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plotting_recharge.R
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# Plots key figures for the superwell manuscript
#
# Hassan Niazi, June 2023
# load libraries ----
{
library(tidyverse)
library(sf)
library(ggplot2)
# library(tmap)
library(RColorBrewer)
library(psych)
}
# load data ----
{
# df_in <- read_csv("inputs/inputs.csv")
# sf_in <- st_read("inputs/shapefiles/inputs.shp") %>% st_make_valid()
# colnames(sf_in) <- c(colnames(df_in), "geometry") # correct col names in sf
df_in_R <- read_csv("inputs/inputs_recharge_lakes.csv")
sf_in_R <- st_read("inputs/shapefiles/inputs_recharge_plus_lakes.shp") %>% st_make_valid()
colnames(sf_in_R) <- c(colnames(df_in_R), "geometry")
mappings_all <- read_csv("processing/basin_country_region_mapping.csv")
figs_dir <- "processing/figures/withRecharge/"
out_dir <- "outputs/superwell_py_deep_C_all_B_all_G_all_"
# main scenario, to load lesser rows n_max = 1000
df_0.3PD_0.25DL <- read_csv(paste0(out_dir, '0.3PD_0.25DL_0.2RR.csv')) %>% rename("GridCellID" = "grid_id")
print(paste0("Number of Grid Cells Processed: ", length(unique(df_0.3PD_0.25DL$GridCellID)),
" out of ", length(unique(df_in_R$GridCellID)),
" (", round(length(unique(df_0.3PD_0.25DL$GridCellID))*100/length(unique(df_in_R$GridCellID)), 1), "%)"))
# base plot format
my_theme <- function () {
theme_bw() +
theme(
strip.background = element_blank(),
strip.text = element_text(face = "bold"),
# axis.title = element_text(face="bold"),
legend.position = c(0.91, 0.12),
# legend.direction = "horizontal",
legend.background = element_blank(),
plot.title = element_text(hjust = 0.5),
panel.grid.major = element_blank(),
plot.tag = element_text(),
plot.tag.position = c(0.01 , 0.99),
plot.margin = margin(t = 1, # Top margin
r = 0, # Right margin
b = 0, # Bottom margin
l = 3) # Left margin
)
}
add_geometry <- function (df) {
df %>% left_join(select(sf_in_R, c("GridCellID", "geometry")), by = "GridCellID") %>% st_as_sf()
}
saveformat <- ".png"
}
# read in all scenarios ----
# only read in for cost curves and volume statistics
{
df_0.3PD_0.05DL <- read_csv(paste0(out_dir, '0.3PD_0.05DL_0.2RR.csv')) %>% rename("GridCellID" = "grid_id")
df_0.3PD_0.4DL <- read_csv(paste0(out_dir, '0.3PD_0.4DL_0.2RR.csv')) %>% rename("GridCellID" = "grid_id")
df_0.6PD_0.05DL <- read_csv(paste0(out_dir, '0.6PD_0.05DL_0.2RR.csv')) %>% rename("GridCellID" = "grid_id")
df_0.6PD_0.25DL <- read_csv(paste0(out_dir, '0.6PD_0.25DL_0.2RR.csv')) %>% rename("GridCellID" = "grid_id")
df_0.6PD_0.4DL <- read_csv(paste0(out_dir, '0.6PD_0.4DL_0.2RR.csv')) %>% rename("GridCellID" = "grid_id")
}
# make a list of all scenarios
# scen_list <- c("0.3PD_0.05DL", "0.3PD_0.25DL", "0.3PD_0.4DL", "0.6PD_0.05DL", "0.6PD_0.25DL", "0.6PD_0.4DL")
# scen_names_list <- c("0.3PD_0.05DL", "0.3PD_0.25DL", "0.3PD_0.4DL", "0.6PD_0.05DL", "0.6PD_0.25DL", "0.6PD_0.4DL")
scen_names_list_labels <- c("0.3PD_0.05DL", "0.3PD_0.25DL", "0.3PD_0.4DL", "0.6PD_0.05DL", "0.6PD_0.25DL", "0.6PD_0.4DL")
scen_list <- list("0.3PD_0.05DL" = df_0.3PD_0.05DL,
"0.3PD_0.25DL" = df_0.3PD_0.25DL,
"0.3PD_0.4DL" = df_0.3PD_0.4DL,
"0.6PD_0.05DL" = df_0.6PD_0.05DL,
"0.6PD_0.25DL" = df_0.6PD_0.25DL,
"0.6PD_0.4DL" = df_0.6PD_0.4DL)
# output data ----
df_0.3PD_0.25DL %>% left_join(select(sf_in_R, c("GridCellID", "geometry")), by = "GridCellID") -> df_out
# clean data
# # determine unique combinations of country and GridCellID in df and in df_in_R
# df %>% as.data.frame() %>% select(COUNTRY, OriginalOb) %>% unique() -> df_unique
# df_in_R %>% select(CNTRY_NAME, OBJECTID) %>% unique() -> df_in_unique
# # determine the difference in both unique dataframes df and df_in_R
# df_unique %>% anti_join(df_in_unique, by = c("COUNTRY" = "CNTRY_NAME", "OriginalOb" = "OBJECTID")) -> df_diff
# high-level analysis ----
# number of cells affected by lakes processing
df_0.3PD_0.25DL %>% left_join(df_in_R %>% select("GridCellID", "Grid_area", "Lake_area"), by = "GridCellID") -> lakes_affected
paste0("Number of Grid Cells Affected by Lakes Processing: ", length(unique((lakes_affected %>% filter(Grid_area > grid_area))$GridCellID)),
" out of ", length(unique(df_in_R$GridCellID)),
" (", round(length(unique((lakes_affected %>% filter(Grid_area > grid_area))$GridCellID))*100/length(unique(df_in_R$GridCellID)), 1), "%)")
print(paste0("Number of Skipped Grid Cells with Large Lakes: ", length(unique((df_in_R %>% filter(Lake_area > 0.9 * Grid_area))$GridCellID)),
" out of ", length(unique(df_in_R$GridCellID)),
" (", round(length(unique((df_in_R %>% filter(Lake_area > 0.9 * Grid_area))$GridCellID))*100/length(unique(df_in_R$GridCellID)), 1), "%)"))
# on volumes
df_in_R %>% mutate(available_volume_allcells = Porosity * Grid_area * (Aquifer_thickness - Depth_to_water),
ponded_depth_avail = Porosity * (Aquifer_thickness - Depth_to_water)) -> df_in_vol
df_in_R %>% mutate(Aquifer_thickness = case_when(Aquifer_thickness > 1000 ~ 1000,
TRUE ~ Aquifer_thickness),
available_volume_allcells = Porosity * Grid_area * (Aquifer_thickness - Depth_to_water),
ponded_depth_avail = Porosity * (Aquifer_thickness - Depth_to_water)) -> df_in_vol_screened
# prep data for Caroline SAWS ----
# one with years aggregated
df_in_vol_screened %>% filter(Country == "United States") %>%
select(GridCellID, Continent, Country, GCAM_basin_ID, Basin_long_name, Grid_area, available_volume_allcells) %>%
left_join(df_out %>% filter(country == "United States") %>%
select(GridCellID, year_number, volume_produced_allwells, total_cost_allwells) %>%
# replace na with 0
group_by(GridCellID) %>% summarise(tot_volume_produced_allwells = sum(volume_produced_allwells),
tot_cost_allwells = sum(total_cost_allwells),
unit_cost = tot_cost_allwells / tot_volume_produced_allwells),
by = "GridCellID") %>% add_geometry() %>%
replace_na(list(tot_volume_produced_allwells = 0, tot_cost_allwells = 0, unit_cost = 0)) -> df_out_SAWS
# write df_out_SAWS as .nc
names(df_out_SAWS)
# write_sf(df_out_SAWS, paste0("C:/Users/niaz981/OneDrive - PNNL/PNNL/Papers/Caroline_SAWS/superwell_saws.shp"))
# outputs with years
# explore using a grid cell
df_out %>% filter(country == "United States") %>% # cell: 89461
select(GridCellID, year_number, continent, country, gcam_basin_id, Basin_long_name, grid_area_dry = grid_area,
