Vegetation#

Unresolved and resolved vegetation on the ground and roofs

PALM represents vegetation in two ways: as flat, vertically unresolved vegetation types or as resolved vegetation described by the 3d distribution of the density of the vegetation. Unresolved vegetation is represented by vegetation types such as short_grass or evergreen_shrubs together with the leaf area index (LAI). The LAI with unit \(\mathrm{m}^2/\mathrm{m}^2\) is defined as the one-sided area of leaves per ground area. The unresolved vegetation is used for vegetation that does not cover the full height of a grid cell. Resolved vegetation is defined by the leaf area density (LAD) and the basal area density (BAD) fields. The LAD with unit \(\mathrm{m}^2/\mathrm{m}^3\) is defined as the one-sided area of leaves per volume, while the BAD is defined as the area of branches per volume. This approach is preferred for vegetation that covers at least the height of a grid cell.

The LAI can be reproduced from the LAD by vertically summing over the LAD multiplied by the vertical grid spacing \(\Delta z=\)dz:

\[ \mathrm{LAI} = \sum_k \mathrm{LAD}_k \, \mathrm{\Delta z} \, . \]

Unresolved vegetation#

palm_csd requires the direct input of the different vegetation types that PALM supports. Additionally, the LAI can be supplied, which is then used instead of the default values in PALM. Note that vegetation types that represent high (grown) vegetation feature large roughness lengths (\(z_0> 1\,\mathrm{m}\), \(z_{0,h} > 1\,\mathrm{m}\)) and are therefore not suitable for the vertical grid spacing typically used in building-resolving simulations. If the vertical grid spacing is close to or smaller than the roughness lengths, PALM will crash or does not provide meaningful results. Thus, if replace_high_vegetation_types is True, high vegetation types are replaced by resolved vegetation depending on the vegetation_height and generate_vegetation_patches as described below or by short_grass for the remaining pixels.

The vegetation type can be supplied as vector polygon file(s) in surfaces or as raster file vegetation_type.

In the vector case, the vegetation type is defined by a column which only includes the numerical values of the vegetation types.

input:
  files:
    surfaces: vegetation.shp
  columns:
    vtyp: vegetation_type

ALKIS surface data
Surface polygons and their attributes. The vegetation type is derived from the values of BEZEICH, which does not include the vegetation type directly.

Alternatively, the vegetation type can be derived from a column that includes strings or values that need to be mapped to PALM's vegetation types and that possibly also include other types.

input_01:
  files:
    surfaces: Nutzung_Flaechen.shp
  columns:
    BEZEICH:
      AX_Bahnverkehr: bare_soil
      AX_Friedhof: short_grass
      AX_Gehoelz: evergreen_shrubs
      AX_Halde: bare_soil
      AX_Heide: tall_grass
      AX_Landwirtschaft: crops_mixed_farming
      AX_Moor: bogs_marsches
      AX_Sumpf: bogs_marsches
      AX_TagebauGrubeSteinbruch: bare_soil
      AX_UnlandVegetationsloseFlaeche: bare_soil
      AX_Wald: mixed_forest_woodland

This maps the BEZEICH column with its values (e.g. AX_Bahnverkehr, AX_Heide, etc.) to the respective vegetation types.

Vegetation type
Vegetation type raster.

In the raster case, the raster consists directly of the numerical vegetation type values.

input:
  files:
    vegetation_type: vegetation_type.tif

Resolved vegetation#

palm_csd can generate LAD and BAD fields from two input variants: single tree input and vegetation patch input. In the single tree case, LAD and BAD are generated from detailed information of individual trees, while in the vegetation patch case, when detailed information is missing, LAD is generated from the vegetation height, LAI and patch type information.

Single trees#

"Single tree parameters" "Single tree shapes" Heldens et al. (2020) 1

The LAD and BAD fields are generated from single tree information if generate_single_trees is True. A single tree is defined by its height, crown diameter, trunk diameter, crown height (all in \(\mathrm{m}\), left figure), shape and LAI.

Single tree vector point input
Single tree vector points and their attributes crown diameter (kronendurch), tree height (baumhoehe) and trunk diameter (stammdurch). The lighter points are street trees, the darker points are park trees.*

The input quantities can be specified as a or several vector point files trees with the columns tree_height, tree_crown_diameter, tree_lai, tree_trunk_diameter, tree_shape or as separate corresponding raster files. In addition, the tree_type column or input file give the species of the tree. Vector point input allows robust reprojection and adjustment to different output resolutions, while it is not guaranteed that these steps preserve single raster pixels. Note that as soon as a vector point is defined or a pixel in one of the raster inputs is defined, this point is assumed to be a single tree even if the other attributes are missing.

