R/metaMQ.R
In maibrittbergh/dischanalyst: Analyses Hydrological Data, especially Discharge Data
Defines functions
metaMQ
Documented in
metaMQ
#' Area-wide MQ-Trend
#'
#'
#'@description Function creates dataframe. Stations in metadataset are filtered by the given timeframe. To guarantee comparability between stations the measurement series of the stations are adapted to the time frame and shortened to the of the time frame.
#'
#'
#' @param Startyear numeric; Startyear of timerange.
#' @param Endyear numeric; Endyear of timerange.
#' @param metadata data.frame; Overview of GRDC-Dataset. The metadata can be created by \link[dischanalyst]{metadata_grdc} function
#' @param data list; Contains all stations that the discharge analysis should consider. List can be created by \link[dischanalyst]{grdc_list}. Each entry of the list contains the existing discharge measurements (as numeric) and the corresponding dates (as character) for the station.
#'
#'@import zyp
#'@import stats
#'
#' @return dataframe; metadata of stations whose measurement series are at least as long as the given time frame. The Dataframe includes different approaches to calculate the trend of the annual Normalized MQ (within the whole Year/the Spring/the Summer/the Autumn/the Winter)
#' \describe{
#' \item{intercept_zyp}{intercept created by \link[zyp]{zyp.trend.vector}}
#' \item{slope_zyp}{slope created by \link[zyp]{zyp.trend.vector}}
#' \item{sig_zyp}{significance (Kendall's P-Value) for the final detrended time-series}
#' \item{intercept_ls}{intercept created by \link[stats]{lm}}
#' \item{slope_ls}{slope created by \link[stats]{lm}}
#' }
#' @export
#'
#' @examples
#' \dontrun{
#' MQtf1820_2019=dataset(1820, 2019, metadata, data)
#'}
#'
#'
#'
#'
metaMQ=function(Startyear, Endyear, metadata, data){
dataset=which(metadata$startyear<=Startyear)
#stationnames that fit in startyear
ld=length(dataset)
starthydro=rep(0,ld )
for ( i in 1:ld){
starthydro[i]=as.numeric(substr(metadata$startday[dataset[i]], 6,7))
start=which(starthydro<12)
}
dataset=dataset[start]
datasetnames=metadata$station[dataset]
#Stations ending at least with October within Endyear
datasetend=which(metadata$endyear>=Endyear)
lde=length( datasetend)
endhydro=rep(0,lde )
for ( i in 1:lde){
endhydro[i]=as.numeric(substr(metadata$endday[datasetend[i]], 6,7))
end=which(endhydro>9)
}
datasetend=datasetend[end]
datasetendnames=metadata$station[datasetend]
#stationnames that fit in endyear
vec=rep(F,length(dataset))
for ( i in 1:length(dataset) ){
vec[i]=is.element(dataset[i],datasetend)
}
stations=dataset[which(vec==T)]
#stations that fit in start as well as endyear
######
l=length(stations)
# calculate for Year ------------------------------------------------------
Yslopezyp=rep(0, l)
Yinterceptzyp=rep(0, l)
Ysigzyp=rep(0, l)
Yslopelm=rep(0, l)
Yintlm=rep(0, l)
Wslopezyp=rep(0, l)
Winterceptzyp=rep(0, l)
