#hazard — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #hazard, aggregated by home.social.
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Where The Water Once Flowed - Rivers And Lakes Dry Up As Droughts Take Toll
(Surreal images of scorched landscapes where water once flowed, as severe droughts lead to dwindling water supplies around the world.)
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https://www.reuters.com/pictures/where-water-once-flowed-rivers-lakes-dry-up-droughts-take-toll-2026-08-14/ <-- shared media article
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[damn!]
#water #hydrology #hydrography #waterresources #rivers #lakes #photojournalism #drought #climatechange #extremeweather #heatwave #temperature #watershortage #global #risk #hazard #infrastructure #watermanagement #global #impacts #watersecurity #humanimpacts #publichealth -
Climate Denial Didn’t Disappear.
It Moved From The Diagnosis To The Treatment
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https://www.forbes.com/sites/we-dont-have-time/2026/08/05/climate-denial-didnt-disappear-it-moved-from-the-diagnosis-to-the-treatment/ <-- shared media article
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H/T @dyckron
"As climate disasters intensify, many governments are retreating from climate action instead of accelerating it. The article argues that this resembles denial in advanced illness; leaders accept the diagnosis while rejecting the treatment. Drawing on medicine, sociology and climate psychology, it identifies four stages of modern denial: denying the crisis, its cause, the solutions or responsibility for acting. Extreme weather alone does not build support; people must connect events to climate change and believe action is possible. Fossil fuel interests exploit this need by framing delay as affordability, competitiveness or pragmatism. The answer is to link disasters clearly to their cause, pair warnings with credible action, and present climate policy as protection rather than sacrifice..."
#climatechange #humanimpacts #risk #hazard #publicsafety #publicsafety #denial #climatechangedenial #climateaction #extremeweather -
WeatherNext – [An] AI Model Achieves Breakthrough In Forecasting Cyclones
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https://deepmind.google/blog/weathernext-ai-model-achieves-breakthrough-in-forecasting-cyclones/ <-- shared technical Google DeepMind blog post
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https://doi.org/10.1038/s41586-026-10953-2 <-- shared paper
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https://deepmind.google/science/weatherlab/ <-- shared data
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https://github.com/google-deepmind/weathernext <-- shared GitHub repository
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H/T @juliet Rothenberg | Product Director of Earth & Resilience AI at Google
[this post should not be considered an endorsement of a particular organisation or their approach]
“[The Google WeatherNext AI team] are showing how the WeatherNext AI model from Google DeepMind and Google Research has achieved state-of-the-art accuracy in predicting a cyclone's track, intensity, and wind structure. On average, the WeatherNext Cyclones model gives forecasters an extra day’s worth of predictive accuracy- delivering an advance equivalent to roughly a decade of historical meteorological progress 🌀
Here is how WeatherNext is transforming cyclone forecasting:
• Gaining an Extra Day of Advanced Warning: WN 3-day forecasts are as good as what prior models were able to provide for 2-day forecasts, giving critical time for emergency response.
• Overcoming Traditional Trade-offs: WN bridges the gap between massive global atmospheric currents (which steer a cyclone's path) and fine-grained thermodynamic processes around its core (which drive its intensity) into a single AI model.
• Unprecedented Ensemble Scale: Using Functional Generative Networks (FGNs), WN now generates 1,000-member ensembles in less than a minute on a TPU to capture rare, consequential tail-risks like sudden rapid intensification – which means forecasters can see a broader range of possible scenarios.
• Real-World Impact: During the 2025 Atlantic hurricane season, the WN model helped the National Hurricane Center (NHC) make a historic forecast for Hurricane Melissa by predicting rapid intensification and landfall five days in advance.
[The] teams are open sourcing the operationalized models (WeatherNext Cyclones and WeatherNext 2), alongside a compact version (WeatherNext 2-mini) that can run on a single TPU in a free public Colab notebook – all with a goal of empowering local organizations worldwide.
