Anomalous changes
Jens Daniel Müller
05 July, 2021
Last updated: 2021-07-05
Checks: 7 0
Knit directory: emlr_obs_analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | c330a09 | jens-daniel-mueller | 2021-07-05 | rerun with observations |
1 Data preparation
Version_IDs <- list.files(
path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
pattern = "v_3")
# Version_IDs <- Version_IDs[1:4]1.1 Read files
Following sensitivity cases were considered:
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 = "")
# read inventory file
cant_inv <-
read_csv(paste(path_version_data,
"cant_inv.csv",
sep = ""))
cant_inv <- cant_inv %>%
mutate(Version_ID = i_Version_IDs)
# read anomaly inventory file
anom_cant_inv <-
read_csv(paste(path_version_data,
"anom_cant_obs_inv_bias.csv",
sep = ""))
anom_cant_inv <- anom_cant_inv %>%
mutate(Version_ID = i_Version_IDs)
# read section file
cant_section <-
read_csv(paste(path_version_data,
"cant_zonal.csv",
sep = ""))
cant_section <- cant_section %>%
mutate(Version_ID = i_Version_IDs)
# read section file
anom_cant_section <-
read_csv(paste(path_version_data,
"anom_cant_obs_section_bias.csv",
sep = ""))
anom_cant_section <- anom_cant_section %>%
mutate(Version_ID = i_Version_IDs)
# read budget file
anom_cant_budget <-
read_csv(paste(path_version_data,
"anom_cant_obs_inv_budget.csv",
sep = ""))
anom_cant_budget <- anom_cant_budget %>%
mutate(Version_ID = i_Version_IDs)
# read local parametrization files
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$MLR_basins,
rarefication = params_local$rarefication,
rarefication_threshold = params_local$rarefication_threshold,
vif_max = params_local$vif_max
)
cant_inv <- full_join(cant_inv, params_local)
cant_section <- full_join(cant_section, params_local)
anom_cant_inv <- full_join(anom_cant_inv, params_local)
anom_cant_section <- full_join(anom_cant_section, params_local)
anom_cant_budget <- full_join(anom_cant_budget, params_local)
# read tref file
tref <- read_csv(paste(path_version_data,
"tref.csv",
sep = ""))
duration <- sort(tref$median_year)[2] - sort(tref$median_year)[1]
eras <- paste0(sort(tref$median_year)[1],"-", sort(tref$median_year)[2])
cant_inv <- cant_inv %>%
mutate(duration = duration,
eras = eras)
cant_section <- cant_section %>%
mutate(duration = duration,
eras = eras)
anom_cant_inv <- anom_cant_inv %>%
mutate(duration = duration,
eras = eras)
anom_cant_section <- anom_cant_section %>%
mutate(duration = duration,
eras = eras)
anom_cant_budget <- anom_cant_budget %>%
mutate(duration = duration,
eras = eras)
# bind inv files
if (exists("cant_inv_all")) {
cant_inv_all <- bind_rows(cant_inv_all, cant_inv)
}
if (!exists("cant_inv_all")) {
cant_inv_all <- cant_inv
}
# bind section files
if (exists("cant_section_all")) {
cant_section_all <- bind_rows(cant_section_all, cant_section)
}
if (!exists("cant_section_all")) {
cant_section_all <- cant_section
}
### for anomalies
# bind inv files
if (exists("anom_cant_inv_all")) {
anom_cant_inv_all <- bind_rows(anom_cant_inv_all, anom_cant_inv)
}
if (!exists("anom_cant_inv_all")) {
anom_cant_inv_all <- anom_cant_inv
}
# bind section files
if (exists("anom_cant_section_all")) {
anom_cant_section_all <- bind_rows(anom_cant_section_all, anom_cant_section)
}
if (!exists("anom_cant_section_all")) {
anom_cant_section_all <- anom_cant_section
}
# bind budget files
if (exists("anom_cant_budget_all")) {
anom_cant_budget_all <- bind_rows(anom_cant_budget_all, anom_cant_budget)
}
if (!exists("anom_cant_budget_all")) {
anom_cant_budget_all <- anom_cant_budget
}
}[1] "v_301"
[1] "v_302"
[1] "v_303"
[1] "v_304"
[1] "v_305"
[1] "v_306"
[1] "v_307"
[1] "v_308"
[1] "v_309"
[1] "v_310"
[1] "v_311"
[1] "v_312"
[1] "v_313"
[1] "v_314"
[1] "v_315"
[1] "v_316"
[1] "v_317"
[1] "v_318"
[1] "v_319"
[1] "v_320"
[1] "v_321"
[1] "v_322"
[1] "v_323"rm(cant_inv, cant_section)
rm(anom_cant_inv, anom_cant_section, anom_cant_budget)1.1.1 Format
# cant_inv_all_long <- cant_inv_all %>%
# pivot_longer(emlr_anom:nss_projection,
# names_to = "estimate",
# values_to = "delta_cant_offset")
#
#
# cant_section_all_long <- cant_section_all %>%
# pivot_longer(emlr_anom:nss_projection,
# names_to = "estimate",
# values_to = "delta_cant_offset")
# unique(cant_inv_all$Version_ID)2 Eras vs basin split
Here we compare the impact of the basin separation approach on the anomaly detection, separately for each compared eras.
