library(dplyr) library(ggplot2) library(gfranges)
Load predicted values
pred_temp_do <- readRDS(here::here("analysis/tmb-sensor-explore/models/predicted-DO.rds")) predicted1 <- pred_temp_do %>% filter(ssid == 4) predicted <- list(predicted1, predicted1)
Define subset of the spatial range
do <- plot_facet_map(predicted[[1]], "do_est", transform_col = no_trans) + labs(fill = "ml/L") + ggtitle("Bottom DO") print(do)
Biannual changes starting 2008 and ending 2018
out1 <- make_vector_data(predicted, variable_names = c("do_est", "temp"), ssid = c(4), start_time = 2008, end_time = 2010, input_cell_size = 2, scale_fac = 1, delta_t_total = 2, delta_t_step = 2, indices = c(1, 2), # match_logic = c(">=", "=="), # plus_minus = c(1, 1)) # vector of plus/minus threshold(s) to define climate match. min_thresholds = c(0.5, 3), max_thresholds = c(Inf, 1), round_fact = 10 ) out1$do_est.units_per_decade <- out1$do_est.units_per_decade / 5 out1$temp.units_per_decade <- out1$temp.units_per_decade / 5 gvocc1a <- plot_vocc(out1, vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 50, NA_label = "*", fill_col = "do_est.units_per_decade", fill_label = "ml/L DO", high_fill = "Steel Blue 4", low_fill = "Red 3", raster_alpha = 1, vec_alpha = 0.5, axis_lables = FALSE, raster_limits = c(-2, 2)) gvocc1b <- plot_vocc(out1, vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 50, NA_label = "*", fill_col = "temp.units_per_decade", fill_label = "°C", raster_alpha = 1, vec_alpha = 0.5, axis_lables = FALSE, # transform_col = fourth_root_power, raster_limits = c(-2, 2)) gvocc1 <- gridExtra::grid.arrange(gvocc1b, gvocc1a, nrow = 1, top = "Change between 2008 and 2010")
out2 <- make_vector_data(predicted, variable_names = c("do_est", "temp"), ssid = c(4), start_time = 2010, end_time = 2012, input_cell_size = 2, scale_fac = 1, delta_t_total = 2, delta_t_step = 2, indices = c(1, 2), min_thresholds = c(0.5, 3), max_thresholds = c(Inf, 1), round_fact = 10) out2$do_est.units_per_decade <- out2$do_est.units_per_decade / 5 out2$temp.units_per_decade <- out2$temp.units_per_decade / 5 gvocc2a <- plot_vocc(out2, vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 50, NA_label = "*", fill_col = "do_est.units_per_decade", fill_label = "ml/L DO", high_fill = "Steel Blue 4", low_fill = "Red 3", raster_alpha = 1, vec_alpha = 0.5, axis_lables = FALSE, raster_limits = c(-2, 2)) gvocc2b <- plot_vocc(out2, vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 50, NA_label = "*", fill_col = "temp.units_per_decade", fill_label = "°C", raster_alpha = 1, vec_alpha = 0.5, axis_lables = FALSE, # transform_col = fourth_root_power, raster_limits = c(-2, 2) ) gvocc2 <- gridExtra::grid.arrange(gvocc2b, gvocc2a, nrow = 1, top = "Change between 2010 and 2012")
out3 <- make_vector_data(predicted, variable_names = c("do_est", "temp"), ssid = c(4), start_time = 2012, end_time = 2014, input_cell_size = 2, scale_fac = 1, delta_t_total = 2, delta_t_step = 2, indices = c(1, 2), min_thresholds = c(0.5, 3), max_thresholds = c(Inf, 1), round_fact = 10) out3$do_est.units_per_decade <- out3$do_est.units_per_decade / 5 out3$temp.units_per_decade <- out3$temp.units_per_decade / 5 View(out3) gvocc3a <- plot_vocc(out3, vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 50, NA_label = "*", fill_col = "do_est.units_per_decade", fill_label = "ml/L DO", high_fill = "Steel Blue 4", low_fill = "Red 3", raster_alpha = 1, vec_alpha = 0.5, axis_lables = FALSE, raster_limits = c(-2, 2)) gvocc3b <- plot_vocc(out3, vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 50, NA_label = "*", fill_col = "temp.units_per_decade", fill_label = "°C", raster_alpha = 1, vec_alpha = 0.5, axis_lables = FALSE, # transform_col = fourth_root_power, raster_limits = c(-2, 2)) gvocc3 <- gridExtra::grid.arrange(gvocc3b, gvocc3a, nrow = 1, top = "Change between 2012 and 2014")
out4 <- make_vector_data(predicted, variable_names = c("do_est", "temp"), ssid = c(4), start_time = 2014, end_time = 2016, input_cell_size = 2, scale_fac = 1, delta_t_total = 2, delta_t_step = 2, indices = c(1, 2), min_thresholds = c(0.5, 3), max_thresholds = c(Inf, 1), round_fact = 10) out4$do_est.units_per_decade <- out4$do_est.units_per_decade / 5 out4$temp.units_per_decade <- out4$temp.units_per_decade / 5 gvocc4a <- plot_vocc(out4, vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 50, NA_label = "*", fill_col = "do_est.units_per_decade", fill_label = "ml/L DO", high_fill = "Steel Blue 4", low_fill = "Red 3", raster_alpha = 1, vec_alpha = 0.5, axis_lables = FALSE ) gvocc4b <- plot_vocc(out4, vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 50, NA_label = "*", fill_col = "temp.units_per_decade", fill_label = "°C", raster_alpha = 1, vec_alpha = 0.5, axis_lables = FALSE, # transform_col = fourth_root_power, raster_limits = c(-2, 2)) gvocc4 <- gridExtra::grid.arrange(gvocc4b, gvocc4a, nrow = 1, top = "Change between 2014 and 2016")
out5 <- make_vector_data(predicted, variable_names = c("do_est", "temp"), ssid = c(4), start_time = 2016, #skip_time = 2016, end_time = 2018, input_cell_size = 2, scale_fac = 1, delta_t_total = 2, delta_t_step = 2, indices = c(1, 2), min_thresholds = c(0.5, 3), max_thresholds = c(Inf, 1), round_fact = 10) out5$do_est.units_per_decade <- out5$do_est.units_per_decade / 5 out5$temp.units_per_decade <- out5$temp.units_per_decade / 5 gvocc5a <- plot_vocc(out5, vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 50, NA_label = "*", fill_col = "do_est.units_per_decade", fill_label = "ml/L DO", high_fill = "Steel Blue 4", low_fill = "Red 3", raster_alpha = 1, vec_alpha = 0.5, axis_lables = FALSE ) gvocc5b <- plot_vocc(out5, vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 50, NA_label = "*", fill_col = "temp.units_per_decade", fill_label = "°C", raster_alpha = 1, vec_alpha = 0.5, axis_lables = FALSE, # transform_col = fourth_root_power, raster_limits = c(-2, 2)) gvocc5 <- gridExtra::grid.arrange(gvocc5b, gvocc5a, nrow = 1, top = "Change between 2016 and 2018")
out6 <- make_vector_data(predicted, variable_names = c("do_est", "temp"), ssid = c(4), start_time = 2014, skip_time = 2016, end_time = 2018, input_cell_size = 2, scale_fac = 1, delta_t_total = 4, delta_t_step = 2, indices = c(1, 2), min_thresholds = c(0.5, 3), max_thresholds = c(Inf, 1), round_fact = 10) out6$do_est.units_per_decade <- out6$do_est.units_per_decade / 5 out6$temp.units_per_decade <- out6$temp.units_per_decade / 5 gvocc6a <- plot_vocc(out6, vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 50, NA_label = "*", fill_col = "do_est.units_per_decade", fill_label = "ml/L DO", high_fill = "Steel Blue 4", low_fill = "Red 3", raster_alpha = 1, vec_alpha = 0.5, axis_lables = FALSE, raster_limits = c(-2, 2)) gvocc6b <- plot_vocc(out6, vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 50, NA_label = "*", fill_col = "temp.units_per_decade", fill_label = "°C", raster_alpha = 1, vec_alpha = 0.5, axis_lables = FALSE, raster_limits = c(-2, 2)) gvocc6 <- gridExtra::grid.arrange(gvocc6b, gvocc6a, nrow = 1, top = "Change between 2014 and 2018")
