Instructions for running the wind multiplier code

Setting up the code repository

The Wind multipliers code is managed through GitHub. To get a copy of the code, either git clone, or download the repository. To clone the repository using git (recommended), open up a command line window (e.g. cmd on Windows, or Terminal on Mac), change to the directory where you would like the code to be installed, then type

git clone https://github.com/GeoscienceAustralia/Wind_multipliers Wind_multipliers

This will initialise a git repository called Wind_multipliers in the folder you are in.

Dependencies

• Python (3.7 preferred), Numpy, Scipy, GDAL, netcdf4-python;

• For parallel execution, mpi4py is required;

In order for the code to run successfully, you will need to install a number of python packages. These are listed in the requirements.txt file in the code’s home directory. To install these packages with pip, use:

pip install -r requirements.txt

Input datasets

The wind multipliers code requires four input datasets:

• Landcover classification:

  The landcover classification dataset is used to calculate the change in wind speed over varying landcover surfaces. The input landcover classification dataset must be a classified dataset, broken into desired landcover categories, such as urban, forest, grassland etc. The classification categories should be integer values (but this is not required). The interpretation of each landcover type is outlined in the accompanying terrain_table. The National Dynamic Land Cover Dataset of Australia Version 2.0 can be used if a higher resolution dataset is not available.

  In order to update local wind multiplier dataset in 2021, the Land Cover Classification Scheme (LCCS) which is last updated for 2015 by Digital Earth Australia as a single-band GeoTiff with 25m spatial resolution, is collated to be used. This dataset is then overlaid with mesh block-derived dataset (2016) in the preprocessing stage for improving the categories with “Natural surface” types.

• Digital elevation model:

  The DEM dataset is used to calculate topography and shielding parameters. The 1 second Shuttle Radar Topography Mission (SRTM) Smoothed Digital Elevation Models (DEM-S) Version 1.0 is available to use as an input.

• Mesh blocks:

  The administrative boundaries based on PSMA Australia version 2016 is used for processing urban areas.

• Settlement types:

  The settlement types based on 2016 census is sourced from Australian Bureau of Statistic (ABS) involved counting dwellings, place of Enumeration as well as UCL by STRD dwelling structure. This dataset is used for processing urban areas along with mesh block input dataset in the preprocessing stage.

All input datasets can be placed in the input folder within Wind_multipliers, however can be placed anywhere that can be accessed by the code. The path to these datasets is set in the configuration file. Previously, both landcover and DEM datasets need to be in *.img format, however this changed with the recent code release.

Note

The lowest resolution of the input Landcover and DEM datasets will determine the resolution of the calculated wind multipliers.

Configuration file

Before running all_multipliers.py to produce terrain, shielding and topographic multipliers, the configuration file multiplier_conf.cfg, in the code home directory, needs to be configured for the preprocessing step with the following options:

• settlement_data the location of the settlement types dataset to be used

• settlement_cat the category that defines the attribute in the settlement types for merge

• land_use_data the location of the meshblock dataset to be used

• land_use_cat the category that defines the attribute in the meshblock dataset for merge

• crop_mask the location of the layer for cropping the outcome of preprocessing step including vector and raster layers - set the parameter to None for continental coverage

• input_topo In order to map to the topographic file and takes the inputValues.dem_data, needs to be set to True.

• topo_crop Set this option to True for cropping the outcome of preprocessing step to the AOI set in crop_mask otherwise, default is False.

Running pre_process.py in preprocessing step will produce merged mesh block with settlement types as both vector and raster (i.e. shapefile and GeoTiff) layers. The second part of preprocessing step is to overlay merged meshblock raster layer with LCCS dataset in order to improve the categories with “Natural surface” types. For this step, the areas with “Natural surface” types in LCCS dataset will be identified and their code will be replaced with the corresponding and approperiate code in the merged mesh block dataset using the LCCS_meshblock_continent.py. The output of this step is an updated LCCS layer that is going to be used as the terrain_data for producing multipliers.

• root: the working directory of the task.

• upwind_length: the upwind buffer distance

• terrain_data: the location of the terrain dataset to be used

• terrain_table: the location of the csv table outlining the format of the terrain dataset to be read in

• dem_data: the location of the DEM dataset to be used

Assuming having the merged shapefile from the preporcess script, There is also one optional step between preprocessing and generating local wind multipliers using rasterize.py. This step can be used to rasterise merged meshblock from shapefile to GeoTiff on a given topography file.

There are two required arguments with -i and -t flags for shapefile and topography inputs.Two other agruments are optional with -a and -c flags for attribute to rasterise and crop mask respectively. At the moment rasterise is based on CAT value set in the preprocess script. If -i and -t are given, it will create the rasterised file (test.tiff) on the extend and resolution of the topography file (same projection). By adding the -c option, it will generate the same test.tiff but also two other files test_crop.tiff and [your topo filename]_crop.tiff being the cropped version of the files. It works with random shapes (not necessary rectangular one).

terrain_table

The terrain table is a csv file that provides the ‘key’ for reading in the terrain dataset. The use of the terrain table means that any input landcover dataset can be used, with any classification method. The csv file requires the following headings:

• CATEGORY: refers to the classification category used in the input terrain dataset

• DESCRIPTION: of the classification category

• ROUGHNESS_LENGTH_m: of the classification category

• SHIELDING: parameter for urban land cover types. Other land cover types should be set to 1.0.

An example of the terrain table that would be used for the National Dynamic Landcover Dataset has been included in the code.

‘’’ CATEGORY,DESCRIPTION,ROUGHNESS_LENGTH_m,SHIELDING 1,’City buildings’,2,0.85 2,’Forest’,1,1 3,’High density (industrial) buildings’,0.8,0.88 4,’Small town centres’,0.4,0.9 5,’Suburban/wooded country’,0.2,1 6,’Orchard, open forest’,0.08,1 7,’Long grass with few trees’,0.06,1 8,’Crops’,0.04,1 9,’Open rough water, airfields, uncut grass etc.’,0.02,1 10,’Cut grass’,0.008,1 11,’Desert (stones),roads’,0.006,1 12,’Mudflats/salt evaporators/sandy beaches’,0.004,1 13,’Snow surface’,0.002,1

# ‘CATEGORY’ refers to the classification category in the # input terrain dataset. # ‘DESCRIPTION’ of the classification category # ‘ROUGHNESS_LENGTH_m’ of the classification category # ‘SHIELDING’ parameter for urban land cover types. Other land cover types should be set to 1.0. # This example is taken from “AS/NZ Standarts 1170.2 - # Structural design actions, Part 2: Wind Actions - # Supplement 1 (2002)” ‘’’

Running the code

The script for preprocessing mesh blocks and settlement types dataset is pre_process.py. This script merges settlement and land use data using a common merging attribute.

To run pre_process.py type

python pre_process.py -c multiplier_conf.cfg

from the code home directory.

The script for rasterizing merged mesh block dataset is rasterize.py.

To run rasterize.py type

python rasterize.py -c multiplier_conf.cfg -i <path to merged shapefile> -t <path to topography file>

from the code directory.

The script for deriving terrain, shielding and topographic multipliers is all_multipliers.py. This script links four modules: terrain, shielding, topographic and utilities.

To run all_multipliers type

python all_multipliers.py -c multiplier_conf.cfg

from the code home directory.

This software implements parallelisation using mpi4py for MPI handling. To run it in parallel mode, use

mpirun -np ncpu python all_mulitpliers.py

where ncpu is the number of CPUs adopted.

The results are located under output folder (created automatically during the process) under root directory.