total_thickness, depth_to_water, aqfr_sat_thickness, total_well_length, drawdown, total_head,
initial_available_volume = available_volume, recharge, volume_produced_allwells, total_cost_allwells, unit_cost, geometry) %>%
filter(GridCellID == 89461) %>%
right_join(df_in_vol_screened %>% filter(Country == "United States") %>%
select(GridCellID, Continent, Country, GCAM_basin_ID, Basin_long_name, Grid_area, available_volume_allcells),
by = "GridCellID") -> df_out_SAWS_test
# final one
df_in_vol_screened %>% filter(Country == "United States") %>%
select(GridCellID, Continent, Country, GCAM_basin_ID, Basin_long_name, Grid_area, available_volume_allcells) %>%
left_join(df_out %>% filter(country == "United States") %>% # cell: 89461
# select(GridCellID, year_number, continent, country, gcam_basin_id, Basin_long_name, grid_area_dry = grid_area,
select(GridCellID, year_number, grid_area_dry = grid_area,
total_thickness, depth_to_water, aqfr_sat_thickness, total_well_length, drawdown, total_head,
adj_available_volume = available_volume, recharge, volume_produced_allwells, total_cost_allwells, unit_cost)
, by = "GridCellID") %>%
mutate(available_volume_allcells = round(available_volume_allcells, 1),
adj_available_volume = round(adj_available_volume, 1),
status = if_else(rowSums(is.na(.)) > 0, "screened", "simulated"),
across(where(is.numeric), ~ replace_na(., 0))) %>%
add_geometry() -> df_out_SAWS_year
# write_sf(df_out_SAWS_year, paste0("C:/Users/niaz981/Downloads/Modelling/GCAM/superwell_saws_year.shp"), delete_layer = TRUE)
# start analysis
#
# plot(df_in_vol$available_volume_allcells, df_in_vol$ponded_depth_avail)
{
# global available volume
print(paste0("Global Available Volume from inputs = ",
round(sum(unique(df_in_vol$available_volume_allcells)) * 1e-15, 3), " million km3"))
print(paste0("Global Available Volume from screened inputs = ",
round(sum(unique(df_in_vol_screened$available_volume_allcells)) * 1e-15, 3), " million km3"))
print(paste0("Global Accessible Volume in 0.3PD 0.05DL = ",
round(sum(unique(df_0.3PD_0.05DL$available_volume)) * 1e-15, 3), " million km3"))
print(paste0("Global Accessible Volume in 0.3PD 0.25DL = ",
round(sum(unique(df_0.3PD_0.25DL$available_volume)) * 1e-15, 3), " million km3"))
print(paste0("Global Accessible Volume in 0.3PD 0.40DL = ",
round(sum(unique(df_0.3PD_0.4DL$available_volume)) * 1e-15, 3), " million km3"))
print(paste0("Global Accessible Volume in 0.6PD 0.05DL = ",
round(sum(unique(df_0.6PD_0.05DL$available_volume)) * 1e-15, 3), " million km3"))
print(paste0("Global Accessible Volume in 0.6PD 0.25DL = ",
round(sum(unique(df_0.6PD_0.25DL$available_volume)) * 1e-15, 3), " million km3"))
print(paste0("Global Accessible Volume in 0.6PD 0.40DL = ",
round(sum(unique(df_0.6PD_0.4DL$available_volume)) * 1e-15, 3), " million km3"))
# global volume produced
print(paste0("Global Produced Volume in 0.3PD 0.05DL = ",
round(sum(df_0.3PD_0.05DL$volume_produced_allwells) * 1e-15, 4), " million km3"))
print(paste0("Global Produced Volume in 0.3PD 0.25DL = ",
round(sum(df_0.3PD_0.25DL$volume_produced_allwells) * 1e-15, 4), " million km3"))
print(paste0("Global Produced Volume in 0.3PD 0.40DL = ",
round(sum(df_0.3PD_0.4DL$volume_produced_allwells) * 1e-15, 4), " million km3"))
print(paste0("Global Produced Volume in 0.6PD 0.05DL = ",
round(sum(df_0.6PD_0.05DL$volume_produced_allwells) * 1e-15, 4), " million km3"))
print(paste0("Global Produced Volume in 0.6PD 0.25DL = ",
round(sum(df_0.6PD_0.25DL$volume_produced_allwells) * 1e-15, 4), " million km3"))
print(paste0("Global Produced Volume in 0.6PD 0.40DL = ",
round(sum(df_0.6PD_0.4DL$volume_produced_allwells) * 1e-15, 4), " million km3"))
}
## summary stats of available, accessible and pumped volumes ----
# since there are duplicate available vol values, we need to group by grid cell before unique
length(unique(df_in_vol$GridCellID))
length(unique(df_in_vol$available_volume_allcells))
global_avail <- df_in_vol %>% select(GridCellID, available_volume_allcells) %>% group_by(GridCellID) %>% unique() %>% ungroup() %>%
summarise(available = sum(available_volume_allcells) * 1e-15)
# define get function to get the data frame from the environment
get_scen <- function(x) eval(as.name(x), envir = .GlobalEnv)
# function to calculate accessible and pumped volumes
calculate_stats <- function(scen_list) {
vols <- data.frame(scenario = character(),
accessible = numeric(),
pumped = numeric()
)
scen_names_list <- scen_list
for (scenario in scen_names_list) {
# get the data frame loaded in the environment if it is equal to scenario name
df <- get_scen(paste0("df_", scenario))
# length(unique(df$available_volume)) - nrow(df %>% select(GridCellID, available_volume) %>% group_by(GridCellID) %>% unique())
# accessible <- sum(unique(df$available_volume)) * 1e-15 # assuming every grid cell has a different accessible volume
accessible <- as.numeric(df %>% select(GridCellID, available_volume) %>% group_by(GridCellID) %>% unique() %>% ungroup() %>%
summarise(available = sum(available_volume) * 1e-15))
# length(unique(df_0.6PD_0.4DL$GridCellID)) - length(unique(df_0.6PD_0.4DL$volume_produced_allwells))
pumped <- sum(df$volume_produced_allwells) * 1e-15
# pumped <- df %>% select(GridCellID, cumulative_vol_produced_allwells) %>% group_by(GridCellID) %>% filter(cumulative_vol_produced_allwells == max(cumulative_vol_produced_allwells)) %>% ungroup() %>% summarise(pumped = sum(cumulative_vol_produced_allwells) * 1e-12)
vols <- as.data.frame(rbind(vols, data.frame(scenario, accessible, pumped)))
}
return(vols)
}
# Getting fractions
fractions <- calculate_stats(scen_names_list_labels) %>%
mutate(available = global_avail$available,
accessible_perc = (accessible*100)/available,
pumped_perc = (pumped*100)/available,
pumped_accessible_perc = (pumped*100)/accessible)
# round everything but "scenario" columns in in fractions to 3 decimal places
fractions[, -1] <- round(fractions[, -1], 3)
print(fractions)
# write fraction as a csv
write.csv(fractions, "outputs/fractions_R.csv", row.names = FALSE)
# mean and std dev across scenarios
sapply(fractions[, -1], function(x) c(mean = mean(x), sd = sd(x)))
# timeseries by scenario ----
# some tests
df_0.3PD_0.05DL %>% select(year_number, volume_produced_allwells, total_cost_allwells, unit_cost) %>%
