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#spatiotemporal — Public Fediverse posts

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

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

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

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

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

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

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

    #electricalresistivitytomography #waterstorage #subsurface #interstitial #groundwater #earthobservation
  4. 💧🌏 Greg Cocks @GregCocks ·

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

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

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

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

    Rising Waters Swamp Lake Naivasha [Kenya] [earth observation]
    --
    science.nasa.gov/earth/earth-o <-- shared NASA 2026 technical article
    --
    africasacountry.com/2026/04/th <-- shared 2026 technical article
    --
    carrzee.org/wp-content/uploads <-- shared technical report
    --
    earth.gsfc.nasa.gov/gwm/lake/91 <-- shared charting, lake water levels
    --
    doi.org/10.11648/j.ajrs.201806 <-- shared 2018 paper, ‘An Assessment of the Role of Water Hyacinth in the Water Level Changes of Lake Naivasha Using GIS and Remote Sensing’
    --
    apnews.com/article/kenya-water <-- shared 2025 media article, ‘How the invasive water hyacinth is threatening fishermen’s livelihoods on … Kenyan [Lake Naivasha]…’
    --
    apnews.com/article/kenya-risin <-- shared 2025 media article
    --
    sei.org/features/revisiting-th <-- shared 2023 technical article
    --
    theguardian.com/world/2022/mar <-- shared 2022 media rticle
    --
    “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

    #kenya #lakenaivasha #africa #water #hydrology #hydrography
  7. 💧🌏 Greg Cocks @[email protected] ·

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

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

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

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

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

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

    Spatiotemporal Distribution, Climatic Factors, And Seasonal Precipitation Patterns Characterizing 66 Years Of Widespread Shallow Landslide Events In Piedmont (Northwestern Italy)
    --
    doi.org/10.3389/feart.2026.188 <-- shared paper
    --
    “Widespread shallow landslide events constitute one of the primary drivers of recurrent societal and economic losses in mountain regions. Although the influence of climate variability on landslide frequency and magnitude has been widely recognized, long-term, event-level analyses linking spatiotemporal landslide patterns to precipitation climatology at the regional scale remain scarce. This study presents a statistical analysis of 128 widespread shallow landslide events recorded in Piedmont, northwestern Italy, over the 66-year period 1960–2025, cross-referenced against the regional precipitation climatology. Events were characterized by season, primary physiographic unit, provincial coverage, and an ordinal magnitude index (scale 1–7) encoding combined spatial extent and estimated landslide count. Results indicate that no statistically significant monotonic trend in event frequency was detected over the study period (mean rate: 1.97 events yr⁻1); however, a moderate positive correlation was established between event magnitude and the number of provinces affected (R2 = 0.51, p < 0.00), validating the magnitude index as a proxy for spatial footprint. Mean event magnitude reached its highest value in the 2010s (3.71), while all five events of magnitude ≥ 6 occurred in autumn. The summer fraction of the catalogue increased markedly, from 6% in the 1960s to 31% in the 2000s and 25% in the 2020s, concurrent with stable or declining summer mean precipitation totals, consistent with Clausius–Clapeyron amplification of convective intensity under documented regional warming. Cross-analysis with regional records identifies three tiers of rainfall–landslide coupling: a stationary direct seasonal coupling; a non-stationary, strengthening intensity-mediated summer coupling; and a structural susceptibility-mediated spatial decoupling whereby the driest provinces generate the highest landslide occurrence frequencies due to the lower triggering thresholds characteristic of Tertiary Piedmont Basin sedimentary environments. A post-hoc assessment of the triggering thresholds used for regional shallow landslide early warning system demonstrates superior detection performance for high-magnitude autumn events (hit rate up to 89% for the 2000–2025 sub-period) and identifies sub-daily convective accumulation windows in summer as the primary domain requiring threshold recalibration…”
    #massmovement #landslide #engineeringgeology #Italy #Piedmont #NorthernItaly #weather #rainfall #precipitation #climate #risk #hazard #corrleation #relationship #earlywarningsystems #damage #loss #community #infrastructure #mountain #spatiotemporal #mapping #spatialanalysis #statistics #geostatistics #climatology #regional #scale #weatherpatterns #physiography #geomorphology #water #hydrology #hydrogeomorphology #geology #soils

    #massmovement #landslide #engineeringgeology #italy #piedmont #northernitaly
  11. 💧🌏 Greg Cocks @[email protected] ·

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

    #massmovement #soil #water #hydrology #hydrography #geology
  12. 💧🌏 Greg Cocks @[email protected] ·

