#spatiotemporal — Public Fediverse posts on home.social

💧🌏 Greg Cocks @[email protected] · 2026-09-16 · 03:49 UTC

HAZUS Estimated Average Annualized Losses for Earthquakes in the United States and its Territories [FEMA]
--
https://www.fema.gov/sites/default/files/documents/fema_HAZUS_p-366-earthquake-loss-report_082026.pdf <-- shared document/report
--
https://www.fema.gov/flood-maps/tools-resources/flood-map-products/HAZUS/resources/loss-studies <-- shared resource, ‘National HAZUS Natural Hazard Loss Studies’
--
https://www.fema.gov/sites/default/files/documents/fema_HAZUS_p-366-overview_082026.pdf <-- shared document, ‘Informing Earthquake Risk with FEMA P-366 - Overview of the Report and How to Use It’
--
http://alturl.com/rzafx <-- shared web map & data portal, ‘HAZUS - P-366 Earthquake Risk/Loss Severity Viewer’
--
H/T @Kishor Jaiswal
“The study reveals that the United States faces $15.8 billion per year in average annualized earthquake losses - an increase of about 7% since the 2023 update, largely driven by rising property values and growth in hazard prone areas. Yet the message is not all caution: modern seismic codes are working, with states like California seeing ~15% reductions in expected losses thanks to improved building practices.
This update incorporates the 2023 USGS National Seismic Hazard Model, new population and building stock data, and advancements in HAZUS 6.1, offering communities a clearer picture of where risk is rising—and where targeted mitigation, such as retrofitting older unreinforced masonry and non ductile concrete buildings, can deliver substantial benefits.
BOTTOM LINE: Earthquake risk is evolving, but smarter design, science-informed planning, and focused mitigation continue to shift the nation toward greater resilience…”
#HAZUS #risk #hazard #naturalhazard #earthquake #geology #loss #cost #economics #development #infrastructure #buildingcodes #engineeringdesign #seismic #seismiccodes #seismology #USGS #NationalSeismicHazardModel #NSHM #population #demographics #hazardanalysis #mitigation #riskreduction #earthquakerisk #spatialanalysis #spatiotemporal #policy #planning #GIS #spatial #mapping #parameters #naturalhazard #USA #national #humanimpacts #P366 #EstimatedAnnualizedEarthquakeLosses #metrics #buildingexposure #opendata #model #modeling #assets #economicloss #SpectralAcceleration #AverageAnnualizedLoss #AverageAnnualizedLossRatios
#FEMA | #USGS

#hazus #risk #hazard #naturalhazard #earthquake #geology

💧🌏 Greg Cocks @[email protected] · 2026-09-16 · 03:49 UTC

HAZUS Estimated Average Annualized Losses for Earthquakes in the United States and its Territories [FEMA]
--
https://www.fema.gov/sites/default/files/documents/fema_HAZUS_p-366-earthquake-loss-report_082026.pdf <-- shared document/report
--
https://www.fema.gov/flood-maps/tools-resources/flood-map-products/HAZUS/resources/loss-studies <-- shared resource, ‘National HAZUS Natural Hazard Loss Studies’
--
https://www.fema.gov/sites/default/files/documents/fema_HAZUS_p-366-overview_082026.pdf <-- shared document, ‘Informing Earthquake Risk with FEMA P-366 - Overview of the Report and How to Use It’
--
http://alturl.com/rzafx <-- shared web map & data portal, ‘HAZUS - P-366 Earthquake Risk/Loss Severity Viewer’
--
H/T @Kishor Jaiswal
“The study reveals that the United States faces $15.8 billion per year in average annualized earthquake losses - an increase of about 7% since the 2023 update, largely driven by rising property values and growth in hazard prone areas. Yet the message is not all caution: modern seismic codes are working, with states like California seeing ~15% reductions in expected losses thanks to improved building practices.
This update incorporates the 2023 USGS National Seismic Hazard Model, new population and building stock data, and advancements in HAZUS 6.1, offering communities a clearer picture of where risk is rising—and where targeted mitigation, such as retrofitting older unreinforced masonry and non ductile concrete buildings, can deliver substantial benefits.
BOTTOM LINE: Earthquake risk is evolving, but smarter design, science-informed planning, and focused mitigation continue to shift the nation toward greater resilience…”
#HAZUS #risk #hazard #naturalhazard #earthquake #geology #loss #cost #economics #development #infrastructure #buildingcodes #engineeringdesign #seismic #seismiccodes #seismology #USGS #NationalSeismicHazardModel #NSHM #population #demographics #hazardanalysis #mitigation #riskreduction #earthquakerisk #spatialanalysis #spatiotemporal #policy #planning #GIS #spatial #mapping #parameters #naturalhazard #USA #national #humanimpacts #P366 #EstimatedAnnualizedEarthquakeLosses #metrics #buildingexposure #opendata #model #modeling #assets #economicloss #SpectralAcceleration #AverageAnnualizedLoss #AverageAnnualizedLossRatios
#FEMA | #USGS

