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#modeling — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #modeling, aggregated by home.social.

  1. 💧🌏 Greg Cocks @GregCocks ·

    Nature-Based Strategies And Mechanisms That Mitigate Coastal Floods And Minimize Induced Flooding
    --
    doi.org/10.1016/j.csr.2026.105 <-- shared paper
    philiporton.com/wp-content/upl
    --
    nyc.gov/site/escr/about/resili <-- shared home page, Resiliency and Flood Protection, East Side Coastal Resiliency [NYC]
    --
    H/T @philip Orton | Associate Research Professor at Stevens Institute of Technology
    “How do coastal nature-based solutioNBS
    ns reduce flooding in one area and not induce flooding elsewhere? Conversely, what characteristics and processes can lead gray infrastructure to cause flooding outside of their protected area?”
    --
    “HIGHLIGHTS:
    • Coastal flood mitigation is studied with hydrodynamic and idealized semi-analytical modeling.
    • Mechanisms of flood wave dissipation, storage and reflection are quantified and evaluated.
    • A storm surge barrier for Jamaica Bay, New York, stops but reflects a 100-year flood wave.
    • Nature-based strategies can attenuate a storm surge with minimal induced flooding.
    • Partial reflection shifts the reflected storm surge wave phase, limiting water level increases..."

    #stormsurge #tide #floodmitigation #naturebasedsolutions #stormsurgebarriers #reflection
  2. 💧🌏 Greg Cocks @[email protected] ·

    Nature-Based Strategies And Mechanisms That Mitigate Coastal Floods And Minimize Induced Flooding
    --
    doi.org/10.1016/j.csr.2026.105 <-- shared paper
    philiporton.com/wp-content/upl
    --
    nyc.gov/site/escr/about/resili <-- shared home page, Resiliency and Flood Protection, East Side Coastal Resiliency [NYC]
    --
    H/T @philip Orton | Associate Research Professor at Stevens Institute of Technology
    “How do coastal nature-based solutioNBS
    ns reduce flooding in one area and not induce flooding elsewhere? Conversely, what characteristics and processes can lead gray infrastructure to cause flooding outside of their protected area?”
    --
    “HIGHLIGHTS:
    • Coastal flood mitigation is studied with hydrodynamic and idealized semi-analytical modeling.
    • Mechanisms of flood wave dissipation, storage and reflection are quantified and evaluated.
    • A storm surge barrier for Jamaica Bay, New York, stops but reflects a 100-year flood wave.
    • Nature-based strategies can attenuate a storm surge with minimal induced flooding.
    • Partial reflection shifts the reflected storm surge wave phase, limiting water level increases..."
    #stormsurge #tide #floodmitigation #naturebasedsolutions #stormsurgebarriers #reflection #NewYorkCity #newyork #NYC #water #model #hydrodynamic #modeling #coast #coastal #grayinfrastructure #infrastructure #mitigation #sustainability #sealevelrise #SLR #extremeweather #waterlevel #increase #inundation #distribution #movingtheproblem #JamaicaBay #casestudy #tides #tidal #floodplain #marsh #inlet #restoration #surgebarrier #levee #floodwavereflection #amplification #dissipation #engineering #naturalsystems

    #stormsurge #tide #floodmitigation #naturebasedsolutions #stormsurgebarriers #reflection
  3. 💧🌏 Greg Cocks @[email protected] ·

    HAZUS Estimated Average Annualized Losses for Earthquakes in the United States and its Territories [FEMA]
    --
    fema.gov/sites/default/files/d <-- shared document/report
    --
    fema.gov/flood-maps/tools-reso <-- shared resource, ‘National HAZUS Natural Hazard Loss Studies’
    --
    fema.gov/sites/default/files/d <-- shared document, ‘Informing Earthquake Risk with FEMA P-366 - Overview of the Report and How to Use It’
    --
    alturl.com/rzafx <-- shared web map & data portal, ‘HAZUS - P-366 Earthquake Risk/Loss Severity Viewer’
    --
    H/T @Kishor Jaiswal
    “The study reveals that the United States faces $15.8 billion per year in average annualized earthquake losses - an increase of about 7% since the 2023 update, largely driven by rising property values and growth in hazard prone areas. Yet the message is not all caution: modern seismic codes are working, with states like California seeing ~15% reductions in expected losses thanks to improved building practices.
    This update incorporates the 2023 USGS National Seismic Hazard Model, new population and building stock data, and advancements in HAZUS 6.1, offering communities a clearer picture of where risk is rising—and where targeted mitigation, such as retrofitting older unreinforced masonry and non ductile concrete buildings, can deliver substantial benefits.
    BOTTOM LINE: Earthquake risk is evolving, but smarter design, science-informed planning, and focused mitigation continue to shift the nation toward greater resilience…”
    #HAZUS #risk #hazard #naturalhazard #earthquake #geology #loss #cost #economics #development #infrastructure #buildingcodes #engineeringdesign #seismic #seismiccodes #seismology #USGS #NationalSeismicHazardModel #NSHM #population #demographics #hazardanalysis #mitigation #riskreduction #earthquakerisk #spatialanalysis #spatiotemporal #policy #planning #GIS #spatial #mapping #parameters #naturalhazard #USA #national #humanimpacts #P366 #EstimatedAnnualizedEarthquakeLosses #metrics #buildingexposure #opendata #model #modeling #assets #economicloss #SpectralAcceleration #AverageAnnualizedLoss #AverageAnnualizedLossRatios
    #FEMA | #USGS

    #hazus #risk #hazard #naturalhazard #earthquake #geology
  4. 💧🌏 Greg Cocks @[email protected] ·

    HAZUS Estimated Average Annualized Losses for Earthquakes in the United States and its Territories [FEMA]
    --
    fema.gov/sites/default/files/d <-- shared document/report
    --
    fema.gov/flood-maps/tools-reso <-- shared resource, ‘National HAZUS Natural Hazard Loss Studies’
    --
    fema.gov/sites/default/files/d <-- shared document, ‘Informing Earthquake Risk with FEMA P-366 - Overview of the Report and How to Use It’
    --
    alturl.com/rzafx <-- shared web map & data portal, ‘HAZUS - P-366 Earthquake Risk/Loss Severity Viewer’
    --
    H/T @Kishor Jaiswal
    “The study reveals that the United States faces $15.8 billion per year in average annualized earthquake losses - an increase of about 7% since the 2023 update, largely driven by rising property values and growth in hazard prone areas. Yet the message is not all caution: modern seismic codes are working, with states like California seeing ~15% reductions in expected losses thanks to improved building practices.
    This update incorporates the 2023 USGS National Seismic Hazard Model, new population and building stock data, and advancements in HAZUS 6.1, offering communities a clearer picture of where risk is rising—and where targeted mitigation, such as retrofitting older unreinforced masonry and non ductile concrete buildings, can deliver substantial benefits.
    BOTTOM LINE: Earthquake risk is evolving, but smarter design, science-informed planning, and focused mitigation continue to shift the nation toward greater resilience…”
    #HAZUS #risk #hazard #naturalhazard #earthquake #geology #loss #cost #economics #development #infrastructure #buildingcodes #engineeringdesign #seismic #seismiccodes #seismology #USGS #NationalSeismicHazardModel #NSHM #population #demographics #hazardanalysis #mitigation #riskreduction #earthquakerisk #spatialanalysis #spatiotemporal #policy #planning #GIS #spatial #mapping #parameters #naturalhazard #USA #national #humanimpacts #P366 #EstimatedAnnualizedEarthquakeLosses #metrics #buildingexposure #opendata #model #modeling #assets #economicloss #SpectralAcceleration #AverageAnnualizedLoss #AverageAnnualizedLossRatios
    #FEMA | #USGS

    #hazus #risk #hazard #naturalhazard #earthquake #geology
  5. 💧🌏 Greg Cocks @[email protected] ·

    HAZUS Estimated Average Annualized Losses for Earthquakes in the United States and its Territories [FEMA]
    --
    fema.gov/sites/default/files/d <-- shared document/report
    --
    fema.gov/flood-maps/tools-reso <-- shared resource, ‘National HAZUS Natural Hazard Loss Studies’
    --
    fema.gov/sites/default/files/d <-- shared document, ‘Informing Earthquake Risk with FEMA P-366 - Overview of the Report and How to Use It’
    --
    alturl.com/rzafx <-- shared web map & data portal, ‘HAZUS - P-366 Earthquake Risk/Loss Severity Viewer’
    --
    H/T @Kishor Jaiswal
    “The study reveals that the United States faces $15.8 billion per year in average annualized earthquake losses - an increase of about 7% since the 2023 update, largely driven by rising property values and growth in hazard prone areas. Yet the message is not all caution: modern seismic codes are working, with states like California seeing ~15% reductions in expected losses thanks to improved building practices.
    This update incorporates the 2023 USGS National Seismic Hazard Model, new population and building stock data, and advancements in HAZUS 6.1, offering communities a clearer picture of where risk is rising—and where targeted mitigation, such as retrofitting older unreinforced masonry and non ductile concrete buildings, can deliver substantial benefits.
    BOTTOM LINE: Earthquake risk is evolving, but smarter design, science-informed planning, and focused mitigation continue to shift the nation toward greater resilience…”
    #HAZUS #risk #hazard #naturalhazard #earthquake #geology #loss #cost #economics #development #infrastructure #buildingcodes #engineeringdesign #seismic #seismiccodes #seismology #USGS #NationalSeismicHazardModel #NSHM #population #demographics #hazardanalysis #mitigation #riskreduction #earthquakerisk #spatialanalysis #spatiotemporal #policy #planning #GIS #spatial #mapping #parameters #naturalhazard #USA #national #humanimpacts #P366 #EstimatedAnnualizedEarthquakeLosses #metrics #buildingexposure #opendata #model #modeling #assets #economicloss #SpectralAcceleration #AverageAnnualizedLoss #AverageAnnualizedLossRatios
    #FEMA | #USGS

    #fema #averageannualizedlossratios #averageannualizedloss #spectralacceleration #economicloss #assets
  6. 💧🌏 Greg Cocks @GregCocks ·

    HAZUS Estimated Average Annualized Losses for Earthquakes in the United States and its Territories [FEMA]
    --
    fema.gov/sites/default/files/d <-- shared document/report
    --
    fema.gov/flood-maps/tools-reso <-- shared resource, ‘National HAZUS Natural Hazard Loss Studies’
    --
    fema.gov/sites/default/files/d <-- shared document, ‘Informing Earthquake Risk with FEMA P-366 - Overview of the Report and How to Use It’
    --
    alturl.com/rzafx <-- shared web map & data portal, ‘HAZUS - P-366 Earthquake Risk/Loss Severity Viewer’
    --
    H/T @Kishor Jaiswal
    “The study reveals that the United States faces $15.8 billion per year in average annualized earthquake losses - an increase of about 7% since the 2023 update, largely driven by rising property values and growth in hazard prone areas. Yet the message is not all caution: modern seismic codes are working, with states like California seeing ~15% reductions in expected losses thanks to improved building practices.
    This update incorporates the 2023 USGS National Seismic Hazard Model, new population and building stock data, and advancements in HAZUS 6.1, offering communities a clearer picture of where risk is rising—and where targeted mitigation, such as retrofitting older unreinforced masonry and non ductile concrete buildings, can deliver substantial benefits.
    BOTTOM LINE: Earthquake risk is evolving, but smarter design, science-informed planning, and focused mitigation continue to shift the nation toward greater resilience…”

    |

    #hazus #risk #hazard #naturalhazard #earthquake #geology
  7. 💧🌏 Greg Cocks @[email protected] ·

