# Script used to produce MERRAclim # The steps described follow the Methods Section from the Data Descriptor: "MERRAclim, A high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling" # by Greta C. Vega, Luis R. Pertierra, Miguel Ángel Olalla-Tárraga ################################################################################ ################################################################################ #Step 1: DATA DOWNLOAD ## We downloaded the variables T2M (Temperature at 2m in Kelvin degrees) and QV2M (Specific Humidity at 2m in kg of water/kg of air) from the dataset MAT1NXSLV from the Global Modeling and Assimilation Office available at: https://disc.sci.gsfc.nasa.gov/mdisc/ ## Download files have the following name: ## MERRA300.prod.assim.tavg1_2d_slv_Nx.20100101.hdf.nc ## This file correspond to the 1st of January of 2010 and contains 24 values, one for each hour of the day ################################################################################ ################################################################################ #Step 2: OPENING AND EXTRACTION OF THE MAXIMUM AND MINIMUM VALUES (in R) #Set working directory where the download files are stored setwd("C:\\MERRA\\download") #Load packages library(RNetCDF) library(raster) library(rgdal) years <- c("1981", "1982", "1983","1984","1985", "1986", "1987", "1988","1989","1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010") months <- c("01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12") hours <- seq(1:24) print (paste(Sys.time(),"start")) #one year for (y in 1:length(years)) { for (m in 1:length(months)) { s<-stack() #one month pat<-paste("assim.tavg1_2d_slv_Nx.",years[y],months[m], sep="") #print(pat) #debug listfiles <- list.files(pattern = pat) #listfiles is the list of the days in the month m for( f in 1:length(listfiles)) { #Open data file fname<-listfiles[f] # f is one file that represents one day fid<-open.nc(fname) # #Read data dat<-read.nc(fid) #Close file close.nc(fid) #this loop extracts all the hours of the day for (h in 1: length(hours)) { mat<-dat$T2M[,,h] #here we chose an hour of the day r<-flip(raster(t(mat)), direction = 2) s<-addLayer(s,r) } #at the end of this loop s is a raster stack of the 24 hours of day f } #at the end of this loop s is a raster stack of the 24 hours of all the days of month m #the min and max can be calculated. minM <-min(s) maxM <-max(s) extent(minM)<-c(-180, 180, -90, 90) extent(maxM)<-c(-180, 180, -90, 90) projection(minM)<-"+proj=longlat +datum=WGS84" projection(maxM)<-"+proj=longlat +datum=WGS84" minfname<-paste("min",years[y],months[m], ".tif", sep="") maxfname<-paste("max",years[y],months[m], ".tif", sep="") writeRaster(minM, minfname,format="GTiff") writeRaster(maxM, maxfname,format="GTiff") # For each month of each year two rasters are written one with the minimum and another one with the maximum removeTmpFiles(h=0) } } print (paste(Sys.time(),"Step 2 done")) ################################################################################ ################################################################################ #Step 3: COMPUTING THE BIOCLIMATIC VARIABLES FOR EACH VERSION OF HUMIDITY (in R) #Load packages library(dismo) library(raster) ## (a) The monthly mean of the humidity (QV2M) has to be computed. It is computed as (max+min)/2, i.e. the mean of max and min. #Set working directory where the humidity max and mean of each month computed in Step 2 are stored setwd("C:\\MERRA\\QV2M_max_min") years <- c("1981", "1982", "1983","1984","1985", "1986", "1987", "1988","1989","1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010") months <- c("01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12") for (y in 1:length(years)) { for (m in 1:length(months)) { s<-stack() pat<-paste(years[y],months[m], sep="") listfiles <- list.files(pattern = pat) s<-addLayer(s,raster(listfiles[1]),raster(listfiles[2])) res<-mean(s) QV2M_mean<-paste("QV2M_mean",years[y],months[m], ".tif", sep="") writeRaster(res, QV2M_mean,format="GTiff") } } ## (b) Once the three versions of Humidity are computed the bioclimatic variables are computed for the three versions #Set working directory to where the min and max of temperature and the min, mean and max of humidity are stored setwd("C:\\MERRA\\QV2M_T2M_max_mean_min") ###This is using the Humidity (QV2M) max for (y in 1:length(years)) { listyear<-list.files(pattern = years[y]) #max Temperature janmax<-raster(listyear[37]) febmax<-raster(listyear[38]) marmax<-raster(listyear[39]) aprmax<-raster(listyear[40]) maymax<-raster(listyear[41]) junmax<-raster(listyear[42]) julmax<-raster(listyear[43]) augmax<-raster(listyear[44]) sepmax<-raster(listyear[45]) octmax<-raster(listyear[46]) novmax<-raster(listyear[47]) decmax<-raster(listyear[48]) maxTemp<-stack(janmax,febmax,marmax,aprmax,maymax,junmax,julmax,augmax,sepmax,octmax,novmax,decmax) #min Temperature janmin<-raster(listyear[49]) febmin<-raster(listyear[50]) marmin<-raster(listyear[51]) aprmin<-raster(listyear[52]) maymin<-raster(listyear[53]) junmin<-raster(listyear[54]) julmin<-raster(listyear[55]) augmin<-raster(listyear[56]) sepmin<-raster(listyear[57]) octmin<-raster(listyear[58]) novmin<-raster(listyear[59]) decmin<-raster(listyear[60]) minTemp<-stack(janmin,febmin,marmin,aprmin,maymin,junmin,julmin,augmin,sepmin,octmin,novmin,decmin) #Humidity janprec<-raster(listyear[1]) febprec<-raster(listyear[2]) marprec<-raster(listyear[3]) aprprec<-raster(listyear[4]) mayprec<-raster(listyear[5]) junprec<-raster(listyear[6]) julprec<-raster(listyear[7]) augprec<-raster(listyear[8]) sepprec<-raster(listyear[9]) octprec<-raster(listyear[10]) novprec<-raster(listyear[11]) decprec<-raster(listyear[12]) prec<-stack(janprec,febprec,marprec,aprprec,mayprec,junprec,julprec,augprec,sepprec,octprec,novprec,decprec) bios<-biovars(prec,minTemp,maxTemp) outname<-paste("C:\\MERRA\\BIOvariables\\dismoMERRA_T2M_QV2M\\withMaxQV2M\\",years[y],"maxQV2M_bios.tif",sep="") writeRaster(bios,filename=outname,bylayer=TRUE,suffix="names") } ###This is using the QV2M mean for (y in 1:length(years)) { listyear<-list.files(pattern = years[y]) #max Temperature janmax<-raster(listyear[37]) febmax<-raster(listyear[38]) marmax<-raster(listyear[39]) aprmax<-raster(listyear[40]) maymax<-raster(listyear[41]) junmax<-raster(listyear[42]) julmax<-raster(listyear[43]) augmax<-raster(listyear[44]) sepmax<-raster(listyear[45]) octmax<-raster(listyear[46]) novmax<-raster(listyear[47]) decmax<-raster(listyear[48]) maxTemp<-stack(janmax,febmax,marmax,aprmax,maymax,junmax,julmax,augmax,sepmax,octmax,novmax,decmax) #min