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glacierSCA_functions.R
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#
# Glacier Trend Functions
# MODDRFS & MODSCAG data input
# Available:
# https://snow-data.jpl.nasa.gov/moddrfs
# https://snow-data.jpl.nasa.gov/modscag
# Date: 11/12/2019
#
# # # # # # # # # # # #
library(raster)
library(rgdal)
library(rgeos)
library(ggplot2)
library(reshape2)
library(dplyr)
##### LOAD PATHS ###############################################################################
load.paths <- function(macpaths=FALSE){
# Specify whether using mac or not
if(macpaths){
rfpath <<- "~/FOLDER_RF/"
scapath <<- "/FOLDER_SCA/"
} else{
rfpath <- 'F:/HiMAT/MATTO/FOLDER_RF/'
scapath <- 'F:/HiMAT/MATTO/FOLDER_SCA/'
tfpath <- 'F:/HiMAT/MATTO/TOPO_FORCING/'
}
}
##### LOAD EXTRA DATA ###############################################################################
load.data <- function(macpaths=FALSE){
if(macpaths){
rgi <<- readOGR(dsn="/RGI_GLACIERS", layer="rgi50_josh_mb")
dpath <- "/DEM_REGION.tif"
demL <<- raster(dpath)
} else{
rgi <<- readOGR(dsn="F:/HiMAT/MATTO/RGI_GLACIERS", layer="rgi50_josh_mb")
dpath <- "F:/HiMAT/MATTO/DEM_REGION.tif"
demL <<- raster(dpath)
}
}
##### LOAD NEW CSV ###############################################################################
mb.csv.new <- function(macpaths=FALSE){
#
# LOAD IN MASS BALANCE DATA .CSV
# adds field for:
# rgi <region>, hydro <sub_bas>, and <sub_bas2> (with some basins aggregated)
#
if(macpaths){
mb_path <- "~/MAURER_GLACIERMB.csv"
bpath = "RGI_SUBREGION.shp"
} else{
mb_path <- "F:/HiMAT/MATTO/MAURER_GLACIERMB.csv"
bpath = "F:/HiMAT/MATTO/RGI_SUBREGION.shp"
}
#
# READ IN MB .CSV
mb = read.csv(mb_path)
mb$Object.ID = gsub("'",'', mb$Object.ID) # Fix character string of glacier name
mb$Object.ID = gsub("[[:punct:]]","_",mb$Object.ID)
mb = mb[!is.na(mb$lon),]
mbc = mb[mb$category == levels(mb$category)[1],] # Only clean ice glaciers
mbg = mbc[,c(1,7,10,12,16,18)] # only keep area2016, meanElev, meanAspect, meanELevChj, and geoMassBal
mbg.coords = mbc[,4:5]
#
# EXTRACT REGION | Tibet 8 | West H 12 | Central H 13 | East H 14
sp.mb = SpatialPointsDataFrame(mbg.coords, mbg, proj4string = CRS("+proj=longlat +datum=WGS84"))
basin = readOGR(bpath)
sp.mb2 = spTransform(sp.mb,crs(basin))
mbg$region = as.factor(extract(basin, sp.mb2)$poly.ID)
#
# CHANGE REGION 8 - TIBET
mbg$region[mbg$region == '8' & sp.mb$lon > 86] <- 14 # east_H
mbg$region[mbg$region == '8' & sp.mb$lon < 86] <- 13 # central_H
mbg$region = as.factor(as.character(mbg$region))
# | West H 12 | Central H 13 | East H 14 | #
#
# get hydrobasin
hydro <- readOGR("~/HMA_HYDROBASINS.shp")
mb.bas = over(sp.mb2, hydro)
mbg$sub_bas = as.factor(mb.bas$SUB_BAS)
mbg$sub_bas[is.na(mbg$sub_bas)] <- as.factor(61023)
mbg$sub_bas2 = mbg$sub_bas
# aggregate subbasins (higher n)
# add all 61009 -> 61012 | 45006 -> 45009 | 45052 -> 45042 | 45050 -> 45042
# 45036 -> 45037 | 45035 -> 45020 | 45058 -> 45062 | NA -> 61023
mbg$sub_bas2[mbg$sub_bas2 == "61009"] <- "61012"; mbg$sub_bas2[mbg$sub_bas2 == "45006"] <- "45009"
mbg$sub_bas2[mbg$sub_bas2 == "45052"] <- "45042"; mbg$sub_bas2[mbg$sub_bas2 == "45050"] <- "45042"
mbg$sub_bas2[mbg$sub_bas2 == "45036"] <- "45037"; mbg$sub_bas2[mbg$sub_bas2 == "45035"] <- "45020"
mbg$sub_bas2[mbg$sub_bas2 == "45058"] <- "45062"; mbg$sub_bas2[mbg$sub_bas2 == "45070"] <- "45044"
mbg$sub_bas2 <- as.factor(mbg$sub_bas2)
return(mbg)
}
##############################################################################################################################
### TREND FUNCTIONS ################################################################################################################
##############################################################################################################################
## DONE
##############################################################################################################################
glacier.var <- function(glacier_id, varpath, rgi, demL, noZero=TRUE, threshold=NULL){
#
# FOR GIVEN GLACIER #
# create VARIABLES for glacier_i #
# mask with inner polygon #
# #
# Returns: #
# vstk | velv | vdates
#
# READ IN VARIABLE STACK
var_match = list.files(varpath, full.names = T, pattern = paste0(glacier_id,".*grd"))
v_stk <- stack(var_match)
#
# FIND RGI SHAPEFILE AND MASK FOR INSIDE POLYGON
shape = rgi[gsub("[[:punct:]]", "_", rgi@data$RGIId) == glacier_id,]
cent_distance <- res(v_stk)[1]/2
suppressWarnings(inner_shape <- buffer(shape,width=-cent_distance))
#
# DEM
dem <- crop(demL,v_stk)
