood poses serious threat to the livelihood of all living beings. Due to the adverse effects of climate change and anthropogenic activities, significant changes in the occurrence of extreme floods are happening day-by-day. An accurate flood susceptibility map plays a crucial role to adopt proper adaptation and mitigation strategies in protecting the vulnerable communities. This study performs a flood susceptibility mapping of the Jagatsinghpur district lying in the delta region of the Mahanadi River basin in the eastern part of India. This river basin has suffered from numerous recurring floods of variable extremities since the 1960s. A major concern arose when the frequency of extreme floods in this delta increased drastically post the 2000s. This study considered several key factors affecting flood occurrence like rainfall, topographic wetness index, land use/land cover, distance from river, elevation, slope and drainage density. The map layers of all these factors are integrated in the Geographic Information System (GIS) platform, wherein the Analytical Hierarchy Process (AHP) is used to develop and evaluate the flood susceptibility maps. The findings suggest that more than one-third of the study area falls into the low to high flood susceptibility zone. Nearly 40% of the area is under very low to low zone, and a small portion fell under the high to very high flood prone zone. The study serves as a preliminary study towards flood risk management and provides critical insights for the decision makers to develop appropriate disaster risk reduction strategies and strengthen the flood management policies.

How to cite: Khatun, A., Baruah, S., and Chatterjee, C.: Assessing Flood Susceptibility using Geospatial Techniques and Analytical Hierarchy Process in an Indian Catchment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12404, https://doi.org/10.5194/egusphere-egu25-12404, 2025.

Accurate spatial distribution of rainfall during extreme weather events is crucial for hydrological analysis and flood forecasting. Despite the availability of numerous neural network-based models for spatiotemporal rainfall interpolation, challenges remain due to the limited number of rain gauges and the presence of missing values in the recorded data. These limitations introduce significant uncertainties into existing models. This study focuses on the Ijzer Basin in Belgium, using 20 years of data collected at 15-minute intervals, including rainfall, humidity, and temperature measurements et. etc. By training several neural network models on these data, we aim to identify the most accurate model for rainfall interpolation. Results indicate that Long Short-Term Memory (LSTM) networks demonstrate superior performance compared to other models in capturing the spatial distribution of rainfall.

How to cite: Xiao, W.: Rainfall Interpolation Analysis in the Ijzer Basin Based on Neural Networks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12497, https://doi.org/10.5194/egusphere-egu25-12497, 2025.

The Himalayan region, characterized by its rugged terrain, distinctive geography, and active tectonics, ranks among the most landslide-prone zones globally. Landslide susceptibility and hazard mapping are critical tools to mitigate future risks and devise effective management strategies. This study uses data-driven statistical approaches to evaluate co-seismic landslide susceptibility in District Hattian, NW Himalayas, Pakistan. A comprehensive co-seismic landslide inventory comprising 349, 393, and 735 landslide events from 2005, 2007, and 2012, respectively, was utilized to train, test and validate predictive models. 
Thirteen landslide causative factors (LCFs), including topographic, environmental, geologic, and anthropogenic variables, were analyzed to determine their influence on landslide occurrence. Three data-driven statistical models i.e., Weight of Evidence (WoE), Information Value (IV), and Frequency Ratio (FR) were employed to develop landslide susceptibility maps (LSMs). Model training used 70% of the landslide inventory, while 30% was reserved for validation. Model performance was evaluated using Receiver Operating Characteristic-Area Under Curve (ROC-AUC) metrics and predictive accuracy assessments. Among the models, the WoE approach outperformed well among the other models as ROC-AUC SRC scores of 84.4, 84.2, and 85.3 for 2005, 90.4, 86.4, and 87.2 for 2007, and 81.9, 86.7, and 85.9 for 2012 for WoE, FR, and IV models, respectively. PRC scores of the WoE, FR, and IV models were recorded as 85.7, 89.4, and 82.5 for 2005, 87.5, 77.5, and 80.4 for 2007, and 80.7, 88.3, and 87.7 for 2012. For the validation of long-term predictivity, efficiency models are checked by comparing the generated LSMs with newly recorded landslide events. The 2005 model was validated using 2007 data, the 2007 model with 2012 data, and the 2012 model with 2024 data. Results revealed a gradual decline in the predictive accuracy of the LSMs model of all three approaches over time; however, WoE consistently outperformed from the IV and FR models, maintaining robust predictive capabilities even after 12 years.
This study highlights that landslide-prone zones in District Hattian exhibit persistent mass movement activity and underscores the urgent need for proactive landslide management to minimize life loss and economic damage in this tectonically active region. The integration of advanced susceptibility modelling techniques with real-time validation offers a reliable framework for hazard assessment and risk mitigation. Policymakers and stakeholders are encouraged to implement targeted interventions, such as optimized land-use planning, the establishment of early warning systems, and increased community awareness programs, to enhance resilience against landslide hazards in the NW Himalayas.

How to cite: Riaz, M. T., Wani, S., Basharat, M., Riaz, M. T., and Manocha, A. R.: Evaluating the Efficiency and Predictive Accuracy of Temporal Susceptibility Models for Co-Seismic Landslides Using Real-Time Validation: A Case Study from the NW Himalayas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13226, https://doi.org/10.5194/egusphere-egu25-13226, 2025.

Drought is an increasing hydroclimatic threat in the Mediterranean, profoundly impacting water resources and ecosystems. Andalusia (Spain) is highly vulnerable due to climatic variability and prolonged dry periods. Effective drought management requires methods to assess impacts on groundwater and surface water systems, which in turn threaten ecological and socio-economic resilience. While socio-economic impacts are more analysed, environmental effects are overlooked due to delayed onset or unclear links to drought. However, drought-induced degradation of natural resources and hydrology-linked ecosystem services can exacerbate challenges in agroforestry, livestock, and tourism. Examining the environmental dimensions of hydrological drought risk is therefore essential.

This research takes a first step in analysing the impacts of drought on water-related ecosystem services. It specifically investigates hydrological and hydrogeological anomalies and examines their spatial and temporal dynamics across varying levels of drought severity. This study defines hydrological anomalies by leveraging high-resolution, open-access data from Copernicus and other datasets available on Google Earth Engine. These include estimates of soil moisture, groundwater storage, terrestrial water storage, flows and evapotranspiration that can be obtained from GLDAS 2.2, FLDAS, CERRA-Land, etc. In situ measurements, such as piezometric and streamflow records, are also integrated to validate findings and provide a robust basis for analysis of the impacts on water systems. Machine learning algorithms are then used to model the complex linkages between the identified hydrological anomalies and the climatic conditions, measured with well-known drought indices like the Standardized Precipitation-Evapotranspiration Index (SPEI) at different scales.

A pilot study in an Andalusian sub-basin with minimal anthropogenic influence serves as a testbed for developing a scalable methodology to evaluate the impacts of short and long-term drought conditions on groundwater and surface water. In line with related relevant research, correlation analyses run for this pilot highlight strong associations between hydrological variables and drought indices. A rapid response of surface water systems to short-term droughts is observed, while groundwater displays delayed, yet significant changes linked to drought, reflecting its buffering capacity and resilience.

This research highlights the potential of tested datasets for assessing drought impacts on water systems and demonstrates the value of open-source hydrological data for improving drought risk assessment and predictive tools. However, the study also reveals limitations regarding spatial resolution, which constrain detailed-scale assessments. On the one hand, the follow-up research will expand the performed analysis to additional sub-basins across Andalusia to compare results. On the other hand, similar modelling methodologies will be applied to understand how the identified droughts and associated anomalies in surface and groundwater systems propagate, leading to a reduction in the provision of ecosystem services. This will include exploring ecological impacts such as failures to maintain ecological flows, declines in extension of wetlands, or anomalies in primary productivity and ecosystem functioning in natural areas.

How to cite: Serrano Acebedo, P., Limones Rodríguez, N., and Aguilar Alba, M.: Unveiling environmental dimensions of hydrological drought in Southern Spain using open-source data and machine learning techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13798, https://doi.org/10.5194/egusphere-egu25-13798, 2025.

Within minutes of any major global earthquake, the U.S. Geological Survey (USGS) Ground Failure product (GFP) provides summary alert levels and spatial estimates of landslide and liquefaction hazard and population exposure. Since the GFP went live in September 2018, 187 events have had an elevated alert level (yellow, orange, or red), indicating limited to extensive hazard and exposure. These events include well-known ground-failure triggering earthquakes such as the 2023 Türkiye-Syria earthquake sequence, the 2021 Nippes, Haiti earthquake, as well as numerous other events. In many cases, the GFP proved to be valuable by estimating the potential extent of these hazards and their overlap with the local population.  In this presentation, we discuss how the product has performed since it was deployed and how it has been used for situational awareness, planning, and reconnaissance. Significant users of the GFP include scientists, the media, emergency responders, and the public. We also discuss operational considerations, such as how moving the GFP to the cloud has improved speed and reliability. We conclude with an overview of enhancements under development, such as model regionalization, road obstruction estimation, fatality estimation, ongoing hazard information, model updating, and integration into other USGS impact products, such as Prompt Assessment of Global Earthquakes for Response (PAGER) and ShakeCast.

How to cite: Allstadt, K. E., Thompson, E. M., Wald, D. J., Hunsinger, H. E., Haynie, K. L., Hearne, M., Bürgi, P. M., Ellison, S. M., Engler, D. T., Jaiswal, K. S., Marano, K., and Lin, K.: Performance and Future Directions of the USGS Near-real-time Earthquake-triggered Ground Failure Product, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13829, https://doi.org/10.5194/egusphere-egu25-13829, 2025.

As stated in The IPCC Sixth Assessment Report, heavy rainfall events of unprecedented scale have occurred in recent years increasingly in terms of both frequency and intensity because of global climate change. As a matter of course, greater attention must be devoted to flooding caused by heavier-than-ever rainfall events. This flooding includes both levee breach and inland water rise effects.

In Japan, T-year hydrological events, such as 100-year-rainfall events with a return period of 100 years as estimated from frequency analysis, have been used conventionally as targets of river improvement plans. In fact, "Guidelines for Small and Medium-Sized River Planning” have been consulted when hydrological quantities are estimated. Nevertheless, the flow chart in the guideline drawn by the MLIT (*) has been discounted completely in work by Kuzuha et al. (2021, 2022a,b,c). In fact, it is most inappropriate to use the SLSC as the criterion for validating stochastic models; it is also inappropriate for usage of the Jack-knife or bootstrap method. Mizuki and Kuzuha (2023) present related supporting details.

As described in this paper, we intend to present other issues which must be urgently resolved: The fact that the precipitation population has not been stationary. It must be regarded as non-stationary because of global climate change.

Explanations of frequency analysis based on the non-stationarity of the precipitation population have been presented in the literature by Hayashi et al. (2015) and by Shimizu et al. (2018). We have considered different approaches than theirs. Ours predict future T-year hydrological events under the condition of non-stationary precipitation population, as presented below. In other words, those approaches can be adapted to recent quite heavier rainfall data.

• We use d4PDF data (2015) data. In fact, d4PDF data were calculated using climate simulations of 50 ensemble members. Each ensemble member has climate data obtained during 1951–2010: we can use annual maximum rainfall of 3,000 years. We specifically examined the area around Kumano city, Mie prefecture and analyzed the annual maximum around Kumano.
• First, we calculated the annual maximum 1-hour precipitation at Kumano described above.
• For example, there are 50 annual maximum 1-hour precipitation events in 1951, because there are 50 ensemble members. Therefore, we can estimate 100-year rainfall in 1951 using 50 data and the Gumbel distribution. We can estimate time-variational 100-year rainfall during 1951 and 2010.
• The blue line in the figure shows the time variational 100-year rainfall between 1951 and 2010.
• The orange line represents future 100-year rainfall calculated using the triple exponential smoothing method.

At the presentation, we intend to show other approaches which can be useful to predict future 100-year precipitation.

* MLIT: The Ministry of Land, Infrastructure, Transport and Tourism, Japan

How to cite: Ohashi, R., Mizuki, C., and Kuzuha, Y.: Estimation approach for T-year hydrological events using non-stationary data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14551, https://doi.org/10.5194/egusphere-egu25-14551, 2025.

On the 30th of November 2022, a major rockfall event occurred in the Triassic dolostones of Castrocucco cliff (Maratea, Southern Italy), mobilising a volume of about 8000 m³ (Minervino Amodio et al. 2024) and destroying the underlying SS18 national road with no fatalities. The SS18 has critical importance in an area of high tourist, landscape, and historical interests, and determined the planning of a bypass tunnel to avoid the cliff, which has been affected by recurring instability events in the last decades (Pellicani et al. 2016). However, before the tunnel could be completed, the safe reopening of the road was critical for the region. For this reason, a high-resolution monitoring system was developed, enabling the timely road closure to the traffic in case of new failure (Santo and Massaro 2024).

In this study, we describe the geo-structural investigation and reconstruction of the rockfall kinematics and triggering factors, as well as the susceptibility analysis carried out to develop the monitoring system that allowed the road to reopen. Such a system consisted of a network of sensors placed in the areas and on the rock blocks that showed high levels of susceptibility to rockfalls. The data collection was performed through field and digital surveys. The latter was carried out on Virtual Outcrop Models (VOM) following drone photo acquisition. Successively, the rock block trajectories were simulated under static and seismically induced conditions with different block volume scenarios. These results, integrated with the real-time deformation data recorded by the sensors, will enhance the mitigation plan further. Moreover, the developed methodological approach and workflow could be applied to similar situations where critical road infrastructures lie in areas of high susceptibility to rockfall.

Minervino Amodio A, Corrado G, Gallo IG, Gioia D, Schiattarella M, Vitale V and Robustelli G (2024) Three-dimensional rockslide analysis using unmanned aerial vehicle and lidar: The Castrocucco case study, Southern Italy. Remote Sensing, 16 (12), 2235. doi: 10.3390/rs16122235

Pellicani R, Spilotro G and Van Westen CJ (2016) Rockfall trajectory modeling combined with heuristic analysis for assessing the rockfall hazard along the Maratea SS18 coastal road (Basilicata, Southern Italy). Landslides, 13: 985-1003. doi: 10.1007/s10346-015-0665-3

Santo A and Massaro L (2024) Landslide monitoring and maintenance plan along infrastructure: The example of the Maratea major rockfall (Southern Italy). Landslides. doi: 10.1007/s10346-024-02409-3

How to cite: Massaro, L., Falcone, G., Urciuoli, G., and Santo, A.: Rockfall susceptibility and trajectory simulations for enhanced monitoring and early warning systems along roads: the Maratea landslide case study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16734, https://doi.org/10.5194/egusphere-egu25-16734, 2025.

Barak River is one of the highly meandering rivers in India causing several problems to society during flooding events. In this study geomorphological changes of an urban meander loop, situated at the main city of Silchar Assam, India was carried out. Based on the adopted analysis, it was found that meander length, meander width, meander ratio, wavelength showed an increasing trend while sinuosity and radius of curvature shows a decreasing trend.  The land use and land cover were also analyzed of this urban meander loop and found that settlement increased gradually by 16.1798 % and waterbodies, dense vegetation and agricultural land decreased by 0.5732 %, 2.5832 % and 13.1558%, respectively. Autoregressive integrated moving average (ARIMA) model was employed for the prediction and the results recommended that shifting of channel in the urban meander loop fluctuated unexpectedly either to rightwards or leftwards. Observed and predicted values of showed a determination coefficient (R² = 0.8). The final step of the research was to generate the predicted values of channel shifting up to 2030.      

How to cite: Annayat, W., Samantaray, S., and Yaseen, Z. M.: Geomorphological transformation and prediction of urban meander loop: A case study of Barak River, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17185, https://doi.org/10.5194/egusphere-egu25-17185, 2025.

Changing climate is enhancing the occurrence and intensity of natural disasters, profoundly impacting human lives, livelihoods, infrastructure, and economic growth. Modelling and prediction of deadly high-mountain slope failure hazards such as snow, ice, and rock avalanches have always been challenging. Current in-situ sensor-based approaches for slope failure predictions of hanging glaciers and rock faces are quite limited in their spatial continuity and extent and there is also a research gap on linking the pre-collapse slope movements with subsequent avalanche runouts. Earth observation datasets can offer a viable alternative for quantifying and monitoring pre-collapse dynamics at larger spatial scales. For the catastrophic 2021 rock-ice collapse in Chamoli, India, several studies had reported some anomalous movements weeks-to-months prior to the collapse. However, we need more analyses to understand how common such pre-collapse anomalous movements are before we can even start considering investigating them as potential precursors for effective avalanche predictions. To fill this research gap, using satellite remote sensing datasets and digital elevation models, we investigated several high-mountain slope failure events (e.g., Piz Scerscen in 2024, Piz Cengalo Bondo in 2017) of varying magnitudes and nature (i.e., rockfall, rock-ice avalanche, and ice avalanche) in different topographical and climate settings. While we were able to quantify pre-collapse dynamics for these events, we also observed variations in the occurrence and magnitude of anomalous movements prior to the events. These preliminary findings are encouraging and the future research and results from such analyses can bridge the knowledge gap on the detection and modelling capabilities, ultimately enhancing resilience to mountain hazards.

How to cite: Sam, L., Bhardwaj, A., and Temadri, P.: Quantifying pre-collapse dynamics of hanging rock-ice masses using remote sensing datasets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17719, https://doi.org/10.5194/egusphere-egu25-17719, 2025.

Sweden, known for its abundant water resources, has recently experienced drought events with significant socio-economic and environmental impacts, revealing existing vulnerabilities in the society. Future climatic projections indicate changes in precipitation and temperature patterns, stressing the need for improved drought risk management. The vulnerability component of risk is often less studied than the hazard component, primarily due to its inherent complexity. Drought vulnerability is highly context-dependent, shaped by the interplay of social, ecological, and hydroclimatic factors. In the context of a changing climate, assessing drought vulnerability is becoming increasingly important. However, such assessments are scarce in Nordic regions.

To address this gap, this study quantifies vulnerability factors related to coping capacity, adaptive capacity, and susceptibility, and integrates them to map drought vulnerability hotspots across Sweden. Based on a stakeholder-validated set of vulnerability factors for water-dependent sectors (including agriculture, forestry, energy, water supply, and environmental management), municipal-level data sources were screened to identify and quantify relevant vulnerability indicators. A probabilistic approach was employed to assess the sensitivity of regional vulnerability patterns to the weighting of vulnerability factors. The resulting spatial distribution of relative vulnerability reflects the heterogeneous socio-hydrological systems across municipalities and highlights the importance of sustainable local economic adaptation to water availability in reducing sensitivity and mitigating drought impacts. Our vulnerability assessment provides valuable insights for local and regional planners, supporting the effective allocating of resources and the development of targeted drought mitigation strategies at municipal level. The findings underscoring the need for context-specific assessments to account for regional and sectoral differences in drought vulnerability. Furthermore, the results emphasize the complexity of drought risk and the challenges of integrating diverse vulnerability factors in diverse socio-hydrological contexts.

How to cite: Canedo Rosso, C., Stenfors, E., Teutschbein, C., and Nyberg, L.: Drought vulnerability assessment in Sweden, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20010, https://doi.org/10.5194/egusphere-egu25-20010, 2025.

Landslides affect millions of people annually in the mountainous regions of Latin America, resulting in significant economic, human and structural losses (Carrasco et al., 2011). The San José de Aloburo landslide, located in Imbabura-Ecuador, occurred in November 2021, significantly changing the landscape as well as the increase of the substantial damage to the locality. Vásquez et al. (2021) characterized it as a complex rotational landslide, highlighting its geomorphological and stratigraphical particularities. This study aims to integrate geophysical and geological approaches to further analyze the internal structure and physical properties of the materials involved in the landslide.

The methodology included the application of electrical resistivity tomography (ERT) profiles (Perrone, 2014), using low-cost equipment, suitable for the economic context of the region. It allowed to identify variations in the subsurface resistivity. Stratigraphic columns were constructed also to analyze the interlaying and composition of the displaced geological strata. In addition, a granulometric analysis was carried out on a representative sample to evaluate the particle size distribution.

The results reveal significant variations in resistivity associated with the distribution of the displaced materials and the presence of complex internal morphology. Likewise, the integration of geophysical and geological data allowed a more precise delineation of the rupture zone, the depth of displacement and the characteristics of the materials involved. These findings provide valuable information for understanding landslide processes in the region and monitoring this type of events with the additional advantage of being economically accessible.

Keywords: Landslides, Electrical Resistivity Tomography (ERT), Geophysical Integration

References:

Carrasco, J., et al. (2011). Impactos del cambio climático, adaptación y desarrollo en las regiones montañosas de América latina. Ministerio
de Relaciones Exteriores, Gobierno de Chile-Alianza para las Montañas-FAO-Banco Mundial.

Perrone, A., et al. (2014) Electrical resistivity tomography technique for landslide investigation: A review. Earth-Science Reviews, 135 , 65-82.

Vázquez, Y., et al. (2021). Informe técnico sobre el movimiento en masa ocurrido en san José de Aloburo (noviembre/2021), Pimampiro,

Imbabura. Escuela de Ciencias de la Tierra, Energía y Ambiente, Yachay Tech.

How to cite: Mayacela-Salazar, B., Torres-Ramirez, R., and Perez-Roa, R.: Landslide evaluation applying electrical tomography techniques: study case San José de Aloburo, Pimampiro,Imbabura, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20680, https://doi.org/10.5194/egusphere-egu25-20680, 2025.

Roto-translational landslides are characterized by two movement types at different landslide parts, i.e., rotational movement at the headscarp and translational movement at the toe. They are widely distributed in clay formations with planar or subhorizontal layers, posing threats to human life and infrastructure. Due to the different shapes of the sliding surfaces, the kinematics of roto-translational landslides show complicated patterns with varying spatial and temporal distributions. Forecasting the rapid sliding of roto-translational landslides presents challenges, as they often manifest as unnoticed slowly movement. The sliding surfaces of the roto-translational landslides feature concave-upward shape at the landslide head and a planar shape at the landslide accumulation zone, leading to complex deformation mechanisms. Roto-translational landslides usually exhibit creep deformation along sliding surfaces, showing transverse cracks on the ground surfaces. However, the scarcity of experimental data has significantly hindered a deep understanding of their failure mechanisms. Our research probes into the rotational failure phenomena of landslides in stiff clay formations, utilizing geotechnical centrifuge modelling and laboratory creep tests. Our findings reveal that rotational failures in model slopes are exclusively triggered under conditions of an undrained boundary at the basal shear zone. The post-failure behaviour is characterized by a settlement at the slope crest and a pronounced bulge at the toe, resulting in complex rotational movements along the basal sliding surface. Moreover, our laboratory experiments illuminate the creep behaviour of shear-zone materials under undrained conditions. In particular, samples with a high initial water content under sustained loading are highly susceptible to a quick transition into tertiary creep, leading to accelerated failure. These experimental insights substantially advance our understanding of the rotational failure patterns observed in clay-based landslides.

How to cite: Peng, X., Kang, X., and Wu, W.: Centrifuge modelling of a roto-translational landslide in stiff clay formation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21656, https://doi.org/10.5194/egusphere-egu25-21656, 2025.

The seismic risk assessment has gained significant popularity in recent years due to the increasing development of infrastructure and urbanization in seismically active locations across the globe. Earthquakes pose serious issues as natural events because of their unpredictability and the extensive harm they may do to infrastructure, buildings, and people's lives. Ground motion at the time of the earthquake can depend on several local sites and event characteristics, such as the size of the seismic event, the depth of the earthquake focus, the distance from the epicentre, the local geology and soil conditions. However, traditional probabilistic seismic hazards using ergodic ground motion models do not consider these variations, leading to a further less accurate damage or risk assessment. Hence, the present work aims to perform a comprehensive seismic risk assessment by incorporating three-dimensional physics-based numerical modelling, which explicitly incorporates the path and site-specific characteristics that cater for non-ergodicity. Here, physics-based ground motion has been simulated for controlling events corresponding to typical sites present in Shimla city, Himachal Pradesh, India. Furthermore, to assess the associated risk for the region exposure, data of the building inventory of Shimla has been gathered using Google Street View (GSV) images, and for the classification of the building inventory to different building typologies, deep machine learning-based Convolution neural network (CNN) models are trained. The developed CNN model has shown great precision in identifying the building class for the region. After classification, suitable well-known fragility functions are mapped to each class, and subsequent risk is calculated. Finally, the results developed using physics-based hazard are compared with the conventional empirical approach. The study results will provide the respective stakeholders with the technical knowledge for the region's hazard and subsequent risk.

How to cite: Shukla, S. S., Choudhary, R., and Jaya, D.: Seismic risk assessment using 3D physics-based seismic hazard: A case study for Shimla city, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-408, https://doi.org/10.5194/egusphere-egu25-408, 2025.

Protecting human health is a fundamental priority in contemporary society. According to the World Health Organization (WHO) Constitution, "Health is a state of complete physical, mental, and social well-being, and not merely the absence of disease or infirmity."  While the physical health of older adults often receives considerable attention after flooding events, their mental and social well-being remains underexplored. 

The 2021 floods in the Ahr Valley, Germany, had a devastating impact on local communities, particularly on older adults who are more vulnerable to the aftermath of natural disasters. This study explores the perceptions of floods among individuals aged 65 and older, focusing on their mental health and social well-being. Using a mixed-methods approach, we conducted surveys and in-depth interviews to collect first-hand data on their experiences and coping mechanisms. Our findings highlight the multifaceted challenges faced by this population, including heightened psychological distress, disruption of social networks, and concerns over long-term recovery.

This research underscores the need for targeted interventions to address the mental and social health needs of older adults in disaster-affected areas. By enhancing scientific understanding of the complex interplay between natural disasters and public health, the study aims to inform policymakers, healthcare providers, and social workers, ultimately improving the quality and effectiveness of post-disaster health services for older adults.

How to cite: Song, C., Atun, F., Blanford, J., and Anthonj, C.: Perception of the 2021 floods and their mental health, and social well-being among older adults in the Ahr Valley, Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-454, https://doi.org/10.5194/egusphere-egu25-454, 2025.

The Nearest Neighbour declustering technique is utilized to differentiate dependent events, such as aftershocks and foreshocks from independent events, such as isolated and mainshocks events . The estimated background field could either be stationary or non-stationary over time and may exhibit patterns that depend on both space and time. Any residual deviations from a time-stationary and spatiotemporally-independent Poisson point field could offer insights into regional loading processes and merit further investigation (Zaliapin and Ben-Zion, 2020). We apply the adopted technique on the Southern California region, an area that includes four significant events with magnitudes greater than 7, over the years 1981- 2021 and the catalog's completeness ranges between magnitudes 2 to 3 (Zaliapin and Benzion, 2020). For generating the complete background set, both outdegree and closeness centrality yielded nearly identical mainshock node counts for background detection in our study region, highlighting the robustness of these centrality measures.   In a tree network, hierarchy identification might not be straightforward, but utilizing centrality can aid in placing elements accurately. Higher centrality values indicate a simpler structure compared to lower centrality values. Although the traditional highest magnitude method produces results almost similar to those of the centrality measure from network analysis, the network-based approach offers new possibilities for future research in the study of earthquake sequences and their evolution. In a spatially inhomogeneous, temporally homogeneous Poisson process (SITHP), there is a strictly positive probability that two events may occur arbitrarily close to each other  and NN method works better for declustering with this condition (Luen and Stark, 2012). In this study, three temporal statistical tests have been conducted: the Conditional Chi square(CC) test, the Brown-Zhao(BZ) test, and the Kolmogorov smirnov (KS) test on the complete background set. It was found that the KS test, which assumes the time series follows a uniform distribution and does not require any adjusting parameters, is more reliable than the other two tests(requires more tuning constants). For almost all magnitude cut-offs, the temporal tests fail the null hypothesis; however, for a magnitude of 3.4, the temporal test is satisfied, but the space time test ( Luen and Stark test) fails the null hypothesis. For the nearest neighbour (NN) method, the null hypothesis is rejected for all magnitude ranges in our study region. Consequently, it can be concluded that NN declustering is not effective for this dataset or the number of data points is low. Notably, the Luen and Stark space time test yielded a value of 0 for most magnitudes, except for magnitudes 4 and 4.2. This suggests two potential scenarios: either the earthquakes are inadequately declustered, leading to some background events being overlooked or there is another possibility that this model is not fit for the Poisson process and suggesting a need for an alternate conditional model.

References:

Luen, B., & Stark, P. B. (2012). Poisson tests of declustered catalogues. Geophysical journal international, 189(1), 691-700. https://doi.org/10.1111/j.1365-246X.2012.05400.x.

Zaliapin, I., & Ben‐Zion, Y. (2020). Earthquake declustering using the nearest‐neighbour approach in space‐time‐magnitude domain. Journal of Geophysical Research: Solid Earth, 125(4), e2018JB017120. https://doi.org/10.1029/2018JB017120.

How to cite: seal, A. and Jana, N.:  Statistical Analysis on Background Seismicity of Southern California Region: Application of Nearest Neighbour Declustering and Network  Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-693, https://doi.org/10.5194/egusphere-egu25-693, 2025.

According to the well-known Lithosphere Ionosphere Coupling (LAIC) mechanism, tectonic activity during the earthquake preparation period produces anomalies at the ground level which propagate upwards in the troposphere as Acoustic or Standing gravity waves. Thus observing the frequency content of the ionospheric turbidity in a well extended area, in space and time, around an earthquake event we will observe a decrease of the higher limit of the turbidity frequency band. In this article we review the repeated observational results of TEC turbulent band upper limit (TBUL) on the occasion of strong earthquakes. Regorus earthquake risk estimation can not be extracted from our result since the characteristics of each event is diferent(i.e Magnitude ,epicentral distance of  the nearest GPS station ect..). Nevertheless continuous monitoring of the TEC (TBUL) fo and the alarming for further investigation by comparing with the TBUL of distant stations and with the results of  seismical monitoring, as well as with the results of other near earth surface precursor methods,  if the  TBUL tend to around 0.001Hz..

How to cite: Contadakis, M. E.: Ionospheric turbulence modulation by intense seismic activity as a tool of  Earthquake risk mitigation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1196, https://doi.org/10.5194/egusphere-egu25-1196, 2025.

Climate change has intensified droughts in many parts of the world, severely impacting different sectors. In particular, the agricultural sector is highly sensitive to precipitation deficits and the resulting soil moisture deficit, leading to a drastic reduction in crop productivity. There is an urgent need to ensure access to food for a growing population in future, making it essential to address agricultural drought induced crop yield losses. Multimodal satellite and reanalysis climate data archives, coupled with advancements in machine learning, offer a promising avenue to address this issue, but studies are often limited to the calculation of drought indices. In order to produce actionable insights and allow for time to prepare for drought-related food production deficits, specific information on crop losses is needed. Therefore, this study demonstrates the potential of the machine learning algorithm Random Forest (RF) for annual crop yield forecasting using multimodal datasets, for two agriculturally important drought-prone regions in India and Germany. Using 11 climate variables from ERA5 data and PKU GIMMS NDVI (version 1.2) from 1990 to 2021, an RF model was trained to predict crop yields for two common crops across the study sites. The model was evaluated at different spatial scales and the spatial transferability of the model was also tested, using Root Mean Square Error (RMSE; absolute error metric) and Mean Absolute Percentage Error (MAPE; relative error metric). Feature importance was also assessed across scales and across different study sites, using the mean decrease in impurity as a post-hoc explainability tool. Results show that different features are important for accurate crop yield predictions in different regions, for different crops, and across different space-time scales. Spatial transferability requires retraining the model with local data, due to the strong influence of local climatic and agricultural conditions as well as data availability. Findings pave the way for long lead time predictions of drought impacts on agricultural productivity purely open source data, contributing directly to improving global food security equitably, as the methods are equally applicable in data-rich and data-poor contexts. 

How to cite: Sahu, H., Garg, P. K., Vijay, S., and Dasgupta, A.: Estimating drought impacts on crop yield using AI and EO, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2768, https://doi.org/10.5194/egusphere-egu25-2768, 2025.

This study investigates the collective narratives of informal settlement communities in Southeast Asia through the lens of participatory mapping, thereby elucidating geonarratives that encapsulate their lived experiences of extreme urban heat. As urban environments increasingly confront the challenges of rising temperatures—particularly evident in cities such as Bangkok in Thailand, and Quezon City in the Philippines—the integration of community perspectives into risk assessments becomes paramount. The heightened vulnerability of informal settlements to these climatic stressors necessitates a thorough examination of the insights provided by residents. Through participatory mapping exercises and focus group discussions, this research actively engages community members in articulating their lived experiences and adaptive strategies in response to extreme heat. The findings reveal that while these communities develop coping mechanisms to mitigate the impacts of heat, such strategies may inadvertently intensify their vulnerabilities and impose additional burdens. The geonarratives that emerge from these collective stories illustrate the interplay between vulnerabilities and adaptive capacities, illuminating the complexities of resilience. By fostering an inclusive participatory framework, this research enables community members to identify the local conditions and challenges that shape their resilience and coping strategies. By prioritising the voices of marginalised populations, this study underscores the necessity of integrating community insights into urban planning and climate adaptation strategies, thereby enhancing resilience in the face of escalating climate risks.

How to cite: Macagba, S. F. A. and Delina, L.: Geonarratives of Resilience and Coping: Understanding Lived Experiences of Urban Extreme Heat in Southeast Asia’s Informal Settlement Communities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2880, https://doi.org/10.5194/egusphere-egu25-2880, 2025.

Quantifying the vulnerability of roads caused by debris flows is crucial for regional hazard mitigation in remote areas. However, the changing climate has increased the uncertainties in providing reliable vulnerability assessment due to the altered pattern of rainfall. Such change has induced the increased frequency and magnitude of debris flows, impacting the safe operation of highways. In this case, a reliable method was developed to help on the improvement of vulnerability quantification with the involvement of AI and Flo-2D simulation techniques before applying this proposed framework to a case study in the Gyirong Zangbo Basin, Tibet, China. In detail, a deep learning model was developed to estimate the physical vulnerability of roads in the event of a future debris flow with the consideration of a series of factors, including spatial locations of roads to the debris-flow channel, debris-flow catchment area (A_c), length of main channel (L), topographic relief (R), mean slope of main channel (J), and rainfall (P). After that, debris-flow simulations were performed to validate the physical vulnerability assessment results, which can further benefit the accurate quantification of economic loss on a regional scale. Here, in addition to the direct loss of the damaged roads, the indirect loss caused by the damaged roads was also estimated using a complex network theoretical approach that account for regional socioeconomic development and the time needed for road restoration. Overall, this study can form part of an early warning system to assist on the effective management of debris flows on a regional scale in mountainous areas.

How to cite: Qiu, C.: Vulnerability quantification of roads caused by future debris flows in mountainous areas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3505, https://doi.org/10.5194/egusphere-egu25-3505, 2025.

This study addresses slope stability challenges at the All-India Radio Telecommunication Tower site in Kodagu, Coorg, Karnataka, India. The hillock supporting the tower exhibited signs of instability following the monsoon of 2022, prompting the need for effective reclamation strategies to prevent future landslides. A detailed spatial analysis was conducted using open-source Digital Elevation Models (DEM) and the Scoop 3D spatial Limit Equilibrium Method (LEM) tool to identify critical regions susceptible to failure. To ensure robust and sustainable slope stabilization, geocell retaining walls were selected as an innovative solution. This technique promotes biotechnical stabilization by integrating structural reinforcement with natural vegetation, aligning with sustainability principles. The three-dimensional geometry of the proposed solution was modelled, and Finite Element Method (FEM) simulations were performed using PLAXIS 3D to evaluate the design’s performance under static and pseudo-static conditions, both with and without reinforcement. The analysis revealed that the geocell-based retaining system significantly enhances the slope's stability, achieving a Factor of Safety improvement of more than 10%. This solution not only addresses immediate stability concerns but also aligns with the United Nations Sustainable Development Goals (SDG) 9 and 11, emphasizing resilient infrastructure and sustainable urban development. The study concludes by recommending the implementation of this hybrid geocell retaining system to effectively mitigate future landslides and protect the telecommunication tower site.

How to cite: Menon, V. and Kolathayar, S.: Innovative Geocell-Based Slope Stabilization for Sustainable Protection: A Case Study of a Radio Tower Site in Kodagu, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3939, https://doi.org/10.5194/egusphere-egu25-3939, 2025.

Mining conflicts sustainable environment and causes disturbances for the livelihoods of people. Given the adverse impact on environment, indigenous community including Sami people and domesticated reindeer, it is of critical importance to peruse mining expansion and reclamation in Lapland, Finland. For the first time, this study employs a spatial-temporal deep learning architecture called ConvoLSTM, which enables accurate predictions of mining activities by capturing spectral, spatial, and temporal dependencies. Our custom model integrates a 2-Dimensional Convolutional Neural Network (2D-CNN) with a Long Short-Term Memory (LSTM) component. Using 10-meter Sentinel-2 imagery, we generated time-series land use/land cover (LULC) maps from 2015 to 2024 to track changes in mining extent. The performance of the spatial-temporal model was carefully evaluated against a Random Forest (RF) and a standalone 2D-CNN model, where it achieved superior accuracy. In the post-analysis phase, the Change Vector Analysis (CVA) technique was applied to quantify the magnitude and direction of change in mining activities over the past decade. The unique contribution of this study lies in implementing a custom spatial-temporal deep learning model to map decade-long mining activities and detect changes using publicly available satellite data. The resulting time-series maps demonstrate significant conversion of forest land and bare soil into mining areas, highlighting the rapid expansion of mining activities in Lapland which indicates a growing environmental concern in the arctic-boreal forest region. These findings offer critical insights and a valuable resource for policymakers, researchers, and reindeer herders, facilitating informed decision-making for sustainable environmental management and natural resource conservation in Finland.

Keywords: Mining Mapping, Environmental Impact, Remote Sensing, Deep Learning, CVA. 

How to cite: Hasan, I. and Liu, D.: Quantifying Surface Mining Expansion and Reclamation Using Deep Learning-based ConvoLSTM Model and Satellite Images: A Case Study in Lapland Region of Finland., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4913, https://doi.org/10.5194/egusphere-egu25-4913, 2025.

The radon-deficit technique has proven to be a valuable tool for environmental site characterization, particularly in detecting subsurface organic contamination. This work highlights its successful application in two contaminated sites, validated by consulting firms and supported by independent data collection campaigns. In the first case study, the technique effectively identified previously undetected DNAPL (Dense Non-Aqueous Phase Liquid) accumulations and optimized the placement of monitoring wells. Similarly, in the second case, radon-deficit data delineated areas potentially impacted by LNAPL (Light Non-Aqueous Phase Liquid) contamination, refining the sampling approach and complementing existing geochemical methods.

Building on these findings, a study is underway to integrate long-term radon data with machine learning (ML) techniques. By analysing environmental variables such as soil moisture, temperature, and atmospheric conditions, this approach aims to reduce the uncertainties inherent in radon-deficit data interpretation. Preliminary results indicate that ML models, such as Random Forest and Artificial Neural Networks, can enhance the predictive accuracy and reliability of the technique, paving the way for standardized protocols in site assessments. This integration represents a significant step toward more robust and scalable applications of radon-deficit methods in environmental monitoring.

How to cite: Barrio-Parra, F., Lorenzo Fernández, D., Cecconi, A., Serrano-García, H., Izquierdo-Díaz, M., and De Miguel García, E.: Application of Radon-Deficit Technique for Site Characterization and Machine Learning Integration: Case Studies and Emerging Insights, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5524, https://doi.org/10.5194/egusphere-egu25-5524, 2025.

In territories with complex orography such as the Macaronesian archipelagos of Madeira, Azores, Canary Islands, and Cape Verde, the spatial resolution of the Coupled Model Intercomparison Project Phase 6 (CMIP6) is not sufficient to account for all the atmospheric phenomena that occur in these archipelagos with such a complex microclimatic structure. This research presents a climatic dataset at a spatial resolution of 3x3 km² in all the Macaronesian archipelagos derived from high-resolution regional climate simulations performed with Weather Research and Forecasting (WRF) model, applying the pseudo-global warming (PGW) method. The dataset is focused on the following parameters: temperature, precipitation, solar radiation, wind, and cloud coverage. Meteorological stations (ECAD and METAR) and reanalysis ERA5 data were used for the validation of the model results in the recent past period (1982–2019). We worked with two periods for future projections (2030–2059 and 2070–2099) under two representative scenarios (SSP2.6 and SSP8.5). These indicators include annual and seasonal statistics and variability for each parameter. The dataset aims to support regional climate adaptation strategies, contributing to the broader scientific understanding of climate in insular environments.

How to cite: Barrancos, J., Carrillo, J., Tondreau, P. S., Expósito, F. J., Pérez, J. C., González, A., and Díaz, J. P.: Pseudo–Global Warming climate projections at convection-permitting resolution in the Macaronesia region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7204, https://doi.org/10.5194/egusphere-egu25-7204, 2025.

Subsidence is a geological phenomenon that continuously affects Mexico City. Over time, the impact of this phenomenon has been extensively studied using various methodologies, primarily at a regional scale. In recent years, efforts have shifted toward mapping subsidence at a local scale using technologies such as photogrammetry and LiDAR. These studies aim to establish a reference database to validate or complement regional-scale initiatives.

Field-based studies on subsidence often involve identifying problematic areas and analyzing topographical changes and structural damage over time. However, it is crucial to quantify and understand the limitations and capabilities of these techniques to establish a reference framework and ensure the reliability of the obtained data. Currently, precision methodologies are within everyone's reach thanks to technologies like photogrammetry and LiDAR from smartphones.

To achieve this, two controlled experiments (one conducted in the field and one in a laboratory setting) were carried out, in which 3D reconstructions of a box with known dimensions were made. Ten photogrammetry and ten LiDAR surveys were performed to compare the measurements obtained from the digital model with those taken from the physical object.

In the laboratory experiments, the average percentage error using photogrammetry was 1.03% (0.20 cm). Specifically, the error for a 16-cm-tall box was 1.44% (0.27 cm), while for a 20-cm-tall box, it was 0.61% (0.12 cm). For LiDAR, the average percentage error was 1.51% (0.27 cm), with errors of 1.50% (0.26 cm) for the 16-cm box and 1.52% (0.27 cm) for the 20-cm box. In field experiments, photogrammetry yielded an average percentage error of 0.88% (0.3 cm), whereas LiDAR showed an average error percentage of 2.17% (0.62 cm).

These findings confirm LiDAR and photogrammetry's potential for high-precision subsidence monitoring, providing a robust and accessible validation method. Utilizing mobile devices such as the iPhone 13 Pro Max extends the reach of these methodologies, enabling more accessible and practical research in urban contexts where subsidence poses significant challenges to infrastructure and quality of life.

How to cite: Vidal, G., Ramírez, N. L., Jácome, M. P., López, N., Reyes, T. A., and Yépez, F. D.: Accuracy Analysis of Photogrammetry and LiDAR Point Clouds Using an iPhone 13 Pro Max, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7731, https://doi.org/10.5194/egusphere-egu25-7731, 2025.

This study presents the site characterization of 133 selected stations in the National Accelerometer Network of Greece. All available earthquake recordings for various distances, magnitudes and azimuths around the station are compiled and processed to estimate a stable and reliable average Horizontal -to-Vertical Spectral Ratio (eHVSR). The earthquake records used have magnitude range 4 ≤ M < 6, with focal depth ranging from 0 to 40km and hypocentral distances 12 km ≤ R_hyp ≤ 300 km. Using the Diffuse Filed Concept for earthquakes (DFCe), and incorporating limited a priori geological information, available for the area around the station, the estimated eHVSRs were utilized in an inversion framework to estimate the best misfit velocity profiles down to seismological bedrock (where Vs>=3km/sec). Comparisons of these estimated velocity profiles with existing ones for the selected stations based on other geophysical or/and geotechnical methods, revealed good agreement, encouraging broader application of this methodology for the rest of stations.

In accordance with recommendations from the SERA project, seven key indicators were calculated for each of the 133 stations and are presented as follows: (1) Resonance frequency (f₀), (2) Shear wave velocity of the uppermost 30 meters (Vs₃₀), (3) Surface geology description, (4) Current EC8 site class, (5) Depth of the seismological bedrock (H_3km/s), (6) Depth of the engineering bedrock (H_0.8km/s) and (7) V_SZ full profiles where available. Such comprehensive site characterization of accelerometer stations enhances regional and international strong-motion databases (e.g. ESM db) and contributes to exploiting earthquake recordings to their full potential for engineering and seismological applications.

How to cite: Chatzianagnostou, E. and Theodoulidis, N.: 1D Site characterization of National Accelerometer stations in Greece based on earthquake recordings and the Diffuse Filed Concept (DFCe), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8382, https://doi.org/10.5194/egusphere-egu25-8382, 2025.

The Working Group on Disasters (WGDisasters) has been established since 2013 by the Committee on Earth Observation Satellites (CEOS, https://ceos.org) to ensure the sustained coordination of disaster-related activities undertaken by the CEOS Agencies as well as to act as an interface between CEOS and the community of stakeholders / users involved in risk management and disaster reduction.

In this framework, CEOS WGDisasters has initiated, promoted and supported a series of concrete actions for Disaster Risk Management (DRM) and Disaster Risk reduction (DRR) oriented to disaster monitoring, preparedness and prevention. These actions have been translated in single-hazard Pilot and Demonstrator projects (currently focusing on fires, floods, landslide, volcanoes and seismic hazards) as well as multi-hazards projects as the Recovery Observatory (RO) and Supersites for Geohazard Supersites and Natural Laboratories (GSNL).

Since 2012 ASI participates and contributes to the above-mentioned initiatives in terms of project selection and evaluation (as part of Data Coordination Team); data provision of COSMO-SkyMed, SAOCOM (only within the ASI Zone of Exclusivity defined in agreement with CONAE within SIASGE program) and PRISMA images; scientific activities in DRM and RO projects.

In coordination with WG members and CEOS Agencies, ASI has delivered more than 20.000 EO products until now and is actively involved in demonstrating novel scientific products based on a tailored exploitation of COSMO-SkyMed radar images. Several showcases will be presented at the time of the conference dealing with volcano monitoring (e.g. Mount Agung in Indonesia, Sierra Negra at Galapagos, St. Vincent in Caribbean), seismic activities (e.g. 2023 Turkey-Syria earthquake), multi-hazards “Supersite” initiatives (e.g. Reykjanes Peninsula, Kilauea and Mauna Loa volcanoes in Hawaii, Nyamuragira and Nyiragongo volcanoes) and RO initiative (e.g. 2016 Hurricane Matthew and 2021 Hurricane Grace in Haiti).

How to cite: Montuori, A., Tapete, D., Frulla, L., Czaran, L., Eddy, A., Virelli, M., Pari, G., and Zoffoli, S.: The Italian Space Agency Contribution to CEOS WGDisasters for Disaster Monitoring and Response, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8599, https://doi.org/10.5194/egusphere-egu25-8599, 2025.

This communication presents a unified modeling framework for human-caused wildfire ignitions across representative European regions (pilot sites, PS), aiming to enhance understanding of ignition drivers and support wildfire risk management. Our approach models ignition probability at a fine spatial resolution (100 m), identifies key influencing factors, and enables cross-regional comparisons.

