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Boreal.drought.R
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Boreal.drought.R
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00011111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111100
00# DESCRIPTION: ##
00# ##
00# This R script includes processed data, and generates graphs associated with ##
00# the manuscript "How tree species, tree size and topographical location influenced ##
00# tree transpiration in northern boreal forests during the historic 2018 drought ##
00# ##
00# NOTE: This data and associated scripts are an extract from a larger script and ##
00# database, and consequently, the files might contains additional data that ##
00# was not included in the manuscript. Additionally, some data are already fully ##
00# processed, and the R scripts provided are for reference purposes only ##
00# ##
00# Manuscript DOI: https://doi.org/10.1111/gcb.15601 ##
00# ##
00# ##
00# SAP FLOW DETAILS: ##
00# - Method: Heat dissipation. Granier 1985 ##
00# - Corrections: Wounding drift & Radial profiles ##
00# ##
00# WHOLE-TREE CONDUCTANCE METHOD: ##
00# - Phillips & Oren, 1998 ##
00# ##
00# KRYCKLAN Sites ##
00# ##
00# Created by: Jose GL, March 1, 2019 ##
00# Last update/edit: ##
00# Mar 15, 2021 ##
00011111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111100
00011111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111100
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
#
# Files required
#
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
# LIST OF FILES REQUIRED ----------------------------------------------------------------
"stk.sf.met.csv"
"daily.data.csv"
"all.nodes.sapflow.csv"
"wound.drift.fd.csv"
"all.nodes.raw.dat"
"longterm_drought.csv"
"all.eco.met.dat"
"allometric_data.dat"
"tree.diam.info.csv"
"par.estimates.csv"
"histo_data.csv"
# END OF SECTION ----
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
#
# LIBRARIES - PACKAGES
#
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
# INSTALL - LIBRARIES & PACKAGES ----------------------------------------------------------------
library(robustbase)
library(scPDSI)
library(lubridate)
library(data.table)
library(dplyr)
library(SPEI)
library(rbin)
library(minpack.lm)
library(biwt)
library(lme4)
# SET - WORKING DIRECTORY ----------------------------------------------------------------
# Get working directory and files
getwd()
setwd("Data/")
# END OF SECTION ----
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
#
# ENVIRONMENTAL DATA
#
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
# OPEN - METEOROLOGICAL AND ECOSYSTEMS DATA ----------------------------------------------------------------
all.eco.met = read.table(file="Data/all.eco.met", header=T, sep = ",")
#Add date to database
all.eco.met["Date"] <- (Date= as_datetime(seq(as.POSIXct("2016/01/01 00:00:00", tz="UTC"), as.POSIXct("2020/12/30 23:30:00", tz="UTC"), by = "30 min")))
rm(Date)
#max(grep("11:30 PM$", eco_met_678$TIMESTAMP_30))
#tail(eco_met_678$TIMESTAMP_30, 1)
# ESTIMATE - REFERENCE ET & VPD ----------------------------------------------------------------
#Estimate saturation vapor pressure & VPD [both in kPa]
SVP = 0.6108 * exp((17.27*all.eco.met$Ta_1_1_1)/(all.eco.met$Ta_1_1_1+237.3))
VPD = SVP * (1-all.eco.met$RH_1_1_1/100)
#Estimate slope component
D = (4098* SVP)/(all.eco.met$Ta_1_1_1 + 237.1)^2
plot(D)
# Estimate atmospheric pressure for a given elevation
elev = 275
P = 101.3*((293-0.0065*elev)/293)^5.26
#Estimate psychrometric constant
#Latent heat vaporizaton = 2.45 MJ K-1
#Specific heat at constant pressure = 1.013 10-3 MK K-1 C-1
#Ratio of molecular weight of water vapour/dry air = 0.622
g = (0.001013*P)/(0.622*2.45)
# Estimate average of all soil heat flux sensors
all.eco.met["G_All"] <-rowMeans(all.eco.met[grep("^G_1|^G_2|^G_3|^G_4", names(all.eco.met))], na.rm = TRUE)