cant_inv_all_sub <- cant_inv_all %>%
filter(rarefication == "none",
inv_depth == params_global$inventory_depth_standard)
# unique(cant_inv_all_sub$Version_ID)
cant_section_all_sub <- cant_section_all %>%
filter(rarefication == "none")
# unique(cant_section_all_sub$Version_ID)2.1 Column inventories
2.1.1 Maps
cant_inv_all_sub %>%
group_split(eras) %>%
# head(1) %>%
map(
~ p_map_cant_inv(
df = .x,
var = "cant_inv",
subtitle_text = paste("Eras:", unique(.x$eras))
) +
facet_grid(MLR_basins ~ data_source) +
theme(axis.text = element_blank(),
axis.ticks = element_blank())
)[[1]]
[[2]]
[[3]]
[[4]]
2.2 Zonal sections
2.2.1 Sections
cant_section_all_sub %>%
group_split(eras, data_source, MLR_basins) %>%
# head(1) %>%
map(
~ p_section_zonal(
df = .x,
var = "cant_mean",
plot_slabs = "n",
col = "continuous",
subtitle_text = paste("Eras:", unique(.x$eras),
"| data source:", unique(.x$data_source),
"| MLR Basins:", unique(.x$MLR_basins))
)
)legend_title <- expression(atop(Delta * C[ant, pos],
(mu * mol ~ kg ^ {
-1
})))
cant_section_all_sub %>%
group_split(eras, basin_AIP) %>%
# head(1) %>%
map(
~ ggplot(data = .x) +
geom_contour_filled(aes(lat, depth, z = cant_mean),
breaks = c(params_global$breaks_cant_pos)) +
scale_fill_scico_d(
palette = "acton",
drop = FALSE,
name = legend_title
) +
guides(fill = guide_colorsteps(barheight = unit(8, "cm"),
show.limits = TRUE)) +
# scale_y_sqrt() +
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(100,500,seq(1000,5000,1000))) +
#scale_y_reverse() +
scale_x_continuous(breaks = seq(-100, 100, 40),
limits = c(-85, 85)) +
facet_grid(MLR_basins ~ data_source) +
coord_cartesian(expand = 0) +
labs(title = paste("Eras", unique(.x$eras),
"| Basin:", unique(.x$basin_AIP)))
)[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
[[11]]
[[12]]
2.3 Budgets
2.3.1 Regional
cant_budget_all_sub %>%
filter(inv_depth == params_global$inventory_depth_standard) %>%
ggplot(aes(estimate, cant_pos_total, fill = basin_AIP)) +
scale_fill_brewer(palette = "PuBuGn") +
geom_col(col = "black") +
facet_grid(MLR_basins ~ eras) +
theme(axis.text.x = element_text(angle = 90))2.3.2 Era additive
cant_budget_all_sub_wide <- cant_budget_all_sub %>%
filter(inv_depth == params_global$inventory_depth_standard) %>%
select(-c(duration, Version_ID)) %>%
pivot_wider(names_from = eras,
values_from = cant_pos_total)
cant_budget_all_sub_wide <- cant_budget_all_sub_wide %>%
mutate(`1994-2014 (sum)` = `1994-2005` + `2005-2014`)
cant_budget_all_sub_long <- cant_budget_all_sub_wide %>%
select(-c(`1994-2005`, `2005-2014`)) %>%
pivot_longer(c(`1994-2014`, `1994-2014 (sum)`),
names_to = "eras",
values_to = "cant_pos_total")
cant_budget_all_sub_long %>%
ggplot(aes(eras, cant_pos_total, fill = basin_AIP)) +
scale_fill_brewer(palette = "PuBuGn") +
geom_col(col = "black") +
facet_grid(MLR_basins ~ estimate) +
theme(axis.text.x = element_text(angle = 90))3 Rarefication and VIF
cant_inv_all_sub <- cant_inv_all %>%
filter(rarefication == "coarse_grid" |
Version_ID == "v_202") %>%
mutate(
rarefication_threshold = as.character(rarefication_threshold),
rarefication_threshold = if_else(rarefication == "none",
"none",
rarefication_threshold),
