Risk assessment: Extreme temperature#
A workflow from the CLIMAAX Handbook and MULTI_infrastructure GitHub repository.
See our how to use risk workflows page for information on how to run this notebook.
In this notebook, the calculation of risk associated with the variable under examination will be shown. Pandas dataframes will be presented which, in the example, show nine airports - the operator will need to modify the list of airports by adding/modifying names. The same concept applies to exposure and vulnerability indicators - indicators will be presented that can be expanded or modified by the user. The only mandatory thing is that the framework was conceptualized for at least two airports
import os
import pandas as pd
import numpy as np
Exposure and Vulnerability#
# Define the airports
airports = ['Milano Malpensa', 'Milano Linate', 'Bergamo Orio al Serio', 'Roma Fiumicino', 'Roma Ciampino',
'Napoli Capodichino', 'Palermo Punta Raisi', 'Catania Fontanarossa', 'Cagliari Elmas']
Folder to save the csv file where average exposure and vulnerability are going to be stored#
csv_folder_path = '/work/cmcc/dg07124/climax/indicators/cordex2/vulnerability_exposure'
if not os.path.exists(csv_folder_path):
os.makedirs(csv_folder_path)
Air Land -Side Components#
The various airports components, divided into landside and airside areas, were considered as exposed samples in accordance with De Vivo et al. (2022). More specifically, the air side components include the structures used for the movement of aircraft, such as runways, taxiways, tower, and aprons. On the other hand, the land side components refer to the public access areas such as offices, terminals, airports access systems and parking areas.
Air side |
Unit of measurement |
|---|---|
Runaway |
Area [m^2] |
Taxiways |
Area [m] |
Tower |
Height [m] |
Apron |
Area [m^2] |
Land side |
Unit of measurement |
|---|---|
Terminal |
Area [m^2] |
Offices and other buildings |
Area [m^2] |
Airport_accesses_systems |
Area [m^2] |
Carparks |
Number |
# Define the values for each indicator and airport
air_land_side_components = {
'Runways': [470400, 159742, 145530, 396000, 9900, 118260, 292650, 109575, 126000],
'Taxiways': [250.5, 156, 183, 300, 301, 99, 368, 159, 604],
'Tower': [80, 47, 30, 56, 60, 40, 25, 12, 28],
'Apron': [1319000, 387000, 190000, 797250, None, 200000, 148000, 180000, 156000],
'Terminals': [315000, 85050, 53025, 354300, 20950, 30700, 35400, 43310, 41290],
'Offices_and_other_buildings': [26715, 13527, 7075, 29520, 5950, 1915, 4180, 6920, 5030],
'Airport_accesses_systems': [31660, None, None, None, None, None, None, None, None],
'Carparks': [15000, 3000, 8000, 20100, 1220, 1500, 1364, 1800, 2133]
}
The aim of normalization processes is to transform the values of these indicators, which are measured at different scales and in different units, into comparable values considered on a common scale (Master Adapt, 2018). We used the Min-Max method to transform all values to scores in a range from 0 to 1, where the value 0 represents the optimal level, while the value 1 reflects the most critical estimates:
# Function to normalize data while handling NaN values and ZeroDivisionError
def normalize_data(indicator_values, airports):
normalized_data = {}
for indicator, values in indicator_values.items():
# Filter out None (NaN) values for normalization
valid_values = [value for value in values if value is not None]
# Check if we have more than one unique value for normalization
if len(valid_values) > 1:
# Get the min and max values for the valid values
min_val = min(valid_values)
max_val = max(valid_values)
# Check for zero division (when min == max)
if min_val == max_val:
normalized_values = [None if value is not None else None for value in values]
else:
# Normalize the values
normalized_values = [
(value - min_val) / (max_val - min_val) if value is not None else None
for value in values
]
else:
# If all values are the same or all values are NaN, set all normalized values to NaN
normalized_values = [None if value is not None else None for value in values]
normalized_data[indicator] = normalized_values
return normalized_data
# Dictionary to store the dynamic DataFrames
dynamic_dataframes = {}
# Function to display data in a DataFrame format and store the avg_exposure with a unique name
def display_data(indicator_values, airports, df_name):
# Create a DataFrame from the original data
df = pd.DataFrame(indicator_values, index=airports)
print("Original Data:")
display(df)
# Normalize the data
normalized_data = normalize_data(indicator_values, airports)
# Convert the normalized data to a DataFrame
normalized_df = pd.DataFrame(normalized_data, index=airports)
# Calculate the average normalized value for each airport
normalized_df['average'] = normalized_df.mean(axis=1)
print("Normalized Data:")
display(normalized_df)
# Separate the "Average" column into another DataFrame
avg_df = normalized_df[['average']]
print("Avg components:")
# Store the avg_exposure_df_temp in the dynamic_dataframes dictionary with the provided name
dynamic_dataframes[df_name] = avg_df
print(f"Stored DataFrame with name: {df_name}")
# Display the air land side components
display_data(air_land_side_components, airports, 'avg_df_exposure')
Original Data:
| Runways | Taxiways | Tower | Apron | Terminals | Offices_and_other_buildings | Airport_accesses_systems | Carparks | |
|---|---|---|---|---|---|---|---|---|
| Milano Malpensa | 470400 | 250.5 | 80 | 1319000.0 | 315000 | 26715 | 31660.0 | 15000 |
| Milano Linate | 159742 | 156.0 | 47 | 387000.0 | 85050 | 13527 | NaN | 3000 |
| Bergamo Orio al Serio | 145530 | 183.0 | 30 | 190000.0 | 53025 | 7075 | NaN | 8000 |
| Roma Fiumicino | 396000 | 300.0 | 56 | 797250.0 | 354300 | 29520 | NaN | 20100 |
| Roma Ciampino | 9900 | 301.0 | 60 | NaN | 20950 | 5950 | NaN | 1220 |
| Napoli Capodichino | 118260 | 99.0 | 40 | 200000.0 | 30700 | 1915 | NaN | 1500 |
| Palermo Punta Raisi | 292650 | 368.0 | 25 | 148000.0 | 35400 | 4180 | NaN | 1364 |
| Catania Fontanarossa | 109575 | 159.0 | 12 | 180000.0 | 43310 | 6920 | NaN | 1800 |
| Cagliari Elmas | 126000 | 604.0 | 28 | 156000.0 | 41290 | 5030 | NaN | 2133 |
Normalized Data:
| Runways | Taxiways | Tower | Apron | Terminals | Offices_and_other_buildings | Airport_accesses_systems | Carparks | average | |
|---|---|---|---|---|---|---|---|---|---|
| Milano Malpensa | 1.000000 | 0.300000 | 1.000000 | 1.000000 | 0.882106 | 0.898388 | None | 0.729873 | 0.830052 |
| Milano Linate | 0.325390 | 0.112871 | 0.514706 | 0.204099 | 0.192290 | 0.420648 | None | 0.094280 | 0.266326 |
| Bergamo Orio al Serio | 0.294528 | 0.166337 | 0.264706 | 0.035867 | 0.096220 | 0.186923 | None | 0.359110 | 0.200527 |
| Roma Fiumicino | 0.838436 | 0.398020 | 0.647059 | 0.554441 | 1.000000 | 1.000000 | None | 1.000000 | 0.776851 |
| Roma Ciampino | 0.000000 | 0.400000 | 0.705882 | NaN | 0.000000 | 0.146169 | None | 0.000000 | 0.208675 |
| Napoli Capodichino | 0.235309 | 0.000000 | 0.411765 | 0.044406 | 0.029249 | 0.000000 | None | 0.014831 | 0.10508 |
| Palermo Punta Raisi | 0.614007 | 0.532673 | 0.191176 | 0.000000 | 0.043348 | 0.082050 | None | 0.007627 | 0.210126 |
| Catania Fontanarossa | 0.216450 | 0.118812 | 0.000000 | 0.027327 | 0.067077 | 0.181308 | None | 0.030720 | 0.09167 |
| Cagliari Elmas | 0.252117 | 1.000000 | 0.235294 | 0.006832 | 0.061017 | 0.112842 | None | 0.048358 | 0.245209 |
Avg components:
Stored DataFrame with name: avg_df_exposure
Exposure calculation#
# Accessing the avg_exposure_temp DataFrames
avg_df_exposure = dynamic_dataframes['avg_df_exposure']
normalized_avg_path = os.path.join(csv_folder_path, f'avg_df_exposure.csv')
avg_df_exposure.to_csv(normalized_avg_path)
display(avg_df_exposure)
| average | |
|---|---|
| Milano Malpensa | 0.830052 |
| Milano Linate | 0.266326 |
| Bergamo Orio al Serio | 0.200527 |
| Roma Fiumicino | 0.776851 |
| Roma Ciampino | 0.208675 |
| Napoli Capodichino | 0.10508 |
| Palermo Punta Raisi | 0.210126 |
| Catania Fontanarossa | 0.09167 |
| Cagliari Elmas | 0.245209 |
Calculation of Vulnerability#
Vulnerability reflects “the propensity or predisposition of a system to be adversely affected. It encompasses a variety of concepts and elements including sensitivity or susceptibility to harm and lack of capacity to cope and adapt opportunities, or to respond to consequences” (IPCC, 2022).
