Plot Wflow forcing

hydroMT provides a simple interface to model forcing data from which we can make beautiful plots:

• Forcing model layers are saved to model forcing attribute as a dict of xarray.DataArray

Load dependencies

import pandas as pd
import xarray as xr
import numpy as np
from os.path import join, dirname
import os
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib import cm, colors

import hydromt

Read the model

root = "wflow_piave_subbasin"
mod = hydromt.WflowModel(root, mode="r")

Plot model forcing

Here we plot the model basin average forcing.

# read wflow forcing; mask region outside the basin and compute the basin average
# NOTE: only very limited forcing data is available from the artifacts
ds_forcing = xr.merge(mod.forcing.values()).where(mod.staticmaps["wflow_subcatch"] > 0)
ds_forcing = ds_forcing.mean(dim=[ds_forcing.raster.x_dim, ds_forcing.raster.y_dim])

# plot axes labels
_ATTRS = {
    "precip": {
        "standard_name": "precipitation",
        "unit": "mm.day-1",
        "color": "darkblue",
    },
    "pet": {
        "standard_name": "potential evapotranspiration",
        "unit": "mm.day-1",
        "color": "purple",
    },
    "temp": {"standard_name": "temperature", "unit": "degree C", "color": "orange"},
}

plt.style.use("seaborn")  # set nice style

n = len(ds_forcing.data_vars)
kwargs0 = dict(sharex=True, figsize=(6, n * 3))

fig, axes = plt.subplots(n, 1, **kwargs0)
axes = [axes] if n == 1 else axes
for i, name in enumerate(ds_forcing.data_vars):
    df = ds_forcing[name].squeeze().to_series()
    attrs = _ATTRS[name]
    longname = attrs.get("standard_name", "")
    unit = attrs.get("unit", "")
    if name == "precip":
        axes[i].bar(df.index, df.values, facecolor=attrs["color"])
    else:
        df.plot.line(ax=axes[i], x="time", color=attrs["color"])
    axes[i].set_title(longname)
    axes[i].set_ylabel(f"{longname}\n[{unit}]")

# save figure
# fn_out = join(mod.root, "figs", "forcing.png")
# plt.savefig(fn_out, dpi=225, bbox_inches="tight")