Budgets
Jens Daniel Müller
16 July, 2022
Last updated: 2022-07-16
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Knit directory: emlr_obs_analysis/analysis/
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1 Read files
# identify required version IDs
Version_IDs <-
list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = "v_G0")2 Bias thresholds
global_bias_rel_max <- 12.5
global_bias_rel_max[1] 12.5regional_bias_rel_max <- 30
regional_bias_rel_max[1] 302.1 Global
for (i_Version_IDs in Version_IDs) {
# i_Version_IDs <- Version_IDs[1]
print(i_Version_IDs)
path_version_data <-
paste(path_observations,
i_Version_IDs,
"/data/",
sep = "")
# load and join data files
dcant_budget_global <-
read_csv(paste(path_version_data,
"dcant_budget_global.csv",
sep = ""))
dcant_budget_global_mod_truth <-
read_csv(paste(
path_version_data,
"dcant_budget_global_mod_truth.csv",
sep = ""
))
dcant_budget_global_bias <-
read_csv(paste(path_version_data,
"dcant_budget_global_bias.csv",
sep = ""))
dcant_budget_global <- bind_rows(dcant_budget_global,
dcant_budget_global_mod_truth)
dcant_budget_global <- dcant_budget_global %>%
mutate(Version_ID = i_Version_IDs)
dcant_budget_global_bias <- dcant_budget_global_bias %>%
mutate(Version_ID = i_Version_IDs)
params_local <-
read_rds(paste(path_version_data,
"params_local.rds",
sep = ""))
params_local <- bind_cols(
Version_ID = i_Version_IDs,
MLR_basins = params_local$MLR_basins,
tref1 = params_local$tref1,
tref2 = params_local$tref2,
gap_filling = params_local$gap_filling,
rarefication = params_local$rarefication,
rarefication_threshold = params_local$rarefication_threshold,
MLR_predictors = str_c(params_local$MLR_predictors, collapse = "+"),
vif_max = params_local$vif_max
)
tref <- read_csv(paste(path_version_data,
"tref.csv",
sep = ""))
params_local <- params_local %>%
mutate(
median_year_1 = sort(tref$median_year)[1],
median_year_2 = sort(tref$median_year)[2],
duration = median_year_2 - median_year_1,
period = paste(median_year_1, "-", median_year_2)
)
if (exists("dcant_budget_global_all")) {
dcant_budget_global_all <-
bind_rows(dcant_budget_global_all, dcant_budget_global)
}
if (!exists("dcant_budget_global_all")) {
dcant_budget_global_all <- dcant_budget_global
}
if (exists("dcant_budget_global_bias_all")) {
dcant_budget_global_bias_all <-
bind_rows(dcant_budget_global_bias_all,
dcant_budget_global_bias)
}
if (!exists("dcant_budget_global_bias_all")) {
dcant_budget_global_bias_all <- dcant_budget_global_bias
}
if (exists("params_local_all")) {
params_local_all <- bind_rows(params_local_all, params_local)
}
if (!exists("params_local_all")) {
params_local_all <- params_local
}
}[1] "v_G001"
[1] "v_G002"
[1] "v_G003"
[1] "v_G004"
[1] "v_G005"
[1] "v_G006"# params_local_all <-
# params_local_all %>%
# mutate(period = factor(period, c("1994 - 2004", "2004 - 2014", "1994 - 2014")))
rm(
dcant_budget_global,
dcant_budget_global_bias,
dcant_budget_global_mod_truth,
params_local,
tref
)
dcant_budget_global_all <- full_join(dcant_budget_global_all,
params_local_all)
dcant_budget_global_bias_all <-
full_join(dcant_budget_global_bias_all,
params_local_all)dcant_budget_global_all <- dcant_budget_global_all %>%
filter(estimate == "dcant",
method == "total") %>%
