lecture (9)
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Lecture (9). Frequency Analysis and Probability Plotting. Flood Frequency Analysis. Statistical Methods to evaluate probability exceeding a particular outcome - P (X >20,000 cfs) = 10% Used to determine return periods of rainfall or flows - PowerPoint PPT PresentationTRANSCRIPT
Lecture (9)Lecture (9)
Frequency Analysis Frequency Analysis and and
Probability Plotting Probability Plotting
Flood Frequency Analysis
• Statistical Methods to evaluate probability exceeding a particular outcome - P (X >20,000 cfs) = 10%
• Used to determine return periods of rainfall or flows
• Used to determine specific frequency flows for floodplain mapping purposes (10, 25, 50, 100 yr)
• Used for datasets that have no obvious trends
• Used to statistically extend data sets
Basics of Probabilistic Prediction of Peak Events
• “What has happened and the frequency of events in a record are the best indicators of what can happen and its probability of happening in the future.”
• Requires a hydrological record at a station.
• The record is analyzed to estimate the probability of events of various sizes, as if they were independent of one another.
• Then it is extrapolated to larger, rarer floods.
• BUT most hydrologic records are short and non-stationary (i.e. conditions of climate and watershed condition change during the recording period). Magnitude of this problem varies, but needs to be checked in each case.
Return Period (Recurrence Interval)Return Period (Recurrence Interval)
• It is the average period in which a certain magnitude of a hydrological event will be once equalled or exceeded.
1
1/
i ii
r
m mP Q Q
n n
T P
the Weibull formula
mi is the rank of the ith event in a set of n events
Tr is the recurrence interval (yr). The long-term average interval between events greater than Qi
Return period (Recurrence interval) cont.
• E.g. if the probability of exceeding 20 m3/sec in any year at a station is 0.01,
It means that: there should be on average 10 events larger than 20 m3/sec in 1000 years, if the conditions affecting events at the site do not change.
• The average recurrence interval between events is 100 years, so we refer to such a discharge as “the 100-year event”.
• The events will not occur regularly every 100 years
How to compute the Return Period
1. Arrange the data in ascending or descending order of magnitude ignoring their chronological sequence.
2. Each observation in the data has the same relative frequency of occurrence.
3. For n observations, fr=1/n.4. For the min value of data we assign 100%.5. For the next smallest of the data we assign
100%- (1/n)*100%.6. Next, 100%- 2(1/n)*100%, …, etc. and end up
of (1/n)*100% at the max value.Conversely, we can do the same form the max
value to the min value (ascending).
Flood Frequency Curve• a flood-frequency curve: It is a plot of the calculated
values of Tr against Qi.
Flood-frequency curve (with error bars) for the Skykomish River, at Gold Bar, WA
(from US Geological Survey gauging records)
Exceedence probability
Plotting PositionPlotting Position
It is related to the the return period. It can be expressed in terms of relative frequency of occurrence or the probability that a hydrological event of a given magnitude will occur.
The plotting position should satisfy some conditions (Gumbel, 1960):
1. The probability that the magnitude of a hydrological variable, X, equals or exceeds an indicated value, a, once in a return period of Tr years is,
2. The probability should be as close as possible to the observed frequency.
3. Neither zero or one.
1( )
r
p X aT
Methods to compute p and Tr Methods to compute p and Tr
Where, m = rank or order of X (m=1 for the largest value, 2 for
the second largest value, …, n for the smallest value.n= sample size.
( ) , , 1m
p X a m n pn
1( ) , 1, 0
mp X a m p
n
0.5 1( ) , 1,
2
mp X a m p
n n
( ) , 0< 11
mp X a p
n
Methods to compute p and Tr (Cont.)Methods to compute p and Tr (Cont.)
0.3( ) , Benard and Bos-Levenback,1953
0.4
mp X a
n
0.44( ) , Gringorten, 1963
0.12
mp X a
n
Choosing Theoretical Probability Distribution of an Event
Note that this procedure involves fitting a theoretical probability distribution to an observed sample drawn from an imaginary (but not well-understood) population
The “true” theoretical probability distribution of the events is not known, and we have no reason to believe it is simple or has only 1 or parameters.