Weather affects everyone. By combining advanced AI with the real-world expertise of human forecasters, we can build a collaborative ecosystem that saves lives and helps communities adapt to a changing climate…”
#Google #DeepMind #GoogleResearch #AI #ensembles #FunctionalGenerativeNetworks #WeatherNext #cyclone #operationalised #model #modeling #forecasting #spatialanalyis #spatiotemporal #track #intensity #windstructure #hurricane #weather #climate #metrology #cyclonetrack #risk #hazard #emergencyresponse #planning #tool #earlywarning #scale #magnitude #path #track #thermodynamic #scenarios #opensource #impacts #tropicalcyclones #WeatherNextCyclones #weathermodel #atmospheric #predictions #mitigation #warning #robust #publicsafety #infrastructure
@Google | @WeatherNext -
Spatiotemporal Distribution, Climatic Factors, And Seasonal Precipitation Patterns Characterizing 66 Years Of Widespread Shallow Landslide Events In Piedmont (Northwestern Italy)
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https://doi.org/10.3389/feart.2026.1884300 <-- shared paper
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“Widespread shallow landslide events constitute one of the primary drivers of recurrent societal and economic losses in mountain regions. Although the influence of climate variability on landslide frequency and magnitude has been widely recognized, long-term, event-level analyses linking spatiotemporal landslide patterns to precipitation climatology at the regional scale remain scarce. This study presents a statistical analysis of 128 widespread shallow landslide events recorded in Piedmont, northwestern Italy, over the 66-year period 1960–2025, cross-referenced against the regional precipitation climatology. Events were characterized by season, primary physiographic unit, provincial coverage, and an ordinal magnitude index (scale 1–7) encoding combined spatial extent and estimated landslide count. Results indicate that no statistically significant monotonic trend in event frequency was detected over the study period (mean rate: 1.97 events yr⁻1); however, a moderate positive correlation was established between event magnitude and the number of provinces affected (R2 = 0.51, p < 0.00), validating the magnitude index as a proxy for spatial footprint. Mean event magnitude reached its highest value in the 2010s (3.71), while all five events of magnitude ≥ 6 occurred in autumn. The summer fraction of the catalogue increased markedly, from 6% in the 1960s to 31% in the 2000s and 25% in the 2020s, concurrent with stable or declining summer mean precipitation totals, consistent with Clausius–Clapeyron amplification of convective intensity under documented regional warming. Cross-analysis with regional records identifies three tiers of rainfall–landslide coupling: a stationary direct seasonal coupling; a non-stationary, strengthening intensity-mediated summer coupling; and a structural susceptibility-mediated spatial decoupling whereby the driest provinces generate the highest landslide occurrence frequencies due to the lower triggering thresholds characteristic of Tertiary Piedmont Basin sedimentary environments. A post-hoc assessment of the triggering thresholds used for regional shallow landslide early warning system demonstrates superior detection performance for high-magnitude autumn events (hit rate up to 89% for the 2000–2025 sub-period) and identifies sub-daily convective accumulation windows in summer as the primary domain requiring threshold recalibration…”
#massmovement #landslide #engineeringgeology #Italy #Piedmont #NorthernItaly #weather #rainfall #precipitation #climate #risk #hazard #corrleation #relationship #earlywarningsystems #damage #loss #community #infrastructure #mountain #spatiotemporal #mapping #spatialanalysis #statistics #geostatistics #climatology #regional #scale #weatherpatterns #physiography #geomorphology #water #hydrology #hydrogeomorphology #geology #soils -
The Portrait of Flood Risk in Italy - Past, Present and Future, From 1870 to 2100
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https://doi.org/10.1029/2026GL122987 <-- shared paper
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“ABSTRACT: Among European countries, Italy ranks as one of the most susceptible to flood risk. While this figure is already substantial, climate change and rapid urbanization in flood-prone areas have been identified as the two main drivers expected to elevate the number of individuals at risk. This study offers a comprehensive assessment of these two drivers of flood risk in Italy over 230 years, from 1870 to 2100, focusing on how they interact to increase risk. Using the large-scale flood risk model RESCUE-FR, [the authors] analyze[d] the population at risk under the 200-year return period scenario to provide a targeted assessment of population risk, how it has evolved in the past, and its projection in the future. [Their] findings indicate that while historical flood risk in Italy has primarily been influenced by population growth and migration into at-risk areas, future projections suggest that climate change will become the dominant driver of flood risk.
