home.social

#spatialanalysis — Public Fediverse posts

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

fetched live
  1. 💧🌏 Greg Cocks @[email protected] ·

    SOME SHARED SPATIAL/GIS ROLES & OPPORTUNITIES
    --
    w/e August 14th, 2026 | GLOBAL but North America focused (as currently based there)
    --
    earthstuff.substack.com/p/some
    --
    • I am NOT associated with HR/recruiting in ANY way
    • Therefore, these are NOT endorsements, and there are no ‘referrals’ or the like for me
    • I am simply sharing SOME roles I have seen – in the hope that they are of use to someone
    • As such, I am not (and indeed can not) in any way ‘vet’ these roles (i.e., you are advised to conduct your own due diligence due to hideous, potential risks like fraudulent postings, ‘ghost jobs’, résumé/ATS ‘improvement’ scammers, etc)
    • Further, roles listed are in no specific order (i.e., simply as I ‘encountered’ them during the week)
    • There is ZERO cost to access this list of spatial / GIS roles
    • I am not (really) ‘mining’ US Federal government roles - with the ‘hiring freeze’, uncertainty, etc
    • You can subscribe to the (free) weekly LinkedIn newsletter www.linkedin.com/build-relation/newsletter-follow?entityUrn=7397678824928366594 or also for free at earthstuff.Substack.com - if so inclined

    #fridayjobs #gischat #jobsonfriday #GIS #spatial #mapping #cartography #geography #job #jobopenings #hiring #newjob #employment #roles #GISJobs #NotARecruiter #fridayspatialjobs #scholarship #PhD #PostDoc #CareersInSpatial #career #openroles #Geomatics #GIS #Innovation #Growth #caveatNONemptor #LetsGetYouHired #GISCareer #GISStudents #PhD #postdoc #hiringalert #GISjobs #careeropportunity #NowHiring #spatialanalysis #GIS #Geospatial #GISSpecialist #Geomatics #OpenToWork

    #fridayjobs #gischat #jobsonfriday #gis #spatial #mapping
  2. 💧🌏 Greg Cocks @GregCocks ·

    SOME SHARED SPATIAL/GIS ROLES & OPPORTUNITIES
    --
    w/e August 14th, 2026 | GLOBAL but North America focused (as currently based there)
    --
    earthstuff.substack.com/p/some
    --
    • I am NOT associated with HR/recruiting in ANY way
    • Therefore, these are NOT endorsements, and there are no ‘referrals’ or the like for me
    • I am simply sharing SOME roles I have seen – in the hope that they are of use to someone
    • As such, I am not (and indeed can not) in any way ‘vet’ these roles (i.e., you are advised to conduct your own due diligence due to hideous, potential risks like fraudulent postings, ‘ghost jobs’, résumé/ATS ‘improvement’ scammers, etc)
    • Further, roles listed are in no specific order (i.e., simply as I ‘encountered’ them during the week)
    • There is ZERO cost to access this list of spatial / GIS roles
    • I am not (really) ‘mining’ US Federal government roles - with the ‘hiring freeze’, uncertainty, etc
    • You can subscribe to the (free) weekly LinkedIn newsletter www.linkedin.com/build-relation/newsletter-follow?entityUrn=7397678824928366594 or also for free at earthstuff.Substack.com - if so inclined

    #fridayjobs #gischat #jobsonfriday #gis #spatial #mapping
  3. 💧🌏 Greg Cocks @[email protected] ·

    Impact Of Urbanization Driven Land Use And Land Cover Change On Ecological Environmental Quality In Rupandehi Nepal Assessed Using The Remote Sensing Ecological Index
    --
    doi.org/10.1007/s44288-026-006 <-- shared paper
    --
    kathmandupost.com/money/2026/0 <-- shared media article
    --
    H/T@ Gaurav Parajulim
    “[The authors] studied how the ecological quality of Nepal's Rupandehi District has changed over three decades (1993–2023), using satellite imagery and the Remote Sensing Ecological Index (RSEI) to track the health of the landscape year by year and to understand how urbanization-driven land use change has reshaped it.
    What [they] found tells a nuanced story: as Butwal and Bhairahawa grew and built-up land expanded, ecological quality shifted in ways that a single number can't capture, some areas recovered, others declined, and the patterns rarely moved in a straight line…”
    --
    “Rapid urbanization and population growth are major drivers of land use and land cover (LULC) change and can substantially alter ecological environmental quality (EEQ). This study assessed the spatiotemporal dynamics of LULC and their effect on EEQ in Rupandehi District, Nepal, over a 30-year period (1993–2023). Four ecological indicators representing greenness, wetness, dryness, and heat were derived from Landsat imagery in Google Earth Engine (GEE), and LULC was classified using a Support Vector Machine (SVM). The Remote Sensing Ecological Index (RSEI) was then constructed from these indicators using Principal Component Analysis (PCA) in ArcGIS Pro, and its spatial structure was examined using global and local spatial autocorrelation. The mean RSEI followed a non-linear trajectory, rising from 0.59 in 1993 to 0.635 in 2004, declining to 0.55 in 2013, and recovering to 0.67 in 2023, indicating an overall improvement in EEQ with a temporary mid-period decline. Over the same period, built-up areas expanded substantially and agricultural land declined, whereas forest cover fluctuated but showed a slight net increase by 2023, and barren land decreased markedly. Higher EEQ was concentrated in the forested northern hills, while lower values occurred in the urban centers of Butwal and Bhairahawa, closely matching the spatial pattern of LULC change. The results indicate that ecological quality reflects the combined influence of all land cover classes rather than any single class. This study provides a transferable and reproducible workflow for long-term ecological assessment based on openly available Landsat data, with the analysis code shared in a public repository, offering practical guidance for sustainable land management and environmentally responsible urban development...”
    #GIS #spatial #mapping #RemoteSensing #GIS #RSEI #EnvironmentalMonitoring #Nepal #Research #GoogleEarthEngine #ArcGIS #EcologicalQuality #spatialautocorrelation #ecology #environment #earthobservation #RemoteSensingEcologicalIndex #landscape #urbanisation #urban #development #landuse #change #spatialanalysis #spatiotemporal

    #gis #spatial #mapping #remotesensing #rsei #environmentalmonitoring
  4. 💧🌏 Greg Cocks @GregCocks ·

    Impact Of Urbanization Driven Land Use And Land Cover Change On Ecological Environmental Quality In Rupandehi Nepal Assessed Using The Remote Sensing Ecological Index
    --
    doi.org/10.1007/s44288-026-006 <-- shared paper
    --
    kathmandupost.com/money/2026/0 <-- shared media article
    --
    H/T@ Gaurav Parajulim
    “[The authors] studied how the ecological quality of Nepal's Rupandehi District has changed over three decades (1993–2023), using satellite imagery and the Remote Sensing Ecological Index (RSEI) to track the health of the landscape year by year and to understand how urbanization-driven land use change has reshaped it.
    What [they] found tells a nuanced story: as Butwal and Bhairahawa grew and built-up land expanded, ecological quality shifted in ways that a single number can't capture, some areas recovered, others declined, and the patterns rarely moved in a straight line…”
    --
    “Rapid urbanization and population growth are major drivers of land use and land cover (LULC) change and can substantially alter ecological environmental quality (EEQ). This study assessed the spatiotemporal dynamics of LULC and their effect on EEQ in Rupandehi District, Nepal, over a 30-year period (1993–2023). Four ecological indicators representing greenness, wetness, dryness, and heat were derived from Landsat imagery in Google Earth Engine (GEE), and LULC was classified using a Support Vector Machine (SVM). The Remote Sensing Ecological Index (RSEI) was then constructed from these indicators using Principal Component Analysis (PCA) in ArcGIS Pro, and its spatial structure was examined using global and local spatial autocorrelation. The mean RSEI followed a non-linear trajectory, rising from 0.59 in 1993 to 0.635 in 2004, declining to 0.55 in 2013, and recovering to 0.67 in 2023, indicating an overall improvement in EEQ with a temporary mid-period decline. Over the same period, built-up areas expanded substantially and agricultural land declined, whereas forest cover fluctuated but showed a slight net increase by 2023, and barren land decreased markedly. Higher EEQ was concentrated in the forested northern hills, while lower values occurred in the urban centers of Butwal and Bhairahawa, closely matching the spatial pattern of LULC change. The results indicate that ecological quality reflects the combined influence of all land cover classes rather than any single class. This study provides a transferable and reproducible workflow for long-term ecological assessment based on openly available Landsat data, with the analysis code shared in a public repository, offering practical guidance for sustainable land management and environmentally responsible urban development...”

    #gis #spatial #mapping #remotesensing #rsei #environmentalmonitoring
  5. 💧🌏 Greg Cocks @[email protected] ·

    Geospatial Analysis of Carbon Offset Projects - A Broader Scientific Outlook
    --
    eldhollow.com/blogs/geospatial <-- shared technical blog
    --
    [this post should not be considered an endorsement of a particular organisation, rather scrutinising the spatial use case & technical approach]
    H/T @kyle Arvisais | Forest Carbon Scientist
    “Geospatial analysis is at the core of [the H/T’s company’s] project quality assessments, and [the author is] constantly finding ways to make the pipeline better and ways to use it. [They are] obviously not the only one who uses these types of tools, but to be perfectly honest, the quality of models [they have] seen over the years has been all over the place.
    This blog makes a casual introduction to [their] pipeline while talking about the field at large…”
    --
    “The world has committed to protecting and restoring nature at an unprecedented scale. Whether that commitment delivers what it promises comes down project execution on the ground. Local socioeconomics and forest ecology intertwine to create complex challenges for projects to overcome during implementation, and at the end of the day, projects boil all of these complexities down to one single unit: the carbon credit. So the question becomes: can we actually measure what is happening to a forest, accurately and honestly, and everywhere at once?
    For a long time, the honest answer has been no. Historically, many forest carbon projects overstated their impact. Usually it was because the baseline was too generous, or because the measurements underneath were flawed. For anyone with a stake in nature markets, that uncertainty is one of the core risks.
    Robust geospatial analysis can help mitigate that risk. If you treat a carbon credit as what it really is, a scientific claim, then we can hold it to that standard and assess it objectively. [Their] geospatial pipeline turns satellite data and ground truth data into models about how much forest is standing, how it is changing, and what might put it at risk in the future. The pipeline does this anywhere on Earth…”
    #GIS #spatial #mapping #usecase #carbonoffset #spatialanalysis #spatiotemporal #qualityassessment #objectivity #projectpipeline #model #modeling #application #nature #environment #ecosystems #ecology #local #regional #factors #socioeconomics #forestecology #vegetation #forest #tree #carboncredit #climatechange #climatecrisis #forestcarbonprojects #global

    #gis #spatial #mapping #usecase #carbonoffset #spatialanalysis
  6. 💧🌏 Greg Cocks @GregCocks ·