select(-c(estimate, method)) %>%
rename(dcant = value)
dcant_budget_global_all_depth <- dcant_budget_global_all
dcant_budget_global_all <- dcant_budget_global_all %>%
filter(inv_depth == params_global$inventory_depth_standard)
dcant_budget_global_bias_all <- dcant_budget_global_bias_all %>%
filter(estimate == "dcant") %>%
select(-c(estimate))
dcant_budget_global_bias_all_depth <- dcant_budget_global_bias_all
dcant_budget_global_bias_all <- dcant_budget_global_bias_all %>%
filter(inv_depth == params_global$inventory_depth_standard)2.2 Regional
# Version_IDs <- Version_IDs[1:length(Version_IDs)-1]
for (i_Version_IDs in Version_IDs) {
# i_Version_IDs <- Version_IDs[1]
print(i_Version_IDs)
path_version_data <-
paste(path_observations,
i_Version_IDs,
"/data/",
sep = "")
# load and join data files
dcant_budget_basin_AIP <-
read_csv(paste(path_version_data,
"dcant_budget_basin_AIP.csv",
sep = ""))
dcant_budget_basin_AIP_mod_truth <-
read_csv(paste(
path_version_data,
"dcant_budget_basin_AIP_mod_truth.csv",
sep = ""
))
dcant_budget_basin_AIP <- bind_rows(dcant_budget_basin_AIP,
dcant_budget_basin_AIP_mod_truth)
dcant_budget_basin_AIP_bias <-
read_csv(paste(path_version_data,
"dcant_budget_basin_AIP_bias.csv",
sep = ""))
dcant_slab_budget_bias <-
read_csv(paste0(path_version_data,
"dcant_slab_budget_bias.csv"))
dcant_slab_budget <-
read_csv(paste0(path_version_data,
"dcant_slab_budget.csv"))
dcant_budget_basin_AIP <- dcant_budget_basin_AIP %>%
mutate(Version_ID = i_Version_IDs)
dcant_budget_basin_AIP_bias <- dcant_budget_basin_AIP_bias %>%
mutate(Version_ID = i_Version_IDs)
dcant_slab_budget <- dcant_slab_budget %>%
mutate(Version_ID = i_Version_IDs)
dcant_slab_budget_bias <- dcant_slab_budget_bias %>%
mutate(Version_ID = i_Version_IDs)
if (exists("dcant_budget_basin_AIP_all")) {
dcant_budget_basin_AIP_all <-
bind_rows(dcant_budget_basin_AIP_all, dcant_budget_basin_AIP)
}
if (!exists("dcant_budget_basin_AIP_all")) {
dcant_budget_basin_AIP_all <- dcant_budget_basin_AIP
}
if (exists("dcant_budget_basin_AIP_bias_all")) {
dcant_budget_basin_AIP_bias_all <-
bind_rows(dcant_budget_basin_AIP_bias_all,
dcant_budget_basin_AIP_bias)
}
if (!exists("dcant_budget_basin_AIP_bias_all")) {
dcant_budget_basin_AIP_bias_all <- dcant_budget_basin_AIP_bias
}
if (exists("dcant_slab_budget_all")) {
dcant_slab_budget_all <-
bind_rows(dcant_slab_budget_all, dcant_slab_budget)
}
if (!exists("dcant_slab_budget_all")) {
dcant_slab_budget_all <- dcant_slab_budget
}
if (exists("dcant_slab_budget_bias_all")) {
dcant_slab_budget_bias_all <-
bind_rows(dcant_slab_budget_bias_all,
dcant_slab_budget_bias)
}
if (!exists("dcant_slab_budget_bias_all")) {
dcant_slab_budget_bias_all <- dcant_slab_budget_bias
}
}[1] "v_G001"
[1] "v_G002"
[1] "v_G003"
[1] "v_G004"
[1] "v_G005"
[1] "v_G006"rm(
dcant_budget_basin_AIP,
dcant_budget_basin_AIP_bias,
dcant_budget_basin_AIP_mod_truth,
dcant_slab_budget,
dcant_slab_budget_bias
)
dcant_budget_basin_AIP_all <- full_join(dcant_budget_basin_AIP_all,
params_local_all)
dcant_budget_basin_AIP_bias_all <-
full_join(dcant_budget_basin_AIP_bias_all,
params_local_all)
dcant_slab_budget_all <- full_join(dcant_slab_budget_all,
params_local_all)
dcant_slab_budget_bias_all <-
full_join(dcant_slab_budget_bias_all,