PLAIN LANGUAGE SUMMARY: Italy is one of the European countries most at risk of flooding. This study examines the impact of two risk factors on flood risk in Italy over the long term, from 1870 to 2100: climate change and the evolution of population in areas prone to flooding. Using a large-scale flood risk model, [they] simulated different scenarios for different time periods, such as with and without climate change, to estimate how each factor contributes to the number of people exposed to floods in the past and future. [Their] results show that population growth and migration into flood-prone areas were the main reasons for the increased risk in the past. In the future, however, climate change is likely to become the dominant factor, putting more people at risk. Understanding how these factors interact can help communities to plan more effectively for floods and reduce the number of people affected…”
#flood #flooding #risk #hazard #Italy #Europe #national #history #historic #cost #damage #infrastructure #floodrisk #population #urbanisation #development #climatechange #extremeweather #dominantfactor #floodprone #national #regional #spatialanalysis #spatiotemporal #model #RESCUEFR #modeling #factors #parameters #drivers #publicsafety -
Many new additions to:
1. Buy a Hazard https://www.ilankelman.org/buyahazard.html
2. Animal Signs https://www.ilankelman.org/animal-signs.html
#hazard #hazards #animal #animals #DisastersAreNotNatural #NoNaturalDisasters (avoid the phrases #NaturalDisaster #NaturalDisasters) #DRR #DisasterRiskReduction #DisasterRisk #RiskReduction #DisasterRiskManagement #disaster #disasters #warning #warnings #WarningSign #WarningSigns #EWS #EarlyWarningSystems -
@tomshardware how much of that, if any, might be preserved in Archive.org?
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Socio-Hydrology Modeling Captures How Inequalities Impact Community Flood Resilience
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https://doi.org/10.1029/2025WR041393 <-- shared paper
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https://www.americanprogress.org/article/how-fema-can-build-rural-resilience-through-disaster-preparedness/ <-- shared technical/opinion article
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https://www.carbonbrief.org/us-flooding-increase-will-disproportionately-impact-black-and-low-income-groups <-- shared technical/opinion article
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https://headwaterseconomics.org/natural-hazards/unequal-impacts-of-flooding/ <-- shared technical/opinion article
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https://youtu.be/8jVRsD8wgMM?si=qBHchWZWkYRGbTdE <-- shared opinion video
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https://fedcommunities.org/lower-income-neighborhoods-face-greater-flood-risk-tougher-recovery/ <-- shared technical/opinion article
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H/T @matthew Preisser | PhD Civil Engineering, Natural Hazard Modeler and Socio-Hydrologists
“How can we better understand the dynamic feedbacks between the environment and society?
Socio-hydrology models are often built around the assumption that cities act as homogeneous entities, without capturing the variable capacity of communities with different underlying socioeconomic characteristics to respond to and recover from disasters.
In this study, [the authors] developed a disaggregated approach to model community-specific adaptive capacity, allowing [them] to examine how inequalities influence flood recovery and resilience. This framework provide[d] a basis for exploring hypotheses about the relationships between growth, inequality, and community resilience in the face of flood hazards.
