Stochastic Methods in Hydrology (250MAG008) – Course 2025/26 PDF
Syllabus
Learning Objectives
By the end of the course, students will be able to: Understand the fundamentals of statistics applied to hydrology and hydrogeochemistry. Manipulate and analyze hydrological data using programming tools such as Python and scientific libraries. Apply descriptive and inferential statistical techniques to environmental datasets. Identify and quantify temporal and spatial patterns in hydrological data. Estimate return periods and fit extreme value models for risk assessment. Develop hydrological forecasting models based on observational data. Apply multivariate analyses such as principal component analysis (PCA) in hydrogeochemical contexts. Understand and apply geostatistical methods for spatial interpolation and simulation of hydrological variables. Interpret statistical results within the framework of real-world engineering and environmental science problems.
Total hours of student work
| Hours | Percentage | |||
|---|---|---|---|---|
| Supervised Learning | Large group | 45h | 100.00 % | |
| Self Study | 80h | |||
Teaching Methodology
There are 3 hours per week of face-to-face classes in the classroom. In these classes, concepts and content are explained, with examples and problem solving. Applied sessions that require the use of computer tools take place in the computer lab, unless all students prefer to use their laptops in the classroom. The main and supplementary support materials will be provided through the ATENEA virtual campus.
Grading Rules
The evaluation calendar and grading rules will be approved before the start of the course.
Practical assignments are proposed to be completed at home during the course, and an exam is held at the end of the semester. The final grade is calculated as FG = 0.6 * EG + 0.4 * PG, where EG is the exam grade and PG is the average grade of the practical assignments.
Bibliography
Basic
- Freeze, R. Allan; Cherry, John A. Groundwater. Englewood Cliffs (N.J.): Prentice-Hall, cop. 1979. ISBN 9780133653120.
- McKinney, Wes. Python for Data Analysis: Data Wrangling with Pandas, Numpy, and Jupyter. 3rd ed. Sebastopol, CA: O'Reilly Media, 2022. ISBN 109810403X.
- Cressie, Noel A.C. Statistics for Spatial Data. Rev. ed. New York: Wiley, 1993. ISBN 0471002550.
- Helsel, Dennis R.; Hirsch, Robert M.; Ryberg, Karen R.; Archfield, Stacey A.; Gilroy, Edward J. Statistical Methods in Water Resources. USGS, 2020.