mutate(unit_cost_calc = total_cost_allwells/volume_produced_allwells,
diff_uc = unit_cost_calc - unit_cost) -> a
a %>% group_by(year_number) %>%
summarise(global_vol_prod = sum(volume_produced_allwells),
global_total_cost = sum(total_cost_allwells),
global_unit_cost = mean(unit_cost)) %>%
mutate(global_unit_cost_calc = global_total_cost/global_vol_prod,
diff_uc_global = global_unit_cost_calc - global_unit_cost) -> b
plot(b$year_number, b$global_vol_prod, type = "l", xlab = "Year", ylab = "Global Volume Produced (km3)")
plot(b$year_number, b$global_total_cost, type = "l", xlab = "Year", ylab = "Global Total Cost ($)")
plot(b$year_number, b$global_unit_cost, type = "l", xlab = "Year", ylab = "Global Unit Cost ($/km3)")
plot(b$year_number, b$global_unit_cost_calc, type = "l", xlab = "Year", ylab = "Global Unit Cost ($/km3)")
# plot drawdown vs depth to water to see the difference between water depth decrease at well head and over water table decrease
plot(df_0.3PD_0.05DL$depth_to_water, df_0.3PD_0.05DL$drawdown, xlab = "Depth to Water (m)", ylab = "Drawdown (m)", pch = 20, cex = 0.5)
# global timeseries df by scenario
# function to calculate global timeseries df
calculate_global_timeseries <- function(scen_list) {
global_timeseries <- data.frame()
for (scenario in scen_list) {
# get the data frame loaded in the environment if it is equal to scenario name
df <- get(paste0("df_", scenario))
# calculate global timeseries df
df %>% group_by(year_number) %>%
summarise(global_vol_prod = sum(volume_produced_allwells) * 1e-9,
global_dep_vol_frac_avg = mean(depleted_vol_fraction),
global_aqfr_sat_thickness_avg = mean(aqfr_sat_thickness),
global_transmissivity_avg = mean(transmissivity),
global_depth_to_water_avg = mean(depth_to_water),
global_drawdown_avg = mean(drawdown),
global_total_head_avg = mean(total_head),
global_total_well_length_avg = mean(total_well_length),
global_energy = sum(energy),
global_energy_avg = mean(energy),
global_power = sum(power),
global_power_avg = mean(power),
global_energy_cost = sum(energy_cost),
global_energy_cost_avg = mean(energy_cost),
global_nonenergy_cost = sum(nonenergy_cost),
global_nonenergy_cost_avg = mean(nonenergy_cost),
global_number_of_wells_avg = mean(number_of_wells),
global_well_installation_cost_avg = mean(well_installation_cost),
global_annual_capital_cost_avg = mean(annual_capital_cost),
global_maintenance_cost_avg = mean(maintenance_cost),
global_total_cost_perwell = sum(total_cost_perwell),
global_total_cost_perwell_avg = mean(total_cost_perwell),
global_total_cost = sum(total_cost_allwells),
global_total_cost_avg = mean(total_cost_allwells),
energy2nonenergy_ratio = sum(energy_cost)/sum(nonenergy_cost),
global_unit_cost_avg = mean(unit_cost)) %>%
mutate(scenario = scenario) -> df
global_timeseries <- as.data.frame(rbind(global_timeseries, df))
}
return(global_timeseries)
}
global_timeseries <- calculate_global_timeseries(scen_names_list_labels)
# make sure scenarios are not reordered
global_timeseries$scenario <- factor(global_timeseries$scenario,
levels = c("0.3PD_0.05DL", "0.6PD_0.05DL", "0.3PD_0.25DL",
"0.6PD_0.25DL", "0.3PD_0.4DL", "0.6PD_0.4DL"))
# plot timeseries of all variables in global_timeseries as figure. x = year_number, y = variable, color = scenario
global_timeseries_long <- tidyr::pivot_longer(global_timeseries, cols = -c(year_number, scenario), names_to = "variable", values_to = "value")
ggplot(global_timeseries_long, aes(x = year_number, y = value, color = scenario, linetype = scenario)) +
geom_line(size = 1) +
facet_wrap(~ variable, scales = "free_y") +
labs(title = "Time Series of Global Variables", x = "Year", y = "Value") +
scale_color_brewer(palette = "Paired") +
scale_linetype_manual(values = c("solid", "dashed", "solid", "dashed", "solid", "dashed")) +
my_theme() +
theme(legend.position = c(0.875, 0.06), legend.box = "horizontal")
ggsave(paste0(figs_dir, "timeseries/global_timeseries.png"), width = 16, height = 9, units = "in", dpi = 300)
# plot(global_timeseries$year_number, global_timeseries$energy2nonenergy_ratio, type = "l", xlab = "Year", ylab = "Global Energy to Nonenergy Cost Ratio")
# function to plot timeseries by scenario. Arguments y variable, y label, and legend position
plot_timeseries <- function(y_var, multiplier, y_lab, legend_pos, tag, logbool = F) {
gg <- ggplot(global_timeseries) +
geom_line(aes(x = year_number, y = get(y_var) * multiplier, color = scenario, linetype = scenario), linewidth = 1) +
labs(x = 'Pumping Year', y = y_lab, color = "Scenario", linetype = "Scenario", tag = tag) +
# scale_x_sqrt(breaks = c(1, 5, 10, 25, 50, 100, 300), expand = c(0.01,0.01)) + # Setting breaks at squares and labels at the square roots
# scale_x_log10(expand = c(0, 0.035), breaks = scales::log_breaks(n = 10)) + #-0.12
# annotation_logticks(base = 10, sides = "b") +
scale_color_brewer(palette = "Paired") +
scale_linetype_manual(values = c("solid", "dashed", "solid", "dashed", "solid", "dashed")) +
theme_bw() +
theme(
strip.background = element_blank(),
strip.text = element_text(face = "bold"),
# axis.title = element_text(face="bold"),
legend.position = if (legend_pos == "r") {c(0.875, 0.78)} else {c(0.125, 0.78)}, # c(0.875, 0.78) for right c(0.125, 0.78) for left
# legend.direction = "horizontal",
legend.background = element_blank(),
legend.title = element_text(face = "bold"),
plot.title = element_text(hjust = 0.5),
panel.grid.major = element_blank(),
plot.tag = element_text(),
plot.tag.position = c(0.01 , 0.99),
legend.key.size = unit(0.2, "in"),
legend.text = element_text(size = 8),
legend.key.width = unit(0.4, "in"),
plot.margin = margin(t = 1, # Top margin
r = 0, # Right margin
b = 0, # Bottom margin
l = 3) # Left margin
)
if (logbool == T) {
gg <- gg + scale_x_log10(expand = c(0, 0.035), breaks = scales::log_breaks(n = 10)) + #-0.12
annotation_logticks(base = 10, sides = "b")
}
ggsave(paste0(figs_dir, "timeseries/time_", y_var, "_all_scen.png"), width = 6, height = 4, units = "in", dpi = 300)
return(gg)
}
{ # for SI
