Risk assessment for airport infrastructure

Risk assessment for airport infrastructure#

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.

Preparation work#

Import libraries#

import os

import matplotlib.pyplot as plt
import contextily as ctx
import numpy as np
import pandas as pd
import scipy.spatial
import xarray as xr

Area of interest#

Specify the same name as in the the hazard assessment to continue with the results of these notebooks.

region_name = 'IT'

Path configuration#

# Path to the folder containing all indicator data
indicators_path = f'data_{region_name}/indicators/uerra'

Step 1: Define the airports#

List the airports and their coordinates.

airports = [
    'Milano Malpensa', 'Milano Linate', 'Bergamo Orio al Serio',
    'Roma Fiumicino', 'Roma Ciampino', 'Napoli Capodichino',
    'Palermo Punta Raisi', 'Catania Fontanarossa', 'Cagliari Elmas'
]

Tip

Enter the locations of your choice here. At least two locations must be defined for the methodology to work.

def to_dataframe(dct):
    idx = pd.Index(airports, name='airport')
    return pd.DataFrame(dct, index=idx, dtype=float)

# List coordinates in the order of the locations from above (°N, °E)
coords = to_dataframe({
    'latitude': [45.63, 45.45, 45.67, 41.80, 41.80, 40.88, 38.18, 37.47, 39.25],
    'longitude': [8.73, 9.28, 9.71, 12.25, 12.59, 14.29, 13.10, 15.07, 9.06]
})

# Check that the coordinates are correctly associated with the airports
coords
latitude longitude
airport
Milano Malpensa 45.63 8.73
Milano Linate 45.45 9.28
Bergamo Orio al Serio 45.67 9.71
Roma Fiumicino 41.80 12.25
Roma Ciampino 41.80 12.59
Napoli Capodichino 40.88 14.29
Palermo Punta Raisi 38.18 13.10
Catania Fontanarossa 37.47 15.07
Cagliari Elmas 39.25 9.06 
fig, ax = plt.subplots(1, 1)
ax.scatter(coords.longitude, coords.latitude, color='k')
ax.set_xlabel('Longitude [°E]')
ax.set_ylabel('Latitude [°N]')
ctx.add_basemap(ax, crs='EPSG:4326', source=ctx.providers.CartoDB.Positron, attribution=False)
../../../../_images/f1b78779a480942a95dca01a150eb4f8069af19414e45387509c62c347ca48eb.png

Step 2: Component normalization#

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 use 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:

def normalize_columns(df):
    return (df - df.min()) / (df.max() - df.min())

Step 3: Hazard#

Load the hazard indicator data produced by the hazard assessment notebook and extract the information for the airport locations specified above.

Hide code cell source

 
def load_all(files):
    '''Load data based on a name-to-file mapping'''
    das = []
    for name, file in files.items():
        path = os.path.join(indicators_path, file)
        if name == '*':
            ds = xr.open_dataset(path)
            das.extend(ds.data_vars.values())
        else:
            da = xr.open_dataarray(path)
            das.append(da.rename(name))
    return xr.merge(das, compat='override')

def extract_points(ds, points):
    '''Extract data from the dataset for a set of points'''
    grid = np.column_stack([
        ds['latitude'].values.flatten(),
        ds['longitude'].values.flatten()
    ])
    tree = scipy.spatial.cKDTree(grid)
    _, closest_idx = tree.query(coords.values, k=1)
    idx_y, idx_x = np.unravel_index(closest_idx, ds['latitude'].shape)
    return (
        ds
        .isel(
            x=xr.DataArray(idx_x, dims='airport'),
            y=xr.DataArray(idx_y, dims='airport')
        )
        # Clean-up table: remove coordinates not needed (extend as required)
        .drop_vars(['latitude', 'longitude', 'x', 'y', 'height', 'time', 'quantile'], errors='ignore')
        .assign_coords({'airport': coords.index})
        .to_dataframe()
    )

