Lecture

Precipitation and Hydrologic Design

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In course

ENV-221: Hydrology for engineers

This is an introductory course to key concepts and methods in physical and engineering hydrology. ...

Description

This lecture focuses on methods to define the design storm for a given location and precipitation duration. It covers statistical analyses of precipitation data, empirical methods, Weibull plotting position, and probability distributions for precipitation extremes. The instructor explains the Weibull plotting position, empirical distribution of rainfall maxima, Gumbel distribution, and estimating its parameters. The lecture also discusses the areal distribution of precipitation, spatial reduction factor, intensity-duration-frequency relationships, and parameterizations of IDF curves. It concludes with examples of rainfall intensity maps and how to relate return period to rainfall depth.

Instructors (3)

Andrea Rinaldo

Paolo Benettin

I was born in Padova, Italy, in 1986. After high school, I started the Environmental Engineering program at the University of Padova (Bachelor and Master), where I followed the 'soil protection' curriculum. During my Master studies I spent a semester as Erasmus student at Wageningen University (Wageningen, the Netherlands) to follow courses in Hydrology. After graduation, I decided to bring my education further and I started a Ph.D. at the civil and environmental engineering department at the University of Padova. My Ph.D. thesis focused on hydrologic transport and travel time distributions at the catchment scale. My contribution to the subject included both new theoretical formulations and extensive numerical applications to real catchments. During my Ph.D., I also spent 7 months as visiting research fellow at Virginia Tech University (Blacksburg, USA). Since I arrived at EPFL as post-doc in 2015, I started being involved in field and experimental activities, which allowed me to learn about practical experimental problems. In 2017 I had the opportunity to by hired by the ECHO laboratory as a permanent scientist, conducting research in the field of catchment hydrology.

Sara Bonetti

Expertise Ecohydrological and geomorphological modeling Education PhD | Civil and Environmental Engineering 2018 – 2018 Duke University Teaching & PhD PhD Students Benjamin Andre De Blois Kargere, Qiming Sun, Frédéric Laurent Lardet, Taiqi Lian, Felix Bauer Courses Ecohydrological modeling ENV-411 This course provides the theoretical basis for understanding and modeling the interactions between the hydrologic cycle, vegetation, soil, climate, and human society. Hydrology

Official source

Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics

Related concepts (31)

Generalized extreme value distribution

In probability theory and statistics, the generalized extreme value (GEV) distribution is a family of continuous probability distributions developed within extreme value theory to combine the Gumbel, Fréchet and Weibull families also known as type I, II and III extreme value distributions. By the extreme value theorem the GEV distribution is the only possible limit distribution of properly normalized maxima of a sequence of independent and identically distributed random variables.

Fréchet distribution

The Fréchet distribution, also known as inverse Weibull distribution, is a special case of the generalized extreme value distribution. It has the cumulative distribution function where α > 0 is a shape parameter. It can be generalised to include a location parameter m (the minimum) and a scale parameter s > 0 with the cumulative distribution function Named for Maurice Fréchet who wrote a related paper in 1927, further work was done by Fisher and Tippett in 1928 and by Gumbel in 1958.

Estimation theory

Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component. The parameters describe an underlying physical setting in such a way that their value affects the distribution of the measured data. An estimator attempts to approximate the unknown parameters using the measurements.

Maximum likelihood estimation

In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable. The point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate. The logic of maximum likelihood is both intuitive and flexible, and as such the method has become a dominant means of statistical inference.

Statistics

Statistics (from German: Statistik, () "description of a state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal".

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Related concepts (31)

Generalized extreme value distribution

Fréchet distribution

Estimation theory

Maximum likelihood estimation

Statistics