Title:	Analyte Flux and Load from Estimates of Concentration and Discharge
Version:	1.0.0
Description:	Flux (mass per unit time) and Load (mass) are computed from timeseries estimates of analyte concentration and discharge. Concentration timeseries are computed from regression between surrogate and user-provided analyte. Uncertainty in calculations is estimated using bootstrap resampling. Code for the processing of acoustic backscatter from horizontally profiling acoustic Doppler current profilers is provided. All methods detailed in Livsey et al (2020) <doi:10.1007/s12237-020-00734-z>, Livsey et al (2023) <doi:10.1029/2022WR033982>, and references therein.
License:	GPL (≥ 3)
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.2.3
Depends:	R (≥ 2.10)
Imports:	data.table, graphics, imputeTS, mice, signal, stats, TideHarmonics, utils
Suggests:	knitr, rmarkdown
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2023-10-17 00:03:34 UTC; livseyd
Author:	Daniel Livsey [aut, cre, cph]
Maintainer:	Daniel Livsey <livsey.daniel@gmail.com>
Repository:	CRAN
Date/Publication:	2023-10-18 14:00:02 UTC

Tools For Computing Loads From Real-time Estimates of Concentration and Discharge with Code for Processing Acoustic Backscatter

Description

Code for computing loads from real-time estimates of concentration and discharge and processing of acoustic backscatter from acoustic Doppler velocimeters (ADVM) and acoustic Doppler current profilers (ADCP) per Livsey (in review)

Functions

acoustic_backscatter_processing Process acoustic backscatter from horizontally profiling ADVM/ADCP

attenuation_of_sound_by_water Computes attenuation of sound in water for acoustic backscatter processing

bootstrap_regression Compute regression parameters with uncertainty from analyte(surrogate(s))

butterworth_tidal_filter Estimate non-tidal variabilty using butterworth filter of Rulh and Simpson (2005)

compute_load Compute analyte load from surrogate, regression, and discharge data

ctd2sal Convert conductance to salinity in PSU

estimate_timeseries Estimate timeseries with uncertainty using outputs from bootstrap_regression() and timeseries of surrogate(s)

ExampleCode Process acoustic backscatter from synthetic data detailed in Livsey et al (in review)

ExampleCodeSCI Compute load from synthetic data using "Sediment Composition Index" (SCI) after Livsey et al (2023)

hADCPLoads Process acoustic backscatter from user data and compute load using list generated by import_Data()

import_data Import data from files in user-specified folder for use in hADCPLoads()

impute_data impute non-tidal or tidal data using ARIMA and decision tree algorithms

linear_interpolation_with_time_limit Interpolate timeseries onto new timestep, Nan returned when nearest observation exceeds time limit specified

near_field_correction Compute near-field correction for acoustic backscatter processing

speed_of_sound Compute speed of sound in water for acoustic backscatter processing

surrogate_to_analyte_interpolation Pair surrogate timeseries t(x) to analyte sample times t(y)

Author(s)

Daniel Livsey (2023) ORCID: 0000-0002-2028-6128

References

Livsey, D.N. (in review). National Industry Guidelines for hydrometric monitoring–Part 12: Application of acoustic Doppler velocity meters to measure suspended-sediment load. Bureau of Meteorology. Melbourne, Australia.

Computes sediment load per guideline from ExampleData

Description

Computes sediment load per guideline from ExampleData

Usage

ExampleCode()

Value

list with data frames of estimated concentration and flux along with data used in regression and surrogate timeseries

Author(s)

Daniel Livsey (2023) ORCID: 0000-0002-2028-6128

References

Livsey, D.N. (in review). National Industry Guidelines for hydrometric monitoring–Part 12: Application of acoustic Doppler velocity meters to measure suspended-sediment load. Bureau of Meteorology. Melbourne, Australia.

See Also

realTimeloads Package help file

Examples

Output <- ExampleCode()

Computes sediment load from optical and acoustic backscatter measurements

Description

Computes sediment load per guideline from optical and acoustic backscatter measurements combined to the "Sediment Composition Index" (SCI) per Livsey et al (2023)

Usage

ExampleCodeSCI()

Value

total load with uncertainty computed from estimates of concentration from SCI

Author(s)

Daniel Livsey (2023) ORCID: 0000-0002-2028-6128

References

Livsey, D.N. (in review). National Industry Guidelines for hydrometric monitoring–Part 12: Application of acoustic Doppler velocity meters to measure suspended-sediment load. Bureau of Meteorology. Melbourne, Australia.

