Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

434
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...
434
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

286
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...
286
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

528
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...
528
Gradually Varying Flow01:29

Gradually Varying Flow

411
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...
411
Rapidly Varying Flow01:24

Rapidly Varying Flow

451
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...
451
Plane Potential Flows01:23

Plane Potential Flows

877
Plane potential flows simplify fluid motion by assuming the fluid to be irrotational and incompressible. These characteristics allow these flows to be described by a velocity potential function, ϕ, representing the flow speed in a given direction, and a stream function, ψ, that visualizes the flow path, both governed by Laplace's equation. These parameters help in estimating flow patterns, velocity distributions, and pressure fields around various hydraulic structures.
Uniform...
877

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A New Era of Collaborative MODFLOW Development.

Ground water·2026
Same author

Multi-Objective Optimization of a Hydro-Economic Model in an Over-Allocated Agricultural Basin.

Ground water·2026
Same author

Analytical Solutions for Steady-State Flow in Heterogeneous Leaky Aquifers: Verification against MODFLOW 6.

Ground water·2026
Same author

A New Groundwater Energy Transport Model for the MODFLOW Hydrologic Simulator.

Ground water·2025
Same author

Parameter ESTimation With the Gauss-Levenberg-Marquardt Algorithm: An Intuitive Guide.

Ground water·2024
Same author

Towards Improved Remedial Outcomes in Categorical Aquifers with an Iterative Ensemble Smoother.

Ground water·2023

Related Experiment Video

Updated: Jan 17, 2026

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
13:07

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression

Published on: January 15, 2022

4.4K

MF6-ADJ: A Non-Intrusive Adjoint Sensitivity Capability for MODFLOW 6.

Mohamed Hayek, Jeremy T White1, Katherine H Markovich2

  • 1INTERA Incorporated, Fort Collins, CO.

Ground Water
|September 25, 2025
PubMed
Summary
This summary is machine-generated.

MF6-ADJ offers a non-intrusive adjoint sensitivity analysis for MODFLOW 6 groundwater models. This efficient method significantly speeds up complex model calibration and diagnostics without altering core simulation code.

More Related Videos

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.5K
Microtensiometer for Confocal Microscopy Visualization of Dynamic Interfaces
08:05

Microtensiometer for Confocal Microscopy Visualization of Dynamic Interfaces

Published on: September 9, 2022

2.8K

Related Experiment Videos

Last Updated: Jan 17, 2026

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
13:07

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression

Published on: January 15, 2022

4.4K
Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.5K
Microtensiometer for Confocal Microscopy Visualization of Dynamic Interfaces
08:05

Microtensiometer for Confocal Microscopy Visualization of Dynamic Interfaces

Published on: September 9, 2022

2.8K

Area of Science:

  • Hydrogeology
  • Computational Hydrology
  • Numerical Modeling

Background:

  • Adjoint sensitivity analysis is efficient for evaluating parameter influence in hydrologic models.
  • Intrusive adjoint implementations require extensive code modification, hindering adoption.
  • A non-intrusive approach is needed for broader accessibility and maintainability.

Purpose of the Study:

  • Introduce MF6-ADJ, a non-intrusive adjoint sensitivity tool for MODFLOW 6.
  • Enable efficient sensitivity analysis without altering the forward model code.
  • Support complex groundwater modeling workflows.

Main Methods:

  • Leveraged the MODFLOW 6 Application Programming Interface (API) for non-intrusive interaction.
  • Supported confined/unconfined flow, structured/unstructured grids, and Standard/Newton-Raphson solvers.
  • Computed sensitivities of heads, fluxes, and residuals for key parameters.

Main Results:

  • MF6-ADJ computes sensitivities at each node for detailed analysis.
  • Achieved excellent agreement with analytical solutions and finite-difference methods.
  • Demonstrated speedups of hundreds to tens of thousands of times compared to direct methods.

Conclusions:

  • MF6-ADJ provides an accessible and maintainable solution for adjoint sensitivity analysis.
  • Enables efficient and scalable sensitivity analysis in complex groundwater modeling.
  • Facilitates improved model diagnostics and calibration.