Wsigzyp=rep(0, l)
Wslopelm=rep(0, l)
Wintlm=rep(0, l)
Spslopezyp=rep(0, l)
Spinterceptzyp=rep(0, l)
Spsigzyp=rep(0, l)
Spslopelm=rep(0, l)
Spintlm=rep(0, l)
Sslopezyp=rep(0, l)
Sinterceptzyp=rep(0, l)
Ssigzyp=rep(0, l)
Sslopelm=rep(0, l)
Sintlm=rep(0, l)
Aslopezyp=rep(0, l)
Ainterceptzyp=rep(0, l)
Asigzyp=rep(0, l)
Aslopelm=rep(0, l)
Aintlm=rep(0, l)
for ( i in 1:l ){
datan=data[[stations[i]]]
min=paste(Startyear, "-11") #calc min of dataset
min=sub(" -", "-", min)
min=min(grep(min, datan[,1]))
max=paste(Endyear, "-10")
max=sub(" -", "-", max)
max=max(grep(max, datan[,1]))
datak=datan[min:max,]
MEAN=(mean(datak[,2]))
years=(Startyear):(Endyear)
ls=length(years)
lm=length(years)-1
MQ=rep(0,lm)
NMQ=rep(0,lm)
for (t in 1:lm){
yearmin=years[t]
yearmax=years[t+1]
min=paste(yearmin,"-11")
min=sub(" -", "-",min)
min
max= paste(yearmax,"-10")
max=sub(" -", "-",max)
start=min(grep(min, datak[,1]))
end=max(grep(max, datak[,1]))
h=datak[start:end, ]
mean=(mean(h[,2]))
MQ[t]=mean
NMQ[t]=(mean/MEAN)
}
hyears=years[-length(years)]
df=data.frame(hyears, MQ, NMQ)
zyp=zyp.trend.vector(df$MQ, df$hyears, "yuepilon")
linmod=lm(df$MQ~df$hyears, na.action = "na.exclude")
Yslopezyp[i]=zyp[2]
Yinterceptzyp[i]=zyp[11]
Ysigzyp[i]=zyp[6]
Yslopelm[i]=as.numeric(linmod$coefficients[2])
Yintlm[i]=as.numeric(linmod$coefficients[1])
# Winter ------------------------------------------------------------------
MQ=rep(0,lm)
NMQ=rep(0,lm)
for (t in 1:lm){
yearmin=years[t]
yearmax=years[t+1]
min=paste(yearmin,"-11")
min=sub(" -", "-",min)
min
max= paste(yearmax,"-01")
max=sub(" -", "-",max)
start=min(grep(min, datak[,1]))
end=max(grep(max, datak[,1]))
h=datak[start:end, ]
mean=(mean(h[,2]))
MQ[t]=mean
NMQ[t]=(mean/MEAN)
}
hyears=years[-length(years)]
wdf=data.frame(hyears, MQ, NMQ)
zyp=zyp.trend.vector(wdf$MQ, wdf$hyears, "yuepilon")
linmod=lm(wdf$MQ~wdf$hyears, na.action = "na.exclude")
Wslopezyp[i]=zyp[2]
Winterceptzyp[i]=zyp[11]
Wsigzyp[i]=zyp[6]
Wslopelm[i]=as.numeric(linmod$coefficients[2])
Wintlm[i]=as.numeric(linmod$coefficients[1])
# Spring ------------------------------------------------------------------
MQ=rep(0,lm)
NMQ=rep(0,lm)
for (t in 2:ls){
yearmin=years[t]
min=paste(yearmin,"-02")
min=sub(" -", "-",min)
min
max= paste(yearmin,"-04")
max=sub(" -", "-",max)
start=min(grep(min, datak[,1]))
end=max(grep(max, datak[,1]))
h=datak[start:end, ]
mean=(mean(h[,2]))
MQ[t]=mean
NMQ[t]=(mean/MEAN)
}
MQ=MQ[-1]
NMQ=NMQ[-1]
hyears=years[-1]
spdf=data.frame(hyears, MQ, NMQ)
zyp=zyp.trend.vector(spdf$MQ, spdf$hyears, "yuepilon")
linmod=lm(spdf$MQ~spdf$hyears, na.action = "na.exclude")
Spslopezyp[i]=zyp[2]
Spinterceptzyp[i]=zyp[11]
Spsigzyp[i]=zyp[6]
Spslopelm[i]=as.numeric(linmod$coefficients[2])
Spintlm[i]=as.numeric(linmod$coefficients[1])
# Summer renew ------------------------------------------------------------
MQ=rep(0,lm)
NMQ=rep(0,lm)
for (t in 2:ls){