Temperature#

data_temp = load_all({
    'Days Above 35°C': 'Temp_DaysAbove35.nc',
    'Days Above 40°C': 'Temp_DaysAbove40.nc',
    'Days Above 45°C': 'Temp_DaysAbove45.nc',
})

indicators_temp = extract_points(data_temp, coords)
indicators_temp
Days Above 35°C Days Above 40°C Days Above 45°C
airport
Milano Malpensa 0.000000 0.000000 0.0
Milano Linate 0.533333 0.000000 0.0
Bergamo Orio al Serio 0.433333 0.000000 0.0
Roma Fiumicino 0.166667 0.000000 0.0
Roma Ciampino 3.400000 0.000000 0.0
Napoli Capodichino 3.433333 0.000000 0.0
Palermo Punta Raisi 1.266667 0.100000 0.0
Catania Fontanarossa 8.933333 1.133333 0.2
Cagliari Elmas 6.700000 0.266667 0.0

Combine the indicator columns into a single indicator for the temperature hazard by taking the mean over all columns at each location.

hazard_temp = normalize_columns(indicators_temp).mean(axis=1, skipna=True)

# Quick bar chart of the temperature hazard indicator
hazard_temp.plot.barh(title='Hazard: Temperature', xlim=(0, 1))

<Axes: title={'center': 'Hazard: Temperature'}, ylabel='airport'>
../../../../_images/538fd5239dbd7c2920b980af715855e0bb9f9b29ac9700f815a456f563776930.png

Precipitation#

Repeat the steps for the precipitation indicators:

data_precip = load_all({
    '*': 'Precip_return_levels_gumbel.nc',
    'Precip_P99': 'Precip_P99.nc',
    'Precip_P999': 'Precip_P999.nc'
})

indicators_precip = extract_points(data_precip, coords)
indicators_precip
return_period_2_y return_period_5_y return_period_10_y return_period_20_y return_period_30_y return_period_50_y return_period_100_y return_period_150_y return_period_200_y return_period_500_y Precip_P99 Precip_P999
airport
Milano Malpensa 77.240410 97.857880 111.508438 124.602386 132.135010 141.551163 154.251877 161.657974 166.906250 183.601303 48.059845 94.042389
Milano Linate 56.678417 70.069450 78.935478 87.439987 92.332420 98.448204 106.697311 111.507561 114.916313 125.759735 34.706484 63.180023
Bergamo Orio al Serio 49.203640 59.805862 66.825455 73.558823 77.432358 82.274467 88.805618 92.614090 95.312943 103.898109 33.550508 51.886977
Roma Fiumicino 50.875408 69.739494 82.229156 94.209549 101.101570 109.716927 121.337532 128.113785 132.915726 148.190964 27.939688 60.487923
Roma Ciampino 43.058388 56.674309 65.689232 74.336563 79.311157 85.529648 93.917290 98.808327 102.274323 113.299850 26.153749 52.537727
Napoli Capodichino 60.708721 85.161652 101.351616 116.881424 125.815338 136.983170 152.046585 160.830429 167.055054 186.855865 35.195312 84.431564
Palermo Punta Raisi 56.251366 81.191704 97.704376 113.543732 122.655724 134.046158 149.409836 158.368759 164.717453 184.912949 35.360001 88.106552
Catania Fontanarossa 58.424789 85.944069 104.164230 121.641449 131.695663 144.263901 161.216263 171.101578 178.106750 200.390549 26.499609 81.429756
Cagliari Elmas 32.551186 44.475163 52.369877 59.942673 64.299118 69.744881 77.090256 81.373528 84.408836 94.064301 17.672501 37.748158 
hazard_precip = normalize_columns(indicators_precip).mean(axis=1, skipna=True)

# Quick bar chart of the precipitation hazard indicator
hazard_precip.plot.barh(title='Hazard: Precipitation', xlim=(0, 1))

<Axes: title={'center': 'Hazard: Precipitation'}, ylabel='airport'>
../../../../_images/2004fddd88f1b1f07e570d74c553669c68c9825d222920a02d725a337894fcbd.png

Step 4: Exposure#

The various airports components, divided into landside and airside areas, were considered as exposed samples in accordance with De Vivo et al. (2022).

