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COLD
MODEL
You will find on this page all the information relating to the constitution of the model for the "Cold" hazard for the analyzes carried out on Bat-ADAPT:
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The Europe climate exposure analysis model for the "Cold" hazard
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The France climate exposure analysis model for the "Cold" hazard
The analysis of climate exposure to the “Extreme cold” hazard in Europe is based on 2 climate indicators:
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The number of frost days
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Heating degree days
The combination of this information makes it possible to assess the climatic exposure of an area, compared to the climatic exposure in the rest of Europe.
Definition of the indicator: This indicator represents the number of days in the year when the minimum daily temperature is below 0°C.
Indicator ID: EU_FD
Mesh: ~150km
Source :IPCC Interactive Atlas
Data processing: The climate data was downloaded in NetCDF format from the IPCC's interactive atlas, for the 3 scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) and the 3 time horizons retained (short, medium and long term), i.e. 9 data sets. Then, cartographic processing using the QGIS software enabled the transition from point data to data covering the whole of Europe. A legend was applied in view of the minimums and maximums existing on all the datasets.
Scope: Europe
Definition of the indicator: The number of heating degree-days (orheating degree days) is a measure of the heating requirement over the year. It is calculated in relation to a reference threshold (15.5°C in the IPCC statistics), and therefore depends on the excess temperature below this threshold, where heating is necessary. If the average temperature for a day is greater than or equal to this threshold, the number of degree-days over the day is zero. Otherwise, it is equal to the difference between the threshold and the average temperature of the day.
Indicator ID: EU_HD
Mesh: ~150km
Source :IPCC Interactive Atlas
Data processing: The climate data was downloaded in NetCDF format from the IPCC's interactive atlas, for the 3 scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) and the 3 time horizons retained (short, medium and long term), i.e. 9 data sets. Then, cartographic processing using the QGIS software enabled the transition from point data to data covering the whole of Europe. A legend was applied in view of the minimums and maximums existing on all the datasets.
Scope: Europe
In order to obtain a single exposure index revealing the climatic risk situation of a building in an area, the previous indicators have been combined under the following formula:
This formula puts the risk in a given location into perspective with the risk existing in the whole of Europe.
These are therefore compared risk ratios, weighted according to their importance, added accordingly, then normalized, to obtain an indicator varying from 0 to 100% with 0% being the minimum risk encountered in Europe from 2020 to 2100 for the 3 scenarios, and 100% the maximum risk, over this same period, for the 3 scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5).
The exposure reliability index is assigned as follows:
5/5 Models used in the IPCC report empirically verified
4/5 Models used in the IPCC report
3/5 Other models - Very reliable
2/5 Other models - Moderately reliable
1/5 Other models - Unreliable
CLIMATE EXPOSURE ANALYSIS MODEL IN FRANCE
COLD HAZARD
The analysis of climatic exposure to the “Cold” hazard in France is based on 2 climatic indicators and 1 territorial indicator:
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The number of cold wave days
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The number of frost days
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The number of declaration of natural disasters for Cold
The combination of this information makes it possible to assess the climatic exposure of an area, compared to the climatic exposure in the rest of France.
Definition of the indicator: Number of days over the year when the minimum daily temperature is lower than normal by more than 5°C for at least 5 consecutive days.
Indicator ID: FR_TNCWD
Mesh: ~8km (Safran grid)
Source :DRIAS - Meteo France
Data processing: The climate data was uploaded in CSV format to the DRIAS command space, for the 3 scenarios (RCP2.6, RCP4.5, RCP8.5) and the 5 time horizons retained (2020, 2030, 2050, 2070 and 2090), i.e. 15 data sets. Statistical processing was done to smooth the climatic variability. This involves realizing, for each time horizon chosen, the median over 20 years (10 years before and 10 after) of the climate indicator. Cartographic processing via the QGIS software allowed the transition from point data to data covering the whole of France. A legend was applied in view of the minimums and maximums existing on all the datasets.
Scope: Metropolitan France
Definition of the indicator: Number of days in the year when the minimum daily temperature is below 0°C.
Indicator ID: FR_TNFD
Mesh: ~8km (Safran grid)
Source :DRIAS - Meteo France
Data processing: The climate data was uploaded in CSV format to the DRIAS command space, for the 3 scenarios (RCP2.6, RCP4.5, RCP8.5) and the 5 time horizons retained (2020, 2030, 2050, 2070 and 2090), i.e. 15 data sets. Statistical processing was done to smooth the climatic variability. This involves realizing, for each time horizon chosen, the median over 20 years (10 years before and 10 after) of the climate indicator. Cartographic processing via the QGIS software allowed the transition from point data to data covering the whole of France. A legend was applied in view of the minimums and maximums existing on all the datasets.
Scope: Metropolitan France
Number of declaration of natural disasters for the cold hazard
Definition of the indicator: This indicator indicates the number of declaration of natural disaster (CatNat) having been published in each municipality of metropolitan France, for a hazard of the severe cold type, all types combined, i.e. disasters linked to cold weather ("Weight of the snow"). The Gaspar database lists all CatNat for compensation purposes in the face of natural disasters. In fact, the state of natural disaster recorded by decree may give rise to the right to cover for the insured against the effects of these disasters on the property covered by the insurance contracts. The count of these decrees therefore makes it possible to report on the historical loss experience of the municipality vis-à-vis floods.
Indicator ID: FR_CatNat_GF
Mesh: communal
Source: Georisques - BD Gaspar
Data processing: The Gaspar database lists all of the CatNat on French territory, at the level of the municipality. A selection of the municipalities on the metropolitan territory was made, and a second selection was made to keep only the municipalities concerned by a Risk Prevention Plan for a type of flood. Finally, a count of the occurrence of each municipality was made.
Scope: Metropolitan France
In order to obtain a single exposure index revealing the climatic risk situation of a building in an area, the previous indicators have been combined under the following formula:
This formula puts the risk in a given location in perspective with the risk existing in the whole of France.
These are therefore compared risk ratios, weighted according to their importance, added accordingly, then normalized, to obtain an indicator varying from 0 to 100% with 0% being the minimum risk encountered in France from 2020 to 2100 for the 3 scenarios, and 100% the maximum risk, over this same period, for the 3 scenarios (RCP2.6, RCP4.5, RCP8.5).
The exposure reliability index is assigned as follows:
5/5 Models used in the IPCC report empirically verified
4/5 Models used in the IPCC report
3/5 Other models - Very reliable
2/5 Other models - Moderately reliable
1/5 Other models - Unreliable
COLD HAZARD
In order to calculate the vulnerability of buildings to the “Extreme cold” hazard, a certain amount of information is necessary. In order to collect them, a form is available to complete the characteristics of your project.
If no information is transmitted, the vulnerability defaults to 100%, with zero reliability.
The building vulnerability matrix for the “Extreme cold weather” hazard will soon be available for download in Excel format. This allows you to know the intermediate steps of the calculations performed in R4RE.
The vulnerability ranges between 0% and 100%. The greater the vulnerability, the more the building is subject to adaptation.
The vulnerability reliability index is calculated from the number of questions answered compared to the total number of questions. It varies from 0 to 5.