be_rahi.RdRahi 2010 solution for calculating barometric efficiency.
be_rahi(dat, dep = "wl", ind = "baro", lag_space = 1, inverse = TRUE)
| dat | data that has the independent and dependent variables (data.table) |
|---|---|
| dep | name of the dependent variable column (character). This is typically the name for the column holding your water level data. |
| ind | name of the independent variable column (character). This is typically the name for the column holding your barometric pressure data. |
| lag_space | space between difference calculation in number of observations |
| inverse | whether the barometric relationship is inverse (TRUE means that when the barometric pressure goes up the measured water level goes down (vented transducer, depth to water), FALSE means that when the barometric pressure goes up so does the measured pressure (non-vented transducer)) (logical). |
barometric efficiency using Rahi's method
Rahi, K. A. (2010). Estimating the hydraulic parameters of the Arbuckle-Simpson aquifer by analysis of naturally-induced stresses (Doctoral dissertation, Oklahoma State University).
library(data.table) datetime <- seq.POSIXt(as.POSIXct("2016-01-01 12:00:00"), as.POSIXct("2016-01-05 12:00:00"), by='hour' ) baro <- sin(seq(0, 2 * pi, length.out = length(datetime))) noise <- rnorm(length(datetime), sd = 0.01) wl <- -0.4 * baro + noise dat <- data.table(baro, wl, datetime) be_rahi(dat, dep = 'wl', ind = 'baro', lag_space = 1)#> [1] 0.4274496