    Geospatial Analysis of Carbon Offset Projects - A Broader Scientific Outlook
    --
    eldhollow.com/blogs/geospatial <-- shared technical blog
    --
    [this post should not be considered an endorsement of a particular organisation, rather scrutinising the spatial use case & technical approach]
    H/T @kyle Arvisais | Forest Carbon Scientist
    “Geospatial analysis is at the core of [the H/T’s company’s] project quality assessments, and [the author is] constantly finding ways to make the pipeline better and ways to use it. [They are] obviously not the only one who uses these types of tools, but to be perfectly honest, the quality of models [they have] seen over the years has been all over the place.
    This blog makes a casual introduction to [their] pipeline while talking about the field at large…”
    --
    “The world has committed to protecting and restoring nature at an unprecedented scale. Whether that commitment delivers what it promises comes down project execution on the ground. Local socioeconomics and forest ecology intertwine to create complex challenges for projects to overcome during implementation, and at the end of the day, projects boil all of these complexities down to one single unit: the carbon credit. So the question becomes: can we actually measure what is happening to a forest, accurately and honestly, and everywhere at once?
    For a long time, the honest answer has been no. Historically, many forest carbon projects overstated their impact. Usually it was because the baseline was too generous, or because the measurements underneath were flawed. For anyone with a stake in nature markets, that uncertainty is one of the core risks.
    Robust geospatial analysis can help mitigate that risk. If you treat a carbon credit as what it really is, a scientific claim, then we can hold it to that standard and assess it objectively. [Their] geospatial pipeline turns satellite data and ground truth data into models about how much forest is standing, how it is changing, and what might put it at risk in the future. The pipeline does this anywhere on Earth…”

    #gis #spatial #mapping #usecase #carbonoffset #spatialanalysis
  7. 💧🌏 Greg Cocks @[email protected] ·

    WHEN YOU ‘JUST’ WANT TO COLLECT SOME SPATIAL DATA! 😊
    --
    Australian Researchers Fitted Five Magpies With GPS Trackers To Map Their Movements, But The Birds Began Removing One Another’s Harnesses Within Minutes, Turning A Failed Trial Into An Unexpected Observation Of Cooperation
    (When researchers tried to track Australian magpies, the birds turned the tables—systematically removing each other's harnesses and revealing something far more interesting than GPS coordinates.)
    --
    youtu.be/mM0j_GybEKw?si=GE3sFw <-- shared video
    --
    spacedaily.com/t-australian-re <-- shared technical article
    --
    dx.doi.org/10.20938/afo39007011 <-- shared paper
    --
    abc.net.au/news/2022-02-22/mag <-- shared media article
    --
    [the Aussie magpies need SWAG on their GPS harness!]
    “Researchers in Queensland fitted five Australian magpies with tiny GPS trackers, hoping to map their movements and social relationships. The [mischief] had other plans.
    Within about ten minutes, an untagged adult female was using her bill to work at the harness on a juvenile. The tracker came off. Nearby, another magpie was helping a second tagged bird on a powerline. Devices that had been designed to stay attached for weeks disappeared within minutes to days.
    [The author] like[s] this story partly because it resists the tidy distinction between a successful experiment and a failed one. The researchers lost the movement data they went looking for, but the birds produced an observation nobody had planned…”
    --
    “Recent advances in tracking technology have enabled devices such as Global Positioning Systems (GPS) loggers to be used on a wide variety of birds. Although there are established ethical considerations to these processes, different species may react differently to particular devices and attachments. Thus, pilot studies are still of utmost importance in this field. Here, [the authors] describe one such study trialling a novel harness design for GPS tracking devices on Australian Magpies Gymnorhina tibicen. Despite previous testing demonstrating the strength and durability of the harness, devices were removed within minutes to hours of initial fitting. Notably, removal was observed to involve one bird snapping another bird’s harness at the only weak point, such that the tracker was released. This behaviour demonstrates both cooperation and a moderate level of problem solving, providing potential further evidence of the cognitive abilities of this species. To our knowledge, this is the first study to report the conspecific removal of GPS trackers, and should be considered when planning future tracking studies especially on highly social species…”
    #rescuebehaviour #altruism #WhereTheAnimalsAre #wildlife #tracking #spatialanalysis #bird #magpie #Australia #Queensland #research #GPS #tracker #harness #removal #ecosystems #GIS #logging #monitoring #spatial #mapping #social #species #behaviour #cooperation #problemsolving #cognitiveabilities #conspecific #biology

    #rescuebehaviour #altruism #wheretheanimalsare #wildlife #tracking #spatialanalysis
  8. 💧🌏 Greg Cocks @GregCocks ·

    WHEN YOU ‘JUST’ WANT TO COLLECT SOME SPATIAL DATA! 😊
    --
    Australian Researchers Fitted Five Magpies With GPS Trackers To Map Their Movements, But The Birds Began Removing One Another’s Harnesses Within Minutes, Turning A Failed Trial Into An Unexpected Observation Of Cooperation
    (When researchers tried to track Australian magpies, the birds turned the tables—systematically removing each other's harnesses and revealing something far more interesting than GPS coordinates.)
    --
    youtu.be/mM0j_GybEKw?si=GE3sFw <-- shared video
    --
    spacedaily.com/t-australian-re <-- shared technical article
    --
    dx.doi.org/10.20938/afo39007011 <-- shared paper
    --
    abc.net.au/news/2022-02-22/mag <-- shared media article
    --
    [the Aussie magpies need SWAG on their GPS harness!]
    “Researchers in Queensland fitted five Australian magpies with tiny GPS trackers, hoping to map their movements and social relationships. The [mischief] had other plans.
    Within about ten minutes, an untagged adult female was using her bill to work at the harness on a juvenile. The tracker came off. Nearby, another magpie was helping a second tagged bird on a powerline. Devices that had been designed to stay attached for weeks disappeared within minutes to days.
    [The author] like[s] this story partly because it resists the tidy distinction between a successful experiment and a failed one. The researchers lost the movement data they went looking for, but the birds produced an observation nobody had planned…”
    --
    “Recent advances in tracking technology have enabled devices such as Global Positioning Systems (GPS) loggers to be used on a wide variety of birds. Although there are established ethical considerations to these processes, different species may react differently to particular devices and attachments. Thus, pilot studies are still of utmost importance in this field. Here, [the authors] describe one such study trialling a novel harness design for GPS tracking devices on Australian Magpies Gymnorhina tibicen. Despite previous testing demonstrating the strength and durability of the harness, devices were removed within minutes to hours of initial fitting. Notably, removal was observed to involve one bird snapping another bird’s harness at the only weak point, such that the tracker was released. This behaviour demonstrates both cooperation and a moderate level of problem solving, providing potential further evidence of the cognitive abilities of this species. To our knowledge, this is the first study to report the conspecific removal of GPS trackers, and should be considered when planning future tracking studies especially on highly social species…”

    #rescuebehaviour #altruism #wheretheanimalsare #wildlife #tracking #spatialanalysis
  9. 💧🌏 Greg Cocks @[email protected] ·

    Classification And Conceptualization Of Karst Recharge Processes Through Spectral And Change Point Analysis Of Drip Water Dynamics
    --
    doi.org/10.1029/2025WR042816 <-- shared paper
    --
    H/T @ Danyang Sun | UNSW-PhD student
    “… [The authors] analysed one year of drip water monitoring data from 46 monitoring sites across six karst regions in southeastern Australia. By integrating fast Fourier analysis, cross-wavelet transform and change point analysis, [they] identified five characteristic recharge behaviours and developed a conceptual framework linking temporal drip dynamics with recharge mechanisms. [They] hope this framework will contribute to a better understanding of recharge heterogeneity in karst systems and support future groundwater research under a changing climate…”
    #karst #Australia #water #hydrology #underground #subsurface #recharge #dynamics #spectral #changepoint #cave #dripwater #analysis #spatiotemporal #groundwater #research #climatechange #extremeweather #flow #storage #vadose #epikarst #watertable #aquifer #percolation #rainfall #precipitation #climate #lithology #geology #spatialanalysis

    #karst #australia #water #hydrology #underground #subsurface
  10. 💧🌏 Greg Cocks @GregCocks ·

    Classification And Conceptualization Of Karst Recharge Processes Through Spectral And Change Point Analysis Of Drip Water Dynamics
    --
    doi.org/10.1029/2025WR042816 <-- shared paper
    --
    H/T @ Danyang Sun | UNSW-PhD student
    “… [The authors] analysed one year of drip water monitoring data from 46 monitoring sites across six karst regions in southeastern Australia. By integrating fast Fourier analysis, cross-wavelet transform and change point analysis, [they] identified five characteristic recharge behaviours and developed a conceptual framework linking temporal drip dynamics with recharge mechanisms. [They] hope this framework will contribute to a better understanding of recharge heterogeneity in karst systems and support future groundwater research under a changing climate…”

    #karst #australia #water #hydrology #underground #subsurface
  11. Mbongowo J Mbuh @[email protected] ·

    Where does nitrogen and phosphorus runoff risk concentrate across North Dakota? I screened every HUC-12 watershed in the state and put it in a dashboard a manager can actually read.
    #GIS #WaterQuality #Agriculture #SpatialAnalysis

    #gis #waterquality #agriculture #spatialanalysis
  12. 💧🌏 Greg Cocks @[email protected] ·

    SOME SHARED SPATIAL/GIS ROLES & OPPORTUNITIES
    --
    w/e August 7th, 2026 | GLOBAL but North America focused (as currently based there)
    --
    earthstuff.substack.com/p/some
    --
    • I am NOT associated with HR/recruiting in ANY way
    • Therefore, these are NOT endorsements, and there are no ‘referrals’ or the like for me
    • I am simply sharing SOME roles I have seen – in the hope that they are of use to someone
    • As such, I am not (and indeed can not) in any way ‘vet’ these roles (i.e., you are advised to conduct your own due diligence due to hideous, potential risks like fraudulent postings, ‘ghost jobs’, résumé/ATS ‘improvement’ scammers, etc)
    • Further, roles listed are in no specific order (i.e., simply as I ‘encountered’ them during the week)
    • There is ZERO cost to access this list of spatial / GIS roles
    • I am not (really) ‘mining’ US Federal government roles - with the ‘hiring freeze’, uncertainty, etc
    • You can subscribe to the (free) weekly LinkedIn newsletter www.linkedin.com/build-relation/newsletter-follow?entityUrn=7397678824928366594 or also for free at earthstuff.Substack.com - if so inclined

    #fridayjobs #gischat #jobsonfriday #GIS #spatial #mapping #cartography #geography #job #jobopenings #hiring #newjob #employment #roles #GISJobs #NotARecruiter #fridayspatialjobs #scholarship #PhD #PostDoc #CareersInSpatial #career #openroles #Geomatics #GIS #Innovation #Growth #caveatNONemptor #LetsGetYouHired #GISCareer #GISStudents #PhD #postdoc #hiringalert #GISjobs #careeropportunity #NowHiring #spatialanalysis #GIS #Geospatial #GISSpecialist #Geomatics #OpenToWork

    #fridayjobs #gischat #jobsonfriday #gis #spatial #mapping
  13. 💧🌏 Greg Cocks @GregCocks ·

