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prepare_obs_for_comparison.R
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prepare_obs_for_comparison.R
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##############################################################################################
#' title Workflow to NCAR CLM Observational data
#' author
#' Hannah Holland-Moritz (hhollandmoritz AT gmail.com)
#'
#' description
#' Workflow for collating NIWOT LTER data in preparation to compare observations
#' to CLM output.
##############################################################################
##############################################################################
# Dependencies
##############################################################################
rm(list = ls())
#Call the R HDF5 Library
packReq <- c("magrittr","EML", "dplyr", "ggplot2",
"purrr", "tidyr", "lubridate","RCurl")
#Install and load all required packages
lapply(packReq, function(x) {
print(x)
if (require(x, character.only = TRUE) == FALSE) {
install.packages(x)
library(x, character.only = TRUE)
}})
#Setup Environment
options(stringsAsFactors = F)
##############################################################################
#Workflow parameters
##############################################################################
#### Output Options ####
# 1) Base directory for output
# 2) Directory to download observation data to
# 3) Location of the tvan data that was used to create forcing files
# location of tvan data with soil information; Note: Tvan soil temperature data
# probes from East tower do not work, so please give west tower tvan data location
# I'm trying to make it so we don't have to keep changing this...
user = 'jayka'
if (user == 'wwieder') {
DirOutBase <- paste0("~/Desktop/Working_files/Niwot/CLM/OBS/data")
DirDnld = "~/Desktop/Working_files/Niwot/CLM/OBS/NWT_lter_obs_downloads"
tvan_data_fp <- "~/Desktop/Working_files/Niwot/CLM/datav20200824T1008/data/tvan_forcing_data_precip_mods_both_towers_2007-05-11_2020-08-11.txt"
tvan_data_soil <- "~/Desktop/Working_files/Niwot/Tvan_out_new/filtered_data/tvan_West_2007-05-09_19-00-00_to_2020-08-11_00-30-00_flux_P.csv"
}
if (user == 'jayka') {
DirOutBase <- paste0("~/Desktop/Working_files/Niwot/CLM/OBS/data")
DirDnld = "~/Desktop/Working_files/Niwot/CLM/OBS/NWT_lter_obs_downloads"
tvan_data_fp <- "~/Desktop/Working_files/Niwot/CLM/data/data/tvan_forcing_data_precip_mods_both_towers_2007-05-11_2021-11-08.txt"
tvan_data_soil <- "~/Desktop/Working_files/Niwot/Tvan_out_new/AmeriFlux_readyData/tvan_West_2007-05-09_19-00-00_to_2021-11-08_00-30-00_flux_P.csv"
} else {
DirOutBase <- paste0("~/Downloads/OBS/data")
DirDnld = "~/Downloads/CLM/OBS/NWT_lter_obs_downloads"
tvan_data_fp <- "~/Downloads/CLM/datav20200816T1808/data/tvan_forcing_data_precip_mods_both_towers_2007-05-11_2020-08-11.txt"
tvan_data_soil <- "~/Downloads/Tvan_out_new/filtered_data/tvan_West_2007-05-09_19-00-00_to_2020-08-11_07-30-00_flux_P.csv"
}
# Should a newer version of EDI data be downloaded if one is available?
getNewData = TRUE
##############################################################################
# Static workflow parameters - these are unlikely to change
##############################################################################
#Check if directory exists and create if not
if(!dir.exists(DirOutBase)) dir.create(DirOutBase, recursive = TRUE)
if(!dir.exists(DirDnld)) dir.create(DirDnld, recursive = TRUE)
# the NWT LTER EDI id for observational data from the saddle
saddle_catch_sensntwk <- "210" # Saddle catchment sensor network data, 2017 - ongoing.
saddle_snow_depth_data <- "31" # Snow depth data for Saddle grid, 1992 - ongoing
saddle_productivity_data <- "16" # Aboveground net primary productivity data for Saddle (contains veg community classification for grid points) grid, 1992 - ongoing
saddle_sensntwk_veg <- "191" # Plot vegetation surveys at the Sensor Network, 2017 - ongoing
saddle_sensntwk_phenocam <- "192" # GCC data from Saddle sensor network phenocams, 2017 - ongoing
# Other possibly useful datasets:
# 211: Above-ground biomass for Sensor Node Array from 2017 to 2018, yearly
# Has plots corresponding to sensor soil moisture node, also describes plot
# biomass.
# 16: Aboveground net primary productivity for saddle grid
# not in edi yet: tvan soil temp/moisture from West tower.
##############################################################################
# Helper functions - for downloading and loading data
##############################################################################
# Functions for downloading LTER Precip data are from Sarah Elmendorf's
# utility_functions_all.R script
# https://github.com/NWTlter/long-term-trends/blob/master/utility_functions/utility_functions_all.R
# function to determine current version of data package on EDI
getCurrentVersion <- function(edi_id){
require(magrittr)
versions = readLines(paste0('https://pasta.lternet.edu/package/eml/knb-lter-nwt/', edi_id),
warn = FALSE) %>%
as.numeric() %>% (max)
packageid = paste0('knb-lter-nwt.', edi_id, '.', versions)
return(packageid)
}
#function to download the EML file from EDI
getEML <- function(packageid){
require(magrittr)
myurl <- paste0("https://portal.edirepository.org/nis/metadataviewer?packageid=",
packageid,
"&contentType=application/xml")
#myeml<-xml2::download_html(myurl)%>%xml2::read_xml()%>%EML::read_eml()
myeml <- xml2::read_xml(paste0("https://portal.edirepository.org/nis/metadataviewer?packageid=",
packageid,
"&contentType=application/xml")) %>% EML::read_eml()
}
# Function for downloading from EDI
download_EDI <- function(edi_id, dest_dir, getNewData = TRUE) {
# This section heavily borrowed from Sarah Elmendorf's generic_timeseries_workflow.R script
# https://github.com/NWTlter/long-term-trends/blob/master/plotting_scripts/generic_timeseries_workflow.R
# Depends on getCurrentVersion() and getEML()
packageid = getCurrentVersion(edi_id)
# Check if files already exist; if they do, load the file paths and column classes
# or warn the user if column classes can't be loaded
if (any(grepl(packageid, list.files(dest_dir)) == TRUE)) {
writeLines(paste0("Most recent package version ",
packageid, " is already downloaded. Nothing to do."))
