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pixel_vol_swe_calc.R
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pixel_vol_swe_calc.R
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## writing the code for multiply each raster swe value by its pixel area
## and then summing the values to get total vol swe
## started 7/20/2020
library(BiocManager)
library(rhdf5)
library(tidyverse)
library(rgeos)
library(rasterVis) #https://oscarperpinan.github.io/rastervis/#levelplot
library(rgdal)
library(raster)
library(data.table)
library(gtools)
library(parallel)
setwd("/Volumes/john_tarricone/UNR_summer_20/margulis/swe/daily_data/WY_2014")
# Define your file paths here.
hdf_path <- "/Volumes/john_tarricone/UNR_summer_20/margulis/swe/hdf" #create path
hdf_name <- "SN_SWE_WY2014.h5"
pixel_area <-raster("/Volumes/john_tarricone/UNR_summer_20/margulis/static/rasters/SNSR_pixel_area.tif")
pix_vol_swe <-function(a,b){return((a*1e-6)*(b*1e-6))}
vol_swe<-function(x){
swe_by_pixel <- overlay(x, pixel_area, fun= pix_vol_swe )
vol_swe <-cellStats(swe_by_pixel,'sum', digits=9, na.rm=TRUE)
print(vol_swe)
}
## create 8 functions which read in the hdfs, convert to brick, and then breack into raster list
open_hdf_as_raster_list_c1 <- function( hdf_name ) {
hdf_name %>%
file.path( hdf_path , . ) %>%
h5read(., "/SWE", index = list(NULL,NULL,1:50)) %>%
brick(., xmn=-123.3, xmx=-117.6, ymn=35.4, ymx=42,CRS("+proj=leac +ellps=clrk66")) %>%
as.list(.)
}
open_hdf_as_raster_list_c2 <- function( hdf_name ) {
hdf_name %>%
file.path( hdf_path , . ) %>%
h5read(., "/SWE", index = list(NULL,NULL,51:100)) %>%
brick(., xmn=-123.3, xmx=-117.6, ymn=35.4, ymx=42,CRS("+proj=leac +ellps=clrk66")) %>%
as.list(.)
}
open_hdf_as_raster_list_c3 <- function( hdf_name ) {
hdf_name %>%
file.path( hdf_path , . ) %>%
h5read(., "/SWE", index = list(NULL,NULL,101:150)) %>%
brick(., xmn=-123.3, xmx=-117.6, ymn=35.4, ymx=42,CRS("+proj=leac +ellps=clrk66")) %>%
as.list(.)
}
open_hdf_as_raster_list_c4 <- function( hdf_name ) {
hdf_name %>%
file.path( hdf_path , . ) %>%
h5read(., "/SWE", index = list(NULL,NULL,151:200)) %>%
brick(., xmn=-123.3, xmx=-117.6, ymn=35.4, ymx=42,CRS("+proj=leac +ellps=clrk66")) %>%
as.list(.)
}
open_hdf_as_raster_list_c5 <- function( hdf_name ) {
hdf_name %>%
file.path( hdf_path , . ) %>%
h5read(., "/SWE", index = list(NULL,NULL,201:250)) %>%
brick(., xmn=-123.3, xmx=-117.6, ymn=35.4, ymx=42,CRS("+proj=leac +ellps=clrk66")) %>%
as.list(.)
}
open_hdf_as_raster_list_c6 <- function( hdf_name ) {
hdf_name %>%
file.path( hdf_path , . ) %>%
h5read(., "/SWE", index = list(NULL,NULL,251:300)) %>%
brick(., xmn=-123.3, xmx=-117.6, ymn=35.4, ymx=42,CRS("+proj=leac +ellps=clrk66")) %>%
as.list(.)
}
open_hdf_as_raster_list_c7 <- function( hdf_name ) {
hdf_name %>%
file.path( hdf_path , . ) %>%
h5read(., "/SWE", index = list(NULL,NULL,301:330)) %>%
brick(., xmn=-123.3, xmx=-117.6, ymn=35.4, ymx=42,CRS("+proj=leac +ellps=clrk66")) %>%
as.list(.)
}
open_hdf_as_raster_list_c8 <- function( hdf_name ) {
hdf_name %>%
file.path( hdf_path , . ) %>%
h5read(., "/SWE", index = list(NULL,NULL,331:365)) %>%
brick(., xmn=-123.3, xmx=-117.6, ymn=35.4, ymx=42,CRS("+proj=leac +ellps=clrk66")) %>%
as.list(.)