temperature janmin<-raster(listyear[49]) febmin<-raster(listyear[50]) marmin<-raster(listyear[51]) aprmin<-raster(listyear[52]) maymin<-raster(listyear[53]) junmin<-raster(listyear[54]) julmin<-raster(listyear[55]) augmin<-raster(listyear[56]) sepmin<-raster(listyear[57]) octmin<-raster(listyear[58]) novmin<-raster(listyear[59]) decmin<-raster(listyear[60]) minTemp<-stack(janmin,febmin,marmin,aprmin,maymin,junmin,julmin,augmin,sepmin,octmin,novmin,decmin) #Humidity janprec<-raster(listyear[13]) febprec<-raster(listyear[14]) marprec<-raster(listyear[15]) aprprec<-raster(listyear[16]) mayprec<-raster(listyear[17]) junprec<-raster(listyear[18]) julprec<-raster(listyear[19]) augprec<-raster(listyear[20]) sepprec<-raster(listyear[21]) octprec<-raster(listyear[22]) novprec<-raster(listyear[23]) decprec<-raster(listyear[24]) prec<-stack(janprec,febprec,marprec,aprprec,mayprec,junprec,julprec,augprec,sepprec,octprec,novprec,decprec) bios<-biovars(prec,minTemp,maxTemp) outname<-paste("C:\\MERRA\\BIOvariables\\dismoMERRA_T2M_QV2M\\withMeanQV2M\\",years[y],"meanQV2M_bios.tif",sep="") writeRaster(bios,filename=outname,bylayer=TRUE,suffix="names") } ###This is using the QV2M min for (y in 1:length(years)) { listyear<-list.files(pattern = years[y]) #max Temperature janmax<-raster(listyear[37]) febmax<-raster(listyear[38]) marmax<-raster(listyear[39]) aprmax<-raster(listyear[40]) maymax<-raster(listyear[41]) junmax<-raster(listyear[42]) julmax<-raster(listyear[43]) augmax<-raster(listyear[44]) sepmax<-raster(listyear[45]) octmax<-raster(listyear[46]) novmax<-raster(listyear[47]) decmax<-raster(listyear[48]) maxTemp<-stack(janmax,febmax,marmax,aprmax,maymax,junmax,julmax,augmax,sepmax,octmax,novmax,decmax) #min temperature janmin<-raster(listyear[49]) febmin<-raster(listyear[50]) marmin<-raster(listyear[51]) aprmin<-raster(listyear[52]) maymin<-raster(listyear[53]) junmin<-raster(listyear[54]) julmin<-raster(listyear[55]) augmin<-raster(listyear[56]) sepmin<-raster(listyear[57]) octmin<-raster(listyear[58]) novmin<-raster(listyear[59]) decmin<-raster(listyear[60]) minTemp<-stack(janmin,febmin,marmin,aprmin,maymin,junmin,julmin,augmin,sepmin,octmin,novmin,decmin) #Humidity janprec<-raster(listyear[25]) febprec<-raster(listyear[26]) marprec<-raster(listyear[27]) aprprec<-raster(listyear[28]) mayprec<-raster(listyear[29]) junprec<-raster(listyear[30]) julprec<-raster(listyear[31]) augprec<-raster(listyear[32]) sepprec<-raster(listyear[33]) octprec<-raster(listyear[34]) novprec<-raster(listyear[35]) decprec<-raster(listyear[36]) prec<-stack(janprec,febprec,marprec,aprprec,mayprec,junprec,julprec,augprec,sepprec,octprec,novprec,decprec) bios<-biovars(prec,minTemp,maxTemp) outname<-paste("C:\\MERRA\\BIOvariables\\dismoMERRA_T2M_QV2M\\withMinQV2M\\",years[y],"minQV2M_bios.tif",sep="") writeRaster(bios,filename=outname,bylayer=TRUE,suffix="names") } ################################################################################ ################################################################################ #STEP 4: MERGING BIOS BY DECADES AND VERSION ##For each version (each in one folder) the mean of the bios is calculated by decade #Load packages library(rgdal) library(raster) #set the