velv <<- mask(resample(dem, v_stk), inner_shape)
#
# MASK VAR FOR ALL YEARS
vstk <- mask(v_stk, inner_shape)
#
# DELETE ALL 0vals
if(noZero){vstk[vstk==0] <- NA}
# THRESHOLD
if (!is.null(threshold)){
# add threshold
# cat("! Error threshold for RF not dependent on SCA !\n SCA threshold valid")
vstk[vstk < threshold] <- NA
print(paste("SCA threshold of >", threshold))
}
#
# EXTRACT DATES
v_n = substring(names(vstk),2,8)
vdates <- as.Date(v_n,format="%Y%j")
#
# EXCLUDE 2017, 2018 VALS
# didx <- format(vdates, "%Y") != "2018"
didx <- format(vdates, "%Y") != "2018" & format(vdates, "%Y") != "2017"
vdates <<- vdates[didx]
vstk <<- vstk[[which(didx)]]
#
# cat("Created variables: \n vstk | velv | vdates \n <glacier i> \n")
# print(glacier_id)
}
## New - 04/22/2020
##############################################################################################################################
glacier.var2 <- function(glacier_id, varpath, rgi, demL, makeZero=TRUE, threshold=NULL){
#
# FOR GIVEN GLACIER #
# create VARIABLES for glacier_i #
# mask with inner polygon #
# #
# Returns: #
# vstk | velv | vdates | shape
#
# READ IN VARIABLE STACK
var_match = list.files(varpath, full.names = T, pattern = paste0(glacier_id,".*grd"))
v_stk <- stack(var_match)
#
# FIND RGI SHAPEFILE AND MASK FOR INSIDE POLYGON
shape <<- rgi[gsub("[[:punct:]]", "_", rgi@data$RGIId) == glacier_id,]
cent_distance <- res(v_stk)[1]/2
suppressWarnings(inner_shape <- buffer(shape,width=-cent_distance))
#
# DEM
dem <- crop(demL,v_stk)
velv <<- mask(resample(dem, v_stk), inner_shape)
#
# MASK VAR FOR ALL YEARS
vstk <- mask(v_stk, inner_shape)
#
# THRESHOLD
if (!is.null(threshold)){
# add threshold
# cat("! Error threshold for RF not dependent on SCA !\n SCA threshold valid")
vstk[vstk < threshold] <- NA
print(paste("SCA threshold of >", threshold))
}
#
# MAKE NA VALUES WITHIN glacier 0
if(makeZero){vstk[is.na(vstk)] <- 0;vstk <- mask(v_stk, inner_shape)}
#
# EXTRACT DATES
v_n = substring(names(vstk),2,8)
vdates <- as.Date(v_n,format="%Y%j")
#
# EXCLUDE 2017, 2018 VALS
# didx <- format(vdates, "%Y") != "2018"
didx <- format(vdates, "%Y") != "2018" & format(vdates, "%Y") != "2017"
vdates <<- vdates[didx]
vstk <<- vstk[[which(didx)]]
#
# cat("Created variables: \n vstk | velv | vdates \n <glacier i> \n")
# print(glacier_id)
}
## DONE
##############################################################################################################################
glacier.var.vals <- function(df_glaciers, varpath, rgi, demL, threshold=NULL){
strt = Sys.time(); ci=ncol(df_glaciers)
o_names <- names(df_glaciers)
df_glaciers[,10:50] <- NA # was 38
## LOOP THROUGH EACH GLACIER
# !!!!
# quick fix (last name has no SCA)
for (i in 1:(length(df_glaciers$Object.ID)-1)){
g_name <- df_glaciers$Object.ID[i]
glacier.var2(g_name, varpath, rgi, demL, threshold=threshold)
# have vstk, vdates, velv
# OUTSIDE FUNCTIONS
trend_vals = year.season.trends(vstk, vdates, velv)
# add values to data frame
df_glaciers[i,(ci+1)] <- mean(getValues(vstk),na.rm=T) # mean over all years
df_glaciers[i,(ci+2)] <- sum(getValues(vstk),na.rm=T) # cumulative sum
df_glaciers[i,((ci+3):(ci+41))] <- trend_vals # was 29
# if(vvar=="RF"){
# # RF specific
#
# } else{
# # SCA specific
# }
print(i)
# add values to dataframe
# SCA %*n_pix
# Min_sca*n_pix
# RF frequency (# maxima)
#
# cumulative
if (i%%20==0){
end = Sys.time() - strt
print(end)
}
}
# add names (move to end)
names(df_glaciers) = c(o_names,c("tot_mean","tot_sum","y_t","y_p","y_r2","mam_t","mam_p","mam_r2","jja_t",
"jja_p","jja_r2","mamj_t","mamj_p","mamj_r2","jaso_t",
"jaso_p","jaso_r2","aso_t","aso_p","aso_r2","amj_t",
"amj_p","amj_r2","na_t","na_p","na_r2","nm_t","nm_p","nm_r2",
"sa_yt","sa_yp","sa_yr2","sa_mamt","sa_mamp","sa_mamr2",
"sa_amjt","sa_amjp","sa_amjr2","sa_jjat","sa_jjap","sa_jjar2"))
#12 more for sca
end = Sys.time() - strt
print(end)
return(df_glaciers)
}
## DONE
##############################################################################################################################
year.season.trends <- function(vstk, vdates, velv){
# INDEX YEARS, MONTHS, MAM, JJA
yid = format(vdates,"%Y"); yidx = as.numeric(yid)-2000
mid = format(vdates,"%m"); midx = as.numeric(mid)
mami = midx == 3 | midx == 4 | midx == 5; jjai = midx == 6 | midx == 7 | midx == 8
mamji = midx == 3 | midx == 4 | midx == 5 | midx == 6; jasoi = midx == 7 | midx == 8 | midx == 9 | midx == 10
amji = midx == 4 | midx == 5 | midx == 6; asoi = midx == 8 | midx == 9 | midx == 10
mam = yidx[mami] # March, April, May etc.
