Python for environmental modelling of mixed coniferous forests dominated by Norway spruce (Picea abies [L.] Karst.) and Swiss stone pine (Pinus cembra L.)

Coniferous forests exhibits distinctive ecological and botanical properties that contribute to our understanding of the environmental evolution and dynamics of Earth's landscapes. Their capacity to regulate water balance offers a possible explanation of forest hydrology as essential source of water. This role is expected to become even more crucial under climate change and anthropogenic pressure, respectively. Their various components provide intrinsic mechanism for regulating water cycle and for evapotranspiration partitioning either at the boundary between the ecotones or at the basin level on forested terrain. However, the presence of fog and the role of forest age are challenged due to the potential for high impact factor in eco-hydrological processes. This work applies methods of Python-based data modelling and statistical analysis. Using data modelling, we show experimentally that forest age, height of canopy, daily meteorological factors (fog and humidity) and presence of epiphytes (lichens) have all input on the water balance in the coniferous forests. The meteorological variables were investigated using fieldwork and included evapotranspiration, precipitation, temperature and water pressure deficit. Additionally, the paper proved that fog indirectly contributes to ecosystem water availability because it favours the growth of lichens which influence water cycle through inherent water retention capacity. Technically, this study offers a Python-based modelling of the observed large environmental-climatic dataset at the South Tyrol, Italy. The libraries included Matplotlib, Pandas, and NumPy for data processing and visualization.