Advanced Flood Modeller
Key Benefits
Delivered through a series of presentations, hands-on practicals, and open discussion sessions
Presented from a practical point of view, by experienced modellers with a background in hydraulic modelling
Course materials include presentation slides, practical examples and datasets, and a glossary of common terminology
Includes a Flood Modeller licence for attendees to access the software on their personal laptop
Suitable for users with experience in flood modelling, preferably attendees will have previously attended the courses Introduction to 1D river modelling and Introduction to 2D modelling
What you will learn
This course will provide you with a solid understanding of advanced features within Flood Modeller, including tools to increase productivity. By the end of the course, you will have experience troubleshooting model issues, alongside the ability to critically review the elements of 1D-2D linked models.
You will be provided with details of the underlying equations and numerics within the solvers and know how the settings provided impact simulation results.
You will gain knowledge of the advanced parameters used by the software, learning the importance of selecting appropriate parameters and the degree of uncertainty that can result in inappropriate values.
The course covers comprehensive model review details, and you will gain experience in using built-in tools to review models and identify any problems.
You will gain an understanding of the model diagnostic information available and how to use it to debug failing models at initialisation and runtime. You will also investigate methods for adjusting models to fit calibration data.
The course will provide you with experience in using both the Simulation Builder and Batch Runner tools to increase productivity when requiring results for multiple return periods, for instance.
You will learn best practices when modelling 1D structures and how to review the suitability of these within models. You will have discussed aspects of uncertainty within modelling, when to have trust in your results, and how to improve results.