Vulnerability is composed by sensitivity and adaptive capacity
Sensitivity#
Sensitivity is intended as the degree to which the system is (positively or negatively) affected by climate variability or climate change.
Indicator |
Unit of measurement |
|---|---|
Soil Sealing |
Area [%] |
Passenger |
Number |
Building in bad condition |
Number or Absence/Presence |
Age building |
Age |
Air traffic |
Number |
Parking accesses |
Number |
Staff work outside airport |
Number |
# Define the sensitivity indicators and their values
sensitivity_indicators = {
'Soil_sealing': [1235, 300, 300, 1590, 133, 217, 391, 225, 246],
'Passengers': [27000000, 7000000, 13857257, 43532573, 5879496, 10860068, 7018087, 10223113, 4747806],
'Buildings_bad_conditions_or_maintenance': [None, None, None, None, None, None, None, None, None],
'Age_buildings': [73, 82, 83, 63, 105, 111, 61, 97, 84],
'Air_traffic': [201050, 85730, 95377, 309783, 52253, 82577, 54243, 73494, 39691],
'Parking_accesses': [None, None, None, None, None, None, None, None, None],
'Staff_work': [1997.1, 855.9, 1044, 2363.9, 1013.1, None, 283, 183, 146]
}
# Execute the function to display the data for Sensitivity Indicators
display_data(sensitivity_indicators, airports, 'avg_df_sensitivity')
Original Data:
| Soil_sealing | Passengers | Buildings_bad_conditions_or_maintenance | Age_buildings | Air_traffic | Parking_accesses | Staff_work | |
|---|---|---|---|---|---|---|---|
| Milano Malpensa | 1235 | 27000000 | None | 73 | 201050 | None | 1997.1 |
| Milano Linate | 300 | 7000000 | None | 82 | 85730 | None | 855.9 |
| Bergamo Orio al Serio | 300 | 13857257 | None | 83 | 95377 | None | 1044.0 |
| Roma Fiumicino | 1590 | 43532573 | None | 63 | 309783 | None | 2363.9 |
| Roma Ciampino | 133 | 5879496 | None | 105 | 52253 | None | 1013.1 |
| Napoli Capodichino | 217 | 10860068 | None | 111 | 82577 | None | NaN |
| Palermo Punta Raisi | 391 | 7018087 | None | 61 | 54243 | None | 283.0 |
| Catania Fontanarossa | 225 | 10223113 | None | 97 | 73494 | None | 183.0 |
| Cagliari Elmas | 246 | 4747806 | None | 84 | 39691 | None | 146.0 |
Normalized Data:
| Soil_sealing | Passengers | Buildings_bad_conditions_or_maintenance | Age_buildings | Air_traffic | Parking_accesses | Staff_work | average | |
|---|---|---|---|---|---|---|---|---|
| Milano Malpensa | 0.756349 | 0.573735 | None | 0.24 | 0.597422 | None | 0.834618 | 0.600425 |
| Milano Linate | 0.114619 | 0.058069 | None | 0.42 | 0.170457 | None | 0.320078 | 0.216644 |
| Bergamo Orio al Serio | 0.114619 | 0.234872 | None | 0.44 | 0.206174 | None | 0.404888 | 0.280111 |
| Roma Fiumicino | 1.000000 | 1.000000 | None | 0.04 | 1.000000 | None | 1.000000 | 0.808 |
| Roma Ciampino | 0.000000 | 0.029179 | None | 0.88 | 0.046510 | None | 0.390955 | 0.269329 |
| Napoli Capodichino | 0.057653 | 0.157594 | None | 1.00 | 0.158783 | None | NaN | 0.343508 |
| Palermo Punta Raisi | 0.177076 | 0.058535 | None | 0.00 | 0.053878 | None | 0.061770 | 0.070252 |
| Catania Fontanarossa | 0.063143 | 0.141172 | None | 0.72 | 0.125154 | None | 0.016682 | 0.21323 |
| Cagliari Elmas | 0.077557 | 0.000000 | None | 0.46 | 0.000000 | None | 0.000000 | 0.107511 |
Avg components:
Stored DataFrame with name: avg_df_sensitivity
# Accessing the avg_exposure_temp DataFrames
avg_df_sensitivity = dynamic_dataframes['avg_df_sensitivity']
display(avg_df_sensitivity)
| average | |
|---|---|
| Milano Malpensa | 0.600425 |
| Milano Linate | 0.216644 |
| Bergamo Orio al Serio | 0.280111 |
| Roma Fiumicino | 0.808 |
| Roma Ciampino | 0.269329 |
| Napoli Capodichino | 0.343508 |
| Palermo Punta Raisi | 0.070252 |
| Catania Fontanarossa | 0.21323 |
| Cagliari Elmas | 0.107511 |
Adaptative capacity#
Adaptive capacity is “the ability of systems, institutions, humans and other organisms to adjust to potential damage, to take advantage of
Following the list of Adaptive capacity indicator considered in this work This indicatori are defined by the classes reported below and included in De Vivo et al. (2021), if the indicator is presence/absence its value will be 0.9/0.1 respectively
Risk awareness: Initiatives for mitigation to climate change:
Adherence to “NetZero2050” Programme (neutrality3+)
Certification ISO 50001 and initiatives of “energy saving”
Neutrality 3+ Airport carbon accreditation
Neutrality 4+ Airport carbon accreditation; EP-100 intelligent use of energy from “The Climate Group”- Certification ISO 50001
Level 2 Airport carbon accreditation, environmental improvement program
Commitment to obtaining certfiication
Initiatives for adaptation to climate change:
Efficient drainage system
Present
Insurance policy for extreme events
Absent
Monitoring and alarm system
Present
Present (but alert wind shear)
Absent
Bioinfiltration and permeable pavements
Present
Absent
Risk awareness (and initiatives of mitigation)
Risk management framework
Development of local (Urban) climate change mitigation and adaptation strategies
Sustainability Report 2019
gesap environmental report
Absent
Guidelines for adaptation plan to climate change
plan (PACC) and regional strategy for adaptation to climate change
Regional plan under development
Absent
Sustainable Energy and Climate Action Plan (Covenant of Mayors)
Sustainable Energy and Climate Action Plan (Covenant of Mayors) municipality of Catania
Regional climate change adaptation strategy
Class No. |
Description |
Value Rane |
Indicator Range (0-1) |
|---|---|---|---|
1 |
Optimal |
0 - 0.2 |
0.1 |
2 |
Quite Positive |
0.2 - 0.4 |
0.3 |
3 |
Neutral |
0.4 - 0.6 |
0.5 |
4 |
Quite Negative |
0.6 - 0.8 |
0.7 |
5 |
Critical |
0.8 - 1 |
0.9 |
# Define the sensitivity indicators and their values
adaptative_indicators = {
'Vegetation_zone': [None, None, None, None, None, None, None, None, None],
'Initiatives_for_optimization_of_energy ': [0.3, 0.3, 0.3, 0.1, 0.1, 0.3, 0.5, 0.7, 0.7],
'Green_wall': [0.1, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9],
'Green_roof': [0.1, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9],
'Heat_resistent_coating': [0.9, 0.9, 0.9, 0.1, 0.9, 0.9, 0.9, 0.9, 0.9],
