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9 results for “GregCocks”

  1. Bits On Tape @[email protected]

    🔬 The best bytes of #science & #tech across the #fediverse

    “Table Mountain Radio Quiet Zone - In Place Since 1954 In Colorado - Is ‘Suddenly In The Media’ | Colorado Cell Complaints Pile Up As Table Mountain Radio Quiet Zone Limits Squeeze AT&T, Verizon And T-Mobile
    --
    its․nti…”

    techhub.social/@GregCocks/1174

    🤖 via RSS feed. Not an endorsement.

    #science #tech #fediverse
  2. 💧🌏 Greg Cocks @gregcocks

    NOAA’s National Mussel Watch Program [USA]
    --
    coastalscience.noaa.gov/scienc <-- shared National Mussel Watch Program home page
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    doi.org/10.1007/s10661-008-067 <-- shared 2008 paper
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    H/T @NOAA Centers for Coastal Ocean Science
    “Major hurricanes bring more than just storm surge - they can shift coastal pollution in an instant. That’s when NOAA’s Mussel Watch Program steps in!
    “For 40 years, MWP has provided critical baseline data to assess how severe storms impact our coastal environments. Following major hurricanes like Katrina, Rita, and Idalia, MWP researchers deployed to collect tissue and sediment samples, comparing before-and-after data to track pollution changes and safeguard coastal health…”
    --
    “PAPER’S ABSTRACT: Hurricane Katrina made landfall on August 29, 2005 and caused widespread devastation along the central Gulf Coast states. Less than a month later Hurricane Rita followed a similar track slightly west of Katrina’s. A coordinated multi-agency response followed to collect water, sediment and tissue samples for a variety of chemical, biological and toxicological indicators. The National Oceanic and Atmospheric Administration’s National Status and Trends Program (NS&T) participated in this effort by measuring chemical contamination in sediment and oyster tissue as part of the Mussel Watch Program, a long-term monitoring program to assess spatial and temporal trends in a wide range of coastal pollutants. This paper describes results for contaminants measured in oyster tissue collected between September 29 and October 10, 2005 and discusses the results in the context of Mussel Watch and its 20-year record of chemical contamination in the region and the nation. In general, levels of metals in oyster tissue were higher then pre- hurricane levels while organic contaminants were at or near record lows. No contaminant reported here exceeded the FDA action level for food safety…”

    @NOAA | @NOAA Centers for Coastal Ocean Science

    #40yearsofmwp #mwp #musselwatchprogram #usa #pollution #chemical
  3. 💧🌏 Greg Cocks @[email protected]

    Spatiotemporal Distribution Of Microplastics In The Protected Transboundary Thaya/Dyje River (Central Europe) - Implications Of Hydrological Connectivity For Microplastic Monitoring
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    doi.org/10.3389/fenvs.2026.194 <-- shared paper
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    phys.org/news/2025-04-alarming <-- shared technical article
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    ehn.org/plastic-waste-hides-in <-- shared technical media article
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    bbc.com/news/av/world-europe-6 <-- shared media article
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    H/T @Ronald Pöppl | Senior Researcher (BOKU University Vienna)
    “Microplastic (MP) concentrations in rivers are highly dynamic, varying across space, depth, and hydrological conditions, yet conventional monitoring approaches often capture only a fraction of this variability. This study investigated the spatio-temporal distribution of microplastics along a 30 km reach of the hydrologically regulated Thaya/Dyje River within the transboundary Thayatal/Podyjí National Park. A total of 35 large-volume water samples (approximately 5 m3 each) were collected during November 2024 and July 2025, including longitudinal, lateral, near-bed, and tributary sampling. A comprehensive QA/QC procedure, including blank correction and recovery experiments (n = 22), was applied to support the reliability of the dataset. Mean MP concentrations were relatively low, averaging 8.18 ± 5.32 MP m−3 in the Thaya/Dyje River and 3.14 ± 0.94 MP m−3 in tributaries entering the park. However, pronounced longitudinal, lateral, and inter-campaign variability was observed. MP concentrations showed relationships with flow velocity and travel-time-corrected discharge, while the direction and strength of hydrological relationships differed between campaigns. The relationship between flow velocity and MP concentration also differed significantly between campaigns suggesting shifts between retention- and remobilization-dominated transport. Thus, transport patterns varied with hydrological conditions, including antecedent flow histories, rather than following simple downstream accumulation. These findings highlight the importance of spatially resolved, large-volume sampling for riverine microplastic monitoring and show that variations in transport, temporary storage, remobilization, and incomplete mixing can be interpreted within a hydrological connectivity framework. The relatively low contamination levels also offer a baseline for future monitoring of microplastic pollution in protected and comparable freshwater environments…”
    #spatial #mapping #GIS #spatiotemporal #spatialanalysis #microplastics #Transboundary #Thaya #Dyje #Thayatal #Podyjí #europe #CzechRepublic #Austria #River #water #nationalpark #hydrology #waterquality #waterresources #HydrologicalConnectivity #monitoring #nurdles #HiddenPollution #pollution #PathogenCarriers #sampling #instrumentation #transport #network #flow #streamflow #riverine #remobilisation #contamination #freshwater #environments #habitat #ecosystems #ecology #humanimpacts