#hazus #risk #hazard #naturalhazard #earthquake #geology

💧🌏 Greg Cocks @[email protected] · 2026-09-16 · 03:49 UTC

HAZUS Estimated Average Annualized Losses for Earthquakes in the United States and its Territories [FEMA]
--
https://www.fema.gov/sites/default/files/documents/fema_HAZUS_p-366-earthquake-loss-report_082026.pdf <-- shared document/report
--
https://www.fema.gov/flood-maps/tools-resources/flood-map-products/HAZUS/resources/loss-studies <-- shared resource, ‘National HAZUS Natural Hazard Loss Studies’
--
https://www.fema.gov/sites/default/files/documents/fema_HAZUS_p-366-overview_082026.pdf <-- shared document, ‘Informing Earthquake Risk with FEMA P-366 - Overview of the Report and How to Use It’
--
http://alturl.com/rzafx <-- shared web map & data portal, ‘HAZUS - P-366 Earthquake Risk/Loss Severity Viewer’
--
H/T @Kishor Jaiswal
“The study reveals that the United States faces $15.8 billion per year in average annualized earthquake losses - an increase of about 7% since the 2023 update, largely driven by rising property values and growth in hazard prone areas. Yet the message is not all caution: modern seismic codes are working, with states like California seeing ~15% reductions in expected losses thanks to improved building practices.
This update incorporates the 2023 USGS National Seismic Hazard Model, new population and building stock data, and advancements in HAZUS 6.1, offering communities a clearer picture of where risk is rising—and where targeted mitigation, such as retrofitting older unreinforced masonry and non ductile concrete buildings, can deliver substantial benefits.
BOTTOM LINE: Earthquake risk is evolving, but smarter design, science-informed planning, and focused mitigation continue to shift the nation toward greater resilience…”
#HAZUS #risk #hazard #naturalhazard #earthquake #geology #loss #cost #economics #development #infrastructure #buildingcodes #engineeringdesign #seismic #seismiccodes #seismology #USGS #NationalSeismicHazardModel #NSHM #population #demographics #hazardanalysis #mitigation #riskreduction #earthquakerisk #spatialanalysis #spatiotemporal #policy #planning #GIS #spatial #mapping #parameters #naturalhazard #USA #national #humanimpacts #P366 #EstimatedAnnualizedEarthquakeLosses #metrics #buildingexposure #opendata #model #modeling #assets #economicloss #SpectralAcceleration #AverageAnnualizedLoss #AverageAnnualizedLossRatios
#FEMA | #USGS

#fema #averageannualizedlossratios #averageannualizedloss #spectralacceleration #economicloss #assets

💧🌏 Greg Cocks @GregCocks · 2026-09-16 · 03:49 UTC

HAZUS Estimated Average Annualized Losses for Earthquakes in the United States and its Territories [FEMA]
--
https://www.fema.gov/sites/default/files/documents/fema_HAZUS_p-366-earthquake-loss-report_082026.pdf <-- shared document/report
--
https://www.fema.gov/flood-maps/tools-resources/flood-map-products/HAZUS/resources/loss-studies <-- shared resource, ‘National HAZUS Natural Hazard Loss Studies’
--
https://www.fema.gov/sites/default/files/documents/fema_HAZUS_p-366-overview_082026.pdf <-- shared document, ‘Informing Earthquake Risk with FEMA P-366 - Overview of the Report and How to Use It’
--
http://alturl.com/rzafx <-- shared web map & data portal, ‘HAZUS - P-366 Earthquake Risk/Loss Severity Viewer’
--
H/T @Kishor Jaiswal
“The study reveals that the United States faces $15.8 billion per year in average annualized earthquake losses - an increase of about 7% since the 2023 update, largely driven by rising property values and growth in hazard prone areas. Yet the message is not all caution: modern seismic codes are working, with states like California seeing ~15% reductions in expected losses thanks to improved building practices.
This update incorporates the 2023 USGS National Seismic Hazard Model, new population and building stock data, and advancements in HAZUS 6.1, offering communities a clearer picture of where risk is rising—and where targeted mitigation, such as retrofitting older unreinforced masonry and non ductile concrete buildings, can deliver substantial benefits.
BOTTOM LINE: Earthquake risk is evolving, but smarter design, science-informed planning, and focused mitigation continue to shift the nation toward greater resilience…”
#HAZUS #risk #hazard #naturalhazard #earthquake #geology #loss #cost #economics #development #infrastructure #buildingcodes #engineeringdesign #seismic #seismiccodes #seismology #USGS #NationalSeismicHazardModel #NSHM #population #demographics #hazardanalysis #mitigation #riskreduction #earthquakerisk #spatialanalysis #spatiotemporal #policy #planning #GIS #spatial #mapping #parameters #naturalhazard #USA #national #humanimpacts #P366 #EstimatedAnnualizedEarthquakeLosses #metrics #buildingexposure #opendata #model #modeling #assets #economicloss #SpectralAcceleration #AverageAnnualizedLoss #AverageAnnualizedLossRatios
#FEMA | #USGS

