Description Usage Arguments Details Value Author(s) See Also Examples
Functions for simplifying the calculation of physical indices for a timeseries of observation data.
Can usually be called directly on data loaded directly using load.ts
and
load.bathy
.
1 2 3 4 5 6 7 8 9 10 11 | ts.meta.depths(wtr, slope = 0.1, na.rm=FALSE, ...)
ts.thermo.depth(wtr, Smin = 0.1, na.rm=FALSE, ...)
ts.schmidt.stability(wtr, bathy, na.rm=FALSE)
ts.lake.number(wtr, wnd, wnd.height, bathy, seasonal=TRUE)
ts.uStar(wtr, wnd, wnd.height, bathy, seasonal=TRUE)
ts.internal.energy(wtr, bathy, na.rm=FALSE)
|
wtr |
A data frame of water temperatures (in Celsius). Loaded using |
slope |
The minimum density gradient (kg/m^3/m) that can be called the thermocline |
Smin |
The minimum density gradient cutoff (kg/m^3/m) defining the metalimion |
bathy |
A data frame containing hypsometric data. Loaded using |
wnd |
A data frame of wind speeds (in m/s). Loaded using |
wnd.height |
Height of the anemometer above the lake surface in meters |
seasonal |
Boolean indicating if seasonal thermocline should be used in calculation. |
na.rm |
Boolean indicated if step-by-step removal of NA's should be tried. If false, a timestep with any NA values will return an NA value. If true, best effort will be made to calculate indices despite NA values. |
... |
Additional parameters passed to underlying base function (e.g., index=TRUE for thermo.depth) |
These are wrapper functions that accept a timeseries of data and call the core
physical metric functions (like schmidt.stability
) on each timestep.
Returns a data frame with the timeseries of calculated derivatives. All include a ‘datetime’ column, but derivative columns differ between functions.
Luke Winslow
For loading input data load.ts
, load.bathy
.
For the underlying functions operating at each timestep meta.depths
,
thermo.depth
, schmidt.stability
, lake.number
, internal.energy
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | #Get the path for the package example file included
exampleFilePath <- system.file('extdata', 'Sparkling.daily.wtr', package="rLakeAnalyzer")
#Load
sparkling.temp = load.ts(exampleFilePath)
#calculate and plot the metalimnion depths
m.d = ts.meta.depths(sparkling.temp)
plot(m.d$datetime, m.d$top, type='l', ylab='Meta Depths (m)', xlab='Date', col='blue')
lines(m.d$datetime, m.d$bottom, col='red')
#calculate and plot the thermocline depth
t.d = ts.thermo.depth(sparkling.temp)
plot(t.d$datetime, t.d$thermo.depth, type='l', ylab='Thermocline Depth (m)', xlab='Date')
|
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