Contributors: B-Open and TCDF
Issued by: B-Open
Issued Date: 28/07/2023
Ref: C3S3_430a – ECDE maintenance and development
Official reference number service contract: 2021/C3S2_430a_BOPEN
Acronyms
1. Introduction
1.1. Executive summary
This dataset provides a series of climate indices derived from reanalysis and model simulations data hosted on the CDS that are made available through interactive visualisation apps on the European Climate Data Explorer hosted on EEA’s Climate-adapt site. These indices describe how climate variability and change of essential climate variables can impact sectors such as health, agriculture, forestry, energy, tourism, or water and coastal management.
The indices have either been calculated through a specific CDS Toolbox workflow using CDS dataset in input or directly retrieved From the CDS when already available. In this way both the calculations and the resulting data are fully traceable. As they come from different datasets the underlying climate simulations data differ in their technical specifications (type and number of climate and impact models involved, bias-corrected or not, periods covered etc.). An effort was made in the dataset selection to limit the heterogeneity of the underlying dataset as Ideally the indices should come from the same dataset with identical specifications.
The indices related to temperature, precipitation and wind (20 out of 30) were calculated from atmospheric variables from the same datasets: Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections and ERA5 single levels. The other indices are directly available from CDS dataset generated by sector specific projects.
1.2. Scope of documentation
This document provides a description of the indices included in the European Climate Data Explorer and details of the dataset and the methodology used to produce them. First, the product requirements (section 2.1) against which this dataset was developed is presented, followed by the definition of the indicators (2.2 product overview) and the input datasets (2.3 Input datasets) used to calculate them. Thereafter, the methodology (2.4 Method) to produce the indicators forming this catalogue entry is described.
1.3. Version history
This is the first version of the dataset.
2. Product description
2.1. Product requirements
The development of those indices was driven by the European Environment Agency (EEA) to address informational needs of climate change adaptation national initiatives across the EU and partner countries as expressed by user requirements and stakeholder consultations. They are relevant for adaptation planning at the European and national level and cover the hazard categories introduced by the IPCC and the European Topic Centre on Climate Change Impacts, Vulnerability and Adaptation (ETC-CCA). They are made available interactively through CDS Toolbox public visualisation apps on the European Climate Data Explorer hosted on EEA’s Climate-adapt site.
2.2. Product overview
2.2.1. Data Description
Table 1: Overview of key characteristics of the ECDE indices.
Data type | Grid |
Projection | Regular latitude-longitude grid |
Horizontal coverage | Europe |
Horizontal resolution | 0.25° x 0.25° |
Vertical resolution | Surface |
Vertical coverage | Single level |
Temporal coverage | 1940-2100 |
Temporal resolution | Monthly, seasonal and yearly |
File format | NetCDF 4 |
Conventions | Climate and Forecast (CF) Metadata Convention v1.6, Attribute Convention for Dataset Discovery (ACDD) v1.3 |
Available versions | v1.0 |
Update frequency | Annual |
2.2.2. Indices description
Table 2: Description of the ECDE indices.