params_local_all)dcant_budget_basin_AIP_all <- dcant_budget_basin_AIP_all %>%
filter(estimate == "dcant",
method == "total") %>%
select(-c(estimate, method)) %>%
rename(dcant = value)
dcant_budget_basin_AIP_all_depth <- dcant_budget_basin_AIP_all
dcant_budget_basin_AIP_all <- dcant_budget_basin_AIP_all %>%
filter(inv_depth == params_global$inventory_depth_standard)
dcant_budget_basin_AIP_bias_all <- dcant_budget_basin_AIP_bias_all %>%
filter(estimate == "dcant") %>%
select(-c(estimate))
dcant_budget_basin_AIP_bias_all_depth <- dcant_budget_basin_AIP_bias_all
dcant_budget_basin_AIP_bias_all <- dcant_budget_basin_AIP_bias_all %>%
filter(inv_depth == params_global$inventory_depth_standard)2.3 Steady state
for (i_Version_IDs in Version_IDs) {
# i_Version_IDs <- Version_IDs[1]
print(i_Version_IDs)
path_version_data <-
paste(path_observations,
i_Version_IDs,
"/data/",
sep = "")
# load and join data files
dcant_obs_budget <-
read_csv(paste0(path_version_data,
"anom_dcant_obs_budget.csv"))
dcant_obs_budget <- dcant_obs_budget %>%
mutate(Version_ID = i_Version_IDs)
if (exists("dcant_obs_budget_all")) {
dcant_obs_budget_all <-
bind_rows(dcant_obs_budget_all, dcant_obs_budget)
}
if (!exists("dcant_obs_budget_all")) {
dcant_obs_budget_all <- dcant_obs_budget
}
}[1] "v_G001"
[1] "v_G002"
[1] "v_G003"
[1] "v_G004"
[1] "v_G005"
[1] "v_G006"rm(dcant_obs_budget)
dcant_obs_budget_all <- full_join(dcant_obs_budget_all,
params_local_all)2.4 Atm CO2
co2_atm <-
read_csv(paste(path_preprocessing,
"co2_atm.csv",
sep = ""))3 Global
3.1 Individual cases
3.1.1 Absoulte values
legend_title = expression(atop(Delta * C[ant],
(mu * mol ~ kg ^ {
-1
})))
dcant_budget_global_all %>%
ggplot(aes(period, dcant, col = MLR_basins)) +
geom_jitter(width = 0.05, height = 0) +
scale_color_brewer(palette = "Dark2", name = "basin\nseparation") +
facet_grid(. ~ data_source) +
ylim(0,NA) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank())
3.1.2 Biases
dcant_budget_global_bias_all %>%
ggplot(aes(period, dcant_bias, col = MLR_basins)) +
geom_hline(yintercept = 0) +
scale_color_brewer(palette = "Dark2", name = "basin\nseparation") +
labs(y = expression(atop(Delta * C[ant] ~ bias,
(mu * mol ~ kg ^ {-1})))) +
geom_point()
p_global_bias <-
dcant_budget_global_bias_all %>%
ggplot() +
geom_hline(yintercept = global_bias_rel_max * c(-1,1),
linetype = 2) +
geom_hline(yintercept = 0) +
scale_color_brewer(palette = "Dark2", name = "basin\nseparation") +
labs(y = expression(Delta * C[ant] ~ bias ~ ("%")),
title = "Model-based assesment") +
theme(axis.title.x = element_blank()) +
geom_point(aes(period, dcant_bias_rel, col = MLR_basins),
alpha = 0.7) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank())
p_global_bias
4 Regional
4.1 Individual cases
4.1.1 Absoulte values
dcant_budget_basin_AIP_all %>%
ggplot(aes(period, dcant, col = MLR_basins)) +
geom_jitter(width = 0.05, height = 0) +
scale_color_brewer(palette = "Dark2", name = "basin\nseparation") +
facet_grid(basin_AIP ~ data_source) +
ylim(0,NA) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank())
4.1.2 Biases
dcant_budget_basin_AIP_bias_all %>%
ggplot(aes(period, dcant_bias, col=MLR_basins)) +
geom_hline(yintercept = 0) +
geom_point() +
facet_grid(basin_AIP ~ .)
