Extremes in temperature, especially heat extremes, are already one of the deadliest meteorological events and they are projected to increase in intensity and frequency due to rising CO2 concentrations. The resulting risks of extreme temperature events to society may increase dramatically with large regional differences, and society will need to adapt locally if the worst impacts are to be avoided. Specifically on health impacts from extremes in temperature, exposure to cold and heat remains as one of the leading causes of deaths from natural hazards globally, with the total (cold and heat)-related deaths projected to increase in a warming world. In recent years, extreme heat events in particular have tested the preparedness of public health services, with a recent study estimating over 70,000 heat-related excess deaths in Europe alone during the summer of 2022. While our understanding of city-level temperature-related health impacts in the present climate has improved, how future health-burden in a warming climate can evolve continues to remain a daunting challenge, especially when accounting for adaptation and changes in future socio-demographic factors at different spatial scales. Moreover, warming trends and the associated health risks vary regionally and are often associated with uncertainties emanating both from modelling strategies in health-impacts assessments and the projected temperatures from climate models.

This session therefore welcomes a broad range of new research addressing the challenge of extreme heat and its impacts, with studies focusing on the Global South particularly welcome. Suitable contributions may: (i) assess definitions, the drivers and underlying processes of extreme heat in observations and/or models; (ii) explore the diverse socio-economic impacts of extreme heat events including vulnerability and exposure for example, on aspects relating to human health, economic productivity, or biodiversity; (iii) address forecasting and monitoring of extreme heat at seasonal to sub-seasonal time scales; (iv) focus on societal adaptation to extreme heat, including the implementation of anticipatory action, heat-health early warning systems, and effective heat adaptation and management solutions; (v) introduce transdisciplinary research frameworks to assess societal relevant heat extremes and their impacts.

With global climate change affecting the frequency and severity of extreme meteorological and hydrological events, it is particularly necessary to develop models and methodologies for a better understanding and forecasting of present-day weather induced hazards. Future changes in the event characteristics as well as changes in vulnerability and exposure are among the further factors for determining risks for infrastructure and society, and for the development of suitable adaptation measures. This session considers extreme events that lead to disastrous hazards induced by severe weather and climate change. These can, e.g., be tropical or extratropical rain- and wind-storms, hail, tornadoes or lightning events, but also (toxic) floods, long-lasting periods of drought, periods of extremely high or of extremely low temperatures, etc. Papers are sought which contribute to the understanding of their occurrence (conditions and meteorological development), to the augmentation of risks and impacts due to specific sequences of extremes, for example droughts, heavy rainfall and floods, to assessment of their risk (economic losses, infrastructural damages, human fatalities, pollution), and their future changes, to studies of recent extreme events occurring in 2023, to the ability of models to reproduce them and methods to forecast them or produce early warnings, to proactive planning focusing on damage prevention and damage reduction. In order to understand fundamental processes, papers are also encouraged that look at complex extreme events produced by combinations or sequences of factors that are not extreme by themselves. The session serves as a forum for the interdisciplinary exchange of research approaches and results, involving meteorology, hydrology, environmental effects, hazard management and applications like insurance issues.

Worldwide, frequency and intensity of extreme floods is increasing, causing dire consequences in terms of loss of life and properties. Cutting-edge monitoring and simulation technologies have become instrumental for guiding flood risk management. A range of mechanistic hydrological and hydrodynamic computational models as well as data-driven models (e.g., Artificial Intelligence “AI” and Machine Learning “ML”) are available to inform flood risk assessment and management, including prevention and preparedness. Such techniques provide a platform for the scientific community to explore the drivers of flood risk and to build up effective approaches for flood risk mitigation. Furthermore, recent advances in airborne remote sensing (include Drone “UAV”) and spaceborne remote sensing help to enhance the accuracy and efficiency of flood monitoring such as inundation mapping in real-time and offline mode.
The objective of this session to invite fundamental and applied research studies carried out through Remote Sensing (e.g., Drone “UAV", satellites), Mechanistic Hydrologic/Hydraulic/Hydrodynamic modelling, and Data-driven AI and ML, including their associated uncertainties for flood inundation mapping, flood hazard mapping, risk assessment, and flood risk management. Particular topics such as 1D, 2D and 3D modelling for flood risk assessment, Emergency Action Planning (EAP), Evacuation planning, Dam Break Analysis (DBA) are also welcome. The scope of the session also covers uncertainty quantification and sensitivity analyses at all stages of flood risk modelling.
Invited Speaker: Dr. Roos Wood from University of Bristol, UK. (https://research-information.bris.ac.uk/en/persons/ross-a-woods)