#hazus #risk #hazard #naturalhazard #earthquake #geology

💧🌏 Greg Cocks @[email protected] · 2026-09-16 · 03:49 UTC

HAZUS Estimated Average Annualized Losses for Earthquakes in the United States and its Territories [FEMA]
--
https://www.fema.gov/sites/default/files/documents/fema_HAZUS_p-366-earthquake-loss-report_082026.pdf <-- shared document/report
--
https://www.fema.gov/flood-maps/tools-resources/flood-map-products/HAZUS/resources/loss-studies <-- shared resource, ‘National HAZUS Natural Hazard Loss Studies’
--
https://www.fema.gov/sites/default/files/documents/fema_HAZUS_p-366-overview_082026.pdf <-- shared document, ‘Informing Earthquake Risk with FEMA P-366 - Overview of the Report and How to Use It’
--
http://alturl.com/rzafx <-- shared web map & data portal, ‘HAZUS - P-366 Earthquake Risk/Loss Severity Viewer’
--
H/T @Kishor Jaiswal
“The study reveals that the United States faces $15.8 billion per year in average annualized earthquake losses - an increase of about 7% since the 2023 update, largely driven by rising property values and growth in hazard prone areas. Yet the message is not all caution: modern seismic codes are working, with states like California seeing ~15% reductions in expected losses thanks to improved building practices.
This update incorporates the 2023 USGS National Seismic Hazard Model, new population and building stock data, and advancements in HAZUS 6.1, offering communities a clearer picture of where risk is rising—and where targeted mitigation, such as retrofitting older unreinforced masonry and non ductile concrete buildings, can deliver substantial benefits.
BOTTOM LINE: Earthquake risk is evolving, but smarter design, science-informed planning, and focused mitigation continue to shift the nation toward greater resilience…”
#HAZUS #risk #hazard #naturalhazard #earthquake #geology #loss #cost #economics #development #infrastructure #buildingcodes #engineeringdesign #seismic #seismiccodes #seismology #USGS #NationalSeismicHazardModel #NSHM #population #demographics #hazardanalysis #mitigation #riskreduction #earthquakerisk #spatialanalysis #spatiotemporal #policy #planning #GIS #spatial #mapping #parameters #naturalhazard #USA #national #humanimpacts #P366 #EstimatedAnnualizedEarthquakeLosses #metrics #buildingexposure #opendata #model #modeling #assets #economicloss #SpectralAcceleration #AverageAnnualizedLoss #AverageAnnualizedLossRatios
#FEMA | #USGS

#hazus #risk #hazard #naturalhazard #earthquake #geology

💧🌏 Greg Cocks @GregCocks · 2026-09-16 · 03:42 UTC

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

#fuzzygammamodel #ahp #fieldofview #spatialanalysis #observationtowers #forestfire

💧🌏 Greg Cocks @[email protected] · 2026-09-16 · 03:42 UTC

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

#fuzzygammamodel #ahp #fieldofview #spatialanalysis #observationtowers #forestfire

💧🌏 Greg Cocks @[email protected] · 2026-09-11 · 03:19 UTC

Sub-Seasonal Forecasting Of Cropland Productivity Anomalies Using Satellite Soil Moisture In Water-Limited Environments
--
https://doi.org/10.1016/j.rse.2026.115645 <-- shared paper
--
H/T @Adebowale Daniel Adebayo | Doctoral Candidate | Geospatial Data Scientist
“When rainfall fails and evaporative demand climbs, root-zone soil moisture is where the deficit registers first and where it carries forward, weeks before crop condition reflects it. In this study [link above], [the authors] used SMAP root-zone soil moisture to forecast crop productivity anomalies across drought-prone croplands of Eastern and Southern Africa, and [they] quantified how much predictive value soil moisture actually carries, over what lead times, and under which hydroclimatic conditions. The contribution of soil moisture was negligible at short leads but grew steadily out to 40 days, and it concentrated in water-limited croplands where soil moisture and vegetation are most tightly coupled. During the 2024 southern African El Niño drought, the soil moisture informed model resolved the spatial pattern of productivity anomalies roughly a month in advance.
Beyond the results themselves, this work is a pointer to how much predictive information soil moisture holds. Leveraging the temporal record of SMAP-related products together with the 100–200 metre resolution NISAR will deliver gives us, [believes the H/T], a clear path to attempt field-scale drought forecasting in the smallholder landscapes where early warning matters most…”
#agriculture #crops #foodsecurity #cropland #productivity #forecasting #rootzone #soilmoisture #SMAP #NIRV #subseasonal #prediction #EasternAfrica #SouthernAfrica #africa #vegetation #anomaly #precipitation #ET #drought #extremeweather #water #hydrology #hydroclimate #productivity #arid #waterlimited #spatialanalysis #spatiotemporal #model #modeling #vegetation #Africa #ElNino #ElNiño #mitigation #prediction #forecast #smallholders #earlywarning #RZSM #SoilMoistureActivePassive #NearInfraredReflectance #NIR #remotesensing #earthobservation

