AQUACROP irrigation synthetic dataset

This example demonstrates how to load and visualize datasets related to evapotranspiration (ETa) and land cover classification (CLC) for a synthetic dataset generated using AQUACROP. The datasets are loaded using Pooch for remote file management. Two visualizations are created: one showing land cover classification (CLC) and another displaying the time series of evapotranspiration (ETa).

import pooch
import xarray as xr 
import matplotlib.pyplot as plt 
from matplotlib.animation import FuncAnimation
from IPython.display import HTML

Step 1: Download datasets using Pooch Pooch is used to manage the dataset downloads from the specified GitHub repository.

pooch_Majadas = pooch.create(
    path=pooch.os_cache("AQUACROP_project"),
    base_url="https://github.com/BenjMy/test_AQUACROP_centum_dataset/raw/refs/heads/main/",
    registry={
        "ds_analysis_EO_0.netcdf": None,
    },
)

AQUACROP_ET_filename = pooch_Majadas.fetch('ds_analysis_EO_0.netcdf')
AQUACROP_ds = xr.load_dataset(AQUACROP_ET_filename)

# AQUACROP_ds = xr.open_dataset(f'../data/synthetic/ds_analysis_EO_0.netcdf')

import numpy as np
_, index = np.unique(AQUACROP_ds['time'], return_index=True)
AQUACROP_ds = AQUACROP_ds.isel(time=index)
AQUACROP_ds['ACT. ETRA'].plot.imshow(x="x", y="y", 
                                        col="time", 
                                        col_wrap=4,
                                        )

Downloading file 'ds_analysis_EO_0.netcdf' from 'https://github.com/BenjMy/test_AQUACROP_centum_dataset/raw/refs/heads/main/ds_analysis_EO_0.netcdf' to '/home/runner/.cache/AQUACROP_project'.

/tmp/ipykernel_1997/1458405863.py:10: FutureWarning: In a future version of xarray decode_timedelta will default to False rather than None. To silence this warning, set decode_timedelta to True, False, or a 'CFTimedeltaCoder' instance.
  AQUACROP_ds = xr.load_dataset(AQUACROP_ET_filename)

<xarray.plot.facetgrid.FacetGrid at 0x7f2786223370>