See Also

realTimeloads Package help file

Examples

Output <- ExampleCodeSCI()

Example data used to demonstrate computation of real-time sediment loads from horizontal acoustic Doppler current profiler (hADCP)

Description

Synthetic dataset from modeled sediment transport and acoustic scattering detailed in the Appendices of Livsey (in review) Following dataframes are provided in list

Usage

ExampleData

Format

Site , Site, site datum, and ADCP elevation information

Site_name

Site name (string)

Site_number

Unique site code (string)

ADCP_elevation_above_bed_m

Elevation of the ADCP above the bed (m)

ADCP_elevation_above_gauge_datum_m

Elevation of the ADCP above local gauge datum (m)

Distance_of_gauge_datum_below_thalweg_m

Distance from local gauge datum to lower point in cross-section (m)

Start_date_and_time

Installation date of ADCP (time, POSIXct)

End_date_and_time

Date if/when ADCP is moved vertically (time, POSIXct)

Comment

User comment (string)

ADCP , ADCP readings except acoustic backscatter

Site_number

Unique site code (string)

time

Date and time (time, POSIXct)

Ensemble

Measurment ensemble number (integer)

Accoustic_Frequency_kHz

Acoustic frequency of ADCP (kHz)

Transducer_radius_m

Radius of ADCP transducer (m)

Beam_angle_degrees

Angle of beam relative to normal (degrees)

Beam_aspect_ratio

Ratio of beam radius to beam length (-)

Number_of_Cells

Number of measurement cells along beam (integer)

Bin_Size_m

Cell width measured normal to ADCP (m)

Blanking_distance_m

Blanking distance measured normal to ADCP (m)

Instrument_serial_number

Serial number of ADCP instrument (string)

CPU_serial_number

Serial number of ADCP CPU (string)

Ambient_Noise_Level_Beam_1_Counts

Ambient noise level for beam 1 (counts)

Ambient_Noise_Level_Beam_2_Counts

Ambient noise level for beam 2 (counts)

Distance_to_Bin_1_mid_point_m

Reported distance normal to ADCP to midpoint of bin/cell (m)

Speed_of_sound_m_per_s

Speed of sound used by ADCP in the field (m/s)

Temperature_degC

Temperature recorded by ADCP (degrees C)

Pressure_dbar

Pressure recorded by ADCP (dBar)

Distance_to_surface_m

Distance to water surface reported by vertical beam of ADCP (m)

Power_supply_voltage

Power to ADCP (V)

Echo_Intensity , Acoustic backscatter measurements from ADCP

Site_number

Unique site code (string)

time

Date and time (time, POSIXct)

Echo_Intensity_Counts_cell_n

Acoustic backscatter in nth cell (counts)

Sonde , Conductivity, temperature, and depth from sonde

time

Date and time (time, POSIXct)

Water_Temperature_degC

Temperature (degrees C)

Conductivity_uS_per_cm

Conductivity (microS/cm)

Pressure_dbar

Pressure (dbar)

Turbidity_FNU

Turbidity (FNU)

Site_number

Unique site code (string)

Height , River height in meters referenced to gauge datum

time

Date and time (time, POSIXct)

Height_m

Water surface elevation above gauge datum (m)

Site_number

Unique site code (string)

Discharge, Discharge timeseries in cubic meters per second

time

Date and time (time, POSIXct)

Discharge_m_cubed_per_s

Dischage (cubic meters per second)

Site_number

Unique site code (string)

Sediment_Samples , Measured sediment concentration in milligrams per liter (SSC or TSS)

time

Date and time (time, POSIXct)

SSCxs_mg_per_liter

Concentration of suspended-sediment in milligrams per liter, depth-averaged and velocity weighted average for cross-section

SSCpt_mg_per_liter

Concentration of suspended-sediment in milligrams per liter, measured at-a-point at elevation of hADCP

Site_number

Unique site code (string)

Examples

data(ExampleData) # lazy-load ony, unable to inspect contents in Rstudio

names(ExampleData) # load data for inspection in Rstudio and view names of items in list

Author(s)

Daniel Livsey (2023) ORCID: 0000-0002-2028-6128

Source

Livsey, D.N. (in review). National Industry Guidelines for hydrometric monitoring–Part 12: Application of acoustic Doppler velocity meters to measure suspended-sediment load. Bureau of Meteorology. Melbourne, Australia.