# Estimate potential evapotranspiration
# We have two measurements per hour
ET0 = (0.408 * D * (((all.eco.met$NetRad_1_2_1*0.0036)-(all.eco.met$G_All*0.0036)) + g * (18.75 / (all.eco.met$Ta_1_1_1 + 273)) * 0 * VPD)) / (D + g * (1 + 0.24 * 0))
# Air density
pa = P /(287.058 *all.eco.met$Ta_1_1_1 + 273.15)
###///////// Section to adjust ET0 to a forest (paused at the moment)
rs = 100/0.5*2.95
ra = (log(20-20*(2/3)/20*0.123)*log(20-20*(2/3)/20*0.123))/( 0.41^2*0.5)
#plot(pa, ylim=c(0,10))
plot(all.eco.met$Ta_1_1_1)
ET0.f = D * (((all.eco.met$NetRad_1_2_1*0.0036)-(all.eco.met$G_All*0.0036)) + (((1.225*0.001013*VPD/ra)))/
D+g*(1+(rs/ra)))
rm(pa)
###/////////
# NetRad is in W/m^2 and we need MJ m^-2 h^-1
# 1 watt = 1 Joule/s
# There are 3600 s in an hour
# There are 1 000 000 Joules in a MJ
# e.g. 1000 watt/m^2 * 3600 s == 3 600 000 J m^-2 h^-1 == 3.6 MJ m^-2 h^-1
# Or I just multiply by 0.0036 and get the same number
# 1000 * 0.0036 = 3.6
# We have two measurements per hour
# To add up all hours in a day, each needs to be divided over two
# Or simply divide the multiplier by two:
# 1000 watt/m^2 * 0.0018 = 1.8 MJ m^-2 (per measurement)
# "Per measurement" units can be easily added up
all.eco.met["ETo_mm"]<- ifelse(ET0 < 0, 0, ET0/2)
all.eco.met["ET0.f"] <- ET0.f/2
all.eco.met["VPD_kPa"]<- VPD
all.eco.met["PP_ET"] <- all.eco.met$P_1_1_1 - all.eco.met$ETo_mm
all.eco.met["DOY_cont"] <- rep(seq(1,NROW(all.eco.met)/48, 1), times=1, each=48)
par(mar=c(4,4, 2, 2))
plot(aggregate(cbind(ET0.f)~DOY_cont, data=all.eco.met, FUN=sum), type="l", lty=1, col="red")
lines(aggregate(cbind(ETo_mm)~DOY_cont, data=all.eco.met, FUN=sum), col="blue")
#ETo_mm <-subset(all.eco.met, select=c("Date", "ETo_mm"))
names(all.eco.met)
#write.table(ETo_mm, "ETo_mm_mar_15_2021.csv", sep=",", row.names=F)
rm(ETo_mm)
rm(D, ET0, SVP, VPD, elev, P, g)
rm(ET0.f, oa, ra, rs, temp, mod.col)
# Testing different VPD formulas (all should match)
function(testrandom_vpd){
SVP = 0.6108 * exp(17.27*all.eco.met$Ta_1_1_1/(all.eco.met$Ta_1_1_1+237.3))
VPD = SVP * (1-all.eco.met$RH_1_1_1/100)
all.eco.met["VPD_kPa_2"]<- VPD
SVP = 610.78 * exp(all.eco.met$Ta_1_1_1 /(all.eco.met$Ta_1_1_1+237.3)*17.2694 )
VPD = SVP *(1-all.eco.met$RH_1_1_1/100)/1000
all.eco.met["VPD_kPa_3"]<- VPD
par(mar = c(3, 3, 0, 0))
junecol <- ifelse(all.eco.met$Month==6, "red", "grey")
plot(all.eco.met$Date, all.eco.met$VPD_kPa)
lines(subset(all.eco.met, all.eco.met$Year==2017, select=c("Date", "VPD_kPa_2")), col="red")
lines(all.eco.met$Date, SVP, col="blue")
vpd_agg <- aggregate(cbind(VPD_kPa, VPD_kPa_2, VPD_kPa_3)~Hour,
data=all.eco.met,FUN=mean, na.rm=T)
plot(vpd_agg$Hour, vpd_agg$VPD_kPa, col="green")
lines(vpd_agg$Hour, vpd_agg$VPD_kPa_2, col="red")
lines(vpd_agg$Hour, vpd_agg$VPD_kPa_3, col="blue")
}
# ADD - HOUR, DOY, DOY_cont, MONTH AND YEAR ----------------------------------------------------------------
NROW(all.eco.met)
names(all.eco.met)
all.eco.met["DOY_cont"] <- rep(seq(1,NROW(all.eco.met)/48, 1), times=1, each=48)
all.eco.met["DOEY"] <- lubridate::day(all.eco.met$Date)
all.eco.met["Month"] <- lubridate::month(all.eco.met$Date)
all.eco.met["Hour"] <- lubridate::hour(all.eco.met$Date)
all.eco.met["Year"] <- lubridate::year(all.eco.met$Date)
# ADD - BINNED VPD & PAR TO - "all.eco.met" ----------------------------------------------------------------
# Binned are used to explore the data, at reduced computing power, and help identify trends
labels = seq(0,4, 0.02)
all.eco.met["VPD_0.2"] <- labels[cut(all.eco.met$VPD_kPa, breaks = seq(0,4.02, 0.02),labels = labels,right = TRUE)]
labels = seq(0,2100, 50)
all.eco.met["PAR_50"] <- labels[cut(all.eco.met$PPFD_IN_1_2_1, breaks = seq(0,2150, 50),labels = labels,right = TRUE)]
rm(labels)
# ESTIMATE - MEAN VOLUMETRIC WATER CONTENT & SOIL SATURATION ----------------------------------------------------------------
names(all.eco.met)
all.eco.met["VWC_5"] <- (rowMeans(all.eco.met[c(29,34,39,44)], na.rm = TRUE)/100)
plot(all.eco.met$Date, all.eco.met$VWC_5)
all.eco.met$VWC_5[all.eco.met$VWC_5<0.15] <- NA
all.eco.met["VWC_10"] <- (rowMeans(all.eco.met[c(30,35,40,45)], na.rm = TRUE)/100)
