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~' After obtaining results using the averaged monthly precipitation data, calcula- <br />tions were repeated using "extreme worst case" precipitation data. The maximum <br />annual precipitation for a 35 year period of record was distributed among 12 <br />months using long-term average monthly distributions. With [his new precipita- <br />tion input, [he calculations were repeated. For [his hypothetical worst case <br />year, 8.75 cm of percolation were calculated. <br />Next, 35 years of precipitation data were analyzed for frequency of occurrence of <br />annual precipitation totals. This was done to determine the frequency of occur- <br />rence and the amount of percolation expected over a long time period. Annual <br />precipitation totals with recurrence intervals of 4, 5, 10, 20, 36, and 50 years <br />were selected. Monthly distributions of precipitation were done as in the worse <br />case calculations. These precipitation inputs were then used in [he model to <br />calculate corresponding annual percolation rates (Table 4.3-10, columns 2-5 ). <br />Probability class intervals were formed using [he difference between the probabi- <br />lilies of the interval end members. For example, events with recurrence inter- <br />vale between 10 and 20 years were assigned a probability of .10-.OS = .OS (see <br />columns 7 and 8 in Table 4.3-10). Percolation quantities for each probability <br />class interval were calculated by averaging the percolation values for the class <br />interval end points (see column 9). <br />The estimated most probable annual percolation amount was calculated as follows: <br />A ~° QPiQi <br />i = 1 <br />Where A =annual percolation amount <br />Pi = probability of even[ within the i[h class interval occurring (column <br />8) <br />Qi = percolation quantity for the ith class interval (column 9). <br /><~ <br />4-48 <br />