NAME

KEYWORDS

SYNOPSIS

DESCRIPTION

r.manning converts a land cover raster to a Manning's roughness coefficient raster for use in hydraulic and hydrological modeling.

Manning's roughness coefficient (Manning's n) quantifies the resistance to flow over a surface and is a key parameter in hydraulic simulations such as flood modeling, overland flow, and channel flow calculations.

The input parameter specifies the land cover raster map. The tool supports two standard land cover classification systems specified by landcover: NLCD (National Land Cover Database) used in the United States, and ESA WorldCover used globally.

Manning's coefficients for ESA WorldCover are based on values used by QGIS Manning's Roughness Generator plugin by Azzam. For NLCD, the source parameter selects the source of Manning's n coefficients:

• kalyanapu: Values from Kalyanapu et al. (2009), suitable for shallow overland flow modeling (flow depths in mm to cm range). Available for NLCD only. This is the default for NLCD.
• hecras: Values from the HEC-RAS 2D User's Manual, suitable for deep flow and floodplain modeling (flow depths > 0.3 m). Available for NLCD.

The method parameter controls which roughness estimate to use:

• low: Lower bound estimate (minimal surface resistance)
• medium: Typical conditions (default)
• high: Upper bound estimate (maximum surface resistance)
• random: Uniform random value between low and high bounds

For custom land cover classifications, use landcover=custom with a rules file in CSV format.

NOTES

Source of values

Kalyanapu et al. (2009) does not include ranges for Manning's n values. These were estimated using 0.75/1.33 multipliers on the original single values to reflect ranges from Chow (1959). These multipliers were also used to estimate default values from ranges in the HEC-RAS 2D User's Manual.

Kalyanapu et al. (2009) does not include NLCD value for cultivated crops, this tool uses conventional tillage from McCuen (2005).

Custom rules file

For custom land cover classifications, provide a CSV file with the rules parameter. Two formats are supported:

Single value (used for all methods):

# code,n
1,0.035
2,0.120
3,0.050

Three values (low, medium, high):

# code,n_low,n_medium,n_high
1,0.025,0.035,0.045
2,0.080,0.120,0.160
3,0.040,0.050,0.060

Lines starting with # are treated as comments.

Uncertainty Analysis

Manning's n values are inherently uncertain due to spatial and temporal variability in vegetation density, surface conditions, and flow characteristics. The method=random option generates spatially uniform random values between low and high bounds for each land cover class, which can be used for Monte Carlo uncertainty analysis.

EXAMPLES

Basic usage with NLCD land cover

Import NLCD into North Carolina sample dataset and convert it to Manning's n using Kalyanapu values for shallow overland flow:

r.manning input=nlcd_landcover output=mannings_n \
          landcover=nlcd source=kalyanapu

Figure: Manning's n values for 2024 NLCD using Kalyanapu et al.

Deep flow modeling

Use HEC-RAS values for flood modeling:

r.manning input=nlcd_landcover output=mannings_n \
          landcover=nlcd source=hecras

ESA WorldCover for global applications

r.manning input=worldcover output=mannings_n \
          landcover=worldcover

Conservative high roughness estimate

r.manning input=nlcd_landcover output=mannings_n \
          landcover=nlcd source=kalyanapu method=high

Stochastic Manning's n values

Generate random Manning's n values for stochastic simulation:

from grass.tools import Tools

tools = Tools()

for i in range(100):
    tools.r_manning(
        input="nlcd_landcover",
        output=f"mannings_n_{i}",
        landcover="nlcd",
        source="kalyanapu",
        method="random",
        seed=i,
    )

Integration with r.sim.water

Use the output with GRASS hydrological simulation:

r.manning input=nlcd_landcover output=mannings_n \
          landcover=nlcd source=kalyanapu

r.sim.water elevation=dem dx=dx dy=dy \
            man=mannings_n depth=water_depth

REFERENCES

• HEC-RAS 2D User's Manual, Version 6.6. U.S. Army Corps of Engineers, Hydrologic Engineering Center. https://www.hec.usace.army.mil/software/hec-ras/documentation.aspx
• Kalyanapu, A. J., Burian, S. J., and McPherson, T. N. (2009). Effect of land use-based surface roughness on hydrologic model output. Journal of Spatial Hydrology, 9(2), 51-71. https://scholarsarchive.byu.edu/josh/vol9/iss2/2/
• McCuen, R. H. (2005). Hydrologic Analysis and Design (3rd ed.). Pearson Prentice Hall.
• Chow, V. T. (1959). Open-Channel Hydraulics. McGraw-Hill.
• Azzam, A. QGIS Manning's Roughness Generator plugin. https://github.com/mabdazzam/mannings_roughness_generator

SEE ALSO

r.sim.water, r.recode

AUTHORS

Anna Petrasova, NCSU Center for Geospatial Analytics

SOURCE CODE

Available at: r.manning source code (history)

Latest change: Thursday Mar 12 16:41:53 2026 in commit: 3ddc671dc85d6fcd278d5abecfb3c0ddfd1b146c

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