We calibrated Random Forest models using historical fire records and geospatial datasets, including land cover, accessibility, population density, and dead fine-fuel moisture content (DFMC). Models were developed individually for each PS and compared to a comprehensive model integrating all PS. Spatial autocorrelation effects on model performance were also evaluated.

Model performance was robust, with AUC values ranging from 0.70 to 0.89. DFMC anomaly emerged as the most influential variable across all PS. Among human-related factors, proximity to the Wildland-Urban Interface was most significant, followed by distance to roads, population density, and wildland coverage. The full model achieved an AUC of 0.81, highlighting mean DFMC and anomaly as dominant ignition drivers modulated by accessibility and population density. Local model performance, however, dropped by 0.10 AUC in regions such as Southern Sweden and Attica, Greece.

These findings underscore the importance of integrating fine-scale spatial and environmental data for wildfire ignition modeling. The developed models provide valuable insights into wildfire ignition hazards and support the implementation of targeted mitigation policies in fire-prone European landscapes.

How to cite: Gelabert Vadillo, P. J., Jiménez-Ruano, A., Ochoa, C., Alcasena, F., Sjöström, J., Marrs, C., Ribeiro, L. M., Palaiologou, P., Bentué-Martínez, C., Chuvieco, E., Vega-García, C., and Rodrigues, M.: Modeling Human-Caused Wildfire Ignition Probability Across Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11004, https://doi.org/10.5194/egusphere-egu25-11004, 2025.

In 2023, Otis strengthened from a slight tropical storm into a major hurricane (Category 5) within only about 12 hours before it made landfall. The storm slammed into Mexico's coast with maximum sustained winds of over 165 mph and hurricane-force winds extending up to 30 miles from its center. The SICT (Secretariat of Infrastructure, Communications and Transportation) warned of a total closure of the Mexico-Acapulco highway in the Chilpancingo-Acapulco section. Faced with reports of hundreds of landslides through the lines, the SICT deployed more than 1000 workers, 100 vehicles and 300 pieces of heavy machinery in the hope of “restoring traffic as soon as possible and providing safety to users.” Unfortunately, predictions could not anticipate close enough the Otis destructive force.

Ensuring the proper functioning of road infrastructure is a fundamental aspect in risk management. Landslides have the potential to impair critical transportation infrastructure, particularly road networks in the hilly regions in Mexico. Recognizing the extremely changing climate conditions in the Mexican Pacific coasts are becoming increasingly difficult to predict, in this research advanced technologies are integrated into an intelligent digital scenario to simulate and control this linear infrastructure before, during and after extreme rainfalls occur.

The strategic roads digital twin comprises i. dynamic susceptibility maps, ii. satellite radar information of control points (the landslides pathologies are easily detected through them), iii. an artificial intelligence slope stability calculator (in near real-time) for pointing incipient instability, and iv. a semi-immersive scenario for analyzing future states based on the information of pluvial stations and control points, once this information is analyzed with the intelligent calculator. For communicate the input conditions, the aggravating factors and the future responses, a digital twin of potentially affected road sections (detected on the dynamic maps) is developed. Simulate scenarios before rainfall increases, help to make informed maintenance and risk prevention decisions in road infrastructure in areas with high geotechnical complexity and strong seasonal rainfall patterns. Exploiting precalculated extremely dangerous conditions, this digital twin can serve as an early warning system because it is programmed for immediate communication of graduated alarms that announce the proximity to dangerous states.

How to cite: García, S., Trejo, P., and Ángeles, B.: A digital twin for management of landslides and slope incidents on strategic road infrastructure, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14209, https://doi.org/10.5194/egusphere-egu25-14209, 2025.

The Amazon Basin (AB), the largest hydrographic basin in the world, spans across seven countries in South America. It constitutes a highly intricate system, rich in natural resources, and is marked by substantial biological heterogeneity. The AB plays a pivotal role in the regulation of environmental processes, serving a key component of the global hydrological cycle and climate systems. Understanding the increasing frequency, intensity and spatial extent of extreme drought events in this region is vital for safeguarding the regional ecosystem. This study aims to classify extreme drought events in the AB using the Standardized Precipitation-Evapotranspiration Index (SPEI), derived from ERA5 reanalysis data, covering the period from 1980 to 2024. To assess both agricultural and hydrological droughts, this research incorporates the accumulation periods of 6 and 12 months (SPEI-6 and SPEI-12). The ranking methodology accounts for various SPEI time scales, the extent of the affected area, and the average SPEI intensity within that area. The results highlight that the 2023/24 drought episode was the most intense ever recorded in the AB, with over 90% (80%) of the region affected for the month of January for SPEI-6 (SPEI-12), surpassing known past mega-events, such as the 2005, 2010 and 2015/16 episodes. These extreme conditions were observed across all timespans. Specifically, for January 2024 under the SPEI-6 and for September 2024 under the SPEI-12, more than half of the AB was categorized as experiencing exceptional drought, as established by the 1st percentile of the SPEI distribution. Furthermore, the results underscore the persistence of consecutive periods of drought, especially since the beginning of 2020. With the climate projections indicating continued warming in the region, increased evapotranspiration and lower rates of rainfall are expected, potentially leading to even drier periods. This marks the significance of studies focused on understanding the development and impacts of droughts, as they play a critical role in the mitigation of future environmental risks.

How to cite: Albuquerque, R., Monteiro dos Santos, D., Miranda, V., Gouveia, C., Liberato, M., Trigo, R., Peres, L., and Libonati, R.: Ranking of extreme drought events in the Amazon Basin between 1980 and 2024, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14311, https://doi.org/10.5194/egusphere-egu25-14311, 2025.

Geohazard chains in watersheds often involve a series of interconnected events, such as landslides that propagate along slopes, intrude into river channels, form landslide dams, and result in dam breaches and outburst flooding. Because the sub-processes within a geohazard chain are coupled, one or more of these events can trigger subsequent ones, leading to larger spatial and temporal scales than isolated disasters. This results in more destructive power and a wider impact area. In this study, a numerical case study focuses on the most recent geohazard chain event: the 2018 Baige landslide in Sichuan Province, China. This event can be divided into several sub-processes based on the coupling order within the chain. The first landslide formed a landslide dam, followed by another landslide at the same location, which overlapped with the first, creating a higher dam. This ultimately led to a larger-scale dam breach and outflow.
To simulate this sequence, a series of validated depth-averaged models for geohazard chains was employed, along with a standard LxF central differencing scheme to retain high resolution and avoid Riemann characteristic decomposition. The landslide propagation was modeled using a visco-inertial friction law. The numerical predictions were verified against field measurements from the literature, demonstrating the feasibility of using μ(K) visco-inertial rheology to simulate large-scale landslides and landslide dam formations. The overtopping failure of the two overlapping landslide dams and the subsequent outburst flooding were successfully simulated using the proposed model. Maximum discharge results indicate the model's capability to capture the interaction between dam breaches and outburst floods. The numerical findings, validated by existing literature, provide a reliable assessment for emergency relief and hazard mitigation. This modeling framework is expected to contribute to improved mitigation strategies for geohazard chains.

How to cite: Xie, Y., Zhou, G., Cui, K. F. E., Lu, X., and Li, N.: Numerical study of  2018 Baige landslides induced geohazards chain and dynamic proesses, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15456, https://doi.org/10.5194/egusphere-egu25-15456, 2025.

Although the European Forest Fire Information System (EFFIS), provided by the Copernicus Emergency Management Service, offers three different methods for determining forest fire danger, the Canadian method is usually used and accepted in Croatia. The Canadian Fire Weather Index (FWI) estimates the forest fire danger level based on meteorological parameters (air temperature, humidity, wind speed and precipitation amount) related to 12 UTC for the given day at the meteorological station or to a grid point of a numerical weather prediction model.

Thanks to the EFFIS statistics portal, it is possible to see the extent to which Croatia has been at risk from forest fires in recent years based on the areas burned and the number of fires. The Copernicus Climate Change Service (C3S) provides a much more detailed overview of the burned areas. The combination of data from the Climate Change Service and the Emergency Management Service can provide a better overview of forest fires in Croatia. The forest fire danger levels are analyzed spatially between different regions such as the continental, mountainous and Adriatic parts of Croatia. In order to find an appropriate duration of the fire season, the forest fires within and outside the fire season are listed. The aim of the spatio-temporal analysis is to show the most endangered areas and the seasonal trend of forest fires in Croatia.

How to cite: Škurić Kuraži, D. and Herceg Bulić, I.: Spatio-temporal analysis of forest fires in Croatia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15531, https://doi.org/10.5194/egusphere-egu25-15531, 2025.

Wildfires are an intensifying global challenge, driven by climate change, which increases their frequency, severity, and spatial extent. Accurate wildfire risk assessment and forecasting are essential for effective mitigation, resource allocation, and long-term planning. The Canadian Fire Weather Index (FWI) is a widely used fire danger rating system that integrates four primary daily meteorological variables—24-hour accumulated precipitation, wind speed, relative humidity, and temperature—into six components representing fuel moisture, ignition probability, and fire spread potential. Its temporal "memory" feature, which tracks moisture changes over time, makes it particularly valuable for capturing wildfire dynamics.

However, the FWI reliance on specific daily input data at noon poses challenges for its application in regions or scenarios lacking such precise temporal measurements. To address this limitation, FWI proxies computed using daily mean data offer a practical alternative. Yet, these proxies often lack the fidelity required to fully replicate the FWI values.

This study focuses on enhancing the emulation of the original FWI using daily mean data and other proxy variables by leveraging advanced deep learning techniques. We explore a spectrum of architectures, ranging from conventional machine learning models to state-of-the-art approaches like convolutional neural networks (CNNs) and Convolutional Long Short-Term Memory (ConvLSTM) networks. These models are tailored to capture the spatial and temporal complexities of wildfire behavior while maintaining robustness in the face of variable data availability.

Our research centers on the Iberian Peninsula, a Mediterranean region highly vulnerable to extreme wildfire events. By utilizing high-resolution, geo-referenced datasets, we validate the ability of these models to emulate the original FWI with high accuracy. To enhance model interpretability, we integrate eXplainable Artificial Intelligence (XAI) techniques, providing actionable insights into the decision-making processes and addressing concerns about the "black box" nature of deep learning.

This work demonstrates how daily data, combined with cutting-edge deep learning methods, can effectively emulate the FWI, offering a scalable and reliable solution for wildfire risk prediction in regions where traditional inputs are unavailable. The proposed models bridge the gap between limited data availability and the growing need for precise fire danger indices, enabling improved assessment and planning for wildfire-prone regions.

By advancing the science of wildfire modeling through daily data-driven approaches, this study contributes to a deeper understanding of spatial and temporal wildfire dynamics. It highlights the potential of integrating geoscience, climatology, and artificial intelligence to develop practical tools for wildfire risk mitigation, resilience, and decision-making in a rapidly changing climate.

Acknowledgments: This research work is part of R+D+i project CORDyS (PID2020-116595RB-I00) with funding from the Spanish Ministry of Science, Innovation and Universities MCIN/AEI/10.13039/501100011033. O.M. has received the research grant PRE2021-100292 funded by MCIN/AEI /10.13039/501100011033.

How to cite: Mirones, Ó., Baño-Media, J., and Bedia, J.: Daily Data-Driven Emulation of the Fire Weather Index: Deep Learning Solutions for Wildfire Risk Prediction, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16116, https://doi.org/10.5194/egusphere-egu25-16116, 2025.

Urgent Computing (UC) refers to the use of High-Performance Computing (HPC) and High-Performance Data Analytics (HPDA) and Artificial Intelligence (AI) modules during or immediately following emergencies. It typically integrates complex end-to-end workflows with scalable computing resources, where multiple model realizations are necessary to account for input and model uncertainties, all under strict time-to-solution constraints. Enabling urgent HPC in unpredictable events such as earthquakes can significantly enhance resilience and response efforts. The temporal horizon for UC usually spans from minutes to a few hours, providing decision-makers with rapid estimates of the potential outcomes of emergency scenarios. In particular, high-resolution synthetic ground motions for earthquakes can complement the tools used by seismological services for impact analysis. Here, the Urgent Computing Integrated Services for Earthquakes (UCIS4EQ) is proposed as an innovative UC seismic workflow designed to rapidly generate synthetic estimates of the consequences (such as synthetic time histories, shakemaps, PGA/PGV, among other proxies) of moderate to large earthquakes (M > 6). Over the last six years, UCIS4EQ has been developed from scratch and received contributions within the framework of three European projects (DT-GEO, eFlows4HPC, and ChEESE CoE). In this work, we demonstrate the technological maturity of UCIS4EQ and its operational readiness in collaboration with the Mexican Seismological Service (SSN). Furthermore, this work addresses the challenges we face to reach operational maturity addressing the specific requirements of a seismological service for an urgent computing framework providing reliable outcomes for decision making with global coverage.

How to cite: Monterrubio-Velasco, M., Boehm, C., Iglesias, A., Diez, G., Bhihe, C., Esquivel, L., Zamora, N., Tuinstra, K., and de la Puente, J.: Earthquake Shaking Simulation Workflow for Urgent Computing Services: Challenges and Advances, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17470, https://doi.org/10.5194/egusphere-egu25-17470, 2025.

To address the challenge of inconsistent line-of-sight (LOS) deformation datum derived from interferometric measurements of different Synthetic Aperture Radar (SAR) images—and the significant variation in LOS direction between near-range and far-range within the same image—this contribution proposes an InSAR deformation datum  connection method with a fixed LOS direction. The method combines Bayesian inference and the Markov Random Field (MRF) model, integrating InSAR and GNSS deformation data to achieve unified deformation datum for adjacent and even different-orbit SAR interferometric results.

A simulation experiments, using Sentinel-1 imaging parameters and GNSS velocity field data, and a real-world validation with InSAR data of the 2023 Southern Turkey earthquake are conducted. In the simulation, the root-mean-square error  of LOS displacement rate difference in the overlapping regions of adjacent-track SAR images decreased 99%. In the real-world experiment, the root-mean-square error of displacement difference reduced from 20 mm to 8 mm, demonstrating the effectiveness of the proposed method.

We have three key contributions：(1) Unified Deformation datum: Successfully realize an InSAR deformation datum connection with fixed LOS direction in SAR images; (2) Adjacent-Track Stitching: Achieve seamless stitching of adjacent-track SAR deformation results from a single data source; (3) Real-Data Validation: Reduce the mean displacement difference in overlapping regions of adjacent-track SAR images of the 2023 Southern Turkey earthquake from 20 mm to 8 mm.

How to cite: Bian, W., Motagh, M., and Wu, J.: InSAR Deformation Datum Connection with A Fixed Line-of-Sight Direction: A Bayesian inference and the Markov Random Field (MRF) model integration, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19686, https://doi.org/10.5194/egusphere-egu25-19686, 2025.

Entrainment plays a vital role in shaping debris flow deposits, influencing their morphology and dynamics. Our study utilized a small-scale flume experiment to investigate the effects of water content (w/c), sediment composition, and bed morphology on granular flow behavior. Sixteen experiments were conducted with varying w/c levels (20–50%) and erodible bed configurations, analyzing deposit morphology in terms of width, thickness, and runout length. The results revealed distinct morphological patterns across different w/c levels. At low w/c levels (20–24%), deposits formed broad, shorter lobes with minimal scouring, resulting in cone-shaped structures. Moderate w/c (~28%) increased flow mobility, leading to thicker deposits near the flume bed due to reduced entrainment. At higher w/c levels (30–50%), deposits shifted farther downstream, characterized by greater entrainment volumes and extended runout distances. While higher w/c reduced deposit thickness, it significantly increased deposit width, highlighting the combined effects of w/c and entrainment. The study identified a clear relationship between entrainment and flow mobility, with greater entrainment volumes producing wider and flatter deposits. Water content was found to be the primary factor influencing deposit thickness, emphasizing its critical role in sediment transport dynamics. The deposits were poorly sorted and exhibited a bedding structure similar to natural debris flows, validating the experimental approach. This research presents an effective and scalable method for studying granular flow behavior over erodible beds, offering valuable insights into sediment transport processes and bridging mesoscale experiments with practical applications in natural hazard mitigation and geotechnical engineering.

How to cite: Satyam, N., Pandey, N. K., and Basumatary, B.: Entrainment-driven changes in runout deposition of debris flows at small scale , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19948, https://doi.org/10.5194/egusphere-egu25-19948, 2025.

Urbanization continues to accelerate, driving global warming change and, at more local scale, land cover changes. In cities, new surface materials, buildings, roads and changes to the surface morphology alter airflow and heat exchange between the urban surface and the atmosphere. As a result, cities are almost always warmer than their surroundings rural area in a phenomenon known as Urban Heat Island (UHI) that could represent a hazard for city inhabitants. Consequently, it is important to evaluate the magnitude of the UHI and understand the urban characteristics involved in its formation process.

The aim of the present study is to assess the Surface Urban Heat Island (SUHI) in Bolzano urban area evaluating its correlation with the urban morphology and its biophysical characteristics. The indices considered to describe the urban morphology are Building Coverage Ratio (BRC), Building Volume Density (BVD), Mean Building Height (MBH), Green Space Ratio (GRS), and Sky View Factor (SVF) at 30 m resolution. The biophysical indices considered are albedo, Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST) at 30 m resolution.

The morphological indices were calculated starting from building, green area, land cover data, and DEM, whereas biophysical indices were derived from Landsat 8/9 OLI/TIRS satellite images. Two images, one for the summer season and one for the winter season, were selected based on air temperature and absence of clouds: 07/19/2022 during a 7-days period of very high temperatures and 02/14/2021 during a 7-days period of very low temperatures. Subsequently, a linear model analysis was fitted, setting the Urban Heat Island Intensity (UHII) as the dependent variable and the morphological and biophysical indices as independent variables.

Results showed how some indices were positive or negative correlated with the UHII both in summer and winter, whereas other had a different behavior depending on the season.
Results regarding summer period highlighted UHII positive correlations with most of the morphological indices and negative correlation with most biophysical indices. In contrast, in winter, all the biophysical indices were positive correlated with the UHII. Moreover, most morphological indices were positive correlated with it.

Understanding which urban characteristics impact more in the SUHI formation is crucial for improving city environment and people health and this study set a first step into it.

This study was carried out within the RETURN Extended Partnership and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005) – SPOKE TS 1.

How to cite: Dalla Vecchia, C., Dalle Vedove, L., Vigato, T., Zandonella Callegher, C., and Giussani, F.: Surface Urban Heat Island in Bolzano (Italy): Evaluating the Role of Morphometric and Biophysical Characteristics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20251, https://doi.org/10.5194/egusphere-egu25-20251, 2025.

The Chamoli Glacial flood happened in the Indian state of Uttarakhand on the 7^th of February 2021. This disaster was triggered by a rockslide-induced glacial burst near the Ronti peak. The event unleashed a massive debris flow that devastated the area’s critical infrastructure, including the Rishiganga and Tapovan Vishnugad hydropower projects. The event underscored the vulnerability of the fragile Himalayan geology, challenges in development, disaster preparedness and early warning systems.

PARATUS project's forensic approach is based on the combination of three specific forensic methodologies: Investigation of Disasters (FORIN), Post Event Review Capability (PERC), and Detecting Disaster Root Causes (DKKV). The forensic analysis investigates the disaster’s causes, multi-dimensional impacts and responses, highlighting the key vulnerabilities across physical, socio-cultural, economic and institutional dimensions. The study identifies poor infrastructure resilience, environmental degradation and limited emergency response capacity as major contributors to the severity of the disaster. Cascading effects such as sedimentation and artificial lake formation further exacerbated the risks. The immediate aftermath saw significant disruptions in transportation and communication networks, hindering rescue operations despite the swift deployment of ground and aerial relief to the affected population.

In the recovery phase, coordinated efforts under India’s National Disaster Management Plan facilitated relief and reconstruction. However, challenges associated with the long-term rehabilitation of the people affected by the disaster still persist. The governmental institutions are currently focusing on building resilience through slope stabilization, improved early warning systems and sustainable infrastructure development. Addressing systemic vulnerabilities, including governance gaps and socio-economic inequities remains a critical step toward mitigating future risks. This forensic analysis builds on existing scientific literature and institutional reports revealed by the Government of India to assess and emphasize the necessity of integrating multi-hazard approaches and localized strategies for disaster risk reduction in vulnerable mountainous regions like the central Himalayas.

How to cite: Ghosh, P., den Bout, B. V., Westen, C. V., and Atun, F.: Chamoli Glacial Burst: Investigating the vulnerability of the Himalayan geology with the support of Forensic Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20476, https://doi.org/10.5194/egusphere-egu25-20476, 2025.

The U.S. NOAA National Centers for Environmental Information (NCEI) has more than 3,700 tsunami marigram (tide gauge) records in both image and paper format, capturing worldwide observations of more than 390 tsunami events from 1854 to 1994. The majority of these tsunami marigram records were scanned to high-resolution digital TIFF images during the U.S. NOAA Climate Data Modernization Program (CDMP) which ran from 2000 to 2011. Additional, uncatalogued physical records exist on microfilm rolls and paper at the David Skaggs Research Center (DSRC) in Boulder, Colorado, USA. For many tsunami events prior to 1994, data resides only on the marigram records, making them of great historical significance. Six of the 13 uncatalogued microfilm rolls have been scanned by NCEI to produce 3,548 TIFF images. During 2025, we will be working to catalog, archive, and make these images discoverable and accessible online. We will identify any duplicates by comparing to the existing catalog of marigrams already archived at NCEI. Given the large number of uncatalogued images, we are exploring automated approaches to harvesting metadata from the images to aid in cataloging. We will present the project background, goals, and initial results of this effort.

How to cite: Sweeney, A. and Radio, E.: Cataloging historical tsunami marigrams from microfilm images, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20966, https://doi.org/10.5194/egusphere-egu25-20966, 2025.

Gravitational mass movements are recurrent events in Brazil, usually triggered by intense rainfall. When such rainfall events occur in urban areas, particularly on slopes, they often result in disasters, causing loss of human lives, social impacts, and economic damage. Thus, mapping and monitoring landslide susceptible areas are extremely important, as well as the implementation of a system capable of predicting their occurrence in advance. In this context, this study aims to assess the efficiency of the TRIGRS numerical model as a component of a prediction system for landslides on urban slopes. As a first step, the influence of the drainage network, which is altered due to urbanization on slopes, will be analyzed in relation to the safety factor, moisture profile, and pore pressure. The drainage network was calculated using a digital terrain model derived from LIDAR data. The TRIGRS model was applied to a small watershed located in the municipality of Campos do Jordão, São Paulo, Brazil. During the 72 hours analyzed period, two heavy rainfall events stroke the area and landslides were registered. The registered landslides show the model efficiency on the identification of the most susceptible areas, because they happened in areas identified by TRIGRS as extremely susceptible to landslides. The combined geotechnical and geophysical methodology for soil characterization and the use of more realistic drainage network feeding the TRIGRS has shown to be useful urban planning and early warning systems. This study is part of Brazilian Council for Scientific and Technological Development (CNPq) Project coordinated by GEGEP/UFPE, with the participation of Cemaden, and in collaboration under development with the National Research Council of Italy (CNR). It aims to implement a methodological procedure.

How to cite: Ernesto de Moraes, M. A., M. Mendes, R., Bortolozo, C. A., Metodiev, D., das Dores S. Medeiros, M., R. M. Andrade, M., S. G. Mendes, T., and Q. Coutinho, R.: Effects of Drainage Network on the Identification of Landslide-Susceptible Areas Using the TRIGRS Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21040, https://doi.org/10.5194/egusphere-egu25-21040, 2025.

Compound hazards, such as the sequential occurrence of Tropical Cyclones (TC) and humid heatwaves in close succession, are more destructive than individual and isolated occurrences of each hazard. While landfalling TCs cause catastrophic consequences from storm surges, strong winds, heavy rain, and pluvial flooding, they are often compounded by anomalous heat. The TC-heat joint occurrence raises significant concerns for public health and critical infrastructure, particularly since powerful TCs may lead to major power outages. For example, TC Remal in May 2024 damaged the coastlines of India and Bangladesh, bordering the Bay of Bengal (BoB), impacting > 10 million people without access to electricity and shelter, with an estimated damage totaling $600 million. For the eastern coast of India, with many small to large port cities, including two major urban agglomerates, Kolkata and Chennai, with populations > 10 million, the likelihood of TC-heat joint occurrence has not been assessed so far. We analyze 251 landfalling TCs on the eastern coast of India between 1982 and 2023. We show that ~16% of terrestrial humid heatwave peaks are compounded by the landfalling TCs, and ~8% of moist heat follows TCs. Further, we show the relative increase in peak wet-bulb temperature in TC-compounded heatwaves is as high as around 7−10% in pre-monsoon (April−May) and post-monsoon (October−December) seasons compared to heatwaves not compounded by the TCs. An anomalous rise in TC-compounded heatwave peaks is more pronounced and often exceeds terrestrial heatwave peaks during the post-monsoon season. Although the annual counts of landfalling TCs over BoB show a decreasing trend, our observational analysis of precursor coincidence rate confirms the increased likelihood of TC-compounded humid heat stress, preconditioned by strong to severe marine heat waves. The derived insights highlight a need to prepare adaptation planning for unprecedented compound tropical cyclones and extreme heat hazards when such sequential hazards are expected to occur more frequently in a warming climate. 

How to cite: Ganguli, P. and Lin, N.: Mapping Compound Hazard Potential of Tropical Cyclone and Anomalous Heat in Eastern Coast of India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-215, https://doi.org/10.5194/egusphere-egu25-215, 2025.

The unprecedented heatwave over India in May- June 2024 has raised significant concerns regarding its underlying dynamics and potential impacts. This study investigates the prolonged heatwave event by combining dynamical diagnostics and numerical simulations using the Global Forecast System (GFS) model used by the India Meteorological Department (IMD). We first analyze the synoptic conditions and large-scale atmospheric patterns contributing to the persistence and intensity of the heatwave. The dynamical diagnostics reveal the role of Hadley Cell expansion, blocking high-pressure systems and regional thermodynamic conditions in sustaining extreme temperatures. Further, IMD’s GFS model products evaluate and validate the heatwave event, focusing on capturing temperature anomalies' spatial and temporal evolution. The model outputs are validated against observational data, demonstrating high accuracy in reproducing the critical characteristics of the heatwave. The results indicate that the combined effect of rapid solar insolation and anomalous atmospheric circulation patterns played a crucial role in the development and persistence of the heatwave. The study also highlights the importance of high-resolution numerical simulations in understanding complex meteorological phenomena and provides insights into improving heatwave prediction and preparedness strategies. This comprehensive analysis of the May-June 2024 heatwave over India would contribute to the broader understanding of extreme weather events in the context of climate variability and change.

How to cite: Amat, H. B., Lohan, N., and Pattanaik, D. R.: Dynamical Diagnostics of the Prolonged May-June 2024 Heatwave Over India: Evaluation and Validation using IMD’s GFS Model. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-772, https://doi.org/10.5194/egusphere-egu25-772, 2025.

The West African Sahel has suffered an unprecedented hot season in the first half of 2024. This preliminary observational study characterises this extreme heat in the urban context of Ouagadougou, one of the major cities in the region where significant death casualty was reported. Using data from the national meteorological agency and the European reanalysis (ERA5), the study investigated both the daytime (Tmax) and nighttime temperatures (Tmin) with the 1991-2020 as reference period. The results show that the average monthly Tmax anomaly ranged from 1.42 °C in January 2024 to 2.41 °C in June 2024, versus -0.4 °C in  January 2024 and 2.05 °C in June 2024 for Tmin, showing that the heat was more important in 2024 than in the historical period. Using the heat wave definition of the Burkina Faso Red Cross heat wave early action protocol, a total of four (one) daytime (nighttime) heat waves were recorded in the city between March and May. This is to be compared with a historical frequency of one event every four years. The longest daytime heat lasted six days with Tmax reaching a maximum of 44.5°C. The unique nighttime heat wave was twice as long as the longest daytime heat wave, persisting for 13 days between late April and early May, a record in the city. From a spatial perspective, the heat was not evenly distributed as some neighbourhoods were significantly hotter than the rest of the city. Furthermore, the initial findings of a household survey conducted in the city corroborated the unprecedentedness of the situation as most respondents reported having never experienced such heat levels in the past with considerable impacts on their health and livelihoods. These results underscore the need for more efforts towards heat wave risk management in African cities.

How to cite: Wendkuni Ghislain, N., Kiswendsida H., G., Dazangwende E., P., and Thomas R., B.: Identification and characterization of 2024 Heat waves in Burkina Faso., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-817, https://doi.org/10.5194/egusphere-egu25-817, 2025.

We calculate the hourly wet bulb globe temperature (WBGT) values for the last 50 years over Western Africa to assess the emergence of new heatwave hotspots and the interplay between moist and dry heatwaves. In the formulation used, WBGT is derived using the grided data from ERA5 and ERA5-HEAT: 2-m air temperature, relative humidity, 10-m wind speed and mean radiant temperature (a measure of incidence of radiation on a body), and is thus representative of outdoor conditions.

We find that the heat stress estimated through WBGT does not peak over the same geographical regions as the air temperature, suggesting an important role of humidity in intensifying heatwaves over certain regions. While the highest temperatures are reached in the northern Sahel and Saharan regions, the highest heat stress values are found further to the south, in the region bordering Senegal, Mauritania and Mali and in southwest Niger. These are the same regions where the WBGT threshold of 33 °C (conditions dangerous even at resting metabolic rates (MR) < 115 W) have recently been crossed for up to 40 hours per year.

The duration of exposure to WBGT > 30 °C (conditions dangerous at light physical activity, MR < 180 W) has been increasing over almost the entire West Africa, at rates from 30 to 100 hours/decade. Over the Senegal - Mauritania - Mali border and southern Niger, exposure to WBGT > 33 °C has been increasing by 1-4 hours/decade.

Dangerous WBGT thresholds can be crossed at a wide range of temperatures and are often not associated with the highest temperature percentiles. For example, in Niamey, a WBGT of 30 °C has been crossed in the temperature range from ~ 29 to 44 °C, in Thies (Dakar) and Ouagadougou from ~ 28 to 43 °C, and in Abidjan from ~ 28 - 36 °C.  In September 2019 and July 2020, in Niamey we find the first occurrences of air temperatures below 36 °C being associated with very dangerous heat stress values (WBGT > 33 ° C).

We conclude that for much of continental West Africa, and particularly for the Senegal - Mauritania - Mali border region and southern Niger, extreme heat alerts should at a minimum include indicators accounting for temperature and humidity, in order to capture the dangerous moist heatwave conditions occurring at temperatures well below the highest temperature percentiles. More complex indicators that additionally account for wind and radiation are very desirable for estimates of outdoor safety.

How to cite: Cvijanovic, I., Sultan, B., Mbengue, A., and Lavaysse, C.: Emergence of new heat stress hotspots over the West Africa, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3128, https://doi.org/10.5194/egusphere-egu25-3128, 2025.

Heatwaves have become more frequent and more intense under the influence of climate change, resulting in increased impacts on human health, infrastructure and economic activities. However, heatwaves climatic characteristics do not always inform properly on the actual human and material impacts resulting from heatwaves. In other terms, heatwaves with a high intensity in the climatological sense may not be equivalently as intense in terms of impacts. In this study, we empirically investigate, in Europe, the link between the climatic characteristics of heatwaves and their impacts, as listed in the EM-DAT disaster database. We apply indices available in the literature to characterize heatwaves for the 1950-2021 period found in the ERA5 and E-OBS datasets. We also propose new indices, combining meteorological and demographic data, that we compare to the existing ones, and to the heatwave’s impacts. We show that including demographic data in the heatwaves indices is key to ensure that heatwave climatic indices reflect more accurately the impacts of heatwaves. We also investigate the top 10 heatwaves that are considered extreme based on our best performing index but are not in the impact data-base and find references of their impacts for 8 of them, meaning that this type of methodology could be used to enrich existing impact databases by flagging events of interest.

How to cite: Mandonnet, L., Jézéquel, A., D'Andrea, F., and Vallet, A.: Extreme heatwaves in Europe 1950-2021: analysis of the links between meteorology, population, and impacts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3676, https://doi.org/10.5194/egusphere-egu25-3676, 2025.

Increased frequency of extreme urban heat and its exposure to urban populations is one of the challenges presented by climate change, especially in urban clusters. Due to the rapid but unequal development, heat exposure disproportionately increased in the underdeveloped regions compared to the developed regions in urban agglomeration. To address this issue, it is crucial to clarify the spatial pattern of heat health risk (HHR) inequality for urban heat resilience. However, analyses for the disparity of HHR inequality often used a single scale, neglecting important spatial context effects at other scales. Moreover, the rationale of HHR inequality remains unclear. Here, we took the well-developed and highly urbanized Yangtze River Delta (YRD) region as a case study and employed multiscale approaches to examine how and why the HHR inequality varied at and within the regional scale. We first assessed HHR using a comprehensive assessment framework at a 1km grid level. Then, we quantified the inequality between regions using local Moran's I and KS distance. Therefore, we utilized the Gini coefficient and Bayes quantile regression to quantify inequality and identify its drivers within the regional scale. Finally, we proposed a conceptual framework to inform policymaking in regions with different patterns of multiscale equality. Our results found that the HHR in YRD exhibited significant spatial inequality at the regional scale (Moran’s I=0.562, P<0.001) and within the regional scale (Gini coefficient: 0.27-0.54). Higher population concentrations and building densities often led to higher HHR. In high HHR areas, intra-regional inequality was often lower due to high and coordinated socioeconomic levels (Gini coefficient: 0.27-0.34). Additionally, in areas with low and medium levels of risk, healthcare resource availability and local temperatures had a greater impact on intra-regional inequities, which varied at different levels of inequality. This study contributes to a better understanding of multiscale HHR inequality, which helps optimize heat risk management strategies and regional sustainable development.

How to cite: Wu, H., Zhao, C., Zhu, Y., and Pan, Y.: A multiscale examination of heat health risk inequality and its drivers in mega-urban agglomeration: A case study in the Yangtze River Delta, China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4661, https://doi.org/10.5194/egusphere-egu25-4661, 2025.

Heat stress events in China are becoming increasingly frequent and severe, exacerbated by the nation’s rapidly aging population. Elderly individuals, as a particularly vulnerable group, face heightened risks and have a reduced ability to withstand such hazards. However, research on the long-term dynamics of heat stress risks among the elderly remains limited. This study addresses this gap by employing the Intergovernmental Panel on Climate Change (IPCC) risk framework to evaluate the evolving heat health risks for the elderly in China since the start of the 21st century. By integrating satellite remote sensing, meteorological observations, and socio-ecological statistics, the study captures the dynamic interplay between hazards, vulnerabilities, and exposure. The findings reveal that the combination of rising heat hazard days and a worsening aging population has led to a steady increase in the elderly population exposed to heat stress, amplifying both vulnerability and exposure risks over time. Regional disparities in comprehensive risk are striking, driven by differing levels of aging and socio-economic development across China. The central region consistently exhibits the highest and fastest-growing comprehensive heat risk, while the northwest and northeast maintain the lowest risk levels. Conversely, socio-economically advanced provinces in the east, such as Shanghai and Beijing, show declining risk levels due to significant reductions in vulnerability facilitated by rapid social progress. This study provides a dynamic and regionally nuanced perspective on the intersection of aging and heat stress risks, offering critical insights to inform targeted policies and resource allocation. Its findings hold particular relevance for developing countries navigating the dual challenges of climate change and aging populations.

How to cite: Liu, C., Chen, S., Huang, J., Zhang, C., Lian, L., Jiang, Y., and Tu, X.: Dynamic assessment of heat stress risks among the aging population in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4780, https://doi.org/10.5194/egusphere-egu25-4780, 2025.

Humid heat is a serious risk to human health, reducing the body’s ability to cool through sweating. The intensity, frequency and impact of humid heat extremes will increase under climate change, particularly in tropical and sub-tropical ‘hot spots’, such as equatorial Africa and the Indian subcontinent, which are highly populated, and already very hot and humid. Research on the meteorological drivers of humid heat extremes is immature compared to that for dry heatwaves. Here an overview of the latest results from the Humid heat extremes in the Global (Sub)Tropics (H2X) project will be presented. We have shown that rainfall is a key ingredient for humid heat, and its role varies depending on the type of land-atmosphere coupling regime. In moisture-limited environments, mostly in the semi-arid sub-tropics, humid heat extremes occur during or immediately after rainfall through increased evaporation into a shallower boundary layer. In energy-limited environments, mostly in the moist tropics, humid heat extremes occur during the easing of rainfall through increased solar heating. Our most recent work focuses on atmospheric waves as a source of predictability. Equatorial Kelvin waves modulate humid heat, where the convergent phase of the wave (in the 850hPa winds) brings rainfall, followed by increased solar heating in the divergent phase. A similar process occurs over the Sahel region of west Africa within African Easterly Waves. We have also performed a set of idealised experiments with the Met Office Unified Model to quantify the role of surface moisture sources such as lakes, wetlands, and patches of wet soil from rainfall on evaporation, mesoscale circulations, and humid heat. Our results are valuable for identifying processes that must be well represented in weather and climate models for accurate weather forecasts and climate projections, and for informing early warning system development.

How to cite: Birch, C., Jackson, L., Chagnaud, G., Hidayat, A., Taylor, C., Law, J., and Marsham, J.: Meteorological drivers of humid heat extremes across the global (sub)tropics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5617, https://doi.org/10.5194/egusphere-egu25-5617, 2025.

Extreme heat can substantially impact worker productivity, causing fatigue, loss of focus, and illness in the workplace. As extreme heat increases under climate change, substantial reductions in labour productivity are expected. According to some models, economic costs from decreased worker productivity will be larger than any other climate related impacts, and corporations are therefore interested in understanding their potential exposure and vulnerability to heat stress in the future. Workplace regulations (e.g. ISO) and epidemiological studies have previously been combined to develop continuous functions relating workplace heat stress to labour productivity. In this work, we utilise productivity loss functions with climate model projections for future extreme heat exposure to assess changes in labour productivity loss. We present a new modelling framework for providing labour productivity loss projections for several scenarios (SSPs), work intensities (low, moderate, and high), and regions, specifically designed for financial services. We include a model of air conditioning availability, with which the productivity loss estimates can be scaled according to the likely adoption and use of cooling systems in the workplace.  

How to cite: Starr, A., Woodhouse, S., Leach, N., Brennan, J., Reveley, G., Woodcock, C., Ramesh, K., Stables, J., Ramsamy, L., Sullivan, P., and Padilha, V. L.: Estimates of labour productivity loss from climate model projections of extreme heat: Implications for financial services  , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6084, https://doi.org/10.5194/egusphere-egu25-6084, 2025.

The frequency, intensity and duration of heatwaves are expected to increase in Toronto, Canada due to both climate change and the urban heat island effect. This poses a greater health risk to those who are most vulnerable to heat among a population of almost three million residents. Therefore, designing and implementing appropriate heat management measures requires information about how heat vulnerability is distributed across the city. To fill the knowledge gap, two distinct methods are examined in this study to quantitatively measure the spatial distribution of heat vulnerability in Toronto. Both heat vulnerability indices (HVIs) consist of three dimensions, exposure, sensitivity and adaptive capacity, that are aggregated from remotely sensed land surface temperature measurements and socio-economic census data. The first method uses principal component analysis to derive an HVI, while the second, simpler method assigns equal weight to each input variable to derive an HVI. The HVIs display a similar U-shaped pattern of high heat vulnerability across Toronto, with low heat vulnerability areas primarily located along the Lake Ontario shoreline and throughout the fluvial ravine system. Further cluster analysis reinforces this spatial pattern. Notably, this study highlights that low-income tower block communities are significantly more vulnerable to heat than the city average. The qualitative consistency between the two HVI methods allows for ease of adoption of the simpler, equal-weight method for future use by the city. Integration of HVI updates into municipal operations can allow city planners and managers to monitor and visualize heat vulnerability consistently over time, develop decision-support tools for heat emergency preparedness and response and assess the effectiveness of heat adaptation strategies.

How to cite: Smith, K., Bu, S., Masoud, F., and Sheinbaum, A.: Spatial distribution of heat vulnerability in Toronto, Canada, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6795, https://doi.org/10.5194/egusphere-egu25-6795, 2025.

How to cite: Carniel, C. E., Wille, J. D., and Fischer, E.: Higher coastal heat stress maxima in storm-resolving GCMs, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8553, https://doi.org/10.5194/egusphere-egu25-8553, 2025.

Heatwaves and prolonged periods of extremely high temperatures are increasingly recognized for their significant impact across various regions and societal sectors, such as energy resources management, human health or agriculture. So far, the scientific community has focused on better understanding summer heatwaves with the use of various definitions and climatic indices. In this context, we introduce the Warm Spell Magnitude Index daily (WSMId) for extending beyond the warm period. This index is designed to capture the characteristics of consecutive days with anomalous high temperatures throughout the year, ultimately allowing us to investigate the various drivers behind prolonged periods of extreme heat.

WSMId builds upon the Heat Wave Magnitude Index daily (HWMId) by Russo et al. (2015), adapting a similar mathematical framework to identify heatwave-like events across multiple seasons. Calibration is based on the ERA5 reanalysis, focusing on the most intense summer European heatwaves of the past decades. Through this exercise, we aim in ensuring that the new index can meaningfully quantify episodes of anomalous warmth in non-summer periods, such as “false spring effects,” “extended summer periods,” and clustering of heatwaves.

This work could allow us to explore the underlying drivers behind these phenomena, aiming at enhancing our understanding of regional heatwave dynamics and their broader implications. We envision that WSMId will offer as a valuable tool for climate scientists, with potential applications in seasonal forecasting, development of climate adaptation strategies, and impact studies.

References

Russo, S., Sillmann, J., & Fischer, E. M. (2015). Top ten European heatwaves since 1950 and their occurrence in the coming decades. Environmental Research Letters, 10(12), 124003. https://doi.org/10.1088/1748-9326/10/12/124003

How to cite: Liakakos, A., Zittis, G., Tyrlis, E., and Hadjinicolaou, P.: Development and Application of the Warm Spell Magnitude Index daily (WSMId) in historical European heatwaves, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8843, https://doi.org/10.5194/egusphere-egu25-8843, 2025.

There is mounting evidence that heatwaves are increasing in frequency and duration around the world. South Asia is highly vulnerable to heatwaves due to its high heat exposure and limited adaptive capacity. Besides, South Asia is also a global aerosol hotspot. Many governments in South Asia plan to reduce aerosol emissions. However, any change to aerosol concentrations may also modify heatwave characteristics through ‘direct’ influences on the radiation budget and surface heat fluxes, and through ‘indirect’ impacts, such as on cloud formation and atmospheric circulation. Hence, lowering aerosol concentrations – while good for human respiratory health – may increase heat stress.

Previous studies have shown that aerosols can affect heat stress by influencing temperature, humidity, wind speed and radiation. However, the process of how aerosols affect heat stress has not been explained in detail. Our research intends to reveal the specific process of aerosols affecting heat stress from the perspective of surface energy balance, using WRF model as the initial methods. Improving understanding of the mechanisms of aerosol influence on heatwaves will help improve the long-range prediction capability for extreme heat. The results from the research will also be of interest to policy makers and disaster risk reduction communities, as it will help characterize the evolving public health burden to expect as temperature rises and aerosol loadings change in the future.

Our preliminary results show that the presence or absence of aerosol emissions affects surface temperature, 2-m temperature, and sensible heat flux in South Asia, with different patterns being observed during the daytime and nighttime.

How to cite: Meng, Y., Matthews, T., Sun, T., and Irvine, P.: Aerosol Modulated Heat Stress in South Asia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9670, https://doi.org/10.5194/egusphere-egu25-9670, 2025.

According to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment (AR6) report, continued global warming and changes in the climate system will increase the likelihood of severe impacts on both people and ecosystems. Accurately assessing the impacts of extreme heat has become a pressing research challenge, particularly as heatwave and heat-health warning systems evolve. Identifying key indicators of heat-related mortality and morbidity is critical to mitigating these impacts. Despite its importance, a comprehensive national-level assessment of heat vulnerability in India remains underdeveloped, leaving a significant research gap in heatwave disaster management. Moreover, the notion of vulnerability has evolved continuously, especially with updates in IPCC assessments, and it varies across communities, societies, regions, and countries and changes over time. This study analyses district-level (640 districts) vulnerability in India based on multiple methodologies, including the Simple Average Method, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the Principal Component 1 (PC1) method, and the Cutter and Finch (2008) approach, supported by a refined set of vulnerability indicators. Key data sources include the Census of India (CoI), alongside other datasets. The spatial distribution of vulnerability indicates that North and Eastern India are the most susceptible to heat related vulnerability. In the context of India’s agrarian economy, this mapping is crucial for supporting the livelihoods of farmers and outdoor workers, who are among the most susceptible to extreme heat events. This study underscores the urgent need for a robust, methodologically comprehensive national heat vulnerability assessment to guide targeted policy interventions and enhance resilience against the escalating threat of extreme heat in India. The vulnerability map developed will serve as a foundational tool for creating a comprehensive risk map by integrating hazard and exposure, enabling targeted interventions to mitigate heat-related risks.  

Keywords: Heatwave, Heat Stress, India, IPCC, Vulnerability.

How to cite: Shilin, A. and Karmakar, S.: Unveiling Heat Vulnerability Across India: A Multi-Method Analysis of District-Level Indicators in the Context of Climate Change., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10921, https://doi.org/10.5194/egusphere-egu25-10921, 2025.

The increase of heatwaves has been observed due to the influence of a changing climate on the planet. Among populations, this phenomenon is associated with a rise in various types of cardiovascular diseases, particularly among vulnerable groups such as the elderly and individuals with pre-existing conditions. This presents a growing concern for healthcare systems and urban management. Although the literature predominantly highlights these events on the European continent, it is known that heatwaves are not confined to this region. Thus, the objective of this study is to evaluate the temporal evolution of the phenomenon through an index developed by LAMMOC/UFF to detect extreme heatwaves in the 26 Brazilian capitals and the Federal District. For this purpose, ERA5 reanalysis data from January 1950 to September 2024 were utilized. The index was calculated hourly and aggregated on a daily and monthly basis. Initially, the Mann-Kendall test was employed to assess the trends in the time series. It was observed that 19 of the analyzed cities exhibited a positive trend, two showed no trend (Florianópolis and Fortaleza), and six coastal cities (primarily in northeastern region, except for Teresina and Recife) located in a region with barotropic conditions did not show occurrences of extreme heat waves during the study period. Subsequently, a correlation matrix between the time series was analyzed, along with clustering, identifying three distinct groups: one less affected by extreme heat waves, a second intermediate group with a positive trend, and a third group that exhibited the most significant positive trends. The time series were then divided, applying a clustering technique, into three distinct periods: 1950–1974 (P1), 1975–1999 (P2), and 2000–2024 (P3). This allowed the calculation of the average accumulated values for these three periods across all evaluated cities to observe the percentage differences. For instance, from P1 to P3, in the southeastern region, the cities of Rio de Janeiro and São Paulo presented an approximate 613% and 643% increase in extreme heatwave occurrences, while in the south region, Porto Alegre had an increase of 557%. In the midwest, Campo Grande and Cuiabá presented an increase of 983% and 671% and in the northeast Recife and Teresina presented an increase of 217% and 257%. Furthermore, the third cluster displayed the highest trends and averages, encompassing most cities of the Northern region. Therefore, cities such as Macapá, Rio Branco, Manaus and Belém experienced an increase of  3154%, 1339%, 1100% and 1028%, respectively. Notably, it was observed that this third cluster predominantly comprised cities in the northern region of the country, situated within the Amazon biome. These findings call for further investigation into the relationship between this trend and factors such as increasing deforestation and forest fires in the region. These alarming results highlight the urgent challenges in environmental, healthcare, and urban management driven by climate change and extreme events, emphasizing the need for investments in municipal health, resilience, and climate mitigation and adaptation strategies.

