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#streamflow — Public Fediverse posts

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

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  1. 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

  2. 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…”

  3. Coupled Hydrological And Public Health Risks From Urban Flooding - Integrated Remote Sensing, Machine Learning, And Hydrodynamic–Ecological Modelling
    --
    doi.org/10.1016/j.jhydrol.2026 <-- shared paper
    --
    youtu.be/VHzYLvSYR7k?si=5oGGPe <-- recent overview video created about the research
    --
    doi.org/10.1016/j.wroa.2025.10 <-- share (earlier) paper
    --
    H/T @RAHUL DEOPA | Research Scholar (IIT Roorkee)
    “… [U]rban floods are not merely hydraulic events; they also transport sewage, pathogens, and other contaminants across streets and communities, leading to significant public health risks…
    How do we quantify microbial contamination in near real time during a flood event, when emergency conditions make field sampling unsafe, sparse, or even impossible?...
    [The authors] explored whether Earth observation data, combined with machine learning, could bridge this critical monitoring gap. By combining Landsat-derived water surface temperature, machine learning, a coupled MIKE+ Flood–ECO Lab hydrodynamic–ecological model, and Quantitative Microbial Risk Assessment (QMRA), [they] estimated microbial concentrations (𝘌. 𝘤𝘰𝘭𝘪), simulated their fate and transport during floods, and quantified the associated human health risks.
    The takeaway: predicting flood risk isn't just about where the water goes; it's about what it's carrying and who it puts in harm's way. Earth observation and machine learning can help close that gap when it matters most, during the emergency, not weeks after…”
    #publichealth #risk #hazard #watersecurity #Floodrisk #Humanhealthrisk #Urbanflooding #Hydrodynamicmodelling #waterquality #model #modeling #SupportVectorRegression #flood #flooding #urban #city #sewage #pathogens #contaminant #disease #streets #community #quantification #remotesensing #GIS #spatial #mapping #earthobservation #spatialanalysis #water #hydrology #climatechange #extremeweather #spatiotemporal #AI #machineleraning #fateandtransport #hydrodynamic #microbial #rainfall #drainage #streamflow #topography #hydrogeomorphology #Delhi #India #floodplain

  4. Coupled Hydrological And Public Health Risks From Urban Flooding - Integrated Remote Sensing, Machine Learning, And Hydrodynamic–Ecological Modelling
    --
    doi.org/10.1016/j.jhydrol.2026 <-- shared paper
    --
    youtu.be/VHzYLvSYR7k?si=5oGGPe <-- recent overview video created about the research
    --
    doi.org/10.1016/j.wroa.2025.10 <-- share (earlier) paper
    --
    H/T @RAHUL DEOPA | Research Scholar (IIT Roorkee)
    “… [U]rban floods are not merely hydraulic events; they also transport sewage, pathogens, and other contaminants across streets and communities, leading to significant public health risks…
    How do we quantify microbial contamination in near real time during a flood event, when emergency conditions make field sampling unsafe, sparse, or even impossible?...
    [The authors] explored whether Earth observation data, combined with machine learning, could bridge this critical monitoring gap. By combining Landsat-derived water surface temperature, machine learning, a coupled MIKE+ Flood–ECO Lab hydrodynamic–ecological model, and Quantitative Microbial Risk Assessment (QMRA), [they] estimated microbial concentrations (𝘌. 𝘤𝘰𝘭𝘪), simulated their fate and transport during floods, and quantified the associated human health risks.
    The takeaway: predicting flood risk isn't just about where the water goes; it's about what it's carrying and who it puts in harm's way. Earth observation and machine learning can help close that gap when it matters most, during the emergency, not weeks after…”

  5. 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

  6. 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..."

  7. 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

  8. Watching A 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..."

    @noaa

  9. 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

  10. 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…”

  11. Recent Upper Colorado River Streamflow Declines Driven by Loss of Spring Precipitation
    --
    dx.doi.org/10.1029/2024GL109826 <-- shared 2024 paper
    --
    sciencedaily.com/releases/2024 <-- shared technical article
    --
    [it would be interesting to see the paper’s author’s updated views/analysis after the 2025/2026 (lack of) winter / record-low snowpack in the Rockies, feeding the Colorado R., etc]


    @University of Washington

  12. Low flow streamflows across the US on Friday, April 10, 2026. All gauges shown on the map below are at the 24th percentile or lower.

    #streamflow #drought #water

    dashboard.waterdata.usgs.gov/a

  13. Low flow streamflows across the US on Friday, April 10, 2026. All gauges shown on the map below are at the 24th percentile or lower.

    #streamflow #drought #water

    dashboard.waterdata.usgs.gov/a

  14. Computing Discharge Using the Entropy-Based Probability Concept [#USGS]
    --
    doi.org/10.3133/tm3A26 <-- shared USGS publication
    --
    "... PLAIN LANGUAGE SUMMARY: This report describes the steps and the theory to compute the speed and flow of water in streams using the probability concept…”
    #water #hydrology #monitoring #measurement #calculation #estimation #discharge # computation #EntropyBasedProbability #probabilityConcept #research #testing #meanchannelvelocity #USGS #streamgaging #channelvelocity #metrology #season #USA #streamflow #timeseries #hydrospatial #spatial
    @USGS

  15. Computing Discharge Using the Entropy-Based Probability Concept [#USGS]
    --
    doi.org/10.3133/tm3A26 <-- shared USGS publication
    --
    "... PLAIN LANGUAGE SUMMARY: This report describes the steps and the theory to compute the speed and flow of water in streams using the probability concept…”
    # computation
    @USGS