#agriculture #crops #foodsecurity #cropland #productivity #forecasting

💧🌏 Greg Cocks @[email protected] · 2026-09-10 · 04:21 UTC

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

#gis #spatial #mapping #remotesensing #earthobservation #interferometricsyntheticapertureradar

💧🌏 Greg Cocks @[email protected] · 2026-09-09 · 04:14 UTC

Cities In Great Britain Most Vulnerable To Extreme Heat Revealed
(OS index examines which ‘urban heat islands’ suffer the most – and which cope the best with rising temperatures)
--
https://www.theguardian.com/environment/2026/sep/08/cities-great-britain-most-vulnerable-extreme-heat-revealed <-- shared media article
--
https://www.ordnancesurvey.co.uk/news/new-urban-heat-islands-analysis <-- shared technical article, OS Heat Vulnerability Index
--
H/T @joe Clarkson | Campaign Manager - Diffusion PR
“[The H/T] work[ed] … closely with Ordnance Survey (OS) on new research into which of Britain's cities are most vulnerable to retaining extreme heat, and how that vulnerability shifts between now and the turn of the century as record temperatures continue to climb…
The newly commissioned OS Heat Vulnerability Index scored 71 cities across Britain using #4EI satellite temperature readings, @Met Office climate modelling projections from 2040 to 2100, and Ordnance Survey data on the makeup of natural and made environments in our cities. Put together, it measures how global warming will impact future heat vulnerability, and how urban heat islands form; a process in which hard surfaces absorb solar radiation through the day to keep centres warm overnight, while rural environments cool.
Anyone who has been unfortunate enough to stand on a Tube platform or walk through central London this summer, or tried to sleep through a heatwave in a flat that hasn't cooled in months already knows what retained heat feels like. But it's more than an uncomfortable feeling. Extreme heat is claiming an increasing number of lives each year, and location-based intelligence like this is essential to support decisions on how we effectively mitigate and protect against climate change to save lives and protect critical infrastructure.
What this analysis does is put a number on extreme heat, and shows where it's heading. As Britain's climate continues to warm, the capacity of our cities to cool themselves naturally will only matter more…”
#ClimateChange #Heat #UrbanHeat #Heatwave #Climate #Warming #Infrastructure #London #Portsmouth #Resilience #OrdnanceSurvey #Geospatial #Data #extremeweather #extremeweather #publichealth #publicsafety #deaths #UK #GreatBritain #OS #HeatVulnerabilityIndex #vulnerability #spatialanalysis #mapping #model #modeling #analysis #spatiotemporal #climate #climatemodeling #spatial #mapping #remotesensing #earthobservation #solarradiation #natural #manmade #retainedheat #mitigation #planning #policy #infrastructure #concrete #asphalt #buildings #hardsurfaces #globalwarming #urbanheatislands #cities #urban
@Ordnance Survey

#4ei #climatechange #heat #urbanheat #heatwave #climate

💧🌏 Greg Cocks @[email protected] · 2026-09-04 · 02:28 UTC

To Predict Tree Death, Scientists Tapped Gamma Rays To Peer Underground
(Airborne radiation sensors could help forecast and prevent drought-driven tree mortality_
--
https://www.science.org/content/article/predict-tree-death-scientists-tapped-gamma-rays-peer-underground <-- shared technical article
--
https://doi.org/10.1029/2026GL122182 <-- shared paper
--
H/T @hannah Richter
“Over an 18-month period starting in 2023, the dense forests of Western Australia [WA] experienced a record-setting drought. Jarrah trees towering 35 metres high died off in patchy brown splotches, turning 400 square kilometres - 3% of the forest - into brittle, fire-prone stands. The event led researchers to wonder whether there was a better way to predict where such die-offs might occur both there and in other forests, a problem that has long been tricky to solve because important factors such as soil depth are hidden underground…
Now, those same researchers have unveiled a surprising new tool for predicting tree mortality: gamma rays [link above.] Resulting from the natural decay of the potassium-40 isotope from granite-rich bedrock, the radiation acts as a proxy for soil depth, which in turn signals how much water a tree can access during drought. The new method could be applied to other highly weathered soils, which cover one-third of Earth’s ice-free land...”
--
"... PLAIN LANGUAGE SUMMARY: During a record-breaking drought and heat event in 2023–2024, forests in southwestern Australia experienced widespread, patchy die-off. While we know that extreme weather triggers these events, it is often a hidden factor, the thickness of soil and the depth to underlying bedrock, that determines which trees live or die. Trees growing in shallow soil over solid rock are highly vulnerable due to limited water storage. Here, [they] show how to map these hidden zones from the air using gamma rays that are naturally emitted by potassium in the ground. Like southwestern Australia, many parts of the world have highly weathered soils where potassium has been washed out of the upper layers of soil. However, [they] showed that higher potassium areas signal that potassium-rich bedrock is closer to the surface and this is sensitive for tens of meters. By comparing gamma ray maps with ground-based geophysical surveys and satellite data, [they] showed that these potassium hotspots accurately predict where forests are most likely to experience die-off during a drought. These types of soils cover about one-third of the Earth's land, so the method provides a powerful new tool for managers to identify and protect vulnerable forests from future, hotter droughts…”
#GIS #spatial #mapping #spatialanalysis #spatiotemporal #Australia #WesternAustralia #WA #forests #vegetation #bush #jarrah #karri #drought #heat #extremedrought #extremeweather #climatechange #water #waterresources #dieoff #soil #weathering #erosion #moisture #nutrients #airborne #gammarays #GRS #granite #gneiss #bedrock #geology #potassium40 #potassium #K #remotesensing #earthobservation #groundwater #interstitial #subsurface #waterstorage #electricalresistivitytomography