    SOME SHARED SPATIAL/GIS ROLES & OPPORTUNITIES
    --
    w/e August 7th, 2026 | GLOBAL but North America focused (as currently based there)
    --
    earthstuff.substack.com/p/some
    --
    • I am NOT associated with HR/recruiting in ANY way
    • Therefore, these are NOT endorsements, and there are no ‘referrals’ or the like for me
    • I am simply sharing SOME roles I have seen – in the hope that they are of use to someone
    • As such, I am not (and indeed can not) in any way ‘vet’ these roles (i.e., you are advised to conduct your own due diligence due to hideous, potential risks like fraudulent postings, ‘ghost jobs’, résumé/ATS ‘improvement’ scammers, etc)
    • Further, roles listed are in no specific order (i.e., simply as I ‘encountered’ them during the week)
    • There is ZERO cost to access this list of spatial / GIS roles
    • I am not (really) ‘mining’ US Federal government roles - with the ‘hiring freeze’, uncertainty, etc
    • You can subscribe to the (free) weekly LinkedIn newsletter www.linkedin.com/build-relation/newsletter-follow?entityUrn=7397678824928366594 or also for free at earthstuff.Substack.com - if so inclined

    #fridayjobs #gischat #jobsonfriday #gis #spatial #mapping
  14. 💧🌏 Greg Cocks @[email protected] ·

    Coastal Flooding At Predictable Hours
    --
    doi.org/10.1038/s41467-026-757 <-- shared paper
    --
    H/T @bruna Alves | Nature Communications | Editor
    “When do coastal floods happen? ⏱️🌊
    A new study [link above] shows that, in many tide-dominated coastal regions, flooding tends to occur at specific and recurring times of day. Rather than being completely random, flood events often cluster around predictable hours driven by local tidal patterns.
    By analysing coastal flood observations from the UK and the US, [the authors] show[ed] that the timing of flood events can be highly structured, with some locations experiencing floods disproportionately during certain parts of the day.
    This adds a temporal dimension to coastal flood risk. We often focus on how frequently floods occur, how severe they are, and where they happen. This study highlights that when they occur may also matter, particularly as rising sea levels increase the frequency of coastal flooding.
    The findings provide a useful perspective for coastal adaptation, risk communication, and emergency planning…”
    --
    “Flooding is typically perceived as a sudden and unpredictable hazard. Here, [they] show that recurrent flooding can occur at highly predictable times in tidally dominated coastal systems. This predictability stems from phase-locking of tidal constituents and constituent pairs with the solar day, causing peak tides to recur at consistent local times set by regional tidal propagation. Using tide-gauge records from the United States and the United Kingdom, [they] quantif[ied] the intraday timing of coastal flood events and show strong clustering at specific hours, particularly where semidiurnal or mixed tides dominate. For example, floods in Boston cluster around noon and midnight, whereas in southern California they occur in the morning. Sites with stronger non-tidal variability show weaker clustering. This temporal predictability extends beyond nuisance flooding to larger consequential events involving inundation, road closures and infrastructural damage, highlighting opportunities for anticipatory risk communication, emergency planning and time-sensitive coastal adaptation…”
    #coast #coastal #flood #flooding #spatialanalysis #spatiotemporal #time #statistics #geostatistics #tide #tidal #timing #temporal #floodrisk #risk #hazard #sealevel #sealevelrise #climatechange #emergency #planning #tideguage #UK #USA #innundation #infrastructure #transportation #riskcommunication

    #coast #coastal #flood #flooding #spatialanalysis #spatiotemporal
  15. 💧🌏 Greg Cocks @GregCocks ·

    Coastal Flooding At Predictable Hours
    --
    doi.org/10.1038/s41467-026-757 <-- shared paper
    --
    H/T @bruna Alves | Nature Communications | Editor
    “When do coastal floods happen? ⏱️🌊
    A new study [link above] shows that, in many tide-dominated coastal regions, flooding tends to occur at specific and recurring times of day. Rather than being completely random, flood events often cluster around predictable hours driven by local tidal patterns.
    By analysing coastal flood observations from the UK and the US, [the authors] show[ed] that the timing of flood events can be highly structured, with some locations experiencing floods disproportionately during certain parts of the day.
    This adds a temporal dimension to coastal flood risk. We often focus on how frequently floods occur, how severe they are, and where they happen. This study highlights that when they occur may also matter, particularly as rising sea levels increase the frequency of coastal flooding.
    The findings provide a useful perspective for coastal adaptation, risk communication, and emergency planning…”
    --
    “Flooding is typically perceived as a sudden and unpredictable hazard. Here, [they] show that recurrent flooding can occur at highly predictable times in tidally dominated coastal systems. This predictability stems from phase-locking of tidal constituents and constituent pairs with the solar day, causing peak tides to recur at consistent local times set by regional tidal propagation. Using tide-gauge records from the United States and the United Kingdom, [they] quantif[ied] the intraday timing of coastal flood events and show strong clustering at specific hours, particularly where semidiurnal or mixed tides dominate. For example, floods in Boston cluster around noon and midnight, whereas in southern California they occur in the morning. Sites with stronger non-tidal variability show weaker clustering. This temporal predictability extends beyond nuisance flooding to larger consequential events involving inundation, road closures and infrastructural damage, highlighting opportunities for anticipatory risk communication, emergency planning and time-sensitive coastal adaptation…”

    #coast #coastal #flood #flooding #spatialanalysis #spatiotemporal
  16. 💧🌏 Greg Cocks @[email protected] ·

    The Latest Data Confirms - Forest Fires Are Getting Worse
    --
    wri.org/insights/global-trends <-- shared technical article
    --
    alturl.com/efp6m <-- shared (focused) #GlobalNatureWatch web map
    --
    science.nasa.gov/earth/explore <-- shared NASA technical article, ‘Wildfires and Climate Change’
    --
    doi.org/10.3389/frsen.2022.825 <-- shared paper
    --
    doi.org/10.1073/pnas.2505418122 <-- shared paper
    --
    doi.org/10.1088/1748-9326/add6 <-- shared paper
    --
    globalnaturewatch.org/dashboar <-- shared Global Nature Watch dashboard
    --
    youtu.be/-0-pv1Bqm-U?si=IHcZJN <-- shared overview video
    --
    grist.org/wildfires/the-us-has <-- shared technical article, ‘Fire is responsible for a quarter of US forest loss since 2021’
    --
    nytimes.com/2026/04/29/climate <-- shared media article
    --
    H/T @ World Resources Institute
    [‘topical’ - Europe, North America, indeed globally, more & more…]
    “New data shows that forest fires are getting worse, burning more than twice as much tree cover today as they did 20 years ago, largely due to climate change…
    The latest data [2nd link above] confirms [that] forest fires are becoming more widespread and destructive around the globe. Updated data from researchers [3rd link above] shows that between 2001 and 2025 forest fires now burn over twice as much tree cover each year as they did two decades ago, and more than three times as much in the tropics.
    This increased fire activity has been starkly visible in recent years. Record-setting blazes are becoming the norm, with four of the five worst years for global forest fires occurring since 2021. As fires worsen - including in historically low-risk areas, like rainforests - they are becoming an increasingly prevalent driver of global forest loss…”
    #GlobalForestWatch #GlobalNatureWatch #deforestation #fire #wildfire #forest #vegetation #climatechange #risk #hazard #loss #ecosystems #GIS #spatial #mapping #remotesensing #earthobservation #spatialanalysis #spatiotemporal #global #worldwide #forestfire #damage #destruction #fireactivity #forestLOSS
    @WRI | @Global Nature Watch

    #globalnaturewatch #globalforestwatch #deforestation #fire #wildfire #forest
  17. 💧🌏 Greg Cocks @GregCocks ·

    The Latest Data Confirms - Forest Fires Are Getting Worse
    --
    wri.org/insights/global-trends <-- shared technical article
    --
    alturl.com/efp6m <-- shared (focused) web map
    --
    science.nasa.gov/earth/explore <-- shared NASA technical article, ‘Wildfires and Climate Change’
    --
    doi.org/10.3389/frsen.2022.825 <-- shared paper
    --
    doi.org/10.1073/pnas.2505418122 <-- shared paper
    --
    doi.org/10.1088/1748-9326/add6 <-- shared paper
    --
    globalnaturewatch.org/dashboar <-- shared Global Nature Watch dashboard
    --
    youtu.be/-0-pv1Bqm-U?si=IHcZJN <-- shared overview video
    --
    grist.org/wildfires/the-us-has <-- shared technical article, ‘Fire is responsible for a quarter of US forest loss since 2021’
    --
    nytimes.com/2026/04/29/climate <-- shared media article
    --
    H/T @ World Resources Institute
    [‘topical’ - Europe, North America, indeed globally, more & more…]
    “New data shows that forest fires are getting worse, burning more than twice as much tree cover today as they did 20 years ago, largely due to climate change…
    The latest data [2nd link above] confirms [that] forest fires are becoming more widespread and destructive around the globe. Updated data from researchers [3rd link above] shows that between 2001 and 2025 forest fires now burn over twice as much tree cover each year as they did two decades ago, and more than three times as much in the tropics.
    This increased fire activity has been starkly visible in recent years. Record-setting blazes are becoming the norm, with four of the five worst years for global forest fires occurring since 2021. As fires worsen - including in historically low-risk areas, like rainforests - they are becoming an increasingly prevalent driver of global forest loss…”

    @WRI | @Global Nature Watch

    #globalnaturewatch #globalforestwatch #deforestation #fire #wildfire #forest
  18. 💧🌏 Greg Cocks @[email protected] ·

    From Fragmentation To Integration - A Review Of Data–Model Integration In Land Subsidence Research
    --
    doi.org/10.1016/j.ancene.2026. <-- shared paper
    --
    H/T @manonzero current drought conditions, increasing pressure on ecosystems, and ongoing climate adaptation challenges, land subsidence is receiving growing attention worldwide.
    For anyone wanting to learn more about land subsidence, or get a refresher, [the authors] provide an overview of the processes involved, the ways it can be measured or estimated, the models used to simulate it, and how observations and models can be combined, [their] new [#openaccess] review paper [link above] may be of interest!
    A central message of the paper is that understanding and managing land subsidence requires bringing these different sources of information together…”
    --
    “HIGHLIGHTS:
    • Presents a comprehensive synthesis that unifies all major elements of integral land-subsidence research.
    • Defines the methodological steps needed for full integration and positions them within the broader challenges posed by subsidence.
    • Brings together and distills the key components of data, modelling, and integration into a coherent framework.
    ABSTRACT: Land subsidence, the sinking of the Earth’s surface, is a multi-faceted hazard driven by both natural and anthropogenic factors, and poses significant risk to environments, ecosystems, and society. Despite decades of growing research output, substantial gaps persist between investigations on the diversity of causes, reflected in data and model insufficiency. These gaps hinder the understanding of the issue and impede the effectiveness of mitigation measures. Many studies have urged to include all identified subsidence processes acting in a single area in an integral framework, for which a complex analysis has not systematically been outlined before. Therefore, [they] focus here on bridging the gaps between various technical research disciplines involved. [They] stress the urgency for an integral approach that combines observations with subsurface information of all known subsidence processes in an area, and [they] appeal for utilizing them in physics-guided data integrations. Only then can all subsidence drivers be understood, and effective mitigation measures designed. [They] outline the elements for an integral approach in categorical tables and schematized figures, and [they] discuss the main opportunities and challenges in a stepwise workflow. Leveraging these opportunities naturally leads to more robust, scalable, and policy-relevant solutions and fosters a more sustainable future…”
    #Land #subsidence #processes #Verticallandmotion #SLR #sealevel #sealevelrise #relativesealevelrise #modeling #data #model #InSAR #elevation #holistic #GIS #spatial #mapping #climate #drought #extremeweather #climatechange #climateadaption #ecosystems #measurement #monitoring #research #review #framework #overview #risk #hazard #anthropogenic #mitigation #engineering #water #hydrology #policy #planning #design #remotesensing #spatialanalysis #spatiotemporal

