Bivariate return periods

In one dimension the return period of an event is defined as the average time interval before a certain quantile level (return level) is exceeded

3.11. BIVARIATE FREQUENCY ANALYSIS 37 (section A.2.3). This is a familiar concept in hydrology which is often used in connection with hydrological design and frequency analysis. The concept of return period can be generalised into two dimensions, but then it is no longer unambiguous and several classes and subclasses of bivariate return periods exist. This arise because there are no natural maxima in a bivariate distribution. In section 3.6.3 this was solved by introducing componentwise maxima, and since bivariate distributions are included in the definition of bivariate return periods, the variables in bivariate return periods are also defined componentwise. In view of this issue three classes of return periods exist, these classes are

• Independent return periods • Joint return periods

• Conditional exceedance probabilities

Independent return periods are used when the variables are independent or the design criterion is only dependent of one variable. The return period is then equal to the univariate return period of the variable of interest. Joint return periods are used when the bivariate distribution is a joint distribution. Within this class subclasses that depends of the design criterion exist. The first sub-class is defined by a generalisation of the univariate return period, and is given by

T (x, y) = 1

1 − F (x, y) , (3.40)

where T(x,y) is the joint return period. This return period is valid for events where X ≥ x or Y ≥ y.

The second sub-class of joint return periods is given by

T′(x, y) = 1

1 − F (x) − F (y) + F (x, y), (3.41)

which is valid for events where X ≥ x and Y ≥ y. Be aware that with the definitions of joint return periods that are used here, different combinations of values of the variables can give the same return period. A comparison of the joint return periods with the univariate return periods based on the same variables show that the following inequalities apply

T (x, y) ≤ min(TX, TY) ≤ max(TX, TY) ≤ T′(x, y) (3.42)

where TX and TY are the marginal return periods for the variables x and

y (Yue and Rasmussen, 2002). This means that by using univariate return periods instead of joint return periods for the first subclass (equation 3.40), the return period for the event of interest will be overestimated. On the other hand by using univariate return periods instead of joint return periods for the second subclass (equation 3.41), the return period for the event of interest will be underestimated. If these result are used for hydrological design, the planned construction is build weaker than intended by the constructional

requirement. As a consequence the construction might not withstand the events it was designed for.

If the distributions of the different pairs of flood characteristics are given conditionally, the conditional exceedance probabilities can be calculated. The reason for using exceedance probabilities and not return periods is that with the use of conditional distributions the concept of return period is no longer meaningful. In joint distributions with componentwise block maxima there is one observation of each variable in each block, whereas in conditional distributions only the observations that satisfy the conditional condition are used. Thus in conditional distributions the time interval between the observations are not fixed. Since the time resolution of the return period is defined from the time resolution of the block, the observations used for determining the return period must satisfy the block condition, which is one observation in each block. Clearly, conditional distributions will not satisfy this condition. Also within this class there exist sub-classes depending on the design criterion of the analysis. The first sub-class is defined for the design criterion X ≥ x given Y = y, and is given by

T (x|y) = 1

1 − FX|Y(x|y), (3.43)

where F_{X|Y(x|y) = P r[X ≤ x|Y = y] is the conditional cumulative}

distribution function, and is defined as F_{X|Y(x|y) =} Z x −∞ f_{X|Y(u|y)du =} Rx −∞f (u, y)du fY(y) . (3.44)

For the event Y ≥ y given X = x, the conditional return period is given by an equation equivalent to equation 3.43, but with an exchange of the variables x and y.

The second sub-class of conditional return periods are useful in hydrological applications and is valid for the design criterion X ≥ x given Y ≥ y. The exceedance probability for this event can be written as

S_{X|Y(x|y) = S(X > x|Y > y) =} 1 − FX(x) − FY(y) + F (x, y)

1 − FY(y) , (3.45)

where S is the survival function. The return period for this event is then given as

T′_{(x|y) =} 1

S_X|Y(x|y) =

1 − FY(y)

1 − FX(x) − FY(y) + F (x, y) . (3.46)

By defining the conditional return period in this way, the value of the conditional variable can be given as an exceedance probability or

an n-year univariate return period. This is an advantage because

constructional requirements in hydrological design often are given as exceedance probabilities or return periods of the variable or variables

of interest. For the event Y > y given X > x, the conditional

3.11. BIVARIATE FREQUENCY ANALYSIS 39

with an exchange of the variables x and y. A comparison of the

conditional exceedance probabilities in the second subclass (equation 3.46) with univariate exceedance probabilities based on the same variables show that by using univariate exceedance probabilities the event of interest is overestimated.

For the classes joint return periods and conditional exceedance probabilities there exist more than the two sub-classes in each class given in this theses. Other sub-classes can be defined by changing the design criterion for the analysis. For more information of bivariate return periods, see Yue and Rasmussen (2002).

Chapter 4

Results and discussion

4.1

Flood characteristics