File:Pueblo culture area summer pdsi 1051 1-1.png

From Wikimedia Commons, the free media repository
Jump to navigation Jump to search

Original file (5,481 × 5,075 pixels, file size: 10.25 MB, MIME type: image/png)

Captions

Captions

Pueblo culture area summer PDSI 1051

Summary

[edit]
Description
English: Pueblo culture area summer PDSI 1051. PDSI is Palmers Drought Index.
Date
Source Own work
Author Merikanto

This image is based on chalsa cruos, chelsa trace21ka dem and north american drought atlas version 2.

North American drought atlas NADA

Cook, E.R., Seager, R., Heim, R.R., Vose, R.S., Herweijer, C., and Woodhouse, C. 2010. Megadroughts in North America: Placing IPCC projections of hydroclimatic change in a long-term paleoclimate context. Journal of Quaternary Science, 25(1), 48-61. doi: 10.1002/jqs.1303 NOAA Study Page: https://www.ncei.noaa.gov/access/paleo-search/study/19119 JSON Metadata: https://www.ncei.noaa.gov/access/paleo-search/study/search.json?xmlId=16785 DIF Metadata: http://www1.ncdc.noaa.gov/pub/data/metadata/published/paleo/dif/xml/noaa-recon-19119.xml ISO Metadata: http://www1.ncdc.noaa.gov/pub/data/metadata/published/paleo/iso/xml/noaa-recon-19119.xml DOI: https://doi.org/10.25921/xyj1-3836

Chelsa cruts v 1.0

Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017): Climatologies at high resolution for the earth’s land surface areas. Scientific Data. 4, 170122.

https://chelsa-climate.org/chelsacruts/

Chelsa trace21k

Karger, D.N., Nobis, M.P., Normand, S., Graham, C.H., Zimmermann, N. (2023) CHELSA-TraCE21k – High resolution (1 km) downscaled transient temperature and precipitation data since the Last Glacial Maximum. Climate of the Past. https://doi.org/10.5194/cp-2021-30

https://chelsa-climate.org/chelsa-trace21k/

"R" code

    1. nada pdsi, chelsa cruts
  3. 21.01.2024 v 0000.0002

library(stringr)

library(raster) library(terra) library(ncdf4)

library(pals)

library(mlr3) library(mlr3learners)

mlr3_downscale_pdsi<-function (ext1, xdim1, ydim1) {

inprname1<-"./indata5/prec_07.tif" inprname2<-"./indata5/prec_08.tif" inprname3<-"./indata5/tmean_07.tif"


insmallr1<-raster("./resu1/minipdsi.tif") accupr10<-raster(inprname1) accupr20<-raster(inprname2) accupr30<-raster(inprname3)

#plot(accupr10)

#stop(-1)

covo0<-insmallr1

accupr1 <- crop(x = accupr10, y = ext1) accupr2 <- crop(x = accupr20, y = ext1) accupr3 <- crop(x = accupr30, y = ext1)

#xdim1=200 #ydim1=200

sabluna1<-raster(nrow=xdim1,ncol=ydim1)

extent(sabluna1)<-extent(accupr2)

accupr1[is.na(accupr1)] <- -0 accupr2[is.na(accupr2)] <- -0 accupr3[is.na(accupr3)] <- -0

#covo0[is.na(covo0)] <- -0

covo1<-raster::resample(covo0, sabluna1, method="bilinear")

ak1<-raster::resample(accupr1, sabluna1, method="bilinear") ak2<-raster::resample(accupr2, sabluna1, method="bilinear") ak3<-raster::resample(accupr3, sabluna1, method="bilinear")

ck1<-raster::resample(accupr1, covo0, method="bilinear") ck2<-raster::resample(accupr2, covo0, method="bilinear") ck3<-raster::resample(accupr3, covo0, method="bilinear")

#plot(ak4)

#stop(-1)

adf1<-data.frame(cbind(values(ak1), values(ak2), values(ak3) )) cdf1<-data.frame(cbind(values(covo0),values(ck1), values(ck2), values(ck3) ))

names(adf1)<-c("a","b", "c") names(cdf1)<-c("re","a", "b", "c")

print(head(cdf1))

re<-cdf1

#tsk_peba = tsk("cdf1")

tsk_peba = as_task_regr(cdf1, target = "re", id = "peba")

