tdm_cal.sfd | R Documentation |
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
)
input |
An |
genus |
Optional, character vector specifying genus-specific calibration
data that should be used (e.g., |
species |
Optional, character vector of species specific calibration data that should be used,
e.g. |
study |
Optional character vector of study specific calibration data that
should be used (e.g., |
wood |
Optional, character vector of wood type specific calibration
data that should be used (one of |
calib |
Optional |
a |
Optional, numeric value for the calibration curve ( |
b |
Optional, numeric value for the calibration curve ( |
decimals |
Integer, the number of decimals of the output (default = 6). |
make.plot |
Logical; if |
df |
Logical; If |
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
.
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.
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 \Sexpr[results=rd]{tools:::Rd_expr_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 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.agrformet.2019.03.012")}
#calculating sap flux density
## Not run:
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
## End(Not run)
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