Heatwave hazard assessment using EuroHEAT methodology

Heatwave hazard assessment using EuroHEAT methodology#

Description of the assessment#

Heatwave based on the EuroHEAT project approach#

The heatwave hazard assessment presented in this page is directly based on an existing dataset of heat wave days on CDS. You can also view these results directly on the CDS using an application that shows aggregated results for the NUTS3 administrative regions. This notebook provides an example on how to extract this data for any grid cell of the dataset (resolution of 12 x 12km) in the EU.

Methodology is based on the EuroHEAT project (source) with two options for defining heatwaves:

Health-related EU-wide definition: for the summer period of June to August, heat waves were defined as days in which the maximum apparent temperature (Tappmax) exceeds the threshold (90th percentile of Tappmax for each month) and the minimum temperature (Tmin) exceeds its threshold (90th percentile of Tmin for each month) for at least two days. The apparent temperature is a measure of relative discomfort due to combined heat and high humidity, developed based on physiological studies on evaporative skin cooling. It can be calculated as a combination of air and dew point temperature.
National heat-wave definition: each nation uses a different methodology e.g. for Belgium, a heatwave-day is a day on which both the three-day running daily minimum and maximum temperature exceeds a threshold of 18.2 °C and 29.6 °C between April and September. This condition has to be met for three consecutive days. For this example, the source of the heat wave definition is the Belgian federal public health agency. See this page for more national definitions.

Advantages of using this methodology and dataset:

Pre-computed indicators - less data that needs to be downloaded and processed (in comparison with the Peseta-IV and Xclim hazard workflows).
Data available on a 12x12km grid for years 1986-2085 for the whole EU.
The heatwave definition is based on both maximum and minimum daily temperatures, so it accounts for the effect of minimum temperature on the severity of a heat wave (e.g. when temperature significantly drops during the nights thus making the heatwave less severe).

Disadvantages:

No possibility of changing thresholds for temperature and number of days in the heatwave definition.
Only contains values aggregated per year.
National heat wave definitions are not available for all EU states (only a selection).

Step 1. Preparation work#

Import packages#

Find out more about the libraries used in this workflow:

zipfile - Provides tools for creating, reading, writing, and extracting ZIP files.
os - Provides functions for interacting with the operating system, such as file and directory manipulation.
cdsapi - A library to request data from the datasets listed in the CDS catalogue.
numpy - A powerful library for numerical computations in Python, widely used for array operations and mathematical functions.
xarray - Introduces labels in the form of dimensions, coordinates, and attributes on top of raw NumPy-like arrays for a more intuitive experience.
rasterio - A library for reading and writing geospatial raster data.
plotly - A Python Graphing Library.
ipyleaflet - Enables interactive maps in Jupyter notebooks using the Leaflet.js library.
ipywidgets - Provides interactive HTML widgets for Jupyter notebooks.
localtileserver - Serves local raster tiles for visualization n Leaflet maps.

import os
import zipfile

import cdsapi
import numpy as np
import xarray as xr
import plotly.graph_objects as go
from ipyleaflet import Map, DrawControl, Marker, LayersControl
import ipywidgets as widgets
from localtileserver import get_leaflet_tile_layer, TileClient
from IPython.display import display

# Set host forwarding for remote jupyterlab sessions
if 'JUPYTERHUB_SERVICE_PREFIX' in os.environ:
    os.environ['LOCALTILESERVER_CLIENT_PREFIX'] = f"{os.environ['JUPYTERHUB_SERVICE_PREFIX']}/proxy/{{port}}"

Create a directory structure#

# Define the directory for storing data
workflow_folder = 'Heatwave_hazard_EuroHEAT'

# Define directories for data and results within the previously defined workflow directory
data_dir = os.path.join(workflow_folder,'data')
results_dir = os.path.join(workflow_folder,'results')

# Check if the workflow directory exists, if not, create it along with subdirectories for data and results
if not os.path.exists(workflow_folder):
    os.makedirs(workflow_folder)
    os.makedirs(os.path.join(data_dir))
    os.makedirs(os.path.join(results_dir))

Step 2: Retrieving data on heat wave occurence#

Download data from the CDS#

You can download data from the CDS with the API. If this is the first time you do this, you will need to register on CDS and set your API key. Check [How to change your API KEY] and change the KEY in the code cell below.

URL = "https://cds-beta.climate.copernicus.eu/api"
KEY = None # put here your key!

c = cdsapi.Client(url=URL, key=KEY)

Heat wave data based on National definitions from EuroHEAT [source]#

# Define zip file's absolute path
zip_path = os.path.join(data_dir, 'heat_spells_country_1986_2085.zip')

# Heat waves and cold spells in Europe derived from climate projections
c.retrieve(
    'sis-heat-and-cold-spells',
    {
        'variable': 'heat_wave_days',
        'definition': 'country_related',
        'experiment': [
            'rcp4_5', 'rcp8_5',
        ],
        'ensemble_statistic': [
            'ensemble_members_average', 'ensemble_members_standard_deviation',
        ],
        'format': 'zip',
    },
    zip_path)

# Unzip the downloaded files so they are ready for computing 
with zipfile.ZipFile(zip_path, 'r') as zObject:
    zObject.extractall(path=data_dir)

# Remove the downloaded zip file after unpacking
os.remove(zip_path)

Conclusion for the hazard assessment result based on EuroHEAT#

The results show the projected heatwave occurrence per year. We can plot the results based on the health-related thresholds or national thresholds for the heatwave definition.
The health-related data are available for the whole EU, but the national-related data are available only for a few EU states.
The workflow allows to evaluate the projected trend in the heat wave occurence over time under two climate change scenarios at a local resolution of 12 x 12 km. Please note that this data does not include the urban heat-island effect and therefore is not fully representative for urban environments. Extra corrections for urban locations are necessary to include the increased temperatures within built environment.

Authors#

Main contributors:

Martin Kuban, KAJO services (martin.kuban@kajoservices.com)
Milan Kalas, KAJO services (milan.kalas@kajoservices.com)
Natalia Aleksandrova (review and editing)

Heatwave hazard assessment using EuroHEAT methodology

Contents

Heatwave hazard assessment using EuroHEAT methodology#

Description of the assessment#

Heatwave based on the EuroHEAT project approach#

Step 1. Preparation work#

Import packages#

Create a directory structure#

Step 2: Retrieving data on heat wave occurence#

Download data from the CDS#

Heat wave data based on National definitions from EuroHEAT [source]#

Plotting the data#

Prepare data for plotting#

Select the location for plotting#

Plot results for heatwaves defined using national thresholds#

Conclusion for the hazard assessment result based on EuroHEAT#

Authors#

Heatwave hazard assessment using EuroHEAT methodology

Contents

Heatwave hazard assessment using EuroHEAT methodology#

Description of the assessment#

Heatwave based on the EuroHEAT project approach#

Step 1. Preparation work#

Import packages#

Create a directory structure#

Step 2: Retrieving data on heat wave occurence#

Download data from the CDS#

Downloading the health-related dataset from EuroHEAT [source]#

Heat wave data based on National definitions from EuroHEAT [source]#

Plotting the data#

Prepare data for plotting#

Select the location for plotting#

Plot results for heatwave data defined based on EU-wide health-related thresholds#

Plot results for heatwaves defined using national thresholds#

Conclusion for the hazard assessment result based on EuroHEAT#

Authors#