This vignette describes a proof of concept for summarizing and reporting dissolved oxygen (DO) data for the Peconic Estuary. Two continuous USGS monitoring stations collect DO data at locations in the Peconic Estuary: USGS stations 01304562 in the Peconic River and 01304200 in Orient Harbor. Data at these stations can be downloaded using the read_pepdo() function.

exdat <- read_pepdo(site = c('01304562', '01304200'), nms = c('Peconic R.', 'Orient Harbor'), startDate = '2020-06-01', endDate = '2020-06-30')

The dodat data object included with the package provides DO data from 2013 to 2020 at the two sites. This takes several minutes to download using the read_pepdo() function, so it is included with the package for ease of use.

head(dodat)
#>            site            DateTime do_mgl
#> 1 Orient Harbor 2013-01-01 00:00:00   11.2
#> 2 Orient Harbor 2013-01-01 00:06:00   11.2
#> 3 Orient Harbor 2013-01-01 00:12:00   11.2
#> 4 Orient Harbor 2013-01-01 00:18:00   11.2
#> 5 Orient Harbor 2013-01-01 00:24:00   11.2
#> 6 Orient Harbor 2013-01-01 00:30:00   11.2

A plot of the raw data:

ggplot(dodat, aes(x = DateTime, y = do_mgl)) + 
  geom_point(size = 0.7) + 
  geom_hline(yintercept = 4.8, linetype = 'dashed', colour = 'red') + 
  geom_hline(yintercept = 3, linetype = 'dashed', colour = 'red') +
  facet_wrap(~site, ncol = 1) +
  theme_minimal() + 
  labs(
    y = 'Dissolved oxygen (mg/L)', 
    x = NULL, 
    title = 'Dissolved oxygen time series at Orient Harbor and Peconic River', 
    subtitle = 'Dashed lines are chronic and acute threhsolds (4.8, 3 mg/L)'
  )

Dissolved oxygen data typically show daily and seasonal variation. Summaries of condition should account for variation at both temporal scales. Further, the impact of low dissolved oxygen concentrations on biota can vary depending on both magnitude and duration of hypoxia/anoxia. As such, condition assessment could also consider how often concentrations fall below a thresholds and for how long. The assessments below speak to each of these needs.

First, the data can be summarized from the continuous (~6 minute observations) to daily averages using the anlz_dodlpep() function. A user-specified threshold can be supplied to the thr argument to summarize the data relative to a value of interest, the default value being the acute threshold of 3 mg/L (an alternative may be the chronic threshold of 4.8 mg/L, [1]).

# 3 mg/l is acute, 4.8 mg/l is chronic
dlysum <- anlz_dodlypep(dodat, thr = 3)
dlysum
#> # A tibble: 5,844 x 7
#>    site          Date          yr    mo do_mgl below below_cumsum
#>    <fct>         <date>     <dbl> <dbl>  <dbl> <dbl>        <dbl>
#>  1 Peconic River 2013-01-01  2013     1   11.0     0            0
#>  2 Peconic River 2013-01-02  2013     1   11.3     0            0
#>  3 Peconic River 2013-01-03  2013     1   11.6     0            0
#>  4 Peconic River 2013-01-04  2013     1   11.6     0            0
#>  5 Peconic River 2013-01-05  2013     1   11.7     0            0
#>  6 Peconic River 2013-01-06  2013     1   11.8     0            0
#>  7 Peconic River 2013-01-07  2013     1   12.0     0            0
#>  8 Peconic River 2013-01-08  2013     1   12.1     0            0
#>  9 Peconic River 2013-01-09  2013     1   12.0     0            0
#> 10 Peconic River 2013-01-10  2013     1   11.7     0            0
#> # … with 5,834 more rows

The last three columns show the daily summarized data. The do_mgl column shows the average DO concentration for each day, the below column shows a 1 (yes) or 0 (no) if the concentration was below the threshold value at any point during a day, and the below_cumsum column shows a cumulative tally of the number of days in each month at which dissolved oxygen fell below the threshold at any point during a day. By default, the anlz_dodlypep() function will impute missing daily dissolved oxygen values to the average for the year, month, site combination. This is often necessary to create summary values that make sense. For example, if a month has incomplete data, the maximum below_cumsum value will not show 30 or 31 days even if every day in the observed record is below the threshold.

The data can also be summarized by month using the anlz_domopep() function. Summarizing by month distills the information into a simple format for plotting. Internally, the anlz_domopep() function uses the anlz_dodlypep() function to first summarize results by day, which are then summarized by month.

mosum <- anlz_domopep(dodat, thr = 3)
mosum
#> # A tibble: 192 x 6
#>    site             yr mo    do_mgl below_ave below_maxrun
#>    <fct>         <dbl> <ord>  <dbl>     <dbl>        <dbl>
#>  1 Peconic River  2013 Jan    11.6      0                0
#>  2 Peconic River  2013 Feb    11.9      0                0
#>  3 Peconic River  2013 Mar    11.7      0                0
#>  4 Peconic River  2013 Apr     9.55     0                0
#>  5 Peconic River  2013 May     7.90     0                0
#>  6 Peconic River  2013 Jun     5.73     0.3              5
#>  7 Peconic River  2013 Jul     3.12     0.871           11
#>  8 Peconic River  2013 Aug     3.13     0.774           15
#>  9 Peconic River  2013 Sep     4.81     0.633            6
#> 10 Peconic River  2013 Oct     6.03     0.258            7
#> # … with 182 more rows

The last three columns show the monthly summarized data, where do_mgl is the average of all daily DO averages across the month, below_ave is the proportion of days in a month when concentrations in a given day fell below the threshold (1 would mean all days had an instance of DO below the threshold, 0 would mean none), and below_maxrun is the maximum number of sequential days in a month when concentrations in a given day fell below the threshold (30 or 31, depending on month, would indicate all days in a month had an instance of DO below the threshold).

The below_ave and below_maxrun columns summarize the DO data differently depending on how hypoxia/anoxia conditions can be described relative to potential impacts on biological resources. The below_ave column summarizes undersaturation relative to only the number of times hypoxia conditions occurred, whereas the below_maxrun column summarizes undersaturation relative to both the number of instances and duration of hypoxia. In other words, biota may be stressed differently depending on the number of times hypoxia occurs vs how long it may persist. The two measures may indicate similar information, but not always depending on characteristics of the DO time series.

The summarized monthly data can be plotted with the show_domatrix() function. In this example, the default plot shows the proportion of days in a month when concentrations in a given day fell below a threshold of 3 mg/L.

show_domatrix(dodat, thr = 3, show = 'below_ave')

The maximum number of sequential days in a month when concentrations in a given day fell below the threshold can also be plotted by changing the show argument.

show_domatrix(dodat, thr = 3, show = 'below_maxrun')

The threshold can also be changed. For example, the chronic threshold of 4.8 mg/L shows conditions relative to a more conservative threshold.

show_domatrix(dodat, thr = 4.8, show = 'below_ave')

Plotting the results provides insights into hypoxia patterns at the site depending on how under-saturated conditions were summarized. Additional considerations may include:

  1. Relevance of a threshold to biological indicator
  2. Spatial extent of the indicators
  3. Breakpoints for management decisions

[1] USEPA, Ambient aquatic life water quality criteria for dissolved oxygen (saltwater): Cape cod to cape hatteras, Office of Water, Office of Research; Development, US Environmental Protection Agency, Washington, DC, 2000.