Thesis
Monitoring the temporal variation of Riparian vegetation roughness
University of Iowa
Master of Science (MS), University of Iowa
Spring 2026
Abstract
Manning’s roughness coefficient (n) is widely used to estimate flow resistance in open-channel hydraulics, yet it is conventionally treated as a static parameter dependent only on boundary material. In vegetated alluvial channels, this assumption does not account for temporal variability in roughness associated with unsteady flow conditions and seasonal changes in riparian vegetation. This investigation evaluates how Manning’s n varies within individual storm events and across seasons, and develops a framework for predicting roughness using observable hydraulic and vegetation characteristics. A field investigation was conducted at Clear Creek near Oxford, IA, where stage, velocity, and vegetation data were collected during eight storm events between 2024 and 2025. Manning’s n was computed at incremental elevations to allow direct comparison between the rising and falling limbs of hydrographs (Event variability) and across storm events occurring in different seasons (Seasonal variability). Results showed that n exhibited a change in roughness of up to 60% within storm events, such that computed roughness values on the rising limb were consistently higher than those on the falling limb at equivalent stages. Seasonal variability was also observed, with higher (up to 25%) and more variable roughness during the growing season compared to the non-growing season. These effects were most pronounced at elevations where flow interacts with riparian vegetation, indicating that vegetation is the primary driver of roughness variability.
A vegetation-based modeling framework was developed using a drag coefficient (Cd) formulation related to the product of velocity and hydraulic radius (VR) in order to incorporate this behavior into roughness prediction. Separate Cd – VR relationships were derived for growing and non-growing seasons and for rising and falling hydrograph limbs to account for both event- and seasonal-scale variability. Model validation using independent storm events demonstrated strong predictive capability during the growing season, with most predicted n values within ±10% of measured values. Performance during the non-growing season showed greater variability due to limited data availability.
These results demonstrate that treating Manning’s n as a constant parameter induces error in flow resistance estimation in vegetated channels. Incorporating time-dependent roughness through vegetation-based modeling improves the ability to accurately predict flow behavior under unsteady conditions and has implications for predicting flood magnitude and timing in natural systems.
Details
- Title: Subtitle
- Monitoring the temporal variation of Riparian vegetation roughness
- Creators
- Anthony John L Lamoreux
- Contributors
- Priscilla Williams (Advisor)Marian Muste (Committee Member)Allen Bradley (Committee Member)
- Resource Type
- Thesis
- Degree Awarded
- Master of Science (MS), University of Iowa
- Degree in
- Civil and Environmental Engineering
- Date degree season
- Spring 2026
- Publisher
- University of Iowa
- Number of pages
- xviii, 175 pages
- Copyright
- Copyright 2026 Anthony John L Lamoreux
- Language
- English
- Date submitted
- 04/28/2026
- Description illustrations
- illustrations (some color)
- Description bibliographic
- Includes bibliographical references (page 168-175).
- Public Abstract (ETD)
Engineers use Manning’s n to estimate how fast water moves through rivers and streams. In most cases, this value is treated as constant, meaning it does not change over time. But in real channels, especially those with vegetation, conditions are always changing.
This investigation examined how flow resistance changes in a stream near Oxford, IA during multiple storm events across different seasons. The results showed that roughness is not constant. It changes by 60% during a storm and also shifts by 25% throughout the year as vegetation grows and decays. When water levels rise, plants interact with the flow in ways that increase resistance. As water levels fall, that resistance changes again. These differences were most noticeable where flowing water reaches vegetation along the banks of the channel.
To better represent these changes, this study developed a method that adjusts roughness based on flow conditions and vegetation. Instead of using a single fixed value, the approach reflects how the channel actually behaves. This method more closely matches field measurements than traditional approaches.
This matters because roughness directly affects how we predict flooding. If roughness is treated as constant, models can under- or over-estimate how high floodwaters will rise and how quickly they will move. By better representing how rivers behave during storms, this work can help improve flood predictions and support more reliable planning and design.- Academic Unit
- Civil and Environmental Engineering
- Record Identifier
- 9985177072902771
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