Simple simulation
Running a simple simulation
Here is a first simple simulation of the coupled energy balance on a leaf over one meteorological time-step:
using PlantBiophysics, PlantSimEngine
meteo = Atmosphere(T = 22.0, Wind = 0.8333, P = 101.325, Rh = 0.4490995)
leaf = ModelList(
Monteith(),
Fvcb(),
Medlyn(0.03, 12.0),
status = (Ra_SW_f = 13.747, sky_fraction = 1.0, aPPFD = 1500.0, d = 0.03)
)
run!(leaf,meteo)
leaf
PlantSimEngine.DependencyGraph{Dict{Symbol, PlantSimEngine.SoftDependencyNode}, Any}(Dict{Symbol, PlantSimEngine.SoftDependencyNode}(:energy_balance => Monteith{Float64, Int64}
), Dict{Symbol, Any}())TimeStepTable{Status{(:Ra_SW_f, :sky_fracti...}(1 x 17):
╭─────┬─────────┬──────────────┬─────────┬─────────┬─────────┬─────────┬────────
│ Row │ Ra_SW_f │ sky_fraction │ d │ Tₗ │ Rn │ Ra_LW_f │ ⋯
│ │ Float64 │ Float64 │ Float64 │ Float64 │ Float64 │ Float64 │ Floa ⋯
├─────┼─────────┼──────────────┼─────────┼─────────┼─────────┼─────────┼────────
│ 1 │ 13.747 │ 1.0 │ 0.03 │ 17.9974 │ 26.3127 │ 12.5657 │ -198. ⋯
╰─────┴─────────┴──────────────┴─────────┴─────────┴─────────┴─────────┴────────
11 columns omitted
Now let's describe what is happening here.
PlantSimEngine
PlantBiophysics is nothing but an extension of PlantSimEngine.jl. What it really does is implementing biophysical models for PlantSimEngine. So when you use PlantBiophysics, you'll also need to import PlantSimEngine too.
Meteorology
The first line of the simulation is calling Atmosphere
. Atmosphere
is a structure used to describe what are the meteorological conditions in the atmosphere surrounding the leaf, such as the air temperature and humidity, the wind speed or the pressure. It comes from the PlantMeteo.jl package, but it is also exported by PlantSimEngine.
ModelList
The next command is using ModelList
(from PlantSimEngine), which helps us associate models (e.g. Monteith()
) to processes (e.g. energy_balance
). Currently PlantBiophysics.jl
implements three processes: the energy balance, the photosynthesis, and the stomatal conductance. For each of these processes, we can choose a model that will be used for its simulation. The package provides processes and models, but you can also implement your own by following the tutorial here.
In our example we use the Monteith et al. (2013) model implementation for the energy balance (Monteith()
), the Farquhar et al. (1980) model for the photosynthesis (Fvcb()
), and the Medlyn et al. (2011) model for the stomatal conductance (Medlyn(0.03, 12.0)
). All are available from PlantBiophysics.jl
.
Each model has its own structure used to provide the parameter values. For example the stomatal conductance model of Medlyn et al. (2011) need two parameters: g0
and g1
. We pass both values when calling the structure here: Medlyn(0.03, 12.0)
. In our example, we use the default values for the two other models used, so they are called without passing any argument.
Then we provide the initializations for some variables in the status keyword argument: Ra_SW_f = 13.747, sky_fraction = 1.0, aPPFD = 1500.0, d = 0.03
. The variables that need to be initialized depend on the combination of models we are using. One way to know which variables should be instantiated is to use to_initialize
from PlantSimEngine.jl
:
to_initialize(ModelList(Monteith(), Fvcb(), Medlyn(0.03, 12.0)))
(photosynthesis = (:aPPFD,), energy_balance = (:Ra_SW_f, :d, :sky_fraction))
It returns a list of the variables that need to be initialized for each independent process. If some processes are coupled, it only returns the ones from the root process that calls the others.
When we know which parameters have to be initialized, we can get the list of the parameters for each model by looking at its field names:
fieldnames(Fvcb)
(:Tᵣ, :VcMaxRef, :JMaxRef, :RdRef, :TPURef, :Eₐᵣ, :O₂, :Eₐⱼ, :Hdⱼ, :Δₛⱼ, :Eₐᵥ, :Hdᵥ, :Δₛᵥ, :α, :θ)
Or look into the documentation of the structure (e.g. ?Fvcb
) or the documentation of the process (e.g. ?AbstractPhotosynthesisModel
) to get more information such as the units.
Model coupling
PlantSimEngine
handles all model coupling and the order of execution of the processes. The user only needs to provide the list of models and the initializations. The package takes care of the rest by building a dependency graph and executing the processes in the right order considering the soft and hard dependencies. You can take a look at these concepts in the PlantSimEngine documentation.
Results
The results of the computations are stored in the status
field of the model list. To get the value for each given variable we can just index the object like so:
leaf[:A]
1-element Vector{Float64}:
32.00581672793857
Another simpler way to get all the results at once is to use DataFrame
:
using DataFrames
DataFrame(leaf)
Row | Ra_SW_f | sky_fraction | d | Tₗ | Rn | Ra_LW_f | H | λE | Cₛ | Cᵢ | A | Gₛ | Gbₕ | Dₗ | Gbc | iter | aPPFD | timestep |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Int64 | Float64 | Int64 | |
1 | 13.747 | 1.0 | 0.03 | 17.9974 | 26.3127 | 12.5657 | -198.479 | 224.791 | 350.022 | 312.074 | 32.0058 | 1.34468 | 0.0204721 | 0.879389 | 0.640402 | 1 | 1500.0 | 1 |
Or simply by printing the object:
leaf
PlantSimEngine.DependencyGraph{Dict{Symbol, PlantSimEngine.SoftDependencyNode}, Any}(Dict{Symbol, PlantSimEngine.SoftDependencyNode}(:energy_balance => Monteith{Float64, Int64}
), Dict{Symbol, Any}())TimeStepTable{Status{(:Ra_SW_f, :sky_fracti...}(1 x 17):
╭─────┬─────────┬──────────────┬─────────┬─────────┬─────────┬─────────┬────────
│ Row │ Ra_SW_f │ sky_fraction │ d │ Tₗ │ Rn │ Ra_LW_f │ ⋯
│ │ Float64 │ Float64 │ Float64 │ Float64 │ Float64 │ Float64 │ Floa ⋯
├─────┼─────────┼──────────────┼─────────┼─────────┼─────────┼─────────┼────────
│ 1 │ 13.747 │ 1.0 │ 0.03 │ 17.9974 │ 26.3127 │ 12.5657 │ -198. ⋯
╰─────┴─────────┴──────────────┴─────────┴─────────┴─────────┴─────────┴────────
11 columns omitted
Wrap-up
We learned to run a simple simulation, along with some details about the simulation, the structures and some helper functions.
Next, we'll learn to run a simulation over several time-steps.