    HAZUS Estimated Average Annualized Losses for Earthquakes in the United States and its Territories [FEMA]
    --
    fema.gov/sites/default/files/d <-- shared document/report
    --
    fema.gov/flood-maps/tools-reso <-- shared resource, ‘National HAZUS Natural Hazard Loss Studies’
    --
    fema.gov/sites/default/files/d <-- shared document, ‘Informing Earthquake Risk with FEMA P-366 - Overview of the Report and How to Use It’
    --
    alturl.com/rzafx <-- shared web map & data portal, ‘HAZUS - P-366 Earthquake Risk/Loss Severity Viewer’
    --
    H/T @Kishor Jaiswal
    “The study reveals that the United States faces $15.8 billion per year in average annualized earthquake losses - an increase of about 7% since the 2023 update, largely driven by rising property values and growth in hazard prone areas. Yet the message is not all caution: modern seismic codes are working, with states like California seeing ~15% reductions in expected losses thanks to improved building practices.
    This update incorporates the 2023 USGS National Seismic Hazard Model, new population and building stock data, and advancements in HAZUS 6.1, offering communities a clearer picture of where risk is rising—and where targeted mitigation, such as retrofitting older unreinforced masonry and non ductile concrete buildings, can deliver substantial benefits.
    BOTTOM LINE: Earthquake risk is evolving, but smarter design, science-informed planning, and focused mitigation continue to shift the nation toward greater resilience…”
    #HAZUS #risk #hazard #naturalhazard #earthquake #geology #loss #cost #economics #development #infrastructure #buildingcodes #engineeringdesign #seismic #seismiccodes #seismology #USGS #NationalSeismicHazardModel #NSHM #population #demographics #hazardanalysis #mitigation #riskreduction #earthquakerisk #spatialanalysis #spatiotemporal #policy #planning #GIS #spatial #mapping #parameters #naturalhazard #USA #national #humanimpacts #P366 #EstimatedAnnualizedEarthquakeLosses #metrics #buildingexposure #opendata #model #modeling #assets #economicloss #SpectralAcceleration #AverageAnnualizedLoss #AverageAnnualizedLossRatios
    #FEMA | #USGS

    #hazus #risk #hazard #naturalhazard #earthquake #geology
  8. 💧🌏 Greg Cocks @GregCocks ·

    Using Analytic Hierarchy Process to Determine Optimal Locations for Fire Observation Towers in Iran’s Shafarood Forest
    --
    alturl.com/8jkgq <-- shared technical article
    --
    en.wikipedia.org/wiki/Hyrcania <-- shared wiki page, Hyrcanian Forests جنگل‌های هیرکانی in Iran
    --
    H/T @Dionatas Martin
    “… Th[is] article explores how …GIS and the Analytic Hierarchy Process (AHP) were used to identify the best locations for fire observation towers in a forested region of Iran. By combining elevation, slope, road access, and visibility analysis, the research team created a data-driven model to maximize monitoring coverage and support forest protection efforts.
    … The methodology could be applied to everything from emergency management and environmental monitoring to public infrastructure and asset management.
    That's the power of GIS: turning complex questions into informed decisions…”
    --
    “Forests have been seriously affected by fire; therefore, it is necessary to detect fire from the observation towers. Currently, the number of observation towers and the effectiveness of their distribution is unknown. The aim of the current research is to determine the current and potential areas that can be seen from the observation towers. Another goal of this research is to propose a new network to maximize its effectiveness. The analysis of the field of view in …GIS software was done by integrating the digital height model and the road network map, vegetation cover, etc. to determine the entire surface of a field of view. It was found that the observation towers resulting from the designed model approximately cover the studied area for the supervision of the security staff. In addition to the designed model, an experienced expert staff took a close look at the field with GPS and according to the experiences, selected places for the construction of an observation tower and placed them on the designed model, and then witnessed the approximate coverage of the field visit…”

    #fuzzygammamodel #ahp #fieldofview #spatialanalysis #observationtowers #forestfire
  9. 💧🌏 Greg Cocks @[email protected] ·

    Using Analytic Hierarchy Process to Determine Optimal Locations for Fire Observation Towers in Iran’s Shafarood Forest
    --
    alturl.com/8jkgq <-- shared technical article
    --
    en.wikipedia.org/wiki/Hyrcania <-- shared wiki page, Hyrcanian Forests جنگل‌های هیرکانی in Iran
    --
    H/T @Dionatas Martin
    “… Th[is] article explores how …GIS and the Analytic Hierarchy Process (AHP) were used to identify the best locations for fire observation towers in a forested region of Iran. By combining elevation, slope, road access, and visibility analysis, the research team created a data-driven model to maximize monitoring coverage and support forest protection efforts.
    … The methodology could be applied to everything from emergency management and environmental monitoring to public infrastructure and asset management.
    That's the power of GIS: turning complex questions into informed decisions…”
    --
    “Forests have been seriously affected by fire; therefore, it is necessary to detect fire from the observation towers. Currently, the number of observation towers and the effectiveness of their distribution is unknown. The aim of the current research is to determine the current and potential areas that can be seen from the observation towers. Another goal of this research is to propose a new network to maximize its effectiveness. The analysis of the field of view in …GIS software was done by integrating the digital height model and the road network map, vegetation cover, etc. to determine the entire surface of a field of view. It was found that the observation towers resulting from the designed model approximately cover the studied area for the supervision of the security staff. In addition to the designed model, an experienced expert staff took a close look at the field with GPS and according to the experiences, selected places for the construction of an observation tower and placed them on the designed model, and then witnessed the approximate coverage of the field visit…”
    #fuzzygammamodel #AHP #fieldofview #spatialanalysis #observationtowers #forestfire #bushfire #fire #model #modeling #risk #hazard #alldataisspatial #GIS #spatial #mapping #ShafaroodForest #HyrcanianForest #GilmanProvince #Iran #forest #spatialanalysis #spatiotemporal #infrastructure #naturalresources #conservation #sustainability #observation #sightlines #elevation #fieldtesting #RangerGord #AnalyticHierarchyProcess #datadriven #monitoring #usecase

    #fuzzygammamodel #ahp #fieldofview #spatialanalysis #observationtowers #forestfire
  10. 💧🌏 Greg Cocks @[email protected] ·

    Using Analytic Hierarchy Process to Determine Optimal Locations for Fire Observation Towers in Iran’s Shafarood Forest
    --
    alturl.com/8jkgq <-- shared technical article
    --
    en.wikipedia.org/wiki/Hyrcania <-- shared wiki page, Hyrcanian Forests جنگل‌های هیرکانی in Iran
    --
    H/T @Dionatas Martin
    “… Th[is] article explores how …GIS and the Analytic Hierarchy Process (AHP) were used to identify the best locations for fire observation towers in a forested region of Iran. By combining elevation, slope, road access, and visibility analysis, the research team created a data-driven model to maximize monitoring coverage and support forest protection efforts.
    … The methodology could be applied to everything from emergency management and environmental monitoring to public infrastructure and asset management.
    That's the power of GIS: turning complex questions into informed decisions…”
    --
    “Forests have been seriously affected by fire; therefore, it is necessary to detect fire from the observation towers. Currently, the number of observation towers and the effectiveness of their distribution is unknown. The aim of the current research is to determine the current and potential areas that can be seen from the observation towers. Another goal of this research is to propose a new network to maximize its effectiveness. The analysis of the field of view in …GIS software was done by integrating the digital height model and the road network map, vegetation cover, etc. to determine the entire surface of a field of view. It was found that the observation towers resulting from the designed model approximately cover the studied area for the supervision of the security staff. In addition to the designed model, an experienced expert staff took a close look at the field with GPS and according to the experiences, selected places for the construction of an observation tower and placed them on the designed model, and then witnessed the approximate coverage of the field visit…”
    #fuzzygammamodel #AHP #fieldofview #spatialanalysis #observationtowers #forestfire #bushfire #fire #model #modeling #risk #hazard #alldataisspatial #GIS #spatial #mapping #ShafaroodForest #HyrcanianForest #GilmanProvince #Iran #forest #spatialanalysis #spatiotemporal #infrastructure #naturalresources #conservation #sustainability #observation #sightlines #elevation #fieldtesting #RangerGord #AnalyticHierarchyProcess #datadriven #monitoring #usecase

    #fuzzygammamodel #ahp #fieldofview #spatialanalysis #observationtowers #forestfire
  11. 💧🌏 Greg Cocks @[email protected] ·

    Using Analytic Hierarchy Process to Determine Optimal Locations for Fire Observation Towers in Iran’s Shafarood Forest
    --
    alturl.com/8jkgq <-- shared technical article
    --
    en.wikipedia.org/wiki/Hyrcania <-- shared wiki page, Hyrcanian Forests جنگل‌های هیرکانی in Iran
    --
    H/T @Dionatas Martin
    “… Th[is] article explores how …GIS and the Analytic Hierarchy Process (AHP) were used to identify the best locations for fire observation towers in a forested region of Iran. By combining elevation, slope, road access, and visibility analysis, the research team created a data-driven model to maximize monitoring coverage and support forest protection efforts.
    … The methodology could be applied to everything from emergency management and environmental monitoring to public infrastructure and asset management.
    That's the power of GIS: turning complex questions into informed decisions…”
    --
    “Forests have been seriously affected by fire; therefore, it is necessary to detect fire from the observation towers. Currently, the number of observation towers and the effectiveness of their distribution is unknown. The aim of the current research is to determine the current and potential areas that can be seen from the observation towers. Another goal of this research is to propose a new network to maximize its effectiveness. The analysis of the field of view in …GIS software was done by integrating the digital height model and the road network map, vegetation cover, etc. to determine the entire surface of a field of view. It was found that the observation towers resulting from the designed model approximately cover the studied area for the supervision of the security staff. In addition to the designed model, an experienced expert staff took a close look at the field with GPS and according to the experiences, selected places for the construction of an observation tower and placed them on the designed model, and then witnessed the approximate coverage of the field visit…”
    #fuzzygammamodel #AHP #fieldofview #spatialanalysis #observationtowers #forestfire #bushfire #fire #model #modeling #risk #hazard #alldataisspatial #GIS #spatial #mapping #ShafaroodForest #HyrcanianForest #GilmanProvince #Iran #forest #spatialanalysis #spatiotemporal #infrastructure #naturalresources #conservation #sustainability #observation #sightlines #elevation #fieldtesting #RangerGord #AnalyticHierarchyProcess #datadriven #monitoring #usecase

    #usecase #monitoring #datadriven #analytichierarchyprocess #rangergord #fieldtesting
  12. 💧🌏 Greg Cocks @[email protected] ·

