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#engineeringgeology — Public Fediverse posts

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  1. Compound Hydrogeomorphic Cascades And Rapid Upstream To Downstream Hazard Coupling In The Eastern Himalaya
    --
    doi.org/10.1038/s41598-026-529 <-- shared paper
    --
    doi.org/10.1007/s11600-022-009 <-- shared paper
    --
    H/T @Kuldeep Dutta | Geology-Earth Science
    “… In hilly regions transitioning rapidly to low gradient alluvial plains, localized hydrometeorological triggers can instantly scale into devastating basin wide disasters. This study dissects the September 2020 cascading hazard in parts of the Arunachal Pradesh-Assam corridor to quantify the rapid coupling between upstream hillslopes and downstream floodplains.
    Check out the [attached graphical abstract figure] for an integrated visual workflow of the entire disaster continuum from hillslope failure to floodplain transformation...”
    --
    “Extreme precipitation in the Eastern Himalaya is increasingly associated with coupled hillslope-floodplain hazards. This study examines the 17th-18th September 2020 rainfall event in Arunachal Pradesh initiating landslides and its downstream impacts in Assam, India, using multi-sensor satellite data and long-term rainfall records. Sentinel-2 imagery was used to map landslides and debris flows, Sentinel-1 SAR data to delineate flood extent, and IMD gridded rainfall (1996–2020) to analyse rainfall spell characteristics. The event triggered widespread slope failures, localized landslide damming, and a subsequent breach, generating sediment-laden flows that inundated ~ 100 km² of the Dhemaji floodplain. A backscatter-derived Relative Flood Volume Index (RFVI) indicates spatial variability in inundation intensity, although it does not represent absolute flood volume. Rainfall analysis suggests that antecedent wetness from preceding spells preconditioned slopes, while peak daily rainfall (> 170 mm day−1) initiated landsliding. Power-law scaling shows negligible dependence of intensity on duration (R2 ≈ 0.0004), whereas cumulative rainfall exhibits a stronger relationship with duration (R2 ≈ 0.54). These results indicate distinct roles of rainfall intensity and accumulation in controlling landslide initiation and downstream flooding, respectively, highlighting the importance of compound rainfall forcing in rapid hydrogeomorphic cascades…”
    #EarthScience #RemoteSensing #Himalayas #NaturalHazards #ClimateChange #ScientificReports #GeospatialAnalysis #DisasterMitigation #Landslide #trigger #Flooding #massmovement #extremeweather #engineeringgeology #floodplain #innundation #hillslope #fluvial #pluvial #alluvial #sediment #sedimentation #hydrometeorology #ArunachalPradesh #Assam #India #Brahmaputra #risk #hazard #geology #engineeringgeology #remotesensing #earthobservation #spatialanalysis #spatiotemporal #disaster #hydrogeomorphology #workflow

  2. Compound Hydrogeomorphic Cascades And Rapid Upstream To Downstream Hazard Coupling In The Eastern Himalaya
    --
    doi.org/10.1038/s41598-026-529 <-- shared paper
    --
    doi.org/10.1007/s11600-022-009 <-- shared paper
    --
    H/T @Kuldeep Dutta | Geology-Earth Science
    “… In hilly regions transitioning rapidly to low gradient alluvial plains, localized hydrometeorological triggers can instantly scale into devastating basin wide disasters. This study dissects the September 2020 cascading hazard in parts of the Arunachal Pradesh-Assam corridor to quantify the rapid coupling between upstream hillslopes and downstream floodplains.
    Check out the [attached graphical abstract figure] for an integrated visual workflow of the entire disaster continuum from hillslope failure to floodplain transformation...”
    --
    “Extreme precipitation in the Eastern Himalaya is increasingly associated with coupled hillslope-floodplain hazards. This study examines the 17th-18th September 2020 rainfall event in Arunachal Pradesh initiating landslides and its downstream impacts in Assam, India, using multi-sensor satellite data and long-term rainfall records. Sentinel-2 imagery was used to map landslides and debris flows, Sentinel-1 SAR data to delineate flood extent, and IMD gridded rainfall (1996–2020) to analyse rainfall spell characteristics. The event triggered widespread slope failures, localized landslide damming, and a subsequent breach, generating sediment-laden flows that inundated ~ 100 km² of the Dhemaji floodplain. A backscatter-derived Relative Flood Volume Index (RFVI) indicates spatial variability in inundation intensity, although it does not represent absolute flood volume. Rainfall analysis suggests that antecedent wetness from preceding spells preconditioned slopes, while peak daily rainfall (> 170 mm day−1) initiated landsliding. Power-law scaling shows negligible dependence of intensity on duration (R2 ≈ 0.0004), whereas cumulative rainfall exhibits a stronger relationship with duration (R2 ≈ 0.54). These results indicate distinct roles of rainfall intensity and accumulation in controlling landslide initiation and downstream flooding, respectively, highlighting the importance of compound rainfall forcing in rapid hydrogeomorphic cascades…”