rarefication_threshold = if_else(rarefication_threshold == "5",
"05",
rarefication_threshold)
)
cant_inv_all_long_sub <- cant_inv_all_long %>%
filter(rarefication == "coarse_grid" |
Version_ID == "v_202") %>%
mutate(
rarefication_threshold = as.character(rarefication_threshold),
rarefication_threshold = if_else(rarefication == "none",
"none",
rarefication_threshold),
rarefication_threshold = if_else(rarefication_threshold == "5",
"05",
rarefication_threshold)
)
cant_section_all_sub <- cant_section_all %>%
filter(rarefication == "coarse_grid" |
Version_ID == "v_202") %>%
mutate(
rarefication_threshold = as.character(rarefication_threshold),
rarefication_threshold = if_else(rarefication == "none",
"none",
rarefication_threshold),
rarefication_threshold = if_else(rarefication_threshold == "5",
"05",
rarefication_threshold)
)
cant_section_all_long_sub <- cant_section_all_long %>%
filter(rarefication == "coarse_grid" |
Version_ID == "v_202") %>%
mutate(
rarefication_threshold = as.character(rarefication_threshold),
rarefication_threshold = if_else(rarefication == "none",
"none",
rarefication_threshold),
rarefication_threshold = if_else(rarefication_threshold == "5",
"05",
rarefication_threshold)
)
cant_budget_all_sub <- cant_budget_all %>%
filter(rarefication == "coarse_grid" |
Version_ID == "v_202") %>%
mutate(
rarefication_threshold = as.character(rarefication_threshold),
rarefication_threshold = if_else(rarefication == "none",
"none",
rarefication_threshold),
rarefication_threshold = if_else(rarefication_threshold == "5",
"05",
rarefication_threshold)
)3.1 Column inventories
3.1.1 Maps rarefication
cant_inv_all_long_sub %>%
filter(vif_max == 1000) %>%
group_split(eras) %>%
# head(1) %>%
map(
~ p_map_cant_inv_offset(
df = .x,
var = "delta_cant_offset",
# col = "divergent",
subtitle_text = paste("Rarefication test | Eras:", unique(.x$eras)),
breaks = seq(-8,8,1)
) +
facet_grid(rarefication_threshold ~ estimate) +
theme(axis.text = element_blank(),
axis.ticks = element_blank())
)3.1.2 Maps VIF
cant_inv_all_long_sub %>%
filter(rarefication_threshold == 10) %>%
group_split(eras) %>%
# head(1) %>%
map(
~ p_map_cant_inv_offset(
df = .x,
var = "delta_cant_offset",
# col = "divergent",
subtitle_text = paste("VIF test | Eras:", unique(.x$eras)),
breaks = seq(-8,8,1)
) +
facet_grid(vif_max ~ estimate) +
theme(axis.text = element_blank(),
axis.ticks = element_blank())
)3.1.3 Bias correlation
axis_limit <- 10
cant_inv_all_sub %>%
filter(vif_max == 1000) %>%
group_split(eras) %>%
# head(1) %>%
map(
~ ggplot(data = .x, aes(emlr_bias, emlr_anom)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_bin2d(binwidth = 1) +
scale_fill_viridis_c(trans = "log10") +
coord_equal(
xlim = c(-axis_limit, axis_limit),
ylim = c(-axis_limit, axis_limit)
) +
labs(title = paste("Eras:", unique(.x$eras))) +
facet_grid(rarefication_threshold ~ basin_AIP)
)
cant_inv_all_sub %>%
filter(vif_max == 1000) %>%
group_split(eras) %>%
# head(1) %>%
map(
~ ggplot(data = .x, aes(emlr_anom, nss_mod_truth)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_bin2d(binwidth = 1) +
scale_fill_viridis_c(trans = "log10") +
coord_equal(
