Urban Heat Island - TARGET
Note
WORK IN PROGRESS. NOT READY.
Introduction
In this tutorial you will generate input data for the TARGET model and simulate spatial (and temporal) variations of air temperature for an area in Gothenburg, Sweden.
The TARGET tool is an implementation of the Python version of TARGET application with the same name. The original TARGET model was presented in Broadbent at al. 2019.
This tutorial makes use of local high resolution detailed spatial data. If this kind of data is unavailable, other datasets derived from e.g. Open Street Map can be exploited. However, it is strongly recommended to go through this tutorial before moving on to more user-specific datasets.
Objectives
To perform and analyse intra urban temperature variations within an area is Gothenburg, Sweden using the TARGET model.
Initial Steps
UMEP is a Python plugin used in conjunction with QGIS. To install the software and the UMEP plugins see the getting started section in the UMEP manual. For this tutorial you will need both UMEP and UMEP for Processing.
Loading and analyzing the spatial data
All the geodata used in this tutorial can be downloaded from here.
Unzip and place in a folder that you have read and write access to.
Start by loading all the raster and vector datasets into an empty QGIS project. All layers are in SWEREF99 TM (EPSG:3006) CRS.
The order in the Layers Panel determines what layer is visible. You can choose to show a layer (or not) with the tick box. You can modify layers by right-clicking on a layer in the Layers Panel and choose Properties. Note for example that that CDSM (vegetation) is given as height above ground (meter) and that all non-vegetated pixels are set to zero. This makes it hard to get an overview of all 3D objects (buildings and trees). QGIS default styling for a raster is using the 98 percentile of the values. Therefore, not all the range of the data is shown in the layer window to the left.
Right-click on your CDSM layer and go to Properties > Style and choose Singleband pseudocolor with a min value of 0 and max of 35. Choose a colour scheme of your liking.
Go to Transparency and add an additional no data value of 0. Click ok.
Now put your CDSM layer at the top and your DSM layer second in your Layers Panel. Now you can see both buildings and vegetation 3D object in your map canvas.
The land cover grid comes with a specific QGIS style file.
Right-click on the land cover layer (torgg_lc) and choose Properties. At the bottom left of the window there is a Style-button. Choose Load Style and open landcoverstyle.qml (shipped with the data for this tutorial) and click OK.
Make only your land cover class layer visible to examine the spatial variability of the different land cover classes.
The land cover grid has already been classified into the seven different classes used in most UMEP applications (see Land Cover Reclassifier). If you have a land cover dataset that is not UMEP formatted you can use the Land Cover Reclassifier found at UMEP > Pre-processor > Urban Land Cover > Land Cover Reclassifier in the menu-bar to reclassify your data. TARGET land cover infromation is in somewhat different classes that the standard UMEP classes. The updated Land Cover Reclassifier includes possibilities to pre-process data into all nine classes used by TARGET. In the table below you see the classes used and how they relate to the standard UMEP classes:
TARGET class
UMEP class
Comment
roof
buildings
road
paved
Total impervious surface in UMEP
watr
water
conc
NA
Part of paved. Impervious in TARGET is both divided up between road and concrete.
Veg
Conifer and Deciduous trees
TARGET only have on class of trees whereas UMEP as two.
dry
grass
TARGET divides grass into two classes either dry or completely wet.
irr
grass
TARGET divides grass into two classes either dry or completely wet.
NA
bare soil
TARGET has no bare soil cless. Bare soil becomes dry grass if UMEP land cover is used.
A polygon grid (300 m x 300 m) to define the study area and individual grids is included (gbgcity_grid_300m.shp). Such a grid can be produced directly from the Processing Toolbox in QGIS (Create Grid) or an external grid can be used.
Load the vector layer gbgcity_grid_300m.shp into your QGIS project.
In the Style tab in layer Properties, choose a Simple fill and set Fill style to No Brush, to be able to see the spatial data within each grid.
Also, add the label IDs for the grid to the map canvas in Properties > Labels > Single Labels to make it easier to identify the different grid squares later on in this tutorial.