The air side components include the structures used for the movement of aircraft, such as runways, taxiways, tower, and aprons:

Air side

Unit of measurement

Runways

Area [m²]

Taxiways

Area [m²]

Tower

Height [m]

Apron

Area [m²]

The land side components refer to the public access areas such as offices, terminals, airports access systems and parking areas:

Land side

Unit of measurement

Terminal

Area [m²]

Offices and other buildings

Area [m²]

Airport accesses systems

Area [m²]

Carparks

Number

Define the values for each indicator and airport: (insert None where data is not available)

# Specify the values in the order of the locations defined above
air_land_side_components = to_dataframe({
    '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]
})

# Check that values are associated correctly to the locations
air_land_side_components
Runways Taxiways Tower Apron Terminals Offices_and_other_buildings Airport_accesses_systems Carparks
airport
Milano Malpensa 470400.0 250.5 80.0 1319000.0 315000.0 26715.0 31660.0 15000.0
Milano Linate 159742.0 156.0 47.0 387000.0 85050.0 13527.0 NaN 3000.0
Bergamo Orio al Serio 145530.0 183.0 30.0 190000.0 53025.0 7075.0 NaN 8000.0
Roma Fiumicino 396000.0 300.0 56.0 797250.0 354300.0 29520.0 NaN 20100.0
Roma Ciampino 9900.0 301.0 60.0 NaN 20950.0 5950.0 NaN 1220.0
Napoli Capodichino 118260.0 99.0 40.0 200000.0 30700.0 1915.0 NaN 1500.0
Palermo Punta Raisi 292650.0 368.0 25.0 148000.0 35400.0 4180.0 NaN 1364.0
Catania Fontanarossa 109575.0 159.0 12.0 180000.0 43310.0 6920.0 NaN 1800.0
Cagliari Elmas 126000.0 604.0 28.0 156000.0 41290.0 5030.0 NaN 2133.0

As for the hazard, we normalize each component and combine them into a single indicator for exposure by taking the mean over all components for each location.

air_land_side_normalized = normalize_columns(air_land_side_components)

# Combine into a single indicator
exposure = air_land_side_normalized.mean(axis=1, skipna=True)

# Quick bar plot of the exposure indicator
ax = exposure.plot.barh(title='Exposure', xlim=(0, 1))
../../../../_images/bc7743a29ced07a9eae4d48d06c330cfbc96b8adfe011eeac296f32ed8f211d2.png

Step 5: 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 of 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

Underground infrastructures

Number (or Area)

Flooded areas

Number (or %)

Define the sensitivity indicators and their values: (use None where data is not available)

sensitivity_indicators = to_dataframe({
    '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],
    'Underground_infrastructures': [30300, 14700, 5700, 26250, 2250, 0, 0, 8650, 0],
    'Flooded_areas': [None, None, None, None, None, None, None, None, None],
})

sensitivity_normalized = normalize_columns(sensitivity_indicators)
sensitivity_normalized
Soil_sealing Passengers Buildings_bad_conditions_or_maintenance Age_buildings Air_traffic Parking_accesses Staff_work Underground_infrastructures Flooded_areas
airport
Milano Malpensa 0.756349 0.573735 NaN 0.24 0.597422 NaN 0.834618 1.000000 NaN
Milano Linate 0.114619 0.058069 NaN 0.42 0.170457 NaN 0.320078 0.485149 NaN
Bergamo Orio al Serio 0.114619 0.234872 NaN 0.44 0.206174 NaN 0.404888 0.188119 NaN
Roma Fiumicino 1.000000 1.000000 NaN 0.04 1.000000 NaN 1.000000 0.866337 NaN
Roma Ciampino 0.000000 0.029179 NaN 0.88 0.046510 NaN 0.390955 0.074257 NaN
Napoli Capodichino 0.057653 0.157594 NaN 1.00 0.158783 NaN NaN 0.000000 NaN
Palermo Punta Raisi 0.177076 0.058535 NaN 0.00 0.053878 NaN 0.061770 0.000000 NaN
Catania Fontanarossa 0.063143 0.141172 NaN 0.72 0.125154 NaN 0.016682 0.285479 NaN
Cagliari Elmas 0.077557 0.000000 NaN 0.46 0.000000 NaN 0.000000 0.000000 NaN