# try to load the file path and column class information
rds.fp <- list.files(dest_dir, pattern = ".RDS", full.names = TRUE)
if (length(rds.fp) == 0) {
warning(paste0("RDS file not found in download directory.",
" This means that no column class information is available,",
" so column classes will be set to NA."))
csv_fp_colclass_list <- list(csv = list.files(dest_dir,
pattern = paste0(packageid, ".{1,}csv"), full.names = T),
colclasses = NA)
} else {
csv_fp_colclass_list <- readRDS(rds.fp)
}
return(csv_fp_colclass_list)
# Check if a more recent version is available; if getNewData is set to false,
# load column classes and file paths as above.
} else if (getNewData == FALSE) {
writeLines(paste0("A more recent version of the data (version ",
packageid, ") is available. ",
"But since you have specified getNewData = FALSE, ",
"the latest version will not be downloaded."))
if (length(rds.fp) == 0) {
warning(paste0("RDS file not found in download directory.",
" This means that no column class information is available,",
" so column classes will be set to NA."))
csv_fp_colclass_list <- list(csv = list.files(dest_dir,
pattern = paste0(packageid, ".{1,}csv"), full.names = T),
colclasses = NA)
} else {
csv_fp_colclass_list <- readRDS(rds.fp)
}
return(csv_fp_colclass_list)
}
# Download the files if they need to be downloaded; extract the column classes from the
# attributes and save the file paths and column classes of the data in the dest_dir
else {
writeLines(paste0("Downloading package ", packageid, " from EDI."))
myeml = getEML(packageid)
# Create output directory for data
ifelse(!dir.exists(file.path(dest_dir)),
dir.create(file.path(dest_dir)), FALSE)
### eml reading and downloading of csv
if (is.null(names(myeml$dataset$dataTable))) {
attributeList = lapply(myeml$dataset$dataTable, function(x){
EML::get_attributes(x$attributeList)
})
names(attributeList) = lapply(myeml$dataset$dataTable, function(x){
x$physical$objectName})
if (getNewData) {
#download all the datatables in the package
csv_list <- list()
csv_list <- lapply(myeml$dataset$dataTable, function(x){
url_to_get = x$physical$distribution$online$url$url
download.file(url_to_get,
destfile = paste0(dest_dir, "/",
packageid, "_",
x$physical$objectName),
method = "curl")
output_csv <- paste0(dest_dir, "/",
packageid, "_",
x$physical$objectName)
return(output_csv)
#print(output_csv_file)
})
output_csv_file <- csv_list
# Get column classes
colclasses <- list()
colclasslist <- lapply(names(attributeList),
function(x) {
attributeList[[x]]$attributes = attributeList[[x]]$attributes %>%
dplyr::mutate(
col_classes = case_when(domain=='textDomain' ~'col_character()',
domain=='dateTimeDomain' ~'col_date()',
domain=='numericDomain' ~'col_number()',
domain=='enumeratedDomain' ~'character()')) %>%
dplyr::mutate(
colclasses=case_when(domain=='textDomain' ~'character',
domain=='dateTimeDomain' ~'Date',
domain=='numericDomain' ~'numeric',
domain=='enumeratedDomain' ~'character'))
# replace "Date" with "Character" since R has a problem
# with loading in Dates that aren't formatted exactly as
# one wants
attributeList[[x]]$attributes$colclasses <- gsub("Date",
"character",
attributeList[[x]]$attributes$colclasses)
return(attributeList[[x]]$attributes$colclasses)
})
names(colclasslist) <- names(attributeList)
}
}else{
#if only one data table
attributeList = list(EML::get_attributes(myeml$dataset$dataTable$attributeList))
names(attributeList) = myeml$dataset$dataTable$physical$objectName
if (getNewData) {
url_to_get = myeml$dataset$dataTable$physical$distribution$online$url$url
download.file(url_to_get,
destfile = paste0(dest_dir, "/",
packageid, "_",
myeml$dataset$dataTable$physical$objectName),
method = "curl")
output_csv_file <- paste0(dest_dir, "/",
packageid, "_",
myeml$dataset$dataTable$physical$objectName)
}
# Get column classes for the data
#map eml types to R col classes
attributeList[[1]]$attributes <- attributeList[[1]]$attributes %>%
dplyr::mutate(col_classes=case_when(domain=='textDomain' ~'col_character()',
domain=='dateTimeDomain' ~'col_date()',
domain=='numericDomain' ~'col_number()',
domain=='enumeratedDomain' ~'character()')) %>%
dplyr::mutate(colclasses=case_when(domain=='textDomain' ~'character',
domain=='dateTimeDomain' ~'Date',
domain=='numericDomain' ~'numeric',
domain=='enumeratedDomain' ~'character'))
colclasslist <- attributeList[[1]]$attributes$colclasses
}
# Also save the full xml
write_eml(myeml, file = paste0(dest_dir, "/", packageid, ".xml"))
# Save the output list as R-object so column classes are preserved
csv_fp_colclass_list <- list(csv = output_csv_file, colclasses = colclasslist)
saveRDS(csv_fp_colclass_list, file = paste0(dest_dir, "/", packageid,
"_files_and_colclasses",
".RDS"))
writeLines(paste0("Downloaded data can be found in: ", dest_dir))
return(csv_fp_colclass_list)
}
}
################################################################################
# Download Data
################################################################################
# Saddle sensor network
message(paste0("Downloading Saddle Catchment sensor network data, please cite: \n",
"Morse, J. and Niwot Ridge LTER. 2020. Saddle catchment sensor network data, 2017- ongoing. ver 2. Environmental Data Initiative. https://doi.org/10.6073/pasta/9415ac5a669c11c6501612a94f90e04a (Accessed ",Sys.Date(), ")"))