working directory in the max folder setwd("C:\\MERRA\\BIOvariables\\dismoMERRA_T2M_QV2M\\withMaxQV2M") bioclim<-c("bio1.tif","bio2.tif","bio3.tif","bio4.tif","bio5.tif","bio6.tif","bio7.tif","bio8.tif","bio9.tif","bio10.tif","bio11.tif","bio12.tif","bio13.tif","bio14.tif","bio15.tif","bio16.tif","bio17.tif","bio18.tif","bio19.tif") for (bio in 1:length(bioclim)) #loop is run per bioclimatic variable { listfiles <- list.files(pattern = bioclim[bio]) s<-stack(listfiles [1:10]) res<-mean(s) outname<-paste("max_80s_bio",bio, ".tif", sep="") writeRaster(res, outname,format="GTiff") s<-stack(listfiles [11:20]) res<-mean(s) outname<-paste("max_90s_bio",bio, ".tif", sep="") writeRaster(res, outname,format="GTiff") s<-stack(listfiles [21:30]) res<-mean(s) outname<-paste("max_00s_bio",bio, ".tif", sep="") writeRaster(res, outname,format="GTiff") } #set the working directory in the mean folder setwd("C:\\MERRA\\BIOvariables\\dismoMERRA_T2M_QV2M\\withMeanQV2M") bioclim<-c("bio1.tif","bio2.tif","bio3.tif","bio4.tif","bio5.tif","bio6.tif","bio7.tif","bio8.tif","bio9.tif","bio10.tif","bio11.tif","bio12.tif","bio13.tif","bio14.tif","bio15.tif","bio16.tif","bio17.tif","bio18.tif","bio19.tif") for (bio in 1:length(bioclim)) #loop is run per bioclimatic variable { listfiles <- list.files(pattern = bioclim[bio]) s<-stack(listfiles[1:10]) res<-mean(s) outname<-paste("mean_80s_bio",bio, ".tif", sep="") writeRaster(res, outname,format="GTiff") s<-stack(listfiles[11:20]) res<-mean(s) outname<-paste("mean_90s_bio",bio, ".tif", sep="") writeRaster(res, outname,format="GTiff") s<-stack(listfiles[21:30]) res<-mean(s) outname<-paste("mean_00s_bio",bio, ".tif", sep="") writeRaster(res, outname,format="GTiff") } #set the working directory in the mean folder setwd("C:\\MERRA\\BIOvariables\\dismoMERRA_T2M_QV2M\\withMinQV2M") bioclim<-c("bio1.tif","bio2.tif","bio3.tif","bio4.tif","bio5.tif","bio6.tif","bio7.tif","bio8.tif","bio9.tif","bio10.tif","bio11.tif","bio12.tif","bio13.tif","bio14.tif","bio15.tif","bio16.tif","bio17.tif","bio18.tif","bio19.tif") for (bio in 1:length(bioclim)) #loop is run per bioclimatic variable { listfiles <- list.files(pattern = bioclim[bio]) s<-stack(listfiles[1:10]) res<-mean(s) outname<-paste("min_80s_bio",bio, ".tif", sep="") writeRaster(res, outname,format="GTiff") s<-stack(listfiles[11:20]) res<-mean(s) outname<-paste("min_90s_bio",bio, ".tif", sep="") writeRaster(res, outname,format="GTiff") s<-stack(listfiles[21:30]) res<-mean(s) outname<-paste("min_00s_bio",bio, ".tif", sep="") writeRaster(res, outname,format="GTiff") } ## Convert the bioclim raster into points # Set the working directory for the max folder 80s setwd("C:\\MERRA\\BIOvariables\\dismoMERRA_T2M_QV2M\\withMaxQV2M\\80s") listfiles <- list.files(pattern="max_80s_bio") listfiles #hay que comprobar que el nombre de las bio aparece en orden for (l in 1:length(listfiles)) { r<-raster(listfiles[l]) #nameRaster<-names(r)[1] #outLay<-paste("points_",nameRaster, seq="") p<-rasterToPoints(r,spatial=T) writeOGR(p, dsn = '.', layer = names(r), driver = "ESRI Shapefile") } #for the max folder 90s setwd("C:\\MERRA\\BIOvariables\\dismoMERRA_T2M_QV2M\\withMaxQV2M\\90s") listfiles <- list.files(pattern="max_90s_bio") listfiles #hay que comprobar que el nombre de las bio aparece