What happens when we remove pines from ED?

Objective

1. What happens when pines are removed from the baseline?

Markdown set up

Process ED Results

In this analysis we are going to be comparing the ED test and the ED no pine runs, although these runs should be

# Run the drake plans that process the data. 
# source(file.path(DRAKE_DIR, 'process-outputs.R')) ## KALYN NEED to figure out the correct way to execute this! usgh # so freaking annoying 
# Import the output files 
no_pines <- readRDS(file.path(OUTPUT_DIR, "no_pines-data.rds"))
test     <- readRDS(file.path(OUTPUT_DIR, "test-data.rds"))

1. Comapre Baseline

First make a data frame of the baseline results.

baseline_df <- rbind(no_pines$ABG[scn == "no_pines-baseline", ], test$ABG[scn == "test-baseline"])
baseline_df$pft_name <- gsub(pattern = 'Temperate broadleaf,', replacement = 'Temp. broad', x = baseline_df$pft_name )
baseline_df$pft_name <- gsub(pattern = 'successional|-successional', replacement = '', x = baseline_df$pft_name)
baseline_df$pft_name <- gsub(pattern = 'Northern North American', replacement = 'N NA ', x = baseline_df$pft_name)
baseline_df_AGB <- baseline_df
baseline_LAI <- rbind(no_pines$LAI[scn == "no_pines-baseline", ], test$LAI[scn == "test-baseline"])
baseline_LAI$pft_name <- gsub(pattern = 'Temperate broadleaf,', replacement = 'Temp. broad', x = baseline_LAI$pft_name )
baseline_LAI$pft_name <- gsub(pattern = 'successional|-successional', replacement = '', x = baseline_LAI$pft_name)
baseline_LAI$pft_name <- gsub(pattern = 'Northern North American', replacement = 'N NA ', x = baseline_LAI$pft_name)

ggplot(data = baseline_df) + 
  geom_point(aes(datetime, value, color = scn)) + 
  THEME + 
  facet_grid(pft_name ~ scn, scales = "free") +
  labs(title = 'Above Ground Biomass', 
       y = unique(baseline_df$unit))

ggplot(data = baseline_LAI) + 
  geom_point(aes(datetime, value, color = scn)) + 
  THEME + 
  facet_grid(pft_name~scn, scales = "free") +
  labs(title = 'LAI', 
       y = unique(baseline_LAI$unit))

Removing the pines does not impact the above ground biomass and the LAI values, this is somewhat expected considering the test runs I did for Alexey.

2. What happens when SLA values are changed?

First make a data frame of the results.

SLA_df <- rbind(no_pines$ABG[scn %in% c("no_pines-UMBS_SLA", "no_pines-baseline"), ], test$ABG[scn == "test-UMBS_SLA"])
SLA_df$pft_name <- gsub(pattern = 'Temperate broadleaf,', replacement = 'Temp. broad', x = SLA_df$pft_name )
SLA_df$pft_name <- gsub(pattern = 'successional|-successional', replacement = '', x = SLA_df$pft_name)
SLA_df$pft_name <- gsub(pattern = 'Northern North American', replacement = 'N NA ', x = SLA_df$pft_name)
SLA_df_AGB <- SLA_df
SLA_LAI <- rbind(no_pines$LAI[scn %in% c("no_pines-UMBS_SLA", "no_pines-baseline"), ], test$LAI[scn == "test-UMBS_SLA"])
SLA_LAI$pft_name <- gsub(pattern = 'Temperate broadleaf,', replacement = 'Temp. broad', x = SLA_LAI$pft_name )
SLA_LAI$pft_name <- gsub(pattern = 'successional|-successional', replacement = '', x = SLA_LAI$pft_name)
SLA_LAI$pft_name <- gsub(pattern = 'Northern North American', replacement = 'N NA ', x = SLA_LAI$pft_name)

ggplot(data = SLA_df_AGB) + 
  geom_point(aes(datetime, value, color = scn)) + 
  THEME + 
facet_wrap("pft_name", scales = "free", ncol = 1) +
  labs(title = 'Above Ground Biomass', 
       y = unique(SLA_df_AGB$unit))

ggplot(data = SLA_LAI) + 
  geom_line(aes(datetime, value, color = scn)) + 
  THEME + 
  facet_grid(pft_name~scn, scales = "free") +
  labs(title = 'LAI', 
       y = unique(SLA_LAI$unit))