Main Variables | ||
Variable | Units | Description |
Heat and cold | ||
Mean temperature | °C | The temperature of air at 2m above the surface. |
Growing degree days | °C day-1 | The cumulative sum of daily degrees above a daily mean temperature of 5°C. |
Heating degree days | °C day-1 | The cumulative sum of daily degrees below a daily mean temperature of 15.5°C. |
Cooling degree days | °C day-1 | The cumulative sum of daily degrees above a daily mean temperature of 22°C. |
Tropical nights | day | The count of days with daily minimum temperature above 20°C. |
Hot days | day | The count of days with daily maximum temperature above a 30°C threshold (also 35°C or 40°C°). |
Warmest three-day period | °C | The highest daily mean temperature averaged over a three-day window over a year. |
Heatwave days | day | The count of climatological hot days in a year. A climatological heatwave is a period of at least three consecutive days exceeding the 99th percentile of the daily maximum temperatures of the May to September season during a reference period. |
High UTCI days | day | The count of days when UTCI remains above 32°C. UTCI stands for Universal Thermal Climate Index and is an equivalent to temperature (°C) corresponding to a measure of the human physiological response to meteorological conditions that also takes into consideration the clothing adaptation of the population in response to outdoor temperature. It is based on four surface variables: air temperature, relative humidity, wind speed and mean radiant temperature. |
Frost days | day | The monthly, seasonal and yearly count of days with daily minimum temperature below 0°C. |
Maximum temperature | °C | The maximum value of daily maximum temperature over a month, season or year. |
Minimum temperature | °C | The minimum value of daily minimum temperature over a month, season or year. |
Wet and dry | ||
Total precipitation | mm period-1 | Total precipitation is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. |
Maximum consecutive five-day precipitation | mm 5-days-1 | Maximum five-day cumulated total precipitation taken over a month, a season or a year. |
Extreme precipitation total | mm | The total sum in a year of daily precipitation values exceeding the 99th percentile of the reference period. |
Frequency of extreme precipitations | day | The count of days with precipitation above the extreme precipitation threshold defined as the 95th percentile of total precipitation of rainy days over 1981-2010. |
Flood recurrence | m3 s-1 | Ensemble mean and distribution of flood recurrence. The River Flood index is defined as the 50-year flood recurrence. Other values from several recurrence periods (10, 5 and 2 years) are also shown as complementary information. |
Mean river discharge | m3 s-1 | The mean annual daily river discharge over a 30 year period. |
Aridity actual | Dimensionless | The monthly mean value of the ratio between actual evapotranspiration and precipitation over a 30 year period. Here actual evapotranspiration is the modelled evapotranspiration computed only with available water. |
Consecutive dry days | day | The longest period of consecutive days with daily precipitation below 1 mm in a year, season or month. |
Duration of meteorological droughts | month | The count of months in a year with anomalously low precipitation conditions based on the 3-month Standardised Precipitation Index (SPI-3) relative to a reference period, here 1981-2010. |
Magnitude of meteorological droughts | Dimensionless | The cumulative severity of drought events in a year based on the 3-month Standardised Precipitation Index (SPI-3) relative to a reference period, here 1981-2010. A drought event starts when SPI-3 values fall below -1 for at least two consecutive months and ends when the index returns positive. The magnitude of the event is defined as the sum of SPI-3 absolute values in the months of the drought episodes. |
Mean soil moisture | Dimensionless | Soil moisture is the water stored in the soil and is affected by precipitation, temperature, soil characteristics, and more. The mean soil moisture is defined as the monthly mean values of soil moisture in the root zone as the fraction of the field capacity volume over a 30 year period. |
Fire weather index | Dimensionless | The Fire Weather Index (FWI) is a meteorologically based index used worldwide to estimate fire danger. It is developed by the Canadian Forestry Service to estimate forest fire ignition and spread conditions based on several weather variables (temperature, precipitation, relative humidity, and wind speed). |
Days with high fire danger | day | The count of days in a period with a Fire Weather Index (FWI) value greater than 30 (Number of days) based upon the European Forest Fire Information System (EFFIS) classification. |
Mean wind speed | m s-1 | Magnitude of the two-dimensional (u and v components) horizontal air velocity at 10 metres averaged over a month a season or a year. |
Extreme wind speed days | day | The count of days over a month, season or year with 10m wind speed above the extreme threshold defined as the 98th percentile of 10m wind speed over 1981-2010. |
Snow and ice | ||
Snowfall amount | mm | The cumulative snowfall precipitation during the winter sports season (November to April). |
Coastal | ||
Relative sea level rise | cm | The annual mean sea level relative to the 1986-2005 reference period. |
Extreme sea level | m | The Total water level for a return period of 100 years estimated over 30-year periods (1951-1980, 1985-2014 and 2021-2050). |
2.2.3. Input datasets by indicator
The table below provides the input dataset used to derive each indicator. Each single dataset is described in more detail in section 2.3. For conciseness and clarity we have used a consistent set of short names for the datasets in the table below and in section 2.3. The Product User Guide (PUG) column provides a link to the specific indicator PUG for further information.
Table 3: List of the ECDE indices datasets.
