Monthly Climate statistics

DOPA provides monthly precipitation and temperature statistics.

Monthly Precipitation

Monthly Precipitation mean values (mm) statistics have been computed for each terrestrial Protected Areas (PA) of size >= 50 km2 (WDPA, October 2017) using the WorldClim 2 dataset.

The data sources are the following ones:

  • UNEP-WCMC and IUCN (2017). Protected Planet: The World Database on Protected Areas (WDPA) [On-line], [October/2017], Cambridge, UK: UNEP-WCMC and IUCN. Available at: www.protectedplanet.net.
  • Fick, S.E. and R.J. Hijmans (2017). Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology. doi:10.1002/joc.5086. Available at: www.worldclim.org/version2 (Release 1, June 2016)


Precipitation Python code:

# Import arcpy module
import arcpy
 
# Check out any necessary licenses
arcpy.CheckOutExtension("spatial")
 
# Load required toolboxes
arcpy.ImportToolbox("Model Functions")
 
 
# Local variables:
WDPA_poly_Oct2017_50 = "E:\\xxx\\WDPA_October2017_Public\\WDPA_Oct2017_Public.gdb\\WDPA_poly_Oct2017_50"
wc2_0_2_5m_prec_12_tif = "E:\\xxx\\wc2.0_2.5m_prec\\wc2.0_2.5m_prec_12.tif"
wc2_0_2_5m_prec_11_tif = "E:\\xxx\\wc2.0_2.5m_prec\\wc2.0_2.5m_prec_11.tif"
wc2_0_2_5m_prec_10_tif = "E:\\xxx\\wc2.0_2.5m_prec\\wc2.0_2.5m_prec_10.tif"
wc2_0_2_5m_prec_09_tif = "E:\\xxx\\wc2.0_2.5m_prec\\wc2.0_2.5m_prec_09.tif"
wc2_0_2_5m_prec_08_tif = "E:\\xxx\\wc2.0_2.5m_prec\\wc2.0_2.5m_prec_08.tif"
wc2_0_2_5m_prec_07_tif = "E:\\xxx\\wc2.0_2.5m_prec\\wc2.0_2.5m_prec_07.tif"
wc2_0_2_5m_prec_06_tif = "E:\\xxx\\wc2.0_2.5m_prec\\wc2.0_2.5m_prec_06.tif"
wc2_0_2_5m_prec_05_tif = "E:\\xxx\\wc2.0_2.5m_prec\\wc2.0_2.5m_prec_05.tif"
wc2_0_2_5m_prec_04_tif = "E:\\xxx\\wc2.0_2.5m_prec\\wc2.0_2.5m_prec_04.tif"
wc2_0_2_5m_prec_03_tif = "E:\\xxx\\wc2.0_2.5m_prec\\wc2.0_2.5m_prec_03.tif"
wc2_0_2_5m_prec_02_tif = "E:\\xxx\\wc2.0_2.5m_prec\\wc2.0_2.5m_prec_02.tif"
wc2_0_2_5m_prec_01_tif = "E:\\xxx\\wc2.0_2.5m_prec\\wc2.0_2.5m_prec_01.tif"
I_WDPA_poly_Oct2017_50_WDPAID = "I_WDPA_poly_Oct2017_50_WDPAID"
prec__Value___3_ = "E:\\xxx\\Default.gdb\\prec_%Value%"
prec__Value___2_ = "E:\\xxx\\Default.gdb\\prec_%Value%"
prec__Value_ = "E:\\xxx\\Default.gdb\\prec_%Value%"
prec__Value___13_ = "E:\\xxx\\Default.gdb\\prec_%Value%"
prec__Value___4_ = "E:\\xxx\\Default.gdb\\prec_%Value%"
prec__Value___5_ = "E:\\xxx\\Default.gdb\\prec_%Value%"
prec__Value___6_ = "E:\\xxx\\Default.gdb\\prec_%Value%"
prec__Value___7_ = "E:\\xxx\\Default.gdb\\prec_%Value%"
prec__Value___8_ = "E:\\xxx\\Default.gdb\\prec_%Value%"
prec__Value___9_ = "E:\\xxx\\Default.gdb\\prec_%Value%"
prec__Value___10_ = "E:\\xxx\\Default.gdb\\prec_%Value%"
prec__Value___11_ = "E:\\xxx\\Default.gdb\\prec_%Value%"
 