plot(all.eco.met$Date, all.eco.met$VWC_10)
all.eco.met$VWC_10[all.eco.met$VWC_10<0.15] <- NA
all.eco.met["VWC_15"] <- (rowMeans(all.eco.met[c(31,36,41,46)], na.rm = TRUE)/100)
par(mar=c(4, 4, 0, 0))
plot(all.eco.met$Date, all.eco.met$VWC_15)
all.eco.met$VWC_15[all.eco.met$VWC_15<0.19] <- NA
all.eco.met["VWC_30"] <- (rowMeans(all.eco.met[c(32,37,42,47)], na.rm = TRUE)/100)
par(mar=c(4, 4, 0, 0))
plot(all.eco.met$Date, all.eco.met$VWC_30)
all.eco.met$VWC_30[all.eco.met$VWC_30<0.14] <- NA
all.eco.met["VWC_50"] <- (rowMeans(all.eco.met[c(33,38)], na.rm = TRUE)/100)
plot(all.eco.met$Date, all.eco.met$VWC_50)
all.eco.met$VWC_50[all.eco.met$VWC_50<0.1] <- NA
all.eco.met["VWC0.15"] <- rowMeans(all.eco.met[grep("VWC_5$|VWC_10$|VWC_15", names(all.eco.met))], na.rm = TRUE)
plot(all.eco.met$Date, all.eco.met$VWC0.15)
all.eco.met$VWC0.15[all.eco.met$VWC0.15<0.18] <- NA
all.eco.met["VWC0.30"] <- rowMeans(all.eco.met[grep("VWC_5$|VWC_10$|VWC_15|VWC_30", names(all.eco.met))], na.rm = TRUE)
plot(all.eco.met$Date, all.eco.met$VWC0.30)
all.eco.met$VWC0.15[all.eco.met$VWC0.15<0.18] <- NA
# Estimate soil saturation from 0-15 cm
soilsat <- (all.eco.met$VWC0.15-0.19)/(0.31-0.19)
all.eco.met["S0.15"] <- ifelse(soilsat<0, 0,
ifelse(soilsat>1, 1, soilsat))
rm(soilsat)
plot(all.eco.met$Date, all.eco.met$S0.15)
# Estimate missing soil saturation using LOESS
# Method works well if only a small fraction of data is missing
all.eco.met["ID"] <- seq(1:NROW(all.eco.met))
x <- all.eco.met$ID
y <- all.eco.met$S0.15
lo <- loess(y ~ x, na.rm=T, family="gaussian", control=loess.control(surface="direct", statistics="approximate"), span=0.01)
all.eco.met["S0.15_loess"] <- ifelse(predict(lo,x,na.rm=T)<0,0, predict(lo,x,na.rm=T))
all.eco.met["S0.15.nomiss"] <- ifelse(is.na(all.eco.met$S0.15), all.eco.met$S0.15_loess, all.eco.met$S0.15)
rm(x, y, lo)
# Estimate missing VPD using LOESS
x <- all.eco.met$ID
y <- all.eco.met$VPD_kPa
lo <- loess(y ~ x, na.rm=T, family="gaussian", control=loess.control(surface="direct", statistics="approximate"), span=0.001)
all.eco.met["VPD_kPa_loess"] <- ifelse(predict(lo,x,na.rm=T)<0, 0, predict(lo,x,na.rm=T))
all.eco.met["S0.15.nomiss"] <- ifelse(is.na(all.eco.met$VPD_kPa), all.eco.met$VPD_kPa_loess, all.eco.met$S0VPD_kPa.15)
plot(all.eco.met$Date, all.eco.met$VPD_kPa_loess,type="l", col="red", ylim=c(0, 3))
lines(all.eco.met$Date, all.eco.met$VPD_kPa, col="blue")
rm(x, y, lo)
par(mar=c(4, 4, 2, 2 ))
plot(all.eco.met$Date, all.eco.met$S0.15_loess, type="l", col="red", lwd=3)
lines(all.eco.met$Date, all.eco.met$S0.15, col="blue", lwd=3)
# ADD - CONTINUOS WEEK ----------------------------------------------------------------
names(all.eco.met)
all.eco.met["Week"] <- ifelse(all.eco.met$Year==2016,lubridate::week(all.eco.met$Date),
ifelse((all.eco.met$Year==2017),lubridate::week(all.eco.met$Date)+53,
ifelse((all.eco.met$Year==2018),lubridate::week(all.eco.met$Date)+106,
ifelse((all.eco.met$Year==2019),lubridate::week(all.eco.met$Date)+159,
lubridate::week(all.eco.met$Date)+212))))
#plot(all.eco.met$Hour, all.eco.met$ETo_mm)
#plot(all.eco.met$Week, all.eco.met$P_1_1_1)
#plot(all.eco.met$Week, all.eco.met$ETo_mm)
# ESTIMATE - OVERCAST DAYS----------------------------------------------------------------
# [Paused, Feb 19, 2021. Might not be needed]
# Aggregates by day and week to extract max ETo and max PAR
names(all.eco.met)
temp_agg <- aggregate(cbind(Week/48, VPD_kPa, ETo_mm, PPFD_IN_1_2_1)~DOY_cont,
data=all.eco.met,FUN=sum, na.rm=T)
temp_agg_week <- aggregate(cbind(V1, VPD_kPa, ETo_mm)~V1,
data=temp_agg,FUN=max, na.rm=T)
temp_week <- subset(all.eco.met, select=c("Week", "DOY_cont"))
colnames(temp_agg)[c(2)]<-c("Week")
colnames(temp_agg_week)[c(2)]<-c("Week")
temp_agg["ETo_max_mm"] <- merge(temp_agg, temp_agg_week, by="Week", all.x=T, all.y=, sort=T )[8]
temp_agg["PPFD_IN_1_2_1_max"] <- merge(temp_agg, temp_agg_week, by="Week", all.x=T, all.y=, sort=T )[5]
names(temp_agg)
x <- temp_agg$DOY_cont
