# Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity
## 4 Discussion
[PERSON] _et al._[20] have demonstrated the use of ray tracing for scenes consisting of several millions of elements and for scenes comprising a stand of individual trees at needle-level resolution. For cases where the data size exceeds computer memory and the scenes exhibit some repetition in structure (e.g., scenes that represent fractal geometries or a collection of identical objects), techniques known as instantiation exist to reduce memory requirements (e.g., [35]). To ensure that absorption probabilities at the level of individual scene elements are accurately sampled, a higher structural fidelity of scenes requires the simulation of a larger numbers of photons and requires the use of advanced, optimized ray tracing algorithms. Increasing scene complexity in ray tracing simulations also provides for studies that investigate the physical mechanisms behind empirical observations of canopy reflectance and its linkages with leaf optical properties [65] and can further be used to analyze the distribution of radiation within shoots and needles [64]. While the current study focused on light transport, no links were established between short- and long-wave radiation and evapotranspiration. Forest canopies provide a level of insulation to extreme weather conditions and establish more temperate, humid conditions closer to ground level [66]. The current model does not simulate heat transfer with canopy depth or the effects of latent heat transfer resulting from evapotranspiration, causing overestimates of drought and temperature stresses on the lower parts of the canopy. The effects on stand-level gross primary productivity may be relatively small, however, since the largest photosynthetic contributions come from the upper canopy (e.g., [59]). Efforts to include thermal energy transfer may incorporate radiosity modeling [25; 52] or using 1D radiative transfer equations, acknowledging that the variability of temperature within the canopy space is relatively small [61] compared to light gradients [67]. In addition, no leaf-level measurements of photosynthetic parameters were made in this study, and as a result, no validation or inference around the variability of photosynthetic parameters within the canopy space can be made at this stage. For comparison, [PERSON] _et al._[17] estimate that physiological variability among needles contributes to over 6% of measurements of electron transport rate--_i.e._, analogous to measurements of quantum yield--owing to differences caused by factors including-shoot morphology and self-shading, leaf acclimation [61] and changes in hydraulic conductance with height [68]. In addition, the modifier functions did not consider hysteresis to changes in temperature and relative humidity. It is likely, however, that photosynthesis responds more quickly to precipitation than to dry conditions. For example, for a drought to have a significant impact on photosynthesis, a weather record of several weeks may need to be considered, whereas a sudden event of heavy rain after a prolonged period of dry conditions affects the growth of vegetation more quickly. Other important considerations that are currently omitted in our model are the co-limitation effect of nitrogen availability on electron transport and photosynthetic capacity [61], as well as the acclimation of leaves to seasonal temperature fluctuations [69] and the effects of transient changes in incident radiation on photosynthetic down-regulation [70].