Plotting the data set on various types of graph paper with different scales, designed to represent various theoretical probability distributions as straight lines, yields graphs of different shapes, which when extrapolated beyond the limits of measurement predict a range of events.
Normal Prob Paper converts
the Normal CDF S curve into
a straight line on a prob scale
Normal Probability Paper
Log-normal Probability Paper
Weibull Probability Paper
Example 1( ) , 0< 1
1
mp X a p
n
Example 2
Graphical Plot of Richmond Data
Example 3
Year Rank Ordered cfs
1940 1 42,700
1925 2 31,100
1932 3 20,700
1966 4 19,300
1969 5 14,200
1982 6 14,200
1988 7 12,100
1995 8 10,300
2000 …… …….
Flood frequency plots of same record on different probability papers
Log-probability paper
Log-extreme value paper
Gumbel Type III
How to Choose a Theoretical Probability Distribution ?
• One common approach to choice of flood frequency plotting paper is to choose one on which the observed data plot as a straight line that can be extrapolated to estimate rare, large events.
• A second is to choose one of the common ones, but this still leads to different predictions among analysts
Some Guidelines
• Take Mean and Variance (S.D.) of ranked data
• Take Skewness Cs of data (3rd moment about mean)
• If Cs near zero, assume normal dist’n
• If Cs large, convert Y = Log x - (Mean and Var of Y)
• Take Skewness of Log data - Cs(Y)
• If Cs near zero, then fits Lognormal
• If Cs not zero, fit data to Log Pearson III
Siletz River Example 75 data points
Mean 20,452 4.2921
Std Dev 6089 0.129
Skew 0.7889 - 0.1565
Coef of Variation
0.298 0.03
Original Q Y = Log Q
Siletz River Example - Fit Normal and LogN
Normal Distribution Q = Qm + z SQ
Q100 = 20452 + 2.326(6089) = 34,620 cfsMean + z (S.D.)
Where z = std normal variate - tables Log N Distribution Y = Ym + k SY
Y100 = 4.29209 + 2.326(0.129) = 4.5923
k = freq factor and Q = 10Y = 39,100 cfs
Log Pearson Type III
Log Pearson Type III Y = Ym + k SY
K is a function of Cs and Recurrence IntervalTable 3.4 lists values for pos and neg skews
For Cs = -0.15, thus K = 2.15
Y100 = 4.29209 + 2.15(0.129) = 4.567
Q = 10Y = 36,927 cfs for LP IIIPlot several points on Log Prob paper
• What is the prob that flow exceeds
some given value - 100 yr value• Plot data with plotting position
formula P = m/n+1 , m = rank, n = #• Log N dist’n plots as straight line
LogN Prob Paper for CDF
Straight Line Fits Data Well
Mean
LogN Plot of Siletz R.
Various Fits of CDFsLP3 has curvatureLN is straight line
Siletz River Flow Data
n ..., 3, 2, 1, 0,x
)1()!(!
! P(x)
xnx ppxnx
n
The probability of getting x successes followed by n-x failuresis the product of prob of n independent events: px (1-p)n-x
This represents only one possible outcome. The number of waysof choosing x successes out of n events is the binomial coeff. Theresulting distribution is the Binomial or B(n,p).
Risk Assessment (Binomial Distribution)
The probability of at least one success in n years, where the probability of success in any year is 1/T, is called the RISK.
Prob success = p = 1/T and Prob failure = 1-p
RISK = 1-P(0) = 1 - Prob(no success in n years)
= 1 - (1-p) n
= 1 - (1 - 1/T) n
Reliability = (1 - 1/T) n
Risk and Reliability
What is the probability of at least one 50 yr flood in a 30 year advance period, where the probability of success in any year is P=1/T = 0.02
RISK = 1 - (1 - 1/T)n = 1 - (1 - 0.02)30
= 1 - (0.98)30 = 0.455 or 46%If this is too large a risk, then increase design level to the 100 year where p = 0.01
RISK = 1 - (0.99)30 = 0.26 or 26%
Risk Example