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#catchments — Public Fediverse posts

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

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  1. Comparing Multi-Model Mosaic And Multi-Model Combination Methods To Simulate Streamflow Across The Contiguous USA
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    doi.org/10.5194/hess-30-3945-2 <-- shared paper
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    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... 👀”
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    “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

  2. Physically-Based Hydrologic Modeling Using GRASS GIS - r.topmodel [tutorial]
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    workshop.isnew.info/omu-2024-r <-- link to technical resource / workshop / tutorial
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    “This workshop will introduce r.topmodel (Cho 2000), the GRASS GIS module for a physically-based hydrologic model called TOPMODEL (Beven 1984)..."
    #GIS #spatial #mapping #water #hydrology #Hydrologic #model #Modeling #GRASS #GRASSGIS #topmodel #workshop #tutorial #onlinelearning #TopographyModel #catchments #R #SAGA #module #ISPSO #particleswarmoptimization #algorithm #continuingeducation

  3. Avoid Backtracking And Burn Your Inputs - CONUS-Scale Watershed Delineation Using OpenMP
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    doi.org/10.1016/j.envsoft.2024 <-- shared paper
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    “HIGHLIGHTS
    • A memory-efficient watershed delineation algorithm was introduced.
    • The new algorithm uses a node-skipping depth-first search to save memory.
    • Both input and output data are stored in a shared matrix to reduce required memory.
    • It performed 95% faster than its CPU benchmark algorithm using 33% less memory.
    • It can solve 50% larger problems than what the CPU benchmark algorithm can handle..."
    #GIS #spatial #mapping #hydrology #water #code #algorithm #watershed #delineation #OpenMP #opensource #memory #efficient #CPU #benchmark #MESHED #model #modeling #largescale #catchments

  4. Vermont researchers have developed a #MachineLearning model to improve the National #Water Model's #streamflow predictions in mountainous areas, setting a precedent for nationwide application in similar terrains.

    "Assessing and Improving National Water Model Performance in #Montane Headwater #Catchments" Thursday afternoon at #AGU23.

    🔗🗓️ bit.ly/agu23121406

  5. "#Watershed models confirm that #dust nutrients delivered to mountain #catchments are transported to #freshwater systems in sufficient amounts to influence the #ecology."

    Join #University of #Utah-based Cluster Member Janice Brahney for a Dust Alliance for North America webinar discussing this as part of Dust composition and effects in the #American #West.

    Details and registration: bit.ly/45j10UE

  6. Superb resource on monitoring of global #water, including fabulous data explorer on #climate, #soil waters, #catchments and #rivers and also an annual report on global water in 2022 in the context of #climatechange and La nina.
    Find out more on:
    wenfo.org/globalwater/

  7. As snow falls and daylight dims, the final sessions of #AGU22 begin. We're settling in for a few hours of Catchment and Critical Zone Science – Understanding Ecosystems Through Monitoring, Analysis, and Experimentation.

    🔗 🗓️ : bit.ly/3hDEYch

    Convened by Big Data Cluster collaborator Jamie Shanley and features a wide range of #CriticalZone research questions looking at #watershed #catchments

    #hydrology #biogeosciences

  8. CW: Coal, climate change, environment

    8 Coal Mining Projects in Great Barrier Reef Catchments Exempted From Environmental Impact Statements - #EcoWatch

    "Eight #coal mining projects located in the #floodplains and #catchments of the #GreatBarrierReef have been considered exempt from providing #environmental impact reports by the #Queensland government, and six of the projects have already earned state approval."

    ecowatch.com/coal-projects-gre