#spatiotemporal — Public Fediverse posts

To Predict Tree Death, Scientists Tapped Gamma Rays To Peer Underground
(Airborne radiation sensors could help forecast and prevent drought-driven tree mortality_
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https://www.science.org/content/article/predict-tree-death-scientists-tapped-gamma-rays-peer-underground <-- shared technical article
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https://doi.org/10.1029/2026GL122182 <-- shared paper
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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...”
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"... 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

Rising Waters Swamp Lake Naivasha [Kenya] [earth observation]
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https://science.nasa.gov/earth/earth-observatory/rising-waters-swamp-lake-naivasha/ <-- shared NASA 2026 technical article
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https://africasacountry.com/2026/04/the-human-cost-of-kenyas-expanding-lakes <-- shared 2026 technical article
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https://carrzee.org/wp-content/uploads/2021/11/kenyarisingwatermenr-scoping-report-latest-5-07-21.pdf <-- shared technical report
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https://earth.gsfc.nasa.gov/gwm/lake/91 <-- shared charting, lake water levels
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https://doi.org/10.11648/j.ajrs.20180602.13 <-- shared 2018 paper, ‘An Assessment of the Role of Water Hyacinth in the Water Level Changes of Lake Naivasha Using GIS and Remote Sensing’
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https://apnews.com/article/kenya-water-hyacinth-invasive-fishing-2ea35d0203bc995a36ddbe32386c1141 <-- shared 2025 media article, ‘How the invasive water hyacinth is threatening fishermen’s livelihoods on … Kenyan [Lake Naivasha]…’
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https://apnews.com/article/kenya-rising-lakes-naivasha-baringo-cbfbb0b670f831b9a310309a810a0075 <-- shared 2025 media article
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https://www.sei.org/features/revisiting-the-rising-rift-valley-lakes-a-glimpse-into-kihotos-struggles/ <-- shared 2023 technical article
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https://www.theguardian.com/world/2022/mar/17/kenya-quiet-slide-underwater-great-rift-valley-lakes-east-africa-flooding <-- shared 2022 media rticle
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“Kenya's Lake Naivasha has long been a place of change and reinvention…
Now the lake faces another major change: rapidly fluctuating water levels. The name Naivasha comes from a Maasai word meaning "that which heaves," an apt description of the freshwater lake over the past 25 years. Satellite altimetry measurements of the lake's depth indicate an increase of about 7 metres (23 feet) since 2010, roughly the height of a two-story building. Over the same period, Landsat observed a roughly 40 percent increase in the lake's area, adding 50 kilometres² (19 miles²) of water, equivalent to roughly 15 Central Parks.
The human and economic toll of the rising water levels is considerable..”
#Kenya #LakeNaivasha #Africa #water #hydrology #hydrography #risingwater #waterlevels #GIS #spatial #mapping #spatialanalysis #spatiotemporal #humanimpacts #farming #agriculture #infrastructure #damage #community #satellite #altimetry #economy #cost #impact #change #flood #flooding #innundation
@nasa

Annual High-Resolution Global Ambient Population Estimates From 1975 To 2024
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https://doi.org/10.31223/X5MV3R <-- shared paper
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https://landscan.ornl.gov/ <-- shared webmap and data link (via download -> LandScan Mosaic Time Series)
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https://gee-community-catalog.org/projects/landcast/ <-- shared link to GEE community catalog collection ‘LandScan Mosaic Annual Global Ambient Population Time Series’
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H/T @andrew Zimmer || Geographer | Research Scientist @ Oak Ridge National Laboratory
“LSM-TS provides annual global estimates of ambient population from 1975–2024 at ~90 m resolution, creating a spatially and temporally consistent reconstruction of population change designed for longitudinal analysis. Reconstruction of historical populations are driven by current LandScan Mosaic building-level population distributions, scaled by built-surface change from #GHSL … and calibrated to annual national-level estimates from U.S. Census Bureau…”
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“LandScan is a globally recognized, R&D100-winning population data platform developed at Oak Ridge National Laboratory (ORNL). Its datasets provide human population distribution estimates down to 100 meter grid resolution. LandScan supports disaster response, humanitarian aid, environmental analysis, and urban planning by providing insights into where people live and how they move…”
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“Gridded population data support assessments of human exposure, settlement change, infrastructure demand, and access to services, yet global datasets combining annual coverage over multiple decades with fine spatial resolution remain limited. LandScan Mosaic Time Series provides 50 annual estimates of global ambient population distribution at 3 arc-second resolution from 1975 through 2024. The series is anchored to the 2024 LandScan Mosaic surface, produced through a building-level population modeling workflow. Historical surfaces are reconstructed using annualized changes in built surface and derived first-level administrative population trajectories. Every layer, including 2024, is normalized to administrative targets scaled to annual country totals from the U.S. Census Bureau International Database. The dataset is distributed as 50 single-band Cloud Optimized GeoTIFFs on an identical WGS84 grid, with values representing estimated persons per cell. Its common grid and methodology support fine-scale longitudinal analysis of ambient population distribution while documented validation and limitations guide appropriate reuse…”
#demographics #change #spatiotemporal #1975 #2024 #population #change #grid #global #LandScan #POPGRID #LSMTS #ambientpopulation #GHSL #OakRidge #ORNL #usecase #opendata #disasterresponse #humanitarianaid #environmentalanalysis #urbanplanning #humanexposure #settlementchange #city #rural #infrastructure #demand #accesstoservices #GIS #spatial #mapping #building #outline #model #modeling
@OAK Ridge National Laboratory | National Security Sciences at ORNL | @POPGRID Data Collaborative