#lrn_rpart = lrn("regr.rpart") #mlr_learners$get("regr.lm") # lrn_rpart = lrn("regr.rpart") 

	#mlr_learners$get("regr.ranger")

#lrn_rpart = lrn("regr.ranger")

mlr_learners$get("regr.svm") lrn_rpart = lrn("regr.svm")

# mlr_learners$get("regr.glmnet") # lrn_rpart = lrn("regr.glmnet")


lrn_rpart$train(tsk_peba)

lrn_rpart$model

splits = partition(tsk_peba) splits

prediction = lrn_rpart$predict_newdata(adf1)

#prediction

#prediction$response

red1<-as.numeric(prediction$response)

#print(red1)

red2<-t(matrix(red1, nrow=xdim1, ncol=ydim1)) #print(red2)

#image(red2)

#red2[red2<0]=0

rout1<-raster(red2) crs(rout1)<-crs(sabluna1) extent(rout1)<-extent(sabluna1)

  1. plot(rout1, col=rev(parula(100)))
  1. contour(rout1, add=T)
  1. plot(covo1)
  2. contour(covo1, add=T)
  return(rout1)

}

get_base_dem_1<-function(cutext1) { # url1="https://biogeo.ucdavis.edu/data/worldclim/v2.1/base/wc2.1_30s_elev.zip" # download.file(url = url1,destfile = './indata3/elev.zip') # unzip("./indata3/elev.zip")

dir.create("./indata6") url1<-"https://os.zhdk.cloud.switch.ch/envicloud/chelsa/chelsa_V1/chelsa_trace/orog/CHELSA_TraCE21k_dem_0_V1.0.tif" destfile1<-"./indata6/CHELSA_TraCE21k_dem_0_V1.0.tif"

  1. system("gdal_translate ")

download.file(url = url1,destfile =destfile1)

r1<-raster(destfile1)

  1. plot(r1)

rout1<-raster::crop(r1, cutext1) crs(rout1)<-"lonlat"

writefile1<-"./indata6/dem.tif"

writeRaster(rout1, writefile1, format="GTiff", overwrite=TRUE)

r1<-raster(writefile1) plot(r1)

#sabluna1 <- raster(ncol=1200, nrow=960) #extent(sabluna1)<-extent(r1) ##crs(sabluna1)<-crs(r1) return(r1) }

convert_cruts_rasters_to_celsius_mm_and_create_tmean<-function(sourcepath1, destpath1, year1) {

  1. quit("yes")

prefix1="CHELSAcruts" svar1="tmin" suffix1="V.1.0.tif" sytamp1=as.character(year1)

dvar1=svar1 #month1=1

coef1=0.1 ofset1=-273.15

for (month1 in 1:12) { smonth1=str_pad(month1, 2, pad = "0") smonth2=as.character(month1)

filename1<-paste0(prefix1,"_",svar1,"_", smonth2,"_",sytamp1, "_",suffix1 ) filename2<-paste0(prefix1,"_",svar1,"_",sytamp1, "_", smonth1,"_",suffix1 )

sourcename1<-paste0(sourcepath1, filename1) destname1<-paste0(destpath1,dvar1, "_",smonth1,".tif") print(sourcename1) print(destname1) rin1<-raster(sourcename1) #plot(rin1) rout1<-(rin1*coef1)+ofset1 #plot(rout1) raster::writeRaster(rout1,destname1, overwrite=TRUE)

}

prefix1="CHELSAcruts" svar1="tmax" suffix1="V.1.0.tif" sytamp1=as.character(year1)

dvar1=svar1 #month1=1

coef1=0.1 ofset1=-273.15 for (month1 in 1:12) { smonth1=str_pad(month1, 2, pad = "0") smonth2=as.character(month1)

filename1<-paste0(prefix1,"_",svar1,"_", smonth2,"_",sytamp1, "_",suffix1 ) filename2<-paste0(prefix1,"_",svar1,"_",sytamp1, "_", smonth1,"_",suffix1 )

sourcename1<-paste0(sourcepath1, filename1) destname1<-paste0(destpath1,dvar1, "_",smonth1,".tif") print(sourcename1) print(destname1) rin1<-raster(sourcename1) #plot(rin1) rout1<-(rin1*coef1)+ofset1 #plot(rout1) raster::writeRaster(rout1,destname1, overwrite=TRUE)