    Using Analytic Hierarchy Process to Determine Optimal Locations for Fire Observation Towers in Iran’s Shafarood Forest
    --
    alturl.com/8jkgq <-- shared technical article
    --
    en.wikipedia.org/wiki/Hyrcania <-- shared wiki page, Hyrcanian Forests جنگل‌های هیرکانی in Iran
    --
    H/T @Dionatas Martin
    “… Th[is] article explores how …GIS and the Analytic Hierarchy Process (AHP) were used to identify the best locations for fire observation towers in a forested region of Iran. By combining elevation, slope, road access, and visibility analysis, the research team created a data-driven model to maximize monitoring coverage and support forest protection efforts.
    … The methodology could be applied to everything from emergency management and environmental monitoring to public infrastructure and asset management.
    That's the power of GIS: turning complex questions into informed decisions…”
    --
    “Forests have been seriously affected by fire; therefore, it is necessary to detect fire from the observation towers. Currently, the number of observation towers and the effectiveness of their distribution is unknown. The aim of the current research is to determine the current and potential areas that can be seen from the observation towers. Another goal of this research is to propose a new network to maximize its effectiveness. The analysis of the field of view in …GIS software was done by integrating the digital height model and the road network map, vegetation cover, etc. to determine the entire surface of a field of view. It was found that the observation towers resulting from the designed model approximately cover the studied area for the supervision of the security staff. In addition to the designed model, an experienced expert staff took a close look at the field with GPS and according to the experiences, selected places for the construction of an observation tower and placed them on the designed model, and then witnessed the approximate coverage of the field visit…”
    #fuzzygammamodel #AHP #fieldofview #spatialanalysis #observationtowers #forestfire #bushfire #fire #model #modeling #risk #hazard #alldataisspatial #GIS #spatial #mapping #ShafaroodForest #HyrcanianForest #GilmanProvince #Iran #forest #spatialanalysis #spatiotemporal #infrastructure #naturalresources #conservation #sustainability #observation #sightlines #elevation #fieldtesting #RangerGord #AnalyticHierarchyProcess #datadriven #monitoring #usecase

    #fuzzygammamodel #ahp #fieldofview #spatialanalysis #observationtowers #forestfire
  13. Hacker News @[email protected] ·

    Cartesian – AI 3D Modeling for Design

    formas.ai/cartesian

    Comments: news.ycombinator.com/item?id=4

    #HackerNews #AI #3D #Modeling #Design #Cartesian #Formas

    #hackernews #ai #3d #modeling #design #cartesian
  14. Hacker News @[email protected] ·

    Cartesian – AI 3D Modeling for Design

    formas.ai/cartesian

    Comments: news.ycombinator.com/item?id=4

    #HackerNews #AI #3D #Modeling #Design #Cartesian #Formas

    #hackernews #ai #3d #modeling #design #cartesian
  15. Hacker News @[email protected] ·

    Cartesian – AI 3D Modeling for Design

    formas.ai/cartesian

    Comments: news.ycombinator.com/item?id=4

    #HackerNews #AI #3D #Modeling #Design #Cartesian #Formas

    #hackernews #ai #3d #modeling #design #cartesian
  16. Hacker News @[email protected] ·

    Cartesian – AI 3D Modeling for Design

    formas.ai/cartesian

    Comments: news.ycombinator.com/item?id=4

    #HackerNews #AI #3D #Modeling #Design #Cartesian #Formas

    #formas #cartesian #design #modeling #3d #ai
  17. Hacker News @[email protected] ·

    Cartesian – AI 3D Modeling for Design

    formas.ai/cartesian

    Comments: news.ycombinator.com/item?id=4

    #HackerNews #AI #3D #Modeling #Design #Cartesian #Formas

    #hackernews #ai #3d #modeling #design #cartesian
  18. 💧🌏 Greg Cocks @GregCocks ·

    The Cooperative National Geologic Map [USGS, USA]
    --
    ngmdb.usgs.gov/nationalgeology/ <-- shared US web map / data portal
    --
    usgs.gov/programs/national-coo <-- share overview, USGS National Cooperative Geologic Mapping Program
    --
    ngmdb.usgs.gov/ <-- shared National Geologic Map Database
    --
    stategeologists.org/ <-- shared home of the Association of American State Geologists
    --
    usgs.gov/news/national-news-re <-- shared USGS technical news release
    --
    fox6now.com/news/new-usgs-map- <-- shared media video
    --
    H/T @kari Stockdale | Emergency Spill Responder in Upstate NY || @wayne R. Belcher, PhD, RG (OR) | Hydrogeologist
    “Our Nation's geology informs decisions about resource management, critical mineral assessment, water quality modeling, infrastructure development, and risks posed by natural hazards. The geologic layers were compiled using a standardized process, funded by the USGS National Cooperative Geologic Mapping Program [2nd link above]. For more site-specific information, refer to the detailed geologic maps, and resources available in the National Geologic Map Database [3rd link above] and from the Association of American State Geologists [4th link above]…”

    @USGS | @Association of American State Geologists

    #publicscience #cooperativenationalgeologicmap #geology #geologymapsmonday #opendata #usa
  19. 💧🌏 Greg Cocks @[email protected] ·

    The Cooperative National Geologic Map [USGS, USA]
    --
    ngmdb.usgs.gov/nationalgeology/ <-- shared US web map / data portal
    --
    usgs.gov/programs/national-coo <-- share overview, USGS National Cooperative Geologic Mapping Program
    --
    ngmdb.usgs.gov/ <-- shared National Geologic Map Database
    --
    stategeologists.org/ <-- shared home of the Association of American State Geologists
    --
    usgs.gov/news/national-news-re <-- shared USGS technical news release
    --
    fox6now.com/news/new-usgs-map- <-- shared #publicscience media video
    --
    H/T @kari Stockdale | Emergency Spill Responder in Upstate NY || @wayne R. Belcher, PhD, RG (OR) | Hydrogeologist
    “Our Nation's geology informs decisions about resource management, critical mineral assessment, water quality modeling, infrastructure development, and risks posed by natural hazards. The geologic layers were compiled using a standardized process, funded by the USGS National Cooperative Geologic Mapping Program [2nd link above]. For more site-specific information, refer to the detailed geologic maps, and resources available in the National Geologic Map Database [3rd link above] and from the Association of American State Geologists [4th link above]…”
    #CooperativeNationalGeologicMap #geology #GeologyMapsMonday #opendata #USA #publicscience #fedscience #fedservice #publicgood #resourcemanagement #criticalminerals #waterquality #model #modeling #infrastructure #development #risk #usecase #hazard #naturalhazard #NationalGeologicMapDatabase #GIS #spatial #mapping #Nation
    @USGS | @Association of American State Geologists

    #publicscience #cooperativenationalgeologicmap #geology #geologymapsmonday #opendata #usa
  20. 💧🌏 Greg Cocks @[email protected] ·

    The Cooperative National Geologic Map [USGS, USA]
    --
    ngmdb.usgs.gov/nationalgeology/ <-- shared US web map / data portal
    --
    usgs.gov/programs/national-coo <-- share overview, USGS National Cooperative Geologic Mapping Program
    --
    ngmdb.usgs.gov/ <-- shared National Geologic Map Database
    --
    stategeologists.org/ <-- shared home of the Association of American State Geologists
    --
    usgs.gov/news/national-news-re <-- shared USGS technical news release
    --
    fox6now.com/news/new-usgs-map- <-- shared #publicscience media video
    --
    H/T @kari Stockdale | Emergency Spill Responder in Upstate NY || @wayne R. Belcher, PhD, RG (OR) | Hydrogeologist
    “Our Nation's geology informs decisions about resource management, critical mineral assessment, water quality modeling, infrastructure development, and risks posed by natural hazards. The geologic layers were compiled using a standardized process, funded by the USGS National Cooperative Geologic Mapping Program [2nd link above]. For more site-specific information, refer to the detailed geologic maps, and resources available in the National Geologic Map Database [3rd link above] and from the Association of American State Geologists [4th link above]…”
    #CooperativeNationalGeologicMap #geology #GeologyMapsMonday #opendata #USA #publicscience #fedscience #fedservice #publicgood #resourcemanagement #criticalminerals #waterquality #model #modeling #infrastructure #development #risk #usecase #hazard #naturalhazard #NationalGeologicMapDatabase #GIS #spatial #mapping #Nation
    @USGS | @Association of American State Geologists

    #publicscience #cooperativenationalgeologicmap #geology #geologymapsmonday #opendata #usa
  21. 💧🌏 Greg Cocks @[email protected] ·

    The Cooperative National Geologic Map [USGS, USA]
    --
    ngmdb.usgs.gov/nationalgeology/ <-- shared US web map / data portal
    --
    usgs.gov/programs/national-coo <-- share overview, USGS National Cooperative Geologic Mapping Program
    --
    ngmdb.usgs.gov/ <-- shared National Geologic Map Database
    --
    stategeologists.org/ <-- shared home of the Association of American State Geologists
    --
    usgs.gov/news/national-news-re <-- shared USGS technical news release
    --
    fox6now.com/news/new-usgs-map- <-- shared #publicscience media video
    --
    H/T @kari Stockdale | Emergency Spill Responder in Upstate NY || @wayne R. Belcher, PhD, RG (OR) | Hydrogeologist
    “Our Nation's geology informs decisions about resource management, critical mineral assessment, water quality modeling, infrastructure development, and risks posed by natural hazards. The geologic layers were compiled using a standardized process, funded by the USGS National Cooperative Geologic Mapping Program [2nd link above]. For more site-specific information, refer to the detailed geologic maps, and resources available in the National Geologic Map Database [3rd link above] and from the Association of American State Geologists [4th link above]…”
    #CooperativeNationalGeologicMap #geology #GeologyMapsMonday #opendata #USA #publicscience #fedscience #fedservice #publicgood #resourcemanagement #criticalminerals #waterquality #model #modeling #infrastructure #development #risk #usecase #hazard #naturalhazard #NationalGeologicMapDatabase #GIS #spatial #mapping #Nation
    @USGS | @Association of American State Geologists

    #publicscience #cooperativenationalgeologicmap #geology #geologymapsmonday #opendata #usa
  22. 💧🌏 Greg Cocks @[email protected] ·

    The Cooperative National Geologic Map [USGS, USA]
    --
    ngmdb.usgs.gov/nationalgeology/ <-- shared US web map / data portal
    --
    usgs.gov/programs/national-coo <-- share overview, USGS National Cooperative Geologic Mapping Program
    --
    ngmdb.usgs.gov/ <-- shared National Geologic Map Database
    --
    stategeologists.org/ <-- shared home of the Association of American State Geologists
    --
    usgs.gov/news/national-news-re <-- shared USGS technical news release
    --
    fox6now.com/news/new-usgs-map- <-- shared #publicscience media video
    --
    H/T @kari Stockdale | Emergency Spill Responder in Upstate NY || @wayne R. Belcher, PhD, RG (OR) | Hydrogeologist
    “Our Nation's geology informs decisions about resource management, critical mineral assessment, water quality modeling, infrastructure development, and risks posed by natural hazards. The geologic layers were compiled using a standardized process, funded by the USGS National Cooperative Geologic Mapping Program [2nd link above]. For more site-specific information, refer to the detailed geologic maps, and resources available in the National Geologic Map Database [3rd link above] and from the Association of American State Geologists [4th link above]…”
    #CooperativeNationalGeologicMap #geology #GeologyMapsMonday #opendata #USA #publicscience #fedscience #fedservice #publicgood #resourcemanagement #criticalminerals #waterquality #model #modeling #infrastructure #development #risk #usecase #hazard #naturalhazard #NationalGeologicMapDatabase #GIS #spatial #mapping #Nation
    @USGS | @Association of American State Geologists

    #nation #mapping #spatial #gis #nationalgeologicmapdatabase #naturalhazard
  23. Nicolas Fränkel 🇪🇺🇺🇦🇬🇪 @[email protected] ·

    A short but fun post about the gotchas of #modeling the world's #geography in #softwaresystems.

