Characterising forest landscapes using a stationary and a roving tower eddy covariance system: linking point data to landscape-level characterisations of growth and yield

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Eddy covariance-based net ecosystem productivity (NEP) measurements taken over growing forests provide the best possible measure of stand wood production. Stand-level forest models based on forest transition rules or modelled interactions between individual trees (gap models) provide generalisations of stand growth and wood production at various levels of accuracy. Most of these models rely on historical tree data for model formulation-calibration, for which growth behavior is well documented. In a changing climate regime, tree growth behavior is most likely to change, rendering future forest growth projections with forest transition models unsuitable. Also, landscape application of gap models is prohibitive because of the huge amount of computer resources needed to calibrate and run the model for a landscape of potentially tens of thousands stands, many with different attributes. Landscape application of existing ecosystem-based NEP models is also not practical, because of the models' complexity and data requirements. Because of these limitations, the proposed work is to develop a forest G&Y by extending an existing 10-parameter NEP model to the landscape by linking the model's monthly growth projections to local biophysical conditions. These conditions are to be derived by combining remote sensing-based images of temperature and leaf area with landscape-level simulations of solar radiation and soil water content. Forest ecosystem characterisation and model parameter setting are to be based mostly on information collected by a stationary base station and roving tower system. The 37-m stationary tower will provide long term base weather and ecological data. This information along with NEP and weather data collected at forest sites with a roving tower will be used to derive the required forest-specific growth parameters for different forest cover, and extend 3-4 week-long time series of NEP for application at the landscape level. The proposed work provides a major scientific innovation in G&Y modelling.