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Advances in Water Resources Engineering pp 247–295Cite as

Hydraulic Modeling Development and Application in Water Resources Engineering

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Part of the book series: Handbook of Environmental Engineering ((HEE,volume 14))

Abstract

The use of modeling has become widespread in water resources engineering and science to study rivers, lakes, estuaries, and coastal regions. For example, computer models are commonly used to forecast anthropogenic effects on the environment, and to help provide advanced mitigation measures against catastrophic events such as natural and dam-break floods. Linking hydraulic models to vegetation and habitat models has expanded their use in multidisciplinary applications to the riparian corridor. Implementation of these models in software packages on personal desktop computers has made them accessible to the general engineering community, and their use has been popularized by the need of minimal training due to intuitive graphical user interface front ends. Models are, however, complex and nontrivial, to the extent that even common terminology is sometimes ambiguous and often applied incorrectly. In fact, many efforts are currently under way in order to standardize terminology and offer guidelines for good practice, but none has yet reached unanimous acceptance. This chapter provides a view of the elements involved in modeling surface flows for the application in environmental water resources engineering. It presents the concepts and steps necessary for rational model development and use by starting with the exploration of the ideas involved in defining a model. Tangible form of those ideas is provided by the development of a mathematical and corresponding numerical hydraulic model, which is given with a substantial amount of detail. The issues of model deployment in a practical and productive work environment are also addressed. The chapter ends by presenting a few model applications highlighting the need for good quality control in model validation.

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Notes

  1. 1.

    The independent variables are the inputs and the forcing quantities, such as boundary conditions and spatial coordinates; the dependent variables are the outputs and effects, such as flow velocity and water surface elevation.

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Acknowledgments

The author is grateful to Chad Wagner, USGS, Louisville, KY, for kindly providing the bathymetry and experimental measurements used for the Olmsted Lock and Dam case, and for his assistance in data preparation and interpretation. The model developed and presented in this work (System for Transport and River Modeling, SToRM) was supported by the National Research Program of the US Geological Survey. The SToRM model and corresponding documentation are available for free download from the iRIC Project Web pages at http://i-ric.org/en/.

Author information

Authors and Affiliations

  1. US Geological Survey Geomorphology and Sediment Transport Laboratory, 4620 Technology Drive, Suite 400, 80403, Golden, CO, USA

    Francisco J.M. Simões PhD

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  1. Francisco J.M. Simões PhD
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Editor information

Editors and Affiliations

  1. Colorado State University, Fort Collins, Colorado, USA

    Chih Ted Yang

  2. Rutgers University, New Brunswick, New Jersey, USA

    Lawrence K. Wang

Glossary

CFD

Short for computational fluid dynamics, it is a discipline that uses numerical methods and algorithms to solve fluid mechanics problems with computers.

CGNS

A standard for the storage and retrieval of digital data produced in CFD applications. It stands for CFD general notation system.

Computational cell

Each individual point, or volume, of the lattice (computational grid) transforming the continuous real-world domains into its discrete counterpart, suitable for numerical evaluation and implementation on digital computers.

Digital terrain model

Also called a digital elevation model (DEM), it is a 3D digital representation of a terrain’s surface.

Eddy viscosity

A method to model the transfer of momentum caused by turbulent eddies that is mathematically similar to momentum transfer due to molecular diffusion, and that consists in replacing the fluid viscosity ν by an effective turbulent viscosity, ν t , also called the eddy viscosity.

Froude number (Fr)

A dimensionless quantity defined as the ratio of a characteristic velocity to a gravitational wave velocity.

Godunov scheme

A conservative finite-volume numerical scheme used in the solution of partial differential equations, which solves exact or approximate Riemann problems at inter-cell boundaries.

Graphical user interface

Type of computer-user interface that allows the user to interact with a computer program using pointing hardware devices, graphical icons, and other visual indicators.

Hydrology

The study of flow of water over the Earth’s surface

k–ε model

A turbulence model based on solving two differential transport equations, one for the turbulent kinetic energy k and the other for the rate of turbulent dissipation ε.

Model

A idealized representation of a system.

MUSCL

Short for modified upwind scheme for conservation laws, it is a method to describe (reconstruct) the states of the variables in a computational cell based on the cell-averaged states (and their gradients) obtained in the previous time step.

Navier–Stokes equations

Partial differential equations arising from Newton’s second law (conservation of momentum) that describe the motion of fluids.

Runge–Kutta methods

A family of implicit and explicit iterative methods used in temporal discretization for the approximation of solutions of ordinary differential equations.

Supercritical flow

A flow whose velocity is larger than the wave velocity, therefore with Fr > 1.

TVD

Total variation diminishing (TVD) is a property of certain discretization schemes used to solve hyperbolic partial differential equations that do not increase the total variation of the solution from one time step to the next.

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Simões, F. (2015). Hydraulic Modeling Development and Application in Water Resources Engineering. In: Yang, C., Wang, L. (eds) Advances in Water Resources Engineering. Handbook of Environmental Engineering, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-319-11023-3_6

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  • DOI: https://doi.org/10.1007/978-3-319-11023-3_6

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