#gis #spatial #mapping #spatialanalysis #spatiotemporal #australia

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

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

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

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

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

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

💧🌏 Greg Cocks @[email protected] · 2026-08-14 · 03:26 UTC

Impact Of Urbanization Driven Land Use And Land Cover Change On Ecological Environmental Quality In Rupandehi Nepal Assessed Using The Remote Sensing Ecological Index
--
https://doi.org/10.1007/s44288-026-00650-y <-- shared paper
--
https://kathmandupost.com/money/2026/02/18/rupandehi-s-continued-urban-sprawl-comes-at-a-cost-for-agriculture-in-the-periphery <-- 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

💧🌏 Greg Cocks @[email protected] · 2026-08-14 · 03:20 UTC

Geospatial Analysis of Carbon Offset Projects - A Broader Scientific Outlook
--
https://www.eldhollow.com/blogs/geospatial-analysis-of-carbon-offset-projects <-- 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

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

Flood And Landslide Susceptibility Assessment And Multi Hazard Interaction Mapping Using Machine Learning And GIS For Sustainable Settlement Planning In Nepal
--
https://doi.org/10.1007/s44288-026-00670-8 <-- 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

💧🌏 Greg Cocks @[email protected] · 2026-08-05 · 04:54 UTC

Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain [Iran]
--
https://doi.org/10.3390/geosciences15030110 <-- 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

💧🌏 Greg Cocks @[email protected] · 2026-08-04 · 04:55 UTC

[Open] Data Related To Flood Mapping [Canada]
--
https://natural-resources.canada.ca/science-data/science-research/natural-hazards/flood-mapping/data-related-flood-mapping <-- shared link to technical details
--
https://app.geo.ca/en-ca/map-browser/record/a13a2575-5bda-4bfd-a9b1-5bd2dd583f09 <-- shared map/data-portal link, Canada Flood Map Inventory (CFM)
--
https://open.canada.ca/data/en/dataset/1074f781-85d3-4c86-86cb-fd1c339197dc <-- shared data-portal link, Canada Flood Susceptibility Index
--
https://doi.org/10.3390/ECWS-7-14235 <-- shared (2023) paper
--
https://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

💧🌏 Greg Cocks @[email protected] · 2026-07-10 · 22:36 UTC

Compound Hydrogeomorphic Cascades And Rapid Upstream To Downstream Hazard Coupling In The Eastern Himalaya
--
https://doi.org/10.1038/s41598-026-52915-8 <-- shared paper
--
https://doi.org/10.1007/s11600-022-00943-z <-- shared paper
--
H/T @Kuldeep Dutta | Geology-Earth Science
“… In hilly regions transitioning rapidly to low gradient alluvial plains, localized hydrometeorological triggers can instantly scale into devastating basin wide disasters. This study dissects the September 2020 cascading hazard in parts of the Arunachal Pradesh-Assam corridor to quantify the rapid coupling between upstream hillslopes and downstream floodplains.
Check out the [attached graphical abstract figure] for an integrated visual workflow of the entire disaster continuum from hillslope failure to floodplain transformation...”
--
“Extreme precipitation in the Eastern Himalaya is increasingly associated with coupled hillslope-floodplain hazards. This study examines the 17th-18th September 2020 rainfall event in Arunachal Pradesh initiating landslides and its downstream impacts in Assam, India, using multi-sensor satellite data and long-term rainfall records. Sentinel-2 imagery was used to map landslides and debris flows, Sentinel-1 SAR data to delineate flood extent, and IMD gridded rainfall (1996–2020) to analyse rainfall spell characteristics. The event triggered widespread slope failures, localized landslide damming, and a subsequent breach, generating sediment-laden flows that inundated ~ 100 km² of the Dhemaji floodplain. A backscatter-derived Relative Flood Volume Index (RFVI) indicates spatial variability in inundation intensity, although it does not represent absolute flood volume. Rainfall analysis suggests that antecedent wetness from preceding spells preconditioned slopes, while peak daily rainfall (> 170 mm day−1) initiated landsliding. Power-law scaling shows negligible dependence of intensity on duration (R2 ≈ 0.0004), whereas cumulative rainfall exhibits a stronger relationship with duration (R2 ≈ 0.54). These results indicate distinct roles of rainfall intensity and accumulation in controlling landslide initiation and downstream flooding, respectively, highlighting the importance of compound rainfall forcing in rapid hydrogeomorphic cascades…”
#EarthScience #RemoteSensing #Himalayas #NaturalHazards #ClimateChange #ScientificReports #GeospatialAnalysis #DisasterMitigation #Landslide #trigger #Flooding #massmovement #extremeweather #engineeringgeology #floodplain #innundation #hillslope #fluvial #pluvial #alluvial #sediment #sedimentation #hydrometeorology #ArunachalPradesh #Assam #India #Brahmaputra #risk #hazard #geology #engineeringgeology #remotesensing #earthobservation #spatialanalysis #spatiotemporal #disaster #hydrogeomorphology #workflow

