#riskassessment — Public Fediverse posts on home.social

BGDon 🇨🇦 🇺🇸 👨‍💻 @[email protected] · 2026-09-09 · 19:03 UTC

Hey you know what? S_IT happens in space!

Sept 4, The US Space Force (USSF) reported 43 debris objects from the breakup Chinese satellite "Yaogan-50" that launched on March 15, 2026.

Cataloging space debris is one of USSF's core missions.

Yaogan-50 successfully reached its operational orbit but apparently experienced an "anomaly" as they say in space lingo. The fragments are at altitudes between 372 and 683 miles (600 and 1,100 km).

Yaogan-50 is a series of remote-sensing and reconnaissance satellites. Suggestions are these satellites are used for military surveillance and intelligence. China claims they are focused on national land surveys, crop yield estimation, and disaster prevention. https://gizmodo.com/chinese-satellite-breaks-apart-in-rare-orbit-that-could-hold-debris-for-centuries-2000808645

Learn more about Space Collision Avoidance Technolgies in Space in the TechAptitude post: https://techaptitude.substack.com/p/satellite-collision-avoidance-technology #Space #SpaceJunk #SpaceDebris #SSA #USSF #CollisionAvoidance #Conjunction #SpaceFlight #SpaceCraft #Satellite #China #Reconnaissance #RiskAssessment #Yaogan50

#space #spacejunk #spacedebris #ssa #ussf #collisionavoidance

BGDon 🇨🇦 🇺🇸 👨‍💻 @BrentD · 2026-09-09 · 19:03 UTC

Hey you know what? S_IT happens in space!

Sept 4, The US Space Force (USSF) reported 43 debris objects from the breakup Chinese satellite "Yaogan-50" that launched on March 15, 2026.

Cataloging space debris is one of USSF's core missions.

Yaogan-50 successfully reached its operational orbit but apparently experienced an "anomaly" as they say in space lingo. The fragments are at altitudes between 372 and 683 miles (600 and 1,100 km).

Yaogan-50 is a series of remote-sensing and reconnaissance satellites. Suggestions are these satellites are used for military surveillance and intelligence. China claims they are focused on national land surveys, crop yield estimation, and disaster prevention. https://gizmodo.com/chinese-satellite-breaks-apart-in-rare-orbit-that-could-hold-debris-for-centuries-2000808645

Learn more about Space Collision Avoidance Technolgies in Space in the TechAptitude post: https://techaptitude.substack.com/p/satellite-collision-avoidance-technology #Space #SpaceJunk #SpaceDebris #SSA #USSF #CollisionAvoidance #Conjunction #SpaceFlight #SpaceCraft #Satellite #China #Reconnaissance #RiskAssessment #Yaogan50

#space #spacejunk #spacedebris #ssa #ussf #collisionavoidance

Eric Maugendre on adapting @[email protected] · 2026-08-09 · 08:03 UTC

Society must anticipate: prepare not for this year's climate but for the future climates. 🧵

#futures #risks #risk #riskManagement #riskAssessment #livability #habitability #climatePolicy #policy #adaptation #plantations #forestry #climateDrift #climateChange #climateFailure #climateCollapse #drought #heatwaves #naturalDisasters #resilience #agriculture #megafires #wildfire #wildfires

#futures #risks #risk #riskmanagement #riskassessment #livability

Eric Maugendre on adapting @[email protected] · 2026-07-27 · 08:24 UTC

Accepting a hypothesis depends on the importance of being mistaken. It can be considered a judgment with ethics attached.
Without accounting for multiple values and preferences in policy design, scientific insights risk becoming politicized, potentially reinforcing dominant parties’ interests in funneling action while shifting risks to vulnerable populations.
Roger A. Pielke Jr. advocated for taking the role of saying "How about these other ideas?" instead of taking a position on currently debated topics.

ref. (2007). "The Honest Broker: Making Sense of Science in Policy and Politics" 🧩 🧵

#policy #uncertainty #uncertainties #robustness #probabilities #futures #anticipation #IAMs #science #modelling #modeling #risks #riskAssessment #unknowns #governance #bias #workCollectives #institutions #Pielke

#policy #uncertainty #uncertainties #robustness #probabilities #futures

💧🌏 Greg Cocks @[email protected] · 2026-06-17 · 05:25 UTC

Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
--
https://doi.org/10.1038/s44304-026-00217-4 <-- shared paper
--
https://doi.org/10.1038/s41598-025-22051-w <-- shared (earlier) paper
--
https://doi.org/10.1080/2150704X.2025.2488532 <-- 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

#kinnaur #permafrost #distribution #gis #spatial #mapping

💧🌏 Greg Cocks @[email protected] · 2026-06-17 · 05:25 UTC

Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
--
https://doi.org/10.1038/s44304-026-00217-4 <-- shared paper
--
https://doi.org/10.1038/s41598-025-22051-w <-- shared (earlier) paper
--
https://doi.org/10.1080/2150704X.2025.2488532 <-- 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

#kinnaur #permafrost #distribution #gis #spatial #mapping

💧🌏 Greg Cocks @[email protected] · 2026-06-17 · 05:25 UTC

Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
--
https://doi.org/10.1038/s44304-026-00217-4 <-- shared paper
--
https://doi.org/10.1038/s41598-025-22051-w <-- shared (earlier) paper
--
https://doi.org/10.1080/2150704X.2025.2488532 <-- 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

#kinnaur #permafrost #distribution #gis #spatial #mapping

💧🌏 Greg Cocks @[email protected] · 2026-06-17 · 05:25 UTC

Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
--
https://doi.org/10.1038/s44304-026-00217-4 <-- shared paper
--
https://doi.org/10.1038/s41598-025-22051-w <-- shared (earlier) paper
--
https://doi.org/10.1080/2150704X.2025.2488532 <-- 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

#avalanche #downstream #publicsafety #hep #infrastructure #riskassessment

💧🌏 Greg Cocks @GregCocks · 2026-06-17 · 05:25 UTC

Permafrost Distribution, Degradation, And Potential Mass Movement Cascades In The Western Himalaya Using Machine Learning And Numerical Models
--
https://doi.org/10.1038/s44304-026-00217-4 <-- shared paper
--
https://doi.org/10.1038/s41598-025-22051-w <-- shared (earlier) paper
--
https://doi.org/10.1080/2150704X.2025.2488532 <-- 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