dcant_budget_basin_AIP_bias_all %>%
ggplot() +
geom_tile(aes(y = 0, height = regional_bias_rel_max * 2,
x = period, width = Inf,
fill = "bias\nthreshold"), alpha = 0.5) +
geom_hline(yintercept = 0) +
scale_fill_manual(values = "grey70", name = "") +
scale_color_brewer(palette = "Dark2", name = "basin\nseparation") +
labs(y = expression(Delta ~ C[ant] ~ bias)) +
theme(axis.title.x = element_blank()) +
geom_jitter(aes(period, dcant_bias_rel, col = MLR_basins),
width = 0.05, height = 0) +
facet_grid(. ~ basin_AIP)
p_regional_bias <-
dcant_budget_basin_AIP_bias_all %>%
ggplot() +
geom_hline(yintercept = regional_bias_rel_max * c(-1,1),
linetype = 2) +
geom_hline(yintercept = 0) +
scale_color_brewer(palette = "Dark2", name = "basin\nseparation") +
labs(y = expression(Delta * C[ant] ~ bias ~ ("%")),
title = "Model-based assesment") +
theme(axis.title.x = element_blank()) +
geom_point(aes(period, dcant_bias_rel, col = MLR_basins),
alpha = 0.7) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank()) +
facet_grid(. ~ basin_AIP) +
theme(
strip.background = element_blank(),
strip.text.x = element_blank()
)
p_regional_bias
4.2 Slab budgets
4.2.1 Absolute values
dcant_slab_budget_all %>%
filter(data_source == "obs") %>%
ggplot(aes(MLR_basins, dcant, fill = gamma_slab)) +
geom_hline(yintercept = 0, col = "red") +
geom_col() +
scale_fill_scico_d(direction = -1) +
facet_grid(basin_AIP ~ period)
dcant_slab_budget_all %>%
filter(data_source == "obs") %>%
group_by(basin_AIP) %>%
group_split() %>%
map(
~ ggplot(data = .x,
aes(MLR_basins, dcant, fill = gamma_slab)) +
geom_hline(yintercept = 0) +
geom_col() +
scale_fill_scico_d(direction = -1) +
labs(title = paste("data_source:", unique(.x$basin_AIP))) +
facet_grid(gamma_slab ~ period)
)[[1]]
[[2]]
[[3]]
4.2.2 Bias
dcant_slab_budget_bias_all %>%
group_by(basin_AIP) %>%
group_split() %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(gamma_slab, dcant_bias, fill = gamma_slab)) +
geom_col() +
coord_flip() +
scale_x_discrete(limits = rev) +
scale_fill_scico_d(direction = -1) +
facet_grid(period ~ MLR_basins) +
labs(title = paste("data_source:", unique(.x$basin_AIP)))
)[[1]]
[[2]]
[[3]]
4.2.3 Spread
dcant_slab_budget_all %>%
filter(data_source != "mod_truth") %>%
group_by(data_source, basin_AIP, gamma_slab, period) %>%
summarise(dcant_range = max(dcant) - min(dcant)) %>%
ungroup() %>%
group_split(basin_AIP) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(gamma_slab, dcant_range, fill = gamma_slab)) +
geom_col() +
coord_flip() +
scale_x_discrete(limits = rev) +
scale_fill_scico_d(direction = -1) +
facet_grid(period ~ data_source) +
labs(title = paste("data_source:", unique(.x$basin_AIP)))
)[[1]]
[[2]]
[[3]]
5 Ensemble
5.1 Global
dcant_budget_global_all_in <- dcant_budget_global_all %>%
filter(data_source %in% c("mod", "obs"))
dcant_budget_global_ensemble <- dcant_budget_global_all_in %>%
group_by(data_source, period, tref2) %>%
summarise(dcant_mean = mean(dcant),
dcant_sd = sd(dcant),
dcant_range = max(dcant)- min(dcant)) %>%
ungroup()5.1.1 Mean
legend_title = expression(Delta * C[ant]~(PgC))
ggplot() +
geom_col(data = dcant_budget_global_ensemble,
aes(x = period,
y = dcant_mean),
fill = "darkgrey") +
geom_errorbar(
data = dcant_budget_global_ensemble,
aes(
x = period,
y = dcant_mean,
ymax = dcant_mean + dcant_sd,
ymin = dcant_mean - dcant_sd
),
width = 0.1
) +
geom_point(
data = dcant_budget_global_all,
aes(period, dcant, col = MLR_basins),
alpha = 0.7,
position = position_jitter(width = 0.2, height = 0)
) +
scale_y_continuous(limits = c(0,70), expand = c(0,0)) +
scale_color_brewer(palette = "Dark2", name = "basin\nseparation") +
facet_grid(. ~ data_source) +
labs(y = legend_title) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank())
p_global_dcant <- ggplot() +
geom_col(data = dcant_budget_global_ensemble %>%
filter(data_source == "obs"),
aes(x = period,
y = dcant_mean),
fill = "darkgrey") +
geom_point(
data = dcant_budget_global_all %>%
filter(data_source == "obs"),
aes(period, dcant, col = MLR_basins),
alpha = 0.7,
