Local Flood Mapping

In the context of this notebook we referred to “local flood mapping” as performing the computation on you own machine, as opposed to remote processing which will be explained in the next notebook.

We will begin by loading the flood module from the dask_flood_mapper. You could use the default Dask settings, but it is often good to fine tune te settings to you machine’s specifications. We can do this by setting the Dask scheduler through the Client of dask.distributed.

from dask_flood_mapper import flood
from dask.distributed import Client

Here we set the Dask Scheduler with the Client, where we avoid inter-worker communication which is common for working with numpy and xarray in this case.

client = Client(processes=False, threads_per_worker=2, n_workers=1, memory_limit="12GB")
client

Client

Client-32ec02fa-0b3d-11f0-87f6-00224804924a

Connection method: Cluster object	Cluster type: distributed.LocalCluster
Dashboard: http://10.1.0.77:8787/status

Cluster Info

LocalCluster

31bd2758

Dashboard: http://10.1.0.77:8787/status	Workers: 1
Total threads: 2	Total memory: 11.18 GiB
Status: running	Using processes: False

Scheduler Info

Scheduler

Scheduler-5239a49c-98ec-4df1-9d25-9bcfe195f708

Comm: inproc://10.1.0.77/2038/1	Workers: 1
Dashboard: http://10.1.0.77:8787/status	Total threads: 2
Started: Just now	Total memory: 11.18 GiB

Workers

Worker: 0

Comm: inproc://10.1.0.77/2038/4	Total threads: 2
Dashboard: http://10.1.0.77:37761/status	Memory: 11.18 GiB
Nanny: None
Local directory: /tmp/dask-scratch-space/worker-x0dymlaq

No we are ready to map a flood. As an example we use here storm Babet which hit the Danish and Northern coast of Germany at the 20^th of October 2023 Wikipedia. We target an area around Zingst at the Baltic coast of Northern Germany.

time_range = "2022-10-11/2022-10-25"
bbox = [12.3, 54.3, 13.1, 54.6]
fd = flood.decision(bbox=bbox, datetime=time_range).compute()
fd

sigma naught datacube processed

harmonic parameter datacube processed

projected local incidence angle processed

<xarray.DataArray 'decision' (time: 8, y: 1048, x: 2793)> Size: 187MB
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  * time         (time) datetime64[ns] 64B 2022-10-11T05:25:01 ... 2022-10-23...
    spatial_ref  int64 8B 0
Attributes:
    _FillValue:  nan

xarray.DataArray

'decision'

• time: 8
• y: 1048
• x: 2793

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• Coordinates: (4)
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    (x)
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    axis :

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  • y
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  • spatial_ref
    ()
    int64
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    crs_wkt :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

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    reference_ellipsoid_name :

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    longitude_of_prime_meridian :

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    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984

    grid_mapping_name :

    latitude_longitude

    spatial_ref :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    GeoTransform :

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    array(0)

• Indexes: (3)
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  • y
    PandasIndex

    PandasIndex(Index([54.599952005319224,  54.59966542625796,  54.59937884719668,
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  • time
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• Attributes: (1)

  _FillValue :

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Since flood.decision does not make a distinction between Sentinel-1 observations over land or over sea, we need to mask pixels over water. For the example here we will load a mask distributed along with this package. But in general this step is left to the user’s own discretion.

from importlib.resources import files
import xarray as xr

data_text = files("dask_flood_mapper.data").joinpath("wcover.tif")
wcover = xr.open_dataarray(data_text, band_as_variable=True)

fd = fd.where(wcover != 80)
fd

<xarray.DataArray 'decision' (time: 8, y: 857, x: 1716)> Size: 94MB
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Coordinates:
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  * time         (time) datetime64[ns] 64B 2022-10-11T05:25:01 ... 2022-10-23...
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Attributes:
    _FillValue:  nan

xarray.DataArray

'decision'

• time: 8
• y: 857
• x: 1716

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• Coordinates: (4)
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  • y
    (y)
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    axis :

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    units :

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  • time
    (time)
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    2022-10-11T05:25:01 ... 2022-10-...

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  • spatial_ref
    ()
    int64
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    crs_wkt :

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    semi_minor_axis :

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    inverse_flattening :

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    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984

    grid_mapping_name :

    latitude_longitude

    spatial_ref :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    GeoTransform :

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    array(0)

• Indexes: (3)
  • x
    PandasIndex

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  • y
    PandasIndex

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  • time
    PandasIndex

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                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (1)

  _FillValue :

  nan

Now we are ready to view our results. We use here hvplot to create an animation of the flood’s extent over time.

import hvplot.xarray


fd.hvplot.image(
    x="x",
    y="y",
    rasterize=True,
    geo=True,
    tiles=True,
    project=True,
    cmap=["rgba(0, 0, 1, 0.1)", "darkred"],
    cticks=[(0, "non-flood"), (1, "flood")],
    frame_height=400,
)

WARNING:param.Image00418: Image dimension(s) x and y are not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.

WARNING:param.Image00418: Image dimension(s) x and y are not evenly sampled to relative tolerance of 0.001. Please use the QuadMesh element for irregularly sampled data or set a higher tolerance on hv.config.image_rtol or the rtol parameter in the Image constructor.

client.close()