File:Maya area drought index pdsi yearv 1418 1.svg

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Captions

Captions

Maya-area drought index PDSI on year 1418

Summary[edit]

Description
English: Maya-area drought index PDSI on year 1418
Date
Source Own work
Author Merikanto

This plot is based on Mexican drought atlas

David W. Stahle, Edward R. Cook, Dorian J. Burnette, Jose Villanueva, Julian Cerano, Jordan N. Burns, Daniel Griffin, Benjamin I. Cook, Rodolfo Acuna, Max C.A. Torbenson, Paul Sjezner, Ian M. Howard. 2016. The Mexican Drought Atlas: Tree-ring reconstructions of the soil moisture balance during the late pre-Hispanic, colonial, and modern eras. Quaternary Science Reviews, 149, 34-60. doi: 10.1016/j.quascirev.2016.06.018

https://www.ncei.noaa.gov/access/paleo-search/study/20353

Homegape of Mexican drought atlas, 2014

http://drought.memphis.edu/MXDA/

R script to process pre-processed data

Separators to semicolon ;

    1. mexico drought hisrtorical pdsi data

library(raster) library(ncdf4) library(terra) library(pals) ##palettes

library(RColorBrewer)

    1. year to map plot
  1. year1=1418
  2. year1=1946
    1. year1=2004

year1=1418

    1. coordinates where from take data
    1. tikal
  2. pox1=-89.622778
  3. poy1=17.221944
    1. uxmal

pox1=-89.771389 poy1=20.359444


gp0<-read.csv("./northdata1/mex/mxda-grid-points_1.csv", 

               header = F, sep = ";")
  1. print(head(gp0))

lons1<-as.vector(gp0$V2) lats1<-as.vector(gp0$V3)

  1. print(lons1)
  2. print(lats1)


  1. quit(-1)


data0 <- read.csv("./northdata1/mex/mxda-recon-jja-scpdsi-1400-2012_2.csv", 

               header = F, sep = ";")
  1. print(head(data0))

d0<-as.matrix(data0)

  1. print(d0)
  1. print (dim(d0))
  1. stop(-1)
  1. Periods of frequent drought centered at the years 1483, 1533, 1571, 1601, 1650, 1691, 1730, 1783, 1818, and 1860 have been identified.

n1=year1-1400

d10<-as.numeric(d0[n1,])

  1. print(d10)

d1<-d10[-1]

  1. print(d1)

len1=length(d1)

  1. print(len1)
  1. print (length(lons1))

df1<-data.frame(cbind(lons1, lats1, d1))

names(df1)<-c("x","y","pdsi")

  1. print (head(df1))

r1 <- rasterFromXYZ(df1)


sr1<-rast(df1)

  1. plot(sr1)

col1<-colFromX(sr1, pox1) row1<-rowFromY(sr1, poy1)

x1<-xFromCol(sr1, col1)

y1<-yFromRow(sr1, row1)

  1. print(x1)
  2. print(y1)

idxe1 <- match(df1$x, x1) idxe2 <- match(df1$y, y1)

  1. print(idx1)

idxe3<-idxe1*idxe2

  1. print(idxe3)

idx1<-which(idxe3==1)

years1<-data0[,1] pdsis1<-data0[,1+idx1]

pd1<-data.frame(cbind(years1, pdsis1)) names(pd1)<-c("year", "pdsi")

print (head(pd1, 32))

print("idx year pdsi") print(idx1) print(years1[idx1]) print(pdsis1[idx1])

print(pd1$year[1+idx1])

  1. lowestyears1<-pd1[pd1$pdsi<-1,]
  2. print(lowestyears1)
  1. lowest1<-pd1[as.numeric(pd1$pdsi) <-3.0]
  1. print(lowest1)
  1. quit(-1)

minidx1<-which.min(pdsis1)

  1. print(minidx1)

minyear1<-minidx1+1400

print(minyear1)

  1. print(years1)
  1. plot(r1)

pal <- colorRampPalette(c("brown","yellow","green","blue"))

plot(years1, pdsis1, type="l")

  1. lines

plot(r1, 

col=pal(256),

main="Maya area annual P/PET percent", 

       xlab="longitude",
       ylab="latitude")
       #sub="Aridity P/PET %")

contour(r1, add=T, alpha=50) 

points( c(-89.622778), c(17.221944), col = "red",pch=2, cex=2, lwd=1, add=T)
points( c(-89.771389), c(20.359444), col = "red",pch=2, cex=2, lwd=1, add=T)

text(c(-89.622778 ),c(17.221944 ), labels=c("Tikal"), cex= 1, pos=3) text(c(-89.771389), c(20.359444), labels=c("Uxmal"), cex= 1, pos=3)

dev.off()