  3. A Century Of Landslide Records In Calabria, Southern Italy, Looking For Changes And Trends Through A Dynamic Analysis
    --
    doi.org/10.5194/nhess-26-3077- <-- shared paper / brief communication
    --
    doi.org/10.5194/nhess-15-2313- <-- shared 2015 paper that this communication updates/adds-to
    --
    doi.org/10.1007/s12665-023-108 <-- shared paper
    --
    H/T @StefanoLuigiGariano
    “This study updates an article published in NHESS journal in 2015 [link above] and investigates long-term changes in landslide-triggering rainfall conditions in Calabria (southern Italy) over 1921–2020. A catalogue of 3,006 rainfall events associated with landslides (RELs) was reconstructed using 9,530 landslide records and daily rainfall measurements from 318 gauges. Rainfall thresholds were calculated for 15 30-year moving windows to investigate the triggering conditions of the RELs. Results show a marked increase in the number of RELs after 2009, shifts in seasonal occurrence, and decreasing rainfall duration and cumulative amounts. Triggering rainfall shows an overall decreasing trend over the years…”
    #Calabria #Italy #massmovement #records #landslides #geology #engineeringgeology #spatiotemporal #spatialanalysis #rainfall #precipitation #extremeweather #trigger #monitoring

  4. A Century Of Landslide Records In Calabria, Southern Italy, Looking For Changes And Trends Through A Dynamic Analysis
    --
    doi.org/10.5194/nhess-26-3077- <-- shared paper / brief communication
    --
    doi.org/10.5194/nhess-15-2313- <-- shared 2015 paper that this communication updates/adds-to
    --
    doi.org/10.1007/s12665-023-108 <-- shared paper
    --
    H/T @StefanoLuigiGariano
    “This study updates an article published in NHESS journal in 2015 [link above] and investigates long-term changes in landslide-triggering rainfall conditions in Calabria (southern Italy) over 1921–2020. A catalogue of 3,006 rainfall events associated with landslides (RELs) was reconstructed using 9,530 landslide records and daily rainfall measurements from 318 gauges. Rainfall thresholds were calculated for 15 30-year moving windows to investigate the triggering conditions of the RELs. Results show a marked increase in the number of RELs after 2009, shifts in seasonal occurrence, and decreasing rainfall duration and cumulative amounts. Triggering rainfall shows an overall decreasing trend over the years…”

  5. Compound Hydrogeomorphic Cascades And Rapid Upstream To Downstream Hazard Coupling In The Eastern Himalaya
    --
    doi.org/10.1038/s41598-026-529 <-- shared paper
    --
    doi.org/10.5194/esurf-13-1281- <-- shared paper
    --
    doi.org/10.1007/s11069-025-077 <-- shared paper
    --
    [I recognise that the photo is instead for the floods, etc in Lubra, Nepal - but felt it better showed the hydrogeomorphical setting (sic) for the 'casual' post viewer...]
    H/T @Kuldeep Dutta
    “In hilly regions transitioning rapidly to low gradient alluvial plains, localized hydrometeorological triggers can instantly scale into devastating basin wide disasters. This study dissects the September 2020 cascading hazard in parts of the Arunachal Pradesh-Assam corridor to quantify the rapid coupling between upstream hillslopes and downstream floodplains…”
    --
    “Extreme precipitation in the Eastern Himalaya is increasingly associated with coupled hillslope-floodplain hazards. This study examines the 17th-18th September 2020 rainfall event in Arunachal Pradesh initiating landslides and its downstream impacts in Assam, India, using multi-sensor satellite data and long-term rainfall records. Sentinel-2 imagery was used to map landslides and debris flows, Sentinel-1 SAR data to delineate flood extent, and IMD gridded rainfall (1996–2020) to analyse rainfall spell characteristics. The event triggered widespread slope failures, localized landslide damming, and a subsequent breach, generating sediment-laden flows that inundated ~ 100 km2 of the Dhemaji floodplain. A backscatter-derived Relative Flood Volume Index (RFVI) indicates spatial variability in inundation intensity, although it does not represent absolute flood volume. Rainfall analysis suggests that antecedent wetness from preceding spells preconditioned slopes, while peak daily rainfall (> 170 mm/day/) initiated landsliding. Power-law scaling shows negligible dependence of intensity on duration (R² ≈ 0.0004), whereas cumulative rainfall exhibits a stronger relationship with duration (R² ≈ 0.54). These results indicate distinct roles of rainfall intensity and accumulation in controlling landslide initiation and downstream flooding, respectively, highlighting the importance of compound rainfall forcing in rapid hydrogeomorphic cascades...”
    #EarthScience #RemoteSensing #Himalayas #NaturalHazards #ClimateChange #ScientificReports #GeospatialAnalysis #DisasterMitigation #Landslide #Flooding #alluvial #fluvial #water #hydrology #hydrography #flood #flooding #spatialanalysis #spatiotemporal #mountain #plain #hydrometeorological #hydrogeomorphology #ArunachalPradesh #Assam #India #hillslope #floodplain #rainfall #precipitation #extremeweather #engineeringgeology #massmovement #landslide #debrisflow #risk #hazard #monitoring #GIS #spatial #mapping #remotesensing #satellite #Sentinel #sedimentation #humanimpacts #infrastructure #damage #cost #economics #public #safety #model #modeling #downstream

