# eee2eye

## Calculating E:I Values from δ18O-H\~2\~O (‰)

Please see ‘EI-Calculations.pdf’ in the ‘man/ei_calcs’ folder for a step-by-step description of the Isotope Mass Balance used to calculate E:I.

Briefly, this function calculates E:I ratios, based on δ18O-H\~2\~O data. Environmental conditions (i.e. evaporation rate, humidity, temperatures, etc.) are set for the sub-arctic around Yellowknife, NT.

The function is based on a table with the following input parameters per sample:

• dL (‰) = -11.77, steady-state lake isotope value (measured value from field)
• dI (‰) = -20.7, source water, likely precipitation (value from Gibson 2001 and GNIP 1999)
• dP (‰) = -23, average value during evaporation season (signal of rain)
• temp (C) = 14.3, average temp. on lake (from Gibson & Reid, 2010)
• humid (dec) = 0.68, relative humidity (from Gibson & Reid, 2010)
• k = 0.7, estimated for our area (see ‘Note’ at bottom to calculate an approximate value)

## Installation

``````remotes::install_github("paukes/eee2eye")
``````

## Example E:I Calculation

``````library(eee2eye)
``````

Add E:I ratios to the `data.frame` of field data:

``````# create example database
ei_input <- data.frame(dL_permille = c(-11.77, -15.67, -18.23),
dI_permille = c(-20.7, -18.2, -20.2),
dP_permille = c(-23, -28, -32),
temp_C = c(14.3, 12.1, 8.9),
h_dec = c(0.68, 0.71, 0.58),
k = c(0.7, 0.72, 0.65))

ei_input <- eee2eye(ei_input, 'dL_permille', 'dI_permille', 'dP_permille', 'temp_C', 'h_dec', 'k')
``````

Add E:I ratios to a `data.frame` of field data when not all input values are known or estimated for each field site so common values can be specified:

``````ei_input <- eee2eye(ei_input, 'dL_permille', -20.7, 'dP_permille', 14.3, 0.68, 0.7)
``````

## Example WRT Calculation

``````# create example database
ei_input <- data.frame(E.I = c(0.2042, 0.3138, 0.1838),
e_myr = c(0.3965, 0.3965, 0.3965),
SA_m2 = c(315900, 300825, 589950),
V_m3 = c(2466000, 3004064, 5712829))

ei_input <- eee2eye_WRT(ei_input, 'E.I', 'e_myr', 'SA_m2', 'V_m3')
``````

## A Note About the Seasonality Value (`k`):

`k` is a very difficult parameter to quantify in that we don’t know much about it. For this reason we created a small function that you could approximate a `k` value based on the decimal latitude of your sampling site:

``````k_season <- function(x) {
k <- (((90 - x) / 90 ) * 0.5) + 0.5
return(k)
}
``````

paukes/eee2eye documentation built on July 18, 2021, 8:09 a.m.