[Their] results highlighted] the importance of considering community-level dynamics when developing flood mitigation and disaster response strategies that balance economic growth with equity. Many challenges remain in applying socio-hydrology models to real-world settings, but this work takes a step toward incorporating more realistic representations of socioeconomic inequality into human–water systems while preserving the generality and flexibility that make conceptual models useful…”
#risk #hazard #water #hydrology #flood #society #flooding #USA #SocioHydrology #naturalhazard #socioeconomic #population #demographics #infrastructure #damage #disaster #disaggregated #model #modeling #community #adaptiveresponse #growth #inequality #communityresilience #floodhazard #floodmitigation #disasterresponse #equity #city #town #rural #urban #economy #cost -
Coastal Flooding At Predictable Hours
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https://doi.org/10.1038/s41467-026-75710-5 <-- shared paper
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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…”
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“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 -
The Latest Data Confirms - Forest Fires Are Getting Worse
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https://www.wri.org/insights/global-trends-forest-fires <-- shared technical article
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http://alturl.com/efp6m <-- shared (focused) #GlobalNatureWatch web map
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https://science.nasa.gov/earth/explore/wildfires-and-climate-change/ <-- shared NASA technical article, ‘Wildfires and Climate Change’
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https://doi.org/10.3389/frsen.2022.825190 <-- shared paper
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https://doi.org/10.1073/pnas.2505418122 <-- shared paper
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https://doi.org/10.1088/1748-9326/add606 <-- shared paper
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https://globalnaturewatch.org/dashboards/global/ <-- shared Global Nature Watch dashboard
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https://youtu.be/-0-pv1Bqm-U?si=IHcZJNiVphosbeVt <-- shared overview video
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https://grist.org/wildfires/the-us-has-lost-a-quarter-of-its-forest-cover-to-fire-since-2001/ <-- shared technical article, ‘Fire is responsible for a quarter of US forest loss since 2021’
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https://www.nytimes.com/2026/04/29/climate/wri-report-forest-loss.html <-- shared media article
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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 -
From Fragmentation To Integration - A Review Of Data–Model Integration In Land Subsidence Research
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https://doi.org/10.1016/j.ancene.2026.100567 <-- shared paper
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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…”
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“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 -
Flood And Landslide Susceptibility Assessment And Multi Hazard Interaction Mapping Using Machine Learning And GIS For Sustainable Settlement Planning In Nepal
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https://doi.org/10.1007/s44288-026-00670-8 <-- shared paper
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H/T @Narayan Thapa | Earth Data Modeling
“Nepal lies within an active seismic zone and is influenced by most dynamic climatic systems in the world. It faces compounding floods and landslide threats. Impacts are worst where multi-hazard interactions create spatially linked corridors. Despite frequent co-occurrence, national-scale assessments remain limited. This study presents machine learning and GIS-based approach to map nationwide susceptibility to floods, landslides, and identify their potential interaction zones, and delineate critical multi-hazard flow zones through spatial adjacency analysis. Using Google Earth Engine, the Random Forest model integrates topographic, climatic, environmental, and hydrological datasets to overcome subjective expert-driven methods. The model achieved strong predictive accuracy (AUC: 0.84 for floods, 0.85 for landslides). The results showed 19% of Nepal’s lowlands are medium to very highly susceptible to inundation, threatening approximately 900,000 people and over 3.4 million buildings; whilst in the hilly terrains, 40% is susceptible to slope-failure endangering 200,000 people and about 0.6 million buildings. K-means clustering followed by spatial adjacency analysis identified four spatial zonation: 81% of national area as low-hazard zone, 9% as flood-only zone, 5% as landslide-only zone, and 5% as interaction zones. Critical multi-hazard flow zone covering 7,588 km² represents spatially connected corridors linking interaction zones to downstream flood-prone populated areas, affecting 88 km² built-up land and 1,722 km² cropland. These zones represent susceptibility-based spatial connectivity rather than physically simulated cascading processes. These findings support recommendations for risk-informed land-use planning, resilient infrastructure development and climate adaptation aligned to sustainable development and investment risk screening…”