# Process: Iterate Feature Selection
arcpy.IterateFeatureSelection_mb(WDPA_poly_Oct2017_50, "WDPAID #", "false")
 
# Process: Zonal Statistics as Table
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_prec_12_tif, prec__Value___3_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (2)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_prec_11_tif, prec__Value___2_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (3)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_prec_10_tif, prec__Value_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (4)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_prec_09_tif, prec__Value___13_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (5)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_prec_08_tif, prec__Value___4_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (6)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_prec_07_tif, prec__Value___5_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (7)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_prec_06_tif, prec__Value___6_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (8)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_prec_05_tif, prec__Value___7_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (9)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_prec_04_tif, prec__Value___8_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (10)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_prec_03_tif, prec__Value___9_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (11)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_prec_02_tif, prec__Value___10_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (12)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_prec_01_tif, prec__Value___11_, "DATA", "MEAN")
 
 




Monthly Temperature

Monthly temperature values (mm) (min, mean, max) statistics have been computed for each terrestrial Protected Areas (PA) of size >= 50 km2 (WDPA, October 2017) using the WorldClim 2 dataset.

The data sources are the following ones:
  • UNEP-WCMC and IUCN (2017). Protected Planet: The World Database on Protected Areas (WDPA) [On-line], [October/2017], Cambridge, UK: UNEP-WCMC and IUCN. Available at: www.protectedplanet.net.
  • Fick, S.E. and R.J. Hijmans (2017). Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology. doi:10.1002/joc.5086. Available at: www.worldclim.org/version2 (Release 1, June 2016)



TMax Python code:

# Import arcpy module
import arcpy
 
# Check out any necessary licenses
arcpy.CheckOutExtension("spatial")
 
# Load required toolboxes
arcpy.ImportToolbox("Model Functions")
 
 
# Local variables:
WDPA_poly_Oct2017_50 = "E:\\xxx\\WDPA_October2017_Public\\WDPA_Oct2017_Public.gdb\\WDPA_poly_Oct2017_50"
wc2_0_2_5m_tmax_12_tif = "E:\\xxx\\wc2.0_2.5m_tmax\\wc2.0_2.5m_tmax_12.tif"
wc2_0_2_5m_tmax_11_tif = "E:\\xxx\\wc2.0_2.5m_tmax\\wc2.0_2.5m_tmax_11.tif"
wc2_0_2_5m_tmax_10_tif = "E:\\xxx\\wc2.0_2.5m_tmax\\wc2.0_2.5m_tmax_10.tif"
wc2_0_2_5m_tmax_09_tif = "E:\\xxx\\wc2.0_2.5m_tmax\\wc2.0_2.5m_tmax_09.tif"
wc2_0_2_5m_tmax_08_tif = "E:\\xxx\\wc2.0_2.5m_tmax\\wc2.0_2.5m_tmax_08.tif"
wc2_0_2_5m_tmax_07_tif = "E:\\xxx\\wc2.0_2.5m_tmax\\wc2.0_2.5m_tmax_07.tif"
wc2_0_2_5m_tmax_06_tif = "E:\\xxx\\wc2.0_2.5m_tmax\\wc2.0_2.5m_tmax_06.tif"
wc2_0_2_5m_tmax_05_tif = "E:\\xxx\\wc2.0_2.5m_tmax\\wc2.0_2.5m_tmax_05.tif"
wc2_0_2_5m_tmax_04_tif = "E:\\xxx\\wc2.0_2.5m_tmax\\wc2.0_2.5m_tmax_04.tif"
wc2_0_2_5m_tmax_03_tif = "E:\\xxx\\wc2.0_2.5m_tmax\\wc2.0_2.5m_tmax_03.tif"
wc2_0_2_5m_tmax_02_tif = "E:\\xxx\\wc2.0_2.5m_tmax\\wc2.0_2.5m_tmax_02.tif"
wc2_0_2_5m_tmax_01_tif = "E:\\xxx\\wc2.0_2.5m_tmax\\wc2.0_2.5m_tmax_01.tif"
I_WDPA_poly_Oct2017_50_WDPAID = "I_WDPA_poly_Oct2017_50_WDPAID"
tmax__Value___3_ = "E:\\xxx\\Default.gdb\\tmax_%Value%"
tmax__Value___2_ = "E:\\xxx\\Default.gdb\\tmax_%Value%"
tmax__Value_ = "E:\\xxx\\Default.gdb\\tmax_%Value%"
tmax__Value___13_ = "E:\\xxx\\Default.gdb\\tmax_%Value%"
tmax__Value___4_ = "E:\\xxx\\Default.gdb\\tmax_%Value%"
tmax__Value___5_ = "E:\\xxx\\Default.gdb\\tmax_%Value%"
tmax__Value___6_ = "E:\\xxx\\Default.gdb\\tmax_%Value%"
tmax__Value___7_ = "E:\\xxx\\Default.gdb\\tmax_%Value%"
tmax__Value___8_ = "E:\\xxx\\Default.gdb\\tmax_%Value%"
tmax__Value___9_ = "E:\\xxx\\Default.gdb\\tmax_%Value%"
tmax__Value___10_ = "E:\\xxx\\Default.gdb\\tmax_%Value%"
tmax__Value___11_ = "E:\\xxx\\Default.gdb\\tmax_%Value%"
 