'Insurance_policy_for_extreme_events': [0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9],
'Guidelines_for_adaptation_climate_change': [0.3, 0.3, 0.3, 0.5, 0.5, 0.9, 0.3, 0.3, 0.3],
'Evening departures': [None, None, None, None, None, None, None, None, None]
}
# Create the DataFrame for the adaptive indicators
adaptative_df= pd.DataFrame(adaptative_indicators, index=airports)
# Add a column with the average of all indicators for each airport
adaptative_df['average'] = adaptative_df.mean(axis=1)
display(adaptative_df)
# Extract a column the average column
avg_df_adaptative = adaptative_df[['average']]
display(avg_df_adaptative)
| Vegetation_zone | Initiatives_for_optimization_of_energy | Green_wall | Green_roof | Heat_resistent_coating | Insurance_policy_for_extreme_events | Guidelines_for_adaptation_climate_change | Evening departures | average | |
|---|---|---|---|---|---|---|---|---|---|
| Milano Malpensa | None | 0.3 | 0.1 | 0.1 | 0.9 | 0.9 | 0.3 | None | 0.433333 |
| Milano Linate | None | 0.3 | 0.9 | 0.9 | 0.9 | 0.9 | 0.3 | None | 0.7 |
| Bergamo Orio al Serio | None | 0.3 | 0.9 | 0.9 | 0.9 | 0.9 | 0.3 | None | 0.7 |
| Roma Fiumicino | None | 0.1 | 0.9 | 0.9 | 0.1 | 0.9 | 0.5 | None | 0.566667 |
| Roma Ciampino | None | 0.1 | 0.9 | 0.9 | 0.9 | 0.9 | 0.5 | None | 0.7 |
| Napoli Capodichino | None | 0.3 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | None | 0.8 |
| Palermo Punta Raisi | None | 0.5 | 0.9 | 0.9 | 0.9 | 0.9 | 0.3 | None | 0.733333 |
| Catania Fontanarossa | None | 0.7 | 0.9 | 0.9 | 0.9 | 0.9 | 0.3 | None | 0.766667 |
| Cagliari Elmas | None | 0.7 | 0.9 | 0.9 | 0.9 | 0.9 | 0.3 | None | 0.766667 |
| average | |
|---|---|
| Milano Malpensa | 0.433333 |
| Milano Linate | 0.7 |
| Bergamo Orio al Serio | 0.7 |
| Roma Fiumicino | 0.566667 |
| Roma Ciampino | 0.7 |
| Napoli Capodichino | 0.8 |
| Palermo Punta Raisi | 0.733333 |
| Catania Fontanarossa | 0.766667 |
| Cagliari Elmas | 0.766667 |
Vulnerability calculation#
As mentioned before, the Vulnerability is the mean between the sensitivity and adaptive capacity
# Create the vulnerability DataFrame by calculating the average of both 'Average' columns
vulnerability_df = pd.DataFrame()
# Combine the 'Average' columns from both DataFrames and calculate the new average
vulnerability_df['average'] = (avg_df_sensitivity['average'] + avg_df_adaptative['average']) / 2
normalized_avg_path = os.path.join(csv_folder_path, f'avg_df_vulnerability.csv')
vulnerability_df.to_csv(normalized_avg_path)
# Display the resulting vulnerability DataFrame
vulnerability_df
| average | |
|---|---|
| Milano Malpensa | 0.516879 |
| Milano Linate | 0.458322 |
| Bergamo Orio al Serio | 0.490055 |
| Roma Fiumicino | 0.687333 |
| Roma Ciampino | 0.484664 |
| Napoli Capodichino | 0.571754 |
| Palermo Punta Raisi | 0.401793 |
| Catania Fontanarossa | 0.489948 |
| Cagliari Elmas | 0.437089 |
Risk Calculation for Extreme Temperature#
The risk will be computed by moltiplication of three components: R = H * E * V
UERRA DATASETS:#
Extreme Temperature
UERRA Dataset#
In the cell below the nearest point to the airport was selected. In the framework the normalization of all the indicator was made, then a mean to obtain a single value
hazard_uerra_temp_path = '/work/cmcc/dg07124/climax/indicators/hazards_csv/hazard_uerra_temp.csv'
hazard_uerra_temp = pd.read_csv(hazard_uerra_temp_path, index_col='Airports')
# Standardize the airport names to Title Case
hazard_uerra_temp.index = hazard_uerra_temp.index.str.title()
hazard_uerra_temp['average'] = pd.to_numeric(hazard_uerra_temp['average'], errors='coerce')
hazard_uerra_temp = hazard_uerra_temp.sort_index()
hazard_uerra_temp
| average | |
|---|---|
| Airports | |
| Bergamo Orio Al Serio | 13.090606 |
| Cagliari Elmas | 15.807716 |
| Catania Fontanarossa | 17.416528 |
| Milano Linate | 13.321168 |
| Milano Malpensa | 12.487971 |
| Napoli Capodichino | 14.670617 |
| Palermo Punta Raisi | 13.622919 |
| Roma Ciampino | 14.587898 |
| Roma Fiumicino | 12.443477 |
avg_df_exposure.index = avg_df_exposure.index.str.title()
avg_df_exposure['average'] = pd.to_numeric(avg_df_exposure['average'], errors='coerce')
avg_df_exposure = avg_df_exposure.sort_index()
avg_df_exposure
| average | |
|---|---|
| Bergamo Orio Al Serio | 0.200527 |
| Cagliari Elmas | 0.245209 |
| Catania Fontanarossa | 0.091670 |
| Milano Linate | 0.266326 |
| Milano Malpensa | 0.830052 |
| Napoli Capodichino | 0.105080 |
| Palermo Punta Raisi | 0.210126 |
| Roma Ciampino | 0.208675 |
| Roma Fiumicino | 0.776851 |
vulnerability_df.index = vulnerability_df.index.str.title()
vulnerability_df['average'] = pd.to_numeric(vulnerability_df['average'], errors='coerce')
vulnerability_df = vulnerability_df.sort_index()
vulnerability_df
| average | |
|---|---|
| Bergamo Orio Al Serio | 0.490055 |
| Cagliari Elmas | 0.437089 |
| Catania Fontanarossa | 0.489948 |
| Milano Linate | 0.458322 |
| Milano Malpensa | 0.516879 |
| Napoli Capodichino | 0.571754 |
| Palermo Punta Raisi | 0.401793 |
| Roma Ciampino | 0.484664 |
| Roma Fiumicino | 0.687333 |
# Now multiply the 'average' column of each DataFrame
uerra_risk_temp = avg_df_exposure['average'] * hazard_uerra_temp['average'] * vulnerability_df['average']
# Convert the Series to a DataFrame
uerra_risk_temp_df = pd.DataFrame(uerra_risk_temp)
# Rename the column
uerra_risk_temp_df.columns = ['Uerra Risk Temperature']
uerra_risk_temp_df
| Uerra Risk Temperature | |
|---|---|
| Bergamo Orio Al Serio | 1.286406 |
| Cagliari Elmas | 1.694239 |
| Catania Fontanarossa | 0.782242 |
| Milano Linate | 1.626026 |
| Milano Malpensa | 5.357799 |
| Napoli Capodichino | 0.881409 |
| Palermo Punta Raisi | 1.150143 |
| Roma Ciampino | 1.475383 |
| Roma Fiumicino | 6.644263 |
RISK CLASSES#
To evaluate the risk classes for each of the airport the Quantile Method was used. Classifying each airport in terms of quantile with respect to all other airport taken into account
# Function to calculate the quantile of the risk for all 9 airports
def classify_dataframe(df, column_name):
"""
Classifies a given dataframe column into quantiles and assigns categorical classes.