Mapping Flood Agents In The Northern Pantanal Wetland Using Multiple Spatio-Temporal GIS And Remote Sensing Techniques
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https://doi.org/10.1007/s13157-026-02082-3 <-- shared paper
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H/T @renato Gatto de Morais | Geógrafo | Mestre em Recursos Hídricos | Doutorando em Geografia
“Combining MODIS data, TRMM, river discharge, a digital terrain model, and multinomial logistic regression, [the authors] identified three predominant classes of flood origin: local rainfall, bank overflow, and mixed areas. The model achieved a Nagelkerke Pseudo-R² of 0.63 and a classification accuracy of up to 81.2% (10-fold cross-validation). To the best of [their] knowledge, this is the first explicit approach to mapping flood agents for this wetland, a component that has historically remained unmapped despite its recognized influence on habitats and biodiversity…”
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“The mapping of functional hydrologic units is crucial for enhancing our understanding of flooding and hydroecological processes in large wetlands. These units are typically defined by flooding frequency, duration, and magnitude, but another important hydrologic characteristic is the origin of flooding, known as the flood agent. This study presents an empirical framework utilizing remote sensing and GIS procedures for modeling flood agents in the northern Pantanal wetland. Eleven spatial data layers were derived from multi-year MODIS flood maps, daily rainfall estimates from the TRMM 3B42 grid, in situ discharge data, the BEST Digital Terrain Model, and a hydrographic network layer. These layers were tested for their predictive power in a multinomial logistic regression mapping model. Model performance metrics, along with qualitative validation of mapping outcomes using in situ flooding measurements, and vegetation and soil data from field test sites, support the plausibility of the proposed mapping scheme. However, they also highlight the challenges of flood agent mapping in large tropical wetlands…”
#GIS #spatial #mapping #MODIS #TRMM #riverdischarge #digitalterrainmodel #multinomiallogisticregression #geostatistics #Pantanal #Cuiaba #Brazil #water #hydrology #spatialanalysis #spatiotemporal #remotesensing #earthobservation #flood #flooding #source #type #floodagent #tropical #wetland #habitat #biodiversity #ecosystem #hydrologicunit #hydroecology #model #modeling #rainfall #precipitation #weather #climate #discharge #network #metrics

Anthropogenic River Flows
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https://doi.org/10.1088/3033-4942/ae94a0 <-- shared technical perspective
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H/T @Homero Paltán | Water, Energy, Land & Infrastructure Systemic Risks | Lecturer
“Are global river flows no longer natural? Can we really talk about natural rivers? [The authors] started asking ourselves these questions a few years ago over wonderful discussion sessions.
[They] argue that global river systems have entered a new phase, one of Anthropogenic River Flows, where flow regimes arise from the persistent interaction of:
• the physical alteration of river systems,
• the institutional and socio-technical organisation of water use and allocation
• broader global and systemic processes operating across scales.
In the Anthropocene, these domains do not operate independently because a change in one alters how the other two function. Global water risks and river flow properties then become an emergent property of these interactions.
As a result, [the authors] call for a rethinking of how we study, understand, and manage river flows to better reflect this new reality…”
#water #hydrology #Anthropogenic #River #Flow #spatial #mapping #spatialanalysis #spatiotemporal #flowregimes #risk #waterrisk #global #climatechange #watersecurity #baseline #humanimpacts #magnitude, #seasonality #variability #waterquality #sociohydrology #hydroclimate #regulation #waterresources #engineering #governance #pumping #alteration

Modeling Climate Change Impacts On Blue And Green Water In The Ethiopian Upper Blue Nile Basin
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https://doi.org/10.1016/j.ejrh.2026.103871 <-- shared paper
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H/T @Dessalegn worku Ayalew
“The present study assesses the impacts of climate change on blue and green water in the Kessie Watershed of the Ethiopian Upper Blue Nile Basin using the SWAT+ model.
[The authors] set up [a] SWAT+ model using quality-controlled and homogenized observational climate time series… and calibrated it using a multisite calibration approach. [They] selected CMIP6 climate models for future simulations and bias-correction methods through a comprehensive performance assessment... Building on these previous studies, the present research further evaluates the reliability of combining robust climate-model selection with optimal bias-correction methods to improve the reliability of hydrological simulations. [They] then used the best-performing climate models, bias-corrected using the optimal methods, to project future changes in blue and green water in the study area…
KEY FINDINGS:
• SWAT+ effectively represented hydrological processes across multiple gauging stations in the Ethiopian Upper Blue Nile Basin.
• Ensembles of CMIP6 climate models improved the reliability of hydrological simulations compared with individual climate models.
• Optimized climate-model selection reduced biases in hydrological simulations more than bias correction alone.
• Arbitrary selection of climate models can degrade hydrological simulations, even when their outputs are bias-corrected using robust methods.
• Both blue and green water are projected to increase under future climate change in the Ethiopian Upper Blue Nile Basin.
• Blue water exhibits greater seasonality and climate sensitivity than green water flow and green water storage.
The study also provides sustainable water management options for adapting to the impacts of climate change, with implications for water resource planning and management in the Upper Blue Nile Basin…”
#Bluewater #Greenwater #CMIP6 #GCMs #Modelensemble #SSPscenarios #SWAT #Ethopia #UpperNile #Nile #NileBasin #gaging #gauging #Africa #climatechange #impacts #water #hydrology #KessieWatershed #EthiopianUpperBlueNileBasin #model #modeling #spatialanalysis #spatiotemporal #CMIP6 #waterresources #watermanagement #ecosystem #habitat #environment

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

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

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

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
--
https://doi.org/10.1038/s41586-026-10953-2 <-- shared paper
--
https://deepmind.google/science/weatherlab/ <-- shared data
--
https://github.com/google-deepmind/weathernext <-- shared GitHub repository
--
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