    #spatial #mapping #gis #spatiotemporal #spatialanalysis #microplastics
  4. 💧🌏 Greg Cocks @[email protected]

    Analysing Operational Aspects Of River Ice Monitoring With Earth Observation For Flood Early Warning In Canada
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    doi.org/10.1144/gh2025-18 <-- shared paper
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    phys.org/news/2014-04-river-ic <-- shared technical article
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    gwf.usask.ca/impc/science/rese <-- shared technical article
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    H/T @arjen Haag | Sr. Researcher / consultant bij Deltares (Operational Water Management & Early Warning)
    “[The authors] assess operational and spatiotemporal constraints in the use of satellite data (specifically the Radarsat Constellation Mission; RCM) for the purpose of river ice monitoring and ice jam early warning, applied over three Canadian provinces with operational #DelftFEWS platforms. The main take-away is simple and obvious, but can never be stressed enough: know the expected latency of any data you (or other users) rely on, optimize retrieval and processing based on this and align it with operational use…”
    --
    “River ice jams are a major contributor to flood risk in cold regions. Satellite remote sensing (i.e. Earth observation) is used across Canada to provide timely information on the status of river ice. Methods and algorithms to classify various stages of river ice from the Radarsat Constellation Mission (RCM) are available, but the operational implementation of these, especially the integration into larger flood forecasting and early warning systems, requires specific expertise, software and computational resources. [The authors] describe the setup designed and employed operationally for the relevant agencies of Alberta, the Northwest Territories and the Yukon in Canada, as well as a framework to analyse various factors relevant for operational monitoring purposes. This includes the spatiotemporal coverage of the data and the timeliness (latency) with which these become available, critical elements in the support of decision-making relating to floods. This analysis reveals that while most images have a relatively low latency, often well below the 2 h threshold expected for images relating to disaster management purposes, there is also a large variation across time and space, with images also exceeding this threshold. This should be considered in the design and operations of monitoring systems, to ensure all relevant imagery is included…”
    #water #hydrography #cryosphere #ice #icejam #flood #flooding #forecasting #earlywarning #risk #hazard #Canada #GIS #spatial #mapping #remotesensing #earthobservation #RadarsatConstellationMission #RCM #Alberta #Yukon #NorthwestTerritories #monitoring #spatialanalysis #spatiotemporal #planning #imagery #imageryanalysis #naturalhazard #disastermanagement
    @Canadian Space Agency | @natural Resources Canada (NRCan) | @NRCan Science

    #delftfews #water #hydrography #cryosphere #ice #icejam
  5. 💧🌏 Greg Cocks @gregcocks