How to cite: Galves, V. L., Cataldi, M., Alcoforado Sphaier, L., and da Fonseca Aguiar, L.: Temporal Evolution and Intensification of Extreme Heatwaves in Brazil’s capitals: Insights from the XHW Index (1950–2024), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12454, https://doi.org/10.5194/egusphere-egu25-12454, 2025.

Since the early 2000s, Spain has experienced a consistent increase in the frequency and severity of heatwaves. Studies indicate that the number of days with extreme heat summer temperatures has increased, particularly in regions such as Andalusia, Valencia, and Madrid. The 2003 heatwave, regarded as a pivotal event in Europe, resulted in over 70,000 additional deaths across European countries, with Spain contributing almost 20% to this statistic (approximately 13,000 deaths). Further studies emphasise that extreme heatwave events, like the 2003 episode, are no longer exceptional phenomena but are becoming recurrent, even in areas where such events were rare before 1980. These extreme heatwave episodes can pose a serious risk to human health, even leading to severe heat illnesses such as heatstroke, hyperthermia, and critical dehydration. Furthermore, they can also exacerbate pre-existing pathologies like cardiovascular and respiratory diseases. This results in a substantial increase in hospitalisations and even fatalities. This study aims to employ a novel Extreme Heatwave (XHW) characterization index, based on the human body's response to water loss during such events, to evaluate the temporal evolution of these occurrences across Spain, with a special focus on the 13 most populated cities (> 300,000 inhabitants). The analysis utilised ERA5 reanalysis (~25 km resolution) for the period 1950–2024. The results showed that, through the application of a k-means clustering technique, XHW occurrences in Spain could be categorised into three distinct periods: 1950–1977 (P1), 1978–2002 (P2), and 2003–2024 (P3). For this assessment, the daily XHW index values were accumulated monthly and then annually at each grid point of the reanalysis data subsequently interpolated using Bilinear Interpolation for the 13 Spanish cities. The highest percentage increases in cumulative XHW index values were observed in Andalusia, exceeding 1000% in certain grid points when comparing the sums of P3 and P1. Some regions and cities, such as Madrid and Barcelona, experienced virtually no XHW episodes during P1, but these became relatively frequent (in over 80% of the years) during P3. In certain cities and regions, particularly in the Southwest of the country, it was found that during P3, one in every four summer days, on average, presented an XHW index value greater than zero. The results revealed and quantified an alarming scenario regarding the increased intensity and frequency of XHW episodes in Spain. This trend is compounded by the broader context of climate change, which coincides with the warming of the North Atlantic Ocean near the western coast of the Iberian Peninsula and the Mediterranean Sea along the remaining coastal regions of the country. Such developments are likely to exacerbate this situation further in the coming years, potentially precipitating a severe crisis in the Spanish public healthcare system. 

How to cite: Cataldi, M., Victalino Galves, V. L., Alcoforado Sphaier, L., and Garnés-Morales, G.: Quantifying the Escalation of Heatwave Events in Spain: A Study Based on the new XHW Index, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12585, https://doi.org/10.5194/egusphere-egu25-12585, 2025.

Temperature rise, amplified by climate change, has a direct impact on human health, exacerbating the risk of heat-related illnesses, such as heat stroke and dehydration. The ((HI) is a parameter that combines air temperature and relative humidity to assess the degree to which the human body perceives heat. In this study, we used the Steadman Man-Kendall HI equation, Sen's slope estimator to evaluate monthly and annual heat index trends for Kano and Bamako between 1992 and 2022. The results indicated that the heat index exhibited a positive trend of 0.01°C yr^-1, although this trend was less statistically significant with a p-value greater than 0.05. In contrast, a significant negative trend was observed in Bamako, with an annual change of approximately -0.06°C yr^-1. It was also observed that the highest heat index values, demonstrating the risk of heat exhaustion, were recorded between April and May, ranging from 30 and 41°C in Kano and 31 to 42°C in Bamako. In contrast, December, January, and February were the coolest months for both cities, with HI values ranging from 23 °C to 28°C in Kano and 25 °C to 28°C in Bamako. These findings underscore the need for policymakers to adopt adaptive strategies to address the health challenges posed by the extreme Heat Index in vulnerable regions.

How to cite: Dicko, K., Umaru, E. T., Sanogo, S., Loewner, R., and Okhimamhe, A. A.: Assessing changes in heat index associated with climate variability in Sahelian Cities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12625, https://doi.org/10.5194/egusphere-egu25-12625, 2025.

Mass gatherings in India, encompassing religious and cultural events, draw millions of participants annually and are increasingly vulnerable to the impacts of climate change, particularly intensifying heat stress during summer months. This study evaluates the projected risks of heat stress at six prominent locations: Ajmer, Amritsar, Delhi, Haridwar, Prayagraj, and Ujjain. Wet-bulb temperature (Tw), a reliable indicator of heat stress, is used to assess the impacts of rising temperature and humidity levels. Simulations from dynamically downscaled bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets, under SSP2-4.5 and SSP5-8.5 scenarios, are used from to capture the spatiotemporal variability of heat stress throughout the 21st century. Results indicate a substantial increase in maximum Tw during the summer months (March to June) across all sites. By mid-century, Tw is projected to reach or exceed “danger” levels, with Prayagraj and Delhi identified as high-risk zones. Extreme danger thresholds are anticipated at these locations by the late 21st century under the SSP5-8.5 scenario. Haridwar, while showing the lowest risk among the study sites, is not immune to the impacts of heat stress. Focusing on the intersection of climate change and public health in densely populated regions, the findings highlight the urgent need for adaptation and mitigation strategies to protect public health during future mass gatherings in India.

How to cite: Dutta, S., Samanta, D., and Deb, P.: Intensified Heat Stress Risks at Indian Mass Gathering Sites Due to Climate Change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12636, https://doi.org/10.5194/egusphere-egu25-12636, 2025.

In this study we assess the occurrence of summertime heatwaves (HW) and their underlaying atmospheric drivers under current and past climate conditions for Sweden using a weather regime classification based on optimized fuzzy rules. It uses daily mean 500hPa geopotential height of ERA5 reanalysis at 0.5° spatial resolution as atmospheric input data for the period of 1940 to 2022. Daily anomalies of 500hPa geopotential height at each grid over the Euro-Atlantic region have been computed as daily deviations from the long-term climatology of 1981-2010. Daily mean temperature from station data over the same 30-year period, distributed in the whole of Sweden, serve as predictand to reflect the variability of local climate. They help to optimize pre-defined fuzzy rules describing individual weather regimes (WRs). A set of twelve temperature-induced WRs are classified as daily timeseries for the years of 1940 to 2022.

HWs are investigated using the Excess Heat Factor (EHF) and the NDQ90 index when daily maximum temperature exceeds the 90^th percentile over the reference period based on ERA5 reanalysis data. The EHF is a measure of heatwave intensity related to human health impacts and consists of two indices describing the deviation of the three-day mean air temperature from the long-term 95^th percentile-based climatology and the short-term anomaly of the previous 30 days. The Chi-square test is used to study the significance of the classified WR along with the co-occurrence of a HW. 

For the case-study of Stockholm, 985 HW events are detected by the NDQ90 index from 1940 to 2022 during May to August. Nearly 83% of detected HWs is found to coincide with the occurrence of four types of anticyclonic WRs. One type of anticyclone explains nearly 40% of the detected summertime HW events. During August, it particularly explains 47% of detected HWs. It is likely because of the strong high-pressure system situated over the North Sea and southern Scandinavia that caused the warmer-than-average temperatures over northern Europe. Similar results are found when using the EHF. In addition, we present how this approach can be extended to investigate the linkages of leading WRs and the occurrence of detected HWs in major European cities. 

How to cite: Klehmet, K. and Yang, W.: Current and past atmospheric heat wave precursors for Sweden: A Machine Learning-based weather regime approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13141, https://doi.org/10.5194/egusphere-egu25-13141, 2025.

Amidst unprecedented rising global temperatures, this study investigates the historical context of heatwave (HW) events in Eastern Europe. The record-breaking 2023 summer, featuring a HW lasting for 19 days in the south-eastern part of Romania, extending up to Ukraine, necessitates a deeper understanding of past extreme events. Utilizing statistical methods on long-term station data spanning from 1885 to 2023, we aim to detect and analyze historical HWs, particularly focusing on events predating 1960. This extended timeframe allows for a more comprehensive assessment of noteworthy extremes compared to recent decades. We used both a percentile-based threshold and a fixed absolute temperature threshold to identify HW events. Our analysis identifies two critical periods with increased HW frequency and intensity: 1920–1965 and 1980–2023, respectively, highlighting the most extreme events in August 1946, August 1952, July 2012, June 2019, and August 2023. Furthermore, reanalysis data shows that historical HWs, similar to the 2023 event, were associated with large-scale European heat extremes linked to high-pressure systems and they were accompanied by extreme drought, thus leading to compound extreme events. We find that while a clear trend emerges towards more frequent HWs from the 1980s onward, the analysis also uncovers substantial HW activity on daily timescales throughout the 1885-1960 period. Moreover, we highlight the intertwined impacts of climate change and multidecadal internal variability on HW patterns, with evidence suggesting that both contribute to the increasing frequency and intensity of these extreme events. Attribution analysis reveals that the extreme summer temperatures observed in 2023, would not have been possible in the absence of anthropogenic climate change. Regardless of future warming levels, such temperatures will occur every year by the end of the century. Our research highlights the value of extending the historical record for a more nuanced understanding of HW behavior and suggests that extreme heat events, comparable to those experienced in recent decades, have occurred throughout the analyzed period.

How to cite: Ionita-Scholz, M., Vaideanu, P., Antonescu, B., Roibu, C., Ma, Q., and Nagavciuc, V.: Examining the Eastern European extreme summer temperatures of 2023 from a long-term perspective: the role of natural variability vs. anthropogenic factors , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15014, https://doi.org/10.5194/egusphere-egu25-15014, 2025.

In recent decades, ecosystems have faced extreme climatic events, such as high temperatures, which have significantly impacted human health, wildfires, and agricultural losses. These heat extremes are expected to continue increasing in frequency and intensity, necessitating a deeper understanding and close monitoring of these events.

The primary objective of this study is to quantify and compare the sectoral impacts of 1.5 °C and 2.0 °C global warming scenarios on human health, agriculture, and wildfire risks using high-resolution climate simulations. Health impacts were assessed using the Health Heat Index (HHI), while wildfire risk was analyzed using the Forest Fire Danger Index (FFDI), which evaluates the duration and frequency of fire seasons in terrestrial ecosystems. Agricultural impacts were quantified by estimating crop heat stress during thermal-sensitive periods, calculating anthesis heat stress (AHS), and normalizing production damage indices.

These analysis reveals a significant increase in areas exposed to critical levels of heat stress, affecting human health, ecosystems, and food security. Our results show a significant rise in risks measured by the HHI And the analysis of the FFDI reveals also an extension in the duration and frequency of fire-prone seasons. In agriculture, the assessment of heat stress during sensitive periods, particularly through the Anthesis Heat Stress Index (AHS), highlights worsening production losses.

These findings highlight the urgency of adopting ambitious mitigation strategies to minimize risks to vulnerable populations and preserve ecosystems and food security.

How to cite: Essoussi, S., El Morjani, Z. E. A., and Sadiq, A.: Assessing the Effects of Extreme Heat on Health, Agriculture, and Ecosystemsin Morocco under different levels of climate warming, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16229, https://doi.org/10.5194/egusphere-egu25-16229, 2025.

The impacts of extreme heat and other climate extremes have intensified with climate change, compounded by international pressures from geopolitical tensions that exacerbate socioeconomic inequalities and harm human wellbeing. This study examines factors affecting life expectancy in the United Kingdom, focusing on the relative influence of socioeconomic characteristics and climate change variables, such as heat and flood exposure. The primary research question explored whether wealth could offset the adverse effects of climate change on life expectancy. The analysis covered various age groups, from 0–9 years to 80–89 years, across UK local authority regions. For instance, among 40–49-year-olds, the life expectancy gap between low- and high-life expectancy regions in England was 2 years, 9 months, and 24 days (84.0 vs. 85.97 years). Socioeconomic factors and climate change indicators, including heatwave occurrence, accounted for 77% of this disparity, while extreme rainfall showed no significant effect. The findings highlight that climate change, particularly through the rising frequency of heatwaves, has significantly influenced life expectancy in certain UK regions. This highlights the critical need to address the interplay between climate risks and socioeconomic inequalities to safeguard public health and wellbeing.

How to cite: Brimicombe, C. and Otto, I.: Heat exposure can reduce life expectancy even across wealthy regions: a UK case study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16500, https://doi.org/10.5194/egusphere-egu25-16500, 2025.

As climate change intensifies, heat extremes pose an increasing threat to human health, leading to heightened morbidity and mortality particularly among vulnerable populations.

Physiological evidence demonstrates that high humidity exacerbates heat strain by impairing the body’s ability to thermoregulate through sweat evaporation. Therefore, metrics such as the wet bulb temperature, which combine both temperature and humidity effects, are commonly used to assess heat-related health risks, highlighting tropical and other humid regions as particularly at risk, while regions dominated by dry heat in the mid-to high latitudes appear comparatively less affected.

However, the tens of thousands of excess deaths caused by prolonged exposure to high ambient temperatures during e.g. recent record-breaking European heatwaves in 2003 and 2022 suggest that heatwave characteristics beyond temperature and humidity might need to be accounted for to accurately capture severe health impacts under dry heat conditions.

Here, we propose to enhance global heat stress assessments by addressing emerging spatio-temporally compounding features of heat extremes, which could greatly aggravate health impacts but due to their complexity are often not considered. Preliminary results will be presented from our efforts to develop a standardized, impact-focused heat stress metric that provides more robust and consistent assessments across diverse regional and climatic settings by integrating the cumulative effects of prolonged and sequential heat exposure.

How to cite: Langer, R. and Kornhuber, K.: Towards Improved Global Heat Stress Projections by Accounting for Spatio-Temporally Compounding Risks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19232, https://doi.org/10.5194/egusphere-egu25-19232, 2025.

Recent research has elucidated that the implementation of heat-health prevention plans has been effective in reducing heat-related mortality in Europe. However, it remains largely unresolved whether heat alerts issued based on weather forecasts, which constitute a core element of these plans, independently contribute to the observed mortality reductions. In this contribution, we will present preliminary results from a study based on daily heat alert data and all-cause mortality series from major cities in several European countries, including Germany, Poland, and Portugal. We used random forest classification to identify days, which would have likely experienced a heat alert in the time period before the implementation of the early warning system. Subsequently, we applied time-series regression methods, including distributed lag non-linear models, combined with a difference-in-difference approach to assess whether actual heat alerts are associated with a reduction in all-cause mortality. City-specific results were pooled using mixed-effect meta-regression techniques. Based on a reduced dataset analysed so far, we found strong geographic heterogeneity, with evidence for a protective effect of heat alerts seen only in part of the cities. The pooled relative risks were close to 1, suggesting that the implementation of heat alerts alone cannot explain the mortality reductions associated with the introduction of more comprehensive heat-health prevention plans. Future research will need to investigate other elements of heat-health prevention plans to identify the most effective preventive measures as Europe faces further escalating heat due to climate change.

How to cite: Huber, V., Kořistková, M., Breitner-Busch, S., Feldbusch, H., Schneider, A., and Urban, A.: The effectiveness of heat alerts issued by national weather services in preventing heat-related mortality in Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19725, https://doi.org/10.5194/egusphere-egu25-19725, 2025.

How to cite: Popov, H. and Stepanyuk, O.: Heat Waves over the Balkans: A statistical analysis, towards a predictive ML model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20371, https://doi.org/10.5194/egusphere-egu25-20371, 2025.

Countless heat records were broken in recent years, leading to thousands of heat-related deaths. This raises the question of how much worse heat-related mortality could become in coming years if a potential worst-case heatwave lasts for several weeks or reaches unprecedented intensity. Here, we develop impact storylines for worst-case heatwaves and associated heat-related mortality in France, Germany, and Switzerland. 

We compare several physical climate storyline approaches to quantify plausible extreme heatwaves and combine these with empirical heat-mortality relationships. The storylines are based on (a) using a Single-Model Initial Condition Large Ensemble (SMILE), (b) ensemble boosting, and by looking for the most extreme events (UNSEEN approach) in the initialized (c) 45-day sub-seasonal re-forecast and (d) 7-months seasonal forecasting system using the ECMWF Integrated Forecast System (IFS).

In all four approaches we find physically consistent week-long heatwaves possible in the climate of 2020 that exceed the observed 7-day record temperatures by more than 5°C and associated mortality impacts exceeding the observed maximum by 30-90%. Even more severe consequences would arise from possible five-week heat periods of unprecedented intensity, which would lead to more than a doubling of impacts. Developing these impact storylines can inform the stress-testing of socio-economic systems for preparing appropriate emergency response capacities.

How to cite: Vicedo Cabrera, A. M., Luthi, S., Huber, V., Pascal, M., Beyerle, U., Pyrina, M., Domeisen, D., and Fischer, E.: Storylines for month-long heatwaves and associated heat-related mortality impacts over Western Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20537, https://doi.org/10.5194/egusphere-egu25-20537, 2025.

The extent to which changes in labour force outcomes can be attributed to historic climate change is currently unknown. Here, we combine robust estimates of the impact of climatic stressors on labour supply, labour productivity, and a combined metric of the two - effective labour, with novel historic climate forcing data to quantify the effect of historic climate change on labour. We do this at the global and regional level, taking explicit account of heterogeneity of working conditions. Glob- ally, effective labour in outdoor working conditions was 1.8 percentage points lower in 1901-2019 than it would have been without climate change. The attributable declines have increased over time, rising to 3.6 percentage points between 2001 and 2019. The highest declines have been in the relatively lower-income regions of Western Africa, South-Eastern Asia, and Middle Africa, where climate change has increased workforce inequalities. We also estimate the economic effects through impacts on GDP and find up to a 12% GDP loss for some regions. Our findings can help improve the design of better labour protections, improve worker health, enhance productivity and economic growth, and inform better climate adaptation and resilience.

How to cite: Dasgupta, S.: Attribution of historical changes in labour outcomes to climate change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20600, https://doi.org/10.5194/egusphere-egu25-20600, 2025.

Exceptional rainfall hit West Central Africa in October 2019. We analyzed regional moisture and Moist Static Energy (MSE) budgets to understand the underlying mechanisms, focusing on dynamic and thermodynamic effects. The moisture budget analysis revealed that precipitation anomalies were primarily driven by dynamic effects. In the north of the region, horizontal moisture advection induced by horizontal wind anomalies dominated, while vertical moisture advection was key in the south. Thermodynamic effects, though secondary, contributed up to 35% in the north and 15% in the south. The MSE balance showed that anomalous vertical motion was dominated by dynamic effects in the north, particularly wet enthalpy advection induced by horizontal wind anomalies. West of the Congo Basin, increased net energy balance was the primary driver of vertical motion changes. Horizontal and vertical MSE advection appeared less significant. Strong MSE balance anomalies in the north were linked to its meridional component, especially meridional wind anomalies in the dynamic effect and meridional latent heat anomalies in the thermodynamic effect. Our findings suggest that both dynamic and thermodynamic effects must be considered to adequately anticipate such extreme events. Understanding these mechanisms could enhance forecasts and projections, ultimately improving the region's resilience to extreme weather.

How to cite: Kenfack, K., Marra, F., Yepdo Djomou, Z., Angennes Djiotang Tchotchou, L., Tchio Tamoffo, A., and Appolinaire Vondou, D.: Dynamic and thermodynamic contribution to the October 2019 exceptional rainfall in West Central Africa, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-347, https://doi.org/10.5194/egusphere-egu25-347, 2025.

Extreme precipitation events are climatic hazard phenomena that can lead to riverine floods, flash floods, and landslides, posing significant threats to society and the environment. In this study, we examine the effects of climate change on the frequency and intensity of extreme precipitation events in the Middle East and North Africa (MENA) region. Our analysis focuses on daily precipitation simulations at a spatial resolution of approximately 50 km, provided by both regional and global climate models. Detecting changes in extreme precipitation events is challenging due to their high variability in time and space. Therefore, we employ the Simplified Meta-statistical Extreme Value (SMEV) method, an advanced extreme precipitation frequency analysis approach that enables a more robust frequency analysis of extreme precipitation by reducing uncertainty compared to traditional methods. We find that, while average precipitation levels exhibit heterogeneous changes across the MENA region, a general intensification of extreme precipitation is expected. Notably, the intensification is stronger for rarer events. Our results also indicate that changes in both average and extreme precipitation across the MENA region are spatially non-uniform, with some areas experiencing intensification while others show a downward change, with regional variability in change strength. The strongest intensification in extreme precipitation is projected over central Africa and the northern Arabian Sea region. We also find that the regional patterns in extreme and average precipitation changes are not always aligned. Notably, mean annual precipitation is expected to decline over the Mediterranean, while extreme precipitation return levels are projected to intensify in much of the area.

How to cite: Goldschmidt, Y., Marra, F., and Morin, E.: The impact of climate change on the frequency and intensity of extreme precipitation events in the Middle East Nort Africa (MENA) region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-460, https://doi.org/10.5194/egusphere-egu25-460, 2025.

Previous studies, based on satellite optical data and VLF network observations, determined that lightning superbolts (SBs) that have exceptionally high peak currents occur predominantly over the oceans. Satellite measurements were categorized according to optical intensity out of which the highest 1% were categorized as SBs and occurred predominantly over the NW Pacific near the coast of Japan. In contrast, VLF measurements were categorized according to their energy, out of which the highest 0.001% of the cloud-to-ground lightning strikes (CG) were categorized as SBs and occurred predominantly over the oceans (>90%) and during the wintertime of the northern hemisphere.

This study analyzed the spatial-temporal distribution of 3.8·10⁹ CG strokes observed during 2018-2023 by the Earth Networks Total Lightning Network (ENTLN). It was determined that the proportion of high peak current (I_peak) CG over the oceans compared to land was greater than 1 starting at PC>80 kA and no more than ~2 for PC=180-310 kA. Above 200-kA, 67% of the CG occurred over the oceans. The percent of PC>200 kA from the total CG (PC>2 kA) is ~0.3%. The percent of the total CG is 0.001% at PC~935 kA, where the sea-to-land ratio is only ~1.2. Over the annual cycle, CG with PC>200 kA was not observed at all during the months of May-September in both hemispheres, while during the rest of the year, most of the events over land occurred in the southern hemisphere at a ratio of 4.4:1 relative to the northern hemisphere, while over the oceans there was a 1:1 ratio between hemispheres. Finally, the hourly distribution of CG over land with PC>200 kA is consistent with the shape of the Carnegie curve as determined for lightning in general in previous studies. The hourly distribution over the oceans exhibits a higher number of events from midnight until 06:30 local time and is relatively constant and low throughout the daytime until 17:30 and afterward increases up to the midnight maximum frequency. These results demonstrate the small contribution of CG with PC>200 kA over the oceans to the atmospheric electric field variations in the Carnegie curve. 

How to cite: Asfur, M., yair, Y., and Silverman, J.: The sea-land ratio of extremely strong cloud-to-ground lightning is significantly smaller than previously estimated, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-793, https://doi.org/10.5194/egusphere-egu25-793, 2025.

The Middle East, with its diverse and complex geographical and environmental conditions, is one of the world's most vulnerable regions to climate change making it particularly susceptible to extreme precipitation events (EPE). These events often are driven by complex interactions between tropical and extratropical weather systems, seasonal variability, and atmospheric rivers. This study presents a specified probability map of extreme precipitation events across the Middle East region. It highlights areas at a higher risk due to heavy rainfall events and extreme events like flash floods.

The study includes 80 years of ERA5 reanalysis data (1941–2020) and three high-resolution AR6 models under two scenarios: the middle-of-the-road scenario (SSP2-4.5) and the pessimistic, high-emissions scenario (SSP5-8.5) for the period 2021–2050. By considering the trends of 10 certain indices (EPIs) based on both historical precipitation data and future projections, The findings categorize areas into four distinct risk levels, ranging from no risk to high risk. The statistical significance of EPIs was assessed using the nonparametric Mann–Kendall test.

Extreme precipitation events in the Middle East are influenced by various climatic and meteorological factors, and certain areas are more susceptible to these events. The outcome of this analysis show several regions of high risks in north and west part of Iran, northern and central Turkey, central Iraq, and eastern Saudi Arabia. Regions closer to the Caspian Sea and Persian Gulf exhibit higher vulnerability, while low-to-medium risk regions involve parts of Syria, Jordan and southern Iran. Countries such as Egypt and southern Saudi Arabia have just a very slight chance of hazard. In the SSP2-4.5 scenario, the general risk map closely resembles the risk based on historical data. However, in the pessimistic scenarios, most regions that were in the low-to-medium risk classes shift toward high risk.

These findings give confidence to the potential impacts of flooding and infrastructure challenges in regions unfamiliar to dealing with heavy rainfall. This information is important for water management strategies that are part of preparing for climate change impacts, which clearly emphasize the rise in extreme weather patterns across the Middle East. 

Keywords: Precipitation, Extreme events, Heavy rainfall, Risk assessment, Middle East

How to cite: Fakour, P. and Ustrnul, Z.: Assessing Susceptible Areas for Extreme Precipitation in the Middle East: Insights from Historical Data and Their Shifts Under Future Projections, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-903, https://doi.org/10.5194/egusphere-egu25-903, 2025.

Compound Dry and Hot Events (CDHEs) are increasingly recognized as critical
challenges of the 21st century, marked by the simultaneous occurrence of prolonged
dry periods and high temperatures. These events, more severe than individual
extremes, adversely impact water resources, agriculture, public health, and
infrastructure. Globally, CDHEs are becoming more frequent, longer, and intense,
particularly in climate-sensitive regions, including India. Urban areas like New Delhi,
with its high population density, limited water resources, and susceptibility to extreme
weather, are particularly vulnerable. This study examines CDHEs in New Delhi over 66
years (1958–2023) using monthly precipitation and temperature data from the
TerraClimate database. Standardized indices like the Standardized Compound Event
Indicator (SCEI), Standardized Precipitation Index (SPI), and Standardized
Temperature Index (STI), along with the joint probability density function (JPDF), are
applied to assess trends in frequency and severity. The analysis reveals a marked
increase in CDHEs post-1990, linked to regional climate changes, accelerated
urbanization, and evolving land-use patterns. Future projections under +2°C and +4°C
global warming scenarios indicate a substantial rise in both the frequency and severity
of CDHEs, posing critical threats to urban systems, including water scarcity, heat stress,
and economic losses. The results also suggest potential interactions between extreme
temperatures and declining rainfall, which could amplify the vulnerability of socio-
economic sectors. This research underscores the urgent need for integrated climate
adaptation and mitigation strategies tailored to urban environments. By linking historical
patterns of CDHEs with future projections, the study provides a comprehensive
perspective on hydroclimatic extremes in New Delhi. These findings are crucial for
informing urban planning, resource management, and policymaking aimed at enhancing
resilience against the cascading impacts of compound extremes in a rapidly warming
world.

How to cite: Sahu, V., Swarnkar, S., and Raj, C.: Understanding Compound Dry-Hot Events in New Delhi: HistoricalTrends and Projections under +2°C and +4°C Warming Scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1044, https://doi.org/10.5194/egusphere-egu25-1044, 2025.

This lecture provides a critical analysis of drought indices, emphasizing their role in evaluating drought severity while addressing the challenges associated with their application. It highlights the inherent complexity of drought assessment, given the multifaceted nature of drought phenomena, the various types of drought, and the intricate mechanisms underlying their development. A central focus is the distinction between drought and aridity, as well as between drought metrics and indices—concepts that are frequently misunderstood or conflated.

Particular attention is given to atmospheric drought indices, especially those incorporating atmospheric evaporative demand (AED). These indices are crucial for assessing water stress but have faced criticism for certain limitations. One notable issue is the "index-impact gap," where atmospheric drought indices often indicate more severe droughts than those reflected in hydrological and ecological metrics derived from Earth System Models (ESMs), particularly in future climate scenarios. Atmospheric indices do not directly account for soil moisture or vegetation dynamics. Nonetheless, AED reflects atmospheric conditions rather than direct water reservoirs and fluxes, making AED-based indices valuable for understanding atmospheric drivers of drought. This value is reinforced by AED's critical role in intensifying drought through increased evaporation, heightened plant water stress, and reduced photosynthesis.

The lecture further focuses into the uncertainties inherent in ESM projections of ecological and hydrological variables, such as soil moisture and runoff. These uncertainties arise because ESMs often underestimate drought severity due to challenges in simulating complex hydrological and physiological processes. The difficulties stem from limitations in modelling plant physiology, water cycles, and ecosystem responses, compounded by biases in key variables such as evapotranspiration. While ESM outputs are valuable for drought assessments, relying exclusively on them risks producing misleading conclusions.

This issue connects with the role of rising atmospheric CO₂ concentrations, a factor commonly incorporated into ESM simulations, which adds another layer of complexity. Elevated CO₂ levels can enhance plant water-use efficiency and photosynthesis but also introduce uncertainties regarding their impacts on evapotranspiration and soil moisture. These dynamics generate complex feedbacks with AED and other variables, further complicating drought severity assessments, particularly in future ESM simulations.

To address these challenges, the lecture advocates for an integrated approach that combines atmospheric drought indices with hydrological and ecological metrics. Such an approach ensures that the intensifying role of AED under global warming is neither overlooked nor overstated, thereby improving the accuracy of drought assessments, especially in the context of future climate scenarios.

How to cite: Vicente Serrano, S. M.: On the Use of Drought Indices for Drought Severity Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1572, https://doi.org/10.5194/egusphere-egu25-1572, 2025.

Extreme windstorms are among the most destructive and costly extreme weather phenomena in the European region. Extreme wind speeds can directly damage or destroy structures like power pylons. Indirect damages are caused by falling trees, which can break power lines or block roads and railways. Assessing long-term trends in windstorm losses and attributing them to climatic and socio-economic changes requires comprehensive and systematic collection of historical information. Here, we present windstorm impact data for Europe that is part of the HANZE (Historical Analysis of Natural HaZards) database.

The dataset covers windstorms that have occurred in 42 European countries between 1950 and 2020. The data was obtained by extensive data-collection from more than 800 sources ranging from news reports through government databases to scientific papers. The dataset includes 1358 events characterized by at least one impact statistic: area affected (forest felled by wind), fatalities, persons affected (loss of electricity) or economic loss. Economic losses are presented both in the original currencies and price levels as well as inflation and exchange-rate adjusted to the 2020 value of the euro. The spatial footprint of affected areas is consistently recorded using subnational units corresponding, to the European Union’s Nomenclature of Territorial Units for Statistics (NUTS), level 2 and 3. Daily start and end dates, information on causes of the event, notes on data quality issues or associated non-wind impacts, and full bibliography of each record supplement the dataset. The database can be viewed, filtered and visualized online: https://naturalhazards.eu. The dataset is designed to be complementary to HANZE-Exposure, a high-resolution model of historical exposure changes (such as population and asset value), and be easily usable in statistical and spatial analyses, including multi-hazard studies.

How to cite: Terefenko, P., Paprotny, D., Śledziowski, J., and Giza, A.: Database of windstorm impacts in Europe, 1950–2020, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1600, https://doi.org/10.5194/egusphere-egu25-1600, 2025.

Severe convective weather systems, characterized by their small spatial scale and rapid, violent development, frequently give rise to disasters such as rainstorms, lightning, gales, and hail. Accurate forecast of such systems has long been a challenging issue of weather forecasting and a dilemma for disaster prevention and mitigation in Shanghai. This research introduces an intelligent forecasting technology for severe convective rainfall systems in Shanghai, encompassing adaptive radar observation of strong convection targets, identification and prediction of typical convective features, machine learning-based correction of numerical prediction errors, and system integration.

In this technology, to address the problem of unbalanced samples with a scarcity of heavy rainfall cases, an autoencoder for noise reduction and ordinal boosting regression module is designed. A FocalLoss method is employed to weight the Loss function, thereby transforming the regression task of precipitation values into multiple classification tasks. An adaptive scale selection method is constructed to better represent the heavy rainfall system. In this method, the spatio-temporal scales of convective systems are adaptively selected, enabling targeted correction of heavy rainfall prediction.

Finally, an intelligent monitoring and early warning system capable of predicting the three-dimensional structure and evolution of strong convective systems has been established and put into operation. This system was evaluated during the flood seasons of 2022-2023. The results indicate that the TS score for 24-hour heavy rainfall (50mm) was significantly enhanced compared to the operational numerical weather prediction system of Shanghai Meteorological Service (SMS). This technology has been extended to application in urban disaster prevention and mitigation.

How to cite: Ma, L.: An Intelligent and Targeted Forecasting Technology for Severe Convective Rainfall Systems in Shanghai, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1914, https://doi.org/10.5194/egusphere-egu25-1914, 2025.

How to cite: Zhou, M.: A real-time storm surge prediction system for the Guangdong–Hong Kong–Macao Greater Bay Area under the background of typhoons: model setup and validation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1929, https://doi.org/10.5194/egusphere-egu25-1929, 2025.

Flash floods represent the most devastating natural hazard in Mediterranean Spain, resulting in significant fatalities and economic losses. Their characterization has been hindered by the absence of a comprehensive database. This study addresses this challenge by compiling and analyzing data using established methodologies. The analysis is structured into two parts. The first part explores extreme daily rainfall patterns in Mediterranean and semi-arid climatic zones during the extended warm season. It utilizes data from 99 flash floods to investigate spatial and temporal distributions and derive envelope curves. The findings reveal that the spatial and temporal patterns of extreme flash floods closely align with those of extreme daily precipitation. The envelope curves are consistent with other regions. In the semi-arid region, flash floods exhibit higher magnitudes, but its envelope curve declines more steeply with increasing drainage size, reflecting distinct climatic and physiographic factors. The second part examines 13 major flash flood events using high-resolution hydrometeorological data. These events are characterized based on climate, basin morphology, precipitation, runoff ratio, lag time, and flashiness. The results confirm previous observations regarding relief ratio, rainfall intensity, and flashiness. However, runoff coefficients are lower than those in other European regions due to the high initial soil storage capacity, which prolongs lag times in smaller basins. In larger basins, flow hydraulics lead to reduced lag times that fall below the lower limit of the European envelope curve. These findings contribute to the expansion of the European flash flood database and provide insights for enhancing flood risk management strategies in Mediterranean Spain.

How to cite: Amengual, A.: Study of Extreme Flash Flood Events in Mediterranean Spain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2174, https://doi.org/10.5194/egusphere-egu25-2174, 2025.

Drought is one of the most complex natural disasters, which has serious socioeconomic and ecological impacts across the world. With a changing climate, not only drought events have occurred more frequently, but also the characteristics of drought propagation have been changed. Under the joint effects of climate change and human activities, the assumption on the stationarity of hydrological time series has been overturned, which is of great significance in the field of hydrology. However, the current research on drought propagation is generally based on the assumption of sequence stationarity, in which related results may be biased. Therefore, it is necessary to construct a nonstationary standardized drought index to explore the dynamics of drought propagation and its driving factors for further understanding the mechanism of drought propagation. The Generalized Additive Models for Location, Scale, and Shape (GAMLSS) were applied in Luanhe River Basin to construct a time-varying drought index. The seasonal propagation characteristics from meteorological to hydrological drought were examined based on conditional probability, and the moving window was utilized to explore the dynamic change of propagation characteristics. The driving factors were investigated by using the variable importance in projection. The results indicated that using a time-varying drought index was more reasonable than using a stationary assumption; the propagation time showed a significant downward trend; hydrological drought was more likely to be triggered by meteorological drought in autumn and winter; and the precipitation, decreasing runoff, and increasing evaporation were the main factors affecting the seasonal propagation characteristics. These findings are valuable for clarifying the nonstationary characteristics of drought propagation and its seasonal dynamics, providing scientific support for drought early warning systems.

How to cite: Dai, M., Feng, P., Li, J., and Shi, X.: Drought propagation dynamics and driving factors from a nonstationarity perspective, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2629, https://doi.org/10.5194/egusphere-egu25-2629, 2025.

Meteorological drought poses significant challenges in Morocco, underscoring the need for accurate precipitation data to monitor and assess drought characteristics, particularly given the limited availability of ground-based measurements. This study evaluates the utility of two Satellite Precipitation Products (SPPs) for drought monitoring across Morocco: the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). The assessment utilizes the Standardized Precipitation Evapotranspiration Index (SPEI) at multiple spatial and temporal scales. Ground-based monthly precipitation data from 27 stations spanning 1987 to 2017 served as the reference for evaluating the satellite-derived products. SPEIs derived from the SPPs and reanalysis data were compared with those based on ground observations to analyze drought trends and characteristics across Morocco. Performance metrics, including correlation coefficient (CC), mean error (ME), root mean square error (RMSE), relative bias, and mean absolute error (MAE), were used for evaluation. The findings show a strong correlation between satellite-derived and observed precipitation data, with low estimation errors overall, though RMSE values indicate some dispersion, particularly in mountainous regions. Both CHIRPS and PERSIANN-CDR effectively capture drought occurrences and characteristics across Morocco, albeit with slight discrepancies compared to ground-based data. PERSIANN-CDR exhibits particularly high accuracy in simulating drought events, and both products effectively illustrate the progression and trends of droughts, providing valuable tools for drought monitoring and management in Morocco.

How to cite: Hadri, A., Rachdane, M., Iazza, K., El Khalki, E. M., Hanadé, I., and Saidi, M. E.: Evaluating Satellite-Derived Precipitation Products for Drought Monitoring in Morocco, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4083, https://doi.org/10.5194/egusphere-egu25-4083, 2025.

Extratropical cyclones (ETC) generate hazardous weather conditions, such as windstorms, which often result in substantial societal impacts. The Royal Netherlands Meteorological Institute (KNMI) has developed high-resolution wind climatology for the Netherlands using downscaled reanalysis data (2.5 × 2.5 km²) from 1979 to 2021, and they created ensembles for various climate scenarios based on IPCC projections. While these datasets offer valuable insights into windstorm hazards (e.g., maximum wind gust, maximum hourly wind speed), further evaluation of trends and probabilistic analysis are needed to create hazard maps tailored to specific return periods.

Our study addresses this research gap by exploring the application of extreme value theory (EVT) to windstorm hazard data to assess the likelihood of extreme wind speeds at specific locations. We analyse thresholds, seasonal variations, and storm frequency trends. Historical records and projections are analysed. A key novelty is the use of high-resolution post-disaster insurance claims data on the losses of windstorm. These data are used to create a risk model that converts windstorm hazards into quantifiable risks, such as expected annual damage and high-resolution risk maps (2.5 × 2.5 km2). This risk-based approach provides insights for stakeholders and decision-makers, aiding in the design of strategies to mitigate and adapt to windstorm impacts.

How to cite: Fonseca Cerda, M. S., de Moel, H., Aerts, J., Botzen, W., and Haer, T.: Windstorm Hazard and Risk Maps for the Netherlands in a Changing Climate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4336, https://doi.org/10.5194/egusphere-egu25-4336, 2025.

Extreme temperature changes from one day to the next, whether warming or cooling, have profound impacts on human health, ecosystems, and socio-economic, and their potential future changes can result in even more significant challenges. In the previous study, we quantified the physical processes—advection, adiabatic, and diabatic heating or cooling—that drive extreme day-to-day temperature (DTDT) changes in present-day climate. However, the role of these processes in projected changes of DTDT extremes under future warming scenarios has remained unexplored. This study addresses this gap by examining these processes globally using the Community Earth System Model (CESM) Large Ensemble under a high-emission scenario together with Lagrangian backward trajectory calculations. The findings reveal that reduced temperature changes due to advection and altered diabatic processes primarily drive the projected decreases in DTDT extremes in the extratropics during December-February (DJF) and June-August (JJA). In contrast, increases in DTDT extremes in the tropics are primarily caused by enhanced local processes, such as radiative heating during DJF, and mostly by intensified diabatic processes during JJA, with advection playing a minor role. Therefore, there is a strong need for adaptive strategies and informed decision-making in response to climate change, particularly in underdeveloped countries in the tropics and subtropics.

How to cite: Hamal, K. and Pfahl, S.: Physical Processes Leading to Extreme Day-to-day Temperatures Changes: Future Climate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4416, https://doi.org/10.5194/egusphere-egu25-4416, 2025.

After an exceptionally wet October 2019, the city of Bafoussam in the Cameroon Highlands was hit by a devastating landslide on 29 October, resulting in around 50 deaths. This study examines the atmospheric drivers leading up to this fatal event on a sub-monthly scale. Leveraging long-term station rainfall data from Bafoussam and the nearby city of Dschang, three marked wet spells during October 2019 are identified, the multi-day rainfall amounts of which exceed the maximum value within the historical data of the stations. Using ERA5 reanalysis data of the European Centre for Medium-Range Weather Forecasts to explore the meteorological background, favourable conditions in each of these wet spells were created by moist southwesterlies from an anomalously warm eastern equatorial Atlantic, induced by cyclonic-anticyclonic vortex couplets over the eastern Gulf of Guinea. The release of the intense rainfalls is associated with strong moisture flux convergence (MFC), likely through an interaction between the southwesterlies and prevailing easterlies from central Africa. On the large scale, the Saharan heat low, extending anomalously far to the northeast towards Libya during large parts of October 2019, appears to have facilitated the recurrence of such vortex couplets by establishing an environmental setting usually found during peak monsoon in August. Eventually, a tropical-extratropical interaction caused the wettest period of the month over the Cameroon Highlands. Dry and initially cool airmasses were advected equatorward from the Mediterranean towards the study region, generating the last strong episode of MFC linked with the landslide event. Subsequently, tropical-extratropical interactions were also involved in the termination of the rainy season. This study highlights not only the importance of the extratropics for rainfall variability in the African inner tropics, but also points to the hitherto understudied role of recurring vortex couplets over western tropical Africa and the Gulf of Guinea for multi-day wet spells.

How to cite: Vondou, D. A., Maranan, M., Fink, A., and Knippertz, P.: Meteorological conditions leading to a catastrophic, rain-induced landslide in Cameroon in October 2019, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4516, https://doi.org/10.5194/egusphere-egu25-4516, 2025.

The present research poster considered  a comprehensive case study of the medicane ‘Daniel’. The storm  struck Central Greece, particularly Thessaly, between September 4-7, 2023. The event was primarily triggered by omega blocking, where a high-pressure system became trapped between two low-pressure zones, leading to severe weather conditions and extreme precipitation. The following severe flooding caused loss of lives, widespread destructioon of road infrastructure, property damage and devastation to agricultural lands.

 A combination of upper-air and surface data was employed to perform in-depth analysis of the atmospheric dynamics before, during, and after the event. Wind patterns, pressure systems, and temperature variations, which contributed to the formation and intensification of the medicane were the key meteorological factors taken into consideration. The Weather Research and Forecasting Model (WRF) was utilized in various simulation scenarios to simulate the event’s behavior, providing valuable insights into its development, progression and the associated extreme weather conditions.

Overall, the case study highlights the critical importance of an deeper understanding of the meteorological factors driving such phenomena, as well as the role of simulations in forecasting and minimizing their impacts. Hopefully, the present effort will contribute to the further implementation of robust early-warning systems and enhance governmental preparedness to safeguard citizens from future extreme weather events.

How to cite: Chantziara, A. N. and Kioutsioukis, I.: Medicane Daniel in Greece: A Model Evaluation Study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4698, https://doi.org/10.5194/egusphere-egu25-4698, 2025.

How to cite: Poulston, A., Ashcroft, J., Koch, M., and Ertl, G.: Flood risk from AI-based, seasonal weather forecasts for the River Elbe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5729, https://doi.org/10.5194/egusphere-egu25-5729, 2025.

Extreme rainfall events are becoming increasingly frequent in Northeast Brazil (NEB). The state of Alagoas, located on the eastern coast of the region, is one of the most affected areas in recent years, with records of high-magnitude events over the past four years. These events cause significant socioeconomic impacts, resulting in considerable human and material losses, underscoring the importance of a deeper understanding to mitigate short-term risks better. This study aims to investigate the synoptic and mesoscale conditions driving the extreme precipitation event occurred on May 6-7, 2024, which rainfall totals surpassed 270 mm/day across multiple areas of Alagoas, marking the highest 24-hour rainfall accumulation in the region this century. The study also evaluated the ability of the global Model for Prediction Across Scales (MPAS) to perform simulation for the extreme precipitation event, using a variable grid of 60-3 km and convection-permitting parameterization through two microphysics schemes: WSM6 and Thompson. Infrared channel (10.35 µm) images from the GOES-16 satellite were used to monitor the cloudiness development. ERA5 global reanalysis data were utilized to evaluate the synoptic conditions as a first analysis step. Observed precipitation data from MERGE/INPE, S-band meteorological radar, and rain gauges operated by CEMADEN were used to analyze accumulated precipitation. Synoptic analysis, through streamlines, revealed strong wind shear between 200 and 850 hPa, which was responsible for developing a Squall Line that propagated and reached Alagoas on May 6. The propagation of eastward-moving cloudiness towards the study area was observed on the same day, resulting from an Easterly Wave Disturbance (EWD) identified through the transport of kinetic energy originating near the African continent (1°E; 20°S). The displacement and intensification of this system towards NEB were confirmed by the intense vertical integrated moisture transport convergence (1000–200 hPa) over time, enhancing convection as it encountered the mesoscale system over the continent. As confirmed by anomalies, Sea Surface Temperature (SST) played an essential role in intensifying vertical motions, which were unusually high for this time of year. Overall, the EWD trough axis propagating along the trade winds, combined with intense moisture convergence, symbolized the intensification of upward movements in the region, where the dynamic conditions necessary for the development of the extreme precipitation event were established. The simulations showed that the MPAS underestimated the intensity of precipitation associated with the extreme event, although the simulations predicted values exceeding 50 mm/day in the most affected area. The results show similar performance in reproducing weather variables, with slightly better results for the WSM6 run. Preliminary results provide valuable insights into the performance of MPAS, emphasizing the need for further evaluation using additional physical parameterizations and alternative model configurations to enhance its predictive accuracy.

How to cite: Lyra, M., Herdies, D., Gomes, H., Pendharkar, J., Silva, M. C., Silva, F., Gomes, H., Lins, M., Figueroa, S. N., Mantovani Jr, J., Ramirez, E., Quadro, M., Coelho, W., Vendrasco, É., and Calvetti, L.: Multiscale analysis and simulations of an extreme rainfall event in Northeast Brazil with the MPAS model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6378, https://doi.org/10.5194/egusphere-egu25-6378, 2025.

