R/T_timing.r
In tsangyp/StreamThermal: Calculating metrics that characterize stream thermal regime
#' A function characterizes timing of stream temperature
#'
#' This function summarize the timing of a variety metrics.
#' @param sitedata stream monitoring site data in SiteID, Date(in as.Date format),
#' MaxT, MinT, MeanT.
#' @param TlengthPortion portion of length that required for calculating metric summaries,
#' defaulty=2/3
#' @param SeasonSp define spring season, default as March, April, and May, c(3,4,5)
#' @param SeasonSu define summer season, default as June, July, August, c(6,7,8)
#' @param SeasonFa define fall season, default as September, October, November, c(9,10,11)
#' @param SeasonWi define winter season, default as December, Januray, February, c(12,1,2)
#' @keywords timing
#' @export
#' @examples
#' install.packages("dataRetrieval")
#' library(dataRetrieval)
#' ExUSGSStreamTemp<-readNWISdv("01382310","00010","2011-01-01","2011-12-31",c("00001","00002","00003"))
#' sitedata<-subset(ExUSGSStreamTemp, select=c("site_no","Date","X_00010_00001","X_00010_00002","X_00010_00003"))
#' names(sitedata)<-c("siteID","Date","MaxT","MinT","MeanT")
#' T_timing(sitedata)
T_timing <- function(sitedata, TlengthPortion=2/3,
SeasonSp=c(3,4,5), SeasonSu=c(6,7,8),
SeasonFa=c(9,10,11),SeasonWi=c(12,1,2)){
library(zoo)
SiteInfo<-sitedata[1,1]
# timing of maximum mean and maximum temperature
# take average if multiple years
#Monthly------------------------------------------------------------------------
TimingMonth<-function(sitedata,y,TlengthPortion){
JDmaxMaxT_mo<-c()
JDminMinT_mo<-c()
JDmaxMeanT_mo<-c()
# find months
mo<-as.numeric(format(sitedata$Date,"%m"))
for(jj in 1:12){
i_mo<-which(mo==jj)
if (length(i_mo)>=30*TlengthPortion){
#there are times, more than one day has the maximum, we decide to pick the first day of them
i_MaxDmax<-which(sitedata$MaxT[i_mo]==max(na.omit(sitedata$MaxT[i_mo])))[1]
i_MinDmin<-which(sitedata$MinT[i_mo]==min(na.omit(sitedata$MinT[i_mo])))[1]
i_MaxDmean<-which(sitedata$MeanT[i_mo]==max(na.omit(sitedata$MeanT[i_mo])))[1]
JDmaxMaxT_temp<-julian(sitedata$Date[i_mo[i_MaxDmax]],origin = as.Date(paste(y,"-01-01",sep="")))
JDminMinT_temp<-julian(sitedata$Date[i_mo[i_MinDmin]],origin = as.Date(paste(y,"-01-01",sep="")))
JDmaxMeanT_temp<-julian(sitedata$Date[i_mo[i_MaxDmean]],origin = as.Date(paste(y,"-01-01",sep="")))
}else{
JDmaxMaxT_temp<-NA
JDminMinT_temp<-NA
JDmaxMeanT_temp<-NA
}
JDmaxMaxT_mo<-c(JDmaxMaxT_mo,JDmaxMaxT_temp)
JDminMinT_mo<-c(JDminMinT_mo,JDminMinT_temp)
JDmaxMeanT_mo<-c(JDmaxMeanT_mo,JDmaxMeanT_temp)
}
SiteMonthlyMetrics<-c(JDmaxMaxT_mo,JDminMinT_mo,JDmaxMeanT_mo)
SiteMonthlyMetrics<-matrix(SiteMonthlyMetrics,nrow=1)
return(SiteMonthlyMetrics)
}
#average multiple years
JD<-c()
m_years<-unique(format(sitedata$Date,"%Y"))
for (y in m_years){
i_year<-which(format(sitedata$Date,"%Y")==y)
sitedata_year<-sitedata[i_year,]
JD_temp<-TimingMonth(sitedata_year,y,TlengthPortion)