Process acoustic backscatter from hADCP

Description

Processes acoustic backscatter from horizontally profiling ADCP (hADCP). Returns attenuation of sound due to water and suspended-sediment. Applies all corrections to acoustic backscatter detailed in the guideline.

Usage

acoustic_backscatter_processing(
  Site,
  ADCP,
  Height,
  Sonde,
  Echo_Intensity_Beam_1,
  Echo_Intensity_Beam_2,
  Instrument_Noise_Level = NULL,
  Include_Rayleigh = FALSE,
  Include_near_field_correction = TRUE
)

Arguments

Value

List with processed data, all variable names and units are written-out in list items, see Livsey (in review) for details of each variable

Author(s)

Daniel Livsey (2023) ORCID: 0000-0002-2028-6128

References

Livsey, D.N. (in review). National Industry Guidelines for hydrometric monitoring–Part 12: Application of acoustic Doppler velocity meters to measure suspended-sediment load. Bureau of Meteorology. Melbourne, Australia.

Examples

InputData <- realTimeloads::ExampleData
Site <- InputData$Site
ADCP <- InputData$ADCP
Height <- InputData$Height
Sonde <- InputData$Sonde
EIa <- InputData$Echo_Intensity
# example code assumes backscatter is equal across beams
EIb <- InputData$Echo_Intensity
Output <- acoustic_backscatter_processing(Site,ADCP,Height,Sonde,EIa,EIb)

Compute attenuation of sound in water given frequency, temperature, and salinity

Description

Computes attenuation of sound in water per Ainslie and McColm (1998)

Usage

attenuation_of_sound_by_water(freq, temp, sal)

Arguments

Value

attenuation of sound in water (dB/m), divide by 20*log10(exp(1)) to convert to Nepers/m

Author(s)

Daniel Livsey (2023) ORCID: 0000-0002-2028-6128

References

Ainslie, M. A., & McColm, J. G. (1998). A simplified formula for viscous and chemical absorption in sea water. The Journal of the Acoustical Society of America, 103(3), 1671-1672.

Author modified Matlab code from David Schoellhamer

Examples

InputData <- realTimeloads::ExampleData
freq <- InputData$ADCP$Accoustic_Frequency_kHz*1000
cond <-InputData$Sonde$Conductivity_uS_per_cm
temp <- InputData$Sonde$Water_Temperature_degC
dbar <- InputData$Sonde$Pressure_dbar
sal <- ctd2sal(cond,temp,dbar)
aw <- attenuation_of_sound_by_water(freq,temp,sal) # dB/m
awNp <- attenuation_of_sound_by_water(freq,temp,sal)/(20*log10(exp(1))) # Np/m

Regression parameters estimated using bootstrap resampling

Description

Computes uncertainty in regression parameters of y(x) after Rustomji and Wilkinson (2008)

Usage

bootstrap_regression(Calibration, fit_eq, fit_glm = FALSE)

Arguments

Value

list with bootstrap regression parameters and list output from stats::lm()

Warning

User should inspect regression residuals and relevant statistics to ensure model form is reasonable, suggested reading: regression diagnostics in Statistical Methods in Water Resources (https://doi.org/10.3133/tm4a3).

One can call plot(fit) to view various regression diagnostic plots

Note

Bias Correction Factor (BCF) is only relevant when analyte is transformed to log units, see https://doi.org/10.3133/tm4a3 to convert a model that used log(analyte) back to linear units use: analyte = 10^(f(surrogates)) x BCF

Author(s)

