MODELLING THE CARBON SINK IN ITALIAN FOREST ECOSYSTEMS USING ANCILLARY DATA, REMOTE SENSING DATA AND PRODUCTIVITY MODELS
The modeling approach
The methodology applied within the project is based on the use of two models, C-Fix and BIOME-BGC, whose outputs are integrated to operatively estimate forest GPP, NPP and NEE all over the Italian territory. In the following paragraphs the two models are briefly described together with the integration methodology.
The model C-Fix
C-Fix is a Monteith type parametric model driven by temperature, radiation and the fraction of absorbed photosynthetically active radiation (fAPAR), quantified through its generalized relationship with the normalized difference vegetation index (NDVI) (Veroustraete et al., 2002; Veroustraete et al., 2004). NDVI, which is mathematically defined as NDVI=[NIR-R]/[NIR+R] where NIR stands for Near Infrared Reflectance (0.7-1.1 μm wavelength) and R stands for Red reflectance (0.6-0.7 μm wavelength), is an indicator of plant photosynthetic activity and particularly of fAPAR (Baret & Guyot, 1991; Bannari et al., 1995).
C-Fix combines NDVI-derived fAPAR with field based estimates of incoming solar radiation and air temperature in order to simulate total photosynthesis (Veroustraete et al., 2004). The model is conceptually simple and generally applicable, and can use inputs averaged over different time periods (most commonly ten-day to monthly). The annual GPP (g C/m2/year) of a forest ecosystem can be computed as:
where ε is the radiation use efficiency, Tcor i is a factor accounting for the dependence of photosynthesis on air temperature Ti, fAPARi is the fraction of absorbed PAR, and Radi is the solar incident PAR, all referred to month i. fAPAR can be derived from the top of canopy NDVI according to the linear equation proposed by Myneni and Williams, (1994).
Maselli et al., (2009a) have recently proposed a modification of C-Fix aimed at improving the model performance in Mediterranean areas, which are characterized by a long summer dry season during which vegetation growth is limited by water availability (Bolle et al., 2006). This new version includes an additional water stress index, Cws, that limits photosynthesis in case of short-term water stress. Cws can vary between 0.5 (when short-term water shortage reduces photosynthesis to half of its potential value) and 1 (when there is no water shortage and photosynthesis reduction) (Maselli et al., 2009a).
This modification is completed by the use of the MODIS temperature correction factors (Heinsch et al., 2003) in place of the original factors proposed by Veroustraete et al., (2002). The former are bounded only by low temperature, while the latter also by high temperature. Since water stress is usually associated with high temperature, the second bound is partly redundant with that brought by Cws. More generally, the upper bound is physiologically useless when using the water stress index, as plants can easily cope with high temperature if there is no water limitation (Odum, 1971). Since the MODIS temperature correction factors are about 15% higher than the corresponding C-Fix factors, their use implies a reduction of the ε used in Eq.(1) from 1.4 to 1.2 g C/MJ APAR (Maselli et al., 2010).
The model BIOME-BGC
BIOME-BGC is a bio-geochemical model developed at the University of Montana to estimate the storage and fluxes of water, carbon and nitrogen within terrestrial ecosystems (Running and Hunt, 1993). It requires daily climate data, information on the general environment (i.e. soil, vegetation and site conditions) and parameters describing the ecophysiological characteristics of vegetation. BIOME-BGC is capable of finding a quasi-climax equilibrium with local eco-climatic conditions through the spin-up phase, whose aim is to quantify the initial amount of all carbon and nitrogen pools; after that, it simulates all respiration and allocation processes corresponding to the requested simulation years (White et al., 2000; Churkina et al., 2003).
The modelling of quasi-climax condition has important consequences on the simulated carbon budget. The sum of all simulated respirations becomes in fact nearly equivalent to GPP, which makes annual net primary productivity (NPP) approach heterotrophic respiration (Rhet) and NEE tend to zero. Also, such modelling renders the obtained GPP estimates similar to those produced by C-Fix, which are descriptive of all ecosystem components (Maselli et al., 2009a).
The version of the model currently used includes complete parameter settings for six main biome types (White et al., 2000). These settings have been recently modified to adapt to Mediterranean ecosystems, which show eco-climatic features markedly different from those for which the model was originally developed (Chiesi et al., 2007; Chiesi et al., 2011a; Chiesi et al., 2011b). Following an auto-ecological criterion (Chiesi et al., 2007), Tuscany forests were grouped into six ecosystem types, for which the vegetation parameters of BIOME-BGC were calibrated by the use of GPP estimates derived from C-Fix. More specifically, the calibration consisted of slightly modifying the BIOME-BGC eco-physiological parameters related to stomata conductance, which control all main transpiration and production processes (Maselli et al., 2009b).
Elaboration and integration of model outputs
Forest GPP computed by the two models is an expression of total, or potential, ecosystem productivity. More specifically, the GPP estimates of C-Fix represent the total photosynthesis of all green plants present in a pixel (Veroustraete et al., 2002), while those of BIOME-BGC express the total photosynthetic potential of an ecosystem in equilibrium with the environment (White et al., 2000). In reality, when this equilibrium is not reached within the tree compartment of an ecosystem due to natural or, most often, human-induced factors, the other compartments (herbs, brushes) can complement the photosynthetic activity of trees, and the almost maximum photosynthetic potential can be still attained (Waring and Running, 2007). This makes the GPP estimates of the two models practically inter-comparable and opens the possibility of using the more accurate GPP estimates of C-Fix to both calibrate BIOME-BGC and stabilize its outputs (Chiesi et al., 2007). In addition, the GPP estimates of both models need no correction to be compared to tower flux measurements of total photosynthesis, which equally comprise the contribution of all ecosystem compartments.