en orden for (l in 1:length(listfiles)) { r<-raster(listfiles[l]) #nameRaster<-names(r)[1] #outLay<-paste("points_",nameRaster, seq="") p<-rasterToPoints(r,spatial=T) writeOGR(p, dsn = '.', layer = names(r), driver = "ESRI Shapefile") } #for the max folder 00s setwd("C:\\MERRA\\BIOvariables\\dismoMERRA_T2M_QV2M\\withMaxQV2M\\00s") listfiles <- list.files(pattern="max_00s_bio") listfiles #hay que comprobar que el nombre de las bio aparece en orden for (l in 1:length(listfiles)) { r<-raster(listfiles[l]) #nameRaster<-names(r)[1] #outLay<-paste("points_",nameRaster, seq="") p<-rasterToPoints(r,spatial=T) writeOGR(p, dsn = '.', layer = names(r), driver = "ESRI Shapefile") } #for the mean folder 80s setwd("C:\\MERRA\\BIOvariables\\dismoMERRA_T2M_QV2M\\withMeanQV2M\\80s") listfiles <- list.files(pattern="mean_80s_bio") listfiles #It is important to check that the Bio names are in order for (l in 1:length(listfiles)) { r<-raster(listfiles[l]) #nameRaster<-names(r)[1] #outLay<-paste("points_",nameRaster, seq="") p<-rasterToPoints(r,spatial=T) writeOGR(p, dsn = '.', layer = names(r), driver = "ESRI Shapefile") } #for the mean folder 90s setwd("C:\\MERRA\\BIOvariables\\dismoMERRA_T2M_QV2M\\withMeanQV2M\\90s") listfiles <- list.files(pattern="mean_90s_bio") listfiles #It is important to check that the Bio names are in order for (l in 1:length(listfiles)) { r<-raster(listfiles[l]) #nameRaster<-names(r)[1] #outLay<-paste("points_",nameRaster, seq="") p<-rasterToPoints(r,spatial=T) writeOGR(p, dsn = '.', layer = names(r), driver = "ESRI Shapefile") } #for the mean folder 00s setwd("C:\\MERRA\\BIOvariables\\dismoMERRA_T2M_QV2M\\withMeanQV2M\\00s") listfiles <- list.files(pattern="mean_00s_bio") listfiles #It is important to check that the Bio names are in order for (l in 1:length(listfiles)) { r<-raster(listfiles[l]) #nameRaster<-names(r)[1] #outLay<-paste("points_",nameRaster, seq="") p<-rasterToPoints(r,spatial=T) writeOGR(p, dsn = '.', layer = names(r), driver = "ESRI Shapefile") } #for the min folder 80s setwd("C:\\MERRA\\BIOvariables\\dismoMERRA_T2M_QV2M\\withMinQV2M\\80s") listfiles <- list.files(pattern="min_80s_bio") listfiles #It is important to check that the Bio names are in order for (l in 1:length(listfiles)) { r<-raster(listfiles[l]) #nameRaster<-names(r)[1] #outLay<-paste("points_",nameRaster, seq="") p<-rasterToPoints(r,spatial=T) writeOGR(p, dsn = '.', layer = names(r), driver = "ESRI Shapefile") } #for the min folder 90s setwd("C:\\MERRA\\BIOvariables\\dismoMERRA_T2M_QV2M\\withMinQV2M\\90s") listfiles <- list.files(pattern="min_90s_bio") listfiles #It is important to check that the Bio names are in order for (l in 1:length(listfiles)) { r<-raster(listfiles[l]) #nameRaster<-names(r)[1] #outLay<-paste("points_",nameRaster, seq="") p<-rasterToPoints(r,spatial=T) writeOGR(p, dsn = '.', layer = names(r), driver = "ESRI Shapefile") } #for the min folder 00s setwd("C:\\MERRA\\BIOvariables\\dismoMERRA_T2M_QV2M\\withMinQV2M\\00s") listfiles <- list.files(pattern="min_00s_bio") listfiles #It is important to check that the Bio names are in order for (l in 1:length(listfiles)) { r<-raster(listfiles[l]) #nameRaster<-names(r)[1] #outLay<-paste("points_",nameRaster, seq="") p<-rasterToPoints(r,spatial=T) writeOGR(p, dsn = '.', layer = names(r), driver = "ESRI Shapefile") } ################################################################################ ################################################################################ #Step 5: INTERPOLATION (in Python with arcpy; code only shown for Mean version) # Import system modules import arcpy from arcpy import env from arcpy.sa import * ## Set Workspace to Mean version 80s decade env.workspace = "C:/MERRA/BIOvariables/dismoMERRA_T2M_QV2M/withMeanQV2M/80s" pointFiles = arcpy.ListFiles("*.shp") zFields = ["min_bio1","min_bio10", "min_bio11", "min_bio12", "min_bio13", "min_bio14", "min_bio15", "min_bio16", "min_bio17", "min_bio18", "min_bio19", "min_bio2", "min_bio3", "min_bio4", "min_bio5", "min_bio6", "min_bio7", "min_bio8", "min_bio9"] resolution = "10m_" zipped = zip (pointFiles,zFields) for file in pointFiles: field_list = arcpy.ListFields(file) nfield = field_list[2].name # Set local variables inPntFeat = file zField = nfield #zf cellSize = 0.16662 splineType = "REGULARIZED" weight = 0.1 # Check out the ArcGIS Spatial Analyst extension license arcpy.CheckOutExtension("Spatial") # Execute Spline outSpline = Spline(inPntFeat, zField, cellSize, splineType, weight) # Save the output outname = "C:/MERRA/BIOvariables/dismoMERRA_T2M_QV2M/withMeanQV2M/80s/" +resolution + file + ".tif" outSpline.save(outname) print "completed 10m" resolution = "5m_" for file in pointFiles: field_list = arcpy.ListFields(file) nfield = field_list[2].name # Set local variables inPntFeat = file zField = nfield #zf cellSize = 0.08331 splineType = "REGULARIZED" weight = 0.1 # Check out the ArcGIS Spatial Analyst extension license arcpy.CheckOutExtension("Spatial") # Execute Spline outSpline = Spline(inPntFeat, zField, cellSize, splineType, weight) # Save the output outname = "C:/MERRA/BIOvariables/dismoMERRA_T2M_QV2M/withMeanQV2M/80s/" +resolution + file + ".tif" outSpline.save(outname) print "completed 5m MEAN 80S" resolution = "2_5m_" for file in pointFiles: field_list = arcpy.ListFields(file) nfield = field_list[2].name # Set local variables inPntFeat = file zField = nfield #zf cellSize = 0.041655 splineType = "REGULARIZED" weight = 0.1 # Check out the ArcGIS Spatial Analyst extension license arcpy.CheckOutExtension("Spatial") # Execute Spline outSpline = Spline(inPntFeat, zField, cellSize, splineType, weight) # Save the output outname = "C:/MERRA/BIOvariables/dismoMERRA_T2M_QV2M/withMeanQV2M/80s/" +resolution + file + ".tif" outSpline.save(outname) print "completed 2.5m MEAN 80S" ## Set Workspace to Mean version 90s decade env.workspace = "C:/MERRA/BIOvariables/dismoMERRA_T2M_QV2M/withMeanQV2M/90s" pointFiles = arcpy.ListFiles("*.shp") zFields = ["min_bio1","min_bio10", "min_bio11", "min_bio12", "min_bio13", "min_bio14", "min_bio15", "min_bio16", "min_bio17", "min_bio18", "min_bio19", "min_bio2", "min_bio3", "min_bio4", "min_bio5", "min_bio6", "min_bio7", "min_bio8", "min_bio9"] resolution = "10m_" for file in pointFiles: field_list = arcpy.ListFields(file) nfield = field_list[2].name # Set local variables inPntFeat = file zField = nfield #zf cellSize = 0.16662 splineType = "REGULARIZED" weight = 0.1 # Check out the ArcGIS Spatial Analyst extension license arcpy.CheckOutExtension("Spatial") # Execute Spline outSpline = Spline(inPntFeat, zField, cellSize, splineType, weight) # Save the output outname = "C:/MERRA/BIOvariables/dismoMERRA_T2M_QV2M/withMeanQV2M/90s/" +resolution + file + ".tif" outSpline.save(outname) print "completed 10m" resolution = "5m_" for file in pointFiles: field_list = arcpy.ListFields(file) nfield = field_list[2].name # Set local variables inPntFeat = file zField = nfield #zf cellSize = 0.08331 splineType = "REGULARIZED" weight = 0.1 # Check out the ArcGIS Spatial Analyst extension license arcpy.CheckOutExtension("Spatial") # Execute Spline outSpline = Spline(inPntFeat, zField, cellSize, splineType, weight) # Save the output outname = "C:/MERRA/BIOvariables/dismoMERRA_T2M_QV2M/withMeanQV2M/90s/" +resolution + file + ".tif" outSpline.save(outname) print "completed 5m MEAN 90S" resolution = "2_5m_" for file in pointFiles: field_list = arcpy.ListFields(file) nfield = field_list[2].name # Set local variables inPntFeat = file zField = nfield #zf cellSize = 0.041655 splineType = "REGULARIZED" weight = 0.1 # Check out the ArcGIS Spatial Analyst extension license arcpy.CheckOutExtension("Spatial") # Execute Spline outSpline = Spline(inPntFeat, zField, cellSize, splineType, weight) # Save the output outname = "C:/MERRA/BIOvariables/dismoMERRA_T2M_QV2M/withMeanQV2M/90s/" +resolution + file + ".tif" outSpline.save(outname) print "completed 2.5m MEAN 90S" ## Set Workspace to Mean version 00s decade env.workspace = "C:/MERRA/BIOvariables/dismoMERRA_T2M_QV2M/withMeanQV2M/00s" pointFiles = arcpy.ListFiles("*.shp") zFields = ["min_bio1","min_bio10", "min_bio11", "min_bio12", "min_bio13", "min_bio14", "min_bio15", "min_bio16", "min_bio17", "min_bio18", "min_bio19", "min_bio2", "min_bio3", "min_bio4", "min_bio5", "min_bio6", "min_bio7", "min_bio8", "min_bio9"] resolution = "10m_" #zipped = zip (pointFiles,zFields) for file in pointFiles: field_list = arcpy.ListFields(file) nfield = field_list[2].name # Set local variables inPntFeat = file zField = nfield #zf cellSize = 0.16662 splineType = "REGULARIZED" weight = 0.1 # Check out the ArcGIS Spatial Analyst extension license arcpy.CheckOutExtension("Spatial") # Execute Spline outSpline = Spline(inPntFeat, zField, cellSize, splineType, weight) # Save the output outname = "C:/MERRA/BIOvariables/dismoMERRA_T2M_QV2M/withMeanQV2M/00s/" +resolution + file + ".tif" outSpline.save(outname) print "completed 10m" resolution = "5m_" for file in pointFiles: field_list = arcpy.ListFields(file) nfield = field_list[2].name # Set local variables inPntFeat = file zField = nfield #zf cellSize = 0.08331 splineType = "REGULARIZED" weight = 0.1 # Check out the ArcGIS Spatial Analyst extension license arcpy.CheckOutExtension("Spatial") # Execute Spline outSpline = Spline(inPntFeat, zField, cellSize, splineType, weight) # Save the output outname = "C:/MERRA/BIOvariables/dismoMERRA_T2M_QV2M/withMeanQV2M/00s/" +resolution + file + ".tif" outSpline.save(outname) print "completed 5m MEAN 00S" resolution = "2_5m_" #zipped = zip (pointFiles,zFields) for file in pointFiles: field_list = arcpy.ListFields(file) nfield = field_list[2].name # Set local variables inPntFeat = file zField = nfield #zf cellSize = 0.041655 splineType = "REGULARIZED" weight = 0.1 # Check out the ArcGIS Spatial Analyst extension license