Lightning is the energetic manifestation of electrical breakdown in the atmosphere, occurring as a result of charge separation processes operating on micro and macro-scales, leading to strong electric fields within thunderstorms. Lightning is associated with tropical storms and severe weather, torrential rains and flash floods. It has significant effects on various atmospheric layers and drives the fair-weather electric field. It is a strong indicator of convective processes on regional and global scales, potentially associated with climate change. Lightning produces nitrogen oxides, which are a precursor to ozone production. Thunderstorms and lightning are essential parts of the Global Electrical Circuit (GEC) and control the fair weather electric field. They are also associated with the production of energetic radiation up to tens of MeV on time scales from sub-millisecond (Terrestrial Gamma-ray Flashes) to tens of seconds (gamma-ray glows).

This session seeks contributions from research in atmospheric electricity with emphasis on:

Atmospheric electricity in fair weather and the global electrical circuit
Effects of dust and volcanic ash on atmospheric electricity
Thunderstorm dynamics and microphysics
Middle atmospheric Transient Luminous Events
Energetic radiation from thunderstorms and lightning
Experimental investigations of lightning discharge physics processes
Remote sensing of lightning and related phenomena by space-based sensors
Thunderstorms, flash floods, tropical storms and severe weather
Connections between lightning, climate and atmospheric chemistry
Modeling of thunderstorms and lightning
Now-casting and forecasting of thunderstorms using machine learning and AI
Regional and global lightning detection networks
Lightning Safety and its societal effects

Hydrological extremes (floods and droughts) have major impacts on society and ecosystems and are projected to increase in frequency and severity with climate change. These events at opposite ends of the hydrological spectrum are governed by different processes that operate on different spatial and temporal scales and require different approaches and indices to characterize them. However, there are also many similarities and links between the two types of extremes which are increasingly being studied.
This session on hydrological extremes aims to bring together the flood and drought communities to learn from the similarities and differences between flood and drought research. We aim to improve the understanding of the processes governing both types of hydrological extremes, develop robust methods for modelling and analyzing floods and droughts, assess the influence of global change on hydro-climatic extremes, and study the socio-economic and environmental impacts of both types of extremes.
We welcome submissions that present insightful flood and/or drought research, including case studies, large-sample studies, statistical hydrology, and analyses of flood or drought non-stationarity under the effects of climate-, land cover-, and other anthropogenic changes. Studies that investigate both extremes are of particular interest. We especially encourage submissions from early-career researchers.

Extreme hydro-meteorological events drive many hydrologic and geomorphic hazards, such as floods, landslides and debris flows, which pose a significant threat to modern societies on a global scale. The continuous increase of population and urban settlements in hazard-prone areas in combination with evidence of changes in extreme weather events lead to a continuous increase in the risk associated with weather-induced hazards. To improve resilience and to design more effective mitigation strategies, we need to better understand the triggers of these hazards and the related aspects of vulnerability, risk, mitigation and societal response.
This session aims at gathering contributions dealing with various hydro-meteorological hazards that address the aspects of vulnerability analysis, risk estimation, impact assessment, mitigation policies and communication strategies. Specifically, we aim to collect contributions from academia, industry (e.g. insurance) and government agencies (e.g. civil protection) that will help identify the latest developments and ways forward for increasing the resilience of communities at local, regional and national scales, and proposals for improving the interaction between different entities and sciences.
Contributions focusing on, but not limited to, novel developments and findings on the following topics are particularly encouraged:
- Physical and social vulnerability analysis and impact assessment of hydro-meteorological hazards
- Advances in the estimation of socioeconomic risk from hydro-meteorological hazards
- Characteristics of weather and precipitation patterns leading to high-impact events
- Relationship between weather and precipitation patterns and socio-economic impacts
- Socio-hydrological studies of the interplay between hydro-meteorological hazards and societies
- Hazard mitigation procedures
- Strategies for increasing public awareness, preparedness, and self-protective response
- Impact-based forecast, warning systems, and rapid damage assessment.
- Insurance and reinsurance applications