jja = yidx[jjai]
amj = yidx[amji]
mamj = yidx[mamji]
jaso = yidx[jasoi]
aso = yidx[asoi]
# variables
# if(ncol(df_glaciers)!=9){cat("! Incorrect column index !\n> ncol != 9 ");stop()}
# MAIN FUNCTIONS
fun_time = 'mean' # joining images over time
fun_spatial = 'mean' # over glacier area
#
# EXTRA FUNCTIONS
fun_lm_time = function(x) { if (is.na(x[1])){ NA } else {lm(x ~ time)$coefficients[2] }}
fun_count_na = function(i, ...) sum(!is.na(i))
fun_count = function(i, ...) sum((i) < mean(i))
fun_count_mean = function(x) {
comp <- x[1]
main <- x[2:length(x)]
length(main[main >= comp])
#sum(main >= comp,na.rm=T)
}
# YEARLY/SEASONAL STACKS (ALL SAME NLAYERS)
#
yearly_means_stk0 <- stackApply(vstk,indices=yidx, fun_time)
yearly_means_stk <- cellStats(stackApply(vstk,indices=yidx, fun_time), fun_spatial)
mam_means_stk <- cellStats(stackApply(vstk[[which(mami)]],indices=mam, fun_time), fun_spatial)
jja_means_stk <- cellStats(stackApply(vstk[[which(jjai)]],indices=jja, fun_time), fun_spatial)
mamj_means_stk <- cellStats(stackApply(vstk[[which(mamji)]],indices=mamj, fun_time), fun_spatial)
jaso_means_stk <- cellStats(stackApply(vstk[[which(jasoi)]],indices=jaso, fun_time), fun_spatial)
aso_means_stk <- cellStats(stackApply(vstk[[which(asoi)]],indices=aso, fun_time), fun_spatial)
amj_means_stk <- cellStats(stackApply(vstk[[which(amji)]],indices=amj, fun_time), fun_spatial)
#
# odd
#monthly_means_stk <- stackApply(vstk,indices=midx, mean) # 12 layers (months)
#year_month_means_stk <- stackApply(vstk,indices=(yidx + midx/100), mean)
#
# NCELL COUNT !NAs
years_ncell_na <- stackApply(vstk, indices = yidx, fun_count_na)
years_ncell_na[years_ncell_na==0] <- NA
years_ncell_na <- cellStats(years_ncell_na, fun_spatial)
#
# NCELLS ABOVE MEAN
for (i in 1:nlayers(yearly_means_stk0)){
vmeans = vstk[[which(yidx==i)]]
#vvm <- calc(vmeans, mean)
vvm <- yearly_means_stk0[[i]]
vmeans2 = stack(vvm, vmeans)
r = calc(vmeans2, fun_count_mean)
# get rid of NA
r[is.na(vmeans[[1]])] = NA
if (i==1){vms = r}else{vms=stack(vms,r)}
}
ncells_peryear_over_mean = cellStats(vms,mean)
## ADD SCA (Should add 'type'=SCA/RF)
# sca_weight_area <- (cellStats(vstk,mean)/100)*cellStats(vstk,fun_count_na)*(0.5^2)
sca_weight_area <- cellStats(vstk,mean)
sca_area_yr <- with(data.frame(value=sca_weight_area,factor=yidx), tapply(value, factor, mean,na.rm=T))
sca_area_mam <- with(data.frame(value=sca_weight_area[which(mami)],factor=mam), tapply(value, factor, mean,na.rm=T))
sca_area_amj <- with(data.frame(value=sca_weight_area[which(amji)],factor=amj), tapply(value, factor, mean,na.rm=T))
sca_area_jja <- with(data.frame(value=sca_weight_area[which(jjai)],factor=jja), tapply(value, factor, mean,na.rm=T))
# TO ADD
# difference between max and min SCA every year
# day of min SCA
# TREND VALS
# create trends and summaries
ys <- 1:nlayers(yearly_means_stk0)
vals_list = list()
#
fit <- lm(yearly_means_stk~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$coefficients[2,4],summary(fit)$r.squared)
#
fit <- lm(mam_means_stk~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$coefficients[2,4],summary(fit)$r.squared)
#
fit <- lm(jja_means_stk~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$coefficients[2,4],summary(fit)$r.squared)
#
fit <- lm(mamj_means_stk~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$coefficients[2,4],summary(fit)$r.squared)
#
fit <- lm(jaso_means_stk~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$coefficients[2,4],summary(fit)$r.squared)
#
fit <- lm(aso_means_stk~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$coefficients[2,4],summary(fit)$r.squared)
#
fit <- lm(amj_means_stk~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$coefficients[2,4],summary(fit)$r.squared)
#
fit <- lm(years_ncell_na~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$coefficients[2,4],summary(fit)$r.squared)
#
fit <- lm(ncells_peryear_over_mean~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$coefficients[2,4],summary(fit)$r.squared)
# NEW FOR SCA 12
#
fit <- lm(sca_area_yr~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$coefficients[2,4],summary(fit)$r.squared)
#
fit <- lm(sca_area_mam~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$coefficients[2,4],summary(fit)$r.squared)
#
fit <- lm(sca_area_amj~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$coefficients[2,4],summary(fit)$r.squared)
#
fit <- lm(sca_area_jja~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$coefficients[2,4],summary(fit)$r.squared)
return(unlist(vals_list))
}
##############################################################################################################################
############# CORRELLATIONS FUNCTIONS ##################
## DONE
##############################################################################################################################
