short term wave

8/2/2019 short term wave

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57

Chapter 3

Short-Term Wave Analysis

3.1 Introduction

In analysis of wave data, it is important to distinguish between short-

term and long-term wave analysis. Short-term analysis refers to analysisof waves that occur within one wave train1 or within one storm (Figs. 2.3

and 3.1). Long-term analysis refers to the derivation of statistical

distributions that cover many years. To emphasize the difference between

the two, they have been arranged into two separate chapters. Short-term

wave analysis is discussed here and long-term wave analysis in Ch. 4.

It was stated in Ch. 2 that the complex sea surface appears to defy

scientific analysis. A number of simplifying assumptions must be made to

describe short-term recordings of the water surface and research has

shown that a number of excellent approximations can be made (Goda,

1970, 1985, 2000). Because there are many sizes of waves in any wave

record we will need to resort to statistical analysis.We define z as the instantaneous water level related to a datum, and

as the difference between the instantaneous water and the mean water

level; the values of z and are functions of location (x, y) and time (t). A

water level record such as shown in Fig. 3.1 therefore represents the

process z(t) at a specific location. Water level records are normally not

continuous, because they are recorded digitally. Thus z is only sampled

at sampling intervals of t. A record of length tR then consists of N

samples zj taken at times jt, where 1 j N.

1 Series of waves.


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58 Introduction to Coastal Engineering and Management

Fig. 3.1 Water level record

The water level record in Fig. 3.1 is one realization of the process z(t).

We will call this z1(t). To understand the relevant terminology, imagine abasin of water with a wave generator at one end. We start up the wave

generator steered by a certain drive signal, and after 5 minutes we

measure the water level in the middle of the basin for 30 seconds. That

produces a short-term record z(t) as shown in Fig. 3.1. Now we shut off

the generator and wait for the water to become quiet. Then we start the

generator up again (with the same drive signal) and after 5 minutes we

measure another 30 second record at the same location. The second

record is a second realization, z2(t) of the same process. We could repeat

this many times to produce an infinite number of realizations (records) of

the process z(t), which in this case represents water surface fluctuations

in the middle of a basin, after 5 minutes of wave generation. Three

realizations of this process are shown in Fig. 3.2. The complete set of K

realizations zk(t) is called an ensemble.

We can take all the values of z at t = jt in the k realizations and

calculate statistical parameters such as ensemble mean jz and ensemble

standard deviation j, where

2, ,

1 1

1 1( )

K K

j k j j k j j

k k

z z and z zK K

= =

= = (3.1)

Ensemble skewness and kurtosis can also be determined. If none of

these ensemble parameters vary in time, the process is called stationary;

if only the ensemble mean and standard deviation are constant, the system

- 0 . 0 4

- 0 . 0 3

- 0 . 0 2

- 0 . 0 1

0

0 .0 1

0 .0 2

0 .0 3

0 .0 4

0 5 1 0 1 5 2 0 2 5 3 0

T i m e ( s e c )

WaterLevel(m)


3/31

Chapter 3 Short-Term Wave Analysis 59

Fig. 3.2 Ensemble of three realizations of a stationary and ergodic process

-0. 04

-0. 03

-0. 02

-0. 01

0

0. 01

0. 02

0. 03

0. 04

0 5 1 0 1 5 2 0 2 5 3 0

Time (sec)

WaterLevel(m)

- 0 . 0 4

- 0 . 0 3

- 0 . 0 2

- 0 . 0 1

0

0 . 0 1

0 . 0 2

0 . 0 3

0 . 0 4

0 5 1 0 1 5 2 0 2 5 3 0

Time (sec)

WaterLevel(m)

- 0 . 0 4

- 0 . 0 3

- 0 . 0 2

- 0 . 0 1

0

0 . 0 1

0 . 0 2

0 . 0 3

0 . 0 4

0 5 1 0 1 5 2 0 2 5 3 0

Time (sec)

WaterLevel(m)


4/31


is said to be weakly stationary. If the time average for each realization

)t(z k is equal to the ensemble average kz , where

( )kz t ,1 1 1

1 1 1N K N

j k k j

j k j

z and z zN K N= = =

= = (3.2)

the process is called ergodic. For the ensemble in Fig. 3.2, the process is

stationary and ergodic. It is stationary because, for example, jz and j,

do not increase with time and ergodic because )(tz k for each realization

is equal to kz , the ensemble average.

What does this mean in practice? A water level record is always only

a single realization of the process to be studied. We have no other

realizations. We cannot turn off and later re-play the same situation as

we could in the wave basin example. Therefore any record is only an

approximation of the process. Weak stationarity can only be inferred

from this single wave record if z and do not vary with time (there is no

trend in the mean water level and the wave heights). Finally, with only

one realization, we can never show that the process is ergodic; we simply

must assume ergodicity as also discussed in Kinsman (1965).