y <- temp_agg$ETo_max_mm
lo <- loess(y~x, family = c("gaussian"), control = loess.control(surface = c("direct"),
statistics = c("approximate")),span=0.01)
temp_agg["ETo_loess"] <- summary(lo)$fitted
par(mar=c(4, 4, 2, 2 ))
plot(temp_agg$DOY_cont, temp_agg[,4], type="l", col="red", lwd=3)
lines(temp_agg$DOY_cont, temp_agg[,8], col="blue", lwd=3)
legend = c("Max ETo", "ETo")
legend("topleft", legend = legend, col = c("blue", "red"), lty=1, lwd=3
,bty="n",ncol=1, cex=1)
rm(legend)
temp_agg["Overcast"] <- ifelse(temp_agg$ETo_mm<temp_agg$ETo_loess *0.9, "Overcast", "Clear")
temp_agg["Overcast.range"] <- temp_agg$ETo_mm/temp_agg$ETo_loess
plot(temp_agg$DOY_cont, temp_agg$Overcast.range)
tmp_col = ifelse(temp_agg$Overcast=="Clear", "Blue", "red")
plot(temp_agg[,1], temp_agg[,4], type="p", col=tmp_col)
temp_week <- merge(temp_week, temp_agg, by="DOY_cont", all.x=T, all.y=, sort=T)
all.eco.met["ETo_max_loess"] <- temp_week$ETo_loess
all.eco.met["Overcast"] <- temp_week$Overcast
all.eco.met["PPFD_IN_1_2_1_max"] <- temp_week$PPFD_IN_1_2_1_max
all.eco.met["Overcast.range"] <- temp_week$Overcast.range
rm(temp_agg, temp_agg_week, temp_week, x, y, surface, temp, lo, tmp_col)
# ESTIMATE: Daily means ----------------------------------------------------------------
names(all.eco.met)
daily.data <- aggregate(cbind(Date)~DOY_cont,
data=all.eco.met, FUN = min, na.action = na.pass)
daily.data$Date <- date(as.POSIXct(daily.data$Date, origin="1970/01/1", tz="UTC"))
all.eco.met["Date_DOY"] <- date(all.eco.met$Date)
daily.means <- aggregate(cbind(Ta_1_1_1, RH_1_1_1, PPFD_IN_1_2_1, VPD_kPa,
VWC_5, VWC_10, VWC_15, VWC_30, VWC_50, VWC0.15, S0.15)~Date_DOY,
data=all.eco.met, FUN = mean, na.action = na.pass)
#daily.means["Date"] <- date(as.POSIXct(daily.means$Date, origin="1970/01/1", tz="UTC"))
daily.data$Date
daily.means$Date
names(daily.data)
names(daily.means)
# Change name of variable for "merge"
colnames(daily.means)[1]<-c("Date")
#daily.data <- merge(daily.data, daily.means, by="Date", all.x=T, all.y=F)
rm(daily.means)
NROW(daily.data)
NROW(daily.means)
summary_Fd.Q["Date_DOY"] <- date(summary_Fd.Q$Date)
daily.sums <- aggregate(cbind(ETo_mm, All_Q, PPFD_IN_1_2_1.sm = PPFD_IN_1_2_1, VPD_kPa.sm =VPD_kPa, P_1_1_1,
Pines_Q, Pine_15DIAM_Q,Pine_25DIAM_Q,Pine_.25DIAM_Q,
Spruce_Q, Spruce_15DIAM_Q, Spruce_25DIAM_Q, Spruce_.25DIAM_Q,
Birch_Q, Birch_15DIAM_Q, Birch_25DIAM_Q)~Date_DOY, data=summary_Fd.Q, FUN = sum, na.action = na.omit)
# Change name of variable for "merge"
colnames(daily.sums)[1]<-c("Date")
#daily.sums["Date"] <- date(as.POSIXct((daily.sums$Date/48)-42300, origin="1970/01/1", tz="UTC"))
# Add daylength-corrected VPD
names(summary_Fd.Q)
summary_Fd.Q_daylength <- subset(summary_Fd.Q, summary_Fd.Q$PPFD_IN_1_2_1>20, select=c("Date","PPFD_IN_1_2_1", "VPD_kPa", "DOY_cont", "Month", "Year" ),na.action=na.omit)
summary_Fd.Q_daylength["Date"] <- date(as.POSIXct(summary_Fd.Q_daylength$Date, origin="1970/01/1", tz="UTC"))
temp <- aggregate(cbind(DOY_cont, PPFD_IN_1_2_1, VPD_kPa)~Date, data=summary_Fd.Q_daylength, FUN=function(x) c(mean = mean(x), lgt = length(x)), na.action=na.omit)
# Estimate daylength-corrected VPD: Dz
temp["Dz"] <- temp$VPD_kPa[,1]*(temp$VPD_kPa[,2]/48)
# Estimate daylength-corrected PAR: PARz
temp["PARz"] <- temp$PPFD_IN_1_2_1[,1]*(temp$PPFD_IN_1_2_1[,2]/48)
as.numeric(temp$Date)
as.numeric(daily.data$Date)
as.numeric(daily.means$Date)
#The part below is no longer needed
#temp["Date"] <- date(as.POSIXct(temp[["Date"]][,1], origin="1970/01/1", tz="UTC"))
#temp["DOY_cont"] <- temp[["DOY_cont"]][,1]
#daily.data["DOY"] <- lubridate::yday(daily.data$Date)
#daily.data["Year"] <- lubridate::year(daily.data$Date)
temp.date <- date(seq(as.POSIXct("2016/01/01", tz="UTC"),as.POSIXct("2019/12/31", tz="UTC"), by = "day"))
daily.data <- setNames(as.data.frame(matrix(ncol = 1, nrow = NROW(temp.date))),c("Date"))
daily.data["Date"] <- temp.date
rm(temp.date)
daily.data <- merge(daily.data, daily.sums, by="Date", all.x=T, all.y=F)