The climate change and related phenomena is one of the biggest challenges of today’s civilisation. The data shows an upward trend of occurrence of meteorological, hydrological, and climatic phenomena worldwide, with the largest increase observed in extreme hydrological phenomena, such as floods and mass movement. Intense rainfall and resulting floods can lead to erosion and transport of large amount of natural and anthropogenic material and thus influencing the spatial distribution of chemical elements on Earth surface. The redistribution of potentially toxic elements (PTEs) is of special concern as they are largely persistent, non-biodegradable, and many are known to accumulate along the food chain. The environmental consequences of remobilization and redistribution of pollutants during flood events are not yet widely recognized and understood.

An extreme rainfall and floods severely affected Slovenia (EU) at the beginning of August 2023, resulting in more than 10,000 landslides and spatial redistribution of large quantity of sediments, including heavily polluted ones. One of the most affected areas was the Carinthia region at the north of Slovenia. This area is strongly impacted by a 300-years of lead and zinc mining in the Mežica area. Although the mining activities have ceased 30 years ago, the environment (e.g., soil, floodplains) is still heavily contaminated with Pb, Zn, Cd, Mo, and other PTEs, and there are several mine waste deposits prone to erosion. The Geological Survey of Slovenia has been studying the geochemical characteristics of the wider Mežica area for several decades. Levels of PTEs in stream sediments were regularly monitored (every 3 years) since 2013.  

To determine the potential influence of extreme rainfall and floods on redistribution of PTEs in the environment that have been previously contaminated with PTEs, the following samples in the Mežica area in 2023 were collected: (1) stream sediment samples before the extreme weather event in August as a part of regular monitoring, (2) repeated samples of stream sediment after heavy storm at the end of July and after the extreme event in August at selected monitoring locations, (3) flood sediment samples along the Meža Valley after the extreme weather event in August. All samples were prepared (dried at 35 °C and sieved <0.125 mm) and analysed (determination of PTEs levels by ICP-MS after aqua regia digestion) by the same methods.

The comparison of PTEs levels in stream sediments from a decade long monitoring with flood sediment and stream sediment sampled after an extreme flood event illuminate the complexity of redistribution processes during such events, which may result in increase or decrease of PTEs levels. For example, the median levels of As, Cd, Mo, Pb, and Zn in flood sediments were higher than their median levels in stream sediment during usual hydrological conditions indicating erosion of contaminated areas and mine waste deposits dominated over erosion of non-contaminated materials. On the other hand, levels of PTEs at some specific sampling locations were much lower after the extreme flood event than before, indicating higher erosion of non-contaminated materials that may lead to the dilution effect.

How to cite: Gaberšek, M., Gosar, M., Miler, M., and Bavec, Š.: The influence of extreme flood event on redistribution of potentially toxic elements: a preliminary results from a former mining area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8859, https://doi.org/10.5194/egusphere-egu25-8859, 2025.

Convection is a vital process which helps to redistribute energy in the Earth atmosphere and is often conducive to cloud formation connected to severe weather events worldwide. Hail production can occur in these severe events highly impacting infrastructures and properties. Italy and the Mediterranean Basin in general are witnessing an increasing trend in the number of occurrences of such events in the last decades, thus calling for an advancement in the observational capability and retrieval methodologies for the analysis of convective storm associated to hail production.

This work focuses on the analysis of few case studies occurred in Italy in August and September 2024. The aim is to evaluate the complementarity and the effectiveness of active and passive, GEO and LEO satellite instruments and satellite-based retrieval algorithms in convection and hailstorm identification. Convective clouds are analysed through the convection products from the EUMATSAT Satellite Application Facility in support to nowcasting and very short range forecasting computed using Meteosat Rapid Scan Service (RSS) data. Further information about updrafts and overshooting tops is acquired from the EarthCARE Cloud Profiling Radar (i.e., reflectivity and vertical velocity of cloud particles). The Multi-sensor Approach for Satellite Hail Advection (MASHA), a new multi-instrument technique conceived for real-time tracking of hail-bearing clouds, completed the set of analysis tools. It combines the hail probabilities computed through the Global Precipitation Measuring PMW sensor constellation, with the high temporal rate acquisition of GEO infrared brightness temperatures (IR-BT) from the Meteosat RSS. Exploiting constantly updated relationships between spatio-temporal co-located IR-BTs and PWM hail probabilities, MASHA monitors the evolution of hail-bearing systems at high spatio-temporal resolution (i.e., 4-5 km and 5 min., respectively).

How to cite: Cattani, E., Vermi, F., Monte, G., Galfione, A., Battaglia, A., and Laviola, S.: Hailstorm characterization with a synergistic active and passive, GEO and LEO observation strategy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8964, https://doi.org/10.5194/egusphere-egu25-8964, 2025.

How does climate change impact extreme events and which is the future change of their dynamics? How will the ongoing and future changing climate control the evolution and intensification of severe storms? These are among the most frequent and significant questions for the scientific community, stakeholders and decision-making structures. The project tackles these open issues by investigating hailstorms in the Mediterranean region through the synergistic application of satellite observations, meteorological reanalysis and climatic modelling. Focusing on determining the atmospheric variables most relevant for the formation and intensification of hail-bearing storms, we delineate specific metrics describing the hail formation potentially applicable at operational level. The proposal stems from the 22-yearlong database of hail episodes described by Laviola et al. (2022), whereby events associated with large and extreme hail (above 2 and 10 cm in diameter, respectively) were preliminarily identified and shown to be on a 30% increase trend. Extending and refining this climatology at daily scale, the large-scale and mesoscale atmospheric scenarios that trigger hail events in the central Mediterranean area are investigated through a cluster analysis with the use of meteorological reanalysis data in the recent past. Hail-prone conditions are associated with the optimization of a hail-proxy index based on environmental variables extracted from global and regional reanalysis products. Such index and the reference hail-prone conditions are then be investigated in the ensemble of climate model projections to outline the future evolution of hail-precursors triggering and sustaining deep convection over the Mediterranean basin to the end of the century. This investigation will be also exploited to identify the environmental key variables controlling hail hazards in the recent past, and prospect future changes of storm extremization. The first-year results presented in this work delineate a new paradigm of knowledge for better understanding the effects of climate change on hailstorms by using hail-bearing convective systems as a driver for evaluating the potential impact of future changes in the Mediterranean basin. 

Reference
Laviola S., G. Monte, E. Cattani, V. Levizzani, 2022: Hail Climatology in the Mediterranean Basin Using the GPM Constellation (1999-2021). Remote Sensing, 14(17), 4320.  https://doi.org/10.3390/rs14174320

How to cite: Laviola, S., Arnone, E., Budillon, G., Monte, G., Cattani, E., Cortesi, N., and Capozzi, V. and the other authors of the Hail Hazard in the Mediterranean (H2Med) PNRR Project: Hail Hazard in the Mediterranean (H2Med), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9333, https://doi.org/10.5194/egusphere-egu25-9333, 2025.

The Multi-sensor Approach for Satellite Hail Advection (MASHA) is a new multi-instrument technique conceived for real-time tracking of hail-bearing clouds. MASHA can identify hail clouds from satellite measurements and monitor the evolution of hail-bearing systems every 5 min, combining the strength of the MicroWave Cloud Classification-Hail (MWCC-H) method to detect hail through the whole GPM sensor constellation (Laviola et al., 2020a-b) with the high temporal rate of the Meteosat Rapid Scan Service (MSG-RSS). This opens the way to operational applications of MASHA method by offering an unprecedented support to the nowcasting of hailstorms and to regional numerical weather predictions.

Recent applications experimented the ingestion in the MASHA scheme of lightning strikes and radar hail indices. This new configuration of the final products significantly refines the reconstruction of hail maps when the GPM constellation overpasses are missing. The result is a near-real time, more consistent and high-resolution hail map described by a proper Hail Severity Index (HSI). Recent applications demonstrate the ability of the MASHA technique to identify severe flash flood events in mountain catchments. These results draw new perspectives to optimally investigate hydro-meteorological events over mountain areas where more traditional methodologies might underestimate the severity of events. Thus, the MASHA scheme provides a useful tool in support to nowcasting systems of hailstorms and severe weather over complex areas.

References

Laviola S., V. Levizzani, R. R. Ferraro, and J. Beauchamp: Hailstorm Detection by Satellite Microwave Radiometers. Remote Sens. 2020a, 12(4), 621; https://doi.org/10.3390/rs12040621.

Laviola S., G. Monte, V. Levizzani, R. R. Ferraro, and J. Beauchamp: A new method for hail detection from the GPM constellation. A prospective for a global hailstorm climatology. Remote Sens. 2020b, 12(21), 3553; https://doi.org/10.3390/rs12213553.

How to cite: Laviola, S., Vermi, F., Monte, G., and Cattani, E.: Multi-sensor Approach for Satellite Hail Advection (MASHA): a new technique to support the nowcasting of hailstorms, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9621, https://doi.org/10.5194/egusphere-egu25-9621, 2025.

The recent flood event in Valencia (Spain) in October 2024 has revealed the need for real-time flood forecasts. Flood forecasts are based on meteorological forecasts that supply the feasible precipitation for the coming hours and a hydrological model to simulate the rainfall-runoff processes in the catchment. Distributed hydrological models require several parameters to simulate basin processes, though estimating their values accurately in each cell remains a challenge. Calibration processes that compare the hydrological model results with observations, in order to identify the best model parameter values, usually have an inherent uncertainty due to errors in the data, initial conditions and the simplified nature of the models. Furthermore, usually there is not a single set of parameter values that can characterise the hydrological response in all flood events. Therefore, the model calibration should consider diverse flood events, to optimize model performance under varying conditions.

This study presents the calibration and application of the Real-time Interactive Basin Simulator (RIBS) distributed hydrological model combined with precipitation forecasts based on the analog method to supply flood forecasts in the Francolí River Basin located in the Catalonia region in Northeast Spain.

First, observed rainfall and streamflow data recorded at gauging stations in the catchment for a set of real flood events have been used to calibrate the RIBS model. Five flood events were identified and used in the hydrological model calibration. The Nash–Sutcliffe Efficiency (NSE) coefficient showed good agreement between observed and simulated hydrographs for some events with values in the range 0.6179-0.9114.

Second, an ensemble of spatially distributed precipitation forecasts were used as input data to the calibrated hydrological model in the Francolí catchment. A set of five past events were used. A set of 10 meteorological analogs associated with the flood event was generated for each of the events analysed. The search for meteorological analogs was conducted using the 500 hPa and 1000 hPa geopotential height fields as predictors, and the similarity metric was based on a combination of Euclidean distance and Pearson spatial correlation. The generated set of analogs for each event can be used as an ensemble for generating a probabilistic precipitation field forecast for the region. The accuracy and reliability of the analog forecasts were assessed comparing the hydrological model outputs with the streamflow and precipitation observations at the gauging stations considered in the study. The best analog for each event obtained a Root Mean Square Error (RMSE) value ranging from 0.894 to 6.344, emphasizing performance variability.

The method proposed supplies a probabilistic flood forecast at the Francolí catchment outlet. This method improves the knowledge about the hydrological catchment response in flood events, supplying a probabilistic forecast. The method proposed enables more accurate flood predictions that can be used to supply informed response actions.

How to cite: Carril Rojas, D., Guzzon, C., Mediero, L., Garrote, L., Llasat, M. C., and Marcos Matamoros, R.: A flood forecasting method in the Francolí River Basin by using a distributed hydrological model and an analog-based precipitation forecast, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9626, https://doi.org/10.5194/egusphere-egu25-9626, 2025.

Hail is a weather phenomenon that can cause significant damage to material goods, crops, infrastructure, and motor vehicles, resulting in human injuries. Large and severe hail is often associated with severe convective storms, which are strongly related to atmospheric instability and the formation of intense thunderstorms. Predicting these thunderstorms, including their initial timing, location, and intensity, remains one of the most challenging aspects of modern weather forecasting. Current numerical weather forecast models often fall short in resolution, making accurate forecasting difficult. Therefore, meteorologists use convective indices as additional tools to predict thunderstorm development; these indices are considered valuable predictors for forecasting the occurrence of thunderstorms.

Convective indices are calculated from radiosondes' vertical temperature and water vapour profiles. In this research, we analysed 13 convective (or stability) indices derived from radiosonde measurements collected at the meteorological station Košutnjak in Belgrade, Serbia (φ = 44°46′ N, λ = 20°25′ E, h = 203 m above sea level) during days when at least one rocket-launching station, as part of the hail suppression system of the Republic Hydrometeorological Service of Serbia (RHMZ), reported occurrences of large and severe hail. Data for the warm season (April to October) at 12 UTC was gathered from 2002 to 2020. The term 12 UTC was selected based on the fact that approximately 96% of all hail events in Serbia, recorded from 1975 to 2009, occurred between 12:00 and 24:00 local time (UTC + 1). To statistically assess if there is a monotonic upward or downward trend, the Mann–Kendall test was used. If there is a trend, its magnitude is calculated using the Sen’s slope.

Recently, discussions about extreme weather conditions have increased. In response, we have focussed our attention on convective indices related to the occurrence of large and severe hail, defined as hailstones with a diameter of 21 mm or more. During the period analysed, days with such extreme hail accounted for 20% of all hail days. From 2005 to 2020, we identified significant monotonic trends in six out of thirteen convective indices: a decreasing trend for the Lifted Index (LI) and Boyden Index (BI), and increasing trends for the Severe Weather Threat Index (SWEAT), K Index (KI), Totals (TT), and Convective Available Potential Energy (CAPE). We could not conclude that days that meet the previously established threshold criteria for stability indices are becoming more frequent.

The number of hail days featuring hailstones with diameters between 21 mm and 30 mm and between 36 mm and 50 mm has been decreasing over time. Yet, the calculated coefficients of determination for both linear regression equations (R² = 0.108 and R² = 0.068, respectively) indicate that these trends are not significant. The number of days with hailstones between 31 mm and 35 mm also did not increase significantly (R² = 0.024). The overall frequency of extreme hail days has therefore not increased.

This research was supported by the Science Fund of the Republic of Serbia, No. 7389, Project „Extreme weather events in Serbia - analysis, modelling and impacts” - EXTREMES

How to cite: Vujovic, D. and Vuckovic, V.: Convective indices and their trends on days with observed large and severe hail in Serbia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9798, https://doi.org/10.5194/egusphere-egu25-9798, 2025.

Hail-proxy indices have been developed over the past years to overcome shortages in hail parameterization in meteorological and climate models. They are mainly focused on reconstructing hail climatology or the frequency of occurrence of hail events during the present or past climate (Prein and Holland 2018, Torralba et al, 2023). Because of their sensitivity to spatio-temporal resolution, they are, however, not specifically designed to simulate long-term changes in the occurrence of large hail events under future climate scenarios.

In this study, we present a novel methodology tailored for CMIP6 climate models under various SSPs scenarios, and in synergy with higher resolution ERA5 reanalysis. Our approach is based on 34 commonly employed hail predictors: their probability distribution functions (pdf) are compared to the hail-conditioned pdf during observed large hail events (hail diameter >2 cm), in order to identify all 3-hourly intervals during the hail season (April to November) in which hailstones might form. These intervals are then combined with coarser GCM trends (individually for each quantile of the pdf) to project future changes in the frequency of hailstorms. The proposed technique provides a simple yet robust framework for assessing future changes in the occurrence of large hail events.

Such a trend-based scaling was rigorously validated using a multi-model ensemble of CMIP6 historical daily simulations and ERA5 reanalysis data. In order to assess the method over the Mediterranean basin and nearby lands, the newly released satellite dataset MASHA was exploited (Laviola et al, 2022). MASHA is the first large hail dataset derived from passive microwave observations; it offers a 3-hourly time resolution and a 1°×1° spatial resolution over the whole Mediterranean basin [5W-35E, 25N-50N] during 1999-2023.

Results of the assessment revealed a good agreement between the simulated and observed average monthly frequency of large hail events and their intradaily variability, highlighting the reliability of the index and its usefulness for climate change projections.

How to cite: Cortesi, N., Arnone, E., Cassardo, C., Monte, G., Capozzi, V., and Laviola, S.: Future changes in the occurrence of large hail events in the Mediterranean region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10730, https://doi.org/10.5194/egusphere-egu25-10730, 2025.

Flood events are significant hydrological phenomena that can lead to severe human and economic damages. In the Seine River basin (France), the floods of May-June 2016 resulted in four fatalities and economic losses estimated between 0.8 and 1.25 billion euros, according to the French reinsurance fund. This event followed an unusually wet month of May, marked by intense rainfall concentrated in the southern part of the basin, primarily within the Loing River watershed. At the outlet of this watershed, an unprecedented peak discharge of nearly 500 m³.s^-1 was recorded, with a return period now estimated to be between 400 and 1,000 years. The latest IPCC report emphasizes the increasing frequency and severity of extreme weather events driven by climate change, highlighting the need for a better understanding of the hydrological processes leading to major floods to improve forecasting and mitigation efforts.

Given that flood dynamics typically result from a combination of processes including river overflow, subsurface runoff and groundwater discharge, a coupled surface and groundwater hydrological application of the Loing River watershed is currently being developed. The CaWaQS modeling platform is used to (i) simulate the key hydrological processes within each component of the Loing hydrosystem (soil, river system, vadose zone, and aquifer system) and (ii) generate daily key variables of interest, such as distributed discharge and hydraulic heads. The simulation of surface flow processes relies on a reservoir-based conceptual approach, utilizing sets of seven calibration parameters (or production-functions), distributed according to the intersection of soil and land use types. An initial simulation solely based on production-function parameters inherited from the CaWaQS-Seine basin regional application (15 production-functions, 105 parameters) led to largely underestimated flows. As a result, local specificities such as the extensive artificial drainage of arable lands was incorporated through four additional functions, bringing the total number of parameters to 133. Bayesian inversion and frequency analysis of observed discharge data were used to calibrate AET fluxes and effective rainfall partionning into runoff and infiltration flows, although this approach was still insufficient to accurately represent flood dynamics. To address this, the CaWaQS source code was enhanced to explicitly incorporate Dunne-type runoff processes related to soil saturation. A new calibration was based on analogies between physical and conceptual parameters, reducing the number of parameters requiring adjustment from 57 to just 2: drainage efficiency and kinematic porosity. These two parameters, initially not spatially discretized, are calibrated using an automated screening procedure.

This revised conceptualization and its associated fitting methodology enable the simulation of runoff processes triggered by soil saturation and drainage in agricultural areas, providing a differentiated assessment of their impact on the hydrosystem. It also allows a more accurate representation of flow dynamics during flood events.

How to cite: Girod, L.-M., Flipo, N., and Gallois, N.: Modeling dunnian runoff dynamics during flood events in the Loing watershed (France), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10792, https://doi.org/10.5194/egusphere-egu25-10792, 2025.

Between 29 and 30 October 2024, Spain experienced one of the most intense and destructive natural disasters in its history, predominantly affecting the Valencian Community but also parts of the Murcia region and the province of Albacete. The floods impacted approximately 75 municipalities, affecting over 400,000 inhabitants, damaging around 100,000 homes and 137,000 vehicles, and resulting in a total of 232 fatalities across Spain, 224 of which occurred in the province of Valencia alone. This extreme meteorological event not only recorded the highest rainfall accumulation in Spain’s history, with 771.8 mm in just 14 hours at the Túris station in Valencia but also highlighted inefficiencies in the authorities’ ability to convey extreme danger alerts to the population. The State Meteorological Agency (AEMET) issued the alert at 07:36 on 29 October, but it was passed on by the local authorities only 20:11, approximately 12 hours after the event started and the onset of precipitation, which significantly increased the risk to the population. In this study, simulations were conducted using the NCAR/MPAS model with a global resolution mesh of approximately 92 km, which converged to a finer mesh centred on Spain with a resolution of 25 km. The resolution increase was smoothed due to the numerical scheme used by the model, which employs Voronoi hexagons. The MPAS was initialised with initial conditions from the NCEP/NOAA dataset, obtained at 00Z for the period 23–29 October 2024. The study aimed to evaluate how far in advance it would have been possible to predict the configuration and position of the centre of the Cut-Off Low (DANA), the atmospheric phenomenon responsible for the extreme precipitation totals. The goal was to determine how early the risk associated with the DANA could have been identified, regardless of the precipitation totals forecasted by the model, focusing solely on the atmospheric phenomenon itself. The MPAS simulations revealed that as early as 24 October, the DANA configuration could be identified, based not only on the position of its vorticity centre at 500 hPa but also on the intense moisture transport at 850 hPa originating from the Mediterranean, which surface temperature was approximately 2–3°C above its average, directed towards the Valencian region. This pattern persisted in all simulations initialised between 24 and 29 October, with some precipitation cores showing accumulations of 200–300 mm between 29 and 30 October in the Valencian region. Thus, this study encourages reflection on the extent to which meteorology should rely on precipitation totals forecasted by atmospheric models when issuing alerts and warnings, or whether such alerts could instead be guided by the configuration of specific atmospheric phenomena. This approach could potentially increase lead times, as forecasting wind fields generally involves lower uncertainty compared to precipitation. Such an increase in lead time could be crucial to save lives in extreme weather events like this one.

How to cite: Ventura de sa, R., Cataldi, M., Raluy Lopez, E., Cristian Segado Moreno, L., Von Ruette, J., Sanchez Marroquin, A., and Chiva Polvillo, B.: Forecasting Cut-Off Lows Events with MPAS: A Study of Valencia's Historic Rainfall in October 2024     , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10875, https://doi.org/10.5194/egusphere-egu25-10875, 2025.

Global warming will lead to strong drought challenges in China. Exploring the spatiotemporal patterns of and changes in meteorological drought in China in the future is therefore of great significance for minimizing drought risks and for mitigating agricultural losses. It is crucial to consider the drought seasonality and aggregation while exploring the spatiotemporal patterns of and changes in meteorological drought in China. This study applied the ST-Moran scatterplot method to identify the drought spatiotemporal aggregation areas (DSTAAs) in China during 2021-2100 under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5 emission scenarios). Based on the identification results, we further analyzed the spatiotemporal patterns of and changes in drought in different seasons, agricultural regions, and time periods in China, and the detailed drought conditions on the Northeast China Plain. The results highlight that: (1) The drought will abate, become slightly worse, and become significantly worse over time under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. (2) Seasonally, the main drought seasons exhibit a transition trend from spring-winter to summer-autumn over time. As the emission level increases, this transition trend becomes increasingly evident. Detailed results in the Northeast China Plain confirm this seasonal transition trend in China and indicate that droughts in the major grain-producing areas in summer require more attention for preparedness and mitigation. (3) Spatially, the Northeast China Plain, Qinghai Tibet Plateau, and the northern arid and semiarid region have the largest number of significant DSTAAs. These results will support relevant institutions in formulating strategies for drought preparedness and mitigation.

How to cite: Wang, Z., Cheng, C., and Yang, J.: More evident trend of main drought seasons transition from spring‑winter to summer‑autumn in future China with higher emission scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10884, https://doi.org/10.5194/egusphere-egu25-10884, 2025.

On 29 October 2024 torrential rainfall exceeding locally 300 mm within less than 24 h, triggered devastating flash floods in the province of Valencia in Spain. Rainfall sums equivalent to more than half a year’s total precipitation occurred within just a few hours.  In this region, more than 150 low-cost weather observation devices, referred to as personal weather stations (PWSs), are located. The network density of PWSs in this region is seven times higher than that of the Spanish Meteorological Agency (AEMET), being able to provide more detailed insights in the rainfall dynamics. Another advantage is that rainfall observations from PWSs are available near real-time for everyone.

In this study we used rainfall observations from PWSs to get local insights into the rainfall event of October 29. Several PWSs measured already more than 180 mm of rainfall in parts of the Magro catchment (1661 km²) in the morning, consequently generating a flash flood in the upstream parts of this rapidly responding catchment. Areal rainfall maps, based on interpolating the PWS data, indicated daily catchment averaged rainfall sums exceeding 150 mm d^-1 across an area of more than 2500 km². Daily rainfall sums recorded by the PWSs showed a slight underestimation of the rainfall with a bias of 4% and a high correlation (r = 0.96) when compared to reported rainfall from AEMET.

This presentation shows the relevance of utilizing PWSs for near real-time rainfall monitoring and potentially flood early warning systems.

How to cite: Rombeek, N., Hrachowitz, M., Wüthrich, D., and Uijlenhoet, R.: Insights from personal weather stations in the rainfall dynamics preceding and during the 29 October 2024 Valencia floods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10932, https://doi.org/10.5194/egusphere-egu25-10932, 2025.

The increasing frequency and severity of extreme storms and floods in the Eastern Mediterranean under climate change pose significant challenges for modern societies. These events often trigger cascading effects that extend far beyond the immediate disaster zone, disrupting interconnected systems such as power, transportation, and communication networks. Despite advancements in flood risk management and growing awareness of cascading hazards, the mechanisms driving these interdependencies and their broader impacts remain poorly understood. This study investigates the cascading effects triggered by the catastrophic Storm Daniel, which struck Thessaly, Greece, in September 2023, as a case study to explore the nature, scale, and ways of impact propagation.

This work also provides an analysis of cascading effects, based on evidence on historical storm and flood disaster impacts in the Mediterranean region, identifying the interactions between primary hazards (flooding, landslides, erosion) and secondary consequences as well as the diverse sectors that suffer impacts. The analysis reveals different propagation mechanisms of these effects, highlighting the vulnerability of interconnected systems as well as the vulnerability of the natural and the built environment. The cascading effects identified underscore systemic risks of modern societies posed by extreme events, particularly in urban areas with dense, interdependent, and critical infrastructure.

The findings contribute to the growing body of literature on cascading disasters, addressing critical knowledge gaps in understanding how extreme weather events propagate through modern societal systems. These insights are particularly relevant in the context of climate change, which is expected to amplify the frequency and intensity of such events.

How to cite: Diakakis, M., Kapris, I., Gogou, M., Sarantopoulou, A., Filis, C., Nastos, P., Vassilakis, E., Konsolaki, A., and Lekkas, E.: Cascading effects of extreme storms and floods: Evidence on impact propagating mechanisms., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11513, https://doi.org/10.5194/egusphere-egu25-11513, 2025.

The frequency and severity of extreme meteorological and hydrological events, including tropical cyclones (TCs), are being reshaped by global climate change, posing significant challenges to infrastructure resilience and disaster management. This study evaluates the performance of satellite-derived quantitative precipitation estimates (QPEs) for hydrological applications during landfalling TCs, focusing on the interplay between localized rainfall and flooding. Using globally available datasets, we analyze eight TCs, including Hurricane Charley (2004) and Hurricane Michael (2018), to address three critical questions: (i) the reliability of satellite QPEs during TC scenarios, (ii) variability among gridded precipitation products (ground-based, radar, and satellite), and (iii) the implications of these differences for surface hydrology and flood risk.

Results indicate that the IMERG, satellite product underpredicts precipitation at higher quantiles but aligns well with ground-based and radar-derived products at lower quantiles. Urban areas exhibit the largest discrepancies in runoff estimates, with errors up to 18 mm, while agricultural and forested regions show more stable performance. Along TC tracks, IMERG reliably estimates hydrological variables in 90% of scenarios, with errors ranging from 0 to 10 mm. These findings underscore the utility of satellite QPEs like IMERG in understanding and forecasting short-term hydrological impacts of TCs, even amidst variations in precipitation intensity and location.

This research highlights the critical role of satellite precipitation products in addressing global disparities in real-time flood prediction systems, informing infrastructure planning, and mitigating societal vulnerability to extreme events. It contributes to the broader effort of enhancing early warning systems and proactive disaster risk management in the face of evolving climate extremes.

How to cite: Tiwari, A., Cherkauer, K., Marks, F., Tung, W., and Niyogi, D.: Enhancing Hydrological Insights for Tropical Cyclones Using Satellite Precipitation Data., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11548, https://doi.org/10.5194/egusphere-egu25-11548, 2025.

Recent years have seen floods of unprecedented intensity, leading to large loss of life, including in Spain (October 2024), Libya (September 2023) and Germany, Belgium, Luxembourg and the Netherlands (July 2021). The potential for conventional methods to underestimate extreme events was vividly and tragically illustrated in the devastating flooding of the Ahrtal associated with the latter event.

As well as the intensification of rainfall resulting from global heating, the anomalous behaviour of extreme floods may result from changes in hydrological processes, such as a transition to infiltration excess overland flow (Mushtaq et al, 2023). There is evidence of a large reduction in response time as rainfall intensity increases, for some catchments (Faulkner and Benn, 2019).

Empirical methods of flood frequency estimation have potential for estimating extreme floods (Bertola et al.,2023; Merz et al., 2022). However, they have limited ability to deal with changes in the physical processes of flood generation. Similarly, conceptual hydrological models can struggle to represent the impact of heterogeneous, nonlinear or otherwise complicated processes.

We investigate and benchmark the ability of a physically-based model (SHETRAN) to simulate extreme events beyond the range of observed conditions, examining how it represents changes in hydrological processes as the rainfall becomes more extreme, up to the probable maximum precipitation. Using a case study in the headwaters of the River Wye in Wales, UK, we find that the model represents the expected acceleration and intensification of runoff-generating processes. It does so partly by routing more runoff over the ground surface.

The findings are important for testing the resilience of society to extreme hazards, in a time of rapid environmental change.

Bertola, M., Blöschl, G., Bohac, M. et al. (2023). Megafloods in Europe can be anticipated from observations in hydrologically similar catchments. Nat. Geosci. 16, 982–988. https://doi.org/10.1038/s41561-023-01300-5.

Faulkner, D. and Benn, J. (2019). Reservoir flood estimation: the way ahead. Dams and Reservoirs, https://doi.org/10.1680/jdare.19.00028.

Merz, B., Basso, S., Fischer, S., Lun, D., Blöschl, G., Merz, R., et al. (2022). Understanding heavy tails of flood peak distributions, Water Resources Research, 58, e2021WR030506.

Mushtaq, S., Miniussi, A., Merz, R., Tarasova, L., Marra, F., & Basso, S. (2023). Prediction of extraordinarily high floods emerging from heterogeneous flow generation processes. Geophysical Research Letters, 50, e2023GL105429. https://doi.org/10.1029/2023GL105429.

How to cite: Faulkner, D., Rohrmueller, I., and Griffith, H.: Can physically-based models represent changes in hydrological processes expected during megafloods?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11977, https://doi.org/10.5194/egusphere-egu25-11977, 2025.

Tornadoes are significant meteorological hazards, causing extensive damage to infrastructure and loss of life. Their small spatial scale (approximately 1km or less), short lifespan (order of 1000s) and, highly nonlinear chaotic behaviour makes their prediction problematic using current operational weather predictions and climate models. Developing methods to overcome these limitations is crucial for providing reliable early warnings and forecasts through civil protection services and determining whether human-induced climate change will affect the frequency and intensity of tornadoes. We estimate the expected occurrence of tornadoes using a set of empirical formulas based on meteorological parameters extracted from the ERA5 reanalysis for the period 2000-2024 and compare these estimates to the actual number of observed tornadoes, as recorded by the Strom Prediction Center (SPC) (https://www.spc.noaa.gov/wcm/#dat) for USA  and the European Severe weather database (ESWD) https://www.essl.org/cms/ for Europe. The formulas incorporate W_MAX (updraft maximum parcel vertical velocity, linked to the Convective Available Potential Energy CAPE), WS₇₀₀ (the wind shear in the lower troposphere), LCL (the lifting condensation level), SRH₉₀₀ (low-level storm relative helicity) and provide a probability for the occurrence of tornadoes (see Ingrosso, et al. https://doi.org/10.5194/nhess-23-2443-2023 for details). Results show a good capability of reproducing the seasonal cycle of tornadoes in the USA and some skill to simulate their interannual variability, with a score depending on season and larger in spring. Results are not satisfactory for tornadoes in Europe. Reasons for this partial failure need a further investigation. This study is carried out with the financial support of ICSC – Centro Nazionale di Ricerca in High Performance Computing, Big Data and Quantum Computing, funded by European Union – NextGenerationEU… Project code CN_00000033, CUP C83C22000560007.

How to cite: Muhammadi, A. and Lionello, P.: Empirical estimate of intraseasonal and interannual variability of occurrence of tornadoes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13018, https://doi.org/10.5194/egusphere-egu25-13018, 2025.

An important challenge associated with climate change is the increase in the intensities of extreme precipitation events, which produce severe flooding and cause substantial damage to hydraulic infrastructure. This intensification has been particularly evident in recent decades and is attributable to increased daily precipitation and the temporal concentration of rainfall over shorter durations. This results in increased precipitation pulses and, consequently, higher rainfall intensities. In Chile, the National Water Agency (DGA) published standardized duration coefficients in the year 1993, which may no longer align with the current climatological period, potentially underestimating the design parameters for extreme storms.

Using ERA5 reanalysis data, this study investigates changes in duration coefficients for extreme storms across a large area of Chile, covering latitudes from 17°S to 43°S. Two climatological periods are defined: the first from 1964 to 1993, that represent the past climatology, and the second from 2004 to 2023 that represent the current climatology. For each period, duration coefficients are computed by identifying annual extreme precipitation events for specific durations and normalizing these by annual maximum daily precipitation totals. Representative duration coefficients are computed for each period, taking the averages and the 90% exceedance probability envelope, reflecting an adverse scenario. The obtained coefficients are compared with those published by the National Water Agency, and change factors are computed for each hourly duration, using both representative coefficients. The computations were performed for each of the 31 stations with available historical data to enable comparative analysis.

The results indicate that the average 24-hour duration coefficients closely align with the 1.1 value suggested in the national design guidelines. However, significant variance is observed across the years of analysis, with the spatial mean of the exceedance envelope reaching 1.3. In northern Chile, characterized by the Atacama Desert and the Altiplano, ERA5 systematically underestimates short-duration coefficients associated with convective storms. Regarding changes in duration coefficients between the two climatological periods, an increase of up to 20% is observed in central Chile for durations shorter than six hours.

These findings highlight the critical need to regularly update duration coefficients in the context of a changing climate to ensure robust hydraulic infrastructure design and to mitigate risks associated with underestimation in regions experiencing intensified extreme events.

How to cite: Ricchetti, F. and Vargas, X.: Variability of Extreme Precipitation Duration Coefficients in Chile: Implications for Hydraulic Design, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13170, https://doi.org/10.5194/egusphere-egu25-13170, 2025.

On June 21st, 2024, the iconic village of La Bérarde, in the centre of the massif Les Ecrins, French Alps, was destroyed by the mountain stream “Les Etancons”, that flooded the houses  and deposited more than 200,000 m³ of raw material. This catastrophic event was caused by the concomitance of rapid warming with a heavy rain on top of a snowpack unusually thick in a glaciated watershed ranging from 1700 m to 4000 m a.s.l. during which a glacial lake drained simultaneously. Using observations (in-situ and remotely sensed) and the Météo-France modelling chain S2M, reanalysing meteorological and snow conditions in mountainous areas, we  characterize a posteriori this rain-on-snow event and evaluate its contribution to the flood as well as its genuine nature within the last 65 years. Change in precipitation extremes was estimated by fitting a time dependent Generalized Extreme Value model within a Bayesian framework on available data. This analysis suggests an intensification of extreme precipitation in the studied  high alpine region which is undergoing profound landscape changes with permafrost thawing and glacial retreat leading to favorable conditions for the formation of glacial lakes. This concomitance of intense meteorological events within a rapidly changing landscape is a striking reminder of how climate change is reshaping flood risk in high alpine regions.

How to cite: Filhol, S., Dumont, M., Hagenmuller, P., Doussot, F., Nicolas, E., Gascoin, S., and Blanc, A.: An unusual Rain-on-Snow event preconditioning the catastrophic fate of the village La Bérarde, French Alps, June 2024, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13270, https://doi.org/10.5194/egusphere-egu25-13270, 2025.

Floods have major impacts in the Mediterranean regions, but their evolution with climate change is unclear. This issue is related to the inadequacy of climate and hydrological models in terms of spatial and temporal resolutions to simulate flash floods over small basins. This study explores future flood scenarios of 12 Mediterranean basins using meteorological forcings from an ensemble of high-resolution convection-permitting climate models. Results indicate an overall increase in flood intensity across all basins, particularly for the most severe events, but also a strong spatial variability of the climate change signal given the geographic location and catchment characteristics. There is a good agreement between the models towards an increase of hourly rainfall extremes, but these changes are not well correlated with changes in floods, indicating that rainfall intensity alone is a poor predictor of future floods. An overall conclusion towards an increase of floods in this region is limited by the short length of the available high-resolution climate simulations. Longer time series are required to better assess the robustness of the projected changes.

How to cite: Lucas-Picher, P., Poncet, N., Tramblay, Y., Thirel, G., and Caillaud, C.: Projections of extreme rainfall and floods in Mediterranean basins from an ensemble of convection-permitting models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13283, https://doi.org/10.5194/egusphere-egu25-13283, 2025.

Climate change and heat waves are among the most pressing challenges impacting water resources and intensifying wildfire occurrences worldwide. Understanding and predicting these extreme temperature events is crucial for developing effective mitigation strategies. To address this need, we conducted a comprehensive study focusing on heatwave prediction across Iran. This project utilized a novel approach, combining global climate change models with advanced statistical techniques such as copula functions [1]. This methodology enabled the detailed examination and correlation of three critical heatwave parameters: intensity, duration, and frequency. By establishing these interrelationships, the study provided a robust framework for predicting future heatwave characteristics [2].

The projections from our study reveal alarming trends for the future. Under various Shared Socioeconomic Pathways (SSPs), including SSP2.6, SSP4.5, and SSP8.5 [3], the intensity, duration, and frequency of heatwaves are expected to increase significantly by the year 2100. Specifically, the SSP8.5 scenario, which assumes high greenhouse gas emissions and limited mitigation, predicts the most dramatic escalation in these parameters. These findings underscore the urgent need for climate-resilient infrastructure and adaptive planning to safeguard public health, ensure the functionality of essential services, and minimize economic and environmental damage.

The integration of global climate models with copula-based statistical analyses proved to be a powerful tool in capturing the complex dynamics of heatwaves. This approach not only enhanced the accuracy of predictions but also provided valuable insights into the probabilistic behaviour of heatwave events under changing climatic conditions. By leveraging these insights, policymakers and planners can make informed decisions to mitigate risks and enhance resilience against future climate extremes.

Given the accelerating pace of global climate change, the implications of this research extend beyond Iran, offering a framework that can be adapted and applied to other regions facing similar challenges. Proactive measures, informed by predictive models such as ours, are essential to address the multifaceted impacts of heatwaves, from public health crises to disruptions in water and energy systems [4]. This study highlights the critical importance of integrating climate science with policy and infrastructure planning to build a sustainable and resilient future.

References

1        Chen, L., Guo, S., Chen, L., and Guo, S.: ‘Copula Theory’, Copulas and its application in Hydrology and Water Resources, 2019, pp. 13-38

2        Mazdiyasni, O., Sadegh, M., Chiang, F., and AghaKouchak, A.: ‘Heat wave intensity duration frequency curve: A multivariate approach for hazard and attribution analysis’, Scientific reports, 2019, 9, (1), pp. 14117

3        Usta, D.F.B., Teymouri, M., and Chatterjee, U.: ‘Assessment of temperature changes over Iran during the twenty-first century using CMIP6 models under SSP1-26, SSP2-4.5, and SSP5-8.5 scenarios’, Arabian Journal of Geosciences, 2022, 15, (5), pp. 416

4        Marx, W., Haunschild, R., and Bornmann, L.: ‘Heat waves: a hot topic in climate change research’, Theoretical and applied climatology, 2021, 146, (1), pp. 781-800

How to cite: Mirzaei, M. E., Izadi, A., and Alizad, K.: Heat Wave Assessment in Iran's Vulnerable Locations Incorporating Climate Change Scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15165, https://doi.org/10.5194/egusphere-egu25-15165, 2025.

Taiwan's semiconductor industry is a significant contributor to the global semiconductor market and requires substantial water resources for its operations. The severe drought during 2020−2021, which garnered global attention, highlighted the importance of understanding water dynamics. This study examines the link between typhoon activity and drought severity. We analyzed tropical cyclone best-track data and satellite-based precipitation records from 1981 to 2020, using anomalies, correlation matrices, and wavelet coherence to investigate the typhoon-drought relationship, seasonal variations, and long-term trends. Our results reveal a complex relationship: typhoon characteristics near Taiwan, such as frequency, duration, path length, and intensity, positively correlate with drought occurrence and severity on 2- to 4-year cycles. Conversely, in the broader Western North Pacific (WNP), typhoon duration and path length correlate negatively with Taiwan’s drought indices, driven by large-scale atmospheric patterns. Notably, WNP typhoon duration and path length exert a stronger influence on Taiwan’s drought conditions than typhoon frequency, demonstrating significant coherence with multi-year and decadal drought trends. These findings illuminate the intricate dynamics between typhoon activity and drought patterns, offering valuable insights for hydrological management and disaster preparedness in typhoon-prone regions.

How to cite: Le, T.-V. and Liou, Y.-A.: Exploring the Typhoon-Drought Interplay in Taiwan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15246, https://doi.org/10.5194/egusphere-egu25-15246, 2025.

Droughts, as severe climatic phenomena, pose substantial risks to sensitive areas globally. In Taiwan, where critical sectors such as semiconductor manufacturing are particularly vulnerable, the effects of droughts are of great concern. Various satellite indices have been developed to monitor drought status. The Temperature-Vegetation Dryness Index (TVDI), based on the Land Surface Temperature (LST) and Fractional Vegetation Cover (FVC), has been extensively used. The newly-proposed Temperature-Soil Moisture Dryness Index (TMDI), derived from the LST–Normalized Difference Latent Heat Index (NDLI) trapezoidal space, presents a more effective alternative to the TVDI. This study enhances TMDI by incorporating the novel Fractional Surface Water Availability (FSWA), focusing on better edge definition in the LST–FSWA space for drought monitoring. The capabilities of these indices were evaluated against metrics such as Surface Energy Balance Algorithm for Land (SEBAL)-based evapotranspiration (ET), Crop Water Stress Index (CWSI), Gross Primary Productivity (GPP), and in-situ precipitation. Notably, the TMDI showed stronger correlations with ET (r = –0.94) and CWSI (r = 0.93) compared to other indices. Moreover, the TMDI closely aligns with CWSI and GPP and is most responsive to precipitation (r = –0.60). Leveraging CWSI classifications, a novel TMDI threshold is proposed to assess drought conditions across southwestern Taiwan from 2014 to 2021. Generally, the TMDI effectively captures spatiotemporal drought patterns, providing essential insights for water management, irrigation planning, and the achievement of sustainable development goals.

How to cite: Thai, M.-T. and Liou, Y.-A.: Advanced Techniques for Enhanced Drought Monitoring Utilizing the Temperature-Soil Moisture Dryness Index (TMDI), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15247, https://doi.org/10.5194/egusphere-egu25-15247, 2025.

It is evident that windstorms rank among Europe's most destructive natural hazards; however, they have received comparatively less attention from researchers. This can be attributed partly to the absence of a consensus on windiness trends and the variability in reported events depending on the databases used. Research on windstorms is predominantly concentrated in Central Europe, and the assessments conducted are often lacking in detail.

The present study aims to address this gap by establishing a new Swedish windstorm database. The database is based on the 99^th percentile of wind gusts, with the objective of identifying extreme events. While lower percentile thresholds (e.g., the 98^th) are commonly employed, they have been deemed inadequate for regions such as Scandinavia, prompting the selection of a higher threshold. The 99^th percentile has been determined to ensure that Sweden's distinctive climatic and geographical conditions are sufficiently captured in the data.

The employment of this more accurate methodology is instrumental in facilitating a more profound comprehension of the impact of windstorms in Sweden. The identification of areas susceptible to risk assumes a pivotal role in informing efficacious disaster preparedness and mitigation strategies, given the propensity of windstorms to inflict considerable damage.

How to cite: Georgali, E. and Karagiorgos, K.: Winds of change: Mapping Extreme Windstorm Events in Sweden, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18369, https://doi.org/10.5194/egusphere-egu25-18369, 2025.

The ClimXtreme program[1,2], funded by the German Ministry of Education and Research, consists of 25 subprojects investigating extreme events in central Europe focussing on heat/drought, extreme precipitation and wind storms. Extreme events occurring during project runtime are assessed within the research program by forming a post-event assessment group (PostAG). This group aims at rapid response using both pre-defined methods and workflows as well as cutting edge methods of the research program.

The basis for the assessment is the evaluation framework Freva (Free Evaluation System Framework)[3,4] which provides an efficient possibility to handle customisable evaluation systems of large research projects. Several projects and institutions are already using this framework for educational or research purposes. Among them is worthy to mention the  Freva instance hosted at the German weather service (DWD) which allows an exchange of methods and assessment workflows between research projects and the DWD. The Freva instance of ClimXtreme[5], hosted at DKRZ, enables the possibility to access more than 10 million data files from models (e.g. CMIP, CORDEX) and observations (e.g. ERA5, HYRAS, stations). Near-realtime data of observations are operationally updated for the PostAG assessment.

This contribution shows an interplay of the evaluation framework and scripted workflows to rapidly assess extreme events directly after their occurrence with pre-defined methods (e.g. plugins) providing the option to easily expand the analysis by new methodologies developed by the research program.

References:

[1] https://www.fona.de/de/massnahmen/foerdermassnahmen/climxtreme.php
[2] https://www.climxtreme.de/
[3] http://doi.org/10.5334/jors.253
[4] https://github.com/FREVA-CLINT/freva
[5] https://www.xces.dkrz.de/

How to cite: Grieger, J., Kunz, T., Lucio-Eceiza, E. E., and Ulbrich, U.: Post-event assessment of extreme events within an evaluation system framework, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18379, https://doi.org/10.5194/egusphere-egu25-18379, 2025.

While warm season heat waves are extensively studied, winter heat waves, which are extended periods of above-average temperature during winter months, largely remain overlooked. These events can disrupt typical water availability patterns and degrade water quality, particularly in regions already facing environmental stress.

The identification of heat waves varies depending on the context and objective. For example, variable percentile thresholds are used to study heat wave mechanisms, while fixed temperature thresholds are often applied to assess their ecological impact. In the context of biogeochemical processes that govern contaminant retention and mobilization, variations in surface and groundwater flow can be particularly important.

We analyzed the CAMELS‐SE dataset, which includes long-term observations of daily temperature, precipitation and streamflow from 1961 to 2020 across 50 sites in Sweden, along with local groundwater measurements, to identify and quantify heat-wave events using various approaches, and explore their correlation with observed surface and groundwater flow variability.

The main objective of the study is to determine the timing, duration, frequency, and magnitude of winter heat waves in Sweden, and to assess trends in surface and groundwater flows associated with these events. We also explore whether certain specific geographical regions in Sweden are more vulnerable to the effects of winter heat waves.

How to cite: Khan, U. A., Teutschbein, C., Ashraf, F., and Lai, F. Y.: Evaluating approaches to identify winter heat wave events and their hydrological impacts in Sweden, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18944, https://doi.org/10.5194/egusphere-egu25-18944, 2025.

