Refer to the updated 1925-2010 Siletz River data developed in Problems 1 and 2. For consistency, assume that the following moments are valid for the period water years 1925-2010:
| Original Data (cfs) | Log10 Data (log cfs) |
Mean | 20,796 | 4.29217 |
Standard Deviation | 7386 | 0.1527 |
Station Skewness | 1.341 | −0.3510 |
Weighted Skewness |
| −0.2773 |
Problem
Assume that the Siletz River data are lognormally distributed. Find the following.
(a) Peak flow of the 100-yr flood
(b) Peak flow of the 50-yr flood
(c) Probability that a flood will be less than or equal to 30,000 cfs
(d) Return period of the 30,000-cfs flood
Problem 1
This chapter employs Siletz River data for the continuous period 1925-1999. As of this writing, peak flow data through water year 2010 are available on the USGS Oregon surface water data webpage http://waterdata.usgs.gov/or/nwis/sw, including for Gage Number 14305500, which is the Siletz River. You will be asked in different problems in this chapter to update the Chapter 3 examples by using the 86-yr record, 1925-2010.
(a) Download the peak flow data for the Siletz River, and enter the data into a spreadsheet.
(b) Plot the 86 years of peak flows (1925-2010) and a 5-yr running mean vs. their water year, to update Figure 1. Comment in general about the appearance of this time series, in the manner of the discussion of Figure 1.
(c) Using the updated data, develop new relative frequency and cumulative frequency histograms; that is, update Figures 2 and 3.
Figure 1
Time series of annual maximum peak flows for the Siletz River, near Siletz, Oregon. Also shown is the 5-yr running mean, from which longer-term trends can sometimes be discerned. Only quantitative methods of time-series analysis can determine for sure whether or not there are periodicities or nonstationary components in the data, but none are obvious visually.
Figure 2
Relative frequencies (probabilities) for the Siletz River, plotted vs. their class mark.
Figure 3
Cumulative frequency histogram for the Siletz River, plotted vs. class intervals.
Problem 2
(a) Use the data found in Problem 3 to calculate the mean, standard deviation, and skew coefficient (Equation 1, 2, and 3) of the updated Siletz River data (1925-2010).
(b) Repeat part (a) using the logs (base 10) of the Siletz River data.
(c) Develop the weighted skewness of the logs according to the Bulletin 17B protocol. That is, redo Example. At the conclusion of this problem, you should have an updated version of Table 2.
Problem 3
This chapter employs Siletz River data for the continuous period 1925-1999. As of this writing, peak flow data through water year 2010 are available on the USGS Oregon surface water data webpage http://waterdata.usgs.gov/or/nwis/sw, including for Gage Number 14305500, which is the Siletz River. You will be asked in different problems in this chapter to update the Chapter 3 examples by using the 86-yr record, 1925-2010.
(a) Download the peak flow data for the Siletz River, and enter the data into a spreadsheet.
(b) Plot the 86 years of peak flows (1925-2010) and a 5-yr running mean vs. their water year, to update Figure 1. Comment in general about the appearance of this time series, in the manner of the discussion of Figure 1.
(c) Using the updated data, develop new relative frequency and cumulative frequency histograms; that is, update Figures 2 and 3.
Figure 1
Time series of annual maximum peak flows for the Siletz River, near Siletz, Oregon. Also shown is the 5-yr running mean, from which longer-term trends can sometimes be discerned. Only quantitative methods of time-series analysis can determine for sure whether or not there are periodicities or nonstationary components in the data, but none are obvious visually.
Figure 2
Relative frequencies (probabilities) for the Siletz River, plotted vs. their class mark.
Figure 3
Cumulative frequency histogram for the Siletz River, plotted vs. class intervals.
Equation 1
Equation 2
Equation 3
Equation 4
EXAMPLE
MOMENTS OF AN ANNUAL MAXIMUM SERIES
The series of 75 annual maximum flows for the Siletz River is shown in Table 1. Evaluate the mean and standard deviation of the original data and of the logs (base 10) of the data using Equations 1 and 2. Compare the various skewness estimates.
SOLUTION
This exercise is easily performed in a spreadsheet. For example, Excel functions to perform the moment calculations for the column of data in Table 1 are shown below. Moments for log10 DATA are performed on the log10 transformation of the column of flows. Note, for instance, that the log of the mean flow is not equal to the mean of the logs; that is, log (20452) = 4.3107 ≠ 4.2921. (Ample significant figures should be carried when working with logarithms.) The results are presented in Table 2.