    Years ago, a colleague of mine told me about two pockets of foreign land surrounded by Switzerland:
    Campione d'Italia and Büsingen am Hochrhein, respectively Italy and Germany.

    blog.frankel.ch/world-geograph

    #modeling #geography #softwaresystems
  24. 💧🌏 Greg Cocks @[email protected] ·

    Sub-Seasonal Forecasting Of Cropland Productivity Anomalies Using Satellite Soil Moisture In Water-Limited Environments
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    H/T @Adebowale Daniel Adebayo | Doctoral Candidate | Geospatial Data Scientist
    “When rainfall fails and evaporative demand climbs, root-zone soil moisture is where the deficit registers first and where it carries forward, weeks before crop condition reflects it. In this study [link above], [the authors] used SMAP root-zone soil moisture to forecast crop productivity anomalies across drought-prone croplands of Eastern and Southern Africa, and [they] quantified how much predictive value soil moisture actually carries, over what lead times, and under which hydroclimatic conditions. The contribution of soil moisture was negligible at short leads but grew steadily out to 40 days, and it concentrated in water-limited croplands where soil moisture and vegetation are most tightly coupled. During the 2024 southern African El Niño drought, the soil moisture informed model resolved the spatial pattern of productivity anomalies roughly a month in advance.
    Beyond the results themselves, this work is a pointer to how much predictive information soil moisture holds. Leveraging the temporal record of SMAP-related products together with the 100–200 metre resolution NISAR will deliver gives us, [believes the H/T], a clear path to attempt field-scale drought forecasting in the smallholder landscapes where early warning matters most…”
    #agriculture #crops #foodsecurity #cropland #productivity #forecasting #rootzone #soilmoisture #SMAP #NIRV #subseasonal #prediction #EasternAfrica #SouthernAfrica #africa #vegetation #anomaly #precipitation #ET #drought #extremeweather #water #hydrology #hydroclimate #productivity #arid #waterlimited #spatialanalysis #spatiotemporal #model #modeling #vegetation #Africa #ElNino #ElNiño #mitigation #prediction #forecast #smallholders #earlywarning #RZSM #SoilMoistureActivePassive #NearInfraredReflectance #NIR #remotesensing #earthobservation

    #agriculture #crops #foodsecurity #cropland #productivity #forecasting
  25. 💧🌏 Greg Cocks @[email protected] ·

    Remote Sensing Of Tracer Dye Concentrations To Support Dispersion Studies In River Channels
    --
    doi.org/10.1080/24705357.2019. <-- shared paper (Kootenai)
    --
    usgs.gov/publications/remote-s <-- shared USGS publication (Kootenai)
    --
    doi.org/10.5066/P9V3Y334 <-- shared USGS Science Base page [Korea]
    --
    doi.org/10.1080/24705357.2019. <-- shared paper [Korea]
    --
    usgs.gov/data/hyperspectral-im <-- shared USGS publication [Korea]
    -
    timesofindia.indiatimes.com/sc <-- shared media article
    --
    H/T @USGS
    “In September 2017, the US Geological Survey (#USGS) carried out an unusual experiment on Idaho’s Kootenai River. Researchers released 72.57 kg (160 pounds) of Rhodamine WT dye into the river and then followed its movement through the water. The aim was to understand how a substance spreads as it travels through a flowing river.
    According to USGS, the dye was released from a single point at the Kootenai Tribal Fish Hatchery on September 26, 2017. The entire release took just 90 seconds. Researchers then had to track what happened to the dye as it moved downstream and spread across the river.
    They used two methods to follow the plume. Instruments placed in the river measured the dye directly, while an aircraft flew over the Kootenai River the following day, capturing images of the plume from above. Together, the two sets of observations helped researchers study how the dye changed as it moved through the river.
    The aircraft carried a hyperspectral imaging system, which can record information from different parts of the electromagnetic spectrum rather than taking an ordinary photograph. It flew about 1,000 metres above the ground while making several passes along the river. The images showed the changing plume across the river channel…
    The experiment was not simply about photographing a coloured patch moving through the Kootenai River. The researchers were testing whether remote sensing could help measure the concentration of a tracer dye as it spread…”
    --
    #GIS #spatial #mapping #riverchannel #dispersion #tracer #Rhodaminedye #concentration #hyperspectral #imaging #remotesensing #numericalflowmodel #Kootenai #Idaho #FirstNation #Korea #movementtracing #concentration #instrumentation #water #hydrology #hydrography #flow #network #model #modeling #fluiddynamics #ROV #sUAS
    @USGS

    #gis #spatial #mapping #riverchannel #dispersion #tracer
  26. 💧🌏 Greg Cocks @[email protected] ·

    Interferometric Synthetic Aperture Radar (InSAR) For Monitoring Seasonal Snow
    --
    doi.org/10.1029/2025WR042866 <-- shared paper
    --
    eos.org/features/satellite-rad <-- shared technical article
    --
    H/T @Jack Tarricone, PhD | Assistant Research Scientist @ NASA GSFC/UMD ESSIC | Remote Sensing and Snow Hydrology
    “[The authors] review[ed] 25 years of progress in using InSAR to measure changes in snow water equivalent (SWE) and snow depth and discuss[ed] what’s needed to extend these methods to basin-scale snow monitoring with NISAR. [They] hope it’s a useful resource for people interested in snow, SAR/InSAR, remote sensing, and hydrology in general…”
    #GIS #spatial #mapping #remotesensing #earthobservation #InterferometricSyntheticApertureRadar #InSAR #literaturereview #research #history #monitoring #spatialanalysis #spatiotemporal #seasonal #snow #water #hydrology #waterresources #snowpack #snowmelt #ablation #melt #runoff #snowwaterequivalent #SWE #NISAR #snowdepth #basin #snowphase #estimation #change #spatial #GIS #mapping #temporal #model #modeling #algorithm #ecosystems #environment #habitat #agriculture #farming #snowmass #satellite

    #gis #spatial #mapping #remotesensing #earthobservation #interferometricsyntheticapertureradar
  27. 💧🌏 Greg Cocks @[email protected] ·

    A Coastal Exposure Index For Ireland - Relative Hazard Exposure And The Protective Role Of Coastal Habitats
    --
    doi.org/10.1007/s11069-026-083 <-- shared paper
    --
    alturl.com/4e83i <-- shared webmap / data portal
    --
    independent.ie/irish-news/reve <-- shared media article
    --
    [“The index complements site-specific flood and erosion modelling.”]
    H/T @kevin Walsh | PhD Researcher at University College Cork
    “In this paper [1st link above], [the authors] seek to answer the question “what is the relative distribution of exposure to coastal hazards around the Irish coast, and where can coastal habitats potentially reduce exposure?”
    The method [they] chose to achieve this was the Coastal Vulnerability Model (CVM), within the InVEST software suite. This involves combining the key physical and climatic variables which can show us where along the coast might be most adversely affected by extreme winds, powerful waves and surges, and sea level rise.
    It also reveals the potential protection provided by coastal habitats, such as dunes and saltmarsh, as well as populations and sites of cultural heritage located within zones deemed “Very High Exposure”.
    [They] tested variations to the model, including seeing how the results change when certain variables are left out, what happens if we add in other variables, and how ranking choices impact exposure distribution. The results show that these choices, especially the ranking methods, can have large impacts on hazard distribution and the overall interpretation of the results. However, [they] found that the CVM quintile based approach was most suitable for Ireland, as it reveals high exposure sites on the east coast, which tends to be overlooked when different methods are applied.
    [The H/T] also prepared an interactive map, where you can view the results in detail [2nd link above]
    There’s a lot of work ongoing in this space around Ireland, [they] hope this will be a valuable contribution towards sustainable management of our coastline…”
    #spatialanalysis #exposure #coastal #hazards #mitigation #risk #Ireland #Irish #coast #CoastalVulnerabilityModel #CVM #InVEST #model #modeling #spatiotemporal #geostatistics #statistics #spatial #mapping #geomorphology #geology #climate #wind #wave #tidal #erosion #stormsurge #storms #ocean #sealevelrise #climatechange #extremeweather #dunes #saltmarshes #machair #seagrass #habitat #protection #vulnerability #parametersensitivity #exposure #sustainable #populationpressure #CoastalExposureIndex #culturalheritage #coastalzonemanagement #planning #policy #evidencebased #adaption #strategies

    #spatialanalysis #exposure #coastal #hazards #mitigation #risk
  28. 💧🌏 Greg Cocks @[email protected] ·

    Cities In Great Britain Most Vulnerable To Extreme Heat Revealed
    (OS index examines which ‘urban heat islands’ suffer the most – and which cope the best with rising temperatures)
    --
    theguardian.com/environment/20 <-- shared media article
    --
    ordnancesurvey.co.uk/news/new- <-- shared technical article, OS Heat Vulnerability Index
    --
    H/T @joe Clarkson | Campaign Manager - Diffusion PR
    “[The H/T] work[ed] … closely with Ordnance Survey (OS) on new research into which of Britain's cities are most vulnerable to retaining extreme heat, and how that vulnerability shifts between now and the turn of the century as record temperatures continue to climb…
    The newly commissioned OS Heat Vulnerability Index scored 71 cities across Britain using #4EI satellite temperature readings, @Met Office climate modelling projections from 2040 to 2100, and Ordnance Survey data on the makeup of natural and made environments in our cities. Put together, it measures how global warming will impact future heat vulnerability, and how urban heat islands form; a process in which hard surfaces absorb solar radiation through the day to keep centres warm overnight, while rural environments cool.
    Anyone who has been unfortunate enough to stand on a Tube platform or walk through central London this summer, or tried to sleep through a heatwave in a flat that hasn't cooled in months already knows what retained heat feels like. But it's more than an uncomfortable feeling. Extreme heat is claiming an increasing number of lives each year, and location-based intelligence like this is essential to support decisions on how we effectively mitigate and protect against climate change to save lives and protect critical infrastructure.
    What this analysis does is put a number on extreme heat, and shows where it's heading. As Britain's climate continues to warm, the capacity of our cities to cool themselves naturally will only matter more…”
    #ClimateChange #Heat #UrbanHeat #Heatwave #Climate #Warming #Infrastructure #London #Portsmouth #Resilience #OrdnanceSurvey #Geospatial #Data #extremeweather #extremeweather #publichealth #publicsafety #deaths #UK #GreatBritain #OS #HeatVulnerabilityIndex #vulnerability #spatialanalysis #mapping #model #modeling #analysis #spatiotemporal #climate #climatemodeling #spatial #mapping #remotesensing #earthobservation #solarradiation #natural #manmade #retainedheat #mitigation #planning #policy #infrastructure #concrete #asphalt #buildings #hardsurfaces #globalwarming #urbanheatislands #cities #urban
    @Ordnance Survey

