Lecture

Hydrologic Design: Probability & Statistics

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

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 by the instructor covers the application of probability and statistics in hydrologic design, focusing on topics such as return period, risk assessment, and the relationship between empirical data and theoretical concepts. The lecture delves into defining random variables, discrete and continuous distributions, and the calculation of probabilities for hydrological events. It also discusses the importance of estimating return periods for extreme events, the concept of engineering design scale, and the use of Weibull plotting positions in data analysis. Practical examples and generalized design criteria for water-control structures are presented to illustrate the theoretical concepts discussed.

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 (32)

Probability theory

Probability theory or probability calculus is the branch of mathematics concerned with probability. Although there are several different probability interpretations, probability theory treats the concept in a rigorous mathematical manner by expressing it through a set of axioms. Typically these axioms formalise probability in terms of a probability space, which assigns a measure taking values between 0 and 1, termed the probability measure, to a set of outcomes called the sample space.

Complex random variable

In probability theory and statistics, complex random variables are a generalization of real-valued random variables to complex numbers, i.e. the possible values a complex random variable may take are complex numbers. Complex random variables can always be considered as pairs of real random variables: their real and imaginary parts. Therefore, the distribution of one complex random variable may be interpreted as the joint distribution of two real random variables.

Independent and identically distributed random variables

In probability theory and statistics, a collection of random variables is independent and identically distributed if each random variable has the same probability distribution as the others and all are mutually independent. This property is usually abbreviated as i.i.d., iid, or IID. IID was first defined in statistics and finds application in different fields such as data mining and signal processing. Statistics commonly deals with random samples. A random sample can be thought of as a set of objects that are chosen randomly.

Convergence of random variables

In probability theory, there exist several different notions of convergence of random variables. The convergence of sequences of random variables to some limit random variable is an important concept in probability theory, and its applications to statistics and stochastic processes. The same concepts are known in more general mathematics as stochastic convergence and they formalize the idea that a sequence of essentially random or unpredictable events can sometimes be expected to settle down into a behavior that is essentially unchanging when items far enough into the sequence are studied.

Probability distribution

In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment. It is a mathematical description of a random phenomenon in terms of its sample space and the probabilities of events (subsets of the sample space). For instance, if X is used to denote the outcome of a coin toss ("the experiment"), then the probability distribution of X would take the value 0.5 (1 in 2 or 1/2) for X = heads, and 0.

Related lectures (64)

Law of Large Numbers: Strong Convergence

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

Continuous Random Variables

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Elements of Statistics: Probability and Random Variables

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Probability and Statistics

Explores joint random variables, conditional density, and independence in probability and statistics.