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conductivity_snapshotmaps.R
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conductivity_snapshotmaps.R
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# conductivity snapshot maps ---
source('conductivity_summary.R')
devtools::install_github("yutannihilation/ggsflabel")
library(sf)
library(ggsflabel)
library(ggspatial)
library(cowplot)
# add values to names for map ----
lmp_cond_aggyearsite <- lmp_cond_aggyearsite %>%
left_join(., lmp_locs) %>%
mutate(mean_name = round(mean_cond_uScm, digits = 1)) %>%
mutate(Name = paste0(Name, ' (', mean_name, ')'))
# bring in spatial layers ----
lake <- read_sf(file.path(feat_dir, 'hydrography/LS_shore_WGS.shp'))
watershed <- read_sf(file.path(feat_dir, 'watersheds/NH_hydro_Sunapee/Lake_Sunapee_watershed.shp'))
watershed <- st_transform(watershed, crs = 'epsg:4326')
streams <- read_sf(file.path(feat_dir, 'hydrography/streams.shp'))
streams <- st_transform(streams, crs = 'epsg:4326')
waterbodies <- read_sf(file.path(feat_dir, 'hydrography/waterbodies open water.shp'))
waterbodies <- st_transform(waterbodies, crs = 'epsg:4326')
roads <- read_sf(file.path(feat_dir, 'roads/roads_sun_wshed.shp'))
roads <- st_transform(roads, crs = 'epsg:4326')
roads <- st_zm(roads,drop = T)
lmp <- st_as_sf(lmp_locs,
coords = c('lon_dd', 'lat_dd'),
crs = 'epsg:4326')
# 2023 conductivity ----
lmp_cond_2023 <- lmp_cond %>%
right_join(lmp_locs) %>%
filter(year == 2023) %>%
group_by(station, lon_dd, lat_dd, site_type, sub_site_type, Name) %>%
filter(!is.na(value)) %>%
summarize(mean_cond_uScm = mean(value)) %>%
mutate(mean_name = as.integer(mean_cond_uScm)) %>%
mutate(Name = paste0(Name, ' (', mean_name, ')'))
lmp_cond_2023 <- st_as_sf(lmp_cond_2023,
coords = c('lon_dd', 'lat_dd'),
crs = 'epsg:4326')
lmp_cond_2023_stream <- lmp_cond_2023 %>%
filter(sub_site_type == 'tributary')
lmp_cond_2023_lake <- lmp_cond_2023 %>%
filter(site_type == 'lake')
lmp_cond_2023_stream_e = lmp_cond_2023_stream %>%
filter(station == 1415 | station == 1420 | station == 830 | station == 835 |
station == 805 | station == 800 | station == 788 | station == 760)
lmp_cond_2023_stream_w = lmp_cond_2023_stream %>%
filter(station != 1415 & station != 1420 & station != 830 & station != 835 &
station != 805 & station != 800 & station != 788 & station != 760)
st_bbox(watershed)
ggplot() +
geom_sf(watershed, mapping = aes(), fill = 'white') +
geom_sf(streams, mapping = aes(), color = 'dark blue') +
geom_sf(waterbodies, mapping = aes(), fill = 'light blue') +
geom_sf(roads, mapping = aes(), color = 'light grey') +
geom_sf(lmp_cond_2023_stream, mapping = aes(color = mean_cond_uScm), size = 3) +
labs(color = 'average\nsummer\nconductivity\n(uS/cm)') +
scale_color_viridis_c() +
theme_void() +
labs(x = NULL, y = NULL, title = '2023 Tributary Conductivity') +
geom_sf_label_repel(lmp_cond_2023_stream_w, mapping = aes(label = Name), nudge_x = -0.07, nudge_y = -0.01, size = 1.5) +
geom_sf_label_repel(lmp_cond_2023_stream_e, mapping = aes(label = Name), nudge_x = 0.07, nudge_y = -0.001, size = 1.5) +
# facet_grid(. ~ sub_site_type) +
theme(plot.title = element_text(hjust = 0.5, face = 'bold')) +
# theme(strip.text.x = element_text(size = 12, face = "bold")) +
theme(legend.position = 'bottom', legend.title = element_text(size = 10)) +
scale_x_continuous(limits = c(as.numeric(st_bbox(watershed)[1])-0.1, as.numeric(st_bbox(watershed)[3])+0.1))
ggsave(file.path(dump_dir, 'conductivity_2023_tribs_map_labeled.jpg'),
height = 8,
width = 5,
dpi = 600,
units = 'in',
bg = 'white')
# 2014-2023 conductivity ----
lmp_cond_10year <- lmp_cond %>%
right_join(lmp_locs) %>%
filter(year >= 2014) %>%