    #land #subsidence #processes #verticallandmotion #slr #sealevel
  19. 💧🌏 Greg Cocks @GregCocks ·

    From Fragmentation To Integration - A Review Of Data–Model Integration In Land Subsidence Research
    --
    doi.org/10.1016/j.ancene.2026. <-- shared paper
    --
    H/T @manonzero current drought conditions, increasing pressure on ecosystems, and ongoing climate adaptation challenges, land subsidence is receiving growing attention worldwide.
    For anyone wanting to learn more about land subsidence, or get a refresher, [the authors] provide an overview of the processes involved, the ways it can be measured or estimated, the models used to simulate it, and how observations and models can be combined, [their] new [#openaccess] review paper [link above] may be of interest!
    A central message of the paper is that understanding and managing land subsidence requires bringing these different sources of information together…”
    --
    “HIGHLIGHTS:
    • Presents a comprehensive synthesis that unifies all major elements of integral land-subsidence research.
    • Defines the methodological steps needed for full integration and positions them within the broader challenges posed by subsidence.
    • Brings together and distills the key components of data, modelling, and integration into a coherent framework.
    ABSTRACT: Land subsidence, the sinking of the Earth’s surface, is a multi-faceted hazard driven by both natural and anthropogenic factors, and poses significant risk to environments, ecosystems, and society. Despite decades of growing research output, substantial gaps persist between investigations on the diversity of causes, reflected in data and model insufficiency. These gaps hinder the understanding of the issue and impede the effectiveness of mitigation measures. Many studies have urged to include all identified subsidence processes acting in a single area in an integral framework, for which a complex analysis has not systematically been outlined before. Therefore, [they] focus here on bridging the gaps between various technical research disciplines involved. [They] stress the urgency for an integral approach that combines observations with subsurface information of all known subsidence processes in an area, and [they] appeal for utilizing them in physics-guided data integrations. Only then can all subsidence drivers be understood, and effective mitigation measures designed. [They] outline the elements for an integral approach in categorical tables and schematized figures, and [they] discuss the main opportunities and challenges in a stepwise workflow. Leveraging these opportunities naturally leads to more robust, scalable, and policy-relevant solutions and fosters a more sustainable future…”

    #land #subsidence #processes #verticallandmotion #slr #sealevel
  20. 💧🌏 Greg Cocks @[email protected] ·

    Flood And Landslide Susceptibility Assessment And Multi Hazard Interaction Mapping Using Machine Learning And GIS For Sustainable Settlement Planning In Nepal
    --
    doi.org/10.1007/s44288-026-006 <-- shared paper
    --
    H/T @Narayan Thapa | Earth Data Modeling
    “Nepal lies within an active seismic zone and is influenced by most dynamic climatic systems in the world. It faces compounding floods and landslide threats. Impacts are worst where multi-hazard interactions create spatially linked corridors. Despite frequent co-occurrence, national-scale assessments remain limited. This study presents machine learning and GIS-based approach to map nationwide susceptibility to floods, landslides, and identify their potential interaction zones, and delineate critical multi-hazard flow zones through spatial adjacency analysis. Using Google Earth Engine, the Random Forest model integrates topographic, climatic, environmental, and hydrological datasets to overcome subjective expert-driven methods. The model achieved strong predictive accuracy (AUC: 0.84 for floods, 0.85 for landslides). The results showed 19% of Nepal’s lowlands are medium to very highly susceptible to inundation, threatening approximately 900,000 people and over 3.4 million buildings; whilst in the hilly terrains, 40% is susceptible to slope-failure endangering 200,000 people and about 0.6 million buildings. K-means clustering followed by spatial adjacency analysis identified four spatial zonation: 81% of national area as low-hazard zone, 9% as flood-only zone, 5% as landslide-only zone, and 5% as interaction zones. Critical multi-hazard flow zone covering 7,588 km² represents spatially connected corridors linking interaction zones to downstream flood-prone populated areas, affecting 88 km² built-up land and 1,722 km² cropland. These zones represent susceptibility-based spatial connectivity rather than physically simulated cascading processes. These findings support recommendations for risk-informed land-use planning, resilient infrastructure development and climate adaptation aligned to sustainable development and investment risk screening…”
    #GIS #spatial #mapping #GoogleEarthEngine #MachineLearning #RemoteSensing #GeospatialAI #DisasterRiskReduction #MultiHazard #ClimateAdaptation #climatechange #extremeweather #LandUsePlanning #SustainableDevelopment #InfrastructurePlanning #RiskAssessment #NaturalHazards #Nepal #EarthObservation #HinduKushHimalaya #HKH #HinduKush #Himalayas #risk #hazard #assessment #national #regional #spatialanalysis #spatiotemporal #massmovement #landslide #assessment #mitigation #water #hydrology #flood #flooding #sustainability

    #gis #spatial #mapping #googleearthengine #machinelearning #remotesensing
  21. 💧🌏 Greg Cocks @GregCocks ·

    Flood And Landslide Susceptibility Assessment And Multi Hazard Interaction Mapping Using Machine Learning And GIS For Sustainable Settlement Planning In Nepal
    --
    doi.org/10.1007/s44288-026-006 <-- shared paper
    --
    H/T @Narayan Thapa | Earth Data Modeling
    “Nepal lies within an active seismic zone and is influenced by most dynamic climatic systems in the world. It faces compounding floods and landslide threats. Impacts are worst where multi-hazard interactions create spatially linked corridors. Despite frequent co-occurrence, national-scale assessments remain limited. This study presents machine learning and GIS-based approach to map nationwide susceptibility to floods, landslides, and identify their potential interaction zones, and delineate critical multi-hazard flow zones through spatial adjacency analysis. Using Google Earth Engine, the Random Forest model integrates topographic, climatic, environmental, and hydrological datasets to overcome subjective expert-driven methods. The model achieved strong predictive accuracy (AUC: 0.84 for floods, 0.85 for landslides). The results showed 19% of Nepal’s lowlands are medium to very highly susceptible to inundation, threatening approximately 900,000 people and over 3.4 million buildings; whilst in the hilly terrains, 40% is susceptible to slope-failure endangering 200,000 people and about 0.6 million buildings. K-means clustering followed by spatial adjacency analysis identified four spatial zonation: 81% of national area as low-hazard zone, 9% as flood-only zone, 5% as landslide-only zone, and 5% as interaction zones. Critical multi-hazard flow zone covering 7,588 km² represents spatially connected corridors linking interaction zones to downstream flood-prone populated areas, affecting 88 km² built-up land and 1,722 km² cropland. These zones represent susceptibility-based spatial connectivity rather than physically simulated cascading processes. These findings support recommendations for risk-informed land-use planning, resilient infrastructure development and climate adaptation aligned to sustainable development and investment risk screening…”

    #gis #spatial #mapping #googleearthengine #machinelearning #remotesensing
  22. 💧🌏 Greg Cocks @[email protected] ·

    Enhancements To The USGS Landsat Level 2 Surface Temperature And Emissivity Product For Collection3 Reprocessing
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    H/T @terry Sohl | USGS EROS Science Branch Chief
    “HIGHLIGHTS:
    • C3 advances Landsat TIR atmospheric correction, emissivity correction, and uncertainty estimates.
    • C3 enables global surface temperature products, including polar regions.
    • C3 retrievals improve accuracy and consistency across validation sites.
    • Split window and single channel methods diverge at extreme temperature conditions.
    • C3 and Landsat 10 support multi-decadal climate monitoring.
    ABSTRACT: The Landsat program provides the longest continuous global record of thermal infrared observations of the Earth's surface, underpinning critical applications in climate monitoring, water resources, ecosystem dynamics, urban heat analysis, and natural hazard assessment. The release of a global inventory of Landsat Collection 2 Level 2 surface temperature products by the U.S. Geological Survey (USGS) marked a major milestone in operational provision of Landsat thermal infrared analysis-ready data. Ongoing validations and community uses of Collection 2 have identified opportunities to further improve accuracy, uncertainty characterization, and emissivity correction across diverse atmospheric and surface conditions. In preparation for the planned Landsat Collection 3 reprocessing of the Landsat data record in the late 2020s, the USGS is implementing a coordinated set of enhancements to the Level 2 surface temperature products. These include revised emissivity estimation that leverages external datasets, improved atmospheric characterization and uncertainty propagation, expanded dynamic range for high temperature targets, consideration of split window atmospheric correction algorithm for Landsat 8 and 9, and decoupling of thermal infrared processing from visible to shortwave infrared constraints to enable surface temperature retrievals under low or no solar illumination conditions. These changes are designed to improve product quality and consistency across the Landsat record. Beyond near-term performance gains, the Collection 3 design establishes a scalable processing architecture to accommodate the expanded spectral and radiometric measurement capabilities of the forthcoming Landsat 10 mission. By preserving continuity across the Landsat 4–9 record while enabling future algorithm evolution, Landsat Collection 3 will provide a foundation for long-term, multi-decadal Earth system thermal infrared observations…”
    #GIS #spatial #mapping #satellite #remotesening #earthobservation #Landsat #thermalinfrared #TIR #surface #temperature #emissivity #thermal #infrared #analysisreadydata #Collection2 #Collection3 #opendata #atmosphericcorrection #global #climate #monitoring #waterresources #ecosystems #dynamics #urbanheat #spatialanalysis #spatiotemporal #naturalhazards #updates #EROS #USGS
    @USGS EROS | @USGS

    #gis #spatial #mapping #satellite #remotesening #earthobservation
  23. 💧🌏 Greg Cocks @GregCocks ·

    Enhancements To The USGS Landsat Level 2 Surface Temperature And Emissivity Product For Collection3 Reprocessing
    --
    doi.org/10.1016/j.rse.2026.115 <-- shared paper
    --
    H/T @terry Sohl | USGS EROS Science Branch Chief
    “HIGHLIGHTS:
    • C3 advances Landsat TIR atmospheric correction, emissivity correction, and uncertainty estimates.
    • C3 enables global surface temperature products, including polar regions.
    • C3 retrievals improve accuracy and consistency across validation sites.
    • Split window and single channel methods diverge at extreme temperature conditions.
    • C3 and Landsat 10 support multi-decadal climate monitoring.
    ABSTRACT: The Landsat program provides the longest continuous global record of thermal infrared observations of the Earth's surface, underpinning critical applications in climate monitoring, water resources, ecosystem dynamics, urban heat analysis, and natural hazard assessment. The release of a global inventory of Landsat Collection 2 Level 2 surface temperature products by the U.S. Geological Survey (USGS) marked a major milestone in operational provision of Landsat thermal infrared analysis-ready data. Ongoing validations and community uses of Collection 2 have identified opportunities to further improve accuracy, uncertainty characterization, and emissivity correction across diverse atmospheric and surface conditions. In preparation for the planned Landsat Collection 3 reprocessing of the Landsat data record in the late 2020s, the USGS is implementing a coordinated set of enhancements to the Level 2 surface temperature products. These include revised emissivity estimation that leverages external datasets, improved atmospheric characterization and uncertainty propagation, expanded dynamic range for high temperature targets, consideration of split window atmospheric correction algorithm for Landsat 8 and 9, and decoupling of thermal infrared processing from visible to shortwave infrared constraints to enable surface temperature retrievals under low or no solar illumination conditions. These changes are designed to improve product quality and consistency across the Landsat record. Beyond near-term performance gains, the Collection 3 design establishes a scalable processing architecture to accommodate the expanded spectral and radiometric measurement capabilities of the forthcoming Landsat 10 mission. By preserving continuity across the Landsat 4–9 record while enabling future algorithm evolution, Landsat Collection 3 will provide a foundation for long-term, multi-decadal Earth system thermal infrared observations…”