3.2 Short-Term Wave Height Distribution

To determine wave heights, it is necessary to use , the difference

between the water level and the mean water level. It is usual to think of

as a superposition of an infinite number of small waves, each generated

by its own wind eddies at different locations and at different times. The

resulting sea surface is, therefore, the sum of a large number of

statistically independent processes, and common sense would tell us that

it is impossible to predict the exact value of at any time or location. In

other words, is a random variable. The probability that has a certain

value is called the Probability Density Function (PDF), p(). The Central

Limit Theorem states that the PDF for a sum of many independent

variables is Gaussian, which means that p() can be described by the

Normal Distribution. The overall behavior of p() may be summarizedby its mean, , standard deviation, , and some additional statistical


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parameters such as skewness and kurtosis. Most often a two-parameter

normal distribution is used, defining p() by and only. By

definition, = 0 and therefore

2exp

2

-

2

1=)p(

2

(3.3)

where is the standard deviation of the process, (t). It is equal to the

square root of the variance of.

2 2 2

01

1lim

R

R

Nt t2

jt tjR

1= dt

t N

=

==

= = (3.4)

If the wave frequencies all occur within a narrow frequency band, (if

the wave periods do not vary greatly) it may be shown theoretically

(Longuet-Higgins, 1952; Cartwright and Longuet-Higgins, 1956;

Benjamin and Cornell, 1970) that the PDF of the maximum instantaneous

water levels is:

max maxmax 2 2

exp

2-

p( ) =2

(3.5)

If it is assumed that for waves of a narrow frequency band the wave

height H is equal to 2max, then the PDF for H becomes:

2 2exp

21 H -Hp(H) =4 8

(3.6)

Equations 3.5 and 3.6 are known as Rayleigh distributions. The

distribution of Eq. 3.6, multiplied by H is shown in Fig. 3.3.

To determine the Cumulative Distribution Function (CDF) of wave

heights, the Rayleigh distribution is integrated to yield the probability that

any individual wave of height H' is not higher than a specified wave

height H

20exp

2H -HP(H < H) = p(H) dH = 1 -

8

(3.7)


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Fig. 3.3 Rayleigh distribution

The probability of exceedence, the probability that any individual

wave of height H' is greater than a specified wave height H may be

obtained as

2exp

2-HQ(H > H) = 1 - P(H < H) =

8

(3.8)

The functions P and Q are also shown in Fig. 3.3. Research has shown

that for practically all locations the wave height distribution is reasonably

close to a Rayleigh distribution. One exception is in shallow water when

the waves are about to break.

The wave height with a probability of exceedence Q, may be

determined from Eq. 3.8 as

2 2ln ln lnQ1 1

= 8 (- Q) = 8 = 2 2HQ Q

(3.9)

To determine QH , the average height of all the waves that are larger

than HQ in a record or a storm

QH

Q

H p(H) dH

=H Q

(3.10)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 1 2 3 4 5 6 7 8

H/

(H).p(H),P,

Q

(H).p(H)

P

Q


7/31


Equation 3.9 and the numerical evaluation of Eq. 3.10 yield Table 3.1, in

which a number of common wave height definitions are related to .

Table 3.1 Commonly used wave height parameters

Symbol Description Value

H.01 Average of highest 1% of the waves 6.67

H.01 Height, exceeded by 1% of the waves 6.07

H .02 Average of the highest 2% of the waves 6.23

H.02 Height, exceeded by 2% of the waves 5.59

H0.1 Average of highest 10% of the waves 5.09

H0.1 Height exceeded by 10% of the waves 4.29

3/1HHs = Significant wave height (Average height of the

highest 1/3 of the waves)4.0

H Average wave height 2

H0.5 Median wave height 2.35

Hmode Most probable wave height 2.0

H rms N

HHH ....2

3

2

2

2

1 +++ 22

Of all these definitions, Significant Wave Height (Hs) is the most

important2. It is defined as the average of the highest 1/3 of the waves in

a wave train, 3/1H . In terms of significant wave height, four commonly

used relationships based on the Rayleigh distribution are

0.1 0.011.27 ; 1.67

0.63 ; 0.707

s s

s rms s

H H H H

H H H H

= =

= =(3.11)

The meaning of average wave height is self-explanatory. The modal

or most probable wave height is the wave height with the greatest

probability of occurrence. The median wave height has 50% probability,

i.e., half the waves in the wave train are higher and half the waves are

2 This wave height definition was historically chosen as significant because it comes

closest to the traditional estimates of average wave height made by experienced

observers before we had instruments to measure wave heights.


8/31


lower than this wave height. The rms wave height is the constant wave

height that represents the wave energy.

The probability of exceedence for the average wave heights may now

be calculated from their values in Table 3.1, using Eq. 3.8. The results

of this calculation are shown in Table 3.2.