Preparing input data
First we need to derive surface fractions etc. from the geodata for each grid. This is similar as done in many other tutorials e.g. Urban Energy Balance - SUEWS Spatial. We start to calculate roughness parameters based on the building geometry within each grids. Open UMEP > Pre-Processor > Urban Morphology > Morphometric Calculator (Grid). Note: This tool and other has benn moved to UMEP for Processing, e.g. UMEP > Pre-Processor > Urban Morphology:Morphometric Calculator (Grid).
Use the settings as in the figure below and press Run.
When calculation is done, close the plugin.
Note
For mac users, use this workaround: manually create a directory, go into the folder above and type the folder name. It will give a warning “—folder name–” already exists. Do you want to replace it? Click replace.
Fig. 118 The settings for calculating building morphology. Click on image for enlargement.
This operation should have produced 21 different text files; 20 (anisotrophic) that include morphometric parameters from each 5 degree section for each grid and one file (isotropic) that includes averaged values for each of the 20 grids. You can open torggbuild_IMPGrid_isotropic.txt and compare the different values for different grids. Header abbreviations are explained here.
Moving on to land cover fraction calculations for each grid.
Open UMEP > Pre-Processor > Urban Land Cover > Land Cover Fraction (Grid).
Use the settings as in the figure below and press Run.
When calculation is done, close the plugin.
Fig. 119 The settings for calculating land cover fractions
As you noticed, we did not tick in Calculate fractions for TARGET... As our land cover grid only included the seven standard UMEP land cover classes, we will deal with the two extra classes in the next step.
Preparing input data for the TARGET model
Now all input information required is pre-processed apart from the final step which is to create the actual input files and folder structure for TARGET.
Open TARGET Prepare (UMEP > Pre-Processor > Urban Heat Island > TARGET prepare) and use the following settings.
Fig. 120 Settings for the TARGET Prepare plugin (click for a larger image).
Here we add fractions to the two missing classes by ticking in Use standard UMEP land cover…. As you notice, this is a simplification and could be more detailed if a 9-class land cover grid was exploited.
Meteorological forcing data
TARGET requires a meteorological forcing data flie. The TARGET make use of user-specific formatted weather data input. These files could be automatically generated from UMEP standard meteorological files (see Metdata Processor). In this tutorial, you are provided with s dataset from ERA5 covering the year 2018 for the Gothenburg region. This data could have been dowmnload via the Meteorological Data: Downlaod data (ERA5) tool in UMEP but to save some time we have done it for you.
Executing the model
Now, lets run the TARGET model. Open UMEP -> Processor -> Urban Heat Island: TARGET and use the settings below. Before starting the calculation, open the Python Console in QGIS to see a more detailed information from the model while is runs. The period selected is the month of May, 2018.
Fig. 121 Settings for the TARGET main plugin (click for a larger image).
Analysing the results
If you take a look in your output folder, you see a number of UMEP-formatted meteorological files which is the output from the model, one for each grid. First, try to plot grid 11 between May 7 and 17 by opening UMEP -> Post Processor -> Urban Heat Island -> TARGETAnalyzer and use the settings below beofre clicking Plot:
Fig. 122 Settings for the TARGET Post-processing plugin (click for a larger image).
The result should look something like this:
Fig. 123 Above: Wind speed and global radiation from met forcing file (ERA5 in this case). Below: Air temperature from forcing data compared with grid 11 (click for a larger image). You can also try to plot grid 19 and see how vegetation and less buildings affect the result.
Finally, you can also make a spatial grid from your model reults, both as a raster of add output to your grid polygon layer. Open the same tool but in UMEP for Processing and use the following settings:
Fig. 124 Settings for the TARGET Post-processing plugin create an dirunal average temperature difference map (click for a larger image).
Having two different analysers, one access from a menu and one from the Processing Toolbox, might be a bit confusing. There are two reasons for this. Firstly, there are possibilites to interact with the Menu-based plugins in a way that is not possible from the Processing toolbox, i.e. making plots for single grids and make intermediate settings while using the plugin. Secondly, having the plugin as a processing plugin makes it possible to build in into workflows, both using the Model Builder in QGIS and in a Python script.
Tutorial finished.