    #4ei #climatechange #heat #urbanheat #heatwave #climate
  29. 💧🌏 Greg Cocks @[email protected] ·

    On-Demand Global Landsat Evapotranspiration Product - Development, Evaluation, And Dissemination
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    espa.cr.usgs.gov <-- shared (open data) USGS EROS Science Processing Architecture (ESPA) platform
    --
    etdata.org/ <-- OpenET SSEBop platform implementation (water management)
    --
    usgs.gov/landsat-missions/land <-- shared USGS Landsat Collection 2 Provisional Actual Evapotranspiration Science Product
    --
    H/T @mac Friedrichs | Remote Sensing Scientist, KBR | USGS EROS
    “This paper summarizes new achievements in developing and distributing the Global Landsat Level-3 Evapotranspiration (ET) product. It is freely available through the USGS EROS Science Processing Architecture (ESPA) platform (2nd link above). 🛰️ …
    Since the product launch in June 2020, there have been over 1.2 million Landsat-based ET orders around the world. This indicates increasing awareness and application of the ET data to help understand and manage the relationships among food, energy, and water resources. 🌽💧 …
    It features the ESPA workflow and evaluation of the upgraded SSEBop model using a variety of observational datasets and hydrologic regions, and examination against OpenET SSEBop platform implementation (3rd link above.)…”
    --
    “HIGHLIGHTS
    • ESPA platform allows access to on-demand, global, Landsat-based, ET products.
    • SSEBop model has been used to create ET data since 1982 through ESPA.
    • A quick estimation of field-scale crop consumptive water use can be achieved.
    • Numerous orders reflect worldwide extensive interest and utilization of the data.
    • Method, workflow, and performance of the actual ET data are presented in the study..."
    #EROSScienceProcessingArchitecture #climate #global #Evapotranspiration #ET #GIS #spatial #mapping #remotesensing #earthobservation #water #hydrology #opendata #Landsat #OpenET #SSEBop #WaterManagement #Agriculture #USGS #EROS #datadelivery #food #foodsecurity #energy #water #watersecurity #model #modeling #ESPA #farming #cropland #wateruse #waterresources #workflow
    @USGS @EROS

    #erosscienceprocessingarchitecture #climate #global #evapotranspiration #et #gis
  30. 💧🌏 Greg Cocks @[email protected] ·

    The Radiative Effects Of Water Vapour From Terrestrial Evapotranspiration
    --
    doi.org/10.1088/1748-9326/adde <-- shared paper/letter
    --
    zenodo.org/records/15413219 | zenodo.org/records/15416936 <-- shared open data, for “Model information and output for "The radiative effects…” ”
    --
    doi.org/10.1007/s11269-025-041 <-- shared paper
    --
    H/T @jan Umsonst | Earth System Nerd
    “Water vapour accounts for roughly 50% of the modern greenhouse effect. Over continental regions, evapotranspiration (ET) is often limited by water availability. In this study, [the authors] spatially quantify how much of the total atmospheric water vapour evaporated most recently from land and calculate the relative contribution of that water vapour to the atmospheric radiative budget. Using a combination of tracer-enabled Earth system model simulations and radiative transfer calculations, [they were] able to explicitly quantify the 3D distribution of terrestrial vs. oceanic water vapour, and the spatial contribution of each to the surface and top of atmosphere radiative budgets. [They found] that over many continental regions, more than half of the total column-integrated water vapour originates from land ET, and that this vapour contributes up to 30 W/m² of longwave radiation into the surface in the annual mean (about 10% of the total). Understanding how terrestrial ET impacts the base-state of water vapour distribution and the water vapour greenhouse effect is critical to understanding how and where changes in terrestrial ET, driven by climate change, land use, etc, will modify the radiative properties of the atmosphere and thus the climate system…”
    #water #hydrology #greehouseeffect #highperformancecomputing #HPC #evapotranspiration #Radiative #WaterVapour #spatial #spatialanalysis #spatiotemporal #atmosphere #model #modeling #earthsystemmodelling #terrestrial #oceanic #vapour #climatechange #landuse #changes #climatesystem

    #water #hydrology #greehouseeffect #highperformancecomputing #hpc #evapotranspiration
  31. 💧🌏 Greg Cocks @[email protected] ·

    Mapping Flood Agents In The Northern Pantanal Wetland Using Multiple Spatio-Temporal GIS And Remote Sensing Techniques
    --
    doi.org/10.1007/s13157-026-020 <-- shared paper
    --
    H/T @renato Gatto de Morais | Geógrafo | Mestre em Recursos Hídricos | Doutorando em Geografia
    “Combining MODIS data, TRMM, river discharge, a digital terrain model, and multinomial logistic regression, [the authors] identified three predominant classes of flood origin: local rainfall, bank overflow, and mixed areas. The model achieved a Nagelkerke Pseudo-R² of 0.63 and a classification accuracy of up to 81.2% (10-fold cross-validation). To the best of [their] knowledge, this is the first explicit approach to mapping flood agents for this wetland, a component that has historically remained unmapped despite its recognized influence on habitats and biodiversity…”
    --
    “The mapping of functional hydrologic units is crucial for enhancing our understanding of flooding and hydroecological processes in large wetlands. These units are typically defined by flooding frequency, duration, and magnitude, but another important hydrologic characteristic is the origin of flooding, known as the flood agent. This study presents an empirical framework utilizing remote sensing and GIS procedures for modeling flood agents in the northern Pantanal wetland. Eleven spatial data layers were derived from multi-year MODIS flood maps, daily rainfall estimates from the TRMM 3B42 grid, in situ discharge data, the BEST Digital Terrain Model, and a hydrographic network layer. These layers were tested for their predictive power in a multinomial logistic regression mapping model. Model performance metrics, along with qualitative validation of mapping outcomes using in situ flooding measurements, and vegetation and soil data from field test sites, support the plausibility of the proposed mapping scheme. However, they also highlight the challenges of flood agent mapping in large tropical wetlands…”
    #GIS #spatial #mapping #MODIS #TRMM #riverdischarge #digitalterrainmodel #multinomiallogisticregression #geostatistics #Pantanal #Cuiaba #Brazil #water #hydrology #spatialanalysis #spatiotemporal #remotesensing #earthobservation #flood #flooding #source #type #floodagent #tropical #wetland #habitat #biodiversity #ecosystem #hydrologicunit #hydroecology #model #modeling #rainfall #precipitation #weather #climate #discharge #network #metrics

    #gis #spatial #mapping #modis #trmm #riverdischarge
  32. 💧🌏 Greg Cocks @[email protected] ·

    Historic Snowfall Map Shows States Bracing For Significant Snow Amid El Niño [North America]
    --
    mensjournal.com/news/historic- <-- shared technical media article
    --
    cpc.ncep.noaa.gov/products/ana <-- shared link to NOAA release ‘El Niño/Southern Oscillation (Enso) Diagnostic Discussion - issued By Climate Prediction Center/NCEP/NWS | 13 August 2026’
    --
    cnn.com/2026/08/20/weather/sup <-- shared media article
    --
    H/T @Mens Journal
    “The National Oceanic and Atmospheric Association [NOAA] updated its prediction for the El Niño, which is strengthening and now has a greater than 90% chance of a very strong event during the Northern Hemisphere fall and winter 2026-27…
    Additionally, the NOAA revealed that during the October-December 2026 season, there is now a 69% chance of a historic event that would exceed the strength of previous El Niño events dating back to 1950. Historical records from the NOAA only date back to 1950, but other reports suggest this could be one of the strongest El Niños in history.
    This El Niño “is something that’s very unusual, if not a once in a lifetime type of (strength),” according to Nat Johnson, a meteorologist with the Geophysical Fluid Dynamics Laboratory at the National Oceanic and Atmospheric Administration.
    El Niño is a natural climate cycle marked by warmer than average water temperatures along the equator in the Pacific Ocean. The warmer water triggers corresponding shifts in the atmosphere that have a domino-like influence on weather patterns around the globe – usually in ways that are largely predictable well in advance…”
    #weather #climate #USA #forecast #fedscience #fedservice #NOAA #ElNiño #ElNino #snow #snowfall #climatecycle #water #hydrology #rainfall #precipitation #NorthAmerica #atmosphere #weatherpatterns #model #modeling #spatialanalysis #spatiotemporal #spatial #mapping #NorthernHemisphere #global
    @NOAA | @nws

    #weather #climate #usa #forecast #fedscience #fedservice
  33. 💧🌏 Greg Cocks @[email protected] ·

    Modeling Climate Change Impacts On Blue And Green Water In The Ethiopian Upper Blue Nile Basin
    --
    doi.org/10.1016/j.ejrh.2026.10 <-- shared paper
    --
    H/T @Dessalegn worku Ayalew
    “The present study assesses the impacts of climate change on blue and green water in the Kessie Watershed of the Ethiopian Upper Blue Nile Basin using the SWAT+ model.
    [The authors] set up [a] SWAT+ model using quality-controlled and homogenized observational climate time series… and calibrated it using a multisite calibration approach. [They] selected CMIP6 climate models for future simulations and bias-correction methods through a comprehensive performance assessment... Building on these previous studies, the present research further evaluates the reliability of combining robust climate-model selection with optimal bias-correction methods to improve the reliability of hydrological simulations. [They] then used the best-performing climate models, bias-corrected using the optimal methods, to project future changes in blue and green water in the study area…
    KEY FINDINGS:
    • SWAT+ effectively represented hydrological processes across multiple gauging stations in the Ethiopian Upper Blue Nile Basin.
    • Ensembles of CMIP6 climate models improved the reliability of hydrological simulations compared with individual climate models.
    • Optimized climate-model selection reduced biases in hydrological simulations more than bias correction alone.
    • Arbitrary selection of climate models can degrade hydrological simulations, even when their outputs are bias-corrected using robust methods.
    • Both blue and green water are projected to increase under future climate change in the Ethiopian Upper Blue Nile Basin.
    • Blue water exhibits greater seasonality and climate sensitivity than green water flow and green water storage.
    The study also provides sustainable water management options for adapting to the impacts of climate change, with implications for water resource planning and management in the Upper Blue Nile Basin…”
    #Bluewater #Greenwater #CMIP6 #GCMs #Modelensemble #SSPscenarios #SWAT #Ethopia #UpperNile #Nile #NileBasin #gaging #gauging #Africa #climatechange #impacts #water #hydrology #KessieWatershed #EthiopianUpperBlueNileBasin #model #modeling #spatialanalysis #spatiotemporal #CMIP6 #waterresources #watermanagement #ecosystem #habitat #environment

    #bluewater #greenwater #cmip6 #gcms #modelensemble #sspscenarios
  34. 💧🌏 Greg Cocks @[email protected] ·

