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Related Concept Videos

Rapidly Varying Flow01:24

Rapidly Varying Flow

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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...
60
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

63
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...
63
Gradually Varying Flow01:29

Gradually Varying Flow

44
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...
44
Hydraulic Jump: Problem Solving01:16

Hydraulic Jump: Problem Solving

59
To analyze a hydraulic jump in a rectangular channel with a flow speed of 6 meters per second, follow these steps:Calculate Effective Upstream Velocity:When the downstream gate closes, a hydraulic jump forms, traveling upstream at 2 meters per second. This wave speed combines with the initial channel flow velocity, creating an effective upstream velocity.Identify Flow Velocities Before and After the Hydraulic Jump:Upstream of the hydraulic jump, the effective flow velocity includes both the...
59
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

70
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...
70
Underflow Gates01:30

Underflow Gates

48
Underflow gates are vital for controlling water flow in irrigation canals. The three main types of underflow gates — vertical, radial, and drum gates — serve different purposes while ensuring effective flow management. Vertical gates move up and down, generating a free-flowing water jet; radial gates pivot to regulate the flow; and drum gates rotate for precise adjustments. The flow through these gates is influenced by downstream conditions, resulting in free or drowned outflow.Free and...
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Related Experiment Video

Updated: Jun 26, 2025

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation
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Interpretable baseflow segmentation and prediction based on numerical experiments and deep learning.

Qiying Yu1, Chen Shi2, Yungang Bai3

  • 1School of Water Conservancy and Transportation, Zhengzhou University, Henan, China; Xinjiang Institute of Water Resources and Hydropower Research, Xinjiang, 830049, China.

Journal of Environmental Management
|May 11, 2024
PubMed
Summary

This study introduces a novel method combining Grey Wolf Optimizer Digital Filter Method (GWO-DFM) and Long Short-Term Memory (LSTM) to analyze baseflow in high-cold mountains. The findings reveal key climate and land factors influencing baseflow, crucial for water resource management.

Keywords:
Baseflow predictionBaseflow separationGrey wolf optimization digital filterHYSEPLSTM-SHAP

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Area of Science:

  • Hydrology and Water Resource Management
  • Environmental Science
  • Artificial Intelligence in Environmental Modeling

Background:

  • Baseflow is vital for runoff stability in high-cold mountainous regions, but its variability is poorly understood due to data scarcity and complex influencing factors.
  • Selecting appropriate baseflow separation methods and evaluating climate/land surface impacts are significant challenges in these data-scarce environments.

Purpose of the Study:

  • To develop and apply a robust method for baseflow separation and prediction in high-cold mountainous regions.
  • To investigate the influence of meteorological factors and underlying surface changes on baseflow variability and seasonal distribution.
  • To clarify the interpretability of the Long Short-Term Memory (LSTM) model in baseflow forecasting.

Main Methods:

  • Utilized the Grey Wolf Optimizer Digital Filter Method (GWO-DFM) for rapid and optimal baseflow separation, identifying an average of three filter methods as superior.
  • Employed the Long Short-Term Memory (LSTM) neural network model for baseflow prediction, achieving Nash-Sutcliffe efficiency coefficients over 0.78.
  • Integrated 63-year flow data, meteorological data (ERA5-land), and MODIS data (NDVI) for comprehensive analysis.

Main Results:

  • GWO-DFM effectively identified optimal filtering parameters, with the arithmetic average of Chapman, Chapman-Maxwell, and Eckhardt filters proving most suitable.
  • Baseflow sources are primarily linked to precipitation infiltration, glacier frozen soil, and seasonal ponding.
  • Solar radiation, temperature, precipitation, and NDVI were identified as the dominant factors affecting baseflow changes, with solar radiation, temperature, and NDVI showing the most significant influence.

Conclusions:

  • The integrated GWO-DFM and LSTM approach provides a reliable framework for baseflow analysis in data-scarce high-cold mountainous regions.
  • Understanding the complex interplay of climate and land surface factors is essential for predicting baseflow dynamics.
  • This research offers valuable insights for sustainable water resource management in mountainous basins facing environmental changes.