Table 3.2 Probabilities of exceedence of average wave heights

H01.0H 1.0H Hs H Hmode

Q(H>H) 0.004 0.039 0.136 0.456 0.606

The expected value of the maximum wave in a wave train of N wwaves could be estimated by setting Q = 1/Nw in Eq. 3.9. A more

accurate estimate is

-3/2maxH = 2 ln N + + [(ln N ])2 2 ln N

w w

w

O

(3.12)

where (x) denotes expected value of x, is the Euler constant

(= 0.5772) and O(x) denotes terms of order greater than x, i.e. small

terms.

Example 3.1 Calculation of short-term wave heights

To analyze a wave record it must be stationary. Hence, it is normal

to record waves for relatively short time durations (10 to 20 minutes).

A longer record would not be stationary because wind and water

level variations would change the waves. Thus it is usual to record, for

example, 15 minutes every three hours. It is subsequently assumed that

the 15 min. record is representative of the complete three hour recording

interval.

Suppose the analysis of such a record yields

10 sec 1.0T and m= = (3.13)


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We want to calculate significant wave height Hs, average wave height, H ,

average of the highest 1% of the waves 0.01H , and the maximum wave

height in the record. From Table 3.1:

0.014 4.0 , 2 2.5 6.67 6.7sH m H m and H m = = = = = =

With sec10=T , the average number of waves in the 15 min record N w =

90 and Eq. 3.12 yields

max 0.57722 ln (90) 3.00 0.19 3.192 2 ln (90)

Hm

= + = + =

(3.14)

or the expected value of Hmax = (3.19)(2)(1.0) = 6.4 m. This calculated

value of Hmax can be verified against the actual record. If the record isrepresentative of a 3 hour recording interval, then 0.01, ,sT H H and H for

the 3 hours would be the same as above. However, Hmax would be larger

than 6.4 m. For the 3 hour recording interval, Nw = 1080 and Eq. 3.14

yields Hmax,3hrs = 7.8 m.

3.3 Wave Period Distribution

In the above discussion the frequency bandwidth for the waves was

assumed to be small (the wave periods are more or less the same) and in

practice, wave period variability is often ignored. One attempt to define

wave period distribution has been made by Bretschneider (1959) who

postulated that the squares of the wave periods form a Rayleigh

distribution. His expression for the PDF for wave periods is

exp

43

4

TTp(T) = 2.7 -0.675

TT

(3.15)

From this, by integration, the expression for probability of exceedence

of a certain wave period becomes

exp

4

T

T

Q(T >T) = p(T)dt = -0.675 T

(3.16)


10/31


Wave periods are related to wave heights particularly in a growing,

locally-generated sea, where high wave heights are always accompanied

by long wave periods. A joint distribution of wave heights and periods

can be postulated and that is normally assumed to be a joint Rayleigh

distribution.

3.4 Time Domain Analysis of a Wave Record

Wave recordings are time series of water levels that typically look like

Fig. 3.4. They are discrete time series z(t), sampled at N short intervals

oft. The water level recording must first be converted into a discretetime series (t), the fluctuation about mean water level by subtracting the

mean water level from the record.

Although the record is assumed to be stationary, there may be a small

change in mean water level with time, as is the case in Fig. 3.4, where the

water level drops slightly. This could be, for example a result of tides.

Because the record is short (20 min), the water level fluctuation is

assumed to be a linear function of time, .z a bt= + It is determined from

the record by regression analysis and then subtracted, so that

( ).z z z a bt= = + Figure 3.5 presents the time series for Fig. 3.4.

The bottom graph is for the first 120 seconds and shows more detail.

Fig. 3.4 Water level record

1.5

1.7

1.9

2.1

2.3

2.5

2.7

2.9

3.1

3.3

3.5

0 200 400 600 800 1000 1200

Time (sec)

WaterLevel-z(m)


11/31


As an initial analysis of a record, we could simply calculate , using

Eq. 3.4. The wave analysis program WAVAN was used3 and was

found to be 0.28 m for the record in Fig. 3.5. If a Rayleigh distribution of

wave heights is assumed (Tables 3.1 and 3.2), we can determine the

values for the important wave heights for Fig. 3.5.

0.11.12 , 0.70 , 0.79 1.43s rmsH m H m H m and H m= = = =

(3.17)

Fig. 3.5 Wave record for Fig. 3.4

3 An alternate program ZCA was also provided with the software.

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

0 200 400 600 800 1000 1200

Time (sec)

(

m)

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

0 20 40 60 80 100 120

Time (sec)

(

m)


12/31


This initial analysis does not tell us anything about wave period, the

other important wave parameter.

The actual distributions of H and T may be obtained from the record

by analysis of individual waves. Figure 3.6 shows a very short segment

of Fig. 3.5. The earliest definition for wave height H is the vertical

distance between a wave crest and the preceding trough (Fig. 3.6) where

crest and trough are defined as a local maximum and a local minimum in

the record. That definition would result in all the small ripples being

identified as waves. How many waves are there between t = 20 and 60

sec in Fig. 3.6?

