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sdd_rasters_nonleap.R
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sdd_rasters_nonleap.R
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# nov 7th 2020
# make full sierra SCF rasters
# converted script from my orginal max one
library(BiocManager)
library(rhdf5)
library(tidyverse)
library(rgeos)
library(raster)
library(rgdal)
library(parallel)
library(data.table)
library(gtools)
library(terra)
library(spatialEco)
library(EnvStats)
# oct 12
# keeping data in a matrix instead of raster for processing
# this way we can avoid geolocation issues
tempdir <- function() { "/Volumes/jt/projects/margulis/temp" }
tempdir()
#set path and file name for hdf5 SWE file
hdf_path <- "/Volumes/jt/projects/margulis/swe/hdf" #create path
h5closeAll()
h5readAttributes(hdf_file, name = "SWE") #$units = mm
# create snow cover frequency (scf) function
# number of days the amounts days there is snow on the ground vs total days
# set threshold to 5.1 mm or .2 inches because thats the accuracy a SNTOEL site has
# this will also reduce the long tails seen on the data and unrealistically large scf numbers i saw
sdd<-function(x){
# return 0 for values that never reach the 5.1 mm threshold
if (max(x) < 5.1){
return(0)
} else {
# use which function to index the days that meet condition
# reverse the vector
days_vector <-as.numeric(rev(which(x > 5.1)))
# take the difference of each vector point and make positive
# we do this bc we are looking for the first point that meets condition
# and does this for 14 consecutive days
diff_vect <-diff(days_vector*-1)
# use rle function to calc the lengths of continuous number sequences
# if there isnt a 14 days with continuous snow cover, make pixel 0
# this fixes the code bc accounts for pixels which may have had 10 days of sc
# which therefore was out of the previous condition set and caused the error
# found this about by testing different areas, and saw was more in east half
result <- rle(diff_vect)
if (max(result$lengths) < 14){
return(0)
} else{
#represents place on rle vector where condition is met
meets <-as.numeric(min(which(result$lengths >= 14)))
#if its the first rle value, this means day 365 so max of days_vecto0r
if (meets == 1) {
sdd1 <- max(days_vector)
return(sdd1)
} else {
#calculate legnth needed to be cut off days vector from rle
values_to_cut <-result$lengths[1:(meets-1)]
#sum the lengths
index_values_until_start <-as.numeric(sum(values_to_cut))
#subtract that many values off to get your SDD
sdd2 <-max(days_vector[-c(1:index_values_until_start)])
return(sdd2)
}
}
}
}
sdd_raster <- function( hdf_name ) {
path <-file.path( hdf_path , hdf_name )
c1 <-h5read(path, "/SWE", index = list(1:500,1:5701,1:365)) #load in
sdd_c1 <-as.matrix(apply(c1, c(1,2), sdd)) #create matrix with sdd value on z axis
rm(c1)
c2 <-h5read(path, "/SWE", index = list(501:1000,1:5701,1:365))
sdd_c2 <-as.matrix(apply(c2, c(1,2), sdd))
rm(c2)
h5closeAll()
c3 <-h5read(path, "/SWE", index = list(1001:1500,1:5701,1:365))
sdd_c3 <-as.matrix(apply(c3, c(1,2), sdd))
rm(c3)
h5closeAll()
c4 <-h5read(path, "/SWE", index = list(1501:2000,1:5701,1:365))
sdd_c4 <-as.matrix(apply(c4, c(1,2), sdd))
rm(c4)
h5closeAll()
c5 <-h5read(path, "/SWE", index = list(2001:2500,1:5701,1:365))
sdd_c5 <-as.matrix(apply(c5, c(1,2), sdd))
rm(c5)
h5closeAll()
c6 <-h5read(path, "/SWE", index = list(2501:3000,1:5701,1:365))
sdd_c6 <-as.matrix(apply(c6, c(1,2), sdd))
rm(c6)
h5closeAll()
c7 <-h5read(path, "/SWE", index = list(3001:3500,1:5701,1:365))
sdd_c7 <-as.matrix(apply(c7, c(1,2), sdd))
rm(c7)
h5closeAll()
c8 <-h5read(path, "/SWE", index = list(3501:4000,1:5701,1:365)) #load in
sdd_c8 <-as.matrix(apply(c8, c(1,2), sdd)) #creat matrix with sdd value on z axis
rm(c8)
c9 <-h5read(path, "/SWE", index = list(4001:4500,1:5701,1:365)) #load in
sdd_c9 <-as.matrix(apply(c9, c(1,2), sdd)) #creat matrix with sdd value on z axis
rm(c9)
c10 <-h5read(path, "/SWE", index = list(4501:5000,1:5701,1:365)) #load in
sdd_c10 <-as.matrix(apply(c10, c(1,2), sdd)) #creat matrix with sdd value on z axis
rm(c10)
c11 <-h5read(path, "/SWE", index = list(5001:5500,1:5701,1:365)) #load in
sdd_c11 <-as.matrix(apply(c11, c(1,2), sdd)) #creat matrix with sdd value on z axis
rm(c11)
c12 <-h5read(path, "/SWE", index = list(5501:6000,1:5701,1:365)) #load in
sdd_c12 <-as.matrix(apply(c12, c(1,2), sdd)) #creat matrix with sdd value on z axis
rm(c12)
c13 <-h5read(path, "/SWE", index = list(6001:6601,1:5701,1:365)) #load in
sdd_c13 <-as.matrix(apply(c13, c(1,2), sdd)) #creat matrix with sdd value on z axis
rm(c13)
#bind chunks together
full_sdd <-rbind(sdd_c1,sdd_c2,sdd_c3,sdd_c4,sdd_c5,sdd_c6,sdd_c7,
sdd_c8,sdd_c9,sdd_c10,sdd_c11,sdd_c12,sdd_c13)
rast <-raster(full_sdd, xmn=-123.3, xmx=-117.6, ymn=35.4, ymx=42, CRS("+proj=leac +ellps=clrk66"))
plot(rast)
hist(rast)
name <- gsub(".h5", "", hdf_name)
good_name <- gsub("SN_SWE_", "sdd_swe_", name)
setwd("/Volumes/jt/projects/margulis/snow_metric_rasters/sdd")
writeRaster(rast, paste0(good_name, ".tif"))
return(rast)
}
sdd_86<-sdd_raster("SN_SWE_WY1986.h5")
#### apply to hdf list
setwd("/Volumes/jt/projects/margulis/swe/hdf")
files <- list.files(pattern = ".h5")
hdf_list <-mixedsort(sort(files)) #sort in correct order
print(hdf_list)
system.time(raster_list <-lapply(hdf_list, function(x) sdd_raster(x)))
warnings()