    As Above, So Below? A Framework For Integrating Long-Term Water Quantity Trends Reveals Divergent Patterns In Groundwater And Low Streamflow Across The United States
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    doi.org/10.1088/3033-4942/ae89 <-- shared paper
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    usgs.gov/mission-areas/water-r <-- shared @USGS (technical) news release
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    H/T @Renae Logston | Senior Environmental Scientist | Translating Decades of Water Data into Operational Decisions & Measurable Cost Savings
    “The West and Southwest are drying both above and below ground. The Upper Midwest is getting wetter. And in the High Plains and the Mississippi Alluvial Plain, the rivers and the aquifers have been telling different stories for decades.
    That is the regional picture [from the authors (link above). They] compared trends in annual 7-day low streamflow with trends in annual mean depth to groundwater across the U.S., over windows of 2000 to 2020, 1990 to 2020, and 1980 to 2020.
    The national tilt: low streamflow has tended to rise while groundwater levels have tended to fall. At individual sites, the two often disagreed, with a skew toward wetter low flows and deeper water tables.
    The mismatch matters for anyone who treats a river gauge as a stand-in for the aquifer beneath it. In regions with long-term mismatches, like the High Plains, a river’s low-flow trend does not tell you what the water table is doing.
    One methods note worth keeping: agreement improved with longer records and regional grouping, and hydrologic landscape regions (climate, relief, and geology combined) gave the best agreement of the five groupings tested. Window length and grouping are choices, and they shape the answer.
    Where does your system sit: rivers and aquifers moving together, or apart?...”

    @USGS

    #hydrology #groundwater #dataanalysis #timeseries #waterscience #water
  6. 💧🌏 Greg Cocks @[email protected]

    CoSeismic Shallow Landslide Distributions Reveal Patterns Of Regolith Thickness In High Mountains
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    doi.org/10.1029/2026JF009250 <-- shared paper
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    H/T @tristram Hales | Professor of Environmental Hazards
    “[This paper] paper seeks to develop a shallow earthquake-triggered landslide model. It has really pushed [the H/T’s] thinking around the role of regolith in controlling landsliding in high mountains…”
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    “... PLAIN LANGUAGE SUMMARY: Large earthquakes can trigger thousands of landslides, moving regolith (the loose soil and weathered rock that mantles hillslopes) into rivers and valleys. Predicting where these landslides occur is difficult because regolith thickness varies across mountain landscapes and is rarely measured directly. [The authors] developed a new model, ShallowLandslider, that combines slope-stability physics with statistical methods to simulate populations of earthquake-triggered shallow landslides. [They] tested the model in two regions affected by the 2015 Gorkha earthquake in Nepal and compared simulated landslides with a mapped inventory. The[ir] model performed best when it included spatial variations in regolith thickness. In the steep, high-relief landscape, landslide occurrence was reproduced most successfully when regolith thickness decreased with elevation. In the lower-relief landscape, the best results occurred when thicker regolith was concentrated in convergent hollows where sediment accumulates. The simulations also matched observations most closely for cohesion values typical of shallow colluvium, supporting the interpretation that most Gorkha landslides were shallow, regolith-sourced failures. These results show that coseismic landslide patterns depend not only on ground shaking and slope steepness, but also on how regolith is organized across the landscape. Landslide inventories can help evaluate competing hypotheses of regolith distribution where direct observations are sparse…”
    #engineeringgeology #massmovement #landslide #inventory #regolith #thickness #soil #cohesion #colluvium #shallow #geology #coseismic #ShallowLandslider #geomorphology #hydrogeomorphology #earthquake #triggered #earthquaketriggered #Nepal #Gorkha #model #modeling #mountain #slope #steepness #mountainous #erosion #weathering #river #valley #riverine #fluvial #prediction #slopestability #geostatistics #GIS #mapping #spatial #spatialanalysis #spatiotemporal

    #engineeringgeology #massmovement #landslide #inventory #regolith #thickness
  7. 💧🌏 Greg Cocks @[email protected]

