require(openair)
## Loading required package: openair
require(knitr)
## Loading required package: knitr
pauliceia <-readRDS("~/Dissertação/DADOS/pauliceia.rds")
names(pauliceia)
## [1] "date" "NEE" "PAR"
## [4] "Rg" "Tair" "VPD"
## [7] "Ustar" "lai" "swctop"
## [10] "swc1m" "swc2m" "PotRad"
## [13] "daytime" "qc_NEE_fr" "qc_PAR_fr"
## [16] "qc_Rg_fr" "qc_Ustar_fr" "W.m.2"
## [19] "W.m.2.1" "W.m.2.2" "mm"
## [22] "NR" "RG_IN" "RG_OUT"
## [25] "PAR_IN" "PAR_OUT" "FG"
## [28] "TAR" "UR" "PRESS_AWS"
## [31] "VAZAO.m3.s." "DEFLUVIO.mm." "H_final.W.m2."
## [34] "Le_final.W.m2." "RAIN_AWS.mm." "RAIN_VERT.mm."
## [37] "WSPEED_AWS.m.s." "WDIR_AWS.o." "TSOIL_10cm"
## [40] "TSOIL_20cm" "UmidSolo_10cm" "UmidSolo_20cm"
## [43] "UmidSolo_20cm.1" "UmidSolo_80cm" "UmidSolo_150cm"
## [46] "UmidSolo_200cm" "Rn.W.m.2" "Gmed.W.m.2"
## [49] "Storage.W.m.2" "X..Av.W.m.2" "PAR_in"
## [52] "PAR_ref" "Rg_in" "Rg_ref"
## [55] "Rn" "Tar" "Patm"
## [58] "Prec" "Vh" "Wdir"
inday <- format(pauliceia$date, "%Y-%m-%d")
www <- data.frame(date = as.POSIXct(paste(unique(inday)," 00:00:00", sep = ""), tz = "GMT"))
www$Umid10 <- aggregate(pauliceia$UmidSolo_10cm, by = list(inday), FUN = mean, na.rm = F)$x
www$Umid20 <- aggregate(pauliceia$UmidSolo_20cm, by = list(inday), FUN = mean, na.rm = F)$x
www$Umid20.1 <- aggregate(pauliceia$UmidSolo_20cm.1, by = list(inday), FUN = mean, na.rm = F)$x
www$Umid80 <- aggregate(pauliceia$UmidSolo_80cm, by = list(inday), FUN = mean, na.rm = F)$x
www$Umid150 <- aggregate(pauliceia$UmidSolo_150cm, by = list(inday), FUN = mean, na.rm = F)$x
www$Umid200 <- aggregate(pauliceia$UmidSolo_200cm, by = list(inday), FUN = mean, na.rm = F)$x
timePlot(www, names(www[,-1]),key.columns = 4)
Dos dados observados
www$www1 <- www$Umid10/0.39
www$www2 <- rowMeans(www[,2:5], na.rm = T)/0.39
www$www3 <- www$Umid150/0.39
timePlot(www,"www1")
timePlot(www,"www2")
timePlot(www,"www3")
kable(summary(www[which(format(www[,1], "%m" ) == "01"),-(1:7)]) )
|
Min. :0.2697 |
Min. :0.2154 |
Min. :0.3413 |
|
1st Qu.:0.3955 |
1st Qu.:0.3220 |
1st Qu.:0.3509 |
|
Median :0.4488 |
Median :0.3722 |
Median :0.3670 |
|
Mean :0.4387 |
Mean :0.3641 |
Mean :0.3954 |
|
3rd Qu.:0.4905 |
3rd Qu.:0.4151 |
3rd Qu.:0.4327 |
|
Max. :0.6508 |
Max. :0.5327 |
Max. :0.5267 |
|
NA’s :103 |
NA’s :103 |
NA’s :104 |
Dos dados preenchidos
mean.swc1.day <- aggregate(pauliceia$swc1m, by = list(inday) ,
FUN = mean, na.rm = F)$x
mean.ww2.day <- (mean.swc1.day/1000)/0.39
www$ww2 <- mean.ww2.day
timePlot(www, c("www2","ww2"), col = c(2,3),
group = T, lty = 1)
timePlot(selectByDate(www, year = 2009:2010), c("www2","ww2"), col = c(2,3),
group = T, lty = 1)
kable(summary(www[which(format(www[,1], "%m" ) == "01"),-(1:7)]) )
|
Min. :0.2697 |
Min. :0.2154 |
Min. :0.3413 |
Min. :0.1205 |
|
1st Qu.:0.3955 |
1st Qu.:0.3220 |
1st Qu.:0.3509 |
1st Qu.:0.2211 |
|
Median :0.4488 |
Median :0.3722 |
Median :0.3670 |
Median :0.2796 |
|
Mean :0.4387 |
Mean :0.3641 |
Mean :0.3954 |
Mean :0.2759 |
|
3rd Qu.:0.4905 |
3rd Qu.:0.4151 |
3rd Qu.:0.4327 |
3rd Qu.:0.3268 |
|
Max. :0.6508 |
Max. :0.5327 |
Max. :0.5267 |
Max. :0.4221 |
|
NA’s :103 |
NA’s :103 |
NA’s :104 |
NA |
mean.swc2.day <- aggregate(pauliceia$swc2m, by = list(inday) ,
FUN = mean, na.rm = F)$x
mean.ww3.day <- (mean.swc2.day/1000)/0.39
www$ww3 <- mean.ww3.day
timePlot(www, c("www3","ww3"), col = c(2,3),
group = T, lty = 1)
timePlot(selectByDate(www, year = 2009:2010), c("www3","ww3"), col = c(2,3),
group = T, lty = 1)
kable(summary(www[which(format(www[,1], "%m" ) == "01"),-(1:7)]) )
|
Min. :0.2697 |
Min. :0.2154 |
Min. :0.3413 |
Min. :0.1205 |
Min. :0.2869 |
|
1st Qu.:0.3955 |
1st Qu.:0.3220 |
1st Qu.:0.3509 |
1st Qu.:0.2211 |
1st Qu.:0.4353 |
|
Median :0.4488 |
Median :0.3722 |
Median :0.3670 |
Median :0.2796 |
Median :0.5272 |
|
Mean :0.4387 |
Mean :0.3641 |
Mean :0.3954 |
Mean :0.2759 |
Mean :0.5407 |
|
3rd Qu.:0.4905 |
3rd Qu.:0.4151 |
3rd Qu.:0.4327 |
3rd Qu.:0.3268 |
3rd Qu.:0.6440 |
|
Max. :0.6508 |
Max. :0.5327 |
Max. :0.5267 |
Max. :0.4221 |
Max. :0.7828 |
|
NA’s :103 |
NA’s :103 |
NA’s :104 |
NA |
NA |