JD<-rbind(JD,JD_temp)
}
JD<-colMeans(JD,na.rm=TRUE)
SiteMonthlyMetrics<-c(JD)
#Seasonal-----------------------------------------------------------------------
monthdays<-function(month){
if(month==2){
MonthDays<-28
}else if(month==1|month==3|month==5|month==7|month==8|month==10|month==12){
MonthDays<-31
}else{
MonthDays<-30
}
return(MonthDays)
}
TimingSeason<-function(sitedata,season,y,TlengthPortion){
mo<-as.numeric(format(sitedata$Date,"%m"))
i_season<-c()
seasondays<-0
for(ii in season){
i_temp<-which(mo==ii)
i_season<-c(i_season,i_temp)
days<-monthdays(ii)
seasondays<-seasondays+days
}
if(length(i_season)>=seasondays*TlengthPortion){
#there are times, more than one day has the maximum, we decide to pick the first day of them
i_MaxDmax<-which(sitedata$MaxT[i_season]==max(na.omit(sitedata$MaxT[i_season])))[1]
i_MinDmin<-which(sitedata$MinT[i_season]==min(na.omit(sitedata$MinT[i_season])))[1]
i_MaxDmean<-which(sitedata$MeanT[i_season]==max(na.omit(sitedata$MeanT[i_season])))[1]
JDmaxMaxT<-julian(sitedata$Date[i_season[i_MaxDmax]],origin = as.Date(paste(y,"-01-01",sep="")))
JDminMinT<-julian(sitedata$Date[i_season[i_MinDmin]],origin = as.Date(paste(y,"-01-01",sep="")))
JDmaxMeanT<-julian(sitedata$Date[i_season[i_MaxDmean]],origin = as.Date(paste(y,"-01-01",sep="")))
}else{
JDmaxMaxT<-NA
JDminMinT<-NA
JDmaxMeanT<-NA
}
return(c(JDmaxMaxT, JDminMinT, JDmaxMeanT))
}
#average multiple years
Jmaxminmean_sp<-c()
Jmaxminmean_su<-c()
Jmaxminmean_fa<-c()
Jmaxminmean_wi<-c()
m_years<-unique(format(sitedata$Date,"%Y"))
for (y in m_years){
i_year<-which(format(sitedata$Date,"%Y")==y)
sitedata_year<-sitedata[i_year,]
#spring
Jmaxminmean_sp_temp<-TimingSeason(sitedata_year,SeasonSp,y,TlengthPortion)
Jmaxminmean_sp<-rbind(Jmaxminmean_sp,Jmaxminmean_sp_temp)
#summer
Jmaxminmean_su_temp<-TimingSeason(sitedata_year,SeasonSu,y,TlengthPortion)
Jmaxminmean_su<-rbind(Jmaxminmean_su,Jmaxminmean_su_temp)
#fall
Jmaxminmean_fa_temp<-TimingSeason(sitedata_year,SeasonFa,y,TlengthPortion)
Jmaxminmean_fa<-rbind(Jmaxminmean_fa,Jmaxminmean_fa_temp)
#winter
Jmaxminmean_wi_temp<-TimingSeason(sitedata_year,SeasonWi,y,TlengthPortion)
Jmaxminmean_wi<-rbind(Jmaxminmean_wi,Jmaxminmean_wi_temp)
}
Jmaxminmean_sp<-colMeans(Jmaxminmean_sp,na.rm=TRUE)
Jmaxminmean_su<-colMeans(Jmaxminmean_su,na.rm=TRUE)
Jmaxminmean_fa<-colMeans(Jmaxminmean_fa,na.rm=TRUE)
Jmaxminmean_wi<-colMeans(Jmaxminmean_wi,na.rm=TRUE)
SiteSeasonMetrics<-c(Jmaxminmean_sp,
Jmaxminmean_su,
Jmaxminmean_fa,
Jmaxminmean_wi)
#Timing of moving average-------------------------------------------------------
# ID the logest data within the range with missing days no more than 5 days
# find the consecutive data
notmissing<-sitedata[!is.na(sitedata$MeanT),]
constart<-notmissing[c(1,diff(notmissing$Date))>5,]$Date
constart<-c(min(notmissing$Date),constart)
conend<-notmissing[diff(notmissing$Date)>5,]$Date
conend<-c(conend,max(notmissing$Date))
