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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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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.
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