compute_load | R Documentation |
Compute load with uncertainty on concentration estimates from bootstrap regression after Rustomji and Wilkinson (2008)
compute_load(Surrogate, Discharge, Regression, period = NULL)
Surrogate |
data frame with time (PosixCt) and surrogate(s) (x,...) |
Discharge |
data frame with time (PosixCt) and discharge in cubic meters per second |
Regression |
data frame from bootstrap_regression() that determines analyte(surrogate) |
period |
two element vector time (PosixCt) indicating period over which load is computed |
list with data frames of estimated concentration and flux used to compute load (i.e., the sum of flux)
Surrogate and Discharge time series can be on different time steps
If period is NULL, computes load over time in Surrogate
Discharge should be in cubic meters per second
Analyte concentration estimated from surrogate should be in milligrams per second
Daniel Livsey (2023) ORCID: 0000-0002-2028-6128
Rustomji, P., & Wilkinson, S. N. (2008). Applying bootstrap resampling to quantify uncertainty in fluvial suspended sediment loads estimated using rating curves. Water resources research, 44(9).https://doi.org/10.1029/2007WR006088
Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., and Gilroy, E.J., 2020, #' Statistical methods in water resources: U.S. Geological Survey Techniques and Methods, book 4, chap. A3, 458 p. https://doi.org/10.3133/tm4a3
Turbidity_FNU <- realTimeloads::ExampleData$Sonde$Turbidity
TSS_mg_per_l <- realTimeloads::ExampleData$Sediment_Samples$SSCpt_mg_per_liter
Discharge <- realTimeloads::ExampleData$Discharge
Calibration <- data.frame(Turbidity_FNU,TSS_mg_per_l)
time <- realTimeloads::ExampleData$Sonde$time
Surrogate <- data.frame(time,Turbidity_FNU)
Regression = bootstrap_regression(Calibration,'TSS_mg_per_l~Turbidity_FNU')
period <- c(as.POSIXct("2000-02-16 AEST"),as.POSIXct("2000-03-16 AEST"))
Output <- compute_load(Surrogate,Discharge,Regression,period)
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