saddle_catch_sensntwk_data_fp <- download_EDI(edi_id = saddle_catch_sensntwk,
dest_dir = paste0(DirDnld,
"/saddle_sensorntwk_data"),
getNewData = getNewData)
# Download sensor network community
# Download saddle grid snow_depth_data
message(paste0("Downloading Saddle Snow Depth data, please cite: \n",
"Walker, S., J. Morse, and Niwot Ridge LTER. 2020. Snow depth data for Saddle grid, 1992 - ongoing ver 17. Environmental Data Initiative. https://doi.org/10.6073/pasta/8186d641539c37787495804b817e55ed (Accessed ",Sys.Date(), ")"))
saddle_snwdpt_data_fp <- download_EDI(edi_id = saddle_snow_depth_data,
dest_dir = paste0(DirDnld, "/saddle_snow_depth_data"),
getNewData = getNewData)
# Download saddle grid productivity
message(paste0("Downloading Saddle Productivity data, please cite: \n",
"Walker, M., J. Smith, H. Humphries, and Niwot Ridge LTER. 2019. Aboveground net primary productivity data for Saddle grid, 1992 - ongoing. ver 4. Environmental Data Initiative. https://doi.org/10.6073/pasta/34b6a7bbe47f9398ff7f5a748f90e838 (Accessed ",Sys.Date(), ")"))
saddle_prod_data_fp <- download_EDI(edi_id = saddle_productivity_data,
dest_dir = paste0(DirDnld,
"/saddle_productivity_data"),
getNewData = getNewData)
# Download saddle sensor network veg community
message(paste0("Downloading Saddle Productivity data, please cite: \n",
"Elwood, K., W. Reed, and Niwot Ridge LTER. 2020. Plot vegetation surveys at the Sensor Network, 2017 to ongoing ver 2. Environmental Data Initiative. https://doi.org/10.6073/pasta/1b5e99d522f986c2244bf5a25e69d3f5 (Accessed ",Sys.Date(), ")"))
saddle_sensntwk_veg_data_fp <- download_EDI(edi_id = saddle_sensntwk_veg,
dest_dir = paste0(DirDnld,
"/saddle_sensntwk_veg_data"),
getNewData = getNewData)
# Download saddle sensor network phenocam data
message(paste0("Downloading Saddle Catchment phenocam data, please cite: \n",
"Elwood, K., J. Smith, and Niwot Ridge LTER. 2021. Time-lapse camera (phenocam) imagery of Sensor Network plots from 2017 to ongoing. ver 2. Environmental Data Initiative. https://doi:10.6073/pasta/89e8189093392325ee139eccc6b2ff85 (Accessed ",Sys.Date(), ")"))
saddle_sensntwk_phenocam_data_fp <- download_EDI(edi_id = saddle_sensntwk_phenocam,
dest_dir = paste0(DirDnld,
"/saddle_phenocam_data"),
getNewData = getNewData)
################################################################################
# Load Tvan flux data
################################################################################
# Both
tvan_comb <- read.table(file = tvan_data_fp, sep = "\t",
skip = 2, header = FALSE)
tvan_comb_names <- read.table(file = tvan_data_fp, sep = "\t",
header = TRUE, nrows = 1)
tvan_comb_units <- as.character(unname(unlist(tvan_comb_names[1,])))
colnames(tvan_comb) <- names(tvan_comb_names)
plot(tvan_comb$Tsoil)
# convert flux GPP (umol/m2/s to g/m2/s, as in CLM)
tvan_comb$GPP = tvan_comb$GPP * 1e-6 * 12.01
tvan_comb_units[1] = 'gC m-2 s-1'
################################################################################
# Clean and format Tvan Flux data
################################################################################
tvan_comb_mod <- tvan_comb %>%
mutate_all(list(~na_if(., -9999))) %>%
mutate(timestamp = DateTime,
Hour = lubridate::hour(timestamp) +
lubridate::minute(timestamp)/60,
date = lubridate::date(timestamp)) %>%
mutate(DoY = yday(date),
Year = year(date),
month = month(date)) %>%
mutate(MonGroup = ifelse(month %in% c(12,1,2), "DJF",
ifelse(month %in% c(3,4,5), "MAM",
ifelse(month %in% c(6,7,8), "JJA", "SON")))) %>%
#group_by(Year, DoY) %>%
#mutate_at(all_of(c("NEE", "LE", "H", "Ustar", "Tair", "VPD", "rH", "VPD", "U",
# "PRECTmms", "P", "FLDS", "Rg", "radNet", "Tsoil", "GPP")),
# list(daily_mean = mean), na.rm = TRUE) %>%
select(date, timestamp, Year, DoY, Hour, everything())
# Get diurnal fluxes
# set the variables to use for diurnal fluxes
diurnal_flx_vars <- c("radNet", "H", "LE", "GPP")
tvan_comb_mod.diurnal_seasonal <- tvan_comb_mod %>%
select(-timestamp, -date) %>%
select(Hour, DoY, Year, MonGroup, all_of(diurnal_flx_vars)) %>%
group_by(MonGroup, Hour) %>%
summarize_at(all_of(diurnal_flx_vars),
list(houravg = mean, hoursd = sd), na.rm = TRUE) %>%
mutate(ObsSim = "Obs") %>%
mutate(veg_com = "FF")
# Get DoY fluxes
DoY_flx_vars <- c("GPP", "LE",'Tsoil')
tvan_comb_mod.daily <- tvan_comb_mod %>%
select(Hour, DoY, month, Year, all_of(DoY_flx_vars)) %>%
# remove leap days and fix DoY
filter(!(leap_year(Year) & DoY == 60)) %>%
mutate(DoY = if_else(leap_year(Year) & (DoY > 59),
DoY - 1, DoY)) %>%
group_by(DoY) %>%
summarize_at(all_of(DoY_flx_vars),
list(dailyavg = mean, dailysd = sd), na.rm = TRUE) %>%
select(!starts_with("LE")) %>%
mutate(ObsSim = "Obs") %>%
mutate(veg_com = "FF")
plot(tvan_comb_mod.daily$Tsoil_dailyavg,type='l')
# Get July data
jul_30_min_tvan <- tvan_comb_mod %>%
select(-timestamp, -date) %>%
select(Hour, DoY, Year, month, all_of(diurnal_flx_vars)) %>%
filter(month == 7) %>%
group_by(Hour) %>%
summarize_at(all_of(diurnal_flx_vars),
list(houravg = mean, hoursd = sd), na.rm = TRUE) %>%
mutate(ObsSim = "Obs") %>%
mutate(veg_com = "FF")
################################################################################
# Load Saddle Catchment Sensor Network Data
################################################################################
writeLines("Reading in saddle sensor network data...")