# Process: Iterate Feature Selection
arcpy.IterateFeatureSelection_mb(WDPA_poly_Oct2017_50, "WDPAID #", "false")
 
# Process: Zonal Statistics as Table
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmax_12_tif, tmax__Value___3_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (2)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmax_11_tif, tmax__Value___2_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (3)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmax_10_tif, tmax__Value_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (4)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmax_09_tif, tmax__Value___13_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (5)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmax_08_tif, tmax__Value___4_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (6)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmax_07_tif, tmax__Value___5_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (7)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmax_06_tif, tmax__Value___6_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (8)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmax_05_tif, tmax__Value___7_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (9)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmax_04_tif, tmax__Value___8_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (10)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmax_03_tif, tmax__Value___9_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (11)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmax_02_tif, tmax__Value___10_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (12)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmax_01_tif, tmax__Value___11_, "DATA", "MEAN")

TMean Python code:

# Import arcpy module
import arcpy
 
# Check out any necessary licenses
arcpy.CheckOutExtension("spatial")
 
# Load required toolboxes
arcpy.ImportToolbox("Model Functions")
 
 
# Local variables:
WDPA_poly_Oct2017_50 = "E:\\xxx\\WDPA_October2017_Public\\WDPA_Oct2017_Public.gdb\\WDPA_poly_Oct2017_50"
wc2_0_2_5m_tmean_12_tif = "E:\\xxx\\wc2.0_2.5m_tmean\\wc2.0_2.5m_tmean_12.tif"
wc2_0_2_5m_tmean_11_tif = "E:\\xxx\\wc2.0_2.5m_tmean\\wc2.0_2.5m_tmean_11.tif"
wc2_0_2_5m_tmean_10_tif = "E:\\xxx\\wc2.0_2.5m_tmean\\wc2.0_2.5m_tmean_10.tif"
wc2_0_2_5m_tmean_09_tif = "E:\\xxx\\wc2.0_2.5m_tmean\\wc2.0_2.5m_tmean_09.tif"
wc2_0_2_5m_tmean_08_tif = "E:\\xxx\\wc2.0_2.5m_tmean\\wc2.0_2.5m_tmean_08.tif"
wc2_0_2_5m_tmean_07_tif = "E:\\xxx\\wc2.0_2.5m_tmean\\wc2.0_2.5m_tmean_07.tif"
wc2_0_2_5m_tmean_06_tif = "E:\\xxx\\wc2.0_2.5m_tmean\\wc2.0_2.5m_tmean_06.tif"
wc2_0_2_5m_tmean_05_tif = "E:\\xxx\\wc2.0_2.5m_tmean\\wc2.0_2.5m_tmean_05.tif"
wc2_0_2_5m_tmean_04_tif = "E:\\xxx\\wc2.0_2.5m_tmean\\wc2.0_2.5m_tmean_04.tif"