    Challenges In The Use Of Local Data For Regional Scale Mapping Of C And N Stocks In The Continuous Permafrost Zone At The Yukon Coastal Plain | Heatwave Risks To Tipping Point Of Permafrost
    --
    doi.org/10.5194/soil-12-113-20 <-- shared paper
    --
    doi.org/10.1038/s41558-026-026 <-- shared paper
    --
    theguardian.com/environment/20 <-- shared media article
    --
    cbc.ca/news/canada/north/perma <-- shared media article
    --
    [putting together two different ‘sorts’/focuses of research/reporting, but…]
    H/T @gustaf Hugelius | Professor at Stockholm University
    --
    “Permafrost soils are particularly vulnerable to climate change. To assess and improve estimations of carbon (C) and nitrogen (N) budgets it is necessary to accurately map soil carbon and nitrogen in the permafrost region. In particular, soil organic carbon (SOC) stocks have been predicted and mapped by many studies from local to pan-Arctic scales. Several studies have been carried out at the Canadian Beaufort Sea coast, though no regional maps of terrestrial carbon stocks based on spatial modelling has been conducted yet. This study combines available field data from the Canadian Yukon coastal plain and uses it to map regional SOC and N stocks using the machine learning algorithm random forest and environmental variables based on remote sensing data. [The authors] developed models using the data for the entire region and separate models for the coastal mainland area and Qikiqtaruk Herschel Island. Each model was used to map SOC and N stocks for its respective area. [They] assessed the performance of the different random forest models by using crossvalidation. [They] further assessed model results using the Area of Applicability (AOA) method and the quantile regression forest approach, comparing the results and discussing their implications within the context of both methods. [They] explore[d] local differences in soil properties and how soil data distribution across the region affects the accuracy of the predictions of SOC and N stocks..."
    #permafrost #soils #geology #climatechange #temperature #thawing #melting #emissions #CO2 #methane #carbon #nitrogen #GIS #spatial #mapping #Qikiqtaruk #HerschelIsland #Yukon #Canada #soilorganiccarbon #SOC #arctic #cryosphere #BeaufortSea #coast #coastal #machinelearning #model #modeling #remotesensing #earthobservation #carbonstocks #island #mainland #spatialanalysis #scale

    #permafrost #soils #geology #climatechange #temperature #thawing
  35. 💧🌏 Greg Cocks @[email protected] ·

    Comparing DESIS Hyperspectral and Landsat 10 Simulated Superspectral Data for Crop Type Classification in California’s Central Valley
    --
    doi.org/10.3390/rs18142282 <-- shared paper
    --
    usgs.gov/publications/comparin <-- shared USGs publication page
    --
    H/T @USGS
    “How can we get better at classifying crops from space? 🛰️🌽
    Some types of satellite imagery are better at distinguishing crops than others. A USGS study compared two approaches and found one produced more accurate crop maps.
    Here's what the researchers found:
    • Hyperspectral imagery achieved 86% accuracy, compared to 75% for simulated superspectral imagery.
    • Using just 14 carefully selected hyperspectral bands produced nearly the same results as using all 60 DLR Earth Sensing Imaging Spectrometer (DESIS) bands, showing those wavelengths contain much of the information needed to distinguish crop types.
    • Using superspectral imagery on the upcoming Landsat 10 mission will allow for routine tracking of crops and enhance finer crop mapping.
    • The findings help scientists identify which wavelengths provide the most useful information for crop mapping and future remote sensing applications.
    Better crop maps will help governments and scientists track global agriculture, monitor the current crop season, and study agricultural trends…”
    --
    “HIGHLIGHTS:
    • What are the main findings?
    - The 14 DESIS hyperspectral narrowbands (10 nm) aligned with the Landsat 10 (formerly Landsat Next) spectral dataset produced similar accuracy results to the full 60-band DESIS hyperspectral dataset for classifying crop types. These 14 DESIS narrowbands resulted in higher accuracy than the 14 simulated Landsat 10 superspectral broadbands.
    - When using DESIS narrowbands, Support Vector Machine (SVM) resulted in higher accuracy than Random Forest (RF).
    • What are the implications of the main findings?
    - A carefully selected set of 14 DESIS hyperspectral narrowbands (10 nm) can achieve classification accuracy comparable to those obtained using all 60 DESIS narrowbands across the 400–1000 nm range. These 14 strategically positioned narrowbands classified crop types with higher classification accuracy than the corresponding 14 Landsat 10 superspectral broadbands within the same spectral range.
    - This study underscores the importance of multi-temporal imagery across the full crop-growing season for achieving more detailed and accurate crop type classifications. Such temporal coverage is more feasible with the planned Landsat 10 routine acquisition of broadband imagery than with task-based hyperspectral collections…”
    #hyperspectral #superspectral #optimalbands #randomforest #supportvectormachine #agriculture #crops #croptype #classifaction #croplands #California #CentralValley #GIS #spatial #mapping #remotesensing #earthobservation #imagery #DESIS #Landsat #Landsat10 #satellite #spatialanalysis #spatiotemporal #global #AI #machinelearning #model #modeling #SupportVectorMachine #SVM #RandomForest #RF #GoogleEarthEngine
    @USGS

    #hyperspectral #superspectral #optimalbands #randomforest #supportvectormachine #agriculture
  36. 💧🌏 Greg Cocks @[email protected] ·

    The Portrait of Flood Risk in Italy - Past, Present and Future, From 1870 to 2100
    --
    doi.org/10.1029/2026GL122987 <-- shared paper
    --
    “ABSTRACT: Among European countries, Italy ranks as one of the most susceptible to flood risk. While this figure is already substantial, climate change and rapid urbanization in flood-prone areas have been identified as the two main drivers expected to elevate the number of individuals at risk. This study offers a comprehensive assessment of these two drivers of flood risk in Italy over 230 years, from 1870 to 2100, focusing on how they interact to increase risk. Using the large-scale flood risk model RESCUE-FR, [the authors] analyze[d] the population at risk under the 200-year return period scenario to provide a targeted assessment of population risk, how it has evolved in the past, and its projection in the future. [Their] findings indicate that while historical flood risk in Italy has primarily been influenced by population growth and migration into at-risk areas, future projections suggest that climate change will become the dominant driver of flood risk.
    PLAIN LANGUAGE SUMMARY: Italy is one of the European countries most at risk of flooding. This study examines the impact of two risk factors on flood risk in Italy over the long term, from 1870 to 2100: climate change and the evolution of population in areas prone to flooding. Using a large-scale flood risk model, [they] simulated different scenarios for different time periods, such as with and without climate change, to estimate how each factor contributes to the number of people exposed to floods in the past and future. [Their] results show that population growth and migration into flood-prone areas were the main reasons for the increased risk in the past. In the future, however, climate change is likely to become the dominant factor, putting more people at risk. Understanding how these factors interact can help communities to plan more effectively for floods and reduce the number of people affected…”
    #flood #flooding #risk #hazard #Italy #Europe #national #history #historic #cost #damage #infrastructure #floodrisk #population #urbanisation #development #climatechange #extremeweather #dominantfactor #floodprone #national #regional #spatialanalysis #spatiotemporal #model #RESCUEFR #modeling #factors #parameters #drivers #publicsafety

    #flood #flooding #risk #hazard #italy #europe
  37. Jocelyn Etienne @[email protected] ·

    RE: biologists.social/@Rxiv_mechan

    "The arrival of #machineLearning approaches that predict cellular behavior at scale and integrate a wide array of #biologicalData types makes the analytical demand on #mechanisticModels even more pressing. Prediction is increasingly available without #mechanistic #understanding. The central challenge is no longer building models that reproduce #biological behavior, but building #models from which causal structure can be inferred."

    #science #AI #AIinResearch #modeling #modelling #biology

    #machinelearning #biologicaldata #mechanisticmodels #mechanistic #understanding #biological
  38. 💧🌏 Greg Cocks @[email protected] ·

    Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain [Iran]
    --
    doi.org/10.3390/geosciences150 <-- shared paper
    --
    H/T @Geosciences MDPI
    “This study applies advanced machine learning algorithms to map flood susceptibility in northwest Iran. The results demonstrate strong predictive performance, with the Locally Weighted Linear model delivering the highest accuracy and providing valuable guidance for flood-risk management and disaster mitigation…”
    --
    “Flooding is one of the most significant natural hazards in Iran, primarily due to the country’s arid and semi-arid climate, irregular rainfall patterns, and substantial changes in watershed conditions. These factors combine to make floods a frequent cause of disasters. In this case study, flood susceptibility patterns in the Marand Plain, located in the East Azerbaijan Province in northwest Iran, were analyzed using five machine learning (ML) algorithms: M5P model tree, Random SubSpace (RSS), Random Forest (RF), Bagging, and Locally Weighted Linear (LWL). The modeling process incorporated twelve meteorological, hydrological, and geographical factors affecting floods at 485 identified flood-prone points. The data were analyzed using a geographic information system, with the dataset divided into 70% for training and 30% for testing to build and validate the models. An information gain ratio and multicollinearity analysis were employed to assess the influence of various factors on flood occurrence, and flood-related variables were classified using quantile classification. The frequency ratio method was used to evaluate the significance of each factor. Model performance was evaluated using statistical measures, including the Receiver Operating Characteristic (ROC) curve. All models demonstrated robust performance, with an area under the ROC curve (AUROC) exceeding 0.90. Among the models, the LWL algorithm delivered the most accurate predictions, followed by RF, M5P, Bagging, and RSS. The LWL-generated flood susceptibility map classified 9.79% of the study area as highly susceptible to flooding, 20.73% as high, 38.51% as moderate, 29.23% as low, and 1.74% as very low. The findings of this research provide valuable insights for government agencies, local authorities, and policymakers in designing strategies to mitigate flood-related risks. This study offers a practical framework for reducing the impact of future floods through informed decision-making and risk management strategies…”
    #FloodSusceptibility #FloodRisk #MachineLearning #GIS #NaturalHazards #DisasterManagement #FloodModeling #Hydrology #EnvironmentalMonitoring #RiskAssessment #GeospatialAnalysis #ClimateResilience #GIS #spatial #mapping #Iran #MarandPlain #EastAzerbaijan #machinelearning #AI #floodhazard #floodvulnerability #flood #flooding #water #hydrography #hydrology #model #modeling #risk #hazard #rainfall #precipitation #extremeweather #spatialanalysis #spatiotemporal #modelperformance #policy #planning #mitigation #design #riskmanagement

    #floodsusceptibility #floodrisk #machinelearning #gis #naturalhazards #disastermanagement
  39. 💧🌏 Greg Cocks @[email protected] ·

