Forest Modelling Lab.

For initialization, the 3D-CMCC-FEM requires as INPUT data:

• the initial stand conditions: species name, age, mean height, diameter at breast height (DBH), number of trees. The initial data are aggregated per classes (height classes, cohorts and species) by a pre-processing activity as following: (1) the relative values of diameters class is associated for each species, (2) the corresponding value of height class is assigned for each diameter class, and (3) the relative age is assigned for each height class (Collalti et al. 2014).

• species-speficic parameters, which are mostly based on species-specific eco-physiological and allometric characteristics and can be partially derived from forest inventories and literature (Collalti et al. 2019).

• meteorological forcing data: daily maximum (Tmax, °C) and minimum air temperature (Tmin, °C), soil temperature (Tsoil, °C), vapour pressure deficit (hPa), global solar radiation (MJ m−2 day−1 ) and precipitation amount (mm day−1 ). In addition, the model uses the day-time (Tday, °C) and night-time (Tnight, °C) average temperature (as optional) (Collalti et al. 2016).

• annual atmospheric CO2 concentration and nitrogen deposition (Collalti et al. 2018)

• soil (e.g. soil depth, sand, clay and silt percentages) and topographic information (e.g. elevation)

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The main OUTPUT data of the 3D-CMCC-FEM (either at daily, monthly or annual scale) are: Gross Primary Productivity (GPP), Net Primary Productivity (NPP), and state variables such as evapotranspiration (ET), Leaf Area Index (LAI) and rain interception. Results are obtained either at class-level (species, diameter, height, or age class level), layer-level (as sum of all tree height classes in the same layer), and grid level (as sum of all classes). The model provides information to support decision-making in forest management planning, such as mean annual volume increment (MAI), current volume increment (CAI), basal area, and DBH.

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