Runup dataset¶
This notebook is adapted from the py-wave-runup examples from Vitousek et al. (2017).
In this example, we will evaluate the compiled wave runup observations provided by Power et al (2018).
Power, H.E., Gharabaghi, B., Bonakdari, H., Robertson, B., Atkinson, A.L., Baldock, T.E., 2018. Prediction of wave runup on beaches using Gene-Expression Programming and empirical relationships. Coastal Engineering. https://doi.org/10.1016/j.coastaleng.2018.10.006
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import mean_squared_error, r2_score
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
Let’s import the Power et al (2018) runup data, which is included in this package.
import pandas as pd
# Manually define names for each column
names = [
"dataset",
"beach",
"case",
"lab_field",
"hs",
"tp",
"beta",
"d50",
"roughness",
"r2",
]
df = pd.read_csv('../pracenv/dataset/power18.csv', encoding="utf8", names=names, skiprows=1)
print(df.head())
dataset beach case lab_field hs tp beta d50 \
0 ATKINSON2017 AUSTINMER AU24-1 F 0.739477 6.4 0.102 0.000445
1 ATKINSON2017 AUSTINMER AU24-2 F 0.729347 6.4 0.101 0.000445
2 ATKINSON2017 AUSTINMER AU24-3 F 0.729347 6.4 0.115 0.000445
3 ATKINSON2017 AUSTINMER AU24-4 F 0.719217 6.4 0.115 0.000445
4 ATKINSON2017 AUSTINMER AU24-5 F 0.719217 6.4 0.115 0.000445
roughness r2
0 0.001112 1.979
1 0.001112 1.862
2 0.001112 1.695
3 0.001112 1.604
4 0.001112 1.515
Iribarren number¶
Important
The Iribarren number or the surf similarity parameter \(\epsilon\) (Battjes, 1974) is a dimensionless parameter that has been used widely to describe beach and surf zone morphodynamics and frequently features in runup predictors (e.g., Battjes, 1971, Roos and Battjes, 1976; Holman and Sallenger, 1985, Holman, 1986; Van der Meer and Stam, 1992; Stockdon et al., 2006).
where
and \(tan \beta\) = beach slope, \(H_0\) = deep water wave height, \(\lambda_0\) = deep water wavelength, \(T\) = wave period, \(g\) = acceleration due to gravity, and the subscript ‘0’ denotes deep water conditions.
First we write a Python function to compute this number:
def iribarrenNb(Tp,Hs,beta):
Lp = 9.81 * (Tp ** 2) / (2 * np.pi)
zeta = beta / (Hs / Lp)**(0.5)
return zeta
We call the function with the Power et al (2018) runup dataset
iribarren = iribarrenNb(Tp=df.tp, Hs=df.hs, beta=df.beta)
# Append a new column at the end of our iribarren values
df["iribarren"] = iribarren
Plotting Powell (2018) dataset¶
Ratio of 2% exceedance (R2%) and the significant wave height (Hs) plotted against the Iribarren number for the ten datasets.
# Set figure parameter
plt.figure(figsize=(8,6))
# Plot vertical dot lines between sediment classes
# define the line like this [x1,x2],[y1,y2]
plt.plot([0.5, 0.5],[0, 5.],'k--',linewidth=1)
plt.plot([1.5, 1.5],[0, 5.],'k--',linewidth=1)
plt.axis([0,3.5,0,4.2]);
# Plot beach types
plt.text(0.1, 3., 'dissipative', fontsize=10, rotation='vertical')
plt.text(0.6, 3., 'intermediate', fontsize=10, rotation='vertical')
plt.text(1.6, 3., 'reflective', fontsize=10, rotation='vertical')
# Plot measured data
ids1 = np.where(df.iribarren<0.5)[0]
plt.plot(df.iribarren[ids1],df.r2[ids1]/df.hs[ids1],'ko',markerfacecolor=[1.0, 0.5, 1.0], markersize=7)
ids2 = np.where(np.logical_and(df.iribarren>=0.5,df.iribarren<1.5))[0]
plt.plot(df.iribarren[ids2],df.r2[ids2]/df.hs[ids2],'ko',markerfacecolor=[1.0, 0.5, 0.5], markersize=7)
ids3 = np.where(df.iribarren>=1.5)[0]
plt.plot(df.iribarren[ids3],df.r2[ids3]/df.hs[ids3],'ko',markerfacecolor=[0.0, 0.5, 0.5], markersize=7)
plt.xlabel('Iribarren number $\epsilon$',fontsize=12)
plt.ylabel('$R_{2\%}/H_s$',fontsize=12)
plt.show()
plt.close()
# Set figure parameter
plt.figure(figsize=(8,6))
# Plot vertical dot lines between sediment classes
# define the line like this [x1,x2],[y1,y2]
plt.axis([0,7,0,12]);
# Plot measured data
plt.plot(df.hs[ids1],df.r2[ids1],'ko',markerfacecolor=[1.0, 0.5, 1.0], markersize=7,label='dissipative')
plt.plot(df.hs[ids2],df.r2[ids2],'ko',markerfacecolor=[1.0, 0.5, 0.5], markersize=7,label='intermediate')
plt.plot(df.hs[ids3],df.r2[ids3],'ko',markerfacecolor=[0.0, 0.5, 0.5], markersize=7,label='reflective')
plt.legend(loc=2, fontsize=10)
plt.xlabel('$H_s$ (m)',fontsize=12)
plt.ylabel('$R_{2\%}$ (m)',fontsize=12)
plt.show()
plt.close()
