Carte des variables environnementales finales

Boris Leroy

2024-02-14

Chargement des données

library(sf)
## Linking to GEOS 3.11.2, GDAL 3.7.2, PROJ 9.3.0; sf_use_s2() is TRUE
library(ggplot2)
library(terra)
## terra 1.7.71
fra <- st_read("data/fra.gpkg")

Reading layer fra' from data sourceC:_PNA_Corse.gpkg’ using driver `GPKG’ Simple feature collection with 1 feature and 168 fields Geometry type: MULTIPOLYGON Dimension: XY Bounding box: xmin: -61.79784 ymin: -21.37078 xmax: 55.8545 ymax: 51.08754 Geodetic CRS: WGS 84

env_corse <- rast("data/env_corse_total_sync.tif")

1. Données climatiques : source de données CHELSA

Description et méta-données

Téléchargement : www.chelsa-climate.org

Intervalle temporel : 1981-2010

Type de données : Raster

Résolution initiale : 0.0083333°

Méta-données : https://chelsa-climate.org/wp-admin/download-page/CHELSA_tech_specification_V2.pdf

Références bibliographiques :

  • Brun, P., Zimmermann, N.E., Hari, C., Pellissier, L., Karger, D. (2022): Data from: CHELSA-BIOCLIM+ A novel set of global climate-related predictors at kilometre-resolution. EnviDat. https://doi.org/10.16904/envidat.332

  • Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth’s land surface areas. EnviDat. https://doi.org/10.16904/envidat.228.v2.1

  • Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122

  • Brun, P., Zimmermann, N.E., Hari, C., Pellissier, L., Karger, D.N. (preprint): Global climate-related predictors at kilometre resolution for the past and future. Earth Syst. Sci. Data Discuss. https://doi.org/10.5194/essd-2022-212

Licence : Creative Commons Zero - No Rights Reserved (CC0 1.0)

Description des variables : Les variables bioclimatiques (variables bio1 à bio19) sont décrites dans Karger et al. 2017. Les autres variables sont décrites dans Brun et al. (2022). La variable de température minimale de la saison estivale pour les chiroptères a été calculée à partir des valeurs mensuelles de température minimale pour les mois de mai à septembre.

Illustration des variables

vars <- xlsx::read.xlsx("data/chelsa_variable_names.xlsx",
                  sheetIndex = 1)

for(i in 1:54){
    plot(env_corse[[i]],
         col = viridis::plasma(12),
         main = vars$longname[i])
    plot(st_geometry(fra), add = TRUE,
         border = grey(0.3))
    print(knitr::kable(vars[i, ]))
}

vars longname unit scale offset explanation
bio1 mean annual air temperature °C 0.1 -273.15 mean annual daily mean air temperatures averaged over 1 year

vars longname unit scale offset explanation
2 bio2 mean diurnal air temperature range °C 0.1 0 mean diurnal range of temperatures averaged over 1 year

vars longname unit scale offset explanation
3 bio3 isothermality °C 0.1 0 ratio of diurnal variation to annual variation in temperatures

vars longname unit scale offset explanation
4 bio4 temperature seasonality °C/100 0.1 0 standard deviation of the monthly mean temperatures

vars longname unit scale offset explanation
5 bio5 mean daily maximum air temperature of the warmest month °C 0.1 -273.15 The highest temperature of any monthly daily mean maximum temperature

vars longname unit scale offset explanation
6 bio6 mean daily minimum air temperature of the coldest month °C 0.1 -273.15 The lowest temperature of any monthly daily mean maximum temperature

vars longname unit scale offset explanation
7 bio7 annual range of air temperature °C 0.1 0 The difference between the Maximum Temperature of Warmest month and the Minimum Temperature of Coldest month

vars longname unit scale offset explanation
8 bio8 mean daily mean air temperatures of the wettest quarter °C 0.1 -273.15 The wettest quarter of the year is determined (to the nearest month)

vars longname unit scale offset explanation
9 bio9 mean daily mean air temperatures of the driest quarter °C 0.1 -273.15 The driest quarter of the year is determined (to the nearest month)

vars longname unit scale offset explanation
10 bio10 mean daily mean air temperatures of the warmest quarter °C 0.1 -273.15 The warmest quarter of the year is determined (to the nearest month)

vars longname unit scale offset explanation
11 bio11 mean daily mean air temperatures of the coldest quarter °C 0.1 -273.15 The coldest quarter of the year is determined (to the nearest month)

vars longname unit scale offset explanation
12 bio12 annual precipitation amount kg m-2 year-1 0.1 0 Accumulated precipitation amount over 1 year

vars longname unit scale offset explanation
13 bio13 precipitation amount of the wettest month kg m-2 month-1 0.1 0 The precipitation of the wettest month.