# global_vol_prod = sum(volume_produced_allwells) * 1e-9,
# global_dep_vol_frac_avg = mean(depleted_vol_fraction),
#
# global_aqfr_sat_thickness_avg = mean(aqfr_sat_thickness),
# global_transmissivity_avg = mean(transmissivity),
#
# global_depth_to_water_avg = mean(depth_to_water),
# global_drawdown_avg = mean(drawdown),
# global_total_head_avg = mean(total_head),
# global_total_well_length_avg = mean(total_well_length),
#
# global_energy = sum(energy),
# global_energy_avg = mean(energy),
# global_power = sum(power),
# global_power_avg = mean(power),
#
# global_energy_cost = sum(energy_cost),
# global_energy_cost_avg = mean(energy_cost),
#
# global_number_of_wells_avg = mean(number_of_wells),
# global_well_installation_cost_avg = mean(well_installation_cost),
# global_annual_capital_cost_avg = mean(annual_capital_cost),
# global_maintenance_cost_avg = mean(maintenance_cost),
# global_nonenergy_cost = sum(nonenergy_cost),
# global_nonenergy_cost_avg = mean(nonenergy_cost),
#
# global_total_cost_perwell = sum(total_cost_perwell),
# global_total_cost_perwell_avg = mean(total_cost_perwell),
# global_total_cost = sum(total_cost_allwells),
# global_total_cost_avg = mean(total_cost_allwells),
#
# energy2nonenergy_ratio = sum(energy_cost)/sum(nonenergy_cost),
# global_unit_cost_avg = mean(unit_cost)
# volume and hydraulics
plot_timeseries("global_vol_prod", 1e-3, expression('Global Volume Produced (1,000 km'^3*')'), "r", "(a)", T)
plot_timeseries("global_dep_vol_frac_avg", 1, expression('Depleted Volume Fraction (-)'), "l", "(b)", T)
# T = Kb
plot_timeseries("global_aqfr_sat_thickness_avg", 1, expression('Aquifer Saturated Thickness (m)'), "l", "(a)", T)
plot_timeseries("global_transmissivity_avg", 86400, expression('Transmissivity (m'^2*'/day)'), "r", "(b)", T)
# depths
plot_timeseries("global_depth_to_water_avg", 1, expression('Global Mean Depth to Water (m)'), "l", "(a)")
plot_timeseries("global_drawdown_avg", 1, expression('Global Mean Drawdown (m)'), "r", "(b)")
plot_timeseries("global_total_head_avg", 1, expression('Global Mean Total Head (m)'), "l", "(c)")
plot_timeseries("global_total_well_length_avg", 1, expression('Global Mean Total Well Length (m)'), "l", "(d)")
# energy and power
plot_timeseries("global_energy", 1e-12, expression('Global Energy (million GWh)'), "r", "(a)", T) # K to giga, million
plot_timeseries("global_energy_avg", 1e-6, expression('Global Mean Energy (GWh)'), "l", "(b)", T)
plot_timeseries("global_power", 1e-6, expression('Global Power (GW)'), "r", "(c)", T)
plot_timeseries("global_power_avg", 1e-6, expression('Global Mean Power (GW)'), "l", "(d)", T)
# energy and non energy costs
plot_timeseries("global_energy_cost", 1e-12, expression('Global Energy Cost (trillion $)'), "r", "(a)", T)
plot_timeseries("global_energy_cost_avg", 1e-6, expression('Global Mean Energy Cost (million $)'), "l", "(b)", T)
plot_timeseries("global_nonenergy_cost", 1e-12, expression('Global Nonenergy Cost (trillion $)'), "r", "(c)", T)
plot_timeseries("global_nonenergy_cost_avg", 1e-6, expression('Global Mean Nonenergy Cost (million $)'), "l", "(d)", T)
# nonenergy costs components
plot_timeseries("global_number_of_wells_avg", 1e-3, expression('Global Mean Number of Wells (x1000)'), "r", "(a)", F)
plot_timeseries("global_well_installation_cost_avg", 1e-6, expression('Global Mean Well Installation Cost (million $)'), "l", "(b)", F)
plot_timeseries("global_annual_capital_cost_avg", 1e-6, expression('Global Mean Annual Capital Cost (million $)'), "r", "(c)", F)
plot_timeseries("global_maintenance_cost_avg", 1e-6, expression('Global Mean Maintenance Cost (million $)'), "r", "(d)", F)
# total costs
plot_timeseries("global_total_cost_perwell", 1e-6, expression('Global Total Cost per Well (million $)'), "r", "(a)", T)
plot_timeseries("global_total_cost_perwell_avg", 1e-6, expression('Global Mean Total Cost per Well (million $)'), "l", "(b)", T)
plot_timeseries("global_total_cost", 1e-12, expression('Global Total Cost (trillion $)'), "r", "(c)", T)
plot_timeseries("global_total_cost_avg", 1e-6, expression('Global Mean Total Cost (million $)'), "l", "(d)", T)
# cost ratios
plot_timeseries("energy2nonenergy_ratio", 1, expression('Global Energy to Nonenergy Cost Ratio (-)'), "l", "(a)")
plot_timeseries("global_unit_cost_avg", 1, expression('Global Mean Unit Cost ($/m'^3*')'), "l", "(b)")
}
# IGNORE THE FOLLOWING. THE FUNCTION ABOVE IS NOW USED TO GENERATE THE PLOTS
### timeseries volume produced by scenario ----
ggplot(global_timeseries) +
geom_line(aes(x = year_number, y = global_vol_prod * 1e-3, color = scenario, linetype = scenario), linewidth = 1) +
labs(x = 'Pumping Year', y = expression('Global Volume Produced (1,000 km'^3*')'), color = "Scenario", linetype = "Scenario", tag = "(c)") +
# scale_x_sqrt(breaks = c(1, 5, 10, 25, 50, 100, 300), expand = c(0.01,0.01)) + # Setting breaks at squares and labels at the square roots
scale_x_log10(expand = c(0, 0.035), breaks = scales::log_breaks(n = 10)) + #-0.12
annotation_logticks(base = 10, sides = "b") +
scale_color_brewer(palette = "Paired") +
scale_linetype_manual(values = c("solid", "dashed", "solid", "dashed", "solid", "dashed")) +
theme_bw() +
theme(
strip.background = element_blank(),
strip.text = element_text(face = "bold"),
# axis.title = element_text(face="bold"),
legend.position = c(0.875, 0.78),
# legend.direction = "horizontal",
legend.background = element_blank(),
legend.title = element_text(face = "bold"),
plot.title = element_text(hjust = 0.5),
panel.grid.major = element_blank(),
plot.tag = element_text(),
plot.tag.position = c(0.01 , 0.99),
legend.key.size = unit(0.2, "in"),
legend.text = element_text(size = 8),
legend.key.width = unit(0.4, "in"),
plot.margin = margin(t = 1, # Top margin
r = 0, # Right margin
b = 0, # Bottom margin
l = 3) # Left margin
)
ggsave(paste0(figs_dir, "global_vol_prod_all_scen.png"), width = 6, height = 4, units = "in", dpi = 300)
# cumulative volume produced
ggplot(global_timeseries %>%
group_by(scenario) %>% mutate(global_vol_prod_cumsum = cumsum(global_vol_prod) * 1e-6)) +
geom_line(aes(x = year_number, y = global_vol_prod_cumsum, color = scenario, linetype = scenario), linewidth = 1) +
labs(x = 'Pumping Year', y = expression('Global Cumulative Volume Produced (mln km'^3*')'), color = "Scenario", linetype = "Scenario", tag = "(d)") +
# scale_x_sqrt(breaks = c(1, 5, 10, 25, 50, 100, 300), expand = c(0.01,0.1)) + # Setting breaks at squares and labels at the square roots
scale_x_log10(expand = c(0, 0.035), breaks = scales::log_breaks(n = 10)) + #-0.12
annotation_logticks(base = 10, sides = "b") +
scale_color_brewer(palette = "Paired") +
