Topography
Topographic information is obviously an important input to WAsP’s flow models. Specifically, WAsP’s terrain model takes as input maps of surface elevation, surface roughness, and optionally displacement height.
In PyWAsP, instead of dealing with surface roughness maps and displacement height maps as completely separate layers, we deal with them as land cover maps, where patches of land are associated with specific land cover classes, each having specific surface characteristics (i.e. the surface roughness and displacement height).
Maps of surface roughness can be read by WindKit, but are converted directly to land cover classes and an associated look-up table of surface characteristics.
To start working with topographic data, we will import xarray, matplotlib, windkit, and pywasp.
In [1]: import xarray as xr
In [2]: import matplotlib.pyplot as plt
In [3]: import windkit as wk
In [4]: import pywasp as pw
Topography I/O
Reading and writing topography files is handled by WindKit. Currently, windkit supports the following file formats:
Format |
Raster/Vector |
Read |
Write |
|---|---|---|---|
|
Raster |
yes |
yes |
|
Raster |
yes |
yes |
|
Raster |
yes |
yes |
|
Vector |
yes |
yes |
|
Vector |
yes |
yes |
|
Vector |
yes |
yes |
- All raster maps, regardless of the format, are read and written using:
windkit.read_raster_map()windkit.raster_map_to_file().
- Similarly, all vector maps are read and written using:
windkit.read_vector_map()windkit.vector_map_to_file()
Raster Maps
To start, let’s read some raster elevation data. The data covers part of the west coast of Jutland, Denmark:
In [5]: elev_map_raster = wk.read_raster_map(
...: "source/tutorials/data/elev.tif",
...: map_type="elevation"
...: )
...:
In [6]: print(elev_map_raster)
<xarray.DataArray 'elevation' (south_north: 800, west_east: 800)>
[640000 values with dtype=float64]
Coordinates:
* west_east (west_east) float64 7.818 7.818 7.819 ... 8.482 8.483 8.483
* south_north (south_north) float64 56.11 56.11 56.11 ... 56.77 56.77 56.77
crs int64 0
Attributes:
description: height above sea level of a point on the surface of an or...
long_name: Terrain Elevation
standard_name: ground_level_altitude
units: m
grid_mapping: crs
history: 2024-02-16T10:49:44:\twindkit==0.7.1.dev17+ga2ef68d\twk.r...
Since the data is a xr.DataArray it is easy to visualize:
In [7]: elev_map_raster.plot(cmap="viridis", vmin=0.0, vmax=60.0);
The raster is in latitude-longitude projection (EPSG:4326).
To reproject the data to a new raster in UTM coordinates, windkit.spatial.warp() can be used:
In [8]: elev_map_raster = wk.spatial.warp(elev_map_raster, to_crs="EPSG:25832", method="cubic")
In [9]: elev_map_raster.plot(cmap="viridis", vmin=0.0, vmax=60.0);
Warping to a new raster can create cells with missing data along the edges, so to clip the raster to only include real data, we will create a bounding box and clip to its bounds:
In [10]: bbox = wk.spatial.BBox.from_cornerpts(
....: minx=433_000,
....: miny=6_245_000.0,
....: maxx=468_000,
....: maxy=6_280_000,
....: crs="EPSG:25832"
....: )
....:
In [11]: elev_map_raster = wk.spatial.clip(elev_map_raster, bbox)
In [12]: elev_map_raster.plot(cmap="viridis", vmin=0.0, vmax=60.0);
While WindKit and PyWAsP have great support for working with both raster and vector maps, PyWAsP works best with
vector maps as the input. Therefore, PyWAsP enables conversion from raster maps to vector maps, through the
pywasp.io.rastermap_to_vectormap() function.
In [13]: elev_map_vector = pw.io.rastermap_to_vectormap(elev_map_raster)
In [14]: fig, ax = plt.subplots(1, 1, figsize=(6, 6))
In [15]: elev_map_raster.plot(ax=ax, cmap="Greys_r", vmin=0.0, vmax=60.0, add_colorbar=False)
In [16]: elev_map_vector.plot("elev", ax=ax, legend=True, legend_kwds={"label": "Elevation [m]"});
Vector Maps
To further illustrate how to work with vector maps, let’s read some land cover data from the Serra Santa Luzia site.
Since the data comes in a .map, which does not hold information about the
coordinate reference system, and, because it can hold both roughness and elevation information, we
have to provide the crs value and ask for the “roughness” part of the data via the map_type="roughness" argument.
Remember, the roughness map is converted to land cover classes by windkit.
In [17]: lc_map_vector, lc_tbl = wk.read_vector_map(
....: "source/tutorials/data/SerraSantaLuzia.map",
....: map_type="roughness",
....: crs="EPSG:32629",
....: )
....:
In [18]: print(lc_map_vector)
geometry id_left id_right
0 LINESTRING (520129.500 4608000.000, 520157.100... 0 0
1 LINESTRING (522602.400 4608000.000, 522613.500... 1 0
2 LINESTRING (518024.600 4608000.000, 518062.300... 0 1
3 LINESTRING (518062.300 4608074.500, 518160.500... 1 1
4 LINESTRING (522376.200 4608000.000, 522411.700... 2 1
.. ... ... ...
