#engineeringgeology — Public Fediverse posts

CoSeismic Shallow Landslide Distributions Reveal Patterns Of Regolith Thickness In High Mountains
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https://doi.org/10.1029/2026JF009250 <-- shared paper
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H/T @tristram Hales | Professor of Environmental Hazards
“[This paper] paper seeks to develop a shallow earthquake-triggered landslide model. It has really pushed [the H/T’s] thinking around the role of regolith in controlling landsliding in high mountains…”
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“... PLAIN LANGUAGE SUMMARY: Large earthquakes can trigger thousands of landslides, moving regolith (the loose soil and weathered rock that mantles hillslopes) into rivers and valleys. Predicting where these landslides occur is difficult because regolith thickness varies across mountain landscapes and is rarely measured directly. [The authors] developed a new model, ShallowLandslider, that combines slope-stability physics with statistical methods to simulate populations of earthquake-triggered shallow landslides. [They] tested the model in two regions affected by the 2015 Gorkha earthquake in Nepal and compared simulated landslides with a mapped inventory. The[ir] model performed best when it included spatial variations in regolith thickness. In the steep, high-relief landscape, landslide occurrence was reproduced most successfully when regolith thickness decreased with elevation. In the lower-relief landscape, the best results occurred when thicker regolith was concentrated in convergent hollows where sediment accumulates. The simulations also matched observations most closely for cohesion values typical of shallow colluvium, supporting the interpretation that most Gorkha landslides were shallow, regolith-sourced failures. These results show that coseismic landslide patterns depend not only on ground shaking and slope steepness, but also on how regolith is organized across the landscape. Landslide inventories can help evaluate competing hypotheses of regolith distribution where direct observations are sparse…”
#engineeringgeology #massmovement #landslide #inventory #regolith #thickness #soil #cohesion #colluvium #shallow #geology #coseismic #ShallowLandslider #geomorphology #hydrogeomorphology #earthquake #triggered #earthquaketriggered #Nepal #Gorkha #model #modeling #mountain #slope #steepness #mountainous #erosion #weathering #river #valley #riverine #fluvial #prediction #slopestability #geostatistics #GIS #mapping #spatial #spatialanalysis #spatiotemporal

Integrated Multi-Criteria Decision Analysis And Bivariate Statistical Modeling For Landslide Susceptibility Zonation In The Northwestern Ethiopian Highlands
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https://doi.org/10.1007/s44475-026-00073-w <-- shared paper
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https://www.trtafrika.com/english/article/cd1fd3dd04f8 <-- shared (example) media article
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https://www.linkedin.com/pulse/how-climate-change-triggered-ethiopias-biggest-landslide-2ruwe/ <-- shared LI technical article
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https://www.eastafricanreview.com/2024/08/27/landslide-crisis-escalates-in-northern-ethiopia-historic-gonder-devastated-urgent-global-support-needed/ <-- shared media article
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H/T @Yohannes Dessalegn Girma | Production Geologist at Allied Gold Corporation
“Landslides represent a critical geo-environmental hazard in the Ethiopian highlands, specifically within the Telemt, Janamora, and Beyeda districts, where they frequently result in fatalities and the destruction of vital infrastructure and agricultural land. This study integrated Geographic Information Systems (GIS) and remote sensing data to perform landslide susceptibility mapping (LSM) using two distinct modeling approaches: the Frequency Ratio (FR) and the Analytical Hierarchy Process (AHP). A total of 140 data points (98 for training (70%) and 42 for testing (30%)) were considered for modeling and validation. Eleven geo-environmental conditioning factors: Slope, rainfall, lithology, density of lineaments, curvature, aspect, normalized difference vegetation index, land use/land cover, distance to drainage, drainage density, and soil were analyzed to evaluate their influence on landslide occurrence. According to the AHP model, 13.69% and 26.91% of the study area were classified as very high and high susceptibility zones, respectively. In comparison, the FR model identified 12.09% as very high and 23.78% as high susceptibility zones. Both models underscored that steep slopes, porphyritic basalt unit, high lineament density, proximity to drainage systems, and concave terrain curvature are the primary drivers of slope instability. Conversely, gentle slopes, sandstone unit, and dense vegetation cover were strongly associated with low-susceptibility regions. Model performance was validated using the Receiver Operating Characteristic (ROC) curve, yielding an Area Under the Curve (AUC) value of 0.812 for the AHP model and 0.793 for the FR model, alongside a prediction rate AUC value of 77.4% (0.774) for the AHP model and 70.4% for the FR model, confirming the predictive reliability of both methods. These results provide essential geospatial intelligence for policy-makers and stakeholders to guide disaster risk management, sustainable land-use planning, and targeted rehabilitation efforts in the region…”
#massmovement #landslide #Ethiopia #water #rainfall #precipitation #climatechange #GIS #spatial #mapping #spatialanalysis #topography #geology #soils #hydrogeomorphology #model #modeling #engineeringgeology #flowchart #methodology #fieldvalidation #risk #hazard #publicsafety #riskassessment #remotesensing #earthobservation #rockfall #debrisflow #earthflow #slope #aspect #landcover #landuse #drainage #hydrology #susceptibility