To define wave height more realistically, zero down-crossing wave

height, Hd, is defined as the vertical distance between the maximum

and minimum water levels that lie between two subsequent zero

down-crossings (in which crosses zero on the way down). Similarly

zero up-crossing wave height, Hu, is the difference between maximum

and minimum water levels between two subsequent zero up-crossings.

These definitions are also shown in Fig. 3.6. They disregard the small

ripples that do not cross the mean water level. Example 3.2 presents the

zero crossing analysisof the wave record of Fig. 3.5 and compares the

results with the values in Eq. 3.17.

Fig. 3.6 Wave height definitions

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

20 30 40 50 60

Time (sec)

Eta(m)

H

HuH

dx

xx

xx x

(

m)


13/31


Example 3.2 Zero crossing analysis of Fig. 3.5

The zero up-and down crossing wave heights in Fig. 3.5 were

determined using WAVAN or ZCA. The two estimates were virtually

identical. The following wave statistics were obtained by averaging the

up- and down-crossing results

0.11.05 , 0.68 , 0.76 1.30s rmsH m H m H m and H m= = = =

(3.18)

When these calculated values are compared with Eq. 3.17 it is seen

that Eq. 3.17 overpredicts for this record.The wave heights were also grouped into 10 bins and the histogram

of the wave heights is shown in Fig. 3.7a. This distribution can be

compared with the Rayleigh distribution. It would also be possible to

plot the cumulative distribution function (P, as in Eq. 3.7) or the

probability of exceedence (Q, as in Eq. 3.8). However, since the wave

height distribution is expected to be Rayleigh, it is best tocompare waveheights directly with this theoretically expected distribution. Equation

3.8 may be re-written as

2

ln( )

2 2

HQ

=

(3.19)

Thus,

1/2{ ln( )} (2 2 )2 2

HR Q or H R

= = = (3.20)

where R is called the Rayleigh parameter. A true Rayleigh distribution

would plot as a straight line with zero intercept and a slope of 2 2 on a

graph of H versus R. Values of R were calculated for each wave

height bin and H versus R was plotted in Fig. 3.7b. The solid line in

Fig. 3.7b represents the initial analysis, combining = 0.28 m with

Tables 3.1 and 3.2. This analysis clearly overpredicts the actually

measured values of H.


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a) b)

Fig. 3.7 Histogram and Rayleigh distribution

The best fit line through the measured values has a slope of 0.69 and

from this; using Eq. 3.20, another estimate for may be computed

z =0.69 / (2 2 )=0.24m . This version is called z since it was

determined by zero-crossing analysis. An estimate of average wave

period may beobtained by dividing the record length (tR = 1200 seconds)

by the number of waves (Nw = 197) to find T 6.1= seconds.

Finally Hmax was found to be 1.56 m from the record. This can

be compared to the theoretical value of Hmax = 1.92m, obtained from

Eq. 3.12, using = 0.28 m. Using z = 0.24 m yields a more reasonable

value of Hmax = 1.65 m.

3.5 Frequency Domain Analysis of a Wave Record

A completely different type of analysis, based on wave frequencies, is

called wave spectrum analysis. We use the statistical assumptions that

the wave record is both stationary and ergodic. Although these

assumptions are necessary to perform a statistically correct analysis, in

practice we have no choice but to assume that records are short enough to

be both stationary and ergodic.

To express the time signal in terms of frequency, we can use a

Fourier series summation for each value ofj

0

5

10

15

20

25

30

35

40

0 .0 8 0 .2 3 0 .3 9 0 .5 5 0 .7 0 0 .8 6 1 .0 1 1 .1 7 1 .3 3 1 .4 8

Wave Height - H (m)

NumberofWaves

y = 0.6854x

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0.0 0.5 1.0 1.5 2.0

Rayleigh Parameter - R

WaveHeight-H

(m)

H=0.69R


15/31


1 1

cos(2 ) sin (2 )j o n n j n n jn n

a a f t b f t

= =

= + + (3.21)

Using Eulers relationship

cos sinie i

= + (3.22)

Eq. 3.21 becomes

2 ( )n ji f t

j n

n

C e

=

= (3.23)

where Cn is a complex coefficient

1 1; ( ); ( ) *

2 2o o n n n n n n nC a C a ib C a ib C = = = + = (3.24)

Equation 3.23 expresses the time signal in terms of discrete

frequency components and is known as a Discrete Fourier Transform

(DFT). The series in Eqs. 3.21 and 3.23 are infinite. However, a wave

record, such as in Fig. 3.5 is neither infinitely long, nor continuous. The

water level is sampled only at N specific times, t apart. There are N

values of (j) at times tj = jt, where 1 j N. As a result, the frequency

domain is also not continuous. The smallest frequency that can bedefined from a record of length tR is fmin = 1/tR. To provide the most

accurate representation in the frequency domain (best resolution) we will

use this smallest possible frequency as the frequency increment.