conend<-conend[1:length(constart)]
condays<-conend-constart
i_longest<-which(condays==max(condays))
## timing of maximum moving average of daily mean(a), daily maximum (b) and daily range
# 1-- maximum of 30 days moving average of daily a. mean b. maximum c.daily range
# 2-- maximum of 21 days moving average of daily a. mean b. maximum c.daily range
# 3-- maximum of 14 days moving average of daily a. mean b. maximum c.daily range
# 4-- maximum of 7 days moving average of daily a. mean b. maximum c.daily range
# 5-- maximum of 3 days moving average of daily a. mean b. maximum c.daily range
# 6-- maximum of 1 days moving average of daily a. mean b. maximum c.daily range <- no need YPT 2012.7.14
if(i_longest>=1){
for(jj in i_longest){
i_start<-which(sitedata$Date==constart[jj])
i_end<-which(sitedata$Date==conend[jj])
sitedata_consec5<-sitedata[i_start:i_end,]
windowdays<-c(30, 21, 14, 7, 3)
JDMMAMeanT<-vector("list", length(windowdays))
JDMMAMaxT<-vector("list", length(windowdays))
JDMMADRT<-vector("list", length(windowdays))
SiteMovingMetrics<-data.frame(1)
# maximum moving average of (a,b,c) in different moving windows
for (dd in 1:length(windowdays)){
# each site might have multiple years
m_years<-unique(format(sitedata$Date[i_start:i_end],"%Y"))
for(y in m_years){
i_year<-which(format(sitedata_consec5$Date,"%Y")==y)
#YPT 2015.2.20--
#moving metrics is used to characterizing thermal pattens in warmest time
#therefore, make sure the moving window include at least one summer month
mo<-as.numeric(format(sitedata_consec5$Date[i_year],"%m"))
#--YPT 2015.2.24 decide to take out the limitation of checking summer months
#if(sum(mo%in%SeasonSu)>=1){
# a. daily mean
if(length(na.omit(sitedata_consec5$MeanT[i_year]))>=windowdays[dd]){
zoo_MeanT<-zoo(sitedata_consec5$MeanT[i_year],as.Date(sitedata_consec5$Date[i_year]))
MovingMeanT<-rollmean(na.omit(zoo_MeanT),windowdays[dd],align="center")
i_movingmeanT<-index(MovingMeanT)
i_MMmeanT<-i_movingmeanT[which(MovingMeanT==max(MovingMeanT))]
#MaxMovingAMeanT[[dd]]<-c(MaxMovingAMeanT[[dd]],max(MovingMeanT))
JDMMAMeanT[[dd]]<-c(JDMMAMeanT[[dd]],julian(i_MMmeanT,origin = as.Date(paste(y,"-01-01",sep=""))))
}
# b. daily maximum
if(length(na.omit(sitedata_consec5$MaxT[i_year]))>=windowdays[dd]){
zoo_MaxT<-zoo(sitedata_consec5$MaxT[i_year],as.Date(sitedata_consec5$Date[i_year]))
MovingMaxT<-rollmean(na.omit(zoo_MaxT),windowdays[dd],align="center")
i_movingmaxT<-index(MovingMaxT)
i_MMmaxT<-i_movingmaxT[which(MovingMaxT==max(MovingMaxT))]
#MaxMovingAMaxT[[dd]]<-c(MaxMovingAMaxT[[dd]],max(MovingMaxT))
JDMMAMaxT[[dd]]<-c(JDMMAMaxT[[dd]],julian(i_MMmaxT,origin = as.Date(paste(y,"-01-01",sep=""))))
}
# c. daily range
DRT<-sitedata_consec5$MaxT[i_year]-sitedata_consec5$MinT[i_year]
if(length(na.omit(DRT))>=windowdays[dd]){
zoo_DRT<-zoo(DRT,as.Date(sitedata_consec5$Date[i_year]))
MovingDRT<-rollmean(na.omit(zoo_DRT),windowdays[dd],align="center")