In case of vector point input, the tree_type can also be derived from a column including the tree species name as text by setting tree_type_name. The content of this column is compared with the species name used in the tree default table. If one of the tree attributes is missing, default values from this table are used. Note that if use_vegetation_height_for_trees is True, the vegetation height field is used in the case of a missing tree height. Similarly, if use_lai_for_trees is True, the LAI field is used in the case of a missing tree LAI. If the LAI field's value at the tree location is also missing and estimate_lai_from_vegetation_height is True, the LAI is estimated from the vegetation height \(h\) using

\[ \mathrm{LAI} = \lambda_\mathrm{LAI} \cdot h \]

with the parameter \(\lambda_\mathrm{LAI}\) as defined in lai_per_vegetation_height. Ifestimate_lai_from_vegetation_height is False, either lai_low_vegetation_default or lai_high_vegetation_default is used, depending on height_high_vegetation_lower_threshold and if these values are defined.

For a vector point input with two separate input files the input section could look like this:

input_1:
  trees:
    - trees_street.shp
    - trees_park.shp
  columns:
    baumhoehe: tree_height
    kronendurch: tree_crown_diameter
    stammdurch: tree_trunk_diameter
    art_bot: tree_type_name

Vegetation patches#

In order to capture tree-like vegetation for areas without single tree information, palm_csd generates LAD cells from vegetation patches if generate_vegetation_patches is True; BAD cells are currently not generated. A vegetation patch is identified for each pixel if one of the following conditions is met:

  1. The vegetation height is larger or equal than a fraction of the vertical grid spacing.
  2. If replace_high_vegetation_types is True, a high vegetation vegetation type with either undefined vegetation height or a vegetation height larger or equal than a fraction of the vertical grid spacing.
  3. A defined patch type with either undefined vegetation height or a vegetation height larger or equal than a fraction of the vertical grid spacing.

Vegetation height raster
Vegetation height raster.

The vegetation height can be supplied as a raster file vegetation_height or as a column in a vector polygon file mapped to vegetation_height. For each identified vegetation patch, if vegetation_height is not defined, patch_height_default is assumed.

The vegetation_type can be supplied, as discussed above, as raster, vector polygon file or as a column in a vector polygon file mapped to vegetation_type.

The patch type is the tree type of the vegetation patch. It can be supplied as a column in a vector polygon file mapped to patch_type or as a raster file patch_type. If the patch type is missing, the negative vegetation type is used in case of a high vegetation vegetation type. If the pixel is not of a high vegetation vegetation type, tree type 0 is used.

The LAI can be supplied as a column in a vector polygon file mapped to lai or as the raster file lai. If LAI values are missing and estimate_lai_from_vegetation_height is True, the LAI is estimated from the vegetation height \(h\) using

\[ \mathrm{LAI} = \lambda_\mathrm{LAI} \cdot h \]

with the parameter \(\lambda_\mathrm{LAI}\) as defined in lai_per_vegetation_height. Ifestimate_lai_from_vegetation_height is False, either lai_low_vegetation_default or lai_high_vegetation_default is used, depending on height_high_vegetation_lower_threshold and if these values are defined.

The threshold fraction of the vertical grid spacing can be adjusted with height_rel_resolved_vegetation_lower_threshold.

For each identified vegetation patch pixel, a continuous vertical LAD profile is assumed according to the chosen lad_method and discretized for the PALM grid. Metal2003 for the profile in Markkanen et al. (2003)3 and LM2004 for the profile in Lalic and Mihailovic (2004)2 are implemented. The profiles and their parameters are detailed below.

With this, the configuration for vegetation patches could look like this:

settings:
  lad_alpha: 5.0
  lad_beta: 3.0
  lad_method: Metal2003
  lai_high_vegetation_default: 6.0
  lai_low_vegetation_default: 3.0
  patch_height_default: 10.0
input:
  files:
    lai: Berlin_leaf_area_index.tif
    patch_type: Berlin_patch_type.tif
    vegetation_height: Berlin_vegetation_patch_height.tif
    vegetation_type: Berlin_vegetation_type.tif
domain:
  generate_vegetation_patches: True

LAD profiles#

For lad_method: Metal2003, the assumed profile (up to a normalization constant) is given by

\[ \mathrm{LAD}_{\mathrm{Metal2003}}(z) \propto \left(\frac{z}{h}\right)^{\alpha-1} \left(1-\frac{z}{h}\right)^{\beta-1} \, \]

where \(\alpha\) (lad_alpha) and \(\beta\) (lad_beta) are the shape parameters of the profile.

