Webinar: Roughing it for water modellers

Using the Manning equation in hydraulic modelling

The empirical Manning equation is one of the most commonly applied approaches in water modelling. Where did it come from, what does it mean for your model, and what other options do you have?

Selecting a proper roughness coefficient is one of the most challenging and consequential steps of hydraulic modelling. Please join us for a discussion about the background of the Manning’s roughness coefficient along with tips, tricks, and pitfalls to look out for in selecting representative values for open channels, pipes, and more.

Date: Wednesday, 26 February 2020

Time: 10:00am (Australia/Sydney; find your local time)

Format: 30min presentation + 30min Q&A

Cost: Free

Chair: Trevor Pillar, National Partnerships Manager

Resources: Webcasts and other documents will be available here

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Presenters:

Robert Czachorski

OHM Advisors

Robert is a principal of OHM Advisors and president of H2Ometrics, a cloud-based smart water and sewer data application platform. During his career, Robert has performed and managed large water resour... Read more

Chris Goodell

Kleinschmidt Associates

Chris is the Principal Consultant for Hydraulics and Hydrology at Kleinschmidt Associates. Chris specializes in water resources hydraulic engineering. He has a background in both hydraulic design and... Read more

Krey Price

Surface Water Solutions

Educated at the University of California at Berkeley, Krey is a civil engineer and project manager with international experience in water resources. He is engaged in computational modelling, engineeri... Read more

Resources:

video

Webinar: Roughing it for water modellers

Water Modelling & GIS

26 Feb 2020

Details:

Attendees-Registrants Map

Fudge factor (noun, informal): a figure included in a calculation to account for some unquantified but significant phenomenon or to ensure a desired result.

By definition, the Manning’s roughness coefficient is a fudge factor. So how do we justify using a subjective number that cannot be derived in our advanced modelling systems? And how can the selection process be improved to provide better confidence in modelling results?


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