Pastas is an open source Python package to analyse hydro(geo)logical time series. Time series modelling is a powerful tool for understanding groundwater dynamics, offering a data-driven approach to analysing hydrological processes. This course provides a comprehensive introduction to time series modelling techniques using Pastas, focusing on lumped-parameter models that use impulse response functions to describe groundwater level fluctuations. Through a combination of theory and hands-on exercises, participants will learn to investigate key hydrological influences such as precipitation, evaporation, and groundwater pumping using Pastas and Jupyter Notebooks.
Designed for groundwater researchers and practitioners, this four-session course presented by the primary developers of Pastas, provides participants with the skills to integrate time series models into their hydrogeological studies. By working through practical case studies, attendees will gain experience in recognising when time series analysis is applicable and how to construct models to interpret groundwater behaviour. This structured learning approach ensures a balance between conceptual understanding and applied problem-solving.
By the end of the course, participants will have the confidence to implement simple time series models in real-world groundwater investigations. You will be able to assess groundwater recharge, evaluate pumping effects, and identify dominant hydrological drivers with a quantitative approach.
Onno Ebbens is a Dutch geohydrologist who graduated from Delft University of Technology (TU Delft) in 2015 with a degree in Water Management. His master's thesis focused on utilising time series analy... Read more
Raoul is a hydrologist focusing on groundwater related problems and developing open-source software to solve them. He leads and is involved in developing open-source software such as Pastas, PyEt and ... Read more
Course Overview
This course provides a comprehensive learning experience by the primary developers of Pastas, an open source Python package in modelling and analysing groundwater level time series. Participants will learn how to extract valuable insights into groundwater systems using Pastas and Python, with practical hands-on exercises in Jupyter Notebooks.
To know more about the theoretical background of Pastas, read the article published in Groundwater, a Journal of The National Groundwater Association.
Learning Outcomes
In this course, you will be able to:
Analyse time series with the Python package Pandas.
Model groundwater level (GWL) time series using Pastas and Python scripts.
Understand how to develop different model structures to simulate GWLs.
Learn about lumped-parameter groundwater models and impulse response functions.
Course Outline
Session 1: Analysing time series with Pandas
Load different types of time series data into Pandas Series and DataFrames.
Analyse the time series data.
Plot the time series data to gain good understanding of the data.
Correct missing values and errors in the time series.
Prepare time series to use as input into Pastas models.
Session 2: Setting up your first Pastas Model
Learn about impulse response functions.
How to set up a basic Pastas model.
How to perform a basic calibration.
Plot and analyse the results.
Understand the meaning of model parameters and statistics.
Session 3: Different model structures for Pastas
Explore how to add different stresses (i.e., pumping, river levels) to the model.
Understand the different options to account for precipitation and potential evaporation.
Build more complex models and compare the results of different models.
Session 4: Model calibration and analysis
Learn how to perform better calibration by choosing different settings.
Learn how to model the residuals using a noise model.
Analyse the model residuals on their statistical assumptions.
Introduction into exploring model uncertainties.
Format
4 x 2hr live interactive sessions from 4 – 6 PM Sydney Time
Session 1: Thursday, 3 July
Session 2: Thursday, 10 July
Session 3: Thursday, 17 July
Session 4: Thursday, 24 July
Ability to ask questions and have them answered in a live forum;
Set of Jupyter notebooks and working model download/s;
Ability to access all the online course materials for a further 30 days after the final live session of the course;
Sessions are recorded & uploaded to the learning platform (LMS – within 2 business days after the session), if you cannot attend live.
Pre-requisites
A basic understanding of Python programming, including working with Jupyter notebooks.
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