  6. Compound Hydrogeomorphic Cascades And Rapid Upstream To Downstream Hazard Coupling In The Eastern Himalaya
    --
    doi.org/10.1038/s41598-026-529 <-- shared paper
    --
    doi.org/10.5194/esurf-13-1281- <-- shared paper
    --
    doi.org/10.1007/s11069-025-077 <-- shared paper
    --
    [I recognise that the photo is instead for the floods, etc in Lubra, Nepal - but felt it better showed the hydrogeomorphical setting (sic) for the 'casual' post viewer...]
    H/T @Kuldeep Dutta
    “In hilly regions transitioning rapidly to low gradient alluvial plains, localized hydrometeorological triggers can instantly scale into devastating basin wide disasters. This study dissects the September 2020 cascading hazard in parts of the Arunachal Pradesh-Assam corridor to quantify the rapid coupling between upstream hillslopes and downstream floodplains…”
    --
    “Extreme precipitation in the Eastern Himalaya is increasingly associated with coupled hillslope-floodplain hazards. This study examines the 17th-18th September 2020 rainfall event in Arunachal Pradesh initiating landslides and its downstream impacts in Assam, India, using multi-sensor satellite data and long-term rainfall records. Sentinel-2 imagery was used to map landslides and debris flows, Sentinel-1 SAR data to delineate flood extent, and IMD gridded rainfall (1996–2020) to analyse rainfall spell characteristics. The event triggered widespread slope failures, localized landslide damming, and a subsequent breach, generating sediment-laden flows that inundated ~ 100 km2 of the Dhemaji floodplain. A backscatter-derived Relative Flood Volume Index (RFVI) indicates spatial variability in inundation intensity, although it does not represent absolute flood volume. Rainfall analysis suggests that antecedent wetness from preceding spells preconditioned slopes, while peak daily rainfall (> 170 mm/day/) initiated landsliding. Power-law scaling shows negligible dependence of intensity on duration (R² ≈ 0.0004), whereas cumulative rainfall exhibits a stronger relationship with duration (R² ≈ 0.54). These results indicate distinct roles of rainfall intensity and accumulation in controlling landslide initiation and downstream flooding, respectively, highlighting the importance of compound rainfall forcing in rapid hydrogeomorphic cascades...”