#earthscience #remotesensing #himalayas #naturalhazards #climatechange #scientificreports

💧🌏 Greg Cocks @[email protected] · 2026-07-09 · 05:39 UTC

A Century Of Landslide Records In Calabria, Southern Italy, Looking For Changes And Trends Through A Dynamic Analysis
--
https://doi.org/10.5194/nhess-26-3077-2026 <-- shared paper / brief communication
--
https://doi.org/10.5194/nhess-15-2313-2015 <-- shared 2015 paper that this communication updates/adds-to
--
https://doi.org/10.1007/s12665-023-10844-z <-- shared paper
--
H/T @StefanoLuigiGariano
“This study updates an article published in NHESS journal in 2015 [link above] and investigates long-term changes in landslide-triggering rainfall conditions in Calabria (southern Italy) over 1921–2020. A catalogue of 3,006 rainfall events associated with landslides (RELs) was reconstructed using 9,530 landslide records and daily rainfall measurements from 318 gauges. Rainfall thresholds were calculated for 15 30-year moving windows to investigate the triggering conditions of the RELs. Results show a marked increase in the number of RELs after 2009, shifts in seasonal occurrence, and decreasing rainfall duration and cumulative amounts. Triggering rainfall shows an overall decreasing trend over the years…”
#Calabria #Italy #massmovement #records #landslides #geology #engineeringgeology #spatiotemporal #spatialanalysis #rainfall #precipitation #extremeweather #trigger #monitoring

#calabria #italy #massmovement #records #landslides #geology

💧🌏 Greg Cocks @[email protected] · 2026-07-02 · 19:07 UTC

Optical, Radar, And Hybrid Indices To Detect Farming Practices In Europe
--
https://doi.org/10.1016/j.rse.2026.115553 <-- shared paper
--
“HIGHLIGHTS:
• [they] compare[d] Sentinel-1 and Sentinel-2 time series to detect farming practices.
• HyBRIS index is introduced, temporally weighting BSI and VH/VV into a daily index.
• Time-series minima and maxima are used to predict sowing, harvest, and tillage.
• Validation is performed across several years, crop types, and European locations.
• Phenology detection is improved compared to HRL-Cropland.
ABSTRACT: Arable farming practices dictate both crop cycles and soil dynamics, and are central to agriculture's environmental impact and its mitigation. Sowing and harvesting mark the beginning and end of the growing season, while tillage modifies soil structure during the dormant period. Although well-established methods exist for delineating the growing season using phenology and optical data, the detection of farming practices, particularly tillage, remains underexplored. This study investigates the strengths of radar and optical data to retrieve sowing, harvest, and tillage dates at the field level, and proposes a novel Hybrid Bare Soil Radar Index (HyBRIS). Based on Sentinel-1 and Sentinel-2, HyBRIS merges optical and radar data into a single index using a temporally weighted mean. Local minima and maxima of the time series are used to detect farming practices across European sites. Validation is carried out against a reference dataset comprising 238 fields in 11 EU countries, including 462 sowing, 374 harvest, and 388 tillage events covering more than 40 crop types over 8 years. Compared to the Copernicus High Resolution Layer Croplands product (HRL-Cropland), the proposed method based on HyBRIS time series improved sowing and harvest dates detection (MAE 26 and 23 days, respectively). Additionally, this method enabled tillage dates estimation during dormant periods (MAE = 28 days), but tended to overestimate the number of tillage events (producer's accuracy = 97%, user's accuracy = 70%). Incorporating soil moisture data is advised for reducing false positives. The results highlight the potential of optical, radar, and hybrid indices for monitoring agricultural management and supporting environmental stewardship…”
#Sowing #Harvest #tillage #tillagedetection #cropland #CroplandManagement #remotesensing #earthobservation #sentinel #Copernicus #cropland #satellite #optical #radar #sensor #landuse #landcover #landsurface #phenology #agricultural #monitoring #GIS #spatial #mapping #spatialanalysis #spatiotemporal #arable #farming #agriculture #soil #substrate #environment #sustainability #environmentalstewardship #growingseason #Europe #region #model #modeling