i_movingDRT<-index(MovingDRT)
i_MMDRT<-i_movingDRT[which(MovingDRT==max(MovingDRT))]
#MaxMovingADRT[[dd]]<-c(MaxMovingADRT[[dd]],max(MovingDRT))
JDMMADRT[[dd]]<-c(JDMMADRT[[dd]],julian(i_MMDRT,origin = as.Date(paste(y,"-01-01",sep=""))))
}
#}
}
JDMMAMeanT[[dd]]<-mean(JDMMAMeanT[[dd]])
JDMMAMaxT[[dd]]<-mean(JDMMAMaxT[[dd]])
JDMMADRT[[dd]]<-mean(JDMMADRT[[dd]])
SiteMovingMetrics<-data.frame(SiteMovingMetrics,
JDMMAMeanT[[dd]],
JDMMAMaxT[[dd]],
JDMMADRT[[dd]])
}
SiteMovingMetrics<-data.frame(SiteMovingMetrics[,2:length(SiteMovingMetrics)])
}
}else{
SiteMovingMetrics<-as.data.frame(matrix(rep(NA,15),ncol=15))
}
SiteMovingMetrics<-as.numeric(SiteMovingMetrics)
# collect all the metrics-----------------------------------------------------
SiteMetrics<-matrix(c(SiteMonthlyMetrics,SiteSeasonMetrics,SiteMovingMetrics),nrow=1,ncol=63)
colnames(SiteMetrics)<-c(
paste("JDmaxMaxT",1:12,sep=""),paste("JDminMinT",1:12,sep=""),paste("JDmaxMeanT",1:12,sep=""),
"JDmaxMaxTSp","JDminMinTSp","JDmaxMeanTSp",
"JDmaxMaxTSu","JDminMinTSu","JDmaxMeanTSu",
"JDmaxMaxTFa","JDminMinTFa","JDmaxMeanTFa",
"JDmaxMaxTWi","JDminMinTWi","JDmaxMeanTWi",
"JDM30MAMeanT","JDM30MAMaxT","JDM30MADRT",
"JDM21MAMeanT","JDM21MAMaxT","JDM21MADRT",
"JDM14MAMeanT","JDM14MAMaxT","JDM14MADRT",
"JDM7MAMeanT","JDM7MAMaxT","JDM7MADRT",
"JDM3MAMeanT","JDM3MAMaxT","JDM3MADRT")
SiteMetrics<-data.frame(SiteInfo,SiteMetrics,stringsAsFactors=FALSE)
return(SiteMetrics)
}
tsangyp/StreamThermal documentation built on June 1, 2019, 1:54 a.m.
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#' A function characterizes timing of stream temperature
#'
#' This function summarize the timing of a variety metrics.
#' @param sitedata stream monitoring site data in SiteID, Date(in as.Date format),
#' MaxT, MinT, MeanT.
#' @param TlengthPortion portion of length that required for calculating metric summaries,
#' defaulty=2/3
#' @param SeasonSp define spring season, default as March, April, and May, c(3,4,5)
#' @param SeasonSu define summer season, default as June, July, August, c(6,7,8)
#' @param SeasonFa define fall season, default as September, October, November, c(9,10,11)
#' @param SeasonWi define winter season, default as December, Januray, February, c(12,1,2)
#' @keywords timing
#' @export
#' @examples
#' install.packages("dataRetrieval")
#' library(dataRetrieval)
#' ExUSGSStreamTemp<-readNWISdv("01382310","00010","2011-01-01","2011-12-31",c("00001","00002","00003"))
#' sitedata<-subset(ExUSGSStreamTemp, select=c("site_no","Date","X_00010_00001","X_00010_00002","X_00010_00003"))
#' names(sitedata)<-c("siteID","Date","MaxT","MinT","MeanT")
#' T_timing(sitedata)
T_timing <- function(sitedata, TlengthPortion=2/3,
SeasonSp=c(3,4,5), SeasonSu=c(6,7,8),
SeasonFa=c(9,10,11),SeasonWi=c(12,1,2)){
library(zoo)
SiteInfo<-sitedata[1,1]
# timing of maximum mean and maximum temperature
# take average if multiple years
#Monthly------------------------------------------------------------------------