group_by(station, lon_dd, lat_dd, site_type, sub_site_type, Name) %>%
filter(!is.na(value)) %>%
summarize(mean_cond_uScm = mean(value)) %>%
mutate(mean_name = as.integer(mean_cond_uScm)) %>%
mutate(Name = paste0(Name, ' (', mean_name, ')'))
lmp_cond_10year <- st_as_sf(lmp_cond_10year,
coords = c('lon_dd', 'lat_dd'),
crs = 'epsg:4326')
lmp_cond_10year_stream <- lmp_cond_10year %>%
filter(sub_site_type == 'tributary')
lmp_cond_10year_lake <- lmp_cond_10year %>%
filter(site_type == 'lake')
lmp_cond_10year_stream_e = lmp_cond_10year_stream %>%
filter(station == 1415 | station == 1420 | station == 830 | station == 835 |
station == 805 | station == 800 | station == 788 | station == 760)
lmp_cond_10year_stream_w = lmp_cond_10year_stream %>%
filter(station != 1415 & station != 1420 & station != 830 & station != 835 &
station != 805 & station != 800 & station != 788 & station != 760)
st_bbox(watershed)
ggplot() +
geom_sf(watershed, mapping = aes(), fill = 'white') +
geom_sf(streams, mapping = aes(), color = 'dark blue') +
geom_sf(waterbodies, mapping = aes(), fill = 'light blue') +
geom_sf(roads, mapping = aes(), color = 'light grey') +
geom_sf(lmp_cond_10year_stream, mapping = aes(color = mean_cond_uScm), size = 3) +
labs(color = 'average\nconductivity\n2014-2023\n(uS/cm)') +
scale_color_viridis_c() +
theme_void() +
labs(x = NULL, y = NULL, title = '2014-2023 Tributary Conductivity') +
geom_sf_label_repel(lmp_cond_10year_stream_w, mapping = aes(label = Name), nudge_x = -0.07,nudge_y = -0.01, size = 1.75) +
geom_sf_label_repel(lmp_cond_10year_stream_e, mapping = aes(label = Name), nudge_x = 0.07,nudge_y = -0.001, size = 1.75) +
# facet_grid(. ~ sub_site_type) +
theme(plot.title = element_text(hjust = 0.5, face = 'bold'),
legend.position = 'bottom',
legend.title = element_text(size = 10)) +
theme(strip.text.x = element_text(size = 12, face = "bold")) +
scale_x_continuous(limits = c(as.numeric(st_bbox(watershed)[1])-0.1, as.numeric(st_bbox(watershed)[3])+0.1))
ggsave(file.path(dump_dir, 'conductivity10year_tribs_map_labeled.jpg'),
height = 6,
width = 5,
dpi = 600,
units = 'in',
bg = 'white')
## in-lake conductivity ----
# 2014-2023 conductivity ----
lmp_cond_10year_lake_e = lmp_cond_10year_lake %>%
filter(station == 110 | station == 200 | station == 210 | station == 220 |
station == 230 | station == 90)
lmp_cond_10year_lake_w = lmp_cond_10year_lake %>%
filter(station != 110 & station != 200 & station != 210 & station != 220 &
station != 230 & station != 90)
st_bbox(watershed)
ggplot() +
geom_sf(watershed, mapping = aes(), fill = 'white') +
geom_sf(streams, mapping = aes(), color = 'dark blue') +
geom_sf(waterbodies, mapping = aes(), fill = 'light blue') +
geom_sf(roads, mapping = aes(), color = 'light grey') +
geom_sf(lmp_cond_10year_lake, mapping = aes(color = mean_cond_uScm), size = 3) +
labs(color = 'average\nconductivity\n2014-2023\n(uS/cm)') +
scale_color_viridis_c() +
theme_void() +
labs(x = NULL, y = NULL, title = '2014-2023 In-Lake Conductivity') +
geom_sf_label_repel(lmp_cond_10year_lake_w, mapping = aes(label = Name), nudge_x = -0.07,nudge_y = -0.01, size = 1.75) +
geom_sf_label_repel(lmp_cond_10year_lake_e, mapping = aes(label = Name), nudge_x = 0.07,nudge_y = -0.001, size = 1.75) +
# facet_grid(. ~ sub_site_type) +
theme(plot.title = element_text(hjust = 0.5, face = 'bold'),
legend.position = 'bottom',
legend.title = element_text(size = 10)) +
theme(strip.text.x = element_text(size = 12, face = "bold")) +
scale_x_continuous(limits = c(as.numeric(st_bbox(watershed)[1])-0.1, as.numeric(st_bbox(watershed)[3])+0.1))
ggsave(file.path(dump_dir, 'conductivity10year_lake_map_labeled.jpg'),
height = 6,
width = 5,
dpi = 600,
units = 'in',
bg = 'white')