Main Variables |
| |
Variable | Dataset | Product User Guide |
Heat and cold | ||
Mean temperature | Add link when Confluence PUG finalised | |
Growing degree days | Add link when Confluence PUG finalised | |
Heating degree days | Add link when Confluence PUG finalised | |
Cooling degree days | Add link when Confluence PUG finalised | |
Tropical nights | Add link when Confluence PUG finalised | |
Hot days | Add link when Confluence PUG finalised | |
Warmest three-day period | Add link when Confluence PUG finalised | |
Heatwave days - Climatological | Add link when Confluence PUG finalised | |
High UTCI days | Add link when Confluence PUG finalised | |
Frost days | Add link when Confluence PUG finalised | |
Maximum temperature | Add link when Confluence PUG finalised | |
Minimum temperature | Add link when Confluence PUG finalised | |
Wet and dry | ||
Total precipitation | Add link when Confluence PUG finalised | |
Maximum consecutive five-day precipitation | Add link when Confluence PUG finalised | |
Extreme precipitation total | Add link when Confluence PUG finalised | |
Frequency of extreme precipitation | Add link when Confluence PUG finalised | |
Flood recurrence | Add link when Confluence PUG finalised | |
Mean river discharge | Add link when Confluence PUG finalised | |
Aridity actual | Add link when Confluence PUG finalised | |
Consecutive dry days | Add link when Confluence PUG finalised | |
Duration of meteorological droughts | Add link when Confluence PUG finalised | |
Magnitude of meteorological droughts | Add link when Confluence PUG finalised | |
Mean soil moisture | Add link when Confluence PUG finalised | |
Fire weather index | Add link when Confluence PUG finalised | |
Days with high fire danger | Add link when Confluence PUG finalised | |
Mean wind speed | Add link when Confluence PUG finalised | |
Extreme wind speed days | Add link when Confluence PUG finalised | |
Snow and ice | ||
Snowfall amount | Add link when Confluence PUG finalised | |
Coastal | ||
Relative sea level rise | Add link when Confluence PUG finalised | |
Extreme sea level | Add link when Confluence PUG finalised |
2.3. Input datasets
2.3.1. ERA5 and derived products.
The ERA5 reanalysis is regarded as a good proxy for observed atmospheric conditions and currently covers 01/01/1959 to near real time and is regularly extended as ERA5 data become available.
The historical values of the indices were evaluated from hourly data from the ERA5 reanalysis (ERA5 single levels) whenever possible. This was the case for the indices related to temperature, precipitation and wind (20 out of 30). For the remaining indicators, values over the historical period (either simulated or from reanalysis when available) were used directly.
The historical values of Days with high fire danger and Fire Weather indices are from the Fire danger indices historical data from the Copernicus Emergency Management Service dataset, that is based on the ERA5 reanalysis and updated in near real time. It is produced by the Copernicus Emergency Management Service (CAMS) for the Global ECMWF Fire Forecasting model (GEFF) and the European Forest Fire Information System (EFFIS). More technical specifications can be found in the dataset documentation.
2.3.2. SIS Energy
The simulated indices related to temperature, precipitation and wind (20 out of 30) were calculated from daily atmospheric variables in the same climate projections dataset: Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections. It is a set of bias-adjusted EURO-CORDEX projections composed of 9 GCM-RCM simulations at 0.25° x 0.25° spatial resolution, 3-hourly temporal resolution and cover emission scenarios RCP4.5 and RCP8.5. The 9 combinations of the 5 GCMs with the 5 RCMs is given in Table 1. More technical specifications can be found in the dataset documentation.
Table 4: The 9 GCM/RCM combinations of the Climate and energy indicators for Europe from 2005 to 2100 derived from climate projection dataset
Global Climate Model | Regional Climate Model | Ensemble member |
EC-EARTH | HIRHAM5 | r3i1p1 |
EC-EARTH | RACMO22E | r1i1p1 |
EC-EARTH | RCA4 | r12i1p1 |
HadGEM2-ES | RACMO22E | r1i1p1 |
HadGEM2-ES | RCA4 | r1i1p1 |
IPSL-CM5A-MR | WRF381P | r1i1p1 |
MPI-ESM-LR | CCLM4-8-17 | r1i1p1 |
MPI-ESM-LR | RCA4 | r1i1p1 |
NORESM1-M | HIRHAM5 | r1i1p1 |
2.3.3. ERA5 Heat
The High UTCI Day index data are from Thermal comfort indices derived from ERA5 reanalysis. As indicated, it is based on surface variables from the ERA5 reanalysis (ERA5 single levels) and inherits the same spatial (0.25° x 0.25°) and temporal resolution (hourly). There are no climate projections of UTCI at the moment. More technical details can be found in the dataset documentation.