#GIS #spatial #mapping #GoogleEarthEngine #MachineLearning #RemoteSensing #GeospatialAI #DisasterRiskReduction #MultiHazard #ClimateAdaptation #climatechange #extremeweather #LandUsePlanning #SustainableDevelopment #InfrastructurePlanning #RiskAssessment #NaturalHazards #Nepal #EarthObservation #HinduKushHimalaya #HKH #HinduKush #Himalayas #risk #hazard #assessment #national #regional #spatialanalysis #spatiotemporal #massmovement #landslide #assessment #mitigation #water #hydrology #flood #flooding #sustainability -
Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain [Iran]
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https://doi.org/10.3390/geosciences15030110 <-- shared paper
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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…”
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“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 -
[Open] Data Related To Flood Mapping [Canada]
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https://natural-resources.canada.ca/science-data/science-research/natural-hazards/flood-mapping/data-related-flood-mapping <-- shared link to technical details
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https://app.geo.ca/en-ca/map-browser/record/a13a2575-5bda-4bfd-a9b1-5bd2dd583f09 <-- shared map/data-portal link, Canada Flood Map Inventory (CFM)
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https://open.canada.ca/data/en/dataset/1074f781-85d3-4c86-86cb-fd1c339197dc <-- shared data-portal link, Canada Flood Susceptibility Index
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https://doi.org/10.3390/ECWS-7-14235 <-- shared (2023) paper
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https://doi.org/10.1002/2017WR020917 <-- shared (2017) paper
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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 -
Using Geospatial Analysis and Explainable Machine Learning to Examine Risk Factors of Out-of-Hospital Cardiac Arrest Survival Outcome
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https://openscholar.uga.edu/record/26998/files/Zhang_uga_0077E_16145.pdf <-- shared technical publication / dissertation
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https://www.aag.org/award-grant/william-l-garrison-award-for-best-dissertation-in-computational-geography/ <-- shared @AAG William L. Garrison Award for Best Dissertation in Computational Geography
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[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 -
Coupled Hydrological And Public Health Risks From Urban Flooding - Integrated Remote Sensing, Machine Learning, And Hydrodynamic–Ecological Modelling
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https://doi.org/10.1016/j.jhydrol.2026.135999 <-- shared paper
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https://youtu.be/VHzYLvSYR7k?si=5oGGPeH6T0dFusfY <-- recent overview video created about the research
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https://doi.org/10.1016/j.wroa.2025.100396 <-- share (earlier) paper
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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 -
There are many industrial workers who use hazardous materials in their daily tasks. They wear protective equipment and follow safety protocols, as prescribed by applicable regulations.
Today, #IT practitioners use AI as a matter of course. AI has the potential to cause catastrophic harm globally. Its invidious impact is insidious to a great extent: once its harmful consequences have become discernible, the damage is irreversible. For example, the presidential elections in 2016 and 2024.
Obviously, #AI qualifies at least as a #hazard material, in the context of the current #IT practice.
So, where are the necessary protective #regulations?
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Clear Cut Logging Can Dramatically Increase Flood Risk
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https://theconversation.com/new-study-finds-clear-cut-logging-can-dramatically-increase-flood-risk-284825 <-- shared technical media article
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https://doi.org/10.1016/j.foreco.2026.123895 <-- shared paper
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H/T @Daniel Pierce | Ramshackle Pictures
“Here is a clear and simple breakdown of some of the latest bombshell findings out of the UBC Hydrology Lab… using a probabilistic framework, known as attribution science in the climate world. [The researchers] studied two watersheds in BC's Okanagan Valley near Summerland. In this area, they found that climate change was actually making the frequency of floods go down, but forest harvesting counteracted the effects of climate change and increased the flood risk by 10-fold (!!!) turning a 20-year flood into a flood that occurs every two years. [The H/T] truly want to know how the BC government and timber industry are responding to this new science internally. They're absolutely silent on it in public as they continue business as usual…”
#Nonstationary #Probabilisticphysics #Foresthydrology #Floodfrequencyanalysis #Snowmelt #Causalinference #flood #flooding #forest #forestry #water #hydrology #OkanaganValley #watershed #BC #Canada #BritishCanada #clearcut #logging #harvesting #practices #climatechange #extremeweather #humanimpacts #caseexamples #floodrisk #risk #hazard #probabilistic #framework #attributionscience #policy #planning