  7. Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
    --
    doi.org/10.1038/s44304-026-002 <-- shared paper
    --
    doi.org/10.1038/s41598-025-220 <-- shared (earlier) paper
    --
    doi.org/10.1080/2150704X.2025. <-- shared (earlier) paper
    --
    H/T @abhinav Alangadan
    “Can we develop a first-order understanding of permafrost degradation and glacial lakes exposed to degradation-induced mass movements in the Himalaya?
    [The authors] tried to address this question. The study [first link above] integrates machine learning, statistical modeling, and numerical modeling to investigate high-resolution permafrost distribution, potential degradation, and associated mass-movement hazards in the Kinnaur district of Himachal Pradesh, India.
    Using rock glaciers as proxies, [they] generated a high-resolution permafrost distribution using machine learning, while potential degradation zones were delineated using the 0°C isotherm as a first-order indicator. [They] further identified glacial lakes located near potentially degrading permafrost zones and reconstructed their bathymetry. A detailed scenario-based GLOF process-chain simulation was then carried out for Kashang Lake using r.avaflow and HEC-RAS.
    [Their] results indicate that seven glacial lakes in #Kinnaur are located close to potentially degrading permafrost zones. The simulations further show that a potential GLOF from Kashang Lake could inundate critical downstream infrastructure, including nearly 11 km of National Highway 5…”
    #permafrost #distribution #GIS #spatial #mapping #Himalayas #India #Kinnaur #HimachalPradesh #KashangLake #massmovement #engineeringgeology #machinelearning #AI #model #modeling #numericalmodel #glaciallakes #glaciet #glacial #glaciallakeoutburstflood #GLOF #cryosphere #geostatistics #rockglaciers #GeoAI #bathymetry #processchainsimulation #HECRAS #avaflow #risk #hazard #mitigation #riskassessment #infrastructure #HEP #publicsafety #downstream #avalanche

  8. Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
    --
    doi.org/10.1038/s44304-026-002 <-- shared paper
    --
    doi.org/10.1038/s41598-025-220 <-- shared (earlier) paper
    --
    doi.org/10.1080/2150704X.2025. <-- shared (earlier) paper
    --
    H/T @abhinav Alangadan
    “Can we develop a first-order understanding of permafrost degradation and glacial lakes exposed to degradation-induced mass movements in the Himalaya?
    [The authors] tried to address this question. The study [first link above] integrates machine learning, statistical modeling, and numerical modeling to investigate high-resolution permafrost distribution, potential degradation, and associated mass-movement hazards in the Kinnaur district of Himachal Pradesh, India.
    Using rock glaciers as proxies, [they] generated a high-resolution permafrost distribution using machine learning, while potential degradation zones were delineated using the 0°C isotherm as a first-order indicator. [They] further identified glacial lakes located near potentially degrading permafrost zones and reconstructed their bathymetry. A detailed scenario-based GLOF process-chain simulation was then carried out for Kashang Lake using r.avaflow and HEC-RAS.
    [Their] results indicate that seven glacial lakes in are located close to potentially degrading permafrost zones. The simulations further show that a potential GLOF from Kashang Lake could inundate critical downstream infrastructure, including nearly 11 km of National Highway 5…”

  9. 🚨 FEMA’s Hazus v7.2 Is Here — A Major Upgrade For Disaster Risk Modeling
    --
    fema.gov/flood-maps/products-t <-- shared link to FEMA HAZUS download, documentation, use case, etc
    --
    [I used to work some with Hazus back in the back, but my career changed path; I still appreciate its strength and unity of purpose (sic) #alldataisspatial]
    H/T @Laban "L.J." Johnson | Founder, LJ Learn & Concordia Initiative | Crisis Support · Leadership Development · Community Resilience | Bridging worlds to help people rise
    “FEMA’s Hazus GIS platform has been updated with a new ArcGIS Pro–based version, bringing faster, more powerful tools for estimating losses from floods, hurricanes, earthquakes, and other natural hazards.
    Key updates in Hazus 7.2 include:
    • Streamlined workflows for flood and hurricane modeling
    • New Earthquake ShakeMap integration using USGS data
    • Expanded and improved results exports and reporting (including geodatabase outputs)
    • Stronger security with known vulnerabilities addressed
    • Performance improvements and optimized installation process
    • [Significantly enhanced and comprehensive summary reports for flood and earthquake are now available for download.]
    • Full integration with ArcGIS Pro (3.4–3.6) for a modern GIS experience
    This release represents a significant step forward in how hazard planners, emergency managers, and GIS professionals analyze and prepare for disaster impacts…”
    --
    “FEMA’s Hazus program provides software, data, methods, and guidance for estimating risk from natural hazards. Hazus can estimate building damages, economic losses, displaced households, casualties, debris generation and more resulting from a natural hazard event and can be used in all phases of emergency management…”
    #HAZUS #fedservice #fedscience #oublicgood #publicsafety #emergencyresponse #software #spatialdata #GIS #spatial #mapping #risk #hazard #riskassessment #naturalhazard #humanimpacts #earthquake #wildfire #spatialanalysis #spatiotemporal #flood #flooding #cost #damage #economic #publicsafety #publichealth #emergencymanagement #opensource #opendata #tsunami #tornado #hurricane #ShakeMap #infrastructure #planning #policy #preparedness #impacts #geology #engineeringgeology #remotesensing #earthobservation
    @FEMA