With the ongoing expansion of urban development into underground spaces and the increasing occurrence of extreme rainfall events, the risk of flooding in these areas has heightened. Stairs, as critical connections within underground spaces, markedly influence water inflow and evacuation during flooding events. Therefore, the impact of different stair types on the hydrodynamic characteristics of water flow in underground spaces is worth studying. This paper constructed physical models of stairs at various underground locations to investigate two types of stairs and their hydrodynamic characteristics and the risk. The findings revealed that at low flow velocities, both stair types lowered water flow in the upper section; at medium flow velocities, the main impact was observed in the lower section; and at high flow velocities, the reduction effects were weaker. For the stair types allowing lateral outflow, increasing the lateral slit height enhanced outflow volume, thereby decreasing water flow on the stairs. However, excessively high slit gaps did not substantially increase lateral outflow and may introduce safety hazards, such as the risk of children falling through. Additionally, a comprehensive analysis of flow velocity and water depth reveals that, in addition to the higher risk in the lower section, particular attention must be given to the turning point between the stair and the rest platform. The intricate vortex flow pattern at this location, characterized by elevated flow velocities and water depths, results in a risk level surpassing that of the jet flow.

How to cite: Dai, Z. and Huang, G.: An experimental investigation into the effects of underground stairs types on hydrodynamics characteristics and risk., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-402, https://doi.org/10.5194/egusphere-egu25-402, 2025.

Flood events are among the most devastating natural hazards, presenting multi-risks to infrastructure, ecosystems, and communities. Beyond the immediate impact of inundation, the entrainment and transport of large wood (LW) during these events amplify their destructive potential. Mobilized LW can obstruct critical infrastructure such as bridges, leading to increased backwater effects, exacerbating flooding, and causing structural damage. However, LW also plays a vital ecological role, contributing to habitat formation, nutrient cycling, and riverine biodiversity. As such, it cannot simply be removed without ecological consequences. Understanding the dynamics of LW entrainment, transport, and deposition is crucial for balancing flood risk reduction with the preservation of ecosystem functions.

This study addresses the challenge of modeling LW dynamics in rivers, with a specific focus on the Allier River in France. For this purpose, the study utilizes the ORSA2D_WT model, an Eulerian-Lagrangian two-way coupled approach, which integrates the two-dimensional Shallow Water Equations (SWE) with the Discrete Element Method (DEM). This hybrid model allows for a representation of entrainment thresholds, transport pathways, and inelastic collisions between LW elements and obstacles such as riverbanks and infrastructure.

This research integrates extensive field data, numerical simulations, and experimental findings to enhance predictions of wood mobilization during flood events. Field data collected from the Allier River, France (2020–2024), provides a robust basis for model improvement. This dataset includes Radio Frequency Identification (RFID)-tracked LW positions over multiple years, high-resolution Digital Terrain Models, granulometric sediment analyses and LW characteristics such as size, shape, density and burial conditions.

By combining numerical simulations with extensive field data, this study aims to refine the model’s ability to predict LW mobilization and transport across different flood scenarios, from moderate flows to extreme flood events. Furthermore, the study seeks to enhance the understanding of how environmental factors, such as LW properties and sediment dynamics, influence LW behavior during floods. The outcomes of this research will contribute to the development of a more accurate and reliable hydrodynamic model coupled with a LW transport model, offering insights into how the dynamics of LW affect riverine systems during flood events.

How to cite: ennouini, W., Persi, E., Ravazzolo, D., Petaccia, G., Sibilla, S., Hortobágyi, B., and Piégay, H.: Floods and Large Wood in Rivers: Exploring Their Dynamic Interactions Through Numerical Modelling and Field Data on the Allier River, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-535, https://doi.org/10.5194/egusphere-egu25-535, 2025.

Floods are the most common natural disaster, and recent studies suggests that their frequency and magnitude will increase due to climate change. Factors such as demographic growth, urbanization, and land consumption contribute to heightened vulnerability for structures, infrastructure, and populations, elevating the risk of cascading incidents. In this context, flood events can lead to multiple simultaneous releases of hazardous materials, causing severe harm to both the environment and human health. In these cases, the term Natech accident is used, referring to industrial accidents triggered by natural events, for which a multi-risk approach is required. Natech accidents caused by floods require particular attention, as the high velocities of water can rapidly transport pollutants to areas far from the point of emission. The need to focus on this issue is further justified by the fact that many chemical and petrochemical plants are in flood-prone areas, making them particularly vulnerable to the risk of failure following a flood. In the context of emergency management, having access to rapid-response models for assessing the fate and transport of spills is crucial for evaluating their trajectory and for planning recovery interventions. Additionally, these models are key for generating risk maps for various spill scenarios. Within Natech risk management, particular attention is given to oil spills in water, as they introduce additional complexity due to the unique behaviour of this substance in water and their potential toxicity, as well as the risk of cascading events (i.e. environmental contamination, fires, explosions). The need to develop specific models for simulating oil spills in floodwaters is particularly important, as the existing literature provides numerous models for offshore spills, but knowledge regarding fluvial systems is still limited.

This study presents the initial results from the implementation of oil spill routing within the CAESAR-LISFLOOD flood inundation model, which addresses the challenge of solving a simplified shallow water equations using a straightforward numerical approach. This results in a model that is computationally efficient while still being grounded in a solid physical framework. The model is enhanced with a module that simulates the dispersion of oil in floodwaters, accounting for the key processes that influence oil movement in a river system. This implementation allows the model to track the behaviour of an oil slick after a spill in areas with complex topography. It provides valuable insights into the dynamics of the spill, the changes in the slick’s thickness over time, and the extent of the affected area. The model was tested on a case study in Italy, where several simulations were performed for multiple spill scenarios, demonstrating the model’s effectiveness, its ability to accurately simulate the oil spill propagation, as well as its computational efficiency.

How to cite: Di Fluri, P., Wilson, M., and Domeneghetti, A.: A new physically-based numerical model to simulate flood triggered oil spills, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-697, https://doi.org/10.5194/egusphere-egu25-697, 2025.

The Saint-Venant equations are extensively employed to model water flow in channels, particularly when a comprehensive analysis is necessary. This study presents a mesh-free approach utilizing Physics-Informed Neural Networks (PINNs) to address the 1D Saint-Venant equations under diverse initial and boundary conditions. PINNs provide substantial benefits compared to conventional hydrodynamic models by enabling predictions at any location within the computational domain without the necessity for predefined computational points or cross-sections. This versatility is especially advantageous for applications necessitating elevated spatial resolution or dynamic adaptability. In contrast to traditional machine learning (ML) methods, Physics-Informed Neural Networks (PINNs) do not necessitate labelled data. Their loss function integrates the residual error of the governing partial differential equations (PDEs) with initial and boundary conditions, thereby ensuring predictions that are physically consistent. This addresses the interpretability deficit frequently linked to machine learning models. The constructed PINNs architecture was evaluated on four test cases that exemplify various channel geometries and flow conditions. The first scenario pertains to a horizontal bed exhibiting a constant upstream velocity. The second case analyses a rectangular channel with a constant slope and dynamic inflow, whereas the third and fourth cases comprise channels with changing slopes and widths. These scenarios reflect real-world water transport channels. In cases 1 and 2, the maximum depth error was ±0.08 m relative to numerical solutions, with the most significant errors occurring at the points of initial water arrival. In cases 3 and 4, the maximum depth errors were ±0.2 m and ±0.4 m, respectively. These findings indicate that PINNs can accurately reproduce numerical solutions without the necessity of a computational mesh. The adaptability of PINNs in sampling collocation points eliminates the necessity for re-simulating the model when results are needed at new locations. This study underscores the efficacy of PINNs for real-time water resource management and flood forecasting, particularly where conventional methods may be computationally expensive or inflexible. Future research will investigate the expansion of the PINNs framework to encompass higher-dimensional Saint-Venant equations and the incorporation of stochastic inputs to address uncertainties in flow conditions.

How to cite: Sasanapuri, S. K., Chadrika Thulaseedharan, D., and Ashwini Kumar, G.: Physics-Informed Neural Networks: A Novel Framework for Solving 1D Saint-Venant Equations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-767, https://doi.org/10.5194/egusphere-egu25-767, 2025.

This study investigates the seepage behavior of homogeneous earthen and rockfill dams during transient conditions, focusing on the temporal variation in seepage discharge as influenced by material composition and structural characteristics. Using experimental modeling, seepage rates were analyzed over 200 minutes, revealing distinct patterns between the two dam types. For the earthen dam, the initial seepage rate was approximately 1 cm³/sec. This discharge decreased steadily over time, stabilizing at around 0.5 cm³/sec by the end of the observation period. The consistent decline reflects the lower permeability of earthen materials, which results in a controlled redistribution of water as the hydraulic gradient diminishes and seepage pathways stabilize. In contrast, the rockfill dam exhibited significantly higher initial seepage rates, starting at 2.5 cm³/sec due to its highly porous structure and larger void spaces that facilitate rapid water movement. Over the 200-minute observation period, the seepage rate decreased gradually, stabilizing at approximately 1.2 cm³/sec. This slower decline and higher stabilization point highlight the greater permeability of rockfill materials, allowing prolonged seepage flow before reaching equilibrium. The comparison of seepage dynamics underscores the impact of dam material properties on hydraulic performance under transient conditions. Earthen dams, with their steady reduction in seepage, are wellsuited for scenarios requiring controlled seepage management. However, the need for proper drainage systems to handle pore pressure buildup remains critical. Conversely, rockfill dams are effective at managing high initial seepage rates but may require additional seepage control measures, such as enhanced drainage systems or impermeable barriers, to ensure long-term stability and prevent structural compromise. These findings emphasize the importance of designing tailored seepage management strategies that account for the unique material properties and structural behaviors of each dam type. For earthen dams, measures to manage gradual seepage and stabilize pore pressures are essential, while for rockfill dams, addressing prolonged seepage flow and high initial rates is critical. This study provides valuable insights into the optimization of dam designs to enhance safety and efficiency, particularly under dynamic hydraulic conditions such as fluctuating reservoir levels or rapid drawdown scenarios. By highlighting the contrasting seepage behaviors of earthen and rockfill dams, this research contributes to the development of resilient and efficient water-retaining structures capable of withstanding diverse environmental and operational challenges.

How to cite: Shrivastava, S., N Khatri, V., and Pasupuleti, S.: Seepage Dynamics in Homogeneous Earthen and Rockfill Dams: Insights from Experimental Modeling Under Transient Conditions., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1056, https://doi.org/10.5194/egusphere-egu25-1056, 2025.

Efficient evacuation during storm floods remains a critical challenge for coastal cities, primarily due to its dynamic and complex nature involving flood progression, human behavioral uncertainties, and emergency resource constraints.  Current evacuation models inadequately capture these multifaceted dynamics, limiting effective emergency planning. This study introduces an Agent-based Dynamic Coastal Flood Evacuation (DCFE) model that comprehensively simulates the interactions among flood dynamics, human behavioral responses, GIS-based transportation networks, and shelter systems.  Using Shanghai as a case study, we evaluate city-scale evacuations during a 1,000-year return period storm event. Our analysis shows that issuing warnings 12 hours before flood peak reduces casualty rates by over 25% compared to scenarios without early warning, while optimized decision-making can double evacuation efficiency. The results further reveal critical spatial disparities in evacuation performance due to inequitable shelter distribution. This integrated approach provides practical guidelines for enhancing evacuation strategies in coastal megacities worldwide.

How to cite: Yang, Y.: Dynamic Flood Evacuation Modelling for Coastal Cities: A Case Study of Shanghai, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1829, https://doi.org/10.5194/egusphere-egu25-1829, 2025.

Flood hazard map is necessary to develop strategies for mitigation of flood damages and sustainable management of catchments especially in drylands with flash flood and megafloods. Here, we applied multiplicative long short-term memory (mLSTM) deep learning model to map flood hazard in an arid catchment – Shamil-Minab plain – in southern Iran. In order to, variables controlling flood hazard consisting of variables extracted from digital elevation model (DEM) (e.g., curvature, plan curvature, profile curvature, slope, stream power index (SPI), topographic position index (TPI)), normalized difference vegetation index (NDVI), hydrological variables (e.g., river density, distance from river), land use, lithology and soil types were mapped spatially. An inventory map for flood was generated according to field survey and historical data. Inventory map provides training and test datasets for building the predictive flood models. Finally, mLSTM model used to map flood hazard in the study area, and its performance was assessed by accuracy measures. The results shown that 27%, 19.7% and 26% of total area were belonged to very low, low and moderate hazard classes, whereas high and very high hazard classes were occupied 15.9% and 11.4% of total study area, respectively. The combination of our suggested methodology with MCDM models can be useful to map flood risk, and to mitigate destructive consequences of floods in drylands.

How to cite: Gholami, H., Firouzy, S., Mohammadifar, A., and Golzari, S.: mLSTM deep learning model to map flood hazard in an arid catchment  , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1924, https://doi.org/10.5194/egusphere-egu25-1924, 2025.

This study utilized AR6 daily rainfall data for Miaoli County, Taiwan, sourced from the National Science and Technology Center for Disaster Reduction (NCDR) through the "Taiwan Climate Change Projection Information and Adaptation Knowledge Platform" (TCCIP). Flood hazard maps were generated for the baseline period (1995–2014) and future projections (2081–2100), considering four greenhouse gas emission pathways: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Social vulnerability composite index data for Miaoli County, developed by NCDR under the "Disaster Reduction Dynamic Data" initiative, were utilized to construct vulnerability maps. Village-level population data were retrieved from the Miaoli County Household Registration Service Platform to create exposure maps. By integrating hazard, vulnerability, and exposure data, flood disaster risk levels for each village were assessed. The results reveal that flood disaster risk in Miaoli County escalates with increasing greenhouse gas emissions under future scenarios. These findings highlight a growing vulnerability to flooding in the county, emphasizing the need for proactive measures. The outcomes of this study provide critical insights for the Miaoli County Government, supporting the identification of high-risk villages and the prioritization of resources for flood mitigation infrastructure. This strategic approach aims to effectively reduce the threat of flood disasters under changing climate conditions.

How to cite: Liu, W.-C., Liu, H.-M., and Huang, W.-C.: Utilizing AR6 Daily Rainfall Data to Assess Climate Change Impacts on Flood Risk in Miaoli County, Taiwan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2360, https://doi.org/10.5194/egusphere-egu25-2360, 2025.

Fluvial floods occur when the water level in a watercourse rises and exceeds the capacity of the river banks, thus flooding the adjacent floodplain. The aim of this study is to map and assess fluvial flood hazard at catchment scale as well as municipal scale using the rainfall-runoff modeling, hydraulic modeling, and geographic information systems (GIS). As the study area, we selected the Gidra River Basin, which is located in western Slovakia. Moreover, we selected twelve municipalities from the studied basin based on the condition that urban area of the municipality is completely or partially located in the studied basin, i.e. can be significantly affected during a fluvial flood event. The Stochastic Rainfall Generator model was used to synthetically generate rainfall time series based on the observed annual maxima daily rainfall from the Častá and Cífer rainfall stations for the period 1990 – 2020. In order to calculate the design peak discharge with 100-year return period, we used the Continuous Simulation Model for Small and Ungauged Basins. The estimated design peak discharges were calculated for five cross-sections in the Gidra River Basin, which were considered independent for hydraulic modeling. Hydraulic modeling was performed with 1D steady-state flow conditions using the Hydrologic Engineering Center's River Analysis System. We modeled selected river sections of the main Gidra River and its tributary named Štefanovský potok. Both river sections were selected because they are listed as critical river sections for possible occurrence of fluvial floods in the last cycle of the Preliminary Flood Risk Assessment in Slovakia from 2018, which was elaborated under the Directive 2007/60/EC of the European Parliament and of the Council of 23 October 2007 on the assessment and management of flood risks. The obtained results from the catchment-scale hydraulic modeling, i.e. flood extent, flow depth, and flow velocity for Q₁₀₀ flood scenario, created the basis for the subsequent municipal-scale assessment. In order to distinguish the flood hazard at municipal scale, we calculated the fluvial flood hazard index (FFHI) using the flood extent, average flow depth, and average flow velocity in each municipality as indicators. First, we normalized the values of these indicators using the maximum method and then we used equal weighting of indicators to combine them to the final FFHI. Based on the obtained results, the highest fluvial flood hazard was recorded in the municipalities of Cífer, Budmerice, and Jablonec, which are located in the central part of the studied basin, but also in the municipalities of Píla and Častá at the upper part of the basin. The resulting FFHI at municipal level was compared with the number of previous fluvial floods in the studied municipalities in the period 1996 – 2024, where a very good agreement was achieved. Acknowledgment: Funded by the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia under the project No. 09I03-03-V03-00085.

How to cite: Vojtek, M. and Vojteková, J.: Mapping fluvial flood hazard at catchment and municipal scale: a case study from Slovakia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3314, https://doi.org/10.5194/egusphere-egu25-3314, 2025.

The increasing frequency and severity of climatic-driven extreme events such as heavy precipitation, floods, and droughts have raised global concerns due to their substantial social, economic, and environmental impacts. Among these, floods are considered the most devastating, causing extensive damage to life, property, and infrastructure.

This study focuses on assessing flood risks in Afghanistan, a country highly vulnerable to climatic disasters due to decades of conflict, environmental degradation, and limited mitigation capacities. Using remote sensing (RS) data and geographic information systems (GIS) techniques, the study evaluates flood hazard and vulnerability as key components of flood risk at the sub-basin and provincial levels. Principal component analysis (PCA) is employed to identify governing environmental, climatic and social indicators of flood risk. Additionally, the Analytical Hierarchy Process (AHP) is used to rank and prioritize the relative importance of various indicators in the hazard and vulnerability index, ensuring logical consistency through a systematic evaluation and minimizing bias by reducing subjective influence in decision-making.

The findings reveal that the eastern and northeastern regions of Afghanistan, mainly overlying the Amu and Kabul River basins, are severely exposed to very high flood hazards. This is primarily due to the combined effects of precipitation, topography, and drainage characteristics, all of which contribute to rapid runoff and increased flooding potential. The vulnerability assessment indicates that the densely populated rural areas in the northern and eastern regions are more susceptible to flood risk. Significant land use changes further intensify vulnerability, increasing the exposure of communities to flooding.  Overall, the study identifies key flood-prone areas, providing essential guidance for policymakers. These findings offer a roadmap for resource allocation with an aim of developing targeted mitigation strategies, ultimately enhancing community preparedness and building a sustainable adaptive capacity to manage future flood risks effectively.

How to cite: Ishanch, Q., Mishra, K., Zarfl, C., and Fitzsimmons, K.: Flood Risk Assessment Using Morphometric and Hydrological Analysis in Afghanistan: An Integrated RS and GIS Approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3596, https://doi.org/10.5194/egusphere-egu25-3596, 2025.

Coastal flooding is caused by a complex interplay of various factors, including storm surges, wave overtopping, river flooding due to heavy rainfall, and inland water inundation. To predict and prepare for potential coastal flooding, coastal inundation predicton maps estimating flood depth and area under various hypothetical scenarios have been developed and utilized. However, most existing coastal inundation predicton maps have limitations in comprehensively considering the diverse factors contributing to coastal flooding. This study aims to overcome these limitations by incorporating multiple flood-inducing factors in coastal areas. Specifically, numerical modeling using ADCIRC and empirical formulas from EurOtop 2018 were applied to predict flooding caused by storm surges and wave overtopping. Additionally, 660 to 735 hypothetical typhoon scenarios were developed and applied for different coastal regions. To account for the impacts of future climate change, sea-level rise projections based on the SSP 5-8.5 climate scenario for the year 2100 were also included. The resulting coastal inundation predicton maps, which integrate multiple factors, were developed for four return periods: 50, 100, 150, and 200 years. These maps can serve as essential tools for developing disaster prevention policies and assessing coastal flood risks, contributing to minimizing flood damage in coastal regions.

How to cite: Lee, H.-Y., Jeong, K.-Y., Cho, W.-H., Park, J.-J., Seo, G.-H., and Hogan, P.: Development of Integrated Coastal Inundation Prediction Maps Considering Multiple Factors and Climate Change Impacts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5380, https://doi.org/10.5194/egusphere-egu25-5380, 2025.

Fast flood modelling for pluvial flood management in Innsbruck, Austria

The increasing frequency and intensity of precipitation events leads to urban flooding, often causing significant damage to infrastructure and property. This phenomenon, known as pluvial flooding, arises when heavy precipitation exceeds the capacity of urban drainage systems, leading to surface water accumulation. Climate change is expected to exacerbate this issue, emphasizing the urgent need for efficient predictive models to mitigate the associated risks and impacts.

Traditional hydrodynamic models, such as coupled 1D-2D simulations, offer highly detailed flood assessments by simulating both surface runoff and sewer network interactions. However, these models are computationally demanding, requiring significant resources and time, making them unsuitable for real-time flood forecasting and decision-making during extreme weather events.

To address these limitations, fast flood models like the dynamic CA-ffé model, based on Jamali et al. (2019) and further developed by Gholami Korzani and Deletic (2023), provide a practical alternative. These models efficiently integrate surface flow and sewer network dynamics, enabling accurate flood forecasting at a much lower computational cost. Previously validated in smaller Australian catchments, the dynamic CA-ffé model has demonstrated its ability to provide timely and accurate urban flood simulations, significantly improving flood forecasting and risk management.

To address flooding challenges in Innsbruck, a larger, mountainous catchment area (~50 km²), the dynamic CA-ffé model was adapted based on the model approach of Gholami Korzani and Deletic (2023). This model approach combines a cellular automata-based 2D simulation with a 1D sewer network model using SWMM (Stormwater Management Model). By synchronizing data exchanges between surface runoff and sewer discharge at regular intervals, the model achieves faster and more accurate flood predictions, enabling high-resolution urban flood forecasting.

Adapting the model to Innsbruck required adjustments to account for the city's complex mountainous terrain and boundary conditions. Additional case-specific modifications were implemented to ensure compatibility with the larger and more challenging catchment area. The model was tested using historical flood events and validated against fire brigade records and photo documentations, as no prior citywide flood models were available for comparison.

The model's fast computation times allow the simulation of different flood scenarios, including assessments of the effects of climate change. These simulations will help to identify flood risks and inform heavy rainfall management strategies. Initial results confirm the model's ability to simulate urban-scale flooding, while highlighting challenges in adapting the approach to larger and more topographically complex study areas, such as land-use based runoff coefficients and the use of multiple rain gauges for precipitation data.

Funding:

BlueGreenCities (project No. KR21KB0K00001), funded by the Austrian Climate and Energy Fund from October 2022 until September 2025

Early Stage Funding (project: FFMFF) funded by the Vice-Rectorate for Research of the University of Innsbruck from November 2023 until October 2024.

References:

Gholami Korzani, M., Deletic, A., 2023. Dynamic CA-ffé: a hybrid 1D/2D fast flood evaluation model for urban floods. Sydney.

Jamali, B., Bach, P.M., Cunningham, L., Deletic, A., 2019. A Cellular Automata Fast Flood Evaluation (CA-ffé) Model. Water Resources Research 55, 4936–4953. https://doi.org/10.1029/2018WR023679

How to cite: Hauser, M., Rauch, N., Gholami Korzani, M., Deletic, A., and Kleidorfer, M.: Fast flood modelling for pluvial flood management in Innsbruck, Austria, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6551, https://doi.org/10.5194/egusphere-egu25-6551, 2025.

How to cite: Pellerin, J., Hayes, S., Chastko, K., Ballard, M., Bourke, R., and Van de Valk, J.: Canada-wide Modelling – Analysis of Model Accuracy to Drive Appropriate Use and Risk Reduction Program Development, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7327, https://doi.org/10.5194/egusphere-egu25-7327, 2025.

Floods are globally catastrophic, with profound impacts on India’s environment, agriculture, and infrastructure. Between 1953 and 2010, floods annually affected 7.2 million hectares and 3.2 million people. Odisha, particularly the Mahanadi basin, ranks seventh in flood vulnerability, with over 90\% of its annual rainfall occurring during the monsoon. Synoptic systems from the Bay of Bengal exacerbate rainfall, causing frequent and severe floods. Despite existing forecasting systems, downstream regions remain inadequately protected, highlighting the need for more accurate predictive mechanisms.

Recent advancements in Machine Learning (ML) and Deep Learning (DL) offer new opportunities for flood forecasting. Long Short-Term Memory (LSTM) models excel at capturing intricate temporal patterns in rainfall and streamflow data, outperforming conventional methods. Innovations like hybrid LSTMs and Spatio-Temporal Attention (STA) mechanisms enhance their performance, while novel architectures such as Temporal Convolutional Networks (TCNs) and Bi-LSTMs improve long-term predictions.

This study introduces a Kolmogorov-Arnold Network (KAN)-enhanced LSTM model for five-day-ahead flood prediction in the Mahanadi basin. KAN leverages learnable activation functions and spline representations, improving accuracy, interpretability, and computational efficiency. Evaluated against traditional LSTMs using metrics such as Nash-Sutcliffe Efficiency (NSE), time-to-peak prediction, and convergence speed, the KAN-enhanced model consistently outperformed standard LSTMs. It demonstrated a 12\% improvement in NSE, superior peak timing, and 20\% faster convergence, offering crucial advancements for early warning systems.

By integrating KAN's ability to model non-linear relationships with LSTM’s strength in sequential data analysis, this framework addresses complex hydrological dynamics, providing reliable flood forecasts. These findings underscore the potential of KAN-enhanced architectures to revolutionize flood prediction, offering scalable and interpretable solutions for flood-prone regions.

How to cite: Sarkar, S., Dey, A., Chatterjee, C., and Mitra, P.: KAN-Enhanced LSTM for Accurate and Scalable Flood Forecasting: A Case Study of the Mahanadi Basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8031, https://doi.org/10.5194/egusphere-egu25-8031, 2025.

There is currently no pre-existing map of embankments, dikes, levees and merlons in the Upper Seine River basin as existing data is limited to specific local studies. Developing a comprehensive map of these features is needed for effective flood risk management and planning. Embankments and merlons play a significant role in shaping the basin’s hydrological and geomorphological dynamics, influencing water flow patterns, floodplain connectivity, and sediment transport. Accurate mapping enables authorities to identify areas where these structures may exacerbate flood risks or disrupt natural floodplain functions, which are essential for mitigating flood impacts. Leveraging advancements made in GIS and LiDAR DEM data, an automated tool was developed to detect and map these features within the floodplain. Their detection was made possible by extracting morphometric parameters related to relative height, slope, and convexity. The mapped features were then characterized to evaluate their potential impact on flood dynamics. This characterization considers three parameters: the valley’s width, the embankment’s position within the valley and its elevation. The tool demonstrates encouraging outcomes, with good detection accuracy and a characterization protocol consistent with field observations. These findings establish a scalable methodology that can be applied to geomorphologically similar regions, providing a framework for improved floodplain management.

How to cite: Dereppe, J., Laurent, V., and Arnaud-Fassetta, G.: Mapping embankments and merlons in the Upper Seine River basin : A GIS-based approach for comprehensive floodplain dynamics., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8185, https://doi.org/10.5194/egusphere-egu25-8185, 2025.

As climate change drives sea level rise and raises critical questions about the design and efficacy of flood defences, there is an increasing demand for high-resolution coastal flood data. As a commercial provider of flood risk data to the insurance and banking sector, we have users that emphasise accuracy. For instance, these users seek not only to determine if floodwaters might reach a property but also whether they will exceed the height of the doorstep. While advancements in flood modelling continue to meet these growing needs, a persistent challenge remains: how can we balance the need for high accuracy with the practical constraints of production costs and the need for timely data delivery?

Focussing on coastal flood mapping examples in the UK, our presentation will compare outputs from two ends of the modelling spectrum: complex full hydrodynamic modelling and a simplified projection approach. Simplified approaches often face criticism for their limitations, but we will argue that they can provide valuable – as well as timely and efficient – outputs, when applied appropriately and with a clear understanding of their constraints.

This work aims to explore the importance of balancing advanced and simplified techniques, offering insights into the factors that most significantly influence flood modelling outputs. For example, we examine whether the choice of input data (e.g., the terrain data, the extreme sea levels) has a greater impact on results than the modelling approach itself (hydrodynamic compared to projection modelling techniques). By highlighting the trade-offs and opportunities, we aim to contribute to a more nuanced understanding of how to optimise coastal flood risk data production to meet user needs.

How to cite: Raven, E., Charge, J., Liam, H., James, S., and Rhiannon, W.: Balancing complexity and efficiency in coastal flood risk modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8901, https://doi.org/10.5194/egusphere-egu25-8901, 2025.

In lowland areas that are susceptible to flooding, for example due to river levee breaches, the presence of linear elements such as levees of minor channels or road/railway embankments can significantly influence the inundation dynamics. Being more elevated than the surrounding land, these embankments can provide an obstacle to flood propagation, reducing the flood extent, but sometimes increasing the water depths. To obtain accurate flood simulations, and hence reliable flood hazard maps, the correct representation of embankments in the computational domain is therefore crucial.

Numerical simulations for flood hazard assessment usually rely on the unrealistic assumption that the minor embankments in the floodable area are able to withstand the interaction with floodwaters without collapsing. However, especially when overtopped, these elements can be eroded or collapse, no longer limiting the flood propagation. The assumption of “non-erodible” embankments can thus lead to misestimating the flood hazard, but studies in literature about this issue are lacking.

In this work, a preliminary analysis on how flood hazard mapping in lowland areas can be influenced by the possible failure of minor embankments is carried out, focusing on a case study in Italy. The results of two-dimensional numerical simulations of flood scenarios performed considering the minor embankments either as non-erodible or as erodible are compared. The most important problem in simulating the case of erodible embankments is the difficulty in including failure criteria to predict their collapse. While this is indeed a limitation for roads and railways, for which the large variability in building materials and structure prevents the definition of reliable failure criteria, it can be presumed that the available models developed to predict the breaching of earthen dams/levees can be applied to the levees of irrigation and drainage channels as well. For this reason, the study focuses on an area that is crossed only by the earthen levees of minor channels, and a simple erosion model that can automatically predict the breaching of these elements due to overtopping is adopted. The scenario considered for the analysis is the inundation induced by a levee breach in a nearby river.

Results show that, when assuming “non-erodible” embankments, the flood extent can be underestimated, while the maximum water depth and the flood hazard classification can be overestimated upstream of the erodible embankments and underestimated downstream. Moreover, the flood arrival time can be anticipated in the downstream areas. Overall, despite being case-specific, the analysis suggests that the unphysical assumption of “non-erodible” embankments in lowland areas can significantly influence the flood hazard assessment, possibly underestimating it, and this limitation should be kept in mind by flood risk management authorities.

This work is part of the project "MORFLOOD" (PNRR-M4C2 - I1.1-Avviso MUR n.104 del 02-02-2022 - PRIN2022 - Project code 2022SJ2NJ9 - CUP Code D53D23004860006 - Funded by the European Union-NextGenerationEU).

How to cite: Dazzi, S.: Failure of minor embankments in inundated areas: influence on the flood hazard estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9269, https://doi.org/10.5194/egusphere-egu25-9269, 2025.

Calibration and validation are necessary steps in the field application of physically-based models for floods and landslides. These processes validate the model assumptions and adjust the parameterization to align with historical observations. However, calibration remains a time-consuming task due to the lack of globally available datasets directly linked to the calibration schemes of physically-based models.

As part of the FastFlood.org and FastSlide.org rapid modeling platforms, we have developed a built-in calibration scheme that leverages global observational datasets and data-driven approaches. This approach links the results of global calibration datasets to a standardized calibration scheme for flood and landslide models, enabling automatic calibration globally based on these datasets.

In this paper, we outline the setup, global dataset processing workflows, and the initial outcomes of this research. The global datasets are derived from multiple observation types. For example, discharge data for return period events in rivers is based on GloFAS reanalysis data, bias-corrected using an extreme-value analysis performed globally on GRDC discharge station time series. Flood extents from the Global Surface Water Explorer are included but corrected for the underrepresentation of flash flood events in the observational data. For landslide processes, landslide inventory collections and data-driven global landslide susceptibility maps are utilized to provide built-in calibration functionality.

To streamline the calibration process, specific automated schemes have been developed to preprocess these global datasets, enabling direct comparison with model outputs without user intervention. Furthermore, we explore some initial results of this calibration scheme, comparing its accuracy and relative performance against other calibration methods.

How to cite: van den Bout, B., Kolaparambil, F., Katarya, S., van Westen, C., and Meijvogel, D.: Global automated calibration procedures for the FastFlood and FastSlide rapid hazard models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9325, https://doi.org/10.5194/egusphere-egu25-9325, 2025.

Hydrodynamic models solving various forms of the shallow water equations are commonly used to assess flow depths and velocities during floods. Recent advances have extended these models to incorporate elements of the catchment such as sewer systems, culverts, bridges, and weirs. However, many flood models still represent buildings as raised elevations or with increased roughness coefficients, blocking water from entering building areas. This can lead to an overestimation of flood depths and extents, as these storage areas are not considered. Until now, the cumulative effects of flow into buildings on city-scale inundation have not been simulated using physically based models. This study develops and implements a building indoor-inundation model, comparing it with simulations that neglect this effect (storage and retention inside buildings) to quantify the differences in flood depth and extent.

A hydrodynamic model was coupled with the indoor-inundation model to estimate flow into and out of buildings (through windows, doors, etc.). This model incorporates building geometry, including walls, doors, and stairwells, to determine flow throughout the building. It also accounts for the transition between pressurized and non-pressurized flow, allowing water to move from lower to upper floors. The building indoor-inundation model was coupled with the 2-D diffusive wave model P-DWave, which simulates surface flood inundation outside the buildings. Water is exchanged between the two models at run-time in a bi-directional manner, with water flowing both into and out of the buildings.

Flood simulations were conducted for the city of Baiersdorf in northern Bavaria, which experienced flash floods in 2007 caused by heavy rainfall, resulting in over 86 million euros in damages. The event was recreated using the dual-drainage model PD-Wave/SWMM to simulate interactions between overland flow and the sewer system. Three test cases were modeled: buildings represented with increased roughness coefficients, buildings raised above the floodplain, and buildings modeled using the presented hydrodynamic approach. The results show that modeling the flow into and out of buildings has a moderate to significant impact on both flood depths and extents, highlighting the importance of including building inundation in urban flood modeling.

How to cite: Johnson, T. G. and Leandro, J.: Assessing the Impact of Building Indoor-Inundation on Flood Depth and Extent at City Scale: A Novel 2-D Coupled Hydrodynamic and Building Inundation Model for Urban Flood Modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10398, https://doi.org/10.5194/egusphere-egu25-10398, 2025.

Traditional flood modelling approaches, which rely on deterministic methods, often fail to account for the inherent uncertainties of flood events, such as discharge estimates or flood defence parameters. Probabilistic flood modelling addresses this gap by quantifying the likelihood of various scenarios, based on the probability of occurrence of its inputs. However, the large number of required numerical simulations makes this framework computationally expensive. In this study, we explore the use of a multi-scale graph neural networks inspired by finite volume methods to accelerate probabilistic flood simulations. This approach is applied to several dike rings in the Netherlands - regions enclosed by levees - considering uncertainties in dike breach locations and inflow discharges. To improve the reliability of the model, we select among the output simulations only the ones that approximately preserve the total flood volumes over time, as calculated from the inflow boundary conditions. Our model generates thousands of flood scenarios with orders-of-magnitude speedups compared to traditional methods. The resulting output maps provide the expected frequencies of inundation extents for specific water depths, offering a robust tool for efficient and comprehensive flood risk assessment.

How to cite: Bentivoglio, R., Isufi, E., Jonkman, S. N., and Taormina, R.: Probabilistic flood modelling with multi-scale hydraulic-based graph neural networks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10951, https://doi.org/10.5194/egusphere-egu25-10951, 2025.

This study investigates flood-prone areas in the Loukous Basin, Northern Morocco, utilizing machine learning and remote sensing techniques to analyze and map zones at risk. Renowned for its agricultural significance, the region features low-lying, flat terrain, an active river system, and oceanic influences, all of which exacerbate flooding risks. Additionally, climate change poses increasing challenges, intensifying extreme weather events and altering precipitation patterns that further threaten this vulnerable region. The research aims to enhance flood prevention strategies and mitigate economic and human losses by identifying and prioritizing highly vulnerable zones. Results consistently highlight significant flood susceptibility along the Loukous River and its adjacent plains, areas characterized by lowland topography, high drainage density, proximity to canals, and intensive agricultural activity. While spatial variations exist among the models, a strong consensus emerges regarding zones of low and very high vulnerability, emphasizing the need for tailored interventions. These findings provide critical insights for integrating agricultural development planning with flood risk management in the Basin of Loukous. They underscore the importance of adaptive strategies that consider the compounded effects of climate change, such as improved land-use practices, enhanced drainage systems, and sustainable water management. This study establishes a robust scientific foundation for implementing targeted measures to reduce flood impacts, safeguard livelihoods, and build resilience in this economically vital and environmentally sensitive region, with broader implications for similar flood-prone areas worldwide.

How to cite: Mekkaoui, O., Morarech, M., En-negady, I., Bouramtane, T., and Akka, H.: Evaluating Flood Vulnerability in the Loukous Basin, Northern Morocco: Integrating Machine Learning, Remote Sensing, and Climate Change Impacts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12481, https://doi.org/10.5194/egusphere-egu25-12481, 2025.

Floods are considered one of the most damaging natural disasters known to humankind, and their severity has increased significantly due to the impacts of climate change and global warming in recent decades. Floods have become more unpredictable and erratic due to the influence of extreme hydroclimatic events. Therefore, obtaining near-real-time and accurate flood inundation maps of such events is essential for effective flood emergency response. These can be achieved easily by leveraging multi-source satellite imagery and remotely sensed data. The freely accessible satellite imagery and remotely sensed products can provide essential information that can significantly reduce the resources needed to create flood inundation maps and improve the accuracy of mapping and monitoring systems. This study integrated high-resolution satellite imagery and multiple remote sensing data to improve the flood inundation mapping technique in a data-scare South Asian watershed. The study considered the 2008 Bihar flood caused by the embankment breach of the Koshi River as a case study. This study used Landsat satellite's surface reflectance data to map the flood inundation using the commonly used water index known as the Modified Normalized Difference Water Index (MNDWI). MNDWI is a commonly used water classification technique to detect open surface water features using surface reflectance data sensed by the satellite. Further, the Normalized Difference Vegetation Index (NDVI), permanent water bodies, and Height Above the Nearest Drainage (HAND) datasets are used to mask the MNDWI map (initial flood inundation map) and improve the accuracy of the inundation map. In addition, different thresholding values of the final masked MNDWI map are applied to obtain more accurate and robust flood inundation maps with fewer false positive and false negative pixels.

How to cite: Aryal, A., Sinha, R., and Lakshmi, V.: Improving the Flood Inundation Mapping Technique using Satellite Imagery and Remote Sensing Data: A Case Study of the Bihar Flood Caused by the Koshi Embankment Breach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12576, https://doi.org/10.5194/egusphere-egu25-12576, 2025.

Over the last twenty years, floods have represented the most predominant natural disasters occurred worldwide. Just in 2024, more than 15 European countries from Italy to Poland and from Spain to the Czech Republic experienced severe floods leading to catastrophic impacts. Between September 17 and 20, the Lamone River basin in the Emilia-Romagna Region in Northern Italy was hit by extreme precipitations and a levee-breach-induced inundation caused the flooding of urban settlements and crops near the Traversara village, an area already affected by huge floods no later than May 2023.

In the present work, the hydrological model Rhyme (River HYdrological ModEl) and the hydrodynamic model PARFLOOD are adopted to reconstruct the hydrological processes that occurred over the watershed and the dynamic of the flooding event. The spatially explicit Rhyme model enabled the description of the rainfall-runoff processes at the catchment scale by using as meteorological forcing hourly rainfall, daily cumulative potential evapotranspiration, and daily average temperatures. Due to the availability of a 16 year-series of water levels recorded at a gauging station located at the basin outlet and stage-discharge relationships, the model was calibrated from 2008 until 2024 using a Markov Chain Monte Carlo algorithm.

The flow hydrographs estimated by the hydrological model for the September 2024 event were then provided as inflow conditions to the hydrodynamic model PARFLOOD, which is a 2D parallel finite volume scheme. The breach opening on the left levee of the Lamone River was modelled by adopting a geometric approach and information about the breach characteristics (e.g. opening time and length) was provided through direct observations. The resulting flooding maps showed that after a few hours of overflowing, the levee-breach-induced flood affected the village of Traversara, urban settlements, crops, and vineyards in less than 10 hours. Moreover, the numerical results highlighted how minor channel embankments spread in the domain confined the flood propagation to the west, thus avoiding the flooding of a highly densely populated area.

Over the last two years, the Lamone River basin was affected by extreme precipitations that in many gauge stations exceeded the 500-year return period and broke historical records. Focusing on the September 2024 event, the close match between the resulting flooded areas and the observed ones, and the fair agreement between the water levels recorded at three gauge stations along the river and the resulting ones, highlighted the capability of the numerical models here adopted to support the assessment of extreme events and increase the preparedness for at-risk populations.

How to cite: Ferrari, A., Passadore, G., Vacondio, R., Carniello, L., Pivato, M., Crestani, E., Carraro, F., Aureli, F., Carta, S., and Mignosa, P.: Reconstruction of the September 2024 extreme flood on the Lamone River in Northern Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13123, https://doi.org/10.5194/egusphere-egu25-13123, 2025.

Bridges worldwide face significant risks from flood-related hazards, with hydraulic actions being the primary cause of structural damage, service disruption, and catastrophic failure. Bridge owners and regulatory agencies must assess multiple hydraulic risks, including scour, uplift forces, drag effects, debris impact, and deck displacement, all of which can compromise the load-carrying capacity of a bridge. The assessment of hydraulic actions on highway bridges in many parts of the world still relies on simplified or qualitative methods, whereby hydraulic models are yet to be embedded within guidance.

In the United Kingdom, the CS469 standard governs the assessment of hydraulic actions on highway bridges, providing guidance for risk evaluation and management strategies. The CS469 methodology calculates hydraulic flow characteristics at critical cross-sections within channels and bridge crossings utilising Bernoulli theorem and specific energy. While computationally efficient, this simplified approach relies on non-physical approximations. This fundamental limitation introduces substantial uncertainty into risk and vulnerability assessments, potentially compromising the reliability of management decisions.

This study presents an alternative approach utilising 2D HEC-RAS hydraulic models with bridges modelled as 1D elements within flow areas. The proposed methodology crucially maintains compatibility with existing data requirements from CS469 while adhering to open-source principles, requiring only publicly available data or information from existing assessments. This approach ensures cost-effectiveness and accessibility for bridge management teams while providing significantly improved accuracy.

Comparative analyses between the 2D HEC-RAS model and traditional CS469 calculations for six case study bridges revealed substantial differences in hydraulic response. The 2D model showed water depths up to 138% higher and flow velocities 64% lower than CS469 estimates. These differences significantly impact scour risk assessments, with HEC-RAS models typically predicting scour depths up to 2.3m lower (averaging 1.5m reduction) compared to simplified equations, resulting in more realistic risk classifications.

While hydraulic vulnerability assessments showed limited variation, the CS469 approach only considers threshold values without quantifying effects. Our findings demonstrate that numerical hydraulic simulations provide more accurate risk estimations with comparable resource requirements, suggesting that future revisions of risk assessment guidelines should prioritise this methodology. This advancement would enhance the accuracy and reliability of bridge risk assessments, ultimately improving infrastructure resilience and safety management strategies.

How to cite: Panici, D., Kripakaran, P., and Brazier, R.: A comparative analysis for assessment of hydrodynamic actions at bridges of 2D hydraulic models and traditional highway standards, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13884, https://doi.org/10.5194/egusphere-egu25-13884, 2025.

The risk associated with river floods has escalated significantly due to the increasing frequency and intensity of extreme precipitation events, compounded by the complex interplay of flood-generating mechanisms under a changing climate. Accurately estimating flood risks poses a formidable challenge, as these mechanisms often interact and exhibit varying influences across spatial and temporal scales. An in-depth understanding of flood-generating processes is critical for improving hydrological modeling, flood frequency analysis, and risk management strategies in diverse climatic regions. Despite India being one of the most flood-prone countries globally, a systematic classification of hydrometeorological flood-generating processes remains largely absent. Understanding the role of catchment and climate attributes in flood generation is crucial for advancing our ability to predict and manage flood risks. This study proposes a robust framework to classify flood-generating processes into three primary categories: long rainfall floods, short rainfall floods, and excess rain floods. The analysis focuses on major river basins across India, offering insights into region-specific flood dynamics. To achieve this, we leverage the CAMELS-IND dataset, a comprehensive repository of hydrological and meteorological data, covering 471 catchments across India from 1980 to 2020. Using a peaks-over-threshold (POT) approach, we identify significant flood events and assess their characteristics. We then employ a Light Gradient Boosting Machine (LightGBM) model, an advanced machine learning algorithm known for its efficiency and accuracy, to evaluate the contribution of various climate and catchment attributes in triggering these floods. To enhance interpretability, we integrate Shapley additive explanations (SHAP), which provide a localized and global understanding of the model's predictions, highlighting the relative importance of each attribute. Our findings underscore the dominant role of climate attributes, such as precipitation intensity, antecedent soil moisture, and temperature, in determining the spatial distribution of flood-generating processes across diverse climatic zones. Catchment attributes, including soil type, slope, and land use, also contribute but to a lesser extent. These insights have significant implications for flood risk management, particularly in ungauged catchments, and can enhance the accuracy of hydrological and hydrodynamic models under changing climatic conditions.

How to cite: Tripathi, V., Deopa, R., and Mohanty, M.: Unraveling Hydrometeorological Drivers of Floods: A Data-Driven Analysis Across India's River Catchments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15096, https://doi.org/10.5194/egusphere-egu25-15096, 2025.

The rising likelihood and severity of flooding caused by climate change heightens the demand for detailed and precise hydrodynamic models. Flood modeling commonly involves analyses done either in a simplified watershed-scale 1D model, a detailed local-scale 2D model, or a combination of both approaches. However, the laborious setup and high data requirements hinder detailed watershed-scale modeling, particularly in urban areas like Hong Kong, where intricate drainage systems pose additional challenges. Alternative methods have been developed to consider underground drainage capacity such as subtracting the water volume held by pipes from the surface runoff and representing it with an equivalent infiltration rate. However, these methods do not give sufficient information on the flow movement underground. Enabled by Hong Kong’s extensive datasets, this study attempts the integration of the digitized urban drainage system into a watershed-scale hydrodynamic model. Hong Kong Island, with a land area of 99.5 square kilometers, is set as the study area. This domain covers 35,259 conduits, 32,622 junctions, and 31 rain gages. A case study is adopted using the September 2023 black rainstorm event to demonstrate the model’s capability to map surface flood inundations and describe the dynamics of the underground drainage system at once. Observed flood depths during the event are then used for results validation. Large-scale urban drainage models like this may aid decision makers in flood risk assessment and emergency action planning.

How to cite: Canlas, A., Zhang, L. M., and He, J.: Large-scale Urban Drainage Modelling for Hong Kong Island, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15323, https://doi.org/10.5194/egusphere-egu25-15323, 2025.