The socio-economic impacts associated with floods are increasing. Floods represent the most frequent and most impacting, in terms of the number of people affected, among the weather-related disasters: nearly 0.8 billion people were affected by inundations in the last decade, while the overall economic damage is estimated to be more than $300 billion.
In this context, remote sensing represents a valuable source of data and observations that may alleviate the decline in field surveys and gauging stations, especially in remote areas and developing countries. The implementation of remotely-sensed variables (such as digital elevation model, river width, flood extent, water level, flow velocities, land cover, etc.) in hydraulic modelling promises to considerably improve our process understanding and prediction. During the last decades, an increasing amount of research has been undertaken to better exploit the potential of current and future satellite observations, from both government-funded and commercial missions, as well as many datasets from airborne sensors carried on airplanes and drones. In particular, in recent years, the scientific community has shown how remotely sensed variables have the potential to play a key role in the calibration and validation of hydraulic models, as well as provide a breakthrough in real-time flood monitoring applications. With the proliferation of open data and more Earth observation data than ever before, this progress is expected to increase.
We encourage presentations related to flood monitoring and mapping through remotely sensed data including: - Remote sensing data for flood hazard and risk mapping, including commercial satellite missions as well as airborne sensors (aircraft and drones);
- Remote sensing techniques to monitor flood dynamics;
- The use of remotely sensed data for the calibration, or validation, of hydrological or hydraulic models;
- Data assimilation of remotely sensed data into hydrological and hydraulic models;
- Improvement of river discretization and monitoring based on Earth observations;
- River flow estimation from remote sensing.

Drought and water scarcity affect many regions of the Earth, including areas generally considered water rich. A prime example is the severe 2022 European drought, caused by a widespread and persistent lack of precipitation combined with a sequence of heatwaves from May onwards. The projected increase in the severity and frequency of droughts may lead to an increase of water scarcity, particularly in regions that are already water-stressed, and where overexploitation of available water resources can exacerbate the consequences droughts have. This may lead to (long-term) environmental and socio-economic impacts. Drought Monitoring and Forecasting are recognised as one of three pillars of effective drought management, and it is, therefore, necessary to improve both monitoring and sub-seasonal to seasonal forecasting for droughts and water availability, and to develop innovative indicators and methodologies that translate the data and information to underpin effective drought early warning and risk management.

This session addresses statistical, remote sensing and physically-based techniques, aimed at monitoring, modelling and forecasting hydro-meteorological variables relevant to drought and water scarcity. These include, but are not limited to: precipitation, snow cover, soil moisture, streamflow, groundwater levels, and extreme temperatures. The development and implementation of drought indicators meaningful to decision-making processes, and ways of presenting and integrating these with the needs and knowledges of water managers, policymakers and other stakeholders, are further issues that are addressed and are invited to submit to this session. Contributions focusing on the interrelationship and feedbacks between drought and water scarcity, hydrological impacts, and society are also welcomed. The session aims to bring together scientists, practitioners and stakeholders in the fields of hydrology and meteorology, as well as in the fields of water resources and drought risk management. Particularly welcome are applications and real-world case studies, both from regions that have long been exposed to significant water stress, as well as regions that are increasingly experiencing water shortages due to drought and where drought warning, supported by state-of-the-art monitoring and forecasting of water resources availability, is likely to become more important in the future.

