{tealeaves} is a lightweight R package to model leaf temperature using leaf energy balance. It uses the R package units to ensure that parameters are properly specified and transformed before calculations. It allows separate lower and upper surface conductances to heat and water vapour, so sensible and latent heat loss are calculated for each surface separately. It’s straightforward to model leaf temperature over environmental gradients such as light, air temperature, humidity, and wind, or trait gradients such as leaf size or stomatal conductance.
From CRAN
install.packages("tealeaves")
or from GitHub
install.packages("remotes")
remotes::install_github("cdmuir/tealeaves")
And load tealeaves
library("tealeaves")
The {tealeaves} package solves for leaf temperature given a set of environmental conditions and leaf traits by balancing the leaf energy budget. There are two main steps to using {tealeaves}:
tleaf
and tleaves
for single and multiple parameter sets,
respectively).In this vignette, I’ll show you how to:
Note that my examples use a lot tidyverse syntax, especially the pipe
operator %>%
. These are not required to use {tealeaves}, but you can
learn more about the the tidyverse here and
the %>%
more specifically here.
You can use the default parameter settings and solve for leaf
temperature in a single leaf using the make_*()
functions and
tleaf()
.
library(magrittr)
library(tealeaves)
# Leaving the make_* functions empty will automatically default to defaults
# parameters.
leaf_par <- make_leafpar() # leaf parameters
enviro_par <- make_enviropar() # environmental parameters
constants <- make_constants() # physical constants
T_leaf <- tleaf(leaf_par, enviro_par, constants, quiet = TRUE)
T_leaf %>% knitr::kable()
T_leaf | value | convergence | R_abs | S_r | H | L | E | Ar | Gr | Re | g_bw |
---|---|---|---|---|---|---|---|---|---|---|---|
301.4181 [K] | 0 | 0 | 1363.813 [W/m^2] | 907.9499 [W/m^2] | 107.3552 [W/m^2] | 348.5078 [W/m^2] | 0.00794791 [mol/m^2/s] | 0.0048272 | 788182.4 | 12778.08 | 0.0297282 |
You can look at default parameters settings in the manual (run
?make_parameters
). These defaults are reasonable, but of course you
will probably want to use different choices and allow some parameters to
vary. Here, I’ll demonstrate how to replace a default. In the next
section, I’ll show you how to set up a gradient of parameter values over
which to solve for leaf temperature.
# Use the `replace` argument to replace defaults. This must be a named list, and
# each named element must have the proper units specified. See `?make_parameters`
# for all parameter names and proper units.
# First, we'll change stomatal conductance to 4 umol / (m^2 s Pa)
leaf_par <- make_leafpar(
replace = list(
g_sw = set_units(4, "umol/m^2/s/Pa")
)
)
# Next, we'll change the air temperature to 25 degree C (= 298.15 K)
enviro_par <- make_enviropar(
replace = list(
T_air = set_units(298.15, "K")
)
)
# Physical constants probably do not need to be replaced in most cases,
# that's why we call them 'constants'!
constants <- make_constants()
T_leaf <- tleaf(leaf_par, enviro_par, constants, quiet = TRUE)
T_leaf %>% knitr::kable()
T_leaf | value | convergence | R_abs | S_r | H | L | E | Ar | Gr | Re | g_bw |
---|---|---|---|---|---|---|---|---|---|---|---|
302.1106 [K] | 0 | 0 | 1363.813 [W/m^2] | 916.3234 [W/m^2] | 130.0263 [W/m^2] | 317.4631 [W/m^2] | 0.007244854 [mol/m^2/s] | 0.0055546 | 903289.3 | 12752.29 | 0.0297445 |
In the previous two examples, I used the tleaf
function to solve for a
single parameter set. In most cases, you’ll want to solve for many
parameter sets. The function tleaves
generalizes tleaf
and makes it
easy to solve for multiple parameter sets using the same argument
structure. All you need to do is specify multiple values for one or more
leaf or environmental parameters and tleaves
uses the
tidyr::crossing
function to fit all combinations[^1].
# As before, use the `replace` argument to replace defaults, but this time we
# enter multiple values
# First, we'll change stomatal conductance to to 2 and 4 umol / (m^2 s Pa)
leaf_par <- make_leafpar(
replace = list(
g_sw = set_units(c(2, 4), "umol/m^2/s/Pa")
)
)
# Next, we'll change the air temperature to 20 and 25 degree C (= 293.15 and 298.15 K)
enviro_par <- make_enviropar(
replace = list(
T_air = set_units(c(293.15, 298.15), "K")
)
)
constants <- make_constants()
# Now there should be 4 combinations (high and low g_sw crossed with high and low T_air)
T_leaves <- tleaves(leaf_par, enviro_par, constants, progress = FALSE,
quiet = TRUE, set_units = FALSE)
T_leaves %>%
dplyr::select(T_air, g_sw, T_leaf) %>%
knitr::kable()
T_air | g_sw | T_leaf |
---|---|---|
293.15 [K] | 2 [umol/m^2/Pa/s] | 300.2217 [K] |
298.15 [K] | 2 [umol/m^2/Pa/s] | 304.2065 [K] |
293.15 [K] | 4 [umol/m^2/Pa/s] | 298.3474 [K] |
298.15 [K] | 4 [umol/m^2/Pa/s] | 302.1106 [K] |
It can take a little while to model many different parameter sets. If
you have multiple processors available, you can speed things up by
running simulations in parallel. In the tealeaves
function, simply use
the parallel = TRUE
argument to simulate in parallel. Here I’ll
provide an example looking at how leaf-to-air temperature differentials
change with air temperature.
# NOTE: not evaluated here because it caused error on CRAN. Copy-and-paste to run on your own machine.
# We'll use the `replace` argument to enter multiple air temperatures and two light levels
leaf_par <- make_leafpar()
enviro_par <- make_enviropar(
replace = list(
S_sw = set_units(c(300, 1000), "W/m^2"),
T_air = set_units(seq(273.15, 313.15, length.out = 10), "K")
)
)
constants <- make_constants()
tl <- tleaves(leaf_par, enviro_par, constants, progress = FALSE, quiet = TRUE,
parallel = TRUE)
tl$T_air %<>% drop_units() # for plotting
tl$T_leaf %<>% drop_units() # for plotting
tl %<>% dplyr::mutate(Light = dplyr::case_when(
round(drop_units(S_sw), 0) == 300 ~ "Shade",
round(drop_units(S_sw), 0) == 1000 ~ "Sun"
))
# Plot T_air versus T_leaf - T_air at different light levels
library(ggplot2)
ggplot(tl, aes(T_air, T_leaf - T_air, color = Light)) +
geom_line() +
xlab("Air Temperature [K]") +
ylab("Leaf - Air Temperature [K]") +
theme_minimal() +
NULL
I welcome comments, criticisms, and especially contributions! GitHub issues are the preferred way to report bugs, ask questions, or request new features. You can submit issues here:
https://github.com/cdmuir/tealeaves/issues
tealeaves
in R doing
citation(package = 'tealeaves')
[^1]: Since optimization is somewhat time-consuming, be careful about
crossing too many combinations. Use progress = TRUE
to show
progress bar with estimated time remaining.