  1. system("pdf2svg Rplots.pdf maya_area_pdsi.svg")

crs(r1)<-"lonlat"

writeRaster(r1 , filename="mexico_pdsi.nc", bandorder='BSQ',format="NetCDF", overwrite=TRUE)


GMT6 bash unix plotting script

    1. mexico drought hisrtorical pdsi data

library(raster) library(ncdf4) library(terra) library(pals) ##palettes

library(RColorBrewer)

    1. year to map plot
  1. year1=1418
  2. year1=1946
    1. year1=2004

year1=1418

    1. coordinates where from take data
    1. tikal
  2. pox1=-89.622778
  3. poy1=17.221944
    1. uxmal

pox1=-89.771389 poy1=20.359444


gp0<-read.csv("./northdata1/mex/mxda-grid-points_1.csv", 

               header = F, sep = ";")
  1. print(head(gp0))

lons1<-as.vector(gp0$V2) lats1<-as.vector(gp0$V3)

  1. print(lons1)
  2. print(lats1)


  1. quit(-1)


data0 <- read.csv("./northdata1/mex/mxda-recon-jja-scpdsi-1400-2012_2.csv", 

               header = F, sep = ";")
  1. print(head(data0))

d0<-as.matrix(data0)

  1. print(d0)
  1. print (dim(d0))
  1. stop(-1)
  1. Periods of frequent drought centered at the years 1483, 1533, 1571, 1601, 1650, 1691, 1730, 1783, 1818, and 1860 have been identified.

n1=year1-1400

d10<-as.numeric(d0[n1,])

  1. print(d10)

d1<-d10[-1]

  1. print(d1)

len1=length(d1)

  1. print(len1)
  1. print (length(lons1))

df1<-data.frame(cbind(lons1, lats1, d1))

names(df1)<-c("x","y","pdsi")

  1. print (head(df1))

r1 <- rasterFromXYZ(df1)


sr1<-rast(df1)

  1. plot(sr1)

col1<-colFromX(sr1, pox1) row1<-rowFromY(sr1, poy1)

x1<-xFromCol(sr1, col1)

y1<-yFromRow(sr1, row1)

  1. print(x1)
  2. print(y1)

idxe1 <- match(df1$x, x1) idxe2 <- match(df1$y, y1)

  1. print(idx1)

idxe3<-idxe1*idxe2

  1. print(idxe3)

idx1<-which(idxe3==1)

years1<-data0[,1] pdsis1<-data0[,1+idx1]

pd1<-data.frame(cbind(years1, pdsis1)) names(pd1)<-c("year", "pdsi")

print (head(pd1, 32))

print("idx year pdsi") print(idx1) print(years1[idx1]) print(pdsis1[idx1])

print(pd1$year[1+idx1])

  1. lowestyears1<-pd1[pd1$pdsi<-1,]
  2. print(lowestyears1)
  1. lowest1<-pd1[as.numeric(pd1$pdsi) <-3.0]
  1. print(lowest1)
  1. quit(-1)

minidx1<-which.min(pdsis1)

  1. print(minidx1)

minyear1<-minidx1+1400

print(minyear1)

  1. print(years1)
  1. plot(r1)

pal <- colorRampPalette(c("brown","yellow","green","blue"))

plot(years1, pdsis1, type="l")

  1. lines

plot(r1, 

col=pal(256),

main="Maya area annual P/PET percent", 

       xlab="longitude",
       ylab="latitude")
       #sub="Aridity P/PET %")

contour(r1, add=T, alpha=50) 

points( c(-89.622778), c(17.221944), col = "red",pch=2, cex=2, lwd=1, add=T)
points( c(-89.771389), c(20.359444), col = "red",pch=2, cex=2, lwd=1, add=T)

text(c(-89.622778 ),c(17.221944 ), labels=c("Tikal"), cex= 1, pos=3) text(c(-89.771389), c(20.359444), labels=c("Uxmal"), cex= 1, pos=3)

dev.off()

  1. system("pdf2svg Rplots.pdf maya_area_pdsi.svg")

crs(r1)<-"lonlat"

writeRaster(r1 , filename="mexico_pdsi.nc", bandorder='BSQ',format="NetCDF", overwrite=TRUE)


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