g.match.rf.sca <- function(glacier_id, rgi, demL, noZero=TRUE, threshold = NULL,
rfpath, scapath, tfpath){
#
# #
# FOR GIVEN GLACIER #
# create matching SCA and RF data for glacier_i #
# mask with inner polygon #
# #
# Returns: #
# rsub | ssub | elv | match_dates #
# #
#
# READ RF FILE
rf_match <- list.files(rfpath, full.names = T, pattern = paste0(glacier_id,".*grd"))
rfstk = stack(rf_match)
#
# RGI SHAPEFILE MASK (inner)
shape <<- rgi[gsub("[[:punct:]]", "_", rgi@data$RGIId) == glacier_id,]
cent_distance <- res(rfstk)[1]/2
suppressWarnings(inner_shape <<- buffer(shape,width=-cent_distance))
#
# DEM
dem <- crop(demL,rfstk)
elv <<- mask(resample(dem, rfstk), inner_shape)
#
# find matching SCA for glacier
sca_match = list.files(scapath, full.names = T, pattern = paste0(glacier_id,".*grd"))
if (!is.na(sca_match)){scastk = try(stack(sca_match))} else{print("!!! SCA_glacier_match does not exist!")}
#
# mask for all years
rf_shp = mask(rfstk, inner_shape)
sca_shp = mask(scastk, inner_shape)
#
# DELETE ALL 0vals
if(noZero){rf_shp[rf_shp==0] <- NA;sca_shp[sca_shp==0] <- NA}
#
# OVERLAP SCA & RF DATES
rf_n = substring(names(rf_shp),2,8)
sca_n = substring(names(sca_shp),2,8)
#
# MATCH RF & SCA (Keep all RF)
# EXCLUDE 2018 DATES (& 2017)
mixd = intersect(rf_n, sca_n)
match_dates <- as.Date(mixd,format="%Y%j")
didx <- format(match_dates, "%Y") != "2018" & format(match_dates, "%Y") != "2017"
match_dates <<- match_dates[didx]
mixd <- mixd[didx]
#
# SUBSET
rsub <<- rf_shp[[match(mixd,rf_n)]]
ssub <<- sca_shp[[match(mixd,sca_n)]]
#
if (!is.null(threshold)){
# add threshold
if(length(threshold)>1){
rsub[ssub < threshold[1]] <- NA
ssub[ssub < threshold[2]] <- NA
rsub <<- rsub; ssub <<- ssub
cat("Threshold of >", threshold[1], "SCA to RF \n ","Threshold of >", threshold[2], "SCA to SCA")
} else{
rsub[ssub < threshold] <- NA
ssub[ssub < threshold] <- NA
rsub <<- rsub; ssub <<- ssub
print(paste("Threshold of >", threshold, "to all"))
}
}
#
# cat("Created variables: \n rsub | ssub | elv | match_dates ")
# print(paste("<glacier_id>",glacier_id))
}
## NEW 06/23/2020
##############################################################################################################################
g.match.rf.sca2 <- function(glacier_id, rgi, demL, noZero=TRUE, threshold = NULL,
rfpath, scapath, tfpath){
#
# #
# FOR GIVEN GLACIER #
# create matching SCA and RF data for glacier_i #
# mask with inner polygon #
# #
# Returns: #
# rsub | ssub | elv | match_dates #
# #
#
# READ RF FILE
rf_match <- list.files(rfpath, full.names = T, pattern = paste0(glacier_id,".*grd"))
rfstk = stack(rf_match)
#
# RGI SHAPEFILE MASK (inner)
shape <<- rgi[gsub("[[:punct:]]", "_", rgi@data$RGIId) == glacier_id,]
cent_distance <- res(rfstk)[1]/2
suppressWarnings(inner_shape <<- buffer(shape,width=-cent_distance))
#
# DEM
dem <- crop(demL,rfstk)
elv <<- mask(resample(dem, rfstk), inner_shape)
#
# find matching SCA for glacier
sca_match = list.files(scapath, full.names = T, pattern = paste0(glacier_id,".*grd"))
if (!is.na(sca_match)){scastk = try(stack(sca_match))} else{print("!!! SCA_glacier_match does not exist!")}
#
# mask for all years
rf_shp = mask(rfstk, inner_shape)
sca_shp = mask(scastk, inner_shape)
#
# DELETE ALL 0vals
# if(noZero){rf_shp[rf_shp==0] <- NA;sca_shp[sca_shp==0] <- NA}
#
# OVERLAP SCA & RF DATES
rf_n = substring(names(rf_shp),2,8)
sca_n = substring(names(sca_shp),2,8)
#
# MATCH RF & SCA (Keep all RF)
# EXCLUDE 2018 DATES (& 2017)
mixd = intersect(rf_n, sca_n)
match_dates <- as.Date(mixd,format="%Y%j")
didx <- format(match_dates, "%Y") != "2018" & format(match_dates, "%Y") != "2017"
match_dates <<- match_dates[didx]
mixd <- mixd[didx]
#
# SUBSET
rsub <<- rf_shp[[match(mixd,rf_n)]]
ssub <<- sca_shp[[match(mixd,sca_n)]]
#
if (!is.null(threshold)){
# add threshold
if(length(threshold)>1){
rsub[ssub < threshold[1]] <- NA
ssub[ssub < threshold[2]] <- NA
cat("Threshold of >", threshold[1], "SCA to RF \n ","Threshold of >", threshold[2], "SCA to SCA")
} else{
rsub[ssub < threshold] <- NA
ssub[ssub < threshold] <- NA
print(paste("Threshold of >", threshold, "to all"))
}
}
# MAKE NA VALUES WITHIN glacier 0 (NEW NEW)
if(noZero){
rsub[is.na(rsub)] <- 0;rsub <- mask(rsub, inner_shape)
ssub[is.na(ssub)] <- 0;ssub <- mask(ssub, inner_shape)
rsub <<- rsub; ssub <<- ssub
print("...applied 0-mask")
}
#
# cat("Created variables: \n rsub | ssub | elv | match_dates ")
# print(paste("<glacier_id>",glacier_id))
}
## DONE
#####################################################################################
glacier.corr.year <- function(df_glaciers, rgi,demL,noZero = TRUE,threshold = NULL,
rfpath,scapath,tfpath){
#
# Data frame of cross correlation between RF and SCA
#
strt = Sys.time(); ci=ncol(df_glaciers)
o_names <- names(df_glaciers)