#sowing #harvest #tillage #tillagedetection #cropland #croplandmanagement

💧🌏 Greg Cocks @[email protected] · 2026-07-02 · 01:32 UTC

Advancing Detailed Flood Hazard Identification in Alberta, Canada - Insights from Two Recent Flood Studies
--
https://doi.org/10.3390/w18131592 <-- shared paper
--
“The increasing frequency of floods and the severity of their consequences for public safety, infrastructure, and the economy demand improved methods for flood hazard identification. Flood studies that include flood hazard mapping are critical tools for informing emergency response and flood recovery, as well as for land use and mitigation planning. The methodology for such flood studies has evolved, and access to more powerful computational resources and high-resolution base data has contributed to the increased use of two-dimensional hydraulic modelling, where one-dimensional modelling previously was the default. However, local-scale flood studies face real-world constraints, including sparse data, challenging hydrologic conditions, and budget limitations, which can hinder the application of advanced techniques. This study addresses these challenges through innovative, practice-driven solutions in two case studies in Alberta, Canada: a small, partly channelised prairie stream network (Wolf Creek, Lacombe) and a laterally dynamic river on a distributary delta (Swan River, Kinuso). Three core components of flood hazard studies are described: field survey data collection, regional hydrology assessment, and hydraulic modelling. Key findings include demonstrating that LiDAR-derived terrain models alone cannot capture channel conveyance, the importance of low-flow calibration in the absence of high-water marks, the selection of a modelling methodology based on bathymetric and topographic features within a study area, and the development of inflow hydrographs for unsteady-state simulation in flat floodplains…”
#FloodMapping #FloodRisk #Hydrology #HydraulicModeling #HECRAS #WaterResources #Alberta #Resilience #RiverSurvey #spatialanlaysis #spatiotemporal #floodhazardmapping #HECRAS #model #modeling #remotesensing #LiDAR #bathymetry #floodfrequencyanalysis #unsteadysimulation #FHIMP #FHIP #WoldCreek #Lacombe #SwanRiver #Kinuso #Alberta #Canada #localscale #provincialfloodstudy # prairie #stream #river #flood #flooding #water #hydrology #risk #hazard #watershed #publicsafety #cost #damage #economics #infrastructure #use #practicedriven #floodhazard #survey #hydraulic #terrainmodels #hydrogeomorphology #topography #elevation #floodplain
@Alberta Environment and Protected Areas | @Government of Alberta | @Barr Engineering

#floodmapping #floodrisk #hydrology #hydraulicmodeling #hecras #waterresources

💧🌏 Greg Cocks @[email protected] · 2026-06-26 · 21:30 UTC

How Space Weather Could Bust The AI Boom
--
https://spacenews.com/how-space-weather-could-bust-the-ai-boom/ <-- shared technical article
--
https://futurism.com/artificial-intelligence/ai-data-centers-electric-grid-meltdown <-- shared technical article, “AI Data Centers Pushing Electric Grid Into Meltdown”
--
https://doi.org/10.1146/annurev-earth-032524-012356 <-- shared 2026 paper, “Magnetic Storms and Geoelectric Hazards”
--
#AI #datcenters #infrastructure #impacts #solarstorms #spaceweather #risk #hazards #overloading #electricity #energy #powersupply #energygrid #vulnerable #transmission #energy #demand #consumers #geoelectrical #geomagnetism #blackout #damage #cost #economics #equipment #transformers #carringtonevent #NERC #grid #reliability #electricaldemand #utilities #magneticstorm #electromagneticinduction #extremeevent #historicalevent #hazardanalysis #spaceweather #history #Carrington #geoelectric #humanimpacts #risk #hazard #monitoring #network #geology #geomagnetism #impedance #rock #soil #utilities #electricaltransmission #powerlines #magnetotelluric #sensor #blackout #brownout #energy #geoelectrichazard #geoelectric #GIS #spatial #mapping #spatialanalysis #spatiotemporal #model #modeling #geomagnetism #geomagneticstorm #telecommunication #electronics #hardened #geography #mitigation #preparedness #geomorphology #geomorphometry #surfacegeology #cost #economics #disaster #impacts #technology #InternetOfThings #internet #USA #review #CONUS #numericalmodeling #realtimemonitoring #AIBoom #Bust
@North American Electric Reliability Corporation (NERC)

