Spatially distributed estimates of Manning’s roughness within floodplain areas of the conterminous United States [scripts and datasets]
| Authors: | |
|---|---|
| Owners: | Gabriel Barinas |
| Type: | Resource |
| Storage: | The size of this resource is 713.7 MB |
| Created: | Nov 01, 2024 at 6:45 p.m. |
| Last updated: | Jan 30, 2025 at 9:08 p.m. |
| Published date: | Jan 30, 2025 at 9:08 p.m. |
| DOI: | 10.4211/hs.5656632a4a4c4b2e96d591b7fc0e2a94 |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
|---|---|
| Views: | 190 |
| Downloads: | 2 |
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Abstract
Floodplain roughness, quantified through Manning’s coefficient n, is a critical parameter in hydrological models for predicting flood dynamics and managing water resources. Traditional methods to determine n rely on roughness values based principally on generalized land cover types and fail to capture the spatial and structural variability of floodplains, leading to inaccuracies during flood events. This study presents a novel approach to estimating floodplain roughness across the conterminous United States (CONUS) by integrating high-resolution remotely sensed canopy height and biomass data from NASA’s Global Ecosystem Dynamics Investigation mission and other spatially distributed data to map Manning’s roughness more accurately across a range of environments. We train a machine learning model (random forest regression) on a dataset of 4,927 roughness estimates from 804 sites to provide the estimates of n at 17.8 million reaches within the Notational Hydrography Database across CONUS. This approach results in a new CONUS wide n database with an R² of 0.51, a root mean squared error of 0.084, and a mean absolute percentage error of 122%, indicating its ability to capture much of the variability in floodplain roughness across CONUS. Canopy height and biomass were identified as the most influential predictors, highlighting the importance of vegetation structure in shaping floodplain dynamics. These results demonstrate the potential for integrating remote sensing data with machine learning models to enhance flood risk assessment and improve the accuracy of hydrological models.
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Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| National Aeronautics and Space Administration | GEDI | |
| Oregon State University |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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