tdm_dt.max | R Documentation |
Determine zero flow conditions (\Delta T_{max}
; or \Delta V_{max}
)
according to four methods; namely,
1) predawn (pd
),
2) moving-window (mw
),
3) double regression (dr
),
and 4) environmental-dependent (ed
) as applied in Peters et al. 2018.
The function can provide (\Delta T_{max}
values and subsequent K values for all methods.
All outputs are provided in a list
including the input data and calculated outputs.
tdm_dt.max(input, methods = c("pd","mw","dr","ed"),
zero.end = 8*60,
zero.start = 1*60,
interpolate = FALSE, det.pd = TRUE,
max.days = 7,
ed.window = 2*60,
vpd.input,
sr.input,
sel.max,
criteria = c(sr = 30, vpd = 0.1, cv = 0.5),
df = FALSE)
input |
An |
methods |
Character vector of the requested |
zero.end |
Numeric, optionally defines the end of the predawn period. Values should be in minutes (e.g. predawn conditions until 08:00 = 8 * 60). When not provided, the algorithm will automatically analyse the cyclic behaviour of the data and define the day length. |
zero.start |
Numeric, optionally defines the beginning of the predawn period. Values should be in minutes (e.g., 01:00 = 1*60). |
interpolate |
Logical: if |
det.pd |
Logical; if |
max.days |
Numeric, defines the number of days which the |
ed.window |
Numeric, defines the length of the period considered for assessing the
environmental conditions and stable |
vpd.input |
An |
sr.input |
An |
sel.max |
Optional |
criteria |
Numeric vector, thresholds for the |
df |
Logical; if |
There are a variety of methods which can be applied to determine zero-flow conditions.
Zero-flow conditions are required to calculate K = (\Delta T_{max} - \Delta T) / \Delta T
.
A detailed description on the methods is provided by Peters et al. (2018).
In short, the pd
method entails the selection of daily maxima occurring prior to sunrise.
This method assumes that during each night zero-flow conditions are obtained.
The algorithm either requires specific times within which it searches for a maximum,
or it analyses the cyclic pattern within the data and defines this time window.
The mw
method uses these predawn \Delta T_{max}
values
and calculates the maximum over a multi-day moving time-window (e.g., 7 days).
The dr
methods is applied by calculating the mean over predawn \Delta T_{max}
with a specified multi-day window, removing all values below the mean,
and calculating a second mean over the same multi-day window and uses these values as \Delta T_{max}
.
The ed
method selects predawn \Delta T_{max}
values based upon 2-hour averaged environmental
conditions prior to the detected time for the predawn \Delta T_{max}
.
These environmental conditions include low vapour pressure deficit (in kPa
) and low solar irradiance
(e.g., in W m-2). In addition, the coefficient of variation (cv) of predawn \Delta T_{max}
are scanned for low values to
ensure the selection of stable zero-flow conditions.
A named list
of zoo
time series or data.frame
objects in the appropriate format for further processing.
List items include:
\Delta T_{max}
time series as determined by the pd
method.
\Delta T_{max}
time series as determined by the mw
method.
\Delta T_{max}
time series as determined by the dr
method.
\Delta T_{max}
time series as determined by the ed
method.
daily predawn \Delta T_{max}
as determined by pd
.
daily predawn \Delta T_{max}
as determined by mw
.
daily predawn \Delta T_{max}
as determined by dr
.
daily predawn \Delta T_{max}
as determined by ed
.
exact predawn \Delta T_{max}
values detected with pd
.
exact predawn \Delta T_{max}
values detected with ed
.
\Delta T
input data.
data.frame
of the applied environmental and variability criteria used within ed
.
data.frame
of applied methods to detect \Delta T_{max}
.
K
values calculated by using the pd
method.
K
values calculated by using the mw
method.
K
values calculated by using the dr
method.
K
values calculated by using the ed
method.
## Not run:
#perform Delta Tmax calculations
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 = 6, df = FALSE)
input[which(input<0.2)]<- NA
output.max <- tdm_dt.max(input, methods = c("pd", "mw", "dr"),
det.pd = TRUE, interpolate = FALSE,
max.days = 10, df = FALSE)
str(output.max)
plot(output.max$input, ylab = expression(Delta*italic("V")))
lines(output.max$max.pd, col = "green")
lines(output.max$max.mw, col = "blue")
lines(output.max$max.dr, col = "orange")
points(output.max$all.pd, col = "green", pch = 16)
legend("bottomright", c("raw", "max.pd", "max.mw", "max.dr"),
lty = 1, col = c("black", "green", "blue", "orange") )
## End(Not run)
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