    @USGS EROS | @USGS

    #gis #spatial #mapping #satellite #remotesening #earthobservation
  24. 💧🌏 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
  25. 💧🌏 Greg Cocks @GregCocks ·

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

    [Open] Data Related To Flood Mapping [Canada]
    --
    natural-resources.canada.ca/sc <-- shared link to technical details
    --
    app.geo.ca/en-ca/map-browser/r <-- shared map/data-portal link, Canada Flood Map Inventory (CFM)
    --
    open.canada.ca/data/en/dataset <-- shared data-portal link, Canada Flood Susceptibility Index
    --
    doi.org/10.3390/ECWS-7-14235 <-- shared (2023) paper
    --
    doi.org/10.1002/2017WR020917 <-- shared (2017) paper
    --
    H/T @Michael DePue | VP & AtkinsRéalis Fellow for Water Resources Engineering | PE, PMP, CFM
    “At the Canadian Water Resources Association National Conference in Winnipeg, colleagues shared insights from Canada's Flood Hazard Identification and Mapping Program. This initiative has seen over 400 flood mapping projects and more than 1,000 flood hazard maps produced, supported by a substantial investment of $164.2 million from 2024 to 2028.
    Two key datasets:
    • The Canada Flood Map Inventory, which records the locations of flood hazard maps and provides information on how to access them.
    • The national Flood Susceptibility Index, a machine-learning assessment of flood-prone areas, including regions that have not been mapped in detail.
    When these two layers are combined on a single screen, it becomes clear where future mapping efforts should be directed — specifically, areas with high susceptibility that currently lack detailed maps…”
    #water #hydrography #flood #flooding #risk #hazard #model #modeling #fedscience #publicsafety #humaninpacts #opendata #Canada #GIS #spatial #mapping #damage #infrastructure #floodmapping #prediction #spatialanalysis #spatiotemporal #historic #current #future #preduction #extremeweather #metrology #rainfall #precipitation #atmosphericriver #FloodMapInventory #CFM #floodhazard #FloodSusceptibilityIndex #floodprone #research #susceptibility
    @NRCAN

    #water #hydrography #flood #flooding #risk #hazard
  27. 💧🌏 Greg Cocks @GregCocks ·

    [Open] Data Related To Flood Mapping [Canada]
    --
    natural-resources.canada.ca/sc <-- shared link to technical details
    --
    app.geo.ca/en-ca/map-browser/r <-- shared map/data-portal link, Canada Flood Map Inventory (CFM)
    --
    open.canada.ca/data/en/dataset <-- shared data-portal link, Canada Flood Susceptibility Index
    --
    doi.org/10.3390/ECWS-7-14235 <-- shared (2023) paper
    --
    doi.org/10.1002/2017WR020917 <-- shared (2017) paper
    --
    H/T @Michael DePue | VP & AtkinsRéalis Fellow for Water Resources Engineering | PE, PMP, CFM
    “At the Canadian Water Resources Association National Conference in Winnipeg, colleagues shared insights from Canada's Flood Hazard Identification and Mapping Program. This initiative has seen over 400 flood mapping projects and more than 1,000 flood hazard maps produced, supported by a substantial investment of $164.2 million from 2024 to 2028.
    Two key datasets:
    • The Canada Flood Map Inventory, which records the locations of flood hazard maps and provides information on how to access them.
    • The national Flood Susceptibility Index, a machine-learning assessment of flood-prone areas, including regions that have not been mapped in detail.
    When these two layers are combined on a single screen, it becomes clear where future mapping efforts should be directed — specifically, areas with high susceptibility that currently lack detailed maps…”

    @NRCAN

    #water #hydrography #flood #flooding #risk #hazard
  28. 💧🌏 Greg Cocks @[email protected] ·

    Using Geospatial Analysis and Explainable Machine Learning to Examine Risk Factors of Out-of-Hospital Cardiac Arrest Survival Outcome
    --
    openscholar.uga.edu/record/269 <-- shared technical publication / dissertation
    --
    aag.org/award-grant/william-l- <-- shared @AAG William L. Garrison Award for Best Dissertation in Computational Geography
    --
    [again, way outside any expertise I might have, but fascinating spatial analysis use case…]
    H/T @Jielu Zhang | Postdoctoral Researcher @ Harvard University
    “[The authors] Ph.D. dissertation "Using Geospatial Analysis and Explainable Machine Learning to Examine Risk Factors of Out-of-Hospital Cardiac Arrest Survival Outcome" [1st link above] has received the 2026 biennial William L. Garrison Award for Best Dissertation in Computational Geography from the American Association of Geographers… [2nd link above.]
    In [their] research, [they] develop[ed] Explainable GeoAI and Causal GeoAI methods that combine geographic data and artificial intelligence to expose and ameliorate health disparities by delivering models that not only predict risks but also illuminate how and where to intervene. While [their] dissertation focused on cardiovascular disease, these approaches are broadly applicable to public health, medicine, urban planning, environmental exposure, and resilience research…”
    #explainable #causal #AI #model #modeling #PublicHealth #GIS #spatial #mapping #spatialanalysis #spatiotemporal #AAG2026 #AAG #Award #geostatistics #Georgia #health #risk #hazard #cardiacarrest #cardiovacscular #usecase #metrics #midocine #urbanplanning #resilience #survival #OutofHospital #PhD #Dissertation #CardiacArrest #AutomatedExternalDefibrillator #SpatialOptimization #GeographicallyExplainableArtificialIntelligence #GeoAI #GeoXAI #SpatiallyAwareCausalInference #OverlayedSpatioTemporalOptimization #healthcare #medical #intervention #GIS #spatial #mappingt #spatialanalysis #spatiotemporal #heart #heartattack #AED #survival #survivaloutcomes #machinelearning #AI #publichealth #healthgeographers #counterfactual #explainable #deeplearning #model #modeling

    #explainable #causal #ai #model #modeling #publichealth
  29. 💧🌏 Greg Cocks @GregCocks ·

    Using Geospatial Analysis and Explainable Machine Learning to Examine Risk Factors of Out-of-Hospital Cardiac Arrest Survival Outcome
    --
    openscholar.uga.edu/record/269 <-- shared technical publication / dissertation
    --
    aag.org/award-grant/william-l- <-- shared @AAG William L. Garrison Award for Best Dissertation in Computational Geography
    --
    [again, way outside any expertise I might have, but fascinating spatial analysis use case…]
    H/T @Jielu Zhang | Postdoctoral Researcher @ Harvard University
    “[The authors] Ph.D. dissertation "Using Geospatial Analysis and Explainable Machine Learning to Examine Risk Factors of Out-of-Hospital Cardiac Arrest Survival Outcome" [1st link above] has received the 2026 biennial William L. Garrison Award for Best Dissertation in Computational Geography from the American Association of Geographers… [2nd link above.]
    In [their] research, [they] develop[ed] Explainable GeoAI and Causal GeoAI methods that combine geographic data and artificial intelligence to expose and ameliorate health disparities by delivering models that not only predict risks but also illuminate how and where to intervene. While [their] dissertation focused on cardiovascular disease, these approaches are broadly applicable to public health, medicine, urban planning, environmental exposure, and resilience research…”

    #explainable #causal #ai #model #modeling #publichealth
  30. 💧🌏 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
  31. 💧🌏 Greg Cocks @GregCocks ·

    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
  32. 💧🌏 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
  33. 💧🌏 Greg Cocks @GregCocks ·

    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…”

    @Federal Office for the Environment FOEN | @Federal Office of Meteorology and Climatology MeteoSwiss

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

    SOME SHARED SPATIAL/GIS ROLES & OPPORTUNITIES
    --
    w/e July 31st, 2026 | GLOBAL but North America focused (as currently based there)
    --
    open.substack.com/pub/earthstu
    --
    • I am NOT associated with HR/recruiting in ANY way
    • Therefore, these are NOT endorsements, and there are no ‘referrals’ or the like for me
    • I am simply sharing SOME roles I have seen – in the hope that they are of use to someone
    • As such, I am not (and indeed can not) in any way ‘vet’ these roles (i.e., PLEASE do your ‘due diligence’ / use your judgement (with the hideous prevalence of ‘ghost jobs’, ‘fishing’, fraudulent so-called recruiters, etc))
    • Further, roles listed are in no specific order (i.e., simply as I ‘encountered’ them during the week)
    • There is ZERO cost to access this list of spatial / GIS roles
    • I am not (really) ‘mining’ US Federal government roles - with the ‘hiring freeze’, uncertainty, etc
    • You can subscribe to the (free) weekly LinkedIn newsletter www.linkedin.com/build-relation/newsletter-follow?entityUrn=7397678824928366594 or also for free at earthstuff.Substack.com - if so inclined

    #fridayjobs #gischat #jobsonfriday #GIS #spatial #mapping #cartography #geography #job #jobopenings #hiring #newjob #employment #roles #GISJobs #NotARecruiter #fridayspatialjobs #scholarship #PhD #PostDoc #CareersInSpatial #career #openroles #Geomatics #GIS #Innovation #Growth #caveatNONemptor #LetsGetYouHired #GISCareer #GISStudents #PhD #postdoc #hiringalert #GISjobs #careeropportunity #NowHiring #spatialanalysis #GIS #Geospatial #GISSpecialist #Geomatics #OpenToWork

    #fridayjobs #gischat #jobsonfriday #gis #spatial #mapping
  35. 💧🌏 Greg Cocks @GregCocks ·

    SOME SHARED SPATIAL/GIS ROLES & OPPORTUNITIES
    --
    w/e July 31st, 2026 | GLOBAL but North America focused (as currently based there)
    --
    open.substack.com/pub/earthstu
    --
    • I am NOT associated with HR/recruiting in ANY way
    • Therefore, these are NOT endorsements, and there are no ‘referrals’ or the like for me
    • I am simply sharing SOME roles I have seen – in the hope that they are of use to someone
    • As such, I am not (and indeed can not) in any way ‘vet’ these roles (i.e., PLEASE do your ‘due diligence’ / use your judgement (with the hideous prevalence of ‘ghost jobs’, ‘fishing’, fraudulent so-called recruiters, etc))
    • Further, roles listed are in no specific order (i.e., simply as I ‘encountered’ them during the week)
    • There is ZERO cost to access this list of spatial / GIS roles
    • I am not (really) ‘mining’ US Federal government roles - with the ‘hiring freeze’, uncertainty, etc
    • You can subscribe to the (free) weekly LinkedIn newsletter www.linkedin.com/build-relation/newsletter-follow?entityUrn=7397678824928366594 or also for free at earthstuff.Substack.com - if so inclined