vars longname unit scale offset explanation
14 bio14 precipitation amount of the driest month kg m-2 month-1 0.1 0 The precipitation of the driest month.

vars longname unit scale offset explanation
15 bio15 precipitation seasonality kg m-2 0.1 0 The Coefficient of Variation is the standard deviation of the monthly precipitation estimates expressed as a percentage of the mean of those estimates (i.e. the annual mean)

vars longname unit scale offset explanation
16 bio16 mean monthly precipitation amount of the wettest quarter kg m-2 month-1 0.1 0 The wettest quarter of the year is determined (to the nearest month)

vars longname unit scale offset explanation
17 bio17 mean monthly precipitation amount of the driest quarter kg m-2 month-1 0.1 0 The driest quarter of the year is determined (to the nearest month)

vars longname unit scale offset explanation
18 bio18 mean monthly precipitation amount of the warmest quarter kg m-2 month-1 0.1 0 The warmest quarter of the year is determined (to the nearest month)

vars longname unit scale offset explanation
19 bio19 mean monthly precipitation amount of the coldest quarter kg m-2 month-1 0.1 0 The coldest quarter of the year is determined (to the nearest month)

vars longname unit scale offset explanation
20 cmi_max Maximum monthly climate moisture index kg m-2 month-1 0.1 0 The climate moisture index of the month with the highest precipitation surplus

vars longname unit scale offset explanation
21 cmi_mean Mean monthly climate moisture index kg m-2 month-1 0.1 0 Average monthly climate moisture index over 1 year

vars longname unit scale offset explanation
22 cmi_min Minimum monthly climate moisture index kg m-2 month-1 0.1 0 The climate moisture index of the month with the highest precipitation deficit

vars longname unit scale offset explanation
23 cmi_range Annual range of monthly climate moisture index kg m-2 month-1 0.1 0 Difference between maximum and minimum monthly climate moisture index

vars longname unit scale offset explanation
24 gdd0 Growing degree days heat sum above 0°C °C 0.1 0 heat sum of all days above the 0°C temperature accumulated over 1 year.

vars longname unit scale offset explanation
25 gdd5 Growing degree days heat sum above 5°C °C 0.1 0 heat sum of all days above the 5°C temperature accumulated over 1 year.

vars longname unit scale offset explanation
26 gdd10 Growing degree days heat sum above 10°C °C 0.1 0 heat sum of all days above the 10°C temperature accumulated over 1 year.

vars longname unit scale offset explanation
27 gsl growing season length TREELIM number of days - - Length of the growing season

vars longname unit scale offset explanation
28 gsp Accumulated precipiation amount on growing season days TREELIM kg m-2
gsl-1 0.1 0 precipitation sum accumulated on all days during the growing season based on TREELIM (https://doi.org/10.1007/s00035-014-0124-0)

vars longname unit scale offset explanation
29 gst Mean temperature of the growing season TREELIM °C 0.1 -273.15 Mean temperature of all growing season days based on TREELIM (https://doi.org/10.1007/s00035-014- 0124-0)

vars longname unit scale offset explanation
30 hurs_max Maximum monthly near surface relative humidity % 0.01 0 The highest monthly near-surface relative humidity

vars longname unit scale offset explanation
31 hurs_mean Mean monthly near-surface relative humidity % 0.01 0 Average monthly near-surface relative humidity over 1 year

vars longname unit scale offset explanation
32 hurs_min Minimum monthly near surface relative humidity % 0.01 0 The lowest monthly near-surface relative humidity

vars longname unit scale offset explanation
33 hurs_range Annual range of monthly near surface relative humidity % 0.01 0 Difference between maximum and minimum near-surface relative humidity

vars longname unit scale offset explanation
34 ngd0 Number of growing degree days number of days - - Number of days at which tas > 0°C

vars longname unit scale offset explanation
35 ngd5 Number of growing degree days number of days - - Number of days at which tas > 5°C

vars longname unit scale offset explanation
36 ngd10 Number of growing degree days number of days - - Number of days at which tas > 10°C

vars longname unit scale offset explanation
37 npp Net primary productivity g C m-2 yr-1 0.1 0 Calculated based on the ‘Miami model’, Lieth, H., 1972. “Modelling the primary productivity of the earth. Nature and resources”, UNESCO, VIII, 2:5-10.

vars longname unit scale offset explanation
38 pet_penman_max Maximum monthly potential evapotranspiration kg m-2 month-1 0.01 0 The highest monthly potential evaporation; calculated with the Penman-Monteith equation.

vars longname unit scale offset explanation
39 pet_penman_mean Mean monthly potential evapotranspiration kg m-2 month-1 0.01 0 Average monthly potential evaporation over 1 year; calculated with the Penman-Monteith equation.

vars longname unit scale offset explanation
40 pet_penman_min Minimum monthly potential evapotranspiration kg m-2 month-1 0.01 0 The lowest monthly potential evaporation; calculated with the Penman-Monteith equation.