arcpy.CheckOutExtension("Spatial") # Execute Spline outSpline = Spline(inPntFeat, zField, cellSize, splineType, weight) # Save the output outname = "C:/MERRA/BIOvariables/dismoMERRA_T2M_QV2M/withMeanQV2M/00s/" +resolution + file + ".tif" outSpline.save(outname) print "completed 2.5m MEAN 00S" ################################################################################ ################################################################################ #STEP 6: REDUCING DEPTH OF PIXEL OF RASTERS (in R) library(raster) setwd("C:\\MERRA\\BIOvariables\\dismoMERRA_T2M_QV2M\\") folders<-c( "10m_max_00s", "10m_max_80s", "10m_max_90s", "10m_mean_00s", "10m_mean_80s", "10m_mean_90s", "10m_min_00s", "10m_min_80s", "10m_min_90s", "5m_max_00s", "5m_max_80s", "5m_max_90s", "5m_mean_00s", "5m_mean_80s", "5m_mean_90s", "5m_min_00s", "5m_min_80s", "5m_min_90s", "2_5m_max_00s", "2_5m_max_80s", "2_5m_max_90s", "2_5m_mean_00s", "2_5m_mean_80s", "2_5m_mean_90s", "2_5m_min_00s", "2_5m_min_80s", "2_5m_min_90s", "30s_max_00s", "30s_max_80s", "30s_max_90s", "30s_mean_00s", "30s_mean_80s", "30s_mean_90s", "30s_min_00s", "30s_min_80s", "30s_min_90s") bios<-c("bio1","bio2","bio3","bio4", "bio5", "bio6", "bio7", "bio8", "bio9", "bio10", "bio11", "bio12", "bio13", "bio14", "bio15","bio16", "bio17", "bio18", "bio19") rbios<-rep(NA,19) class(rbios)<-"character" print (paste(Sys.time()," start")) for (folder in 1: length(folders)) { rbios<-paste(folders[folder],"_",bios,".tif", sep="") for (rastername in 1:11) # para las variables de temperatura { rasterfile<-rbios[rastername] r<-raster(paste("E:\\MERRAClim\\DataDryads\\", folders[folder],"\\",rasterfile, sep="" )) writeRaster(round(r,0), filename=rasterfile, datatype='INT2S',format="GTiff") } for (rastername in 12:19) # para las variables de humedad { rasterfile<-rbios[rastername] r<-raster(paste("E:\\MERRAClim\\DataDryads\\", folders[folder],"\\",rasterfile, sep="" )) outR<-round(r*100000,0) writeRaster(outR,filename=rasterfile, datatype='INT4U',format="GTiff") } } paste(Sys.time()," end") ################################################################################ ################################################################################ #STEP 7: ADAPTATION FROM PRECIPITATION TO HUMIDITY (in R, code only shown for Mean version at 2.5 minutes resolution from the 80s decade) ##BIO12 has to be divided by 12 ##BIO18 has to be divided by 3 ##BIO19 has to be divided by 3 ##BIO16 has to be divided by 3 ##BIO17 has to be divided by 3 #load packages library(raster) # Set the working directory where all the ras setwd("C:\\MERRA\\BIOvariables\\dismoMERRA_T2M_QV2M\\2_5m_mean_80s\\") bio12<-raster("2_5m_mean_80s_bio12.tif") newbio12<-bio12/12 writeRaster(newbio12, "2_5m_mean_80s_bio12_tbg.tif",format="GTiff") bio16<-raster("2_5m_mean_80s_bio16.tif") newbio16<-bio16/3 writeRaster(newbio16, "2_5m_mean_80s_bio16_tbg.tif",format="GTiff") bio17<-raster("2_5m_mean_80s_bio17.tif") newbio17<-bio17/3 writeRaster(newbio17, "2_5m_mean_80s_bio17_tbg.tif",format="GTiff") bio18<-raster("2_5m_mean_80s_bio18.tif") newbio18<-bio18/3 writeRaster(newbio18, "2_5m_mean_80s_bio18_tbg.tif",format="GTiff") bio19<-raster("2_5m_mean_80s_bio19.tif") newbio19<-bio19/3 writeRaster(newbio19, "2_5m_mean_80s_bio19_tbg.tif",format="GTiff")