    #fridayjobs #gischat #jobsonfriday #gis #spatial #mapping
  36. 💧🌏 Greg Cocks @[email protected] ·

    Unraveling The Drivers Of Water Shortage Across Spatial Scales And Sectors In Colorado's West Slope River Basins
    --
    doi.org/10.1029/2026EF008137 <-- shared paper
    --
    ['sorry' about your Kentucky Bluegrass, almonds, etc... /s]
    H/T Sai Veena Sunkara | Postdoctoral Associate
    “…Colorado’s West Slope basins provide nearly 70% of the inflows to Lake Powell and are also essential to communities, agriculture, industry, hydropower, and downstream Colorado River users.
    To examine the wide range of possible futures, [they] simulated 2.1 million years, defining 20,000 plausible scenarios applying changes to streamflow, snowmelt timing, drought persistence, and agricultural, municipal, and industrial water demand.
    A key finding is that there is 𝗻𝗼 𝘀𝗶𝗻𝗴𝗹𝗲 𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝗳𝘂𝘁𝘂𝗿𝗲 𝘄𝗮𝘁𝗲𝗿 𝘀𝗵𝗼𝗿𝘁𝗮𝗴𝗲𝘀. The most influential drivers vary by basin, sector, and water user. In some areas, shortages are driven primarily by persistent low-flow conditions or changing snowmelt timing. In others, increasing municipal, industrial, or irrigation demand plays a larger role. This suggests that adaptation strategies must be tailored to specific basins and users rather than relying on a single, system-wide solution. Other major findings are
    • West Slope deliveries to Lake Powell could fall more than 50% below the current median baseline
    • Storage in major West Slope reservoirs could decline 40–55% below historical medians
    These results underscore the need for water-planning approaches that account for deep uncertainty, persistent drought, shifting snowmelt patterns, and sector-specific demand…”
    #Colorado #waterallocation #StateMod #USWest #USA #WesternSlope #waterresources #watersecurity #watershortage #drought #snowmelt #rainfall #precipitation #riverbasin #water #hydrography #hydrology #reasons #agriculture #industry #hydropower #streamflow #surfacewater #municipal #irrigation #adaptationstrategies #planning #policy #mitigation #ColoradoRiver #basins #climatechange #extremeweather #populationpressure #waterdemand #waterrights #model #modeling #HiddenMarkovModel #stochastic #projecteddemand #ColoradoRiverBasin #wateruse #spatial #mapping #spatialanalysis #spatiotemporal #strategy

    #colorado #waterallocation #statemod #uswest #usa #westernslope
  37. 💧🌏 Greg Cocks @GregCocks ·

    Unraveling The Drivers Of Water Shortage Across Spatial Scales And Sectors In Colorado's West Slope River Basins
    --
    doi.org/10.1029/2026EF008137 <-- shared paper
    --
    ['sorry' about your Kentucky Bluegrass, almonds, etc... /s]
    H/T Sai Veena Sunkara | Postdoctoral Associate
    “…Colorado’s West Slope basins provide nearly 70% of the inflows to Lake Powell and are also essential to communities, agriculture, industry, hydropower, and downstream Colorado River users.
    To examine the wide range of possible futures, [they] simulated 2.1 million years, defining 20,000 plausible scenarios applying changes to streamflow, snowmelt timing, drought persistence, and agricultural, municipal, and industrial water demand.
    A key finding is that there is 𝗻𝗼 𝘀𝗶𝗻𝗴𝗹𝗲 𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝗳𝘂𝘁𝘂𝗿𝗲 𝘄𝗮𝘁𝗲𝗿 𝘀𝗵𝗼𝗿𝘁𝗮𝗴𝗲𝘀. The most influential drivers vary by basin, sector, and water user. In some areas, shortages are driven primarily by persistent low-flow conditions or changing snowmelt timing. In others, increasing municipal, industrial, or irrigation demand plays a larger role. This suggests that adaptation strategies must be tailored to specific basins and users rather than relying on a single, system-wide solution. Other major findings are
    • West Slope deliveries to Lake Powell could fall more than 50% below the current median baseline
    • Storage in major West Slope reservoirs could decline 40–55% below historical medians
    These results underscore the need for water-planning approaches that account for deep uncertainty, persistent drought, shifting snowmelt patterns, and sector-specific demand…”

    #colorado #waterallocation #statemod #uswest #usa #westernslope
  38. 💧🌏 Greg Cocks @[email protected] ·

    Coupled Hydrological And Public Health Risks From Urban Flooding - Integrated Remote Sensing, Machine Learning, And Hydrodynamic–Ecological Modelling
    --
    doi.org/10.1016/j.jhydrol.2026 <-- shared paper
    --
    youtu.be/VHzYLvSYR7k?si=5oGGPe <-- recent overview video created about the research
    --
    doi.org/10.1016/j.wroa.2025.10 <-- share (earlier) paper
    --
    H/T @RAHUL DEOPA | Research Scholar (IIT Roorkee)
    “… [U]rban floods are not merely hydraulic events; they also transport sewage, pathogens, and other contaminants across streets and communities, leading to significant public health risks…
    How do we quantify microbial contamination in near real time during a flood event, when emergency conditions make field sampling unsafe, sparse, or even impossible?...
    [The authors] explored whether Earth observation data, combined with machine learning, could bridge this critical monitoring gap. By combining Landsat-derived water surface temperature, machine learning, a coupled MIKE+ Flood–ECO Lab hydrodynamic–ecological model, and Quantitative Microbial Risk Assessment (QMRA), [they] estimated microbial concentrations (𝘌. 𝘤𝘰𝘭𝘪), simulated their fate and transport during floods, and quantified the associated human health risks.
    The takeaway: predicting flood risk isn't just about where the water goes; it's about what it's carrying and who it puts in harm's way. Earth observation and machine learning can help close that gap when it matters most, during the emergency, not weeks after…”
    #publichealth #risk #hazard #watersecurity #Floodrisk #Humanhealthrisk #Urbanflooding #Hydrodynamicmodelling #waterquality #model #modeling #SupportVectorRegression #flood #flooding #urban #city #sewage #pathogens #contaminant #disease #streets #community #quantification #remotesensing #GIS #spatial #mapping #earthobservation #spatialanalysis #water #hydrology #climatechange #extremeweather #spatiotemporal #AI #machineleraning #fateandtransport #hydrodynamic #microbial #rainfall #drainage #streamflow #topography #hydrogeomorphology #Delhi #India #floodplain

    #publichealth #risk #hazard #watersecurity #floodrisk #humanhealthrisk
  39. 💧🌏 Greg Cocks @GregCocks ·

    Coupled Hydrological And Public Health Risks From Urban Flooding - Integrated Remote Sensing, Machine Learning, And Hydrodynamic–Ecological Modelling
    --
    doi.org/10.1016/j.jhydrol.2026 <-- shared paper
    --
    youtu.be/VHzYLvSYR7k?si=5oGGPe <-- recent overview video created about the research
    --
    doi.org/10.1016/j.wroa.2025.10 <-- share (earlier) paper
    --
    H/T @RAHUL DEOPA | Research Scholar (IIT Roorkee)
    “… [U]rban floods are not merely hydraulic events; they also transport sewage, pathogens, and other contaminants across streets and communities, leading to significant public health risks…
    How do we quantify microbial contamination in near real time during a flood event, when emergency conditions make field sampling unsafe, sparse, or even impossible?...
    [The authors] explored whether Earth observation data, combined with machine learning, could bridge this critical monitoring gap. By combining Landsat-derived water surface temperature, machine learning, a coupled MIKE+ Flood–ECO Lab hydrodynamic–ecological model, and Quantitative Microbial Risk Assessment (QMRA), [they] estimated microbial concentrations (𝘌. 𝘤𝘰𝘭𝘪), simulated their fate and transport during floods, and quantified the associated human health risks.
    The takeaway: predicting flood risk isn't just about where the water goes; it's about what it's carrying and who it puts in harm's way. Earth observation and machine learning can help close that gap when it matters most, during the emergency, not weeks after…”

    #publichealth #risk #hazard #watersecurity #floodrisk #humanhealthrisk
  40. 💧🌏 Greg Cocks @[email protected] ·

    Sacred Sites As Refuges For Native Plant Diversity In An Urbanized Tropical Landscape [Madagascar]
    --
    doi.org/10.1038/s44284-026-004 <-- shared paper
    --
    H/T @nicolas Salliou | Senior scientist at ETH Zürich
    “When [the authors] investigated the potential for blue-green infrastructures for the capital of Madagascar,… a large team surveyed plant communities along a rural-urban gradient.
    They found that 12 Malagasy "sacred" hill settlements, situated in the core of a pre-colonial kingdom, are particularly precious as they host a high diversity of native plants. A key insights when one considers that Madagascar is a unique hotspot for biodiversity and human cultures…”
    --
    “In fragmented and intensively managed remnant urban green spaces, enabling natural regeneration is crucial to sustain ecosystem functioning and associated services. Natural regeneration therefore offers an important lens into ecological resilience in human-modified landscapes. Here [the authors] present one of the most comprehensive field-based assessments of urban plant diversity in the Afrotropics, comprising over 25,000 naturally regenerating woody plant individuals of 206 species (39% native) across 329 vegetation patches in Antananarivo, Madagascar. Landscape-level native species diversity declined with greater building complexity, density and socio-economic development, whereas exotic species showed no consistent associations with determinants of urban characteristics. Native species richness was highest in ‘sacred’ forest fragments, many of which contain historic remains dating back to pre-colonial times. Notably, seven of the ten most species-rich patches were located in such sites. [Their] findings highlight the potentially important role of these sites in sustaining native species diversity and indicate how socio-cultural factors can present both opportunities and barriers for tropical urban conservation and restoration...”
    #sacredsites #antananarivo #madagascar #vegetation #nativeplant #urban #greenspace #tropical #hillsettlements #spatialanalysis #survey #ecosystem #biodiversity #ecology #regeneration #humanimpacts #GIS #spatial #mapping #afrotropics #forest #forestfragments #precolonial #sustainability #diversity #native #sociocultural #restoration

    #sacredsites #antananarivo #madagascar #vegetation #nativeplant #urban
  41. 💧🌏 Greg Cocks @GregCocks ·

    Sacred Sites As Refuges For Native Plant Diversity In An Urbanized Tropical Landscape [Madagascar]
    --
    doi.org/10.1038/s44284-026-004 <-- shared paper
    --
    H/T @nicolas Salliou | Senior scientist at ETH Zürich
    “When [the authors] investigated the potential for blue-green infrastructures for the capital of Madagascar,… a large team surveyed plant communities along a rural-urban gradient.
    They found that 12 Malagasy "sacred" hill settlements, situated in the core of a pre-colonial kingdom, are particularly precious as they host a high diversity of native plants. A key insights when one considers that Madagascar is a unique hotspot for biodiversity and human cultures…”
    --
    “In fragmented and intensively managed remnant urban green spaces, enabling natural regeneration is crucial to sustain ecosystem functioning and associated services. Natural regeneration therefore offers an important lens into ecological resilience in human-modified landscapes. Here [the authors] present one of the most comprehensive field-based assessments of urban plant diversity in the Afrotropics, comprising over 25,000 naturally regenerating woody plant individuals of 206 species (39% native) across 329 vegetation patches in Antananarivo, Madagascar. Landscape-level native species diversity declined with greater building complexity, density and socio-economic development, whereas exotic species showed no consistent associations with determinants of urban characteristics. Native species richness was highest in ‘sacred’ forest fragments, many of which contain historic remains dating back to pre-colonial times. Notably, seven of the ten most species-rich patches were located in such sites. [Their] findings highlight the potentially important role of these sites in sustaining native species diversity and indicate how socio-cultural factors can present both opportunities and barriers for tropical urban conservation and restoration...”

