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CHIRPS Visualisations

This notebook produces the three Visualisations-section plots (Vis1-3) for the CHIRPS data source page: district-level rainfall anomalies, province-vs-district annual rainfall, and extreme wet-season counts, all for Tete Province, Mozambique.

Inputs (from ../data/, produced by CHIRPS_Download&Process.ipynb): - moz_boundaries/moz_admin0.shp (country boundary, from the extracted HDX download) - PRCPTOT_anomaly_CHIRPS-3.0-0p05-rnl_201601-202512_vs_199101-202012_TeteProvince_districts.gpkg - PRCPTOT_annual_CHIRPS-3.0-0p05-rnl_198101-202512_TeteProvince.csv - PRCPTOT_annual_CHIRPS-3.0-0p05-rnl_198101-202512_TeteProvince_districts.csv - PRCPTOT_DJF_extreme_seasons_CHIRPS-3.0-0p05-rnl_201601-202512_TeteProvince_districts.gpkg

Outputs: Vis1_CHIRPS_AnnualRainfallAnomaly_Districts_TeteProvince_2016-2025.png, Vis2_CHIRPS_AnnualRainfall_ProvinceDistricts_Tete_1981-2025.png, Vis3_CHIRPS_ExtremeDJF_Districts_TeteProvince_2016-2025.png, saved to ../images/.

Step 1: Setup

Show the code 
import os
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib.colors import TwoSlopeNorm

os.makedirs('../images', exist_ok=True)

Vis1: District-level rainfall anomaly map

How mean annual rainfall in each district over 2016-2025 compares to the 1991-2020 baseline, as a percentage anomaly.

Show the code 
tete_districts_anomaly = gpd.read_file(
    '../data/PRCPTOT_anomaly_CHIRPS-3.0-0p05-rnl_201601-202512_vs_199101-202012_TeteProvince_districts.gpkg'
)
countries = gpd.read_file('../data/moz_boundaries/moz_admin0.shp')
Show the code 
# Centre the colour scale on 0% (no change), with -10% to +20% as the range
norm = TwoSlopeNorm(vmin=-10, vcenter=0, vmax=20)

fig, ax = plt.subplots(figsize=(10, 8))

# Mozambique outline in the background for context
countries.plot(ax=ax, facecolor='#d0d0d0', edgecolor='black', linewidth=1)

tete_districts_anomaly.plot(
    column='pct_anomaly',
    cmap='RdBu',
    legend=True,
    norm=norm,
    legend_kwds={'label': 'Rainfall Anomaly (%)', 'orientation': 'horizontal'},
    ax=ax,
    edgecolor='black',
    linewidth=0.5,
)

# Label each district at its centre point
for idx, row in tete_districts_anomaly.iterrows():
    ax.annotate(
        row['adm2_name'],
        xy=(row.geometry.centroid.x, row.geometry.centroid.y),
        ha='center', fontsize=7
    )

# Zoom the map to just Tete Province, with a small buffer
buf = 0.5
ax.set_xlim([tete_districts_anomaly.total_bounds[0] - buf,
             tete_districts_anomaly.total_bounds[2] + buf])
ax.set_ylim([tete_districts_anomaly.total_bounds[1] - buf,
             tete_districts_anomaly.total_bounds[3] + buf])

ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')
ax.tick_params(axis='both', labelsize=8)
ax.grid(True, linewidth=0.3, color='grey', alpha=0.5)

ax.set_title(
    'Mean Annual Rainfall Anomaly by District, Tete Province\n'
    '2016-2025 relative to 1991-2020 baseline (CHIRPS v3)',
    fontsize=12
)

plt.savefig(
    '../images/Vis1_CHIRPS_AnnualRainfallAnomaly_Districts_TeteProvince_2016-2025.png',
    dpi=300, bbox_inches='tight'
)
plt.show()

Vis2: Province vs district annual rainfall time series

Annual rainfall for Tete Province as a whole (bold blue line), with each individual district shown as a thin grey line in the background, so district-level spread around the province mean is visible.

