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SMP_per_scenario.R
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source('create_output/figures_for_MS/benchmark_figure_BCI_2008to2014.R')
#show NPP per pft per scenario
#load libraries
library(ncdf4)
#library(ncdf.tools)
from_new_data <- T
library(magrittr)
library(tidyverse)
library(lubridate)
library(scales)
#library(plotrix)
library(ggplot2)
library(reshape2)
source("utils/supporting_funcs.R")
source("create_output/figure_formatting.R")
source("parameter_files/bci_parameters.R")
if(from_new_data == T){
#name the run
run_type <- "ED2" # keep this as ED2
emulate_ED2 <- T
patch_run_type <- "one" #"many" #one or "many"
synthetic_patches <- F # T or F
run_name <- "SMP_BASE"
start_date <- "2001-01-01"
end_date <- "2040-12-31"
n_PFTs <- 4
soil_layer <- 15 # 15 is 6 cm, 16 is 2 cm deep
#set path to driver data
runs <- c("ED2_output/", "ED2_ENSO/", "ED2_wet/","ED2_dryDS/analy_dry75/")
for(r in runs){
driver_data_path <- paste0("~/cloud/gdrive/rec_submodel/data/",r)
source("clean_input/prep_driver_data_ED2_bci.R")
#get precip data
precip <- ED2_data1 %>% select(yr, month) %>% distinct() %>%
arrange(yr,month) %>%
add_column(rain) %>%
mutate(run = basename(driver_data_path))
#write_csv(precip,path = paste0("temp/","precip_",basename(driver_data_path),".csv"))
write_csv(input_data %>% select(yr,month,pft,NPP,SMP) %>% mutate(run = basename(driver_data_path)),path = paste0("temp/",basename(driver_data_path),".csv"))
print(paste("wrote csv for",r))
}
}
precip_data <- read_csv("temp/precip_ED2_output.csv") %>%
rbind(read_csv("temp/precip_ED2_ENSO.csv")) %>%
rbind(read_csv("temp/precip_ED2_wet.csv")) %>%
rbind(read_csv("temp/precip_analy_dry75.csv"))
NPPdata <- read_csv("temp/ED2_output.csv") %>%
rbind(read_csv("temp/ED2_ENSO.csv")) %>%
rbind(read_csv("temp/ED2_wet.csv")) %>%
rbind(read_csv("temp/analy_dry75.csv")) %>% str()
left_join(precip_data, by = c("yr","month","run"))
SMP_data <- read_csv("temp/ED2_output.csv") %>%
rbind(read_csv("temp/ED2_ENSO.csv")) %>%
rbind(read_csv("temp/ED2_wet.csv")) %>%
rbind(read_csv("temp/analy_dry75.csv"))
SMP_data %>% group_by(run, pft) %>% summarize(x = length(SMP))
day <- rep(1:(56520/4),16)
# SMP_data %>%
# add_column(day = day) %>%
# filter(pft == "LD_DT") %>%
# #filter(run == "ED2_ENSO", pft == "LD_DT") %>%
# #mutate(x = row_number()) %>% pull(row)
# ggplot(aes(day,SMP/1e5)) +
# geom_line() +
# facet_wrap(~run)
#
#create a dataframe with the vertical line information
vh_line <- data.frame(
xintercept = c(9, 29),
run = rep("SYN-ENSO", 2)
)
NPP_fig <- NPPdata %>%
rownames_to_column(var = "day") %>%
filter(as.numeric(day) %% 200 == 0) %>%
mutate(simYr = as.numeric(stringr::str_sub(yr, start = 1, end = 4)) - 2000) %>%
mutate(run = case_when(
run == "analy_dry75" ~ "DRY-DS",
run == "ED2_ENSO" ~ "SYN-ENSO",
run == "ED2_output" ~ "BASE",
run == "ED2_wet" ~ "WET"
)) %>%
mutate(run = factor(run,levels = c("BASE","SYN-ENSO","WET","DRY-DS"))) %>%
ggplot(aes(simYr,NPP,color = pft)) +
geom_smooth(se = F, ) +
geom_vline(data = vh_line, aes(xintercept = xintercept), linetype='dashed', size=1) +
scale_y_continuous(limits = c(0.5,1.3), breaks = c(0.5,0.75,1,1.25)) +
ylab(expression(paste("NPP ", "(g C m"^"-2","day"^"-1",")")))+