WeatherNext – [An] AI Model Achieves Breakthrough In Forecasting Cyclones
--
https://deepmind.google/blog/weathernext-ai-model-achieves-breakthrough-in-forecasting-cyclones/ <-- shared technical Google DeepMind blog post
--
https://doi.org/10.1038/s41586-026-10953-2 <-- shared paper
--
https://deepmind.google/science/weatherlab/ <-- shared data
--
https://github.com/google-deepmind/weathernext <-- shared GitHub repository
--
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

WeatherNext – [An] AI Model Achieves Breakthrough In Forecasting Cyclones
--
https://deepmind.google/blog/weathernext-ai-model-achieves-breakthrough-in-forecasting-cyclones/ <-- shared technical Google DeepMind blog post
--
https://doi.org/10.1038/s41586-026-10953-2 <-- shared paper
--
https://deepmind.google/science/weatherlab/ <-- shared data
--
https://github.com/google-deepmind/weathernext <-- shared GitHub repository
--
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

Widespread Landslide Activity in an Extreme Wet Season and Implications for Regional Sediment Management, Eastern San Francisco Bay Area, California
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https://doi.org/10.1029/2026EA005227 <-- shared paper
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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…”
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"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

Geospatial Analysis of Carbon Offset Projects - A Broader Scientific Outlook
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https://www.eldhollow.com/blogs/geospatial-analysis-of-carbon-offset-projects <-- shared technical blog
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[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…”
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“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

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

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

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

[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

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

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

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

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

Impact Of Reservoir Storage On Propagation From Meteorological To Hydrological Drought
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https://doi.org/10.1016/j.jhydrol.2026.136061 <-- shared paper
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H/T @DrAjayGupta | Post Doctoral Fellow, IIT Bombay | Ph.D. in Hydrology, IIT Roorkee | Commonwealth Split-site Fellow, University of Birmingham I M.Tech in Water Resources Engineering, NIT Silchar | B.E. in Civil Engineering, PCE Nagpur.
“🌍 Why is this important?
While reservoirs are widely recognized for mitigating drought impacts, their role in controlling how drought propagates through the hydrological cycle has remained largely unexplored. In this study, [the authors] investigate how reservoir storage influences the transition of drought from meteorological to agricultural to reservoir to streamflow drought across the semi-arid Krishna River Basin, India.
🔍 THIS STUDY ADDRESSES TWO KEY RESEARCH QUESTIONS:
✅ How do drought propagation time (initiation, peak, and termination) change from meteorological to agricultural, reservoir, and streamflow droughts across different timescales and threshold values?
✅ How does reservoir storage influence drought propagation between upstream and downstream reservoirs using the Downstreamness concept?
📌 KEY FINDINGS
🔹 Drought propagation differs substantially across drought types because each component of the hydrological system responds at different rates.
🔹 Reservoirs significantly delay the propagation of drought by buffering water deficits, particularly between agricultural and streamflow drought.
🔹 Mild and moderate upstream reservoir droughts rarely propagate downstream, whereas severe upstream droughts consistently transmit downstream, leading to longer duration, greater severity, and delayed onset.
🔹 The downstreamness analysis reveals dynamic shifts in water storage between upstream and downstream reservoirs throughout drought development and recovery, providing valuable insights for reservoir operation and basin-scale drought management…”
--
“HIGHLIGHTS
• Reservoir storage impact on drought propagation from meteorological-to-hydrological drought.
• Drought propagation timeframe: initiation, peak and termination are checked.
• Impact assessment using hydrological connection: upstream to downstream reservoirs.
• Severe upstream droughts propagate downstream with increased duration and severity.
• During drought periods water-storage concentration shifts from downstream to upstream..."
#Drought #DroughtPropagation #Reservoirs #WaterResources #WaterManagement #RiverBasinManagement #KrishnaRiverBasin #Downstreamness #India #climatechange #reservoir #storage #hydrology #water #hydrologiccycle #watersecurity #planning #policy #KrishnaRiver #weather #climate #metrology #agriculture #farming #streamflow #model #modeling #spatiotemporal #spatialanalysis

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

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

Beyond The 100-Year Flood - Probabilistic Flood Hazard Assessment For King And Pierce Counties Under Future Climate Scenarios
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https://doi.org/10.5194/nhess-26-3231-2026 <-- shared #openacess paper
--
[part of my old stomping ground as an engineering geologist]
H/T @Kees Nederhoff
“Flood maps are usually built from a single design storm. For King and Pierce Counties in the Pacific Northwest (USA), [the authors] tried the opposite - simulate 82 years of actual coastal and river conditions (plus 18 synthetic years) with SFINCS and let the statistics fall out cell by cell. That took about 5,400 yearly simulations and 194,000 CPU hours on USGS's Hovenweep HPC. Worth it!
The design-event shortcut turns out to hide a real hazard. A deterministic 10-year event underestimated flood depths by up to half a meter compared to the continuous runs.
The bigger surprise [to the authors] was how one-sided the climate signal is. One metre of sea level rise takes King County's expected annual flooded area from 161 --> 787 hectares, almost a factor of five. Changes in storminess over the same horizon barely register. And somewhere between 100 and 150 cm of SLR, land that never floods today starts flooding fast. If you plan adaptation in Puget Sound, that threshold matters more than any single return-period map.
[They] also propose Expected Annual Flooded Area (EAFA) as a probability-weighted alternative to the binary "inside or outside the 100-year zone" label…”
#USGS #supercomputing #Hovenweep #HPC #coast #coastal #PNW #Seattle #PacificNorthwest #risk #hazard #riskmanagement #model #modeling #CFRM #deterministic #probabilistic #climatechange #extremeweather #fedscience #WA #KingCounty #PierceCounty #WashingtonState #USA #flood #flooding #compoundflooding #floodmaps #SFINCS #storm #weather #climate #climatechange #rainfall #precipitation #sealevel #sealevelrise #SLR #100yearflood #floodhazardmapping #returnperiods #pluvial #fluvial #spatialanalysis #spatiotemporal #remotesensing #streamgage #history #historicflooding #projections #predictions
#USGS