A coefficient of variation of the flow data of 30% indicates wide variability of the flows, as is evident in Figure 1. Considering the logi0 values, using the regional data from Figure 4 gives Cm = 0.0 for the north-central coastline of Oregon. A weighted average using Equation 4 gives an alternative estimate for the skewness of the logs of −0.1242, somewhat less in magnitude than the station value given by Equation 3. Which value is more nearly correct could be determined from a study of other stations in the region; the practical effect of the small difference in this case is minor. For purposes of examples in this chapter, the weighted value of −0.1242 will be used [Equation 4]. The weighted average skewness is computed as follows:
For the Siletz River data for Oregon, using Equation 4, Cm = 0.0 and | Cs | = 0.1565. Therefore,
A = − 0.33 + 0.08(0.1565) = − 0.31748
B = 0.94 -0.26(0.1565) = 0.899315
and V(Cm) = 0.302 for the map.
Finally,
and 1 − W = 0.207
and
Cw = 0.793(−0.1565) + (0.207)(0.0) = − 0.1242.
Table 2
Computed Moments for Annual Maximum Floods for the Siletz River, Near Siletz, Oregon, Water Years 1925-1999.
| Excel Function | Original Data (cfs) | login Data (log cfs) |
Number of data points | COUNT | 75 | 75 |
Mean [Equation 1] | AVERAGE | 20,452 cfs | 4.2921 |
Variance [Equation 2)] | VAR | 37,079,690 cfs2 | 0.01665 |
Standard deviation | STDEV | 6089 cfs | 0.12905 |
Coefficient of variation |
| 0.298 | 0.030 |
Skewness [Equation 3] | SKEW | 0.7889 | -0.1565 |
Weighted skewness [Equation (4)] |
|
| -0.1242 |
Table 1
Data and Freauency Analysis Computations for the Siletz River, at Siletz, Oreaon
Wate Year | Peak Flow (cfs) | Water Year | Peak Flow (cfs) | Water Year | Peak Flow (cfs) |
1925 | 1 8,800 | 1950 | 16,400 | 1975 | 21,500 |
1926 | 16,800 | 1951 | 16,600 | 1976 | 23,600 |
1927 | 19,500 | 1952 | 19,400 | 1977 | 8630 |
1928 | 30,700 | 1953 | 24,600 | 1978 | 23,100 |
1929 | 11,200 | 1954 | 21,900 | 1979 | 16,600 |
1930 | 11,500 | 1955 | 21,200 | 1980 | 14,500 |
1931 | 34,100 | 1956 | 22,700 | 1981 | 26,500 |
1932 | 21,800 | 1957 | 20,900 | 1982 | 21,400 |
1933 | 19,800 | 1958 | 22,200 | 1983 | 18,300 |
1934 | 28,700 | 1959 | 14,200 | 1984 | 11,300 |
1935 | 1 5,000 | 1960 | 14,200 | 1985 | 13,600 |
1936 | 1 9,600 | 1961 | 24,400 | 1986 | 17,100 |
1937 | 16,100 | 1962 | 20,900 | 1987 | 20,000 |
1938 | 30,100 | 1963 | 26,300 | 1988 | 19,200 |
1939 | 17,800 | 1964 | 19,700 | 1989 | 15,400 |
1940 | 21 ,400 | 1965 | 32,200 | 1990 | 17,200 |
1941 | 1 3,200 | 1966 | 19,500 | 1991 | 20,500 |
1942 | 25,400 | 1967 | 19,100 | 1992 | 10,800 |
1943 | 26,500 | 1968 | 18,600 | 1993 | 12,000 |
1944 | 12,800 | 1969 | 14,500 | 1994 | 18,300 |
1945 | 22,400 | 1970 | 17,200 | 1995 | 18,800 |
1946 | 21 ,600 | 1971 | 18,100 | 1996 | 34,700 |
1947 | 28,000 | 1972 | 31,800 | 1997 | 22,700 |
1948 | 21 ,900 | 1973 | 19,700 | 1998 | 16,800 |
1949 | 29,000 | 1974 | 20,900 | 1999 | 40,500 |
Data were downloaded for USGS Gage Number 14305500 from the USGS website, http://water. usgs.gov/nwis/. The continuous record from 1925 has been used.
Figure 4
Generalized skew coefficients of logarithms of annual maximum streamflow. (From IACWD, 1982.)
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