scale_linetype_manual(values = c("solid", "dashed", "solid", "dashed", "solid", "dashed")) +
theme_bw() +
theme(
strip.background = element_blank(),
strip.text = element_text(face = "bold"),
# axis.title = element_text(face="bold"),
legend.position = c(0.125, 0.78),
# legend.direction = "horizontal",
legend.background = element_blank(),
legend.title = element_text(face = "bold"),
plot.title = element_text(hjust = 0.5),
panel.grid.major = element_blank(),
plot.tag = element_text(),
plot.tag.position = c(0.01 , 0.99),
legend.key.size = unit(0.2, "in"),
legend.text = element_text(size = 8),
legend.key.width = unit(0.4, "in"),
plot.margin = margin(t = 1, # Top margin
r = 0, # Right margin
b = 0, # Bottom margin
l = 3) # Left margin
)
ggsave(paste0(figs_dir, "global_vol_prod_all_scen_cumulative.png"), width = 6, height = 4, units = "in", dpi = 300)
### timeseries energy to nonenergy cost ratio by scenario ----
ggplot(global_timeseries) +
geom_line(aes(x = year_number, y = energy2nonenergy_ratio, color = scenario, linetype = scenario), linewidth = 1) +
labs(x = 'Pumping Year', y = expression('Global Energy to Nonenergy Cost Ratio)'), color = "Scenario", linetype = "Scenario", tag = "(c)") +
# scale_x_sqrt(breaks = c(1, 5, 10, 25, 50, 100, 300), expand = c(0.01,0.01)) + # Setting breaks at squares and labels at the square roots
scale_x_log10(expand = c(0, 0.035), breaks = scales::log_breaks(n = 10)) + #-0.12
annotation_logticks(base = 10, sides = "b") +
scale_color_brewer(palette = "Paired") +
scale_linetype_manual(values = c("solid", "dashed", "solid", "dashed", "solid", "dashed")) +
theme_bw() +
theme(
strip.background = element_blank(),
strip.text = element_text(face = "bold"),
# axis.title = element_text(face="bold"),
legend.position = c(0.125, 0.78),
# legend.direction = "horizontal",
legend.background = element_blank(),
legend.title = element_text(face = "bold"),
plot.title = element_text(hjust = 0.5),
panel.grid.major = element_blank(),
plot.tag = element_text(),
plot.tag.position = c(0.01 , 0.99),
legend.key.size = unit(0.2, "in"),
legend.text = element_text(size = 8),
legend.key.width = unit(0.4, "in"),
plot.margin = margin(t = 1, # Top margin
r = 0, # Right margin
b = 0, # Bottom margin
l = 3) # Left margin
)
ggsave(paste0(figs_dir, "global_energy2nonenergy_ratio_all_scen.png"), width = 6, height = 4, units = "in", dpi = 300)
### timeseries total cost by scenario ----
ggplot(global_timeseries) +
geom_line(aes(x = year_number, y = global_total_cost * 1e-9, color = scenario, linetype = scenario), linewidth = 1) +
labs(x = 'Pumping Year', y = expression('Global Total Cost (billion $)'), color = "Scenario", linetype = "Scenario", tag = "(c)") +
# scale_x_sqrt(breaks = c(1, 5, 10, 25, 50, 100, 300), expand = c(0.01,0.01)) + # Setting breaks at squares and labels at the square roots
scale_x_log10(expand = c(0, 0.035), breaks = scales::log_breaks(n = 10)) + #-0.12
annotation_logticks(base = 10, sides = "b") +
scale_color_brewer(palette = "Paired") +
scale_linetype_manual(values = c("solid", "dashed", "solid", "dashed", "solid", "dashed")) +
theme_bw() +
theme(
strip.background = element_blank(),
strip.text = element_text(face = "bold"),
# axis.title = element_text(face="bold"),
legend.position = c(0.875, 0.78),
# legend.direction = "horizontal",
legend.background = element_blank(),
legend.title = element_text(face = "bold"),
plot.title = element_text(hjust = 0.5),
panel.grid.major = element_blank(),
plot.tag = element_text(),
plot.tag.position = c(0.01 , 0.99),
legend.key.size = unit(0.2, "in"),
legend.text = element_text(size = 8),
legend.key.width = unit(0.4, "in"),
plot.margin = margin(t = 1, # Top margin
r = 0, # Right margin
b = 0, # Bottom margin
l = 3) # Left margin
)
ggsave(paste0(figs_dir, "global_total_cost_all_scen.png"), width = 6, height = 4, units = "in", dpi = 300)
### timeseries unit cost ----
ggplot(global_timeseries) +
geom_line(aes(x = year_number, y = global_unit_cost, color = scenario, linetype = scenario), linewidth = 1) +
labs(x = 'Pumping Year', y = expression('Global Unit Cost ($/m'^3*')'), color = "Scenario", linetype = "Scenario", tag = "(c)") +
# scale_x_sqrt(breaks = c(1, 5, 10, 25, 50, 100, 300), expand = c(0.01,0.01)) + # Setting breaks at squares and labels at the square roots
scale_x_log10(expand = c(0, 0.035), breaks = scales::log_breaks(n = 10)) + #-0.12
annotation_logticks(base = 10, sides = "b") +
scale_color_brewer(palette = "Paired") +
scale_linetype_manual(values = c("solid", "dashed", "solid", "dashed", "solid", "dashed")) +
theme_bw() +
theme(
strip.background = element_blank(),
strip.text = element_text(face = "bold"),
# axis.title = element_text(face="bold"),
# legend.position = c(0.875, 0.78),
legend.position = c(0.125, 0.78),
# legend.direction = "horizontal",
legend.background = element_blank(),
legend.title = element_text(face = "bold"),
plot.title = element_text(hjust = 0.5),
panel.grid.major = element_blank(),
plot.tag = element_text(),
plot.tag.position = c(0.01 , 0.99),
legend.key.size = unit(0.2, "in"),
legend.text = element_text(size = 8),
legend.key.width = unit(0.4, "in"),
plot.margin = margin(t = 1, # Top margin
r = 0, # Right margin
b = 0, # Bottom margin
l = 3) # Left margin
)
ggsave(paste0(figs_dir, "global_unit_cost_all_scen.png"), width = 6, height = 4, units = "in", dpi = 300)
# cumulative unit cost
# this probably doesn't mean anything in reality
ggplot(global_timeseries %>%
group_by(scenario) %>% mutate(global_unit_cost_cumsum = cumsum(global_unit_cost))) +
geom_line(aes(x = year_number, y = global_unit_cost_cumsum, color = scenario, linetype = scenario), linewidth = 1) +
labs(x = 'Pumping Year', y = expression('Global Cumulative Unit Cost ($/m'^3*')'), color = "Scenario", linetype = "Scenario", tag = "(d)") +
# scale_x_sqrt(breaks = c(1, 5, 10, 25, 50, 100, 300), expand = c(0.01,0.1)) + # Setting breaks at squares and labels at the square roots
scale_x_log10(expand = c(0, 0.035), breaks = scales::log_breaks(n = 10)) + #-0.12
annotation_logticks(base = 10, sides = "b") +
scale_color_brewer(palette = "Paired") +
scale_linetype_manual(values = c("solid", "dashed", "solid", "dashed", "solid", "dashed")) +
theme_bw() +
theme(
strip.background = element_blank(),
strip.text = element_text(face = "bold"),