3. What happens when Vcmax values are changed?

Vcmax_df <- rbind(no_pines$ABG[scn %in% c("no_pines-UMBS_Vcmax", "no_pines-baseline"), ], test$ABG[scn == "test-UMBS_Vcmax"])
Vcmax_df$pft_name <- gsub(pattern = 'Temperate broadleaf,', replacement = 'Temp. broad', x = Vcmax_df$pft_name )
Vcmax_df$pft_name <- gsub(pattern = 'successional|-successional', replacement = '', x = Vcmax_df$pft_name)
Vcmax_df$pft_name <- gsub(pattern = 'Northern North American', replacement = 'N NA ', x = Vcmax_df$pft_name)
Vcmax_ABG <- Vcmax_df
Vcamx_LAI <- rbind(no_pines$LAI[scn %in% c("no_pines-UMBS_Vcmax", "no_pines-baseline"), ], test$LAI[scn == "test-UMBS_Vcmax"])
Vcamx_LAI$pft_name <- gsub(pattern = 'Temperate broadleaf,', replacement = 'Temp. broad', x = Vcamx_LAI$pft_name )
Vcamx_LAI$pft_name <- gsub(pattern = 'successional|-successional', replacement = '', x = Vcamx_LAI$pft_name)
Vcamx_LAI$pft_name <- gsub(pattern = 'Northern North American', replacement = 'N NA ', x = Vcamx_LAI$pft_name)

ggplot(data = Vcmax_ABG) + 
  geom_point(aes(datetime, value, color = scn)) + 
  THEME + 
facet_wrap("pft_name", scales = "free", ncol = 1) +
  labs(title = 'Above Ground Biomass', 
       y = unique(SLA_df_AGB$unit))

ggplot(data = Vcamx_LAI) + 
  geom_line(aes(datetime, value, color = scn)) + 
  THEME + 
  facet_grid(pft_name~scn, scales = "free") +
  labs(title = 'LAI', 
       y = unique(SLA_LAI$unit))

4. What happens when both SLA and Vcmax are changed?

Vcmax_df <- rbind(no_pines$ABG[scn %in% c("no_pines-Vcmax-SLA", "no_pines-baseline"), ], test$ABG[scn == "test-UMBS_Vcmax"])
Vcmax_df$pft_name <- gsub(pattern = 'Temperate broadleaf,', replacement = 'Temp. broad', x = Vcmax_df$pft_name )
Vcmax_df$pft_name <- gsub(pattern = 'successional|-successional', replacement = '', x = Vcmax_df$pft_name)
Vcmax_df$pft_name <- gsub(pattern = 'Northern North American', replacement = 'N NA ', x = Vcmax_df$pft_name)
SLA_Vcmax_ABG <- Vcmax_df
Vcamx_LAI <- rbind(no_pines$LAI[scn %in% c("no_pines-Vcmax-SLA", "no_pines-baseline"), ], test$LAI[scn == "test-UMBS_Vcmax"])
Vcamx_LAI$pft_name <- gsub(pattern = 'Temperate broadleaf,', replacement = 'Temp. broad', x = Vcamx_LAI$pft_name )
Vcamx_LAI$pft_name <- gsub(pattern = 'successional|-successional', replacement = '', x = Vcamx_LAI$pft_name)
Vcamx_LAI$pft_name <- gsub(pattern = 'Northern North American', replacement = 'N NA ', x = Vcamx_LAI$pft_name)
SLA_Vcmax_LAI <- Vcamx_LAI

ggplot(data = SLA_Vcmax_ABG) + 
  geom_point(aes(datetime, value, color = scn)) + 
  THEME + 
facet_wrap("pft_name", scales = "free", ncol = 1) +
  labs(title = 'Above Ground Biomass', 
       y = unique(SLA_df_AGB$unit))

ggplot(data = SLA_Vcmax_LAI) + 
  geom_line(aes(datetime, value, color = scn)) + 
  THEME + 
  facet_grid(pft_name~scn, scales = "free") +
  labs(title = 'LAI', 
       y = unique(SLA_LAI$unit))