Intra-annual variability of precipitation
A comparison between the seasonality of Tel Aviv and London
Let's shift the months according the the hydrological year:
Seasonality Index, Walsh and Lawler (1981)
$R=$ mean annual precipitation
$m_i$ precipitation mean for month $i$
$$ SI = \displaystyle \frac{1}{R} \sum_{n=1}^{n=12} \left| m_i - \frac{R}{12} \right| $$
| $SI$ | Precipitation Regime |
|---|---|
| <0.19 | Precipitation spread throughout the year |
| 0.20-0.39 | Precipitation spread throughout the year, but with a definite wetter season |
| 0.40-0.59 | Rather seasonal with a short dry season |
| 0.60-0.79 | Seasonal |
| 0.80-0.99 | Marked seasonal with a long dry season |
| 1.00-1.19 | Most precipitation in <3 months |
# import packages
import numpy as np
import pandas as pd
from calendar import month_abbr
# load data
month_numbers = np.arange(1,13)
month_names = [month_abbr[i] for i in month_numbers]
def monthly_mean(station_name, freq):
# import daily data
df = pd.read_csv(station_name + '_' + freq + '.csv', sep=",")
# make 'DATE' the dataframe index
df['DATE'] = pd.to_datetime(df['DATE'])
df = df.set_index('DATE')
# print(df.index[0], df.index[-1])
if freq == 'daily':
# resample data by month
df_month = df['PRCP'].resample('M').sum() # sum is labeled at the last day of the month
df_month = df_month/10 # PRCP is given in tens of mm (see readme)
if freq == 'monthly':
df_month = df['PRCP']
# calculate monthly mean
monthly_mean = np.array([]) # empty array
for m in month_numbers: # cycle over months (1, 2, 3, etc)
this_month_all_indices = (df_month.index.month == m) # indices in df_month belonging to month m
this_month_mean = df_month[this_month_all_indices].mean() # this is the monthly mean
monthly_mean = np.append(monthly_mean, this_month_mean) # append
# make new df and return it
df_return = pd.DataFrame({'monthly rainfall (mm)':monthly_mean,
'month names':month_names,
'month number':month_numbers
})
return df_return
# load monthly mean
df_london = monthly_mean("LONDON HEATHROW", 'monthly')
df_telaviv = monthly_mean("TEL AVIV READING", 'monthly')
#collapse-hide
def walsh_index(df):
m = df["monthly rainfall (mm)"]
R = df["monthly rainfall (mm)"].sum()
SI = np.sum(np.abs(m-R/12)) / R
return SI
london_index = walsh_index(df_london)
telaviv_index = walsh_index(df_telaviv)
print("Seasonality index (Walsh and Lawler, 1981)")
print(f"London: {london_index:.2f}")
print(f"Tel Aviv: {telaviv_index:.2f}")