Adaptative capacity#

These indicators are defined by the classes reported below and included in De Vivo et al. (2022). If the indicator is present or absent, its value will be 0.9 or 0.1, respectively.

Risk awareness: Initiatives for mitigation to climate change#

  1. Adherence to “NetZero2050” Programme (neutrality 3+)

  2. Certification ISO 50001 and initiatives of “energy saving”

  3. Neutrality 3+ Airport carbon accreditation

  4. Neutrality 4+ Airport carbon accreditation; EP-100 intelligent use of energy from “The Climate Group”- Certification ISO 50001

  5. Level 2 Airport carbon accreditation, environmental improvement program

  6. Commitment to obtaining certfiication

Initiatives for adaptation to climate change#

  • Efficient drainage system

    1. Present

  • Insurance policy for extreme events

    1. Absent

  • Monitoring and alarm system

    1. Present

    2. Present (but alert wind shear)

    3. Absent

  • Bioinfiltration and permeable pavements

    1. Present

    2. Absent

  • Risk awareness (and initiatives of mitigation)

    1. Risk management framework

    2. Development of local (Urban) climate change mitigation and adaptation strategies

    3. Sustainability Report 2019

    4. gesap environmental report

    5. Absent

Guidelines for adaptation plan to climate change#

  1. Plan (PACC) and regional strategy for adaptation to climate change

  2. Regional plan under development

  3. Absent

  4. Sustainable Energy and Climate Action Plan (Covenant of Mayors)

  5. Sustainable Energy and Climate Action Plan (Covenant of Mayors) municipality of Catania

  6. Regional climate change adaptation strategy

Class No.

Description

Value Range

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 adaptive capacity indicators and their values: (use None where data is not available)

adaptive_indicators = to_dataframe({
    '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],
    'Initiatives_mitigation_climate_change_risk_awareness': [0.3, 0.3, 0.3, 0.1, 0.1, 0.3, 0.5, 0.7, 0.7],
    'Efficient_drainage_system': [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
    'Monitoring_and_alarm_system': [0.1, 0.1, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
    'Bioinfiltration_and_permeable_pavements': [0.9, 0.9, 0.1, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9],
    '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]
})

# As defined in the example, the indicators are already normalized into the range [0, 1]
adaptive_normalized = adaptive_indicators
adaptive_normalized
Vegetation_zone Initiatives_for_optimization_of_energy Initiatives_mitigation_climate_change_risk_awareness Efficient_drainage_system Monitoring_and_alarm_system Bioinfiltration_and_permeable_pavements Green_wall Green_roof Heat_resistent_coating Insurance_policy_for_extreme_events Guidelines_for_adaptation_climate_change Evening_departures
airport
Milano Malpensa NaN 0.3 0.3 0.1 0.1 0.9 0.1 0.1 0.9 0.9 0.3 NaN
Milano Linate NaN 0.3 0.3 0.1 0.1 0.9 0.9 0.9 0.9 0.9 0.3 NaN
Bergamo Orio al Serio NaN 0.3 0.3 0.1 0.3 0.1 0.9 0.9 0.9 0.9 0.3 NaN
Roma Fiumicino NaN 0.1 0.1 0.1 0.3 0.9 0.9 0.9 0.1 0.9 0.5 NaN
Roma Ciampino NaN 0.1 0.1 0.1 0.3 0.9 0.9 0.9 0.9 0.9 0.5 NaN
Napoli Capodichino NaN 0.3 0.3 0.1 0.3 0.9 0.9 0.9 0.9 0.9 0.9 NaN
Palermo Punta Raisi NaN 0.5 0.5 0.1 0.3 0.9 0.9 0.9 0.9 0.9 0.3 NaN
Catania Fontanarossa NaN 0.7 0.7 0.1 0.3 0.9 0.9 0.9 0.9 0.9 0.3 NaN
Cagliari Elmas NaN 0.7 0.7 0.1 0.3 0.9 0.9 0.9 0.9 0.9 0.3 NaN

Vulnerability calculation#

We combine the sensitivity and adaptive capacity indicators by taking the mean at each location, then calculate a single vulnerability indicator by taking the mean of these two components.