png( file = "figs/biannual_temp-do-_change-4-asym3-inf.png", res = 600, units = "in", width = 8, height = 20 ) gridExtra::grid.arrange(gvocc1, gvocc2, gvocc3, gvocc4, gvocc5, gvocc6, nrow = 6, top = grid::textGrob("VOCC vectors for <0.5 ml/L biannual decrease in bottom DO and -3 to +1 °C")) dev.off()
Cumulative change between last two decades
VOCC for declining levels of DO
unique(predicted1$year) starttime1 <- Sys.time() declining_var <- "do_est" min_threshold <- 0.2 out0.5d <- make_vector_data(predicted1, variable_names = c(declining_var), #ssid = c(4), start_time = 2008, skip_time = 2016, input_cell_size = 2, scale_fac = 1, delta_t_total = 6, delta_t_step = 2, indices = c(1, 1, 1, 2, 2), min_thresholds = c(min_threshold), max_thresholds = c(Inf), round_fact = 10 ) endtime1 <- Sys.time() time1 <- round(starttime1 - endtime1) saveRDS(out0.5d, file = paste0("data/", "vocc-", ssid_string, declining_var, min_threshold, "decline.rds"))
do4 <- plot_vocc(out0.5d, fill_col = "var_1_s", fill_label = "ml/L", raster_alpha = 1, vec_aes = NULL, #viridis_option = "A", raster_limits = c(0, 5 ) ) do4 <- do4 + ggtitle("Mean DO 2008-2012") do4
voccd4 <- plot_vocc(out0.5d, vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 100, fill_col = "units_per_decade", fill_label = "ml/L DO\nper decade", raster_alpha = 1, vec_alpha = 0.75, axis_lables = FALSE, viridis_option = "C", viridis_dir = -1, NA_label = "*", high_fill = "Steel Blue 4", low_fill = "Red 3", min_vec_plotted = 2, raster_limits = c(-1.75, 1.75)) voccd4 <- voccd4 + ggtitle("VOCC <1 ml/L decline (excludes 2016)") voccd4
increasing_var <- "temp" max_threshold <- 0.5 out0.5t <- make_vector_data(predicted1, variable_names = c(increasing_var), #ssid = c(4), start_time = 2008, skip_time = 2016, input_cell_size = 2, scale_fac = 1, delta_t_total = 6, delta_t_step = 2, indices = c(1, 1, 1, 2, 2), min_thresholds = c(Inf), max_thresholds = c(max_threshold), round_fact = 10 ) saveRDS(out0.5t, file = paste0("data/", "vocc-", ssid_string, increasing_var, max_threshold, "increase.rds"))
temp4 <- plot_vocc(out0.5t, fill_col = "var_1_s", fill_label = "°C ", raster_alpha = 1, vec_aes = NULL, viridis_option = "C", raster_limits = c(4.5, 11) ) temp4 <- temp4 + ggtitle("Mean temperature 2008-2012") temp4
vocct4 <- plot_vocc(out0.5t, vec_aes = "distance", vec_lwd_range = c(0.2, 0.5), max_vec_plotted = 100, fill_col = "units_per_decade", fill_label = "°C per \ndecade", vec_alpha = 0.75, raster_alpha = 1, min_vec_plotted = 2, white_zero = TRUE, raster_limits = c(-0.5, 2)) vocct4 <- vocct4 + ggtitle("VOCC <0.5°C increase (excludes 2016)") vocct4
png( file = "figs/vocc-temp-do-2008-2018-4-asym-1.png", res = 600, units = "in", width = 7, height = 7 ) gridExtra::grid.arrange(temp4, do4, vocct4, voccd4, nrow = 2) dev.off()
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