yearmin=years[t]
min=paste(yearmin,"-05")
min=sub(" -", "-",min)
max= paste(yearmin,"-07")
max=sub(" -", "-",max)
start=min(grep(min, datak[,1]))
end=max(grep(max, datak[,1]))
h=datak[start:end, ]
mean=(mean(h[,2]))
MQ[t]=mean
NMQ[t]=(mean/MEAN)
}
MQ=MQ[-1]
NMQ=NMQ[-1]
hyears=years[-1]
sdf=data.frame(hyears, MQ, NMQ)
zyp=zyp.trend.vector(sdf$MQ, sdf$hyears, "yuepilon")
linmod=lm(sdf$MQ~sdf$hyears, na.action = "na.exclude")
Sslopezyp[i]=zyp[2]
Sinterceptzyp[i]=zyp[11]
Ssigzyp[i]=zyp[6]
Sslopelm[i]=as.numeric(linmod$coefficients[2])
Sintlm[i]=as.numeric(linmod$coefficients[1])
# Autumn ------------------------------------------------------------------
MQ=rep(0,ls)
NMQ=rep(0,ls)
for (t in 2:ls){
yearmin=years[t]
min=paste(yearmin,"-08")
min=sub(" -", "-",min)
min
max= paste(yearmin,"-10")
max=sub(" -", "-",max)
start=min(grep(min, datak[,1]))
end=max(grep(max, datak[,1]))
h=datak[start:end, ]
mean=(mean(h[,2]))
MQ[t]=mean
NMQ[t]=(mean/MEAN)
}
MQ=MQ[-1]
NMQ=NMQ[-1]
hyears=years[-1]
adf=data.frame(hyears, MQ, NMQ)
zyp=zyp.trend.vector(adf$MQ, adf$hyears, "yuepilon")
linmod=lm(adf$MQ~adf$hyears, na.action = "na.exclude")
Aslopezyp[i]=zyp[2]
Ainterceptzyp[i]=zyp[11]
Asigzyp[i]=zyp[6]
Aslopelm[i]=as.numeric(linmod$coefficients[2])
Aintlm[i]=as.numeric(linmod$coefficients[1])
}
# metadata ----------------------------------------------------------------
meta=data.frame( metadata$station[stations] , metadata$river[stations] , metadata$longitude[stations],
metadata$latitude[stations],
Yslopezyp, #Year
Yinterceptzyp,
Ysigzyp,
Yslopelm,
Yintlm,
Wslopezyp,#Winter
Winterceptzyp,
Wsigzyp,
Wslopelm,
Wintlm,
Spslopezyp, #Spring
Spinterceptzyp,
Spsigzyp,
Spslopelm,
Spintlm,
Sslopezyp, #Summer
Sinterceptzyp,
Ssigzyp,
Sslopelm,
Sintlm,
Aslopezyp, #Autumn
Ainterceptzyp,
Asigzyp,
Aslopelm,
Aintlm
)
colnames(meta)[c(1,2,3,4)]=c("station","river", "longitude","latitude" )
return(meta)
}
maibrittbergh/dischanalyst documentation built on May 13, 2022, 1:52 a.m.
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Defines functions metaMQ
Documented in metaMQ
#' Area-wide MQ-Trend
#'
#'
#'@description Function creates dataframe. Stations in metadataset are filtered by the given timeframe. To guarantee comparability between stations the measurement series of the stations are adapted to the time frame and shortened to the of the time frame.
#'
#'
#' @param Startyear numeric; Startyear of timerange.
#' @param Endyear numeric; Endyear of timerange.
#' @param metadata data.frame; Overview of GRDC-Dataset. The metadata can be created by \link[dischanalyst]{metadata_grdc} function
#' @param data list; Contains all stations that the discharge analysis should consider. List can be created by \link[dischanalyst]{grdc_list}. Each entry of the list contains the existing discharge measurements (as numeric) and the corresponding dates (as character) for the station.