#colnames(daily.data)[3]<-c("DOY_cont")
#daily.data <- merge(daily.data, daily.means, by="Date", all.x=T, all.y=F)
daily.data["Dz"] <- merge(daily.data, temp, by="Date", all.x=T, all.y=F)$Dz
daily.data["PARz"] <- merge(daily.data, temp, by="Date", all.x=T, all.y=F)$PARz
rm(daily.sums, daily.means)
#I think the stuff below is not needed
names(daily.data)
# Plot test to see if things work
plot(daily.data$Dz, daily.data$Pines_Q)
rm(temp, temp.2, temp.3, temp.4)
plot(daily.data$Date.x,daily.data$S0.15)
# ADD: Long-term drought data to daily.data ----------------------------------------------------------------
names(daily.data)
daily.data["Year"] <- lubridate::year(daily.data$Date)
daily.data["Month"] <- lubridate::month(daily.data$Date)
daily.data["DOY"] <- lubridate::yday(daily.data$Date)
daily.data["Week"] <- ifelse(daily.data$Year==2016,lubridate::week(daily.data$Date),
ifelse((daily.data$Year==2017),lubridate::week(daily.data$Date)+53,
ifelse((daily.data$Year==2018),lubridate::week(daily.data$Date)+106,
lubridate::week(daily.data$Date)+159)))
longterm_drought$Date
temp.drought <- subset(longterm_drought, longterm_drought$Year>2015)
temp.drought["Week"] <- ifelse(temp.drought$Year==2016,lubridate::week(temp.drought$Date),
ifelse((temp.drought$Year==2017),lubridate::week(temp.drought$Date)+53,
ifelse((temp.drought$Year==2018),lubridate::week(temp.drought$Date)+106,
lubridate::week(temp.drought$Date)+159)))
as.Date(temp.drought$Date)
daily.data["spei"] <- merge(daily.data, temp.drought, by="Week", all.x= T, all.y=T)["spei"]
rm(temp.drought)
plot(daily.data$Date, daily.data$spei)
write.table(daily.data, "Data/daily.data.csv", sep=",", row.names=F)
# END OF SECTION ----
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
#
# LONG-TERM ENVIRONMENTAL DATA
#
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
# OPEN - LONG_TERM DATA ----------------------------------------------------------------
# Open files
histo_data = read.table(file="Data/histo_data.csv",header=T,sep=",")
names(histo_data)
histo_data["Date"] <- as_datetime(seq(as.POSIXct("1985/01/1 00:00:00", tz="UTC"), as.POSIXct("2020/12/30 23:30:00", tz="UTC"), by = "day"))
# Add Year, Month, Week and DOY
histo_data["Year"] <- lubridate::year(histo_data$Date)
histo_data["Month"] <- lubridate::month(histo_data$Date)
histo_data["Week"] <- lubridate::week(histo_data$Date)
histo_data["DOY"] <- lubridate::yday(histo_data$Date)
# ADD - ETo TO LONG_TERM DATA ----------------------------------------------------------------
names(all.eco.met)
temp.eto <- aggregate(cbind(Date, ETo_mm, Ta_1_1_1, RH_1_1_1)~DOY_cont, data=all.eco.met, FUN=sum, na.action = na.omit)
temp.eto["Date"] <- aggregate(cbind(Date, ETo_mm)~DOY_cont, data=all.eco.met, FUN=min, na.action = na.omit)["Date"]
head(temp.eto$Date, 5)
temp.eto$Date <- as.POSIXct(temp.eto$Date, origin="1970/01/1", tz="UTC")
histo_data <- merge(histo_data, temp.eto, by="Date", all.x=T)
#par(mar=c(1,1,1,1))
plot(histo_data$ETo_mm)
tail(histo_data$ETo_mm, 50)
rm(temp.eto)
# ADD - MISSING PP TO histo_data ----------------------------------------------------------------
#histo_data$precip[is.na(histo_data$precip)] <- 0
histo_data$precip[is.na(histo_data$precip)&histo_data$Year>2015]
histo_data$Date <- lubridate::date(histo_data$Date)
histo_data["precip.16.20"] <- NA
daily.data$Date
histo_data$precip.16.19 <- merge(histo_data, daily.data, by="Date", all.x=T, all.y = F)["P_1_1_1.no.mis"]
tail(histo_data$precip.16.19, 300)
str(histo_data)
histo_data["precip.nomiss"] <- NA
histo_data$precip.nomiss <- unlist(ifelse(histo_data$precip<0,histo_data$precip.16.19, histo_data$precip))
# COUNT - DAYS WITHOUT PP ----------------------------------------------------------------
# All pp under 0.1 = 0
histo_data["precip.adj"] <- NA
histo_data$precip.adj <- ifelse(histo_data$precip<0.1&is.na(histo_data$precip), 0, histo_data$precip)
histo_data$precip[is.na(histo_data$precip)]
names(histo_data)
head(histo_data, 30)
