Statistical calculations pmp12/6/2023 ![]() Engineers therefore seek additional information to overcome the limitations of deterministic physical reasoning and probabilistic approaches. ![]() These approaches have been criticized ( Klemeš 1986, 1987, 2000), for example, on the basis that very long return periods (e.g., for 1000 or 10 000 years) can only be estimated from available 50- or 100-yr observational records with very high uncertainty, which may make these estimates unsuitable for engineering applications. These approaches involve the fitting of probability distribution models to recorded storm precipitation amounts and extrapolating the tails of these models to very low exceedance probabilities. ![]() Probabilistic approaches using statistical frequency analysis offer a plausible alternative for estimating extremes for a given return period. Nevertheless, estimates of such rare extremes are needed for engineering practice, for example, in dam spillway design. In the case of extreme precipitation, current knowledge of storm mechanisms remains insufficient to allow a precise evaluation of limiting values and very rare extreme precipitation. While we have developed an impressive ability to describe climate and hydrologic systems from both dynamic and thermodynamic perspectives, for practical purposes, we do not yet have the ability to analyze and describe the upper bounds of the intensity of many types of extremes based on physical reasoning. A difference of this magnitude may have serious implications in engineering design. Estimates from the operational approach are 15% larger on average over North America than those obtained when accounting for the dependence between precipitation efficiency and precipitable water extremes realistically. In contrast, our results suggest weaker than complete dependence. ![]() Specifically, in the climate simulated by CanRCM4, the operational approach applied to 50-yr data records produces a value that is similar to the value that is obtained in our approach when assuming complete dependence between extreme precipitation efficiency and extreme precipitable water. Results based on a 50-yr Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) simulation over North America reveal that operational PMP estimates are highly uncertain and suggest that the assumption that PMP events have maximum moisture availability may not be valid. This 1) allows us to evaluate estimates from the operational procedure relative to an estimate of a plausible distribution of PMP values, 2) enables an evaluation of the uncertainty of these values, and 3) provides clarification of the impact of the assumption that a PMP event occurs under conditions of maximum moisture availability. We therefore propose a probabilistic framework based on a bivariate extreme value distribution to aid in the interpretation of these PMP values. The usual operational procedure for obtaining PMP values, which is based on a moisture maximization approach, produces a single PMP value without an estimate of its uncertainty. Probable maximum precipitation (PMP) is the key parameter used to estimate the probable maximum flood (PMF), both of which are important for dam safety and civil engineering purposes.
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |