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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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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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DeepBase: A Deep Learning-based Daily Baseflow Dataset across the United States.

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This study generates a daily baseflow dataset for the contiguous United States using deep learning. This crucial data enhances water resource management and hydrological predictions, especially for extreme events.

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Area of Science:

  • Hydrology
  • Water Resources Management
  • Environmental Science

Background:

  • High-quality baseflow data is essential for accurate water resources modeling and management.
  • Baseflow, originating from groundwater and delayed sources, is critical for understanding streamflow, especially during dry periods and surface-groundwater interactions.

Purpose of the Study:

  • To generate a comprehensive daily baseflow dataset for the contiguous United States (CONUS) spanning 1981-2022.
  • To address the scarcity of accessible, high-quality daily baseflow data for both gauged and ungauged basins.
  • To provide a valuable resource for earth scientists, environmental managers, and water resource professionals.

Main Methods:

  • Utilized deep learning algorithms to estimate daily baseflow.
  • Developed a dataset covering 1661 basins across the CONUS.
  • Data spans from 1981 to 2022, offering a long-term perspective.

Main Results:

  • Generated a novel, high-quality daily baseflow dataset for the CONUS.
  • The dataset enhances baseflow estimation capabilities for both gauged and ungauged basins.
  • Provides a benchmark for future hydrological studies and water resource management.

Conclusions:

  • The generated dataset significantly advances understanding of the water cycle and baseflow contributions.
  • This resource is vital for improving hydrological predictions and managing water resources, particularly concerning extreme events like droughts and floods.
  • Establishes a new standard for baseflow data, fostering further research and effective water management strategies.