2.3.4. SIS Operational Water Service
The Flood recurrence, Mean river discharge, Aridity actual, and Duration of Soil moisture Draughts index data are from the Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections dataset. It is a set of 30-year statistics from two hydrological models forced by 8 bias-adjusted multi-model simulations from the EURO-CORDEX experiment. The hydrological models are from the Swedish Meteorological and Hydrological Institute (SMHI, E-HYPEgrid model) and Wageningen University (VIC-WIR Model). The hydrological simulations are either gridded (5km x 5km) or at catchment scale and cover scenarios RCP4.5 and RCP8.5. The 8 combinations of the 5 GCMs with the 5 RCMs is given in Table 1. More technical details about the hydrological models can be found in the dataset documentation and in the following Hydrological model specification.
Table 7: The 8 GCM/RCM combinations of the water related indices dataset.
Global Climate Model | Regional Climate Model |
EC-EARTH | CCLM4-8-17 |
EC-EARTH | RACMO22E |
EC-EARTH | RCA4 |
HadGEM2-ES | RCA4 |
HadGEM2-ES | RACMO22E |
MPI-ESM-LR | RCA4 |
MPI-ESM-LR | REMO2009 |
MPI-ESM-LR | REMO2009 |
2.3.5. SIS EU Tourism
The Days with high fire danger and Fire Weather index data are from the Fire danger indicators for Europe from 1970 to 2098 derived from climate projections dataset. It is a set of 6 bias-adjusted multi-model simulations from the EURO-CORDEX experiment. These simulations have a daily temporal resolution, a spatial resolution of 0.1° x 0.1° and cover scenarios RCP4.5 and RCP8.5. The 5 combinations of the 5 GCMs with 1 RCM is given in Table 1. More technical specifications can be found in the dataset documentation.
Table 8: The 5 GCM/RCM combinations of the Fire danger indicators dataset
Global Climate Model | Regional Climate Model |
CNRM-CM5 | RCA4 |
EC-EARTH | RCA4 |
HadGEM2-ES | RCA4 |
IPSL-CM5A-M | RCA4 |
MPI-ESM-LR | RCA4 |
Note: The Fire Weather Index values were bias-corrected in this project to be compatible with the CEMS reanalysis FWI. See subsection 2.4.2 Indicator calculation.
The Snowfall amount index data (both historical and simulated) are from the Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections dataset. The dataset is based on the UERRA reanalysis and a set of 9 bias-adjusted multi-model simulations from the EURO-CORDEX experiment. The index data simulations have an annual temporal resolution, a spatial resolution over NUTS3 regions, a vertical resolution of 100m and cover scenarios RCP4.5 and RCP8.5. More technical specifications can be found in the dataset documentation.
Table 9: The 9 GCM/RCM combinations of the snow indicators dataset.
Global Climate Model | Regional Climate Model |
CNRM-CM5 | RCA4 |
CNRM-CM5 | ALADIN53 |
EC-EARTH | RCA4 |
HadGEM2-ES | RCA4 |
IPSL-CM5A-M | RCA4 |
IPSL-CM5A-M | WRF331F |
MPI-ESM-LR | RCA4 |
MPI-ESM-LR | REMO2009 |
MPI-ESM-LR | RCA4 |
2.3.6. SIS European Storm Surges
The Relative sea level rise and the Extreme sea level index data are from the Global sea level change time series from 1950 to 2050 derived from reanalysis and high resolution CMIP6 climate projections dataset. It is based on a set of simulations produced with the Global Tide and Surge Model (GTSM) of Deltares, a global 2D hydrodynamic model which incorporates tides, surges and mean sea-levels dynamically. Both the historical and future GTSM simulations include sea level rise data as input that are also available in the dataset. The Relative sea level rise field is annual and spatially-varying at 1° x 1° resolution and is relative to the 1986-2005 reference period based on RCP8.5 (not SSPs as for the GTSM simulations). The field is the median result of a probabilistic model that computes and combines processes affecting sea level. As such, it is model independent and only one field (the median) of SLR is provided in the dataset. More technical specifications can be found in the dataset documentation.