Therefore f = 1/tR and we define fn = nf. The highest frequency that

can be defined from a time series with increments t is the Nyquist

frequency

1

2 2 2N

r

N Nf f

t t= = =

(3.25)

This results in the finite discrete Fourier transform (FDFT)

2[2 ( )]

12

n j

N

i f t

j nN

n

F e

= +

= (3.26)


16/31


Because Fn = F-n*, Eq. 3.26 may also be written as:

1[2 ( )]

0

n j

Ni f t

j n

n

F e

=

= (3.27)

The inverse of Eq. 3.27 is

[2 ( )]

1

1n j

Ni f t

n j

j

F eN

=

= (3.28)

Equations 3.27 and 3.28 form a FDFT pair that permits us to switch

between the time and frequency domains. Equation 3.28 allows us to

calculate the complex frequency function Fn from a real time function j

and Eq. 3.27 permits calculation of the real time function j from thecomplex frequency function Fn. The complex variable Fn may also be

expressed as

ni

n nF F e

= (3.29)

where

2 2 11 tan2

nn n n n

n

bF a b and

a

= + =

(3.30)

Substitution of Eq. 3.29 into Eq. 3.27 results in

1[2 ( ) ]

0

n j n

Ni f t

j nn F e

==

(3.31)In practice waves have only positive frequencies, only frequencies

lower than fN can be defined, and j is real. Therefore, using Eq. 3.22,

we can rewrite Eq. 3.31 as

1

0

/2 /2

0 0

cos(2 )

2 cos(2 ) cos(2 )

N

j n n

n

N N

n n n n

n n

F ft

F ft A ft

=

= =

=

= =

(3.32)

where

2n nA F= (3.33)


17/31


Here nA is called the amplitude spectrum and n the phase

spectrum. In standard wave spectrum analysis only the amplitude

spectrum nA is calculated. In effect, n is assumed to be a random

variable - < n < resulting in the random phase model. This

unfortunate assumption loses all phase relationships between the N terms,

which means for example, that wave groups are not reproduced when

calculating j from nA using random n. Resonance and reflection

patterns are also not reproduced.

Parsevals theorem can be used to calculate from the amplitude

spectrum nA because

1 /22 2 2

2 2

0 0

1

2

N N

n n n

n n nC F A

= = == = = = (3.34)

Thus the variance at any frequency can be expressed as

2 21 1( ) ( )

2 2n n n nS f df A or S f A

df= = (3.35)

where S(f ) is known as the wave variance spectral density function or

wave spectrum. Variance is a statistical term. In physical terms, wave

energy density is

2E = g (3.36)

and hence wave energy distribution as a function of frequency is

( ) ( )E f = g S f (3.37)Wave spectra for Fig. 3.5 were computed using WAVAN and are

shown in Fig. 3.84. Because we always have only one realization of the

process and the record length (tR) is finite, resulting in finite increments

of frequency (f ), the calculated value of S(f ) is always an estimate of

the true S(f ).

The wave spectrum for the record in Fig. 3.5, using Eqs. 3.32 to 3.35,

is shown in Fig. 3.8a. This spectrum gives the maximum possible

resolution and distinguishes between frequencies that are f = 1/tR =

1/1200 = 0.00083 Hz apart. It contains many closely spaced spikes of

4 An alternate program WSPEC was also provided with the software.


18/31


a) b)

c) d)

Fig. 3.8 Wave spectra of the wave record in Fig. 3.5

wave energy. Physically, such very local energy peaks are not possible.

They are a result of the uncertainties in our estimates and therefore the

wave spectrum is smoothed.

Smoothing can be done by averaging S(f ) over frequency ranges

longer than f so that

/22

/2

1( )

m M

n m f

m M

S f A where df M f df

=

+

=

= = (3.38)

where df is the resolution of the spectrum and Mf denotes how many

values off are averaged. The results for Mf= 6 and 12 (df = 0.005 and

0.01 Hz) are shown in Figs. 3.8b and c. Smoothing produces a more

regular spectrum. The amount of smoothing to be used depends on the

purpose of the analysis. If a general impression of a wave field is needed,

Fig. 3.8c is most useful. If specific frequencies need to be identified, then

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0. 00 0. 05 0.1 0 0. 15 0. 20 0.2 5 0. 30 0. 35 0. 40

Frequency (Hz)

S(f)(m2/Hz)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

Frequency (Hz)

S(f)(m2/Hz)

0.0

0.5

1.0

1.5

2.0

2.5

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

Frequency (Hz)

S(f)(m2/H

z)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

0 .0 0 0 .0 5 0 .1 0 0 .1 5 0 .2 0 0. 25 0 .3 0 0 .3 5 0 .4 0

Frequency (Hz)

S(f)(m2

/Hz)


19/31


less smoothing such as in Fig. 3.8b may be more appropriate. Another

method used for smoothing the spectrum is to divide the record into

shorter sections, compute the spectrum for each section and then average

the results. Figure 3.8d, presents the average of 4 spectra, each for t R.

