Tip
Update a Wflow model: landuse#
Once you have a Wflow model, you may want to update your model in order to use a new landuse map, change a parameter value, add sample locations, use different forcing data, create and run different scenarios etc.
With HydroMT, you can easily read your model and update one or several components of your model using the update function of the command line interface (CLI). Here are the steps and some examples on how to update the landuse map and parameters.
All lines in this notebook which starts with ! are executed from the command line. Within the notebook environment the logging messages are shown after completion. You can also copy these lines and paste them in your shell to get more direct feedback.
Import packages#
In this notebook, we will use some functions of HydroMT to check available datasets and also to plot the landuse maps from the original and updated models. Here are the libraries to import to realize these steps.
[1]:
import numpy as np
import pandas as pd
import hydromt
from hydromt_wflow import WflowModel
# for plotting
import matplotlib as mpl
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
proj = ccrs.PlateCarree() # plot projection
Searching the data catalog for landuse#
In our previous notebook, we built a Wflow model using the GlobCover landuse classification. But we could as well have chosen another one. Let’s what other landuse landcover data are available in HydroMT and choose another landuse classification for our model. For this we will open the data catalog.
You can also directly open and search the HydroMT yaml library by downloading and opening the data_catalog.yml file in hydromt-artifacts or look at the list of data sources in the documentation.
[2]:
# Load the default data catalog of HydroMT
data_catalog = hydromt.DataCatalog('artifact_data')
[3]:
# Check which landuse/lancover sources are available in the DataCatalog
data_table = data_catalog.to_dataframe()
data_table.source_url[data_table["category"] == "landuse & landcover"]
[3]:
name
corine https://land.copernicus.eu/pan-european/corine...
globcover http://due.esrin.esa.int/page_globcover.php
modis_lai https://lpdaac.usgs.gov/products/mcd15a3hv006/
simard https://webmap.ornl.gov/ogc/dataset.jsp?ds_id=...
vito https://land.copernicus.eu/global/products/lc
Name: source_url, dtype: object
Here we can see that we have five data sources in HydroMT related to landuse & landcover properties. Out of these, three are landuse classifications:
globcover (already used in our current model)
corine
vito
The other datasets include a Leaf Area Index dataset (modis_lai) and a canopy height dataset (simard).
Let’s now see how to update our current model in one command line to use the corine classification.
HydroMT CLI update interface#
Using the HydroMT build API, we can update one or several components of an already existing Wflow model. Let’s get an overview of the available options:
[4]:
# Print the options available from the update command
! hydromt update --help
Usage: hydromt update [OPTIONS] MODEL MODEL_ROOT
Update a specific component of a model.
Set an output directory to copy the edited model to a new folder, otherwise
maps are overwritten.
Example usage: --------------
Update (overwrite!) landuse-landcover based maps in a Wflow model: hydromt
update wflow /path/to/model_root -c setup_lulcmaps --opt lulc_fn=vito -d
/path/to/data_catalog.yml -v
Update Wflow model components outlined in an .yml configuration file and
write the model to a directory: hydromt update wflow /path/to/model_root -o
/path/to/model_out -i /path/to/wflow_config.yml -d
/path/to/data_catalog.yml -v
Options:
-o, --model-out DIRECTORY Output model folder. Maps in MODEL_ROOT are
overwritten if left empty.
-i, --config PATH Path to hydroMT configuration file, for the model
specific implementation.
-c, --components TEXT Model methods from configuration file to run
--opt TEXT Method specific keyword arguments, see the method
documentation of the specific model for more
information about the arguments.
-d, --data TEXT Path to local yaml data catalog file OR name of
predefined data catalog.
--dd, --deltares-data Flag: Shortcut to add the "deltares_data" catalog
--fo, --force-overwrite Flag: If provided overwrite existing model files
--cache Flag: If provided cache tiled rasterdatasets
-q, --quiet Decrease verbosity.
-v, --verbose Increase verbosity.
--help Show this message and exit.