sad_sens_data_raw <- vector(length = length(saddle_catch_sensntwk_data_fp$csv),
mode = "list")
sad_sens_data_raw <- lapply(seq_along(saddle_catch_sensntwk_data_fp$csv),
function(x) {
writeLines(paste0("Reading in ",
basename(saddle_catch_sensntwk_data_fp$csv[[x]])))
# replace date with chararacter
tmp_colclasses <- gsub("Date", "character", saddle_catch_sensntwk_data_fp$colclasses[[x]])
read.csv(saddle_catch_sensntwk_data_fp$csv[[x]],
header = T, sep = ",", quot = '"',
as.is = TRUE, na.strings = c("NA", "NaN", ""),
colClasses = tmp_colclasses)
})
names(sad_sens_data_raw) <- basename(unlist(saddle_catch_sensntwk_data_fp$csv))
sad_sens_data_all <- bind_rows(sad_sens_data_raw)
#writeLines("Reading in saddle sensor veg data...")
################################################################################
# Gather vegetation communities for Saddle Network plots
################################################################################
# Using Kelsey Elwood Carter's Community characterizations from her master's thesis
sensor_plot_com <- data.frame(plot = c(9,10,14, 16, 20, 21, 11, 15, 6,7,8,12,13,17,19),
veg_com_long = c(rep("Dry Meadow 1", 3),
rep("Dry Meadow 2", 3),
rep("Dry Meadow 3", 2),
rep("Moist Meadow", 4),
rep("Wet Meadow", 2),
"Subalpine")) %>%
mutate(veg_com = ifelse(grepl("Dry", veg_com_long), "DM",
ifelse(grepl("Moist", veg_com_long), "MM",
ifelse(grepl("Wet", veg_com_long), "WM",
ifelse(grepl("Subalpine", veg_com_long), "SA", NA)))),
plot = as.character(plot))
################################################################################
# Handle Saddle Network Soil Moisture and Temperature data
################################################################################
# Filter out questionable data from sensor network and collapse 10-minute readings
# into 30-minute readings. Also categorize the vegetation communities for each
# site
sad_sens_10min <- sad_sens_data_all %>%
mutate(timestamp = as.POSIXct(date, format = "%Y-%m-%d %H:%M:%OS", tz = "MST"),
date = as.Date(timestamp)) %>%
select(sensornode, timestamp, date, contains("soil")) %>%
#filter(sensornode == 6) %>%
# set flagged values to NA so they won't be used in averages
mutate(soiltemp_5cm_avg = ifelse(!is.na(flag_soiltemp_5cm_avg), NA, soiltemp_5cm_avg),
soiltemp_30cm_avg = ifelse(!is.na(flag_soiltemp_30cm_avg), NA, soiltemp_30cm_avg),
soilmoisture_a_5cm_avg = ifelse(!is.na(flag_soilmoisture_a_5cm_avg), NA,
soilmoisture_a_5cm_avg),
soilmoisture_a_30cm_avg = ifelse(!is.na(flag_soilmoisture_a_30cm_avg), NA,
soilmoisture_a_30cm_avg),
soilmoisture_b_5cm_avg = ifelse(!is.na(flag_soilmoisture_b_5cm_avg), NA,
soilmoisture_b_5cm_avg),
soilmoisture_b_30cm_avg = ifelse(!is.na(flag_soilmoisture_b_30cm_avg), NA,
soilmoisture_b_30cm_avg),
soilmoisture_c_5cm_avg = ifelse(!is.na(flag_soilmoisture_c_5cm_avg), NA,
soilmoisture_c_5cm_avg),
soilmoisture_c_30cm_avg = ifelse(!is.na(flag_soilmoisture_c_30cm_avg), NA,
soilmoisture_c_30cm_avg)) %>%
# Remove flag columns
select(-contains("flag")) %>%
# Remove soil moisture data when temperature <0 (frozen water messes with sensors)
mutate(soilmoisture_a_5cm_avg = ifelse(soiltemp_5cm_avg <= 0, NA,
soilmoisture_a_5cm_avg),
soilmoisture_a_30cm_avg = ifelse(soiltemp_30cm_avg <= 0, NA,
soilmoisture_a_30cm_avg),
soilmoisture_b_5cm_avg = ifelse(soiltemp_5cm_avg <= 0, NA,
soilmoisture_b_5cm_avg),
soilmoisture_b_30cm_avg = ifelse(soiltemp_30cm_avg <= 0, NA,
soilmoisture_b_30cm_avg),
soilmoisture_c_5cm_avg = ifelse(soiltemp_5cm_avg <= 0, NA,
soilmoisture_c_5cm_avg),
soilmoisture_c_30cm_avg = ifelse(soiltemp_30cm_avg <= 0, NA,
soilmoisture_c_30cm_avg)) %>%
# Group times by half-hour so half-hourly averages can be taken
mutate(Time = gsub(".{4}-.{2}-.{2} ", "", timestamp),
cleanTime =
strsplit(Time, ":") %>%
sapply(function(x){
x <- as.numeric(x)
x[1] + x[2]/60 + x[3]/(60*60)
}),
decimalTime = floor(cleanTime * 2)/2)
writeLines(paste0("Collapsing 10-minute soil sensor data into 30-minute chunks, \n",
"this may take a while..."))