position = position_jitter(width = 0.1, height = 0)
) +
geom_errorbar(
data = dcant_budget_global_ensemble %>%
filter(data_source == "obs"),
aes(
x = period,
y = dcant_mean,
ymax = dcant_mean + dcant_sd,
ymin = dcant_mean - dcant_sd
),
width = 0.1
) +
scale_y_continuous(limits = c(0,70), expand = c(0,0)) +
scale_color_brewer(palette = "Dark2", name = "basin\nseparation") +
labs(y = legend_title,
title = "Observation-based results") +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank())
p_global_dcant_bias <-
p_global_dcant / p_global_bias +
plot_layout(guides = 'collect',
heights = c(2,1))
p_global_dcant_bias
# ggsave(plot = p_global_dcant_bias,
# path = "output/publication",
# filename = "Fig_global_dcant_budget.png",
# height = 5,
# width = 5)
rm(p_global_bias, p_global_dcant, p_global_dcant_bias)5.1.2 Mean vs atm CO2
dcant_ensemble <- dcant_budget_global_ensemble %>%
filter(data_source == "obs",
period != "1994 - 2014") %>%
select(year = tref2, dcant_mean, dcant_sd)
tcant_S04 <- bind_cols(year = 1994, dcant_mean = 118, dcant_sd = 19)
tcant_ensemble <- full_join(dcant_ensemble, tcant_S04)
tcant_ensemble <- left_join(tcant_ensemble, co2_atm)
co2_atm_pi <- bind_cols(pCO2 = 280, dcant_mean = 0, year = 1750, dcant_sd = 0)
tcant_ensemble <- full_join(tcant_ensemble, co2_atm_pi)
tcant_ensemble <- tcant_ensemble %>%
arrange(year) %>%
mutate(tcant = cumsum(dcant_mean),
tcant_sd = cumsum(dcant_sd))
tcant_ensemble %>%
ggplot(aes(pCO2, tcant, ymin = tcant - tcant_sd, ymax = tcant + tcant_sd)) +
geom_ribbon(fill = "grey80") +
geom_point() +
geom_line() +
geom_text(aes(label = year), nudge_x = -5, nudge_y = 5) +
labs(x = expression(Atmospheric~pCO[2]~(µatm)),
y = expression(Total~oceanic~C[ant]~(PgC)))
# ggsave(path = "output/publication",
# filename = "Fig_global_dcant_budget_vs_atm_pCO2.png",
# height = 4,
# width = 7)5.1.3 Sum decades
dcant_budget_global_all_in_sum <-
dcant_budget_global_all_in %>%
# filter(period != "1994 - 2014") %>%
arrange(tref1) %>%
group_by(data_source, MLR_basins) %>%
mutate(dcant = dcant + lag(dcant)) %>%
ungroup() %>%
drop_na() %>%
mutate(estimate = "sum")
dcant_budget_global_all_in_sum <-
bind_rows(
dcant_budget_global_all_in_sum,
dcant_budget_global_all_in %>%
filter(period == "1994 - 2014") %>%
mutate(estimate = "direct")
)
ggplot() +
geom_point(
data = dcant_budget_global_all_in_sum,
aes(estimate, dcant, col = MLR_basins),
alpha = 0.7,
position = position_jitter(width = 0, height = 0)
) +
scale_y_continuous(limits = c(0,70), expand = c(0,0)) +
scale_color_brewer(palette = "Dark2", name = "basin\nseparation") +
facet_grid(. ~ data_source) +
theme(axis.text.x = element_text(angle = 45, hjust=1),
axis.title.x = element_blank())5.1.4 Mean bias
dcant_budget_global_ensemble_bias <- full_join(
dcant_budget_global_ensemble %>%
filter(data_source == "mod") %>%
select(period, dcant_mean, dcant_sd),
dcant_budget_global_all %>%
filter(data_source == "mod_truth",
MLR_basins == "2") %>%
select(period, dcant)
)
dcant_budget_global_ensemble_bias <- dcant_budget_global_ensemble_bias %>%
mutate(dcant_mean_bias = dcant_mean - dcant,
dcant_mean_bias_rel = 100 * dcant_mean_bias / dcant)
dcant_budget_global_ensemble_bias %>%
ggplot(aes(period, dcant_mean_bias)) +
geom_hline(yintercept = 0) +
geom_point()
dcant_budget_global_ensemble_bias %>%
ggplot(aes(period, dcant_mean_bias_rel)) +
geom_hline(yintercept = 0) +
geom_point()
5.1.5 Vertical patterns
5.1.5.1 Absoulte values
dcant_budget_global_all_depth %>%
filter(data_source != "mod_truth") %>%
group_by(data_source) %>%
group_split() %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(dcant, MLR_basins, fill=period)) +
geom_vline(xintercept = 0) +
geom_col(position = "dodge") +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ .) +
labs(title = paste("data_source:", unique(.x$data_source)))
)[[1]]
[[2]]
5.1.5.2 Biases
dcant_budget_global_bias_all_depth %>%
ggplot(aes(dcant_bias, MLR_basins, fill = period)) +
geom_vline(xintercept = 0) +
geom_col(position = "dodge") +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ .)