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

    #flood #flooding #risk #hazard #italy #europe
  13. 💧🌏 Greg Cocks @[email protected] ·

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

    #flood #flooding #risk #hazard #italy #europe
  14. 💧🌏 Greg Cocks @[email protected] ·

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

    #flood #flooding #risk #hazard #italy #europe
  15. 💧🌏 Greg Cocks @[email protected] ·

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

    #publicsafety #drivers #parameters #factors #modeling #rescuefr
  16. 💧🌏 Greg Cocks @GregCocks ·

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

    #flood #flooding #risk #hazard #italy #europe
  17. 💧🌏 Greg Cocks @[email protected] ·

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

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

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

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

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

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

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

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

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

    #global #forestcarbonprojects #climatecrisis #climatechange #carboncredit #tree
  22. 💧🌏 Greg Cocks @GregCocks ·

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

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

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

    #karst #australia #water #hydrology #underground #subsurface
  24. 💧🌏 Greg Cocks @[email protected] ·

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

    #karst #australia #water #hydrology #underground #subsurface
  25. 💧🌏 Greg Cocks @[email protected] ·

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

    #karst #australia #water #hydrology #underground #subsurface
  26. 💧🌏 Greg Cocks @[email protected] ·

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

    #spatialanalysis #geology #lithology #climate #precipitation #rainfall
  27. 💧🌏 Greg Cocks @GregCocks ·

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

    #karst #australia #water #hydrology #underground #subsurface
  28. 💧🌏 Greg Cocks @[email protected] ·

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

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

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

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

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

    #gis #spatial #mapping #satellite #remotesening #earthobservation
  31. 💧🌏 Greg Cocks @[email protected] ·

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

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

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

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

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

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

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

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

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

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

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

    #coast #coastal #innundation #climatechange #global #sealevel
  37. 💧🌏 Greg Cocks @[email protected] ·

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

    #drought #droughtpropagation #reservoirs #waterresources #watermanagement #riverbasinmanagement
  38. 💧🌏 Greg Cocks @[email protected] ·

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

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

    Refined Modeling of Arctic Circumpolar Building Stock Increases Estimated Mid-Century Permafrost Degradation Damages
    --
    doi.org/10.1029/2026EF008578 <-- shared paper
    --
    thearcticinstitute.org/climate | thearcticinstitute.org/dwindli <-- shared technical articles
    --
    theguardian.com/cities/2016/oc <-- shared media article
    --
    news.grida.no/new-map-shows-ex <-- shared technical article
    --
    H/T @elias Manos
    “Why the increase?
    Our understanding of climate risk is only as good as our understanding of our exposure to hazards. The better we can account for what is at risk, the better we can measure risk in a changing world.
    In this new study [link above], [they] investigate[d] how damage to the building stock across the Arctic, a key impact of permafrost degradation, is underestimated because of underdeveloped exposure information. With National Science Foundation (NSF) supercomputers and 400 TB of Vantor satellite imagery, [they] detected building footprints across the Arctic and classified their use types using deep learning models. Then, using Polar Geospatial Center's ArcticDEM digital surface model, [they] estimated the total floor space of each residential building. This move from 2D to 3D representation of the building stock was the largest contributor to increased building damage.
    Properly estimating this consequence is necessary for understanding the near future of the Arctic economy. Knowing the magnitude of damages is critical for sustaining the communities and livelihoods of more than 5 million people that call the Arctic home. There are also much broader implications. With the Arctic continuing to emerge as a strategic centerpiece in global affairs and the global economy, accurately quantifying the physical shocks to its built environment will allow researchers to more effectively represent the Arctic in global climate economic models. More precise international policymaking will also be enabled by these improvements.
    Ultimately, this research highlights a similar challenge in completely different regions of the world (e.g., Southeast Asia, Sub-Saharan Africa) where exposure is constantly evolving alongside rapid population growth and urbanization. Building stock information can quickly become outdated as these changes occur; satellite remote sensing and AI are key players in keeping up with these changes and supporting data-driven disaster risk management…”
    #arctic #circumpolar #permafrost #model #modeling #spatialanalysis #spatiotemporal #GIS #spatial #mapping #melting #degradation #damage #cost #economics #risk #hazard #climaterisk #climatechange #remotesensing #HPC #earthobservation #ArcticDEM #buildingfootprint #LLM #AI #machinelearning #engineering #economy #buildingstock #community #policy #planning #geopolitics #risk #management @UConn Research

    #arctic #circumpolar #permafrost #model #modeling #spatialanalysis
  40. 💧🌏 Greg Cocks @[email protected] ·