Water is a strategic issue in drylands, where ecosystems and their inhabitants strongly rely on the scarce and often intermittent water availability or its low quality. The characteristics of drylands increase their vulnerability to climate change and susceptibility to the impact of short- to long-term extreme events and processes, such as floods, droughts, and desertification. These events can reshape the landscape through the mobilisation of surface sediments, deposits of which preserve archives of past Earth system states, including changes in the extent of deserts. Over the last century, anthropogenic modifications of all kinds and intensities have affected surface conditions. In drylands and Mediterranean hydrosystems, agricultural water use is constantly increasing threatening the sustainability of the surface and groundwater reservoirs, and their hydrology is then continuously evolving. Nevertheless, the study of hydroclimatic processes in drylands remains at the periphery of many geoscientific fields. A proper understanding of the hydrological, hydrometeorological and (paleo)climatic processes in these regions is a cornerstone to achieving the proposed sustainable development goals we set for the end of this century.

This session welcomes contributions from scientific disciplines addressing any of the drylands' full range of environmental and water-related processes. The purpose is to foster interdisciplinary research and expand knowledge and methods established in individual subdisciplines. We will address hydrological issues across global drylands, and devote a section of our session to a geographical focus on the Mediterranean region to analyse the changes in hydrologic processes and fluxes unique to that region.

Assessing the impact of climate variability and changes on hydrological systems and water resources is crucial for society to better adapt to future changes in water resources, as well as extreme conditions (floods and droughts). However, important sources of uncertainty have often been neglected in projecting climate impacts on hydrological systems, especially uncertainties associated with internal/natural climate variability. From one model to another, or from a single model realization to another, the impact of diverging trends and sequences of interannual and decadal variability of various internal/natural climate modes (e.g., ENSO, NAO, AMO) could substantially alter the impact of human-induced climate change on hydrological variability and extremes. Furthermore, model findings may contrast with insights that global satellite data provide, e.g. observations of hydrological change often do not support dry-gets-dryer and wet-gets-wetter patterns that global climate models suggest. Therefore, we need to improve both our understanding of how internal/natural climate patterns affect hydrological variability and extremes, and how we communicate these impacts. We also need to better understand how internal/natural climate variations interact with various catchment properties (e.g., vegetation cover, groundwater support) and land-use changes altering them. In this direction, storylines of plausible worst cases, or multiple physically plausible cases, arising from internal climate variability can complement information from probabilistic impact scenarios. In addition, a comparison of satellite data and model output can help close the gap in understanding wetting and drying patterns at the continental scale.

We welcome abstracts capturing recent insights for understanding past or future impacts of internal/natural climate variability on hydrological systems and extremes, as well as newly developed probabilistic and storyline impact scenarios. Results from model intercomparisons using large ensembles are encouraged. We also solicited presentations on improving our observing system (e.g. via new retrieval approaches, data assimilation, or developing new sensor systems) and on developing modelling frameworks.

Karst environments are characterized by distinctive landforms and unique hydrological behaviors. Karst systems are extremely complex, heterogeneous and very difficult to manage, because their formation and evolution are controlled by a wide range of geological, hydrological, geochemical and biological processes, and are extremely variable in time and space. Furthermore, karst systems are highly vulnerable to a variety of hazards, due to the direct connection between the surface and subsurface through the complex networks of conduits and caves.
In karst, any interference is likely to have irreversible impacts and disturb the natural balance of the elements and processes. The great variability and unique connectivity may result in serious engineering problems: on one hand, karst groundwater resources are easily contaminated by pollution because of the rapidity of transmission through conduit flow, and remediation action, when possible, could be very expensive and require a long time; on the other hand, the presence of karst conduits that weakens the strength of the rock mass may lead to serious natural and human-induced hazards. The design and development of engineering projects in karst environments thus should necessarily require: 1) an enhanced understanding of the natural processes governing the initiation and evolution of karst systems through both field and modelling approaches, and 2) specific interdisciplinary approaches aimed at mitigating the detrimental effects of hazardous processes and environmental problems.
This session calls for abstracts on research from karst areas worldwide related to geomorphology, hydrogeology, engineering geology, hazard mitigation in karst environments in the context of climate change and increasing human disturbance.

