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Empirical wave runup models implemented in Python

Project description

Empirical wave runup models implemented in Python for coastal engineers and scientists.
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Background

Wave runup refers to the final part of a wave’s journey as it travels from offshore onto the beach. It is observable by anyone who goes to the beach and watches the edge of the water “runup” and rundown the beach. It is comprised of two components:

  • setup: the height of the time averaged superelevation of the mean water level above the Still Water Level (SWL)

  • swash: the height of the time varying fluctuation of the instantaneous water level about the setup elevation

Setup, swash and other components of Total Water Level (TWL) rise are shown in this handy figure below.

./docs/_static/VitousekDoubling2017Fig1.jpg
Figure from Vitousek et al. (2017) [1]

Wave runup can contribute a significant portion of the increase in TWL in coastal storms causing erosion and inundation. For example, Stockdon et al. (2006) [2] collated data from numerous experiments, some of which showed wave runup 2% excedence heights in excess of 3 m during some storms.

Given the impact such a large increase in TWL can have on coastlines, there has been much research conducted to try improve our understanding of wave runup processes. Although there are many processes which can influence wave runup (such as nonlinear wave transformation, wave reflection, three-dimensional effects, porosity, roughness, permeability and groundwater) [3], many attempts have been made to derive empirical relatinoships based on easily measurable parameters. Typically, empirical wave runup models include:

  • Hs: significant wave height

  • Tp: peak wave length

  • beta: beach slope

This python package attempts to consolidate the work done by others in this field and collate the numerous empirical relationships for wave runup which have been published.

Installation

Installation of py-wave-runup can be done with pip:

pip install py-wave-runup

Usage

from py_wave_runup import models

model_sto06 = models.Stockdon2006(Hs=4, Tp=12, beta=0.1)

model_sto06.R2     # 2.54
model_sto06.setup  # 0.96
model_sto06.sinc   # 2.06
model_sto06.sig    # 1.65

Documentation

Documentation is located at https://py-wave-runup.readthedocs.io.

Contributing

  1. Fork it (https://github.com/chrisleaman/py-wave-runup/fork)

  2. Create your feature branch (git checkout -b feature/fooBar)

  3. Commit your changes (git commit -am 'Add some fooBar)

  4. Push to the branch (git push origin feature/fooBar)

  5. Create a new Pull Request

License

Distributed under the GNU General Public License v3.

References

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