devtools::load_all()
project_path <- here::here()
working_dir <- file.path(project_path, "data", "ewe")
sim_years <- 1985:2017
#### Atlantic Multidecadal Oscillation (AMO) Index: https://psl.noaa.gov/data/timeseries/AMO/
# unsmoothed data with no lag
data <- IP4EBFM::download_amo(
url = "https://psl.noaa.gov/data/correlation/amon.us.long.data",
years = sim_years,
output_dir = file.path(working_dir, "amo_unsmooth_lag0.csv"),
scale_range = c(0, 2)
)
windows()
plot(data$scaled_value, xlab = "Time", ylab = "Atlantic Multidecadal Oscillation Index")
# unsmoothed data with lag 1
data <- IP4EBFM::download_amo(
url = "https://psl.noaa.gov/data/correlation/amon.us.long.data",
years = sim_years - 1,
output_dir = file.path(working_dir, "amo_unsmooth_lag1.csv"),
scale_range = c(0, 2)
)
plot(data$scaled_value, xlab = "Time", ylab = "Atlantic Multidecadal Oscillation Index")
# There are missing data in smoothed data; smoothed data with no lag
data <- IP4EBFM::download_amo(
url = "https://psl.noaa.gov/data/correlation/amon.sm.long.data",
years = sim_years,
output_dir = file.path(working_dir, "amo_smooth_lag0.csv"),
scale_range = c(0, 2)
)
plot(data$scaled_value, xlab = "Time", ylab = "Atlantic Multidecadal Oscillation Index")
# smoothed data with lag 1
data <- data <- IP4EBFM::download_amo(
url = "https://psl.noaa.gov/data/correlation/amon.sm.long.data",
years = sim_years - 1,
output_dir = file.path(working_dir, "amo_smooth_lag1.csv"),
scale_range = c(0, 2)
)
plot(data$scaled_value, xlab = "Time", ylab = "Atlantic Multidecadal Oscillation Index")
#### Kaplan SST average: https://psl.noaa.gov/data/timeseries/AMO/
# Kaplan SST average has the same data format compared to AMO data, we can continue using download_amo_data function.
data <- IP4EBFM::download_amo(
url = "https://psl.noaa.gov/data/correlation/amon.us.long.mean.data",
years = sim_years,
output_dir = file.path(working_dir, "kaplan_sst.csv"),
scale_range = c(0, 5)
)
plot(data$scaled_value, xlab = "Time", ylab = "Kaplan SST average")
#### Precipitation data by states from NCDC (https://www.ncdc.noaa.gov/cag/statewide/time-series/44/pcp/all/1/1985-2018)
# States: Viginia, Maryland, Connecticut, Rhode Island, and Massachusetts
states <- c("VA", "MD", "CT", "RI", "MA")
states_id <- c(44, 18, 6, 37, 19)
# Precipitation
data <- IP4EBFM::download_ncei(
years = sim_years,
states = states,
states_id = states_id,
parameter = "pcp",
output_dir = file.path(working_dir, "precipitation.csv"),
scale_range = c(0, 5)
)
plot(data$raw_value, xlab = "Time", ylab = "Precipitation")
lines(data$scaled_value)
# Palmer drought severity index
data <- IP4EBFM::download_ncei(
years = sim_years,
states = states,
states_id = states_id,
parameter = "pdsi",
output_dir = file.path(working_dir, "palmer_drought_severity_index.csv"),
scale_range = c(0, 2)
)
plot(data$raw_value, xlab = "Time", ylab = "Palmer drought severity index")
lines(data$scaled_value)
#### Chlorophyll a data (have missing data)
# Website: https://coastwatch.pfeg.noaa.gov/erddap/griddap/pmlEsaCCI31OceanColorMonthly_Lon0360.html
# Start and stop points of latitude: 34 and 46
# Start and stop points of longitude: 280 and 295
# Full url: https://coastwatch.pfeg.noaa.gov/erddap/griddap/pmlEsaCCI31OceanColorMonthly_Lon0360.htmlTable?chlor_a%5B(1997-09-04T00:00:00Z):1:(2018-01-01T00:00:00Z)%5D%5B(34):1:(46)%5D%5B(280):1:(295)%5D
# data <- data.table::fread("https://coastwatch.pfeg.noaa.gov/erddap/griddap/pmlEsaCCI50OceanColorMonthly_Lon0360.htmlTable?chlor_a%5B(1997-09-04T00:00:00Z):1:(2018-01-01T00:00:00Z)%5D%5B(34):1:(46)%5D%5B(280):1:(295)%5D", fill=TRUE)
#### Beaufort Assessment Model recruitment deviations
menhaden <- dget(here::here("data", "AtlanticMenhadenSA", "am019.rdat"))
recdev <- cbind(
rep(sim_years, each = 12),
rep(1:12, times = length(sim_years)),
rep(exp(menhaden$t.series$logR.dev[menhaden$t.series$year %in% sim_years]), each = 12)
)
write.csv(recdev, file.path("data", "ewe", "recruitment_deviations.csv"))
#### Ecosim phytoplankton and zooplankton outputs
ewe_output <- read_ewe_output(
file_path = here::here("data", "ewe", "7ages", "ecosim_output", "BA0"),
file_names = "biomass_monthly.csv",
skip_nrows = 8,
plot = FALSE,
figure_titles = NULL,
functional_groups = c(
"StripedBass0",
"StripedBass2_5",
"StripedBass6",
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"SpinyDogfish",
"BluefishJuvenile",
"BluefishAdult",
"WeakfishJuvenile",
"WeakfishAdult",
"AtlanticHerring0_1",
"AtlanticHerring2",
"Anchovies",
"Benthos",
"Zooplankton",
"Phytoplankton",
"Detritus"
),
figure_colors = NULL
)
# Phytoplankton
phytoplankton <- scales::rescale(ewe_output[[1]]$Phytoplankton, to = c(0,2))
write.csv(phytoplankton, file.path("data", "ewe", "phytoplankton.csv"))
# Zooplankton
zooplankton <- scales::rescale(ewe_output[[1]]$Zooplankton, to = c(0,2))
write.csv(phytoplankton, file.path("data", "ewe", "zooplankton.csv"))
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