ModVege with HiResIreland data#

Jouven, M., Carrère, P., and Baumont, R. (2006a). ‘Model predicting dynamics of biomass, structure and digestibility of herbage in managed permanent pastures. 1. Model description’, Grass and Forage Science, vol. 61, no. 2, pp. 112-124. DOI: 10.1111/j.1365-2494.2006.00515.x.
Jouven, M., Carrère, P., and Baumont, R. (2006b). ‘Model predicting dynamics of biomass, structure and digestibility of herbage in managed permanent pastures. 2. Model evaluation’, Grass and Forage Science, vol. 61, no. 2, pp. 125-133. DOI: 10.1111/j.1365-2494.2006.00517.x.
Agri4cast (2023). ‘ModVege’. Available at: https://code.europa.eu/agri4cast/modvege (Accessed: 28 August 2023).

import os
import glob
from datetime import datetime, timezone
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
import xarray as xr
import climag.climag as cplt
from climag import climag_plot

DATA_DIR = os.path.join("data", "ModVege")

# Ireland boundary
GPKG_BOUNDARY = os.path.join("data", "boundaries", "boundaries_all.gpkg")
# ie = gpd.read_file(GPKG_BOUNDARY, layer="NUTS_RG_01M_2021_2157_IE")
ie = gpd.read_file(GPKG_BOUNDARY, layer="NUTS_RG_01M_2021_2157_IE_BBOX_DIFF")

# met station coords
LON_VAL, LAT_VAL = -10.24333, 51.93806  # Valentia Observatory
LON_ROC, LAT_ROC = -8.24444, 51.79306  # Roche's Point
LON_JOH, LAT_JOH = -6.5, 52.29167  # Johnstown Castle
LON_MUL, LAT_MUL = -7.36222, 53.53722  # Mullingar

rcp45#

data = xr.open_mfdataset(
    glob.glob(
        os.path.join(DATA_DIR, "HiResIreland", "rcp45", "EC-EARTH", "*.nc")
    ),
    chunks="auto",
    decode_coords="all",
)

data

<xarray.Dataset>
Dimensions:       (time: 10957, rlat: 113, rlon: 85, bnds: 2)
Coordinates:
  * time          (time) datetime64[ns] 2041-01-01T10:30:00 ... 2070-12-31T10...
    lon           (rlat, rlon) float32 dask.array<chunksize=(113, 85), meta=np.ndarray>
    lat           (rlat, rlon) float32 dask.array<chunksize=(113, 85), meta=np.ndarray>
  * rlon          (rlon) float64 -1.54 -1.505 -1.47 -1.435 ... 1.33 1.365 1.4
  * rlat          (rlat) float64 -1.945 -1.91 -1.875 -1.84 ... 1.905 1.94 1.975
    height_2m     float32 2.0
    time_bnds     (time, bnds) datetime64[ns] dask.array<chunksize=(365, 2), meta=np.ndarray>
    rotated_pole  int32 0
    spatial_ref   int64 0
Dimensions without coordinates: bnds
Data variables: (12/14)
    bm_gv         (time, rlat, rlon) float32 dask.array<chunksize=(365, 113, 85), meta=np.ndarray>
    bm_gr         (time, rlat, rlon) float32 dask.array<chunksize=(365, 113, 85), meta=np.ndarray>
    bm_dv         (time, rlat, rlon) float32 dask.array<chunksize=(365, 113, 85), meta=np.ndarray>
    bm_dr         (time, rlat, rlon) float32 dask.array<chunksize=(365, 113, 85), meta=np.ndarray>
    bm            (time, rlat, rlon) float32 dask.array<chunksize=(365, 113, 85), meta=np.ndarray>
    pgro          (time, rlat, rlon) float32 dask.array<chunksize=(365, 113, 85), meta=np.ndarray>
    ...            ...
    h_bm          (time, rlat, rlon) float32 dask.array<chunksize=(365, 113, 85), meta=np.ndarray>
    env           (time, rlat, rlon) float32 dask.array<chunksize=(365, 113, 85), meta=np.ndarray>
    rep           (time, rlat, rlon) float32 dask.array<chunksize=(365, 113, 85), meta=np.ndarray>
    lai           (time, rlat, rlon) float32 dask.array<chunksize=(365, 113, 85), meta=np.ndarray>
    aet           (time, rlat, rlon) float32 dask.array<chunksize=(365, 113, 85), meta=np.ndarray>
    wr            (time, rlat, rlon) float32 dask.array<chunksize=(365, 113, 85), meta=np.ndarray>
Attributes:
    creation_date:  2023-02-10 19:58:44.032666+00:00
    contact:        nstreethran@ucc.ie
    frequency:      day
    references:     https://github.com/ClimAg

# remove the spin-up year
data = data.sel(time=slice("2042", "2070"))

Seasonal averages#

for var in ["gro", "bm", "pgro", "aet"]:
    climag_plot.plot_averages(
        data=data, var=var, averages="season", boundary_data=ie, cbar_levels=12
    )