#'
#'@import zyp
#'@import stats
#'
#' @return dataframe; metadata of stations whose measurement series are at least as long as the given time frame. The Dataframe includes different approaches to calculate the trend of the annual Normalized MQ (within the whole Year/the Spring/the Summer/the Autumn/the Winter)
#' \describe{
#' \item{intercept_zyp}{intercept created by \link[zyp]{zyp.trend.vector}}
#' \item{slope_zyp}{slope created by \link[zyp]{zyp.trend.vector}}
#' \item{sig_zyp}{significance (Kendall's P-Value) for the final detrended time-series}
#' \item{intercept_ls}{intercept created by \link[stats]{lm}}
#' \item{slope_ls}{slope created by \link[stats]{lm}}
#' }
#' @export
#'
#' @examples
#' \dontrun{
#' MQtf1820_2019=dataset(1820, 2019, metadata, data)
#'}
#'
#'
#'
#'
metaMQ=function(Startyear, Endyear, metadata, data){
dataset=which(metadata$startyear<=Startyear)
#stationnames that fit in startyear
ld=length(dataset)
starthydro=rep(0,ld )
for ( i in 1:ld){
starthydro[i]=as.numeric(substr(metadata$startday[dataset[i]], 6,7))
start=which(starthydro<12)
}
dataset=dataset[start]
datasetnames=metadata$station[dataset]
#Stations ending at least with October within Endyear
datasetend=which(metadata$endyear>=Endyear)
lde=length( datasetend)
endhydro=rep(0,lde )
for ( i in 1:lde){
endhydro[i]=as.numeric(substr(metadata$endday[datasetend[i]], 6,7))
end=which(endhydro>9)
}
datasetend=datasetend[end]
datasetendnames=metadata$station[datasetend]
#stationnames that fit in endyear
vec=rep(F,length(dataset))
for ( i in 1:length(dataset) ){
vec[i]=is.element(dataset[i],datasetend)
}
stations=dataset[which(vec==T)]
#stations that fit in start as well as endyear
######
l=length(stations)
# calculate for Year ------------------------------------------------------
Yslopezyp=rep(0, l)
Yinterceptzyp=rep(0, l)
Ysigzyp=rep(0, l)
Yslopelm=rep(0, l)
Yintlm=rep(0, l)
Wslopezyp=rep(0, l)
Winterceptzyp=rep(0, l)
Wsigzyp=rep(0, l)
Wslopelm=rep(0, l)
Wintlm=rep(0, l)
Spslopezyp=rep(0, l)
Spinterceptzyp=rep(0, l)
Spsigzyp=rep(0, l)
Spslopelm=rep(0, l)
Spintlm=rep(0, l)
Sslopezyp=rep(0, l)
Sinterceptzyp=rep(0, l)
Ssigzyp=rep(0, l)
Sslopelm=rep(0, l)
Sintlm=rep(0, l)
Aslopezyp=rep(0, l)
Ainterceptzyp=rep(0, l)
Asigzyp=rep(0, l)
Aslopelm=rep(0, l)
Aintlm=rep(0, l)
for ( i in 1:l ){
datan=data[[stations[i]]]
min=paste(Startyear, "-11") #calc min of dataset
min=sub(" -", "-", min)
min=min(grep(min, datan[,1]))
max=paste(Endyear, "-10")
max=sub(" -", "-", max)
max=max(grep(max, datan[,1]))
datak=datan[min:max,]
MEAN=(mean(datak[,2]))
years=(Startyear):(Endyear)
ls=length(years)
lm=length(years)-1
MQ=rep(0,lm)
NMQ=rep(0,lm)
for (t in 1:lm){
yearmin=years[t]
yearmax=years[t+1]
min=paste(yearmin,"-11")
min=sub(" -", "-",min)
min
max= paste(yearmax,"-10")
max=sub(" -", "-",max)
start=min(grep(min, datak[,1]))
end=max(grep(max, datak[,1]))
h=datak[start:end, ]
mean=(mean(h[,2]))
MQ[t]=mean
NMQ[t]=(mean/MEAN)
}
hyears=years[-length(years)]
df=data.frame(hyears, MQ, NMQ)
zyp=zyp.trend.vector(df$MQ, df$hyears, "yuepilon")
linmod=lm(df$MQ~df$hyears, na.action = "na.exclude")
Yslopezyp[i]=zyp[2]
Yinterceptzyp[i]=zyp[11]
Ysigzyp[i]=zyp[6]
Yslopelm[i]=as.numeric(linmod$coefficients[2])
Yintlm[i]=as.numeric(linmod$coefficients[1])
# Winter ------------------------------------------------------------------
MQ=rep(0,lm)
NMQ=rep(0,lm)
for (t in 1:lm){
