Categorie
Webinar series

New webinar “Integrating Geomechanical, Environmental, and Remote Sensing Approaches in Data-Scarce Mountain Regions”

The sixth webinar of the LandAware 2026 webinar series From Landslide Hazard Assessment to Early Warning: Integrating Geomechanical, Environmental, and Remote Sensing Approaches in Data-Scarce Mountain Regions”, by Danny Love Wamba Djukem (University of Liège, Belgium) is scheduled for 25 June 2026, 05:00 UTC.

Abstract

Landslide early warning systems are often developed in regions with extensive monitoring networks, long-term datasets, and well-documented landslide inventories. However, many mountainous regions around the world, particularly in Africa and other parts of the Global South, face a very different reality: limited monitoring, sparse environmental data, and incomplete records of past landslides.
This webinar explores how landslide hazard assessment can contribute to the development of early warning strategies in such data-scarce environments. Drawing on case studies from Cameroon, China,
Haiti, it highlights how geomechanical analyses, landslide inventories, physically based modelling, and remote sensing observations can be combined to better understand landslide processes and support forecasting efforts.
The webinar first examines the environmental and triggering factors controlling landslides in tropical mountain regions, using examples from Mount Oku in Cameroon. It then discusses the development and application of site-adaptive approaches for predicting earthquake-induced landslides across different tectonic and climatic settings. Finally, it explores how national landslide inventories, satellite observations, and integrated hazard assessments can provide practical foundations for future landslide early warning systems in regions where traditional monitoring networks remain limited.
The talk argues that effective early warning begins with understanding the landscape, its processes, and its triggers. By integrating geomechanical, environmental, and remote sensing approaches, it is possible to move from hazard assessment toward more operational and scalable warning frameworks, even in data-constrained mountain environments

Bio

Dr. Danny Love Wamba Djukem is an engineering geologist and geohazards researcher specializing in landslide processes, slope stability, earthquake-induced landslides, remote sensing, and hazard assessment in mountainous environments. She holds a Ph.D. in Geotechnics and Geohazards and was previously a Postdoctoral Researcher at Chengdu University of Technology, China. She is currently an Invited Researcher with the Georisk & Environment Group at the University of Liège, Belgium, and is based in Cameroon.
Her research focuses on understanding landslide triggering mechanisms and developing practical approaches for landslide hazard assessment and early warning in data-scarce regions. Her work combines geomechanical analyses, physically based modelling, remote sensing, and landslide inventory development, with applications in Africa, the Caribbean, and Asia.
Her recent studies include landslide hazard assessment in Cameroon, site-adaptive prediction of earthquake-induced landslides, and national-scale landslide inventory development to support disaster risk reduction and resilience planning.

Categorie
Webinar series

Recording of the webinar “From Monitoring to Decision: Lessons Learned from the Mont de La Saxe LEWS” available on YouTube

The recording of the webinar “From Monitoring to Decision: Lessons Learned from the Mont de La Saxe Landslide Early Warning System” by Davide Bertolo (K4T – Italy), held on 28 May 2026, is available on the LandAware YouTube channel.

From Monitoring to Decision: Lessons Learned from the Mont de La Saxe LEWS, by Davide Bertolo
Categorie
Webinar series

Webinar “From Monitoring to Decision: Lessons Learned from the Mont de La Saxe Landslide Early Warning System”

The fifth webinar of the LandAware 2026 webinar series From Monitoring to Decision: Lessons Learned from the Mont de La Saxe Landslide Early Warning System”, by Davide Bertolo (K4T -Knowledge for Tomorrow, Italy) is scheduled for 28 May 2026, 14:00 UTC.

Abstract:
Landslide Early Warning Systems (LEWS) are increasingly based on high-frequency monitoring data and the definition of empirical thresholds. However, operational experience has shown that the exceedance of such thresholds does not always lead to failure, posing significant challenges for decision-makers responsible for issuing warnings.
This presentation discusses the lessons learned from the management of the Mont de La Saxe landslide (Aosta Valley, Italy), one of the most intensively monitored large slope instabilities in the Alps. Between 2012 and 2014, the site experienced critical acceleration phases that required the activation of civil protection measures under conditions of high uncertainty.
The experience highlighted key limitations of threshold-based approaches, particularly in terms of false alarms and decision reliability. In response, a multi-stage decision framework was progressively developed, integrating data from different monitoring systems with field observations and contextual information.
The presentation will focus on the practical challenges faced during the emergency, the evolution of the early warning procedure, and the operational strategies adopted to support timely and reliable decisions. Particular attention will be given to how combining multiple lines of evidence can improve confidence in warning levels and reduce the risk of both false alarms and missed events.
The Mont de La Saxe case provides a concrete example of how early warning systems can evolve from purely data-driven approaches to more structured and transparent decision processes, offering insights that are transferable to other monitored slope instabilities and relevant for international LEWS initiatives such as LandAware.