For lad_method: LM2004, the assumed vertical continuous LAD is given by

\[ \mathrm{LAD}_{\mathrm{LM2004}}(z) = L_\mathrm{m} \left(\frac{h-z_\mathrm{m}}{h-z}\right)^n \exp\mathopen{}\left[n\left(1-\frac{h-z_\mathrm{m}}{h-z}\right)\right] \]

with

\[ n = \begin{cases} 6 & \text{for } 0 \leq z < z_\mathrm{m} \\ 0.5 & \text{for } z_\mathrm{m} \leq z \leq h \end{cases} \, . \]

Here, \(z_\mathrm{m}\) (lad_z_max_rel) is the relative height of the highest LAD value \(L_m\) and a parameter of this profile. \(L_m\) is given by the normalization of the profile to the LAI.

The parameters of these profiles, in particular lad_alpha and lad_beta, are hard to interpret. Thus, we provide an interactive tool to visualize the different profiles the effect of their parameters in tools/plot_lad_profiles.py. In addition to the Python packages installed for palm_csd, it requires the xarray and hvplot packages. The tool can be executed from the command line with

python3 tools/lad_patch_configurator.py

LAD profile tool

In order to discretize the LAD profile for the PALM grid, the LAD is integrated over the vertical grid spacing \(\Delta z\) and averaged over the grid cell height \(z_k\) to \(z_{k+1}\), where \(z_k\) is the lower height of the grid cell and \(z_{k+1}\) is the upper height of the grid cell. The resulting LAD value for each grid cell is given by

\[ \mathrm{LAD}_k = \frac{1}{\Delta z} \int_{z_k}^{z_{k+1}} \mathrm{LAD}(z) \, \mathrm{d}z \,. \]

Vegetation of roofs and walls#

Vegetation on roofs can be added using the building parameters building_fraction and building_lai for wall and roof surfaces as described in the respective building section. For building_fraction, the values for the building_surface_types green_gfl, green_agfl and green_roof can be adjusted. For building_lai, the values for the building_surface_levels gfl, agfl and roof can be set.

If use_lai_for_roofs is True, the general LAI input is used for vegetation on roofs if the specific building_lai input for roof surfaces is not set.

An example configuration using polygon input could look like this:

input_tiergarten:
  files:
    surfaces: buildings.shp
  columns:
    bfrac_gr_r: building_fraction_green_roof
    bfrac_wa_r: building_fraction_wall_roof
domain_root:
  building_fraction:
    wall_agfl: 0.8
    green_agfl: 0.2

Tree database#

Default values for trees if individual parameters are not provided. Default data is derived as mean values from the tree database for Berlin, Germany.