Flood forecasting and reservoir flood control operation are important technical to realize the "four pre" of basin flood control. Taking Shanxi reservoir, Baizhangji reservoir and Zhaoshandu drinking water project in Feiyun River Basin in Zhejiang Province as examples, this paper analyzes the spatial relationship and hydraulic connection of various water conservancy projects in the basin, and establishes a distributed Xin'anjiang model for flood forecasting at important nodes in the basin, which is used as the input of the flood control joint optimization operation model of reservoir groups and solves the model. The results show that during the period of defending against the typhoon Megi, the flood control joint optimization operation of reservoir group considering flood forecast information can further play the potential of reservoir flood control, and the peak shaving effect of Zhaoshandu section in the downstream is obvious.

How to cite: Ren, M., Zhang, Q., and Zhao, L.: Research and application of joint optimal operation of reservoir group flood control considering flood forecast information, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15522, https://doi.org/10.5194/egusphere-egu25-15522, 2025.

Floods are the most frequent disasters, affecting the largest number of people. In 2023, 164 floods were recorded worldwide, killing 7763 people, affecting 32.4 million people, and resulting in 20.4 billion USD losses (CRED 2023 Disasters in Number report). To mitigate flood risk, decision makers and civil protection authorities need reliable information on flood risk assessment, covering all disaster management stages.

In the framework of a Programming Agreement with the Prefecture of Attica, NOA/IAASARS/BEYOND, in cooperation with NTUA/ITIA, developed a holistic multiparameter methodology that was implemented in five flood-stricken river basins at high spatial resolution. The research teams collected all available Earth Observation data, spatial data and technical studies; conducted detailed field visits; and modified the DEM and land cover accordingly. Following rainfall-runoff modeling and hydraulic modeling, the flood hazard was assessed for different scenarios. Vulnerability was considered a weighted estimation of population density, population age, and building characteristics on the basis of the population-housing census at the building block level. Exposure was based on the land value. Flood risk was eventually assessed on the basis of the combination of flood hazard, vulnerability, and exposure. Moreover, critical points, which were identified from the field visits, were also crosschecked with the flood inundation maps. Finally, refuge areas and escape routes were proposed for the worst-case flood scenario. This innovative methodology was applied, among other methods, in the Mandra river basin and was validated with the results of the urban flash flood, which took place in 2017, the deadliest flood in Greece in the last 40 years. BEYOND developed a user-friendly web GIS platform in which all the collected and produced data, including flood risk maps, critical points, refuge areas and escape routes, are made available.

This flood risk assessment methodology was applied, following adaptation, in the Garyllis river basin in Cyprus, within the framework of the EXCELSIOR project, as part of the collaborative activities between ECoE and BEYOND. Data were collected from multiple sources, including satellite missions, governmental portals, in situ measurements, and historical records, at different resolutions. The collected data were calibrated via onsite visits and discussions with relevant actors, harmonized in terms of spatial and temporal resolution and used as inputs to estimate the flood hazard for different return periods. The vulnerability levels of the study area were quantified via the weighted linear combination of population density, population age, and building characteristics at the road level. The exposure levels were quantified in terms of the land value. Flood risk levels were estimated as a product of hazard, vulnerability and exposure levels. The validity of the proposed methodology was evaluated by comparing the critical points identified during the field visits with the estimates of the flood risk levels. Consequently, escape routes and refuge regions are recommended for the most extreme scenario.

This work supports relevant authorities in improving disaster resilience and in implementing the EU Floods Directive 2007/60/EC, the Sendai Framework for Disaster Risk Reduction, the UN SDGs, and the UN Early Warnings for All initiative.

How to cite: Tsouni, A., Panagiotou, C., Sigourou, S., Kountouri, J., Pagana, V., Dimitriadis, P., Iliopoulou, T., Sargentis, G.-F., Mettas, C., Evagorou, E., Ioannidis, R., Chardavellas, E., Dimitrakopoulou, D., Alexopoulos, M. J., Mamasis, N., Koutsoyiannis, D., Hadjimitsis, D., and Kontoes, C. (.: A transferable multicriteria methodology for flood risk assessment: two case studies in Greece and Cyprus, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15732, https://doi.org/10.5194/egusphere-egu25-15732, 2025.

Faced with climate change and the increasing pressure exerted by marine dynamics on the coastline (notably coastal erosion and marine submersion), adapting impacted territories has become a major challenge. Several solutions exist to protect coastal communities. In Quebec, numerous beach nourishments have been completed or are currently underway. This type of solution is increasingly being implemented (Hinkel et al., 2013). However, follow-up studies are necessary to better quantify their impact on the coast, particularly through multidisciplinary approaches (socio-economic, ecological, geomorphological, etc.).

In September 2022, an extratropical storm (Fiona) significantly affected Quebec's coastline. A heritage site of importance (Havre-Aubert, Îles-de-la-Madeleine) experienced significant marine submersion. A beach nourishment had been carried out shortly before this event. In-situ measurements were taken a few days before and after the storm, allowing for the creation of an exceptional dataset on the storm and its impacts on the site.

This dataset was used to perform various numerical simulations with the open-source morphodynamic model XBeach (Roelvink et al., 2009). This model allows for different computation modes: phase-averaged or phase-resolved, as well as a specific mode for gravel beaches (XBeach-G, McCall et al., 2014). All these configurations were used to simulate this storm event with and without the beach nourishment. The results of these simulations are compared and discussed.

Our results show that the nourishment played a protective role by significantly reducing marine submersion and damage to infrastructure. Under the impact of the storm, the nourishment rapidly adjusted towards a Dean-type equilibrium profile. A reprofiling of the nourishment was observed without significant sediment loss offshore.

Hinkel, J., Nicholls, R. J., Tol, R. S., Wang, Z. B., Hamilton, J. M., Boot, G., ... & Klein, R. J. (2013). A global analysis of erosion of sandy beaches and sea-level rise: An application of DIVA. Global and Planetary change, 111, 150-158.

McCall, R. T., Masselink, G., Poate, T. G., Roelvink, J. A., Almeida, L. P., Davidson, M., & Russell, P. E. (2014). Modelling storm hydrodynamics on gravel beaches with XBeach-G. Coastal Engineering, 91, 231-250.

Roelvink, D., Reniers, A., Van Dongeren, A. P., De Vries, J. V. T., McCall, R., & Lescinski, J. (2009). Modelling storm impacts on beaches, dunes and barrier islands. Coastal engineering, 56(11-12), 1133-1152.

How to cite: Caulet, C., Bernatchez, P., Savoie-Ferron, F., Sauvé, P., St-Onge, S., and McKinnon, R.: Protective role of a gravel-beach nourishment on built-up area during an extra tropical storm (Fiona, September 2022), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16109, https://doi.org/10.5194/egusphere-egu25-16109, 2025.

Understanding flood hazard at scale requires consistently generated flood maps. To build flood maps at scale, automated frameworks must be used that can take input data, transform this into hydrodynamic simulations and process it into output flood maps.

This transformation from inputs into simulations can be influenced by the design of the framework and the choices made by modellers as to how the transformations are made. These choices can be influenced by quality and availability of input data, availability of computational resources and desired outputs. There may not be clear objectively better options, so subjective choices have to be made. Understanding the sensitivity of these choices is key in both making them, and rating the uncertainty of the outputs.

Here we present the outputs from some of the sensitivity testing undertaken when developing a global model framework including both their influence on flood hazard and their influence on other deciding factors (such as computational cost) made when building a global flood map. This presentation includes results from tests that were shown to develop key or interesting results, such as:

• How rivers are split into separate simulations
• Minimum size of fluvial river assessed
• Length of pluvial storm assessed

How to cite: Cooper, A. and Savage, J.: Sensitivity of global flood modelling frameworks to input parameter choices, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17337, https://doi.org/10.5194/egusphere-egu25-17337, 2025.

Flooding ranks among the most devastating natural disasters, often triggered by excessive rainfall, river overflow and storm surges. The consequences can be catastrophic, leading to loss of lives, displacement of communities, demolition of infrastructure, and prolonged economic setbacks. Accurate real-time flood forecasting is essential for providing early warnings, optimizing resource allocation, and implementing mitigation measures. Flood mapping and modelled water levels strongly depend on the river network's connectivity, channel geometry, and interactions with the floodplains. The Mahanadi Delta region in the Odisha state of India is such a dynamic area where the main river splits into several distributaries as it flows downstream, eventually merging with the Bay of Bengal. In this study, a 2D hydraulic model, HEC-RAS, has been utilized to simulate water levels, velocities, and water surface elevations over time for the purpose of identifying overflowing banks and floodplains. To enhance connectivity and improve floodplain representation, surveyed cross-sections of the river network have been merged with an improved Carto-set digital elevation model (DEM). The model has been calibrated and validated using data from various flood years. The flood extent from the model was compared with the satellite-derived flood extent obtained from Sentinel-1. Different performance matrices are used to investigate the model accuracy. The years 2011, 2014, and 2022 witnessed major flood events in recent times.  These events are then simulated, major flood-prone stretches and floodplains are identified, and mitigation measures have been suggested. Developing a modelling framework capable of capturing the movement of water in river networks and floodplains of the Mahanadi River enables disaster management authorities to improve their preparedness and response strategies, significantly reducing potential damage and loss of life.

Keywords: Flood, Mahanadi delta, HEC-RAS, Sentinel-1, Flood mitigation.

How to cite: Singh, V. P. and Kuiry, S. N.: Assessing Flood Risk and Mitigation Strategies in the Mahanadi Delta Using 2D Hydraulic Modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17641, https://doi.org/10.5194/egusphere-egu25-17641, 2025.

The design of flood hydrographs for specific return periods is crucial in hydrological studies, especially in poorly-gauged watersheds. Exploiting the background of the distributed grid-based hydrological model DREAM, to estimate flood hydrographs for assigned return periods we propose the Q_T-DREAM, which incorporates high-resolution geomorphological data to reduce structural uncertainty compared to traditional empirical models. By combining Hortonian and Dunnian infiltration models, Q_T-DREAM allows a spatially-distributed assessment of runoff generation based on local climatic and geomorphological characteristics. Q_T-DREAM was applied on twenty gauged catchments located in Puglia and Basilicata regions (Southern Italy), characterized by climates ranging from semi-arid Mediterranean to humid continental. Flood hydrographs were generated by the model for nested sub-basins for return periods of 30, 200, and 500 years and compared with those calculated using runoff maps of the entire basin. Results demonstrate that the Q_T-DREAM model successfully reproduces the frequency distribution of observed peak floods, ensuring consistency between peak discharges estimated at sub-basin and entire basin scales. This study provides a significant contribution to the development of indirect methodologies for flood estimation in poorly-gauged watersheds, with potential implications for hydraulic risk assessment and flood mitigation planning. In particular, model outputs can be integrated with two-dimensional hydrodynamic models to provide detailed flood hazard mapping, enhancing its applicability for basin-scale flood risk assessment and mitigation.

How to cite: Mesto, F., Gioia, A., Bonelli, R., Ciccone, M., Dal Sasso, S., Giuzio, L., Lombardo, M., Manfreda, S., Margiotta, M. R., Sileo, B., Perrini, P., Totaro, V., Iacobellis, V., Fiorentino, M., and Corbelli, V.: On the Use of the Distributed Hydrological Model QT-DREAM to Reproduce the Frequency Distribution of Observed Flood Peaks in Puglia and Basilicata (Southern Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18066, https://doi.org/10.5194/egusphere-egu25-18066, 2025.

Floods are among the most impactful natural phenomena affecting society. The associated risks are influenced by factors such as urban expansion into high-risk areas, modifications to rivers and watersheds (e.g., artificial channels, flow redirection, and structures like dams and dikes), and the effects of climate change, including more frequent and severe events. In recent years, the application of Artificial Intelligence (AI) in climate and weather risk assessment has gained increased attention due to its ability to handle numerical and categorical variables, uncover nonlinear relationships, and achieve high performance.
In this study, we introduce FloodGuard, an AI-powered tool for flood vulnerability and risk mapping. FloodGuard employs the concept of regionalization in ungauged basins and leverages a flood inventory derived from satellite imagery (e.g., Copernicus Emergency Mapping Service) over extensive areas (e.g., national or continental scales). The methodology selects the most relevant historical flood events and transfers this information to train a Random Forest machine learning model for estimating flood extent and producing a flood exposure map. Inputs to the model include the Geomorphic Flood Index (GFI), the Elevation, the Horizontal Distance to the Nearest River, Precipitation, the NDVI, and information on Land Use and Lithology. Flood prediction map is evaluated using maps generated from hydrological and hydraulic models. To assess vulnerability, we apply a geomorphic approach proposed by Manfreda and Samela (2019). This approach estimates flood depth, which is useful for estimating fast vulnerability levels. Finally flood risk is estimates with a GIS-based model.
The primary objective of this study is to provide a preliminary simple tool to estimate a flood risk and provide risk maps. At the same time, this study evaluates evaluate the transferability of machine learning models from regions with flood records to ungauged areas using satellite observations. Limitations include uncertainties inherent to machine learning models and the lack of association with specific return periods. Preliminary results across Italy demonstrate that the Random Forest model achieves high performance (AUC > 0.9) and exhibits robust generalization capabilities (e.g., combined error (rfp + (1-rtp)) of 0.58).

Keywords: Artificial Intelligence, Machine Learning, Flood risk, Flood vulnerability, GFI. 

This study was carried out within the RETURN Extended Partnership and received funding from the European Union Next-Generation EU (National Recovery and Resilience Plan -NRRP, Mission 4, Component 2, Investment 1.3 -D. D. 1243 2/8/2022, PE0000005). 

How to cite: Saavedra Navarro, J., Zhuang, R., Albertini, C., Samela, C., and Manfreda, S.: FloodGuard: An AI-Powered Tool for Flood Risk and Vulnerability Mapping in Ungauged Basins., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19363, https://doi.org/10.5194/egusphere-egu25-19363, 2025.

Flooding is a common and devastating disaster that happens recurrently in Bangladesh. Climate change, rapid urbanization, and inadequate drainage systems have exacerbated this event in recent years. In 2023, Satkania upazila in Chattogram district was severely affected by flood. Additionally, communities weren’t prepared for this flood, as they hadn’t faced any kind of flood in recent years, as reported by field observation. That severity helped to select Satkania upazila as a study area for this research. This study focuses on developing a GIS-based evacuation route plan to mitigate the adverse impacts of flooding by ensuring safe and efficient evacuation during emergencies. To identify high-risk and low-risk zones, Sentinel-1 SAR imagery was used to create an inundation map. Frequent and severe flooding criteria were used to find the high-risk zones. This area is designated as an assembly point where people can gather before moving to safer places. Low-risk zones were identified as suitable locations for emergency shelters to ensure people's safety during disaster events. ArcGIS network analyst tools are used to perform closest facility analysis and service area analysis. For this analysis, road network, elevation data, and building density were integrated with ArcGIS network analysis. Closest facility analysis helped to optimize the evacuation routes based on minimum travel time and shortest distance. To determine zones to locate emergency shelters, a service area analysis was performed to improve accessibility for vulnerable populations. The inundated map showed that 11.78% area was inundated during the 2023 flood. According to the network analysis, the assembly point that is closest to the emergency shelter is more accessible in both time and distance cases. A total of 3 out of 14 suggested emergency shelters were within 30 min, 7 within 60 min, 3 within 90 min, and 1 within 90 min walk from the assembly point. In terms of distance from the assembly site, there are 3 emergency shelters within 1000 m, 7 within 2000m, 3 within 3000 m, and 1 within 4000 m distance. The result showed the effectiveness of GIS-based route planning to minimize flood causalities and enhance disaster preparedness. This study provides actionable insights to design targeted interventions and response strategies for local governments and disaster management authorities.

How to cite: Ahmed, M., Anny, N. S., Khadem, S., and Baten, T. A.: A GIS-Based Evacuation Route Planning in Flood Inundated Area of Satkania, Chattogram, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20503, https://doi.org/10.5194/egusphere-egu25-20503, 2025.

Flooding is increasingly exacerbated by cascading and compounding risks, necessitating a systematic understanding of natural hazard interrelationships and the influence of anthropogenic processes on these dynamics. Integrating multi-hazard scenarios into flood hazard, impact, and risk models provides a more comprehensive understanding of flood risks by considering interactions such as earthquake-triggered landslides and rainfall-induced flooding, alongside the impacts of urbanisation, land-use change, and climate change. This approach enhances decision-making frameworks, enabling more effective preparation and response. Here, we first illustrate hazard interaction matrices as a methodology to systematically identify potential triggers and secondary hazards by synthesising evidence from local and global literature. We then highlight multi-hazard scenarios through examples from Nairobi (Kenya), İstanbul (Türkiye), the Kathmandu Valley (Nepal), and the Philippines, where frequent and high-magnitude hazards underscore the critical importance of preparedness and mitigation. Effective flood risk management also requires interdisciplinary collaboration among researchers, policymakers, and practitioners, leveraging disaster science methodologies to assess hazard relationships, enhance response strategies, and build resilient communities.

How to cite: Malamud, B. D.: Integrating Multi-Hazard Scenarios for Enhanced Flood Risk Management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21109, https://doi.org/10.5194/egusphere-egu25-21109, 2025.

Floods are one of India’s most catastrophic natural disasters, causing extensive loss of life and property. Recent research highlights that compound floods—arising from the interplay of multiple drivers—pose greater risks than individual flood events. Although compound flood drivers like precipitation and storm surge, precipitation and runoff, and others have been the focus of recent research globally, very limited research has been done on these flood drivers in India. To address this gap, we conducted a comprehensive compound flood analysis of Peninsular India river basins from 1980 to 2023, utilizing precipitation, runoff, and soil moisture data. Extreme events were identified using a certain percentile threshold (95^th and 99^th percentiles) for all the parameters and each parameter was initially subjected to a univariate analysis. The preliminary results indicate that individual drivers provide limited insights of these flood drivers. To address this, we employed a bivariate copula-based approach to estimate joint distributions at varying percentiles (25th, 50th, 75th, 90th, and 95^th percentile). The analysis using copula was focused to determine of exceedance probability, conditional probability, joint return period, and conditional return period for the paired variables: precipitation-runoff, precipitation-soil moisture, and runoff-soil moisture pairs, respectively. Our results illustrate that, especially in instances where there are multiple contributing components, bivariate analyses provide deeper insights into comprehending the complexity of flood dynamics. Additionally, it has been observed that some regions in our research region had shorter return durations and higher exceedance probabilities, suggesting that compound flood events of lower severity occur frequently. Identical patterns were noted for conditional return durations and conditional probabilities. These results underscore the critical importance of understanding the interconnections among flood drivers for effective flood risk estimation. Our study provides valuable insights for enhancing India’s flood management strategies by identifying disaster-prone regions and informing policymakers in the development of targeted mitigation measures.

How to cite: Mukherjee, A., Poonia, V., and Swarnkar, S.: Probabilistic Evaluation of Compound Flooding in Peninsular India: A Copula-Based Analysis of Precipitation, Runoff, and Soil Moisture , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-386, https://doi.org/10.5194/egusphere-egu25-386, 2025.

Convective precipitation plays a crucial role in extreme weather events, significantly influencing regional hydrological patterns, especially in topographically complex areas such as the Greater Alpine Region (GAR). Despite its importance, the study of convective precipitation remains limited due to its high spatial and temporal variability, which poses challenges for accurate observation and representation in climate models. Reanalysis datasets, such as ERA5, offer a valuable resource for overcoming these challenges, providing consistent, high-resolution data derived from both observational records and model outputs. However, the convective component of precipitation in ERA5 remains insufficiently explored, particularly regarding extreme events and seasonal trends. This study investigates the convective component of precipitation in the GAR using the ERA5 reanalysis dataset, focusing on extreme precipitation and their seasonality. By applying extreme precipitation indices from the ETCCDI framework, we identify a significant increase in the convective fraction of precipitation in recent decades, particularly during summer extreme events, along with an extension of the summer convective season. Trends in monthly precipitation are found to be largely driven by changes in the convective component, emphasising its growing influence on regional precipitation patterns. Additionally, the study is extended to CMIP6 global climate models, providing further insight into the representation of convective precipitation in climate projections. This work contributes to advancing the understanding of convective processes in climate models, emphasizing a critical gap in the current representation of precipitation in mountainous regions.

How to cite: Saglietto, G. and Ferguglia, O.: Seasonality change in ERA5 convective precipitation in the Greater Alpine Region., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-847, https://doi.org/10.5194/egusphere-egu25-847, 2025.

The link between climate extremes and river floods is complex and greatly affected by regional characteristics. River discharges are highly dependent on elevation and size of catchment in mountainous regions. This study explores the effects of orography on the precipitation-discharge relationship in the Greater Alpine Region (GAR). We make use of  daily discharge data and several reanalysis and observation datasets. The region is stratified into low (LE), and high (HE) elevation categories to assess variations in discharge responses. The correlation of discharges with precipitation at HE shows stronger relationship during the autumn season (September-November), while LE exhibits a stronger association in summer (June-August). Coarser resolution (>0.25^o) datasets show degradation of the association of precipitation with river discharge at both elevation categories,  although with a larger sensitivity of HE  to decreasing spatial resolution (i.e. 0.10^o to 1^o degree) as compared to the LE category. Significant sensitivity to spatio-temporal scales is found also in the intensity and duration of the climate extremes (ETCCDI indices) and their relationship with discharges in the GAR. This study emphasizes the advantages of high-resolution, multi-scale approaches to understand the intensity and duration of climate extremes and their impacts on river discharges. An improved framework integrating climate and orographic indices is essential to identify the complex relationships governing flood extremes in the GAR. The improved framework will contribute to the development of diagnostic tools and enhance the skill of future flood extreme projections by climate models.

How to cite: Kushwaha, V. K., Lombardo, L., Basso, A., Viglione, A., and Arnone, E.: Elevation dependent effects of precipitation on river discharge at different spatio-temporal scales, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-973, https://doi.org/10.5194/egusphere-egu25-973, 2025.

Forecasting floods (peak flows/quantiles) with significant lead time is crucial for effective water resources management. Traditionally, it has been carried out by forcing meteorological drivers onto the hydrological models. However, season-ahead flood forecasting remains challenging due to the limitations of weather forecasting models and the complexities associated with multiple model-chain linkages. Thus, to circumvent this, we applied a climate-informed approach to forecast season-ahead flood quantiles. Briefly, a climate-informed model comprises 1) selection of predictands, 2) identification of suitable large-scale climate predictors that control the predictands, and 3) derivation of a statistical link between predictands and predictors. In our study, we condition the probability distribution parameters of flood samples with large-scale climate predictors, focusing specifically on sea surface temperature (SST) patterns. The rationale behind this approach lies in the established linkage of SST in the Pacific and Indian Oceans to the Indian Monsoon system. To minimise the anthropogenic signals, we restricted our analysis to the gauging stations without significant reservoir influences by filtering the stations with reservoir indices less than 0.1. Both linear and nonlinear relations between the climate predictors and predictands have been applied in this study. Bayesian inference is used to estimate the parametric values of the Climate-Informed model. Furthermore, the selection of the suitable climate predictor and the nature of their relationship to a specific gauge is based on the widely applicable selection criterion (WAIC). WAIC computes log posterior predictive density and adjusts the overfitting using the effective number of parameters; the model with the least WAIC value is preferred. We assessed the skill of the climate-informed model on flood quantile forecasting by performing a leave-one-out cross-validation technique. Various performance metrics, including both deterministic and probabilistic measures, have been used to assess the prediction skill of the model in reference to the stationary model. Overall, our results suggest that for the majority of the gauges, climate indices have the potential to forecast flood-quantiles season ahead. While this initial forecast can inform decision-makers regarding expected flood quantiles, it is recommended that this method be complemented with traditional approaches that account for local catchment behaviour.

How to cite: Ganapathy, A. and Agarwal, A.: Reliability of Climate Information to Forecast Season-Ahead Flood Quantiles for Indian Catchments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1036, https://doi.org/10.5194/egusphere-egu25-1036, 2025.

In recent years, extreme runoff has been affected by increasing climate change, which causes non-stationary behaviors in extreme runoff series. Climate change is driven by external forcing and internal variability. However, the role of these two factors in runoff variability remains unclear. Taking the historical period as the baseline, this study employs four Single-Model Initial-Condition Large Ensembles (SMILEs) to investigate future changes in extreme runoff represented by annual maximum 1-day runoff (AM1R) over China and to evaluate the impacts of external forcing and internal variability on these changes. A decomposition-based non-stationary frequency analysis method is proposed to estimate the frequency changes of extreme runoff events, which incorporates components of runoff influenced by external forcing and internal variability. Two shared socioeconomic pathways (i.e., SSP2-4.5 and SSP5-8.5) are selected for the future. The results show that the catchments with increased AM1R are more than those with decreased AM1R under SSP-2.4.5 and SSP5-8.5 scenarios for all SMILEs, with the catchments showing decreased AM1R mainly in Qinghai-Tibet Plateau and northeastern China. The impact of external forcing on runoff is stronger than that of internal variability at more than 35% and 62% of catchments for all SMILEs under SSP2-4.5 and SSP5-8.5 scenarios, respectively. The catchments with significant trends of AM1R are mainly in the eastern Qinghai-Tibet Plateau under the SSP2-4.5 scenario, while those are mainly in Qinghai-Tibet Plateau and southwestern China under the SSP5-8.5 scenario. For changes in the frequency of extreme runoff events, corresponding to the 50-yr return level of AM1R in the historical period, the return period is projected to become shorter in at least 66% of catchments for all SMILEs under the two scenarios. The study indicates that extreme runoff events are likely to become more frequent in the future, which is important for the flood prevention policy.

How to cite: Liu, Y. and Chen, J.: Extreme runoff variation and non-stationary frequency analysis based on external forcing and internal variability decomposition, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2720, https://doi.org/10.5194/egusphere-egu25-2720, 2025.

How to cite: Pandidurai, D., Raghuvanshi, A. S., and Agarwal, A.: Atmospheric moisture linkages to flood inducing Multiday extreme precipitation in India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3278, https://doi.org/10.5194/egusphere-egu25-3278, 2025.

Global warming is driving an increase in extreme precipitation events across many regions worldwide, often leading to intensified flooding. However, other changing precipitation characteristics may counterbalance this effect. These include reductions in total event precipitation, precipitation coverage area, duration, and frequency. The interplay of these often-contradictory trends remains poorly understood, with limited mapping and quantification available.
Through a series of studies focusing on the eastern Mediterranean region, we identify this area as susceptible to these contrasting precipitation trends. Our research reveals a decline in average precipitation and the number of wet days, alongside an increase in extreme precipitation events for return periods ranging from 10 to 100 years. Furthermore, storm total precipitation, coverage area, and duration decrease while conditional precipitation intensities rise.
When these trends are incorporated into hydrological models to simulate catchment responses and flood impacts, the role of soil moisture emerges as a critical factor in flood regulation. Due to lower precipitation amounts and wet days number, average soil moisture decreases. Despite heightened precipitation intensity, this leads to diminished runoff in most cases. Additionally, smaller storm sizes reduce runoff-contributing areas, resulting in lower flow discharges within concentrating channels. However, urbanization amplifies these dynamics, as urban areas are more sensitive to increased precipitation intensities due to limited soil moisture regulation. Consequently, in future climate scenarios, the largest runoff events produce higher peak discharges and total runoff compared to historical conditions. In contrast, lower-intensity events exhibit reduced peak and total runoff. These effects are intensified as urban impervious surfaces expand, making precipitation intensity a dominant driver of urban runoff.
Our findings suggest that floods are not universally intensifying, even in the context of more extreme precipitation. The dampening effects of other precipitation properties can offset flood magnitudes, highlighting the complexity of flood behavior under changing climate conditions.

How to cite: Morin, E., Rinat, Y., Armon, M., Goldschmidt, Y., Nussbaum, R., and Marra, F.: Shifting Flood Regimes Under Contradictory Precipitation Trends, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3512, https://doi.org/10.5194/egusphere-egu25-3512, 2025.

This study investigates the relation between Euro-Atlantic large-scale atmospheric circulation and extreme precipitation events (EPEs) in the Great Alpine Region (GAR). We analyze the connection between weather regimes (WRs)—recurrent and quasi-stationary circulation patterns—and EPEs to assess temporal and spatial variations.

The analysis covers the period 1940–2023, using daily geopotential height data at 500 hPa and daily total precipitation data from ERA5 reanalysis. WRs classification mainly follows the methodology outlined by Grams et al. (2017), enabling year-round characterization of atmospheric patterns, which are then linked to average precipitation and EPEs, defined as precipitation exceeding the 95th percentile of the distribution and an intensity greater than 15 mm/day (Q95R15).

Our results show diversities in the average precipitation patterns over the GAR when different regimes occur. In particular, Scandinavian Trough (ScTr), Greenland Blocking (GrBL), Scandinavian Blocking (ScBL) and Atlantic Ridge (AR) seem mostly connected with average precipitation, whose intensity varies according to the season.

Relating WRs and extreme precipitation, we observe that spatially the association between WRs and EPEs varies across GAR sub-regions and depends on the season. We detect higher frequencies of occurrence for ScTr, GrBL, ScBL, AR and Atlantic Trough (ATr) when precipitation above Q95R15 occurs. For instance, during autumn (SON), EPEs are primarily linked to ScTr, ScBL and AR regimes; during winter (DJF) we observe ScTr, GrBL, ScBL, AR and ATr instead. During spring (MAM) and summer (JJA) a clear association is elusive up to now, needing further analysis to be clarified.

Investigations into different sub-periods are ongoing, in order to obtain more insights about how decadal changes due to forced and/or internal variability in the Euro-Atlantic circulation affect the occurrence of EPEs in the GAR.

How to cite: Tessari, I., Giuntoli, I., and Corti, S.: Weather Regimes and Extreme Precipitation in the Great Alpine Region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4110, https://doi.org/10.5194/egusphere-egu25-4110, 2025.

Floods are an ever-present risk to society and economy in Europe, influenced by both climatic and socioeconomic drivers. An accurate and timely attribution of impacts is important for risk management, “loss and damage” debate and public communication in context of climate change. Here, we discuss the opportunities and challenges of operationalizing attribution for European flood impacts in the framework of Horizon Europe project “Compound extremes attribution of climate change: towards an operational service” (COMPASS). The prospective operational service would build upon the framework for attribution of historical flood impacts for 42 European countries. The work so far includes an extensive modelling chain covering both riverine and coastal floods that can reconstruct temporal changes in hazard, exposure and vulnerability to quantify their influence on the observed flood impacts. It considers drivers such as climate change, catchment alteration, population and economic growth, land use change, and evolution of flood precaution and adaptation. High-resolution datasets with long time series are used to first reconstruct each flood event under the factual (historical) scenario, and then under counterfactual scenarios in which a particular climatic or socioeconomic driver is set to 1950 conditions. In this way, the role of each driver can be quantified relative to a common temporal benchmark. In total, 1729 impactful floods occurring between 1950 and 2020 were attributed to the various drivers, highlighting the role of not only climate change (hazard), but particularly population growth (increase in exposure) and adaptation (decrease in vulnerability). Further integration with available operational services, primarily the Copernicus Climate Change Service, would enable timely input data processing for the hydrological and hydrodynamic modelling of riverine and coastal flooding. The approach will be extended to multihazard events, which will be showcased through the use case of extra-tropical cyclone Xynthia, which resulted in major impacts from both coastal flooding and extreme wind speeds in France in 2010.

How to cite: Paprotny, D., Tilloy, A., Terefenko, P., Mengel, M., and Couasnon, A.: Impact attribution of European floods: towards an operational system, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4431, https://doi.org/10.5194/egusphere-egu25-4431, 2025.

Flood event attribution, including the analysis of extreme precipitation and flood peaks, is crucial for understanding how anthropogenic climate change influences these events. This study employs an unconditional attribution approach to quantify changes in the likelihood of the July 2021 flood in the Ahr region, western Germany, in a factual world representing the current climate compared to a pre-industrial counterfactual world without anthropogenic greenhouse gas emissions.

To achieve this, the non-stationary weather generator nsRWG, conditioned on large-scale circulation patterns (CPs) and regional mean daily temperature (t2m), is used to generate 100 realizations of synthetic precipitation and temperature data over a 30-year period for both worlds. The CPs, derived from the classification of mean sea level pressure, and t2m are obtained from the ERA5 reanalysis dataset for the factual world and from natural historic simulations of several CMIP6 GCMs for the counterfactual world. The nsRWG-generated data are further disaggregated to an hourly resolution and fed into the hydrological model mHM, set up for the Ahr basin, to simulate streamflow and derive hourly peak flow. The simulated extreme precipitation and peak flows are analyzed to estimate the likelihood of the July 2021 flood event in each climate state, forming the basis for calculating the probability ratio between the two worlds.

Our model-based results indicate that the likelihood of 1-day and 2-day extreme precipitation of the Ahr event is on average 1.28 and 1.63 times higher, respectively, in the current climate. The flood peak appears to be 1.07 times more likely in the present climate compared to the counterfactual world. These findings suggest that anthropogenic climate change has notably increased the likelihood of events like the July 2021 flood. The use of a weather generator in combination with a hydrological model paves the way towards hydrologic event attribution and sets the stage for further research into attribution of flood impacts.

How to cite: Nguyen, V. D., Merz, B., Han, L., Apel, H., Guan, X., Kreibich, H., and Vorogushyn, S.: Attribution of the July 2021 flood event in the Ahr region to anthropogenic climate change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6938, https://doi.org/10.5194/egusphere-egu25-6938, 2025.

Flood estimates used in engineering design are commonly based on intensity–duration–frequency (IDF) curves derived from historical extreme rainfall. Under global warming, extreme rainfall is increasing, threatening the capacity of existing infrastructure. Hence, there is a need to update our methods of engineering design, namely our design rainfall intensities, for climate change.

One way of adjusting our design inputs for climate change is to incorporate covariates into the fitted probability distributions that describe extreme rainfall. To this end, here we evaluate which large-scale climate driver is best for modelling non-stationarity in IDF curves up to the 100-year design return level. The climate drivers we evaluate include global and continental mean temperature, continental diurnal temperature range, continental dewpoint temperature, continental precipitable water, the Indian Ocean Dipole, the El Niño Southern Oscillation, and the Southern Annular Mode.

Based on the Akaike Information Criteria, precipitable water is the superior covariate, irrespective of storm duration. However, when quantile changes across the historical period are inspected, we find that global temperature is best able to adequately capture the variability in changes across both storm duration and annual exceedance probability. We finish with presenting a case study where extreme rainfalls are projected using a global mean temperature covariate. The implications for flood risk are that, under 4ºC of global warming, flood risk increases by a multiple of eight.

How to cite: Wasko, C., Jayaweera, L., Ho, M., Nathan, R., O'Shea, D., and Sharma, A.: Using global temperature as a covariate to project flood risk, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7489, https://doi.org/10.5194/egusphere-egu25-7489, 2025.

Floods induced by rainstorm events (RSEs) are among the most frequent natural disasters and have a significant impact on ecosystems and human society. While most extensive researches have investigated the magnitude, frequency, and risk of floods, understanding the spatiotemporal evolution of contiguous flood-causing rainstorm events remains largely unexplored in China. Here, we collected historical flood disaster data from the Statistical Yearbook, news reports, and government sources and examined the evolution patterns of spatiotemporally contiguous flood-causing RSEs across China from 2000 to 2020, utilizing the connected component three-dimensional algorithm. Our results indicate that floods mostly occur in southern China (SC), followed by northern China (NC), with less frequency in northwestern China (NWC) and the Qinghai-Tibetan Plateau (TP). The flood-causing RSEs tend to occur with longer durations and higher magnitudes in SC and NC, while in NWC and TP, they are primarily characterized by short-term precipitation processes with lower magnitudes. Moreover, the flood-causing RSEs exhibit distinct evolutionary patterns in different subregions. In NWC and TP, RSEs generally move eastward and southeastward, with relatively longer lifespans, traveling longer distances at faster moving speeds, but covering smaller areal extent and lower accumulated rainfall amounts. In contrast, in both SC and NC, flood-causing rainstorm events are mainly moved in two directions, namely westwards and eastwards. These events have shorter average lifespans, and travel shorter moving distances at slower moving speeds, but have a larger areal extent and huge accumulated rainfall amounts. Our findings significantly enhance our understanding of flood-causing rainstorm characteristics in China.

How to cite: Wang, J. and Guan, X.: Spatiotemporal evolution patterns of flood-causing rainstorm events in China from a 3D perspective, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7964, https://doi.org/10.5194/egusphere-egu25-7964, 2025.

Does a changing climate lead to a higher flash flood hazard?

Flash floods pose a significant natural hazard and are triggered by high-intensity precipitation events occurring in small and steep catchments. The short lead time, high flow velocity, and transportation of debris and sediment of these floods can lead to devastating impacts. 

With the warming climate, the intensity and extent of precipitation events are likely to increase, consequently leading to an expected increase of flash flood hazard. But what do we have to expect, and how can we adapt to future climate scenarios? Simulating extreme rainfall is still highly uncertain under climate change. Because of their coarse spatio-temporal resolution, global circulation models are not suited to investigate the impacts of a warming climate on flash floods. However, new convection-permitting models (regional climate models) for the first time now offer an appropriate spatia-temporal resolution (3x3 km, 1 hour) for flash flood modelling. Based on the COSMO-CLM (COSMO model in CLimate Mode, Rockel et al., 2008; Sørland et al., 2021), we modelled the runoff in all small-scale catchments in Germany for the periods 1971-2000, 2001-2019, and for the period 2030-2100, which is based on the RCP8.5 scenario.

Our results reveal that half of the catchments would produce a flood peak of factor 1.5 or higher under the RCP8.5 scenario compared to the present period (2001-2019) and further enable us to estimate and compare return levels of flood peaks for the RCP8.5 scenario and shed light on regional differences within Germany. This study is the first comprehensive analysis of the (flash) flood response to a warmer climate in Germany.

References:

Rockel, B., A. Will, A. Hense, 2008: The regional climate nmodel COSMO-CLM (CCLM). Meteorol. Z. 17, 347–348, DOI: 10.1127/0941-2948/2008/0309.

Rybka, Harald, et al. "Convection-permitting climate simulations with COSMO-CLM for Germany: Analysis of present and future daily and sub-daily extreme precipitation; Convection-permitting climate simulations with COSMO-CLM for Germany: Analysis of present and future daily and sub-daily extreme precipitation." Meteorologische Zeitschrift 32.2 (2023): 91-111.

Sørland, S.L., C. Schär, D. Lüthi, E. Kjellström, 2018: Bias patterns and climate change signals in GCM-RCM model chains. Env. Res. Lett. 13, 074017, DOI:10.1088/1748-9326/aacc77.

How to cite: Voit, P., Heistermann, M., and Rybka, H.: Does a changing climate lead to a higher flash flood hazard?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8219, https://doi.org/10.5194/egusphere-egu25-8219, 2025.

In this study, the variations of rainfall and river discharges were analyzed in the Chenyulan watershed in Nantou County, central Taiwan. The hydrological data, including rainfall, daily discharges and yearly maximum instantaneous discharge, were collected from the Neimaopu hydrology station for the period from 1972 to 2022, covering approximately 50 years. According to the data analysis, when the rainfall exceeds the average, the river discharges in the Chenyoulan catchment increases, with larger rainfall events leading to more significant changes. Upon comparing the long-term data, it was found that the maximum instantaneous discharge occurred on August 1, 1996, during the Herb Typhoon. Though this event did not coincide with the historical maximum for total rainfall, rainfall intensity or average rainfall intensity, it resulted in the maximum instantaneous discharge.

 All of the rainfall events, daily average discharge and yearly maximum instantaneous discharge are preliminarily analyzed as follows: 1. Rainfall in the catchment shows a positive correlation with river discharge; 2. The increase in rainfall characteristics in the catchment and the increase in discharge are not linearly related; 3. The non-linear reasons for the relationship between rainfall and maximum instantaneous discharge are preliminarily summarized as being related to soil conditions, different rainfall intensity locations and the runoff coefficients of various catchment units; 4. This study will subsequently estimate the average runoff coefficient of the catchment based on the relationship between individual rainfall and discharge, and conclude rational formula.

How to cite: Huang, W.-S., Chen, J.-C., Chien, K.-H., Lai, X.-Z., and Lai, Y.-T.: Impacts of rainfall variability on river discharges characteristics : A Case Study in Chenyulan Watershed, Taiwan, China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10340, https://doi.org/10.5194/egusphere-egu25-10340, 2025.

Floods result from the interplay of climatic drivers, catchment characteristics and river system dynamics. The observed shift to extreme climatic events necessitates a better quantification of their impact on flood generation. Improving our current understanding of flood generation processes in the observed climate provides a pathway to improve flood projections in a warming climate.

This presentation will share insights from our work on river basin scale flooding in India. Using a physical hydrological model, we conducted an event-scale analysis of high flows across multiple river basins in India. The results highlight the significant role of antecedent catchment moisture, as well as the duration and spatial extent of precipitation events, in driving river basin scale flooding. The study also examines and distinguishes the relative importance of large-scale moisture transport, and origin, persistence and direction of propagation of low-pressure systems in triggering localized and widespread floods. Furthermore, we find that prominent flood drivers in a warming climate are similar to those observed in the historical period. Careful attribution of observed flood changes, combined with a thorough assessment of changes in key drivers, is essential for deriving reliable projections of future flood risk.

How to cite: Jayadevan Sobhana, N. and Mishra, V.: On the Changing Role of Climatic Drivers to River Basin Scale Flooding, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10885, https://doi.org/10.5194/egusphere-egu25-10885, 2025.

Among weather-related extreme events in Europe, floods are one of the most disastrous and costliest. Atmospheric blocking episodes (i.e., persistent, quasi-stationary, and self-preserved weather systems that propagate very slowly and interrupt the usual westerly flows) are part of the main weather regimes in the Euro-Atlantic and have been associated with notable flood events across Europe. So far, the relationship between blocking and some high-impact extreme weather events has been established, including the modulation of the odds of heavy precipitation. Yet, a long-term continental relationship between blocking and flooding remains unrevealed, and in particular, the way atmospheric blocking translates into floods. For the 1960-2010 period, this study analyses a pancontinental database of maximum discharge, atmospheric and soil variables from ERA5 and ERA5-Land reanalyses, and a gridded binary blocking index derived from ERA20C. Preliminary results indicate mixed positive and negative anomalies in mean precipitation and wet-spell frequencies in response to blocking, depending on the region. Nonetheless, robustly across Europe, the anomalies in wet-spell duration and total precipitation depth are generally positive under blocking conditions. We present the spatial patterns across Europe induced by atmospheric blocking in anomalies of, e.g., streamflow maxima, rainfall maxima, and root zone moisture excess maxima, pointing out that the patterns between streamflow maxima and moisture excess maxima are significantly correlated but not in the case between streamflow maxima and rainfall maxima. Hence, this research suggests that the effect of atmospheric blocking on floods is acting at the level of the interaction between rainfall and soil moisture. The outcomes presented here unveil a continental and long-term impact of atmospheric blocking in relevant variables for flood generation.

How to cite: Hernandez, D., Bertola, M., Lun, D., Ahrens, B., McPhee, J., and Blöschl, G.: Floods and moisture excesses induced by atmospheric blocking are related at the long-term scale in Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11534, https://doi.org/10.5194/egusphere-egu25-11534, 2025.

Out of all weather-related hazards, flooding has the most widespread impact globally, and the province of Quebec is no exception. In the past decades, dozens of riverside municipalities have felt the socio-economic consequences of flooding firsthand. Most of Quebec is characterised by a cold and humid continental climate, with precipitation year-round. Here, river flooding often takes place in the spring, due to snowmelt. Although important, snowmelt alone is not the only factor influencing flooding in the mid-latitudes. By bringing heavier than normal precipitation with them, extratropical cyclones are also known to be key contributors. The relationship between extratropical cyclones and flooding have been extensively studied on the West Coast of North America, but remains largely unexplored in eastern Canada. Thus, this study aims to link flooding events that have happened in the past 30 years in Quebec to their triggering extratropical cyclones and identify possible characteristics (genesis locations, trajectories, lifetime, progression speed, or precipitation intensity) that set these systems apart. Coupled with financial aid claims data, highlighting the differences between regular vs flood-inducing extratropical cyclones coming through Quebec can help describe the region’s flooding history and better prepare for future events. We also explore the involvement of atmospheric rivers in these extreme events. This analysis is performed using three databases. First, the Quebec Floods Financial Aid Claims Database provides the 14360 financial aid claims filed by individuals or businesses for material loss following flooding, from 1990-2022. Each claim contains the location of the damaged infrastructures, watershed involved, and closest river section. Second, the North American Extratropical Cyclone Catalogue provides extratropical cyclone tracks derived from the ERA5 reanalysis, available every hour from 1979-2020, and includes variables of interest such as precipitation and near surface wind-speeds. Third, the Global Atmospheric River Scale Database gives the occurrence and scale (based on integrated water vapor transport and duration of event) of atmospheric rivers every 6 hours from 1979-2020. By grouping the financial aid claims by location and date, 385 events were identified. Through this analysis, 550 extratropical cyclones (storms) of interest were identified and ranked according to their associated percentage of cumulated rain during the event. Five zones of storm genesis locations were identified: western Canada, Great Lakes and Ontario, US Northern East Coast and Quebec, Central US, and US East Coast. The genesis location of weaker storms was uniformly distributed among the five regions. However, most of the remaining 108 more intense storms were coming from two genesis locations: Central US (48%), and US East Coast (25%). For these two genesis zones, trajectories of stronger storms were found to be different from those of weaker storms. For example, tracks were more likely to move over land going up the US East Coast and go over the Great Lakes when coming from Central US. As for atmospheric rivers, their involvement in flood-events was found to be very high in the winter, and minimal in the summer. The combination of data used in this method offers new insights for investigating flooding events.

How to cite: Gagnon, C., Nadeau, D., Di Luca, A., and Anctil, F.: The role of extratropical cyclones in flooding in Quebec, Canada, from 1990-2020, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12781, https://doi.org/10.5194/egusphere-egu25-12781, 2025.

Convection-permitting regional climate models (CPRCMs) are increasingly recognized for their ability to improve extreme precipitation predictions, yet their application to hydrological modeling in complex terrains remains uncertain. This study evaluates the performance of CPRCMs in predicting hydrological extremes in two basins in Western Norway: Røykenes, dominated by rainfall-induced floods, and Bulken, characterized by snowmelt-induced floods. We compare the capabilities of a high-resolution convection-permitting model (HCLIM3, 3 km resolution) with a coarser regional climate model (HCLIM12, 12 km resolution) in driving two hydrological models: the physically based Weather Research and Forecasting Model Hydrological system (WRF-Hydro) and the conceptual Hydrologiska Byråns Vattenbalansavdelning (HBV) model. Performance was evaluated based on precipitation, temperature, runoff, and hydrological extremes. We found that HCLIM3 exhibited significantly better performance in estimating annual maximum 1-day (Rx1d) and 1-hour (Rx1h) precipitation, with reduced biases compared to HCLIM12. It also showed added value in capturing the probability density distribution of daily and hourly precipitation, as quantified by the Distribution Added Value (DAV) metric. However, both HCLIM3 and HCLIM12 displayed cold biases, especially in mountainous areas. Besides, in the rainfall-dominated Røykenes basin, WRF-Hydro outperformed HBV in simulating extreme flood magnitudes across return periods (5, 10, 20, and 50 years). However, in the snowmelt-dominated Bulken basin, cold biases in HCLIM3 and HCLIM12 introduced uncertainties in snowmelt timing, leading to larger errors. The added value of HCLIM3 was observed in hourly discharge in the Røykenes basin. However, this benefit was less pronounced in the snowmelt-dominated Bulken basin, where temperature sensitivities significantly influenced snowmelt processes. Biases in HCLIM3 and HCLIM12 meteorological forcing propagated through hydrological models, leading to discharge errors, as highlighted by DAV metrics. This research highlights the importance of applying bias correction to CPRCM simulations to improve hydrological modeling of extreme events, especially in mountainous terrains where biases in temperature and precipitation critically affect hydrological processes.

