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Typical Model Studies01:30

Typical Model Studies

295
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.
295
Rapidly Varying Flow01:24

Rapidly Varying Flow

45
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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Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

116
Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
116
Gradually Varying Flow01:29

Gradually Varying Flow

30
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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Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

53
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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Design Example: Design of an Irrigation Channel01:27

Design Example: Design of an Irrigation Channel

56
Trapezoidal channels are widely used in irrigation systems due to their cost-effectiveness and efficiency in conveying water. Trapezoidal channels feature a flat bottom and sloping sides, making them stable and easier to construct compared to other shapes. The bottom width and side slope ratio are determined based on the required flow capacity and site conditions. The side slope is kept gentle for unlined channels to prevent soil erosion.Hydraulic parameters in channel design include the flow...
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Related Experiment Video

Updated: May 24, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

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Data-driven model as a post-process for daily streamflow prediction in ungauged basins.

Jeonghyeon Choi1, Sangdan Kim2

  • 1Forecast and Contral Division, Nakdong River Flood Control Office, Ministry of Environment, 1233-88, Nakdongnam-ro, Saha-gu, Busan, 49300, Republic of Korea.

Heliyon
|March 3, 2025
PubMed
Summary

Improving streamflow prediction in ungauged basins is crucial for water management. This study enhances predictions by using data-driven models (DDMs) as post-processors for hydrological models, boosting accuracy in areas lacking streamflow data.

Keywords:
Data-driven modelHybrid modelPost-processProcess-based modelStreamflow prediction in ungauged basins

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Area of Science:

  • Hydrology
  • Water Resource Management
  • Environmental Science

Background:

  • Streamflow prediction in ungauged basins (PUB) presents significant challenges for water resource planning.
  • Existing data-driven models (DDMs) show promise but require further refinement for improved accuracy and applicability in PUB.
  • Process-based models (PBMs) also face limitations in ungauged basin predictions.

Purpose of the Study:

  • To propose and investigate a novel framework for enhancing PUB performance.
  • To utilize data-driven models (DDMs) as post-processors for both PBMs and other DDMs.
  • To assess the effectiveness of post-processing in improving streamflow prediction accuracy in ungauged basins.

Main Methods:

  • Employed the Parsimonious EcoHydrologic Model (PEHM) as a PBM.
  • Utilized Long Short-Term Memory (LSTM) and Random Forest (RF) as DDMs.
  • Applied RF and LSTM as post-processors to refine streamflow predictions from PEHM and standalone DDMs across 28 Korean basins assumed to be ungauged.

Main Results:

  • Streamflow predictions from PEHM and LSTM were initially generated for ungauged basins.
  • Post-processing with RF significantly improved the accuracy of streamflow predictions.
  • Evaluation of LSTM as a post-processor also indicated potential benefits for PUB.

Conclusions:

  • The proposed framework demonstrates the significant value of DDM post-processing for enhancing streamflow prediction in ungauged basins.
  • This approach offers a viable strategy to improve water resource management and planning in data-scarce regions.
  • Further research into various post-processing techniques can lead to more robust and accurate PUB.