yearmin=years[t]
yearmax=years[t+1]
min=paste(yearmin,"-11")
min=sub(" -", "-",min)
min
max= paste(yearmax,"-01")
max=sub(" -", "-",max)
start=min(grep(min, datak[,1]))
end=max(grep(max, datak[,1]))
h=datak[start:end, ]
mean=(mean(h[,2]))
MQ[t]=mean
NMQ[t]=(mean/MEAN)
}
hyears=years[-length(years)]
wdf=data.frame(hyears, MQ, NMQ)
zyp=zyp.trend.vector(wdf$MQ, wdf$hyears, "yuepilon")
linmod=lm(wdf$MQ~wdf$hyears, na.action = "na.exclude")
Wslopezyp[i]=zyp[2]
Winterceptzyp[i]=zyp[11]
Wsigzyp[i]=zyp[6]
Wslopelm[i]=as.numeric(linmod$coefficients[2])
Wintlm[i]=as.numeric(linmod$coefficients[1])
# Spring ------------------------------------------------------------------
MQ=rep(0,lm)
NMQ=rep(0,lm)
for (t in 2:ls){
yearmin=years[t]
min=paste(yearmin,"-02")
min=sub(" -", "-",min)
min
max= paste(yearmin,"-04")
max=sub(" -", "-",max)
start=min(grep(min, datak[,1]))
end=max(grep(max, datak[,1]))
h=datak[start:end, ]
mean=(mean(h[,2]))
MQ[t]=mean
NMQ[t]=(mean/MEAN)
}
MQ=MQ[-1]
NMQ=NMQ[-1]
hyears=years[-1]
spdf=data.frame(hyears, MQ, NMQ)
zyp=zyp.trend.vector(spdf$MQ, spdf$hyears, "yuepilon")
linmod=lm(spdf$MQ~spdf$hyears, na.action = "na.exclude")
Spslopezyp[i]=zyp[2]
Spinterceptzyp[i]=zyp[11]
Spsigzyp[i]=zyp[6]
Spslopelm[i]=as.numeric(linmod$coefficients[2])
Spintlm[i]=as.numeric(linmod$coefficients[1])
# Summer renew ------------------------------------------------------------
MQ=rep(0,lm)
NMQ=rep(0,lm)
for (t in 2:ls){
yearmin=years[t]
min=paste(yearmin,"-05")
min=sub(" -", "-",min)
max= paste(yearmin,"-07")
max=sub(" -", "-",max)
start=min(grep(min, datak[,1]))
end=max(grep(max, datak[,1]))
h=datak[start:end, ]
mean=(mean(h[,2]))
MQ[t]=mean
NMQ[t]=(mean/MEAN)
}
MQ=MQ[-1]
NMQ=NMQ[-1]
hyears=years[-1]
sdf=data.frame(hyears, MQ, NMQ)
zyp=zyp.trend.vector(sdf$MQ, sdf$hyears, "yuepilon")
linmod=lm(sdf$MQ~sdf$hyears, na.action = "na.exclude")
Sslopezyp[i]=zyp[2]
Sinterceptzyp[i]=zyp[11]
Ssigzyp[i]=zyp[6]
Sslopelm[i]=as.numeric(linmod$coefficients[2])
Sintlm[i]=as.numeric(linmod$coefficients[1])
# Autumn ------------------------------------------------------------------
MQ=rep(0,ls)
NMQ=rep(0,ls)
for (t in 2:ls){
yearmin=years[t]
min=paste(yearmin,"-08")
min=sub(" -", "-",min)
min
max= paste(yearmin,"-10")
max=sub(" -", "-",max)
start=min(grep(min, datak[,1]))
end=max(grep(max, datak[,1]))
h=datak[start:end, ]
mean=(mean(h[,2]))
MQ[t]=mean
NMQ[t]=(mean/MEAN)
}
MQ=MQ[-1]
NMQ=NMQ[-1]
hyears=years[-1]
adf=data.frame(hyears, MQ, NMQ)
zyp=zyp.trend.vector(adf$MQ, adf$hyears, "yuepilon")
linmod=lm(adf$MQ~adf$hyears, na.action = "na.exclude")
Aslopezyp[i]=zyp[2]
Ainterceptzyp[i]=zyp[11]
Asigzyp[i]=zyp[6]
Aslopelm[i]=as.numeric(linmod$coefficients[2])
Aintlm[i]=as.numeric(linmod$coefficients[1])
}
# metadata ----------------------------------------------------------------
meta=data.frame( metadata$station[stations] , metadata$river[stations] , metadata$longitude[stations],
metadata$latitude[stations],
Yslopezyp, #Year
Yinterceptzyp,
Ysigzyp,
Yslopelm,
Yintlm,
Wslopezyp,#Winter
Winterceptzyp,
Wsigzyp,
Wslopelm,
Wintlm,
Spslopezyp, #Spring
Spinterceptzyp,
Spsigzyp,
Spslopelm,
Spintlm,
Sslopezyp, #Summer
Sinterceptzyp,
Ssigzyp,
Sslopelm,
Sintlm,
Aslopezyp, #Autumn
Ainterceptzyp,
Asigzyp,
Aslopelm,
Aintlm
)
colnames(meta)[c(1,2,3,4)]=c("station","river", "longitude","latitude" )
return(meta)
}
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