Update Wflow landuse layers#
[5]:
# NOTE: copy this line (without !) to your shell for more direct feedback
! hydromt update wflow wflow_piave_subbasin -o ./wflow_piave_corine -c setup_lulcmaps --opt lulc_fn=corine -d artifact_data -v
2024-04-23 07:08:10,464 - update - log - INFO - HydroMT version: 0.9.5.dev0
2024-04-23 07:08:10,464 - update - main - INFO - Updating wflow model at /home/runner/work/hydromt_wflow/hydromt_wflow/docs/_examples/wflow_piave_subbasin (r).
2024-04-23 07:08:10,464 - update - main - INFO - Output dir: /home/runner/work/hydromt_wflow/hydromt_wflow/docs/_examples/wflow_piave_corine
2024-04-23 07:08:10,464 - update - main - INFO - User settings:
2024-04-23 07:08:10,800 - update - data_catalog - INFO - Reading data catalog archive artifact_data v0.0.8
2024-04-23 07:08:10,800 - update - data_catalog - INFO - Parsing data catalog from /home/runner/.hydromt_data/artifact_data/v0.0.8/data_catalog.yml
2024-04-23 07:08:10,843 - update - model_api - INFO - Initializing wflow model from hydromt_wflow (v0.5.1.dev0).
2024-04-23 07:08:10,843 - update - data_catalog - INFO - Parsing data catalog from /home/runner/work/hydromt_wflow/hydromt_wflow/hydromt_wflow/data/parameters_data.yml
2024-04-23 07:08:10,852 - update - wflow - INFO - Read grid from /home/runner/work/hydromt_wflow/hydromt_wflow/docs/_examples/wflow_piave_subbasin/staticmaps.nc
2024-04-23 07:08:10,905 - update - wflow - INFO - Reading model intbl files.
2024-04-23 07:08:10,905 - update - wflow - INFO - Reading model table files.
2024-04-23 07:08:10,906 - update - wflow - INFO - Reading model staticgeom files.
2024-04-23 07:08:11,014 - update - wflow - INFO - Read forcing from /home/runner/work/hydromt_wflow/hydromt_wflow/docs/_examples/wflow_piave_subbasin/inmaps.nc
2024-04-23 07:08:11,032 - update - wflow - INFO - Model read
2024-04-23 07:08:11,037 - update - model_api - INFO - setup_lulcmaps.lulc_fn: corine
2024-04-23 07:08:11,037 - update - model_api - INFO - setup_lulcmaps.lulc_mapping_fn: None
2024-04-23 07:08:11,037 - update - model_api - INFO - setup_lulcmaps.lulc_vars: ['landuse', 'Kext', 'N', 'PathFrac', 'RootingDepth', 'Sl', 'Swood', 'WaterFrac']
2024-04-23 07:08:11,037 - update - wflow - INFO - Preparing LULC parameter maps.
2024-04-23 07:08:11,041 - update - rasterdataset - INFO - Reading corine raster data from /home/runner/.hydromt_data/artifact_data/v0.0.8/corine.tif
2024-04-23 07:08:11,103 - update - dataframe - INFO - Reading corine_mapping_default csv data from /home/runner/work/hydromt_wflow/hydromt_wflow/hydromt_wflow/data/lulc/corine_mapping.csv
2024-04-23 07:08:11,224 - update - landuse - INFO - Deriving landuse using nearest resampling (nodata=999).
2024-04-23 07:08:11,338 - update - landuse - INFO - Deriving Kext using average resampling (nodata=-999.0).
2024-04-23 07:08:11,431 - update - landuse - INFO - Deriving N using average resampling (nodata=-999.0).
2024-04-23 07:08:11,526 - update - landuse - INFO - Deriving PathFrac using average resampling (nodata=-999.0).
2024-04-23 07:08:11,620 - update - landuse - INFO - Deriving RootingDepth using average resampling (nodata=-999.0).
2024-04-23 07:08:11,714 - update - landuse - INFO - Deriving Sl using average resampling (nodata=-999.0).
2024-04-23 07:08:11,809 - update - landuse - INFO - Deriving Swood using average resampling (nodata=-999.0).
