The abiotic simple model#

This section walks through the steps in generating and updating the abiotic_simple model.

Required variables#

The abiotic_simple model requires a timeseries of the following variables to initialise and update the model.

Model overview#

The abiotic_simple model is a simple regression model that estimates microclimatic variables based on empirical relationships between leaf area index (LAI) and air temperature, relative humidity, vapour pressure deficit, and horizontal wind speed to derive vertical profiles of these variables from external climate data such as regional climate models or satellite observations. The model also provides information on atmospheric pressure and \(\ce{CO_{2}}\) and soil temperatures at different depths.

This sections describes the workflow of the abiotic_simple model update step. At each time step when the model updates, the run_simple_microclimate() function is called to perform the steps outlined below.

Step 1: Linear regression above ground#

The linear regression for below canopy values (1.5 m) is based on Hardwick et al. (2015) as

\[y = m * LAI + c\]

where \(y\) is the variable of interest, \(m\) is the gradient (see AbioticSimpleBounds) and \(c\) is the intersect which we set to the external data values, see Fig. 6. We assume that the gradient remains constant throughout the simulation.

Abiotic simple step1

Fig. 6 Linear regression between leaf area index (LAI) and abiotic variables at 1.5 m above the ground. The y-axis is intersected at the temperature at reference height. Orange crosses indicate 1.5 m and reference height.#

Step 2: Interpolation above ground#

The values for any other aboveground heights, including but not limited to canopy layers and surface layer, are calculated by logarithmic regression (for wind speed) or exponential regression (for air temperature, relative humidity and vapour pressure deficit) and interpolation between the input at reference height 2 m above the canopy and the 1.5 m values, see Fig. 7.

Abiotic simple step2

Fig. 7 Logarithmic (solid blue curve) and exponential (dashed blue curve) interpolation between abiotic variables at 1.5 m and the reference height 2 m above the canopy. This approach returns values at any height of interest. Orange crosses indicate 1.5 m and reference height as in Fig. 6.#

Step 3: Broadcasting constant atmospheric properties#

The model also broadcasts the reference values for atmospheric pressure and \(\ce{CO2}\) to all atmospheric levels as they are currently assumed to remain constant during one time step.

Step 4: Linear interpolation below ground#

Soil temperatures are interpolated between the surface layer and the temperature at 1 m depth which approximately equals the mean annual temperature, i.e. can assumed to be constant over the year.

Generated variables#

The calculations described above result in the following variables being calculated and saved within the data object, and then updated

Updated variables#

The link below provides the complete set of model variables that are updated at each model step.