../_images/9134e1eff9c99bfa5738d0a07b2389dc37c436bf11fde33a027dca67b8385b0f.png

../_images/de448cecca2dea5c9412f38674dba9fd474288b019d401e76cf05d039dec2d73.png

../_images/132b127447023c7d5e0bdffc6863d1e7c6e2b458df7d7251c3d0ce1bf4ffc5a7.png

../_images/4d4ae11ea0c4cb2c4cd8f7080c9d9b34bf5ff7fdc7780b41e483b2f62e47a8df.png

Point subset#

Valentia Observatory#

cds = cplt.rotated_pole_point(data=data, lon=LON_VAL, lat=LAT_VAL)
data_ie = data.sel({"rlon": cds[0], "rlat": cds[1]}, method="nearest").sel(
    time=slice("2054", "2056")
)

data_ie_df = pd.DataFrame({"time": data_ie["time"]})
for var in data_ie.data_vars:
    data_ie_df[var] = data_ie[var]

data_ie_df.set_index("time", inplace=True)

# configure plot title
plot_title = []
for var in data_ie.data_vars:
    plot_title.append(
        f"{data_ie[var].attrs['long_name']} [{data_ie[var].attrs['units']}]"
    )

data_ie_df.plot(
    subplots=True,
    layout=(5, 3),
    figsize=(15, 11),
    legend=False,
    xlabel="",
    title=plot_title,
)

plt.tight_layout()
plt.show()

../_images/0ddd185e11fad9e1c2729fb0dd95bfcd4a09aff8eea517f8d2f01235c269d5a7.png

Roche’s Point#

cds = cplt.rotated_pole_point(data=data, lon=LON_ROC, lat=LAT_ROC)
data_ie = data.sel({"rlon": cds[0], "rlat": cds[1]}, method="nearest").sel(
    time=slice("2054", "2056")
)

data_ie_df = pd.DataFrame({"time": data_ie["time"]})
for var in data_ie.data_vars:
    data_ie_df[var] = data_ie[var]

data_ie_df.set_index("time", inplace=True)

# configure plot title
plot_title = []
for var in data_ie.data_vars:
    plot_title.append(
        f"{data_ie[var].attrs['long_name']} [{data_ie[var].attrs['units']}]"
    )

data_ie_df.plot(
    subplots=True,
    layout=(5, 3),
    figsize=(15, 11),
    legend=False,
    xlabel="",
    title=plot_title,
)

plt.tight_layout()
plt.show()

../_images/ac5b430e3a0a49fd0eec91e260281e129fe120a18816fd6dc884a69dc7246b03.png

Johnstown Castle#

cds = cplt.rotated_pole_point(data=data, lon=LON_JOH, lat=LAT_JOH)
data_ie = data.sel({"rlon": cds[0], "rlat": cds[1]}, method="nearest").sel(
    time=slice("2054", "2056")
)

data_ie_df = pd.DataFrame({"time": data_ie["time"]})
for var in data_ie.data_vars:
    data_ie_df[var] = data_ie[var]

data_ie_df.set_index("time", inplace=True)

# configure plot title
plot_title = []
for var in data_ie.data_vars:
    plot_title.append(
        f"{data_ie[var].attrs['long_name']} [{data_ie[var].attrs['units']}]"
    )

data_ie_df.plot(
    subplots=True,
    layout=(5, 3),
    figsize=(15, 11),
    legend=False,
    xlabel="",
    title=plot_title,
)

plt.tight_layout()
plt.show()

../_images/34154bb6787a0d41feb590c1b67b1aa563ba1bdf9a6ca1a72da4af61be26469a.png

Mullingar#

cds = cplt.rotated_pole_point(data=data, lon=LON_MUL, lat=LAT_MUL)
data_ie = data.sel({"rlon": cds[0], "rlat": cds[1]}, method="nearest").sel(
    time=slice("2054", "2056")
)

data_ie_df = pd.DataFrame({"time": data_ie["time"]})
for var in data_ie.data_vars:
    data_ie_df[var] = data_ie[var]

data_ie_df.set_index("time", inplace=True)

# configure plot title
plot_title = []
for var in data_ie.data_vars:
    plot_title.append(
        f"{data_ie[var].attrs['long_name']} [{data_ie[var].attrs['units']}]"
    )

data_ie_df.plot(
    subplots=True,
    layout=(5, 3),
    figsize=(15, 11),
    legend=False,
    xlabel="",
    title=plot_title,
)

plt.tight_layout()
plt.show()

../_images/14a97dfcdbd90596d0345f8bca34cf9383afd70f321ef872bd91cb693d3278c0.png

ModVege with HiResIreland data#

rcp45#

Seasonal averages#

Point subset#

Valentia Observatory#

Roche’s Point#

Johnstown Castle#

Mullingar#

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