Modelización matemática de la radiación solar fotosintéticamente activa

Resumen

Photosynthetically active radiation (���) is the component of solar radiation that most influences photosynthesis and plant growth. Vegetation acts as a CO2 sink, mitigating the effects of climate change. Therefore, knowledge of the influence of ��� on plant growth is of essential importance. Mathematical modelling makes allows estimating ��� from different meteorological indices, without the need for specific measuring instruments, since it is not usual to find sensors measuring ��� at radiometric stations. In this thesis, ��� has been mathematically modelled in Burgos (Spain). For this purpose, a statistical study has been performed at this location, analysing the ratio of ��� with the global horizontal irradiance. An exhaustive review of the existing models has been carried out. Thus, 21 of them have been calibrated and validated with experimental data from the 7 radiometric stations belonging to the SURFRAD network (USA). Most of the studies published by other authors focus on results for clear skies, limiting their application to the local area and to those sky conditions. Using machine learning procedures, applied to the experimental data obtained in Burgos, a selection of variables has been made to model the ��� by means of multilinear regressions and neural networks. These studies have made it possible to obtain different mathematical models for each sky type (overcast, partial and clear) classified according to the ISO/CIE standard and, alternatively, using the clearness index (�� ) as a classification parameter. The performance of the latter models, locally fitted for Burgos, has been evaluated against the SURFRAD network measurements obtaining very good results. Therefore, it can be stated that these models may be used at any location, regardless of the climate