The acquired \(K\) values are calculated to sap flux density
(\(SFD\) in \(cm^3 cm^{-2} h^{-1}\)). As many calibration curves exist
(see Peters et al. 2018; Flo et al. 2019), the function provides the option to
calculate \(SFD\) using calibration experiment data from the meta-analyses by
Flo et al. (2019; see cal.data). Additionally,
raw calibration data can be provided or parameters \(a\) and \(b\)
for a specific calibration function (\(aK^b\)) can be provided.
The algorithm determines for each calibration experiment dataset
the calibration curve (\(SFD = aK^b\)) and calculates \(SFD\) from
either the mean of all curves and the 95% confidence interval
of either all curves, or bootstrapped resampled uncertainty around
the raw calibration experiment data when one calibration dataset is selected.
tdm_cal.sfd(
input,
genus,
species,
study,
wood,
calib,
a,
b,
decimals,
make.plot = TRUE,
df = FALSE
)An is.trex-compliant object (zoo vector,
data.frame) of \(K\) values containing a timestamp and a value column.
Optional, character vector specifying genus-specific calibration
data that should be used (e.g., c("Picea", "Larix")). See cal.data
for the specific labels (default = Use all).
Optional, character vector of species specific calibration data that should be used,
e.g. c("Picea abies"). See cal.data for the specific labels (default = Use all).
Optional character vector of study specific calibration data that
should be used (e.g., c("Peters et al. 2018") ). See cal.data
for the specific labels (default= Use all).
Optional, character vector of wood type specific calibration
data that should be used (one of c("Diffuse-porous", "Ring-porous", "Coniferous")).
See cal.data for the specific labels (default= Use all).
Optional data.frame containing raw calibration experiment values.
Required columns include: [ ,1] \(K = K\) values measured with the probe (numeric),
and [,2] \(SFD =\) Gravimetrically measured sap flux density (\(cm^3 cm^{-2} h^{-1}\))) (numeric).
If not provided, literature values are used.
Optional, numeric value for the calibration curve (\(SFD = aK^b\)). No uncertainty can be calculated when this value is provided.
Optional, numeric value for the calibration curve (\(SFD = aK^b\)). No uncertainty can be calculated when this value is provided.
Integer, the number of decimals of the output (default = 6).
Logical; if TRUE, a plot is generated showing
the calibration curve with \(K vs sap flux density\) (\(cm^3 cm^{-2} h^{-1}\))).
Logical; If TRUE, output is provided in a data.frame format
with a timestamp and a value column. If FALSE, output
is provided as a zoo vector object (default = FALSE).
A list containing either a zoo object or data.frame in the appropriate format
for other functionalities (see tdm_dt.max output specifications), as well as
all \(SFD\) values for each method are provided and added to the
is.trex-compliant object (e.g., [['sfd.pd']], [['sfd.mw']])
if this format was provided as an input, and,
finally, a data.frame is provided with the mean and 95% confidence
interval of the applied calibration functions (see [['model.ens']]).
If an individual time series is provided for input with \(K\) values an alternative output is provided:
K values provided as input.
\(SFD\) values calculated for the input according to the mean of the calibration function.
A data.frame providing the mean and 95% confidence interval of the applied calibration function.
A data.frame with the coefficients of calibration function.
The function fits a calibration curve (\(SFD = aK^b\)) through all selected raw calibration data. If multiple studies are provided, multiple calibration curves are fitted. In case a single calibration dataset is provided a bootstrap resampling is applied (n = 100) to determined the mean and 95% confidence interval of the fit. When multiple calibration curves are requested the mean and 95% confidence interval is determined on the fitted functions. The mean and confidence interval are used to calculate \(SFD\) from \(K\).
Peters RL, Fonti P, Frank DC, Poyatos R, Pappas C, Kahmen A, Carraro V, Prendin AL, Schneider L, Baltzer JL, Baron-Gafford GA, Dietrich L, Heinrich I, Minor RL, Sonnentag O, Matheny AM, Wightman MG, Steppe K. 2018. Quantification of uncertainties in conifer sap flow measured with the thermal dissipation method. New Phytologist 219:1283-1299 doi:10.1111/nph.15241
Flo V, Martinez-Vilalta J, Steppe K, Schuldt B, Poyatos, R. 2019. A synthesis of bias and uncertainty in sap flow methods. Agricultural and Forest Meteorology 271:362-374 doi:10.1016/j.agrformet.2019.03.012
#calculating sap flux density
if (FALSE) {
raw <-is.trex(example.data(type="doy"),
tz="GMT",time.format="%H:%M",
solar.time=TRUE,long.deg=7.7459,
ref.add=FALSE)
input <-dt.steps(input=raw,start="2014-05-08 00:00",
end="2014-07-25 00:50",
time.int=15,max.gap=60,decimals=10,df=FALSE)
input[which(input<0.2)]<-NA
input <-tdm_dt.max(input, methods=c("pd","mw","dr"),
det.pd=TRUE,interpolate=FALSE,max.days=10,df=FALSE)
output.data<-tdm_cal.sfd(input,make.plot=TRUE,df=FALSE,
wood="Coniferous", decimals = 6)
str(output.data)
plot(output.data$sfd.pd$sfd,ylim=c(0,10))
lines(output.data$sfd.pd$q025,lty=1,col="grey")
lines(output.data$sfd.pd$q975,lty=1,col="grey")
lines(output.data$sfd.pd$sfd)
output.data$out.param
}