Parameters:
df (pd.DataFrame): Input dataframe with numerical values.
column_name (str): The column name to be classified.
Returns:
pd.DataFrame: Dataframe with quantile values and class categories.
"""
df = df.copy() # Ensure the original dataframe is not modified
# Calculating the quantiles on a scale from 0 to 1
df['Quantile'] = df[column_name].rank(pct=True)
# Defining classification labels
labels = ["Very Low", "Low", "Medium", "High", "Very High"]
# Assigning classes based on quantile values
df['Class'] = pd.qcut(df['Quantile'], q=5, labels=labels)
return df
# Define a function to color the class column based on the classes
def highlight_class_column(s):
colors = {
"Very Low": "background-color: forestgreen",
"Low": "background-color: limegreen",
"Medium": "background-color: yellow",
"High": "background-color: orange",
"Very High": "background-color: red; color: white"
}
return [colors.get(val, "") if s.name == "Class" else "" for val in s]
# Call the function that calculates the quantile
classified_df = classify_dataframe(uerra_risk_temp_df, "Uerra Risk Temperature")
# Apply styling
styled_uerra_risk_temp_df = classified_df.style.apply(highlight_class_column, axis=0)
# Displaying the classified risk
display(styled_uerra_risk_temp_df)
| Uerra Risk Temperature | Quantile | Class | |
|---|---|---|---|
| Bergamo Orio Al Serio | 1.286406 | 0.444444 | Low |
| Cagliari Elmas | 1.694239 | 0.777778 | High |
| Catania Fontanarossa | 0.782242 | 0.111111 | Very Low |
| Milano Linate | 1.626026 | 0.666667 | High |
| Milano Malpensa | 5.357799 | 0.888889 | Very High |
| Napoli Capodichino | 0.881409 | 0.222222 | Very Low |
| Palermo Punta Raisi | 1.150143 | 0.333333 | Low |
| Roma Ciampino | 1.475383 | 0.555556 | Medium |
| Roma Fiumicino | 6.644263 | 1.000000 | Very High |
CORDEX Dataset#
The Risk computation for Cordex dataset follows the same steps as UERRA
RCP26 2021-2050#
hazard_cordex_temp_rcp26_20212050_path = '/work/cmcc/dg07124/climax/indicators/cordex2/temp_avg_ensembles/temp_normalized_temp_rcp26_2021-2050_data.csv'
hazard_cordex_temp_rcp26_20212050 = pd.read_csv(hazard_cordex_temp_rcp26_20212050_path, index_col='Airports')
# Standardize the airport names to Title Case
hazard_cordex_temp_rcp26_20212050.index = hazard_cordex_temp_rcp26_20212050.index.str.title()
hazard_cordex_temp_rcp26_20212050['average'] = pd.to_numeric(hazard_cordex_temp_rcp26_20212050['average'], errors='coerce')
hazard_cordex_temp_rcp26_20212050 = hazard_cordex_temp_rcp26_20212050.sort_index()
hazard_cordex_temp_rcp26_20212050 = hazard_cordex_temp_rcp26_20212050[['average']]
hazard_cordex_temp_rcp26_20212050
| average | |
|---|---|
| Airports | |
| Bergamo Orio Al Serio | 0.245054 |
| Cagliari Elmas | 0.510344 |
| Catania Fontanarossa | 0.792611 |
| Milano Linate | 0.369710 |
| Milano Malpensa | 0.268374 |
| Napoli Capodichino | 0.282604 |
| Palermo Punta Raisi | 0.155084 |
| Roma Ciampino | 0.388948 |
| Roma Fiumicino | 0.320900 |
# Now multiply the 'average' column of each DataFrame
cordex_risk_temp_rcp26_20212050 = avg_df_exposure['average'] * hazard_cordex_temp_rcp26_20212050['average'] * vulnerability_df['average']
cordex_risk_temp_rcp26_20212050
# Convert the Series to a DataFrame
cordex_risk_temp_rcp26_20212050_df = pd.DataFrame(cordex_risk_temp_rcp26_20212050)
# Rename the column
cordex_risk_temp_rcp26_20212050_df.columns = ['Cordex Risk Temp rcp26 2021-2050']
cordex_risk_temp_rcp26_20212050_df
| Cordex Risk Temp rcp26 2021-2050 | |
|---|---|
| Bergamo Orio Al Serio | 0.024081 |
| Cagliari Elmas | 0.054698 |
| Catania Fontanarossa | 0.035599 |
| Milano Linate | 0.045128 |
| Milano Malpensa | 0.115142 |
| Napoli Capodichino | 0.016979 |
| Palermo Punta Raisi | 0.013093 |
| Roma Ciampino | 0.039337 |
| Roma Fiumicino | 0.171347 |
# Call the function that calculates the quantile
classified_df = classify_dataframe(cordex_risk_temp_rcp26_20212050_df, "Cordex Risk Temp rcp26 2021-2050")
# Apply styling
styled_cordex_risk_temp_df = classified_df.style.apply(highlight_class_column, axis=0)
# Displaying the classified risk
display(styled_cordex_risk_temp_df)
| Cordex Risk Temp rcp26 2021-2050 | Quantile | Class | |
|---|---|---|---|
| Bergamo Orio Al Serio | 0.024081 | 0.333333 | Low |
| Cagliari Elmas | 0.054698 | 0.777778 | High |
| Catania Fontanarossa | 0.035599 | 0.444444 | Low |
| Milano Linate | 0.045128 | 0.666667 | High |
| Milano Malpensa | 0.115142 | 0.888889 | Very High |
| Napoli Capodichino | 0.016979 | 0.222222 | Very Low |
| Palermo Punta Raisi | 0.013093 | 0.111111 | Very Low |
| Roma Ciampino | 0.039337 | 0.555556 | Medium |
| Roma Fiumicino | 0.171347 | 1.000000 | Very High |
RCP26 2041-2070#
hazard_cordex_temp_rcp26_20412070_path = '/work/cmcc/dg07124/climax/indicators/cordex2/temp_avg_ensembles/temp_normalized_temp_rcp26_2041-2070_data.csv'
hazard_cordex_temp_rcp26_20412070 = pd.read_csv(hazard_cordex_temp_rcp26_20412070_path, index_col='Airports')
# Standardize the airport names to Title Case
hazard_cordex_temp_rcp26_20412070.index = hazard_cordex_temp_rcp26_20412070.index.str.title()
hazard_cordex_temp_rcp26_20412070['average'] = pd.to_numeric(hazard_cordex_temp_rcp26_20412070['average'], errors='coerce')
hazard_cordex_temp_rcp26_20412070 = hazard_cordex_temp_rcp26_20412070.sort_index()
hazard_cordex_temp_rcp26_20412070 = hazard_cordex_temp_rcp26_20412070[['average']]