How to cite: Li, L. and Xie, K.: Evaluating the added value of convection-permitting regional climate models in simulating hydrological extremes over basins in western Norway, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13633, https://doi.org/10.5194/egusphere-egu25-13633, 2025.

The latest IPCC report (2022) projects an increase in climate risks for all regions of the world, both in frequency and intensity. In particular, on the Spanish Mediterranean coast, catastrophes such as the Gloria event in January 2020, or the tragic floods that occurred in October 2024 in Valencia and Castile-La Mancha, are aligned with these projections. On a smaller geographical scale, flash floods that occurred in the Montsià county (southern Catalonia) in 2018, 2021 and 2023 also point to an increase in frequency in this in this 733 km² region located at the south of the Ebro Delta. This region is a paradigmatic example of a Mediterranean region with a high flood risk. Firstly, it has a high flash flood hazard, as a result of its abrupt orography with steep slopes that favours the existence of numerous steep torrents, as well as the rise of humid air masses from the Mediterranean, especially when they hit perpendicular to the coastline, which helps trigger convection and gives rise to intense rainfall. Likewise, the geographical region in which it is located is favourable to the entry of humid air from remote sources, which contribute to the increase in the intensity and amount of precipitation. Secondly, it has a high flood exposure despite the low population density, but which is multiplied by four in summer and early autumn in some municipalities. Thirdly, it has a high flood vulnerability, a consequence of being divided into three hydrographic basins, managed by three different administrations, which makes coordination difficult, especially regarding flood prevention. This is combined with a low-risk awareness both socially and individually that is joined to the difficulty of predicting and nowcasting the convective events that give rise to the severe flash floods that the region frequently experiences.

During the catastrophic flooding event of October 18–20, 2018, the maximum precipitation recorded in the Montsià region was 312.2 mm, and a daily rainfall of 209.6 mm, with a peak of 30-minute rainfall of 52.4 mm. On September 1, 2021, 251.9 mm were recorded over three hours, with a peak of 30-minute rainfall of 72 mm.  On September 3, 2023, very heavy rainfall was recorded once again in Montsià, with a maximum rainfall of 206 mm/24h and a peak of 30-minute rainfall of 61.4 mm.  In this study we characterize these three catastrophic flash flood events taking into account the complexity that local scale phenomena may have. For this reason, the characteristics of the thunderstorms that gave rise to the catastrophic flash floods are analyzed, to then go on to understand the synoptic and mesoscale context and finish with the search for the moisture source fields at global scale. In order to ascertain whether this increase in frequency in recent years responds to a significant trend, a spatio-temporal analysis in extreme rainfall indicators has been made. To do this, information from multiple data sources has been integrated, including meteorological station observations, weather radar products, lightning detection networks, high-resolution mesoscale model outputs.

How to cite: Marcos-Matamoros, R., Llasat, M. C., Pascual, R., Rigo, T., Insúa-Costa, D., and Crespo, A.: The case of flash floods in Montsià county (Catalonia, Spain): from the source of precipitate water to thunderstorm cells , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15024, https://doi.org/10.5194/egusphere-egu25-15024, 2025.

Given the current warming trend of our climate system, the frequency and intensity of extreme weather events are expected to have a significant impact on flood dynamics. The Clim2FlEx project aims in this evolving context to assess how floods of different natures are linked to climate extremes under potential future climate scenarios.

This work focuses on the European Alps, an optimal natural laboratory for this topic due to the complex hydro-meteorological processes occurring in the region and its unique position at the intersection of the Mediterranean and continental Europe.

The methodology uses an innovative and integrated version of the TUWmodel, combined with a machine-learning-based regionalization approach, HydroPASS. Once the regional model is validated, it will enable hydrological runoff predictions for both current and future scenarios across the Greater Alpine Region. Based on these simulations, we aim to identify flood events in time and space, linking them to climate extreme indices and, ultimately, to the large-scale climatic phenomena driving their dynamics.

At the EGU, we will present the results obtained regarding the performance of the regional model, along with the steps taken, and those planned, for developing the spatio-temporal event detection strategies.

How to cite: Basso, A., Lombardo, L., and Viglione, A.: A distributed rainfall-runoff model to explore the connection between floods and climate extremes in the European Alps, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15233, https://doi.org/10.5194/egusphere-egu25-15233, 2025.

One of the major challenges in hydrological research for estimating design flood events is accounting for the influence of climate change. These changes are reflected in increasingly frequent and intense fluctuations in river water regimes and sediment transport, indirectly affecting riverbed erosion processes. Therefore, assessing the long-term impacts on the lifespan of hydraulic structures (e.g., bridges) is crucial, requiring a comprehensive analysis of the interrelationship between climate change indicators, flood wave characteristics (including peak flow and hydrograph shape), and local riverbed erosion.

The SERIOUS project (Synthetic dEsign hydrographs undeR current and future clImate for local bridge scOUr aSsessment) aims to methodologically link synthetic design hydrographs (SDH) derived from statistical bivariate analysis under current and projected future climate conditions in the continental parts of the Danube River basin to the assessment of climate change impacts on bridge scour at selected pilot sites. The project objectives are to: (1) establish a methodological framework for determining control SDH based on literature reviews and available data in selected pilot areas; (2) apply and improve supervised and/or unsupervised machine learning algorithms to categorize different SDH types based on their shapes and/or topologies; (3) calibrate a regional hydrological model to evaluate climate change projections using historical discharge and water level data from the selected pilot areas; (4) investigate changes in SDH under climate change projections; and (5) develop a methodological framework for evaluating climate change impacts on bridge scour depth. These objectives are supported by the IAHS "Helping Decade" initiative (Working Group 11.1). The proposed project is expected to improve methodologies for determining SDH, serving as critical inputs for designing various engineering structures.

Acknowledgment:

This work has been supported in part by the Croatian Science Foundation under the project SERIOUS (IP-2024-05-1497) and the “Young Researchers’ Career Development Project – Training New Doctoral Students” (DOK-2020-01-5354).

How to cite: Potočki, K., Bekić, D., Bezak, N., Conradt, T., Pintar, D., Šrajbek, M., and Lacko, M.: Synthetic Design Hydrographs Under Current and Future Climate for Local Bridge Scour Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16468, https://doi.org/10.5194/egusphere-egu25-16468, 2025.

The temporal evolution of extreme precipitation is expected to be influenced by the broader impacts of climate change. This is generally considered to be due to the increased water-holding capacity of a warmer atmosphere, as well as alterations in atmospheric circulation patterns. However, gaining a comprehensive understanding of how extreme precipitation has changed in the past has been a challenge due to limited historical data and inherent uncertainties, particularly when examining short-duration rainfall events such as those occurring within a one-hour period.

By analyzing rainfall gauge data from Austria collected during the twentieth century, we observe significant decadal-scale variations in daily extreme precipitation. These variations suggest that the frequency and intensity of daily extreme events are highly variable over time. In contrast, our analysis of hourly extreme precipitation reveals a more consistent and noticeable upward trend over the past four decades. This trend corresponds with the increase in global temperatures, showing a 7% rise in hourly extreme precipitation for every 1°C of warming, which is in line with the Clausius-Clapeyron relationship. This increase in hourly extreme precipitation is consistent across both the northern and southern regions of the Alps, indicating that the effects of warming are widespread across Austria. On the other hand, daily extreme precipitation appears to be more strongly influenced by atmospheric circulation patterns, with a more notable correlation to decadal-scale variations in these patterns. These atmospheric circulation shifts are responsible for driving the weather systems that generate extreme precipitation events, particularly on the daily timescale.

In summary, our findings suggest that thermodynamic changes, such as the increase in temperature, have a more pronounced impact on hourly extreme precipitation than on daily extremes. This highlights the distinct processes at play for different timescales, where the short-term (hourly) extreme events are more closely tied to the fundamental thermodynamic properties of the atmosphere, while longer-term (daily) extremes are influenced more by large-scale atmospheric circulation dynamics.

How to cite: Haslinger, K., Breinl, K., Pavlin, L., Pistotnik, G., bertola, M., Olefs, M., Greilinger, M., Schöner, W., and Blöschl, G.: Controls on the temporal evolution of extreme precipitation in Austria, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17369, https://doi.org/10.5194/egusphere-egu25-17369, 2025.

Extreme weather events often, but not exclusively, occur when the jet stream is highly disturbed and the atmospheric circulation becomes blocked, allowing long-lasting, quasi-stationary and self-sustaining atmospheric weather regimes to develop. The interactions of subtropical, warm and moist air with polar, cold and dry air within the structure of the atmospheric block may then provide the local ingredients for these highly impactful weather events, including persistent rainfall from cut-off low pressure systems causing floods like those in Central Europe in 2024, or in Germany in 2021, or in Greece or Spain in 2023, or short-duration downbursts leading to serious flash flooding as occurred in Liguria, Italy in Oct 2023 breaking the European record for hourly rainfall. This talk will draw on evidence from several published and unpublished studies to examine the mechanisms for such events, from global drivers, through synoptic scale weather regimes to local-scale processes. Identifying the causal pathways for hydroclimatic extremes is important for developing improved methods for event attribution, and for improving climate model projections, since even high-resolution climate models poorly simulate key mechanisms driving these events and likely underestimate future changes.

How to cite: Fowler, H., Davies, P., Whitford, A., Blenkinsop, S., White, C., and Sauter, C.: Blocking patterns are crucial in producing recent extreme summer floods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19877, https://doi.org/10.5194/egusphere-egu25-19877, 2025.

Ice-jam flooding linked with the interactions of hydrological and cryosphere processes is a serious threat to riverine communities in cold regions. This work uses the coupling of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) hydrological model, which represents a wide range of hydrological processes, and the mizuRoute river routing model with that of a river ice model (i.e., RIVICE model) to project of ice-jam floods under changing climatic conditions. In fact, SUMMA and mizuRoute simulate streamflow, which is then passed to RIVICE to model ice formation and dynamics. The dynamics of streamflow simulated by SUMMA / mizuRoute include comprehensive representation of various hydrological processes, while the RIVICE model considers the processes of ice formation, frazil ice dynamics, and accumulation. This coupled modeling framework is applied to the Klondike River in Yukon, Canada, one of the regions historically affected by ice-jam flooding. This study uniquely integrates these models to enable projection of future ice-jam flood scenarios. The simulations are driven by climate projections from the CMIP6 datasets, enabling comprehensive assessments of future freeze-up events and associated flood risks at high spatial and temporal resolution. This work contributes to the increasing value of integrated hydrological and cryospheric modeling, improving flood risk assessments and informing adaptive strategies, such as improved forecasting systems and infrastructure design, for community protection in cold regions.

How to cite: Lindenschmidt, K.-E., Ghoreishi, M., and Eythorsson, D.: Modeling of Ice-jam Flooding: Integrating SUMMA with River Ice Processes for Climate Change Impacts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20525, https://doi.org/10.5194/egusphere-egu25-20525, 2025.

Northeast India (NEI) plays a key role in national development and environmental security due to its ecological diversity, socioeconomic significance, and strategic importance. In addition to being highly susceptible to climatic extremes, it's crucial for the region to build resilience against such challenges. The NEI, a region traditionally known for its heavy rainfall during the monsoon months (June–September), has witnessed a significant shift in its climatic patterns. The monsoon season, once characterized by consistent rainfall, has now transformed into a flood-drought cycle occurring within the same year. Intense bursts of rainfall lead to widespread flooding, followed by prolonged dry spells that verge on drought conditions. While NEI's vulnerability to flooding has been extensively studied, its susceptibility to drought remains underexplored, despite its growing relevance in the region. Therefore, this study presents a spatial drought vulnerability mapping framework designed to enhance this resilience in NEI, including Bangladesh (NEIB)—a geographically and climatologically intertwined region encompassing diverse landscapes from mountains to coastal plains. The study assesses drought vulnerability for the historical period (1981–2014) and projects future vulnerability (2015–2100) under four Shared Socio-Economic Pathways (SSPs), considering different climatic and socio-economic factors. A total of 16 factors like precipitation, temperature, drainage density, land-cover, surface soil-moisture, population density, etc. are used in this integrated framework. These factors fall under four main categories – hydrology, meteorology, socioeconomics, and agriculture, which employ two Multi-Criteria Decision-Making methods: an Analytical Hierarchy Process and a Weighted Aggregate Sum Product Assessment. Out of all the factors, precipitation emerged as the most influential one, followed by potential evapotranspiration and temperature. The spatial drought vulnerability mapping categorizes the NEIB region into five levels of vulnerability: very low, low, moderate, high, and very high. Interestingly, none of the regions in the NEIB fall into the very low or very high vulnerability categories. Regions such as Tripura, Mizoram, West Bengal, and Bangladesh are categorized as highly vulnerable, while Sikkim, Arunachal Pradesh, and Meghalaya demonstrate greater resilience. Future projections indicate a significant shift in vulnerability patterns. Towards the end of the century (2071–2100), under the SSP585 scenario, the area classified as having moderate vulnerability is expected to decrease from ~85% in the historical period (1981–2014) to approximately ~70%, while the proportion of the region categorized as highly vulnerable is anticipated to rise from ~9% to ~25%. Both the methods demonstrated high accuracy and reliability, achieving Area Under the Curve values above 80% based on Receiver Operating Characteristic curves. A sensitivity analysis via the Stillwell Ranking Method indicated comparable performances by criteria suggesting the robustness of the framework that can be applied to other parts of the world. The findings from such a framework will be helpful to promote the need for actions to mitigate future increases in drought severity in susceptible areas, while the resilience of less-impacted regions might be utilized to derive adaptive measures. As challenges from climate continue to evolve, this study provides valuable information for policymakers and stakeholders seeking to increase regional resilience and achieve sustainable development.

How to cite: Rudra Paul, A. and Maity, R.: Spatial Drought Vulnerability Mapping for Regional Climate Resilience: A study over India’s Northeast including Bangladesh, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3949, https://doi.org/10.5194/egusphere-egu25-3949, 2025.

    The Taoyuan Tableland has faced a significant shortage of water resources due to booming socio-economic development in the past decades. The Shihmen Reservoir built in 1964 has been gradually unable to support the Taoyuan Tableland's agricultural and public water demands. By analyzing 1995-2014 rainfall data with the 3-month Standardized Precipitation Index (SPI-III), seven extreme meteorology drought events with the SPI-III less than -2 were found. Using the 0.05° statistically downscaled daily rainfall data provided by the Taiwan Climate Change Projection and Information Platform Project (TCCIP), it is expected to have 40 extreme meteorology drought events in 2041-2060. More drought events in the changing climate will further worsen the water shortage. It is urgent to develop adaptation measures for water resources management to enhance the climatic resilience of the Taoyuan Tableland. Agricultural ponds have been used for temporary water storage to support irrigation for more than 70 years, even earlier than the construction of the Shihmen Reservoir. Deepening agricultural ponds to provide distributed water storage capacity over the tableland is considered one of the effective adaptation measures to reduce the impacts of drought. This study focuses on how to systematically integrate and enhance the capacities of agricultural ponds to achieve a better climate-resilient Taoyuan Tableland.

    Components of the Taoyuan Tableland’s water supply-demand system, including the Shihmen reservoir, agricultural ponds, agricultural districts, and water treatment plants, were integrated to build a water-resource system-dynamic model (WRSDM). Baseline (1995-2014) and SSP5-8.5 projections of 2041-2060 were obtained from the TCCIP. The Taiwan Water Resources Assessment Program to Climate Change (TaiWAP) was used to simulate flow discharges for running the WRSDM. The Deficit Percent Day (DPD) index and the Total Agricultural Deficit (TD_Ag) index are used to evaluate public and agricultural water shortages, respectively. The availability indicator calculated as the ratio of the average time without water shortage to the summation of average time without water shortage and average time of water shortage is used to represent the mean duration of no water shortage in the water resources system. Compared to the baseline (1995-2014), the average TD_Ag will increase by 10.25% and the availability indicator of public water will decrease by 21.51% due to more drought events in the mid-future (2041-2060). By deepening agricultural ponds by 2 meters, the availability indicator of public water will increase by 0.42% in the mid-future which is better than the case without applying any adaptation measure and indicates water shortage impacts to the domestic and industry sectors can be reduced. In addition to deepening agricultural ponds, different adaptation measures (e.g., rotated irrigation schedules, dry farming, reclaimed water, etc.) will be assessed to provide an optimized combination for adaptation policy recommendations in our future studies.

How to cite: Lin, T. Y., Li, M. H., and Jian, C. B.: Assessing Climate Change Impact and Water Resources Adaptation Measures of the Taoyuan Tableland in Taiwan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6162, https://doi.org/10.5194/egusphere-egu25-6162, 2025.

Ongoing climate change, resulting in heavier rainfall and potentially higher flood peaks, can challenge flood risk management in many European regions. In particular, flood design values and flood hazard and risk maps can be challenged by future climate conditions. The devastating July 2021 floods in western Europe highlighted the need for transboundary cooperation in adapting flood risk management to climate change. In the JCAR-ATRACE Initiative (Joint Cooperation programme on Applied scientific Research – Accelerate Transboundary Regional Adaptation to Climate Extremes), we review and synthesize how climate change information is integrated into flood risk management in regions of Germany, the Netherlands, Belgium, and Luxembourg. We assess whether regions have published flood policy papers, developed future climate and flood scenarios, and translated these scenarios to flood hazard and risk maps and/or flood design values. Our findings reveal that while all 17 sub-national regions have adaptation plans addressing climate change, only 6 regions have developed future flood projections, with even fewer (3) incorporating climate-adjusted design values and only one providing flood hazard and risk maps under future climate scenarios. Practices vary widely: for example, Flanders in Belgium uses a full range of emission scenarios (CMIP5 RCP2.6 to RCP8.5), while Baden-Württemberg and Bavaria in Germany rely on the high-end scenario (CMIP5 RCP8.5) only. The Netherlands adopts a robust approach using 33 CMIP6 global climate models and a dynamic adaptation pathway framework to address uncertainties. Some regions like Saxony in Germany argue that the spread of projections is too large to derive design values and emphasize the need for standardized scenarios and methods. In summary, our synthesis highlights substantial gaps in incorporating climate change projections into flood risk management and significant regional variation in approaches. The synthesis will hopefully contribute to cross-border learning and foster uptake of climate change adaptation in flood risk management in Europe.

How to cite: Vorogushyn, S., Macdonald, E., Merz, B., Aerts, J., Dewals, B., Kwadijk, J., Slager, K., Willems, P., and Zoccatelli, D.: Practices to include assessments of future climate change in flood risk management in Germany and the Benelux countries , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8837, https://doi.org/10.5194/egusphere-egu25-8837, 2025.

Title: Masters of the Meuse: Navigating water scarcity in a shared river basin

Overview
Water management in transboundary river basins is one of the most pressing challenges in the face of climate change and competing sectoral demands. Masters of the Meuse is a serious game designed to simulate the complexities of water allocation and governance in the international Meuse River basin, shared by France, Flanders, Wallonia, the Netherlands, and Germany. By assuming the roles of national water managers, players experience firsthand the intricacies of balancing diverse priorities while preventing regional conflicts caused by water scarcity.

Why Participate?
The Meuse River supports nature, agriculture, industry, drinking water, energy production, recreation and cargo shipping. Over 7 million people in the Netherlands and Flanders rely on the Meuse for drinking water, highlighting the river's critical importance. Competing demands, compounded by climate change, increasingly strain the availability and quality of water resources, making effective and transboundary management more urgent. The game provides an interactive platform to explore the complexities of balancing regional priorities, ensuring sustainable water use, and promoting stability. It is especially valuable for policymakers, researchers, and stakeholders in water management.

Objectives
The game aims to:

• Develop a deeper understanding of transboundary water governance.

• Provide an immersive experience in managing water scarcity in the context of climate change and to Illustrate the importance of balancing economic, ecological, and societal priorities.
• Stimulate the international dialogue on how to manage water resources equitably and sustainably and foster collaboration and negotiation skills for conflict prevention.

Gameplay and Insights
Participants represent countries in the Meuse River Basin, each with distinct water needs. The game unfolds over five rounds, each presenting key decisions:

• Water allocation: Distribute limited resources across sectors and river systems.
• Negotiation: Collaborate with neighbouring countries to address cross-border challenges and prevent conflict.
• Event and climate impacts: Respond to disruptions like extreme weather or droughts.
• Conflict management: A shared Conflict Tracker monitors tensions. If one country’s demand exceeds supply, the entire region faces a collective loss.

Success in the game hinges on finding innovative and collaborative solutions that balance national interests with the shared goal of regional stability. This experience simulates the real-world challenges of managing shared water resources in an unpredictable climate.

Relevance to EGU 2025
Masters of the Meuse offers a unique opportunity for researchers, policymakers, and educators to explore the intersection of science, policy, and society. It highlights how hydroclimatic factors, governance frameworks, and negotiation dynamics interact in shared water systems.

Impact
The Meuse River represents the broader challenge of managing shared natural resources globally. By engaging with these issues, participants gain valuable insights into collaborative decision-making and sustainable water use practices.

Join Us
Discover how Masters of the Meuse translates scientific challenges into actionable insights and equips participants with the tools to address the complexities of transboundary water governance. Join us on-site at EGU 2025 in Vienna to experience the game and participate in discussions on its potential applications for research, education, and policymaking.

Together, let’s master the challenges of the Meuse and beyond!

How to cite: van der Ploeg, M. and de Lange, T.: Masters of the Meuse: Navigating water scarcity in a shared river basin , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10500, https://doi.org/10.5194/egusphere-egu25-10500, 2025.

Much of the food supplied to the city of Niamey (1.5 million inhabitants), the capital of Niger, comes from 150 large commercial horticultural sites and 10 vast irrigated perimeters distributed along the Niger River upstream of the city. These areas are threatened by floods, such as the one that devastated paddy fields and horticultural areas in August 2024. To address this problem, a detailed assessment of the river flood risk, expressed in monetary terms, is urgently needed to complement the early flood warning system.

This activity is part of the SLAPIS Sahel project, which aims to develop a more general framework for flood risk management applied to the transboundary Sirba river basin and the nearby Niger river, with the active participation of the water authorities of Burkina Faso and Niger. In this context, this work focuses on the flood risk analysis of the Niger River upstream of the city of Niamey in a multidisciplinary way. To this aim, a hydrological study of the basin was carried out, taking into account the two types of floods that affect the area: floods due to the local rainy season, and dry season events caused by floods upstream in the Guinea-Conakry basin. A hydraulic model was then used to map the extent of flooding, allowing to study the impact and expected damage to the target areas. Daily satellite imagery was used to assess the extent of recent floods and the characteristics of the exposed areas. All these activities were repeated for both the wet and dry seasons, as agricultural production changes and the impacts are different.

This analysis supports the cost-benefit assessment of possible defense structures.

How to cite: Ganora, D., Abraiz, M., Belcore, E., Cannella, G., Housseini Ibrahim, M., Piras, M., Saretto, F., Tiepolo, M., and Vesipa, R.: Flood risk assessment of agricultural areas along the Niger river upstream Niamey, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10808, https://doi.org/10.5194/egusphere-egu25-10808, 2025.

In July 2021 large flooding took place in North-Western Europe. The Geul river, which is shared between the Netherlands, Belgium and Germany, was one of the flooded catchments, with total damages estimated to be €250 million. Since then, there has been a call for additional flood risk reduction measures in the area, including transboundary nature-based solutions in upstream parts of Belgium and local scale flood-proofing of buildings in The Netherlands.

The main novelty of our study is to make an economic trade-off between upstream nature-based solutions (NBS) and downstream building-level measures. For this, we further develop GEB, a coupled agent-based hydrological model and integrate the hydrodynamic model SFINCS into GEB. Furthermore, to calculate high-resolution risk estimates for buildings, we use object-based exposure data from OpenStreetMap and empirically derived vulnerability curves using survey data at the building level. The model allows us to 1) understand current flood risk in the Geul catchment at the object-level and 2) evaluate the effect of several flood risk reduction measures. The model validation shows good performance against observations of flood extent (CSI=0.66), flood depth, and damage of the July 2021 flood.

We then quantify the risk reduction of several nature-based solutions (wetland restoration, reforestation, retention ponds and the conversion of agricultural land to natural grassland) and building-level adaptation measures (wet-proofing and dry-proofing). Moreover, we examine the effect of upstream nature-based solutions on downstream communities. Finally, we perform a cost-benefit analysis (CBA) to gain insight into which combinations of measures are most desirable. Our results show that NBS are especially effective for less extreme floods with high return periods (<1/25). For extreme floods (>1/25), benefit-cost ratios (BCR) may drop to 0.25 or lower. However, these numbers do not account for co-benefits (e.g. tourism). The results can be used by policymakers to design effective flood risk management strategies.

How to cite: Bril, V., de Bruijn, J., de Moel, H., Sadana, T., Busker, T., Botzen, W., and Aerts, J.: Comparing the effectiveness of upstream nature-based solutions with building-level adaptation measures: a case study for the Geul river, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11137, https://doi.org/10.5194/egusphere-egu25-11137, 2025.

The Horn of Africa drylands (HAD) encompassing Kenya, Somalia, and Ethiopia,  recently endured an unprecedented multi-year drought from 2020 to 2023, causing devastating impacts. This study investigates these impacts and the dynamics of human adaptation in response to the drought, comparing it to earlier drought events (i.e., 2016-2018) to identify key lessons. First, drought impact data—covering milk production, trekking distances to water sources, and internally displaced persons (IDPs)—are analyzed over time to provide a detailed overview of drought dynamics. Second, household survey data (n=752) are used to examine community perceptions of the drought period and their adaptation strategies. Finally, agent-based modelling (ABM) simulations explore the interactions between mitigation, adaptation decisions, and drought impacts. The results reveal that, on average, the 2020-2023 drought had more severe impacts than the 2016-2018 drought, although the latter exhibited greater variability in impacts. Communities have adopted various adaptation measures to cope with drought effects; however, limited knowledge and financial resources remain significant barriers to scaling these efforts. ABM simulations indicate that enhancing extension services can boost the adoption of adaptation strategies, leading to increased crop and milk production. Additionally, the simulations suggest that water harvesting can mitigate drought impacts upstream, though it may reduce water availability downstream. These findings highlight the critical need for sustained investments in adaptation measures, timely and well-informed decision-making, and region-specific interventions while carefully considering the trade-offs associated with these strategies. 

How to cite: Schrieks, T., Odongo, R., Streefkerk, I., de Moel, H., Busker, T., Haer, T., MacLeod, D., Michaelides, K., Singer, M., Assen, M., van Loon, A., and Otieno, G.: Drought impacts and community adaptation: perspectives on the 2020-2023 drought in East Africa , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12019, https://doi.org/10.5194/egusphere-egu25-12019, 2025.

The Meuse River Basin, like many transboundary river systems, faces a growing number of challenges, exacerbated by climate change, rapid urbanization and population growth. These pressures not only strain water resources, but also increase the frequency and intensity of hydroclimatic extremes such as floods and droughts. In July 2021, floods in Belgium, Luxembourg, the Netherlands, and Germany caused more than 220 deaths and more than 46 billion euros in economic losses. Post-flood assessment reports revealed significant gaps in communication and coordination, especially across borders. The Meuse River Basin is also increasingly affected by droughts, with river discharges below 20 m3/s recorded at Eijsden (Netherlands) in 2018 and 2022. Amid these challenges, there is a heightened focus on alternative solutions to manage these risks, such as detention basins, floodplain restoration, and nature-based approaches, which could significantly affect land use and resource management. The integration of such local measures presents a valuable opportunity, but also demands careful consideration of how different countries within the basin approach land and water governance.

A major barrier to more effective flood and drought management lies in the fragmented nature of data integration and modeling infrastructure. Evaluation reports have pointed to significant communication and coordination gaps, particularly across borders. They found that disparate data sources are often not sufficiently coordinated or shared across the regions that make up the basin, making it difficult to design and implement unified policies. This lack of integration complicates decision-making, creates gaps that hinder the development of cohesive strategies that are essential for managing the basin’s shared resources, increases the likelihood that conflicting measures will be taken in different jurisdictions, undermining the overall resilience of the basin. Although the International Meuse Commission (IMC) acts as platform for exchange and coordination of river basin water management strategies and guarantor of compliance with EU directives like the Water Framework Directive, it lacks the authority and capacity to ensure efficient information exchange among riparian regions. At present, regions and countries turn to bi- or multilateral agreements and projects independently of the IMC. The Netherlands for instance deploys great diplomatic efforts Belgium in an attempt to improve information sharing with Belgium.

This paper examines the relevance and effectiveness of a river basin organization in a basin where regions tend to prefer bilateral agreements and action guided by local implementation visions. It compares the advantages and disadvantages of a governance structure based primarily on bilateral relations with the river basin approach. It reflects on the IMC framework ostensibly regulated by the Water Framework Directive and its failure to add value to effective transboundary river basin cooperation.

Key Words:

Climate change adaptation, international basin cooperation, knowledge co-production, policy integration, transboundary water governance

How to cite: Telle, A. and Bréthaut, C.: Assessing Fragmented Governance and Data Integration Challenges in the Meuse River Basin: A Review of Transboundary Cooperation Effectiveness, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14407, https://doi.org/10.5194/egusphere-egu25-14407, 2025.

In hazard-to-risk assessment, often given the same natural hazard situations, risk is generalized in terms of scenarios and vulnerability. In reality, even in the same natural hazard situations, vulnerability can be different, considering different natural, social, political and economic aspects. This is also the case of the Prut floodplain, which has long been a hard political border and where two different socio-economic regimes have shaped human-environment interactions over the last 55-75 years. Despite the joint construction of the Stânca-Costești reservoir, predominantly downstream the Romanian side built dikes, after the Second World War, resulting in a lower theoretical vulnerability. On the Moldovan side, the dyke network is not very extensive and especially in the floods after 2000, the vulnerability and risks were greater. We mapped the dike network on both banks of the Prut River on LiDAR data and synthesized the post-2000 flood impact to establish a vulnerability estimation framework.

How to cite: Niculita, M., Bunduc, T., Bejan, I., Chiriac, I., Chelariu, E.-O., Botnari, A., Fedor, A., and Margarint, M. C.: Cross-border hydrological hazard and risk differences in the case of the Prut River for Romania and the Republic of Moldova, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15373, https://doi.org/10.5194/egusphere-egu25-15373, 2025.

In July 2021 an intense and rapid onset rainfall event resulted in severe flooding in Belgium as well as neighbouring countries of Germany, the Netherlands, and Luxembourg. The region of Wallonia in Belgium was severely affected with the Vesdre River valley in the province of Liège being particularly hard-hit, with 39 reported fatalities there. The warning system was significantly criticised in the aftermath of the event. Hence, this work addresses the flood forecasting warning and response system (FFWRS) performance in Belgium for the July 2021 flood with a focus on Wallonia. The analysis is based on an online survey (n=550) and addresses the reception of official warnings, interpretation and trust in the warnings, and response behaviour. We investigate which variables may influence behaviour and situational factors which leads to people receiving an official warning in time before the flood including flood severity experienced and risk perception. We find that among the respondents in Wallonia 33% reported that they had not been warned and while 28% were warned through official channels, many did not know how to respond. From a similar survey conducted in Germany we see comparable results, suggesting that there were similar cross border challenges. A first regression analysis of the Belgian data suggests that respondents whose household was highly affected were less likely to receive an official warning in time which is consistent with testimonies reporting that inhabitants in severely affected areas were particularly surprised by the flood. We also investigate the role of risk perception and flood warning. This points to some of the challenges with effectively early warning for flash floods. Our analysis highlights the need to improve Belgium's flood warning system by ensuring timely issuance of warnings and enhanced public understanding. In addition, with a comparison of results to the Germany data we discuss common challenges but also important differences.

How to cite: Murdock, H. J., Rodriguez Castro, D., Dewals, B., Heidenreich, A., and Thieken, A. H.: Learning from the past to inform flood risk management: Analysis of public survey data in Belgium on flood early warning and response during the July 2021 flood, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15747, https://doi.org/10.5194/egusphere-egu25-15747, 2025.

To tackle systemic drought risks, both short-term and long-term decision making that anticipates climate change and balances the varying needs and availability of water across different sectors is required. Adaptation pathways are a promising approach which can enable this, by indicating how to implement adaptation options progressively depending on how drought risks emerge under different hydrological and societal conditions. However, for adaptation pathways to be effective for managing systemic drought risks, they need to take into consideration cross-sectoral and cross-border effects, and therefore be informed by risk assessments that identify vulnerabilities and underlying risk drivers across multiple sectors. In this presentation we showcase research from the recently published World Drought Atlas, demonstrating how conceptual models of drought risks can integrate with a pathways approach to manage shared impacts and drivers of drought risks across different sectors.

Individual Drought Impact Chains derived from literature were developed for five impacted systems at the global level (water supply, agriculture, hydropower, inland navigation and ecosystems). These were brought together to create a systemic conceptual model that identified cross-sectoral and cross-border impacts and shared underlying drivers and root causes of drought risks across systems. We then showed how different risk management and adaptation measures, which are often designed for a single system, can have positive effects across different, interconnected systems by tackling these shared risk drivers and root causes. The chosen measures covered diverse sectoral needs, focusing on water resource management, land-use management and governance aspects, and included grey infrastructure, early warning systems, Nature-based Solutions and community-based approaches. Finally, the measures were brought together in a pathways approach, demonstrating how different clusters of measures, when implemented progressively and in consideration of one another, can strengthen co-benefits and create synergies across systems. The pathways show how combing measures can be more effective against increasing levels of risk, and also when measures cease to be effective and a shift to a new pathway is needed. The pathways framework additionally supports the timing of when measures should be considered for implementation, avoiding less desirable adaptation decisions until absolutely necessary.

While this methodology was developed in the context of managing systemic and cross-border drought risks, the measures and pathways also have high relevance for flood management. This signals that such an approach cold equally be developed for systemic flood risks, or for managing hydrological extremes from both floods and droughts. By integrating adaptation pathways with a cross-sectoral conceptual model of risks, dynamic adaptation planning is supported that connects the vulnerabilities of multiple systems with prospective, forward looking risk management. This helps to reduce uncertainty and manage trade-offs in decision making. Such an approach shows the benefits of taking a systemic lens towards the management of drought risks.

How to cite: Sparkes, E., Cotti, D., Ramesh, A., Werners, S., and Hagenlocher, M.: Integrating conceptual risk models with an adaptation pathways approach to assess and manage systemic drought risks across sectors and boarders, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17200, https://doi.org/10.5194/egusphere-egu25-17200, 2025.

With global climate change, it is not only getting warmer but precipitation patterns are also shifting. This leads to more intense or prolonged precipitation as well as periods with reduced or no precipitation. Stress testing, a technique originally from engineering, assesses the stability of an object under adverse conditions and has been widely used in the financial sector to evaluate the impact of interacting drivers of change and to plan actions to minimize risks in a standardized and transparent manner.  In the water sector, stress testing has also recently been employed in the Netherlands to map out the vulnerabilities of landscapes and the assets in it to weather extremes. This research aims to advance this stress testing methodology to aid dialogues on improving climate resilience of landscapes. The developed framework serves as a systematic evaluation process designed to assess a system’s behaviour under progressively increasing stress levels linked to a wider variety of hydro-meteorological events. It lists key stressors driving the system and proposes indicators to evaluate performance under stress, including system responses expressed as extend of floods and droughts, shifts in water quality and biodiversity values, and socioeconomic impact. In this research, the methodology is applied by conducting hydrological model experiments to simulate flood and drought scenarios in transboundary catchments. Using a range of stress tests, we explore landscapes’ sensitivity to variations in precipitation patterns and initial conditions. Additionally, the study evaluates potential sponge measures designed to mitigate system stress and enhance its resilience before critical failures occur. By testing these measures, the study assesses their capacity to reduce system pressure, improve adaptability, and enhance resilience to extreme events to limit critical failure or significant operational disruptions.

How to cite: Ruangpan, L., Klein, A., Albert, C., Dussaillant, A., Slager, K., and Penning, E.: Stress testing landscapes’ response to climatic extremes with and without sponge measures, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17389, https://doi.org/10.5194/egusphere-egu25-17389, 2025.

More frequent and more severe low flow events under a changing climate pose significant challenges to water management and impact various sectors such as agriculture, water supply, navigation, energy and recreation. Low flow events naturally occur as a result of periods of drought. While the generation and propagation of low flows will depend on basin characteristics, these are also influenced by human actions, which can aggravate or attenuate their intensity and duration. Here, we focus on understanding of the genesis and propagation of low flows in the Meuse Basin, a transboundary basin shared by France, Belgium, Luxembourg, Germany, and the Netherlands. Characteristics of the different sub-basins of the Meuse were analysed using an extensive 40-year observed streamflow dataset collated from multiple providers across the basin (e.g., Rijkswaterstaat, SPW, EauFrance, ELWAS-WEB, Vlaanderen Waterinfo, Waterschap Limburg). The collated dataset is used to identify low flow periods by comparing daily streamflow to a 20% non-exceedance seasonally adjusted threshold. The degree of human influence is then determined by contrasting indices such as low flow duration and deficit volume between a benchmark naturalised time series and the human-influenced time series. The storage capacity of sub-basins is analysed through annual and seasonal baseflow volumes as well as sub-basin recession constants. The study revealed that sub-basins like the Rur, Amblève and Chiers are high baseflow contributors, though significant human influences are found. This contrasts with the Upper Meuse, which has a lower human influence, albeit with a limited baseflow contribution. Aggravation of low flows due to human influences can be linked to agricultural land use and water abstractions in the basin as well as reservoirs though these can either aggravate or attenuate low flows, depending on how these are operated. These findings provide important insights into the genesis of low flows and water storage in the Meuse. This understanding lays the foundation for proposing tailored adaptation measures at the sub-basin level depending on its characteristics that have the potential to increase the overall basin storage potential and optimise water management; including through Nature-Based Solutions, improved reservoir operations and other infrastructural interventions.

How to cite: Dotta Correa, D., Werner, M., and Cremers, N.: Understanding low flow genesis in the International Meuse Basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18504, https://doi.org/10.5194/egusphere-egu25-18504, 2025.

Using transdisciplinary approaches, PASSAGE brings together a diverse team with the aim of addressing several gaps by co-developing with pastoral communities, local government, and the civil society, inclusive and cross-scale risk narratives and anticipatory action (AA) plans based on predictive multi-hazard impact-based forecasts to effectively build the resilience of pastoral communities. PASSAGE particularly focuses on the transboundary regions within the region as these host the most vulnerable pastoral communities with acute malnutrition levels. 

The current food insecurity over the Greater Horn of Africa region is deeply alarming, with millions among the pastoral communities particularly affected. Whilst this evolving food security crisis has been well monitored and forecasted, the extent of early actions has been demonstrably insufficient to save lives and livelihoods. The goal of PASSAGE, a CLARE-funded project, is to co-produce knowledge for action with all sections of pastoral societies. The project is driven by research questions and activities, which include identifying indicators and triggers that best capture the impacts of drought and extreme temperature on diverse socio-ecological landscapes; estimating the cascading impacts of these hazards on pastoral livelihoods; evaluating the most effective AA to build the resilience of pastoral communities at phased lead times; and defining mechanisms for coordinated transboundary AA plans. The project is at its midway point and we will be sharing some exciting results. 

How to cite: Rowhani, P., Hopling, C., Mohamoud, A., Kathiya, D., Mashango, G., and Ambani, M.: PASSAGE: strengthening PAStoral livelihoodS in the African Greater horn through Effective anticipatory action, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20048, https://doi.org/10.5194/egusphere-egu25-20048, 2025.

Terrestrial gamma-ray flashes (TGFs) are bursts of energetic X- and gamma-rays which are emitted from thunderstorms as the Bremsstrahlung radiation of relativistic electrons. Recently, the ALOFT (Airborne Lightning Observatory for FEGS and TGFs) mission has shown that the emission of such energetic radiation, also including gamma-ray glows and flickering gamma-ray flashes, is more abundant than previously thought. This raises the question how the relativistic electrons and photons interact with the atmosphere and whether they have an impact on the chemical composition while propagating through the atmosphere, potentially relevant for the production of greenhouse gases. The propagation and interaction of relativistic particles with the atmosphere can be studied with particle Monte Carlo collision models requiring cross sections as an input. Whilst there are well established data for photoionization, Compton scattering and pair production, we lack cross sections for photoexcitation, photodissociation or the excitation of air molecules through relativistic electrons which contribute to the chemical activation of the atmosphere. In order to fill this gap of data, we here present a novel numerical tool calculating cross sections for energetic particles propagating in air. We provide an overview of the code structure and present benchmarking cases against well-known cross sections. Additionally, we will present a first application by calculating cross sections for photodissociation for a wide range of energies. In the end, we will give an outlook how this will allow to pave the path for more realistic simulations of energetic phenomena in our atmosphere, relevant for chemical processes.

How to cite: Köhn, C. and van Gemert, H.: Towards the investigation of chemical effects of energetic electrons and photons in the atmosphere, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1217, https://doi.org/10.5194/egusphere-egu25-1217, 2025.

The influence of lightning on the atmospheric electric field (potential gradient, PG) is examined at Xanthi, NE Greece. The data span one year, 01/06/2011 - 31/05/2012. The influence of lightning distance on PG is large, and is evident up to distances of 50 km. At distances shorter than 1 km, the 1-min absolute PG values mean increase is 10 kV/m, while 1-sec values may increase above 20 kV/m for lightning distances below 10 km. It appears that PG increases linearly with decreasing lightning distance. Lightning can cause both positive and negative PG values. It is found that negative PG values increase faster than positive ones as the lightning distance decreases, and mean negative values are at any distance up to 50 km 20% higher than the mean positive ones. It is also examined how synoptic weather types influence lightning frequency and PG values. Circulation Weather Types (CWT) that produce more lightning near Xanthi are ones associated with high 500 hPa geopotential heights over the area and high thickness of the 850-500 hPa isobaric surfaces. Thgey are encountered predominantly during summer, and to a lower extend during spring and autumn. During such systems, when lightning was detected at distances shorter than 100 km from the site, the mean absolute values of PG were 1-1.2 kV/m.

How to cite: Kourtidis, K., Karagioras, A., Kosmadakis, I., and Kotroni, V.: On the influence of lightning distance on the atmospheric electric field, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1630, https://doi.org/10.5194/egusphere-egu25-1630, 2025.

The appearance of transient luminous events (TLEs) in the mesosphere is known to be associated with strong (almost exclusively) positive cloud-to-ground (+CG) strokes with large charge moment change (CMC) values in tropospheric thunderstorms. Nevertheless, despite numerous observational campaigns from ground and space-based platforms, robust theoretical models, and laboratory experiments, there are lingering open questions concerning the exact circumstances for the appearance of sprites, among which is the cause for the observed delay in sprite appearance relative to the onset of the current in the parent stroke. Curiously, seemingly identical +CG discharges with the same CMC that should lead to a mesospheric discharge do not initiate sprites, while sometimes even weaker +CG discharges are able to do so. Previous studies aiming to resolve this issue have investigated different effects, such as mesospheric inhomogeneities, the presence of meteoritic ablation products, discharges in neighboring cloud cells, associative detachment of electrons from atomic oxygen ions, and long continuing currents. Here, we investigate the properties of the parent +CG's continuing current by suggesting piecewise-varying discharge time dependence. We present the results of simulations using a 3D quasi-electrostatic model (Haspel and Yair, 2024) with various patterns of the parent flash discharge current. We show how short, moderate, and long delayed sprites can be incepted due to piecewise-varying discharge current time dependence, and how discharges possessing low iCMC values can still produce electric fields in the mesosphere with magnitudes above the conventional electrical breakdown field. The model is validated by simulating two sprite events observed from the International Space Station during the ILAN-ES campaigns in April 2022 (on AX-1) and February 2024 (on AX-3), showing how a delayed sprite is incepted by a prolonged piecewise pattern of the current in the parent +CG flash.

Haspel, C. and Y. Yair (2024), Numerical Simulations of the region of possible sprite inception in the mesosphere above winter thunderstorms under windshear. Ad. Spa. Res.., 74, 11, 5548-4468, doi:10.1016/j.asr.2024.08.050

How to cite: Yair, Y. and Haspel, C.: Simulating the possible regions of delayed sprite inception above thunderstorms using piecewise-varying lightning current time dependence , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2357, https://doi.org/10.5194/egusphere-egu25-2357, 2025.

The UK’s goal of achieving net zero emissions by 2050 requires the construction of extensive new power infrastructure to accommodate low carbon energy technologies (e.g., offshore wind, nuclear) while mitigating climate risks. Lightning activity poses severe risks to power system security and can result in significant economic losses (Ofgem, 2019; Bialek, 2020). These risks must be mitigated as effectively as possible as new power grid infrastructure is built in the coming years and climate scenarios.

To represent lightning activity, this study employs a newly developed thunder hour dataset from Earth Networks, with a spatial resolution of approximately 5.5 km, specifically designed for climate research (DiGangi et al., 2022). Ten years of monthly historical UK thunder hour data from Earth Networks are analysed to identify lightning climatology trends and support the development of a long-term predictive lightning model. This study differentiates itself from previous UK lightning research by focusing directly on lightning risks impacting the UK’s power grid infrastructure, aiming to offer actionable insights for risk mitigation during the planning of future power assets for National Grid Electricity Transmission (NGET).

Historical lightning damage hotspots are identified by linking power system fault records with spatiotemporal lightning activity characteristics such as peak current and lightning duration from lightning detection and location networks. Analysing lightning activity’s impact on power system line trippings helps improve the grid’s reliability and safety (Li et al., 2024). The novelty of this research lies in its integration of lightning hotspot analysis, informed by lightning climatology trends, with asset distribution to pinpoint high-risk areas for electrical infrastructure, validated through power system failure case studies. These findings offer a basis for improved disaster prevention and mitigation strategies, enhancing grid resilience and safety.

Acknowledgement:

The authors acknowledge the support for the KERAUNIC project (ref: NIA2_NGET0055, National Grid Electricity Transmission, 2024), which focuses on improving the understanding of lightning-induced damage to UK power systems. This research is part of an innovation effort funded through the Network Innovation Allowance (NIA).

References:

Bialek, J. (2020). What does the GB power outage on 9 August 2019 tell us about the current state of decarbonised power systems? Energy Policy, 146, 111821.

DiGangi, E., Stock, M., & Lapierre, J. (2022). Thunder Hours: How Old Methods Offer New Insights into Thunderstorm Climatology. Bulletin of the American Meteorological Society, 103, E548-E569. https://doi.org/10.1175/BAMS-D-20-0198.1.

Li, M., Cheng, S., Wang, J., Cai, L., Fan, Y., Cao, J., & Zhou, M. (2024). Thunderstorm total lightning activity behaviour associated with transmission line trip events of power systems. npj Climate and Atmospheric Science, 7(1), 148.