df_glaciers[,10:68] <- NA # make room for new variables
## LOOP THROUGH EACH GLACIER
# !!!!
# quick fix (last name has no SCA)
for (i in 1:(length(df_glaciers$Object.ID)-1)){
#
g.match.rf.sca(df_glaciers$Object.ID[i],rgi,demL,noZero = TRUE,threshold = threshold,
rfpath,scapath,tfpath)
# ...creates: rsub | ssub | elv | match_dates
#
# CROSS-CORRELATION
rf_vals <- cellStats(rsub,mean)
sca_vals <- cellStats(ssub,mean)
continuous_weeks <- as.Date(seq(match_dates[1], tail(match_dates,1), by="7 days"))
df_corr <- data.frame(time = continuous_weeks)
df_corr$sca <- df_corr$rf <- NA
match_weeks_idx <- match(match_dates,continuous_weeks)
df_corr$rf[match_weeks_idx] <- rf_vals; df_corr$rf <- fill.na.mean(df_corr$rf)
df_corr$sca[match_weeks_idx] <- sca_vals; df_corr$sca <- fill.na.mean(df_corr$sca)
#
## CORRELATION
ccvals <- ccf(df_corr$rf,df_corr$sca, plot = FALSE)
cloess <- loess(ccvals$acf~ccvals$lag, span=0.2)
lines(cloess$fitted~cloess$x, col='red',lwd=2)
cf <- min(cloess$fitted)
lag <- ccvals$lag[which.min(cloess$fitted)]
#
# split by years
yid = format(match_dates,"%Y"); yidx = as.numeric(yid)-2000
#
## AREA
sca_weight_area <- (cellStats(ssub,mean)/100)*cellStats(ssub,fun_count_na)*(0.5^2)
rf_weight_area <- (cellStats(rsub,mean)/100)*cellStats(rsub,fun_count_na)*(0.5^2)
#
sca_mean_yr <- with(data.frame(value=sca_weight_area,factor=yidx), tapply(value, factor, mean,na.rm=T))
sca_sd_yr <- with(data.frame(value=sca_weight_area,factor=yidx), tapply(value, factor, sd,na.rm=T))
sca_min_yr <- with(data.frame(value=sca_weight_area,factor=yidx), tapply(value, factor, min,na.rm=T))
rf_mean_yr <- with(data.frame(value=rf_weight_area,factor=yidx), tapply(value, factor, mean,na.rm=T))
rf_max_yr <- with(data.frame(value=rf_weight_area,factor=yidx), tapply(value, factor, max,na.rm=T))
#
rsr = cellStats(rsub,mean)*(cellStats(ssub,mean)/100)
rf_sca_frac <- with(data.frame(value=rsr,factor=yidx), tapply(value, factor, mean,na.rm=T))
rf_sum_yr <- with(data.frame(value=cellStats(rsub,mean),factor=yidx), tapply(value, factor, sum,na.rm=T))
#
# DAY of MIN/MAX
sca_min_d <- with(data.frame(value=sca_weight_area,factor=yidx), tapply(value, factor, which.min))
sca_min_day <- as.numeric(sapply(1:17,FUN = function(x) format(match_dates[yidx==x][sca_min_d[x]],"%j")))
rf_max_d <- with(data.frame(value=rf_weight_area,factor=yidx), tapply(value, factor, which.max))
rf_max_day <- as.numeric(sapply(1:17,FUN = function(x) format(match_dates[yidx==x][rf_max_d[x]],"%j")))
#
# days over total mean
tm <- mean(cellStats(rsub,mean),na.rm=T)
rf_ndays_overmean <- with(data.frame(value=cellStats(rsub,mean),factor=yidx), tapply(value, factor, function(x) sum(x > tm,na.rm=T)))
tm <- mean(cellStats(ssub,mean),na.rm=T)
sca_ndays_undermean <- with(data.frame(value=cellStats(ssub,mean),factor=yidx), tapply(value, factor, function(x) sum(x < tm,na.rm=T)))
#
# maxima?
rf_maxima <- rep(1,17)
# rf_maxima <- with(data.frame(value=cellStats(rsub,mean),factor=yidx), tapply(value, factor, function(x) length(localMaxima(x))))
#
# QUICK TRENDS
vals_list = list()
ys = 1:17
fit <- lm(sca_min_yr~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$r.squared)
fit <- lm(sca_min_day~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$r.squared)
fit <- lm(rf_mean_yr~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$r.squared)
fit <- lm(rf_max_yr~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$r.squared)
fit <- lm(rf_max_day~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$r.squared)
fit <- lm(rf_sum_yr~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$r.squared)
fit <- lm(rf_ndays_overmean~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$r.squared)
fit <- lm(sca_ndays_undermean~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$r.squared)
fit <- lm(rf_maxima~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$r.squared)
fit <- lm(rf_sca_frac~ys)
vals_list = c(vals_list, coef(fit)['ys'],summary(fit)$r.squared)
# 20 extra trend vals
#
# add values to data frame
df_glaciers[i,(ci+1)] <- mean(getValues(rsub),na.rm=T)
df_glaciers[i,(ci+2)] <- sd(getValues(rsub),na.rm=T)
df_glaciers[i,(ci+3)] <- mean(getValues(ssub),na.rm=T)
df_glaciers[i,(ci+4)] <- sd(getValues(ssub),na.rm=T)
df_glaciers[i,(ci+5)] <- cf
df_glaciers[i,(ci+6)] <- lag
df_glaciers[i,(ci+7)] <- max(cellStats(rsub,fun_count_na),na.rm=T) # max|mean n cell !na
df_glaciers[i,(ci+8)] <- mean(cellStats(rsub,fun_count_na),na.rm=T)
df_glaciers[i,(ci+9)] <- sd(cellStats(rsub,fun_count_na),na.rm=T)
df_glaciers[i,(ci+10)] <- min(cellStats(ssub,fun_count_na),na.rm=T)
df_glaciers[i,(ci+11)] <- mean(cellStats(ssub,fun_count_na),na.rm=T)
df_glaciers[i,(ci+12)] <- sd(cellStats(ssub,fun_count_na),na.rm=T)