Hydroclimatic extremes such as floods, droughts, storms, or heatwaves often affect large regions and can cluster in time, therefore causing large socio-economic damages. Hazard and risk assessments, aiming at reducing the negative consequences of such extreme events, are often performed with a focus on one location despite the spatially compounding nature of extreme events. Also, clustering of extremes in time is often neglected, with potentially severe underestimation of hazard. While spatial-temporal extremes receive a lot of attention by the media, it remains scientifically and technically challenging to assess their risk by modelling approaches. Key challenges in advancing our understanding of spatio-temporal extremes and in developing new modeling approaches include: the definition of multivariate events; the dealing with large dimensions; the quantification of spatial and temporal dependence, together with the introduction of flexible dependence structures; the identification of potential drivers for spatio-temporal dependence; the estimation of occurrence probabilities, and the linking of different spatial and temporal scales. This session invites contributions which help to better understand processes governing spatio-temporal extremes and/or propose new ways of describing and modeling compounding events at different scales.

This session investigates mid-latitude cyclones and storms on both hemispheres. We invite studies considering cyclones in all different stages of their life cycles, from initial generation to the final development, including studies to large- and synoptic-scale conditions influencing cyclones’ growth to a severe storm, their dissipation, and related socioeconomic impacts.
Papers are welcome, which focus also on the diagnostic of observed past and recent trends, as well as on future storm development under changed climate conditions. This will include storm predictability studies on different scales. Finally, the session will also invite studies investigating impacts related to storms: Papers are welcome dealing with vulnerability, diagnostics of sensitive social and infrastructural categories and affected areas of risk for property damages. Which risk transfer mechanisms are currently used, depending on insured and economic losses? Which mechanisms (e.g., new reinsurance products) are already implemented or will be developed in order to adapt to future loss expectations?

Forecasting the weather, in particular severe and extreme weather has always been the most important subject in meteorology. This session will focus on recent research and developments on forecasting techniques, in particular those designed for operations and impact oriented. Contributions related to nowcasting, meso-scale and convection permitting modelling, ensemble prediction techniques, and statistical post-processing are very welcome.
Topics may include:
 Nowcasting methods and systems, use of observations and weather analysis
 Mesoscale and convection permitting modelling
 Remote sensing and data assimilation
 Ensemble prediction techniques
 Ensemble-based products for severe/extreme weather forecasting
 Seamless deterministic and probabilistic forecast prediction
 Post-processing techniques, statistical methods in prediction
 Use of machine learning, data mining and other advanced analytical techniques
 Impact oriented weather forecasting
 Presentation of results from relevant international research projects of EU, WMO, and EUMETNET etc.

Key Words: Forecast technique, nowcasting, ensemble prediction, statistics, AI

Volcanoes are complex systems with potentially catastrophic impacts, able to generate and distribute vast sediment volumes to surrounding environments. Understanding, modelling and forecasting volcanic hazards is challenging, not least because of data issues. There is a need for the construction of robust and reliable models for forecasting volcanic hazards, both syn- and post-eruptive, and management of the resulting risks.

Syn-eruptive hazards include pyroclastic density currents, volcanic plumes and gas theoretically described by computational fluid dynamics, and experimentally modelled. But application of experimental results to large-scale natural processes is only possible via a thorough scaling analysis. Uncertainty is frequently cited as a major problem in volcanic hazard analyses and a plethora of statistical methods have attempted to quantify uncertainty in both hazard modelling and eruption forecasting. The data underlying models for both eruption occurrence and hazard propagation is multi-scale, multi-dimensional and nonlinearly correlated, and often not representative of the volcano's potential behaviour. Additional knowledge is often required to provide the causal links, and to extrapolate outside of the perceived bounds of existing data.

Post-eruption, understanding the origin, transport and emplacement mechanisms of volcanic deposits is fundamental for accurately reconstructing accumulation histories of ancient and modern volcano-sedimentary records, and for assessing future hazards and their potential economic impacts. Many knowledge gaps in these records could be reduced by bringing together multidisciplinary specialists and methods, combining classical field-based work with novel numerical modelling approaches.

Addressing risks from volcanic eruptions requires interactions between volcanologists and decision-makers, and with pre-eruption mitigation activities. These present issues around timeliness, the use of data, administrative responsibilities, and the application of laws.

