Diagnóstico rodadas InLand - Rodada default
Goergen, G.
19 de novembro de 2016
Análise simulação default InLand
Desenvolvido por Goergen, G.
1 Introdução
As análises apresentadas abaixo dizem respeito as saÃdas da simulação default realizada com o Modelo de SuperfÃcie InLand. Para esta rodada, as forçantes do modelo correspondem aos dados observados no sÃtio experimental Reserva Jarú, integrante da rede de observações do projeto LBA-MIP.
2 Pacotes e diretório
## openair knitr geosphere plyr dplyr
## TRUE TRUE TRUE TRUE TRUE
## stringr ggplot2 lubridate lattice latticeExtra
## TRUE TRUE TRUE TRUE TRUE
## tidyr magrittr dygraphs xts data.table
## TRUE TRUE TRUE TRUE TRUE
## tidyr
## TRUE#install.packages("RCurl", dep = T)
#RCurl, PKI, rsconnect. Diretório de trabalho
wd <- "~/Documents/Inland/scriptsR/"
setwd(wd)3 Carregando dados
3.1 Variáveis de entrada do modelo
3.2 SaÃda do modelo
csv.in <- read.csv(file = "../test_01/output/single_point-output.csv", header = T, sep = "")
head(csv.in)## time Swnet Lwnet Qle Qh Qg DelCanHeat
## 1 1 0 -13.248667 45.56675 -1.658783 -6.862923 85666.94
## 2 2 0 9.878327 46.24508 -6.529383 -11.783823 136116.72
## 3 3 0 8.333557 37.88517 -6.469487 -14.094309 92357.75
## 4 4 0 6.251198 29.71090 -5.336092 -15.398129 54820.37
## 5 5 0 6.342670 27.65858 -3.656302 -15.940701 51855.28
## 6 6 0 6.222919 30.84788 -4.547105 -16.133314 59005.38
## DelSurfHeat Evap Qs Qrec Qsb Qt DelSoilMoist
## 1 -999.99 1.815119e-05 0 -999.99 0.004534252 0.004534252 3426.47956
## 2 -999.99 1.842140e-05 0 -999.99 0.004533343 0.004533343 -16.26264
## 3 -999.99 1.509129e-05 0 -999.99 0.004529958 0.004529958 -16.10442
## 4 -999.99 1.183513e-05 0 -999.99 0.004522296 0.004522296 -15.83826
## 5 -999.99 1.101760e-05 0 -999.99 0.004508726 0.004508726 -15.57976
## 6 -999.99 1.228804e-05 0 -999.99 0.004488017 0.004488017 -15.48461
## DelSurfStor DelIntercept WaterTableD VegT BaresoilT AvgSurfT Albedo
## 1 -999.99 -999.99 -999.99 296.9620 298.9697 -999.99 -999.99
## 2 -999.99 -999.99 -999.99 296.2633 298.8991 -999.99 -999.99
## 3 -999.99 -999.99 -999.99 295.9243 298.8142 -999.99 -999.99
## 4 -999.99 -999.99 -999.99 295.7536 298.7216 -999.99 -999.99
## 5 -999.99 -999.99 -999.99 295.6296 298.6267 -999.99 -999.99
## 6 -999.99 -999.99 -999.99 295.4458 298.5318 -999.99 -999.99
## SurfStor fPAR SoilMoist1 SoilMoist2 SoilMoist3 SoilMoist4 SoilMoist5
## 1 -999.99 -999.99 79.74489 124.0568 211.9799 428.9523 860.3843
## 2 -999.99 -999.99 75.43724 119.8337 207.9912 426.2420 859.4943
## 3 -999.99 -999.99 72.46931 116.3156 203.9184 422.6089 857.9056
## 4 -999.99 -999.99 70.43289 113.4133 200.0652 418.4335 855.6092
## 5 -999.99 -999.99 69.02083 111.0206 196.5398 414.0044 852.6684
## 6 -999.99 -999.99 67.92109 109.0143 193.3556 409.5174 849.1812
## SoilMoist6 SoilTemp1 SoilTemp2 SoilTemp3 SoilTemp4 SoilTemp5 SoilTemp6
## 1 1721.361 298.9697 299.0090 299.0100 299.01 299.01 299.01
## 2 1721.218 298.8991 299.0059 299.0099 299.01 299.01 299.01
## 3 1720.895 298.8142 299.0007 299.0098 299.01 299.01 299.01
## 4 1720.320 298.7216 298.9935 299.0096 299.01 299.01 299.01
## 5 1719.441 298.6267 298.9843 299.0092 299.01 299.01 299.01
## 6 1718.220 298.5318 298.9733 299.0088 299.01 299.01 299.01
## SoilWet Ecanop Tveg Esoil Ewater RootMoist
## 1 0.9189844 1.617813e-05 7.054661e-07 1.267598e-06 -999.99 -999.99
## 2 0.8992754 1.654645e-05 4.381715e-07 1.436773e-06 -999.99 -999.99
## 3 0.8827526 1.335390e-05 3.425367e-07 1.394854e-06 -999.99 -999.99
## 4 0.8688376 1.026257e-05 2.948266e-07 1.277735e-06 -999.99 -999.99
## 5 0.8569549 9.524255e-06 2.938876e-07 1.199455e-06 -999.99 -999.99
## 6 0.8463848 1.063031e-05 4.192554e-07 1.238474e-06 -999.99 -999.99
## CanopInt GPP NPP NEE AutoResp HeteroResp ANPP
## 1 0.4737899 0 -5.685146e-08 5.685146e-08 5.685146e-08 0 0
## 2 1.8390781 0 -5.459942e-08 5.459942e-08 5.459942e-08 0 0
## 3 2.2493248 0 -5.403877e-08 5.403877e-08 5.403877e-08 0 0
## 4 2.9447140 0 -5.375957e-08 5.375957e-08 5.375957e-08 0 0
## 5 2.2533937 0 -5.352400e-08 5.352400e-08 5.352400e-08 0 0
## 6 1.5369067 0 -5.679746e-08 5.679746e-08 5.679746e-08 0 0
## BgResp LitterFall CarbPools1 CarbPools2 CarbPools3 CarbPools4
## 1 -999.99 0 0.2725 0.13625 18.5 1e-04
## 2 -999.99 0 0.2725 0.13625 18.5 1e-04
## 3 -999.99 0 0.2725 0.13625 18.5 1e-04
## 4 -999.99 0 0.2725 0.13625 18.5 1e-04
## 5 -999.99 0 0.2725 0.13625 18.5 1e-04
## 6 -999.99 0 0.2725 0.13625 18.5 1e-04
## CarbPools5 CarbPools6 CarbPools7 CarbPools8 CarbPools9 CarbPools10
## 1 2e-04 0.027 1e-04 2e-04 0.0411 0.0052
## 2 2e-04 0.027 1e-04 2e-04 0.0411 0.0052
## 3 2e-04 0.027 1e-04 2e-04 0.0411 0.0052
## 4 2e-04 0.027 1e-04 2e-04 0.0411 0.0052
## 5 2e-04 0.027 1e-04 2e-04 0.0411 0.0052
## 6 2e-04 0.027 1e-04 2e-04 0.0411 0.0052
## CarbPools11 CarbPools12 CarbPools13 TotLivBiom AbvGrndWood LAI Tair
## 1 0.0024 0.418 18.7725 18.90875 18.5 6.5 295.807
## 2 0.0024 0.418 18.7725 18.90875 18.5 6.5 295.886
## 3 0.0024 0.418 18.7725 18.90875 18.5 6.5 295.702
## 4 0.0024 0.418 18.7725 18.90875 18.5 6.5 295.613
## 5 0.0024 0.418 18.7725 18.90875 18.5 6.5 295.364
## 6 0.0024 0.418 18.7725 18.90875 18.5 6.5 295.195
## Qair Wind Rainf Psurf Swdown Lwdown CO2air
## 1 89.069 1.239 0.0003011111 -999.99 0 440.130 -999.99
## 2 89.572 1.782 0.0008272222 -999.99 0 440.600 -999.99
## 3 90.596 1.713 0.0003711111 -999.99 0 439.365 -999.99
## 4 89.950 1.551 0.0006555556 -999.99 0 436.740 -999.99
## 5 90.905 1.304 0.0000000000 -999.99 0 436.270 -999.99
## 6 90.783 1.427 0.0000000000 -999.99 0 435.530 -999.99#str(csv.in)3.3 Configurações da simulação
O arquivo ‘/data/offline/single_point/conf/inland-single_point.infile’ tem informações com a configuração para uma simulação single-point.
#conf <- read.table(file = "../test_01/conf/inland-single_point.infile")
## datas correspondentes ao perÃodo de simulação do modelo
dataini <- "2001010101" #year-month-day-hour
dataend <- "2002123124"
datas <- data.frame(date = seq.POSIXt(from = as.POSIXct(dataini,tz = "GMT","%Y%m%d%H")-3600,
to = as.POSIXct(dataend,tz = "GMT","%Y%m%d%H")-3600,
by = "hour"))
range(datas$date)## [1] "2001-01-01 00:00:00 GMT" "2002-12-31 23:00:00 GMT"dim(datas)## [1] 17520 1dim(csv.in)## [1] 26280 74#Por hora, vou analisar somente os anos de 2001 e 2002. Achar uma solução depois!
sdados <- csv.in %>% filter(time > 8760) #%>% select(Swnet,Lwnet)
inland <- cbind(datas,sdados)4 Visualização gráfica
Componentes do balanço de energia
be <- c("Swnet","Lwnet")
inland %>% timePlot(be, lty = 1, lwd = 2,
avg.time = "day", ylab = "",
main = "Média diária Net long (short) wave radiation")
Fluxos
flx <- c("Qle","Qh","Qg")
inland %>% timePlot(flx, lty = 1, lwd = 2,
avg.time = "day", ylab = "",
key.columns = 3,col = c("black","red","blue"),
main = "Média diária fluxos - Latente, Sensivel, no Solo [W/m2]")
Balanço de água
ba <- c("Evap","Qs","Qrec","Qsb","Qt")
inland %>% timePlot(ba, lty = 1, lwd = 2,
avg.time = "day", ylab = "",
main = "Média diária ET e escoamentos [kg/m2/s]")
Balanço de carbono
bcarb <- c("GPP","NPP", "NEE","AutoResp","HeteroResp")
inland %>% timePlot(bcarb, lty = 1, lwd = 2,
avg.time = "day", ylab = "",
main = "Média diária comp. Balanço de Carbono [kg/m2/s]")
!!!!!!!! F I M !!!!!!!!