Heavy rainfall

Setup

First, we need to import all the necessary libraries. Some of them are specifically developed to handle the download and plotting of the data and are hosted at the indicators set-up repository in GitHub

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

from myst_nb import glue 

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

sys.path.append("../../../../indicators_setup")
from ind_setup.plotting_int import plot_timeseries_interactive
from ind_setup.plotting import plot_bar_probs
from ind_setup.colors import get_df_col
from ind_setup.core import fontsize
from ind_setup.tables import plot_df_table, table_rain_a_summary


sys.path.append("../../../functions")
from data_downloaders import GHCN


from data_downloaders import GHCN, download_oni_index
from ind_setup.plotting_int import plot_oni_index_th
from ind_setup.plotting import plot_bar_probs_ONI, add_oni_cat
import df2img

country = 'Palau'
vars_interest = ['PRCP']

Get Data

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

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if update_data:
    df_country = GHCN.get_country_code(country)
    print(f'The GHCN code for {country} is {df_country["Code"].values[0]}')

    df_stations = GHCN.download_stations_info()
    df_country_stations = df_stations[df_stations['ID'].str.startswith(df_country.Code.values[0])]
    print(f'There are {df_country_stations.shape[0]} stations in {country}')

Obervations from Koror Station

https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/doc/GHCND_documentation.pdf

The data used for this analysis comes from the GHCN (Global Historical Climatology Network)-Daily database.
This a database that addresses the critical need for historical daily temperature, precipitation, and snow records over global land areas. GHCN-Daily is a composite of climate records from numerous sources that were merged and then subjected to a suite of quality assurance reviews. The archive includes over 40 meteorological elements including temperature daily maximum/minimum, temperature at observation time, precipitation and more.

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if update_data:
    GHCND_dir = 'https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/access/'
    id = 'PSW00040309' # Koror Station
    dict_prcp = GHCN.extract_dict_data_var(GHCND_dir, 'PRCP', df_country_stations.loc[df_country_stations['ID'] == id])[0]
    data = dict_prcp[0]['data']#.dropna()
    data.to_pickle(op.join(path_data, 'GHCN_precipitation.pkl'))
else:
    data = pd.read_pickle(op.join(path_data, 'GHCN_precipitation.pkl'))

Analysis

Days over 95 threshold

To have a metric of the changes in the extreme rainfall regime, the number of days over the 95 percentile is analyzed in the following plot

prcentile = 95
threshold = np.round(np.percentile(data['PRCP'].dropna(), prcentile), 2)
print(f'Threshold of {threshold}mm')
data['wet_day_t'] = np.where(data['PRCP'] > threshold, 1, np.where((np.isnan(data['PRCP'])==True), np.nan, 0))

Threshold of 45.5mm

data_2 = data.loc[data['wet_day_t'] == 1][['PRCP']]
data_over_th = data_2.groupby(data_2.index.year).count()
data_over_th.index = pd.to_datetime(data_over_th.index, format = '%Y')

data_over_th['PRCP_below'] = 365 - data_over_th['PRCP'].values

dict_plot = [{'data' : data_over_th, 'var' : 'PRCP', 'ax' : 1, 'label':f'Number of days over threshold: {threshold}mm'},]

fig, ax = plot_bar_probs(x = data_over_th.index.year, y = data_over_th['PRCP'].values, trendline = True,
               figsize = (15, 5), y_label = f'Number of days over {prcentile} percentile')

plt.savefig(op.join(path_figs, 'F7b_Wet_days_95p.png'), dpi=300, bbox_inches='tight')
glue('95_fig', fig, display=False)

Annual maxima

The evolution of the annual maximum precipitation is shown in the following plot.

data_max = data.groupby(data.index.year).max()
data_max.index = pd.to_datetime(data_max.index, format = '%Y')

dict_plot = [{'data' : data_max, 'var' : 'PRCP', 'ax' : 1, 'label':'Annual daily maxima'},]
plot_timeseries_interactive(dict_plot, trendline = True, figsize = (25, 12), label_yaxes = 'Precipitation [mm]');

Table

Table sumarizing different metrics of the data analyzed in the plots above

# df = table_rain_a_summary(data)
# fig = plot_df_table(df.T, figsize = (600, 350),)