Mapping Deforestation Probability And Understanding The Forest Dynamics In Gazipur, Bangladesh
--
https://doi.org/10.1016/j.envc.2026.101568 <-- shared paper
--
"ABSTRACT: Deforestation is a spiralling environmental catastrophe with impervious results for biodiversity, climate change, and human livelihoods, specifically in tropical regions. Being a tropical country, Bangladesh has experienced approximately 40% loss of its forest cover, at Gazipur since 1930, which contains about 86% of the country's Sal (Shorea robusta) forest, ranging approximately 4,300 hectares per year (2001–2010) to over 19,500 hectares per year (2011–2020), exemplifying an intensification of nearly 353%. The objective of this study is to map deforestation probability at the Gazipur district of Dhaka Division, Bangladesh, by utilising machine learning algorithms along with multi-source geospatial data, with the purpose of identifying high-risk zones and facilitating evidence-based forest governance, land-use development, and prioritizing conservation areas. This study integrated twelve conditioning factors, including biophysical, landscape, and anthropogenic. To identify susceptible zones the study trained and assessed five machine learning algorithms; RF, XGBoost, ANN, NB, and MLP and validating the result through different metrics like sensitivity, specificity, precision, accuracy, F1-score, AUC. The performance of the models was evaluated using Wilcoxon signed-rank tests and marginal response curves (MRC) were used to understand factor contributions. In the result, RF achieved highest performance with accuracy of 84% and AUC of 0.93, followed by XGBoost at 83% accuracy and 0.92 AUC. Rainfall and population density were most dominant conditioning factors among models. Pairwise statistical testing resulted that ensemble-based algorithms (RF, XGBoost) generated statistically comparable and significantly higher predictions compared to NB and MLP. Spatial probability maps indicate areas of high and very high risk in the south-western and north-eastern upazilas. The results can be applicable for forest management authorities, urban planners, and policymakers, and correspond with SDG Indicator 15. An inclusive governance framework containing land zoning, ecological area identification, and compliance with industrial EIA is proposed to persuade probability maps into adaptive forest management strategies…”
#deforestation #probability #machinelearning #algorithms #AI #Gazipur #Bangladesh #GIS #spatial #mapping #spatialanalysis #spatiotemporal #rainfall #precipitation #humanimpacts #populationpressure #risk #prediction #RandomForest #conservation #restoration #environment #biodiversity, #climatechange #human #livelihood #tropical #forestcover #sal #forest #vegetation #tree #upazila #spatialprobability #geostatistics #forestmanagement #planning #policy #urbanplanners #governance #zoning #ecology #habitat

GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
--
https://doi.org/10.21203/rs.3.rs-10085674/v1 <-- shared paper
--
https://zenodo.org/records/17627111 <-- shared open data
--
H/T @kyle Davis
“Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”
#IrrigatedAreas #Mapping #GIS #spatial #mapping #spatialanalysis #spatiotemporal #global #irrigation #water #hydrology #hydrography #waterresources #farming #agriculture #opendata #remotesensing #earthobservation #geomorphometry #AI #machinelearning #LLM #model #modeling #WaterManagement #opendata #AgroEcologicalZone #AEZ #cropland #irrigatedareas #foodproduction #wateruse #humanimpacts #EarthObservation #remotesensing #earlywarning #monitoring #FoodandAgricultureOrganizationFAO #FAO
@FAO - Food and Agriculture Organization

GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
--
https://doi.org/10.21203/rs.3.rs-10085674/v1 <-- shared paper
--
https://zenodo.org/records/17627111 <-- shared open data
--
H/T @kyle Davis
“Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”
#IrrigatedAreas #Mapping #GIS #spatial #mapping #spatialanalysis #spatiotemporal #global #irrigation #water #hydrology #hydrography #waterresources #farming #agriculture #opendata #remotesensing #earthobservation #geomorphometry #AI #machinelearning #LLM #model #modeling #WaterManagement #opendata #AgroEcologicalZone #AEZ #cropland #irrigatedareas #foodproduction #wateruse #humanimpacts #EarthObservation #remotesensing #earlywarning #monitoring #FoodandAgricultureOrganizationFAO #FAO
@FAO - Food and Agriculture Organization

GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
--
https://doi.org/10.21203/rs.3.rs-10085674/v1 <-- shared paper
--
https://zenodo.org/records/17627111 <-- shared open data
--
H/T @kyle Davis
“Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”
#IrrigatedAreas #Mapping #GIS #spatial #mapping #spatialanalysis #spatiotemporal #global #irrigation #water #hydrology #hydrography #waterresources #farming #agriculture #opendata #remotesensing #earthobservation #geomorphometry #AI #machinelearning #LLM #model #modeling #WaterManagement #opendata #AgroEcologicalZone #AEZ #cropland #irrigatedareas #foodproduction #wateruse #humanimpacts #EarthObservation #remotesensing #earlywarning #monitoring #FoodandAgricultureOrganizationFAO #FAO
@FAO - Food and Agriculture Organization

GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
--
https://doi.org/10.21203/rs.3.rs-10085674/v1 <-- shared paper
--
https://zenodo.org/records/17627111 <-- shared open data
--
H/T @kyle Davis
“Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”
#IrrigatedAreas #Mapping #GIS #spatial #mapping #spatialanalysis #spatiotemporal #global #irrigation #water #hydrology #hydrography #waterresources #farming #agriculture #opendata #remotesensing #earthobservation #geomorphometry #AI #machinelearning #LLM #model #modeling #WaterManagement #opendata #AgroEcologicalZone #AEZ #cropland #irrigatedareas #foodproduction #wateruse #humanimpacts #EarthObservation #remotesensing #earlywarning #monitoring #FoodandAgricultureOrganizationFAO #FAO
@FAO - Food and Agriculture Organization

GMIA-NEXT - Next-Generation Global Map of Irrigated Areas |
--
https://doi.org/10.21203/rs.3.rs-10085674/v1 <-- shared paper
--
https://zenodo.org/records/17627111 <-- shared open data
--
H/T @kyle Davis
“Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here [they] integrate[d] multi-source Earth observation and environmental datasets and use[d] machine learning to develop a medium-resolution (30 metre) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. [They] subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available [link above] and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management…”
#IrrigatedAreas #Mapping #GIS #spatial #mapping #spatialanalysis #spatiotemporal #global #irrigation #water #hydrology #hydrography #waterresources #farming #agriculture #opendata #remotesensing #earthobservation #geomorphometry #AI #machinelearning #LLM #model #modeling #WaterManagement #opendata #AgroEcologicalZone #AEZ #cropland #irrigatedareas #foodproduction #wateruse #humanimpacts #EarthObservation #remotesensing #earlywarning #monitoring #FoodandAgricultureOrganizationFAO #FAO
@FAO - Food and Agriculture Organization

Watching A #NOAA #Webinar on Flash Droughts
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https://noaaresearch.webex.com/wbxmjs/joinservice/sites/noaaresearch/meeting/download/9b3e684d45ca47fc9469070eabd9a142?MTID=m2fa4a8af7bd8647fc48619af5eeecb5a <-- shared NOAA Summer Science Series individual webinar
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https://www.drought.gov/what-is-drought/flash-drought <-- shared NOAA overview technical article
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https://www.star.nesdis.noaa.gov/star/NOAAScienceSeminars.php <-- subscribe to the NOAA Summer Science Series
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https://doi.org/10.1038/s41612-024-00618-0 <-- shared paper
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https://communities.springernature.com/posts/the-prevalent-life-cycle-of-agricultural-flash-droughts <-- shared technical article (derived from paper above)
H/T @Jeffrey Basara PhD, MBA | Chair and Professor - Department of Environmental, Earth, and Atmospheric Sciences, University of Massachusetts Lowell | Co-Founder - American Prime Sustainable Solutions
[Flash floods? not TOO hard to conceptualise.
Flash drought? harder to 'get my head around', but H/T / presenter does an excellent job!]
"Not all droughts are the same. In some cases, drought rapidly intensifies at subseasonal to seasonal scales with significant impacts to agriculture and water resources along with the increased propensity for heatwaves and wildfires. Like all droughts, flash drought begins with a precipitation deficit. However, both evaporative demand and soil moisture are critical flash drought variables, and identifying and monitoring the desiccation of the terrestrial surface is key for determining flash drought development and associated impacts. While recent advances in knowledge and monitoring of flash drought have occurred, fundamental questions remain in the state of the science. What are the overall mechanistic relationships between atmospheric demand, evaporative stress, terrestrial desiccation, and precipitation that drive the progression of flash drought? Do regional characteristics of the environment impact the evolution of flash drought? What are the scales of predictability for flash drought? Finally, how will flash drought frequency and intensity evolve in a changing climate system"
--
"Flash drought intensifies rapidly due to changes in precipitation, temperature, wind, and radiation. These changes in the weather increase evapotranspiration and lower soil moisture. Flash droughts can cause extensive damage to agriculture, economies, and ecosystems if they are not predicted and discovered early..."
#water #hydrology #fedscience #publicgood #hydrologicdrought #waterdeficit #spatialanalysis #spatiotemporal #watersecurity #risk #hazard #humanimpacts #streamflow #riverflow #groundwater #surfacewater #climate #weather #climatechange #extremeweather #atmosphere #metrology #regional #global #farming #agriculture #fluvial #pluvial #rainfall #precipitation #cloudcover #energy #heat #temperature #ET #evapotranspiration #farming #agriculture #foodsecurity #waterresources #dynamicsystems #watermanagement #flashdrought #drought #susceptibility #monitoring #prediction #model #modeling
@noaa

A Scale-Invariance-Based Algorithm Application For Land Surface Temperature Downscaling In Denmark
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https://doi.org/10.3390/rs18132263 <-- shared paper
--
https://zenodo.org/records/20863040 <-- shared open data for downscaled LST dataset for Copenhagen
--
H/T @CLIM4cities
#urbanclimate #downscaling #landsurfacetemperature #LST #AI #machinelearning #scaleinvariance #residualcorrection #Sentinel #Landsat #satellite #remotesensing #earthobservation #CLIM4cities #UrbanClimate #ClimateServices #MachineLearning #ClimateAdaptation #heatwave #temperature #ontheground #Copenhagen #Denmark #impervioussurface #asphalt #roof #concrete #albedo #heatabsorption #mitigation #urban #urbancentre #treecover #vegetation #urbanheatisland #planning #design #hotspots #monitoring #spatialanalysis #spatiotemporal #model #modeling #usecase #operational #climatechange #extremeweather #evidencebased #adapation #sustainability #urbanplanning #climateresilience #EssentialClimateVariable #ECV
@+ATLANTIC | @Danish Meteorological Institute | @ESA Φ-lab Collaborative Innovation Network | @CLIM4cities