The resolution is now 4/tR = 0.0033 and averaging the four spectra

introduces some further smoothing.

The value of , as computed from the frequency analysis by

integrating the spectrum is denoted as f because it was derived by

frequency analysis. Because Eqs. 3.4 and 3.34 both integrate 2, f for

all four spectra in Fig. 3.8, as well as found using Eq. 3.4, are the same

and equal to 0.28.

Only the basic principles of wave spectrum are presented here. There

are other methods of computing the wave spectra and dealing with

smoothing of the spectra. Further details may be found in the literature,

such as Bendat and Piersol (1966) and ASCE (1974) and Janssen

(1999).

Frequencies that exceed the Nyquist frequency (Eq. 3.25) cannot be

defined as separate frequencies. The energy in these high frequency

waves is superimposed on the spectrum by a process known as aliasing.

Figure 3.9a demonstrates aliasing in the time domain. A wave of

frequency 1.1 Hz is sampled at t = 1.0, for which fN = 0.5 Hz. It is seen

that the sampled signal (square points) does not have a frequency of

1.1 Hz, but of 0.1 Hz. The energy of such a wave component would

therefore become added at 0.1 Hz in a wave spectrum. Aliasing in the

frequency domain is depicted in Fig. 3.9b.

Aliasing can be prevented by filtering frequencies greater than fN

out of the signal. Alternately, if fc is the highest frequency that must

be computed correctly (without aliasing), then it is reasonable to assume

that fc = fN/2. That defines the necessary sampling interval for the record

as t 1/(2fN) = 1/(4fc). For wind waves, if we wish to define

the spectrum correctly for all frequencies f < 2Hz, t should be less than

1/8 sec.


20/31


Fig. 3.9 Aliasing of a wave spectrum by high frequency components

3.6 Parameters Derived from the Wave Spectrum

The moments of the wave spectrum are defined as

0= ( )

f hh

fS f df fm

=

= (3.39)

The zero moment (n = 0) is therefore the area under the spectrum

2

0= ( ) =

f

o ff

S f df m =

= (3.40)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0 0.1 0.2 0.3 0.4 0.5 0.6

Frequency - f (Hz)

S(f)(m2/Hz)

Correct

Spectrum

Aliased

Spectrum

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0 2 4 6 8 10 12 14t

a)

b)


21/31


From the area under the wave spectrum, assuming the wave height

distribution to be Rayleigh, the various wave heights of Table 3.1 may be

estimated as in Eq. 3.17. To distinguish between significant wave height

derived from time domain analysis and its counterpart derived from

frequency analysis, the latter is called the CharacteristicWave Height or

Zero MomentWave Height.

ch moH = H = 4 (3.41)

and for Fig. 3.5, Hmo = (4)(0.28) = 1.12 m.

The representation of the wave energy distribution with frequency is

an improvement over the time-domain analysis methods discussed earlier.

With this information we can study resonant systems such as theresponse of drilling rigs, ships moorings, etc. to wave action, since it is

now known in which frequency bands the forcing energy is concentrated.

It is also possible to separate sea and swell when both occur

simultaneously (Fig. 3.10).

Fig. 3.10 Wave spectrum with sea and swell

The moments of the wave spectrum also define spectral bandwidth

22 2

41 o

m

= m m

(3.42)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0 0.1 0.2 0.3 0.4 0.5 0.6

Frequency - f (Hz)

S(f)

(m2/Hz)

Swell

Sea


22/31


Cartwright and Longuet-Higgins (1956) show that for a narrow

bandwidth ( 0) all wave periods in a wave train are almost the same

and the distribution of is purely Rayleigh5. For 1, the distribution

of is random. This would obviously affect the wave height definitions

used in Tables 3.1 and 3.2. For the record in Fig. 3.5, 2 was calculated

to be 0.65.

Since there are many wave frequencies (or wave periods) represented

in the spectrum it is usual to characterize a wave spectrum by its peak

frequency fp, the frequency at which the spectrum displays its largest

variance (or energy). The peak period is then defined as

1p

pT f

= (3.43)

Other spectrum-based definitions of wave period found in the

literature are

0 01 2

1 2

;m m

T Tm m

= = (3.44)

Theoretically T2 is approximately equal to ,T as obtained by zero

crossing analysis. For the spectra in Fig. 3.8, fp = 1.3 Hz, if the narrow

peak in Fig. 3.8a is discounted. Thus Tp = 7.6 seconds and T1 = 6.4

seconds and T2 = 6.1 seconds. T was also 6.1 seconds in Ex. 3.2. In

Fig. 3.10 we can distinguish peak fp and Tp values for both the sea and

the swell. Rye (1977) indicates that the moments for the spectrum are

functions of the cutoff frequency (the highest frequency considered in the

analysis) and thus , T1 and T2 should be viewed with caution.

Sometimes the angular frequency, , is used to define the frequencies

in the wave spectrum. The total variance for the S() spectrum is

0

1( )

2

2 = S d

=

= (3.45)

The results of the three methods of analysis for the waves of Fig. 3.5

are compared in Table 3.3.

