CLIMATE.FRANCE.R

################################################ LOAD and PROCEED CLIMATIC DATA
source("./R/FUN.climate.R")
## Data extracted from C. Piedallu data base with Radition with cloud cover for
## each plots Piedallu, C, and J Gegout. 2008. “Efficient Assessment of
## Topographic Solar Radiation to Improve Plant Distribution Models.” Agricultural
## and Forest Meteorology 148 (11) (October): 1696–1706.
## doi:10.1016/j.agrformet.2008.06.001.  Temperature and precipitation with
## temperature corrected by elevation based on Piedallu, C., J. C. Gégout, V.
## Perez, F. Lebourgeois, and R. Field. 2012. “Soil Water Balance Performs Better
## Than Climatic Water Variables in Tree Species Distribution Modelling.” Global
## Ecology and Biogeography.
## http://onlinelibrary.wiley.com/doi/10.1111/geb.12012/full.

###### LOAD PIDEALLU DATA
data.CLIM <- read.csv("./data/raw/DataFrance/climate_piedallu/placettesGK_avec_2011.csv", 
    header = T, sep = "\t", stringsAsFactors = FALSE, dec = ",", na.strings = "")
print(names(data.CLIM))

data.CLIM$rumkg_500 <- as.numeric(gsub(",", ".", data.CLIM$rumkg_500))


######################################### COMPUTE CLIMATIC VARIABLES !!!

### NEED TO COMPUTE DDG5 NEED TO COMPUTE SOIL MAX WATER CONTENT NEED TO COMPUTE PET
### NEED TO COMPUTE WATER BUDGET

## remove NA lines
data.CLIM.na <- is.na(data.CLIM[["tmoy6190_1_cor"]])
data.CLIM2 <- data.CLIM[!data.CLIM.na, ]

### apply function sgdd
sgdd.vec <- apply(data.CLIM2[, 82:93], MARGIN = 1, FUN = fun.sgdd)
MeanT.vec <- apply(data.CLIM2[, 82:93], MARGIN = 1, FUN = mean)
plot(MeanT.vec, sgdd.vec)
dim(data.CLIM)

################################################# Max Soil Water Content compute based on Piedallu, C., J. C. Gégout, A. Bruand,
################################################# and I. Seynave. 2011. “Mapping Soil Water Holding Capacity over Large Areas to
################################################# Predict Potential Production of Forest Stands.” Geoderma 160 (3): 355–366.

### read data texture as in Piedallu et al. 2011
data.texture <- read.table("./data/raw/DataFrance/climate_piedallu/texture.txt", 
    header = T)
### load ecological data
load(file = "./data/process/ecologie_tot.Rdata")
head(ecologie_tot)

codeprof <- c(2.5, 10, 20, 30, 40, 50, 60, 70, 80, 92.5)  # code prof en cm
codecaillou <- c(2.5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 97.5)  ## code percentage en %

# compute swhc
swhc <- fun.swhc(affroc = ecologie_tot$affroc, cailloux = ecologie_tot$cailloux, 
    text2 = ecologie_tot$text2, text1 = ecologie_tot$text1, prof2 = ecologie_tot$prof2, 
    prof1 = ecologie_tot$prof1, codeprof, codecaillou, data.texture)
swhc[is.na(ecologie_tot$prof2)] <- NA

### add to ecological data based
ecologie_tot$SWHC <- swhc


################################## COMPUTE PET WITH TURC FORMULA unit to convert radneb61_ from J/cm2/month in
################################## MJ/m2/day /100/30 then to convert in KJ/m2/day as in Nick formula *1000

## RAD and Temperature of the 12 months
radneb61_1to12 <- 66:77
tmoy6190_1_cor.1to12 <- 82:93

### apply in parallel
library(doParallel)
registerDoParallel(cores = 6)  ## affect automaticaly half of the core detected to the foreach