# NOTE: this could probably be made more efficient if handled one file at a time. And then
# joining the 30-minute data together after each is combined
sad_sens_soilmoist_temp <- sad_sens_10min %>%
# Get half-hourly averages
group_by(date, decimalTime, sensornode) %>%
mutate(across(contains("soil"), list(~mean(., na.rm = TRUE)),
.names = "mean_{col}")) %>%
ungroup() %>%
select(sensornode, date, decimalTime, contains("mean_")) %>%
unique() %>%
# Join with vegetation classifications
left_join(sensor_plot_com, by = c("sensornode" = "plot")) %>%
# Remove sub alpine, and non-characterized vegetation communities
filter(!is.na(veg_com)) %>%
filter(veg_com != "SA")
# Subset the data so that the "a" sensor is used, unless that sensor is NA, then
# preferentially use "b", and then "c"
sad_sensnet_soil <- sad_sens_soilmoist_temp %>%
# if a is NA choose b
mutate(soilmoisture_5cm_sensor_letter = ifelse(is.na(mean_soilmoisture_a_5cm_avg),
"b", "a"),
soilmoisture_5cm_avg = ifelse(is.na(mean_soilmoisture_a_5cm_avg),
mean_soilmoisture_b_5cm_avg,
mean_soilmoisture_a_5cm_avg),
soilmoisture_30cm_sensor_letter = ifelse(is.na(mean_soilmoisture_a_30cm_avg),
"b", "a"),
soilmoisture_30cm_avg = ifelse(is.na(mean_soilmoisture_a_30cm_avg),
mean_soilmoisture_b_30cm_avg,
mean_soilmoisture_a_30cm_avg)) %>%
# if b is also NA choose c
mutate(soilmoisture_5cm_sensor_letter = ifelse(is.na(soilmoisture_5cm_avg),
"c", soilmoisture_5cm_sensor_letter),
soilmoisture_5cm_avg = ifelse(is.na(soilmoisture_5cm_avg),
mean_soilmoisture_c_5cm_avg,
soilmoisture_5cm_avg),
soilmoisture_30cm_sensor_letter = ifelse(is.na(soilmoisture_30cm_avg),
"c", soilmoisture_30cm_sensor_letter),
soilmoisture_30cm_avg = ifelse(is.na(soilmoisture_30cm_avg),
mean_soilmoisture_c_30cm_avg,
soilmoisture_30cm_avg)) %>%
# if c is also NA, make everything NA
mutate(soilmoisture_5cm_sensor_letter = ifelse(is.na(soilmoisture_5cm_avg),
NA, soilmoisture_5cm_sensor_letter),
soilmoisture_5cm_avg = ifelse(is.na(soilmoisture_5cm_avg),
NA,
soilmoisture_5cm_avg),
soilmoisture_30cm_sensor_letter = ifelse(is.na(soilmoisture_30cm_avg),
NA, soilmoisture_30cm_sensor_letter),
soilmoisture_30cm_avg = ifelse(is.na(soilmoisture_30cm_avg),
NA,
soilmoisture_30cm_avg)) %>%
select(sensornode, date, decimalTime, mean_soiltemp_5cm_avg, mean_soiltemp_30cm_avg,
soilmoisture_5cm_avg, soilmoisture_30cm_avg, veg_com,
soilmoisture_5cm_sensor_letter,
soilmoisture_30cm_sensor_letter) %>%
# rename to be generic enough to match Tvan soil columns
dplyr::rename(soiltemp_upper_avg = mean_soiltemp_5cm_avg,
soiltemp_lower_avg = mean_soiltemp_30cm_avg,
soilmoisture_upper_avg = soilmoisture_5cm_avg,
soilmoisture_lower_avg = soilmoisture_30cm_avg,
sensnet_soilmoisture_5cm_sensor_letter = soilmoisture_5cm_sensor_letter,
sensnet_soilmoisture_30cm_sensor_letter = soilmoisture_30cm_sensor_letter,
Hour = decimalTime,
plot = sensornode) %>%
mutate(data_set = "Saddle_sensor_network_EDI_210_5cm_30cm_moisttemp_probes",
plot = as.character(plot),
upper_sensor_depth_cm = 5,
lower_sensor_depth_cm = 30) %>%
mutate(soilmoisture_upper_avg = soilmoisture_upper_avg * 100,
soilmoisture_lower_avg = soilmoisture_lower_avg * 100)
# Plotting soil moisture
# sad_sens_soilmoist_temp.nested <- sad_sens_soilmoist_temp %>%
# mutate(timestamp = as.Date(date, origin = paste0(date, " 00:00:00")) +
# lubridate::minutes(decimalTime * 60)) %>%
# #select(sensornode, date, decimalTime, timestamp) %>%
# pivot_longer(contains("soil"), names_to = "Variable", values_to = "Value") %>%
# group_by(veg_com) %>%
# nest()
# sad_sens_soilmoist_temp.plot <- sad_sens_soilmoist_temp.nested %>%
# mutate(plot = map2(data, veg_com, ~ ggplot(data = .x,
# aes(x = timestamp, y = Value)) +
# ggtitle(glue::glue("Vegetation Community: {.y}")) +
# geom_point(alpha = 0.3) +
# facet_wrap(~Variable, scales = "free_y", ncol = 2)
# )
# )
#
# pdf("~/Downloads/test.pdf")
# sad_sens_soilmoist_temp.plot$plot
# dev.off()
################################################################################
# Tvan soil moisture data
################################################################################
# Read in Tvan soil moisture and temperature data
tvan_soil <- read.table(file = tvan_data_soil, sep = ",",