# axis.title = element_text(face="bold"),
legend.position = c(0.125, 0.78),
# legend.direction = "horizontal",
legend.background = element_blank(),
legend.title = element_text(face = "bold"),
plot.title = element_text(hjust = 0.5),
panel.grid.major = element_blank(),
plot.tag = element_text(),
plot.tag.position = c(0.01 , 0.99),
legend.key.size = unit(0.2, "in"),
legend.text = element_text(size = 8),
legend.key.width = unit(0.4, "in"),
plot.margin = margin(t = 1, # Top margin
r = 0, # Right margin
b = 0, # Bottom margin
l = 3) # Left margin
)
ggsave(paste0(figs_dir, "global_unit_cost_all_scen_cumulative.png"), width = 6, height = 4, units = "in", dpi = 300)
# stacked bar charts of costs over time ----
df_0.3PD_0.25DL %>%
select(year_number, annual_capital_cost, maintenance_cost, energy_cost) %>%
mutate(annual_capital_cost = annual_capital_cost * 1e-12,
maintenance_cost = maintenance_cost * 1e-12,
energy_cost = energy_cost * 1e-12) %>%
group_by(year_number) %>% summarise_all(sum) %>% ungroup() %>% mutate(scen = "0.3PD_0.25DL") %>%
pivot_longer(cols = c(annual_capital_cost, maintenance_cost, energy_cost), names_to = "cost_type", values_to = "cost") %>%
mutate(cost_type = case_when(cost_type == "annual_capital_cost" ~ "Capital",
cost_type == "maintenance_cost" ~ "Maintenance",
cost_type == "energy_cost" ~ "Energy"),
cost_type = factor(cost_type, levels = c("Capital", "Maintenance", "Energy")) %>% fct_rev()) %>%
ggplot() +
geom_bar(aes(fill = cost_type, y = cost, x = year_number), position = "stack", stat = "identity") +
labs(x = 'Pumping Year', y = expression('Global Annual Cost (Trillion $)'), fill = "Cost Type", tag = "(a)") +
scale_fill_manual(values = c("#00b0f0", "#f4aa00", "#c10435")) + my_theme() +
theme(legend.position = c(0.825, 0.85),
legend.title = element_text(face = "bold"))
ggsave(paste0(figs_dir, "global_annual_cost_total_0.3PD_0.25DL.png"), width = 6, height = 4, units = "in", dpi = 300)
df_0.3PD_0.25DL %>%
select(year_number, annual_capital_cost, maintenance_cost, energy_cost) %>%
mutate(annual_capital_cost = annual_capital_cost * 1e-6,
maintenance_cost = maintenance_cost * 1e-6,
energy_cost = energy_cost * 1e-6) %>%
group_by(year_number) %>% summarise_all(mean) %>% ungroup() %>% mutate(scen = "0.3PD_0.25DL") %>%
pivot_longer(cols = c(annual_capital_cost, maintenance_cost, energy_cost), names_to = "cost_type", values_to = "cost") %>%
mutate(cost_type = case_when(cost_type == "annual_capital_cost" ~ "Capital",
cost_type == "maintenance_cost" ~ "Maintenance",
cost_type == "energy_cost" ~ "Energy"),
cost_type = factor(cost_type, levels = c("Capital", "Maintenance", "Energy")) %>% fct_rev()) %>%
ggplot() +
geom_bar(aes(fill = cost_type, y = cost, x = year_number), position = "stack", stat = "identity") +
labs(x = 'Pumping Year', y = expression('Global Average Annual Cost (Million $)'), fill = "Cost Type", tag = "(b)") +
scale_fill_manual(values = c("#00b0f0", "#f4aa00", "#c10435")) + my_theme() +
theme(legend.position = c(0.825, 0.85),
legend.title = element_text(face = "bold"))
ggsave(paste0(figs_dir, "global_annual_cost_avg_0.3PD_0.25DL.png"), width = 6, height = 4, units = "in", dpi = 300)
df_0.3PD_0.25DL %>%
select(year_number, number_of_wells, annual_capital_cost, maintenance_cost, energy_cost) %>%
mutate(annual_capital_cost = annual_capital_cost/number_of_wells,
maintenance_cost = maintenance_cost/number_of_wells,
energy_cost = energy_cost/number_of_wells) %>%
group_by(year_number) %>% summarise_all(mean) %>% ungroup() %>% mutate(scen = "0.3PD_0.25DL") %>%
pivot_longer(cols = c(annual_capital_cost, maintenance_cost, energy_cost), names_to = "cost_type", values_to = "cost") %>%
mutate(cost_type = case_when(cost_type == "annual_capital_cost" ~ "Capital",
cost_type == "maintenance_cost" ~ "Maintenance",
cost_type == "energy_cost" ~ "Energy"),
cost_type = factor(cost_type, levels = c("Capital", "Maintenance", "Energy")) %>% fct_rev()) %>%
ggplot() +
geom_bar(aes(fill = cost_type, y = cost, x = year_number), position = "stack", stat = "identity") +
labs(x = 'Pumping Year', y = expression('Global Annual Average Cost per Well ($/well)'), fill = "Cost Type", tag = "(c)") +
scale_fill_manual(values = c("#00b0f0", "#f4aa00", "#c10435")) + my_theme() +
theme(legend.position = c(0.125, 0.85),
legend.title = element_text(face = "bold"))
ggsave(paste0(figs_dir, "global_annual_cost_avgperwell_0.3PD_0.25DL.png"), width = 6, height = 4, units = "in", dpi = 300)
# one grid cell
df_0.3PD_0.25DL %>% filter(GridCellID == "39689") %>% # 72548 for deepen and added wells, 12422 south africa for 199th year, US 96934, Amu Darya 39689 nice balanced graph, Nile 19565
select(year_number, number_of_wells, annual_capital_cost, maintenance_cost, energy_cost) %>%
mutate(annual_capital_cost = annual_capital_cost/number_of_wells,
maintenance_cost = maintenance_cost/number_of_wells,
energy_cost = energy_cost/number_of_wells) %>%
group_by(year_number) %>% summarise_all(mean) %>% ungroup() %>% mutate(scen = "0.3PD_0.25DL") %>%
pivot_longer(cols = c(annual_capital_cost, maintenance_cost, energy_cost), names_to = "cost_type", values_to = "cost") %>%
mutate(cost_type = case_when(cost_type == "annual_capital_cost" ~ "Capital",
cost_type == "maintenance_cost" ~ "Maintenance",
cost_type == "energy_cost" ~ "Energy"),
cost_type = factor(cost_type, levels = c("Capital", "Maintenance", "Energy")) %>% fct_rev()) %>%
ggplot() +
geom_bar(aes(fill = cost_type, y = cost, x = year_number), position = "stack", stat = "identity") +
labs(x = 'Pumping Year', y = expression('Grid Annual Cost per Well ($/well)'), fill = "Cost Type", tag = "(a)") +
scale_fill_manual(values = c("#00b0f0", "#f4aa00", "#c10435")) + my_theme() +
theme(legend.position = c(0.125, 0.85),
legend.title = element_text(face = "bold"))
ggsave(paste0(figs_dir, "grid_annual_cost_perwell_0.3PD_0.25DL.png"), width = 6, height = 4, units = "in", dpi = 300)
# one grid with deepened and added wells
# select grid cell that has both added wells and deepening
df_0.3PD_0.25DL %>%
# select(GridCellID, well_yield, number_of_wells) %>%
select(GridCellID, year_number, well_yield, number_of_wells) %>%
filter(year_number > 19) %>% select(-year_number) %>%
# group_by(GridCellID) %>%
unique() %>%
group_by(GridCellID) %>% mutate(n = n()) %>% ungroup() %>% filter(n > 1) -> df_addedwells
