Methods of estimating floods with return periods of up to one hundred years are reasonably well established, and in the main rely on extrapolation of historical flood data at the site of interest. However, extrapolating the tails of fitted probability distributions to higher return periods is very unreliable and cannot provide a satisfactory basis for extreme flood estimation. The probable maximum flood concept is an alternative approach, which is often used for critical cases such as the location of nuclear power plants, and is viewed as a consequence of a combination of a probable maximum precipitation with the worst possible prevailing catchment conditions. Return periods are not usually quoted although they are implicitly thought to be of the order of tens of thousand of years. There are many less critical situations which still justify greater flood protection than would be provided for an estimated one-hundred year flood. There is therefore a need for techniques which can be used to estimate floods with return periods of up to several thousand years. The predictive approach adopted here involves a combination of a probabilistic storm transposition technique with a physically-based distributed rainfall-runoff model. Extreme historical storms within a meteorologically homogeneous region are, conceptually, moved to the catchment of interest, and their return periods are estimated within a probabilistic framework. Known features of storms such as depth, duration, and perhaps approximate shape will, together with catchment characteristics, determine much of the runoff response. But there are other variables which also have an effect and these include the space-time distribution of rainfall within the storm, storm velocity and antecedent catchment conditions. The effects of all these variables on catchment response are explored.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:386794 |
| Date | January 1994 |
| Creators | Suyanto, Adhi |
| Publisher | University of Newcastle Upon Tyne |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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