Time series modeling of Groundwater levels with Pastas

Description

Course Overview

The course will be held onsite in Milan over three days during the second week of April 2026 (exact dates to be defined).

Time series analysis may be applied to answer many groundwater questions without the need for a regular groundwater model, such as what is the drawdown caused by a pumping station? Or, how long will it take before groundwater levels recover after a period of drought? For a more detailed overview, see: Bakker and Schaars (2019).

This workshop provides a theoretical and practical introduction to time series modeling of groundwater levels using impulse response functions, with hands-on exercises using the open-source Python package Pastas.

Participants will learn how to use Pastas to describe and predict groundwater level fluctuations. By the end of the course, you will be able to:

• Identify and quantify the influence of hydrological forcings such as precipitation, evaporation, groundwater pumping and surface water levels;
• Apply data-driven time series models to solve real-world groundwater problems;
• Choose an appropriate time series model structure taking into account forcings, trends, and nonlinearity;
• Manage and evaluate large datasets efficiently.

Requirements

No previous experience with time series modeling is required, but familiarity with Python is required to successfully complete the in-class exercises performed in Jupyter Notebooks. If your Python level is not sufficient, you can get up to par with some excellent free online material such as this MOOC (https://programming-26.mooc.fi/) or GitHub repository (https://github.com/mbakker7/exploratory_computing_with_python).

Trainers

• Raoul Collenteur (Collenteur HydroConsult GmbH)
• Mark Bakker (Delft University of Technology)
• Davíd Brakenhoff (Artesia / Delft University of Technology)
• Onno Ebbens (Artesia)
• Ruben Caljé (Artesia)
• Martin Vonk (Artesia / Delft University of Technology)

April 2026, Milan (Italy) – dates to be defined