dcant_budget_global_bias_all_depth %>%
ggplot(aes(dcant_bias_rel, MLR_basins, fill = period)) +
geom_vline(xintercept = 0) +
geom_col(position = "dodge") +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ .)
rm(dcant_budget_global_all,
dcant_budget_global_all_depth,
dcant_budget_global_bias_all,
dcant_budget_global_bias_all_depth,
dcant_budget_global_ensemble,
dcant_budget_global_ensemble_bias,
params_local_all)5.2 Regional
dcant_budget_basin_AIP_ensemble <- dcant_budget_basin_AIP_all %>%
filter(data_source %in% c("mod", "obs")) %>%
group_by(basin_AIP, data_source, period) %>%
summarise(dcant_mean = mean(dcant),
dcant_sd = sd(dcant),
dcant_range = max(dcant)- min(dcant)) %>%
ungroup()5.2.1 Mean
dcant_budget_basin_AIP_ensemble %>%
ggplot(aes(period, dcant_mean, col=basin_AIP)) +
geom_pointrange(aes(ymax = dcant_mean + dcant_sd,
ymin = dcant_mean - dcant_sd),
shape = 21) +
facet_grid(. ~ data_source)
p_regional_dcant <- ggplot() +
geom_col(
data = dcant_budget_basin_AIP_ensemble %>%
filter(data_source == "obs"),
aes(x = period,
y = dcant_mean),
fill = "darkgrey"
) +
geom_point(
data = dcant_budget_basin_AIP_all %>%
filter(data_source == "obs"),
aes(period, dcant, col = MLR_basins),
position = position_jitter(width = 0.1, height = 0),
alpha = 0.7
) +
geom_errorbar(
data = dcant_budget_basin_AIP_ensemble %>%
filter(data_source == "obs"),
aes(
x = period,
y = dcant_mean,
ymax = dcant_mean + dcant_sd,
ymin = dcant_mean - dcant_sd
),
width = 0.1
) +
scale_y_continuous(limits = c(0, 35), expand = c(0, 0)) +
scale_color_brewer(palette = "Dark2", name = "basin\nseparation") +
labs(y = legend_title,
title = "Observation-based results") +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank()) +
facet_grid(. ~ basin_AIP)
p_regional_dcant_bias <-
p_regional_dcant / p_regional_bias +
plot_layout(guides = 'collect',
heights = c(2,1))
p_regional_dcant_bias
# ggsave(plot = p_regional_dcant_bias,
# path = "output/publication",
# filename = "Fig_regional_dcant_budget.png",
# height = 5,
# width = 10)
rm(p_regional_bias, p_regional_dcant, p_regional_dcant_bias)5.2.2 Mean bias
dcant_budget_basin_AIP_ensemble_bias <- full_join(
dcant_budget_basin_AIP_ensemble %>%
filter(data_source == "mod") %>%
select(basin_AIP, period, dcant_mean, dcant_sd),
dcant_budget_basin_AIP_all %>%
filter(data_source == "mod_truth",
MLR_basins == "2") %>%
select(basin_AIP, period, dcant)
)
dcant_budget_basin_AIP_ensemble_bias <- dcant_budget_basin_AIP_ensemble_bias %>%
mutate(dcant_mean_bias = dcant_mean - dcant,
dcant_mean_bias_rel = 100 * dcant_mean_bias / dcant)
dcant_budget_basin_AIP_ensemble_bias %>%
ggplot(aes(period, dcant_mean_bias, col = basin_AIP)) +
geom_hline(yintercept = 0) +
geom_point()
dcant_budget_basin_AIP_ensemble_bias %>%
ggplot(aes(period, dcant_mean_bias_rel, col = basin_AIP)) +
geom_hline(yintercept = 0) +
geom_point()
5.2.3 Vertical patterns