Bio:
Davide Bertolo is an engineering geologist with more than 25 years of experience in landslide risk management, monitoring systems, and civil protection decision-making. He has served as Head of the Geological Survey of the Autonomous Region of Aosta Valley (Italy), where he led the development and implementation of advanced monitoring networks for large alpine instabilities, including the Mont de La Saxe landslide.
His work focuses on bridging the gap between data acquisition and decision-making, with particular emphasis on early warning systems and probabilistic approaches to risk management. He is the author of a Bayesian-based early warning methodology that integrates quantitative monitoring data with qualitative field evidence to support transparent and robust decision processes.
Davide is currently founder of K4T, an advisory initiative focused on innovative solutions for geological risk and civil protection, combining domain expertise with advanced data analysis and decision-support methodologies.

Categorie
Webinar series

Recording of the webinar “What makes landslide forecasts actionable?” available on Youtube

The recording of the fourth webinar of the LandAware 2026 webinar series “What Makes Landslide Forecasts Actionable? Lessons from Users of Aotearoa New Zealand’s Trial Products”, by Sara Harrison (Earth Sciences New Zealand), held on 20 April 2026 is available on the LandAware Youtube channel.

What Makes Landslide Forecasts Actionable? Lessons from Users of Aotearoa New Zealand’s Trial Products, by Sara Harrison
Categorie
LandAware network Webinar series

Webinar “What Makes Landslide Forecasts Actionable?”

The fourth webinar of the LandAware 2026 webinar series “What Makes Landslide Forecasts Actionable? Lessons from Users of Aotearoa New Zealand’s Trial Products”, by Sara Harrison (Earth Sciences New Zealand) is scheduled for 30 April 2026, 05:00 UTC.

Dr Sara Harrison is a Hazard and Risk Social Scientist at Earth Sciences New Zealand, specialising in the design and use of people‑centred early warning systems for natural hazards.

Her work focuses on how hazard forecasts, warnings, and decision‑support tools are developed, communicated, and used in real‑world risk management, with a strong emphasis on ensuring science is actionable for those who need it most. In her current role, Sara contributes to national and international projects exploring how trial forecasting products—such as landslide, severe weather, and tsunami hazard and impact forecasts—can be made more usable, trusted, and decision‑relevant. Her work bridges social science and operational hazard modelling, helping ensure that forecast products support effective action rather than simply providing more information.

In this webinar, Sara draws on lessons from users of Aotearoa New Zealand’s trial landslide forecasting products to explore what makes forecasts truly actionable—and how understanding user contexts, decision thresholds, and institutional settings is just as critical as improving technical accuracy.

Categorie
Webinar series

Recording of the webinar on “Atmospheric River Controls on Extreme Rainfall and Landslide Hazard in SE Alaska” available

The recording of the second webinar of the LandAware 2026 webinar series “Atmospheric River Controls on Extreme Rainfall and Landslide Hazard in Southeast Alaska”, by Deanna Nash (Center for Western Weather and Water Extremes, University of California, San Diego, USA), held online on 26 March 2026, is online on the LandAware YouTube channel.

Atmospheric River Controls on Extreme Rainfall and Landslide Hazard in Southeast Alaska, by Deanna Nash
Categorie
LandAware network LATAM TF News from members

1st Webinar of LATin AMerican regional group

News received from
Johnny Vega, Isabela Horta, Elias Garcia-Urquia, Mario Reyes, Graziella Devoli

Estimados colegas

Los invitamos al:
1er Webinar de LandAware Grupo LATAM – 1º Webinar do Grupo LandAware LATAM (en español)
Jueves 9 de abril de 2026 (14:00pm Colombian Time) (16:00 Rio de Janeiro) (13:00 Central American time) (19:00 UTC) (21:00 CEST)

Edier Aristizábal (Universidad Nacional de Colombia) – “La lluvia como detonante de movimientos en masa en el Valle de Aburrá (Colombia)”

Daniel F. Ruiz (Universidad EAFIT) – “Sistemas de alerta temprana de deslizamientos a múltiples escalas: de la implementación local a la regional”

Edier Aristizábal es Ingeniero Geólogo especializado en riesgos geológicos, también asociados con el clima. Formado en la Universidad de Ginebra (Suiza), tiene maestría en ingeniería conseguida en la Universidad de Shimane (Japón), doctorado en Ingeniería con énfasis en recursos hidráulicos de la Universidad Nacional de Colombia, y recientemente desarrolló su postdoctorado en la Universidad de Potsdam (Alemania). Desde el año 2015 se encuentra vinculado al Departamento de Geociencias y Medio Ambiente de la Facultad de Minas como profesor.

Daniel F. Ruiz es ingeniero civil formado en la Universidad Nacional de Colombia y cuenta con estudios de maestría y doctorado en Ingeniería Geotécnica por la Universitat Politècnica de Catalunya (UPC). Actualmente se desempeña como director del programa de Ingeniería Civil de la Universidad EAFIT y participa como asesor técnico en sistemas de alerta como SIATA, SAMA y SIMER, consolidando una trayectoria académica y profesional enfocada en la gestión del riesgo y la ingeniería geotécnica.

