Abstract
The quantification of forest carbon budgets is important for understanding the role of forests in the global climate system. Given the variety of different methodologies (inventories, remote sensing, modelling) and spatial resolutions involved, methods for consistent transfer between scales are needed. In this study, the scaling of variables, which drive the carbon budget, was investigated for a tropical forest in Panama. Based on field inventory data from Barro Colorado Island, spanning 50 ha over 30 years, the distributions of aboveground biomass, biomass gain and mortality were derived at different spatial resolutions, ranging from 10 to 100 m. Methods for fitting parametric distribution functions were compared. Further, it was tested under which assumptions about the distributions a simple stochastic simulation forest model could best reproduce observed biomass distributions at all scales. Also, an analytical forest model for calculating biomass distributions at equilibrium and assuming mortality as a white shot noise process is presented. Scaling exponents of about −0.47 were found for the standard deviations of the biomass and gain distributions, while mortality showed a different scaling relationship with an exponent of −0.3. Lognormal and gamma distribution functions fitted with the moments matching estimation method allowed for consistent parameter transfers between scales. Both forest models (stochastic simulation and analytical solution) were able to reproduce observed biomass distributions across scales, when combined with the derived scaling relationships. The study provides insights about transferring between scales and its effect on frequency distributions of forest attributes, which is particularly important for the increasing efforts to combine information from sources such as inventories, remote sensing and modelling.
Highlights
Forests are inherently dynamic systems which are sensitive to climate patterns and a wide range of disturbance regimes (Lewis et al, 2015)
The three main questions of the study were: 1) How do forest ecosystem attributes such as standing biomass and biomass gains, losses and mortality vary with spatial scale? 2) Can a stochastic simulation forest model reproduce realistic biomass distributions from gain and mortality information at different scales? 3) Can a stochastic analytical forest model reproduce these biomass distributions as well?
3 Results 3.1 Scaling relationships of standard deviations of forest attribute distributions 220 The relationships between spatial scale and standard deviations of the frequency distributions were derived for aboveground biomass (AGB), gain, loss and mortality within the 50-ha forest plot for the scale range from 10 to 100 m
Summary
Forests are an important pool in the global carbon cycle. It is estimated that they store about 45% of the terrestrial carbon, mainly in trees in the form of living aboveground biomass (AGB) and in the soil (Bonan, 2008). Eddy flux towers have typical footprint sizes in the range a few 45 hundred meters, for which they acquire net gas exchange between the land surface and atmosphere (Rebmann et al, 2005) Such field measurements are an important information source for deriving tropical forest carbon budgets at continental scales (Brienen et al, 2015; Hubau et al, 2020). There are spatially implicit differential equation models describing the carbon balance in terms of 65 and aggregated biomass stock and fluxes of productivity and mortality (Fisher et al, 2008). In times where field measurements, remote sensing and model simulations are increasingly used in combination, approaches for harmonizing the different datasets with regard to spatial resolution are required. The three main questions of the study were: 1) How do forest ecosystem attributes such as standing biomass and biomass gains, losses and mortality vary with spatial scale? 2) Can a stochastic simulation forest model reproduce realistic biomass distributions from gain and mortality information at different scales? 3) Can a stochastic analytical forest model reproduce these biomass distributions as well?
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