889 LINESTRING (524303.300 4637000.000, 524300.400... 0 1
890 LINESTRING (519338.000 4636768.000, 519331.300... 3 0
891 LINESTRING (510688.600 4635512.000, 510675.500... 4 9
892 LINESTRING (510166.200 4637000.000, 510167.200... 1 9
893 LINESTRING (526903.900 4637000.000, 526904.100... 1 1
[894 rows x 3 columns]
Vector maps are geopandas.GeoDataFrame objects with shapely.LineString geometries associated with either land cover boundaries or elevation levels.
Here, we have just two columns of data, the geometry column (for the shapely.LineString’s’) and the elevation column “elev”.
Since vector maps are geopandas.GeoDataFrame objects, they are easy to inspect and plot.
To avoid too much clutter, we will only plot lines where some specific classes are present (ocean and one forest category):
In [19]: fig, ax = plt.subplots(1, 1, figsize=(6, 6))
In [20]: ax.set_facecolor("black")
In [21]: cats = [3, 4]
In [22]: mask = lc_map_vector.id_left.isin(cats) | lc_map_vector.id_right.isin(cats)
In [23]: lc_map_vector.loc[mask].plot(
....: "id_left",
....: cmap="tab20",
....: vmin=-0.5,
....: vmax=19.5,
....: linewidth=2,
....: ax=ax
....: );
....:
In [24]: lc_map_vector.loc[mask].plot(
....: "id_right",
....: cmap="tab20",
....: vmin=-0.5,
....: vmax=19.5,
....: ax=ax,
....: linewidth=1,
....: linestyle="--",
....: legend=True,
....: legend_kwds={"label": "Land cover category", "ticks": list(range(13))},
....: );
....:
The way we plot the roughness-change lines, each line has two colors: the index for the “left” and “right” land cover category. The lines represent jumps from one type of land cover to another. The land cover table holds the information about the classes and their associated surface roughnesses and displacement heights:
In [25]: print(lc_tbl)
id = LandCoverID
z0 = Roughness length (m)
d = Displacement height (m)
desc = Description
id z0 d desc
0 0.0300 0.0
1 0.6000 0.0
2 0.7000 0.0
3 0.9000 0.0
4 0.0000 0.0
5 0.1000 0.0
6 1.5000 0.0
7 0.0100 0.0
8 0.5000 0.0
9 0.0500 0.0
10 0.3000 0.0
11 0.2000 0.0
12 0.8000 0.0
Preparing Input for Terrain Modeling
To predict site effects from the Topography data with PyWAsP, the data is bundled together
in the pywasp.wasp.TopographyMap PyWAsP class object.
It takes as input an elevation vector map, a land cover vector map, and a land cover table, as input.
In [26]: elev_map_vector = wk.read_vector_map(
....: "source/tutorials/data/SerraSantaLuzia.map",
....: map_type="elevation",
....: crs="EPSG:32629",
....: )
....:
In [27]: topo_map = pw.wasp.TopographyMap(
....: elev_map_vector,
....: lc_map_vector,
....: lc_tbl
....: )
....:
In [28]: print(topo_map)
Roughness map
id_left id_right geometry
0 0 0 LINESTRING (520129.500 4608000.000, 520157.100...
1 1 0 LINESTRING (522602.400 4608000.000, 522613.500...
2 0 1 LINESTRING (518024.600 4608000.000, 518062.300...
3 1 1 LINESTRING (518062.300 4608074.500, 518160.500...
4 2 1 LINESTRING (522376.200 4608000.000, 522411.700...
.. ... ... ...
889 0 1 LINESTRING (524303.300 4637000.000, 524300.400...
890 3 0 LINESTRING (519338.000 4636768.000, 519331.300...
891 4 9 LINESTRING (510688.600 4635512.000, 510675.500...
892 1 9 LINESTRING (510166.200 4637000.000, 510167.200...
893 1 1 LINESTRING (526903.900 4637000.000, 526904.100...
[894 rows x 3 columns]
Elevation map
elev geometry
0 0.1 LINESTRING (512460.300 4637000.000, 512464.400...
1 0.1 LINESTRING (515280.700 4608000.000, 515277.100...
2 0.1 LINESTRING (517893.500 4616000.000, 517895.900...
3 0.1 LINESTRING (516457.400 4616000.000, 516454.600...
4 0.1 LINESTRING (515413.300 4616000.000, 515412.800...
.. ... ...
577 510.0 LINESTRING (515924.700 4623250.000, 515919.100...
578 520.0 LINESTRING (515950.900 4623300.000, 515944.400...
579 520.0 LINESTRING (516082.000 4623650.000, 516074.800...
580 520.0 LINESTRING (515674.800 4623925.000, 515667.000...
581 530.0 LINESTRING (515972.600 4623375.000, 515967.800...
[582 rows x 2 columns]
Topography maps can be saved to, and loaded from, a ZipFile archive using the :py:method:`pywasp.wasp.TopographyMap.save` and pywasp.wasp.TopographyMap.load() methods.
In WAsP Flow Model we describe the WAsP models in more details and show how terrain effects can be calculated
from a topo_map, like the one above.