}

prefix1="CHELSAcruts" svar1="prec" suffix1="V.1.0.tif" sytamp1=as.character(year1)

dvar1=svar1 #month1=1

coef1=0.1 ofset1=0 for (month1 in 1:12) { smonth1=str_pad(month1, 2, pad = "0") smonth2=as.character(month1)

filename1<-paste0(prefix1,"_",svar1,"_", smonth2,"_",sytamp1, "_",suffix1 ) filename2<-paste0(prefix1,"_",svar1,"_",sytamp1, "_", smonth1,"_",suffix1 )

sourcename1<-paste0(sourcepath1, filename1) destname1<-paste0(destpath1,dvar1, "_",smonth1,".tif") print(sourcename1) print(destname1) rin1<-raster(sourcename1) #plot(rin1) rout1<-(rin1*coef1)+ofset1 #plot(rout1) raster::writeRaster(rout1,destname1, overwrite=TRUE)

}

prefix1="CHELSAcruts" suffix1="V.1.0.tif" sytamp1=as.character(year1)

svar1="tmin" svar2="tmax"

for (month1 in 1:12) { smonth1=str_pad(month1, 2, pad = "0") smonth2=as.character(month1)

sname1<-paste0(destpath1,svar1, "_",smonth1,".tif") sname2<-paste0(destpath1,svar2, "_",smonth1,".tif") destname1<- paste0(destpath1,"tmean", "_",smonth1,".tif") #print(sname1) print(destname1) rin1<-raster(sname1) rin2<-raster(sname2) rout1<-(rin1+rin2)/2 raster::writeRaster(rout1,destname1, overwrite=TRUE)

}

}

get_f_chelsa_cruts<-function(down1,destpath1, destpath2,year1, svar1,svar2, num1, ext1) {

  1. https://os.zhdk.cloud.switch.ch/envicloud/chelsa/chelsa_V1/chelsa_cruts/prec/CHELSAcruts_prec_1_1980_V.1.0.tif

urlbase1<-"https://os.zhdk.cloud.switch.ch/envicloud/chelsa/chelsa_V1/chelsa_cruts"

prefix1="CHELSAcruts"

suffix1="V.1.0.tif" syear1=as.character(year1)

#print("k") print(svar1) #print(".") #stop(-1) urlbase2<-paste0(urlbase1,"/", svar1, "/")

#print(urlbase2)

#stop(-1)


#month1=1 for (month1 in 1:num1) { #smonth1=str_pad(month1, 2, pad = "0") smonth2=as.character(month1)

filename2<-paste0(prefix1,"_",svar2,"_", smonth2,"_",syear1, "_",suffix1 )

url1<-paste0(urlbase2, filename2) print(url1) #stop(-1)

destname1<-paste0(destpath1, filename2) destname2<-paste0(destpath2, filename2)

print(destname1)

if(down1==1) { download.file(url = url1,destfile =destname1) }

r1<-raster(destname1)

# plot(r1)

rout1<-crop(r1, ext1) crs(rout1)<-"lonlat"

#writefile1<-"./indata13/npp.tif"

writeRaster(rout1, destname2, format="GTiff", overwrite=TRUE)

r1<-raster(destname2) #plot(r1)

}

}

nada_pdsi_cut<-function(iname1, cutext1, beginyr1, endyr1) { ncin1 <- nc_open(iname1) lon1 <- ncvar_get(ncin1,"lon") nlon1 <- dim(lon1) lat1 <- ncvar_get(ncin1,"lat") nlat1 <- dim(lat1) time1 <- ncvar_get(ncin1,"time") ntime1 <- dim(time1) pdsi1 <- ncvar_get(ncin1,"pdsi") #npdsi1 <- dim(pdsi1)

minlon1<-min(lon1) maxlon1<-max(lon1) minlat1<-min(lat1) maxlat1<-max(lat1)

ext1<-c(minlon1, maxlon1, minlat1, maxlat1) crs1<-"lonlat"