skip = 2, header = FALSE)
tvan_soil_names <- read.table(file = tvan_data_soil, sep = ",",
header = TRUE, nrows = 1)
tvan_soil_units <- as.character(unname(unlist(tvan_soil_names[1,])))
colnames(tvan_soil) <- names(tvan_soil_names)
# Fix time, rename to match generic names of sensor network soil data, add informational
# columns about the data's origin, and vegetation community
tvan_soil_mod <- tvan_soil %>%
select(time, wc10, wc30, soil_temp, tc30, G) %>%
mutate(timestamp = time,
Hour = lubridate::hour(timestamp) +
lubridate::minute(timestamp)/60,
date = lubridate::date(timestamp)) %>%
select(-time, -timestamp) %>%
rename(soiltemp_upper_avg = soil_temp,
soiltemp_lower_avg = tc30,
soilmoisture_upper_avg = wc10,
soilmoisture_lower_avg = wc30) %>%
mutate(upper_sensor_depth_cm = 10,
lower_sensor_depth_cm = 30) %>%
mutate(veg_com = "FF",
plot = "Tvan_West",
data_set = "Tvan_West_Tower_10cm_30cm_moisttemp_probes")
plot(tvan_soil_mod$date, tvan_soil_mod$soiltemp_upper_avg,pch='.')
#ggplot(tvan_soil_mod, aes(x = DoY, y = soiltemp_upper_avg)) + geom_point()
################################################################################
# Combine Tvan and Sensor Network Soil and Moisture data
################################################################################
soilmoist_temp_comb <- full_join(sad_sensnet_soil, tvan_soil_mod,
by = c("date", "Hour",
"veg_com", "plot", "data_set",
"soiltemp_upper_avg",
"soiltemp_lower_avg",
"soilmoisture_upper_avg",
"soilmoisture_lower_avg",
"upper_sensor_depth_cm",
"lower_sensor_depth_cm"))
# Summarize the data by hour
soilmoist_temp_comb_hrly <- soilmoist_temp_comb %>%
group_by(Hour, veg_com) %>%
mutate_at(all_of(c("soiltemp_upper_avg",
"soiltemp_lower_avg",
"soilmoisture_upper_avg",
"soilmoisture_lower_avg")),
list(~mean(., na.rm = TRUE), ~sd(., na.rm = TRUE))) %>%
ungroup() %>%
mutate(soilmoisture_upper_avg = ifelse(soiltemp_upper_avg < 0, NA,
soilmoisture_upper_avg),
soilmoisture_lower_avg = ifelse(soiltemp_lower_avg < 0, NA,
soilmoisture_lower_avg)) %>%
mutate(data_information = paste0("data = ", data_set, " | ",
"sensnet_5cm_letter = ",
sensnet_soilmoisture_5cm_sensor_letter, " | ",
"sensnet_30cm_letter = ",
sensnet_soilmoisture_30cm_sensor_letter, " | ",
"upr_sens_depth = ", upper_sensor_depth_cm, " | ",
"lwr_sens_depth = ", lower_sensor_depth_cm)) %>%
select(Hour, ends_with("_avg_mean"), ends_with("_avg_sd"), veg_com,
data_information) %>%
unique()
# Summarize the data by daily averages
soilmoist_temp_comb_daily <- soilmoist_temp_comb %>%
mutate(DoY = yday(date),
month = month(date),
year = year(date)) %>%
# remove leap days and fix DoY
filter(!(leap_year(year) & DoY == 60)) %>%
mutate(DoY = if_else(leap_year(year) & (DoY > 59),
DoY - 1, DoY)) %>%
group_by(DoY, veg_com) %>%
mutate(across(all_of(c("soiltemp_upper_avg",
"soiltemp_lower_avg",
"soilmoisture_upper_avg",
"soilmoisture_lower_avg")),
.fns = list(dailyavg = ~mean(., na.rm = TRUE),
dailysd = ~sd(., na.rm = TRUE)))) %>%
ungroup() %>%
mutate(soilmoisture_upper_avg = ifelse(soiltemp_upper_avg < 0, NA,
soilmoisture_upper_avg),
soilmoisture_lower_avg = ifelse(soiltemp_lower_avg < 0, NA,
soilmoisture_lower_avg)) %>%
# construct a data information column with information about which dataset and
# sensor the data came from
mutate(data_information = paste0("data = ", data_set, " | ",
"sensnet_5cm_letter = ",
sensnet_soilmoisture_5cm_sensor_letter, " | ",
"sensnet_30cm_letter = ",
sensnet_soilmoisture_30cm_sensor_letter, " | ",
"upr_sens_depth = ", upper_sensor_depth_cm, " | ",
"lwr_sens_depth = ", lower_sensor_depth_cm)) %>%
select(DoY, month, ends_with("_avg_dailyavg"), ends_with("_avg_dailysd"),
veg_com, data_information) %>%
unique()
names(soilmoist_temp_comb_daily)
ggplot(soilmoist_temp_comb_daily, aes(x = DoY)) +
geom_line(aes(y = soiltemp_upper_avg_dailyavg, color = veg_com))
################################################################################
# Load in data from Saddle Grid (snow depth and productivity)
################################################################################
writeLines("Reading in saddle grid snow depth data...")
