This module provides the tools to perform a simple "flood control ecosystem-service demand" calculation for a region.
The resulting raster map gives the relative contribution of different parts of a landscape to mitigating flooding from storm run-off.
The resulting raster is dimensionless, and the value for each pixel represents:
Where a is each flood-prone area downstream of the pixel, B is the value or number of buildings in the pixel (or some weight thereof) and W is the area of the upstream watershed of that pixel.
To execute this method you will need:
A digital elevation model of your area of interest.
A shapefile containing the point locations of structures in your area. This may be obtained from enhanced 911 (E911) databases.
A shapefile of your area containing soils data from the NRCS Soil Survey Geographic Database (SSURGO).
The simplest syntax to use is:
from FD_Calc import FloodDemandCalculator
FD = FloodDemandCalculator(wkdir)
FD.execute(dem_path, fld_map_path, structure_map_path)
All paths passed as arguments to the function must be absolute paths.
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There are several additional options for using this tool.
If you run into memory problems on your machine, you can try executing piecewise. This lets you run your calculations one sub-watershed at a time.
from FD_Calc import FloodDemandCalculator
import geopandas as gpd
FD = FloodDemandCalculator(wkdir)
FD.execute_piecewise(watersheds_map_path, wshed_id_col, dem_path,
fld_map_path, struct_map_path)
Make sure that your sub-watersheds aren't connected to one another in any way that is important for flood control. If one delineated sub-watershed is below another, the method won't return accurate results.
If you have data in your shapefiles that describes relative risks that different areas will flood, or relative values of structures, you can pass that data to the arguments "area_weight_col" and "struct_weight_col" respectively.
By Default, this package delinates flood-prone areas using a SSURGO soil map.
If you have a different map delineating areas at risk of flooding, you can use this, but you must pass "soil_map_is_SSURGO = FALSE" to the execute method.
To install, download this repository and run
pip install -r requirements.txt
This method was developed by Keri Bryan Watson and is described in Watson, et al (2019), but has not been made available as a single module.
Most geoprocessing operations are carrie out using the WhiteboxTools open-source geoprocessing libary (Lindsay, 2014). https://github.com/jblindsay/whitebox-tools
Lindsay, J. B. (2014, April). The whitebox geospatial analysis tools project and open-access GIS. In Proceedings of the GIS Research UK 22nd Annual Conference, The University of Glasgow (pp. 16-18).
Watson, K. B., Galford, G. L., Sonter, L. J., Koh, I., & Ricketts, T. H. (2019). Effects of human demand on conservation planning for biodiversity and ecosystem services. Conservation Biology, 33(4), 942–952.
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