#landslide — Public Fediverse posts on home.social

Japan @[email protected] · 2026-09-05 · 10:40 UTC

Japan warns of landslides as heavy rain lashes Yakushima Island

Japan’s weather agency on Saturday continued to urge people to exercise maximum caution over the risk of landslides…
#EuropeSays #Japan #JP #heavyrains #landslide #Nihon #weatherwarning #yakushimaisland
https://www.europesays.com/japan/86509/

#yakushimaisland #weatherwarning #nihon #landslide #heavyrains #jp

Bombo Radyo News [Unofficial] @[email protected] · 2026-08-30 · 15:42 UTC

Himalayan Glacier Collapse Unleashes Deadly Floods, Nearly 800 Dead Along Nepal-China Border

https://web.brid.gy/r/https://news.bomboradyo.com/himalayan-glacier-collapse-unleashes-deadly-floods-nearly-800-dead-along-nepal-china-border/?utm_source=rss&utm_medium=rss&utm_campaign=himalayan-glacier-collapse-unleashes-deadly-floods-nearly-800-dead-along-nepal-china-border

#international #china #climatechange #disaster #featured #flashflood

💧🌏 Greg Cocks @[email protected] · 2026-08-18 · 03:21 UTC

Spatiotemporal Distribution, Climatic Factors, And Seasonal Precipitation Patterns Characterizing 66 Years Of Widespread Shallow Landslide Events In Piedmont (Northwestern Italy)
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https://doi.org/10.3389/feart.2026.1884300 <-- shared paper
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“Widespread shallow landslide events constitute one of the primary drivers of recurrent societal and economic losses in mountain regions. Although the influence of climate variability on landslide frequency and magnitude has been widely recognized, long-term, event-level analyses linking spatiotemporal landslide patterns to precipitation climatology at the regional scale remain scarce. This study presents a statistical analysis of 128 widespread shallow landslide events recorded in Piedmont, northwestern Italy, over the 66-year period 1960–2025, cross-referenced against the regional precipitation climatology. Events were characterized by season, primary physiographic unit, provincial coverage, and an ordinal magnitude index (scale 1–7) encoding combined spatial extent and estimated landslide count. Results indicate that no statistically significant monotonic trend in event frequency was detected over the study period (mean rate: 1.97 events yr⁻1); however, a moderate positive correlation was established between event magnitude and the number of provinces affected (R2 = 0.51, p < 0.00), validating the magnitude index as a proxy for spatial footprint. Mean event magnitude reached its highest value in the 2010s (3.71), while all five events of magnitude ≥ 6 occurred in autumn. The summer fraction of the catalogue increased markedly, from 6% in the 1960s to 31% in the 2000s and 25% in the 2020s, concurrent with stable or declining summer mean precipitation totals, consistent with Clausius–Clapeyron amplification of convective intensity under documented regional warming. Cross-analysis with regional records identifies three tiers of rainfall–landslide coupling: a stationary direct seasonal coupling; a non-stationary, strengthening intensity-mediated summer coupling; and a structural susceptibility-mediated spatial decoupling whereby the driest provinces generate the highest landslide occurrence frequencies due to the lower triggering thresholds characteristic of Tertiary Piedmont Basin sedimentary environments. A post-hoc assessment of the triggering thresholds used for regional shallow landslide early warning system demonstrates superior detection performance for high-magnitude autumn events (hit rate up to 89% for the 2000–2025 sub-period) and identifies sub-daily convective accumulation windows in summer as the primary domain requiring threshold recalibration…”
#massmovement #landslide #engineeringgeology #Italy #Piedmont #NorthernItaly #weather #rainfall #precipitation #climate #risk #hazard #corrleation #relationship #earlywarningsystems #damage #loss #community #infrastructure #mountain #spatiotemporal #mapping #spatialanalysis #statistics #geostatistics #climatology #regional #scale #weatherpatterns #physiography #geomorphology #water #hydrology #hydrogeomorphology #geology #soils

#massmovement #landslide #engineeringgeology #italy #piedmont #northernitaly

💧🌏 Greg Cocks @[email protected] · 2026-08-18 · 03:01 UTC

Widespread Landslide Activity in an Extreme Wet Season and Implications for Regional Sediment Management, Eastern San Francisco Bay Area, California
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https://doi.org/10.1029/2026EA005227 <-- shared paper
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H/T @amy East, Ph.D., P.G. | Researcher integrating geoscience and climate-change preparedness
“[This paper (link above) is] a collaboration with [the H/T’s] colleagues from [the] USGS Landslide Hazards Program, who mapped over 8,900 landslides in the eastern San Francisco Bay Area during an extreme wet winter.
How much sediment does such an extreme winter produce, from landslides or in stream discharge? How does that compare with long-term sediment production and landscape denudation rates?
[They] f[o]nd that landslide sediment mobilization is comparable to long-term denudation rates, emphasizing the role of extreme events in long-term sediment production. However, one extreme wet year has a negligible effect toward counteracting ongoing problems of sediment deficit in San Francisco Bay: to keep pace with sea-level rise, extreme wet conditions would need to occur in 50 out of the next 75 years…”
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"PLAIN LANGUAGE SUMMARY: Watersheds will likely produce more sediment in a warmer future with more extreme rain, primarily through landslides in steep terrain. This study examines how an extremely wet season affected sediment production and transport in the eastern San Francisco Bay area, California. By mapping and measuring 8,928 landslides, [they] found that rare, extreme rain conditions are likely responsible for the vast majority of long-term hillslope erosion rates in this region. However, due to long residence times for sediment on hillslopes and in stream channels, a maximum of 1%–2% of that newly mobilized landslide material could have potentially contributed to sediment carried by streams into the Bay that year. Even extremely wet years cannot provide enough sediment for Bay wetlands and shorelines to keep pace with rising sea levels. To meet the demand for sediment in the Bay, such extreme rain and sediment production would need to occur in most years, which is not realistic. To restore wetlands and protect shorelines, managers likely will need to supplement the coastal system with repurposed dredged material…”
#massmovement #soil #water #hydrology #hydrography #geology #soils #geomorphometry #hydrogeomorphology #geomorphology #landslide #masswasting #climatechange #extremeweather #precipitation #rainfall #weather #climate #mapping #engineeringgeology #mapping #SanFrancisco #BayArea #USA #California #fedscience #fedservice #oublicgood #sediment #stream #discharge #extremewinter #sealevelrise #SLR #hillslope #erosion #sedimentation #tidal #wetlands #coast #coastline #shoreline #GIS #spatial #spatialanalysis #spatiotemporal #watershed
#USGS | #USGSLandslideHazardsProgram