xlab(label = "simulation year") +
facet_wrap(~run) +
scale_color_manual(values = pft.cols) +
adams_theme +
theme(panel.spacing = unit(2, "lines")) +
guides(colour = guide_legend(override.aes = list(size = 5)))
makePNG(NPP_fig,path_to_output.x = path_to_MS_figures,file_name = "NPP_figs")
SMP_fig <- SMP_data %>%
arrange(run,pft) %>%
add_column(day = day) %>%
filter(pft == "LD_DT") %>%
mutate(run = case_when(
run == "analy_dry75" ~ "DRY-DS",
run == "ED2_ENSO" ~ "SYN-ENSO",
run == "ED2_output" ~ "BASE",
run == "ED2_wet" ~ "WET"
)) %>%
mutate(run = factor(run,levels = c("BASE","SYN-ENSO","WET","DRY-DS"))) %>%
ggplot(aes(day/365,SMP/1e5)) +
geom_line() +
#geom_smooth(size = 1.8, method = "loess", span = .01, se = F, lty = 1)+
#geom_vline(data = vh_line, aes(xintercept = xintercept), linetype='dashed', size=1) +
#scale_y_continuous(limits = c(0.5,1.3), breaks = c(0.5,0.75,1,1.25)) +
ylab(expression(paste("soil matric potential (MPa)")))+
xlab(label = "simulation year") +
facet_wrap(~run) +
#scale_color_manual(values = pft.cols) +
adams_theme +
theme(panel.spacing = unit(2, "lines")) #+
#guides(colour = guide_legend(override.aes = list(size = 5)))
makePNG(SMP_fig,path_to_output.x = path_to_MS_figures,file_name = "SMP_figs")
#Figure for the SI that shows variation in precipitation for each of the scenarios
precipData <- NPPdata %>%
mutate_at(.vars = "month", .funs = as.numeric) %>%
mutate(season = case_when(
month %in% 1:4 ~ "dry",
month %in% 5:12 ~ "wet"
)) %>%
filter(pft == "LD_DI") %>%
distinct() %>%
mutate(run = case_when(
run == "analy_dry75" ~ "DRY-DS",
run == "ED2_ENSO" ~ "SYN-ENSO",
run == "ED2_output" ~ "BASE",
run == "ED2_wet" ~ "WET"
)) %>%
mutate(simYr = as.numeric(stringr::str_sub(yr, start = 1, end = 4)) - 2000) %>%
group_by(run, simYr, season) %>%
summarise(precip = sum(rain))
precipFig <- precipData %>%
ggplot(aes(simYr,precip,color = season)) +
geom_line() +
scale_color_manual(values = c("brown","blue")) +
geom_vline(data = vh_line, aes(xintercept = xintercept), linetype='dashed', size=1) +
facet_wrap(~run) +
scale_y_log10() +
ylab(label = "total precip. per season (mm)") +
xlab(label = "simulation year") +
adams_theme +
theme(legend.title = element_text()) +
guides(fill=guide_legend(title="Season"))
makePNG(precipFig,path_to_output.x = path_to_MS_figures,file_name = "precipFig")
NPPdata %>%
rownames_to_column(var = "day") %>%
filter(as.numeric(day) %% 200 == 0, pft == "ST_DT") %>%
mutate(simYr = as.numeric(stringr::str_sub(yr, start = 1, end = 4)) - 2000) %>%
mutate(run = case_when(
run == "analy_dry75" ~ "DRY-DS",
run == "ED2_ENSO" ~ "SYN-ENSO",
run == "ED2_output" ~ "BASE",
run == "ED2_wet" ~ "WET"
)) %>%
mutate(run = factor(run,levels = c("BASE","SYN-ENSO","WET","DRY-DS"))) %>%
ggplot(aes(simYr,SMP)) +
geom_line() +
#geom_vline(data = vh_line, aes(xintercept = xintercept), linetype='dashed', size=1) +
#scale_y_continuous(limits = c(0.5,1.3), breaks = c(0.5,0.75,1,1.25)) +
ylab(expression(paste("NPP ", "(g C m"^"-2","day"^"-1",")")))+
xlab(label = "simulation year") +
facet_wrap(~run) +
scale_color_manual(values = pft.cols) +
adams_theme +
theme(panel.spacing = unit(2, "lines")) +
guides(colour = guide_legend(override.aes = list(size = 5)))