In mkoohafkan/rremat: Russian River Estuary data collected by the Largier Lab

Processing Largier Lab Russian River Estuary Data

library(rremat)
library(dplyr)

Processing the bathymetry

This code reproduces the dataframe volumes from the text files in extdata/bathymetry. The volume lookup table is generated from a 1ft x 1ft bathymetry raster with a vertical resolution as defined in bathymetry/zgrid.csv.

# load the bathymetry tables
volpath = system.file("extdata/bathymetry/bathytables", package = "rremat")
bathytables = dir(volpath, pattern = "*.csv$", full.names = TRUE)
bathydat = read_bathymetry_tables(bathytables)
# build the habitat table
wses = seq(2.7, 3.0, by = 0.1)
lookupvalues = setNames(expand.grid(unique(bathydat$bedelev), 
  unique(bathydat$bedelev)), c("elev", "wse"))
lookupvalues = filter(lookupvalues, elev <= wse)
volumes = evalmany(build_habitat_table, e = lookupvalues$elev, 
  w = lookupvalues$wse, method = "set", default.args = list(d = bathydat), 
  packages = "dplyr", clusters = 3)[1:9]
# add bathymetry resolution information
respath = system.file("extdata/bathymetry/bathymetryresolution.csv", 
  package = "rremat")
attr(volumes, "resolution") = read.csv(respath)

The stage-volume curve is generated from the volume lookup table.

stage_volume = volumes  %>% 
  gather(type, habcount, count, count.littoral, count.limnetic, 
    count.epibenthic, count.sublimnetic, count.profundal) %>% 
  group_by(type, wse) %>% summarize(count = sum(habcount))

Processing closure data

information on closures is contained in extdata/closures/closures.csv. This code reproduces the data frame closures which provides meta data for closures occurring during the monitoring periods.

closurepath = system.file("extdata/closures", package = "rremat")
closures = read.csv(paste0(closurepath, "/closures.csv"), 
  stringsAsFactors = FALSE, colClasses = c(start = "Date", end = "Date", 
  code = "factor", note = "character"))

Processing CTD transect meta data

CTD transect meta data is contained in extdata/ctd_meta/transectdatetimes.csv. This code reproduces the dataframe ctdmeta which provides meta data for the transect data described in the following sections.

ctdmpath = system.file("extdata/ctd_meta", package = "rremat")
ctdm = read.csv(paste0(ctdmpath, "/transectdatetime.csv"), 
  stringsAsFactors = FALSE, colClasses = "character")
ctdmeta = data.frame(id = factor(ctdm$id), 
  start = as.POSIXct(paste(ctdm$date, ctdm$start), tz = "US/Pacific"),
  end = as.POSIXct(paste(ctdm$date, ctdm$end), tz = "US/Pacific"), 
  numcasts = as.integer(ctdm$numcasts), note = ctdm$note)
# cross-reference with closures
ctdmeta["code"] = factor(unlist(sapply(as.Date(ctdmeta$start), function(x){ 
  this = as.character(closures$code[which(x >= closures$start & x <= closures$end)])
  if(length(this) > 0)
    this
  else
    "O"  
  })))
# set time zones to UTC
attr(ctdmeta$start, "tzone") = "UTC"
attr(ctdmeta$end, "tzone") = "UTC"

Processing CTD location data

CTD cast location data is contained in extdata/ctd_meta/CTDlocations.csv. Cast ID data is contained in extdata/ctd_meta/RR_station_IDs.mat. This code reproduces the dataframe ctdlocations which provides IDs thalweg distance, and GPS coordinates, for each of the 12 CTD cast locations for the transect data described in the following sections.