#ai #datcenters #infrastructure #impacts #solarstorms #spaceweather

💧🌏 Greg Cocks @[email protected] · 2026-06-25 · 03:43 UTC

Compound Hydrogeomorphic Cascades And Rapid Upstream To Downstream Hazard Coupling In The Eastern Himalaya
--
https://doi.org/10.1038/s41598-026-52915-8 <-- shared paper
--
https://doi.org/10.5194/esurf-13-1281-2025 <-- shared paper
--
https://doi.org/10.1007/s11069-025-07766-3 <-- shared paper
--
[I recognise that the photo is instead for the floods, etc in Lubra, Nepal - but felt it better showed the hydrogeomorphical setting (sic) for the 'casual' post viewer...]
H/T @Kuldeep Dutta
“In hilly regions transitioning rapidly to low gradient alluvial plains, localized hydrometeorological triggers can instantly scale into devastating basin wide disasters. This study dissects the September 2020 cascading hazard in parts of the Arunachal Pradesh-Assam corridor to quantify the rapid coupling between upstream hillslopes and downstream floodplains…”
--
“Extreme precipitation in the Eastern Himalaya is increasingly associated with coupled hillslope-floodplain hazards. This study examines the 17th-18th September 2020 rainfall event in Arunachal Pradesh initiating landslides and its downstream impacts in Assam, India, using multi-sensor satellite data and long-term rainfall records. Sentinel-2 imagery was used to map landslides and debris flows, Sentinel-1 SAR data to delineate flood extent, and IMD gridded rainfall (1996–2020) to analyse rainfall spell characteristics. The event triggered widespread slope failures, localized landslide damming, and a subsequent breach, generating sediment-laden flows that inundated ~ 100 km2 of the Dhemaji floodplain. A backscatter-derived Relative Flood Volume Index (RFVI) indicates spatial variability in inundation intensity, although it does not represent absolute flood volume. Rainfall analysis suggests that antecedent wetness from preceding spells preconditioned slopes, while peak daily rainfall (> 170 mm/day/) initiated landsliding. Power-law scaling shows negligible dependence of intensity on duration (R² ≈ 0.0004), whereas cumulative rainfall exhibits a stronger relationship with duration (R² ≈ 0.54). These results indicate distinct roles of rainfall intensity and accumulation in controlling landslide initiation and downstream flooding, respectively, highlighting the importance of compound rainfall forcing in rapid hydrogeomorphic cascades...”
#EarthScience #RemoteSensing #Himalayas #NaturalHazards #ClimateChange #ScientificReports #GeospatialAnalysis #DisasterMitigation #Landslide #Flooding #alluvial #fluvial #water #hydrology #hydrography #flood #flooding #spatialanalysis #spatiotemporal #mountain #plain #hydrometeorological #hydrogeomorphology #ArunachalPradesh #Assam #India #hillslope #floodplain #rainfall #precipitation #extremeweather #engineeringgeology #massmovement #landslide #debrisflow #risk #hazard #monitoring #GIS #spatial #mapping #remotesensing #satellite #Sentinel #sedimentation #humanimpacts #infrastructure #damage #cost #economics #public #safety #model #modeling #downstream

#earthscience #remotesensing #himalayas #naturalhazards #climatechange #scientificreports

💧🌏 Greg Cocks @[email protected] · 2026-06-18 · 03:09 UTC

Hydroclimate Volatility On A Warming Earth
--
https://doi.org/10.1038/s43017-024-00624-z <-- shared 2025 paper
--
https://newsroom.ucla.edu/releases/floods-droughts-fires-hydroclimate-whiplash-speeding-up-globally <-- shared UCLA article, “Floods, Droughts, Then Fires: Hydroclimate Whiplash Is Speeding Up Globally “
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H/T @Daniel Swain
“Hydroclimate volatility refers to sudden, large and/or frequent transitions between very dry and very wet conditions. In this Review, we examine how hydroclimate volatility is anticipated to evolve with anthropogenic warming. Using a metric of ‘hydroclimate whiplash’ based on the Standardized Precipitation Evapotranspiration Index, global-averaged subseasonal (3-month) and interannual (12-month) whiplash have increased by 31–66% and 8–31%, respectively, since the mid-twentieth century. Further increases are anticipated with ongoing warming, including subseasonal increases of 113% and interannual increases of 52% over land areas with 3 °C of warming; these changes are largest at high latitudes and from northern Africa eastward into South Asia. Extensive evidence links these increases primarily to thermodynamics, namely the rising water-vapour-holding capacity and potential evaporative demand of the atmosphere. Increases in hydroclimate volatility will amplify hazards associated with rapid swings between wet and dry states (including flash floods, wildfires, landslides and disease outbreaks), and could accelerate a water management shift towards co-management of drought and flood risks. A clearer understanding of plausible future trajectories of hydroclimate volatility requires expanded focus on the response of atmospheric circulation to regional and global forcings, as well as land–ocean–atmosphere feedbacks, using large ensemble climate model simulations, storm-resolving high-resolution models and emerging machine learning methods…
#water #hydrology #hydroclimate #whiplash #global #spatialanalysis #spatiotemporal #weatherwhiplash #ecogeomorphology #sustainability #ecology# ###
#water #hydrology #hydroclimate #volatility #dry #wet #drought #flood #flooding #wildfire #landslide #massmovement #whiplash #global #spatialanalysis #spatiotemporal #weatherwhiplash #ecogeomorphology #sustainability #ecology #hydrogeomorphology #climatechange #extremeweather #anthropogenicwarming #climate #weather #connection #StandardizedPrecipitationEvapotranspiration #precipitation #rainfall #research #evapotranspiration #risk #hazard #riskassessment #disease #pandemic #publichealth #publicsafety #waterquality #watersecurity #watermanagement #hydrography #atmospheric #regional #global #forcing #climatemodel #model #modeling #AI #machinelearning