Atentamente

—————————————–

Dear colleagues

You are invited to the the 1st Webinar LandAware LATAM Group (in Spanish)
Tuesday 9th of April 2026 (14:00pm Colombian Time) (16:00 Rio de Janeiro) (13:00 Central American time) (19:00 UTC) (21:00 CEST)

Edier Aristizábal (Universidad Nacional de Colombia) – “La lluvia como detonante de movimientos en masa en el Valle de Aburrá (Colombia)”

Daniel F. Ruiz (Universidad EAFIT) – “Sistemas de alerta temprana de deslizamientos a múltiples escalas: de la implementación local a la regional”

Best regards

Categorie
LandAware network Webinar series

Webinar “Atmospheric River Controls on Extreme Rainfall and Landslide Hazard in Southeast Alaska”

The third webinar of the LandAware 2026 webinar seriesAtmospheric River Controls on Extreme Rainfall and Landslide Hazard in Southeast Alaska”, by Deanna Nash (Center for Western Weather and Water Extremes) is scheduled for 26 March 2026, 15:00 UTC.

Abstract:
Landslides triggered by extreme precipitation during atmospheric rivers (ARs) pose significant hazards to rural and Indigenous communities in Southeast Alaska. Recent research has demonstrated a strong relationship between AR strength and extreme precipitation in the region; however, forecasted AR magnitude and duration alone do not fully explain when impacts occur or provide sufficient context for emergency managers and the public. To address this gap, ongoing collaborative work with the National Weather Service (NWS) in Juneau is focused on developing a forecasting tool that leverages the relationship between AR characteristics and extreme precipitation while also incorporating additional key factors such as freezing level, low-level wind speed and direction, AR orientation, and forecasted precipitation.
Using NOAA’s Global Ensemble Forecast System version 12 (GEFSv12) reforecast dataset, we developed a Model Climate (M-Climate) framework for integrated water vapor transport (IVT), freezing level, low-level winds, and quantitative precipitation forecasts (QPF). M-Climate places these forecast variables within the context of historical reforecasts with the same lead time and time of year. For example, a 95th percentile M-Climate IVT indicates that the ensemble-mean IVT is greater than 95% of reforecast values at that location, lead time, and time of year. By comparing forecasts to analogous forecasts rather than to observations, M-Climate preserves the magnitude of ensemble-mean anomalies that might otherwise be dampened when compared to observed climatology.
Using a catalog of impactful landslides compiled by NWS Juneau, we applied the M-Climate framework to develop the Southeast Alaska Atmospheric River Impact Tool, which highlights when forecasted AR conditions are most likely to lead to impacts such as landslides. The tool is now available operationally to support NWS Juneau forecasters by improving situational awareness and enhancing Impact Decision Support Services (IDSS) messaging before and during high-impact weather events. By linking forecasted AR characteristics to potential impacts, the tool also helps forecasters communicate risk more effectively to weather partners, community leaders, and the public.

Bio:
Deanna Nash, Ph.D., is a Precipitation and Geohazards Scientist at the Center for Western Weather and Water Extremes at the Scripps Institution of Oceanography at the University of California San Diego. Her research focuses on improving understanding and forecasting of meteorological conditions during atmospheric rivers that produce extreme precipitation and increase the risk of flooding and landslides, particularly in mountainous regions of the world. In Southeast Alaska, she contributed to an NSF-funded project through the Coastlines and Peoples Initiative called KUTÍ (one of the Tlingit words for weather), which collaborated closely with forecasters at the National Weather Service Weather Forecast Office in Juneau to develop forecasting tools for extreme atmospheric rivers that are now incorporated into their operational workflows.

Categorie
Webinar series

Recording of the webinar on “Landslide Monitoring at the Slope Scale” available

The recording of the second webinar of the LandAware 2026 webinar series “Landslide Monitoring at the Slope Scale: From Challenges to Early Warning Solutions“, by Andrea Carri (ASE – Advanced Slope Engineering S.r.l.), held online on 26 February 2026, is online on the LandAware YouTube channel.

“Landslide Monitoring at the Slope Scale: From Challenges to Early Warning Solutions“, by Andrea Carri
Categorie
News from members

Final Webinar of the PRIN-ITALERT project

NEWS received from
Stefano Luigi Gariano (CNR, Italy)

The final webinar of the PRIN-ITALERT (Prediction of Rainfall-INduced landslides – Improving multi-scale TerritoriAL Early warning through aRTificial intelligence) project is scheduled on Tuesday, 24 February 2026 at 10:00 am CET (9:00 am UTC).

The webinar will focus on the main findings of the project. After a brief introduction, three main presentations will be delivered:

Landslide data and satellite rainfall products to define reliable tools for landslide prediction, by Stefano Luigi Gariano (Italian National Research Council)
Leveraging hydrological information through machine learning for Landslide Early Warning Systems, by Nunziarita Palazzolo (University of Catania)
A dynamic, machine learning–based early warning model for daily spatio-temporal landslide prediction, Nicola Nocentini (University of Florence)

To join the webinar (no registration needed), use the following link:

More info on the project can be found
here: https://www.irpi.cnr.it/en/project/prin-italert/