    #sacredsites #antananarivo #madagascar #vegetation #nativeplant #urban
  42. Nick Bearman @nickbearman ·

    Want to learn how to visualise and analyze spatial data in the social sciences and build your skills?
    My Intro QGIS and Advanced QGIS courses are coming up, starting next week, with InStats

    instats.org/seminar/introducti
    instats.org/seminar/advanced-q
    nickbearman.com/training-cours

    Any questions, please ask!

    #gis #gischat #research #phd #spatialanalysis
  43. Nick Bearman @[email protected] ·

    Want to learn how to visualise and analyze spatial data in the social sciences and build your #GIS skills?
    My Intro QGIS and Advanced QGIS courses are coming up, starting next week, with InStats

    instats.org/seminar/introducti
    instats.org/seminar/advanced-q
    nickbearman.com/training-cours

    #GISchat #GIS #Research #PhD #SpatialAnalysis

    Any questions, please ask!

    #gis #gischat #research #phd #spatialanalysis
  44. 💧🌏 Greg Cocks @[email protected] ·

    Busy Beavers - The Turbidity Signature Of Ecosystem Engineers At Work
    --
    doi.org/10.1002/hyp.70661 <-- shared paper
    --
    H/T @alan Puttock
    “Beavers are the quintessential ecosystem engineers. In slow-flowing streams, they create complex wetlands with ponds by building dams and canals that can positively impact biodiversity, hydrology and water quality. These activities can interchangeably capture or release sediment along the watercourse. To date this has not been quantified at the resolution of rainfall events or beaver activity. This study used 15-min frequency, sustained monitoring upstream and downstream of a newly establishing beaver wetland to measure episodic changes in water turbidity at an event resolution. Monitoring showed no significant differences between upstream and downstream turbidity over 160 days when the first pair of beavers, known not to be building dams or canals, were resident. Shortly after introduction of another beaver pair, however, dam building, burrows and canal excavations were quickly observed, resulting in the creation of a complex beaver wetland between 2021 and 2024. Monitoring over 375 days during this period showed significant differences. Downstream turbidity was significantly higher overall than upstream: 13.1 Nephelometric Turbidity Units (NTU) compared to 4.2 NTU. Stochastic spikes in downstream turbidity during the study period not recorded upstream were associated with dam building and burrowing. Overall, there was no significant difference in turbidity loads, which was at least partially explained by a reduction in discharge downstream, particularly in higher flows, during the dam building period. This demonstrates a complex system with the trapping of influent sediment, the storing of water and the periodic release of beaver wetland sediment leading to net balance in loads. These results help provide context for other studies which have used temporally discrete sampling campaigns rather than continuous high-frequency monitoring. They provide a unique insight into the downstream impacts of a rapidly developing beaver wetland over its first three and a half years in a landscape that hasn't had beavers for over 400 years…"
    #hydromorphic #water #hydrology #dam #beaverdam #waterquality #biodiversity #ecology #benefits #NatureBasedSolutions #Wetlands #Ecology #Biodiversity #EnvironmentalScience #Wildlife #Ecosystem #bioviversity #conservation #restoration #landscaperecovery #floodmanagement #FloodMitigation #flooding #energy #floodrisk #sustainability #wetlands #hydrography #dams #impoundment #deadwood #waterresources #landscapeengineer #benefits #vegetation #ecology #ecosystem #riversystemsstabilisation #naturalwaterregulation #resilience #valleysreborn #fisheries #invertebrates #extremeweather #floodflows #sediment #baseflow #drought #landmanagement #naturalsystems #landuse #monitoring #spatialanalysis #spatiotemporal

    #hydromorphic #water #hydrology #dam #beaverdam #waterquality
  45. 💧🌏 Greg Cocks @GregCocks ·

    Busy Beavers - The Turbidity Signature Of Ecosystem Engineers At Work
    --
    doi.org/10.1002/hyp.70661 <-- shared paper
    --
    H/T @alan Puttock
    “Beavers are the quintessential ecosystem engineers. In slow-flowing streams, they create complex wetlands with ponds by building dams and canals that can positively impact biodiversity, hydrology and water quality. These activities can interchangeably capture or release sediment along the watercourse. To date this has not been quantified at the resolution of rainfall events or beaver activity. This study used 15-min frequency, sustained monitoring upstream and downstream of a newly establishing beaver wetland to measure episodic changes in water turbidity at an event resolution. Monitoring showed no significant differences between upstream and downstream turbidity over 160 days when the first pair of beavers, known not to be building dams or canals, were resident. Shortly after introduction of another beaver pair, however, dam building, burrows and canal excavations were quickly observed, resulting in the creation of a complex beaver wetland between 2021 and 2024. Monitoring over 375 days during this period showed significant differences. Downstream turbidity was significantly higher overall than upstream: 13.1 Nephelometric Turbidity Units (NTU) compared to 4.2 NTU. Stochastic spikes in downstream turbidity during the study period not recorded upstream were associated with dam building and burrowing. Overall, there was no significant difference in turbidity loads, which was at least partially explained by a reduction in discharge downstream, particularly in higher flows, during the dam building period. This demonstrates a complex system with the trapping of influent sediment, the storing of water and the periodic release of beaver wetland sediment leading to net balance in loads. These results help provide context for other studies which have used temporally discrete sampling campaigns rather than continuous high-frequency monitoring. They provide a unique insight into the downstream impacts of a rapidly developing beaver wetland over its first three and a half years in a landscape that hasn't had beavers for over 400 years…"

    #hydromorphic #water #hydrology #dam #beaverdam #waterquality
  46. 💧🌏 Greg Cocks @[email protected] ·

    Quantifying UK Coastal Flood Exposure Under Future Sea-Level Rise To [2100 and] 2300
    --
    doi.org/10.1038/s41467-026-749 <-- shared paper
    --
    theguardian.com/environment/20 <-- shared media article
    --
    H/T @University of Bristol School of Geographical Sciences
    “🌊 New research on long-term coastal flooding as a result of climate inaction 🌊
    … The researchers found:
    🔷 By 2100 at least an additional 0.5 million people exposed to the 1-in-200 year undefended flood extent - a 25% increase compared to present day.
    🔷 Under the most pessimistic storyline by 2300 involving significant ice-sheet instability, an additional 13 million people could be exposed to the 1-in-200 year undefended flood event.
    🔷 Under some scenarios there is a need for large-scale movement of populations and settlements away from the coast in the coming centuries…”
    --
    “Up to 13 million people in the UK face the long-term risk of coastal flooding due to the climate crisis, scientists have warned, with a ‘reasonable worst-case scenario’ suggesting the need for the large-scale movement of populations away from a dramatically reshaped coast…
    Sea level has risen by about 20cm around the UK in the last century and is accelerating. A further 40cm to 60cm by 2100 is already baked in, meaning about half a million more people will be at risk of their homes being flooded, on top of the 2.5 million already in danger near the coasts. Lincolnshire and the Humber estuary are most in danger…”
    --
    “The latest Intergovernmental Panel on Climate Change assessment report highlighted the potential for more than 15 metres of global sea-level rise by 2300. In this study, [the authors] explore the implications for UK coastal flood exposure by combining national-scale flood modelling with physically-based storylines of UK sea-level rise. By 2100 all storylines show broadly similar results with at least an additional ½ million people exposed to the 1-in-200 year undefended flood extent, which represents a 25% increase compared to present day. Under the most pessimistic storyline by 2300 involving significant ice-sheet instability, an additional 13 million people could be exposed to the 1-in-200 year undefended flood event. This would imply the potential need for large-scale movement of populations and settlements away from the coast in the coming centuries. Given current global emissions pledges, exposure increases by 1.7 million people by 2300, however up to 1 million could be avoided if Paris Agreement targets for greenhouse gas emissions are met…”
    #coast #coastal #innundation #climatechange #global #sealevel #sealevelrise #SLR #flood #flooding #climatechange #population #infrastructure #UK #England #Scotland #Wales #NorthernIreland #Lincolnshire #Humber #estuary #settlement #city #urban #mitigation #planning #policy #infrastructure #risk #hazard #humanimpacts #spatialanalysis #spatiotemporal #GIS #spatial #mapping #coastalflooding #climatecrisis #model #modeling #elevation #DEM

    #coast #coastal #innundation #climatechange #global #sealevel
  47. 💧🌏 Greg Cocks @GregCocks ·

    Quantifying UK Coastal Flood Exposure Under Future Sea-Level Rise To [2100 and] 2300
    --
    doi.org/10.1038/s41467-026-749 <-- shared paper
    --
    theguardian.com/environment/20 <-- shared media article
    --
    H/T @University of Bristol School of Geographical Sciences
    “🌊 New research on long-term coastal flooding as a result of climate inaction 🌊
    … The researchers found:
    🔷 By 2100 at least an additional 0.5 million people exposed to the 1-in-200 year undefended flood extent - a 25% increase compared to present day.
    🔷 Under the most pessimistic storyline by 2300 involving significant ice-sheet instability, an additional 13 million people could be exposed to the 1-in-200 year undefended flood event.
    🔷 Under some scenarios there is a need for large-scale movement of populations and settlements away from the coast in the coming centuries…”
    --
    “Up to 13 million people in the UK face the long-term risk of coastal flooding due to the climate crisis, scientists have warned, with a ‘reasonable worst-case scenario’ suggesting the need for the large-scale movement of populations away from a dramatically reshaped coast…
    Sea level has risen by about 20cm around the UK in the last century and is accelerating. A further 40cm to 60cm by 2100 is already baked in, meaning about half a million more people will be at risk of their homes being flooded, on top of the 2.5 million already in danger near the coasts. Lincolnshire and the Humber estuary are most in danger…”
    --
    “The latest Intergovernmental Panel on Climate Change assessment report highlighted the potential for more than 15 metres of global sea-level rise by 2300. In this study, [the authors] explore the implications for UK coastal flood exposure by combining national-scale flood modelling with physically-based storylines of UK sea-level rise. By 2100 all storylines show broadly similar results with at least an additional ½ million people exposed to the 1-in-200 year undefended flood extent, which represents a 25% increase compared to present day. Under the most pessimistic storyline by 2300 involving significant ice-sheet instability, an additional 13 million people could be exposed to the 1-in-200 year undefended flood event. This would imply the potential need for large-scale movement of populations and settlements away from the coast in the coming centuries. Given current global emissions pledges, exposure increases by 1.7 million people by 2300, however up to 1 million could be avoided if Paris Agreement targets for greenhouse gas emissions are met…”