df_glaciers[i,(ci+13)] <- max(rf_mean_yr,na.rm=T) # RF max|mean area
df_glaciers[i,(ci+14)] <- mean(rf_mean_yr,na.rm=T) # the yr mean of means
df_glaciers[i,(ci+15)] <- sd(rf_mean_yr,na.rm=T)
df_glaciers[i,(ci+16)] <- max(rf_max_yr,na.rm=T)
df_glaciers[i,(ci+17)] <- mean(rf_max_yr,na.rm=T)
df_glaciers[i,(ci+18)] <- sd(rf_max_yr,na.rm=T)
df_glaciers[i,(ci+19)] <- min(sca_mean_yr,na.rm=T) # SCA min|mean area
df_glaciers[i,(ci+20)] <- mean(sca_mean_yr,na.rm=T)
df_glaciers[i,(ci+21)] <- sd(sca_mean_yr,na.rm=T)
df_glaciers[i,(ci+22)] <- min(sca_min_yr,na.rm=T)
df_glaciers[i,(ci+23)] <- mean(sca_min_yr,na.rm=T)
df_glaciers[i,(ci+24)] <- sd(sca_min_yr,na.rm=T)
df_glaciers[i,(ci+25)] <- mean(rf_max_day,na.rm=T) # RF day of max
df_glaciers[i,(ci+26)] <- sd(rf_max_day,na.rm=T)
df_glaciers[i,(ci+27)] <- sort(rf_max_day)[1] # earliest/latest
df_glaciers[i,(ci+28)] <- tail(sort(rf_max_day),1)
df_glaciers[i,(ci+29)] <- mean(sca_min_day,na.rm=T) # SCA day of min
df_glaciers[i,(ci+30)] <- sd(sca_min_day,na.rm=T)
df_glaciers[i,(ci+31)] <- sort(sca_min_day)[1] # earliest/latest
df_glaciers[i,(ci+32)] <- tail(sort(sca_min_day),1)
df_glaciers[i,(ci+33)] <- mean(rf_ndays_overmean,na.rm=T)
df_glaciers[i,(ci+34)] <- sd(rf_ndays_overmean,na.rm=T)
df_glaciers[i,(ci+35)] <- mean(sca_ndays_undermean,na.rm=T)
df_glaciers[i,(ci+36)] <- sd(sca_ndays_undermean,na.rm=T)
df_glaciers[i,(ci+37)] <- mean(rf_maxima,na.rm=T)
df_glaciers[i,(ci+38)] <- mean(rf_sca_frac,na.rm=T)
df_glaciers[i,(ci+39)] <- sd(rf_sca_frac,na.rm=T)
# trends
st= ci+40; en=st+19
df_glaciers[i,(st:en)] <- unlist(vals_list)
#
print(i)
#
if (i%%20==0){
end = Sys.time() - strt
print(end)
}
}
# add names (move to end)
names(df_glaciers) = c(o_names,c("rfmean","rfsd","scamean","scasd","lcorr","lag","n_rfmax","n_rfmean","n_rfsd",
"n_scamax","n_scamean","n_scasd","rfarea_meanmax","rfarea_meanmean","rfarea_meansd",
"rfarea_maxmean","rfarea_maxmax","rfarea_maxsd","scarea_meanmin",
"scarea_meanmean","scarea_meansd","scarea_minmin","scarea_minmean","scarea_minsd",
"rfmax_day_mean","rfmax_day_sd","rfmax_day_first","rfmax_day_last","scamin_day_mean",
"scamin_day_sd","scamin_day_first","scamin_day_last",
"rfover_mean","rfover_sd","sunder_mean","sunder_sd","rf_maxima","rfsca_f_mean","rfsca_f_sd",
"tr_sarea_min","tr2_sarea_min","tr_sday_min","tr2_sday_min","tr_rarea_mean","tr2_rarea_mean",
"tr_rarea_max","tr2_rarea_max","tr_rday_max","tr2_rday_max","tr_rsum","tr2_rsum",
"tr_rover","tr2_rover","tr_sunder","tr2_sunder","tr_rmaxim","tr2_rmaxim",
"tr_rfsca_f","tr2_rfsca_f"))
end = Sys.time() - strt
print(end)
return(df_glaciers)
}
## NEW 04/15/2020
#####################################################################################
glacier.month.trends <- function(df_glaciers, rgi,demL,noZero = TRUE,threshold = NULL,
rfpath,scapath,tfpath, t.func='mean'){
#
# Data frame of cross correlation between RF and SCA
#
strt = Sys.time(); ci=ncol(df_glaciers)
o_names <- names(df_glaciers)
df_glaciers[,10:406] <- NA # make room for new variables
## LOOP THROUGH EACH GLACIER
# !!!!
# quick fix (last name has no SCA)
for (i in 1:(length(df_glaciers$Object.ID)-1)){
#
g.match.rf.sca2(df_glaciers$Object.ID[i],rgi,demL,noZero = TRUE,threshold = threshold,
rfpath,scapath,tfpath)
# ...creates: rsub | ssub | elv | match_dates
#
# Main Values
rf_vals <- cellStats(rsub,mean)
sca_vals <- cellStats(ssub,mean)
#
# split indices
yid = format(match_dates,"%Y"); yidx = as.numeric(yid)-2000
mid = format(match_dates,"%m"); midx = as.numeric(mid)
ymidx = (yidx + midx/100)
ym_sca <- stackApply(ssub,indices=ymidx, mean)
ym_rf <- stackApply(rsub,indices=ymidx, mean)
ymidx_u <- unique(ymidx)
#
## YEAR_VALUES ------------->
sca_mean_yr0 <- with(data.frame(value=sca_vals,factor=yidx), tapply(value, factor, mean,na.rm=T))
sca_mean_yr = mean(sca_mean_yr0,na.rm=T)
sca_sd_yr0 <- with(data.frame(value=sca_vals,factor=yidx), tapply(value, factor, sd,na.rm=T))
sca_sd_yr = mean(sca_sd_yr0,na.rm=T)
sca_min_yr0 <- with(data.frame(value=sca_vals,factor=yidx), tapply(value, factor, min,na.rm=T))
sca_min_yr = mean(sca_min_yr0,na.rm=T)
rf_mean_yr0 <- with(data.frame(value=rf_vals,factor=yidx), tapply(value, factor, mean,na.rm=T))
rf_mean_yr = mean(rf_mean_yr0,na.rm=T)
rf_sum_yr0 <- with(data.frame(value=rf_vals,factor=yidx), tapply(value, factor, sum,na.rm=T))
rf_sum_yr = mean(rf_sum_yr0,na.rm=T)
rf_sd_yr0 <- with(data.frame(value=rf_vals,factor=yidx), tapply(value, factor, sd,na.rm=T))
rf_sd_yr = mean(rf_sd_yr0,na.rm=T)
rf_max_yr0 <- with(data.frame(value=rf_vals,factor=yidx), tapply(value, factor, max,na.rm=T))
rf_max_yr = mean(rf_max_yr0,na.rm=T)
#
ts = 1:16 # replace all (take unique years...)