#pdsi2<-pdsi1[beginyr1:endyr1,,] #slice2<-apply(pdsi2,c(2,3),mean)

slice2<-pdsi1[beginyr1,,] r1<-raster(slice2)

extent(r1)<-ext1 crs(r1) <-crs1

r1<-flip(r1)

r1[r1<-10]<- NA 


   minir1<-crop(r1, cutext1)
   return(minir1)

}

                                                                                                1. 3
                                                                                                1. 3

get_base_dem=1 get_chelsa_cruts=1 convert_and_create_tmeans=1 cut_nada_pdsi=1 downscale_mlr3=1

year2=1151 ## "target year" in NADA year1=1955 ## accurate map year from CHELSA

cutminlon1=-111 cutmaxlon1=-105 cutminlat1=34 cutmaxlat1=38

cutext1<-c(cutminlon1, cutmaxlon1, cutminlat1, cutmaxlat1 )

dir.create("./resu1") dir.create("./indata6") dir.create("./indata4") dir.create("./indata3") dir.create("./indata5")


if(get_base_dem==1) { get_base_dem_1(raster::extent(cutext1)) }

if(get_chelsa_cruts==1) { get_f_chelsa_cruts(1,"./indata3/", "./indata4/",year1, "prec", "prec", 12, cutext1) get_f_chelsa_cruts(1,"./indata3/", "./indata4/",year1, "tmin", "tmin", 12, cutext1) get_f_chelsa_cruts(1,"./indata3/", "./indata4/",year1, "tmax", "tmax", 12, cutext1) }

if(convert_and_create_tmeans==1) { convert_cruts_rasters_to_celsius_mm_and_create_tmean("./indata4/", "./indata5/", year1) }

if(cut_nada_pdsi==1) { r1<-nada_pdsi_cut("./indata1/nada_hd2_cl.nc", cutext1, year2,year2) writeRaster(r1, "./resu1/minipdsi.tif", overwrite=T) }

if(downscale_mlr3==1) { dsr1<-mlr3_downscale_pdsi(cutext1, 256,256) writeRaster(dsr1, "./resu1/dspdsi1.tif", overwrite=T) #plot(dsr1)


plot(dsr1, col=rev(parula(256)) ) contour(dsr1, add=T) }

  1. sabluna1<-raster(ncol=256, nrow=256)
  1. extent(sabluna1)<-cutext1
  2. crs(sabluna1)<-"lonlat"
  1. r1<- "./resu1/minipdsi.tif"
  1. plotr1<-resample(r1, sabluna1)
  1. plot(cr1, col=rev(parula(256)))
  2. contour(cr1, add=T)
  3. plot(plotr1, col=rev(parula(256)) )
  4. contour(plotr1, add=T)

GMT 6.0 code

import numpy as np import math import matplotlib.pyplot as plt

mstar=1 a=0.06

hr=0.05 ## disk aspect ratioo in early disk stahes in inner sys o.05, but later stage s0.02 ... 0.03

  1. hr=0.025

f=10 # separation of protoplanets hill radius sigmab=10 ## pebbles? !! kg m-2 sigmad=8 ## g cm-2! at 1 au 8 10 etc sigmagas=math.pow(a, -0.5)*610

  1. sigmap=5*1e-3*1e-4*math.pow(a, -1) #https://iopscience.iop.org/article/10.3847/1538-4357/ab9eb2/pdf

stokes=0.01 ## 0.01 ... 0.5 ? alpha=1e-3 mplanet=1e-3*1/330

rhill1=math.pow((mplanet/mstar),1/3)*a

    1. snow line
  2. https://arxiv.org/pdf/1505.03516.pdf

a_snow=2.1*math.pow(mstar,1/3 )