Comparative Hydro-Climatic Datasets For Catchment-Wise Linked Water Fluxes And Storage Changes Across South America
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https://doi.org/10.3389/fenvs.2026.1764771 <-- shared paper
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https://doi.org/10.1038/s43247-026-03661-2 <-- shared paper
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https://doi.org/10.1002/joc.6443 <-- shared paper
--
https://www.pik-potsdam.de/en/news/latest-news/from-droughts-to-floods-climate-change-and-migration-in-peru | https://publications.iom.int/books/evaluacion-de-la-evidencia-cambio-climatico-y-migracion-en-el-peru <-- shared 2021 Peru hydroclimate technical article | report
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https://youtu.be/Ngbm0gsmYAw?si=haqV7t15pGkEmJB8 <-- shared overview video
--
#water #GIS #spatial #mappping #spatialanalysis #spatiotemporal #remotesensing #earthobservation #Hydrology #Hydroclimatology #ClimateChange #extremeweather #WaterResources #WaterSecurity uncertainity #SouthAmerica #ClimateData #PeerReview #OpenScience #Hydrometeorology #opendata #datasets #rainfall #precipitation #fluvial #heatwave #temperature #changing #consistency #flood #flooding #drought #riskmanagement #risk #hazard #earthsystems #resilience #waterquality #waterpollution #model #modeling #monitoring #records #hydroclimate #hydrogeomorphology #review #SouthAmerica #planning #policy #sustainability #evapotranspiration #runoff #waterstorage #SAHCD

Optical, Radar, And Hybrid Indices To Detect Farming Practices In Europe
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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

Geospatial Data As Bioethical Evidence
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https://doi.org/10.4401/jgsg-111 <-- shared paper
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https://theconversation.com/gaza-we-analysed-a-year-of-satellite-images-to-map-the-scale-of-agricultural-destruction-248796 <-- shared technical article
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https://www.scientificamerican.com/article/inside-the-satellite-tech-revealing-gazas-destruction/ <-- shared 2023 technical article
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“Satellite imagery now documents systematic patterns of infrastructure destruction at spatial resolutions and temporal cadences that were unavailable during the atrocities of the twentieth century. Whether and how such data may enter bioethical deliberation, however, remains under-theorized. Quantitative remote sensing produces damage percentages, not normative claims, and bridging the two without committing an is-ought fallacy requires an explicit epistemological procedure. The contribution developed here is normative and epistemological rather than empirical. A six-component admissibility framework integrates, for the first time, geospatial evidence, population-level bioethical principlism, and the coherentist verification epistemology of political fact-checking into a single reproducible procedure for the bioethical use of satellite imagery. The first component specifies evidence admissibility criteria tailored to bioethical rather than strictly legal use. The second requires coherentist triangulation across methodologically independent remote sensing studies. The third operates as a bioethical relevance filter mapping infrastructure categories onto population-level social determinants of health. The fourth operationalizes principlism by translating health justice, accountability, solidarity, and sustainability into measurable geospatial observables. The fifth establishes ethical representation safeguards against voyeuristic or dehumanizing uses of destruction imagery. The sixth demands explicit epistemic humility regarding uncertainty, data missingness, and attribution limits. The Gaza conflict provides the case in point. Two independently produced geospatial studies, one based on SAR coherent change detection and one on very-high-resolution optical analysis, converge on extensive damage to civilian healthcare, water, sanitation, and educational infrastructure, and thereby satisfy the coherentist triangulation requirement of the framework. The resulting inference licenses bioethical claims of systematic survival infrastructure degradation while preserving transparent boundaries between what satellite evidence can and cannot establish about genocidal intent…”
#geoethics #GIS #spatial #mapping #remotesensing #earthobservation #imagery #satellite #opendata #conflict #war #military #destruction #infrastructure #spatiotemporal #bioethical #evidence #damagepercentages #spatialanalysis #change #quantitative #metrics #normative #epistemological #admissibility #framework #factchecking #controlled #publicsafety #publichealth #healthjustice #geospatialobservables #observation #uncertainty #controls #boundaries #changedetection #coherentisttriangulation #example #gaza

Impact Of Floods On Surface Water Quality - A Systematic Review And Comprehensive Assessment
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https://doi.org/10.1016/j.jhydrol.2026.135916 <-- shared paper
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https://www.epa.gov/system/files/documents/2024-12/2024-12.pdf <-- shared paper
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“Floods, as extreme flow events, are among the costliest and devastating natural hazards. Among the various domains impacted by flooding, environmental degradation, particularly the deterioration of water quality (WQ), is one of the most impacted yet often overlooked. Therefore, it is essential to understand the nature and source of water pollution associated with flooding. This study aims to evaluate and assess multiple studies conducted globally to determine the impact of floods on WQ. A literature review and assessment of 66 studies published between 2007 and 2026 was conducted using the total comprehensiveness score (TCS). To support the scoring process, studies that scored more than 70% of the maximum achievable TCS (15.4) are considered the most detailed and comprehensive in addressing the objectives of this review. 16 studies achieved a TCS above 15.4, indicating that a limited number of studies incorporate a broader set of factors in this domain. A higher number of studies were conducted post the year 2021, highlighting both scientific progress and a growing focus on WQ impacts from disasters such as floods, beyond the traditionally emphasized socio-economic loss. Among the shortlisted studies, fluvial floods are the most frequently examined, followed by pluvial floods and coastal floods. During fluvial floods, turbidity increased by up to two orders of magnitude, while nutrient concentrations (TN, TP) typically rose by ∼ 10–30%. In contrast, pluvial floods were characterised by dilution-driven decreases in EC and TDS, with DOX, BOD and COD showing variable responses across flood types. This review evaluates flood impacts on WQ, catchment characteristics, and sources of WQ modification. The findings of the research reveal that not all WQ parameters are responsible for WQ degradation during every flood event. Rather, it is a combination of certain parameters that leads to deteriorated WQ. WQ degradation depends on interacting factors such as flood duration, extent, depth, and flow dynamics. In overall, this study provides an overview of the multiple cascading impacts of floods on WQ, along with a detailed perspective on the set of criteria that should be considered in future research…”
#water #hydrology #hydrography #flood #flooding #criteriaassessment #waterpollution #waterquality #parameters #extremeflow #waterresources #extremeweather #waterresources #watermanagement #global #literaturereview #morphology #source #type #watersecurity #research #papers #compilation #humanimpacts #PRISMA #spatiotemporal #fluvial #pluvial #coast #coastal #risk #hazard #riverine #climatechange #EnvironmentalScience #Research #ClimateResilience #floodtype #pollution #naturalhazard