hazard_cordex_temp_rcp26_20412070
| average | |
|---|---|
| Airports | |
| Bergamo Orio Al Serio | 0.151568 |
| Cagliari Elmas | 0.595888 |
| Catania Fontanarossa | 0.824857 |
| Milano Linate | 0.214873 |
| Milano Malpensa | 0.174961 |
| Napoli Capodichino | 0.170859 |
| Palermo Punta Raisi | 0.280882 |
| Roma Ciampino | 0.366586 |
| Roma Fiumicino | 0.234371 |
# Now multiply the 'average' column of each DataFrame
cordex_risk_temp_rcp26_20412070 = avg_df_exposure['average'] * hazard_cordex_temp_rcp26_20412070['average'] * vulnerability_df['average']
cordex_risk_temp_rcp26_20412070
# Convert the Series to a DataFrame
cordex_risk_temp_rcp26_20412070_df = pd.DataFrame(cordex_risk_temp_rcp26_20412070)
# Rename the column
cordex_risk_temp_rcp26_20412070_df.columns = ['Cordex Risk Temp rcp26 2041-2070']
cordex_risk_temp_rcp26_20412070_df
| Cordex Risk Temp rcp26 2041-2070 | |
|---|---|
| Bergamo Orio Al Serio | 0.014894 |
| Cagliari Elmas | 0.063866 |
| Catania Fontanarossa | 0.037047 |
| Milano Linate | 0.026228 |
| Milano Malpensa | 0.075065 |
| Napoli Capodichino | 0.010265 |
| Palermo Punta Raisi | 0.023714 |
| Roma Ciampino | 0.037076 |
| Roma Fiumicino | 0.125144 |
# Call the function that calculates the quantile
classified_df = classify_dataframe(cordex_risk_temp_rcp26_20412070_df, "Cordex Risk Temp rcp26 2041-2070")
# Apply styling
styled_cordex_risk_temp_df = classified_df.style.apply(highlight_class_column, axis=0)
# Displaying the classified risk
display(styled_cordex_risk_temp_df)
| Cordex Risk Temp rcp26 2041-2070 | Quantile | Class | |
|---|---|---|---|
| Bergamo Orio Al Serio | 0.014894 | 0.222222 | Very Low |
| Cagliari Elmas | 0.063866 | 0.777778 | High |
| Catania Fontanarossa | 0.037047 | 0.555556 | Medium |
| Milano Linate | 0.026228 | 0.444444 | Low |
| Milano Malpensa | 0.075065 | 0.888889 | Very High |
| Napoli Capodichino | 0.010265 | 0.111111 | Very Low |
| Palermo Punta Raisi | 0.023714 | 0.333333 | Low |
| Roma Ciampino | 0.037076 | 0.666667 | High |
| Roma Fiumicino | 0.125144 | 1.000000 | Very High |
RCP26 2071-2100#
hazard_cordex_temp_rcp26_20712100_path = '/work/cmcc/dg07124/climax/indicators/cordex2/temp_avg_ensembles/temp_normalized_temp_rcp26_2071-2100_data.csv'
hazard_cordex_temp_rcp26_20712100 = pd.read_csv(hazard_cordex_temp_rcp26_20412070_path, index_col='Airports')
# Standardize the airport names to Title Case
hazard_cordex_temp_rcp26_20712100.index = hazard_cordex_temp_rcp26_20712100.index.str.title()
hazard_cordex_temp_rcp26_20712100['average'] = pd.to_numeric(hazard_cordex_temp_rcp26_20712100['average'], errors='coerce')
hazard_cordex_temp_rcp26_20712100 = hazard_cordex_temp_rcp26_20712100.sort_index()
hazard_cordex_temp_rcp26_20712100 = hazard_cordex_temp_rcp26_20712100[['average']]
hazard_cordex_temp_rcp26_20712100
| average | |
|---|---|
| Airports | |
| Bergamo Orio Al Serio | 0.151568 |
| Cagliari Elmas | 0.595888 |
| Catania Fontanarossa | 0.824857 |
| Milano Linate | 0.214873 |
| Milano Malpensa | 0.174961 |
| Napoli Capodichino | 0.170859 |
| Palermo Punta Raisi | 0.280882 |
| Roma Ciampino | 0.366586 |
| Roma Fiumicino | 0.234371 |
# Now multiply the 'average' column of each DataFrame
cordex_risk_temp_rcp26_20712100 = avg_df_exposure['average'] * hazard_cordex_temp_rcp26_20712100['average'] * vulnerability_df['average']
cordex_risk_temp_rcp26_20712100
# Convert the Series to a DataFrame
cordex_risk_temp_rcp26_20712100_df = pd.DataFrame(cordex_risk_temp_rcp26_20712100)
# Rename the column
cordex_risk_temp_rcp26_20712100_df.columns = ['Cordex Risk Temp rcp26 2071-2100']
cordex_risk_temp_rcp26_20712100_df
| Cordex Risk Temp rcp26 2071-2100 | |
|---|---|
| Bergamo Orio Al Serio | 0.014894 |
| Cagliari Elmas | 0.063866 |
| Catania Fontanarossa | 0.037047 |
| Milano Linate | 0.026228 |
| Milano Malpensa | 0.075065 |
| Napoli Capodichino | 0.010265 |
| Palermo Punta Raisi | 0.023714 |
| Roma Ciampino | 0.037076 |
| Roma Fiumicino | 0.125144 |
# Call the function that calculates the quantile
classified_df = classify_dataframe(cordex_risk_temp_rcp26_20712100_df, "Cordex Risk Temp rcp26 2071-2100")
# Apply styling
styled_cordex_risk_temp_df = classified_df.style.apply(highlight_class_column, axis=0)
# Displaying the classified risk
display(styled_cordex_risk_temp_df)
| Cordex Risk Temp rcp26 2071-2100 | Quantile | Class | |
|---|---|---|---|
| Bergamo Orio Al Serio | 0.014894 | 0.222222 | Very Low |
| Cagliari Elmas | 0.063866 | 0.777778 | High |
| Catania Fontanarossa | 0.037047 | 0.555556 | Medium |
| Milano Linate | 0.026228 | 0.444444 | Low |
| Milano Malpensa | 0.075065 | 0.888889 | Very High |
| Napoli Capodichino | 0.010265 | 0.111111 | Very Low |
| Palermo Punta Raisi | 0.023714 | 0.333333 | Low |
| Roma Ciampino | 0.037076 | 0.666667 | High |
| Roma Fiumicino | 0.125144 | 1.000000 | Very High |
RCP45 2021-2050#
hazard_cordex_temp_rcp45_20212050_path = '/work/cmcc/dg07124/climax/indicators/cordex2/temp_avg_ensembles/temp_normalized_temp_rcp45_2021-2050_data.csv'
hazard_cordex_temp_rcp45_20212050 = pd.read_csv(hazard_cordex_temp_rcp45_20212050_path, index_col='Airports')
# Standardize the airport names to Title Case
hazard_cordex_temp_rcp45_20212050.index = hazard_cordex_temp_rcp45_20212050.index.str.title()
hazard_cordex_temp_rcp45_20212050['average'] = pd.to_numeric(hazard_cordex_temp_rcp45_20212050['average'], errors='coerce')