  16. Have You Ever Wondered What A New Concrete Stilling Well [stream gauge] Looked Like?
    --
    H/T Roy Socolow, USGS
    "While doing research on the USGS stream gaging program for a local presentation, [he] found this beautiful image of a classic stilling well from the Kentucky archives. Intakes and valve stems in perfect condition, hand pump and flush tank ready to fill, no kerosene or funky well water smell, and the clock weights on the Stevens recorder have plenty of travel room. Makes them] think of the good old days climbing down well ladders to “muck out” wells. Many of these “tanks” of instrument shelters still exist with either working intakes or as converted bubble gages. Many others have been decommissioned due to safety issues (falling or confined space hazards). Thanks to the men of the #CCC and #WPA..."
    #gage #streamflow #instrumentation #hydrology #measurement #history #waterresources #stillingwell #recording #USGS #fedscience #fedservice
    @USGS

  17. Have You Ever Wondered What A New Concrete Stilling Well [stream gauge] Looked Like?
    --
    H/T Roy Socolow, USGS
    "While doing research on the USGS stream gaging program for a local presentation, [he] found this beautiful image of a classic stilling well from the Kentucky archives. Intakes and valve stems in perfect condition, hand pump and flush tank ready to fill, no kerosene or funky well water smell, and the clock weights on the Stevens recorder have plenty of travel room. Makes them] think of the good old days climbing down well ladders to “muck out” wells. Many of these “tanks” of instrument shelters still exist with either working intakes or as converted bubble gages. Many others have been decommissioned due to safety issues (falling or confined space hazards). Thanks to the men of the and ..."

    @USGS

  18. Estimating Increased Transient Water Storage With Increases In Beaver Dam Activity
    --
    doi.org/10.3390/w16111515 <-- shared paper
    --
    “Dam building by beaver (Castor spp.) slows water movement through montane valleys, increasing transient water storage and the diversity of residence times. In some cases, water storage created by beaver dam construction is correlated to changes in streamflow magnitude and timing. However, the total amount of additional surface and groundwater storage that beaver dams may create (and, thus, their maximum potential impact on streamflow) has not been contextualized in the water balance of larger river basins..."
    #water #surfacewater #groundwater #infiltration #river #basin #hydrology #natural #beaver #wildlife #habitat #ecosystem #waterstorage #waterresources #waterbalance #streamflow #snowwaterequivalent #snowmelt #beaverdams #geomorphology #montane #landscape #landforms #basin #watershed #drainage #HAND #spatialanalysis #spatiotemporal #model #modeling #spatial #MODFLOW #reservoir

  19. Estimating Increased Transient Water Storage With Increases In Beaver Dam Activity
    --
    doi.org/10.3390/w16111515 <-- shared paper
    --
    “Dam building by beaver (Castor spp.) slows water movement through montane valleys, increasing transient water storage and the diversity of residence times. In some cases, water storage created by beaver dam construction is correlated to changes in streamflow magnitude and timing. However, the total amount of additional surface and groundwater storage that beaver dams may create (and, thus, their maximum potential impact on streamflow) has not been contextualized in the water balance of larger river basins..."

  20. This map shows normal 7-day average streamflow compared to historical streamflow for September 18. The areas in oranges and reds indicate very low streamflows.

    #streamflow #water #drought

    waterwatch.usgs.gov/index.php?

  21. This map shows normal 7-day average streamflow compared to historical streamflow for September 18. The areas in oranges and reds indicate very low streamflows.

    #streamflow #water #drought

    waterwatch.usgs.gov/index.php?

  22. Map of real-time streamflow compared to historical streamflow for September 19. The Northeast, Northwest and Hawaii are some parts of the U.S. with record low streamflows. Parts of the Great Plains have much above normal flows.

    #streamflow #water #drought

    waterwatch.usgs.gov/index.php?

  23. Map of real-time streamflow compared to historical streamflow for September 19. The Northeast, Northwest and Hawaii are some parts of the U.S. with record low streamflows. Parts of the Great Plains have much above normal flows.

    #streamflow #water #drought

    waterwatch.usgs.gov/index.php?

  24. The map of below normal 7-day average streamflow compared to historical streamflow indicates areas where streamflow is much below normal.

    #streamflow #drought

    waterwatch.usgs.gov/index.php?

  25. The map of below normal 7-day average streamflow compared to historical streamflow indicates areas where streamflow is much below normal.

    #streamflow #drought

    waterwatch.usgs.gov/index.php?

  26. USGS Water Data Centers May Soon Close, Threatening States’ Water Management

    "Across the country, the data collected at stream gauges managed by the U.S. Geological Survey are used to implement drought measures when streamflows are low, alert local authorities of floods, help administer water to users on rivers and issue pollution discharge permits required by the Clean Water Act for communities across the country."

    But more than two dozen USGS Water Science Centers that house the employees and equipment to manage those gauges and equipment will soon have their leases terminated after being targeted by the Department of Government Efficiency. Data collected by the centers inform studies of the condition of the country’s water resources and shape local and state water management plans.

    #water #USGS #StreamGauge #StreamFlow #DOGE

    insideclimatenews.org/news/290

  27. USGS Water Data Centers May Soon Close, Threatening States’ Water Management

    "Across the country, the data collected at stream gauges managed by the U.S. Geological Survey are used to implement drought measures when streamflows are low, alert local authorities of floods, help administer water to users on rivers and issue pollution discharge permits required by the Clean Water Act for communities across the country."

    But more than two dozen USGS Water Science Centers that house the employees and equipment to manage those gauges and equipment will soon have their leases terminated after being targeted by the Department of Government Efficiency. Data collected by the centers inform studies of the condition of the country’s water resources and shape local and state water management plans.

    #water #USGS #StreamGauge #StreamFlow #DOGE

    insideclimatenews.org/news/290