Vulnerability indicators are computed separately for each hazard, only taking into account the relevant indicators from sensitivity and adaptive capacity.

def calculate_vulnerability(sensitivity, adaptive_capacity, only=None):
    # Take mean of each category, then average
    return 0.5 * (
        sensitivity[[col for col in sensitivity.columns if (only is None or col in only)]].mean(skipna=True, axis=1)
        + adaptive_capacity[[col for col in adaptive_capacity.columns if (only is None or col in only)]].mean(skipna=True, axis=1)
    )

vulnerability_temp = calculate_vulnerability(sensitivity_normalized, adaptive_normalized, only={
    'Soil_sealing', 'Passengers', 'Buildings_bad_conditions_or_maintenance',
    'Age_buildings', 'Air_traffic', 'Parking_accesses', 'Staff_work',
    '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'
})

vulnerability_precip = calculate_vulnerability(sensitivity_normalized, adaptive_normalized, only={
    'Soil_sealing', 'Passengers', 'Buildings_bad_conditions_or_maintenance',
    'Age_buildings', 'Air_traffic', 'Underground_infrastructures', 'Flooded_areas',
    'Vegetation_zone', 'Initiatives_mitigation_climate_change_risk_awareness',
    'Efficient_drainage_system', 'Insurance_policy_for_extreme_events',
    'Monitoring_and_alarm_system', 'Bioinfiltration_and_permeable_pavements',
    'Guidelines_for_adaptation_climate_change'
})

ax = pd.DataFrame.from_dict({
    'Temperature Vulnerability': vulnerability_temp,
    'Precipitation Vulnerability': vulnerability_precip,
}).plot.barh(xlim=(0, 1))
../../../../_images/9a6cc29b92904ddf841fbadd40a7f3f285760c0f3d43125de3648a210efda815.png

Step 6: Calculate Risk#

The risk (R) will be computed by multiplication of the three components hazard (H), exposure (E) and vulnerability (V):

R = H * E * V

risk_table = pd.DataFrame.from_dict({
    'Temperature hazard': hazard_temp,
    'Precipitation hazard': hazard_precip,
    'Exposure': exposure,
    'Temperature Vulnerability': vulnerability_temp,
    'Precipitation Vulnerability': vulnerability_precip,
    # Calculate the risk by multiplying the components
    'Temperature risk': hazard_temp * exposure * vulnerability_temp,
    'Precipitation risk': hazard_precip * exposure * vulnerability_precip,
})

# Export the table to the data folder
risk_table.to_csv(os.path.join(indicators_path, f'risk_{region_name}.csv'))

# Show the table
risk_table
Temperature hazard Precipitation hazard Exposure Temperature Vulnerability Precipitation Vulnerability Temperature risk Precipitation risk
airport
Milano Malpensa 0.000000 0.958177 0.830052 0.530212 0.533417 0.000000 0.424247
Milano Linate 0.019900 0.418003 0.266326 0.498322 0.341496 0.002641 0.038017
Bergamo Orio al Serio 0.016169 0.226971 0.200527 0.530055 0.285045 0.001719 0.012973
Roma Fiumicino 0.006219 0.484378 0.776851 0.734000 0.623967 0.003546 0.234792
Roma Ciampino 0.126866 0.221027 0.208675 0.544664 0.336328 0.014419 0.015512
Napoli Capodichino 0.128109 0.820619 0.105080 0.621754 0.420736 0.008370 0.036280
Palermo Punta Raisi 0.076675 0.786895 0.210126 0.425126 0.278949 0.006849 0.046123
Catania Fontanarossa 1.000000 0.853814 0.091670 0.496615 0.400161 0.045525 0.031320
Cagliari Elmas 0.328431 0.000000 0.245209 0.443756 0.320422 0.035738 0.000000