# Daily data
sad_snw <- read.csv(saddle_snwdpt_data_fp$csv,
header = T, sep = ",", quot = '"')
writeLines("Reading in saddle productivity data...")
sad_prod <- read.csv(saddle_prod_data_fp$csv[[1]],
header = T, sep = ",", quot = '"')
################################################################################
# Handle Saddle Grid Snow-depth data
################################################################################
# Get saddle grid point vegetation community characterizations
sad_grid_veg_com <- sad_prod %>%
select(grid_pt, veg_class) %>%
dplyr::rename(veg_com = veg_class) %>%
unique()
# Merge saddle snow depth measurements with the saddle vegetation characterizations
# Snow measured in cm at stake and m in the model (SNOW_DEPTH)
sad_snw_mod <- sad_snw %>%
left_join(sad_grid_veg_com, by = c("point_ID" = "grid_pt")) %>%
#select(point_ID, veg_class) %>%
mutate(veg_class = ifelse(is.na(veg_com), "not available", veg_com)) %>%
filter(!(veg_com == "not available")) %>%
filter(veg_com %in% c("DM", "FF", "MM", "SB", "WM")) %>%
mutate(snow_depth = as.numeric(mean_depth),
date = as.Date(date, format = "%Y-%m-%d"),
DoY = lubridate::yday(date),
Year = lubridate::year(date)) %>%
group_by(veg_com, DoY) %>%
mutate(snow_depth_dailyavg = mean(snow_depth, na.rm=TRUE),
snow_depth_dailysd = sd(snow_depth, na.rm = TRUE)) %>%
select(date, DoY, Year, point_ID, snow_depth, snow_depth_dailyavg,
snow_depth_dailysd, veg_com) %>%
ungroup() %>%
mutate(data_information = "Saddle_grid_snow_depth_EDI_31")
# Get DoY averages
sad_snw_daily <- sad_snw_mod %>%
select(DoY, snow_depth_dailyavg, snow_depth_dailysd,
veg_com, data_information) %>%
unique() %>%
dplyr::rename(snow_depth_data_information = data_information)
# Average the snow depth across plots of the same vegetation community, at
# each date
sad_snw_forc_yrs <- sad_snw_mod %>%
filter(Year >= 2007) %>%
group_by(date, veg_com) %>%
mutate(avg_date_depth = mean(snow_depth, na.rm = TRUE),
sd_date_depth = sd(snow_depth, na.rm = TRUE)) %>%
ungroup() %>%
select(date, DoY, Year, avg_date_depth, sd_date_depth, veg_com,
data_information) %>%
unique()
# plot the measurements and doy averages for each community
# sad_snw_mod %>%
# filter(snow_depth > 0) %>%
# ggplot(aes(x = as.Date("2000-01-01", format = "%Y-%m-%d") +
# (DoY - 1))) +
# #geom_point(aes(y = snow_depth), alpha = 0.03) +
# geom_line(data = sad_snw_mod %>%
# select(DoY, doy_avg_depth, veg_com) %>%
# unique(),
# aes(y = doy_avg_depth), color = "red") +
# geom_ribbon(aes(ymin = doy_avg_depth - doy_sd_depth,
# ymax = doy_avg_depth + doy_sd_depth),
# alpha = 0.3) +
# facet_wrap(~veg_com) +
# scale_x_date(date_labels = "%b", date_breaks = "1 month")
#
# sad_snw_mod %>%
# filter(Year >= 2007) %>%
# group_by(date, veg_com) %>%
# mutate(avg_date_depth = mean(snow_depth, na.rm = TRUE),
# sd_date_depth = sd(snow_depth, na.rm = TRUE)) %>%
# ungroup() %>%
# ggplot(aes(x = date)) +
# geom_ribbon(aes(ymin = avg_date_depth - sd_date_depth,
# ymax = avg_date_depth + sd_date_depth),
# alpha = 0.5) +
# geom_line(aes(y = avg_date_depth)) +
# facet_wrap(~veg_com, ncol = 1) +
# scale_x_date(date_labels = "%b-%Y", date_breaks = "1 year") +
# theme(axis.text.x = element_text(angle = 45, hjust = 1))
################################################################################
# Handle Saddle Grid Productivity data
################################################################################
# Yearly Saddle grid Productivity data (gC/m^2)
# CLM needs it in gC/m^2/s
sad_prod_mod <- sad_prod %>%
dplyr::rename(veg_com = veg_class) %>%
select(year, grid_pt, veg_com, NPP, subsample) %>%
#mutate(row = row_number()) %>%
# Separate by subsamples
pivot_wider(names_from = matches("subsample"),
names_prefix = "subsample_",
values_from = matches("NPP")) %>%
# average subsamples
mutate(NPP = rowMeans(select(., starts_with("subsample_")),
na.rm = TRUE))
sad_prod_mod_ann <- sad_prod_mod %>%
group_by(year, veg_com) %>%
mutate(mean_NPP = mean(NPP, na.rm = TRUE),
sd_NPP = sd(NPP, na.rm = TRUE))
# sad_prod_mod %>%
# ggplot(aes(x = veg_com, y = NPP)) +
# geom_boxplot(fill = NA) +
# geom_point(position = position_jitter(width = rel(0.3)))
# Get relevant meadow measurements: -- use purr to put in list; filter out tundra shrub and
# snow fence first
# sad_prod_FF <- sad_prod_sub %>%
# filter(veg_class == "FF")
# sad_prod_SB <- sad_prod_sub %>%
# filter(veg_class == "SB")
# sad_prod_MM <- sad_prod_sub %>%
# filter(veg_class == "MM")
# sad_prod_WM <- sad_prod_sub %>%
# filter(veg_class == "WM")
# sad_prod_DM <- sad_prod_sub %>%
# filter(veg_class == "DM")
################################################################################
# Load Saddle Catchment Phenocam Data
################################################################################
writeLines("Reading in sensor network phenocam data...")