### Identify dates where no precipitatio occurs continuously
histo_data["pp.cont"] <- NA
i = 1
j = NA
y=NA
for(i in 1:NROW(histo_data)){
j <- histo_data$precip.nomiss[i]
if(j>0){y=0}
if(j==0){y=y+1
}else{y=y}
print(y)
histo_data$pp.cont[i] <- y
i=i+1
}
rm(i, j, y)
histo_data["pp.cont.lngt"] <- NA
i = 1
j = NA
y=NA
for(i in 1:NROW(histo_data)){
#j <- histo_data$pp.cont[i-1]
xx <- histo_data$pp.cont[i]
y <- ifelse(xx==0, histo_data$pp.cont[i-1], NA)
histo_data$pp.cont.lngt[i-1] <- y
print(y)
# dt.temp$pp.fix[i] <- y
i=i+1
}
hist.temp <- subset(histo_data, histo_data$pp.cont.lngt>0&histo_data$Month>3&histo_data$Month<11, select=(pp.cont.lngt))
length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==1])/length(hist.temp$pp.cont.lngt)
histogram <- setNames(as.data.frame(matrix(ncol = 3, nrow = 30)),c("ID", "n", "percent" ))
histogram$ID <- seq(1,30)
histogram$n[1] <- length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==1])
histogram$n[2] <- length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==2])
histogram$n[3] <- length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==3])
histogram$n[4] <- length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==4])
histogram$n[5] <- length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==5])
histogram$n[6] <- length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==6])
histogram$n[7] <- length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==7])
histogram$n[8] <- length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==8])
histogram$n[9] <- length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==9])
histogram$n[10] <- length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==10])
histogram$n[11] <- length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==11])
histogram$n[12] <- length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==22])
histogram$n[13] <- length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==23])
histogram$n[14] <- length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==24])
histogram$n[15] <- length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==25])
histogram$n[16] <- length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==26])
histogram$n[17] <- length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==27])
histogram$n[18] <- length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==28])
histogram$n[19] <- length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==29])
histogram$n[20] <- length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==20])
histogram$n[27] <- length(hist.temp$pp.cont.lngt[hist.temp$pp.cont.lngt==27])
histogram$percent <- histogram$n/length(hist.temp$pp.cont.lngt)
is.na(hist.temp$pp.cont.lngt)
plot(NULL, xlim = c(1,12), ylim=c(0,0.5),
ylab="Percent",
xlab="Length of period (days)")
arrows(histogram$ID, 0, histogram$ID, histogram$percent, length=0, angle=50, code=2, lwd= 15, col="blue")
text(histogram$ID, histogram$percent+0.05, round(histogram$percent, 2),col="deeppink", cex=1)
text(histogram$ID, histogram$percent+0.02, paste("n=", histogram$n,sep=""),col="black", cex=0.8)
# End of this script
names(histo_data)
# This also works great
#no.pp.count <- rle(histo_data$precip.adj)$lengths[rle(histo_data$precip.adj)$values==0]
hist(no.pp.count)
rm(no.pp.count)
rm(i, j, y, xx, hist.temp)
rm(no.pp.count)
rm(histogram)
#write.table(no.pp.count, "no.pp.count.csv", sep=",", row.names = F)
# SAVE - HISTORIC DATA ----------
# OPEN - HISTORIC DATA ----------
# PREDICT - LONG_TERM ETo ----------------------------------------------------------------
# Predictions are made with VPD and AirT. VPD is used primarily, and AirT is used when VPD data is missing
summary(histo_data$VPD_data.range)
x = unlist(subset(histo_data, histo_data$VPD_data.range=="Good" , select=c("DATE", "VPD"))[2])
y = unlist(subset(histo_data, histo_data$VPD_data.range=="Good" , select=c("DATE", "ETo_mm"))[2])
# Send model results to variable
plot(x, y)
ETo_model <-suppressWarnings( nlsLM(y ~ b0*(1 - (b1 * exp(-b2 * x ))),