5 The Rayleigh distribution is in fact based on the assumption that 0 (Sec. 3.2).


23/31


Table 3.3 Comparison of analysis methods

Analysis Initial Zero Crossing Frequency

(m) = 0.28 z = 0.24 f = 0.28

N 197

T (sec) T = 6.1 Tp = 7.6

T1 = 6,4

T2 = 6.1

Hs (m) 1.12 1.05 Hmo = 1.11

H (m) 0.70 0.68 0.70

Hrms (m) 0.79 0.76 0.79

0.1

H (m) 1.43 1.30 1.43

Hmax (m) 1.92 1.56 1.92

2 0.65

3.7 Uncertainties in Wave Measurements

At this point, we reflect upon how well we know the basic wave

parameters: wave height, wave period and wave angle. First we define

uncertainty. It quantifies the combination of errors, randomness and

general lack of physical understanding. For most physical quantities,

errors increase with the magnitude of the quantity. For example, the

absolute error in measuring a wave height of 0.5 m will be less than theabsolute error in measuring a wave height of 5 m. For this reason we

normally use a relative error to define the accuracy of our quantities. The

errors in a quantity such as wave height H are assumed to have a normal

distribution with Has its mean value and H as its standard deviation.

The uncertaintyin H is defined as its coefficient of variation

' HHH

= (3.46)

More detail about uncertainties may be found in Thoft-Christensen

and Baker (1982), Ang and Tang (1984), Madsen, Krenk and Lind

(1986), Pilarczyk (1990), Burcharth (1992) and PIANC (1992). Fromthe definition of standard deviation, H is between (1 ' )HH 68% of the


24/31


time, there is a 95% probability that H is between (1 2 ' ),HH and

virtually all values lie between (1 3 ' ).HH

Wave heights are based on measurements of instantaneous water

levels, usually measured offshore, at frequent intervals (e.g. 10 Hz) over

a recording period (e.g. 10 to 20 Minutes). Zero Crossing or Wave

Spectrum Analysis is then used to reduce the instantaneous water level

measurements to one single wave height value (Hs or Hmo) to represent

the complete recording period. Along an exposed coast Hs = 1 m would

be typical. Even for very carefully measured instantaneous water levels,

using the latest equipment Hs = 1 m would contain an absolute error

(standard deviation) H,Measured = 0.05 to 0.1 m (say 0.075 m). The

uncertainty in a 1 m wave height would therefore be H,Measured = 0.075.

The errors in measuring smaller waves would be less and for larger waves

they would be greater. Therefore an uncertainty H,Measured = 0.075 would

not be unreasonable for wave height measurements. This means that for

H = 1 m, there is a 68% probability that 0.92 < H < 1.08 m, a 95%

probability that 0.85 < H < 1.15 m and that almost all values of H lie

between 0.78 < H < 1.22.

Such relatively accurate offshore wave height measurements are

subjected to several conversions before they can be considered useful for

subsequent computations. So far, the value of Hs = 1 m represents 10 to

20 minutes of record. For the 1 m wave height to represent a complete

recording interval of 3 to 6 hours, it must be remembered that the

environmental parameters such as wind speed and direction, water levels,

etc. are not constant over the recording interval. This increases the

uncertainty of the representative wave height values. The additional

uncertainty depends on the variability of the conditions over the recording

interval, but in most cases it would be reasonable to expect the

uncertainty to double so that H,Interval = 0.15.

Uncertainty of measured wave periods T is known to be greater

than for H and reasonable estimates would be T,Measured = 0.1 and

T,Interval = 0.2. Wave direction () is notoriously poorly measured. Even

the best directional instrumentation has difficulty to produce wave

directions within 3 for large, well-formed waves and may be as much


25/31


as 10 wrong for smaller, more irregular waves. Estimates of wave

direction by means other than directional measurement are much worse.

Assuming the values of 3 and 10 to be maximum values of the errors in

angle (assuming these values to be 3 removed from the mean), can

be estimated as 1 in the first case and 3.3 in the second. Thus an

average value of standard deviation is ,Measured = 2. This value of is

independent of the incident wave angle and hence we cannot define .

However, in order to complete our subsequent discussion about

uncertainties, we will relate to an incident wave angle of 10 with

respect to the shoreline. In that case, ,Measured becomes 0.2. Many

times the incident wave angle on a sandy shore is much smaller than 10,

which would result in much higher uncertainty values. When the wave

angle with the shoreline approaches 0 (as is often the case), the

uncertainty for wave angle approaches infinity and the whole discussion

about uncertainty loses its meaning. For the longer interval and = 10,

a reasonable estimate is ,Interval = 4.