Mountain Lions Have Major Ecological Impact Even In Small Preserves
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https://phys.org/news/2026-06-mountain-lions-major-ecological-impact.html <-- shared technical article
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https://doi.org/10.1002/ece3.73775 <-- shared paper
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https://youtu.be/jy-ngOhoNPU?si=WTSMWz9XuaqT8Z4O <-- shared Standford overview video
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https://youtu.be/CzSCu2FOj0Q?si=bb15-e03DLuWhQ4Y <-- shared Stanford overview video
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[not my usual fare to post, but fascinating…]
“#Bigcats have a big impact. A long-term study showed that when mountain lions began regular visits to a small suburban preserve about 45 miles (72 kilometers) south of San Francisco, they changed the behavior of many other animals.
Mountain lions (Puma concolor) started appearing with increasing frequency on trail cameras at Stanford's Jasper Ridge Biological Preserve ('Ootchamin 'Ooyakma) from 2015 to 2020. Researchers documented a corresponding drop in deer activity compared with the prior years of lower or absent puma activity. Vegetation surveys also showed that many woody plants deer like to eat or tend to trample, including young oak trees, began to thrive.
These types of multilevel effects, called trophic cascades, have been studied primarily in large wilderness areas, particularly cascades caused by #apexpredators such as #wolves reintroduced into Yellowstone National Park...”
#mountainlion #cougar #puma #trophiccascade #JasperRidgeBiologicalPreserve #OotchaminOoyakma #monitoring #spatiotemporal #spatialanalysis #trailcamera #deer #rabbit #coyote #bobcat #fox #vegetation #survey #oak #tree #young #sapling #plant #predator #preyabundance #herbivore #health #ecosystem #balance #habitat #mesopredator #crossmapping #nocturnal #GIS #spatial #mapping #ecology #conservation #wilderness
#StanfordUniversity

How Space Weather Could Bust The AI Boom
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https://spacenews.com/how-space-weather-could-bust-the-ai-boom/ <-- shared technical article
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https://futurism.com/artificial-intelligence/ai-data-centers-electric-grid-meltdown <-- shared technical article, “AI Data Centers Pushing Electric Grid Into Meltdown”
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https://doi.org/10.1146/annurev-earth-032524-012356 <-- shared 2026 paper, “Magnetic Storms and Geoelectric Hazards”
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#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)

Compound Hydrogeomorphic Cascades And Rapid Upstream To Downstream Hazard Coupling In The Eastern Himalaya
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https://doi.org/10.1038/s41598-026-52915-8 <-- shared paper
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https://doi.org/10.5194/esurf-13-1281-2025 <-- shared paper
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https://doi.org/10.1007/s11069-025-07766-3 <-- shared paper
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[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…”
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“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

Environment Auckland [New Zealand] Data Portal [incl. spatial]
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https://environmentauckland.org.nz/Data <-- shared link to data portal
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https://ourauckland.aucklandcouncil.govt.nz/news/2025/09/soe-report-2025/ <-- shared 2025 report link
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https://coastalmonitoringac.netlify.app/ <-- shared Auckland Council Beach Monitoring Program page
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[ancetodal: a VERY long time ago I was an intern Engineering Geologist at the Auckland Council, although it was ARC back then 😊 ]
H/T @David Wright | Senior Field Hydrologist, Hydrology And Data Management Team
“This portal contains primary data from Auckland Council’s State of the Environment monitoring programmes.
Te Kaunihera o Tāmaki Makaurau / Auckland Council's Environmental Evaluation and Monitoring Unit carries out environmental monitoring across the region. [They] have been collecting information about Auckland’s environment for more than 30 years and have more than 1,000 monitoring sites across the region. [Their] comprehensive monitoring programmes build a picture of the health of Auckland’s environment, track changes and identify issues...”
#opendata #Auckland #NewZealand #GIS #spatial #mapping #dataportal #localgovernment #publicservice #publicgood #ratepayers #StateoftheEnvironment #monitoringprogrammes #environment #water #hydrology #waterquality #waterresources #intergration #environmentalmonitoring #airquality #coast #coastal #development #construction #engineering #sediment #biology #biodiversity #ecology #habitat #monitoring #spatialanalysis #spatiotemporal #estuary #river #stream #marine #mitigation #identification
@Auckland Council