The session aims at advancing volcanic hazard estimation and response through multidisciplinary approaches including
• Better describing uncertainty in volcanic hazard estimates through the use of statistical, analogue, surrogate and synthetic data
• Field studies of volcanoclastic features in sedimentary records,
• Novel statistical, experimental and computational modelling approaches, and
• Examination of the role of the state in volcanic risk management

Debris flows are among the most dangerous natural hazards that threaten people and infrastructures in both mountainous and volcanic areas. The study of the initiation and dynamics of debris flows, along with the characterization of the associated erosion/deposition processes, is of paramount importance for hazard assessment, land-use planning, design of mitigation measures and early-warning systems. In addition, climate change may expose more mountain areas to higher hazard, and further research is needed to understand the consequences of this.

A growing number of scientists with diverse backgrounds are studying debris flows and lahars. The difficulties in measuring parameters related to their initiation and propagation have progressively prompted research into a wide variety of laboratory experiments and monitoring studies. However, there is a need of improving the quality of instrumental observations that would provide knowledge for more accurate modelling and hazard maps. Nowadays, the combination of distributed sensor networks and remote sensing techniques represents a unique opportunity to gather direct observations of debris flows to better constrain their physical properties. At the same time, computer-aided hazard assessment and mitigation design are undergoing a revolution due to the widespread adoption of AI and of data-driven numerical models.

Scientists working in the field of debris flows are invited to present their recent advancements. In addition, contributions from practitioners and decision makers are also welcome. Topics of the session include field studies and documentation, mechanics of debris-flow initiation and propagation, laboratory experiments, modelling, monitoring, impacts of climate change on debris-flow activity, hazard and risk assessment and mapping, early warning, and alarm systems.

Large mass movements in rock, debris and ice in glacial masses, represent enormous risks. These complex systems are difficult to describe, investigate, monitor and model. Hence a reliable model of these phenomena requires acquisition and analysis of all available data to support successive steps up to the management of Early Warning systems.
Large instabilities affect all materials (rock, weak rocks, debris, ice), from low to high altitudes, evolving as slow or fast complex mass movements. This and the complex dependency on forcing factors result in different types and degrees of hazard and risk. Some aspects of these instabilities are still understudied and debated, because of difficult characterization and few cases thoroughly studied. Regional and temporal distribution, relationships with controlling and triggering factors are poorly understood resulting in poor predictions of behavior and evolution under present and future climates. How will it change their state of activity under future climatic changes? How this will impact on existing structures and infrastructures? How can we improve our predictions? Relationships among geological and hydrological boundary conditions and displacements are associated to evolution in space and time of hydro-mechanical controls . Even for well studied and active phenomena warning thresholds are mostly qualitative, based on semi-empirical approaches. Hence a multidisciplinary approach and robust monitoring data are needed. Many modeling approaches can be applied to evaluate instability and failure, considering triggerings, failure propagation, leading to rapid mass movements . Nevertheless, these approaches are still phenomenological and have difficulty to explain the observed behavior. Impacts of such instabilities on structures represents a relevant risk but also an opportunity in terms of investigations and quantitative measurements of effects on tunnels, dams, roads. Design of these structures and knowledge of their expected performance is fundamental.
We invite to present case studies, sharing views and data, to discuss monitoring and modeling approaches and tools, to introduce new approaches for thresholds definition, including advanced numerical modeling, Machine Learning for streamline and offline data analyses, development of monitoring tools and dating or investigation techniques.

Across the world, a large part of slope instability phenomena of different type (e.g. landslides, rockfalls, debris flows) is recognized to be regulated by weather patterns largely differing in terms of variables (precipitation, temperature, snow melting) and significant time span (from a few minutes up to several months). On the other hand, local modifications induced by human intervention: e.g. socio-economic induced land use/cover changes, reduced soil management due to land abandonment or implementation and maintenance of Nature Based Solutions are recognized playing a key role in slope instability risk. In turn, such local human-induced factors can be strongly influenced by weather dynamics: e.g. hydrological and thermal regime regulate vegetation suitability, then land cover and, in turn, landslide risk.
A clear and robust evaluation about how ongoing and expected global warming and resulting climate change can affect such factors and, therefore, landslide risk represents a clear need for practitioners, communities, and decision-makers.
The Session aims at presenting studies concerning the analysis of the role of climate-related variables and slope-atmosphere interaction on landslide/rockfall triggering/activity and/or effectiveness of protection measures, across different geographical contexts and scales. Test cases and investigations (by exploiting monitoring and modelling) carried out in different geographical contexts in evaluation of ongoing and future landslide activity are welcome. Furthermore, are greatly welcome investigations focused on data-driven approaches (e.g. Machine Learning, AI) through which the variations induced by climate and environmental changes on triggering, dynamics, and hazard are analysed.

