Checking for non-preferred file/folder path names (may take a long time depending on the number of files/folders) ...

This resource contains some files/folders that have non-preferred characters in their name. Show non-conforming files/folders.

This resource contains content types with files that need to be updated to match with metadata changes. Show content type files that need updating.

Spatially distributed estimates of Manning’s roughness within floodplain areas of the conterminous United States [scripts and datasets]

Authors:
Owners:		This resource does not have an owner who is an active HydroShare user. Contact CUAHSI (help@cuahsi.org) for information on this resource.
Type:	Resource
Storage:	The size of this resource is 713.8 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:	62
Downloads:	2
+1 Votes:	Be the first one to this.
Comments:	No comments (yet)

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.

Subject Keywords

Coverage

Spatial

Coordinate System/Geographic Projection:

WGS 84 EPSG:4326

Coordinate Units:

Decimal degrees

North Latitude

50.0736°

East Longitude

-64.3359°

South Latitude

24.0598°

West Longitude

-129.5508°

Content

Learn more about the BagIt download

Select a file to see file type metadata.

readme.txt

Spatially Distributed Estimates of Manning’s Roughness within Floodplain Areas of the Conterminous United States [scripts and datasets]

Overview
This repository contains the code and data associated with the research project titled "Spatially Distributed Estimates of Manning’s Roughness within Floodplain Areas of the Conterminous United States."

Contents

1. Output Files

- fpMannings_prediction.csv: Folder with 18 prediction files, each representing a HUC2 level basin in the WBDHU8 layer of the NHDPlus dataset.
  Columns:
  - NHDPlusID: Reach-level feature ID from the NHDFlowline layer.
  - huc4: HUC4 level basin identifier (WBDHU8 layer).
  - n: Floodplain Manning’s n prediction from the model.

- fpMannings_PredictionStatistics.csv: Statistical summary at the HUC8 level.
  Columns:
  - HUC8: HUC8 level basin identifier.
  - Name: Basin name.
  - n: Predicted Manning’s roughness n.
  - std: Standard deviation.
  - count: Prediction count within HUC8.
  - q25, q50, q75: 25th, 50th (median), and 75th percentiles.

2. Required Files (provided)
- fpMannings_predictionModel.py: Main script to reproduce output datasets and figures.
- getPredictors.py: Supporting script for creating predictor files (requires GEDI, MODIS, and NHD data files).
- fp_mannings.csv: Main Floodplain Manning's n dataset from Barinas et al., 2024.
- VEG_data.csv: GEDI and MODIS sampling data. Columns: site, lat, lon, elev, elevSTD, vegh, veghSTD, MU, PE, PS, SE, V1, V2, FPAR, LAI, FPARstd, LAIstd, IGBP.
- NHD_data.csv: NHD data sampling. Columns: site, lat, lon, NHDPlusID, QEMA, VEMA, VPUID (equivalent to HUC4).

3. Required Files (not provided)
- NHDPlus Data: Reach and watershed features. Available at https://doi.org/10.3133/ofr20191096.
- GEDI Data: Vegetation data. Available at https://doi.org/10.3334/ORNLDAAC/2299 and https://doi.org/10.3334/ORNLDAAC/1952.
- MODIS Data: Additional vegetation data. Available at https://doi.org/10.5067/MODIS/MCD15A2H.061 and https://doi.org/10.5067/MODIS/MCD12Q1.061.

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

Barinas, G. (2025). Spatially distributed estimates of Manning’s roughness within floodplain areas of the conterminous United States [scripts and datasets], HydroShare, https://doi.org/10.4211/hs.5656632a4a4c4b2e96d591b7fc0e2a94

This resource is shared under the Creative Commons Attribution CC BY.

http://creativecommons.org/licenses/by/4.0/

Comments

There are currently no comments