# why is that the cells which reduce the pumping rate don't go beyond 50 years?
df_0.3PD_0.25DL %>% filter(GridCellID == "82509") %>% # 82509 (seems to be better to use), 72548
select(GridCellID, year_number, number_of_wells, well_yield, total_well_length, depth_to_water, total_head, annual_capital_cost, maintenance_cost, energy_cost, total_cost_allwells) -> df_deepen_addedwells
# write_csv(df_0.3PD_0.25DL %>% filter(GridCellID == "72548"), "df_0.3PD_0.25DL_grid_id_72548.csv")
# one grid cell
# read output file of one grid cell
# df_0.3PD_0.25DL_Grid_72548 <- read_csv(paste0(out_dir, '0.3PD_0.25DL_Grid_72548.csv')) %>% rename("GridCellID" = "grid_id")
df_0.3PD_0.25DL_Grid_72548 <- df_0.3PD_0.25DL %>% filter(GridCellID == "72548")
# df_0.3PD_0.25DL
df_0.3PD_0.25DL %>% filter(GridCellID == "72548") %>% # 82509 3 pumping rates, 2450 added wells only, 72548 for deepen and added wells, 12422 south africa for 199th year, US 96934, Amu Darya 39689 nice balanced graph, Nile 19565
#
select(year_number, number_of_wells, annual_capital_cost, maintenance_cost, energy_cost) %>%
mutate(annual_capital_cost = annual_capital_cost/number_of_wells,
maintenance_cost = maintenance_cost/number_of_wells,
energy_cost = energy_cost/number_of_wells) %>%
group_by(year_number) %>% summarise_all(mean) %>% ungroup() %>% mutate(scen = "0.3PD_0.25DL") %>%
pivot_longer(cols = c(annual_capital_cost, maintenance_cost, energy_cost), names_to = "cost_type", values_to = "cost") %>%
mutate(cost_type = case_when(cost_type == "annual_capital_cost" ~ "Capital",
cost_type == "maintenance_cost" ~ "Maintenance",
cost_type == "energy_cost" ~ "Energy"),
cost_type = factor(cost_type, levels = c("Capital", "Maintenance", "Energy")) %>% fct_rev()) %>%
ggplot() +
geom_bar(aes(fill = cost_type, y = cost, x = year_number), position = "stack", stat = "identity") +
labs(x = 'Pumping Year', y = expression('Grid Annual Cost per Well ($/well)'), fill = "Cost Type", tag = "(b)") +
scale_fill_manual(values = c("#00b0f0", "#f4aa00", "#c10435")) + my_theme() +
theme(legend.position = c(0.125, 0.85),
legend.title = element_text(face = "bold"))
# ggsave(paste0(figs_dir, "grid_annual_cost_perwell_0.3PD_0.25DL_addedwells.png"), width = 6, height = 4, units = "in", dpi = 300)
df_0.3PD_0.25DL_Grid_72548 %>% filter(GridCellID == "72548") %>% # 82509 3 pumping rates, 2450 added wells only, 72548 for deepen and added wells, 12422 south africa for 199th year, US 96934, Amu Darya 39689 nice balanced graph, Nile 19565
#
select(year_number, number_of_wells, annual_capital_cost, maintenance_cost, energy_cost) %>%
# mutate(annual_capital_cost = annual_capital_cost/number_of_wells,
# maintenance_cost = maintenance_cost/number_of_wells,
# energy_cost = energy_cost/number_of_wells) %>%
group_by(year_number) %>% summarise_all(mean) %>% ungroup() %>% mutate(scen = "0.3PD_0.25DL") %>%
pivot_longer(cols = c(annual_capital_cost, maintenance_cost, energy_cost), names_to = "cost_type", values_to = "cost") %>%
mutate(cost_type = case_when(cost_type == "annual_capital_cost" ~ "Capital",
cost_type == "maintenance_cost" ~ "Maintenance",
cost_type == "energy_cost" ~ "Energy"),
cost_type = factor(cost_type, levels = c("Capital", "Maintenance", "Energy")) %>% fct_rev()) %>%
ggplot() +
geom_bar(aes(fill = cost_type, y = cost * 1e-6, x = year_number), position = "stack", stat = "identity") +
labs(x = 'Pumping Year', y = expression('Grid Annual Costs (million $)'), fill = "Cost Type", tag = "(d)") +
scale_fill_manual(values = c("#00b0f0", "#f4aa00", "#c10435")) + my_theme() +
theme(legend.position = c(0.125, 0.85),
legend.title = element_text(face = "bold"))
# ggsave(paste0(figs_dir, "grid_annual_cost_0.3PD_0.25DL_addedwells.png"), width = 6, height = 4, units = "in", dpi = 300)
# single well dynamics ----
df_single <- df_0.3PD_0.25DL_Grid_72548
plot_list <- list()
plot_single_well <- function(data, x_var = "year_number", y_var, y_scale = 1, y_label, color = "red", tag = "") {
gg <- ggplot(data, aes_string(x = x_var, y = paste0(y_var, " * ", y_scale))) +
geom_point(color = color) +
labs(x = "Pumping Year", y = y_label, tag = tag) + my_theme()
ggsave(paste0(figs_dir, "singlecell/cell_", y_var, ".png"), plot = gg, width = 6, height = 4, units = "in", dpi = 300)
# list to plot everything in one plot
plot_list[[y_var]] <<- gg
return(gg)
}
{
# depths
plot_single_well(df_single, "year_number", "total_thickness", 1, expression('Total Aquifer Thickness (m)'), "blue2", "(a)")
plot_single_well(df_single, "year_number", "depth_to_water", 1, expression('Depth to Water (m)'), "red2", "(b)")
plot_single_well(df_single, "year_number", "orig_aqfr_sat_thickness", 1, expression('Original Aquifer Saturated Thickness (m)'), "green3", "(c)")
plot_single_well(df_single, "year_number", "total_well_length", 1, expression('Total Well Length (m)'), "orange", "(d)")
plot_single_well(df_single, "year_number", "aqfr_sat_thickness", 1, expression('Aquifer Saturated Thickness (m)'), "purple", "(e)")
plot_single_well(df_single, "year_number", "total_head", 1, expression('Total Head (m)'), "brown", "(f)")
# T=Kb
plot_single_well(df_single, "year_number", "hydraulic_conductivity", 86400, expression('Hydraulic Conductivity (m/day)'), "red2", "(a)")
plot_single_well(df_single, "year_number", "transmissivity", 86400, expression('Transmissivity (m'^2*'/day)'), "green4", "(b)")
# well hydraulics
plot_single_well(df_single, "year_number", "well_yield", 1, expression('Well Yield (m'^3*'/day)'), "orange2", "(a)")
plot_single_well(df_single, "year_number", "areal_extent", 1, expression('Areal Extent (m'^2*')'), "royalblue2", "(b)")
plot_single_well(df_single, "year_number", "number_of_wells", 1, 'Number of Wells', "red2", "(c)")
plot_single_well(df_single, "year_number", "drawdown", 1, expression('Drawdown (m)'), "green3", "(d)")
# volumes
plot_single_well(df_single, "year_number", "volume_produced_perwell", 1e-6, expression('Volume Produced Per Well (million m'^3*')'), "blue3", "(a)")
plot_single_well(df_single, "year_number", "cumulative_vol_produced_perwell", 1e-6, expression('Cumulative Volume Produced Per Well (million m'^3*')'), "red3", "(b)")
plot_single_well(df_single, "year_number", "volume_produced_allwells", 1e-9, expression('Volume Produced All Wells (billion m'^3*')'), "green3", "(c)")
plot_single_well(df_single, "year_number", "cumulative_vol_produced_allwells", 1e-9, expression('Cumulative Volume Produced All Wells (billion m'^3*')'), "orange", "(d)")
plot_single_well(df_single, "year_number", "available_volume", 1e-9, expression('Available Volume (billion m'^3*')'), "purple", "(e)")
plot_single_well(df_single, "year_number", "depleted_vol_fraction", 1, 'Depleted Volume Fraction', "brown", "(f)")
# power and energy
plot_single_well(df_single, "year_number", "power", 1e-6, expression('Power (GW)'), "olivedrab3", "(a)")