#
## YEAR TRENDS
dfyv = data.frame(t = 1:16,sca_mean_yr0,sca_sd_yr0,sca_min_yr0,
rf_mean_yr0,rf_sum_yr0,rf_sd_yr0,rf_max_yr0)
# run trends
dtr1 = dfyv %>% melt(.,id.vars = 't') %>%
group_by(variable) %>%
do(dfit = lm(value~t, data=.)) %>%
tidy(dfit) %>% filter(term=="t")
dtr2 = dfyv %>% melt(.,id.vars = 't') %>%
group_by(variable) %>%
do(dfit = lm(value~t, data=.)) %>% glance(dfit)
# save values
tr_est_yr = dtr1$estimate
tr_pval_yr = dtr1$p.value
tr_ste_yr = dtr1$std.error
tr_sig_yr = dtr2$sigma
tr_r2_yr = dtr2$r.squared
#
## YEAR SCA~RF
cor_scarf = dfyv %>%
dplyr::summarize(cor_mean = round(cor(sca_mean_yr0,rf_mean_yr0),2),
cor_smean_rsum = round(cor(sca_mean_yr0,rf_sum_yr0),2),
cor_smin_rsum = round(cor(sca_min_yr0,rf_sum_yr0),2),
cor_smin_rmean = round(cor(sca_min_yr0,rf_mean_yr0),2),
cor_sd = round(cor(sca_sd_yr0,rf_sd_yr0),2))
# **if cor is significant then rerun with more lm
scarf_mean1 = dfyv %>% do(dfit = lm(sca_mean_yr0~rf_mean_yr0, data=.)) %>%
tidy(dfit) %>% filter(term=="rf_mean_yr0") %>% select(-term,-statistic)
scarf_mean2 = dfyv %>% do(dfit = lm(sca_mean_yr0~rf_mean_yr0, data=.)) %>% glance(dfit)
tr_scarf_mean = c(as.numeric(scarf_mean1),scarf_mean2$r.squared)
# COUNT npix
npix = max(cellStats(ssub,fun_count_na,na.rm=T))
snpix = cellStats(ssub,fun_count_na,na.rm=T); snpix[snpix < (npix*0.25)] = npix # if less than 1/4 of glacier
ss50 = ssub; ss50[ss50 < 50] = NA # exclude pixels less than X
snpix50 = cellStats(ss50,fun_count_na,na.rm=T);snpix50[snpix50 < (npix*0.25)] = npix
spixmin <- with(data.frame(value=snpix,factor=yidx), tapply(value, factor, min,na.rm=T))
spixmin50 <- with(data.frame(value=snpix50,factor=yidx), tapply(value, factor, min,na.rm=T))
# trend for pixels
ppx = data.frame(y=1:16,spixmin) %>%
do(dfit = lm(spixmin~y, data=.)) %>%
tidy(dfit) %>% filter(term=="y") %>% select(-term,-statistic,-std.error)
ppx50 = data.frame(y=1:16,spixmin50) %>%
do(dfit = lm(spixmin50~y, data=.)) %>%
tidy(dfit) %>% filter(term=="y") %>% select(-term,-statistic,-std.error)
# save values
spx_rat = (mean(spixmin,na.rm=T)/npix)
spx_rat50 = (mean(spixmin50,na.rm=T)/npix)
pix_vals = c(npix,spx_rat,spx_rat50)
tr_pix = c(as.numeric(ppx),as.numeric(ppx50))
#
## MONTH DFs ------------->
sca_month <- with(data.frame(value=sca_vals,factor=ymidx), tapply(value, factor, t.func,na.rm=T))
dfmos = cbind(y=1:16,as.data.frame(matrix(sca_month,ncol=12,byrow=TRUE)))
rf_month <- with(data.frame(value=rf_vals,factor=ymidx), tapply(value, factor, t.func,na.rm=T))
dfmor = cbind(y=1:16,as.data.frame(matrix(rf_month,ncol=12,byrow=TRUE)))
#
# MAIN MONTH VALUES
sca_mean_month <- colMeans(dfmos)[2:13]
sca_sd_month <- apply(dfmos, 2, sd)[2:13]
sca_min_month <- apply(dfmos, 2, min)[2:13]
rf_mean_month <- colMeans(dfmor)[2:13]
rf_sum_month <- colSums(dfmor)[2:13]
rf_sd_month <- apply(dfmor, 2, sd)[2:13]
rf_max_month <- apply(dfmor, 2, max)[2:13]
# 1 value
sca_whmin_month <- as.numeric(which.min(sca_min_month))
rf_whmax_month <- as.numeric(which.max(rf_mean_month))
rf_whmaxsum_month <- as.numeric(which.max(rf_sum_month))
#
## MONTH TRENDS
# SCA
dm1 = dfmos %>% melt(.,id.vars = 'y') %>%
group_by(variable) %>%
do(dfit = lm(value~y, data=.)) %>%
tidy(dfit) %>% filter(term=="y") %>% select(-term,-statistic)
dm2 = dfmos %>% melt(.,id.vars = 'y') %>%
group_by(variable) %>%
do(dfit = lm(value~y, data=.)) %>% glance(dfit)
# save values
trsca_est_month = dm1$estimate
trsca_pval_month = dm1$p.value
trsca_ste_month = dm1$std.error
trsca_sig_month = dm2$sigma
trsca_r2_month = dm2$r.squared
# SCA
dm1 = dfmor %>% melt(.,id.vars = 'y') %>%
group_by(variable) %>%
do(dfit = lm(value~y, data=.)) %>%
tidy(dfit) %>% filter(term=="y") %>% select(-term,-statistic)
dm2 = dfmor %>% melt(.,id.vars = 'y') %>%
group_by(variable) %>%
do(dfit = lm(value~y, data=.)) %>% glance(dfit)
# save values (12 values)
trf_est_month = dm1$estimate
trf_pval_month = dm1$p.value
trf_ste_month = dm1$std.error
trf_sig_month = dm2$sigma
trf_r2_month = dm2$r.squared
## YEAR SCA~RF
mrf = dfmor %>%
melt(id.vars="y")
srf = dfmos %>%
melt(id.vars="y")
cor_month = cbind(srf, rvalue = mrf$value) %>% na.omit() %>%
group_by(variable) %>%
dplyr::summarize(corr = round(cor(rvalue,value),2)) %>% select(corr)
dmon1 = cbind(srf, rvalue = mrf$value) %>%
group_by(variable) %>%
do(dfit = lm(value~rvalue, data=.)) %>%
tidy(dfit) %>% filter(term=="rvalue") %>% select(-term,-statistic)
dmon2 = cbind(srf, rvalue = mrf$value) %>%
group_by(variable) %>%
do(dfit = lm(value~rvalue, data=.)) %>% glance(dfit)