    Table Mountain Radio Quiet Zone - In Place Since 1954 In Colorado - Is ‘Suddenly In The Media’ | Colorado Cell Complaints Pile Up As Table Mountain Radio Quiet Zone Limits Squeeze AT&T, Verizon And T-Mobile
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    its.ntia.gov/research/table-mo <-- shared details of the Table Mountain Radio Quiet Zone
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    geodesy.noaa.gov/GRAV-D/2009Wo <-- shared overview of the long-standing research effort
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    timescall.com/2012/03/21/table <-- shared media article
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    wirelessestimator.com/articles <-- shared Oct 5th 2026 technical media article
    --
    [note that it is only one of two designated radio quiet zones in the United States, alongside the Green Bank Quiet Zone in West Virginia]
    “The Table Mountain Field Site and Radio Quiet Zone is located approximately 15 km north of the Department of Commerce campus in Boulder, CO. The site is approximately 4 km (2.5 miles) in a north-south direction by 2.4 km (1.5 miles) east-west and has an area of approximately 680 hectares (1,700 acres). While Table Mountain is owned by the Department of Commerce, ITS has been designated to oversee site management and operations. Table Mountain is expansive enough to support a multitude of radio frequency and non–radio frequency research programs overseen by ITS, NIST, NOAA, and the U.S. Geological Survey.
    To preserve this rare asset, Table Mountain is protected by law as one of only two federally mandated radio quiet zones within the United States where external signals are restricted by state law and federal regulation. The regulations specify field strength limits at various distances and frequencies and recommend advance coordination for transmitters within a n 80 km (50 mi) radius of the site…”
    #Longmont #Boulder #Colorado #Niwot #TableMountain #federalfacility #research #decades #longstanding #TableMountainRadioQuietZone #RadioQuietZone #exclusion #fedscience #fedservice #publicgood #publicsafety #asset #standards #radiofrequency #cellphone #mobilephone #commercial #forprofit #providers #compliance #greatergood #mediascrutiny #media
    @NOAA | @doc | @NTIA | @USGS | @NIST | @FCC

    #longmont #boulder #colorado #niwot #tablemountain #federalfacility
  8. 💧🌏 Greg Cocks @gregcocks

    I could not agree more with Randy!! :)
    (and Charlie is just a _____)
    --
    Indeed, things like this were one of the selection criteria for my new-to-me vehicle...

  9. 💧🌏 Greg Cocks @[email protected]

    Integrated Multi-Criteria Decision Analysis And Bivariate Statistical Modeling For Landslide Susceptibility Zonation In The Northwestern Ethiopian Highlands
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    doi.org/10.1007/s44475-026-000 <-- shared paper
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    trtafrika.com/english/article/ <-- shared (example) media article
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    linkedin.com/pulse/how-climate <-- shared LI technical article
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    eastafricanreview.com/2024/08/ <-- shared media article
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    H/T @Yohannes Dessalegn Girma | Production Geologist at Allied Gold Corporation
    “Landslides represent a critical geo-environmental hazard in the Ethiopian highlands, specifically within the Telemt, Janamora, and Beyeda districts, where they frequently result in fatalities and the destruction of vital infrastructure and agricultural land. This study integrated Geographic Information Systems (GIS) and remote sensing data to perform landslide susceptibility mapping (LSM) using two distinct modeling approaches: the Frequency Ratio (FR) and the Analytical Hierarchy Process (AHP). A total of 140 data points (98 for training (70%) and 42 for testing (30%)) were considered for modeling and validation. Eleven geo-environmental conditioning factors: Slope, rainfall, lithology, density of lineaments, curvature, aspect, normalized difference vegetation index, land use/land cover, distance to drainage, drainage density, and soil were analyzed to evaluate their influence on landslide occurrence. According to the AHP model, 13.69% and 26.91% of the study area were classified as very high and high susceptibility zones, respectively. In comparison, the FR model identified 12.09% as very high and 23.78% as high susceptibility zones. Both models underscored that steep slopes, porphyritic basalt unit, high lineament density, proximity to drainage systems, and concave terrain curvature are the primary drivers of slope instability. Conversely, gentle slopes, sandstone unit, and dense vegetation cover were strongly associated with low-susceptibility regions. Model performance was validated using the Receiver Operating Characteristic (ROC) curve, yielding an Area Under the Curve (AUC) value of 0.812 for the AHP model and 0.793 for the FR model, alongside a prediction rate AUC value of 77.4% (0.774) for the AHP model and 70.4% for the FR model, confirming the predictive reliability of both methods. These results provide essential geospatial intelligence for policy-makers and stakeholders to guide disaster risk management, sustainable land-use planning, and targeted rehabilitation efforts in the region…”
    #massmovement #landslide #Ethiopia #water #rainfall #precipitation #climatechange #GIS #spatial #mapping #spatialanalysis #topography #geology #soils #hydrogeomorphology #model #modeling #engineeringgeology #flowchart #methodology #fieldvalidation #risk #hazard #publicsafety #riskassessment #remotesensing #earthobservation #rockfall #debrisflow #earthflow #slope #aspect #landcover #landuse #drainage #hydrology #susceptibility

    #massmovement #landslide #ethiopia #water #rainfall #precipitation
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