  1. https://people.ast.cam.ac.uk/~wyatt/lecture4_planetformation.pdf
  2. https://ui.adsabs.harvard.edu/abs/2003DPS....35.2507W/abstract
  3. https://arxiv.org/pdf/2203.09759.pdf
  4. https://www.aanda.org/articles/aa/full_html/2015/10/aa26463-15/aa26463-15.html

spla1=0 aexp1=1.5*(2-spla1)

miso_planetesimal=3.3e-3*math.pow(f, 1.5)*math.pow((sigmad/10), 1.5)*math.pow(a,3)*math.pow(mstar, -0.5)

sidmada=10

  1. a=np.linspace(0.01,10,999)

a=np.linspace(0.1,6,999)

sigmad=np.power(a, -1.0)*10.0

s2=np.copy(a) s2=np.where(s2>a_snow,1.0,0.0 )

sidmad=sigmad*s2*4

plt.plot(a,sigmad)

plt.show()

quit(-1)

    1. print(s2)
  1. quit(-1)
  1. mstar=0.1

miso_pt=3.3e-3*math.pow(f, 1.5)*np.power((sigmad/10), 1.5)*np.power(a,3)*math.pow(mstar, -0.5)

  1. plt.xlim(-0.2, 6)

plt.plot(a,sigmad)

  1. plt.plot(a,miso_pt)

plt.show()

quit(-1)

  1. https://www.oca.eu/images/LAGRANGE/EcolesThematiques/chronologiesolarsystem2013/cours/Dullemond_Houches2013.pdf
    1. dead zones alpha=1e-6

sigmagas=1e3 ## OK if 8e-8 mo/yr q=-1/2 # -3/4 p=-1 # -3/4 hr=0.035 ## 1au eta=0.01

  1. print(aexp1)

quit(-1)

  1. https://people.ast.cam.ac.uk/~wyatt/lecture4_planetformation.pdf
  1. miso1_me=3.3e-3*math.pow(f, 1.5)*math.pow((sigmad), 1.5)*math.pow(a,3)

miso1_me=3.3e-3*math.pow(f, 1.5)*math.pow((sigmad/10), 1.5)*math.pow(a,3)

  1. https://ui.adsabs.harvard.edu/abs/2003DPS....35.2507W/abstract
  2. miso1_me_2=2.1e-3*math.pow((sigmad), 1.5)*math.pow(a,3)
    1. --- > mmsn , sigmap=8 g cm-2 , miso 0.05 me
  1. https://arxiv.org/pdf/2203.09759.pdf

miso1_me2=0.1*math.pow((sigmad/5), 1.5)*math.pow(a,3)*math.pow(mstar, -1/2)

  1. https://www.aanda.org/articles/aa/full_html/2015/10/aa26463-15/aa26463-15.html

spla1=1 ## gradient of planetesimals b=10*rhill1 ## number of hilll radiis between planets

  1. https://www.aanda.org/articles/aa/full_html/2015/10/aa26463-15/aa26463-15.html

miso1_me3=0.16*math.pow((b/(10*rhill1)), 3/2)*math.pow((sigmad/10),3/2)*math.pow(a, 1.5*(2-spla1))*math.pow(mstar, -0.5)

Licensing

[edit]
I, the copyright holder of this work, hereby publish it under the following license:
Creative Commons CC-Zero This file is made available under the Creative Commons CC0 1.0 Universal Public Domain Dedication.
The person who associated a work with this deed has dedicated the work to the public domain by waiving all of their rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.

File history

Click on a date/time to view the file as it appeared at that time.

Date/TimeThumbnailDimensionsUserComment
current09:09, 22 January 2024Thumbnail for version as of 09:09, 22 January 20245,481 × 5,075 (10.25 MB)Merikanto (talk | contribs)Uploaded own work with UploadWizard

You cannot overwrite this file.

There are no pages that use this file.

Metadata

This file contains additional information such as Exif metadata which may have been added by the digital camera, scanner, or software program used to create or digitize it. If the file has been modified from its original state, some details such as the timestamp may not fully reflect those of the original file. The timestamp is only as accurate as the clock in the camera, and it may be completely wrong.

Retrieved from "https://commons.wikimedia.org/w/index.php?title=File:Pueblo_culture_area_summer_pdsi_1051_1-1.png&oldid=844202674"