Chlorophyll Concentration

Figure. Change in Chlorophyll from satellite. The map (top) shows the change in annual mean Chlorophyll-a concentration (mg/m3) in the vicinity of Palau over the period 1998-2024 derived from satellite remotely sensed ocean color data. The grey line is the Palau EEZ. The line plot (bottom) shows the change in mean Chlorophyll-a concentration (mg/m3) averaged over the area within the top plot. The black line represents the trend, which is not statistically significant (p < 0.05).

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import warnings
warnings.filterwarnings("ignore")
import os
import os.path as op
import sys

import pandas as pd
import numpy as np
import xarray as xr
import geopandas as gpd
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
from myst_nb import glue 

sys.path.append("../../../../indicators_setup")

from ind_setup.plotting_int import plot_timeseries_interactive, plot_oni_index_th
from ind_setup.plotting import plot_base_map, plot_map_subplots, add_oni_cat, plot_bar_probs, fontsize

sys.path.append("../../../functions")
from data_downloaders import download_ERDDAP_data, download_oni_index
from ocean import process_trend_with_nan

Setup

Define area of interest

#Area of interest
lon_range  = [129.4088, 137.0541]
lat_range = [1.5214, 11.6587]

EEZ shapefile

shp_f = op.join(os.getcwd(), '..', '..','..', 'data/Palau_EEZ/pw_eez_pol_april2022.shp')
shp_eez = gpd.read_file(shp_f)

Download Data

update_data = False
path_data = "../../../data"
path_figs = "../../../matrix_cc/figures"

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base_url = 'https://oceanwatch.pifsc.noaa.gov/erddap/griddap/esa-cci-chla-monthly-v6-0.csv'
dataset_id = 'chlor_a'
if update_data:
    date_ini = '1998-01-01T00:00:00Z'
    date_end = '2024-12-01T00:00:00Z'
    data = download_ERDDAP_data(base_url, dataset_id, date_ini, date_end, lon_range, lat_range)
    data_xr = data.set_index(['latitude', 'longitude', 'time']).to_xarray()
    data_xr['time'] = pd.to_datetime(data_xr.time)
    data_xr = data_xr.coarsen(longitude=2, latitude=2, boundary = 'pad').mean()
    data_xr.to_netcdf(op.join(path_data, f'griddap_{dataset_id}.nc'))
else:
    data_xr = xr.open_dataset(op.join(path_data, f'griddap_{dataset_id}.nc'))

Analysis

Average

fig, ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
im = ax.pcolor(data_xr.longitude, data_xr.latitude, data_xr.mean(dim='time')[dataset_id], transform=ccrs.PlateCarree(), 
                cmap = 'Greens', vmin = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 1), 
                vmax = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 99))
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
plt.colorbar(im, ax=ax, label='chlorophyll (mg/$m^3$)')
glue("average_map", fig, display=False)
plt.savefig(op.join(path_figs, 'F15_chlorophyll_mean_map.png'), dpi=300, bbox_inches='tight')

Change

trend_m, _, _, _, _ = process_trend_with_nan(data_xr[dataset_id])

fig, ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
im = ax.pcolor(data_xr.longitude, data_xr.latitude, 
               trend_m,
                transform=ccrs.PlateCarree(), 
                cmap = 'RdBu_r', 
                vmin = -.07,
                vmax = .07,
                )
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
plt.colorbar(im, ax=ax, label=  dataset_id)
plt.savefig(op.join(path_figs, 'F15_chlorophyll_mean_map_trend.png'), dpi=300, bbox_inches='tight')

Seasonal average

data_month = data_xr.groupby('time.season').mean().sel(season = ['DJF', 'MAM', 'JJA', 'SON'])
im = plot_map_subplots(data_month, dataset_id, shp_eez = shp_eez, cmap = 'Greens', 
                  vmin = np.nanpercentile(data_month.min(dim = 'season')[dataset_id], 1), 
                  vmax = np.nanpercentile(data_month.max(dim = 'season')[dataset_id], 99),
                  figsize = (15,11), sub_plot = [1, 4], cbar_pad = 0.05, cbar = 1)

Seasonal anomaly

data_month = data_xr.groupby('time.season').mean().sel(season = ['DJF', 'MAM', 'JJA', 'SON']) - data_xr.mean(dim='time')
im = plot_map_subplots(data_month, dataset_id, shp_eez = shp_eez, 
                  cmap = 'RdBu_r', vmin=-.05, vmax=.05,
                  figsize = (15,11), sub_plot = [1, 4], cbar_pad = 0.05,
                  cbar = 1)