wc2_0_2_5m_tmean_03_tif = "E:\\xxx\\wc2.0_2.5m_tmean\\wc2.0_2.5m_tmean_03.tif"
wc2_0_2_5m_tmean_02_tif = "E:\\xxx\\wc2.0_2.5m_tmean\\wc2.0_2.5m_tmean_02.tif"
wc2_0_2_5m_tmean_01_tif = "E:\\xxx\\wc2.0_2.5m_tmean\\wc2.0_2.5m_tmean_01.tif"
I_WDPA_poly_Oct2017_50_WDPAID = "I_WDPA_poly_Oct2017_50_WDPAID"
tmean__Value___3_ = "E:\\xxx\\Default.gdb\\tmean_%Value%"
tmean__Value___2_ = "E:\\xxx\\Default.gdb\\tmean_%Value%"
tmean__Value_ = "E:\\xxx\\Default.gdb\\tmean_%Value%"
tmean__Value___13_ = "E:\\xxx\\Default.gdb\\tmean_%Value%"
tmean__Value___4_ = "E:\\xxx\\Default.gdb\\tmean_%Value%"
tmean__Value___5_ = "E:\\xxx\\Default.gdb\\tmean_%Value%"
tmean__Value___6_ = "E:\\xxx\\Default.gdb\\tmean_%Value%"
tmean__Value___7_ = "E:\\xxx\\Default.gdb\\tmean_%Value%"
tmean__Value___8_ = "E:\\xxx\\Default.gdb\\tmean_%Value%"
tmean__Value___9_ = "E:\\xxx\\Default.gdb\\tmean_%Value%"
tmean__Value___10_ = "E:\\xxx\\Default.gdb\\tmean_%Value%"
tmean__Value___11_ = "E:\\xxx\\Default.gdb\\tmean_%Value%"
 
# Process: Iterate Feature Selection
arcpy.IterateFeatureSelection_mb(WDPA_poly_Oct2017_50, "WDPAID #", "false")
 
# Process: Zonal Statistics as Table
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmean_12_tif, tmean__Value___3_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (2)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmean_11_tif, tmean__Value___2_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (3)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmean_10_tif, tmean__Value_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (4)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmean_09_tif, tmean__Value___13_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (5)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmean_08_tif, tmean__Value___4_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (6)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmean_07_tif, tmean__Value___5_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (7)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmean_06_tif, tmean__Value___6_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (8)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmean_05_tif, tmean__Value___7_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (9)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmean_04_tif, tmean__Value___8_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (10)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmean_03_tif, tmean__Value___9_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (11)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmean_02_tif, tmean__Value___10_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (12)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmean_01_tif, tmean__Value___11_, "DATA", "MEAN")
 
 


TMin Python code:

# Import arcpy module
import arcpy
 
# Check out any necessary licenses
arcpy.CheckOutExtension("spatial")
 
# Load required toolboxes
arcpy.ImportToolbox("Model Functions")
 
 
# Local variables:
WDPA_poly_Oct2017_50 = "E:\\xxx\\WDPA_October2017_Public\\WDPA_Oct2017_Public.gdb\\WDPA_poly_Oct2017_50"
wc2_0_2_5m_tmin_12_tif = "E:\\xxx\\wc2.0_2.5m_tmin\\wc2.0_2.5m_tmin_12.tif"
wc2_0_2_5m_tmin_11_tif = "E:\\xxx\\wc2.0_2.5m_tmin\\wc2.0_2.5m_tmin_11.tif"
wc2_0_2_5m_tmin_10_tif = "E:\\xxx\\wc2.0_2.5m_tmin\\wc2.0_2.5m_tmin_10.tif"
wc2_0_2_5m_tmin_09_tif = "E:\\xxx\\wc2.0_2.5m_tmin\\wc2.0_2.5m_tmin_09.tif"
wc2_0_2_5m_tmin_08_tif = "E:\\xxx\\wc2.0_2.5m_tmin\\wc2.0_2.5m_tmin_08.tif"
wc2_0_2_5m_tmin_07_tif = "E:\\xxx\\wc2.0_2.5m_tmin\\wc2.0_2.5m_tmin_07.tif"
wc2_0_2_5m_tmin_06_tif = "E:\\xxx\\wc2.0_2.5m_tmin\\wc2.0_2.5m_tmin_06.tif"
wc2_0_2_5m_tmin_05_tif = "E:\\xxx\\wc2.0_2.5m_tmin\\wc2.0_2.5m_tmin_05.tif"
wc2_0_2_5m_tmin_04_tif = "E:\\xxx\\wc2.0_2.5m_tmin\\wc2.0_2.5m_tmin_04.tif"
wc2_0_2_5m_tmin_03_tif = "E:\\xxx\\wc2.0_2.5m_tmin\\wc2.0_2.5m_tmin_03.tif"
wc2_0_2_5m_tmin_02_tif = "E:\\xxx\\wc2.0_2.5m_tmin\\wc2.0_2.5m_tmin_02.tif"
wc2_0_2_5m_tmin_01_tif = "E:\\xxx\\wc2.0_2.5m_tmin\\wc2.0_2.5m_tmin_01.tif"
I_WDPA_poly_Oct2017_50_WDPAID = "I_WDPA_poly_Oct2017_50_WDPAID"
tmin__Value___3_ = "E:\\xxx\\Default.gdb\\tmin_%Value%"
tmin__Value___2_ = "E:\\xxx\\Default.gdb\\tmin_%Value%"
tmin__Value_ = "E:\\xxx\\Default.gdb\\tmin_%Value%"
tmin__Value___13_ = "E:\\xxx\\Default.gdb\\tmin_%Value%"
tmin__Value___4_ = "E:\\xxx\\Default.gdb\\tmin_%Value%"
tmin__Value___5_ = "E:\\xxx\\Default.gdb\\tmin_%Value%"
tmin__Value___6_ = "E:\\xxx\\Default.gdb\\tmin_%Value%"
tmin__Value___7_ = "E:\\xxx\\Default.gdb\\tmin_%Value%"
tmin__Value___8_ = "E:\\xxx\\Default.gdb\\tmin_%Value%"
tmin__Value___9_ = "E:\\xxx\\Default.gdb\\tmin_%Value%"
tmin__Value___10_ = "E:\\xxx\\Default.gdb\\tmin_%Value%"
tmin__Value___11_ = "E:\\xxx\\Default.gdb\\tmin_%Value%"
 
# Process: Iterate Feature Selection
arcpy.IterateFeatureSelection_mb(WDPA_poly_Oct2017_50, "WDPAID #", "false")
 
# Process: Zonal Statistics as Table
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmin_12_tif, tmin__Value___3_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (2)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmin_11_tif, tmin__Value___2_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (3)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmin_10_tif, tmin__Value_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (4)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmin_09_tif, tmin__Value___13_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (5)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmin_08_tif, tmin__Value___4_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (6)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmin_07_tif, tmin__Value___5_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (7)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmin_06_tif, tmin__Value___6_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (8)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmin_05_tif, tmin__Value___7_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (9)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmin_04_tif, tmin__Value___8_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (10)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmin_03_tif, tmin__Value___9_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (11)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmin_02_tif, tmin__Value___10_, "DATA", "MEAN")
 
# Process: Zonal Statistics as Table (12)
arcpy.gp.ZonalStatisticsAsTable_sa(I_WDPA_poly_Oct2017_50_WDPAID, "wdpa_id_text", wc2_0_2_5m_tmin_01_tif, tmin__Value___11_, "DATA", "MEAN")