hazard_cordex_temp_rcp45_20212050 = hazard_cordex_temp_rcp45_20212050.sort_index()
hazard_cordex_temp_rcp45_20212050 = hazard_cordex_temp_rcp45_20212050[['average']]
hazard_cordex_temp_rcp45_20212050
| average | |
|---|---|
| Airports | |
| Bergamo Orio Al Serio | 0.315651 |
| Cagliari Elmas | 0.341100 |
| Catania Fontanarossa | 0.889613 |
| Milano Linate | 0.441427 |
| Milano Malpensa | 0.286262 |
| Napoli Capodichino | 0.220931 |
| Palermo Punta Raisi | 0.152799 |
| Roma Ciampino | 0.438427 |
| Roma Fiumicino | 0.187835 |
# Now multiply the 'average' column of each DataFrame
cordex_risk_temp_rcp45_20212050 = avg_df_exposure['average'] * hazard_cordex_temp_rcp45_20212050['average'] * vulnerability_df['average']
cordex_risk_temp_rcp45_20212050
# Convert the Series to a DataFrame
cordex_risk_temp_rcp45_20212050_df = pd.DataFrame(cordex_risk_temp_rcp45_20212050)
# Rename the column
cordex_risk_temp_rcp45_20212050_df.columns = ['Cordex Risk Temp rcp45 2021-2050']
cordex_risk_temp_rcp45_20212050_df
| Cordex Risk Temp rcp45 2021-2050 | |
|---|---|
| Bergamo Orio Al Serio | 0.031019 |
| Cagliari Elmas | 0.036558 |
| Catania Fontanarossa | 0.039956 |
| Milano Linate | 0.053882 |
| Milano Malpensa | 0.122817 |
| Napoli Capodichino | 0.013274 |
| Palermo Punta Raisi | 0.012900 |
| Roma Ciampino | 0.044341 |
| Roma Fiumicino | 0.100295 |
# Call the function that calculates the quantile
classified_df = classify_dataframe(cordex_risk_temp_rcp45_20212050_df, "Cordex Risk Temp rcp45 2021-2050")
# Apply styling
styled_cordex_risk_temp_df = classified_df.style.apply(highlight_class_column, axis=0)
# Displaying the classified risk
display(styled_cordex_risk_temp_df)
| Cordex Risk Temp rcp45 2021-2050 | Quantile | Class | |
|---|---|---|---|
| Bergamo Orio Al Serio | 0.031019 | 0.333333 | Low |
| Cagliari Elmas | 0.036558 | 0.444444 | Low |
| Catania Fontanarossa | 0.039956 | 0.555556 | Medium |
| Milano Linate | 0.053882 | 0.777778 | High |
| Milano Malpensa | 0.122817 | 1.000000 | Very High |
| Napoli Capodichino | 0.013274 | 0.222222 | Very Low |
| Palermo Punta Raisi | 0.012900 | 0.111111 | Very Low |
| Roma Ciampino | 0.044341 | 0.666667 | High |
| Roma Fiumicino | 0.100295 | 0.888889 | Very High |
RCP45 2041-2070#
hazard_cordex_temp_rcp45_20412070_path = '/work/cmcc/dg07124/climax/indicators/cordex2/temp_avg_ensembles/temp_normalized_temp_rcp45_2041-2070_data.csv'
hazard_cordex_temp_rcp45_20412070 = pd.read_csv(hazard_cordex_temp_rcp45_20412070_path, index_col='Airports')
# Standardize the airport names to Title Case
hazard_cordex_temp_rcp45_20412070.index = hazard_cordex_temp_rcp45_20412070.index.str.title()
hazard_cordex_temp_rcp45_20412070['average'] = pd.to_numeric(hazard_cordex_temp_rcp45_20412070['average'], errors='coerce')
hazard_cordex_temp_rcp45_20412070 = hazard_cordex_temp_rcp45_20412070.sort_index()
hazard_cordex_temp_rcp45_20412070 = hazard_cordex_temp_rcp45_20412070[['average']]
hazard_cordex_temp_rcp45_20412070
| average | |
|---|---|
| Airports | |
| Bergamo Orio Al Serio | 0.467816 |
| Cagliari Elmas | 0.622728 |
| Catania Fontanarossa | 0.727498 |
| Milano Linate | 0.548227 |
| Milano Malpensa | 0.410582 |
| Napoli Capodichino | 0.241711 |
| Palermo Punta Raisi | 0.007270 |
| Roma Ciampino | 0.428041 |
| Roma Fiumicino | 0.244190 |
# Now multiply the 'average' column of each DataFrame
cordex_risk_temp_rcp45_20412070 = avg_df_exposure['average'] * hazard_cordex_temp_rcp45_20412070['average'] * vulnerability_df['average']
cordex_risk_temp_rcp45_20412070
# Convert the Series to a DataFrame
cordex_risk_temp_rcp45_20412070_df = pd.DataFrame(cordex_risk_temp_rcp45_20412070)
# Rename the column
cordex_risk_temp_rcp45_20412070_df.columns = ['Cordex Risk Temp rcp45 2041-2070']
cordex_risk_temp_rcp45_20412070_df
| Cordex Risk Temp rcp45 2041-2070 | |
|---|---|
| Bergamo Orio Al Serio | 0.045972 |
| Cagliari Elmas | 0.066743 |
| Catania Fontanarossa | 0.032675 |
| Milano Linate | 0.066918 |
| Milano Malpensa | 0.176155 |
| Napoli Capodichino | 0.014522 |
| Palermo Punta Raisi | 0.000614 |
| Roma Ciampino | 0.043291 |
| Roma Fiumicino | 0.130387 |
# Call the function that calculates the quantile
classified_df = classify_dataframe(cordex_risk_temp_rcp45_20412070_df, "Cordex Risk Temp rcp45 2041-2070")
# Apply styling
styled_cordex_risk_temp_df = classified_df.style.apply(highlight_class_column, axis=0)
# Displaying the classified risk
display(styled_cordex_risk_temp_df)
| Cordex Risk Temp rcp45 2041-2070 | Quantile | Class | |
|---|---|---|---|
| Bergamo Orio Al Serio | 0.045972 | 0.555556 | Medium |
| Cagliari Elmas | 0.066743 | 0.666667 | High |
| Catania Fontanarossa | 0.032675 | 0.333333 | Low |
| Milano Linate | 0.066918 | 0.777778 | High |
| Milano Malpensa | 0.176155 | 1.000000 | Very High |
| Napoli Capodichino | 0.014522 | 0.222222 | Very Low |
| Palermo Punta Raisi | 0.000614 | 0.111111 | Very Low |
| Roma Ciampino | 0.043291 | 0.444444 | Low |
| Roma Fiumicino | 0.130387 | 0.888889 | Very High |
RCP45 2071-2100#
hazard_cordex_temp_rcp45_20712100_path = '/work/cmcc/dg07124/climax/indicators/cordex2/temp_avg_ensembles/temp_normalized_temp_rcp45_2071-2100_data.csv'
hazard_cordex_temp_rcp45_20712100 = pd.read_csv(hazard_cordex_temp_rcp45_20712100_path, index_col='Airports')
# Standardize the airport names to Title Case
hazard_cordex_temp_rcp45_20712100.index = hazard_cordex_temp_rcp45_20712100.index.str.title()
