Simulation of Lake Erie Mean Monthly Water Levels

Planning and designing lakeshore structures requires information on the likely range of future lake levels. Historical data present only one set of observed events with no information as to whether future events will be greater or smaller. Using statistics extracted from the historical data set, we...

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Bibliographic Details
Published in:Journal of Great Lakes research 1992, Vol.18 (3), p.481-488
Main Author: Kite, Geoffrey W.
Format: Article
Language:English
Subjects:
data generation
Freshwater
Lake Erie
Statistical model
water levels
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Summary:Planning and designing lakeshore structures requires information on the likely range of future lake levels. Historical data present only one set of observed events with no information as to whether future events will be greater or smaller. Using statistics extracted from the historical data set, we can generate alternate sequences of data with the same properties as those observed. By generating very many sequences we can derive information on the frequency of occurrence of events of varying magnitude, thus providing the information needed for planning. Time series techniques were used to analyze mean monthly levels of Lake Erie at Cleveland, Ohio, and to generate 1,000 time series to estimate confidence limits. Results indicate that the maximum and minimum historical lake levels all fall within the generated upper and lower 95% confidence limits but that maximum and minimum generated data are often significantly beyond the range of the historical data.
ISSN:0380-1330
DOI:10.1016/S0380-1330(92)71313-6