Variations within an individual sea state

1 hr

Variations over a long-term collection of sea states

20 yr

parameters. A mean value usually suffices for most processes, such as water level, current, wind speed, and

direction. Wave parameters require special consideration (see Part II-1). Typically the parameters *H*m0, *T*p,

and *θ*p are used to represent either the total sea state or each major wave component in the sea state, which

may include a locally generated sea and several independent swell components. It is especially important to

note in shallow water design applications that the energy-based parameter *H*m0 and the height-based parameter

choice of parameter must be consistent with the particular design application. For example, *H*m0 is appropriate

for sediment transport and beach erosion applications, but *H*1/3, *H*10, or *H*1 may be preferred for estimating

wave forces on a pier.

(1) Statistical methods for analyzing *long-term *information covering many years are an integral part of

most design applications. These methods deal with the various values assumed by selected statistical

parameters representing short-term information. Typically the largest parameter values are the primary design

concern. For example, the largest value of the statistical parameter *H*m0 to be expected over a 25-year time

interval might be needed.

(2) Extreme events are often highly variable in terms of intensity and sequencing. By definition, they

are rare. Thus, long-term statistical methods must deal with the problem of using a small, variable sample

to estimate parameters that often have a major impact on design. Engineers are continually reminded that

events such as the 100-year extreme storm can by chance occur during a much shorter data collection effort.

Conversely, a 10-year record may not contain any events that equal or exceed the long-term 10-year extreme.

Long-term statistical methods typically address the following two, related problems:

(a) How to extend available information to a longer time period; e.g., how to use 10 years of data to

estimate the 25-year extreme.

(b) How to use a small sample of extreme events to get unbiased extreme estimates and some measure

of confidence or variability that can be expected in the estimates.

(3) In some applications, the preferred approach is to extend the available information base to longer time

periods by generating additional realizations of the process and ensuring that the realizations are statistically

consistent with known information. One adaptation of this approach is described in the following section on

stochastic time histories.

II-8-4

Hydrodynamic Analysis and Design Conditions

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