Visualizing the absolute risk indicators#

Because the hazard, exposure and vulnerability indicators are normalized into the range 0 to 1, the risk indicator as a product of these components also lies in the range 0 to 1. The higher its value, the larger the risk.

ax = risk_table[['Temperature risk', 'Precipitation risk']].plot.barh(title='Risk indicators')
../../../../_images/6e423e7d115559dcfb065807a33ec1a5351b3717fd01091df531cc1e0942ce17.png 
fig, axs = plt.subplots(1, 3, width_ratios=[20, 20, 1], figsize=(8, 4))

for ax, name in zip(axs, ['Temperature risk', 'Precipitation risk']):
    ax.set_title(name)
    sc = ax.scatter(
        coords.longitude,
        coords.latitude,
        c=risk_table[name],
        cmap='RdYlBu_r',
        vmin=0.0,  # minimum value of color scale
        vmax=0.5,  # maximum value of color scale
        edgecolor='black',
    )
    ax.set_xlabel('Longitude [°E]')
    ax.set_ylabel('Latitude [°N]')
    ctx.add_basemap(ax, crs='EPSG:4326', source=ctx.providers.CartoDB.Positron, attribution=False)

fig.colorbar(sc, cax=axs[2], orientation='vertical', label='Risk indicator')
fig.tight_layout()
../../../../_images/10b5700aadb888473d359d9f0e1550451c7d4ace222d26cf792926aa03bd9a53.png

Classify relative risk#

Evaluate the risk classes for each airport with a quantile method, classifying each airport relative to all other airports.

def classify_risk(risk, classes=['Very Low', 'Low', 'Medium', 'High', 'Very High']):
    return pd.qcut(risk, q=len(classes), labels=classes)

risk_classes = pd.DataFrame.from_dict({
    c: classify_risk(risk_table[c]) for c in ['Temperature risk', 'Precipitation risk']
})

def highlight_class_column(s):
    '''Color the table entries based on the risk classes'''
    colors = {
        'Very Low': 'background-color: #2c7bb6; color: white;',
        'Low': 'background-color: #abd9e9;',
        'Medium': 'background-color: #ffffbf;',
        'High': 'background-color: #fdae61;',
        'Very High': 'background-color: #d7191c; color: white;'
    }
    return [colors.get(val, '') for val in s]

risk_classes.style.apply(highlight_class_column, axis=0)
  Temperature risk Precipitation risk
airport    
Milano Malpensa Very Low Very High
Milano Linate Low High
Bergamo Orio al Serio Very Low Very Low
Roma Fiumicino Low Very High
Roma Ciampino High Low
Napoli Capodichino High Medium
Palermo Punta Raisi Medium High
Catania Fontanarossa Very High Low
Cagliari Elmas Very High Very Low

Next steps#

Options to continue with the risk assessment:

  • Other hazard, exposure or vulnerability indicators can be integrated in the risk assessment following the normalisation approach as demonstrated in this notebook. Extend the corresponding location-specific tables created above with appropriate values to expand the scope of the risk asssessment and customise it so it reflects your priorities and interests.

  • Run the risk assessment for different time periods and climate scenarios. Take care, however, when comparing the risk results from different periods/scenarios as the method based on normalisation provides a relative assessment of risk within each workflow application (i.e., within each scenario/period).

  • Run the workflow for multiple climate model projections to obtain an estimate of uncertainty. A simple measure of uncertainty is the standard deviation around the mean of an ensemble of models.

See also

Our data dashboards on EURO-CORDEX model biases and climate scenarios provide relevant information on temperature and precipitation projections for a quick estimation of projection uncertainty.

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