    #coast #coastal #innundation #climatechange #global #sealevel
  48. 💧🌏 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
  49. 💧🌏 Greg Cocks @GregCocks ·

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

    SOME SHARED SPATIAL/GIS ROLES & OPPORTUNITIES
    --
    w/e July 24th, 2026 | GLOBAL but North America focused (as currently based there)
    --
    open.substack.com/pub/earthstu
    --
    • I am NOT associated with HR/recruiting in ANY way
    • Therefore, these are NOT endorsements, and there are no ‘referrals’ or the like for me
    • I am simply sharing SOME roles I have seen – in the hope that they are of use to someone
    • As such, I am not (and indeed can not) in any way ‘vet’ these roles (i.e., PLEASE do your ‘due diligence’ / use your judgement (with the hideous prevalence of ‘ghost jobs’, ‘fishing’, fraudulent so-called recruiters, etc))
    • Further, roles listed are in no specific order (i.e., simply as I ‘encountered’ them during the week)
    • There is ZERO cost to access this list of spatial / GIS roles
    • I am not (really) ‘mining’ US Federal government roles - with the ‘hiring freeze’, uncertainty, etc
    • You can subscribe to the (free) weekly LinkedIn newsletter www.linkedin.com/build-relation/newsletter-follow?entityUrn=7397678824928366594 or also for free at earthstuff.Substack.com - if so inclined

    #fridayjobs #gischat #jobsonfriday #GIS #spatial #mapping #cartography #geography #job #jobopenings #hiring #newjob #employment #roles #GISJobs #NotARecruiter #fridayspatialjobs #scholarship #PhD #PostDoc #CareersInSpatial #career #openroles #Geomatics #GIS #Innovation #Growth #caveatNONemptor #LetsGetYouHired #GISCareer #GISStudents #PhD #postdoc #hiringalert #GISjobs #careeropportunity #NowHiring #spatialanalysis #GIS #Geospatial #GISSpecialist #Geomatics #OpenToWork

    #fridayjobs #gischat #jobsonfriday #gis #spatial #mapping
  51. 💧🌏 Greg Cocks @GregCocks ·

    SOME SHARED SPATIAL/GIS ROLES & OPPORTUNITIES
    --
    w/e July 24th, 2026 | GLOBAL but North America focused (as currently based there)
    --
    open.substack.com/pub/earthstu
    --
    • I am NOT associated with HR/recruiting in ANY way
    • Therefore, these are NOT endorsements, and there are no ‘referrals’ or the like for me
    • I am simply sharing SOME roles I have seen – in the hope that they are of use to someone
    • As such, I am not (and indeed can not) in any way ‘vet’ these roles (i.e., PLEASE do your ‘due diligence’ / use your judgement (with the hideous prevalence of ‘ghost jobs’, ‘fishing’, fraudulent so-called recruiters, etc))
    • Further, roles listed are in no specific order (i.e., simply as I ‘encountered’ them during the week)
    • There is ZERO cost to access this list of spatial / GIS roles
    • I am not (really) ‘mining’ US Federal government roles - with the ‘hiring freeze’, uncertainty, etc
    • You can subscribe to the (free) weekly LinkedIn newsletter www.linkedin.com/build-relation/newsletter-follow?entityUrn=7397678824928366594 or also for free at earthstuff.Substack.com - if so inclined

    #fridayjobs #gischat #jobsonfriday #gis #spatial #mapping
  52. 💧🌏 Greg Cocks @[email protected] ·

    Closure Of The Strait Of Hormuz May Trigger A Bioinvasion Super-Spreader Event
    --
    doi.org/10.1007/s10530-026-038 <-- shared paper / brief report
    --
    theguardian.com/environment/20 <-- shared media article
    --
    ecomagazine.com/news/coastal/i <-- shared technical article
    --
    thedeepdraft.com/2026/06/08/th <-- shared technical article
    --
    youtube.com/watch?v=1aGz7pBF08k <-- shared technicial overview video
    --
    timesofindia.indiatimes.com/sc <-- shared media article
    --
    H/T @alejandro Bortolus | Investigador Principal CONICET
    [“consequences”, fascinating, illuminating and scary frankly!!]
    “The ongoing conflicts in the Middle East are causing substantial humanitarian, economic, and geopolitical impacts and disrupting global trade. While the human and economic effects remain the focus of most immediate concern, a pending ecological crisis is also unfolding. The closure of the Strait of Hormuz on February 28, 2026, stranded an estimated 1,500 ships in the Persian/Arabian Gulf, with hundreds more anchored in the Gulf of Oman. Prolonged stationary periods lead to substantial biofouling penalties for ships, as marine microbes, algae, and invertebrates rapidly colonize and grow on submerged surfaces. This accumulation not only impairs vessel operations but also creates significant environmental risks. A major concern is the high likelihood that these idle ships will facilitate the extensive spread of economically and ecologically damaging invasive species as they re-enter the global shipping network. Given the unprecedented scale and duration of this mass lay-up of ships, conditions are primed for a large-scale, marine bioinvasion “super-spreader” event as regular maritime trade in the region resumes. The combination of extensive biofouling growth and species accumulation, and the sheer number, size, and global reach of affected vessels, has created an immense international biosecurity threat. Here, [they] highlight the biosecurity risks arising from the closure of the Strait and provide recommendations to mitigate them. [They] urge relevant stakeholders worldwide, including ship operators, port authorities, and environmental managers, to prepare now for the backlog of ship biofouling management requirements and to implement appropriate actions to abate this extraordinary biosecurity threat...”
    #bioinvasion #superspreader #spatialanalysis #spatiotemporal #mapping #global #worldwide #conflict #MiddleEast #biofouling #stationary #warmwater #marine #ship #shipping #cargo #transportation #StraitofHormuz #ecology #crisis #vessel #environmental #risk #hazard #economy #impact #humanimpacts #largescale #maritime #trade #international #biosecurity

    #bioinvasion #superspreader #spatialanalysis #spatiotemporal #mapping #global
  53. 💧🌏 Greg Cocks @GregCocks ·

    Closure Of The Strait Of Hormuz May Trigger A Bioinvasion Super-Spreader Event
    --
    doi.org/10.1007/s10530-026-038 <-- shared paper / brief report
    --
    theguardian.com/environment/20 <-- shared media article
    --
    ecomagazine.com/news/coastal/i <-- shared technical article
    --
    thedeepdraft.com/2026/06/08/th <-- shared technical article
    --
    youtube.com/watch?v=1aGz7pBF08k <-- shared technicial overview video
    --
    timesofindia.indiatimes.com/sc <-- shared media article
    --
    H/T @alejandro Bortolus | Investigador Principal CONICET
    [“consequences”, fascinating, illuminating and scary frankly!!]
    “The ongoing conflicts in the Middle East are causing substantial humanitarian, economic, and geopolitical impacts and disrupting global trade. While the human and economic effects remain the focus of most immediate concern, a pending ecological crisis is also unfolding. The closure of the Strait of Hormuz on February 28, 2026, stranded an estimated 1,500 ships in the Persian/Arabian Gulf, with hundreds more anchored in the Gulf of Oman. Prolonged stationary periods lead to substantial biofouling penalties for ships, as marine microbes, algae, and invertebrates rapidly colonize and grow on submerged surfaces. This accumulation not only impairs vessel operations but also creates significant environmental risks. A major concern is the high likelihood that these idle ships will facilitate the extensive spread of economically and ecologically damaging invasive species as they re-enter the global shipping network. Given the unprecedented scale and duration of this mass lay-up of ships, conditions are primed for a large-scale, marine bioinvasion “super-spreader” event as regular maritime trade in the region resumes. The combination of extensive biofouling growth and species accumulation, and the sheer number, size, and global reach of affected vessels, has created an immense international biosecurity threat. Here, [they] highlight the biosecurity risks arising from the closure of the Strait and provide recommendations to mitigate them. [They] urge relevant stakeholders worldwide, including ship operators, port authorities, and environmental managers, to prepare now for the backlog of ship biofouling management requirements and to implement appropriate actions to abate this extraordinary biosecurity threat...”

    #bioinvasion #superspreader #spatialanalysis #spatiotemporal #mapping #global
  54. 💧🌏 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
  55. 💧🌏 Greg Cocks @GregCocks ·

    Global Performance of 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…”

    #remotesensing #global #mapping #earthobservation #gis #spatial
  56. Josep Pueyo-Ros :rstats: @jospueyo ·

    Last week, I calculated several zonal means in big files, 1M cells raster and a >200k polygons vector. I used terra::zonal or stars::aggregate and both took around 30 minutes. Then, I tried zonalstatistics from using {qgisprocess} and took 30 seconds!

    Anyone knows why this huge difference?

    #qgis #rstats #rspatial #spatialanalysis
  57. Josep Pueyo-Ros :rstats: @[email protected] ·

    Last week, I calculated several zonal means in big files, 1M cells raster and a >200k polygons vector. I used terra::zonal or stars::aggregate and both took around 30 minutes. Then, I tried zonalstatistics from #QGIS using {qgisprocess} and took 30 seconds!

    Anyone knows why this huge difference?

    #rstats #rspatial #spatialAnalysis

    #qgis #rstats #rspatial #spatialanalysis
  58. Jakub Nowosad @nowosad ·

    A resource from Jérôme Guélat for anyone working with spatial data in R.

    *Spatial Data Processing with R* combines GIS fundamentals with practical, reusable workflows for analysis and map making. 🗺️

    jguelat.github.io/spatial-r/

    #rspatial #rstats #spatialanalysis #opensource
  59. Jakub Nowosad @[email protected] ·

    A resource from Jérôme Guélat for anyone working with spatial data in R.

    *Spatial Data Processing with R* combines GIS fundamentals with practical, reusable workflows for analysis and map making. 🗺️

    jguelat.github.io/spatial-r/

    #rspatial #rstats #SpatialAnalysis #OpenSource

    #rspatial #rstats #spatialanalysis #opensource
  60. Eugene @[email protected] ·

    🌳 Is Calgary actually as green as it looks?

    While open lawns (28.6% citywide) give Calgary a visual "green appearance", dry summer conditions quickly strip unirrigated grass of its cooling capacity. Meanwhile, true cooling infrastructure — dense and sparse tree canopy — accounts for only ~17%.

    To quantify structural heat risks, I introduced the Solid-to-Tree Ratio index across 193 residential communities:
    🔹 Critical Deficit: Downtown Core (56.2) and Beltline (24.6), where hard surfaces outnumber tree canopy by tens of times.
    🔹 Suburban Pressure: New communities like Redstone (23.8) and Seton (20.8) feature high-density lots with minimal mature shade.
    🔹 Ecological Buffers: River valley communities like Discovery Ridge (0.5) and Wildwood (0.6), where canopy exceeds concrete.

    👇 Link to the full study and interactive dataset:
    datastory.org.ua/calgarys-micr

    #Calgary #YYC #DataScience #GIS #RStats #RemoteSensing #UrbanHeat #DataViz #SpatialAnalysis #MachineLearning #GreennessOfCalgary #FOSSGIS #Alberta #Canada

    #canada #datascience #rstats #alberta #dataviz #yyc