2.4. Method
2.4.1. Input dataset selection
The indices are either retrieved from datasets when available or calculated through a specific CDS Toolbox workflow. In this way both the calculations and the resulting data are fully traceable. As they come from different datasets the underlying climate data differ in their technical specifications (type and number of climate and impact models involved, bias-corrected or not, periods covered etc.). An effort was made in the dataset selection to limit the heterogeneity of the underlying dataset as Ideally the indices should come from the same dataset with identical specifications.
2.4.2. Indicator calculation
The indicators were calculated according to the recommended definitions based on a technical paper from the European Topic Centre on Climate Change Impacts, Vulnerability and Adaptation (ETC/CCA). The indices are either retrieved from datasets when available or calculated through a specific CDS Toolbox workflow. In this way both the calculations and the resulting data are fully traceable.
One exception is the Fire Weather Index (FWI) data of the SIS Tourism dataset. A bias was found between the original values and the values based on reanalysis (CAMS) and it was decided to apply a bias-correction procedure to correct the mean and the variance of the simulated FWI with the mean and variance of the reanalysis based FWI and taking the 1981-2010 period as reference (see Appendix 1).
2.4.3. Regional aggregation
Where relevant the indicators have been aggregated over standard administrative bounderies used by european institution for reporting. The aggregation was performed using the CDS Toolbox aggregation tools. The administrative boundaries available are:
- the NUTS classification (Nomenclature of territorial units for statistics) maintained by Eurostat (see Eurostat (2021) website). NUTS is a hierarchical system for dividing up the economic territory of the EU and the UK. The indicators uses NUTS regions ranging from NUTS0 (country) to NUTS2 (sub-country) and NUTS3 in the specific case of Snowfall amount.
- Europe zones is the number of countries considered for the European domain (27 EU countries, 32 EEA member countries and 38 EEA member and cooperating countries).
- Transnational regions regions involve cooperating regions from several countries of the EU forming bigger areas (e.g. danube, alpine, ionian etc.) to promote better cooperation and regional development within the Union
Appendix 2 lists all the available regional layers.
2.4.4. Limitations
The EEA ECDE initiative gathers in a single dataset a number of indices that are relevant for adaptation planning at the European and national level. Ideally the indices should come from the same dataset with identical specifications, as this was not possible considering the available datasets hosted on the CDS an effort was made in the dataset selection to limit the heterogeneity of the underlying dataset. Out of the 30 ECDE indicators in this catalogue entry, 19 of them use the same input dataset (SIS Energy and ERA5 Single level, see Table 3) and provide a consistent core of indicators. The other indices required the use of different datasets because they require more sector specific data (fire, hydrology, tourism) and are therefore not directly comparable. In particular, differences in future behaviour (trends, variability, etc..) between indicators using different input datasets can be, at least partially, attributed to the underlying climate projection included in each dataset. Besides the possible heterogeneity between indicators it is advised to consult the Product User Guide of individual indicators to understand their own limitations.
2.4.5. Validation and quality assurance
The strategy behind the calculation of the ECDE indicators is to use Quality Assured and validated datasets distributed through the CDS and use fully traceable and repeatable CDS Toolbox workflows to perform the computation. All the datasets used in input and listed in 2.2.3. Input Datasets by Index have gone through the CDS quality assurance and are under C3S governance and scrutiny should any error be found in the future. This strategy eliminates the uncertainties or errors due to the input data or tools used.
The workflows to calculate the indicators and their documentation could be subject to errors and to minimise them the quality assurance procedure described below has been followed. This process included several reviews by different responsible at various steps during the computation and publication of the indicators.
Figure 1: ECDE internal quality assurance and review process
Regarding the validation of specific indicators the validation has mainly been performed by comparing the output with either the already existing indicators available on the ECDE or other publication from the EEA where the same indicators were presented and computed different input data. Even though it was not necessarily possible to check against a numerically identical dataset it was possible to check that the indicators are consistent with other publications.
3. Concluding remarks
The ECDE indicators dataset provides a series of climate indices derived from reanalysis and model simulations data hosted on the CDS. It gathers data both in a gridded format and as regional averages that are used to make the indicators available through interactive visualisation apps on the European Climate Data Explorer hosted on EEA’s Climate-adapt site. It is provided as a dataset to support further analysis by climate change adaptation practitioners that wish to have access to the underlying data of the ECDE.