Index Species Shape Crown height/width ratio(*) Crown diameter (m) Height (m) LAI summer(*) LAI winter(*) Height of maximum LAD (m) LAD/BAD ratio(*) DBH (m)
0 Default 1.0 1.0 4.0 12.0 3.0 0.8 0.6 0.025 0.35
1 Abies 3.0 1.0 4.0 12.0 3.0 0.8 0.6 0.025 0.80
2 Acer 1.0 1.0 7.0 12.0 3.0 0.8 0.6 0.025 0.80
3 Aesculus 1.0 1.0 7.0 12.0 3.0 0.8 0.6 0.025 1.00
4 Ailanthus 1.0 1.0 8.5 13.5 3.0 0.8 0.6 0.025 1.30
5 Alnus 3.0 1.0 6.0 16.0 3.0 0.8 0.6 0.025 1.20
6 Amelanchier 1.0 1.0 3.0 4.0 3.0 0.8 0.6 0.025 1.20
7 Betula 1.0 1.0 6.0 14.0 3.0 0.8 0.6 0.025 0.30
8 Buxus 1.0 1.0 4.0 4.0 3.0 0.8 0.6 0.025 0.90
9 Calocedrus 3.0 1.0 5.0 10.0 3.0 0.8 0.6 0.025 0.50
10 Caragana 1.0 1.0 3.5 6.0 3.0 0.8 0.6 0.025 0.90
11 Carpinus 1.0 1.0 6.0 10.0 3.0 0.8 0.6 0.025 0.70
12 Carya 1.0 1.0 5.0 17.0 3.0 0.8 0.6 0.025 0.80
13 Castanea 1.0 1.0 4.5 7.0 3.0 0.8 0.6 0.025 0.80
14 Catalpa 1.0 1.0 5.5 6.5 3.0 0.8 0.6 0.025 0.70
15 Cedrus 1.0 1.0 8.0 13.0 3.0 0.8 0.6 0.025 0.80
16 Celtis 1.0 1.0 6.0 9.0 3.0 0.8 0.6 0.025 0.80
17 Cercidiphyllum 1.0 1.0 3.0 6.5 3.0 0.8 0.6 0.025 0.80
18 Cercis 1.0 1.0 2.5 7.5 3.0 0.8 0.6 0.025 0.90
19 Chamaecyparis 5.0 1.0 3.5 9.0 3.0 0.8 0.6 0.025 0.70
20 Cladrastis 1.0 1.0 5.0 10.0 3.0 0.8 0.6 0.025 0.80
21 Cornus 1.0 1.0 4.5 6.5 3.0 0.8 0.6 0.025 1.20
22 Corylus 1.0 1.0 5.0 9.0 3.0 0.8 0.6 0.025 0.40
23 Cotinus 1.0 1.0 4.0 4.0 3.0 0.8 0.6 0.025 0.70
24 Crataegus 3.0 1.0 3.5 6.0 3.0 0.8 0.6 0.025 1.40
25 Cryptomeria 3.0 1.0 5.0 10.0 3.0 0.8 0.6 0.025 0.50
26 Cupressocyparis 3.0 1.0 3.0 8.0 3.0 0.8 0.6 0.025 0.40
27 Cupressus 3.0 1.0 5.0 7.0 3.0 0.8 0.6 0.025 0.40
28 Cydonia 1.0 1.0 2.0 3.0 3.0 0.8 0.6 0.025 0.90
29 Davidia 1.0 1.0 10.0 14.0 3.0 0.8 0.6 0.025 0.40
30 Elaeagnus 1.0 1.0 6.5 6.0 3.0 0.8 0.6 0.025 1.20
31 Euodia 1.0 1.0 4.5 6.0 3.0 0.8 0.6 0.025 0.90
32 Euonymus 1.0 1.0 4.5 6.0 3.0 0.8 0.6 0.025 0.60
33 Fagus 1.0 1.0 10.0 12.5 3.0 0.8 0.6 0.025 0.50
34 Fraxinus 1.0 1.0 5.5 10.5 3.0 0.8 0.6 0.025 1.60
35 Ginkgo 3.0 1.0 4.0 8.5 3.0 0.8 0.6 0.025 0.80
36 Gleditsia 1.0 1.0 6.5 10.5 3.0 0.8 0.6 0.025 0.60
37 Gymnocladus 1.0 1.0 5.5 10.0 3.0 0.8 0.6 0.025 0.80
38 Hippophae 1.0 1.0 9.5 8.5 3.0 0.8 0.6 0.025 0.80
39 Ilex 1.0 1.0 4.0 7.5 3.0 0.8 0.6 0.025 0.80
40 Juglans 1.0 1.0 7.0 9.0 3.0 0.8 0.6 0.025 0.50
41 Juniperus 5.0 1.0 3.0 7.0 3.0 0.8 0.6 0.025 0.90
42 Koelreuteria 1.0 1.0 3.5 5.5 3.0 0.8 0.6 0.025 0.50
43 Laburnum 1.0 1.0 3.0 6.0 3.0 0.8 0.6 0.025 0.60
44 Larix 3.0 1.0 7.0 16.5 3.0 0.8 0.6 0.025 0.60
45 Ligustrum 1.0 1.0 3.0 6.0 3.0 0.8 0.6 0.025 1.10
46 Liquidambar 3.0 1.0 3.0 7.0 3.0 0.8 0.6 0.025 0.30
47 Liriodendron 3.0 1.0 4.5 9.5 3.0 0.8 0.6 0.025 0.50
48 Lonicera 1.0 1.0 7.0 9.0 3.0 0.8 0.6 0.025 0.70
49 Magnolia 1.0 1.0 3.0 5.0 3.0 0.8 0.6 0.025 0.60