# save values
tr_scarf_est_month = dmon1$estimate
tr_scarf_pval_month = dmon1$p.value
tr_scarf_ste_month = dmon1$std.error
tr_scarf_sig_month = dmon2$sigma
tr_scarf_r2_month = dmon2$r.squared
# COUNT npix (CHANGE BY MONTH!!!!!!)
snpixm = cellStats(ssub,fun_count_na,na.rm=T)
spixmonth <- with(data.frame(value=snpixm,factor=midx), tapply(value, factor, min,na.rm=T))
snpixm[snpixm < (npix*0.25)] = npix # if less than 1/4 of glacier
spixmonth_c <- with(data.frame(value=snpixm,factor=midx), tapply(value, factor, min,na.rm=T))
snpix50m = cellStats(ss50,fun_count_na,na.rm=T)
spixmonth50 <- with(data.frame(value=snpix50m,factor=midx), tapply(value, factor, min,na.rm=T))
snpix50m[snpix50m < (npix*0.25)] = npix
spixmonth50_c <- with(data.frame(value=snpix50m,factor=midx), tapply(value, factor, min,na.rm=T))
spix_allmonth <- with(data.frame(value=snpixm,factor=ymidx), tapply(value, factor, min,na.rm=T))
dfspix = cbind(y=1:16,as.data.frame(matrix(spix_allmonth,ncol=12,byrow=TRUE)))
# trend for pixels
ppxm = dfspix %>% melt(.,id.vars="y") %>% group_by(variable) %>%
do(dfit = lm(value~y, data=.)) %>%
tidy(dfit) %>% filter(term=="y") %>% select(-term,-statistic,-std.error)
ppx50m = dfspix %>% melt(.,id.vars="y") %>% group_by(variable) %>%
do(dfit = lm(value~y, data=.)) %>%
tidy(dfit) %>% filter(term=="y") %>% select(-term,-statistic,-std.error)
# save values
spxmo_rat = spixmonth/npix
spxmo_rat_c = spixmonth_c/npix
spxmo_rat50_c = spixmonth50_c/npix
tr_pix_month_est = ppxm$estimate
tr_pix_month_pval = ppxm$p.value
# tr_pix50_month_est = ppxm$estimate
# can be saved as a list of values???
# SAVE VALUES
var_yr_mo_ls = list(sca_mean_yr,sca_sd_yr,sca_min_yr, ## YEAR VALUES!!!
rf_mean_yr,rf_sum_yr,rf_sd_yr,rf_max_yr, # first 7 are values
tr_est_yr, # 7 trends (smean,ssd,smin,rmean,rsum,rsd,rmax)
tr_pval_yr,tr_ste_yr,tr_sig_yr,tr_r2_yr, # (all 7 values each)
cor_scarf, # 5 values (means, smean_rsum, smin_rsum, smin_rmean, sd)
tr_scarf_mean, # 4 (estimate, std.error, p.value, r2)
npix,spx_rat,spx_rat50, # 3 (npix, ave_minpix, ave_minpix50)
tr_pix, # 4 (tr_minpix_estimate, tr_minpix_p.value, tr_minpix50_estimate, tr_minpix50_p.value)
sca_mean_month, # MONTH VALUES!!! (12 values)
sca_sd_month,
sca_min_month,
rf_mean_month,
rf_sum_month,
rf_sd_month,
rf_max_month,
sca_whmin_month, # 1 variable
rf_whmax_month, # 1 variable
rf_whmaxsum_month, # 1 variable
trsca_est_month, # MONTHLY TRENDS (12 Vals)
trsca_pval_month,
trsca_ste_month,
trsca_sig_month,
trsca_r2_month,
trf_est_month,
trf_pval_month,
trf_ste_month,
trf_sig_month,
trf_r2_month,
cor_month, # SCARF by MONTH (12 values)
tr_scarf_est_month,
tr_scarf_pval_month,
tr_scarf_ste_month,
tr_scarf_sig_month,
tr_scarf_r2_month,
spxmo_rat,spxmo_rat_c,spxmo_rat50_c,
tr_pix_month_est,
tr_pix_month_pval
)
# add pixels by month
nm_h1 = c("smean","ssd","smin","rmean","rsum","rsd","rmax")
# NAMES
nms = c("sca_mean_yr","sca_sd_yr","sca_min_yr", ## YEAR VALUES!!!
"rf_mean_yr","rf_sum_yr","rf_sd_yr","rf_max_yr", # 1 each
paste0(rep("tr_est_yr",7),nm_h1), # 7 trends (smean,ssd,smin,rmean,rsum,rsd,rmax)
paste0(rep("tr_pval_yr",7),nm_h1),
paste0(rep("tr_ste_yr",7),nm_h1),
paste0(rep("tr_sig_yr",7),nm_h1),
paste0(rep("tr_r2_yr",7),nm_h1),
paste0(rep("cor_scarf",5),c('means', 'smean_rsum', 'smin_rsum', 'smin_rmean', 'sd')),
paste0(rep("tr_scarf_mean",4),c('est', 'ste', 'pval', 'r2')),
'npix','spx_rat','spx_rat50', # 3 (npix, ave_minpix, ave_minpix50)
paste0(rep("tr_pix",4),c('est', 'pval', 'est50', 'pval50')),
paste0(rep("sca_mean_month",12),1:12), # MONTH VALUES!!! (12 values)
paste0(rep("sca_sd_month",12),1:12),
paste0(rep("sca_min_month",12),1:12),
paste0(rep("rf_mean_month",12),1:12),
paste0(rep("rf_sum_month",12),1:12),
paste0(rep("rf_sd_month",12),1:12),
paste0(rep("rf_max_month",12),1:12),
"sca_whmin_month", # 1 variable
"rf_whmax_month", # 1 variable
"rf_whmaxsum_month", # 1 variable
paste0(rep("trsca_est_month",12),1:12), # MONTHLY TRENDS (12 Vals)
paste0(rep("trsca_pval_month",12),1:12),
paste0(rep("trsca_ste_month",12),1:12),
paste0(rep("trsca_sig_month",12),1:12),
paste0(rep("trsca_r2_month",12),1:12),
paste0(rep("trf_est_month",12),1:12),
paste0(rep("trf_pval_month",12),1:12),
paste0(rep("trf_ste_month",12),1:12),
paste0(rep("trf_sig_month",12),1:12),
paste0(rep("trf_r2_month",12),1:12),
paste0(rep("cor_month",12),1:12), # SCARF by MONTH (12 values)
paste0(rep("tr_scarf_est_month",12),1:12),
paste0(rep("tr_scarf_pval_month",12),1:12),
paste0(rep("tr_scarf_ste_month",12),1:12),
paste0(rep("tr_scarf_sig_month",12),1:12),
paste0(rep("tr_scarf_r2_month",12),1:12),
paste0(rep("spxmo_rat",12),1:12),