inst/examples_2rd_Rmd/Verificate_GEOtop_point_simulation.md

In ecor/geotopOptim: An R Plug-in to calibrate soil moisture output of the Distributed Hydrological Model GEOtop

title: "Verification GEOtop point simulation" author: "Johannes Brenner" date: "25. Juni 2015" output: html_document runtime: shiny

This R Markdown document is made interactive using Shiny. To learn more, see Interactive Documents.

```{r, include=FALSE}

if(!require(AnalyseGeotop)) { if(!require("devtools")) { install.packages(devtools) require("devtools") } install_github("AnalyseGeotop", "JBrenn") require("AnalyseGeotop") }

if(!require("dygraphs")) { install.packages(dygraphs) require("dygraphs") }

if(!require("hydroGOF")) { install.packages(hydroGOF) require("hydroGOF") }

if(!require("ggplot2")) { install.packages(ggplot2) require("ggplot2") }

wpath <- '/home/ecor/temp/geotopOptim_tests/B2_BeG_017_DVM_001'

wpath <- '/home/ecor/temp/geotopOptim_tests/B2_BeG_017_DVM_001_test_1'

'/home/ecor/activity/2016/eurac2016/Incarico_EURAC/Simulations/B2/B2_BeG_017_DVM_001_test_2'

TO CHECK ...

wpath <- '/home/ecor/activity/2016/eurac2016/Muntatschini_pnt_1_225_B2_004'

wpath <- "/run/user/1000/gvfs/smb-share:server=sdcalp01.eurac.edu,share=data2/Simulations/Simulation_GEOtop_1_225_ZH/Vinschgau/SimTraining/BrJ/HiResAlp/1D/Montecini_pnt_1_225_B2_007/"

wpath <- "/run/user/1000/gvfs/smb-share:server=sdcalp01.eurac.edu,share=data2/Simulations/Simulation_GEOtop_1_225_ZH/Vinschgau/SimTraining/BrJ/HiResAlp/1D/Montecini_pnt_1_225_P2_003/"

wpath <- "/run/user/1000/gvfs/smb-share:server=sdcalp01.eurac.edu,share=data2/Simulations/Simulation_GEOtop_1_225_ZH/Vinschgau/SimTraining/BrJ/MonaLisa/1D/Kaltern/sim006/"

obs data

load(file.path(wpath, "obs", "observation.RData")) names(observation) <- c("hour", "day") obs <- observation

time(obs$hour) <- as.POSIXlt(time(obs$hour))

sim data ### WORK HERE

if (file.exists(file.path(wpath,"PointOutValidation.RData"))) { load(file.path(wpath,"PointOutValidation.RData")) } else { var_out <- GEOtop_ReadValidationData(wpath = wpath, obs = observation, save_rData = T) }

varout <- var_out

adjust datetime: observations -1 hour

time(obs$hour) <- as.POSIXlt(time(obs$hour)) time(obs$hour)$mday <- time(obs$hour)$mday - 1 time(obs$day) <- time(obs$day) - 1

get hourly lag between obs and sim

ccf_temp <- stats::ccf(var_out[["air_temperature"]], obs$hour[,"air_temperature"], plot = F, na.action = na.pass) lag <- round(ccf_temp$acf[which.max(ccf_temp$acf)],0) time(obs$hour)$hour <- time(obs$hour)$hour + lag

load lookup table

data("lookup_tbl_observation")

#### Interactive Inputs

```{r, echo=FALSE}
inputPanel(
  #textInput(inputId = "simFolder", label = "Simulation folder", value = "")

  selectInput(inputId = "variable", label = "discover variable", choices = dimnames(obs$day)[[2]], selected = "air_temperature"),

  selectInput(inputId = "add_variable", label = "additional variable (sim)", choices = dimnames(obs$day)[[2]], selected = "rainfall_amount"),

  radioButtons(inputId = "aggregation", label = "aggregation", choices = c("hour","day","month"), selected = "day", inline = FALSE),

  radioButtons(inputId = "flux_amount", label = "flux or amount", choices = c("flux","amount"), selected = "flux", inline = FALSE)

  # radioButtons(inputId = "cum", label = "cumulated or time series", choices = c("time series","cumulated over time"), selected = "time series", inline = FALSE)