50 Malus 1.0 1.0 4.5 5.0 3.0 0.8 0.6 0.025 0.30
51 Metasequoia 5.0 1.0 4.5 12.0 3.0 0.8 0.6 0.025 0.50
52 Morus 1.0 1.0 7.5 11.5 3.0 0.8 0.6 0.025 1.00
53 Ostrya 1.0 1.0 2.0 6.0 3.0 0.8 0.6 0.025 1.00
54 Parrotia 1.0 1.0 7.0 7.0 3.0 0.8 0.6 0.025 0.30
55 Paulownia 1.0 1.0 4.0 8.0 3.0 0.8 0.6 0.025 0.40
56 Phellodendron 1.0 1.0 13.5 13.5 3.0 0.8 0.6 0.025 0.50
57 Picea 3.0 1.0 3.0 13.0 3.0 0.8 0.6 0.025 0.90
58 Pinus 3.0 1.0 6.0 16.0 3.0 0.8 0.6 0.025 0.80
59 Platanus 1.0 1.0 10.0 14.5 3.0 0.8 0.6 0.025 1.10
60 Populus 1.0 1.0 9.0 20.0 3.0 0.8 0.6 0.025 1.40
61 Prunus 1.0 1.0 5.0 7.0 3.0 0.8 0.6 0.025 1.60
62 Pseudotsuga 3.0 1.0 6.0 17.5 3.0 0.8 0.6 0.025 0.70
63 Ptelea 1.0 1.0 5.0 4.0 3.0 0.8 0.6 0.025 1.10
64 Pterocaria 1.0 1.0 10.0 12.0 3.0 0.8 0.6 0.025 0.50
65 Pterocarya 1.0 1.0 11.5 14.5 3.0 0.8 0.6 0.025 1.60
66 Pyrus 3.0 1.0 3.0 6.0 3.0 0.8 0.6 0.025 1.80
67 Quercus 1.0 1.0 8.0 14.0 3.1 0.1 0.6 0.025 0.40
68 Rhamnus 1.0 1.0 4.5 4.5 3.0 0.8 0.6 0.025 1.30
69 Rhus 1.0 1.0 7.0 5.5 3.0 0.8 0.6 0.025 0.50
70 Robinia 1.0 1.0 4.5 13.5 3.0 0.8 0.6 0.025 0.50
71 Salix 1.0 1.0 7.0 14.0 3.0 0.8 0.6 0.025 1.10
72 Sambucus 1.0 1.0 8.0 6.0 3.0 0.8 0.6 0.025 1.40
73 Sasa 1.0 1.0 10.0 25.0 3.0 0.8 0.6 0.025 0.60
74 Sequoiadendron 5.0 1.0 5.5 10.5 3.0 0.8 0.6 0.025 1.60
75 Sophora 1.0 1.0 7.5 10.0 3.0 0.8 0.6 0.025 1.40
76 Sorbus 1.0 1.0 4.0 7.0 3.0 0.8 0.6 0.025 1.10
77 Syringa 1.0 1.0 4.5 5.0 3.0 0.8 0.6 0.025 0.60
78 Tamarix 1.0 1.0 6.0 7.0 3.0 0.8 0.6 0.025 0.50
79 Taxodium 5.0 1.0 6.0 16.5 3.0 0.8 0.6 0.025 0.60
80 Taxus 2.0 1.0 5.0 7.5 3.0 0.8 0.6 0.025 1.50
81 Thuja 3.0 1.0 3.5 9.0 3.0 0.8 0.6 0.025 0.70
82 Tilia 3.0 1.0 7.0 12.5 3.0 0.8 0.6 0.025 0.70
83 Tsuga 3.0 1.0 6.0 10.5 3.0 0.8 0.6 0.025 1.10
84 Ulmus 1.0 1.0 7.5 14.0 3.0 0.8 0.6 0.025 0.80
85 Zelkova 1.0 1.0 4.0 5.5 3.0 0.8 0.6 0.025 1.20
86 Zenobia 1.0 1.0 5.0 5.0 3.0 0.8 0.6 0.025 0.40

(*) Preliminary parameter.

  1. Heldens, W., Burmeister, C., Kanani-Sühring, F., Maronga, B., Pavlik, D., Sühring, M., Zeidler, J., Esch, T. 2020. ‘Geospatial Input Data for the PALM Model System 6.0: Model Requirements, Data Sources and Processing’. Geoscientific Model Development 13 (11): 5833–73. doi: 10.5194/gmd-13-5833-2020. ↩

  2. Lalic, B. and Mihailovic, D. T. (2004): An Empirical Relation Describing Leaf-Area Density inside the Forest for Environmental Modeling. Journal of Applied Meteorology, vol. 43, no. 4, pp. 641–645, doi: 10.1175/1520-0450(2004)043<0641:AERDLD>2.0.CO;2. ↩

  3. Markkanen, T., Rannik, Ü., Marcolla, B., Cescatti, A. and Vesala, T. (2003): Footprints and Fetches for Fluxes over Forest Canopies with Varying Structure and Density. Boundary-Layer Meteorology 106, 437–459, doi: 10.1023/A:1021261606719. ↩