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SNODAS_Processing_Code.R
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SNODAS_Processing_Code.R
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# SNODAS Processing
# Colton Padilla
# 10/12/2022
#-----------------#
# Housekeeping ####
#-----------------#
# Set working directory
setwd("C:/Users/coltonp/OneDrive - New Mexico State University/Desktop/GIS/Spatial_R_Code_Shapefiles/SNODAS/LiquidPrecip/SNODAS/SNODAS_LiquidPrecipitation")
# Load packages
library(tidyverse)
library(terra)
# Now list all the files
files <- list.files(".", pattern = ".tif$")
# Now create the raster
snodas <- rast(files)
# Get only numbers in the names
names(snodas) <- gsub("[^[:digit:]]", "", names(snodas)) %>%
as.Date(., format = "%Y%m%d") %>% format.Date(., "%Y%m%d")
# Get unique dates
dates <- unique(names(snodas))
#------------------------#
# Weekly Precip Prior ####
#------------------------#
# Set the writing directory
setwd("C:/Users/coltonp/OneDrive - New Mexico State University/Desktop/Data_For_Callie")
# Create an empty raster
week <- rast(extent = ext(snodas),
resolution = res(snodas),
nlyrs = nlyr(snodas))
# Now loop through and calculate the previous weeks precip
for(i in 1:length(dates)){
# Get set of dates for raster subset
date <- as.Date(dates[i], format = "%Y%m%d")
date_sub <- seq(date - 6, date, "days") %>% format.Date(., "%Y%m%d")
# Subset the raster
rast <- subset(snodas, which(names(snodas) %in% date_sub, arr.ind = T))
# Calculate a sum of precip
week[[i]] <- sum(rast)
# Set the names at the end and save the raster
if(i == length(dates)){
# Names
names(week) <- dates
# Write the raster
writeRaster(week,
filename = "Weekly_Precip.tif",
gdal = "COMPRESS=DEFLATE",
overwrite = T)
}
}
#---------------------------#
# Bi-Weekly Precip Prior ####
#---------------------------#
# Create an empty raster
biweek <- rast(extent = ext(snodas),
resolution = res(snodas),
nlyrs = nlyr(snodas))
# Now loop through and calculate the previous weeks precip
for(i in 1:length(dates)){
# Get set of dates for raster subset
date <- as.Date(dates[i], format = "%Y%m%d")
date_sub <- seq(date - 13, date, "days") %>% format.Date(., "%Y%m%d")
# Subset the raster
rast <- subset(snodas, which(names(snodas) %in% date_sub, arr.ind = T))
# Calculate a sum of precip
biweek[[i]] <- sum(rast)
# Set the names at the end and save the raster
if(i == length(dates)){
# Names
names(biweek) <- dates
# Write the raster
writeRaster(biweek,
filename = "Bi-Weekly_Precip.tif",
gdal = "COMPRESS=DEFLATE",
overwrite = T)
}
}
#-------------------------#
# Monthly Precip Prior ####
#-------------------------#
# Create an empty raster
month <- rast(extent = ext(snodas),
resolution = res(snodas),
nlyrs = nlyr(snodas))
# Now loop through and calculate the previous weeks precip
for(i in 1:length(dates)){
# Get set of dates for raster subset
date <- as.Date(dates[i], format = "%Y%m%d")
date_sub <- seq(date - 27, date, "days") %>% format.Date(., "%Y%m%d")
# Subset the raster
rast <- subset(snodas, which(names(snodas) %in% date_sub, arr.ind = T))
# Calculate a sum of precip
month[[i]] <- sum(rast)
# Set the names at the end and save the raster
if(i == length(dates)){
# Names
names(month) <- dates
# Write the raster
writeRaster(month,
filename = "Monthly_Precip.tif",
gdal = "COMPRESS=DEFLATE",
overwrite = T)
}
}
#--------------------#
# Cumulative Sums ####
#--------------------#
# Create cumulative sums
cumsum <- app(snodas, cumsum)
# Write Rasters
writeRaster(cumsum,
filename = "CumSum_Precip.tif",
gdal = "COMPRESS=DEFLATE",
overwrite = T)