  10. 🚨 FEMA’s Hazus v7.2 Is Here — A Major Upgrade For Disaster Risk Modeling
    --
    fema.gov/flood-maps/products-t <-- shared link to FEMA HAZUS download, documentation, use case, etc
    --
    [I used to work some with Hazus back in the back, but my career changed path; I still appreciate its strength and unity of purpose (sic) ]
    H/T @Laban "L.J." Johnson | Founder, LJ Learn & Concordia Initiative | Crisis Support · Leadership Development · Community Resilience | Bridging worlds to help people rise
    “FEMA’s Hazus GIS platform has been updated with a new ArcGIS Pro–based version, bringing faster, more powerful tools for estimating losses from floods, hurricanes, earthquakes, and other natural hazards.
    Key updates in Hazus 7.2 include:
    • Streamlined workflows for flood and hurricane modeling
    • New Earthquake ShakeMap integration using USGS data
    • Expanded and improved results exports and reporting (including geodatabase outputs)
    • Stronger security with known vulnerabilities addressed
    • Performance improvements and optimized installation process
    • [Significantly enhanced and comprehensive summary reports for flood and earthquake are now available for download.]
    • Full integration with ArcGIS Pro (3.4–3.6) for a modern GIS experience
    This release represents a significant step forward in how hazard planners, emergency managers, and GIS professionals analyze and prepare for disaster impacts…”
    --
    “FEMA’s Hazus program provides software, data, methods, and guidance for estimating risk from natural hazards. Hazus can estimate building damages, economic losses, displaced households, casualties, debris generation and more resulting from a natural hazard event and can be used in all phases of emergency management…”

    @FEMA

  11. Climate Warming and Ice Weakening Trigger Alpine Glacier Collapses - The Marmolada Case [Dolomites, Northern Italy]
    --
    doi.org/10.1029/2025GL121279 <-- shared paper
    --
    doi.org/10.5194/nhess-25-3027- <-- shared technical article
    --
    en.wikipedia.org/wiki/2022_Mar <-- shared Wikipedia page
    --
    [anecdotal – in the early 1990s, I chose to spend a winter as a ski bum/guide, living in Arabba, Dolomites, Sud Tyrol; I still remember the excellent days when I got to ski on the Marmolada]
    --
    “PLAIN LANGUAGE SUMMARY: On 3 July 2022, a portion of the Marmolada glacier, near Punta Rocca, collapsed and caused the death of 11 mountaineers. This dramatic event had a considerable impact on the media, and authorities were concerned about the risk that other collapses might occur in this and other glaciers of the Dolomites, a well-renowned mountain region of the southeastern Alps and one of the UNESCO World Heritage sites. [They] analyzed the possible causes of the collapse by developing a three-dimensional thermo-mechanical model. The analysis concluded that the collapse was caused by increased internal ice temperature and the development of a dense network of fractures, reducing the ice shear strength, with melting water that possibly contributed by increasing the basal pressure. [They] also showed that collapsing conditions can be identified with a simplified model version approximating the sliding basal surface as a plane with a slope equal to the surface slope. The developed approach can be used in hazard identification and risk analysis of mountain glaciers…”
    #EngineeringGeology #RockMechanics #GlacierCollapse #RiskAssessment #italy #Dolomites #SudTyrol #glacier #melting #massmovement #risk #hazard #analysis #Marmolada #glaciercollapse #cryosphere #warning #mitigation #hazardassessment #alpine #climatechange #globalwarming #massloss #instability #model #modeling #thermomechanical #spatialanalysis #temperature #parameters #ice #shearstrength #water #hydrology #hydrography #basal #waterpressure #meltwater #fracturing #riskanalysis #mountainglaciers

