Welcome to Module 4!

This module introduces powerful techniques for model calibration and validation. We will explore ModelSkill, a dedicated Python package for comparing MIKE+ model outputs against observed data.

You’ll see how ModelSkill builds upon MIKE IO, leveraging your existing skills in data handling.

Throughout this module, you will learn to:

• Prepare Observation and ModelResult objects.
• Match observational data with model results.
• Visualize model performance using standard validation plots.
• Quantify model accuracy using statistical skill scores.

This module culminates in a practical homework assignment where you’ll apply these skills to validate a sample MIKE+ model.

Let’s dive in!

Where can I download sample data to follow along?

All of the sample data used in this module is available for download:

• flow_meter_data.dfs0
• flow_meter_data.csv
• model_results.dfs0
• network.res1d