A comparison of models for quantifying growth and standing carbon in UK Scots pine forests

Abstract

© iForest -Biogeosciences and Forestry IntroductionThe combination of both spatial scale and rotation length of forest stands mean that models of forest growth are essential for sustainable management (Blanco et al. 2008). Historically this has been achieved using yield models which use empirical relations between state variables such as top height, basal area, and number of stems to forecast stand development and timber volume production (Vanclay 1994). For example, in the UK there is widespread use of the yield tables of Edwards & Christie (1981) provided through a lookup package ForestYield. Increased emphasis on providing a multifunction forest resource (Nijnik & Mather 2007) has meant that such models have had to be applied to activities such as carbon storage reporting (Dyson et al. 2009). However, forest managers focusing on multi-function management are in turn more likely to deviate from management regimes aimed only at maximising timber production. Another shortcoming of such yield tables is that they fail to be linked causally to the drivers of productivity such as climate and nutrient availability, and are unable to account for changes in these drivers as might be expected in a changing climate (Monserud 2003). A shift to more flexible forest models is appropriate to accommodate for changes in environment or management objectives.Hybrid models combine both empirical and process based modelling approaches: using simple mathematical relationships between stand variables, and representations of the underlying ecophysiological processes in stand development respectively. Through the combination, shortcomings of the empirical and process-based approaches may be ameliorated (Landsberg 2003, Monserud 2003, providing both traditional outputs for forest managers as well as estimates of carbon sequestration, whilst reducing the uncertainty in model outputs that occurs in complex process modelling (Valentine & Mäkelä 2005). Additionally, prediction precision may be improved by using a hybrid approach which can deal with changes in growing environment not represented in empirical models (Pinjuv et al. 2006).There are a number of hybrid models which have been applied for varied species and locations globally (Valentine & Mäkelä 2005, Weiskittel et al. 2009a, Mason et al. 2011. Perhaps the most widely applied hybrid model is Physiological Principles Predicting Growth (3PG) developed by Landsberg & Waring (1997). It uses physiological principles as the basis for a relatively simple process-based model that can provide forest managers with stand variables as well as estimates of carbon fixation. The model works in three stages: derivation of primary production, partitioning the production to above and below ground portions, and derivation of output variables . . However, while 3PG has the potential to be used over large areas where it has not been calibrated (Almeida et al. 2010), it still requires a large number of parameters to be measured or estimated (both climatic and in the stand). This may increase both mode...

Assimilation of repeated woody biomass observations constrains decadal ecosystem carbon cycle uncertainty in aggrading forests

“…In contrast, the mean transit times estimated here for wood are ∼100 years shorter than field estimates [ De Kauwe et al , ]. DALEC lacks an explicit representation of self‐thinning which becomes increasingly important as trees increase in height [ Lonsdale et al , ]. In DALEC self‐thinning is included implicitly in the retrieved woody transit time, potentially explaining this discrepancy.…”

Information Needs of Next-Generation Forest Carbon Models: Opportunities for Remote Sensing Science

“…Hence, process-based models are a more appropriate tool for projections of future conditions. Some efforts have attempted to bridge both empirical models and process-based models [68][69][70][71]; however, these approaches are often too onerous for carbon reporting and management applications. The current limits of process understanding, including their scale-dependent behavior as well as their complicated and cumulative interactions, are the main limits of our ability to model forest carbon via process-based models.…”