Statistical Methods in Hydrology (250822) – Course 2025/26 PDF
Contents
* Sistema de evaluación. * Características del trabajo personal a realizar. * Que aporta la estadística a la hidrología. * Técnicas de análisis de datos en hidrología. La importancia de las técnicas estadísticas. * Caracterización estadística de las variables hidrológicas. * Ajustes de leyes de distribución de probabilidad. * Bondad de un ajuste. Criterios estadísticos objetivos. * Ejemplos con la distribución normal y lognormal
Dedication
2h Large group + 2h 48m Self Study* The concept of return period. Use in hydrology. * Laws of probability distribution of expremos. Gumbel law. GEV. * Graphic setting: need and methodology. * Construction of graphics settings Gaussian, lognormal and Gumbel. * Examples and exercises. * Problem setting extreme values of flow rates. * Probability distributions Log Pearson III, TCEV and SQRT-MAX * Examples and exercises. * The method of GRADEX * Troubleshooting and exercises * flow duration curve. * Applications to detection of drought, farms and hydrological characterization of the impact of climate change. * Uniformity of series. * Curves double cluster. * Comparison with other variables. * Examples and exercises.
Dedication
4h Large group + 6h Medium group + 14h Self Study5. multivariate statistical analysis: * Troubleshooting and exercises fit multiple regression
Dedication
2h Large group + 2h Medium group + 5h 36m Self Study* Basic concepts. And eigenvalues. * Statistical significance. Choosing the number of variables. * Cluster analysis * Application to improve the reconstruction of series. * Application to improve the reconstruction of 2D fields. * Examples and exercises. * Study of the interpolation of the historical rainfall distribution patterns. * Troubleshooting and exercises Principal Components Analysis
Dedication
2h Large group + 2h Medium group + 5h 36m Self Study* Basic concepts. Regionalized variables. * Random Functions. stationary and second order stationary random functions. * variogram. Relationship between semi-variogram and covariance stationary random functions. * Ordinary Kriging. * Examples * Inference variogram. Semi-variogram sample. * semi-variogram models: exponential, spherical, Gaussian and pure nugget. * Exercises * Analysis of advanced structure: scales of variability, anisotropy causes of nugget effect semivariograms stationary and non-stationary. * Introduction to problem solving using IDL * Exercises * Universal Kriging and residual * Cokriging * Kriging with external co-kriging and co-located derived. * Introduction to geostatistical simulation. * Monte Carlo method. sequential simulation. * Tracks to learn more. * Examples * Troubleshooting and exercises and kriging geostatistical analysis
Dedication
6h Large group + 3h Medium group + 12h 36m Self Study* Basic principle for validation * Simple techniques. * Advanced techniques. * Examples of models and validation on real data * Exercises. * Tracks to learn more.
Dedication
2h Large group + 2h 48m Self Study* Concept of uncertainty. Characterization methods of uncertainty. * Impact of uncertainty in forecasting models. * probabilistic forecast. hydrological forecasting ensemble * Probabilistic models of rain forecast * Probabilistic models of rain forecast * Tracks to learn more.
Dedication
2h Large group + 2h 48m Self Study* Resolution of doubts. * Presentation of results of exercises
Dedication
3h Medium group + 4h 11m Self Studyexercises
Dedication
6h Medium group + 3h Laboratory classes + 12h 36m Self Study