TimingMonth<-function(sitedata,y,TlengthPortion){
JDmaxMaxT_mo<-c()
JDminMinT_mo<-c()
JDmaxMeanT_mo<-c()
# find months
mo<-as.numeric(format(sitedata$Date,"%m"))
for(jj in 1:12){
i_mo<-which(mo==jj)
if (length(i_mo)>=30*TlengthPortion){
#there are times, more than one day has the maximum, we decide to pick the first day of them
i_MaxDmax<-which(sitedata$MaxT[i_mo]==max(na.omit(sitedata$MaxT[i_mo])))[1]
i_MinDmin<-which(sitedata$MinT[i_mo]==min(na.omit(sitedata$MinT[i_mo])))[1]
i_MaxDmean<-which(sitedata$MeanT[i_mo]==max(na.omit(sitedata$MeanT[i_mo])))[1]
JDmaxMaxT_temp<-julian(sitedata$Date[i_mo[i_MaxDmax]],origin = as.Date(paste(y,"-01-01",sep="")))
JDminMinT_temp<-julian(sitedata$Date[i_mo[i_MinDmin]],origin = as.Date(paste(y,"-01-01",sep="")))
JDmaxMeanT_temp<-julian(sitedata$Date[i_mo[i_MaxDmean]],origin = as.Date(paste(y,"-01-01",sep="")))
}else{
JDmaxMaxT_temp<-NA
JDminMinT_temp<-NA
JDmaxMeanT_temp<-NA
}
JDmaxMaxT_mo<-c(JDmaxMaxT_mo,JDmaxMaxT_temp)
JDminMinT_mo<-c(JDminMinT_mo,JDminMinT_temp)
JDmaxMeanT_mo<-c(JDmaxMeanT_mo,JDmaxMeanT_temp)
}
SiteMonthlyMetrics<-c(JDmaxMaxT_mo,JDminMinT_mo,JDmaxMeanT_mo)
SiteMonthlyMetrics<-matrix(SiteMonthlyMetrics,nrow=1)
return(SiteMonthlyMetrics)
}
#average multiple years
JD<-c()
m_years<-unique(format(sitedata$Date,"%Y"))
for (y in m_years){
i_year<-which(format(sitedata$Date,"%Y")==y)
sitedata_year<-sitedata[i_year,]
JD_temp<-TimingMonth(sitedata_year,y,TlengthPortion)
JD<-rbind(JD,JD_temp)
}
JD<-colMeans(JD,na.rm=TRUE)
SiteMonthlyMetrics<-c(JD)
#Seasonal-----------------------------------------------------------------------
monthdays<-function(month){
if(month==2){
MonthDays<-28
}else if(month==1|month==3|month==5|month==7|month==8|month==10|month==12){
MonthDays<-31
}else{
MonthDays<-30
}
return(MonthDays)
}
TimingSeason<-function(sitedata,season,y,TlengthPortion){
mo<-as.numeric(format(sitedata$Date,"%m"))
i_season<-c()
seasondays<-0
for(ii in season){
i_temp<-which(mo==ii)
i_season<-c(i_season,i_temp)
days<-monthdays(ii)
seasondays<-seasondays+days
}
if(length(i_season)>=seasondays*TlengthPortion){
#there are times, more than one day has the maximum, we decide to pick the first day of them
i_MaxDmax<-which(sitedata$MaxT[i_season]==max(na.omit(sitedata$MaxT[i_season])))[1]
i_MinDmin<-which(sitedata$MinT[i_season]==min(na.omit(sitedata$MinT[i_season])))[1]
i_MaxDmean<-which(sitedata$MeanT[i_season]==max(na.omit(sitedata$MeanT[i_season])))[1]
JDmaxMaxT<-julian(sitedata$Date[i_season[i_MaxDmax]],origin = as.Date(paste(y,"-01-01",sep="")))
JDminMinT<-julian(sitedata$Date[i_season[i_MinDmin]],origin = as.Date(paste(y,"-01-01",sep="")))
JDmaxMeanT<-julian(sitedata$Date[i_season[i_MaxDmean]],origin = as.Date(paste(y,"-01-01",sep="")))
}else{
JDmaxMaxT<-NA
JDminMinT<-NA
JDmaxMeanT<-NA
}
return(c(JDmaxMaxT, JDminMinT, JDmaxMeanT))
}
#average multiple years
Jmaxminmean_sp<-c()
Jmaxminmean_su<-c()