Mountain regions are a complex system of different glacial, paraglacial and periglacial environments rapidly changing due to global warming. In this context, short-term landscape evolution is affected by glacier motion, by a variety of mass movements including slow rock slope deformations, rock and debris slides, rockfalls, as well as by periglacial features such as rock glaciers. These mass movements are driven be different processes, evolve at different rates and can pose different risks to lives, human activities and infrastructure. The physics of rock slope degradation and the dynamics of failure and transport define the hazards.

In this session we bring together researchers from different communities interested in a better understanding of the physical processes controlling mass movements mass around the world in glacial, paraglacial and periglacial environments, and investigating their evolution in a changing climate. Topics range from state-of-the-art methods for assessing, quantifying, predicting, and protecting against alpine slope hazards across spatial and temporal scales to innovative contributions dealing with mass movement predisposition, detachment, transport, and deposition. The selected contributions are expected to: (i) provide insights from field observations and/or laboratory experiments; (ii) apply statistical methods and/or artificial intelligence to identify and map mass movements; (iii) present new monitoring approaches (in-situ and remote sensing) applied at different spatial and temporal scales; (iv) use models (from conceptual frameworks to theoretical and/or advanced numerical approaches) for the analysis and interpretation of the governing physical processes; (v) develop strategies applicable for hazard assessment and mitigation. We also aim at triggering discussions on effective countermeasures that can be implemented to increase preparedness and risk reduction, and studies that integrate social, structural, or natural protection measures.

The session strives to build a community and to grow networks at EGU and beyond.

Landslides can trigger catastrophic consequences, leading to loss of life and assets. In specific regions, landslides claim more lives than any other natural catastrophe. Anticipating these events proves to be a monumental challenge, encompassing scientific curiosity and vital societal implications, as it provides a means to safeguard lives and property.
This session revolves around methodologies and state-of-the-art approaches in landslide prediction, encompassing aspects like location, timing, magnitude, and the impact of single and multiple slope failures. It spans a range of landslide variations, from abrupt rockfalls to rapid debris flows, and slow-moving slides to sudden rock avalanches. The focus extends from local to global scales.

Contributions are encouraged in the following areas:

Exploring the theoretical facets of predicting natural hazards, with a specific emphasis on landslide prognosis. These submissions may delve into conceptual, mathematical, physical, statistical, numerical, and computational intricacies.
Presenting applied research, supported by real-world instances, that assesses the feasibility of predicting individual or multiple landslides and their defining characteristics, with specific reference to early warning systems and methods based on monitoring data and time series of physical quantities related to slope stability at different scales.
Evaluating the precision of landslide forecasts, comparing the effectiveness of diverse predictive models, demonstrating the integration of landslide predictions into operational systems, and probing the potential of emerging technologies.

Should the session yield fruitful results, noteworthy submissions may be consolidated into a special issue of an international journal.

Landslide early warning systems (LEWS) are cost effective non-structural mitigation measures for landslide risk reduction. For this reason, the design, application and management of LEWS are gaining consensus not only in the scientific literature but also among public administrations and private companies.
LEWS can be applied at different spatial scales of analysis, reliable implementations and prototypal LEWS have been proposed and applied from slope to regional scales.
The structure of LEWS can be schematized as an interrelation of four main components: monitoring, modelling, warning, response. However, tools, instruments, methods employed in the components can vary considerably with the scale of analysis, as well as the characteristics and the aim of the warnings/alerts issued. For instance, at local scale instrumental devices are mostly used to monitor deformations and hydrogeological variables with the aim of setting alert thresholds for evacuation or interruption of services. At regional scale rainfall thresholds are widely used to prepare a timely response of civil protection and first responders. For such systems, hydro-meteorological thresholds built combining different variables represent one of the most promising and recent advancement. Concerning the modeling techniques, analyses on small areas generally allow for the use of physically based models, while statistical models are widely used for larger areas.
This session focuses on LEWS at all scales and stages of maturity (i.e., from prototype to active and dismissed ones). Test cases describing operational application of consolidated approaches are welcome, as well as works dealing with promising recent innovations, even if still at an experimental stage. The session is not focused only on technical scientific aspects, and submissions concerning practical and social aspects are also welcome.