Daniel Livsey (2023) ORCID: 0000-0002-2028-6128

References

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

Examples

# linear model
x <- 1:10
y <- 0.5*x + 10
boot <- bootstrap_regression(data.frame(x,y),"y~x")
# polynomial model, call to I() needed for squaring x in equation string
x <- 1:10
y <- x + x^2
boot <- bootstrap_regression(data.frame(x,y),"y ~ x+I(x^2)")
# power law model
# BCF returned since y is transformed to log units
x <- 1:10
y <- x^0.3
boot <- bootstrap_regression(data.frame(x,y),"log10(y)~log10(x)")
# multivariate model
a <- 1:10
b <- a*2
c <- a^2*b^3
boot <- bootstrap_regression(data.frame(a,b,c),"log10(c)~log10(a)+log10(b)")

Return non-tidal signal in data after Rulh and Simpson (2005)

Description

Applies a Butterworth filter with a 30-hour stop period and a 40-hour pass period

Usage

butterworth_tidal_filter(time, x)

Arguments

Value

non-tidal signal in x with data affected by filter ringing removed

Author(s)

Daniel Livsey (2023) ORCID: 0000-0002-2028-6128

References

Ruhl, C. A., & Simpson, M. R. (2005). Computation of discharge using the index-velocity method in tidally affected areas (Vol. 2005). Denver: US Department of the Interior, US Geological Survey. https://pubs.usgs.gov/sir/2005/5004/sir20055004.pdf

Examples

time <- realTimeloads::ExampleData$Height$time
x <- realTimeloads::ExampleData$Height$Height_m
xf <- butterworth_tidal_filter(time,x)

Compute load with uncertainty on concentration estimates

Description

Compute load with uncertainty on concentration estimates from bootstrap regression after Rustomji and Wilkinson (2008)

Usage

compute_load(Surrogate, Discharge, Regression, period = NULL)

Arguments

Value

list with data frames of estimated concentration and flux used to compute load (i.e., the sum of flux)

Note

Surrogate and Discharge time series can be on different time steps

If period is NULL, computes load over time in Surrogate

Warning

Discharge should be in cubic meters per second

Analyte concentration estimated from surrogate should be in milligrams per second

Author(s)

Daniel Livsey (2023) ORCID: 0000-0002-2028-6128

References

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

Examples

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)

Compute salinity (PSU) from conductivity, water temperature, and depth

Description

Computes salinity from conductivity, water temperature, and depth.

Usage

ctd2sal(cond, temp, dbar)

Arguments

Value

Salinity in PSU

Warning

If specific conductivity is returned from the sonde, the temperature at which specific conductivity is computed should be utilized

Author(s)

Daniel Livsey (2023) ORCID: 0000-0002-2028-6128

References

Fofonoff, N. P., & Millard Jr, R. C. (1983). Algorithms for the computation of fundamental properties of seawater.

Chen, C. T. A., & Millero, F. J. (1986). Thermodynamic properties for natural waters covering only the limnological range 1. Limnology and Oceanography, 31(3), 657-662.

Hill, K., Dauphinee, T., & Woods, D. (1986). The extension of the Practical Salinity Scale 1978 to low salinities. IEEE Journal of Oceanic Engineering, 11(1), 109-112.

Author modified Matlab code from David Schoellhamer

Examples

Sonde <- realTimeloads::ExampleData$Sonde
sal <- ctd2sal(Sonde$Conductivity_uS_per_cm,Sonde$Water_Temperature_degC,Sonde$Pressure_dbar)

Compute timeseries with uncertainty from bootstrap regression

Description

Compute uncertainty on timeseries from bootstrap regression after Rustomji and Wilkinson (2008)

Usage

estimate_timeseries(Surrogate, Regression)

Arguments

Value

list with inputs and uncertainty on timeseries estimated from Regression

Author(s)

Daniel Livsey (2023) ORCID: 0000-0002-2028-6128

References

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

Examples

Turbidity_FNU <- realTimeloads::ExampleData$Sonde$Turbidity
TSS_mg_per_l <- realTimeloads::ExampleData$Sediment_Samples$SSCpt_mg_per_liter
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')
Output <- estimate_timeseries(Surrogate,Regression)

Compute sediment load per guideline using acoustic backscatter from processed hADCP data

Description

Computes sediment load per guideline from user data in list "InputData" generated by function import_data()

Usage

hADCPLoads(InputData)

Arguments

Value

list with data frames of estimated concentration and flux along with data used in regression and surrogate timeseries

Author(s)

Daniel Livsey (2023) ORCID: 0000-0002-2028-6128

References

Livsey, D.N. (in review). National Industry Guidelines for hydrometric monitoring–Part 12: Application of acoustic Doppler velocity meters to measure suspended-sediment load. Bureau of Meteorology. Melbourne, Australia.

