Prep Verna Wellfield data for use in AD and NPS calculations

Usage

util_prepverna(fl, typ, fillmis = T, mincrit = 75)

Arguments

fl: text string for the file path to the Verna Wellfield data
typ: character string for the type of data to prepare, either 'AD' for atmospheric deposition or 'NPS' for nonpoint source. Uses different TP calculation for each type.
fillmis: logical indicating whether to fill missing data with monthly means, see details
mincrit: numeric from 0 to 100, minimum value of the NADP Criteria1 (percent valid sample days) and Criteria3 (percent of precipitation captured by valid samples) completeness flags for a month to be treated as valid, see details. Ignored if either column is absent from fl.

Value

A data frame with total nitrogen and phosphorus estimates as mg/l for each year and month of the input data

Details

Raw data can be obtained from https://nadp.slh.wisc.edu/sites/ntn-FL41/ as monthly observations. Total nitrogen and phosphorus concentrations are estimated from ammonium and nitrate concentrations (mg/L) using the following relationships:

$$TN = NH_4^+ * 0.78 + NO_3^- * 0.23$$ $$TP = \begin{cases} 0.01262 \cdot TN + 0.00110 & \text{if } typ = ``AD" \\ 0.195 & \text{if } typ = ``NPS" \end{cases}$$

The first equation corrects for the % of ions in ammonium and nitrate that is N, and the second is a regression relationship between TBADS TN and TP, applied to Verna for atmospheric deposition estimates. A constant is used for non-point source estimates.

NADP flags each month's completeness with Criteria1 (percent of days with a valid sample) and Criteria3 (percent of precipitation captured by valid samples), both 0-100. A month with either value below mincrit (default 75) is treated as missing, in addition to the raw -9 value, before any gap-filling occurs.

Missing data (-9 values, or values failing the mincrit completeness screen) can be filled using monthly means from the previous five years where data exist for that month. If there are less than five previous years of data for that month, the missing value is not filled.

Years with incomplete seasonal data will be filled with NA values if fillmis = FALSE or filled with monthly means if fillmis = TRUE.

Examples

fl <- system.file('extdata/verna-raw.csv', package = 'tbeploads')
util_prepverna(fl, typ = 'AD')
#> # A tibble: 504 × 4
#>     Year Month TNConc TPConc
#>    <int> <int>  <dbl>  <dbl>
#>  1  1983     1     NA     NA
#>  2  1983     2     NA     NA
#>  3  1983     3     NA     NA
#>  4  1983     4     NA     NA
#>  5  1983     5     NA     NA
#>  6  1983     6     NA     NA
#>  7  1983     7     NA     NA
#>  8  1983     8     NA     NA
#>  9  1983     9     NA     NA
#> 10  1983    10     NA     NA
#> # ℹ 494 more rows