ctdipath = system.file("extdata/ctd_meta", package = "rremat")
ctdi = readMat(file.path(ctdipath, "RR_station_IDs.mat"))
ctdid = with(ctdi$sitekey[,, 1],
    correct_dist(data.frame(station = unlist(stn),
    dist = unlist(dist))))

ctdlpath = system.file("extdata/ctd_meta", package = "rremat")
ctdl = read.csv(paste0(ctdlpath, "/CTDlocations.csv"),
    stringsAsFactors = FALSE, colClasses = "character")
ctdloc = data.frame(station = ctdl$Station.ID,
    name = ctdl$Station.Name,
    latitude = as.numeric(ctdl$Latitude),
    longitude = as.numeric(ctdl$Longitude)
)

ctdlocations = merge(ctdloc, ctdid, by = "station")
ctdlocations['station'] = factor(ctdlocations$station, 1:12)
ctdlocations = ctdlocations[order(ctdlocations$station),]

Processing CTD transect data

This code reproduces the dataframe ctd from the Matlab files in extdata/ctd.

ctdpath = system.file("extdata/ctd", package = "rremat")
ctd.list = Map(function(fpath) read_rrectd(fpath, quiet = TRUE)[[1]], 
  fpath = paste0(ctdpath, "/", dir(ctdpath)))
ctd = correct_dist(depth_from_elev(merge_rrectd(ctd.list)))

Processing gridded CTD data

This code reproduces the dataframe grids from the matlab files in extdata/ctd_interp. These grids may contain extrapolated data including regions outside of the estuary bathymetry and above the observed water-surface elevation. Note that the dist values have already been corrected as per the function correct_dist (see matlab-src/interpctd.m). Volume information and water-surface elevation is added to the gridded data using the volume lookup table and and CTD cast data, respectively. Extrapolated data occurring either outside of the estuary bathymetry (volume == 0) or above the water-surface elevation (elev > wse) is removed.

gridpath = system.file("extdata/ctd_interp", package = "rremat")
grid.list = Map(function(fpath) read_rrectdgrid(fpath, quiet = TRUE)[[1]],
  fpath = paste0(gridpath, "/", dir(gridpath)))
grids = merge_rrectd(grid.list)
grids = join_wse(grids, ctd) %>%
  mutate(wse = round(wse, 1)) %>% 
  filter(elev <= wse) %>%
  join_volume(volumes) %>% 
  filter(volume.total > 0)
#usethis::use_data(grids, overwrite = TRUE)

Processing water level pressure gauge data

This code reproduces the dataframe wll from the Matlab files in extdata/wll.

wllpath = system.file("extdata/wll", package = "rremat")
wll.list = Map(function(fpath) read_rrewll(fpath, quiet = TRUE)[[1]], 
  fpath = paste0(wllpath, "/", dir(wllpath))) 
wll = merge_rrewll(wll.list)
wll = wll[!is.nan(wll$depth),]

Supplemental Data

The following data sets are produced outside of the Largier Lab, but provide critical supplemental data.

Tide data

This code reproduces the dataframe tides generated from files in extdata/tides. The files contain data for the NOAA Point Reyes Station downloaded from the NOAA web API. Those files were created using the function download_tides.

tidespath = system.file("extdata/tides", package = "rremat")
tides.list = Map(read.csv, file = paste0(tidespath, "/", dir(tidespath)),
  stringsAsFactors = FALSE)
tides = merge_tides(tides.list)

Wave data

This code reproduces the dataframe waves generated from files in extdata/waves. The files contain data for the NOAA buoy at Bodega Bay (Station 46013/LLNR 390). Those files were scraped from the NDBC historical data web page.

wavespath = system.file("extdata/waves", package = "rremat")
waves.list = Map(read.csv, file = paste0(wavespath, "/", dir(wavespath)),
  stringsAsFactors = FALSE, encoding = "UTF-8", comment.char = "#")
waves = merge_waves(waves.list)

Streamflow data

This code reproduces the dataframe inflows from the files in extdata/streamflow. The files contain streamflow data for from USGS gauges near Guerneville, CA (Russian River/Station 11467000) and near Cazadero, CA (Austin Creek/Station 11467200). Those files were downloaded from the USGS National Water Information System.

sfpath = system.file("extdata/streamflow", package = "rremat")
sf.list = Map(read.csv, file = paste0(sfpath, "/", dir(sfpath)), 
  stringsAsFactors = FALSE)
inflows = merge_streamflow(sf.list)

mkoohafkan/rremat documentation built on July 3, 2021, 12:06 p.m.