Grant: Discovery Grants Program - Individual

In designing flood-control structures, or in estimating flood damage, decisions are often made on the basis of a short sample of river flow measurements. A statistical frequency distribution is chosen to fit the measurements, and the fitted distribution is then used to estimate future floods. Many methods exist to fit distributions to data. Some of the distributions have a finite upper bound that is a function of the distribution parameters (shape, scale, etc.). Some distribution fitting methods may produce an estimate of this upper bound that is inconsistent with the observed data. This occurs when one or more sample observations exceed the upper bound of the estimated distribution. This makes the estimated upper bound unrealistic. We will investigate this inconsistency of some estimation methods with the observed data and examine its engineering risk consequences. We hope to establish improved guidelines for choosing between statistical distributions and fitting methods for hydrological risk analysis. In hydrological design, the joint occurrence of more than one random variable is often involved. For example, the study of urban drainage systems involves the simultaneous consideration of several random variables such as the intensity, duration, and frequency of rainfall events. The frequency analysis of these variables must be based on their joint frequency distribution. We will develop new methods for estimating the joint occurrence probability of more than one hydrological variable and make these methods available to practitioners. ) Regional flood estimation procedures are useful for improving flood estimation at an engineering site when there is a lack of river flow measurements at the site (""ungauged site""). In ""regional flood frequency modeling"", ""hydrologically homogeneous"" regions are delineated by grouping ""similar"" river basins together. Needed flood estimates at the ungauged site are then obtained by developing ""regional frequency models"". We will pursue research on regional frequency models and how to improve them.