xlim = c(-axis_limit, axis_limit),
ylim = c(-axis_limit, axis_limit)
) +
labs(title = paste("Eras:", unique(.x$eras))) +
facet_grid(rarefication_threshold ~ basin_AIP)
)
cant_inv_all_sub %>%
filter(vif_max == 1000) %>%
group_split(eras) %>%
# head(1) %>%
map(
~ ggplot(data = .x, aes(nss_mod_truth, nss_projection)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_bin2d(binwidth = 1) +
scale_fill_viridis_c(trans = "log10") +
coord_equal(
xlim = c(-axis_limit, axis_limit),
ylim = c(-axis_limit, axis_limit)
) +
labs(title = paste("Eras:", unique(.x$eras))) +
facet_grid(rarefication_threshold ~ basin_AIP)
)3.2 Zonal sections
3.2.1 Sections
cant_section_all_long_sub %>%
filter(vif_max == 1000) %>%
group_split(eras, rarefication_threshold, estimate) %>%
head(1) %>%
map(
~ p_section_zonal(
df = .x,
var = "delta_cant_offset",
plot_slabs = "n",
col = "divergent",
subtitle_text = paste("Eras:", unique(.x$eras),
"| MLR Basins:", unique(.x$MLR_basins),
"| Estimate:", unique(.x$estimate)),
breaks = c(-Inf,seq(-8,8,1), Inf)
)
)legend_title <- expression(atop(Delta * C[ant, pos],
(mu * mol ~ kg ^ {
-1
})))
cant_section_all_long_sub %>%
filter(vif_max == 1000) %>%
group_split(eras, basin_AIP) %>%
# head(1) %>%
map(
~ ggplot(data = .x) +
geom_contour_filled(aes(lat, depth, z = delta_cant_offset),
breaks =c(-Inf,seq(-8,8,1), Inf)) +
scale_fill_scico_d(
palette = "vik",
drop = FALSE,
name = legend_title
) +
guides(fill = guide_colorsteps(barheight = unit(8, "cm"),
show.limits = TRUE)) +
# scale_y_sqrt() +
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(100,500,seq(1000,5000,1000))) +
#scale_y_reverse() +
scale_x_continuous(breaks = seq(-100, 100, 40),
limits = c(-85, 85)) +
facet_grid(rarefication_threshold ~ estimate) +
coord_cartesian(expand = 0) +
labs(title = paste("Rarefication test | Eras", unique(.x$eras),
"| Basin:", unique(.x$basin_AIP)))
)cant_section_all_long_sub %>%
filter(rarefication_threshold == 10) %>%
group_split(eras, basin_AIP) %>%
# head(1) %>%
map(
~ ggplot(data = .x) +
geom_contour_filled(aes(lat, depth, z = delta_cant_offset),
breaks =c(-Inf,seq(-8,8,1), Inf)) +
scale_fill_scico_d(
palette = "vik",
drop = FALSE,
name = legend_title
) +
guides(fill = guide_colorsteps(barheight = unit(8, "cm"),
show.limits = TRUE)) +
# scale_y_sqrt() +
scale_y_continuous(trans = trans_reverser("sqrt"),
breaks = c(100,500,seq(1000,5000,1000))) +
#scale_y_reverse() +
scale_x_continuous(breaks = seq(-100, 100, 40),
limits = c(-85, 85)) +
facet_grid(vif_max ~ estimate) +
coord_cartesian(expand = 0) +
labs(title = paste("VIF test | Eras", unique(.x$eras),
"| Basin:", unique(.x$basin_AIP)))
)3.2.2 Bias correlation
axis_limit <- 20
cant_section_all_sub %>%
filter(vif_max == 1000) %>%
group_split(eras) %>%
# head(1) %>%
map(
~ ggplot(data = .x, aes(emlr_bias, emlr_anom)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_bin2d(binwidth = 1) +
scale_fill_viridis_c(trans = "log10") +
coord_equal(
xlim = c(-axis_limit, axis_limit),
ylim = c(-axis_limit, axis_limit)
) +
labs(title = paste("Eras:", unique(.x$eras))) +
facet_grid(rarefication_threshold ~ basin_AIP)
)
cant_section_all_sub %>%
filter(vif_max == 1000) %>%