)

Time Series Plot

Cumulated timeseries are only provided for daily data aggregation. Check if your variable of interest is a flux or an amount!

```{r, echo=FALSE}

renderDygraph({

name <- input$variable if (grepl("soil_moisture_content",input$variable)) name <- "soil_moisture_content" if (grepl("soil_temperature", input$variable)) name <- "soil_temperature" if (grepl("liquid_soil_water_pressure", input$variable)) name <- "liquid_soil_water_pressure"

add_name <- input$add_variable if (grepl("soil_moisture_content",input$add_variable)) add_name <- "soil_moisture_content" if (grepl("soil_temperature", input$add_variable)) add_name <- "soil_temperature" if (grepl("liquid_soil_water_pressure", input$add_variable)) add_name <- "liquid_soil_water_pressure"

units <- lookup_tbl_observation$unit[lookup_tbl_observation$obs_var==name] add_units <- lookup_tbl_observation$unit[lookup_tbl_observation$obs_var==add_name]

if (input$aggregation=="hour" | input$aggregation=="day") { observation <- obs[[input$aggregation]][,input$variable] } else observation <- obs[["day"]][,input$variable]

simulation <- varout[[input$variable]] add_var <- varout[[input$add_variable]] * (-1)

if (input$aggregation=="day" & input$flux_amount=="flux") simulation <- aggregate(simulation, as.Date(time(simulation)), mean, na.rm=T) if (input$aggregation=="day" & input$flux_amount=="amount") simulation <- aggregate(simulation, as.Date(time(simulation)), sum, na.rm=F) if (input$aggregation=="month" & input$flux_amount=="flux") { simulation <- aggregate(simulation, as.yearmon(time(simulation)), mean, na.rm=T) observation <- aggregate(observation, as.yearmon(time(observation)), mean, na.rm=T) } if (input$aggregation=="month" & input$flux_amount=="amount") { simulation <- aggregate(simulation, as.yearmon(time(simulation)), sum, na.rm=F) observation <- aggregate(observation, as.yearmon(time(observation)), sum, na.rm=F) }

if (input$aggregation=="day") add_var <- aggregate(add_var, as.Date(time(add_var)), sum, na.rm=T) if (input$aggregation=="month") add_var <- aggregate(add_var, as.yearmon(time(add_var)), sum, na.rm=T)

# data <- merge(observation, simulation)

if (input$cum == "cumulated over time" & input$aggregation=="day")

{

if (input$variable=="evapotranspiration" | input$variable=="sensible_heat_flux_in_air" | input$variable=="latent_heat_flux_in_air") {

data <- merge(observation, simulation)

data <- window(x = data, start = as.Date("30-04-2011",format="%d-%m-%Y"), end = as.Date("17-04-2013", format="%d-%m-%Y"))

data <- zoo(na.approx.default(data), time(data))

data <- cumsum(data)

} else {

data <- merge(observation, simulation)

data <- data[!is.na(data$observation),]

data <- cumsum(data)

}

dygraph(data, ylab=paste("[",units,"]",sep="")) %>%

dyRangeSelector() %>%

dyRoller()

} else {

from POSIXlt to POSIXct

time(observation) <- as.POSIXct(time(observation)) time(add_var) <- as.POSIXct(time(add_var)) time(simulation) <- as.POSIXct(time(simulation))

data <- merge(add_var, observation, simulation)
names(data) <- c( "additional var", "observation", "simulation")

dygraph(data, ylab=paste("[",units,"]",sep="")) %>%
  dyRangeSelector() %>%
  dyRoller() %>%
  dySeries(name = "additional var", axis = "y2", stepPlot = TRUE, fillGraph = TRUE, label = paste("[-",add_units,"]",sep=""))

}

})

***

####Summary Table on Goodness of Fit (GOF)

Measures for GOF are given for seasons and for the whole data series. Calculation were performed with the hydroGOF R-Package. Type ?gof in RStudio for information on specific GOFs.