    Impact of River Morphology on River–Groundwater Exchange in Braided River Systems
    --
    doi.org/10.1111/gwat.70092 <-- shared paper
    --
    H/T @thomas Wöhling | Professor at TU Dresden
    “Do you like braided rivers? We think they are soooo beautiful. And they are interesting to study as well. Particularly how these complex and transient systems interact with regional aquifers…”
    --
    “Coupled models of two braided rivers with real, pre- and postflood event morphologies are studied for river–groundwater exchange changes…”
    --
    “Braided river systems are an important source for groundwater recharge, but their complex morphology makes river–groundwater exchange fluxes difficult to estimate. Their river channel morphology changes frequently after floods, which has effects on recharge rates that have rarely been studied in the past. This work aims to isolate the effects of changes in braided river morphology on groundwater recharge for two sections of the Wairau River and Waikirikiri River in New Zealand. For each study site, two different river morphology variants of a fully coupled surface water–groundwater model utilizing high-resolution DEMs of river bathymetry before and after a major flood event were set up while keeping parameterization and boundary conditions the same. The models demonstrate that flood-induced morphology changes in braided river systems alter groundwater recharge. [They] identif[ied] features, both simulated and observed, that explain the direction of change. Features that increase groundwater recharge are a larger braidplain aquifer extent and volume, larger wetted area and, specifically, an increase of areas with high exchange rates in locations of larger gradients between braidplain aquifer and regional aquifer. These factors influence groundwater recharge independent of connection (Wairau River) or disconnection (Waikirikiri River) of the system to the regional aquifer, albeit with different magnitudes. An extension of [their] research to other braided rivers is needed to more broadly generalize [their] findings...”
    #NewZealand #river #morphology #braided #Waikirikiri #Wairau #water #hydrology #hydrography #model #modeling #groundwater #aquifer #waterresources #recharge #infiltration #flood #flow #flooding #hydrogeomorphology #exchangefluxes #surfacewater #remotesensing #DEM #elevation #spatialanalysis #GIS #spatial #mapping #change #dynamic #spatiotemporal #bathymetry

    #newzealand #river #morphology #braided #waikirikiri #wairau
  41. 💧🌏 Greg Cocks @[email protected] ·

    Beyond The 100-Year Flood - Probabilistic Flood Hazard Assessment For King And Pierce Counties Under Future Climate Scenarios
    --
    doi.org/10.5194/nhess-26-3231- <-- 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

    #openacess #usgs #supercomputing #hovenweep #hpc #coast
  42. 💧🌏 Greg Cocks @[email protected] ·

    Mapping Multifunctionality In Remote Patagonian Forest Landscapes Reveals High-Value Ecosystems Beyond Protected Areas
    --
    doi.org/10.1038/s43247-026-035 <-- shared paper
    --
    H/T @Peter Potapov | Researcher at the World Resources Institute (WRI)
    “This paper is] a strong example of multifunctionality analysis applied to conservation planning. The study mapped six ecosystem functions, including carbon storage, nutrient availability, water regulation, erosion control, habitat quality, and ecological connectivity. [The author] combined satellite data, field soil sampling, and spatial modeling for this comprehensive analysis.
    Two findings stand out.
    1. Old-growth forests had the highest multifunctionality index of any land cover type.
    2. 78.5% of the top multifunctionality hotspots fall outside the region's protected areas, even though PAs already cover more than 54% of the territory.
    Together, these results make a clear case for expanding conservation of the remaining Intact Forest Landscapes and primary forests in Patagonia and elsewhere…”
    --
    “Remote forest landscapes provide critical references for understanding ecosystem functions (EFs) under low anthropogenic pressure, yet their capacity to sustain multiple EFs simultaneously remains poorly understood. [They] assessed landscape multifunctionality in western Patagonia by integrating satellite indicators, field data, and spatial modeling. Six EFs (carbon storage, nutrient availability, water regulation, erosion control, habitat quality, and ecological connectivity) were mapped, and their spatial relationships and hotspot distribution within and outside protected areas (PAs) were analyzed. Old-growth and secondary forests showed the highest functional performance. Strong synergies (ρ ≥ 0.6) between carbon storage and nutrient availability covered >50% of the landscape, whereas strong trade-offs (ρ ≤ –0.6) were spatially limited ( < 6%). Notably, 78% of multifunctionality hotspots occurred outside PAs, indicating that high-functional-value areas extend beyond formal conservation boundaries. These findings reveal spatial mismatches between multifunctionality and protection status and provide a replicable framework for integrating multifunctionality into conservation planning under global change…”
    #Patagonia #chile #aysen #coyhaique #landcover #mapping #spatial #spatialpatterns #spatiotemporal #spatialanalysis #forest #vegetation #oldgrowth #secondgrowth #shrubland #grassland #steppe #ecosystem #habitat #nutrients #water #hydrology #erosion #multifunctionality #multifunctionalityanalysis #protectedareas #landuse #conservationplanning #conservation #ecology #carbonstorage #nutrientavailability #waterregulation #erosioncontrol #habitatquality #ecologicalconnectivity #remotesensing #satellite #earthobservation #modeling