phenocam_data_raw <- vector(length = length(saddle_sensntwk_phenocam_data_fp$csv),
mode = "list")
phenocam_data_raw <- lapply(seq_along(saddle_sensntwk_phenocam_data_fp$csv),
function(x) {
writeLines(paste0("Reading in ",
basename(saddle_sensntwk_phenocam_data_fp$csv[[x]])))
# replace date with chararacter
tmp_colclasses <- gsub("Date", "character", saddle_sensntwk_phenocam_data_fp$colclasses[[x]])
read.csv(saddle_sensntwk_phenocam_data_fp$csv[[x]],
header = T, sep = ",", quot = '"',
as.is = TRUE, na.strings = c("NA", "NaN", ""),
colClasses = tmp_colclasses)
})
names(phenocam_data_raw) <- basename(unlist(saddle_sensntwk_phenocam_data_fp$csv))
phenocam_data_filtered <- phenocam_data_raw[[2]]
# Joining veg classifications with phenocam data
phenocam_data_filtered <- left_join(phenocam_data_filtered, sensor_plot_com, by = c("node" = "plot")) %>%
# Remove sub alpine, and non-characterized vegetation communities
filter(!is.na(veg_com)) %>%
filter(veg_com != "SA")
################################################################################
# Reformat data
################################################################################
# Reformatting several data frames to better match with simulation data
# Data frame 1:
# Rename flux variables to match tvan
# Half-hourly fluxes from Tvan; Comparable to the fell-field
# Variables: FSH (tvan), RN (tvan), LE (tvan), GPP (tvan)
tvan_comb_mod.diurnal_seasonal <- tvan_comb_mod.diurnal_seasonal %>%
dplyr::rename(RNET_houravg = radNet_houravg,
RNET_hoursd = radNet_hoursd,
FSH_houravg = H_houravg,
FSH_hoursd = H_hoursd,
EFLX_LH_TOT_houravg = LE_houravg,
EFLX_LH_TOT_hoursd = LE_hoursd)
# July flux summary
jul_30_min_tvan <- jul_30_min_tvan %>%
dplyr::rename(RNET_houravg = radNet_houravg,
RNET_hoursd = radNet_hoursd,
FSH_houravg = H_houravg,
FSH_hoursd = H_hoursd,
EFLX_LH_TOT_houravg = LE_houravg,
EFLX_LH_TOT_hoursd = LE_hoursd)
# Data frame 2:
# Daily averages for each vegetation community
# Variables: GPP (tvan), SoilTemp (tvan/sensor network),
# Soil Moisture (tvan/sensor network), snow depth (saddle grid),
daily_soilmoisttemp_gpp_snwdp <- soilmoist_temp_comb_daily %>%
dplyr::rename(soilmoisture_data_info = data_information) %>%
# join with tvan GPP data
left_join(tvan_comb_mod.daily, by = c("DoY", "veg_com")) %>%
# join with snow depth data
left_join(sad_snw_daily, by = c("DoY", "veg_com")) %>%
mutate(ObsSim = "Obs")
################################################################################
# Combine and write out
################################################################################
# For each time-series of data, write out units and data
# Write out halfhourly fluxes:
writeLines("Writing out diurnal, daily, and annual data.")
# Diurnal-seasonal data
write.table(tvan_comb_mod.diurnal_seasonal,
file = paste0(DirOutBase, "/Diurnal_seasonal_summaries_", "tvan_flux.txt"),
row.names = FALSE, sep = "\t")
# Diurnal-seasonal data
write.table(jul_30_min_tvan,
file = paste0(DirOutBase, "/July_flux_summary_", "tvan_flux.txt"),
row.names = FALSE, sep = "\t")
# DoY data
write.table(daily_soilmoisttemp_gpp_snwdp,
file = paste0(DirOutBase,
"/Daily_soilmoisture_soiltemp_gpp_snwdpth_summaries.txt"),
row.names = FALSE, sep = "\t")
# Annual data
write.table(sad_prod_mod_ann,
file = paste0(DirOutBase,
"/annual_saddle_grid_NPP_summaries.txt"),
row.names = FALSE, sep = "\t")
# Unsummarized data
writeLines("Writing out data that has not been summarized by time.")
# Saddle sensor network soil data
write.table(sad_sensnet_soil,
file = paste0(DirOutBase,
"/sensor_network_soil_data_30_min.txt"),
row.names = FALSE, sep = "\t")
# Tvan soil data
write.table(tvan_soil_mod,
file = paste0(DirOutBase,
"/tvan_soil_data_30_min.txt"),
row.names = FALSE, sep = "\t")
# Snow depth data
write.table(sad_snw_forc_yrs,
file = paste0(DirOutBase,
"/saddle_grid_snow_depth_data_biweekly.txt"),
row.names = FALSE, sep = "\t")
# Productivity
write.table(sad_prod_mod,
file = paste0(DirOutBase,
"/saddle_grid_productivity_data.txt"),
row.names = FALSE, sep = "\t")
# Phenocam data
write.table(phenocam_data_filtered,
file = paste0(DirOutBase,
"/sensor_network_phenocam_data.txt"),
row.names = FALSE, sep = "\t")
print('--- finished with script ---')