hazard_cordex_temp_rcp45_20712100['average'] = pd.to_numeric(hazard_cordex_temp_rcp45_20712100['average'], errors='coerce')
hazard_cordex_temp_rcp45_20712100 = hazard_cordex_temp_rcp45_20712100.sort_index()
hazard_cordex_temp_rcp45_20712100 = hazard_cordex_temp_rcp45_20712100[['average']]
hazard_cordex_temp_rcp45_20712100
| average | |
|---|---|
| Airports | |
| Bergamo Orio Al Serio | 0.459278 |
| Cagliari Elmas | 0.539877 |
| Catania Fontanarossa | 0.838774 |
| Milano Linate | 0.574990 |
| Milano Malpensa | 0.493102 |
| Napoli Capodichino | 0.153590 |
| Palermo Punta Raisi | 0.103647 |
| Roma Ciampino | 0.228306 |
| Roma Fiumicino | 0.081144 |
# Now multiply the 'average' column of each DataFrame
cordex_risk_temp_rcp45_20712100 = avg_df_exposure['average'] * hazard_cordex_temp_rcp45_20712100['average'] * vulnerability_df['average']
cordex_risk_temp_rcp45_20712100
# Convert the Series to a DataFrame
cordex_risk_temp_rcp45_20712100_df = pd.DataFrame(cordex_risk_temp_rcp45_20712100)
# Rename the column
cordex_risk_temp_rcp45_20712100_df.columns = ['Cordex Risk Temp rcp45 2071-2100']
cordex_risk_temp_rcp45_20712100_df
| Cordex Risk Temp rcp45 2071-2100 | |
|---|---|
| Bergamo Orio Al Serio | 0.045133 |
| Cagliari Elmas | 0.057863 |
| Catania Fontanarossa | 0.037673 |
| Milano Linate | 0.070185 |
| Milano Malpensa | 0.211559 |
| Napoli Capodichino | 0.009228 |
| Palermo Punta Raisi | 0.008751 |
| Roma Ciampino | 0.023090 |
| Roma Fiumicino | 0.043327 |
# Call the function that calculates the quantile
classified_df = classify_dataframe(cordex_risk_temp_rcp45_20712100_df, "Cordex Risk Temp rcp45 2071-2100")
# Apply styling
styled_cordex_risk_temp_df = classified_df.style.apply(highlight_class_column, axis=0)
# Displaying the classified risk
display(styled_cordex_risk_temp_df)
| Cordex Risk Temp rcp45 2071-2100 | Quantile | Class | |
|---|---|---|---|
| Bergamo Orio Al Serio | 0.045133 | 0.666667 | High |
| Cagliari Elmas | 0.057863 | 0.777778 | High |
| Catania Fontanarossa | 0.037673 | 0.444444 | Low |
| Milano Linate | 0.070185 | 0.888889 | Very High |
| Milano Malpensa | 0.211559 | 1.000000 | Very High |
| Napoli Capodichino | 0.009228 | 0.222222 | Very Low |
| Palermo Punta Raisi | 0.008751 | 0.111111 | Very Low |
| Roma Ciampino | 0.023090 | 0.333333 | Low |
| Roma Fiumicino | 0.043327 | 0.555556 | Medium |
RCP85 2021-2050#
hazard_cordex_temp_rcp85_20212050_path = '/work/cmcc/dg07124/climax/indicators/cordex2/temp_avg_ensembles/temp_normalized_temp_rcp85_2021-2050_data.csv'
hazard_cordex_temp_rcp85_20212050 = pd.read_csv(hazard_cordex_temp_rcp85_20212050_path, index_col='Airports')
# Standardize the airport names to Title Case
hazard_cordex_temp_rcp85_20212050.index = hazard_cordex_temp_rcp85_20212050.index.str.title()
hazard_cordex_temp_rcp85_20212050['average'] = pd.to_numeric(hazard_cordex_temp_rcp85_20212050['average'], errors='coerce')
hazard_cordex_temp_rcp85_20212050 = hazard_cordex_temp_rcp85_20212050.sort_index()
hazard_cordex_temp_rcp85_20212050 = hazard_cordex_temp_rcp85_20212050[['average']]
hazard_cordex_temp_rcp85_20212050
| average | |
|---|---|
| Airports | |
| Bergamo Orio Al Serio | 0.399005 |
| Cagliari Elmas | 0.327102 |
| Catania Fontanarossa | 1.000000 |
| Milano Linate | 0.576300 |
| Milano Malpensa | 0.410556 |
| Napoli Capodichino | 0.090410 |
| Palermo Punta Raisi | 0.274023 |
| Roma Ciampino | 0.264913 |
| Roma Fiumicino | 0.108295 |
# Now multiply the 'average' column of each DataFrame
cordex_risk_temp_rcp85_20212050 = avg_df_exposure['average'] * hazard_cordex_temp_rcp85_20212050['average'] * vulnerability_df['average']
cordex_risk_temp_rcp85_20212050
# Convert the Series to a DataFrame
cordex_risk_temp_rcp85_20212050_df = pd.DataFrame(cordex_risk_temp_rcp85_20212050)
# Rename the column
cordex_risk_temp_rcp85_20212050_df.columns = ['Cordex Risk Temp rcp85 2021-2050']
cordex_risk_temp_rcp85_20212050_df
| Cordex Risk Temp rcp85 2021-2050 | |
|---|---|
| Bergamo Orio Al Serio | 0.039210 |
| Cagliari Elmas | 0.035058 |
| Catania Fontanarossa | 0.044914 |
| Milano Linate | 0.070345 |
| Milano Malpensa | 0.176144 |
| Napoli Capodichino | 0.005432 |
| Palermo Punta Raisi | 0.023135 |
| Roma Ciampino | 0.026793 |
| Roma Fiumicino | 0.057825 |
# Call the function that calculates the quantile
classified_df = classify_dataframe(cordex_risk_temp_rcp85_20212050_df, "Cordex Risk Temp rcp85 2021-2050")
# Apply styling
styled_cordex_risk_temp_df = classified_df.style.apply(highlight_class_column, axis=0)
# Displaying the classified risk
display(styled_cordex_risk_temp_df)
| Cordex Risk Temp rcp85 2021-2050 | Quantile | Class | |
|---|---|---|---|
| Bergamo Orio Al Serio | 0.039210 | 0.555556 | Medium |
| Cagliari Elmas | 0.035058 | 0.444444 | Low |
| Catania Fontanarossa | 0.044914 | 0.666667 | High |
| Milano Linate | 0.070345 | 0.888889 | Very High |
| Milano Malpensa | 0.176144 | 1.000000 | Very High |
| Napoli Capodichino | 0.005432 | 0.111111 | Very Low |
| Palermo Punta Raisi | 0.023135 | 0.222222 | Very Low |
| Roma Ciampino | 0.026793 | 0.333333 | Low |
| Roma Fiumicino | 0.057825 | 0.777778 | High |
RCP85 2041-2070#
hazard_cordex_temp_rcp85_20412070_path = '/work/cmcc/dg07124/climax/indicators/cordex2/temp_avg_ensembles/temp_normalized_temp_rcp85_2041-2070_data.csv'