    #patagonia #chile #aysen #coyhaique #landcover #mapping
  43. 💧🌏 Greg Cocks @[email protected] ·

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

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

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

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

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

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

    Mapping Sleeping Bees within Their Nest: Spatial and Temporal Analysis of Worker Honey Bee Sleep
    --
    pmc.ncbi.nlm.nih.gov/articles/ <-- shared paper
    --
    H/T Dimitrios A. Karras
    “Bees are not only very important for all ecosystems... They are very cute too....
    However, bee societies are very complex and different sleep patterns exist for the other kinds of bees in the hierarchy..
    In general, bees actually sleep, and many species spend around 5-8 hours resting each day, usually at night when they're not foraging.
    Some solitary bees have been photographed sleeping inside flowers, where they cling onto the petals or stamens with their tiny legs and sometimes even their jaws.
    One of the cutest parts is that tired bees can look almost like they've fallen asleep at work, curled up inside a flower with pollen still covering their bodies…”
    --
    “Patterns of behavior within societies have long been visualized and interpreted using maps. Mapping the occurrence of sleep across individuals within a society could offer clues as to functional aspects of sleep. In spite of this, a detailed spatial analysis of sleep has never been conducted on an invertebrate society. [The authors] introduce the concept of mapping sleep across an insect society, and provide an empirical example, mapping sleep patterns within colonies of European honey bees (Apis mellifera L.). Honey bees face variables such as temperature and position of resources within their colony's nest that may impact their sleep. [They] mapped sleep behavior and temperature of worker bees and produced maps of their nest's comb contents as the colony grew and contents changed. By following marked bees, [they] discovered that individuals slept in many locations, but bees of different worker castes slept in different areas of the nest relative to position of the brood and surrounding temperature. Older worker bees generally slept outside cells, closer to the perimeter of the nest, in colder regions, and away from uncapped brood. Younger worker bees generally slept inside cells and closer to the center of the nest, and spent more time asleep than awake when surrounded by uncapped brood. The average surface temperature of sleeping foragers was lower than the surface temperature of their surroundings, offering a possible indicator of sleep for this caste. [They] propose mechanisms that could generate caste-dependent sleep patterns and discuss functional significance of these patterns…”
    #bee #insect #hive #sleeping #sleep #patterns #monitoring #spatialanalysis #spatiotemporal #spatial #mapping #sleeppatterns #behaviour #society #community #honeybees #ecosystem #colony #infrared #remotesensing #temperature #instrumentation

    #bee #insect #hive #sleeping #sleep #patterns
  47. 💧🌏 Greg Cocks @[email protected] ·

    Compound Hydrogeomorphic Cascades And Rapid Upstream To Downstream Hazard Coupling In The Eastern Himalaya
    --
    doi.org/10.1038/s41598-026-529 <-- shared paper
    --
    doi.org/10.1007/s11600-022-009 <-- shared paper
    --
    H/T @Kuldeep Dutta | Geology-Earth Science
    “… In hilly regions transitioning rapidly to low gradient alluvial plains, localized hydrometeorological triggers can instantly scale into devastating basin wide disasters. This study dissects the September 2020 cascading hazard in parts of the Arunachal Pradesh-Assam corridor to quantify the rapid coupling between upstream hillslopes and downstream floodplains.
    Check out the [attached graphical abstract figure] for an integrated visual workflow of the entire disaster continuum from hillslope failure to floodplain transformation...”
    --
    “Extreme precipitation in the Eastern Himalaya is increasingly associated with coupled hillslope-floodplain hazards. This study examines the 17th-18th September 2020 rainfall event in Arunachal Pradesh initiating landslides and its downstream impacts in Assam, India, using multi-sensor satellite data and long-term rainfall records. Sentinel-2 imagery was used to map landslides and debris flows, Sentinel-1 SAR data to delineate flood extent, and IMD gridded rainfall (1996–2020) to analyse rainfall spell characteristics. The event triggered widespread slope failures, localized landslide damming, and a subsequent breach, generating sediment-laden flows that inundated ~ 100 km² of the Dhemaji floodplain. A backscatter-derived Relative Flood Volume Index (RFVI) indicates spatial variability in inundation intensity, although it does not represent absolute flood volume. Rainfall analysis suggests that antecedent wetness from preceding spells preconditioned slopes, while peak daily rainfall (> 170 mm day−1) initiated landsliding. Power-law scaling shows negligible dependence of intensity on duration (R2 ≈ 0.0004), whereas cumulative rainfall exhibits a stronger relationship with duration (R2 ≈ 0.54). These results indicate distinct roles of rainfall intensity and accumulation in controlling landslide initiation and downstream flooding, respectively, highlighting the importance of compound rainfall forcing in rapid hydrogeomorphic cascades…”
    #EarthScience #RemoteSensing #Himalayas #NaturalHazards #ClimateChange #ScientificReports #GeospatialAnalysis #DisasterMitigation #Landslide #trigger #Flooding #massmovement #extremeweather #engineeringgeology #floodplain #innundation #hillslope #fluvial #pluvial #alluvial #sediment #sedimentation #hydrometeorology #ArunachalPradesh #Assam #India #Brahmaputra #risk #hazard #geology #engineeringgeology #remotesensing #earthobservation #spatialanalysis #spatiotemporal #disaster #hydrogeomorphology #workflow