}
######function which chunk out analysis
big_vol_fun<-function(x){
## c1
rast_list_c1<-open_hdf_as_raster_list_c1("SN_SWE_WY2014.h5")
system.time(results_c1<-mclapply(rast_list_c1, function(x) vol_swe(x), mc.cores = 3, mc.cleanup = TRUE))
results_df_c1 <- data.frame(matrix(unlist(results_c1), nrow=length(results_c1), byrow=T))
colnames(results_df_c1)[1] <- "WY_2014"
print(results_df_c1)
rm(rast_list_c1)
rm(results_c1)
## c2
rast_list_c2<-open_hdf_as_raster_list_c2("SN_SWE_WY2014.h5")
system.time(results_c2<-mclapply(rast_list_c2, function(x) vol_swe(x), mc.cores = 3, mc.cleanup = TRUE))
results_df_c2 <- data.frame(matrix(unlist(results_c2), nrow=length(results_c2), byrow=T))
colnames(results_df_c2)[1] <- "WY_2014"
print(results_df_c2)
rm(rast_list_c2)
rm(results_c2)
## c3
rast_list_c3<-open_hdf_as_raster_list_c3("SN_SWE_WY2014.h5")
system.time(results_c3<-mclapply(rast_list_c3, function(x) vol_swe(x), mc.cores = 3, mc.cleanup = TRUE))
results_df_c3 <- data.frame(matrix(unlist(results_c3), nrow=length(results_c3), byrow=T))
colnames(results_df_c3)[1] <- "WY_2014"
print(results_df_c3)
rm(rast_list_c3)
rm(results_c3)
## c4
rast_list_c4<-open_hdf_as_raster_list_c4("SN_SWE_WY2014.h5")
system.time(results_c4<-mclapply(rast_list_c4, function(x) vol_swe(x), mc.cores = 3, mc.cleanup = TRUE))
results_df_c4 <- data.frame(matrix(unlist(results_c4), nrow=length(results_c4), byrow=T))
colnames(results_df_c4)[1] <- "WY_2014"
print(results_df_c4)
rm(rast_list_c4)
rm(results_c4)
## c5
rast_list_c5<-open_hdf_as_raster_list_c5("SN_SWE_WY2014.h5")
system.time(results_c5<-mclapply(rast_list_c5, function(x) vol_swe(x), mc.cores = 3, mc.cleanup = TRUE))
results_df_c5 <- data.frame(matrix(unlist(results_c5), nrow=length(results_c5), byrow=T))
colnames(results_df_c5)[1] <- "WY_2014"
print(results_df_c5)
rm(rast_list_c5)
rm(results_c5)
## c6
rast_list_c6<-open_hdf_as_raster_list_c6("SN_SWE_WY2014.h5")
system.time(results_c6<-mclapply(rast_list_c6, function(x) vol_swe(x), mc.cores = 3, mc.cleanup = TRUE))
results_df_c6 <- data.frame(matrix(unlist(results_c6), nrow=length(results_c6), byrow=T))
colnames(results_df_c6)[1] <- "WY_2014"
print(results_df_c6)
rm(rast_list_c6)
rm(results_c6)
## c7
rast_list_c7<-open_hdf_as_raster_list_c7("SN_SWE_WY2014.h5")
system.time(results_c7<-mclapply(rast_list_c7, function(x) vol_swe(x), mc.cores = 3, mc.cleanup = TRUE))
results_df_c7 <- data.frame(matrix(unlist(results_c7), nrow=length(results_c7), byrow=T))
colnames(results_df_c7)[1] <- "WY_2014"
print(results_df_c7)
rm(rast_list_c7)
rm(results_c7)
## c8
rast_list_c8<-open_hdf_as_raster_list_c8("SN_SWE_WY2014.h5")
system.time(results_c8<-mclapply(rast_list_c8, function(x) vol_swe(x), mc.cores = 3, mc.cleanup = TRUE))
results_df_c8 <- data.frame(matrix(unlist(results_c8), nrow=length(results_c8), byrow=T))
colnames(results_df_c8)[1] <- "WY_2014"
print(results_df_c8)
rm(rast_list_c8)
rm(results_c8)
results <- rbind(results_df_c1, results_df_c2, results_df_c3,
results_df_c4, results_df_c5, results_df_c6,
results_df_c7, results_df_c8)
#stich data together and save
doWY<-(1:365) #create vector
doWY_df <- data.frame(matrix(unlist(doWY), nrow=length(doWY), byrow=T)) #convert to df
colnames(doWY_df)[1] <- "doWY"
#bind on doWY column
full_year <-cbind(doWY_df, results_14)
write.csv(full_year, row.names = FALSE)
return(results)
}
system.time(results_14<-big_vol_fun("SN_SWE_WY2014.h5"))
write.csv(full_year,"/Volumes/john_tarricone/UNR_summer_20/margulis/volume_results/WY_2014_SN_ts.csv", row.names = FALSE)
ggplot(full_year)+ aes(doWY,WY_2014) +
geom_line(color="darkblue") +
scale_x_continuous(breaks = seq(0,366,50)) +
scale_y_continuous(breaks = seq(0,30,2)) +
labs(title="WY 2014 CA Sierra Nevada Volumetric SWE", y=(expression('Volumetric SWE'~(km^{"3"}))), x="Day of Water Year") +
theme_light(11)
sessionInfo()