Zonal sections
Jens Daniel Müller
27 June, 2022
Last updated: 2022-06-27
Checks: 7 0
Knit directory: emlr_obs_analysis/analysis/
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1 Uncertainty limit
sd_uncertainty_limit <- 1.52 Read files
# identify required version IDs
Version_IDs <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = "v_2i")
# Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
# pattern = "v_20")
#
# Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
# pattern = "v_30")
#
# Version_IDs <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)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_zonal <-
read_csv(paste(path_version_data,
"dcant_zonal.csv",
sep = ""))
dcant_zonal_mod_truth <-
read_csv(paste(path_version_data,
"dcant_zonal_mod_truth.csv",
sep = ""))
dcant_zonal <- bind_rows(dcant_zonal,
dcant_zonal_mod_truth)
dcant_profile <-
read_csv(paste(path_version_data,
"dcant_profile.csv",
sep = ""))
dcant_profile_mod_truth <-
read_csv(paste(path_version_data,
"dcant_profile_mod_truth.csv",
sep = ""))
dcant_profile <- bind_rows(dcant_profile,
dcant_profile_mod_truth)
dcant_budget_basin_AIP_layer <-
read_csv(paste(path_version_data,
"dcant_budget_basin_AIP_layer.csv",
sep = ""))
dcant_zonal_bias <-
read_csv(paste(path_version_data,
"dcant_zonal_bias.csv",
sep = ""))
dcant_zonal <- dcant_zonal %>%
mutate(Version_ID = i_Version_IDs)
dcant_profile <- dcant_profile %>%
mutate(Version_ID = i_Version_IDs)
dcant_budget_basin_AIP_layer <- dcant_budget_basin_AIP_layer %>%
mutate(Version_ID = i_Version_IDs)
dcant_zonal_bias <- dcant_zonal_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_zonal_all")) {
dcant_zonal_all <- bind_rows(dcant_zonal_all, dcant_zonal)
}
if (!exists("dcant_zonal_all")) {
dcant_zonal_all <- dcant_zonal
}
if (exists("dcant_profile_all")) {
dcant_profile_all <- bind_rows(dcant_profile_all, dcant_profile)
}
if (!exists("dcant_profile_all")) {
dcant_profile_all <- dcant_profile
}
if (exists("dcant_budget_basin_AIP_layer_all")) {
dcant_budget_basin_AIP_layer_all <-
bind_rows(dcant_budget_basin_AIP_layer_all,
dcant_budget_basin_AIP_layer)
}
if (!exists("dcant_budget_basin_AIP_layer_all")) {
dcant_budget_basin_AIP_layer_all <- dcant_budget_basin_AIP_layer
}
if (exists("dcant_zonal_bias_all")) {
dcant_zonal_bias_all <- bind_rows(dcant_zonal_bias_all, dcant_zonal_bias)
}
if (!exists("dcant_zonal_bias_all")) {
dcant_zonal_bias_all <- dcant_zonal_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_2i01"
[1] "v_2i02"
[1] "v_2i03"
[1] "v_2i04"
[1] "v_2i05"
[1] "v_2i06"rm(dcant_zonal, dcant_zonal_bias, dcant_zonal_mod_truth,
dcant_budget_basin_AIP_layer,
tref)
dcant_zonal_all <- full_join(dcant_zonal_all,
params_local_all)
dcant_profile_all <- full_join(dcant_profile_all,
params_local_all)
dcant_budget_basin_AIP_layer_all <-
full_join(dcant_budget_basin_AIP_layer_all,
params_local_all)
dcant_zonal_bias_all <- full_join(dcant_zonal_bias_all,
params_local_all)3 Label test
dcant_zonal_all <- dcant_zonal_all %>%
mutate(
label = case_when(
Version_ID == "v_2i01" ~ "no_O2",
Version_ID == "v_2i02" ~ "IO_test_lat",
Version_ID == "v_2i03" ~ "IO_test_lon",
Version_ID == "v_2i04" ~ "reoccupation",
Version_ID == "v_2i05" ~ "tco2_target",
Version_ID == "v_2i06" ~ "adj_filter"
)
)
dcant_profile_all <- dcant_profile_all %>%
mutate(
label = case_when(
Version_ID == "v_2i01" ~ "no_O2",
Version_ID == "v_2i02" ~ "IO_test_lat",
Version_ID == "v_2i03" ~ "IO_test_lon",