#UniversityofBritishColumbia | #GovernmentofBritishColumbia -
Quantifying UK Coastal Flood Exposure Under Future Sea-Level Rise To [2100 and] 2300
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https://doi.org/10.1038/s41467-026-74982-1 <-- shared paper
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https://www.theguardian.com/environment/2026/jul/21/millions-long-term-coastal-flooding-risk-uk-climate-action <-- shared media article
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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…”
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“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…”
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“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 -
NOAA’s SOLAR-1 Enters New Era Of Space Weather Monitoring
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https://www.noaa.gov/news-release/noaas-solar-1-enters-new-era-of-space-weather-monitoring <-- shared technical article
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https://www.spaceweather.gov/ <-- shared NOAA Space Weather Prediction Center (SWPC) home page
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https://www.nesdis.noaa.gov/our-satellites/future-programs/swfo/space-weather-observations-l1-advance-readiness-solar-1 <-- shared SOLAR-1 overview and details page
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https://www.nesdis.noaa.gov/our-environment/space-weather <-- shared overview of space weather and its risk, hazards & impacts
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[I was lucky enough to see the launch from the Kennedy Space Center of GOES-19/U, my first launch - and love reading about the ‘ongoing story’…]
#spaceweather #solar #monitoring #risk #hazard #impacts #humanimpacts #SOLAR1 #SOLAR-1 #SpaceWeatherPredictionCenter #NOAA #NESDIS #earlywarningsystem #monitoring #risk #hazard #mitigation #infrastructure #satellites #electric #powergrid #blackouts #brownouts #transformers #utilities #energy #aviation #nationalsecurity #defence #missileguidance #agriculture #farming #precisionagriculture #GPS #vanigation #transportation #spaceflight #LaGrange #L1 #protection #coronagraph #SWPC #NESDIS #opendata #publicgood #fedservice #fedscience
#NOAA -
Closure Of The Strait Of Hormuz May Trigger A Bioinvasion Super-Spreader Event
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https://doi.org/10.1007/s10530-026-03893-5 <-- shared paper / brief report
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https://www.theguardian.com/environment/2026/jul/29/biodiversity-superspreader-invasive-species-threat-ships-stuck-strait-hormuz-gulf <-- shared media article
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https://ecomagazine.com/news/coastal/idle-ships-in-the-strait-of-hormuz-could-trigger-a-global-bioinvasion-event/ <-- shared technical article
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https://thedeepdraft.com/2026/06/08/the-barnacle-problem-waiting-behind-hormuz/ <-- shared technical article
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https://www.youtube.com/watch?v=1aGz7pBF08k <-- shared technicial overview video
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https://timesofindia.indiatimes.com/science/thousands-of-idle-ships-trapped-near-the-strait-of-hormuz-could-become-floating-nurseries-for-invasive-marine-species-and-spread-them-worldwide-when-trade-resumes/articleshow/132576193.cms <-- shared media article
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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 -
Can Himalayan Crops Help Secure The Future Of Food?
(Exploring how genomic diversity, traditional crops, and regional cooperation can strengthen climate resilience and food security across the Hindu Kush Himalaya [HKH])
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https://substack.com/home/post/p-207443153 <-- shared technical post
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https://openlibrary.substack.com/ <-- shared Substack, “Open Library on Green Economy”
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https://adaptationwithoutborders.org/knowledge-base/adaptation-without-borders/shifting-cooperation-in-the-hindu-kush-himalaya/ <-- shared technical article, “Shifting cooperation in the [HKH]”
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https://www.icimod.org/who-we-are/the-hindu-kush-himalaya/ <-- background on the HKH, International Centre for Integrated Mountain Development (ICIMOD)
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https://doi.org/10.48130/cas-0026-0003 <-- shared paper
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[not at all my technical area - but fascinating, including the spatial component #alldataisspatial]
H/T @jeevan Labh
“As we navigate the intensifying impacts of the 2026 El Niño, the vulnerability of our global food systems require careful evaluation. With this year’s erratic weather patterns driving severe, prolonged droughts in some regions and unseasonal, devastating floods in others, the climate crisis is proving that it is not a distant threat; it is happening right now, in our fields and on our plates.