Late Miocene Euphrates River Drained Into A Partially Desiccated Eastern Mediterranean
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https://doi.org/10.1038/s41561-026-01962-x <-- shared paper
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[the paleogeographic reconstruction is outstanding, including the strength and information conveyed so well in that figure, kudos!]
H/T @lina Jakaitė-Darkšė
“Although the Euphrates River - stretching ~3,000 km across Western Asia - has shaped the region’s geology for millions of years, the timing of its origin and the evolution of its course remain enigmatic. So far, two contrasting hypotheses have been proposed to explain the fluvial system’s Late Neogene path: termination in Anatolia at a palaeo-lake or the Mediterranean, or a southeastward continuation to Arabia. Here [they] use seismic-reflection and topographic data to show that two previously identified sedimentary accumulations - deposited during the terminal phase of the Late Miocene Messinian salinity crisis - resulted from dual riverine systems that drained into a partially desiccated eastern Mediterranean before avulsing toward the Persian Gulf and converging to form the modern Euphrates River. From probabilistic sediment-budget modelling, [they] show that although the latest Messinian drainage basins were an order of magnitude smaller than their present-day extents, the total palaeo-discharge exceeded that of the modern Tigris, Euphrates and Nile rivers combined, indicating intense palaeo-precipitation and high palaeo-relief. These results suggest that plate-margin deformation both controlled the fluvial avulsions that diverted the Euphrates River from the Anatolian–Eurasian Plate to the Arabian Plate, and established the conditions necessary for the development of the alluvial Fertile Crescent…”
#water #hydrology #hydrography #paleogeography #Euphrates #river #Miocene #reconstruction #spatialreconstruction #geology #change #erosion #MiddleEast #spatialanalysis #spatiotemporal #Neogene #Anatolia #paleolake #Mediterranean #Arabia #Messinian #remotesensing #model #modeling #topography #hydrogeomorphology #geomorphology #PersianGulf #sediment #paleodischarge #volume #Tigris #elevation #platetectonics #structuralgeology #platemargin #fluvial #avulsion #FertileCrescent

Decoupling Of Surface Water Storage From Precipitation In Global Drylands Due To Anthropogenic Activity
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https://doi.org/10.1038/s44221-024-00367-7 <-- shared paper
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“The availability of surface water in global drylands is essential for both human society and ecosystems. However, the long-term drivers of change in surface water storage, particularly those related to anthropogenic activities, remain unclear. Here [they] use[d] multi-mission remote sensing data to construct monthly time series of water storage changes from 1985 to 2020 for 105,400 lakes and reservoirs in global drylands. An increase of 2.20 km³ per year in surface water storage is found primarily due to the construction of new reservoirs. For lakes and old reservoirs (constructed before 1983), conversely, the trend in storage is minor when aggregated globally, but they dominate surface water storage trends in 91% of individual global dryland basins. Further analysis reveals that long-term storage changes in these water bodies are primarily linked to anthropogenic factors - including human-induced warming and water-management practices - rather than to precipitation changes, as previously thought. These findings reveal a decoupling of surface water storage from precipitation in global drylands, raising concerns about societal and ecosystem sustainability…”
#water #hydrology #hydrography #waterstorage #waterresources #surfacewater #global #drylands #precipitation #rainfall #watersecurity #ecosystems #habitat #publichealth #anthropogenic #GIS #spatial #mapping #remotesensing #earthobservation #spatiotemporal #spatialanalysis #monitoring #geostatistics #engineering #reservoirs #infrastructure #lakes #waterbodies #globalwarming #climatechange #sustainability #planning #baseline

On The Use Of Rainfall Time Series For Regional Landslide Prediction By Means Of Functional Regression
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https://doi.org/10.1016/j.enggeo.2026.108860 <-- shared paper
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#rainfallinduced #landslide #Functionalregression #Japan #Earlywarningsystem #Spacetimeprediction #spatialanalysis #spatiotemporal #massmovement #engineeringgeology #geomorphology #hydrogeomorphology #geomorphometry #remotesensing #geostatistics #model #modeling #mechanics #rainfall #threshold #precipitation #water #hydrology #risk #hazard #hazardassessment #terrain #landscape #landform #landuse #geology #soil #lithology #functionalGeneralizedAdditiveModel #FGAM #prediction #casestudy #AI #LLM

Magnetic Storms And Geoelectric Hazards
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https://doi.org/10.1146/annurev-earth-032524-012356 <-- shared paper / annual review
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#magneticstorm #electromagneticinduction #extremeevent #historicalevent #hazardanalysis #spaceweather #history #Carrington #geoelectric #humanimpacts #risk #hazard #monitoring #network #geology #geomagnetism #impedance #rock #soil #utilities #electricaltransmission #powerlines #magnetotelluric #sensor #magnetometer #blackout #brownout #energy #geoelectrichazard #geoelectric #geostatics #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

Human Disturbances Dominated The Unprecedentedly High Frequency Of Yellow River Flood Over The Last Millennium
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https://doi.org/10.1126/sciadv.adf8576 <-- shared paper
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https://www.smithsonianmag.com/smart-news/3000-years-humans-alterations-chinas-yellow-river-created-catastrophic-situation-exists-today-180951815/ <-- shared technical article
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#humanimpacts #anthropogenic #water #hydrography #hydrology #flood #flooding #river #catchment #engineering #riverine #YellowRiver #China #climatechange #extremeweather #floodhazard #risk #hazard #history #historic #sediment #reconstruction #spatialanalysis #spatiotemporal #levee #breaches #Holocene #geologic #fluvial #sustainability #planning #management #watermanagement #stress #change #dating #radiocarbon #OSL #sampling #paleoflood #overtopping #slackwater #sedimentation #model #modeling #embankment #riverstage #dams

Non-Linear Morphodynamic Response Of Deltas To Extreme Events
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https://doi.org/10.1029/2025GL120954 <-- shared paper
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#extremeweather #GIS #spatial #mapping #water #hydrography #climatechange #coast #coastal #delta #river #rivers #riverdelta #estuary #estuarine #adapation #flood #flooding #planning #population #demographics #global #morphodynamics #pyDeltaRCM #numericalmodeling #model #modeling #deltamorphology #channel #flow #flowchannel #sedimentation #geomorphology #geomorphometry #spatialanalysis #spatiotemporal #ecology #ecosystems #habitat #floodfrequency #morphometrics