plot_single_well(df_single, "year_number", "energy", 1e-6, expression('Energy (GWh)'), "darkorchid4", "(b)")
# energy cost
plot_single_well(df_single, "year_number", "energy_cost_rate", 1, expression('Electricity Cost Rate (USD/KWh)'), "darkorange", "(a)")
plot_single_well(df_single, "year_number", "energy_cost", 1e-6, expression('Energy Cost (million $)'), "green", "(b)")
# nonenergy cost components
plot_single_well(df_single, "year_number", "well_installation_cost", 1e-6, expression('Well Installation Cost (million $)'), "darkslateblue", "(a)")
plot_single_well(df_single, "year_number", "annual_capital_cost", 1e-6, expression('Annual Capital Cost (million $)'), "cadetblue", "(b)")
plot_single_well(df_single, "year_number", "maintenance_cost", 1e-6, expression('Maintenance Cost (million $)'), "darkmagenta", "(c)")
plot_single_well(df_single, "year_number", "nonenergy_cost", 1e-6, expression('Nonenergy Cost (million $)'), "khaki3", "(d)")
# total costs
plot_single_well(df_single, "year_number", "total_cost_perwell", 1e-6, expression('Total Cost Per Well (million $)'), "mediumpurple", "(a)")
plot_single_well(df_single, "year_number", "total_cost_allwells", 1e-6, expression('Total Cost All Wells (million $)'), "hotpink4", "(b)")
# unit cost
plot_single_well(df_single, "year_number", "unit_cost", 1, expression('Unit Cost (USD/m'^3*')'), "chartreuse3", "(a)")
plot_single_well(df_single, "year_number", "unit_cost_per_acreft", 1, expression('Unit Cost (USD/acre-ft)'), "darkolivegreen4", "(b)")
}
# combined plot of single cell dynamics
combined_plot <-patchwork:: wrap_plots(plot_list, ncol = 6)
# maintain 16:9 ratio but keep the height to 12
ggsave(paste0(figs_dir, "single_well_dynamics.png"), combined_plot, width = 24.88889, height = 14, units = "in", dpi = 300)
# plot(x = df_0.3PD_0.25DL_Grid_72548$year_number, y = df_0.3PD_0.25DL_Grid_72548$energy_cost * 1e-6, type = "p", col = "red", xlab = "Pumping Year", ylab = "Energy Costs (million $)")
# plot(x = df_0.3PD_0.25DL_Grid_72548$year_number, y = df_0.3PD_0.25DL_Grid_72548$annual_capital_cost * 1e-6, type = "p", col = "blue2", xlab = "Pumping Year", ylab = "Capital Costs (million $)")
# # maintenance cost
# plot(x = df_0.3PD_0.25DL_Grid_72548$year_number, y = df_0.3PD_0.25DL_Grid_72548$maintenance_cost * 1e-6, type = "p", col = "green4", xlab = "Pumping Year", ylab = "Maintenance Costs (million $)")
# plot(x = df_0.3PD_0.25DL_Grid_72548$year_number, y = df_0.3PD_0.25DL_Grid_72548$nonenergy_cost * 1e-6, type = "p", col = "blue2", xlab = "Pumping Year", ylab = "Nonenergy Costs (million $)")
# plot(x = df_0.3PD_0.25DL_Grid_72548$year_number, y = df_0.3PD_0.25DL_Grid_72548$total_cost_allwells * 1e-6, type = "p", col = "green4", xlab = "Pumping Year", ylab = "Total Cost (million $)")
# # plot transmissivity
# plot(x = df_0.3PD_0.25DL_Grid_72548$year_number, y = df_0.3PD_0.25DL_Grid_72548$transmissivity * 86400, type = "p", col = "gold", xlab = "Pumping Year", ylab = "Transmissivity (m2/day)")
#
# plot(x = df_0.3PD_0.25DL_Grid_72548$year_number, y = df_0.3PD_0.25DL_Grid_72548$well_yield * 86400, type = "p", col = "dodgerblue", xlab = "Pumping Year", ylab = "Well Yield (m3/day)")
# plot(x = df_0.3PD_0.25DL_Grid_72548$year_number, y = df_0.3PD_0.25DL_Grid_72548$areal_extent * 1e-6, type = "p", col = "darkorange", xlab = "Pumping Year", ylab = "Well Area (km2)")
# plot all input variables -----------------------------------------------
# setting up the plot
plot_input <- sf_in_R %>% # filter(Country == "United States") %>%
mutate(Grid_area_km = Grid_area/10^6, # convert to km2
Lake_area_km = Lake_area/10^6, # convert to km2
Aquifer_thickness = case_when(Aquifer_thickness > 1000 ~ 1000,
TRUE ~ Aquifer_thickness)) %>% # replace all MEAN_thk_m values greater than 1000 with 1000
select(c("Porosity", "Permeability", "Aquifer_thickness",
"Depth_to_water", "Grid_area_km", "Lake_area_km", "Recharge", "WHYClass")) # filter only relevant ones
# rename(c("storativity" = "MEAN_Poros", "permeability" = "MEAN_Perme",
# "grid_area_km" = "CalcArea_m", "total_thickness" = "MEAN_thk_m",
# "depth_to_water" = "MEAN_Depth", "country_name" = "COUNTRY",
# "GridCellID" = "OriginalOb")) # rename to make the join with output smooth
# legendnames <- c("Porosity" = "Porosity (-)", "Permeability" = "Permeability (m2)",
legendnames <- c("Porosity" = "Porosity (-)", "Permeability" = expression('Permeability (m'^2*')'),
"Aquifer_thickness" = "Aquifer Thickness (m)", "Depth_to_water" = "Depth to Water (m)",
"Grid_area_km" = expression('Grid Area (km'^2*')'),
"Lake_area_km" = expression('Lake Area (km'^2*')'),
"Recharge" = "Recharge (m/yr)", "WHYClass" = "WHY Class") # to label legend properly
# see all palettes available: tmaptools::palette_explorer()
pal <- c("Porosity" = "BuPu", "Permeability" = "YlGn",
"Aquifer_thickness" = "YlOrRd", "Depth_to_water" = "YlOrBr",
"Grid_area_km" = "BuGn", "Lake_area_km" = "YlGnBu",
"Recharge" = "Blues", "WHYClass" = "PuBuGn")
# max(plot_input$Permeability)
# plot all input variables using ggplot
for (plot in colnames(plot_input)[4:(length(plot_input) - 5)]) { # plot all input variables except last two
ggplot(plot_input) +
geom_sf(aes(fill = .data[[plot]]), lwd = 0, colour = NA) +
scale_fill_gradientn(name = legendnames[plot],
colors = c(brewer.pal(9, pal[plot])),
values = scales::rescale(sort(c(min(plot_input[[plot]]),
kmeans(plot_input[[plot]], centers = 7)$center, # breaks by kmeans clustering
max(plot_input[[plot]]))))
) +
coord_sf(expand = FALSE) + # makes sure the plot doesn't extend beyond Earth (also plots y axis ticks and crops)
theme_minimal() + theme(plot.margin = grid::unit(c(-4, 0, -4, 0), "cm"),
legend.justification = c(0,0), legend.position = c(0,0),
# legend.title = element_text(face = "bold"),
panel.grid = element_blank())
# save each plot
plotname <- paste0(figs_dir, "map_in_", plot, ".png")
ggsave(plotname, width = 11, height = 3.5, units = "in", dpi = 300)
}
# for WHYClass
ggplot(plot_input) +
geom_sf(lwd = 0, aes(fill = as.factor(WHYClass))) +
scale_fill_manual(name = "WHY Class", values = c("#377eb8", "#e41a1c", "#4daf4a")) +
coord_sf(expand = FALSE) + # makes sure the plot doesn't extend beyond Earth (also plots y axis ticks and crops)
theme_minimal() + theme(panel.grid = element_blank(),
plot.margin = grid::unit(c(-4, 0, -4, 0), "cm"),
legend.justification = c(0,0), legend.position = c(0,0)
# legend.title = element_text(face = "bold")
)
ggsave(paste0(figs_dir, "map_in_WHYClass.png"), width = 11, height = 3.5, units = "in", dpi = 300)
# for recharge
ggplot(plot_input) +
geom_sf(lwd = 0, aes(fill = Recharge), lwd = 0, colour = NA) +
scale_fill_gradientn(name = "Recharge (m/yr)",
colors = c(brewer.pal(9, "Blues")),
values = scales::rescale(sort(c(min(plot_input$Recharge),
kmeans(plot_input$Recharge, centers = 2)$center, # breaks by kmeans clustering