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Identical coursesStochastic Hydrology (B-KUL-I0D13B)
Cannot be taken as part of an examination contractAims
Fundamental knowledge and practical understanding is given for the common techniques of data processing, statistical analysis, time series analysis, data-driven and stochastic modelling in hydrology and water engineering, model uncertainty analysis, and water system control. This knowledge and understanding must allow the students to select and apply most appropriate techniques for such processing, analysis, modelling and control. It also allows them to have an insight in the limitations of these techniques and the corresponding consequences for water management and engineering.
Previous knowledge
Students are supposed to have skills in calculus, mathematics, statistics, and spreadsheet software
Order of Enrolment
This course unit is a prerequisite for taking the following course units:
I0S76A : Thesis Research Project Water Resources Engineering
I0S78A : Research Methods for Data Collection and Processing
Identical courses
This course is identical to the following courses:
I0D13A : Statistics for Water Engineering (No longer offered this academic year)
Is included in these courses of study
- Master of Water Resources Engineering (Leuven et al) 120 ects.
- Master of Water Resources Engineering (abridged programme 60 ECTS) (Leuven et al) 60 ects.
- Courses for Exchange Students Faculty of Bioscience Engineering (Leuven)
- Master in de bio-ingenieurswetenschappen: landbeheer (Leuven) (Major bodem- en watersystemen) 120 ects.
- Master of Bioscience Engineering: Agro- and Ecosystems Engineering (Leuven) (Major Subject: Soil and Water Systems) 120 ects.
Activities
2.5 ects. Stochastic Hydrology: Lectures (B-KUL-I0D13a)
Content
1. Hydrological time series analysis: Subflow filtering in hydrology; Selection of independent extremes from a time series
2. Extreme value analysis: Periodic maxima method, Peak-over-threshold method (POT, PDS); Extreme value distributions for hydrological extremes; Return period calculation; Flood frequency analysis; Low flow frequency analysis; Combined extreme-value-analysis at different time scales / aggregation levels and introduction to IDF, QDF and CDF relationships
3. Data-driven, conceptual and stochastic modelling
4. Model sensitivity and uncertainty analysis: Model residual analysis; Model goodness-of-fit statistics; Model sensitivity analysis; Variance decomposition; Different types of uncertainty sources in mathematical modelling; Calculation of parameter uncertainties and model prediction uncertainties
5. Systems approach in water management; Model based decision support
6. Statistical downscaling for climate change impact analysis
7. Real-time control of water systems
8. Introduction to the application of AI methods in water management and engineering
Course material
Documents on Toledo: course text, slides
2.5 ects. Stochastic Hydrology: PC Class Sessions (B-KUL-I0V91a)
Content
Based on example datasets for water systems, PC class exercises will be given on:
1. Separation of river flow time series in baseflow, interflow and overland flow components
2. Extraction of independent peak flow extremes from a river flow time series
3. Extreme value analysis on river peak flows; flood frequency analysis
4. Data-driven and grey-box model structure identification, calibration and validation
5. Model performance evaluation
6. Model uncertainty analysis
7. Climate change impact analysis
8. Real-time control of a reservoir.
Course material
Exercise descriptions + datasets + software-tools