Jmaxminmean_fa<-c()
Jmaxminmean_wi<-c()
m_years<-unique(format(sitedata$Date,"%Y"))
for (y in m_years){
i_year<-which(format(sitedata$Date,"%Y")==y)
sitedata_year<-sitedata[i_year,]
#spring
Jmaxminmean_sp_temp<-TimingSeason(sitedata_year,SeasonSp,y,TlengthPortion)
Jmaxminmean_sp<-rbind(Jmaxminmean_sp,Jmaxminmean_sp_temp)
#summer
Jmaxminmean_su_temp<-TimingSeason(sitedata_year,SeasonSu,y,TlengthPortion)
Jmaxminmean_su<-rbind(Jmaxminmean_su,Jmaxminmean_su_temp)
#fall
Jmaxminmean_fa_temp<-TimingSeason(sitedata_year,SeasonFa,y,TlengthPortion)
Jmaxminmean_fa<-rbind(Jmaxminmean_fa,Jmaxminmean_fa_temp)
#winter
Jmaxminmean_wi_temp<-TimingSeason(sitedata_year,SeasonWi,y,TlengthPortion)
Jmaxminmean_wi<-rbind(Jmaxminmean_wi,Jmaxminmean_wi_temp)
}
Jmaxminmean_sp<-colMeans(Jmaxminmean_sp,na.rm=TRUE)
Jmaxminmean_su<-colMeans(Jmaxminmean_su,na.rm=TRUE)
Jmaxminmean_fa<-colMeans(Jmaxminmean_fa,na.rm=TRUE)
Jmaxminmean_wi<-colMeans(Jmaxminmean_wi,na.rm=TRUE)
SiteSeasonMetrics<-c(Jmaxminmean_sp,
Jmaxminmean_su,
Jmaxminmean_fa,
Jmaxminmean_wi)
#Timing of moving average-------------------------------------------------------
# ID the logest data within the range with missing days no more than 5 days
# find the consecutive data
notmissing<-sitedata[!is.na(sitedata$MeanT),]
constart<-notmissing[c(1,diff(notmissing$Date))>5,]$Date
constart<-c(min(notmissing$Date),constart)
conend<-notmissing[diff(notmissing$Date)>5,]$Date
conend<-c(conend,max(notmissing$Date))
conend<-conend[1:length(constart)]
condays<-conend-constart
i_longest<-which(condays==max(condays))
## timing of maximum moving average of daily mean(a), daily maximum (b) and daily range
# 1-- maximum of 30 days moving average of daily a. mean b. maximum c.daily range
# 2-- maximum of 21 days moving average of daily a. mean b. maximum c.daily range
# 3-- maximum of 14 days moving average of daily a. mean b. maximum c.daily range
# 4-- maximum of 7 days moving average of daily a. mean b. maximum c.daily range
# 5-- maximum of 3 days moving average of daily a. mean b. maximum c.daily range
# 6-- maximum of 1 days moving average of daily a. mean b. maximum c.daily range <- no need YPT 2012.7.14
if(i_longest>=1){
for(jj in i_longest){
i_start<-which(sitedata$Date==constart[jj])
i_end<-which(sitedata$Date==conend[jj])
sitedata_consec5<-sitedata[i_start:i_end,]
windowdays<-c(30, 21, 14, 7, 3)
JDMMAMeanT<-vector("list", length(windowdays))
JDMMAMaxT<-vector("list", length(windowdays))
JDMMADRT<-vector("list", length(windowdays))
SiteMovingMetrics<-data.frame(1)
# maximum moving average of (a,b,c) in different moving windows
for (dd in 1:length(windowdays)){
# each site might have multiple years
m_years<-unique(format(sitedata$Date[i_start:i_end],"%Y"))
for(y in m_years){
i_year<-which(format(sitedata_consec5$Date,"%Y")==y)
#YPT 2015.2.20--
#moving metrics is used to characterizing thermal pattens in warmest time
#therefore, make sure the moving window include at least one summer month