4. Glossary
Acronym | Term |
C3S | Copernicus Climate Change Service |
CDS | Climate Data Store of the Copernicus Climate Change Service |
GCM | Global Climate Model |
RCP | Representative Concentration Pathway |
RCM | Regional Climate Model |
VM | Virtual Machine |
Term | Definition |
Essential Climate Variable (ECV) | An ECV is a physical, chemical or biological variable or a group of linked variables that critically contributes to the characterization of Earth’ s climate. Source. |
Representative Concentrations Pathway (RCPs) | RCP’s comprise greenhouse gas emission scenarios that have similar radiative forcing characteristics. Source. |
RCP4.5 | RCP4.5 is a stabilisation scenario in which total radiative forcing stabilises at 4.5 W/m2 shortly after the year 2100. Source. |
RCP8.5 | RCP8.5 is representative of scenarios that lead to high greenhouse gas concentration levels and has a radiative forcing of 8.5 W/m2 in the year 2100. Source. |
5. References
Crespi A., Terzi S., Cocuccioni S., Zebisch M., Berckmans J., Füssel H-M (2020) “Climate-related hazard indices for Europe”. European Topic Centre on Climate Change impacts, Vulnerability and Adaptation (ETC/CCA) Technical Paper 2020/1. DOI: https://doi.org/10.25424/cmcc/climate_related_hazard_indices_europe_2020
6. Appendix 1: Fire weather index bias correction
The projected FWI data distributed in the CDS is not bias-corrected against any observation data. To be able to compare the projected FWI data to FWI data derived from reanalysis a bias correction to correct the mean and the variance of the simulated FWI has been implemented.
The implemented bias-correction follows the following steps for each single FWI simulation:
- Compute the monthly climatology of the model for the period 1981 to 2010.
- Compute the monthly standard deviation of the model for the period 1980 to 2021.
- Compute the monthly climatology of the reanalysis derived FWI for the period 1981 to 2010.
- Compute the monthly standard deviation of the reanalysis derived FWI for the period 1980 to 2021.
- For each month:
- Compute the 30 year running mean.
- Detrend: subtract the 30 year running mean to the daily projected values.
- Variance adjustment: multiply the projected detrended daily values by the standard deviation ratio (reanalysis standard deviation divided by the projection standard deviation). For projection standard deviation below 0.1 (corresponding to an FWI close to 0) the ratio is set to 1 to avoid diverging values.
- Retrend: add the 30 year running mean to the variance adjusted values.
- Compute the anomaly between the re-trended values and the projections monthly climatology.
- Sum the anomalies to the reanalysis climatology to obtain the bias corrected values.
7. Appendix 2: Regional layers
The table below lists all the regional layers available and the corresponding code to use in the file naming conventions.
Regions | Region | Code |
NUTS | NUTS 0 | nuts_0 |
NUTS 1 | nuts_1 | |
NUTS 2 | nuts_2 | |
NUTS 3 | nuts_3 | |
Non NUTS | Non NUTS | non_nuts |
Europe Zones
| EEA EU 27 | eea_eu_27 |
EEA EU 32 | eea_eu_32 | |
EEA EU 38 | eea_eu_38 | |
Transnational Regions
| Interreg VI-B Adriatic-Ionian | eea_trans_adriatic_ionian |
Interreg VI-B Alpine Space | eea_trans_alpine_space | |
Interreg VI-B Northern Periphery and Arctic | eea_trans_northern_periphery_and_arctic | |
Interreg VI-B Atlantic Area | eea_trans_atlantic_area | |
Interreg VI-B Baltic Sea Region | eea_trans_baltic_sea_region | |
Interreg VI-B Central Europe | eea_trans_central_europe | |
Interreg VI-B Danube | eea_trans_danube | |
Interreg VI-B Mediterranean (EURO MED) | eea_trans_mediterranean | |
Interreg VI-B North Sea | eea_trans_north_sea | |
Interreg VI-B North West Europe | eea_trans_north_west_europe | |
Interreg VI-B South West Europe (SUDOE) | eea_trans_south_west_europe |