Dry Conditions#
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 os
import os.path as op
import sys
import folium
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from myst_nb import glue
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_dry_summary
sys.path.append("../../../functions")
from data_downloaders import GHCN
from rain_func import consecutive_dry_days, count_consecutive_days
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}')
Observations 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'))
data['wet_day'] = np.where(data['PRCP'] > 1, 1, np.where((np.isnan(data['PRCP'])==True), np.nan, 0))
fig, ax = plot_bar_probs(x = [0, 1], y = data.groupby('wet_day').count()['PRCP'].values, labels = ['Dry Days', 'Wet Days'])
ax.set_title('Distribution of Wet Days', fontsize = fontsize)
ax.set_ylabel('Number of Days', fontsize = fontsize)
Text(0, 0.5, 'Number of Days')
Analysis#
Number of days over and above 1mm threshold#
The following plot analyzes the number of wet (over 1mm) and dry days over time
Dry days are considered those in which precipitation is lower than 1mm.
threshold = 1 #np.percentile(data['PRCP'].dropna(), 90)
data['wet_day_t'] = np.where(data['PRCP'] > threshold, 1, np.where((np.isnan(data['PRCP'])==True), np.nan, 0))
data_th = data.groupby([data.index.year, data.wet_day_t]).count()['PRCP']
data_th = data_th/data.groupby(data.index.year).count()['PRCP'] * 365
Plotting#
fig, ax = plt.subplots(figsize = [15, 5])
data_th.unstack().plot(kind = 'bar', stacked = True, ax = ax, color = get_df_col()[:2], edgecolor = 'white', alpha = .5)
ax.set_ylabel('Number of days', fontsize = fontsize)
Text(0, 0.5, 'Number of days')
The following plots analyze independently the number of wet and dry days over time as well the trend over time which is not significant in both cases.
#Wet days
data2 = data.loc[data['wet_day_t'] == 1]
data2 = data2.groupby(data2.index.year).count()
fig, ax, trend = plot_bar_probs(x = data2.index, y = data2.PRCP.values, trendline = True,
y_label = 'Number of wet days [>1mm]', figsize = [15, 4], return_trend = True)
plt.savefig(op.join(path_figs, 'F7a_Wet_days_1mm.png'), dpi=300, bbox_inches='tight')
glue("trend_wet", float(trend), display=False)
#Dry days
data2 = data.loc[data['wet_day_t'] == 0]
data2 = data2.groupby(data2.index.year).count()
fig, ax, trend = plot_bar_probs(x = data2.index, y = data2.PRCP.values, trendline = True,
y_label = 'Number of dry days [<1mm]', figsize = [15, 4], return_trend = True)
plt.savefig(op.join(path_figs, 'F6a_Number_dry.png'), dpi=300, bbox_inches='tight')
glue('trend_dry', float(trend), display=False)
data = data.groupby(data.index.year).filter(lambda x: len(x) >= 300).dropna()
data['dry_day'] = np.where(data['PRCP'] < threshold, 1, 0)
consecutive_dry_year = data.groupby(data.index.year)['dry_day'].apply(consecutive_dry_days)
data['below_threshold'] = data['PRCP'] < threshold
data['consecutive_days'] = count_consecutive_days(data['below_threshold'])
Number of consecutive dry days#
The following plot represents the average number of consecutive dry days which are considered those in which precipitation is lower than 1mm.
fig, ax = plot_bar_probs(np.unique(data.index.year), data.groupby(data.index.year)['consecutive_days'].mean(),
trendline =True, y_label = 'Mean consecutive dry days [< 1mm]',
figsize = [12, 5])
glue("mean_dry_days_fig", fig, display=False)
The following plot represents the maximum number of dry days which are considered those in which precipitation is lower than 1mm.
fig = plot_bar_probs(np.unique(data.index.year), data.groupby(data.index.year)['consecutive_days'].max(),
trendline =True, y_label = 'Maximum consecutive dry days [< 1mm]',
figsize = [12, 5])
plt.savefig(op.join(path_figs, 'F6b_Consecutive_dry.png'), dpi=300, bbox_inches='tight')
Table#
Table sumarizing different metrics of the data analyzed in the plots above
df = table_rain_dry_summary(data)
fig = plot_df_table(df.T, figsize = (600, 150),)