These uncertainty values are only general indications. They are

heavily influenced by assumed average values for wave heights and

periods, and particularly angles of breaking. The actual values are not as

important, however, as the fact that they clearly indicate that the basic

coastal data wave heights, wave periods and incident wave angles

contain large uncertainties. Since these wave quantities are basic to all

coastal design calculations, the effects of these uncertainties will pervade

all subsequent calculations. The awareness of uncertainties is basic to

our understanding of the fundamental issues of coastal engineering and

management. For example, it explains why we can use small amplitude

wave theory successfully for most design calculations. The discussion

and evaluation of uncertainties will be extended in later chapters.

3.8 Common Parametric Expressions for Wave Spectra

Since the measured spectra show considerable similarity (they

basically consist of a peak and two curves decreasing toward f = 0 andf = ), attempts have been made to formulate parametric expressions.


26/31


Only the most common expressions will be presented here. Phillips

(1958) postulated that for the equilibrium range (for f > fp) the spectral

shape S(f ) is proportional to f-5. He quantified his results as

2 5

4(2 )

-PP = g f

(3.47)

where p denotes the Phillips Function and the Phillips constant is

0.0074P = (3.48)

Pierson and Moskowitz (1964), added a low frequency filter to extend

the Phillips expression over the complete frequency range

( ) P PMPM f =S (3.49)where:

4

5exp

4PM

p

f=

f

(3.50)

The Pierson-Moskowitz spectrum is therefore

42

( )

4 5( )

(2 )

PM /fPM

gf =S e

f

(3.51)

Commonly used expressions for and are:

450.0081;4

PM p= = f (3.52)

The quantity was also related to wind speed U so that this spectrum can

be used to hindcast waves from wind data (Ch. 5)

4

0.742

g=

U

(3.53)

where U is the wind speed. The Pierson-Moskowitz spectrum is valid

for a fully developed sea condition. For developing seas the Jonswap

Spectrumwas proposed by Hasselmann et al (1973). It is essentially an

enhanced Pierson-Moskowitz spectrum as shown in Fig. 3.11.


27/31


Fig. 3.11 Jonswap and Pierson-Moskowitz spectra

A developing seas filter, J, can be assumed so that the Jonswap

spectrum is

( ) P PM J J f =S (3.54)

wherea

eJ= (3.55)

and

2

22

( )p

p

f f

a =f2

(3.56)

Typical values of are

0.07

0.09

p

p

= for f f

= for f f

>(3.57)

The Jonswap expression is therefore

425

44 5( ) ( )

(2 )

a

a

p

e fe J

fJ PM

gf = f =S S e

f

(3.58)

0

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

0.0014

0.0016

0.0018

0 0.5 1 1.5 2

Frequency - f (Hz)

S(f)(m2/Hz)

Jonswap

Pierson-

Moskowitz


28/31


The coefficient can be related to the wave generating conditions

0.22

20.076J

gF=

U

(3.59)

where F is fetch length. Alternately, Mitsuyasu (1980) states

2/7

210

0.0817MgF

=U

(3.60)

and

0.33

22.84

p

gF=f

U

(3.61)

The peak enhancement factor

( )

( )

J p

PM p

fS=

fS (3.62)

has an average value of 3.3 and typically lies between 1 and 7.

Mitsuyasu (1980) postulates

1/7

210

7.0gF

=U

(3.63)

The above development traces the derivation of a parametric

expression for a wave spectrum from the equilibrium spectrum (Phillips)

through the fully developed sea spectrum (PM) to the developing sea

spectrum (Jonswap). But waves have a limiting steepness. Thus, any

wave in a wave train that reaches a limiting steepness will break. This is

known as Spectral Saturation. Bouws et al (1985) modify the Jonswap

spectrum to take spectral saturation into account and produce the TMA

spectrum.

P J dTMA PM( f,d) =S (3.64)

where

tanh2

d

1 2 d

= 2n L

(3.65)


29/31


In deep water, the value of d is one. In other words, the Jonswap

spectrum through its own derivation takes into account the deep water

wave steepness limitation and Eq. 3.63 modifies the Jonswap spectrum

for wave breaking induced in shallow water.

3.9 Directional Wave Spectra

Until now has been considered to be a function of time at a single

location and we learned to calculate S(f ) from such a time series. We

also discussed some parametric expressions for such wave spectra.

However, is also a function of direction (of x and y). Measurement ofwave direction involves correlating spectra for several synoptic, adjacent

records of water levels, pressures and/or velocities. Some discussion may

be found in Sec. 2.3.1, but detailed discussion is beyond the scope of this

book. An example of a Directional WaveSpectrum (a function of both

wave frequency and direction) is shown in Fig. 3.12 and a good

description of directional wave spectra may be found in Goda (1985).

To describe such a spectrum, the simplest approach is

( ) ( ) ( )S f, = S f G (3.66)

G is called the Directional Spreading Function and is measured

counter-clockwise from the wave direction. A necessary condition is

obviously

( ) 1G d =

=

= (3.67)

Two common directional spreading functions used are the

Cos-Squared function

22( ) cos

2

( ) 0

G = for

short term wave

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