    Identifying Agricultural Consumptive-Use Patterns To Support Adaptive Water Management In California’s Santa Clara Valley Via Remote Sensing And Machine Learning
    --
    doi.org/10.1371/journal.pwat.0 <-- shared paper
    --
    H/T @Guillaume Wright | Executive Editor, PLOS
    “💧 With drought [and high temperatures] gripping many areas of the world right now... [the H/T] wanted to highlight a new paper in PLOS Water this week with a very timely focus on hydroclimatic stresses and what can be done to mitigate this through water management practices when it comes to agriculture.
    [The authors] investigate[d] adaptive water management practices in California’s Santa Clara Valley via remote sensing and machine learning techniques. They [found] good evidence for use of customized agricultural water-management plans for irrigation monitoring, conservation planning, and adaptive water management in groundwater-dependent regions such as is found in California…”
    #GIS #spatial #mapping #California #SantaClara #SantaClaraValley #custom #watermanagement #practices #waterresources #agriculture #remotesensing #spatialanalysis #machinelearning #earthobservation #AI #planning #wateruse #efficiency #water #hydrology #irrigation #conservation #adaptivewatermanagement #model #modeling #drought #extremeweather #hydroclimate #stress #crop #cropland #evapotranspiration #ET #NDVI #PRISM #precipitation #rainfall #watermanagementplan #groundwater

    #gis #spatial #mapping #california #santaclara #santaclaravalley
  40. 💧🌏 Greg Cocks @[email protected] ·

    A National-Scale Database Of Groundwater Level Data For Switzerland
    --
    doi.org/10.1038/s41597-026-073 <-- shared paper
    --
    H/T @RaoulCollenteur | Groundwater Hydrologist at Collenteur HydroConsult GmbH
    “Looking for a ready-to-use FAIR dataset with groundwater levels, signatures, and meteorological drivers to test new models and analysis methods to learn from groundwater level data? Why not try [the authors’] new Swiss Groundwater Database with almost 1,000 piezometers in diverse climatological and hydrogeological settings within Switzerland? 💡
    💧 Long groundwater level time series with frequent measurements
    💧 Meteorological drivers included
    💧Unique dataset in terms of hydrogeological data in an alpine setting
    … [They] hope [that they] can develop the database in the future with other variables (i.e., groundwater temperature, spring discharge, etc.) and welcome additions and collaborations to make this happen. 🌊…”
    --
    “Groundwater is a vital component of the global supply of freshwater, playing a critical role for human populations, agriculture, and ecosystems. Due to the complex interactions between groundwater, surface water, climate, and human activity, these systems are frequently studied using advanced data analysis and modeling techniques. The effectiveness of these methods is generally enhanced by the availability and quality of data. In Switzerland, the focus area of this study, groundwater data is fragmented and lacks a standardized nationwide compilation. Consequently, the process of conducting nationwide studies with substantial sample sizes is both resource-intensive and time-consuming. In this paper, [they] introduce the Swiss Groundwater Database, a comprehensive compilation of groundwater time series and associated metadata throughout Switzerland. The current database consists of groundwater level data from 985 monitoring wells, which were completed with additional static and time-varying variables. The environmental characteristics and climate indices were compiled and determined for each monitoring well. The database is designed to facilitate and support large-sample hydrological research related to groundwater in Switzerland and beyond…”
    #water #hydrography #database #GIS #spatial #mapping #groundwater #Switzerland #FAIR #SwissGroundwaterDatabase #opendata #hydrogeology #meteorology #weather #climate #alpine #waterresources #agriculture #ecosystems #humanimpacts #spatialanalysis #spatiotemporal #model #modeling #dataanalysis #nationwide #metadata #monitoring #wells
    @Federal Office for the Environment FOEN | @Federal Office of Meteorology and Climatology MeteoSwiss

    #water #hydrography #database #gis #spatial #mapping
  41. 💧🌏 Greg Cocks @[email protected] ·

    Anatomy Of A Seafloor Spreading Event Captured By In Situ Seismogeodesy
    --
    doi.org/10.1038/s41586-026-107 <-- shared paper
    --
    smithsonianmag.com/smart-news/ <-- shared technical media article
    --
    H/T @Seabed 2030
    “🔍 For the first time, scientists have observed seafloor spreading in real time.
    Seafloor spreading is the process by which new oceanic crust is formed at mid-ocean ridges - a geological process that has shaped entire ocean basins over millions of years.
    During a research expedition in the Indian Ocean, scientists had just deployed a suite of instruments when a series of earthquakes triggered a seafloor spreading event, allowing them to observe the process as it unfolded.
    The findings offer rare new insights into how new oceanic crust forms and how the seafloor continues to evolve…”
    --
    “Earth’s outermost layer - the crust - is constantly renewing itself. It’s broken into giant chunks called tectonic plates that pull apart, push against or slide past one another, creating grand geologic features.
    Underwater mountain ranges, or mid-ocean ridges, for instance, generally take shape where two tectonic plates are moving away from each other. Magma can then bubble up in between, solidifying and turning into new oceanic crust as part of a process called seafloor spreading. Although the phenomenon has created entire ocean basins, it remains quite mysterious because it happens so deep in the water.
    Now, for the first time, scientists have observed this dynamic activity happening in real time. They describe their findings - and their stroke of luck - in a study [link above], shedding light on a mechanism that made roughly two-thirds of Earth’s crust…”
    --
    #Seabed2030 #OceanMapping #Hydrospatial #remotesensing #seafloor #seafloorspreading #oceanic #crust #geology #structuralgeology #IndianOcean #earthquake #midoceanridge #instrumentation #marine #seabed #hydrography #model #modeling #mapping #GIS #spatial #tectonicplates #magma #fortuitous #survey #seismogeodetic #monitoring #submarine #rifting #observation #volcanism #seismicity #dyke #fault #faulting #midoceanridge #MOR

    #seabed2030 #oceanmapping #hydrospatial #remotesensing #seafloor #seafloorspreading
  42. 💧🌏 Greg Cocks @[email protected] ·

    Impact Of Reservoir Storage On Propagation From Meteorological To Hydrological Drought
    --
    doi.org/10.1016/j.jhydrol.2026 <-- shared paper
    --
    H/T @DrAjayGupta | Post Doctoral Fellow, IIT Bombay | Ph.D. in Hydrology, IIT Roorkee | Commonwealth Split-site Fellow, University of Birmingham I M.Tech in Water Resources Engineering, NIT Silchar | B.E. in Civil Engineering, PCE Nagpur.
    “🌍 Why is this important?
    While reservoirs are widely recognized for mitigating drought impacts, their role in controlling how drought propagates through the hydrological cycle has remained largely unexplored. In this study, [the authors] investigate how reservoir storage influences the transition of drought from meteorological to agricultural to reservoir to streamflow drought across the semi-arid Krishna River Basin, India.
    🔍 THIS STUDY ADDRESSES TWO KEY RESEARCH QUESTIONS:
    ✅ How do drought propagation time (initiation, peak, and termination) change from meteorological to agricultural, reservoir, and streamflow droughts across different timescales and threshold values?
    ✅ How does reservoir storage influence drought propagation between upstream and downstream reservoirs using the Downstreamness concept?
    📌 KEY FINDINGS
    🔹 Drought propagation differs substantially across drought types because each component of the hydrological system responds at different rates.
    🔹 Reservoirs significantly delay the propagation of drought by buffering water deficits, particularly between agricultural and streamflow drought.
    🔹 Mild and moderate upstream reservoir droughts rarely propagate downstream, whereas severe upstream droughts consistently transmit downstream, leading to longer duration, greater severity, and delayed onset.
    🔹 The downstreamness analysis reveals dynamic shifts in water storage between upstream and downstream reservoirs throughout drought development and recovery, providing valuable insights for reservoir operation and basin-scale drought management…”
    --
    “HIGHLIGHTS
    • Reservoir storage impact on drought propagation from meteorological-to-hydrological drought.
    • Drought propagation timeframe: initiation, peak and termination are checked.
    • Impact assessment using hydrological connection: upstream to downstream reservoirs.
    • Severe upstream droughts propagate downstream with increased duration and severity.
    • During drought periods water-storage concentration shifts from downstream to upstream..."
    #Drought #DroughtPropagation #Reservoirs #WaterResources #WaterManagement #RiverBasinManagement #KrishnaRiverBasin #Downstreamness #India #climatechange #reservoir #storage #hydrology #water #hydrologiccycle #watersecurity #planning #policy #KrishnaRiver #weather #climate #metrology #agriculture #farming #streamflow #model #modeling #spatiotemporal #spatialanalysis

    #drought #droughtpropagation #reservoirs #waterresources #watermanagement #riverbasinmanagement
  43. Eric Maugendre on adapting @[email protected] ·

    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
  44. 💧🌏 Greg Cocks @[email protected] ·

    Global Performance of #RemoteSensing Based and Reanalysis-Driven Models to Estimate Open Water Evaporation
    --
    doi.org/10.1029/2025WR042363
    --
    “ABSTRACT: Evaporation plays an essential role in the water cycle, influencing local and regional climates while directly impacting water availability in lakes. However, directly measuring evaporation over water bodies remains challenging due to the high costs of installing and maintaining the required in situ instrumentation. Although several remote sensing algorithms have been providing evaporation estimates, the lack of a global validation hinders our understanding of their relative uncertainties and performances across different regions. Here, [they] analyze[d] the performance of a suite of models that leverage satellite data and meteorological reanalysis to estimate evaporation over lakes worldwide. [They] compare[d] three remote sensing-based models, one reanalysis-driven model and one ensemble approach, using in situ observations from 27 lakes representing a diverse range of geographic and climatic regions. [Their] results demonstrate that, overall, the ensemble outperformed any individual model in terms of accuracy, with a RMSE and a bias of 1.3 and 0.3 mm/day, respectively. These findings highlight the benefits of using an ensemble approach to estimate open water evaporation with satellite-based models at the global scale, leveraging the unique strengths of each model. For the individual models, differences in the representation of heat storage changes and advection effects led to lower values of RMSE and bias, depending on the location and depth of the lakes. This study sets the path for future improvement of open water evaporation algorithms globally, while remote sensing techniques are proven satisfactory to monitoring of water loss in lakes globally, an essential step toward effective large-scale water resources management.
    PLAIN LANGUAGE SUMMARY: Water loss through evaporation in lakes and reservoirs directly affects water availability, which highlights the need to monitor these losses. However, measuring evaporation in situ is challenging and expensive. An alternative is to estimate evaporation using remote-sensing models and compare these estimates with in-situ data to verify their accuracy. Here, [they] evaluated four models and their ensemble (the models' mean value) using measurements from 27 lakes and reservoirs worldwide. [They] found that the ensemble presented higher accuracy and consistency than any individual model because it benefits from the strengths of each model. This approach can guide future improvements in estimating open-water evaporation, which is essential for large-scale water-resource management…”
    #global #mapping #earthobservation #GIS #spatial #spatialanalysis #spatiotemporal #model #modeling #water #hydrology #surfacewater #waterbody #lake #reservoir #evaporation #evapotranspiration #watercycle #weather #meteorology #usecase #waterresources #watermanagement #waterloss #regional #estimate #policy #planning #instrumentation #comparasion

    #remotesensing #global #mapping #earthobservation #gis #spatial
  45. 💧🌏 Greg Cocks @[email protected] ·

    GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
    --
    doi.org/10.21203/rs.3.rs-10085 <-- shared paper
    --
    zenodo.org/records/17627111 <-- shared open data
    --
    H/T @kyle Davis
    “Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”
    #IrrigatedAreas #Mapping #GIS #spatial #mapping #spatialanalysis #spatiotemporal #global #irrigation #water #hydrology #hydrography #waterresources #farming #agriculture #opendata #remotesensing #earthobservation #geomorphometry #AI #machinelearning #LLM #model #modeling #WaterManagement #opendata #AgroEcologicalZone #AEZ #cropland #irrigatedareas #foodproduction #wateruse #humanimpacts #EarthObservation #remotesensing #earlywarning #monitoring #FoodandAgricultureOrganizationFAO #FAO
    @FAO - Food and Agriculture Organization

    #irrigatedareas #mapping #gis #spatial #spatialanalysis #spatiotemporal
  46. 💧🌏 Greg Cocks @[email protected] ·

    Watching A #NOAA #Webinar on Flash Droughts
    --
    noaaresearch.webex.com/wbxmjs/ <-- shared NOAA Summer Science Series individual webinar
    --
    drought.gov/what-is-drought/fl <-- shared NOAA overview technical article
    --
    star.nesdis.noaa.gov/star/NOAA <-- subscribe to the NOAA Summer Science Series
    --
    doi.org/10.1038/s41612-024-006 <-- shared paper
    --
    communities.springernature.com <-- shared technical article (derived from paper above)
    H/T @Jeffrey Basara PhD, MBA | Chair and Professor - Department of Environmental, Earth, and Atmospheric Sciences, University of Massachusetts Lowell | Co-Founder - American Prime Sustainable Solutions
    [Flash floods? not TOO hard to conceptualise.
    Flash drought? harder to 'get my head around', but H/T / presenter does an excellent job!]
    "Not all droughts are the same. In some cases, drought rapidly intensifies at subseasonal to seasonal scales with significant impacts to agriculture and water resources along with the increased propensity for heatwaves and wildfires. Like all droughts, flash drought begins with a precipitation deficit. However, both evaporative demand and soil moisture are critical flash drought variables, and identifying and monitoring the desiccation of the terrestrial surface is key for determining flash drought development and associated impacts. While recent advances in knowledge and monitoring of flash drought have occurred, fundamental questions remain in the state of the science. What are the overall mechanistic relationships between atmospheric demand, evaporative stress, terrestrial desiccation, and precipitation that drive the progression of flash drought? Do regional characteristics of the environment impact the evolution of flash drought? What are the scales of predictability for flash drought? Finally, how will flash drought frequency and intensity evolve in a changing climate system"
    --
    "Flash drought intensifies rapidly due to changes in precipitation, temperature, wind, and radiation. These changes in the weather increase evapotranspiration and lower soil moisture. Flash droughts can cause extensive damage to agriculture, economies, and ecosystems if they are not predicted and discovered early..."
    #water #hydrology #fedscience #publicgood #hydrologicdrought #waterdeficit #spatialanalysis #spatiotemporal #watersecurity #risk #hazard #humanimpacts #streamflow #riverflow #groundwater #surfacewater #climate #weather #climatechange #extremeweather #atmosphere #metrology #regional #global #farming #agriculture #fluvial #pluvial #rainfall #precipitation #cloudcover #energy #heat #temperature #ET #evapotranspiration #farming #agriculture #foodsecurity #waterresources #dynamicsystems #watermanagement #flashdrought #drought #susceptibility #monitoring #prediction #model #modeling
    @noaa

    #noaa #webinar #water #hydrology #fedscience #publicgood
  47. 💧🌏 Greg Cocks @[email protected] ·

    National Water Availability Assessment Data Companion Launches Interactive Map
    --
    water.usgs.gov/nwaa-data/ <-- shared USGS resource link
    --
    water.usgs.gov/nwaa-data/inter <-- shared USGS webmap
    --
    H/T @USGS NWDC
    “The National Water Availability Assessment Data Companion (NWDC) delivers national-scale modeled water data underlying the National Water Availability Assessment Report. The NWDC will be continuously updated to include new data used in future National Water Availability Assessment Reports, with planned reports in 2026 and 2030.
    The NWDC also serves information on underlying model methodologies, strengths, and limitations to enable proper use of the data…
    USGS scientific teams develop NWDC models to analyze and represent the complexities of water systems. These models fill gaps where USGS observations are unavailable, covering the conterminous United States (lower 48 states) and soon extending to Alaska, Hawaii, and Puerto Rico.
    All NWDC datasets currently cover past conditions over multiple decades, and are standardized to 12-digit [WBD] hydrologic unit code (HUC12) watersheds and monthly timesteps…”
    #opendata #monitoring #spatialanalysis #spatiotemporal #fedscience #publicgood #water #hydrology #waterresources #watermanagement #change #model #modeling #USA #NationalWaterAvailabilityAssessment #NWDC #CONUS #USGS #USGS_water
    @USGS

    #opendata #monitoring #spatialanalysis #spatiotemporal #fedscience #publicgood
  48. 💧🌏 Greg Cocks @[email protected] ·

    Flood Vulnerability And Load Capacity Assessment Of Historic Masonry Arch Bridges In Ireland Under Changing Climates
    --
    doi.org/10.1016/j.eve.2026.100 <-- shared paper
    --
    H/T @Upaka Rathnayake
    “HIGHLIGHTS:
    • Field surveys and flood modelling were used to evaluate the resilience of eight historic masonry arch bridges in County Offaly, Ireland, under projected fluvial flooding conditions.
    • Results indicate that increased flood levels and hydraulic forces can significantly reduce bridge load-carrying capacity, with potential reductions of up to 40% due to buoyancy effects during extreme flood events.
    • The study demonstrates a clear relationship between flood exposure and structural deterioration, emphasizing the need for structural health monitoring, maintenance strategies, and climate-resilient infrastructure management…
    ABSTRACT: Masonry bridges, predominantly constructed from stone or brick, were a common feature of bridge engineering during the 18th and 19th centuries. However, these historic bridges are still in use today, but they are at risk due to various extreme climate conditions. Thus, these bridges are vulnerable to damage and needy for investigation. This paper offers an in-depth analysis of the projected impacts of fluvial flooding stemming from climate change on a number of masonry arch bridge structures located in county Offaly, Ireland. It evaluates bridge resilience by examining estimated flood levels alongside the overall condition of the structures. These assessments play a crucial role in determining the load-bearing capacity of the bridges and whether adjustment factors should be implemented. Particularly for bridges situated on primary and secondary roads with consistent heavy goods vehicle (HGV) traffic, the potential decrease in load-bearing capabilities warrants significant consideration. This study highlights concerns regarding the resilience of these historic structures and presents a valid argument regarding their suitability for contemporary environmental conditions and present-day activities…”
    #bridge #resilience #climatechange #impacts #loadcarrying #capacity #masonry #archbridges #fluvial #flood #flooding #Ireland #casestudies #transportation #bridges #historicbridges #history #survey #model #modeling #floodmodeling #CountyOffaly #ContaeUíbhFhailí #hydraulics #engineering #chokepoint #constraint #constriction #hydraulicforce #bridgeload #carryingcapacity #buoyancyeffects #damage #structuraldeterioration #structuralhealth #monitoring #maintenance #planning #policy #climateresilience #infrastructure #management #water #hydrography

    #bridge #resilience #climatechange #impacts #loadcarrying #capacity
  49. 💧🌏 Greg Cocks @[email protected] ·

    Remote Sensing And The New Global River Science
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    doi.org/10.1038/s44221-026-006 <-- shared paper
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    “Rivers impact the well-being of humans and the environment. As they increasingly face planetary-scale stressors, it is critically important to monitor and understand rivers at the global scale. As the only synoptic resource for global primary data on rivers, satellite remote sensing has recently begun to provide unprecedented opportunities for the monitoring, understanding, and prediction of global river behaviour. Despite these advances, the role of satellite remote sensing in global river science has still not been fully explored. New satellite systems and algorithms will enable substantial improvements in river measurements, provide new answers to long-standing or newly emerging scientific questions, and eventually update basic knowledge of rivers to advance global river science. In this [paper they] explore how remote sensing has been used to study the world’s rivers, examine challenges and opportunities for further advancing our understanding of rivers using existing and upcoming sensors, and identify possible solutions and future research directions…”
    #GIS #spatial #mapping #water #hydrology #satellite #remotsesensing #earthobservation #hydrography #spatialanalysis #spatiotemporal #physicalgeography #change #river #global #model #modeling #research #hydrogeomorphology #geomorphometry #riverine #humanimpacts #waterquality #waterresources #watermanagement #infrastructure #lake #reservoir #dam #impoundment #canals #avulsion #overbank #flood #flooding #erosion #sedimentation #morphology #network #downstream

    #gis #spatial #mapping #water #hydrology #satellite
  50. 💧🌏 Greg Cocks @[email protected] ·

    NASA's Parker Solar Probe Flew In And Out Of The Solar Corona And Found A Source Of High-Energy Particles That No Existing Model Had Predicted
    (When NASA's Parker Solar Probe passed through the solar corona during perihelion encounters at the heliospheric current sheet, its instruments recorded energetic protons at energies far above what existing models of particle acceleration at that location could account for.)
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    spacedaily.com/n-nasas-parker- <-- shared technical article
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    doi.org/10.3847/2041-8213/ae3c <-- shared paper
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    space.com/high-energy-particle <-- shared technical article
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    youtu.be/Woo4yZby4BU?si=rzzND3 <-- shared space weather overview effects video, #AIslop notwithstanding 🙃 🫠 😉
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    “… A team… at the Southwest Research Institute… and… the University of Maryland’s Institute for Research in Electronics and Applied Physics has published an analysis of those measurements [link above]…, identifying magnetic reconnection at the heliospheric current sheet as the mechanism responsible. The proton energies detected were, in the study’s framing, approximately a thousand times greater than the available magnetic energy per particle that models of this process had predicted.
    The finding matters for two reasons: it identifies a source of energetic particles close to the Sun that was not anticipated, and it raises questions about how far the existing understanding of reconnection-driven particle acceleration extends…
    The Parker measurements suggest that picture needs revision. The magnetic island merging mechanism [the authors] identif[ied] can evidently produce proton populations at energies that challenge the boundary between what reconnection does and what shock acceleration does. Whether this source can explain a significant fraction of observed near-Earth energetic particle events, or whether it contributes mainly at energies and scales that remain confined close to the Sun, is a question the paper does not yet resolve…
    Solar Orbiter, the European Space Agency mission operating in coordination with Parker, offers complementary measurements at somewhat greater distances. Comparing what the two spacecraft measure of the same particle populations as they propagate outward from the Sun will help establish which features originate close to the source and which are modified by the intervening solar wind. The energetic particle observations that puzzled researchers in the pre-Parker era now have a candidate mechanism. Whether that mechanism accounts for a large or small fraction of what actually reaches Earth remains the open question…”
    #sun #solar #solarsystem #heliosphere #Parker #SolarOrbiter #spacecraft #research #monitoring #instrumentatin #spaceweather #solarflare #coronalmassejection #CME #NASA #ESA #magnetism #geomagnetism #proton #energy #particleacceleration #measurement #solarwind #risk #hazard #impacts #corona #perihelion #model #modeling
    @nasa @esa

    #aislop #sun #solar #solarsystem #heliosphere #parker