Show the code 
province_annual = pd.read_csv(
    '../data/PRCPTOT_annual_CHIRPS-3.0-0p05-rnl_198101-202512_TeteProvince.csv'
)
district_annual_loaded = pd.read_csv(
    '../data/PRCPTOT_annual_CHIRPS-3.0-0p05-rnl_198101-202512_TeteProvince_districts.csv',
    index_col=0
)
Show the code 
fig, ax = plt.subplots(figsize=(12, 6))

# Each district as a thin grey line
years = district_annual_loaded.columns.astype(int)
for district in district_annual_loaded.index:
    ax.plot(
        years, district_annual_loaded.loc[district],
        color='grey', alpha=0.4, linewidth=0.8
    )

# Province mean as a bold line
ax.plot(
    province_annual['year'], province_annual['prcptot'],
    color='tab:blue', linewidth=2, label='Tete Province'
)

# 1991-2020 baseline mean, for reference
baseline_mean = province_annual[
    (province_annual['year'] >= 1991) &
    (province_annual['year'] <= 2020)
]['prcptot'].mean()
ax.axhline(
    baseline_mean, color='black', linestyle='--',
    linewidth=1, label=f'1991-2020 baseline mean ({baseline_mean:.0f} mm)'
)

ax.set_xlabel('Year')
ax.set_ylabel('Annual Rainfall (mm)')
ax.set_title(
    'Annual Rainfall for Tete Province and Districts (1981-2025) - CHIRPS v3',
    fontsize=12
)
ax.legend()
ax.grid(axis='y', linewidth=0.5, alpha=0.7)

plt.savefig(
    '../images/Vis2_CHIRPS_AnnualRainfall_ProvinceDistricts_Tete_1981-2025.png',
    dpi=300, bbox_inches='tight'
)
plt.show()

Vis3: Extreme DJF (summer) seasons by district

For each district, how many of the last 10 wet seasons (2016-2025) were “extreme” - i.e. exceeded the 90th percentile of DJF rainfall totals seen in the 1991-2020 baseline.

Show the code 
tete_districts_extreme = gpd.read_file(
    '../data/PRCPTOT_DJF_extreme_seasons_CHIRPS-3.0-0p05-rnl_201601-202512_TeteProvince_districts.gpkg'
)
countries = gpd.read_file('../data/moz_boundaries/moz_admin0.shp')
Show the code 
fig, ax = plt.subplots(figsize=(10, 8))

countries.plot(ax=ax, facecolor='#d0d0d0', edgecolor='black', linewidth=1)

tete_districts_extreme.plot(
    column='n_extreme',
    cmap='Blues',
    legend=True,
    legend_kwds={'label': 'Number of extreme DJF seasons (out of 10)',
                 'orientation': 'horizontal'},
    ax=ax,
    edgecolor='black',
    linewidth=0.5,
    vmin=0, vmax=3
)

for idx, row in tete_districts_extreme.iterrows():
    ax.annotate(
        f'{row["adm2_name"]}\n({int(row["n_extreme"])})',
        xy=(row.geometry.centroid.x, row.geometry.centroid.y),
        ha='center', fontsize=7
    )

buf = 0.5
ax.set_xlim([tete_districts_extreme.total_bounds[0] - buf,
             tete_districts_extreme.total_bounds[2] + buf])
ax.set_ylim([tete_districts_extreme.total_bounds[1] - buf,
             tete_districts_extreme.total_bounds[3] + buf])

# Force the colourbar to show whole numbers only, since these are counts
cbar = ax.get_figure().axes[-1]
cbar.xaxis.set_major_locator(ticker.MaxNLocator(integer=True))

ax.set_title(
    'Number of Extreme DJF Seasons by District, Tete Province\n'
    'Seasons exceeding 90th percentile of 1991-2020 baseline, 2016-2025 (CHIRPS v3)',
    fontsize=12
)
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')
ax.tick_params(axis='both', labelsize=8)
ax.grid(True, linewidth=0.3, color='grey', alpha=0.5)

plt.savefig(
    '../images/Vis3_CHIRPS_ExtremeDJF_Districts_TeteProvince_2016-2025.png',
    dpi=300, bbox_inches='tight'
)
plt.show()
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