This is not, however, the case for actual forest NPP and NEE, since the C-Fix estimates of these variables are intrinsically quite inaccurate, while those of BIOME-BGC are produced assuming a quasi-climax situation. This situation may be completely different from that existing in real forest ecosystems, where the tree compartment can be artificially kept much smaller than the potential one and the possibility of accumulating new woody biomass can therefore be still very high. The capacity of new carbon accumulation in woody components, which mostly determines forest NPP and NEE, is in fact limited by both very low and high levels of existing woody biomass, due to reduced tree photosynthesis and increasing autotrophic and heterotrophic respirations, respectively (Waring and Running, 2007).
The strategy proposed to account for these cases is based on the simplifying assumption that the forest fractions not covered by trees (grasses and shrubs) behave approximately in the same way as trees regarding photosynthesis but do not significantly contribute to both woody NPP and NEE, since they only marginally accumulate woody biomass and tend to a climax equilibrium. A further assumption is that the woody biomass contained in an ecosystem tends to increase as it approaches climax. This implies that tree volume, which is directly related to woody biomass, can be taken as an indicator of ecosystem proximity to equilibrium condition. More specifically, the ratio between actual and potential tree volume is considered as an index of such proximity and is used to correct for photosynthesis and respiration differences with respect to BIOME-BGC simulations.
The first step to apply this approach is the estimation of actual forest volume, which can be carried out by the processing of ground, satellite and ancillary data (Maselli and Chiesi, 2006). Maximum (climax) volume can be obtained by properly converting the stem carbon computed by BIOME-BGC. The availability of both these estimates allows the computation of normalized actual volume, NVA, through division of actual over maximum volume. NVA is easily convertible into actual LAI (LAIA) by exploiting the BIOME-BGC per-species linear relationship between stem and leaf carbon (White et al., 2000). Specifically, LAIA can be calculated by multiplying NVA by BIOME-BGC maximum LAI. LAIA can then be used to obtain actual forest cover (FCA) following Beer’s law.
FCA represents the fraction of photosynthetic radiation usable by the tree canopy. Thus, it is related to the actual photosynthetic capacity of the stand examined, and can be used to convert BIOME-BGC GPP into the GPP of the existing tree compartment. In this way, the relationship between NVA and actual tree GPP (GPPA) becomes asymptotic, accounting for the GPP rise at low volume, due to the rapid tree occupation of the available space, and for the GPP tendency to level off at volume close to the maximum, when canopy competition prevails. The actual forest respirations, which are needed to compute actual NPP and NEE, can be also derived from NVA. In particular, growth respiration is assumed to vary in the same way as GPPA, i.e. with FCA, due to its strong dependence on photosynthesis and to the early investment of plant in growth processes (Running and Hunt, 1993). On the other hand, both maintenance and heterotrophic respirations are considered to vary linearly with NVA. This assumes that tree volume is directly related to the sum of living wood, which mainly determines maintenance respiration, and dead wood, which mainly determines heterotrophic respiration (decomposition) (Waring and Running, 2007).
On these bases, the computation of actual NPP and NEE (NPPA and NEEA) is feasible. In particular, NPPA can be computed as:
NPPA = GPP·FCA/FC – Rgr·FCA/FC - Rmn·NVA
where the term GPP·FCA/FC, Rgr·FCA/FC and Rmn·NVA correspond to the actual GPP, growth and maintenance respirations of the existing tree compartment. The division of GPP and Rgr by FC, which is the forest cover corresponding to BIOME-BGC LAI, accounts for the incomplete canopy cover used by the model to simulate quasi-climax condition.
Similarly, NEEA can be computed following:
NEEA = GPP·FCA/FC – Rgr· FCA/FC - Rmn·NVA - Rhet·NVA
Where Rhet·NVA is the actual heterotrophic respiration of the tree compartment.
Figure 2 shows the dependence of NPPA and NEEA on NVA; NPPA varies from 0 to the NPP predicted by BIOME-BGC, while NEEA approaches 0 for both high and low NVA values, in accordance with the model definition of equilibrium condition.
Figure 2 – Scheme of the dependence of NPPA and NEEA on NVA (see text and Maselli et al., 2009b, for details).
Cited bibliography not linked
Odum, E.P., 1971. Fundamentals of ecology. 3rd ed. Philadelphia. W. B. Saunders.
Running, S.W., and Hunt, E.R., 1993. Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an application for global-scale models. In: Ehleringer, J.R., and Field, C.B. (eds.), Scaling Physiological Processes: Leaf to Globe, Academic Press, San Diego, pp. 141-158.
Waring, H.R, and Running, S.W., 2007. Forest Ecosystems. Analysis at Multiples Scales. 3rd edition. Academic Press, San Diego.
White, M.A., Thornton, P.E., Running, S.W., Nemani, R.R., 2000. Parameterisation and sensitivity analysis of the BIOME-BGC terrestrial ecosystem model: net primary production controls. Earth Inter., 4, 1-85.
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