Version_ID == "v_2i04" ~ "reoccupation",
Version_ID == "v_2i05" ~ "tco2_target",
Version_ID == "v_2i06" ~ "adj_filter"
)
)
dcant_zonal_bias_all <- dcant_zonal_bias_all %>%
mutate(
label = case_when(
Version_ID == "v_2i01" ~ "no_O2",
Version_ID == "v_2i02" ~ "IO_test_lat",
Version_ID == "v_2i03" ~ "IO_test_lon",
Version_ID == "v_2i04" ~ "reoccupation",
Version_ID == "v_2i05" ~ "tco2_target",
Version_ID == "v_2i06" ~ "adj_filter"
)
)
dcant_budget_basin_AIP_layer_all <- dcant_budget_basin_AIP_layer_all %>%
mutate(
label = case_when(
Version_ID == "v_2i01" ~ "no_O2",
Version_ID == "v_2i02" ~ "IO_test_lat",
Version_ID == "v_2i03" ~ "IO_test_lon",
Version_ID == "v_2i04" ~ "reoccupation",
Version_ID == "v_2i05" ~ "tco2_target",
Version_ID == "v_2i06" ~ "adj_filter"
)
)4 Individual cases
4.1 Absoulte values
dcant_zonal_all %>%
filter(data_source %in% c("mod", "obs")) %>%
group_by(basin_AIP) %>%
group_split() %>%
# head(1) %>%
map(
~ p_section_zonal_continous_depth(
df = .x,
var = "dcant",
plot_slabs = "n",
subtitle_text = paste(
"basin:",
unique(.x$basin_AIP)
)
) +
facet_grid(label ~ data_source)
)[[1]]
| Version | Author | Date |
|---|---|---|
| 09b0780 | jens-daniel-mueller | 2022-05-24 |
| c3a6238 | jens-daniel-mueller | 2022-03-08 |
| 9753eb8 | jens-daniel-mueller | 2022-01-26 |
| d7dfc7c | jens-daniel-mueller | 2022-01-18 |
| d5db8cd | jens-daniel-mueller | 2021-11-02 |
| e534f51 | jens-daniel-mueller | 2021-11-02 |
| 7abe2b2 | jens-daniel-mueller | 2021-11-01 |
| 4331a22 | jens-daniel-mueller | 2021-10-29 |
[[2]]
| Version | Author | Date |
|---|---|---|
| 09b0780 | jens-daniel-mueller | 2022-05-24 |
| c3a6238 | jens-daniel-mueller | 2022-03-08 |
| 9753eb8 | jens-daniel-mueller | 2022-01-26 |
| d7dfc7c | jens-daniel-mueller | 2022-01-18 |
| d5db8cd | jens-daniel-mueller | 2021-11-02 |
| e534f51 | jens-daniel-mueller | 2021-11-02 |
| 7abe2b2 | jens-daniel-mueller | 2021-11-01 |
| 4331a22 | jens-daniel-mueller | 2021-10-29 |
[[3]]
| Version | Author | Date |
|---|---|---|
| 09b0780 | jens-daniel-mueller | 2022-05-24 |
| c3a6238 | jens-daniel-mueller | 2022-03-08 |
| 9753eb8 | jens-daniel-mueller | 2022-01-26 |
| d7dfc7c | jens-daniel-mueller | 2022-01-18 |
| d5db8cd | jens-daniel-mueller | 2021-11-02 |
| e534f51 | jens-daniel-mueller | 2021-11-02 |
| 7abe2b2 | jens-daniel-mueller | 2021-11-01 |
| 4331a22 | jens-daniel-mueller | 2021-10-29 |
4.2 Biases
dcant_zonal_bias_all %>%
group_by(basin_AIP) %>%
group_split() %>%
# head(1) %>%
map(
~ p_section_zonal_continous_depth(
df = .x,
var = "dcant_bias",
col = "divergent",
plot_slabs = "n",
subtitle_text = paste("basin:",
unique(.x$basin_AIP)
)
) +
facet_grid(label ~ period)
)[[1]]
| Version | Author | Date |
|---|---|---|
| e09320d | jens-daniel-mueller | 2022-04-12 |
| c3a6238 | jens-daniel-mueller | 2022-03-08 |
| 9753eb8 | jens-daniel-mueller | 2022-01-26 |
| d7dfc7c | jens-daniel-mueller | 2022-01-18 |
| d5db8cd | jens-daniel-mueller | 2021-11-02 |
| e534f51 | jens-daniel-mueller | 2021-11-02 |
| 7abe2b2 | jens-daniel-mueller | 2021-11-01 |
| 4331a22 | jens-daniel-mueller | 2021-10-29 |
[[2]]
| Version | Author | Date |
|---|---|---|
| e09320d | jens-daniel-mueller | 2022-04-12 |
| c3a6238 | jens-daniel-mueller | 2022-03-08 |
| 9753eb8 | jens-daniel-mueller | 2022-01-26 |
| d7dfc7c | jens-daniel-mueller | 2022-01-18 |
| d5db8cd | jens-daniel-mueller | 2021-11-02 |
| e534f51 | jens-daniel-mueller | 2021-11-02 |
| 7abe2b2 | jens-daniel-mueller | 2021-11-01 |
