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eRAMS Overland Manning’s Roughness Coefficient Tool

 

Summary

This tool estimates Manning’s overland flow roughness coefficient (n) based on NLCD landuse layer. This tool was created in CSIP (David et al., 2015) environment. The look up table for NLCD landuse classes and n was created in the python code inside CSIP service. The relationship between n and NLCD landuse layer was established according to the research of Kalyanapu et al. (2010) except non-available classes such as ‘Dwarf Scrub’,  ‘Sedge/Herbaceous’, ‘Lichens’, ‘Moss’ and ‘Cultivated Crop’.  The n of ‘Shrub/Scrub’ class was used for ‘Dwarf Scrub’ class and the n of ‘Grassland/Herbaceous’ was used for ‘Sedge/Herbaceous’, ‘Lichens’, ‘Moss’ classes because they are under same super classes. For the ‘Cultivated Crop’ class, n was determined as 0.04, which was used for normal mature field crops in cultivated area from Hydraulics Design Manual of Oregon Department of Transportation and Highway Division (2014) . Once Manning’s coefficients are read from look up table for each NLCD class, the average for each polygon is calculated by  zonal_stats (Python Software Foundation, 2015).

 

Example

Figure 1. shows the Manning’s roughness coefficient for overland flow calculated for each polygon features and added to boundary polygons vector layer table.

manning

Figure 1. Manning’s overland flow roughness coefficient.

How to use

Click the eRAMS tool icon, Hydrology, General and Overland Manning’s buttons as shown in figure 2. Then, GUI will be displayed as shown in figure 3. In figure 3, click button 1 to create user folder, where user can store and organize files. Then, select or create boundary layer using selection tool or buttons in step 2. After defining boundary layer, create NLCD landuse layer using ‘Build’ and ‘Create’ buttons in step 3 and 4. Next, click ‘Calculate’ button.

manning_order

Figure 2. Selection of Manning’s Coefficient Tool for Overland Flow.

manning_gui

Figure 3. GUI of overland manning’s coefficient tool.

 

API Notes

manning
The input data are landuse raster(.tif) and boundary polygon shapefile. For boundary polygon shapefile, shp, .shx and .dbf files should be posted.

Input Parameters

Parameter Type Description
JSON request JSON {
“metainfo”: {
},
“parameter”: [
{ “name”: “landuse”,
“value”: “landuse.tif”} ,
{
“name”: “boundary”,
“value”: “polygon_boundary.shp”
}
]
}

Output Parameters

Parameter Type Description
results csv zonal statistics of manning’s n

 

References

David, O., Lloyd, W., Arabi, M., and Rojas, K. (2015). Cloud Service Innovation Platform User Manual and Technical Documentation [draft].

Kalyanapu, A. J., Burian, S. J., and McPherson, T. N. (2010). Effect of land use-based surface roughness on hydrologic model output. Journal of Spatial Hydrology, 9(2).

Oregon Department of Transportation Highway Division (2014). Hydraulics Design Manual, Retrieved from https://www.oregon.gov/ODOT/HWY/GEOENVIRONMENTAL/docs/Hydraulics/Hydraulics%20Manual/Chapter_08_Appendix_A.pdf.

Python Software Foundation (2015). Rasterstats 0.7.0, Retrieved from https://pypi.python.org/pypi/rasterstats/0.7.0.