vars longname unit scale offset explanation
41 pet_penman_range Annual range of monthly potential evapotranspiration kg m-2 0.01 0 Difference between maximum and minimum monthly potential evapotranspiration; calculated with the Penman-Monteith equation

vars longname unit scale offset explanation
42 rsds_max Maximum monthly surface downwelling shortwave flux in air MJ m-2 d-1 0.001 0 The highest monthly surface downwelling shortwave flux in air

vars longname unit scale offset explanation
43 rsds_mean Mean monthly surface downwelling shortwave flux in air MJ m-2 d-1 0.001 0 Average monthly surface downwelling shortwave flux in air over 1 year

vars longname unit scale offset explanation
44 rsds_min Minimum monthly surface downwelling shortwave flux in air MJ m-2 d-1 0.001 0 The lowest monthly surface downwelling shortwave flux in air

vars longname unit scale offset explanation
45 rsds_range Annual range of monthly surface downwelling shortwave flux in air MJ m-2 d-1 0.001 0 Difference between maximum and minimum monthly surface downwelling shortwave flux in air

vars longname unit scale offset explanation
46 scd Snow cover days count - - Number of days with snowcover calculated using the snowpack model implementation in from TREELIM (https://doi.org/10.1007/s00035-014- 0124-0)

vars longname unit scale offset explanation
47 sfcWind_max Maximum monthly near surface wind speed m s-1 0.001 0 The highest monthly near-surface wind speed; near surface represents 10 m above ground.

vars longname unit scale offset explanation
48 sfcWind_mean Mean monthly near-surface wind speed m s-1 0.001 0 Average monthly near-surface wind speed over 1 year; near surface represents 10 m above ground.

vars longname unit scale offset explanation
49 sfcWind_min Minimum monthly near surface wind speed m s-1 0.001 0 The lowest monthly near-surface wind speed; near surface represents 10 m above ground.

vars longname unit scale offset explanation
50 sfcWind_range Annual range of monthly near surface wind speed m s-1 0.001 0 Difference between maximum and minimum monthly near-surface wind speed; near surface represents 10 m above ground.

vars longname unit scale offset explanation
51 vpd_max Maximum monthly vapor pressure deficit Pa 0.1 0 The highest monthly vapor pressure deficit

vars longname unit scale offset explanation
52 vpd_mean Mean monthly vapor pressure deficit Pa 0.1 0 Average monthly vapor pressure deficit over 1 year

vars longname unit scale offset explanation
53 vpd_min Minimum monthly vapor pressure deficit Pa 0.1 0 The lowest monthly vapor pressure deficit

vars longname unit scale offset explanation
54 vpd_range Annual range of monthly vapor pressure deficit Pa 0.1 0 Difference between maximum and minimum monthly vapor pressure deficit
plot(env_corse[["tasmin_chiro"]],
     col = viridis::plasma(12),
     main = "Température minimale de la saison estivale")
plot(st_geometry(fra), add = TRUE,
     border = grey(0.3))

2. Données routes

Description et méta-données

Téléchargement : https://geoservices.ign.fr/bdcarto

Intervalle temporel : 2023

Type de données : Vectoriel

Précision : 1:50000 à 1:200000

Méta-données : https://geoservices.ign.fr/documentation/donnees/vecteur/bdcarto

Références bibliographiques :

Licence : licence ouverte Etalab 2.0

Description des variables : Distance à la plus proche cellule possédant une route en mètres

Illustration des variables

plot(env_corse[["distance_routes"]],
     col = viridis::plasma(12),
     main = "Distance aux routes")
plot(st_geometry(fra), add = TRUE,
     border = grey(0.3))

3. Données hydrographiques

Description et méta-données

Téléchargement : https://geoservices.ign.fr/bdtopo

Intervalle temporel : 2023

Type de données : Vectoriel

Précision : 1:2000 à 1:50000

Méta-données : https://geoservices.ign.fr/documentation/donnees/vecteur/bdtopo

Références bibliographiques :

Licence : licence ouverte Etalab 2.0

Description des variables : Les cours d’eau et les plans d’eau ont été fournis par la DREAL de Corse en résolution très fine (environ 0.0026 * 0.0017°), présence-absence par cellule. Ils ont été agrégés à la résolution des variables climatiques (0.0083333°) pour créer deux variables :

  • la proportion de milieux d’eau douce dans chaque cellule de 0.0083333°

  • la distance aux milieux d’eau douce, calculée comme la distance à la cellule la plus proche contenant au moins un milieu d’eau douce, en mètres

Illustration des variables

plot(env_corse[["milieux_eaudouce"]],
     col = viridis::plasma(12),
     main = "Proportion de milieux d'eau douce")
plot(st_geometry(fra), add = TRUE,
     border = grey(0.3))