This crisis is more visible in the Hindu Kush Himalaya (HKH) region. Often called the “water tower of Asia,” this vast mountain range sustains 240 million people across eight countries and provides essential ecosystem services to nearly 2 billion people downstream. When glaciers melt at accelerated rates and monsoons become unpredictable, the narrative often focuses solely on the mountain communities. But the truth is much broader: a disrupted harvest in the high hills of Nepal or Bhutan creates a domino effect. It leads to displaced populations, reduced agricultural output flowing into the Indus, Ganges, and Brahmaputra basins, and ultimately, skyrocketing food prices for families living in the sprawling downstream plains.
The climate crisis respects no borders and recognizes no difference between altitude and sea level. Because this problem affects us all, the solution must protect us all. Fortunately, the means to overcome this challenge are already in our hands, locked within the ancient seeds of the mountains…”
## #GreenEconomy #economy #monoculture #agriculture #farming #crop #cropland #HinduKushHimalaya #himalaya #mountain #HKH #spatial #mapping #elevation #climatechange #foodsecurity #food #extremeweather #weather #ElNiño #drought #rainfall #precipitation #snowmelt #climatecrisis #mountainrange #water #hydrology #ecosystem #glacier #moonsoons #community #harvest #Nepal #Bhutan #watersheds #watersecurity #risk #hazard #waterresources #genes #genomicdiversity #cooperation -
Refined Modeling of Arctic Circumpolar Building Stock Increases Estimated Mid-Century Permafrost Degradation Damages
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https://doi.org/10.1029/2026EF008578 <-- shared paper
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https://www.thearcticinstitute.org/climate-change-geopolitics-monitoring-thawing-permafrost/ | https://www.thearcticinstitute.org/dwindling-arctic-sea-ice-impacts-permafrost-health/ <-- shared technical articles
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https://www.theguardian.com/cities/2016/oct/14/thawing-permafrost-destroying-arctic-cities-norilsk-russia <-- shared media article
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https://news.grida.no/new-map-shows-extent-of-permafrost-in-northern-hemisphere <-- shared technical article
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H/T @elias Manos
“Why the increase?
Our understanding of climate risk is only as good as our understanding of our exposure to hazards. The better we can account for what is at risk, the better we can measure risk in a changing world.
In this new study [link above], [they] investigate[d] how damage to the building stock across the Arctic, a key impact of permafrost degradation, is underestimated because of underdeveloped exposure information. With National Science Foundation (NSF) supercomputers and 400 TB of Vantor satellite imagery, [they] detected building footprints across the Arctic and classified their use types using deep learning models. Then, using Polar Geospatial Center's ArcticDEM digital surface model, [they] estimated the total floor space of each residential building. This move from 2D to 3D representation of the building stock was the largest contributor to increased building damage.
Properly estimating this consequence is necessary for understanding the near future of the Arctic economy. Knowing the magnitude of damages is critical for sustaining the communities and livelihoods of more than 5 million people that call the Arctic home. There are also much broader implications. With the Arctic continuing to emerge as a strategic centerpiece in global affairs and the global economy, accurately quantifying the physical shocks to its built environment will allow researchers to more effectively represent the Arctic in global climate economic models. More precise international policymaking will also be enabled by these improvements.
Ultimately, this research highlights a similar challenge in completely different regions of the world (e.g., Southeast Asia, Sub-Saharan Africa) where exposure is constantly evolving alongside rapid population growth and urbanization. Building stock information can quickly become outdated as these changes occur; satellite remote sensing and AI are key players in keeping up with these changes and supporting data-driven disaster risk management…”
#arctic #circumpolar #permafrost #model #modeling #spatialanalysis #spatiotemporal #GIS #spatial #mapping #melting #degradation #damage #cost #economics #risk #hazard #climaterisk #climatechange #remotesensing #HPC #earthobservation #ArcticDEM #buildingfootprint #LLM #AI #machinelearning #engineering #economy #buildingstock #community #policy #planning #geopolitics #risk #management @UConn Research