  12. Climate Warming and Ice Weakening Trigger Alpine Glacier Collapses - The Marmolada Case [Dolomites, Northern Italy]
    --
    doi.org/10.1029/2025GL121279 <-- shared paper
    --
    doi.org/10.5194/nhess-25-3027- <-- shared technical article
    --
    en.wikipedia.org/wiki/2022_Mar <-- shared Wikipedia page
    --
    [anecdotal – in the early 1990s, I chose to spend a winter as a ski bum/guide, living in Arabba, Dolomites, Sud Tyrol; I still remember the excellent days when I got to ski on the Marmolada]
    --
    “PLAIN LANGUAGE SUMMARY: On 3 July 2022, a portion of the Marmolada glacier, near Punta Rocca, collapsed and caused the death of 11 mountaineers. This dramatic event had a considerable impact on the media, and authorities were concerned about the risk that other collapses might occur in this and other glaciers of the Dolomites, a well-renowned mountain region of the southeastern Alps and one of the UNESCO World Heritage sites. [They] analyzed the possible causes of the collapse by developing a three-dimensional thermo-mechanical model. The analysis concluded that the collapse was caused by increased internal ice temperature and the development of a dense network of fractures, reducing the ice shear strength, with melting water that possibly contributed by increasing the basal pressure. [They] also showed that collapsing conditions can be identified with a simplified model version approximating the sliding basal surface as a plane with a slope equal to the surface slope. The developed approach can be used in hazard identification and risk analysis of mountain glaciers…”

  13. 🥚 ⚠️ Wenn das Ei Alarm schlägt:

    Kugelförmige IoT-Sensoren, sogenannte "Mureneier", können Bewegungen im Hang messen und vor flachen Hangrutschen warnen. Entwickelt und getestet wurde das smarte Frühwarnsystem von Robert Hofmann, einem Geotechnik-Professor der Uni Innsbruck, und seinem Team.

    🆕 uibk.ac.at/de/newsroom/2026/mu

    📖 nature.com/articles/s43247-025

    #Bauingenieurwesen #Geologie #Geotechnik #Geowissenschaften #Naturgefahren #IoT #EngineeringGeology

  14. 🥚 ⚠️ Wenn das Ei Alarm schlägt:

    Kugelförmige IoT-Sensoren, sogenannte "Mureneier", können Bewegungen im Hang messen und vor flachen Hangrutschen warnen. Entwickelt und getestet wurde das smarte Frühwarnsystem von Robert Hofmann, einem Geotechnik-Professor der Uni Innsbruck, und seinem Team.

    🆕 uibk.ac.at/de/newsroom/2026/mu

    📖 nature.com/articles/s43247-025

    #Bauingenieurwesen #Geologie #Geotechnik #Geowissenschaften #Naturgefahren #IoT #EngineeringGeology

  15. Niscemi Landslide—Why Did The Side Of A Town Fall Off? [Engineering Geology | Geotechnical]
    --
    youtu.be/-2tEvuTftbs?si=Pq73A7 <-- shared technical video
    --
    youtu.be/u6JUjA-Fdmc?si=P40a42 <-- shared media video
    --
    theguardian.com/world/2026/jan <-- shared media article
    --
    [The area has suffered two historic landslides, in 1790 and 1997. Trying to comprehend the SCALE of this 2026 event is difficult, but the inline video in the 3rd (media) link does give an indication.]
    #Niscemi #Sicily #Italy #landslide #mechanics #massmovement #pluvial #extremeweather #cyclone #risk #hazard #damage #infrastructure #cost #model #engineeringgeology #geology #geotechnical #water #hydrology #massmovement #overview #slide
    @TheGeoModels #TheGeoModels

  16. Niscemi Landslide—Why Did The Side Of A Town Fall Off? [Engineering Geology | Geotechnical]
    --
    youtu.be/-2tEvuTftbs?si=Pq73A7 <-- shared technical video
    --
    youtu.be/u6JUjA-Fdmc?si=P40a42 <-- shared media video
    --
    theguardian.com/world/2026/jan <-- shared media article
    --
    [The area has suffered two historic landslides, in 1790 and 1997. Trying to comprehend the SCALE of this 2026 event is difficult, but the inline video in the 3rd (media) link does give an indication.]

    @TheGeoModels

  17. Earthquake Prediction Flowchart [#XKCD]
    --
    xkcd.com/3165/ <-- shared XKCD technical cartoon
    --
    “At least people who make religious predictions of the apocalypse have an answer to the question: “Why didn’t you predict any of the other ones that happened recently?”
    #earthquake #prediction #AI #data #flowchart #humour #geology #risk #hazard #engineeringgeology #seismology #model #modeling
    @xkcd

  18. Earthquake Prediction Flowchart [#XKCD]
    --
    xkcd.com/3165/ <-- shared XKCD technical cartoon
    --
    “At least people who make religious predictions of the apocalypse have an answer to the question: “Why didn’t you predict any of the other ones that happened recently?”

    @xkcd