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Rapidly Varying Flow01:24

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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Gradually Varying Flow01:29

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Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
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Watershed Planning within a Quantitative Scenario Analysis Framework
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Towards a Transferable UAV-Based Framework for River Hydromorphological Characterization.

Mónica Rivas Casado1, Rocío Ballesteros González2, José Fernando Ortega3

  • 1School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire MK430AL, UK. m.rivas-casado@cranfield.ac.uk.

Sensors (Basel, Switzerland)
|September 29, 2017
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Summary

Unmanned Aerial Vehicle (UAV) frameworks can harmonize river hydromorphology assessments across diverse European regions. While effective in some areas, further development is needed for broader feature identification and consistent EU-wide application.

Keywords:
artificial neural networkhydromorphologyintercalibrationphotogrammetryunmanned aerial vehiclewater framework directive

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Area of Science:

  • River ecology
  • Geomorphology
  • Environmental monitoring

Background:

  • Harmonizing river hydromorphology characterization across EU member states is challenging due to diverse protocols, partly driven by the EU Water Framework Directive (WFD).
  • Remote sensing offers potential for standardization, but resolution limitations hinder accurate assessment of key hydromorphological features.
  • High-resolution Unmanned Aerial Vehicle (UAV) photography presents a promising solution to overcome remote sensing resolution limitations.

Purpose of the Study:

  • To evaluate the transferability and accuracy of an existing UAV-based hydromorphological characterization framework across different EU eco-regions.
  • To assess the framework's performance in identifying key hydromorphological features using Artificial Neural Networks (ANNs).

Main Methods:

  • Application of a UAV-based framework utilizing automated feature recognition via Artificial Neural Networks (ANNs).
  • Testing the framework across three distinct fluvial settings representing Central-Baltic, Mediterranean, and Very Large Rivers Geographical Intercalibration Groups (GIGs).

Main Results:

  • The UAV framework achieved over 70% accuracy in feature identification in the Central-Baltic and Mediterranean GIGs.
  • Accuracy decreased to 50% in the Very Large Rivers GIG, indicating regional variability in performance.
  • The framework successfully identified features such as vegetation, deep/shallow water, riffles, side bars, and shadows.

Conclusions:

  • The UAV-based framework shows promise for harmonized hydromorphological assessment, demonstrating transferability across different EU GIGs.
  • Further algorithm refinement is necessary for accurate identification of a wider range of features, including chutes, structures, and erosion.
  • An objective, fit-for-purpose hydromorphological characterization framework is needed for consistent EU-wide adoption and data comparability.