| 4331a22 | jens-daniel-mueller | 2021-10-29 |
[[3]]
| Version | Author | Date |
|---|---|---|
| e09320d | jens-daniel-mueller | 2022-04-12 |
| c3a6238 | jens-daniel-mueller | 2022-03-08 |
| 9753eb8 | jens-daniel-mueller | 2022-01-26 |
| d7dfc7c | jens-daniel-mueller | 2022-01-18 |
| d5db8cd | jens-daniel-mueller | 2021-11-02 |
| e534f51 | jens-daniel-mueller | 2021-11-02 |
| 7abe2b2 | jens-daniel-mueller | 2021-11-01 |
| 4331a22 | jens-daniel-mueller | 2021-10-29 |
4.2.1 Density distribution
dcant_zonal_bias_all %>%
ggplot(aes(dcant_bias, col = label)) +
scale_color_brewer(palette = "Dark2") +
geom_vline(xintercept = 0) +
geom_density() +
facet_grid(period ~.) +
coord_cartesian(xlim = c(-10, 10))
4.3 Concentration profiles
4.3.1 Data source
dcant_profile_all %>%
group_split(period) %>%
map(
~ ggplot(data = .x,
aes(
dcant, depth,
col = data_source, fill = data_source
)) +
geom_hline(yintercept = params_global$inventory_depth_standard) +
geom_vline(xintercept = 0) +
geom_ribbon(
aes(xmin = dcant - dcant_sd,
xmax = dcant + dcant_sd),
alpha = 0.2,
col = "transparent"
) +
geom_path() +
scale_y_reverse() +
labs(title = paste("period", unique(.x$period))) +
facet_grid(label ~ basin_AIP)
)[[1]]
4.3.2 Basin separation
dcant_profile_all %>%
group_split(period) %>%
map(
~ ggplot(data = .x,
aes(
dcant, depth,
col = label, fill = label
)) +
geom_hline(yintercept = params_global$inventory_depth_standard) +
geom_vline(xintercept = 0) +
geom_path() +
scale_y_reverse() +
labs(title = paste("period", unique(.x$period))) +
facet_grid(data_source ~ basin_AIP)
)[[1]]
4.3.3 Era
dcant_profile_all %>%
arrange(depth) %>%
filter(period != "1994 - 2014") %>%
group_split(data_source) %>%
map(
~ ggplot(
data = .x,
aes(
dcant,
depth,
col = period,
group = interaction(label, period)
)
) +
geom_hline(yintercept = params_global$inventory_depth_standard) +
geom_vline(xintercept = 0) +
geom_path() +
scale_y_reverse() +
labs(title = paste("data_source", unique(.x$data_source))) +
facet_grid(. ~ basin_AIP)
)[[1]]
[[2]]
[[3]]
4.4 Layer budgets
dcant_budget_basin_AIP_layer_all %>%
filter(estimate == "dcant") %>%
mutate(dcant = value,
inv_depth = fct_inorder(as.factor(inv_depth))) %>%
group_split(period) %>%
# head(1) %>%
map(
~ ggplot(data = .x,
aes(dcant, inv_depth,
fill = label)) +
geom_vline(xintercept = 0) +
geom_col(position = "dodge") +
scale_y_discrete(limits = rev) +
scale_fill_brewer(palette = "Dark2") +
labs(title = paste("period", unique(.x$period))) +
facet_grid(data_source ~ basin_AIP)
)[[1]]
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 isoband_0.2.5 labeling_0.4.2
[25] sass_0.4.0 scales_1.1.1 checkmate_2.0.0 SolveSAPHE_2.1.0
[29] callr_3.7.0 digest_0.6.29 rmarkdown_2.11 oce_1.5-0
[33] pkgconfig_2.0.3 htmltools_0.5.2 highr_0.9 dbplyr_2.1.1
[37] fastmap_1.1.0 rlang_1.0.2 readxl_1.3.1 rstudioapi_0.13
[41] jquerylib_0.1.4 generics_0.1.1 farver_2.1.0 jsonlite_1.7.3
[45] vroom_1.5.7 magrittr_2.0.1 Rcpp_1.0.8 munsell_0.5.0
[49] fansi_1.0.2 lifecycle_1.0.1 stringi_1.7.6 whisker_0.4
[53] yaml_2.2.1 MASS_7.3-55 grid_4.1.2 parallel_4.1.2
[57] promises_1.2.0.1 crayon_1.4.2 haven_2.4.3 hms_1.1.1
[61] seacarb_3.3.0 knitr_1.37 ps_1.6.0 pillar_1.6.4
[65] reprex_2.0.1 glue_1.6.0 evaluate_0.14 getPass_0.2-2
[69] data.table_1.14.2 modelr_0.1.8 vctrs_0.3.8 tzdb_0.2.0
[73] tweenr_1.0.2 httpuv_1.6.5 cellranger_1.1.0 gtable_0.3.0
[77] polyclip_1.10-0 assertthat_0.2.1 xfun_0.29 broom_0.7.11
[81] later_1.3.0 viridisLite_0.4.0 ellipsis_0.3.2