data=histo_data, start=list(b0=1, b1=1, b2=1)))
# Store predicted coefficients for legend
b0_ETo <- summary(ETo_model)$coefficients[1, 1]
b1_ETo <- summary(ETo_model)$coefficients[2, 1]
b2_ETo <- summary(ETo_model)$coefficients[3, 1]
# Created column with predicted ETo
pred.eto <- b0_ETo*(1 - b1_ETo * exp(-b2_ETo *histo_data$VPD))
histo_data["ETo_mm_pred.VPD"] <- ifelse(pred.eto<0, 0, pred.eto)
#summary_DOY["ETo_mm_pred"] <- 2.32924126330696 / (1 + 0.337335339562585 * exp( -0.121242767960978 * summary_DOY$Ta_1_1_1 )) ^ (1 / 0.0807243045601035)
plot(histo_data$ETo_mm, ylim=c(0, 6))
lines(histo_data$ETo_mm_pred.VPD, col="red")
rm(x, y,ETo_model, b0_ETo, b1_ETo, b2_ETo, pred.eto)
# Predict ETo using AirT
summary(histo_data$VPD_data.range)
x = unlist(subset(histo_data, histo_data$VPD_data.range=="Good"&histo_data$AirT>0 , select=c("DATE", "AirT"))[2])
y = unlist(subset(histo_data, histo_data$VPD_data.range=="Good"&histo_data$AirT>0 , select=c("DATE", "ETo_mm"))[2])
# Send model results to variable
plot(x, y)
ETo_model <-suppressWarnings( nlsLM(y ~ b0*(1 - (b1 * exp(-b2 * x ))),
data=histo_data, start=list(b0=-2, b1=0.99, b2=0.02)))
# Store predicted coefficients for legend
b0_ETo <- summary(ETo_model)$coefficients[1, 1]
b1_ETo <- summary(ETo_model)$coefficients[2, 1]
b2_ETo <- summary(ETo_model)$coefficients[3, 1]
# Created column with predicted ETo
pred.eto <- b0_ETo*(1 - b1_ETo * exp(-b2_ETo *histo_data$AirT))
histo_data["ETo_mm_pred_AirT"] <- ifelse(pred.eto<0, 0, pred.eto)
#summary_DOY["ETo_mm_pred"] <- 2.32924126330696 / (1 + 0.337335339562585 * exp( -0.121242767960978 * summary_DOY$Ta_1_1_1 )) ^ (1 / 0.0807243045601035)
rm(pred.eto)
plot(histo_data$ETo_mm, ylim=c(0, 6))
lines(histo_data$ETo_mm_pred_AirT, col="red")
rm(x, y,ETo_model, b0_ETo, b1_ETo, b2_ETo)
# Estimate a continuous ETo
histo_data["ETo_mm.combined"] <- ifelse(is.na(histo_data$ETo_mm_pred.VPD), histo_data$ETo_mm_pred_AirT, histo_data$ETo_mm_pred.VPD)
plot(histo_data$ETo_mm, ylim=c(0, 6))
lines(histo_data$ETo_mm.combined, col="red")
rm(b0_ETo, b1_ETo, b2_ETo, x, y, ETo_model)
# END OF SECTION ----
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
#
# LONG-TERM DROUGHT
#
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
# ADD - LONG_TERM DROUGHT INDEX ----------------------------------------------------------------
# Sum by week
histo_data["ID"] <- NA
histo_data$ID <- 1:NROW(histo_data)
histo_data$ID <- as.numeric(histo_data$ID)
plot(histo_data$Date, histo_data$ETo_mm.combined)
head(histo_data$Date, 5)
names(histo_data)
str(histo_data)
longterm_drought <- aggregate(cbind(precip.nomiss, ETo_mm.combined, Date, ID, DATE)~Week+Year, data=histo_data,FUN=sum, na.rm=T, na.action=NULL)
temp.date <- aggregate(cbind(Date)~Week+Year, data=histo_data,FUN=min, na.rm=F, na.action=NULL)
temp.date["Date"] <- histo_data[!duplicated(histo_data[c("Year", "Week")]),]["Date"]
longterm_drought$Date <- temp.date$Date
longterm_drought$Date <- as.Date(longterm_drought$Date)
rm(temp.date)
longterm_drought.month <- aggregate(cbind(precip.nomiss, ETo_mm.combined, Date, ID)~Month+Year, data=histo_data,FUN=sum, na.rm=T, na.action=NULL)
date.month <- aggregate(cbind(precip.nomiss, ETo_mm.combined, Date, ID)~Month+Year, data=histo_data,FUN=min, na.rm=T, na.action=NULL)
longterm_drought.month$ID <- aggregate(cbind(ID)~Month+Year, data=histo_data,FUN=min, na.rm=T, na.action=NULL)["ID"]
date.month$Date <- as.POSIXct(date.month$Date, origin="1970/01/1", tz="UTC")
date.month$Date <- as.Date(date.month$Date)
longterm_drought.month$Date <-date.month$Date
rm(date.month)
longterm_drought["PP_ET"] <- longterm_drought$precip-longterm_drought$ETo_mm.combined
longterm_drought.month["PP_ET.month"] <- longterm_drought.month$precip-longterm_drought.month$ETo_mm.combined
rm(temp_date)
# Estimate SPEI week
spei_out <- spei(longterm_drought$PP_ET, 1, kernel = list(type = 'rectangular', shift = 0),
distribution = 'log-Logistic', fit="ub-pwm", na.rm = FALSE,