Annual average

data_y = data_xr.resample(time='1YE').mean()
fig = plot_map_subplots(data_y, dataset_id, shp_eez = shp_eez, cmap = 'Greens', vmin = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 1), 
                vmax = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 99), cbar = 1)

Annual anomaly

data_an = data_y - data_xr.mean(dim='time')
fig = plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r', vmin=-.1, vmax=.1, cbar = 1)

Average over area

dict_plot = [{'data' : data_xr.mean(dim = ['longitude', 'latitude']).to_dataframe(), 
              'var' : dataset_id, 'ax' : 1, 'label' : 'Chlorophyll - MEAN AREA'},]

fig, trend = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, return_trend=True, figsize = (25, 12), label_yaxes = 'Chlorophyll (mg/m3)');
fig.write_html(op.join(path_figs, 'F15_chlorophyll_mean_trend.html'), include_plotlyjs="cdn")

Seasonal variability

fig, ax = plt.subplots(figsize=(12,2))
data_xr.mean(dim = ['longitude', 'latitude']).groupby('time.month').mean()[dataset_id].plot(ax = ax, marker = 'o', color = 'k')

[<matplotlib.lines.Line2D at 0x191310920>]

d_p = data_xr.mean(dim = ['longitude', 'latitude']).to_dataframe()

Timeseries at a given point

loc = [7.37, 134.7]
dict_plot = [{'data' : data_xr.sel(longitude=loc[1], latitude=loc[0], method='nearest').to_dataframe(), 
              'var' : dataset_id, 'ax' : 1, 'label' : f'Chlorophyll at [{loc[0]}, {loc[1]}]'},]

fig, ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
ax.plot(loc[1], loc[0], '*', markersize = 12, color = 'royalblue', transform=ccrs.PlateCarree(), label = 'Location Analysis')
ax.legend()

<matplotlib.legend.Legend at 0x1913719a0>

fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, figsize = (25, 12), label_yaxes = 'Chlorophyll (mg/m3)');

ONI index analysis

p_data = 'https://psl.noaa.gov/data/correlation/oni.data'
df1 = download_oni_index(p_data)

lims = [-.5, .5]
plot_oni_index_th(df1, lims = lims)

df1 = add_oni_cat(df1, lims = lims)
df1['ONI'] = df1['oni_cat']
data_xr['ONI'] = (('time'), df1.iloc[np.intersect1d(data_xr.time, df1.index, return_indices=True)[2]].ONI.values)
data_xr['ONI_cat'] = (('time'), np.where(data_xr.ONI < lims[0], -1, np.where(data_xr.ONI > lims[1], 1, 0)))
data_oni = data_xr.groupby('ONI_cat').mean()

Average

fig = plot_map_subplots(data_oni, dataset_id, shp_eez = shp_eez, cmap = 'Greens', 
                  vmin = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 1), 
                  vmax = np.percentile(data_xr.mean(dim = 'time')[dataset_id], 99),
                  sub_plot= [1, 3], figsize = (20, 9),  cbar = True, cbar_pad = 0.1,
                  titles = ['La Niña', 'Neutral', 'El Niño'],)
plt.savefig(op.join(path_figs, 'F15_chlorophyll_ENSO.png'), dpi=300, bbox_inches='tight')

Anomaly

data_an = data_oni - data_xr.mean(dim='time')
fig = plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r', vmin=-.05, vmax=.05,
                  sub_plot= [1, 3], figsize = (20, 9),  cbar = True, cbar_pad = 0.1,
                  titles = ['La Niña', 'Neutral', 'El Niño'],)

Table

from ind_setup.tables import style_matrix, table_ocean
style_matrix(table_ocean(data_xr, trend[0], data_oni, dataset_id))

Key Metrics Summary

Metric	Value
Monthly Average	0.100
Monthly Maximum 01/02/2005	0.166
Monthly Minimum 01/09/1998	0.052
Maximum Annual Average	0.121
Minimum Annual Average	0.089
Rate of change [mg/m3/year]	-0.000
Change between 1998 and 2024 [mg/m3]	-0.000
Average La Niña chlor_a	0.096
Average El Niño chlor_a	0.106
Average Neutral chlor_a	0.103