mo<-as.numeric(format(sitedata_consec5$Date[i_year],"%m"))
#--YPT 2015.2.24 decide to take out the limitation of checking summer months
#if(sum(mo%in%SeasonSu)>=1){
# a. daily mean
if(length(na.omit(sitedata_consec5$MeanT[i_year]))>=windowdays[dd]){
zoo_MeanT<-zoo(sitedata_consec5$MeanT[i_year],as.Date(sitedata_consec5$Date[i_year]))
MovingMeanT<-rollmean(na.omit(zoo_MeanT),windowdays[dd],align="center")
i_movingmeanT<-index(MovingMeanT)
i_MMmeanT<-i_movingmeanT[which(MovingMeanT==max(MovingMeanT))]
#MaxMovingAMeanT[[dd]]<-c(MaxMovingAMeanT[[dd]],max(MovingMeanT))
JDMMAMeanT[[dd]]<-c(JDMMAMeanT[[dd]],julian(i_MMmeanT,origin = as.Date(paste(y,"-01-01",sep=""))))
}
# b. daily maximum
if(length(na.omit(sitedata_consec5$MaxT[i_year]))>=windowdays[dd]){
zoo_MaxT<-zoo(sitedata_consec5$MaxT[i_year],as.Date(sitedata_consec5$Date[i_year]))
MovingMaxT<-rollmean(na.omit(zoo_MaxT),windowdays[dd],align="center")
i_movingmaxT<-index(MovingMaxT)
i_MMmaxT<-i_movingmaxT[which(MovingMaxT==max(MovingMaxT))]
#MaxMovingAMaxT[[dd]]<-c(MaxMovingAMaxT[[dd]],max(MovingMaxT))
JDMMAMaxT[[dd]]<-c(JDMMAMaxT[[dd]],julian(i_MMmaxT,origin = as.Date(paste(y,"-01-01",sep=""))))
}
# c. daily range
DRT<-sitedata_consec5$MaxT[i_year]-sitedata_consec5$MinT[i_year]
if(length(na.omit(DRT))>=windowdays[dd]){
zoo_DRT<-zoo(DRT,as.Date(sitedata_consec5$Date[i_year]))
MovingDRT<-rollmean(na.omit(zoo_DRT),windowdays[dd],align="center")
i_movingDRT<-index(MovingDRT)
i_MMDRT<-i_movingDRT[which(MovingDRT==max(MovingDRT))]
#MaxMovingADRT[[dd]]<-c(MaxMovingADRT[[dd]],max(MovingDRT))
JDMMADRT[[dd]]<-c(JDMMADRT[[dd]],julian(i_MMDRT,origin = as.Date(paste(y,"-01-01",sep=""))))
}
#}
}
JDMMAMeanT[[dd]]<-mean(JDMMAMeanT[[dd]])
JDMMAMaxT[[dd]]<-mean(JDMMAMaxT[[dd]])
JDMMADRT[[dd]]<-mean(JDMMADRT[[dd]])
SiteMovingMetrics<-data.frame(SiteMovingMetrics,
JDMMAMeanT[[dd]],
JDMMAMaxT[[dd]],
JDMMADRT[[dd]])
}
SiteMovingMetrics<-data.frame(SiteMovingMetrics[,2:length(SiteMovingMetrics)])
}
}else{
SiteMovingMetrics<-as.data.frame(matrix(rep(NA,15),ncol=15))
}
SiteMovingMetrics<-as.numeric(SiteMovingMetrics)
# collect all the metrics-----------------------------------------------------
SiteMetrics<-matrix(c(SiteMonthlyMetrics,SiteSeasonMetrics,SiteMovingMetrics),nrow=1,ncol=63)
colnames(SiteMetrics)<-c(
paste("JDmaxMaxT",1:12,sep=""),paste("JDminMinT",1:12,sep=""),paste("JDmaxMeanT",1:12,sep=""),
"JDmaxMaxTSp","JDminMinTSp","JDmaxMeanTSp",
"JDmaxMaxTSu","JDminMinTSu","JDmaxMeanTSu",
"JDmaxMaxTFa","JDminMinTFa","JDmaxMeanTFa",
"JDmaxMaxTWi","JDminMinTWi","JDmaxMeanTWi",
"JDM30MAMeanT","JDM30MAMaxT","JDM30MADRT",
"JDM21MAMeanT","JDM21MAMaxT","JDM21MADRT",
"JDM14MAMeanT","JDM14MAMaxT","JDM14MADRT",
"JDM7MAMeanT","JDM7MAMaxT","JDM7MADRT",
"JDM3MAMeanT","JDM3MAMaxT","JDM3MADRT")
SiteMetrics<-data.frame(SiteInfo,SiteMetrics,stringsAsFactors=FALSE)
return(SiteMetrics)
}
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