See Also

import_data Import data from files in user-specified folder

Examples

# loads example data in package folder extdata
InputData <- import_data()
# import_data(path) can be used to import user data
Output <- hADCPLoads(InputData)

Load data from comma-delimited .txt files to list to be used in function hADCPLoads()

Description

Imports csv files to R, file names, variable names (and units) in csv text files must match variable names used in ExampleData.rda

Usage

import_data(data_folder)

Arguments

Value

list with data frames used in package code, see ?ExampleData for list format

Warning

Synthetic data used in ExampleData only has backscatter for one beam ("ADCP_Echo_Intensity.txt"), for user data, one should have backscatter for two beams with following names: "ADCP_Echo_Intensity_Beam_1.txt" and "ADCP_Echo_Intensity_Beam_2.txt"

Package arguments require variable names and units to match the names and variable units provided (see ?ExampleData, or .txt files in extdata folder)

Suggest saving all csv files in .txt format to ensure time format is not changed when editing/saving csv in Excel

Author(s)

Daniel Livsey (2023) ORCID: 0000-0002-2028-6128

References

Livsey, D.N. (in review). National Industry Guidelines for hydrometric monitoring–Part 12: Application of acoustic Doppler velocity meters to measure suspended-sediment load. Bureau of Meteorology. Melbourne, Australia.

See Also

hADCPLoads Process acoustic backscatter from hADCP and compute load using InputData from import_Data()

Examples

InputData <- import_data() # loads text files provided in package folder "extdata"

Returns x with gaps imputed using ARIMA and Decision Trees, optional uncertainty estimation using Monte Carlo resampling

Description

Returns x with gaps imputed using ARIMA and Decision Trees with option to use harmonic model as predictors for x in decision tree algorithm. Uncertainty on imputed data is estimated using using Monte Carlo (MC) resampling adapting methods of Rustomji and Wilkinson (2008)

Usage

impute_data(
  time,
  x,
  Xreg = NULL,
  ti = NULL,
  hfit = NULL,
  harmonic = FALSE,
  only_use_Xreg = FALSE,
  MC = 1,
  ptrain = 1
)

Arguments

Value

list with x imputed at time or ti, if given. Uncertainty estimated from Monte Carlo simulations

Note

If MC == 1, uncertainty is not evaluated. If ptrain == 1, uncertainty and validation accuracy are not computed

Author(s)

Daniel Livsey (2023) ORCID: 0000-0002-2028-6128

References

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).

van Buuren S, Groothuis-Oudshoorn K (2011). “mice: Multivariate Imputation by Chained Equations in R.” Journal of Statistical Software, 45(3), 1-67. doi:10.18637/jss.v045.i03.

Stephenson AG (2016). Harmonic Analysis of Tides Using TideHarmonics. https://CRAN.R-project.org/package=TideHarmonics.

Moritz S, Bartz-Beielstein T (2017). “imputeTS: Time Series Missing Value Imputation in R.” The R Journal, 9(1), 207–218. doi:10.32614/RJ-2017-009.

Examples

# Impute non-tidal data
time <- realTimeloads::ExampleData$Sediment_Samples$time
xo <- realTimeloads::ExampleData$Sediment_Samples$SSCxs_mg_per_liter
Q <- realTimeloads::ExampleData$Discharge$Discharge_m_cubed_per_s
idata <- sample(1:length(xo),round(length(xo)*0.5),replace=FALSE)
x <- rep(NA,length(xo))
x[idata] <- xo[idata] # simulated samples
flow_concentrtion_ratio <- imputeTS::na_interpolation(Q/x)
Xreg <- cbind(Q,flow_concentrtion_ratio)
Output <- impute_data(time,x,Xreg,MC = 10,ptrain = 0.8)

# Impute tidal data
time <-TideHarmonics::Portland$DateTime[1:(24*90)]
xo <-TideHarmonics::Portland$SeaLevel[1:(24*90)]
idata <- sample(1:length(xo),round(length(xo)*0.5),replace=FALSE)
x <- rep(NA,length(xo))
x[idata] <- xo[idata] # simulated samples
Output <- impute_data(time,x,harmonic = TRUE,MC = 10,ptrain = 0.8)