2024-04-23 07:08:11,904 - update - landuse - INFO - Deriving WaterFrac using average resampling (nodata=-999.0).
2024-04-23 07:08:12,008 - update - wflow - INFO - Write model data to /home/runner/work/hydromt_wflow/hydromt_wflow/docs/_examples/wflow_piave_corine
2024-04-23 07:08:12,009 - update - model_api - INFO - Writing model config to /home/runner/work/hydromt_wflow/hydromt_wflow/docs/_examples/wflow_piave_corine/wflow_sbm.toml
2024-04-23 07:08:12,041 - update - wflow - INFO - Write grid to /home/runner/work/hydromt_wflow/hydromt_wflow/docs/_examples/wflow_piave_corine/staticmaps.nc
2024-04-23 07:08:12,226 - update - wflow - INFO - Writing model staticgeom to file.
2024-04-23 07:08:12,317 - update - wflow - INFO - Write forcing file
2024-04-23 07:08:12,327 - update - wflow - INFO - Process forcing; saving to /home/runner/work/hydromt_wflow/hydromt_wflow/docs/_examples/wflow_piave_corine/inmaps.nc
[########################################] | 100% Completed | 100.99 ms
The example above means the following: run hydromt with:
update wflow: i.e. update a wflow modelwflow_piave_subbasin: original model folder-o ./wflow_piave_corine: output updated model folder-c setup_lulcmaps: model component to update, here setup_lulcmaps for landuse layers--opt lulc_fn=corine: arguments to use when updating the setup_lulcmaps component, all options are described in the docs (setup lulcmaps)-d artifact_data: specify to use the artifact_data catalog-v: give some extra verbosity (2 * v) to display feedback on screen. Now debug messages are provided.
Model comparison#
From the information above, you can see that not only was the landuse map updated but also all wflow landuse-related parameters with it: Kext, N, PathFrac, RootingDepth, Sl, Swood and WaterFrac.
Let’s now have a look at the different landuse maps of our two models and check that they were indeed updated.
[6]:
# Load both models with hydromt
mod0 = WflowModel(root="wflow_piave_subbasin", mode="r")
mod1 = WflowModel(root="wflow_piave_corine", mode="r")
[7]:
df = pd.read_csv("./legends/GLOBCOVER_2009_QGIS.txt", header=None, index_col=0)
# plot globcover map
levels = df.index
colors = (df.iloc[:-1, :4] / 255).values
ticklabs = df.iloc[:-1, 4].values
cmap, norm = mpl.colors.from_levels_and_colors(levels, colors)
ticks = np.array(levels[:-1]) + np.diff(levels) / 2.0
# create new figure
fig = plt.figure(figsize=(14, 7))
ax = fig.add_subplot(projection=proj)
# plot globcover landuse
mask = mod0.grid["wflow_subcatch"] > 0
p = (
mod0.grid["wflow_landuse"]
.raster.mask_nodata()
.plot(ax=ax, cmap=cmap, norm=norm, cbar_kwargs=dict(ticks=ticks))
)
p.axes.set_title("GlobCover LULC")
_ = p.colorbar.ax.set_yticklabels(ticklabs)
[8]:
# plot corine map
df = pd.read_csv(
"./legends/CLC2018_CLC2018_V2018_20_QGIS.txt", header=None, index_col=0
)
# plot globcover map
levels = df.index
colors = (df.iloc[:-1, :4] / 255).values
ticklabs = df.iloc[:-1, 4].values
cmap, norm = mpl.colors.from_levels_and_colors(levels, colors)
ticks = np.array(levels[:-1]) + np.diff(levels) / 2.0
# create new figure
fig = plt.figure(figsize=(14, 7))
ax = fig.add_subplot(projection=proj)
# plot corine landuse
p = (
mod1.grid["wflow_landuse"]
.raster.mask_nodata()
.plot(ax=ax, cmap=cmap, norm=norm, cbar_kwargs=dict(ticks=ticks))
)
p.axes.set_title("Corine LULC")
_ = p.colorbar.ax.set_yticklabels(ticklabs)