```{r, echo=FALSE}

renderDataTable({
 if (input$aggregation=="hour" | input$aggregation=="day")
  {
    observation <- obs[[input$aggregation]][,input$variable]
  } else observation <- obs[["day"]][,input$variable]

  simulation <- varout[[input$variable]]

 if (input$aggregation=="day" & input$flux_amount=="flux") simulation <- aggregate(simulation, as.Date(time(simulation)), mean, na.rm=T) 
  if (input$aggregation=="day" & input$flux_amount=="amount") simulation <- aggregate(simulation, as.Date(time(simulation)), sum, na.rm=F)
  if (input$aggregation=="month" & input$flux_amount=="flux") 
  {
    simulation <- aggregate(simulation, as.yearmon(time(simulation)), mean, na.rm=T)
    observation <- aggregate(observation, as.yearmon(time(observation)), mean, na.rm=T)
  } 
  if (input$aggregation=="month" & input$flux_amount=="amount") 
  {
    simulation <- aggregate(simulation, as.yearmon(time(simulation)), sum, na.rm=F)
    observation <- aggregate(observation, as.yearmon(time(observation)), sum, na.rm=F)
  } 

  time(observation) <- as.POSIXct(time(observation))
  time(simulation) <- as.POSIXct(time(simulation))

  data <- merge(observation, simulation)

  gofs <- gof(sim = data$simulation, obs=data$observation, na.rm = T)
  gofs <- as.data.frame(gofs)
  names(gofs) <- "YEAR"
  gofs$GOF <- dimnames(gofs)[[1]]

  mon <- as.numeric(format(time(data), "%m"))

  datadjf <-  data[mon==12 | mon==1 | mon==2,]
  gofs$DJF <-  c(gof(sim = datadjf$simulation, obs=datadjf$observation, na.rm = T))
  datamam <-  data[mon==3 | mon==4 | mon==5,]
  gofs$MAM <-  c(gof(sim = datamam$simulation, obs=datamam$observation, na.rm = T))
  datajja <-  data[mon==6 | mon==7 | mon==8,]
  gofs$JJA <-  c(gof(sim = datajja$simulation, obs=datajja$observation, na.rm = T))
  datason <-  data[mon==9 | mon==10 | mon==11,]
  gofs$SON <-  c(gof(sim = datason$simulation, obs=datason$observation, na.rm = T))

  gofs <- gofs[,c(2,3,4,5,6,1)]

}, options = list(pageLength=5, lengthMenu=c(5, 10, 15, 20)))

Scatterplot

```{r, echo=FALSE}

renderPlot({

name <- input$variable
if (grepl("soil_moisture_content",input$variable)) name <- "soil_moisture_content"
if (grepl("soil_temperature", input$variable)) name <- "soil_temperature"
if (grepl("liquid_soil_water_pressure", input$variable)) name <- "liquid_soil_water_pressure"

units <- lookup_tbl_observation$unit[lookup_tbl_observation$obs_var==name]

if (input$aggregation=="hour" | input$aggregation=="day")
{
  observation <- obs[[input$aggregation]][,input$variable]
} else observation <- obs[["day"]][,input$variable]

simulation <- varout[[input$variable]]

if (input$aggregation=="day" & input$flux_amount=="flux") simulation <- aggregate(simulation, as.Date(time(simulation)), mean, na.rm=T) 
if (input$aggregation=="day" & input$flux_amount=="amount") simulation <- aggregate(simulation, as.Date(time(simulation)), sum, na.rm=F)
if (input$aggregation=="month" & input$flux_amount=="flux") 
{
  simulation <- aggregate(simulation, as.yearmon(time(simulation)), mean, na.rm=T)
  observation <- aggregate(observation, as.yearmon(time(observation)), mean, na.rm=T)
} 
if (input$aggregation=="month" & input$flux_amount=="amount") 
{
  simulation <- aggregate(simulation, as.yearmon(time(simulation)), sum, na.rm=F)
  observation <- aggregate(observation, as.yearmon(time(observation)), sum, na.rm=F)
}

time(observation) <- as.POSIXct(time(observation)) time(simulation) <- as.POSIXct(time(simulation))

data <- merge(observation, simulation)
names(data) <- c("observation", "simulation")
data <- as.data.frame(coredata(data))

ggplot(data = data, aes(x=observation, y=simulation)) +
  geom_point() +
  geom_abline(intercept=0, slope=1, col=rgb(1,0,0,.5), lwd=2)

}) ```

ecor/geotopOptim documentation built on May 15, 2019, 8:54 p.m.