    #earthscience #remotesensing #himalayas #naturalhazards #climatechange #scientificreports
  48. 💧🌏 Greg Cocks @[email protected] ·

    Comparing Multi-Model Mosaic And Multi-Model Combination Methods To Simulate Streamflow Across The Contiguous USA
    --
    doi.org/10.5194/hess-30-3945-2 <-- shared paper
    --
    H/T @cyril THEBAULT | Postdoctoral Fellow chez Earth Sciences New Zealand
    “[They] compared different multi-model approaches for streamflow simulation using 78 hydrological models across 559 catchments in the United States. [Their] results show that while multi-model combinations can slightly improve accuracy and reduce uncertainty, no single approach performs best everywhere.
    One interesting takeaway is that a carefully selected single model can perform as well as more complex multi-model approaches when it is chosen based on a comparative evaluation rather than simply inherited from legacy operational systems... 👀”
    --
    “The ability to accurately predict streamflow underpins decisions in water management, flood prevention, and sectoral planning. Traditional approaches for streamflow prediction often rely on a single model, thereby overlooking potential benefits from using multiple models. To address this limitation, this study explores alternative methods that select and combine multiple models to enhance streamflow simulations. Specifically, [they] assess[ed] the performance of multi-model mosaic methods that assign a single model to each catchment, and multi-model combination methods that merge multiple models using static or dynamic weighting schemes. The Framework for Understanding Structural Errors (FUSE) is used to create an ensemble of 78 hydrological models, which were applied to 544 catchments from the CAMELS dataset across the contiguous United States. Each of the 78 models is calibrated utilizing a composite objective function, calculated as the average of a high-flow and a low-flow performance metric, to cover a wide range of streamflow conditions. Based on [their] selection of lumped FUSE models, the results show that a carefully chosen single model from a larger ensemble can closely approach the performance of more complex multi-model strategies. Among the multi-model approaches, the combination and mosaic methods show broadly similar overall skill, although the combination approaches deliver slightly higher performance and lower sampling uncertainty. However, per-catchment differences persist, indicating that no single multi-model strategy dominates everywhere. This heterogeneity in performance makes it difficult to determine a priori which multi-model method will best represent streamflow in a given catchment…”
    #water #hydrology #streamflow #USA #CONUS #multimodel #simulation #hydrologic #model #modeling #catchments #watermanagement #waterresources #planning #watersecurity #flood #flooding #prediction #FUSE #CAMELS #spatialanalysis #spatiotemporal

    #water #hydrology #streamflow #usa #conus #multimodel
  49. 💧🌏 Greg Cocks @[email protected] ·

    A Scale-Invariance-Based Algorithm Application For Land Surface Temperature Downscaling In Denmark
    --
    doi.org/10.3390/rs18132263 <-- shared paper
    --
    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

    #urbanclimate #downscaling #landsurfacetemperature #lst #ai #machinelearning
  50. 💧🌏 Greg Cocks @[email protected] ·

    A Scale-Invariance-Based Algorithm Application For Land Surface Temperature Downscaling In Denmark
    --
    doi.org/10.3390/rs18132263 <-- shared paper
    --
    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

    #urbanclimate #downscaling #landsurfacetemperature #lst #ai #machinelearning