Linearly interpolate timeseries time(x) onto new timesetep ti

Description

Linear interpolation limited by time since previous or following reading

Usage

linear_interpolation_with_time_limit(time, x, ti, threshold)

Arguments

Value

a data frame with time (ti), x interpolated from time(x) onto ti, and logical (ibad) if interpolation exceeded threshold

Author(s)

Daniel Livsey (2023) ORCID: 0000-0002-2028-6128

References

Dowle M, and others (2023). data.table: Extension of 'data.frame'. https://cran.r-project.org/web/packages/data.table

Examples

InputData <- realTimeloads::ExampleData
ADCP <- InputData$ADCP
Height <- InputData$Height
# Interpolate river height to ADCP time
time <- realTimeloads::ExampleData$Height$time
x <- realTimeloads::ExampleData$Height$Height_m
ti <-realTimeloads::ExampleData$ADCP$time
threshold <- 1
Output<- linear_interpolation_with_time_limit(time,x,ti,threshold)

Near-field correction of Downing et al (1995)

Description

Computes dimensionless near-field correction

Usage

near_field_correction(freq, c, r, at)

Arguments

Value

Near-field correction (dimensionless)

Warning

See various references cautioning use of near-field correction (e.g., https://doi.org/10.1002/2016WR019695)

Author(s)

Daniel Livsey (2023) ORCID: 0000-0002-2028-6128

References

Downing, A., Thorne, P. D., & Vincent, C. E. (1995). Backscattering from a suspension in the near field of a piston transducer. The Journal of the Acoustical Society of America, 97(3), 1614-1620.

Examples

InputData <- realTimeloads::ExampleData
Sonde<- InputData$Sonde
freq <- InputData$ADCP$Accoustic_Frequency_kHz[1]*1000
S <- ctd2sal(Sonde$Conductivity_uS_per_cm,Sonde$Water_Temperature_degC,Sonde$Pressure_dbar)
c <- speed_of_sound(S,Sonde$Water_Temperature_degC,Sonde$Pressure_dbar)
at <- InputData$ADCP$Transducer_radius_m
r <- seq(0.1,10,0.1)
psi <- near_field_correction(freq,c[1],r,at[1])

Compute speed of sound in water given salinity, temperature, and depth

Description

Computes speed of sound in water per Del grosso (1974)

Usage

speed_of_sound(sal, temp, depth)

Arguments

Value

Speed of sound in water (m/s)

Author(s)

Daniel Livsey (2023) ORCID: 0000-0002-2028-6128

References

Del Grosso, V. A. (1974). New equation for the speed of sound in natural waters (with comparisons to other equations). The Journal of the Acoustical Society of America, 56(4), 1084-1091. Author modified matlab code from David Schoellhamer

Examples

InputData <- realTimeloads::ExampleData
Sonde<- InputData$Sonde
sal <- ctd2sal(Sonde$Conductivity_uS_per_cm,Sonde$Water_Temperature_degC,Sonde$Pressure_dbar)
c <- speed_of_sound(sal,Sonde$Water_Temperature_degC,Sonde$Pressure_dbar)

Interpolate timeseries x(tx) onto y(ty)

Description

Interpolate timeseries x(tx) onto y(ty) with temporal threshold on interpolation

Usage

surrogate_to_analyte_interpolation(tx, x, ty, y, threshold)

Arguments

Value

a data frame with surrogate (x) interpolated onto timestep of analyte (y), interpolated values exceeding threshold are excluded from the output

Author(s)

Daniel Livsey (2023) ORCID: 0000-0002-2028-6128

Examples

tx <- as.POSIXct(seq(0,24*60^2,60*1), origin = "2000-01-01",tz = "Australia/Brisbane")
x <- sin(1:length(tx))
ty <- as.POSIXct(seq(0,24*60^2,60*15), origin = "2000-01-01",tz = "Australia/Brisbane")
y <- seq(0,24*60^2,60*15)
threshold <- 10
calibration <- surrogate_to_analyte_interpolation(tx,x,ty,y,threshold)