National Grid Electricity Transmission. (2024). Knowledge Elicitation of Risks to Assets Under LightNing Impulse Conditions (KERAUnIC). https://smarter.energynetworks.org/projects/nia2_nget0055/.

Ofgem. (2019). Investigation into 9 August 2019 Power Outage. Retrieved from https://www.ofgem.gov.uk/publications/investigation-9-august-2019-power-outage.

How to cite: Bai, X., He, X., Fullekrug, M., Gu, C., Xu, M., Li, B., Souto, L., Chikohora, T., and Dodds, D.: Enhancing Lightning Resilience: Predictive Models and Infrastructure Protection for UK Electric Power Systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3614, https://doi.org/10.5194/egusphere-egu25-3614, 2025.

Thunderclouds and lightning produce high-energy radiation over a wide range of time scales. Terrestrial gamma-ray flashes (TGFs) are brief emissions lasting ~100 µs, consisting of photons with energies ranging from 20 keV to 40 MeV. Simultaneous ground-based measurements of electromagnetic fields and gamma-ray emissions have found TGFs to be associated with the evolutionary phases of both intracloud and cloud-to-ground lightning discharges.

Gamma-ray glows, on the other hand, last from a few seconds to several tens of minutes, typically coincide with the passage of thunderclouds, and are sometimes abruptly terminated by nearby lightning. Photons emitted during gamma-ray glows share the same energy spectrum as TGFs but are less intense. It was recently discovered that thundercloud regions can glow for hours and that gamma glows are more dynamic phenomena than originally thought.

Both types of gamma-ray emissions are believed to be generated via bremsstrahlung by energetic runaway electrons accelerated in the strong electric fields within thunderclouds. However, the connection between TGFs and gamma-ray glows remains not fully understood.

Until now, the only simultaneous gamma ray and radio wave measurements were conducted onboard an airplane during the ALOFT campaign. The TARANIS mission, which was intended to carry a unique set of electromagnetic, particle, gamma ray, and optical instruments, was unfortunately lost due to the failure of the Vega launcher in 2020.

The STRATELEC balloon project (part of the French-US STRATEOLE-2 project of long-duration balloon flights at the tropical tropopause), with precise synchronization of broadband electric field measurements and a gamma-ray detector, will provide a unique opportunity to correlate individual photon detections with electromagnetic pulses emitted by various lightning processes. These coordinated measurements could help answer the following questions:

• a) At which stage of the evolution of lightning discharges are TGFs produced?
• b) Which types of intracloud discharges produce detectable high-energy radiation?
• c) What are the differences in the electromagnetic signatures of lightning processes associated with TGFs and gamma glows?
• d) What are the temporal variations in electromagnetic emissions associated with gamma glows?
• e) Are flickering TGFs truly radio silent?

In this presentation, we introduce the FPGA-based radio receiver RIP (Radio Instrument Package), developed for the STRATELEC balloon project. The receiver is designed to capture and analyze the electromagnetic signatures of various lightning phenomena associated with gamma-ray production, including leader pulses, initial breakdown pulses, compact intracloud discharges, and dart-stepped leader pulses. The anticipated launch is late 2026.

How to cite: Kolmasova, I., Santolik, O., Celestin, S., Defer, E., and Lan, R.: Upcoming broadband electromagnetic balloon measurements related to terrestrial gamma ray flashes and gamma glows , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4072, https://doi.org/10.5194/egusphere-egu25-4072, 2025.

The Lightning Mapping Imager (LMI) onboard the Fengyun-4A (FY-4A) satellite is the first independently developed satellite-borne lightning imager in China. It enables continuous lightning detection in China and surrounding areas, regardless of weather conditions. The FY-4A LMI uses a Charge-Coupled Device (CCD) array for lightning detection, and the accuracy of lightning positioning is influenced by cloud top height (CTH). In this study, we proposed an ellipsoid CTH parallax correction (ECPC) model for lightning positioning applicable to FY-4A LMI. The model utilizes CTH data from the Advanced Geosynchronous Radiation Imager (AGRI) on FY-4A to correct the light-ning positioning data. According to the model, when the CTH is 12 km, the maximum deviation in lightning positioning caused by CTH in Beijing is approximately 0.1177° in the east–west direction and 0.0530° in the north–south direction, corresponding to a horizontal deviation of 13.1558 km, which exceeds the size of a single ground detection unit of the geostationary satellite lightning imager. Therefore, it is necessary to be corrected. A comparison with data from the Beijing Broadband Lightning Network (BLNET) and radar data shows that the corrected LMI data exhibit spatial distribution that is closer to the simultaneous BLNET lightning positioning data. The coordinate differences between the two datasets are significantly reduced, indicating higher consistency with radar data. The correction algorithm decreases the LMI lightning location deviation caused by CTH, thereby improving the accuracy and reliability of satellite lightning positioning data. The proposed ECPC model can be used for the real-time correction of lightning data when CTH is obtained at the same time, and it can be also used for the post-correction of space-based lightning detection with other cloud top height data.

How to cite: Zhang, Y., Qie, X., Cao, D., Yang, J., and Wang, D.: Correction of Parallax Shift Effect Based on Cloud Top Height for FY-4A LMI, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4688, https://doi.org/10.5194/egusphere-egu25-4688, 2025.

About 45 lightning flashes occur per second all around the Earth with a predominant distribution over the continents and along the inter-tropical band. While different types of Transient Luminous Events (TLEs) induced by lightning flashes can be produced above the thunderstorms, Terrestrial Gamma Ray Flashes (TGFs) are bursts of high-energy photons originating from the Earth’s atmosphere in association with thunderstorm activity with a great majority of TGFs occurring in the inter-tropical region. In addition to those radiation bursts, another type of high-energy emission, so-called gamma ray glows, has been observed inside thunderstorms corresponding to significant enhancements of background radiation that last for more than a few seconds. All these connected phenomena remain to be documented both remotely and on an in-situ manner. Balloon-borne missions offer the required in-situ close-range high-altitude measurements of the ambient electrostatic field, conductivity, TGF radiation and lightning occurrence for a better understanding and modeling of these complex phenomena and of their effects on the Earth atmosphere and the global atmospheric electrical circuit.

The STRATELEC (STRatéole-2 ATmospheric ELECtricity) project (Defer et al., 2022), funded by CNES, aims at deploying within the Stratéole-2 framework (Hertzog and Plougonven, 2020) new atmospheric electricity instrumentation on several stratospheric balloons to:

• Document the electrical state of the atmosphere and the production of high-energy radiation through in-situ and remote sensing measurements to reach better understanding and better modeling capabilities of the processes occurring during thunderstorms,
• Identify state-of-the-art and emerging technologies to populate the STRATELEC instrumentation package with new sensors in the perspective of their operation on stratospheric balloons, high altitude aircraft and even low-level drones to eventually propose new balloon and/or space mission concepts,
• Contribute to additional scientific returns on any space mission dedicated to lightning detection (e.g. MTG-LI, GOES-GLM) and more generally to the study of the convection in the Tropics and of electrodynamic couplings in the terrestrial atmosphere-ionosphere-magnetosphere system.

First, we will remind the scientific objectives of the STRATELEC project. Then we will provide an update on the different scientific and technical activities, including the development and the testing of STRATELEC instruments, but also the data analysis méthodology. Finally, we will discuss the way forward for the upcoming and final Stratéole-2 campaign (winter 2026-2027), as well as some initial thoughts on future balloon campaigns.

Hertzog A., and R. Plougonven (2020), Stratéole-2 : des ballons longue durée pour étudier la tropopause tropicale, La Météorologie - n° 108 - février 2020.

Defer, E., et al. (2022), An Overview of the STRATELEC (STRatéole-2 ATmospheric ELECtricity) Project, 25th ESA Symposium on European Rocket and Balloon Programmes and Related Research, 1-5 May 2022, Biarritz, France.

How to cite: Defer, E., Soula, S., Célestin, S., Hazem, Y., Trompier, F., Kolmašová, I., Santolík, O., Lán, R., Berthelier, J.-J., Seran, E., Godefroy, M., Hertzog, A., and Venel, S.: The STRATELEC (STRatéole-2 ATmospheric ELECtricity) project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4814, https://doi.org/10.5194/egusphere-egu25-4814, 2025.

An analysis of lightning strike statistics and spatial distributions in South Korea was conducted throughout 2023 to archive records and to support weather research through the use of radar data. The annual lightning strikes reached 73,341, demonstrating a twofold increase from 36,750 in the previous year but still below the 10-year (2014-2023) average of 93,380. Temporal analysis shows summer recorded the highest number of lightning strikes at 55,258, accounting for 75.35% of annual occurrences, a pattern consistent with the 10-year average. June, October, and December exhibited higher strikes than the 10-year average, while February, March, and August showed significantly lower activity. Spatial distribution examination identified Gyeongsangbuk-do as the dominant region with 12,892 strikes constituting 17.58% of the total. In contrast, Daejeon Metropolitan City recorded the lowest count with 270 strikes, equivalent to 0.37%. The grid investigation revealed high activity zones over the West Sea and around Seoul and Busan, representing increased strikes compared to the 10-year average. Ground-to-cloud discharges prevailed, with high intensities recorded over the South Sea relative to other regions. The five days with the highest number of lightning strikes were identified as 27 June, 11 July, 12 July, 26 July, and 26 October, followed by an analysis of regional strike distribution for each date. This study contributes to an improved understanding of lightning climatology in South Korea, enhancing meteorological forecasting capabilities.

This research was supported by the "Development of radar based severe weather monitoring technology (KMA2021-03121)" of "Development of integrated application technology for Korea weather radar" project funded by the Weather Radar Center, Korea Meteorological Administration.

How to cite: Ko, K., Chu, S., Nam, K.-Y., and Kim, K.-H.: Lightning statistics and spatial distribution in South Korea in 2023, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5416, https://doi.org/10.5194/egusphere-egu25-5416, 2025.

Lightning flashes with continuing current (CC) are a type of cloud-to-ground (CG) flash that pose significant risks, including air quality degradation, damage to electrical systems and the igniting of wildfires.  Understanding CC lightning is important for mitigating its effects and assessing its potential connection to climate change.

In this study, we used a combination of space-based instruments (ASIM and GLM) and ground-based networks (ENTLN and ELF) to systematically identify CC lightning across the Contiguous United States (CONUS) from June 1, 2018, to December 31, 2021.

ASIM, aboard the International Space Station, provides high-resolution optical measurements at dual wavelengths (337.0 nm and 777.4 nm), while GLM offers continuous geostationary monitoring of optical emissions at 777 nm. Ground-based systems like ENTLN and ELF provide complementary radio data.

We utilized two distinct methods to classify lightning flashes as CC or no CC. The first relied on the predictive models of Fairman and Bitzer (2022), based on the optical signal of GLM, while the second utilized a metric derived from Extreme Low Frequency (ELF) magnetic signals.

We found clear differences between optical properties in ASIM dual-wavelength (337.0~nm, 777.4~nm) light curves associated with CC and no CC lightning, indicating potential for identifying CC flashes using ASIM optical recordings.

Results reveal optical and electromagnetic differences between CC and no CC lightning. First, CC flashes have longer-lasting optical emissions, higher power densities, and elevated total energy levels compared to no CC flashes. Second, the processed ELF radio signal can sense the presence of CC and the electrical polarity of lightning flashes. These findings highlight the value of combining space-based optical and ground-based ELF measurements to improve detection and classification of CC lightning.

How to cite: Camino-Faillace, P. A., Gordillo-Vazquez, F. J., Pérez-Invernón, F. J., Montanya, J., Mlynarczyk, J., Torsten, N., Chanrion, O., and Østgaard, N.: Characterizing continuing current lightning using multi instrument observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5624, https://doi.org/10.5194/egusphere-egu25-5624, 2025.

Electrical discharges such as lightning are among the most energetic and remarkable phenomena in planetary atmospheres. Both laboratory experiments and modeling studies have predicted that triboelectric charging of wind-blown particles in dust events on Mars should lead to significant electrification. However, there have been no direct measurements of a Martian electric field or observations of discharges. Here, using acoustic recordings from the SuperCam microphone onboard the Perseverance rover, we report evidence for an atmospheric discharge in a dust devil, based on the electromagnetic and acoustic signatures observed in the microphone signal. This is the first direct detection of a triboelectric discharge in the Mars atmosphere. It shows that the electric field in a dust devil can reach 25 kV/m, which is the expected breakdown threshold of the Mars atmosphere. Electrical discharges on Mars may have implications for dust dynamics, the chemistry of oxidants and methane in the atmosphere, and ultimately robotic and human exploration.

How to cite: Chide, B., Lorenz, R., Montmessin, F., Maurice, S., Parot, Y., Hueso, R., Martinez, G., de Vicente-Retortillo, A., Jacob, X., Lemmon, M., Dubois, B., Meslin, P.-Y., Newman, C., Bertrand, T., Cousin, A., and Wiens, R.: Search for in situ signatures of electric activity on Mars, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5745, https://doi.org/10.5194/egusphere-egu25-5745, 2025.

On 3 August 2021, Sandia launched a flotilla of four Heliotrope solar hot air balloons (Bowman et al., 2020) from Belen regional airport in New Mexico (USA) to coincide with the launch of the Boeing Starliner rocket. These Heliotrope balloons allow level flights between 15 and 25 km altitude for several hours from sunrise to sunset. Despite the cancellation of the rocket launch, the microbarometers on board these balloons were able to record in the stratosphere the acoustic signals emitted by eight chemical explosions and the lightning that occurred in a thunderstorm cell. This storm cell was located between 10 and 40 km from three of the four balloons.

In this presentation, we first identify the individual signals that may be due to lightning. For this we use the method proposed by Farges and Blanc (2010) for ground-based thunder measurements and by Lamb et al. (2018) for the first stratospheric balloon lightning measurements. Signal analysis has enabled us to (i) confirm that the acoustic energy of thunder decreases as the inverse square of the distance, and (ii) identify that the electrostatic mechanism of thunder production in the infrasonic range (Wilson, 1921; Dessler, 1973; Pasko, 2009) is indeed present when the observer is located just above or just below the thundercloud. One of the balloons was equipped with two microbarometers separated vertically by around 30 m. The time difference between the two microbarometers for the arrival of signals from a flash of lightning is characteristic of the angle of incidence of the wave. It can be seen that this time difference evolves as expected as the balloon moves away from the storm cell.

Finally, we show for the first time that with a network of three sensors located in the stratosphere, it is possible to give a 3D localization of the first arrival of lightning signals. An equivalent acoustic source inside the cloud is clearly identified when the discharge is of the intranuage type, whereas the acoustic source is located between the ground and the cloud when the discharge is of the cloud-to-ground type.

SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.

How to cite: Farges, T., Burgos, G., Bowman, D. C., Gainville, O., Albert, S. A., and Le Pichon, A.: Characterisation and localisation of lightning by a flotilla of stratospheric balloons., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5799, https://doi.org/10.5194/egusphere-egu25-5799, 2025.

Several successful attempts have been made so far to utilize the uninterrupted view on the atmosphere from space and discover yet undocumented features of lightning activity and transient luminous events (TLEs), most recently THOR and ILAN-ES (2022-2024). As Hungary is considering sending an astronaut to the International Space Station (ISS) in 2025, an experiment has been proposed that aims at further enriching the existing set of space-borne targeted observations of nighttime electrical phenomena in the atmosphere. This is to be accomplished by an optical camera which is directed to preselected thunderstorm targets by the astronaut. This would be the UHU experiment which has been named after the Eurasian eagle-owl, a nighttime predator bird known for its extremely silent flight and exceptionally sharp eyes. The experiment is planned to be supported by a ground-based global observation and data collection campaign. One utterly desired achievement of the experiment and the accompanying observation campaign would be obtaining optical records of one or more TLEs taken simultaneously from the ISS and from a ground location. The experiment would also serve to elevate public awareness about the benefits of monitoring atmospheric electric parameters in studying the atmosphere and the near-Earth space environment. In this contribution, the motivation and the scientific aims behind organizing yet another TLE observation experiment from the ISS are presented and planning of the experiment as well as the supporting observation campaign are described.

How to cite: Bór, J., Yair, Y., Hegedüs, T., and Jäger, Z.: UHU - another experiment to observe lightning and TLEs from the ISS, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5994, https://doi.org/10.5194/egusphere-egu25-5994, 2025.

M-components are transient enhancements of the channel luminosity occurring simultaneously with the continuing current phase of the cloud-to-ground lightning. They are initiated by the connection of the in-cloud channel to the grounded channel. We have developed a new model of M-component processes, which is based on the numerical solution of the Maxwell’s equations together with the Poisson’s equation for a given thundercloud charge structure. We compute the radiated electric and magnetic fields at various distances from the lightning channel. We model a microsecond-scale electric field pulse emitted during the connection of the in-cloud channel to the grounded channel and compare its waveform with measurements conducted in Florida. The modeled current waveforms at various heights above the ground are the outputs of our model and we compare them with measured luminosity curves. We also show how the M-component simulation results depend on the parameters of the model.

How to cite: Kaspar, P., Kolmasova, I., Marshall, T., Stolzenburg, M., and Santolik, O.: Electromagnetic model of M-components, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6249, https://doi.org/10.5194/egusphere-egu25-6249, 2025.

Lightning Mapping Array (LMA) networks detect very-high-frequency (VHF, 60–66 MHz) emissions from lightning channels inside clouds. This enables the mapping of lightning in three dimensions. The use of real-time LMA data has proven beneficial for forecasting and warning about impending severe weather. Beyond the standard analysis of cloud-to-ground lightning information, the ability to visualize 3D total lightning has provided forecasters with greater knowledge of storm-scale processes.

A network of more than 20 LMA stations has been established in Catalonia (northeastern Iberian Peninsula) thanks to a partnership between the Meteorological Service of Catalonia (SMC) and the Technical University of Catalonia (UPC). Since it began real-time operations during the summer of 2023, it has grown to become Europe's largest LMA network.

To complement classic severe weather signatures observed in weather radar (e.g., storm splitting, BWER, TBSS) and in satellite imagery (e.g., overshooting tops, v-shape), we put our focus here on severe weather signatures observed with the LMA network during the thunderstorm seasons of 2023 and 2024 in Catalonia. Indeed, lightning distribution and evolution can portray complementary information in real-time surveillance, adding confidence to the forecaster and therefore reinforcing the decision-making when issuing alerts for imminent severe weather.

How to cite: Pineda, N., Fabró, F., Rodríguez, O., Romero, D., van der Velde, O., López, J. A., and Montanyà, J.: Total lightning for the early warning: Severe weather signatures from the real-time Lightning Mapping Array network in Catalonia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6293, https://doi.org/10.5194/egusphere-egu25-6293, 2025.

Lightning is a primary driver of natural wildfires globally. In mid- and high-latitude regions, summer thunderstorms are key precursors of lightning-ignited wildfires, contributing substantially to the total burned area. While the influence of meteorological conditions and fuel availability on wildfire occurrence is relatively well understood, the role of the electrical characteristics of lightning in ignition probability remains uncertain. In particular, it is unclear whether the presence of a continuing current lasting tens to hundreds of milliseconds is essential for ignition or whether it significantly affects ignition probability compared to meteorological factors and fuel availability.

In this study, we investigate the factors that increase the probability of wildfire ignition in Contiguous United States (CONUS). We investigate the meteorological conditions during the occurrence of fire-igniting flashes, the value of fire danger indices, the presence of continuing currents detected from space by the Geostationary Lightning Mapper (GLM), and the polarity of the strokes provided by the Earth Networks Lightning Total Network (ENTLN). We found that the lightning ignition efficiency of fire-igniting strokes with continuing current is slightly higher than that of lightning without continuing current. In particular, we report that strokes with continuing currents may have a higher potential to produce wildfires than cloud-to-ground strokes without continuing currents when the conditions for fire ignition and spread are less favorable. Additionally, we find that lightning strokes with continuing currents are associated with smaller burned areas, likely due to less favorable conditions for fire spread.

How to cite: Perez-Invernon, F. J., Moris, J. V., Gordillo-Vázquez, F. J., Zhu, Y., and Lapierre, J.: Assessing the Role of Continuing Current in Fire-Igniting Lightning Strokes with Space-Based Measurements, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7039, https://doi.org/10.5194/egusphere-egu25-7039, 2025.

By combining space-based data from the Lightning Imaging Sensor (LIS) aboard the International Space Station (ISS) with broadband ground-based electromagnetic measurements, we investigate the relationship between electromagnetic emissions from lightning processes and their optical signatures, focusing on the lightning initiation phase. Our case study is based on data from the SLAVIA (Shielded Loop Antenna with Versatile Integrated Amplifier) magnetic detectors at various European locations, as well as on data from the lightning location systems ENTLN (Earth Networks Total Lightning Network), WWLLN (World Wide Lightning Location Network), EUCLID (European Cooperation for Lightning Detection), and from the SAETTA Lightning Mapping Array. Our analysis of 11 lightning flashes from 2020-2023 reveals that the light emitted during the preliminary breakdown stage can be clearly observable from the low Earth orbit, highlighting the potential for space-based systems to detect and study the lightning initiation processes.

How to cite: Kolínská, A., Kolmašová, I., and Santolík, O.: Space-Based Observations of Lightning Initiation: A Multisystem Case Study Combining Optical and Electromagnetic Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7073, https://doi.org/10.5194/egusphere-egu25-7073, 2025.

Lightning is a key atmospheric phenomenon that modulates atmospheric chemistry and impacts terrestrial carbon dynamics through the ignition of wildfires and direct tree mortality. Despite its importance, there is a data gap in publicly available global lightning datasets with high spatial and temporal resolution for scientific use. In this study, we present our progress towards creating a global gridded lightning dataset derived from Vaisala’s Global Lightning Detection Network (GLD360), covering the period from 2019 to 2024, with potential annual updates thereafter. This dataset is produced through a systematic gridding procedure that converts raw GLD360 lightning event data into 0.1º hourly, 0.25º daily, and 0.5º monthly gridded values. It includes key variables such as positive and negative cloud-to-ground and intra-cloud stroke count/density, stroke peak current, stroke location uncertainty, and flash count/density, making it valuable for a wide range of scientific applications. We are evaluating the gridded dataset using local lightning detection networks in Alaska (USA), Spain, South Africa, and the New South Wales and Australian Capital Territory (Australia). Meanwhile, we are comparing stroke density with the Global Lightning Climatology (WGLC) dataset derived from the World Wide Lightning Location Network (WWLLN) and flash density with the Lightning Imaging Sensor/Optical Transient Detector (LIS/OTD). The dataset could be particularly useful for advancing studies on lightning climatology, the role of lightning in wildfire ignition, thunderstorm identification, and other related topics. Its high spatial and temporal resolution also supports regional studies of lightning-related hazards and ecosystem impacts. Our goal is to make this dataset publicly available to the scientific community to facilitate new insights into the role of lightning in the Earth system.

How to cite: Qu, Y., Jones, M. W., Brambleby, E., Hunt, H. G. P., Pérez-Invernón, F. J., Yebra, M., Zhao, L., Moris, J. V., Janssen, T., and Veraverbeke, S.: A new global gridded lightning dataset with high spatial and temporal resolution, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8351, https://doi.org/10.5194/egusphere-egu25-8351, 2025.

How to cite: Fangel-Lloyd, E., Gourbin, P., Dujko, S., Gammelmark, M., Karlsson, S., Jara Jimenez, A. R., and Köhn, C.: ASTRAPÉ: Atmospheric STReamer And relativistic Particle Engine – a GPU-based particle code for pre-exascale supercomputing , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8563, https://doi.org/10.5194/egusphere-egu25-8563, 2025.

Thunderstorm processes represent a challenge for numerical models, as they involve numerous processes of various scales, and explosive events producing exponentially increasing numbers of particles in a very short span of time. A phenomenon called a Relativistic Runaway Electron Avalanche can occur under the right conditions, and lead to the production of a Terrestrial Gamma-Ray Flash (TGF), spanning over tens of microseconds, and during which up to 10¹⁷ electrons and photons are produced for the most intense ones, the weaker ones still producing 10¹² to 10¹⁵ energetic photons. While Monte Carlo models are often used to simulate such processes, runtime typically scales with particle number, which leads to poor performance without a way to limit the number of particles computed. On the other hand, a fluid model may be adapted to deal with large particle densities, but it will struggle to deal with the extreme energies and electric fields, and will lose track of the physics of individual particles, which becomes relevant when submitted to such extreme parameters.

In order to accurately and efficiently simulate all these processes, we are developing a fully parallelized 3-D hybrid model. The code is optimised for massively parallel usage on Graphics Processing Units (GPUs), and uses the AMReX library, a software framework for massively parallel, block-structured codes, allowing us to run in parallel with implemented adaptive mesh refinement (AMR), which further improves the accuracy of the model.

With this model, we are aiming at obtaining a better understanding of lightning processes and TGFs, not only in the current Earth atmosphere, but also in the atmosphere of other celestial bodies, and in mixtures likely to have existed in the environment of Primordial Earth.

How to cite: Gourbin, P., Fangel-Lloyd, E., Dujko, S., Gammelmark, M., Karlsson, S., Jara Jimenez, A. R., van Gemert, H., and Köhn, C.: Towards a hybrid model to simulate lightning and associated energetic events in various atmospheres, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8887, https://doi.org/10.5194/egusphere-egu25-8887, 2025.

Dart leaders are a poorly understood lightning phenomenon where a current pulse propagates quickly (~10^7 m/s) along a previously established, now decayed, plasma channel, resulting in a re-heating of the channel. It is not understood how dart leaders propagate or how they get started. Therefore, in this work we have imaged the beginning of multiple dart leaders with the LOFAR radio telescope. We have observed two interesting phenomena related to the start of dart leaders. Firstly, we regularly observe other discharges, such as needles or `mini’ dart leaders, hundreds of microseconds before the start of the dart leader. The `mini’ dart leaders are particularly fascinating, as they propagate up side branches over a few hundred meters (towards the positive leader branch tip) before stopping. The main dart leader then initiates after the `mini’ dart leader. The exact connection between these preceding discharges (needles and mini-darts) and the main dart leaders, if one triggers the other, or why mini-darts ought to occur at all, are difficult to understand. In addition, previous work has shown that dart leaders tend to start with an exponential rise in VHF power and speed. In this work we find that some dart leaders have a period at their beginning where they propagate relatively slowly with weak VHF emission before a period of exponential growth. In one particular case, a dart leader initiated on a side branch, propagated slowly (~5x10^6 m/s) and weakly for about for about 100 µs until it connected with the main leader branch, and only then accelerated to a high speed (~ 1.7x10^7 m/s) over a period of about 50 µs. Finally, we will attempt to relate our measurements to recent hypothesis that dart leaders are a new kind of propagation that essentially amounts to a heating wave; the dart leader charge pushes a weak current in-front of it that heats up the plasma channel which in turn allows the current to increase further and the main charge packet to move forward.

How to cite: Hare, B., Scholten, O., Lourens, M., Turekova, P., Cummer, S., Dwyer, J., Liu, N., Pantuso, J., Da Silva, C., Sterpka, C., and ter Veen, S.: LOFAR Observations of Dart Leader Starts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8909, https://doi.org/10.5194/egusphere-egu25-8909, 2025.

Central Africa is widely recognized as the most active region for thunderstorms globally, with the highest frequency of lightning strikes occurring near Kifuka in the Democratic Republic of Congo, where over 150 lightning flashes per square kilometer are recorded annually. The absence of accessible early warning systems in many developing countries significantly amplifies the risks associated with lightning. For instance, on August 28, 2020, a catastrophic lightning strike near the Uganda-Democratic Republic of Congo border resulted in the deaths of nine children, with a tenth succumbing while being transported to the hospital. Moreover, the detrimental effects of lightning on critical sectors—such as livestock, forestry, power utilities, aviation, high-tech industries, and public safety—are increasingly evident. A discernible rise in lightning-related fatalities has been observed, potentially attributable to population growth, which increases exposure to thunderstorms, or to changes in thunderstorm frequency driven by climate change. Regardless of the underlying causes, the risk posed to the African population remains significant and appears to be intensifying.

Building on the recent advancements of Denoising Diffusion Probabilistic Models (DDPMs)—which have demonstrated superior performance over adversarial and autoencoder-based frameworks in applications such as image generation, text-to-image synthesis, precipitation nowcasting, and weather forecasting—this research introduces an innovative nowcasting system. The proposed system predicts lightning probabilities up to six hours in advance, with 30-minute intervals, offering a probabilistic and life-saving early warning mechanism tailored for Central Africa.

Specifically, we investigate the potential of DDPMs for lightning nowcasting by adapting spatiotemporal frameworks originally developed for precipitation nowcasting. In essence, diffusion models learn the underlying data distribution Ρ(Χ), where Χ represents the spatiotemporal probability density function of lightning. This is achieved by training the model to reverse a predefined noising process that progressively corrupts the target data with Gaussian noise. Here, the diffusion process has been extended to condition on auxiliary data Υ, such as satellite-derived wavelength imagery, constituting the approach suitable for spatiotemporal conditional nowcasting Ρ(ΧΥ).

As a data source, we leverage recent datasets from the Meteosat Third Generation (MTG) Lightning Imager (LI) over Africa and the Earth Networks Total Lightning Network (ENTLN) to train the model that locally characterizes the stochastic nature of lightning events.

How to cite: Landa, V., Price, C., and Reuveni, Y.: Nowcasting Thunderstorms to Protect Lives in Africa, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9121, https://doi.org/10.5194/egusphere-egu25-9121, 2025.

Convective weather, often associated with heavy precipitation, hail, lightning, and other hazardous phenomena, is highly unpredictable, short-lived, and localized, making forecasting and early warning particularly challenging. The formation of lightning is closely tied to the thermodynamic and microphysical processes within severe convective weather systems (e.g., Qie et al., 2021). Not only does it pose a significant threat to human life and properties, but it has also been recognized by the International Electrotechnical Commission (IEC) as a major hazard to power systems, communication networks, buildings, and electronic devices.

Since the mid-20th century, Doppler weather radars have been widely used to monitor hazardous weather by identifying precipitation, storm structures, and movement. Advances in radar technology, especially the introduction of array weather radar, have further enhanced the precision and timeliness of severe weather nowcasting. Unlike traditional single-antenna radars, array radars use multiple small antennas to form a large, flexible antenna array for rapid and precise beam control. This distributed phased-array system excels in detecting fine-scale flow and intensity fields, offering powerful tools for studying small-scale convective phenomena (e.g., Adachi et al., 2016).

This study utilizes array radar data from Foshan, Guangdong, China, high-precision lightning location data, and ground-based meteorological observation data to identify, track, and forecast severe convective weather. Based on a radar dual-threshold convective storm tracking and identification algorithm (e.g., Tian et al., 2019), combined with a lightning jump algorithm (e.g., Schultz et al., 2017), this nowcasting method monitors the lightning variation characteristics within strong convective cells (CCs), providing indices for severe convective weather. By comparing results with observations and optimizing algorithm parameters, the method improves hit rates, reduces false alarms, and achieves an average lead time of ~22 minutes with a hit rate over 80%, as demonstrated by case studies. This method can be effectively applied to enhance the monitoring and early warning capabilities for severe convective weather, thereby mitigating the impact of lightning and reducing lightning-related disasters for critical infrastructure, particularly power systems.

Acknowledgment

This work was jointly supported by the KERAUNIC project (ref: NIA2_NGET0055, National Grid Electricity Transmission, 2024) under the Network Innovation Allowance (NIA), the Arctic Pavilion Open Research Fund of Nanjing Joint Institute for Atmospheric Sciences under Grant BJG202410 and the China Scholarship Council program under Grant 202305330027.

References

Adachi, T., Kusunoki, K., Yoshida, S., et al. (2016). High-speed volumetric observation of a wet microburst using X-band phased array weather radar in Japan. Monthly Weather Review, 144(10), 3749-3765.

National Grid Electricity Transmission. (2024). Knowledge Elicitation of Risks to Assets Under LightNing Impulse Conditions (KERAUnIC). https://smarter.energynetworks.org/projects/nia2_nget0055

Qie, X., Yuan, S., Chen, Z., et al. (2021). Understanding the dynamical-microphysical-electrical processes associated with severe thunderstorms over the Beijing metropolitan region. Science China Earth Sciences, 64, 10-26.

Schultz, C. J., Carey, L. D., Schultz, E. V., & Blakeslee, R. J. (2017). Kinematic and microphysical significance of lightning jumps versus nonjump increases in total flash rate. Weather and forecasting, 32(1), 275-288.

Tian, Y., Qie, X., Sun, Y., et al. (2019). Total lightning signatures of thunderstorms and lightning jumps in hailfall nowcasting in the Beijing area. Atmospheric Research, 230, 104646.

How to cite: Xu, M., Qie, X., Tian, Y., Fullekrug, M., Gu, C., Bai, X., Ma, S., Liu, Y., Zhao, C., He, X., Li, B., Souto, L., Chikohora, T., and Dodds, D.: A Nowcasting Method for Severe Convective Weather Based on Array Radar and Lightning Jumps, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9498, https://doi.org/10.5194/egusphere-egu25-9498, 2025.

            Although cloud electrification and lightning have been studied for hundreds of years, the field still deals with many open questions [1]. One of the most puzzling examples is that of lightning inititation – neither the mechanism by which a cloud generates enough charge to cause lightning nor the process by which lightning itself is triggered are well understood. In our experiment we aim to gain insight into both questions on the scale of a single particle. We utilize optical tweezers to levitate individual aerosol particles and observe their charging and discharging dynamics over days-to-weeks time periods and with elementary-charge resolution. Our approach allows us to study these processes without losing information to ensemble averages or external interference from other particles or substrates [2], and is applicable to solid and liquid particles in the micrometer size range. Using multi-photon absorption from the trapping laser [3] we can charge the trapped particle at different rates and to different values, observing every charging and discharging event along the way. Additionally, the experiment allows us to control the relative humidity around the particle and to fully discharge the particle using air ions. By studying the charging behavior of the particle and the spontaneous discharges it experiences, we hope to contribute to a better understanding of the microphysical processes involved in lightning initiation and adjacent electrical phenomena in the atmosphere.

This project has received funding from the European Research Council (ERC) under the European Union’s Starting Grant (A. Stoellner, I. Lenton & S. Waitukaitis received funding from ERC No. 949120, C. Muller received funding from ERC No. 805041).

[1] J. R. Dwyer and M. A. Uman, Physics Reports 534, 147 (2014).
[2] F. Ricci, M. T. Cuairan, G. P. Conangla, A. W. Schell, and R. Quidant, Nano Letters 19, 6711 (2019).
[3] A. Ashkin and J. M. Dziedzic, Physical Review Letters 36, 267 (1976).

How to cite: Stoellner, A., Lenton, I., Muller, C., and Waitukaitis, S.: Measuring self-induced corona discharges of individual aerosol particles in an optical trap , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9659, https://doi.org/10.5194/egusphere-egu25-9659, 2025.

Wildfires pose a significant natural disaster risk to populations and contribute to accelerated climate change. As wildfires are also affected by climate change, extreme wildfires are becoming increasingly frequent. Although they occur less frequently globally than those sparked by human activities, lightning-ignited wildfires play a substantial role in carbon emissions and account for the majority of burned areas in certain regions. While existing computational models, especially those based on machine learning, aim to predict lightning-ignited wildfires, they are typically tailored to specific regions with unique characteristics, limiting their global applicability. In this study, we present machine learning models designed to characterize and predict lightning-ignited wildfires on a global scale. Our approach involves classifying lightning-ignited versus anthropogenic wildfires globally over a long timespan, and estimating with high accuracy of over 91% the probability of lightning to ignite a fire based on a wide spectrum of factors such as meteorological conditions and vegetation. Utilizing these models, we analyze seasonal and spatial trends in lightning-ignited wildfires shedding light on the impact of climate change on this phenomenon. Our findings highlight significant global differences between anthropogenic and lightning-ignited wildfires. Moreover, we demonstrate that, even over a short time span of less than a decade, climate change has steadily increased the global risk of lightning-ignited wildfires. We also find that models trained to predict lightning-ignited wildfires and models trained to predict anthropogenic wildfires are very different. This dramatically reduces the predictive performance of models trained on anthropogenic wildfires when applied to lightning-ignited ignitions, and vice versa. This distinction underscores the imperative need for dedicated predictive models and fire weather indices tailored specifically to each type of wildfire.

How to cite: Price, C., Shmuel, A., Glickman, O., Lazebnik, T., and Heifetz, E.: Prediction of Lightning-Ignited Wildfires On A Global Scale based on Explainable Machine Learning Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9775, https://doi.org/10.5194/egusphere-egu25-9775, 2025.

Fog, a reduction in visibility caused by water droplets suspended in the atmosphere, is a weather phenomenon which is linked to atmospheric electrical changes. Measurements of the potential gradient (PG) in particular have been shown to be useful for predicting fog, which has important applications for the aviation industry. The underlying theory behind these changes in PG during and before fog events is still an area of active research. Previously, in many studies of fog and atmospheric electricity, it has been assumed that fog droplets are neutral, for simplicity. However, it is well known that many clouds contain significant layers of space charge, and it is likely that fog droplets may also be charged. In this work, the distribution of charge in fog is studied using both numerical modelling and real-world measurements.

Numerical investigations use an earth-electrode model, in which it is assumed that the earth is a negatively charged surface and that there is a vertical electric field in the atmosphere above the surface. Using a system of 1D electrostatic equations, the steady-state distribution of vertical charge can be found, both in clear air and in a foggy air with prescribed aerosol. The results of these simulations provide the expected electrical charge in an idealised setup, which show appreciable space charge near the surface of the earth, as well as a rapidly decreasing PG with height.

Real-world measurements of the vertical charge distribution in fog up to 55m are made using a miniature electrode sensor and battery powered datalogger which is attached to a tethered balloon. The electrode current is amplified, and changes are apparent if the balloon passes through a sharp vertical gradient in space charge. As a result, vertical profiles of the magnitude and polarity of space charge in the fog layer can be measured and then compared with the modelled ideal case. In this presentation, we will show the measurements made during several fog cases with this setup.

A better understanding through modelling and measurements of the space charge in fog will help to identify cases where PG is especially well suited to fog prediction.

How to cite: Miller, C., Nicoll, K., Westbrook, C., and Harrison, R. G.: Investigating vertical distribution of charge in fog through tethered balloon measurements and modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10228, https://doi.org/10.5194/egusphere-egu25-10228, 2025.

New geostationary satellites, together with ground networks, now provide high-resolution, continuous lightning observations, offering unprecedented insights into lightning activity across vast areas of the globe. In contrast, global climate models (GCMs) lack the spatial resolution and physical processes required to simulate lightning directly, leading to the need for parameterizations and scaling methods. In this study, we present a novel trend-based scaling approach that bridges the gap between coarse-resolution GCM output and high-resolution lightning flash rates to improve projections of lightning activity by the end of the century. The scaling method employs machine learning techniques to identify the atmospheric parameters that best reproduce observed current lightning activity, which are then combined with coarser GCM trends (individually for each quantile of the distribution) to project future lightning changes.

Italy was selected as a case study, using the past 15 years of lightning observations from the LINET network to identify lightning predictors among atmospheric parameters from the ERA5 reanalysis. The best predictors identified include a combination of convective available potential energy, relative humidity, temperature gradients, wind velocity and shear, geopotential height, and freezing level. This model accurately reproduces the spatial distribution and temporal variability of current lightning activity. Trend scaling from multiple future climate scenarios was then applied using CMIP6 projections to evaluate changes in lightning activity across different regions and time periods. 

Our results show that trend-based scaling significantly improves the spatial distribution and intensity of projected lightning flash rates compared to traditional parameterizations. This work provides a practical framework for integrating lightning projections into climate impact studies, enhancing the reliability of lightning future changes under various climate scenarios. The main advantage of the proposed method is that it can be applied to reanalysis datasets of any resolution, offering a flexible tool for assessing lightning-related risks in a warming world.

How to cite: Arnone, E., Cortesi, N., Rubinetti, S., Dietrich, S., and Petracca, M.: Trend-based scaling for high-resolution lightning in climate projections , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10264, https://doi.org/10.5194/egusphere-egu25-10264, 2025.

During the ALOFT flight campaign, July 2023, a novel type of multi-pulse gamma-ray emission from thunderclouds was systematically recorded. Referred to as flickering gamma-ray flashes (FGFs), this type of emission is not linked with lightning leaders and does not coincide with detectable radio emissions [1].

A promising candidate theory for explaining this phenomenon is the relativistic feedback discharge (RFD) developed by Dr. J. Dwyer and his group [2]. Fully self-consistent 3D Monte-Carlo calculations of RFD [3], which take the field quenching by produced currents into account, are quite computationally intensive. In fact, the full physics of RFD has barely been explored outside Dwyer’s group.

Therefore, we developed an independent numerical model especially made to evaluate the capability of the RFD theory to reproduce FGFs. Despite its simplification into a set of spatially-independent ordinary differential equations (ODEs), it applies the most relevant physics: ionisation, electron dynamics, attachment processes, relativistic runaway electron avalanche (RREA), and feedback akin to Dwyer’s theory. The ODEs that we end up solving are analogous to a complexified Lotka-Volterra model which describes a system with oscillations.

In this presentation, we introduce a 0.5D model (i.e., with indirect account of the spatial size of the RREA region) and demonstrate its ability to reproduce emission light curves very alike FGFs under realistic conditions, given the right set of parameters (see below). Furthermore, we show that the same model also reproduces light curves alike terrestrial gamma-ray flashes (TGFs, both single and multiple pulses) and gamma-ray glows (GRGs) for different sets of parameters but still under realistic conditions, hence proving this model to be even more general than originally intended.

With this, we performed a parameter-space exploration, using our model and systematically applying different values for 1) the initial (background) internal electric field strength of the cloud, 2) the characteristic growth time of the external electric field, 3) the vertical size of the high-field region in the cloud, and 4) the maximum change of the external field. The results, as shown in parameter-space diagrams, are qualitatively as expected. TGFs tend to occur for relatively small high-field regions. For larger high-field regions, the model reproduces GRGs in the case of slowly increasing external fields while FGFs and weak TGFs in the case of rapidly increasing external fields. The amplitude and the number of pulses typically scale with the maximum change of the external field. Finally, increasing the value of the initial internal electric field leads to a decrease in the minimum required change in the external field needed to reproduce FGFs, multi-pulse TGFs and GRGs.

References:

[1] Østgaard, N., Mezentsev, A., Marisaldi, M., Grove, J. E., Quick, M., Christian, H., Cummer, S., Pazos, M., Pu, Y., Stanley, M., et al., “Flickering gamma-ray flashes, the missing link between gamma glows and TGFs”, Nature (2023).

[2] Dwyer, J. R., “Relativistic breakdown in planetary atmospheres,” Physics of Plasmas, vol. 14, no. 4, p. 042901 (2007).

[3] Liu, N., Dwyer, D., “Modeling terrestrial gamma ray flashes produced by relativistic feedback discharges”, Journal of Geophysical Research (Space Physics), Vol. 118, no. 5, p. 2359-2376 (2013)

How to cite: Færder, Ø. H., Lehtinen, N., Sarria, D., Marisaldi, M., and Østgaard, N.: A parameter-space exploration of the Relativistic Discharge Model mapping for which conditions ALOFT’s Flickering Gamma-ray Flashes are produced, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10378, https://doi.org/10.5194/egusphere-egu25-10378, 2025.

From rubbing a balloon on one's hair to the dramatic display of volcanic lightning, the triboelectric effect is a widespread phenomenon where contact between objects leads to an exchange of electric charge. Despite its ubiquity, our understanding of the underlying physics remains largely phenomenological. Among the many open questions, one is particularly relevant to earth science and astrophysics: why do objects made of the same material continually exchange electric charge? This effect is especially pronounced in systems involving grains or powders, where frequent collisions can result in a significant buildup of electrostatic potential energy. Such processes can influence the dispersal range of aerosols in the atmosphere, determine whether protoplanetary dust will coalesce, and even trigger thunderstorms during volcanic eruptions or forest fires. Using acoustic levitation, we isolate individual grains and conduct controlled collisions with a substrate, measuring the charge by observing the grain's behavior in electric fields. This method can accurately resolve individual collision events, allowing us to investigate various proposed charging mechanisms and explore in detail what causes the breaking of symmetry between positively and negatively charging samples. We determine that slight variations in surface composition due to molecules recruited from the atmosphere can lead to drastic changes in the charging behavior.

How to cite: Grosjean, G. and Waitukaitis, S.: Investigating the origins of static charge in granular systems of silica and other oxides using acoustic traps, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10837, https://doi.org/10.5194/egusphere-egu25-10837, 2025.

The polarization of VHF radio signals emitted by lightning can help shed light on the intricate science of lighting propagation, through the direction of the corona VHF emission. However, this lightning radio polarization is not easily measured and, thus, understood. Employing the LOFAR radio telescope, we use a near-field beamforming algorithm (TRI-D) that coherently sums antenna voltages while accounting for the antenna function. This allows us to reconstruct VHF source location and polarization in 3 dimensions. In this work, we evaluate the accuracy of these unparalleled results. Performing a Monte Carlo error analysis, we simulate the antenna voltage signal resulting from a point-like dipole, which is then reconstructed with the imager. The difference between the input and the reconstructed source parameters gives us an approximation of the polarization error bars. We find that the polarization error is at maximum 12 degrees. This value fluctuates with varying source location and angle suspended between the polarization vector and the radial vector. We are testing the polarization reconstruction accuracy for radio point-like sources, background noise, and extended sources. We will present a comprehensive report on these results and their interpretation, our technique, and the imaging algorithm.

How to cite: Turekova, P., Hare, B., Scholten, O., Lourens, M., Cummer, S., Dwyer, J., Liu, N., Sterpka, C., and ter Veen, S.: LOFAR Lightning Data: Accuracy in Polarization Reconstruction, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10850, https://doi.org/10.5194/egusphere-egu25-10850, 2025.

Lightning can indicate the location of strong convection in thunderstorms. We develop a lightning data assimilation observational operator based on a 2D-to-3D Bayesian method, which converts the 2D lightning distribution into vertical velocity profiles and RH profiles for each grid point in the plane. The new lightning observational operator provides a good representation of the shape and peak height of the instantaneous vertical velocity profiles in thunderstorms, rather than using a fixed or long-term averaged profile distribution. After 1-hour forecasting, experiments that assimilated both vertical velocity and water vapor still maintained a close vertical distribution to the observations in the lower layers. It also shows significant improvement in heavy rainfall forecasting within 1 hour, with a notable increase in precipitation scores. The improvement in heavy rainfall prediction primarily lies in the positive adjustment of the location of intense rainfall and the enhancement of rainfall intensity.

How to cite: Shaoxuan, D., Xiushu, Q., and Wei, H.: Lightning Assimilation based on a 2D-to-3D Bayesian method for Vertical Velocity and Water Vapor, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11142, https://doi.org/10.5194/egusphere-egu25-11142, 2025.

How to cite: Lourens, M., Hare, B., Scholten, O., Turekova, P., Cummer, S., Dwyer, J., Liu, N., Sterpka, C., and ter Veen, S.: High resolution imaging of negative leader propagation with LOFAR, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11832, https://doi.org/10.5194/egusphere-egu25-11832, 2025.