group_split(eras) %>%
# head(1) %>%
map(
~ ggplot(data = .x, aes(emlr_anom, nss_mod_truth)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_bin2d(binwidth = 1) +
scale_fill_viridis_c(trans = "log10") +
coord_equal(
xlim = c(-axis_limit, axis_limit),
ylim = c(-axis_limit, axis_limit)
) +
labs(title = paste("Eras:", unique(.x$eras))) +
facet_grid(rarefication_threshold ~ basin_AIP)
)
cant_section_all_sub %>%
filter(vif_max == 1000) %>%
group_split(eras) %>%
# head(1) %>%
map(
~ ggplot(data = .x, aes(nss_mod_truth, nss_projection)) +
geom_hline(yintercept = 0) +
geom_vline(xintercept = 0) +
geom_bin2d(binwidth = 1) +
scale_fill_viridis_c(trans = "log10") +
coord_equal(
xlim = c(-axis_limit, axis_limit),
ylim = c(-axis_limit, axis_limit)
) +
labs(title = paste("Eras:", unique(.x$eras))) +
facet_grid(rarefication_threshold ~ basin_AIP)
)3.3 Budgets
3.3.1 Regional
cant_budget_all_sub %>%
filter(vif_max == 1000,
inv_depth == params_global$inventory_depth_standard) %>%
ggplot(aes(as.factor(rarefication_threshold), cant_pos_total, fill = basin_AIP)) +
scale_fill_brewer(palette = "PuBuGn") +
geom_col(col = "black") +
facet_grid(estimate ~ eras) +
theme(axis.text.x = element_text(angle = 90))cant_budget_all_sub %>%
filter(rarefication_threshold == 10,
inv_depth == params_global$inventory_depth_standard) %>%
ggplot(aes(as.factor(vif_max), cant_pos_total, fill = basin_AIP)) +
scale_fill_brewer(palette = "PuBuGn") +
geom_col(col = "black") +
facet_grid(estimate ~ eras) +
theme(axis.text.x = element_text(angle = 90))
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.2
Matrix products: default
BLAS: /usr/local/R-4.0.3/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.0.3/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] ggforce_0.3.3 marelac_2.1.10 shape_1.4.5 metR_0.9.0
[5] scico_1.2.0 patchwork_1.1.1 collapse_1.5.0 forcats_0.5.0
[9] stringr_1.4.0 dplyr_1.0.5 purrr_0.3.4 readr_1.4.0
[13] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.3 tidyverse_1.3.0
[17] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] fs_1.5.0 lubridate_1.7.9 gsw_1.0-5
[4] httr_1.4.2 rprojroot_2.0.2 tools_4.0.3
[7] backports_1.1.10 R6_2.5.0 DBI_1.1.0
[10] colorspace_1.4-1 withr_2.3.0 tidyselect_1.1.0
[13] compiler_4.0.3 git2r_0.27.1 cli_2.1.0
[16] rvest_0.3.6 xml2_1.3.2 isoband_0.2.2
[19] labeling_0.4.2 scales_1.1.1 checkmate_2.0.0
[22] digest_0.6.27 rmarkdown_2.5 oce_1.2-0
[25] pkgconfig_2.0.3 htmltools_0.5.0 dbplyr_1.4.4
[28] rlang_0.4.10 readxl_1.3.1 rstudioapi_0.11
[31] generics_0.0.2 farver_2.0.3 jsonlite_1.7.1
[34] magrittr_1.5 Matrix_1.2-18 Rcpp_1.0.5
[37] munsell_0.5.0 fansi_0.4.1 lifecycle_1.0.0
[40] stringi_1.5.3 whisker_0.4 yaml_2.2.1
[43] MASS_7.3-53 grid_4.0.3 blob_1.2.1
[46] parallel_4.0.3 promises_1.1.1 crayon_1.3.4
[49] lattice_0.20-41 haven_2.3.1 hms_0.5.3
[52] seacarb_3.2.14 knitr_1.30 pillar_1.4.7
[55] reprex_0.3.0 glue_1.4.2 evaluate_0.14
[58] RcppArmadillo_0.10.1.2.0 data.table_1.13.2 modelr_0.1.8
[61] vctrs_0.3.5 tweenr_1.0.2 httpuv_1.5.4
[64] testthat_2.3.2 cellranger_1.1.0 gtable_0.3.0
[67] polyclip_1.10-0 assertthat_0.2.1 xfun_0.18
[70] broom_0.7.5 RcppEigen_0.3.3.7.0 later_1.1.0.1
[73] ellipsis_0.3.1