5.2.3.1 Absoulte values
dcant_budget_basin_AIP_all_depth %>%
filter(data_source != "mod_truth") %>%
group_by(data_source) %>%
group_split() %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(dcant, MLR_basins, fill = basin_AIP)) +
geom_vline(xintercept = 0) +
geom_col() +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ period) +
labs(title = paste("data_source:", unique(.x$data_source)))
)[[1]]
[[2]]
5.2.3.2 Biases
dcant_budget_basin_AIP_bias_all_depth %>%
ggplot(aes(dcant_bias, MLR_basins, fill = basin_AIP)) +
geom_vline(xintercept = 0) +
geom_col() +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ period)
dcant_budget_basin_AIP_bias_all_depth %>%
ggplot(aes(dcant_bias_rel, MLR_basins, fill = basin_AIP)) +
geom_vline(xintercept = 0) +
geom_col(position = "dodge") +
scale_fill_brewer(palette = "Dark2") +
facet_grid(inv_depth ~ period)
6 Steady state
dcant_obs_budget_all %>%
group_by(inv_depth) %>%
group_split() %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(estimate, dcant_pos, fill = basin_AIP)) +
scale_fill_brewer(palette = "Dark2") +
geom_col() +
facet_grid(MLR_basins ~ period) +
labs(title = paste("inventory depth:",unique(.x$inv_depth)))
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3
Matrix products: default
BLAS: /usr/local/R-4.1.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.1.2/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] colorspace_2.0-2 marelac_2.1.10 shape_1.4.6 ggforce_0.3.3
[5] metR_0.11.0 scico_1.3.0 patchwork_1.1.1 collapse_1.7.0
[9] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[13] readr_2.1.1 tidyr_1.1.4 tibble_3.1.6 ggplot2_3.3.5
[17] tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] fs_1.5.2 bit64_4.0.5 lubridate_1.8.0 gsw_1.0-6
[5] RColorBrewer_1.1-2 httr_1.4.2 rprojroot_2.0.2 tools_4.1.2
[9] backports_1.4.1 bslib_0.3.1 utf8_1.2.2 R6_2.5.1
[13] DBI_1.1.2 withr_2.4.3 tidyselect_1.1.1 processx_3.5.2
[17] bit_4.0.4 compiler_4.1.2 git2r_0.29.0 cli_3.1.1
[21] rvest_1.0.2 xml2_1.3.3 labeling_0.4.2 sass_0.4.0
[25] scales_1.1.1 checkmate_2.0.0 SolveSAPHE_2.1.0 callr_3.7.0
[29] digest_0.6.29 rmarkdown_2.11 oce_1.5-0 pkgconfig_2.0.3
[33] htmltools_0.5.2 highr_0.9 dbplyr_2.1.1 fastmap_1.1.0
[37] rlang_1.0.2 readxl_1.3.1 rstudioapi_0.13 jquerylib_0.1.4
[41] generics_0.1.1 farver_2.1.0 jsonlite_1.7.3 vroom_1.5.7
[45] magrittr_2.0.1 Rcpp_1.0.8 munsell_0.5.0 fansi_1.0.2
[49] lifecycle_1.0.1 stringi_1.7.6 whisker_0.4 yaml_2.2.1
[53] MASS_7.3-55 grid_4.1.2 parallel_4.1.2 promises_1.2.0.1
[57] crayon_1.4.2 haven_2.4.3 hms_1.1.1 seacarb_3.3.0
[61] knitr_1.37 ps_1.6.0 pillar_1.6.4 reprex_2.0.1
[65] glue_1.6.0 evaluate_0.14 getPass_0.2-2 data.table_1.14.2
[69] modelr_0.1.8 vctrs_0.3.8 tzdb_0.2.0 tweenr_1.0.2
[73] httpuv_1.6.5 cellranger_1.1.0 gtable_0.3.0 polyclip_1.10-0
[77] assertthat_0.2.1 xfun_0.29 broom_0.7.11 later_1.3.0
[81] ellipsis_0.3.2