hazard_cordex_temp_rcp85_20412070 = pd.read_csv(hazard_cordex_temp_rcp85_20412070_path, index_col='Airports')
# Standardize the airport names to Title Case
hazard_cordex_temp_rcp85_20412070.index = hazard_cordex_temp_rcp85_20412070.index.str.title()
hazard_cordex_temp_rcp85_20412070['average'] = pd.to_numeric(hazard_cordex_temp_rcp85_20412070['average'], errors='coerce')
hazard_cordex_temp_rcp85_20412070 = hazard_cordex_temp_rcp85_20412070.sort_index()
hazard_cordex_temp_rcp85_20412070 = hazard_cordex_temp_rcp85_20412070[['average']]
hazard_cordex_temp_rcp85_20412070
| average | |
|---|---|
| Airports | |
| Bergamo Orio Al Serio | 0.352040 |
| Cagliari Elmas | 0.588853 |
| Catania Fontanarossa | 0.865200 |
| Milano Linate | 0.488185 |
| Milano Malpensa | 0.416816 |
| Napoli Capodichino | 0.097882 |
| Palermo Punta Raisi | 0.272453 |
| Roma Ciampino | 0.288052 |
| Roma Fiumicino | 0.086157 |
# Now multiply the 'average' column of each DataFrame
cordex_risk_temp_rcp85_20412070 = avg_df_exposure['average'] * hazard_cordex_temp_rcp85_20412070['average'] * vulnerability_df['average']
cordex_risk_temp_rcp85_20412070
# Convert the Series to a DataFrame
cordex_risk_temp_rcp85_20412070_df = pd.DataFrame(cordex_risk_temp_rcp85_20412070)
# Rename the column
cordex_risk_temp_rcp85_20412070_df.columns = ['Cordex Risk Temp rcp85 2041-2070']
cordex_risk_temp_rcp85_20412070_df
| Cordex Risk Temp rcp85 2041-2070 | |
|---|---|
| Bergamo Orio Al Serio | 0.034595 |
| Cagliari Elmas | 0.063112 |
| Catania Fontanarossa | 0.038859 |
| Milano Linate | 0.059589 |
| Milano Malpensa | 0.178830 |
| Napoli Capodichino | 0.005881 |
| Palermo Punta Raisi | 0.023002 |
| Roma Ciampino | 0.029133 |
| Roma Fiumicino | 0.046004 |
# Call the function that calculates the quantile
classified_df = classify_dataframe(cordex_risk_temp_rcp85_20412070_df, "Cordex Risk Temp rcp85 2041-2070")
# Apply styling
styled_cordex_risk_temp_df = classified_df.style.apply(highlight_class_column, axis=0)
# Displaying the classified risk
display(styled_cordex_risk_temp_df)
| Cordex Risk Temp rcp85 2041-2070 | Quantile | Class | |
|---|---|---|---|
| Bergamo Orio Al Serio | 0.034595 | 0.444444 | Low |
| Cagliari Elmas | 0.063112 | 0.888889 | Very High |
| Catania Fontanarossa | 0.038859 | 0.555556 | Medium |
| Milano Linate | 0.059589 | 0.777778 | High |
| Milano Malpensa | 0.178830 | 1.000000 | Very High |
| Napoli Capodichino | 0.005881 | 0.111111 | Very Low |
| Palermo Punta Raisi | 0.023002 | 0.222222 | Very Low |
| Roma Ciampino | 0.029133 | 0.333333 | Low |
| Roma Fiumicino | 0.046004 | 0.666667 | High |
RCP85 2071-2100#
hazard_cordex_temp_rcp85_20712100_path = '/work/cmcc/dg07124/climax/indicators/cordex2/temp_avg_ensembles/temp_normalized_temp_rcp85_2071-2100_data.csv'
hazard_cordex_temp_rcp85_20712100 = pd.read_csv(hazard_cordex_temp_rcp85_20712100_path, index_col='Airports')
# Standardize the airport names to Title Case
hazard_cordex_temp_rcp85_20712100.index = hazard_cordex_temp_rcp85_20712100.index.str.title()
hazard_cordex_temp_rcp85_20712100['average'] = pd.to_numeric(hazard_cordex_temp_rcp85_20712100['average'], errors='coerce')
hazard_cordex_temp_rcp85_20712100 = hazard_cordex_temp_rcp85_20712100.sort_index()
hazard_cordex_temp_rcp85_20712100 = hazard_cordex_temp_rcp85_20712100[['average']]
hazard_cordex_temp_rcp85_20712100
| average | |
|---|---|
| Airports | |
| Bergamo Orio Al Serio | 0.538966 |
| Cagliari Elmas | 0.497329 |
| Catania Fontanarossa | 0.799593 |
| Milano Linate | 0.684917 |
| Milano Malpensa | 0.551032 |
| Napoli Capodichino | 0.112875 |
| Palermo Punta Raisi | 0.097831 |
| Roma Ciampino | 0.330814 |
| Roma Fiumicino | 0.161266 |
# Now multiply the 'average' column of each DataFrame
cordex_risk_temp_rcp85_20712100 = avg_df_exposure['average'] * hazard_cordex_temp_rcp85_20712100['average'] * vulnerability_df['average']
cordex_risk_temp_rcp85_20712100
# Convert the Series to a DataFrame
cordex_risk_temp_rcp85_20712100_df = pd.DataFrame(cordex_risk_temp_rcp85_20712100)
# Rename the column
cordex_risk_temp_rcp85_20712100_df.columns = ['Cordex Risk Temp rcp85 2071-2100']
cordex_risk_temp_rcp85_20712100_df
| Cordex Risk Temp rcp85 2071-2100 | |
|---|---|
| Bergamo Orio Al Serio | 0.052964 |
| Cagliari Elmas | 0.053303 |
| Catania Fontanarossa | 0.035913 |
| Milano Linate | 0.083603 |
| Milano Malpensa | 0.236413 |
| Napoli Capodichino | 0.006781 |
| Palermo Punta Raisi | 0.008260 |
| Roma Ciampino | 0.033458 |
| Roma Fiumicino | 0.086109 |
# Call the function that calculates the quantile
classified_df = classify_dataframe(cordex_risk_temp_rcp85_20712100_df, "Cordex Risk Temp rcp85 2071-2100")
# Apply styling
styled_cordex_risk_temp_df = classified_df.style.apply(highlight_class_column, axis=0)
# Displaying the classified risk
display(styled_cordex_risk_temp_df)
| Cordex Risk Temp rcp85 2071-2100 | Quantile | Class | |
|---|---|---|---|
| Bergamo Orio Al Serio | 0.052964 | 0.555556 | Medium |
| Cagliari Elmas | 0.053303 | 0.666667 | High |
| Catania Fontanarossa | 0.035913 | 0.444444 | Low |
| Milano Linate | 0.083603 | 0.777778 | High |
| Milano Malpensa | 0.236413 | 1.000000 | Very High |
| Napoli Capodichino | 0.006781 | 0.111111 | Very Low |
| Palermo Punta Raisi | 0.008260 | 0.222222 | Very Low |
| Roma Ciampino | 0.033458 | 0.333333 | Low |
| Roma Fiumicino | 0.086109 | 0.888889 | Very High |