ref.start=NULL, ref.end=NULL, x=FALSE, params=NULL)
plot(spei_out)
# Save to dataframe
longterm_drought["spei"] <- as.data.frame(spei_out[[2]])
# Estimate SPEI month
spei_out <- spei(longterm_drought.month$PP_ET, 1, kernel = list(type = 'rectangular', shift = 0),
distribution = 'log-Logistic', fit="ub-pwm", na.rm = FALSE,
ref.start=NULL, ref.end=NULL, x=FALSE, params=NULL)
#par(mar = c(10, 10, 10, 10))
#par(mar=c(1,1,1,1))
plot(spei_out)
par("mar")
# Save to dataframe
longterm_drought.month["spei"] <- as.data.frame(spei_out[[2]])
longterm_drought["spei.month"] <- merge(longterm_drought, longterm_drought.month, by="ID", all.x = T)["spei.y"]
#lubridate::date(longterm_drought$Date)
plot(longterm_drought$ID, longterm_drought$spei, type="l", col="blue")
plot(longterm_drought.month$spei, col="red")
rm(spei_out, longterm_drought.month)
# Decided not to estimate the other INDICES
#scPDSI_out <- pdsi(longterm_drought$pp, longterm_drought$ET0_mod.rich_gwth.year, sc=TRUE, AWC=50 )
#longterm_drought["scPDSI"] <- as.data.frame(scPDSI_out[[2]])[1:1749,]
# Replace week 1 and 53 with 0, to close the polygon
# This is done for esthetic purposes. Do not delete if the plot is not a polygon closed every year
longterm_drought$spei <- ifelse(longterm_drought$Week==1, 0, longterm_drought$spei)
longterm_drought$spei <- ifelse(longterm_drought$Week==53, 0, longterm_drought$spei)
longterm_drought.month$spei <- ifelse(longterm_drought.month$Month==1, 0, longterm_drought.month$spei)
longterm_drought.month$spei <- ifelse(longterm_drought.month$Month==12, 0, longterm_drought.month$spei)
# SAVE - LONG_TERM DROUGHT INDEX ----------------------------------------------------------------
names(longterm_drought)
write.table(longterm_drought, "Data/longterm_drought.csv", sep=",", row.names=FALSE)
# OPEN - LONG_TERM DROUGHT INDEX ----------------------------------------------------------------
longterm_drought <- read.table(file="Data/longterm_drought.csv",header=T,sep=",")
names(longterm_drought)
# END OF SECTION ----
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
#
# PARAMETERS
#
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
# This includes a database with parameters fitted for a various models/equations
# They are compiled here in a single file, to simplify the data analysis
# OPEN - PARAMETER DATABASE ----------------------------------------------------------------
par.estimates <- read.table(file="Data/par.estimates.csv",header=T,sep=",")
# END OF SECTION ----
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
#
# TREE DATA
#
000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
# OPEN - DATA FOR FIT ALLOMETRIC EQUATIONS [PINE, SPRUCE, BIRCH] ----------------------------------------------------------------
# Load databases
dt.allomet <- read.table(file="Data/allometric_data.dat",header=T,sep=",", na.strings = "NAN" )
dt.allomet <- subset(dt.allomet, dt.allomet$Outlier=="No")
# Ignore birch data
#dt.allomet_birch = read.table(file="/Users/data/krycklan_birch.csv",header=T,sep=",", na.strings = "NAN" )
colnames(dt.allomet)
# CREATE DATAFRAMES ----------------------------------------------------------------
dt.allomet.df <-data.frame(ID=dt.allomet$ID, species=dt.allomet$species, status=dt.allomet$status,diamclass=dt.allomet$diamclass_cm,
sapdepth = dt.allomet$R_cm, corelength=dt.allomet$corelength_cm, complete_r=dt.allomet$Complete_R,
dbh_cm=dt.allomet$DBH_cm)
#dt.allomet_RA.df <- as.data.frame(dt.allomet_RA)
#rm(dt.allomet_pine)
colnames(dt.allomet.df)
# ESTIMATE SAPWOOD AREA FOR ALL SPECIES ----------------------------------------------------------------
# Estimate sapwood area
dt.allomet.df["ba_m"] <- (0.7854 * (dt.allomet.df$dbh_cm)^2)/10000
plot(dt.allomet.df$dbh_cm ,dt.allomet.df$sapdepth)
# Force-correct these values. Likely outliers
dt.allomet.df$bark.depth.cm[21] <-3
dt.allomet.df$bark.depth.cm[10] <-1
dt.allomet.df["As.core_m"] <- ((0.7854*dt.allomet.df$corelength^2) - (0.7854*(dt.allomet.df$corelength-(dt.allomet.df$sapdepth*2))^2))/10000