In response to global climate change, many important earth-systems-oriented science programs have been established in the past. One such program, the Fluxnet program, studies the response of world forests and other natural ecosystems by measuring biospheric fluxes of carbon dioxide (CO2), water vapour, and energy with eddy-covariance (EC) techniques to assess the role of world ecosystems in offsetting increases in CO2 emissions and related impacts on global climate. The EC methodology has its limitations particularly when weather is inclement and during system stoppages. These limitations create non-trivial problems by creating data gaps in the monitored data stream, diminishing the integrity of the dataset and increasing uncertainty with data interpretation. This Thesis deals with the development of a parsimonious, semi-empirical approach for gap filling of net ecosystem productivity (NEP) data. The approach integrates the effects of environmental controls on diurnal NEP. The approach, because of its limited number of parameters, can be rapidly optimized when appropriate meteorological, site, and NEP target values are provided. The procedure is verified by applying it to several gap-filling case studies, including timeseries collected over balsam fir (Abies Balsamea (L.) Mill.) forests in New Brunswick (NB), Canada and several other forests along a north-south temperaturemoisture gradient from northern Europe to the Middle East. The evaluation showed that the model performed relatively well for most sites; i.e., r2 ranged from 0.68-0.83 and modelling efficiencies, from 0.89-0.97, demonstrating the possibility of applying the model to forests outside NB. Inferior model performance was associated with sites with less than complete input datasets.