#massmovement #soil #water #hydrology #hydrography #geology

💧🌏 Greg Cocks @[email protected] · 2026-08-05 · 05:02 UTC

Flood And Landslide Susceptibility Assessment And Multi Hazard Interaction Mapping Using Machine Learning And GIS For Sustainable Settlement Planning In Nepal
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https://doi.org/10.1007/s44288-026-00670-8 <-- shared paper
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H/T @Narayan Thapa | Earth Data Modeling
“Nepal lies within an active seismic zone and is influenced by most dynamic climatic systems in the world. It faces compounding floods and landslide threats. Impacts are worst where multi-hazard interactions create spatially linked corridors. Despite frequent co-occurrence, national-scale assessments remain limited. This study presents machine learning and GIS-based approach to map nationwide susceptibility to floods, landslides, and identify their potential interaction zones, and delineate critical multi-hazard flow zones through spatial adjacency analysis. Using Google Earth Engine, the Random Forest model integrates topographic, climatic, environmental, and hydrological datasets to overcome subjective expert-driven methods. The model achieved strong predictive accuracy (AUC: 0.84 for floods, 0.85 for landslides). The results showed 19% of Nepal’s lowlands are medium to very highly susceptible to inundation, threatening approximately 900,000 people and over 3.4 million buildings; whilst in the hilly terrains, 40% is susceptible to slope-failure endangering 200,000 people and about 0.6 million buildings. K-means clustering followed by spatial adjacency analysis identified four spatial zonation: 81% of national area as low-hazard zone, 9% as flood-only zone, 5% as landslide-only zone, and 5% as interaction zones. Critical multi-hazard flow zone covering 7,588 km² represents spatially connected corridors linking interaction zones to downstream flood-prone populated areas, affecting 88 km² built-up land and 1,722 km² cropland. These zones represent susceptibility-based spatial connectivity rather than physically simulated cascading processes. These findings support recommendations for risk-informed land-use planning, resilient infrastructure development and climate adaptation aligned to sustainable development and investment risk screening…”
#GIS #spatial #mapping #GoogleEarthEngine #MachineLearning #RemoteSensing #GeospatialAI #DisasterRiskReduction #MultiHazard #ClimateAdaptation #climatechange #extremeweather #LandUsePlanning #SustainableDevelopment #InfrastructurePlanning #RiskAssessment #NaturalHazards #Nepal #EarthObservation #HinduKushHimalaya #HKH #HinduKush #Himalayas #risk #hazard #assessment #national #regional #spatialanalysis #spatiotemporal #massmovement #landslide #assessment #mitigation #water #hydrology #flood #flooding #sustainability

#gis #spatial #mapping #googleearthengine #machinelearning #remotesensing

💧🌏 Greg Cocks @[email protected] · 2026-06-09 · 15:13 UTC

On The Use Of Rainfall Time Series For Regional Landslide Prediction By Means Of Functional Regression
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https://doi.org/10.1016/j.enggeo.2026.108860 <-- shared paper
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#rainfallinduced #landslide #Functionalregression #Japan #Earlywarningsystem #Spacetimeprediction #spatialanalysis #spatiotemporal #massmovement #engineeringgeology #geomorphology #hydrogeomorphology #geomorphometry #remotesensing #geostatistics #model #modeling #mechanics #rainfall #threshold #precipitation #water #hydrology #risk #hazard #hazardassessment #terrain #landscape #landform #landuse #geology #soil #lithology #functionalGeneralizedAdditiveModel #FGAM #prediction #casestudy #AI #LLM

#rainfallinduced #landslide #functionalregression #japan #earlywarningsystem #spacetimeprediction

💧🌏 Greg Cocks @[email protected] · 2026-03-06 · 04:34 UTC

Karst Flash Floods - An Example From The Dinaric Karst (Croatia)
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https://doi.org/10.5194/nhess-6-195-2006 <-- shared (older) paper
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#flood #flooding #pluvial #fluvial #subsurface #risk #hazard #naturalhazard #disaster #flashflood #infrastructure #damage #building #destabilisation #foundations #karstburst #Marina #Dinaric #Croatia #extremeweather #climatechange #intense #prolonged #rainfall #precipitation #speed #fast #caves #conduits #fractures #aquifers #water #hydrology #groundwater #hydrogeology #waterpressure #sinkhole #subsidence #massmovement #landslide #monitoring #mapping #geology #engineeringgeology #hydroseismic #humanimpacts #lossoflife #death #karstterrain #springs #debris #sediment #cavities #riskassessment #Grazalema #Andalusia #Cadíz #Spain #StormLeonard #emergencymanagement #planning

#flood #flooding #pluvial #fluvial #subsurface #risk