Contributions addressing the following topics will be considered positively:
- conventional and innovative slope-scale monitoring systems for early warning purposes
- conventional and innovative regional prediction tools for warning purposes
- innovative on-site instruments and/or remote sensing devices implemented in LEWS
- warning models for warning/alert issuing
- operational applications and performance analyses of LEWS
- communication strategies
- emergency phase management

The global increase in damaging landslide events has attracted the attention of governments, practitioners, and scientists to develop functional, reliable and (when possible) low cost monitoring strategies. Numerous case studies have demonstrated how a well-planned monitoring system of landslides is of fundamental importance for long and short-term risk reduction.
Today, the temporal evolution of a landslide is addressed in several ways, encompassing classical and more complex in situ measurements or remotely sensed data acquired from satellite and aerial platforms. All these techniques are adopted for the same final scope: measure landslide motion over time, trying to forecast future evolution or minimally reconstruct its recent past. Real time, near-real time and deferred time strategies can be profitably used for landslide monitoring, depending on the type of phenomenon, the selected monitoring tool, and the acceptable level of risk.
Novel geophysical methods represent valuable approaches in understanding landslides characteristics, especially when integrated with remote sensing, machine learning techniques and time-lapse surveys.
This session follows the general objectives of the International Consortium on Landslides, namely: (i) promote landslide research for the benefit of society, (ii) integrate geosciences and technology within the cultural and social contexts to evaluate landslide risk, and (iii) combine and coordinate international expertise.
Considering these key conceptual drivers, this session aims to present successful monitoring experiences worldwide based on both in situ and/or remotely sensed data. The integration and synergic use of different techniques is welcomed, as well as newly developed tools or data analysis approaches, including big data management strategies. The session is expected to present case studies in which multi-temporal and multi-platform monitoring data are exploited for risk management and Civil Protection aims with positive effects in both social and economic terms. Specific relevance is given to the evaluation of the impact of landslides on cultural heritage.
The current session includes contributions deriving from the session NH3.3 – 'Landslide Imaging and Monitoring Using Geophysical Methods - Perspectives and Possibilities’.

Landslide Inventory Maps (LIMs) are the basic tool for spatially representing landslides, forming the cornerstone for subsequent analyses in landslide research. Traditional methods of landslide mapping have historically relied on heuristic interpretation, resulting in varied accuracy, coverage, and timeliness. Their reliability is influenced by mapping errors arising from diverse techniques and base data. Recent research emphasizes geographic accuracy, thematic accuracy, and completeness/statistical representativeness as key factors defining the quality of LIMs.
Classification of susceptibility adds to the complexity of mapping efforts. Conventional methods often struggle with differences between the types of landslides due to variations in morphological and environmental factors. The integration of Machine Learning (ML) has revolutionized landslide mapping and modeling. ML's capacity to extract critical patterns from heterogeneous data sources enables precise classification of landslides, addressing challenges faced by conventional methods. Additionally, ML techniques offer a comprehensive view of the landscape and its dynamic changes and a comprehensive solution for assessing and mitigating landslide hazards by addressing challenges related to threshold determination, classification accuracy, and uncertainty evaluation.
We invite contributions addressing:
• Metrics for evaluating mapping accuracy, errors, and uncertainty.
• Statistical modelling of mapping errors and ML-based classification.
• Quality assessment methods for Landslide Inventory Maps.
• Impact of error propagation on susceptibility models, hazard assessment, and risk evaluation.
• Model inter-comparisons
• Relating LIMs quality to use limitations and decision-making at different land-management levels.